From 5ac48ecbf2bb44b0527b4a136c98c11c05641aa3 Mon Sep 17 00:00:00 2001
From: An204o <166038499+An204o@users.noreply.github.com>
Date: Sun, 13 Oct 2024 18:34:21 -0400
Subject: [PATCH 01/22] Week 2 Data Analysis

This is my Week 2 code which includes both hypothesis results and eda recommendations
---
 Copy_of_Data_Analysis_Week_2_Project.ipynb | 2898 ++++++++++++++++++++
 1 file changed, 2898 insertions(+)
 create mode 100644 Copy_of_Data_Analysis_Week_2_Project.ipynb

diff --git a/Copy_of_Data_Analysis_Week_2_Project.ipynb b/Copy_of_Data_Analysis_Week_2_Project.ipynb
new file mode 100644
index 00000000..fce99d88
--- /dev/null
+++ b/Copy_of_Data_Analysis_Week_2_Project.ipynb
@@ -0,0 +1,2898 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyOUecsYgEmf2wHgVFstRrVu",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/An204o/Data-projects/blob/main/Copy_of_Data_Analysis_Week_2_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Loading the csv file for the Cab_Data.csv and checking for its outliers. The boxplot seems to not have any outliers as it is not above the maximum value or below the minimum value."
+      ],
+      "metadata": {
+        "id": "nxz9GK3qZWv2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "\n",
+        "df1 = pd.read_csv('Cab_Data.csv', on_bad_lines='skip')\n",
+        "df1.head()\n",
+        "\n",
+        "df1['profit'] = df1['Price Charged'] - df1['Cost of Trip']\n",
+        "df1.head()\n",
+        "\n",
+        "sns.boxplot(x=df1['profit'])\n",
+        "\n",
+        "plt.savefig('boxplot_new.png')\n",
+        "\n",
+        "#Hypothesis:\n",
+        "#In the code above, I loaded the first dataset, which is Cab_Data.csv, and verified the whether the outliers are presentor not by using the boxplot chart. In the boxplot below,\n",
+        "# it seems that there are no outliers as the minimum is -150.38, and maximum is 1327.6219999999998, so there are no values which are less than minimum or above the maximum.\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "id": "zeCKWNYVOkql",
+        "outputId": "99558f26-5f10-4b9e-caf4-2559e332999f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df1.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "_9QBzdB0dcb-",
+        "outputId": "015153a3-7370-4fc6-cd23-cd0d9654cb2d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Transaction ID  Date of Travel   Company        City  KM Travelled  \\\n",
+              "0        10000011           42377  Pink Cab  ATLANTA GA         30.45   \n",
+              "1        10000012           42375  Pink Cab  ATLANTA GA         28.62   \n",
+              "2        10000013           42371  Pink Cab  ATLANTA GA          9.04   \n",
+              "3        10000014           42376  Pink Cab  ATLANTA GA         33.17   \n",
+              "4        10000015           42372  Pink Cab  ATLANTA GA          8.73   \n",
+              "\n",
+              "   Price Charged  Cost of Trip  profit  \n",
+              "0         370.95       313.635  57.315  \n",
+              "1         358.52       334.854  23.666  \n",
+              "2         125.20        97.632  27.568  \n",
+              "3         377.40       351.602  25.798  \n",
+              "4         114.62        97.776  16.844  "
+            ],
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+              "      <th>Transaction ID</th>\n",
+              "      <th>Date of Travel</th>\n",
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+              "      <td>313.635</td>\n",
+              "      <td>57.315</td>\n",
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+              "      <td>10000012</td>\n",
+              "      <td>42375</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
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+              "      <td>334.854</td>\n",
+              "      <td>23.666</td>\n",
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+              "      <th>2</th>\n",
+              "      <td>10000013</td>\n",
+              "      <td>42371</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>9.04</td>\n",
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+              "      <td>97.632</td>\n",
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+              "      <th>3</th>\n",
+              "      <td>10000014</td>\n",
+              "      <td>42376</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>33.17</td>\n",
+              "      <td>377.40</td>\n",
+              "      <td>351.602</td>\n",
+              "      <td>25.798</td>\n",
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+              "      <td>10000015</td>\n",
+              "      <td>42372</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>8.73</td>\n",
+              "      <td>114.62</td>\n",
+              "      <td>97.776</td>\n",
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+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
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+              "\n",
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+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df1"
+            }
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Bar chart for comparing the average profits of both Pink and Yellow Cab Companies"
+      ],
+      "metadata": {
+        "id": "ocbjQ1Svoc4J"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "average_profit = df1.groupby('Company')['profit'].mean()\n",
+        "\n",
+        "\n",
+        "average_profit.plot(kind='bar', color=['blue', 'red'])\n",
+        "\n",
+        "\n",
+        "plt.xlabel('Company')\n",
+        "plt.ylabel('Profit')\n",
+        "plt.title('Pink and Yellow Cab Companies vs Average Profits')\n",
+        "plt.savefig('barplot_comparison_profits.png')\n",
+        "\n",
+        "#EDA Recommendation\n",
+        "# Based on the exploratory data analysis, it is recommended to further investigate the factors contributing to the higher average profits of Yellow Cabs compared to Pink Cabs.\n",
+        "# This includes analyzing variables such as trip duration, fare rates, customer satisfaction, and the operational strategies of both cab companies.\n",
+        "\n",
+        "# Hypothesis Results\n",
+        "# In the bar chart below, I am comparing the average profits for two cab companies, Yellow and Pink Cabs.\n",
+        "# As you can see, the data indicates that the Yellow Cab company has more average profits, with a maximum profit of approximately $158,\n",
+        "# while the Pink Cab has an average profit of about $63. If this hypothesis holds true, it suggests that XYZ firm should consider investing in the Yellow Cab company\n",
+        "# due to its stronger profitability. This analysis supports the notion that Yellow Cabs may provide a more lucrative investment opportunity."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 532
+        },
+        "id": "A-hBShAXUwUw",
+        "outputId": "1c313882-44c2-4ba3-fa30-a0130a283d17"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df2 = pd.read_csv('City.csv')\n",
+        "df2.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "9HFXuXAzWAro",
+        "outputId": "79e2dd6a-56cc-4256-e5cd-1461fb68f420"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "             City   Population      Users\n",
+              "0     NEW YORK NY   8,405,837    302,149 \n",
+              "1      CHICAGO IL   1,955,130    164,468 \n",
+              "2  LOS ANGELES CA   1,595,037    144,132 \n",
+              "3        MIAMI FL   1,339,155     17,675 \n",
+              "4  SILICON VALLEY   1,177,609     27,247 "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-4ba8c576-5618-4e5e-a93f-5a15ef622129\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Users</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>NEW YORK NY</td>\n",
+              "      <td>8,405,837</td>\n",
+              "      <td>302,149</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>CHICAGO IL</td>\n",
+              "      <td>1,955,130</td>\n",
+              "      <td>164,468</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>LOS ANGELES CA</td>\n",
+              "      <td>1,595,037</td>\n",
+              "      <td>144,132</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>MIAMI FL</td>\n",
+              "      <td>1,339,155</td>\n",
+              "      <td>17,675</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>SILICON VALLEY</td>\n",
+              "      <td>1,177,609</td>\n",
+              "      <td>27,247</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ba8c576-5618-4e5e-a93f-5a15ef622129')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-4ba8c576-5618-4e5e-a93f-5a15ef622129 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-4ba8c576-5618-4e5e-a93f-5a15ef622129');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-407a8eb6-3504-4fb7-869e-56f573ef9ac5\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-407a8eb6-3504-4fb7-869e-56f573ef9ac5')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-407a8eb6-3504-4fb7-869e-56f573ef9ac5 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df2",
+              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"NEW YORK NY\",\n          \"WASHINGTON DC\",\n          \"SACRAMENTO CA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 8,405,837 \",\n          \" 418,859 \",\n          \" 545,776 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Users\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 302,149 \",\n          \" 127,001 \",\n          \" 7,044 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Stacked bar chart with the number of trips in different cities for both yellow and pink cabs"
+      ],
+      "metadata": {
+        "id": "yDvVWeIVu2RW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "\n",
+        "city_company_counts = df1.groupby(['City', 'Company'])['Company'].count().unstack()\n",
+        "\n",
+        "\n",
+        "city_company_counts.plot(kind='bar', stacked=True)\n",
+        "\n",
+        "\n",
+        "plt.xlabel('City')\n",
+        "plt.ylabel('Number of Trips')\n",
+        "plt.title('Most Used Cabs (Pink and Yellow) by City')\n",
+        "\n",
+        "\n",
+        "plt.legend(title='Company')\n",
+        "\n",
+        "\n",
+        "plt.savefig('stacked_bar_city_company.png')\n",
+        "\n",
+        "plt.show()\n",
+        "\n",
+        "#Hypothesis\n",
+        "# As you can see in the stacked bar chart below, the most used cabs are yellow cabs.\n",
+        "# In most of the countries, the most used cab is yellow. For instance, in the city\n",
+        "# New York, the number of trips for pink cabs is approximately 1,600, while the yellow cab\n",
+        "# has 980,000 trips.\n",
+        "\n",
+        "# Based on the stacked bar chart analysis, it appears that yellow cabs are predominantly used\n",
+        "# over pink cabs in New York, as evidenced by the trip counts.\n",
+        "# This indicates a strong preference for yellow cabs among consumers in that area.\n",
+        "# If these trends hold true across other locations, this may reflect a broader market trend\n",
+        "# that XYZ firm should consider when deciding on cab investments.\n",
+        "\n",
+        "#Recommendations:\n",
+        "#Data Visualizations: I would have made a graph such as linear or pie chart to better show the distribution of the values for this particular goal."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 579
+        },
+        "id": "Tc89YuH-Zcg4",
+        "outputId": "1cd1a65c-62e5-4426-aeaa-1574afd6a9e4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df1.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "Mue7qS3FdC55",
+        "outputId": "4f3a1c8e-4a82-4afa-bf0b-9877e6909c9d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Transaction ID  Date of Travel   Company        City  KM Travelled  \\\n",
+              "0        10000011           42377  Pink Cab  ATLANTA GA         30.45   \n",
+              "1        10000012           42375  Pink Cab  ATLANTA GA         28.62   \n",
+              "2        10000013           42371  Pink Cab  ATLANTA GA          9.04   \n",
+              "3        10000014           42376  Pink Cab  ATLANTA GA         33.17   \n",
+              "4        10000015           42372  Pink Cab  ATLANTA GA          8.73   \n",
+              "\n",
+              "   Price Charged  Cost of Trip  profit  \n",
+              "0         370.95       313.635  57.315  \n",
+              "1         358.52       334.854  23.666  \n",
+              "2         125.20        97.632  27.568  \n",
+              "3         377.40       351.602  25.798  \n",
+              "4         114.62        97.776  16.844  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-0f61529b-5bcc-456c-a8f1-b095116fa7aa\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Transaction ID</th>\n",
+              "      <th>Date of Travel</th>\n",
+              "      <th>Company</th>\n",
+              "      <th>City</th>\n",
+              "      <th>KM Travelled</th>\n",
+              "      <th>Price Charged</th>\n",
+              "      <th>Cost of Trip</th>\n",
+              "      <th>profit</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>10000011</td>\n",
+              "      <td>42377</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>30.45</td>\n",
+              "      <td>370.95</td>\n",
+              "      <td>313.635</td>\n",
+              "      <td>57.315</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>10000012</td>\n",
+              "      <td>42375</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>28.62</td>\n",
+              "      <td>358.52</td>\n",
+              "      <td>334.854</td>\n",
+              "      <td>23.666</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>10000013</td>\n",
+              "      <td>42371</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>9.04</td>\n",
+              "      <td>125.20</td>\n",
+              "      <td>97.632</td>\n",
+              "      <td>27.568</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>10000014</td>\n",
+              "      <td>42376</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>33.17</td>\n",
+              "      <td>377.40</td>\n",
+              "      <td>351.602</td>\n",
+              "      <td>25.798</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>10000015</td>\n",
+              "      <td>42372</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>8.73</td>\n",
+              "      <td>114.62</td>\n",
+              "      <td>97.776</td>\n",
+              "      <td>16.844</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f61529b-5bcc-456c-a8f1-b095116fa7aa')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-0f61529b-5bcc-456c-a8f1-b095116fa7aa button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-0f61529b-5bcc-456c-a8f1-b095116fa7aa');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-0ad292d2-411a-4734-ae75-ba6c58245641\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0ad292d2-411a-4734-ae75-ba6c58245641')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-0ad292d2-411a-4734-ae75-ba6c58245641 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df1"
+            }
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The bar chart that represents the most number of trips for both yellow and pink cabs in the year of 1970"
+      ],
+      "metadata": {
+        "id": "I3xr1iPUyaUy"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "\n",
+        "df1['Date of Travel'] = pd.to_datetime(df1['Date of Travel'])\n",
+        "\n",
+        "\n",
+        "df1_1970 = df1[df1['Date of Travel'].dt.year == 1970]\n",
+        "\n",
+        "\n",
+        "company_counts_1970 = df1_1970.groupby('Company')['Company'].count()\n",
+        "\n",
+        "\n",
+        "company_counts_1970.plot(kind='bar', color = ['pink', 'yellow'])\n",
+        "\n",
+        "\n",
+        "plt.xlabel('Company')\n",
+        "plt.ylabel('Number of Trips in 1970')\n",
+        "plt.title('Most Used Cabs (Pink and Yellow) in 1970')\n",
+        "\n",
+        "\n",
+        "plt.savefig('bar_chart_1970.png')\n",
+        "\n",
+        "plt.show()\n",
+        "\n",
+        "# Hypothesis Result for Bar Chart of Most Used Cabs in 1970\n",
+        "\n",
+        "# The bar chart above showcases the number of trips undertaken by Yellow and Pink Cab companies in the year 1970.\n",
+        "# As depicted, Yellow Cabs had significantly more trips compared to Pink Cabs in that year.\n",
+        "# This suggests a higher demand or preference for Yellow Cabs in the market during 1970.\n",
+        "# If this trend holds for other years, it indicates that Yellow Cabs potentially have a stronger market share and possibly higher profitability.\n",
+        "# This information can be useful for XYZ firm in making informed decisions about which cab company to invest in, potentially favoring the Yellow Cab company given their historical dominance.\n",
+        "\n",
+        "#EDA Recommendations:\n",
+        "#We should collect and analyze data on customer demographics (age, gender, income level) to better understand who is using each cab company.\n",
+        "#This could provide insights into targeted marketing strategies."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 532
+        },
+        "id": "kQaKMrE7eRqw",
+        "outputId": "ceedad65-74bd-4261-f1a3-871c62d5c9f6"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df2 = pd.read_csv('City.csv')\n",
+        "df2.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "JPYgpO2sfGkf",
+        "outputId": "b7854f08-5674-4a57-a750-5d236960514f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "             City   Population      Users\n",
+              "0     NEW YORK NY   8,405,837    302,149 \n",
+              "1      CHICAGO IL   1,955,130    164,468 \n",
+              "2  LOS ANGELES CA   1,595,037    144,132 \n",
+              "3        MIAMI FL   1,339,155     17,675 \n",
+              "4  SILICON VALLEY   1,177,609     27,247 "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-7e8d8437-3bda-49c9-9b22-0c73cde27d3b\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Users</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>NEW YORK NY</td>\n",
+              "      <td>8,405,837</td>\n",
+              "      <td>302,149</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>CHICAGO IL</td>\n",
+              "      <td>1,955,130</td>\n",
+              "      <td>164,468</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>LOS ANGELES CA</td>\n",
+              "      <td>1,595,037</td>\n",
+              "      <td>144,132</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>MIAMI FL</td>\n",
+              "      <td>1,339,155</td>\n",
+              "      <td>17,675</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>SILICON VALLEY</td>\n",
+              "      <td>1,177,609</td>\n",
+              "      <td>27,247</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7e8d8437-3bda-49c9-9b22-0c73cde27d3b')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
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+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-7e8d8437-3bda-49c9-9b22-0c73cde27d3b button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-7e8d8437-3bda-49c9-9b22-0c73cde27d3b');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
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+              "            title=\"Suggest charts\"\n",
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+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-8b7768ec-b9a8-4fe4-9a62-0cbd5ac9b727 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df2",
+              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"NEW YORK NY\",\n          \"WASHINGTON DC\",\n          \"SACRAMENTO CA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 8,405,837 \",\n          \" 418,859 \",\n          \" 545,776 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Users\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 302,149 \",\n          \" 127,001 \",\n          \" 7,044 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "city_company_counts = df1.groupby(['City', 'Company'])['Company'].count().unstack()\n",
+        "\n",
+        "\n",
+        "city_company_counts = city_company_counts.reset_index()\n",
+        "\n",
+        "\n",
+        "city_company_counts = city_company_counts.rename(\n",
+        "    columns={'Pink Cab': 'Pink Cab Users', 'Yellow Cab': 'Yellow Cab Users'}\n",
+        ")\n",
+        "\n",
+        "\n",
+        "merged_df = pd.merge(df2, city_company_counts, on='City', how='left')\n",
+        "\n",
+        "\n",
+        "merged_df = merged_df.fillna(0)\n",
+        "\n",
+        "\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "plt.plot(merged_df['City'], merged_df['Pink Cab Users'], label='Pink Cab Users')\n",
+        "plt.plot(merged_df['City'], merged_df['Yellow Cab Users'], label='Yellow Cab Users')\n",
+        "plt.xlabel('City')\n",
+        "plt.ylabel('Number of Users')\n",
+        "plt.title('Number of Users per City for Pink and Yellow Cabs')\n",
+        "plt.xticks(rotation=90)\n",
+        "plt.legend()\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n",
+        "\n",
+        "# Hypothesis Result for Line Graph of Number of Users per City\n",
+        "\n",
+        "# The line graph above visually depicts the number of users for Pink and Yellow Cabs in different cities.\n",
+        "# It seems that the Yellow Cabs consistently have a larger user base across most cities compared to Pink Cabs.\n",
+        "# This indicates a potential market share dominance for Yellow Cabs.\n",
+        "# Notably, in certain cities, the difference in user numbers is significant, which may reflect a strong preference for Yellow Cabs in those areas.\n",
+        "# If these trends persist, it implies a greater demand for Yellow Cabs, potentially contributing to higher profitability for the company.\n",
+        "# This information can guide XYZ firm's investment decisions, possibly favouring the Yellow Cab company due to its extensive user base.\n",
+        "\n",
+        "#EDA Recommendations:\n",
+        "#I can analyze trends and patterns, and observe the overall trends in user counts for both companies across different cities."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 607
+        },
+        "id": "dSmc_aL3VSKR",
+        "outputId": "7bb7ca04-299e-48d7-da61-442d8144dd6f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df3 = pd.read_csv('Transaction_ID.csv')\n",
+        "df3"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "02ORKDVwZI9H",
+        "outputId": "3597cfb2-8880-4357-bac4-1e00390f96d9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "        Transaction ID  Customer ID Payment_Mode\n",
+              "0             10000011      29290.0         Card\n",
+              "1             10000012      27703.0         Card\n",
+              "2             10000013      28712.0         Cash\n",
+              "3             10000014      28020.0         Cash\n",
+              "4             10000015      27182.0         Card\n",
+              "...                ...          ...          ...\n",
+              "307683        10307694      42426.0         Cash\n",
+              "307684        10307695      42819.0         Cash\n",
+              "307685        10307696      44212.0         Card\n",
+              "307686        10307697      44827.0         Card\n",
+              "307687             103          NaN          NaN\n",
+              "\n",
+              "[307688 rows x 3 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-eb77c9c0-5dc0-428e-8314-d40899ad4ed5\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "\n",
+              "    .dataframe thead th {\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Transaction ID</th>\n",
+              "      <th>Customer ID</th>\n",
+              "      <th>Payment_Mode</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>10000011</td>\n",
+              "      <td>29290.0</td>\n",
+              "      <td>Card</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>10000012</td>\n",
+              "      <td>27703.0</td>\n",
+              "      <td>Card</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>10000013</td>\n",
+              "      <td>28712.0</td>\n",
+              "      <td>Cash</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>10000014</td>\n",
+              "      <td>28020.0</td>\n",
+              "      <td>Cash</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>10000015</td>\n",
+              "      <td>27182.0</td>\n",
+              "      <td>Card</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>307683</th>\n",
+              "      <td>10307694</td>\n",
+              "      <td>42426.0</td>\n",
+              "      <td>Cash</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>307684</th>\n",
+              "      <td>10307695</td>\n",
+              "      <td>42819.0</td>\n",
+              "      <td>Cash</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>307685</th>\n",
+              "      <td>10307696</td>\n",
+              "      <td>44212.0</td>\n",
+              "      <td>Card</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>307686</th>\n",
+              "      <td>10307697</td>\n",
+              "      <td>44827.0</td>\n",
+              "      <td>Card</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>307687</th>\n",
+              "      <td>103</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>307688 rows × 3 columns</p>\n",
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+              "        document.querySelector('#df-eb77c9c0-5dc0-428e-8314-d40899ad4ed5 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-eb77c9c0-5dc0-428e-8314-d40899ad4ed5');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-20fa4755-2c55-41d8-95d1-5d6cce0d3b97\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-20fa4755-2c55-41d8-95d1-5d6cce0d3b97')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-20fa4755-2c55-41d8-95d1-5d6cce0d3b97 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_28a52d13-3169-4eeb-9213-8e435274f7de\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df3')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_28a52d13-3169-4eeb-9213-8e435274f7de button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df3');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df3"
+            }
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "df3 = df3.fillna(0)\n",
+        "df3.isnull().sum()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 178
+        },
+        "id": "bbOEMXM0kUdS",
+        "outputId": "653cdb57-f3e8-4173-d6b1-d21ff300a7a0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Transaction ID    0\n",
+              "Customer ID       0\n",
+              "Payment_Mode      0\n",
+              "dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Transaction ID</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Customer ID</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Payment_Mode</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df4 = pd.read_csv('Customer_ID.csv')\n",
+        "df4.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "64BsXf3bkToQ",
+        "outputId": "2fe40639-bd6f-4bf6-db74-ee6cf4a24f4e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Customer ID Gender  Age  Income (USD/Month)\n",
+              "0        29290   Male   28               10813\n",
+              "1        27703   Male   27                9237\n",
+              "2        28712   Male   53               11242\n",
+              "3        28020   Male   23               23327\n",
+              "4        27182   Male   33                8536"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-de4edf0e-d992-4c95-a735-95045f372109\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Customer ID</th>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Income (USD/Month)</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>29290</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>28</td>\n",
+              "      <td>10813</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>27703</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>27</td>\n",
+              "      <td>9237</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>28712</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>53</td>\n",
+              "      <td>11242</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>28020</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>23</td>\n",
+              "      <td>23327</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>27182</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>33</td>\n",
+              "      <td>8536</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-de4edf0e-d992-4c95-a735-95045f372109')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-de4edf0e-d992-4c95-a735-95045f372109 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-de4edf0e-d992-4c95-a735-95045f372109');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-0060794c-844e-40e1-b957-d2d55815f585\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0060794c-844e-40e1-b957-d2d55815f585')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-0060794c-844e-40e1-b957-d2d55815f585 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df4",
+              "summary": "{\n  \"name\": \"df4\",\n  \"rows\": 49171,\n  \"fields\": [\n    {\n      \"column\": \"Customer ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17714,\n        \"min\": 1,\n        \"max\": 60000,\n        \"num_unique_values\": 49171,\n        \"samples\": [\n          32451,\n          524,\n          19349\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 18,\n        \"max\": 65,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          32,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income (USD/Month)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8002,\n        \"min\": 2000,\n        \"max\": 35000,\n        \"num_unique_values\": 23341,\n        \"samples\": [\n          12932,\n          16207\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "merged_df1 = pd.merge(df3, df4, on='Customer ID')\n",
+        "merged_df2 = merged_df1.merge(df1, on='Transaction ID')\n",
+        "merged_df2.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 348
+        },
+        "id": "v8U48YCOFasv",
+        "outputId": "f9516c30-e25a-4ef3-8a41-ddb6cdf4a0a3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Transaction ID  Customer ID Payment_Mode Gender  Age  Income (USD/Month)  \\\n",
+              "0        10000011      29290.0         Card   Male   28               10813   \n",
+              "1        10000012      27703.0         Card   Male   27                9237   \n",
+              "2        10000013      28712.0         Cash   Male   53               11242   \n",
+              "3        10000014      28020.0         Cash   Male   23               23327   \n",
+              "4        10000015      27182.0         Card   Male   33                8536   \n",
+              "\n",
+              "   Date of Travel   Company        City  KM Travelled  Price Charged  \\\n",
+              "0         42377.0  Pink Cab  ATLANTA GA         30.45         370.95   \n",
+              "1         42375.0  Pink Cab  ATLANTA GA         28.62         358.52   \n",
+              "2         42371.0  Pink Cab  ATLANTA GA          9.04         125.20   \n",
+              "3         42376.0  Pink Cab  ATLANTA GA         33.17         377.40   \n",
+              "4         42372.0  Pink Cab  ATLANTA GA          8.73         114.62   \n",
+              "\n",
+              "   Cost of Trip  profit  \n",
+              "0       313.635  57.315  \n",
+              "1       334.854  23.666  \n",
+              "2        97.632  27.568  \n",
+              "3       351.602  25.798  \n",
+              "4        97.776  16.844  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-977a3625-8670-4406-b627-b1b07f9ce9cf\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Transaction ID</th>\n",
+              "      <th>Customer ID</th>\n",
+              "      <th>Payment_Mode</th>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Income (USD/Month)</th>\n",
+              "      <th>Date of Travel</th>\n",
+              "      <th>Company</th>\n",
+              "      <th>City</th>\n",
+              "      <th>KM Travelled</th>\n",
+              "      <th>Price Charged</th>\n",
+              "      <th>Cost of Trip</th>\n",
+              "      <th>profit</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>10000011</td>\n",
+              "      <td>29290.0</td>\n",
+              "      <td>Card</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>28</td>\n",
+              "      <td>10813</td>\n",
+              "      <td>42377.0</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>30.45</td>\n",
+              "      <td>370.95</td>\n",
+              "      <td>313.635</td>\n",
+              "      <td>57.315</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>10000012</td>\n",
+              "      <td>27703.0</td>\n",
+              "      <td>Card</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>27</td>\n",
+              "      <td>9237</td>\n",
+              "      <td>42375.0</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>28.62</td>\n",
+              "      <td>358.52</td>\n",
+              "      <td>334.854</td>\n",
+              "      <td>23.666</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>10000013</td>\n",
+              "      <td>28712.0</td>\n",
+              "      <td>Cash</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>53</td>\n",
+              "      <td>11242</td>\n",
+              "      <td>42371.0</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>9.04</td>\n",
+              "      <td>125.20</td>\n",
+              "      <td>97.632</td>\n",
+              "      <td>27.568</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>10000014</td>\n",
+              "      <td>28020.0</td>\n",
+              "      <td>Cash</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>23</td>\n",
+              "      <td>23327</td>\n",
+              "      <td>42376.0</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>33.17</td>\n",
+              "      <td>377.40</td>\n",
+              "      <td>351.602</td>\n",
+              "      <td>25.798</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>10000015</td>\n",
+              "      <td>27182.0</td>\n",
+              "      <td>Card</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>33</td>\n",
+              "      <td>8536</td>\n",
+              "      <td>42372.0</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>8.73</td>\n",
+              "      <td>114.62</td>\n",
+              "      <td>97.776</td>\n",
+              "      <td>16.844</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-977a3625-8670-4406-b627-b1b07f9ce9cf')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-977a3625-8670-4406-b627-b1b07f9ce9cf button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-977a3625-8670-4406-b627-b1b07f9ce9cf');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-d68e6d85-bbb6-425a-bc1f-ab3e6605fcd0\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d68e6d85-bbb6-425a-bc1f-ab3e6605fcd0')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-d68e6d85-bbb6-425a-bc1f-ab3e6605fcd0 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "merged_df2",
+              "summary": "{\n  \"name\": \"merged_df2\",\n  \"rows\": 71151,\n  \"fields\": [\n    {\n      \"column\": \"Transaction ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25592,\n        \"min\": 10000011,\n        \"max\": 10088433,\n        \"num_unique_values\": 71151,\n        \"samples\": [\n          10047350,\n          10009057,\n          10065991\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21212.115589102177,\n        \"min\": 1.0,\n        \"max\": 60000.0,\n        \"num_unique_values\": 26259,\n        \"samples\": [\n          59647.0,\n          53174.0,\n          24110.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Payment_Mode\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Cash\",\n          \"Card\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 18,\n        \"max\": 65,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          32,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income (USD/Month)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7994,\n        \"min\": 2000,\n        \"max\": 34996,\n        \"num_unique_values\": 16961,\n        \"samples\": [\n          5974,\n          2136\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date of Travel\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 80.38125009707976,\n        \"min\": 42371.0,\n        \"max\": 42661.0,\n        \"num_unique_values\": 291,\n        \"samples\": [\n          42458.0,\n          42631.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Company\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yellow Cab\",\n          \"Pink Cab\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"ATLANTA GA\",\n          \"DENVER CO\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"KM Travelled\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.213689673845838,\n        \"min\": 1.9,\n        \"max\": 48.0,\n        \"num_unique_values\": 874,\n        \"samples\": [\n          4.0,\n          29.43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price Charged\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 288.486993996938,\n        \"min\": 16.38,\n        \"max\": 1978.79,\n        \"num_unique_values\": 49504,\n        \"samples\": [\n          464.18,\n          626.96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cost of Trip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 157.89328330950855,\n        \"min\": 19.2,\n        \"max\": 691.2,\n        \"num_unique_values\": 14297,\n        \"samples\": [\n          434.5704,\n          380.4408\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"profit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 176.18102319112268,\n        \"min\": -198.69800000000004,\n        \"max\": 1336.8700000000001,\n        \"num_unique_values\": 68226,\n        \"samples\": [\n          730.5763999999999,\n          109.31200000000001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "income_company_counts = merged_df2.groupby(['Income (USD/Month)', 'Company'])['Company'].count().unstack()\n",
+        "income_company_counts.dropna(inplace = True)\n",
+        "\n",
+        "income_company_counts = income_company_counts.reset_index()\n",
+        "\n",
+        "\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "\n",
+        "\n",
+        "for company in income_company_counts.columns[1:]:\n",
+        "    plt.bar(income_company_counts['Income (USD/Month)'], income_company_counts[company], label=company)\n",
+        "\n",
+        "plt.xlabel('Income (USD)')\n",
+        "plt.ylabel('Number of Users')\n",
+        "plt.title('Number of Users per Income for Pink and Yellow Cabs')\n",
+        "plt.xticks(rotation=90)\n",
+        "plt.legend()\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n",
+        "plt.savefig('bar_chart_income_new.png')\n",
+        "\n",
+        "# Hypothesis Result for Bar Chart of Number of Users per Income\n",
+        "\n",
+        "# The bar chart above depicts the relationship between customer income and the usage of Pink and Yellow Cabs.\n",
+        "# It appears that across various income levels, Yellow Cabs tend to have a larger user base compared to Pink Cabs.\n",
+        "# This observation reinforces the potential market share dominance of Yellow Cabs.\n",
+        "# It is noticeable that as income levels increase, the user count for both Pink and Yellow Cabs also tends to increase, though the difference in user counts between the two companies seems consistent across different income brackets.\n",
+        "# If this trend continues, it suggests a strong preference for Yellow Cabs among customers regardless of their income level, potentially translating into greater profitability for the Yellow Cab company.\n",
+        "# This information can assist XYZ firm in making investment decisions, potentially favoring the Yellow Cab company due to its extensive user base across different income segments.\n",
+        "\n",
+        "# EDA Recommendations:\n",
+        "# Further examination of the customer demographic, such as locations."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 624
+        },
+        "id": "hXTgM55hGHoS",
+        "outputId": "69875007-f601-4bd4-f238-fc819a904d2b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 0 Axes>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

From 82bb77b4dd6b96f40291da0f29ad3993f1095f78 Mon Sep 17 00:00:00 2001
From: An204o <166038499+An204o@users.noreply.github.com>
Date: Sun, 13 Oct 2024 21:12:09 -0400
Subject: [PATCH 02/22] Add files via upload

---
 Data Intake Report.pdf | Bin 0 -> 53683 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 Data Intake Report.pdf

diff --git a/Data Intake Report.pdf b/Data Intake Report.pdf
new file mode 100644
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GIT binary patch
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From 777e1bf5030e0c83fc683391fd6c21a93db0dae2 Mon Sep 17 00:00:00 2001
From: An204o <166038499+An204o@users.noreply.github.com>
Date: Sat, 19 Oct 2024 20:51:18 -0400
Subject: [PATCH 03/22] Add files via upload

---
 Data Glacier Internship.potx | Bin 0 -> 48753 bytes
 EDA Cab.pptx                 | Bin 0 -> 515194 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 Data Glacier Internship.potx
 create mode 100644 EDA Cab.pptx

diff --git a/Data Glacier Internship.potx b/Data Glacier Internship.potx
new file mode 100644
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diff --git a/EDA Cab.pptx b/EDA Cab.pptx
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From b054f58ee927470df4161d19b52685eab65be76d Mon Sep 17 00:00:00 2001
From: An204o <166038499+An204o@users.noreply.github.com>
Date: Sat, 19 Oct 2024 20:52:39 -0400
Subject: [PATCH 04/22] Delete Data Glacier Internship.potx

---
 Data Glacier Internship.potx | Bin 48753 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 Data Glacier Internship.potx

diff --git a/Data Glacier Internship.potx b/Data Glacier Internship.potx
deleted file mode 100644
index ee656d36d36abc3ebf8d341e8f68b5e3a637d8a3..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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From 5b8a43aa6edd8930f6c167f70534546a77477094 Mon Sep 17 00:00:00 2001
From: An204o <166038499+An204o@users.noreply.github.com>
Date: Tue, 22 Oct 2024 15:37:06 -0400
Subject: [PATCH 05/22] Add files via upload

---
 ...Copy_of_Data_Analysis_Week_2_Project.ipynb | 2925 +++++++++++++++++
 1 file changed, 2925 insertions(+)
 create mode 100644 Copy_of_Copy_of_Data_Analysis_Week_2_Project.ipynb

diff --git a/Copy_of_Copy_of_Data_Analysis_Week_2_Project.ipynb b/Copy_of_Copy_of_Data_Analysis_Week_2_Project.ipynb
new file mode 100644
index 00000000..8864177c
--- /dev/null
+++ b/Copy_of_Copy_of_Data_Analysis_Week_2_Project.ipynb
@@ -0,0 +1,2925 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Loading the csv file for the Cab_Data.csv and checking for its outliers. The boxplot seems to not have any outliers as it is not above the maximum value or below the minimum value."
+      ],
+      "metadata": {
+        "id": "nxz9GK3qZWv2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "#This code loads the dataset by using pandas library\n",
+        "df1 = pd.read_csv('Cab_Data.csv', on_bad_lines='skip')\n",
+        "\n",
+        "#Creating a new column called profit\n",
+        "df1['profit'] = df1['Price Charged'] - df1['Cost of Trip']\n",
+        "\n",
+        "#Using seaborn and drawing boxplot by using the profit column\n",
+        "sns.boxplot(x=df1['profit'])\n",
+        "\n",
+        "#saving the graph in a png form by using savefig()\n",
+        "plt.savefig('boxplot_new.png')\n",
+        "\n",
+        "#Hypothesis:\n",
+        "#In the code above, I loaded the first dataset, which is Cab_Data.csv, and verified the whether the outliers are presentor not by using the boxplot chart. In the boxplot below,\n",
+        "# it seems that there are no outliers as the minimum is -150.38, and maximum is 1327.6219999999998, so there are no values which are less than minimum or above the maximum.\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "id": "zeCKWNYVOkql",
+        "outputId": "99558f26-5f10-4b9e-caf4-2559e332999f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiAAAAGwCAYAAACQB97CAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAhI0lEQVR4nO3deXRU5f3H8c9MksmiJGGRhEAgoCwKwbIUDPizVKhocYFycDkUpfWAIlSoiOhBoBYpuEspougp8vsVpXgAEcVSCiKCLLIJYVcRZAkUkAQkkJB5fn94cpuRQAEz38nyfp3D0bn38tznMcnM25m5E59zzgkAAMCQP9ITAAAAVQ8BAgAAzBEgAADAHAECAADMESAAAMAcAQIAAMwRIAAAwFx0OAYNBoPav3+/qlWrJp/PF45TAACAMuac0/Hjx5WWlia/P7zPUYQlQPbv36/09PRwDA0AAMLsm2++Ub169cJ6jrAESLVq1SR9v4DExMRwnAIAAJSxvLw8paene4/j4RSWACl+2SUxMZEAAQCggrF4+wRvQgUAAOYIEAAAYI4AAQAA5ggQAABgjgABAADmCBAAAGCOAAEAAOYIEAAAYI4AAQAA5ggQAABgjgABAADmCBAAAGCOAAEAAOYIEAAAYI4AAQAA5ggQAABgjgABAADmCBAAAGCOAAEAAOYIEAAAYI4AAQAA5ggQAABgjgABAADmCBAAAGCOAAEAAOYIEAAAYI4AAQAA5ggQAABgjgABAADmCBAAAGCOAAEAAOYIEAAAYI4AAQAA5qIjPQGc28GDB5WbmxvpaZhKSkpSSkpKpKcBAAgzAqScOnjwoH7d514VFpyO9FRMxQRi9bf/+18iBAAqOQKknMrNzVVhwWnlN/qZgnFJZTauP/+Y4nctVX7DGxSMTy6zccuC/1Su9NXHys3NJUAAoJIjQMq5YFySgpfVKvtx45PDMi4AABeCN6ECAABzBAgAADBHgAAAAHMECAAAMEeAAAAAcwQIAAAwR4AAAABzBAgAADBHgAAAAHMECAAAMEeAAAAAcwQIAAAwR4AAAABzBAgAADBHgAAAAHMECAAAMEeAAAAAcwQIAAAwR4AAAABzBAgAADBHgAAAAHMECAAAMEeAAAAAcwQIAAAwR4AAAABzBAgAADBHgAAAAHMECAAAMEeAAAAAcwQIAAAwR4AAAABzBAgAADBHgAAAAHMECAAAMEeAAAAAcwQIAAAwR4AAAABzBAgAADBHgAAAAHMECAAAMEeAAAAAcwQIAAAwR4AAAABzBAgAADBHgAAAAHMECAAAMEeAAAAAcwQIAAAwR4AAAABzBAgAADBHgAAAAHMECAAAMEeAAAAAcwQIAAAwR4AAAABzBAgAADBHgAAAAHMECAAAMEeAAAAAcwQIAAAwR4AAAABzBAgAADBHgAAAAHMECAAAMEeAAAAAcwQIAAAwR4AAAABzBAgAADBHgAAAAHMECAAAMFehAuTUqVPasWOHTp06FempABUeP08AIqlCBciePXvUv39/7dmzJ9JTASo8fp4ARFKFChAAAFA5ECAAAMAcAQIAAMwRIAAAwBwBAgAAzBEgAADAHAECAADMESAAAMAcAQIAAMwRIAAAwBwBAgAAzBEgAADAHAECAADMESAAAMAcAQIAAMwRIAAAwBwBAgAAzBEgAADAHAECAADMESAAAMAcAQIAAMwRIAAAwBwBAgAAzBEgAADAHAECAADMESAAAMAcAQIAAMwRIAAAwBwBAgAAzBEgAADAHAECAADMESAAAMAcAQIAAMwRIAAAwBwBAgAAzBEgAADAHAECAADMESAAAMAcAQIAAMwRIAAAwBwBAgAAzBEgAADAHAECAADMESAAAMAcAQIAAMwRIAAAwBwBAgAAzBEgAADAHAECAADMESAAAMAcAQIAAMwRIAAAwBwBAgAAzBEgAADAHAECAADMESAAAMAcAQIAAMwRIAAAwBwBAgAAzBEgAADAHAECAADMESAAAMAcAQIAAMwRIAAAwBwBAgAAzBEgAADAHAECAADMESAAAMBcdKQnAMBefn6+Jk6cKEnq379/hGdTtfh8PgUCAZ0+fdrbFggE5JzTmTNn5JyTJPn9fgWDQe8Yv98v55z8fr+Sk5OVlJSk7777TkVFRapbt67i4+N1+PBhnTx5UoWFhTpx4oQkKTY2VpIUHx+vQCCgGjVqKCEhQddff71q1KihpUuXasOGDcrNzVUgEFBcXJwSExN15swZBQIBnTlzRjVq1FBUVJRq1KghSfruu+90+vRp1axZU02bNtW2bdu0a9cuSVLHjh3VvXt3bdu2TUePHlWNGjXUsmVLFRUVac6cOdq0aZNiY2OVkJCgqKgopaamKhgMasuWLYqLi1OjRo2Um5urL774QtHR0Tpx4oTi4uJUt25dtW/fXkuWLFF+fr4yMzPVo0cPBQIBFRQUaO7cudq/f7/S0tJ0xx13KBAISJKKioq0ceNGHT16VMnJyZKkY8eOefOKioo667gf7rsYZTVOpMa3RIAAVcyIESO0fPnySE+jynLOhcSHJBUUFJx1XMn4KHm7qKhIR44c0ZEjR7x9hw8fPuf58vPzJX3/oCtJu3fvlqTzfg/s37//PCs4v+zsbL322msh2xISEpSfn+/F1aVYu3at3nvvPe/2smXL9Nprr6lJkybauXOnioqKvH2vvvqqevXqpWuuuUavvPKKcnJySh0zNTVVDz30kCSddVzxvhtuuOGC57h06dIyGSdS41vjJRigCiE+EC7VqlU7a1vPnj3VpUsXnTx5Us45JSQkSPr+2ZiS/9fu8/ku6lzNmjXTlVdeqWAwqG3btik6OlqPPvqoZs2apUcffVSJiYmaMWOGRo0apUaNGqlfv36SpMzMTGVmZsrn86lfv35q1KiRRo0apdGjR6tRo0aaNGmS5s+fr0mTJqlRo0YaPXq0li5dekFzWrp0aZmME6nxI4EAAaqI/Px84gNh4ff7dfLkScXExCgQCHgvdcyaNUuLFy+W3+9XYmKiTp48qerVq2vWrFkhz1iUfGakZMjUrFnTeymlWHR0tLZt26bnn3/e21ZYWKibbrpJNWvW1K233qoZM2bI7//+4W3EiBGaN2+eOnTooAkTJmjChAnKysrS+++/r1GjRik2NlaBQEBPPfWUmjdvroSEBDVv3lxPP/20srKyNHny5JC5lqaoqEivvPKKsrKy9PTTT1/yOJEaP1LK5CWY06dPhzylmJeXVxbDnlPxU4iVWVVY47lU5bWH01tvvRXpKaASat26tdatWydJ3gNg9+7dNXPmTEn/eemodevWWrJkiX7xi1/ogw8+OOd41apV0/HjxyUp5GWmYpmZmVq/fr1GjhzpbQsGg5o7d6569eolSdqyZYt33tdff105OTkaOXKkFyW9e/fWwIEDNW/ePO+xKzs7W61atfLG9Pv93nEbN24M2fdDGzduPOsclzJOpMaPlDIJkHHjxumpp54qi6EuyNixY83OBXt8fYGKo2bNmmdtq1Onzlnb4uLiJEl169bV119/fc7xTp06dUHnO3jwYMj2ku9bOXr0qPfve/fulSQ1bNjQ21b87+f6Oz88rrR9JRXvL3mOSxknUuNHSpkEyBNPPKFHHnnEu52Xl6f09PSyGLpUI0aMUIMGDcI2fnmwe/fuKvtAXBW+vpHw1ltvacmSJZGeBiqZ0p6lOHDgwFnbisNi3759SktLO+d4xaHy386XkpKif//73972kmMWX60jSfXq1dOaNWu0a9cuNW/eXJK8K3bO9XeKFR9X2r6SiveXPMeljBOp8SOlTAIkNjbWu9TLQoMGDdSkSROz88EWX9/wGD58OAGCMrdu3Tr5/X75fD7vn//85z+9/cUvGRS/TLNw4UJNnz5dkyZNKnW84pdfpO+f7Th+/HjIVUKbNm2SJI0ZM0Y9evTwznHHHXd4x1xzzTXeZcz9+vXTypUrNX36dD399NOSpOnTp6tOnTq67bbb9MYbb0iSWrRoETKPYDDoHdeyZcvz/jdo2bKlUlNTvXOUfJnkYsaJ1PiRwptQgSoiPj5eHTt2jPQ0UAkFg0HFx8ersLBQBQUF3iW/PXv21I033qhgMKi8vDwlJCTo22+/Vc+ePc95FUzJADly5MhZlyifOXNGzZo109ChQ71tMTExWrBggQ4fPqx58+bp7rvv9t4DMnbsWN1666369NNPNXjwYA0ePFgrVqzQrbfeqj/+8Y86ffq0CgoKNHr0aG3evFknT57U5s2b9eSTT2rFihUaMGDAf/2cjaioKD300ENasWKFnnzyyUseJ1LjR4rP/ZgLs88hLy9PSUlJys3NVWJiYpmNu2PHDvXv319Tpkyp9P+HXLzW7665XcHLapXZuP7vDuuyLe+V+bhloXhuVeHrG0lcigtrZfE5IKXx+/2lfg5IVFTUBX0OSJ06dTRgwABJZ38OSPG+H/s5IJcyTqTGl8L3+F0aPogMqGLGjh2r/Px8PfbYY97T2bDDJ6HafhJqx44dL+iTUEsed6mfMHrDDTeUyTiRGt8aAQJUQfHx8frd735XZZ5RxLm1b9/+R4/RrVu3s7b98HLQqKgo3XXXXbrrrrt+1LlKexkxEAh4l+D+UFRU1AVdmnqhx1mNE6nxLfEeEAAAYI4AAQAA5ggQAABgjgABAADmCBAAAGCOAAEAAOYIEAAAYI4AAQAA5ggQAABgjgABAADmCBAAAGCOAAEAAOYIEAAAYI4AAQAA5ggQAABgjgABAADmCBAAAGCOAAEAAOYIEAAAYI4AAQAA5ggQAABgjgABAADmCBAAAGCOAAEAAOYIEAAAYI4AAQAA5ggQAABgjgABAADmCBAAAGCOAAEAAOYIEAAAYI4AAQAA5ggQAABgjgABAADmCBAAAGCOAAEAAOYIEAAAYI4AAQAA5ggQAABgjgABAADmCBAAAGCOAAEAAOYIEAAAYI4AAQAA5ggQAABgjgABAADmCBAAAGCOAAEAAOYIEAAAYI4AAQAA5ggQAABgjgABAADmCBAAAGCOAAEAAOYIEAAAYI4AAQAA5ggQAABgjgABAADmCBAAAGCOAAEAAOYIEAAAYI4AAQAA5ggQAABgjgABAADmCBAAAGCOAAEAAOYIEAAAYI4AAQAA5ipUgNSvX19TpkxR/fr1Iz0VoMLj5wlAJEVHegIXIy4uTk2aNIn0NIBKgZ8nAJFUoZ4BAQAAlQMBAgAAzBEgAADAHAECAADMESAAAMAcAQIAAMwRIAAAwBwBAgAAzBEgAADAHAECAADMESAAAMAcAQIAAMwRIAAAwBwBAgAAzBEgAADAHAECAADMESAAAMAcAQIAAMwRIAAAwBwBAgAAzBEgAADAHAECAADMESAAAMAcAQIAAMwRIAAAwBwBAgAAzBEgAADAHAECAADMESAAAMAcAQIAAMwRIAAAwBwBAgAAzBEgAADAHAECAADMESAAAMAcAQIAAMwRIAAAwBwBAgAAzBEgAADAHAECAADMESAAAMAcAQIAAMwRIAAAwBwBAgAAzBEgAADAHAECAADMESAAAMAcAQIAAMwRIAAAwBwBAgAAzBEgAADAHAECAADMESAAAMAcAQIAAMwRIAAAwBwBAgAAzBEgAADAHAECAADMESAAAMAcAQIAAMwRIAAAwBwBAgAAzBEgAADAHAECAADMESAAAMAcAQIAAMwRIAAAwBwBAgAAzBEgAADAHAECAADMRUd6Ajg//6ncsh0v/1jIP8uTsl4rAKD8IkDKqaSkJMUEYqWvPg7L+PG7loZl3B8rJhCrpKSkSE8DABBmBEg5lZKSor/93/8qN7dqPSuQlJSklJSUSE8DABBmBEg5lpKSwoMxAKBS4k2oAADAHAECAADMESAAAMAcAQIAAMwRIAAAwBwBAgAAzBEgAADAHAECAADMESAAAMAcAQIAAMwRIAAAwBwBAgAAzBEgAADAHAECAADMESAAAMAcAQIAAMwRIAAAwBwBAgAAzBEgAADAHAECAADMESAAAMAcAQIAAMwRIAAAwBwBAgAAzBEgAADAHAECAADMESAAAMAcAQIAAMwRIAAAwBwBAgAAzBEgAADAHAECAADMESAAAMBcdDgGdc5JkvLy8sIxPAAACIPix+3ix/FwCkuAHD9+XJKUnp4ejuEBAEAYHT9+XElJSWE9h8+FIXOCwaD279+vatWqyefzhezLy8tTenq6vvnmGyUmJpb1qcsl1syaKyvWzJorq6q85i1btqhp06by+8P7Lo2wPAPi9/tVr1698x6TmJhYZb6oxVhz1cCaqwbWXDVUxTXXrVs37PEh8SZUAAAQAQQIAAAwZx4gsbGxGj16tGJjY61PHTGsuWpgzVUDa64aWHP4heVNqAAAAOfDSzAAAMAcAQIAAMwRIAAAwBwBAgAAzIUlQL7++mvdf//9atiwoeLj43XllVdq9OjRKigoCDlu48aN+p//+R/FxcUpPT1dzz777FljvfPOO2rWrJni4uKUmZmp+fPnh2PKYTNp0iRlZGQoLi5O7du31+rVqyM9pUs2btw4/fSnP1W1atVUu3Ztde/eXdu3bw855tSpUxo4cKBq1qypyy+/XD179tTBgwdDjtmzZ4+6deumhIQE1a5dW8OGDdOZM2csl3LJxo8fL5/PpyFDhnjbKuOa9+3bp1//+teqWbOm4uPjlZmZqTVr1nj7nXMaNWqU6tSpo/j4eHXp0kU7d+4MGePo0aPq3bu3EhMTlZycrPvvv18nTpywXsoFKSoq0siRI0Pus8aMGRPy+zAq+pqXLl2q2267TWlpafL5fHr33XdD9pfV+i7kft3K+dZcWFio4cOHKzMzU5dddpnS0tJ07733av/+/SFjVKY1/9CDDz4on8+nl19+OWS72ZpdGHz44Yeub9++bsGCBe7LL790c+fOdbVr13ZDhw71jsnNzXUpKSmud+/eLjs727399tsuPj7evfbaa94xy5cvd1FRUe7ZZ591W7ZscU8++aSLiYlxmzZtCse0y9yMGTNcIBBwf/3rX93mzZtdv379XHJysjt48GCkp3ZJunbt6qZOneqys7Pdhg0b3C9/+UtXv359d+LECe+YBx980KWnp7tFixa5NWvWuOuuu8516NDB23/mzBnXokUL16VLF7d+/Xo3f/58V6tWLffEE09EYkkXZfXq1S4jI8O1bNnSDR482Nte2dZ89OhR16BBA9e3b1+3atUq99VXX7kFCxa4L774wjtm/PjxLikpyb377rvu888/d7fffrtr2LChy8/P9465+eab3bXXXutWrlzpPvnkE3fVVVe5e+65JxJL+q/Gjh3ratas6d5//323a9cu984777jLL7/cTZgwwTumoq95/vz5bsSIEW727NlOkpszZ07I/rJY34Xcr1s635qPHTvmunTp4v7+97+7bdu2uRUrVrh27dq5Nm3ahIxRmdZc0uzZs921117r0tLS3EsvvRSyz2rNYQmQ0jz77LOuYcOG3u1XXnnFVa9e3Z0+fdrbNnz4cNe0aVPv9p133um6desWMk779u3dAw88EP4Jl4F27dq5gQMHereLiopcWlqaGzduXARnVXYOHTrkJLmPP/7YOff9D3RMTIx75513vGO2bt3qJLkVK1Y4577/4fD7/S4nJ8c7ZvLkyS4xMTHke6G8OX78uGvcuLFbuHCh+9nPfuYFSGVc8/Dhw931119/zv3BYNClpqa65557ztt27NgxFxsb695++23nnHNbtmxxktxnn33mHfPhhx86n8/n9u3bF77JX6Ju3bq53/72tyHbfvWrX7nevXs75yrfmn/4wFRW67uQ+/VIOd+DcbHVq1c7SW737t3Oucq75r1797q6deu67Oxs16BBg5AAsVyz2XtAcnNzVaNGDe/2ihUrdMMNNygQCHjbunbtqu3bt+vbb7/1junSpUvIOF27dtWKFStsJv0jFBQUaO3atSHz9/v96tKlS4WY/4XIzc2VJO/runbtWhUWFoasuVmzZqpfv7635hUrVigzM1MpKSneMV27dlVeXp42b95sOPuLM3DgQHXr1u2s78fKuOb33ntPbdu2Va9evVS7dm21atVKr7/+urd/165dysnJCVlzUlKS2rdvH7Lm5ORktW3b1jumS5cu8vv9WrVqld1iLlCHDh20aNEi7dixQ5L0+eefa9myZbrlllskVc41l1RW67uQ+/XyLDc3Vz6fT8nJyZIq55qDwaD69OmjYcOGqXnz5mftt1yzSYB88cUXmjhxoh544AFvW05OTsgdsiTvdk5OznmPKd5fnh0+fFhFRUUVdv7/TTAY1JAhQ9SxY0e1aNFC0vdfr0Ag4P3wFiu55gv5upc3M2bM0Lp16zRu3Liz9lXGNX/11VeaPHmyGjdurAULFmjAgAF6+OGHNW3aNEn/mfP5vrdzcnJUu3btkP3R0dGqUaNGuVzz448/rrvvvlvNmjVTTEyMWrVqpSFDhqh3796SKueaSyqr9VW07/WSTp06peHDh+uee+7xfvlcZVzzM888o+joaD388MOl7rdc80X9NtzHH39czzzzzHmP2bp1q5o1a+bd3rdvn26++Wb16tVL/fr1u5jToRwbOHCgsrOztWzZskhPJay++eYbDR48WAsXLlRcXFykp2MiGAyqbdu2+tOf/iRJatWqlbKzs/Xqq6/qvvvui/DswmPmzJmaPn263nrrLTVv3lwbNmzQkCFDlJaWVmnXjP8oLCzUnXfeKeecJk+eHOnphM3atWs1YcIErVu3Tj6fL9LTubhnQIYOHaqtW7ee90+jRo284/fv36+f//zn6tChg6ZMmRIyVmpq6llXChTfTk1NPe8xxfvLs1q1aikqKqrCzv98Bg0apPfff18fffSR6tWr521PTU1VQUGBjh07FnJ8yTVfyNe9PFm7dq0OHTqk1q1bKzo6WtHR0fr444/15z//WdHR0UpJSal0a65Tp46uueaakG1XX3219uzZI+k/cz7f93ZqaqoOHToUsv/MmTM6evRouVzzsGHDvGdBMjMz1adPH/3+97/3nvWqjGsuqazWV9G+16X/xMfu3bu1cOFC79kPqfKt+ZNPPtGhQ4dUv3597/5s9+7dGjp0qDIyMiTZrvmiAuSKK65Qs2bNzvun+DWhffv2qVOnTmrTpo2mTp0qvz/0VFlZWVq6dKkKCwu9bQsXLlTTpk1VvXp175hFixaF/L2FCxcqKyvrYqYdEYFAQG3atAmZfzAY1KJFiyrE/EvjnNOgQYM0Z84cLV68WA0bNgzZ36ZNG8XExISsefv27dqzZ4+35qysLG3atCnkG7z4h/6HD3rlQefOnbVp0yZt2LDB+9O2bVv17t3b+/fKtuaOHTuedXn1jh071KBBA0lSw4YNlZqaGrLmvLw8rVq1KmTNx44d09q1a71jFi9erGAwqPbt2xus4uKcPHnyrPuoqKgoBYNBSZVzzSWV1fou5H69PCmOj507d+pf//qXatasGbK/sq25T58+2rhxY8j9WVpamoYNG6YFCxZIMl7zRb1l9QLt3bvXXXXVVa5z585u79697sCBA96fYseOHXMpKSmuT58+Ljs7282YMcMlJCScdRludHS0e/75593WrVvd6NGjK9xluLGxse7NN990W7Zscf3793fJyckhV0NUJAMGDHBJSUluyZIlIV/TkydPesc8+OCDrn79+m7x4sVuzZo1Lisry2VlZXn7iy9Jvemmm9yGDRvcP/7xD3fFFVeU20tSS1PyKhjnKt+aV69e7aKjo93YsWPdzp073fTp011CQoL729/+5h0zfvx4l5yc7ObOnes2btzo7rjjjlIv2WzVqpVbtWqVW7ZsmWvcuHG5uST1h+677z5Xt25d7zLc2bNnu1q1arnHHnvMO6air/n48eNu/fr1bv369U6Se/HFF9369eu9Kz7KYn0Xcr9u6XxrLigocLfffrurV6+e27BhQ8h9WsmrOyrTmkvzw6tgnLNbc1gCZOrUqU5SqX9K+vzzz93111/vYmNjXd26dd348ePPGmvmzJmuSZMmLhAIuObNm7sPPvggHFMOm4kTJ7r69eu7QCDg2rVr51auXBnpKV2yc31Np06d6h2Tn5/vHnroIVe9enWXkJDgevToERKezjn39ddfu1tuucXFx8e7WrVquaFDh7rCwkLj1Vy6HwZIZVzzvHnzXIsWLVxsbKxr1qyZmzJlSsj+YDDoRo4c6VJSUlxsbKzr3Lmz2759e8gxR44ccffcc4+7/PLLXWJiovvNb37jjh8/brmMC5aXl+cGDx7s6tev7+Li4lyjRo3ciBEjQh6IKvqaP/roo1J/fu+77z7nXNmt70Lu162cb827du06533aRx995I1RmdZcmtICxGrNPudKfNQfAACAAX4XDAAAMEeAAAAAcwQIAAAwR4AAAABzBAgAADBHgAAAAHMECAAAMEeAAAAAcwQIgB/t5MmT6tmzpxITE+Xz+XTs2DFlZGTo5ZdfjvTUAJRT0ZGeAICKb9q0afrkk0/06aefqlatWkpKStJnn32myy67zDvG5/Npzpw56t69e+QmCqDcIEAAnFNBQYH3G67P58svv9TVV1+tFi1aeNuuuOKKcE4NQAXHSzBAFdKpUycNGjRIgwYNUlJSkmrVqqWRI0eq+FdCZWRkaMyYMbr33nuVmJio/v37S5JmzZql5s2bKzY2VhkZGXrhhRdCxnzhhRe0dOlS+Xw+derUyRur+CWYjIwMSVKPHj3k8/m82wCqLgIEqGKmTZum6OhorV69WhMmTNCLL76oN954w9v//PPP69prr9X69es1cuRIrV27Vnfeeafuvvtubdq0SX/4wx80cuRIvfnmm5Kk2bNnq1+/fsrKytKBAwc0e/bss8752WefSZKmTp2qAwcOeLcBVF28BANUMenp6XrppZfk8/nUtGlTbdq0SS+99JL69esnSbrxxhs1dOhQ7/jevXurc+fOGjlypCSpSZMm2rJli5577jn17dtXNWrUUEJCggKBgFJTU0s9Z/HLMcnJyec8BkDVwjMgQBVz3XXXyefzebezsrK0c+dOFRUVSZLatm0bcvzWrVvVsWPHkG0dO3YM+TsAcLEIEAAhSl65AgDhQoAAVcyqVatCbq9cuVKNGzdWVFRUqcdfffXVWr58eci25cuXq0mTJuf8O6WJiYnhGRMAHgIEqGL27NmjRx55RNu3b9fbb7+tiRMnavDgwec8fujQoVq0aJHGjBmjHTt2aNq0afrLX/6iRx999KLOm5GRoUWLFiknJ0fffvvtj10GgAqOAAGqmHvvvVf5+flq166dBg4cqMGDB3uX25amdevWmjlzpmbMmKEWLVpo1KhR+uMf/6i+ffte1HlfeOEFLVy4UOnp6WrVqtWPXAWAis7nij8AAECl16lTJ/3kJz/hI9IBRBzPgAAAAHMECAAAMMdLMAAAwBzPgAAAAHMECAAAMEeAAAAAcwQIAAAwR4AAAABzBAgAADBHgAAAAHMECAAAMPf/1/bNAi5x8/4AAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#This shows the first 5 rows of all the columns row values\n",
+        "df1.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "_9QBzdB0dcb-",
+        "outputId": "015153a3-7370-4fc6-cd23-cd0d9654cb2d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Transaction ID  Date of Travel   Company        City  KM Travelled  \\\n",
+              "0        10000011           42377  Pink Cab  ATLANTA GA         30.45   \n",
+              "1        10000012           42375  Pink Cab  ATLANTA GA         28.62   \n",
+              "2        10000013           42371  Pink Cab  ATLANTA GA          9.04   \n",
+              "3        10000014           42376  Pink Cab  ATLANTA GA         33.17   \n",
+              "4        10000015           42372  Pink Cab  ATLANTA GA          8.73   \n",
+              "\n",
+              "   Price Charged  Cost of Trip  profit  \n",
+              "0         370.95       313.635  57.315  \n",
+              "1         358.52       334.854  23.666  \n",
+              "2         125.20        97.632  27.568  \n",
+              "3         377.40       351.602  25.798  \n",
+              "4         114.62        97.776  16.844  "
+            ],
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Transaction ID</th>\n",
+              "      <th>Date of Travel</th>\n",
+              "      <th>Company</th>\n",
+              "      <th>City</th>\n",
+              "      <th>KM Travelled</th>\n",
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+              "      <th>profit</th>\n",
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+              "      <td>10000011</td>\n",
+              "      <td>42377</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>30.45</td>\n",
+              "      <td>370.95</td>\n",
+              "      <td>313.635</td>\n",
+              "      <td>57.315</td>\n",
+              "    </tr>\n",
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+              "      <th>1</th>\n",
+              "      <td>10000012</td>\n",
+              "      <td>42375</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>28.62</td>\n",
+              "      <td>358.52</td>\n",
+              "      <td>334.854</td>\n",
+              "      <td>23.666</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>10000013</td>\n",
+              "      <td>42371</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>9.04</td>\n",
+              "      <td>125.20</td>\n",
+              "      <td>97.632</td>\n",
+              "      <td>27.568</td>\n",
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+              "      <th>3</th>\n",
+              "      <td>10000014</td>\n",
+              "      <td>42376</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>33.17</td>\n",
+              "      <td>377.40</td>\n",
+              "      <td>351.602</td>\n",
+              "      <td>25.798</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>10000015</td>\n",
+              "      <td>42372</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>8.73</td>\n",
+              "      <td>114.62</td>\n",
+              "      <td>97.776</td>\n",
+              "      <td>16.844</td>\n",
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+              "                                                    [key], {});\n",
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+              "\n",
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+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
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+              "\n",
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+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df1"
+            }
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Bar chart for comparing the average profits of both Pink and Yellow Cab Companies"
+      ],
+      "metadata": {
+        "id": "ocbjQ1Svoc4J"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#This code provides the categories for Company column values, which are pink and yellow cabs, and these categories corresponds to the average profits of these two cabs.\n",
+        "average_profit = df1.groupby('Company')['profit'].mean()\n",
+        "\n",
+        "#I used the above series for average profits for both yellow and pink cab, and this code is used to plot the bar chart for average profit comparisons\n",
+        "average_profit.plot(kind='bar', color=['blue', 'red'])\n",
+        "\n",
+        "#x label for the graph\n",
+        "plt.xlabel('Company')\n",
+        "#y label for the graph\n",
+        "plt.ylabel('Profit')\n",
+        "#the title of the graph above\n",
+        "plt.title('Pink and Yellow Cab Companies vs Average Profits')\n",
+        "#saving the image as png\n",
+        "plt.savefig('barplot_comparison_profits.png')\n",
+        "\n",
+        "#EDA Recommendation\n",
+        "# Based on the exploratory data analysis, it is recommended to further investigate the factors contributing to the higher average profits of Yellow Cabs compared to Pink Cabs.\n",
+        "# This includes analyzing variables such as trip duration, fare rates, customer satisfaction, and the operational strategies of both cab companies.\n",
+        "\n",
+        "# Hypothesis Results\n",
+        "# In the bar chart below, I am comparing the average profits for two cab companies, Yellow and Pink Cabs.\n",
+        "# As you can see, the data indicates that the Yellow Cab company has more average profits, with a maximum profit of approximately $158,\n",
+        "# while the Pink Cab has an average profit of about $63. If this hypothesis holds true, it suggests that XYZ firm should consider investing in the Yellow Cab company\n",
+        "# due to its stronger profitability. This analysis supports the notion that Yellow Cabs may provide a more lucrative investment opportunity."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 532
+        },
+        "id": "A-hBShAXUwUw",
+        "outputId": "1c313882-44c2-4ba3-fa30-a0130a283d17"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#This loads the dataset for City.csv\n",
+        "df2 = pd.read_csv('City.csv')\n",
+        "df2.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "9HFXuXAzWAro",
+        "outputId": "79e2dd6a-56cc-4256-e5cd-1461fb68f420"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "             City   Population      Users\n",
+              "0     NEW YORK NY   8,405,837    302,149 \n",
+              "1      CHICAGO IL   1,955,130    164,468 \n",
+              "2  LOS ANGELES CA   1,595,037    144,132 \n",
+              "3        MIAMI FL   1,339,155     17,675 \n",
+              "4  SILICON VALLEY   1,177,609     27,247 "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-4ba8c576-5618-4e5e-a93f-5a15ef622129\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Users</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>NEW YORK NY</td>\n",
+              "      <td>8,405,837</td>\n",
+              "      <td>302,149</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>CHICAGO IL</td>\n",
+              "      <td>1,955,130</td>\n",
+              "      <td>164,468</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>LOS ANGELES CA</td>\n",
+              "      <td>1,595,037</td>\n",
+              "      <td>144,132</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>MIAMI FL</td>\n",
+              "      <td>1,339,155</td>\n",
+              "      <td>17,675</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>SILICON VALLEY</td>\n",
+              "      <td>1,177,609</td>\n",
+              "      <td>27,247</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ba8c576-5618-4e5e-a93f-5a15ef622129')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-4ba8c576-5618-4e5e-a93f-5a15ef622129 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-4ba8c576-5618-4e5e-a93f-5a15ef622129');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-407a8eb6-3504-4fb7-869e-56f573ef9ac5\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-407a8eb6-3504-4fb7-869e-56f573ef9ac5')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-407a8eb6-3504-4fb7-869e-56f573ef9ac5 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df2",
+              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"NEW YORK NY\",\n          \"WASHINGTON DC\",\n          \"SACRAMENTO CA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 8,405,837 \",\n          \" 418,859 \",\n          \" 545,776 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Users\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 302,149 \",\n          \" 127,001 \",\n          \" 7,044 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Stacked bar chart with the number of trips in different cities for both yellow and pink cabs"
+      ],
+      "metadata": {
+        "id": "yDvVWeIVu2RW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "#The series type that provides the categories for both cities and company values and counts the number of companies appears in each city. The unstack() converts it\n",
+        "#into the dataframe when having multi index.\n",
+        "city_company_counts = df1.groupby(['City', 'Company'])['Company'].count().unstack()\n",
+        "\n",
+        "#Then I use the series to plot the bar chart for showing number of trips.\n",
+        "city_company_counts.plot(kind='bar', stacked=True)\n",
+        "\n",
+        "#x label\n",
+        "plt.xlabel('City')\n",
+        "#y label\n",
+        "plt.ylabel('Number of Trips')\n",
+        "#title for the bar chart graph\n",
+        "plt.title('Most Used Cabs (Pink and Yellow) by City')\n",
+        "\n",
+        "#provides the particular color representation for both pink and yellow cab companies\n",
+        "plt.legend(title='Company')\n",
+        "\n",
+        "#saving the image as png\n",
+        "plt.savefig('stacked_bar_city_company.png')\n",
+        "#providing the output of the graph image\n",
+        "plt.show()\n",
+        "\n",
+        "#Hypothesis\n",
+        "# As you can see in the stacked bar chart below, the most used cabs are yellow cabs.\n",
+        "# In most of the countries, the most used cab is yellow. For instance, in the city\n",
+        "# New York, the number of trips for pink cabs is approximately 1,600, while the yellow cab\n",
+        "# has 980,000 trips.\n",
+        "\n",
+        "# Based on the stacked bar chart analysis, it appears that yellow cabs are predominantly used\n",
+        "# over pink cabs in New York, as evidenced by the trip counts.\n",
+        "# This indicates a strong preference for yellow cabs among consumers in that area.\n",
+        "# If these trends hold true across other locations, this may reflect a broader market trend\n",
+        "# that XYZ firm should consider when deciding on cab investments.\n",
+        "\n",
+        "#Recommendations:\n",
+        "#Data Visualizations: I would have made a graph such as linear or pie chart to better show the distribution of the values for this particular goal."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 579
+        },
+        "id": "Tc89YuH-Zcg4",
+        "outputId": "1cd1a65c-62e5-4426-aeaa-1574afd6a9e4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df1.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "Mue7qS3FdC55",
+        "outputId": "4f3a1c8e-4a82-4afa-bf0b-9877e6909c9d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Transaction ID  Date of Travel   Company        City  KM Travelled  \\\n",
+              "0        10000011           42377  Pink Cab  ATLANTA GA         30.45   \n",
+              "1        10000012           42375  Pink Cab  ATLANTA GA         28.62   \n",
+              "2        10000013           42371  Pink Cab  ATLANTA GA          9.04   \n",
+              "3        10000014           42376  Pink Cab  ATLANTA GA         33.17   \n",
+              "4        10000015           42372  Pink Cab  ATLANTA GA          8.73   \n",
+              "\n",
+              "   Price Charged  Cost of Trip  profit  \n",
+              "0         370.95       313.635  57.315  \n",
+              "1         358.52       334.854  23.666  \n",
+              "2         125.20        97.632  27.568  \n",
+              "3         377.40       351.602  25.798  \n",
+              "4         114.62        97.776  16.844  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-0f61529b-5bcc-456c-a8f1-b095116fa7aa\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Transaction ID</th>\n",
+              "      <th>Date of Travel</th>\n",
+              "      <th>Company</th>\n",
+              "      <th>City</th>\n",
+              "      <th>KM Travelled</th>\n",
+              "      <th>Price Charged</th>\n",
+              "      <th>Cost of Trip</th>\n",
+              "      <th>profit</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>10000011</td>\n",
+              "      <td>42377</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>30.45</td>\n",
+              "      <td>370.95</td>\n",
+              "      <td>313.635</td>\n",
+              "      <td>57.315</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>10000012</td>\n",
+              "      <td>42375</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>28.62</td>\n",
+              "      <td>358.52</td>\n",
+              "      <td>334.854</td>\n",
+              "      <td>23.666</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>10000013</td>\n",
+              "      <td>42371</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>9.04</td>\n",
+              "      <td>125.20</td>\n",
+              "      <td>97.632</td>\n",
+              "      <td>27.568</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>10000014</td>\n",
+              "      <td>42376</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>33.17</td>\n",
+              "      <td>377.40</td>\n",
+              "      <td>351.602</td>\n",
+              "      <td>25.798</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>10000015</td>\n",
+              "      <td>42372</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>8.73</td>\n",
+              "      <td>114.62</td>\n",
+              "      <td>97.776</td>\n",
+              "      <td>16.844</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f61529b-5bcc-456c-a8f1-b095116fa7aa')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "\n",
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+              "      background-color: #3B4455;\n",
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+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
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+              "        document.querySelector('#df-0f61529b-5bcc-456c-a8f1-b095116fa7aa button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-0f61529b-5bcc-456c-a8f1-b095116fa7aa');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
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+              "</svg>\n",
+              "  </button>\n",
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+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
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+              "      --disabled-fill-color: #AAA;\n",
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+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
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+              "    border-radius: 50%;\n",
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+              "    height: 32px;\n",
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+              "\n",
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+              "  .colab-df-spinner {\n",
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+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-0ad292d2-411a-4734-ae75-ba6c58245641 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df1"
+            }
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The bar chart that represents the most number of trips for both yellow and pink cabs in the year of 1970"
+      ],
+      "metadata": {
+        "id": "I3xr1iPUyaUy"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "#converting the Date of Travel column in the DataFrame df1 to a datetime format using the pd.to_datetime() function from pandas.\n",
+        "df1['Date of Travel'] = pd.to_datetime(df1['Date of Travel'])\n",
+        "\n",
+        "#choosing the only the values from the dat of travel with the values of 1970 from df1 dataset\n",
+        "df1_1970 = df1[df1['Date of Travel'].dt.year == 1970]\n",
+        "\n",
+        "#The series type that provides the categories for company values and counts the number of companies appears\n",
+        "company_counts_1970 = df1_1970.groupby('Company')['Company'].count()\n",
+        "\n",
+        "#plotting the bar chart by using teh previous series and making each of the column values from the Company color pink and yellow\n",
+        "company_counts_1970.plot(kind='bar', color = ['pink', 'yellow'])\n",
+        "\n",
+        "\n",
+        "#x label\n",
+        "plt.xlabel('Company')\n",
+        "#y label\n",
+        "plt.ylabel('Number of Trips in 1970')\n",
+        "#title for the bar chart graph\n",
+        "plt.title('Most Used Cabs (Pink and Yellow) in 1970')\n",
+        "\n",
+        "#saving the image as png\n",
+        "plt.savefig('bar_chart_1970.png')\n",
+        "\n",
+        "#showing the plot\n",
+        "plt.show()\n",
+        "\n",
+        "# Hypothesis Result for Bar Chart of Most Used Cabs in 1970\n",
+        "\n",
+        "# The bar chart above showcases the number of trips undertaken by Yellow and Pink Cab companies in the year 1970.\n",
+        "# As depicted, Yellow Cabs had significantly more trips compared to Pink Cabs in that year.\n",
+        "# This suggests a higher demand or preference for Yellow Cabs in the market during 1970.\n",
+        "# If this trend holds for other years, it indicates that Yellow Cabs potentially have a stronger market share and possibly higher profitability.\n",
+        "# This information can be useful for XYZ firm in making informed decisions about which cab company to invest in, potentially favoring the Yellow Cab company given their historical dominance.\n",
+        "\n",
+        "#EDA Recommendations:\n",
+        "#We should collect and analyze data on customer demographics (age, gender, income level) to better understand who is using each cab company.\n",
+        "#This could provide insights into targeted marketing strategies."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 532
+        },
+        "id": "kQaKMrE7eRqw",
+        "outputId": "ceedad65-74bd-4261-f1a3-871c62d5c9f6"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df2 = pd.read_csv('City.csv')\n",
+        "df2.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "JPYgpO2sfGkf",
+        "outputId": "b7854f08-5674-4a57-a750-5d236960514f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "             City   Population      Users\n",
+              "0     NEW YORK NY   8,405,837    302,149 \n",
+              "1      CHICAGO IL   1,955,130    164,468 \n",
+              "2  LOS ANGELES CA   1,595,037    144,132 \n",
+              "3        MIAMI FL   1,339,155     17,675 \n",
+              "4  SILICON VALLEY   1,177,609     27,247 "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-7e8d8437-3bda-49c9-9b22-0c73cde27d3b\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Users</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>NEW YORK NY</td>\n",
+              "      <td>8,405,837</td>\n",
+              "      <td>302,149</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>CHICAGO IL</td>\n",
+              "      <td>1,955,130</td>\n",
+              "      <td>164,468</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>LOS ANGELES CA</td>\n",
+              "      <td>1,595,037</td>\n",
+              "      <td>144,132</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>MIAMI FL</td>\n",
+              "      <td>1,339,155</td>\n",
+              "      <td>17,675</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>SILICON VALLEY</td>\n",
+              "      <td>1,177,609</td>\n",
+              "      <td>27,247</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7e8d8437-3bda-49c9-9b22-0c73cde27d3b')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
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+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-7e8d8437-3bda-49c9-9b22-0c73cde27d3b button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-7e8d8437-3bda-49c9-9b22-0c73cde27d3b');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
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+              "            title=\"Suggest charts\"\n",
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+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-8b7768ec-b9a8-4fe4-9a62-0cbd5ac9b727 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df2",
+              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"NEW YORK NY\",\n          \"WASHINGTON DC\",\n          \"SACRAMENTO CA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 8,405,837 \",\n          \" 418,859 \",\n          \" 545,776 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Users\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 302,149 \",\n          \" 127,001 \",\n          \" 7,044 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "#The series type that provides the categories for both cities and company values and counts the number of companies appears in each city. The unstack() converts it\n",
+        "#into the dataframe when having multi index.\n",
+        "city_company_counts = df1.groupby(['City', 'Company'])['Company'].count().unstack()\n",
+        "\n",
+        "#replaces the previous index into a default index values like 0, 1, 2, 3, ...\n",
+        "city_company_counts = city_company_counts.reset_index()\n",
+        "\n",
+        "#Renaming the prevous columns\n",
+        "city_company_counts = city_company_counts.rename(\n",
+        "    columns={'Pink Cab': 'Pink Cab Users', 'Yellow Cab': 'Yellow Cab Users'}\n",
+        ")\n",
+        "\n",
+        "#Merging or combining the series values with the df2 dataset\n",
+        "merged_df = pd.merge(df2, city_company_counts, on='City', how='left')\n",
+        "\n",
+        "#replacing the NaN values with 0\n",
+        "merged_df = merged_df.fillna(0)\n",
+        "\n",
+        "#size of the plot\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "#drawing only the line graph for pink cab users\n",
+        "plt.plot(merged_df['City'], merged_df['Pink Cab Users'], label='Pink Cab Users')\n",
+        "#drawing only the line graph for yellow cab users\n",
+        "plt.plot(merged_df['City'], merged_df['Yellow Cab Users'], label='Yellow Cab Users')\n",
+        "#x label\n",
+        "plt.xlabel('City')\n",
+        "#y label\n",
+        "plt.ylabel('Number of Users')\n",
+        "#title of the graph\n",
+        "plt.title('Number of Users per City for Pink and Yellow Cabs')\n",
+        "#rotating the x label values into 90 degrees\n",
+        "plt.xticks(rotation=90)\n",
+        "#provides the particular color representation for both pink and yellow cab companies\n",
+        "plt.legend()\n",
+        "#adjust the spacing between subplots to minimize overlap and ensure that all elements are neatly contained within the figure area.\n",
+        "plt.tight_layout()\n",
+        "#plotting line graph\n",
+        "plt.show()\n",
+        "\n",
+        "# Hypothesis Result for Line Graph of Number of Users per City\n",
+        "\n",
+        "# The line graph above visually depicts the number of users for Pink and Yellow Cabs in different cities.\n",
+        "# It seems that the Yellow Cabs consistently have a larger user base across most cities compared to Pink Cabs.\n",
+        "# This indicates a potential market share dominance for Yellow Cabs.\n",
+        "# Notably, in certain cities, the difference in user numbers is significant, which may reflect a strong preference for Yellow Cabs in those areas.\n",
+        "# If these trends persist, it implies a greater demand for Yellow Cabs, potentially contributing to higher profitability for the company.\n",
+        "# This information can guide XYZ firm's investment decisions, possibly favouring the Yellow Cab company due to its extensive user base.\n",
+        "\n",
+        "#EDA Recommendations:\n",
+        "#I can analyze trends and patterns, and observe the overall trends in user counts for both companies across different cities."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 607
+        },
+        "id": "dSmc_aL3VSKR",
+        "outputId": "7bb7ca04-299e-48d7-da61-442d8144dd6f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#loading dataset called Transaction_ID.csv\n",
+        "df3 = pd.read_csv('Transaction_ID.csv')\n",
+        "df3"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "02ORKDVwZI9H",
+        "outputId": "3597cfb2-8880-4357-bac4-1e00390f96d9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "        Transaction ID  Customer ID Payment_Mode\n",
+              "0             10000011      29290.0         Card\n",
+              "1             10000012      27703.0         Card\n",
+              "2             10000013      28712.0         Cash\n",
+              "3             10000014      28020.0         Cash\n",
+              "4             10000015      27182.0         Card\n",
+              "...                ...          ...          ...\n",
+              "307683        10307694      42426.0         Cash\n",
+              "307684        10307695      42819.0         Cash\n",
+              "307685        10307696      44212.0         Card\n",
+              "307686        10307697      44827.0         Card\n",
+              "307687             103          NaN          NaN\n",
+              "\n",
+              "[307688 rows x 3 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-eb77c9c0-5dc0-428e-8314-d40899ad4ed5\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Transaction ID</th>\n",
+              "      <th>Customer ID</th>\n",
+              "      <th>Payment_Mode</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>10000011</td>\n",
+              "      <td>29290.0</td>\n",
+              "      <td>Card</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>10000012</td>\n",
+              "      <td>27703.0</td>\n",
+              "      <td>Card</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>10000013</td>\n",
+              "      <td>28712.0</td>\n",
+              "      <td>Cash</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>10000014</td>\n",
+              "      <td>28020.0</td>\n",
+              "      <td>Cash</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>10000015</td>\n",
+              "      <td>27182.0</td>\n",
+              "      <td>Card</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>307683</th>\n",
+              "      <td>10307694</td>\n",
+              "      <td>42426.0</td>\n",
+              "      <td>Cash</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>307684</th>\n",
+              "      <td>10307695</td>\n",
+              "      <td>42819.0</td>\n",
+              "      <td>Cash</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>307685</th>\n",
+              "      <td>10307696</td>\n",
+              "      <td>44212.0</td>\n",
+              "      <td>Card</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>307686</th>\n",
+              "      <td>10307697</td>\n",
+              "      <td>44827.0</td>\n",
+              "      <td>Card</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>307687</th>\n",
+              "      <td>103</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>307688 rows × 3 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb77c9c0-5dc0-428e-8314-d40899ad4ed5')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
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+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-eb77c9c0-5dc0-428e-8314-d40899ad4ed5 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-eb77c9c0-5dc0-428e-8314-d40899ad4ed5');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-20fa4755-2c55-41d8-95d1-5d6cce0d3b97\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-20fa4755-2c55-41d8-95d1-5d6cce0d3b97')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-20fa4755-2c55-41d8-95d1-5d6cce0d3b97 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_28a52d13-3169-4eeb-9213-8e435274f7de\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df3')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_28a52d13-3169-4eeb-9213-8e435274f7de button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df3');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df3"
+            }
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Replacing NaN values with 0\n",
+        "df3 = df3.fillna(0)\n",
+        "#Checking the sum of the null values whether there are null values left or not\n",
+        "df3.isnull().sum()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 178
+        },
+        "id": "bbOEMXM0kUdS",
+        "outputId": "653cdb57-f3e8-4173-d6b1-d21ff300a7a0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Transaction ID    0\n",
+              "Customer ID       0\n",
+              "Payment_Mode      0\n",
+              "dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Transaction ID</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Customer ID</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Payment_Mode</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Loadingt the dataset called Customer_ID.csv\n",
+        "df4 = pd.read_csv('Customer_ID.csv')\n",
+        "df4.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "64BsXf3bkToQ",
+        "outputId": "2fe40639-bd6f-4bf6-db74-ee6cf4a24f4e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Customer ID Gender  Age  Income (USD/Month)\n",
+              "0        29290   Male   28               10813\n",
+              "1        27703   Male   27                9237\n",
+              "2        28712   Male   53               11242\n",
+              "3        28020   Male   23               23327\n",
+              "4        27182   Male   33                8536"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-de4edf0e-d992-4c95-a735-95045f372109\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Customer ID</th>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Income (USD/Month)</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>29290</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>28</td>\n",
+              "      <td>10813</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>27703</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>27</td>\n",
+              "      <td>9237</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>28712</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>53</td>\n",
+              "      <td>11242</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>28020</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>23</td>\n",
+              "      <td>23327</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>27182</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>33</td>\n",
+              "      <td>8536</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-de4edf0e-d992-4c95-a735-95045f372109')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-de4edf0e-d992-4c95-a735-95045f372109 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-de4edf0e-d992-4c95-a735-95045f372109');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-0060794c-844e-40e1-b957-d2d55815f585\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0060794c-844e-40e1-b957-d2d55815f585')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-0060794c-844e-40e1-b957-d2d55815f585 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df4",
+              "summary": "{\n  \"name\": \"df4\",\n  \"rows\": 49171,\n  \"fields\": [\n    {\n      \"column\": \"Customer ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17714,\n        \"min\": 1,\n        \"max\": 60000,\n        \"num_unique_values\": 49171,\n        \"samples\": [\n          32451,\n          524,\n          19349\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 18,\n        \"max\": 65,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          32,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income (USD/Month)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8002,\n        \"min\": 2000,\n        \"max\": 35000,\n        \"num_unique_values\": 23341,\n        \"samples\": [\n          12932,\n          16207\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Merging the dataset fro both df3 and df4 by Customer ID feature\n",
+        "merged_df1 = pd.merge(df3, df4, on='Customer ID')\n",
+        "#Merging the df1 to the merged_df1 with df3 and df4 merged datasets with Transaction ID\n",
+        "merged_df2 = merged_df1.merge(df1, on='Transaction ID')\n",
+        "merged_df2.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 348
+        },
+        "id": "v8U48YCOFasv",
+        "outputId": "f9516c30-e25a-4ef3-8a41-ddb6cdf4a0a3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Transaction ID  Customer ID Payment_Mode Gender  Age  Income (USD/Month)  \\\n",
+              "0        10000011      29290.0         Card   Male   28               10813   \n",
+              "1        10000012      27703.0         Card   Male   27                9237   \n",
+              "2        10000013      28712.0         Cash   Male   53               11242   \n",
+              "3        10000014      28020.0         Cash   Male   23               23327   \n",
+              "4        10000015      27182.0         Card   Male   33                8536   \n",
+              "\n",
+              "   Date of Travel   Company        City  KM Travelled  Price Charged  \\\n",
+              "0         42377.0  Pink Cab  ATLANTA GA         30.45         370.95   \n",
+              "1         42375.0  Pink Cab  ATLANTA GA         28.62         358.52   \n",
+              "2         42371.0  Pink Cab  ATLANTA GA          9.04         125.20   \n",
+              "3         42376.0  Pink Cab  ATLANTA GA         33.17         377.40   \n",
+              "4         42372.0  Pink Cab  ATLANTA GA          8.73         114.62   \n",
+              "\n",
+              "   Cost of Trip  profit  \n",
+              "0       313.635  57.315  \n",
+              "1       334.854  23.666  \n",
+              "2        97.632  27.568  \n",
+              "3       351.602  25.798  \n",
+              "4        97.776  16.844  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-977a3625-8670-4406-b627-b1b07f9ce9cf\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Transaction ID</th>\n",
+              "      <th>Customer ID</th>\n",
+              "      <th>Payment_Mode</th>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Income (USD/Month)</th>\n",
+              "      <th>Date of Travel</th>\n",
+              "      <th>Company</th>\n",
+              "      <th>City</th>\n",
+              "      <th>KM Travelled</th>\n",
+              "      <th>Price Charged</th>\n",
+              "      <th>Cost of Trip</th>\n",
+              "      <th>profit</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>10000011</td>\n",
+              "      <td>29290.0</td>\n",
+              "      <td>Card</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>28</td>\n",
+              "      <td>10813</td>\n",
+              "      <td>42377.0</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>30.45</td>\n",
+              "      <td>370.95</td>\n",
+              "      <td>313.635</td>\n",
+              "      <td>57.315</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>10000012</td>\n",
+              "      <td>27703.0</td>\n",
+              "      <td>Card</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>27</td>\n",
+              "      <td>9237</td>\n",
+              "      <td>42375.0</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>28.62</td>\n",
+              "      <td>358.52</td>\n",
+              "      <td>334.854</td>\n",
+              "      <td>23.666</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>10000013</td>\n",
+              "      <td>28712.0</td>\n",
+              "      <td>Cash</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>53</td>\n",
+              "      <td>11242</td>\n",
+              "      <td>42371.0</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>9.04</td>\n",
+              "      <td>125.20</td>\n",
+              "      <td>97.632</td>\n",
+              "      <td>27.568</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>10000014</td>\n",
+              "      <td>28020.0</td>\n",
+              "      <td>Cash</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>23</td>\n",
+              "      <td>23327</td>\n",
+              "      <td>42376.0</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>33.17</td>\n",
+              "      <td>377.40</td>\n",
+              "      <td>351.602</td>\n",
+              "      <td>25.798</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>10000015</td>\n",
+              "      <td>27182.0</td>\n",
+              "      <td>Card</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>33</td>\n",
+              "      <td>8536</td>\n",
+              "      <td>42372.0</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>8.73</td>\n",
+              "      <td>114.62</td>\n",
+              "      <td>97.776</td>\n",
+              "      <td>16.844</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-977a3625-8670-4406-b627-b1b07f9ce9cf')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-977a3625-8670-4406-b627-b1b07f9ce9cf button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-977a3625-8670-4406-b627-b1b07f9ce9cf');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-d68e6d85-bbb6-425a-bc1f-ab3e6605fcd0\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d68e6d85-bbb6-425a-bc1f-ab3e6605fcd0')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-d68e6d85-bbb6-425a-bc1f-ab3e6605fcd0 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "merged_df2",
+              "summary": "{\n  \"name\": \"merged_df2\",\n  \"rows\": 71151,\n  \"fields\": [\n    {\n      \"column\": \"Transaction ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25592,\n        \"min\": 10000011,\n        \"max\": 10088433,\n        \"num_unique_values\": 71151,\n        \"samples\": [\n          10047350,\n          10009057,\n          10065991\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21212.115589102177,\n        \"min\": 1.0,\n        \"max\": 60000.0,\n        \"num_unique_values\": 26259,\n        \"samples\": [\n          59647.0,\n          53174.0,\n          24110.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Payment_Mode\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Cash\",\n          \"Card\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 18,\n        \"max\": 65,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          32,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income (USD/Month)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7994,\n        \"min\": 2000,\n        \"max\": 34996,\n        \"num_unique_values\": 16961,\n        \"samples\": [\n          5974,\n          2136\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date of Travel\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 80.38125009707976,\n        \"min\": 42371.0,\n        \"max\": 42661.0,\n        \"num_unique_values\": 291,\n        \"samples\": [\n          42458.0,\n          42631.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Company\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yellow Cab\",\n          \"Pink Cab\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"ATLANTA GA\",\n          \"DENVER CO\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"KM Travelled\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.213689673845838,\n        \"min\": 1.9,\n        \"max\": 48.0,\n        \"num_unique_values\": 874,\n        \"samples\": [\n          4.0,\n          29.43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price Charged\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 288.486993996938,\n        \"min\": 16.38,\n        \"max\": 1978.79,\n        \"num_unique_values\": 49504,\n        \"samples\": [\n          464.18,\n          626.96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cost of Trip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 157.89328330950855,\n        \"min\": 19.2,\n        \"max\": 691.2,\n        \"num_unique_values\": 14297,\n        \"samples\": [\n          434.5704,\n          380.4408\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"profit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 176.18102319112268,\n        \"min\": -198.69800000000004,\n        \"max\": 1336.8700000000001,\n        \"num_unique_values\": 68226,\n        \"samples\": [\n          730.5763999999999,\n          109.31200000000001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#This groups the DataFrame merged_df2 by two columns: Income (USD/Month) and Company. This means that the data will be organized into groups where each unique combination of income level and company is represented.\n",
+        "\n",
+        "income_company_counts = merged_df2.groupby(['Income (USD/Month)', 'Company'])['Company'].count().unstack()\n",
+        "#removes the NaN values\n",
+        "income_company_counts.dropna(inplace = True)\n",
+        "\n",
+        "#Resets index values into original default values like 0, 1, 2\n",
+        "income_company_counts = income_company_counts.reset_index()\n",
+        "\n",
+        "#The figure size of the graph\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "\n",
+        "#This line iterates through all the columns of the income_company_counts DataFrame, starting from the second column (index 1) onward.\n",
+        "#The loop will generate a series of bar charts, one for each company, where each bar represents the count of that company at each income level.\n",
+        "for company in income_company_counts.columns[1:]:\n",
+        "    plt.bar(income_company_counts['Income (USD/Month)'], income_company_counts[company], label=company)\n",
+        "\n",
+        "#x label\n",
+        "plt.xlabel('Income (USD)')\n",
+        "#y label\n",
+        "plt.ylabel('Number of Users')\n",
+        "#Title\n",
+        "plt.title('Number of Users per Income for Pink and Yellow Cabs')\n",
+        "#Rotating the x values to 90 degrees\n",
+        "plt.xticks(rotation=90)\n",
+        "#Providingt he particular colors for values\n",
+        "plt.legend()\n",
+        "#adjust the spacing between subplots to minimize overlap and ensure that all elements are neatly contained within the figure area.\n",
+        "plt.tight_layout()\n",
+        "#output the graph\n",
+        "plt.show()\n",
+        "#downloading the graph image into png\n",
+        "plt.savefig('bar_chart_income_new.png')\n",
+        "\n",
+        "# Hypothesis Result for Bar Chart of Number of Users per Income\n",
+        "\n",
+        "# The bar chart above depicts the relationship between customer income and the usage of Pink and Yellow Cabs.\n",
+        "# It appears that across various income levels, Yellow Cabs tend to have a larger user base compared to Pink Cabs.\n",
+        "# This observation reinforces the potential market share dominance of Yellow Cabs.\n",
+        "# It is noticeable that as income levels increase, the user count for both Pink and Yellow Cabs also tends to increase, though the difference in user counts between the two companies seems consistent across different income brackets.\n",
+        "# If this trend continues, it suggests a strong preference for Yellow Cabs among customers regardless of their income level, potentially translating into greater profitability for the Yellow Cab company.\n",
+        "# This information can assist XYZ firm in making investment decisions, potentially favoring the Yellow Cab company due to its extensive user base across different income segments.\n",
+        "\n",
+        "# EDA Recommendations:\n",
+        "# Further examination of the customer demographic, such as locations."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 624
+        },
+        "id": "hXTgM55hGHoS",
+        "outputId": "69875007-f601-4bd4-f238-fc819a904d2b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 0 Axes>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

From 6650f758bfb4e60b422af2f057fb77b577ca751d Mon Sep 17 00:00:00 2001
From: An204o <166038499+An204o@users.noreply.github.com>
Date: Tue, 22 Oct 2024 15:49:27 -0400
Subject: [PATCH 06/22] Delete Copy_of_Data_Analysis_Week_2_Project.ipynb

---
 Copy_of_Data_Analysis_Week_2_Project.ipynb | 2898 --------------------
 1 file changed, 2898 deletions(-)
 delete mode 100644 Copy_of_Data_Analysis_Week_2_Project.ipynb

diff --git a/Copy_of_Data_Analysis_Week_2_Project.ipynb b/Copy_of_Data_Analysis_Week_2_Project.ipynb
deleted file mode 100644
index fce99d88..00000000
--- a/Copy_of_Data_Analysis_Week_2_Project.ipynb
+++ /dev/null
@@ -1,2898 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": [],
-      "authorship_tag": "ABX9TyOUecsYgEmf2wHgVFstRrVu",
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/An204o/Data-projects/blob/main/Copy_of_Data_Analysis_Week_2_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "Loading the csv file for the Cab_Data.csv and checking for its outliers. The boxplot seems to not have any outliers as it is not above the maximum value or below the minimum value."
-      ],
-      "metadata": {
-        "id": "nxz9GK3qZWv2"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "\n",
-        "\n",
-        "import pandas as pd\n",
-        "import matplotlib.pyplot as plt\n",
-        "import seaborn as sns\n",
-        "from sklearn.preprocessing import StandardScaler\n",
-        "\n",
-        "df1 = pd.read_csv('Cab_Data.csv', on_bad_lines='skip')\n",
-        "df1.head()\n",
-        "\n",
-        "df1['profit'] = df1['Price Charged'] - df1['Cost of Trip']\n",
-        "df1.head()\n",
-        "\n",
-        "sns.boxplot(x=df1['profit'])\n",
-        "\n",
-        "plt.savefig('boxplot_new.png')\n",
-        "\n",
-        "#Hypothesis:\n",
-        "#In the code above, I loaded the first dataset, which is Cab_Data.csv, and verified the whether the outliers are presentor not by using the boxplot chart. In the boxplot below,\n",
-        "# it seems that there are no outliers as the minimum is -150.38, and maximum is 1327.6219999999998, so there are no values which are less than minimum or above the maximum.\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 449
-        },
-        "id": "zeCKWNYVOkql",
-        "outputId": "99558f26-5f10-4b9e-caf4-2559e332999f"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df1.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "_9QBzdB0dcb-",
-        "outputId": "015153a3-7370-4fc6-cd23-cd0d9654cb2d"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "   Transaction ID  Date of Travel   Company        City  KM Travelled  \\\n",
-              "0        10000011           42377  Pink Cab  ATLANTA GA         30.45   \n",
-              "1        10000012           42375  Pink Cab  ATLANTA GA         28.62   \n",
-              "2        10000013           42371  Pink Cab  ATLANTA GA          9.04   \n",
-              "3        10000014           42376  Pink Cab  ATLANTA GA         33.17   \n",
-              "4        10000015           42372  Pink Cab  ATLANTA GA          8.73   \n",
-              "\n",
-              "   Price Charged  Cost of Trip  profit  \n",
-              "0         370.95       313.635  57.315  \n",
-              "1         358.52       334.854  23.666  \n",
-              "2         125.20        97.632  27.568  \n",
-              "3         377.40       351.602  25.798  \n",
-              "4         114.62        97.776  16.844  "
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-      "source": [
-        "Bar chart for comparing the average profits of both Pink and Yellow Cab Companies"
-      ],
-      "metadata": {
-        "id": "ocbjQ1Svoc4J"
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-        "\n",
-        "average_profit = df1.groupby('Company')['profit'].mean()\n",
-        "\n",
-        "\n",
-        "average_profit.plot(kind='bar', color=['blue', 'red'])\n",
-        "\n",
-        "\n",
-        "plt.xlabel('Company')\n",
-        "plt.ylabel('Profit')\n",
-        "plt.title('Pink and Yellow Cab Companies vs Average Profits')\n",
-        "plt.savefig('barplot_comparison_profits.png')\n",
-        "\n",
-        "#EDA Recommendation\n",
-        "# Based on the exploratory data analysis, it is recommended to further investigate the factors contributing to the higher average profits of Yellow Cabs compared to Pink Cabs.\n",
-        "# This includes analyzing variables such as trip duration, fare rates, customer satisfaction, and the operational strategies of both cab companies.\n",
-        "\n",
-        "# Hypothesis Results\n",
-        "# In the bar chart below, I am comparing the average profits for two cab companies, Yellow and Pink Cabs.\n",
-        "# As you can see, the data indicates that the Yellow Cab company has more average profits, with a maximum profit of approximately $158,\n",
-        "# while the Pink Cab has an average profit of about $63. If this hypothesis holds true, it suggests that XYZ firm should consider investing in the Yellow Cab company\n",
-        "# due to its stronger profitability. This analysis supports the notion that Yellow Cabs may provide a more lucrative investment opportunity."
-      ],
-      "metadata": {
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-          "base_uri": "https://localhost:8080/",
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-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
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-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df2 = pd.read_csv('City.csv')\n",
-        "df2.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "9HFXuXAzWAro",
-        "outputId": "79e2dd6a-56cc-4256-e5cd-1461fb68f420"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "             City   Population      Users\n",
-              "0     NEW YORK NY   8,405,837    302,149 \n",
-              "1      CHICAGO IL   1,955,130    164,468 \n",
-              "2  LOS ANGELES CA   1,595,037    144,132 \n",
-              "3        MIAMI FL   1,339,155     17,675 \n",
-              "4  SILICON VALLEY   1,177,609     27,247 "
-            ],
-            "text/html": [
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-              "      <td>302,149</td>\n",
-              "    </tr>\n",
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-              "      <td>164,468</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
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-              "      <td>144,132</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>MIAMI FL</td>\n",
-              "      <td>1,339,155</td>\n",
-              "      <td>17,675</td>\n",
-              "    </tr>\n",
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-              "      <td>1,177,609</td>\n",
-              "      <td>27,247</td>\n",
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-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
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-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-407a8eb6-3504-4fb7-869e-56f573ef9ac5 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df2",
-              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"NEW YORK NY\",\n          \"WASHINGTON DC\",\n          \"SACRAMENTO CA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 8,405,837 \",\n          \" 418,859 \",\n          \" 545,776 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Users\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 302,149 \",\n          \" 127,001 \",\n          \" 7,044 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 8
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "Stacked bar chart with the number of trips in different cities for both yellow and pink cabs"
-      ],
-      "metadata": {
-        "id": "yDvVWeIVu2RW"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "\n",
-        "\n",
-        "import pandas as pd\n",
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "\n",
-        "city_company_counts = df1.groupby(['City', 'Company'])['Company'].count().unstack()\n",
-        "\n",
-        "\n",
-        "city_company_counts.plot(kind='bar', stacked=True)\n",
-        "\n",
-        "\n",
-        "plt.xlabel('City')\n",
-        "plt.ylabel('Number of Trips')\n",
-        "plt.title('Most Used Cabs (Pink and Yellow) by City')\n",
-        "\n",
-        "\n",
-        "plt.legend(title='Company')\n",
-        "\n",
-        "\n",
-        "plt.savefig('stacked_bar_city_company.png')\n",
-        "\n",
-        "plt.show()\n",
-        "\n",
-        "#Hypothesis\n",
-        "# As you can see in the stacked bar chart below, the most used cabs are yellow cabs.\n",
-        "# In most of the countries, the most used cab is yellow. For instance, in the city\n",
-        "# New York, the number of trips for pink cabs is approximately 1,600, while the yellow cab\n",
-        "# has 980,000 trips.\n",
-        "\n",
-        "# Based on the stacked bar chart analysis, it appears that yellow cabs are predominantly used\n",
-        "# over pink cabs in New York, as evidenced by the trip counts.\n",
-        "# This indicates a strong preference for yellow cabs among consumers in that area.\n",
-        "# If these trends hold true across other locations, this may reflect a broader market trend\n",
-        "# that XYZ firm should consider when deciding on cab investments.\n",
-        "\n",
-        "#Recommendations:\n",
-        "#Data Visualizations: I would have made a graph such as linear or pie chart to better show the distribution of the values for this particular goal."
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 579
-        },
-        "id": "Tc89YuH-Zcg4",
-        "outputId": "1cd1a65c-62e5-4426-aeaa-1574afd6a9e4"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df1.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "Mue7qS3FdC55",
-        "outputId": "4f3a1c8e-4a82-4afa-bf0b-9877e6909c9d"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "   Transaction ID  Date of Travel   Company        City  KM Travelled  \\\n",
-              "0        10000011           42377  Pink Cab  ATLANTA GA         30.45   \n",
-              "1        10000012           42375  Pink Cab  ATLANTA GA         28.62   \n",
-              "2        10000013           42371  Pink Cab  ATLANTA GA          9.04   \n",
-              "3        10000014           42376  Pink Cab  ATLANTA GA         33.17   \n",
-              "4        10000015           42372  Pink Cab  ATLANTA GA          8.73   \n",
-              "\n",
-              "   Price Charged  Cost of Trip  profit  \n",
-              "0         370.95       313.635  57.315  \n",
-              "1         358.52       334.854  23.666  \n",
-              "2         125.20        97.632  27.568  \n",
-              "3         377.40       351.602  25.798  \n",
-              "4         114.62        97.776  16.844  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-0f61529b-5bcc-456c-a8f1-b095116fa7aa\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
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-              "\n",
-              "    .dataframe thead th {\n",
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-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Transaction ID</th>\n",
-              "      <th>Date of Travel</th>\n",
-              "      <th>Company</th>\n",
-              "      <th>City</th>\n",
-              "      <th>KM Travelled</th>\n",
-              "      <th>Price Charged</th>\n",
-              "      <th>Cost of Trip</th>\n",
-              "      <th>profit</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>10000011</td>\n",
-              "      <td>42377</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>30.45</td>\n",
-              "      <td>370.95</td>\n",
-              "      <td>313.635</td>\n",
-              "      <td>57.315</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>10000012</td>\n",
-              "      <td>42375</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>28.62</td>\n",
-              "      <td>358.52</td>\n",
-              "      <td>334.854</td>\n",
-              "      <td>23.666</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>10000013</td>\n",
-              "      <td>42371</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>9.04</td>\n",
-              "      <td>125.20</td>\n",
-              "      <td>97.632</td>\n",
-              "      <td>27.568</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>10000014</td>\n",
-              "      <td>42376</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>33.17</td>\n",
-              "      <td>377.40</td>\n",
-              "      <td>351.602</td>\n",
-              "      <td>25.798</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>10000015</td>\n",
-              "      <td>42372</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>8.73</td>\n",
-              "      <td>114.62</td>\n",
-              "      <td>97.776</td>\n",
-              "      <td>16.844</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
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-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f61529b-5bcc-456c-a8f1-b095116fa7aa')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
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-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
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-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
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-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
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-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
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-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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-              "      buttonEl.style.display =\n",
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-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-0f61529b-5bcc-456c-a8f1-b095116fa7aa');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
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-              "        await google.colab.output.renderOutput(dataTable, element);\n",
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-              "        element.appendChild(docLink);\n",
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-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
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-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
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-              "    height: 32px;\n",
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-              "    width: 32px;\n",
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-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
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-              "    fill: var(--button-hover-fill-color);\n",
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-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
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-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
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-              "\n",
-              "  @keyframes spin {\n",
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-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
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-              "      border-right-color: var(--fill-color);\n",
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-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-0ad292d2-411a-4734-ae75-ba6c58245641 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df1"
-            }
-          },
-          "metadata": {},
-          "execution_count": 9
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "The bar chart that represents the most number of trips for both yellow and pink cabs in the year of 1970"
-      ],
-      "metadata": {
-        "id": "I3xr1iPUyaUy"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "\n",
-        "\n",
-        "import pandas as pd\n",
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "\n",
-        "df1['Date of Travel'] = pd.to_datetime(df1['Date of Travel'])\n",
-        "\n",
-        "\n",
-        "df1_1970 = df1[df1['Date of Travel'].dt.year == 1970]\n",
-        "\n",
-        "\n",
-        "company_counts_1970 = df1_1970.groupby('Company')['Company'].count()\n",
-        "\n",
-        "\n",
-        "company_counts_1970.plot(kind='bar', color = ['pink', 'yellow'])\n",
-        "\n",
-        "\n",
-        "plt.xlabel('Company')\n",
-        "plt.ylabel('Number of Trips in 1970')\n",
-        "plt.title('Most Used Cabs (Pink and Yellow) in 1970')\n",
-        "\n",
-        "\n",
-        "plt.savefig('bar_chart_1970.png')\n",
-        "\n",
-        "plt.show()\n",
-        "\n",
-        "# Hypothesis Result for Bar Chart of Most Used Cabs in 1970\n",
-        "\n",
-        "# The bar chart above showcases the number of trips undertaken by Yellow and Pink Cab companies in the year 1970.\n",
-        "# As depicted, Yellow Cabs had significantly more trips compared to Pink Cabs in that year.\n",
-        "# This suggests a higher demand or preference for Yellow Cabs in the market during 1970.\n",
-        "# If this trend holds for other years, it indicates that Yellow Cabs potentially have a stronger market share and possibly higher profitability.\n",
-        "# This information can be useful for XYZ firm in making informed decisions about which cab company to invest in, potentially favoring the Yellow Cab company given their historical dominance.\n",
-        "\n",
-        "#EDA Recommendations:\n",
-        "#We should collect and analyze data on customer demographics (age, gender, income level) to better understand who is using each cab company.\n",
-        "#This could provide insights into targeted marketing strategies."
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 532
-        },
-        "id": "kQaKMrE7eRqw",
-        "outputId": "ceedad65-74bd-4261-f1a3-871c62d5c9f6"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df2 = pd.read_csv('City.csv')\n",
-        "df2.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "JPYgpO2sfGkf",
-        "outputId": "b7854f08-5674-4a57-a750-5d236960514f"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "             City   Population      Users\n",
-              "0     NEW YORK NY   8,405,837    302,149 \n",
-              "1      CHICAGO IL   1,955,130    164,468 \n",
-              "2  LOS ANGELES CA   1,595,037    144,132 \n",
-              "3        MIAMI FL   1,339,155     17,675 \n",
-              "4  SILICON VALLEY   1,177,609     27,247 "
-            ],
-            "text/html": [
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-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-8b7768ec-b9a8-4fe4-9a62-0cbd5ac9b727 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df2",
-              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"NEW YORK NY\",\n          \"WASHINGTON DC\",\n          \"SACRAMENTO CA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 8,405,837 \",\n          \" 418,859 \",\n          \" 545,776 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Users\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 302,149 \",\n          \" 127,001 \",\n          \" 7,044 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 11
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "\n",
-        "city_company_counts = df1.groupby(['City', 'Company'])['Company'].count().unstack()\n",
-        "\n",
-        "\n",
-        "city_company_counts = city_company_counts.reset_index()\n",
-        "\n",
-        "\n",
-        "city_company_counts = city_company_counts.rename(\n",
-        "    columns={'Pink Cab': 'Pink Cab Users', 'Yellow Cab': 'Yellow Cab Users'}\n",
-        ")\n",
-        "\n",
-        "\n",
-        "merged_df = pd.merge(df2, city_company_counts, on='City', how='left')\n",
-        "\n",
-        "\n",
-        "merged_df = merged_df.fillna(0)\n",
-        "\n",
-        "\n",
-        "plt.figure(figsize=(10, 6))\n",
-        "plt.plot(merged_df['City'], merged_df['Pink Cab Users'], label='Pink Cab Users')\n",
-        "plt.plot(merged_df['City'], merged_df['Yellow Cab Users'], label='Yellow Cab Users')\n",
-        "plt.xlabel('City')\n",
-        "plt.ylabel('Number of Users')\n",
-        "plt.title('Number of Users per City for Pink and Yellow Cabs')\n",
-        "plt.xticks(rotation=90)\n",
-        "plt.legend()\n",
-        "plt.tight_layout()\n",
-        "plt.show()\n",
-        "\n",
-        "# Hypothesis Result for Line Graph of Number of Users per City\n",
-        "\n",
-        "# The line graph above visually depicts the number of users for Pink and Yellow Cabs in different cities.\n",
-        "# It seems that the Yellow Cabs consistently have a larger user base across most cities compared to Pink Cabs.\n",
-        "# This indicates a potential market share dominance for Yellow Cabs.\n",
-        "# Notably, in certain cities, the difference in user numbers is significant, which may reflect a strong preference for Yellow Cabs in those areas.\n",
-        "# If these trends persist, it implies a greater demand for Yellow Cabs, potentially contributing to higher profitability for the company.\n",
-        "# This information can guide XYZ firm's investment decisions, possibly favouring the Yellow Cab company due to its extensive user base.\n",
-        "\n",
-        "#EDA Recommendations:\n",
-        "#I can analyze trends and patterns, and observe the overall trends in user counts for both companies across different cities."
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 607
-        },
-        "id": "dSmc_aL3VSKR",
-        "outputId": "7bb7ca04-299e-48d7-da61-442d8144dd6f"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1000x600 with 1 Axes>"
-            ],
-            "image/png": 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jplMqPj6eRYsW0b59e4oWLWrfnz9/fp5++mnWrFmTpBt9nz59UjUGe9iwYeTJk4d8+fLRqFEjDh8+zOjRo+nQocNdH1e2bFnq169v/z5Pnjx3/FyeOHGChg0bEhsby6pVq+xd6e/G3d3dPiY/Pj6eCxcu2IcxJNfd+Nlnn03Uu8GWzZZn8+bNRERE8NxzzyVq16NHD/z8/O6Zx6Zw4cIMGzaMixcvMnjwYPtV2tR+ru7m9OnTbN++nR49ehAQEGDfX7FiRZo1a2b/fWZ7nlu3brX3alizZg2PPPIIlStXtnedt41Bv9vwmPv5XNz6ebt06RKRkZH2PLe712cerL9XNmzYwKlTp1KcQUSyBhXdIiJ38PbbbxMXF3fPsd2pcXu3RttSTrlz506yP7lxkiVKlEj0vcVioXjx4vbxqAcPHgSsBVqePHkSfX355ZdER0cTGRmZ6BjBwcEpyn706FFKlSqVZH+ZMmXs96cV25jjhg0b0rFjR9577z1y585Nu3btmD59OtHR0fa2Bw8eJDIyksDAwCSvwdWrV4mIiEh07OSe/5NPPkndunXp3bs3efPmpXPnzvz4448pLsBv/zkBlCxZkuvXr3Pu3DnOnTvH5cuXmTZtWpKMzz77LECKcibH19cXsI4fdbTSpUtTo0YNZs+ebd83e/ZsateuTfHixVN9vHPnznH9+vU7vq8SEhKSzFmQ0tfBpm/fvixevJilS5eyZcsWIiIieP311+/5uOS6IOfMmTPZz2W3bt2IiIhg5cqVPPTQQynKlZCQwKeffkqJEiVwd3cnd+7c5MmThx07diT5jCaXJ2fOnAD2PLbP3+3vPVdX10R/0EiJGjVqACRa0i21n6u7sWW908/d9odCsBbdcXFxhIaGsn//fiIiIqhfvz4NGjRIVHSXLVs2UQF/u/v5XMybN4/atWvj4eFBQEAAefLkYfLkycn+fO71mQcYM2YMu3btomDBgtSsWZN333032T/iiEjWozHdIiJ3ULRoUbp27cq0adOSLKkE3HGCsLut85rcFbo7XbUz7jK++k5sReFHH31E5cqVk21jG0trY+ZM3R4eHoB1OankXL9+PVE7i8XCzz//zPr16/nzzz9ZuHAhPXv2ZOzYsaxfvx4fHx8SEhIIDAxMVBjeKk+ePIm+T+75e3p6smrVKpYvX878+fNZsGABP/zwA40bN2bRokUPPNu17efUtWtXnnnmmWTbVKxY8Z45k1O6dGkAdu7cmehKraN0796dl156iRMnThAdHc369euZMGGCw89zJ6l9v5YoUYKmTZum+jyp+Vx26NCBWbNm8dlnnzFy5MgUHX/EiBG888479OzZk/fff5+AgACcnJwYNGhQsn/cceTvifuR2s+Vo1SvXh0PDw9WrVpFoUKFCAwMpGTJktSvX59JkyYRHR3N6tWrk8wzcLvixYvj4uLCzp07U3Te1atX8+ijj9KgQQMmTZpE/vz5cXV1Zfr06cyZM+e+nssTTzxB/fr1+e2331i0aBEfffQRo0eP5tdff6VVq1b3dUwRyRxUdIuI3MXbb7/Nt99+y+jRo5PcZ7vSdPny5UT70/KKr+1Kto1hGBw6dMheoNkmbvL19b2vQuNuChcunOxszPv27bPfn1p58uTBy8vrjrM879+/Hy8vryQ9AWrXrk3t2rX58MMPmTNnDl26dOH777+nd+/eFCtWjCVLllC3bt0H+oOCk5MTTZo0oUmTJnzyySeMGDGCt956i+XLl9/ztb395wRw4MABvLy87MVJjhw5iI+Pd/jPqW3btowcOZJvv/32vovuu80437lzZwYPHsx3333HjRs3cHV15cknn7yv89zt579v3z6cnJxSNAGY2V588UWKFy/O0KFD8fPzS/aPdLf7+eefefjhh/nqq68S7b98+XKS93tK2D5/Bw8etM/QDhAbG0t4eDiVKlVK9TFv5ajP1a1Z7/Rzz507t30pODc3N/ss7IUKFbK/p+vXr090dDSzZ8/m7NmzSSadvJ2XlxeNGzdm2bJlHD9+/J7vq19++QUPDw8WLlyYaA336dOnJ9s+JZ95sA6deOGFF3jhhReIiIigatWqfPjhhyq6RbI4dS8XEbmLYsWK0bVrV6ZOncqZM2cS3efr60vu3LmTLAszadKkNMsza9asRN0jf/75Z06fPm3/D1u1atUoVqwYH3/8MVevXk3yeFs3x/vxyCOPsHHjxkTLQl27do1p06ZRpEgRypYtm+pjOjs707x5c/7880+OHTuW6L5jx47x559/0rx5c/tVvkuXLiW5sme7om/rYv7EE08QHx/P+++/n+R8cXFxSf5IkpyLFy8m2Xf7ee4mNDQ00bjP48eP8/vvv9ufi7OzMx07duSXX35Jdl3gB/k5hYSE0LJlS7788kvmzp2b5P6YmBheffXVux7D29v7jq9T7ty5adWqFd9++y2zZ8+mZcuW91Ukwn8//99//z3Rkl1nz55lzpw51KtXz94tOKN75513ePXVVxkyZAiTJ0++Z3tnZ+ck7+WffvqJkydP3tf5q1evTp48eZgyZQoxMTH2/TNmzEjRe/5eHPG5ssmfPz+VK1dm5syZiR63a9cuFi1axCOPPJKoff369dmwYQPLly+3F925c+emTJky9j+IpuQPTMOGDcMwDLp165bs78ctW7Ywc+ZMwPrzsVgsiXouHTlyJNnPFNz7Mx8fH5+kW3pgYCBBQUEp+p0iIpmbrnSLiNzDW2+9xTfffMP+/fuTLBXUu3dvRo0aRe/evalevTqrVq3iwIEDaZYlICCAevXq8eyzz3L27FnGjRtH8eLF6dOnD2C9Ovvll1/SqlUrypUrx7PPPstDDz3EyZMnWb58Ob6+vvz555/3de4333yT7777jlatWjFw4EACAgKYOXMm4eHh/PLLL/ZJoVJrxIgR1K5dm6pVq9K3b1+KFCnCkSNHmDZtGhaLhREjRtjbzpw5k0mTJvHYY49RrFgxrly5whdffIGvr6/9P+oNGzakX79+jBw5ku3bt9O8eXNcXV05ePAgP/30E5999hmdOnW6a6bhw4ezatUqWrduTeHChYmIiGDSpEkUKFDgnmuZA5QvX54WLVokWj4IrOsE24waNYrly5dTq1Yt+vTpQ9myZbl48SJbt25lyZIlyRb+KTVr1iyaN29Ohw4daNu2LU2aNMHb25uDBw/y/fffc/r06URrdd+uWrVqLFmyhE8++YSgoCCCg4MTLd3VvXt3+2uYXBGWGh988IF9TfQXXngBFxcXpk6dSnR0NGPGjHmgY6e3jz76iMjISPr370+OHDnuOrlcmzZtGD58OM8++yx16tRh586dzJ49O9Xjr21cXV354IMP6NevH40bN+bJJ58kPDyc6dOn3/cxb+WIz9WtPvroI1q1akVISAi9evWyLxnm5+fHu+++m6ht/fr1+fDDDzl+/Hii4rpBgwZMnTqVIkWKpGjN9jp16jBx4kReeOEFSpcuTbdu3ShRogRXrlxhxYoV/PHHH3zwwQcAtG7dmk8++YSWLVvy9NNPExERwcSJEylevDg7duxIcux7feavXLlCgQIF6NSpE5UqVcLHx4clS5awadMmxo4dm+LXTUQyKbOmTRcRyWhuXf7nds8884wBJFoyzDCsS8r06tXL8PPzM3LkyGE88cQTRkRExB2XDLt9CapnnnnG8Pb2TnK+25cnsy0b9N133xlDhgwxAgMDDU9PT6N169bG0aNHkzx+27ZtRocOHYxcuXIZ7u7uRuHChY0nnnjCWLp06T0z3c3hw4eNTp06Gf7+/oaHh4dRs2ZNY968eUnakcIlw2z27t1rPPnkk0ZgYKDh4uJiBAYGGp07dzb27t2bqN3WrVuNp556yihUqJDh7u5uBAYGGm3atEm0VI/NtGnTjGrVqhmenp5Gjhw5jAoVKhivv/66cerUKXubwoULG61bt07y2KVLlxrt2rUzgoKCDDc3NyMoKMh46qmnjAMHDtzzudie+7fffmuUKFHCcHd3N6pUqWIsX748SduzZ88a/fv3NwoWLGi4uroa+fLlM5o0aWJMmzbN3sb2s//pp5/uee5bXb9+3fj444+NGjVqGD4+Poabm5tRokQJ48UXXzQOHTpkb5fckmH79u0zGjRoYHh6ehpAkuXDoqOjjZw5cxp+fn7GjRs3UpTnbsvDbd261WjRooXh4+NjeHl5GQ8//LCxbt26RG3u9vlM7fmSO+7tS4Yl9764fSms5DLFx8cbTz31lOHi4mLMnTv3jue9efOm8corrxj58+c3PD09jbp16xqhoaFJznGnn/+dliycNGmSERwcbLi7uxvVq1c3Vq1aleSY93K391xKPlcpWTLMMAxjyZIlRt26dQ1PT0/D19fXaNu2rbFnz54k54yKijKcnZ2NHDlyJFou7ttvvzUAo1u3bil+boZhGFu2bDGefvppIygoyHB1dTVy5sxpNGnSxJg5c2aiZdy++uor+2e4dOnSxvTp05P9vKTkMx8dHW289tprRqVKlYwcOXIY3t7eRqVKlYxJkyalKruIZE4Ww0inGThERESyAYvFQv/+/dN1crH0FhcXR1BQEG3btk0yJllEREQS05huERERSZW5c+dy7tw5unfvbnYUERGRDE9jukVERCRFNmzYwI4dO3j//fepUqUKDRs2NDuSiIhIhqcr3SIiIpIikydP5vnnnycwMJBZs2aZHUdERCRT0JhuERERERERkTSiK90iIiIiIiIiaURFt4iIiIiIiEga0URqDpKQkMCpU6fIkSMHFovF7DgiIiIiIiKShgzD4MqVKwQFBeHkdOfr2Sq6HeTUqVMULFjQ7BgiIiIiIiKSjo4fP06BAgXueL+KbgfJkSMHYH3BfX19TU4jIiIiIiIiaSkqKoqCBQvaa8E7UdHtILYu5b6+viq6RUREREREsol7DS/WRGoiIiIiIiIiaURFt4iIiIiIiEgaUdEtIiIiIiIikkY0pltERERERDKM+Ph4YmNjzY4hgqurK87Ozg98HBXdIiIiIiJiOsMwOHPmDJcvXzY7ioidv78/+fLlu+dkaXejoltERERERExnK7gDAwPx8vJ6oCJH5EEZhsH169eJiIgAIH/+/Pd9LBXdIiIiIiJiqvj4eHvBnStXLrPjiADg6ekJQEREBIGBgffd1VwTqYmIiIiIiKlsY7i9vLxMTiKSmO09+SDzDKjoFhERERGRDEFdyiWjccR7UkW3iIiIiIiISBpR0S0iIiIiIpIGevToQfv27VP1GIvFwty5c9Mkz90cOXIEi8XC9u3b0/3cWZ2KbhERERERkfvQo0cPLBYLFosFNzc3ihcvzvDhw4mLiwPgs88+Y8aMGWmeIyYmhjFjxlCpUiW8vLzInTs3devWZfr06Wm+5nmRIkUYN25ckv3vvvsulStXTtNzZxaavVxEREREROQ+tWzZkunTpxMdHc1ff/1F//79cXV1ZciQIfj5+aX5+WNiYmjRogX//PMP77//PnXr1sXX15f169fz8ccfU6VKlSxZ/MbGxuLq6mp2jBTRle7sIuYa7P8btswwO4mIiIiISJbh7u5Ovnz5KFy4MM8//zxNmzbljz/+AJJ2L2/UqBEDBw7k9ddfJyAggHz58vHuu+/e9fjDhg0jf/787NixI9n7x40bx6pVq1i6dCn9+/encuXKFC1alKeffpoNGzZQokQJABYsWEC9evXw9/cnV65ctGnThsOHDyc53r59+6hTpw4eHh6UL1+elStX3t8Lc5uff/6ZChUq4OnpSa5cuWjatCnXrl2z3//ll19SpkwZPDw8KF26NJMmTbLfZ+v6/sMPP9CwYUM8PDyYPXs2R48epW3btuTMmRNvb2/KlSvHX3/95ZC8jqQr3dlFxD74rjO4+0LlruCsH72IiIiIZEyGYXAjNt6Uc3u6Oj/QjNWenp5cuHDhjvfPnDmTwYMHs2HDBkJDQ+nRowd169alWbNmidoZhsHAgQOZN28eq1evpnjx4skeb/bs2TRt2pQqVaokuc/V1dV+NfjatWsMHjyYihUrcvXqVYYOHcpjjz3G9u3bcXL671rsa6+9xrhx4yhbtiyffPIJbdu2JTw8/IHWTz99+jRPPfUUY8aM4bHHHuPKlSusXr0awzDsz2Ho0KFMmDCBKlWqsG3bNvr06YO3tzfPPPOM/ThvvvkmY8eOpUqVKnh4eNCnTx9iYmJYtWoV3t7e7NmzBx8fn/vOmVZUeWUXQZXBwx9uXoaTm6FQbZMDiYiIiIgk70ZsPGWHLjTl3HuGt8DLLfVlkmEYLF26lIULF/Liiy/esV3FihUZNmwYACVKlGDChAksXbo0UdEdFxdH165d2bZtG2vWrOGhhx664/EOHjxIo0aN7pmvY8eOib7/+uuvyZMnD3v27KF8+fL2/QMGDLC3nTx5MgsWLOCrr77i9ddfv+c57uT06dPExcXRoUMHChcuDECFChXs9w8bNoyxY8fSoUMHAIKDg9mzZw9Tp05NVHQPGjTI3gbg2LFjdOzY0X6sokWL3nfGtKSiO7twcoaijWDPXDi8TEW3iIiIiIgDzJs3Dx8fH2JjY0lISODpp5++a5fxihUrJvo+f/78REREJNr38ssv4+7uzvr168mdO/ddz2+7WnwvBw8eZOjQoWzYsIHz58+TkJAAWAvXW4vukJAQ+7aLiwvVq1dn7969KTrHnVSqVIkmTZpQoUIFWrRoQfPmzenUqRM5c+bk2rVrHD58mF69etGnTx/7Y+Li4pKMia9evXqi7wcOHMjzzz/PokWLaNq0KR07dkzy+mYEKrqzk+JN/iu6H/6f2WlERERERJLl6erMnuEtTDt3ajz88MNMnjwZNzc3goKCcHG5e4l1++RfFovFXgDbNGvWjO+++46FCxfSpUuXux6vZMmS7Nu3754527ZtS+HChfniiy8ICgoiISGB8uXLExMTc8/H3o2vry+RkZFJ9l++fNleNDs7O7N48WLWrVvHokWL+Pzzz3nrrbfYsGEDXl5eAHzxxRfUqlUr0TGcnRP/LLy9vRN937t3b1q0aMH8+fNZtGgRI0eOZOzYsXftaWAGTaSWnRRrbL09uQVuXDI3i4iIiIjIHVgsFrzcXEz5Su14bm9vb4oXL06hQoXuWXCn1KOPPsqcOXPo3bs333///V3bPv300yxZsoRt27YluS82NpZr165x4cIF9u/fz9tvv02TJk0oU6YMly4lXw+sX7/evh0XF8eWLVsoU6bMHc9fqlQptmzZkmT/1q1bKVmypP17i8VC3bp1ee+999i2bRtubm789ttv5M2bl6CgIMLCwihevHiir+Dg4Ls+d4CCBQvy3HPP8euvv/LKK6/wxRdf3PMx6U1XurMTvwKQuxSc3w9hK6Fce7MTiYiIiIhIMh577DG++eYbunXrhouLC506dUq23aBBg5g/fz5NmjTh/fffp169euTIkYPNmzczevRovvrqKypWrEiuXLmYNm0a+fPn59ixY7z55pvJHm/ixImUKFGCMmXK8Omnn3Lp0iV69ux5x5wvv/wy9evX58MPP6RDhw7Ex8fz3XffERoaap+BfMOGDSxdupTmzZsTGBjIhg0bOHfunL2Yf++99xg4cCB+fn60bNmS6OhoNm/ezKVLlxg8ePAdzz1o0CBatWpFyZIluXTpEsuXL7/rHwjMoqI7uynW2Fp0H16qoltEREREJAPr1KkTCQkJdOvWDScnp0STiNm4u7uzePFiPv30U6ZOncqrr76Kl5cXZcqUYeDAgZQvXx4nJye+//57+/elSpVi/PjxyU7ANmrUKEaNGsX27dspXrw4f/zxx13HldepU4e///6b4cOHM3bsWJycnKhQoQJLly61jxX39fVl1apVjBs3jqioKAoXLszYsWNp1aoVYO0m7uXlxUcffcRrr72Gt7c3FSpUYNCgQXd9feLj4+nfvz8nTpzA19eXli1b8umnn6b8BU4nFiOlI+/lrqKiovDz8yMyMhJfX1+z49zZwcUwuxP4FYRBO+EBlkMQEREREXGEmzdvEh4eTnBwMB4eHmbHEbG723szpTWgxnRnN4XrgLMbRB6HC4fMTiMiIiIiIpKlqejObty8/1su7NBSc7OIiIiIiIhkcSq6s6NiTay3h5eZm0NERERERCSLU9GdHdmWDjuyGuKizc0iIiIiIiKShanozo7ylgfvQIi9Dsc3mJ1GREREREQky1LRnR05OUGxh63bGtctIiIiIiKSZlR0Z1ca1y0iIiIiIpLmVHRnV0UbWW/P7ICr50yNIiIiIiIiklWp6M6ucuSFvBWs22HLzc0iIiIiIiKSRanozs6K/zuLubqYi4iIiIikq0aNGjFo0CD790WKFGHcuHGm5blfM2bMwN/f3+wYGZqK7uys2C1Ft2GYm0VEREREJBMxDIOmTZvSokWLJPdNmjQJf39/Tpw4YUKylDtz5gwvvvgiRYsWxd3dnYIFC9K2bVuWLk3byZaPHDmCxWJh+/btSe67/Y8RWYGK7uysUAi4eMLVs3B2t9lpREREREQyDYvFwvTp09mwYQNTp0617w8PD+f111/n888/p0CBAiYmvLsjR45QrVo1li1bxkcffcTOnTtZsGABDz/8MP379zc7XpqIj48nISEh3c9ratEdHx/PO++8Q3BwMJ6enhQrVoz3338f45arroZhMHToUPLnz4+npydNmzbl4MGDiY5z8eJFunTpgq+vL/7+/vTq1YurV68marNjxw7q16+Ph4cHBQsWZMyYMUny/PTTT5QuXRoPDw8qVKjAX3/9lTZPPKNwcYci9azbh7V0mIiIiIhIahQsWJDPPvuMV199lfDwcAzDoFevXjRv3pwqVarQqlUrfHx8yJs3L926deP8+fMpPvaxY8do164dPj4++Pr68sQTT3D27FkAIiMjcXZ2ZvPmzQAkJCQQEBBA7dq17Y//9ttvKViw4B2P/8ILL2CxWNi4cSMdO3akZMmSlCtXjsGDB7N+/Xp7u08++YQKFSrg7e1NwYIFeeGFF5LUWgBz586lRIkSeHh40KJFC44fP57i53onhmHw7rvvUqhQIdzd3QkKCmLgwIH2+6Ojo3n11Vd56KGH8Pb2platWqxYscJ+v63r+x9//EHZsmVxd3fn2LFjrFixgpo1a+Lt7Y2/vz9169bl6NGjD5z3TkwtukePHs3kyZOZMGECe/fuZfTo0YwZM4bPP//c3mbMmDGMHz+eKVOmsGHDBry9vWnRogU3b960t+nSpQu7d+9m8eLFzJs3j1WrVtG3b1/7/VFRUTRv3pzChQuzZcsWPvroI959912mTZtmb7Nu3TqeeuopevXqxbZt22jfvj3t27dn165d6fNimKW4lg4TERERkQzGMCDmmjlfqRx2+cwzz9CkSRN69uzJhAkT2LVrF1OnTqVx48ZUqVKFzZs3s2DBAs6ePcsTTzyRomMmJCTQrl07Ll68yMqVK1m8eDFhYWE8+eSTAPj5+VG5cmV7gblz504sFgvbtm2zF8QrV66kYcOGyR7/4sWLLFiwgP79++Pt7Z3k/lvHaDs5OTF+/Hh2797NzJkzWbZsGa+//nqi9tevX+fDDz9k1qxZrF27lsuXL9O5c+cUPde7+eWXX/j000+ZOnUqBw8eZO7cuVSoUMF+/4ABAwgNDeX7779nx44dPP7447Rs2TLRRdrr168zevRovvzyS3bv3k1AQADt27enYcOG7Nixg9DQUPr27YvFYnngvHfikmZHToF169bRrl07WrduDVgnD/juu+/YuHEjYP3Lxrhx43j77bdp164dALNmzSJv3rzMnTuXzp07s3fvXhYsWMCmTZuoXr06AJ9//jmPPPIIH3/8MUFBQcyePZuYmBi+/vpr3NzcKFeuHNu3b+eTTz6xF+efffYZLVu25LXXXgPg/fffZ/HixUyYMIEpU6ak90uTfmzjuo+GQsx1cPMyN4+IiIiISOx1GBFkzrn/dwrckhaidzNt2jTKlSvHqlWr+OWXX5g6dSpVqlRhxIgR9jZff/01BQsW5MCBA5QsWfKux1u6dCk7d+4kPDzcfrV61qxZlCtXjk2bNlGjRg0aNWrEihUrePXVV1mxYgXNmjVj3759rFmzhpYtW7JixYokxbHNoUOHMAyD0qVL3/O53T7Z2wcffMBzzz3HpEmT7PtjY2OZMGECtWrVAmDmzJmUKVOGjRs3UrNmzXue406OHTtGvnz5aNq0Ka6urhQqVMh+vGPHjjF9+nSOHTtGUJD1vfLqq6+yYMECpk+fbn/tY2NjmTRpEpUqVQKsf3CIjIykTZs2FCtWDIAyZcrcd8aUMPVKd506dVi6dCkHDhwA4J9//mHNmjW0atUKsI6HOHPmDE2bNrU/xs/Pj1q1ahEaGgpAaGgo/v7+9oIboGnTpjg5ObFhwwZ7mwYNGuDm5mZv06JFC/bv38+lS5fsbW49j62N7TxZVu6S4PsQxEfD0XVmpxERERERyXQCAwPp168fZcqUoX379vzzzz8sX74cHx8f+5etwD18+PA9j7d3714KFiyYqHt42bJl8ff3Z+/evQA0bNiQNWvWEB8fz8qVK2nUqJG9ED916hSHDh2iUaNGyR7fSMXV/CVLltCkSRMeeughcuTIQbdu3bhw4QLXr1+3t3FxcaFGjRr270uXLp0o6/16/PHHuXHjBkWLFqVPnz789ttvxMXFAdar+/Hx8ZQsWTLR67xy5cpEr7GbmxsVK1a0fx8QEECPHj1o0aIFbdu25bPPPuP06dMPlPNeTL3S/eabbxIVFUXp0qVxdnYmPj6eDz/8kC5dugDW2fQA8ubNm+hxefPmtd935swZAgMDE93v4uJCQEBAojbBwcFJjmG7L2fOnJw5c+au57lddHQ00dHR9u+joqJS9dwzDIvFerV72zfWLuYlmt77MSIiIiIiacnVy3rF2axz3wcXFxdcXKzl1dWrV2nbti2jR49O0i5//vwPFM+mQYMGXLlyha1bt7Jq1SpGjBhBvnz5GDVqFJUqVSIoKIgSJUok+9gSJUpgsVjYt2/fXc9x5MgR2rRpw/PPP8+HH35IQEAAa9asoVevXsTExODldX+vla+vL2Adm367y5cv4+fnB1jHzO/fv58lS5awePFiXnjhBT766CNWrlzJ1atXcXZ2ZsuWLTg7Oyc6ho+Pj33b09MzSdfx6dOnM3DgQBYsWMAPP/zA22+/zeLFixONiXckU4vuH3/8kdmzZzNnzhx7l+9BgwYRFBTEM888Y2a0exo5ciTvvfee2TEcw150azI1EREREckALJZUd/HOSKpWrcovv/xCkSJF7IV4apQpU4bjx49z/Phx+9XuPXv2cPnyZcqWLQtYx11XrFiRCRMm4OrqSunSpQkMDOTJJ59k3rx5dxzPDdarvS1atGDixIkMHDgwybjuy5cv4+/vz5YtW0hISGDs2LE4OVk7Sf/4449JjhcXF8fmzZvtXb/379/P5cuX79htOyAggNy5c7Nly5ZEOaOiojh06FCi7veenp60bduWtm3b0r9/f0qXLs3OnTupUqUK8fHxREREUL9+/ZS8rIlUqVKFKlWqMGTIEEJCQpgzZ06aFd2mdi9/7bXXePPNN+ncuTMVKlSgW7duvPzyy4wcORKAfPnyAdhn6bM5e/as/b58+fIRERGR6P64uDguXryYqE1yx7j1HHdqY7v/dkOGDCEyMtL+5YjZ+UxTtBFggXP7IPKk2WlERERERDK1/v37c/HiRZ566ik2bdrE4cOHWbhwIc8++yzx8fH3fHzTpk2pUKECXbp0YevWrWzcuJHu3bvTsGHDRMNqGzVqxOzZs+2Fa0BAAGXKlOGHH364a9ENMHHiROLj46lZsya//PILBw8eZO/evYwfP56QkBAAihcvTmxsLJ9//jlhYWF88803yc535erqyosvvsiGDRvYsmULPXr0oHbt2ncdzz148GBGjBjB7NmzOXz4MBs3bqRLly7kyZOHDh06ANbZx7/66it27dpFWFgY3377LZ6enhQuXJiSJUvSpUsXunfvzq+//kp4eDgbN25k5MiRzJ8//47nDQ8PZ8iQIYSGhnL06FEWLVrEwYMH03Rct6lF9/Xr1+1/MbFxdna2r50WHBxMvnz5Ei3OHhUVxYYNG+xvhJCQEC5fvsyWLVvsbZYtW0ZCQoJ9IH9ISAirVq0iNjbW3mbx4sWUKlWKnDlz2tvcvgj84sWL7ee5nbu7O76+vom+Mi2vAHioqnU7bLm5WUREREREMrmgoCDWrl1LfHw8zZs3p0KFCgwaNAh/f/8k9U9yLBYLv//+Ozlz5qRBgwY0bdqUokWL8sMPPyRq17BhQ+Lj4xON3W7UqFGSfckpWrQoW7du5eGHH+aVV16hfPnyNGvWjKVLlzJ58mQAKlWqxCeffMLo0aMpX748s2fPtl8gvZWXlxdvvPEGTz/9NHXr1sXHxydJ1tu9/vrrDBs2jNGjR1OxYkU6duyIt7c3y5cvx9PTE7Bezf/iiy+oW7cuFStWZMmSJfz555/kypULsHYT7969O6+88gqlSpWiffv2bNq0iUKFCt3xvF5eXuzbt8++TFrfvn3p378//fr1u2veB2ExUjOK3sF69OjBkiVLmDp1KuXKlWPbtm307duXnj172sc/jB49mlGjRjFz5kyCg4N555132LFjB3v27MHDwwOAVq1acfbsWaZMmUJsbCzPPvss1atXZ86cOYB1rECpUqVo3rw5b7zxBrt27aJnz558+umn9tnL161bR8OGDRk1ahStW7fm+++/Z8SIEWzdupXy5cvf87lERUXh5+dHZGRk5izAl30Iq8ZAuQ7w+HSz04iIiIhINnLz5k3Cw8MJDg62/x9fJCO423szpTWgqWO6P//8c9555x1eeOEFIiIiCAoKol+/fgwdOtTe5vXXX+fatWv07duXy5cvU69ePRYsWJDoCc+ePZsBAwbQpEkTnJyc6NixI+PHj7ff7+fnx6JFi+jfvz/VqlUjd+7cDB06NNFa3nXq1GHOnDm8/fbb/O9//6NEiRLMnTs3RQV3llCssbXoDlsOCfHg5Hzvx4iIiIiIiMhdmXqlOyvJ9Fe642NhdDDEXIE+y+ChamYnEhEREZFsQle6JaNyxJVuU8d0Swbi7ApF/51s4fAyc7OIiIiIiIhkESq65T/FHrbeHlLRLSIiIiIi4ggquuU/xZpYb09shJtR5mYRERERERHJAlR0y38CgiFnMCTEwZE1ZqcRERERkWzGtnSwSEbhiPekqbOXSwZUvAls+hIOL4XSj5idRkRERESyATc3N5ycnDh16hR58uTBzc0Ni8VidizJxgzDICYmhnPnzuHk5ISbm9t9H0tFtyRWrPG/RbfGdYuIiIhI+nByciI4OJjTp09z6tQps+OI2Hl5eVGoUCGcnO6/k7iKbkmsSH1wcoGLYXAx3NrlXEREREQkjbm5uVGoUCHi4uKIj483O44Izs7OuLi4PHCvCxXdkpiHLxSoCcfWWa92B/QyO5GIiIiIZBMWiwVXV1dcXV3NjiLiMJpITZIq1th6qy7mIiIiIiIiD0RFtyRV/N+iO3wVxMeam0VERERERCQTU9EtSeWvDJ45IToKTm4xO42IiIiIiEimpaJbknJyhqIPW7cPLTU3i4iIiIiISCamoluSp3HdIiIiIiIiD0xFtyTPVnSf2grXL5qbRUREREREJJNS0S3J83sI8pQGIwHCV5qdRkREREREJFNS0S13pi7mIiIiIiIiD0RFt9xZsSbW20PLwDDMzSIiIiIiIpIJqeiWOytcB5zdIOoEnD9odhoREREREZFMR0W33JmbFxQKsW4f1tJhIiIiIiIiqaWiW+6u+L9dzDWuW0REREREJNVUdMvd2SZTO7IG4qLNzSIiIiIiIpLJqOiWu8tbHrwDIfY6HFtvdhoREREREZFMRUW33J3FoqXDRERERERE7pOKbrk3+7huTaYmIiIiIiKSGiq65d6KNrLentkJVyNMjSIiIiIiIpKZqOiWe/MJhHwVrNuHl5ubRUREREREJBNR0S0pU0xLh4mIiIiIiKSWim5JmVsnU0tIMDeLiIiIiIhIJqGiW1KmUG1w9YJrERCx2+w0IiIiIiIimYKKbkkZF3coUs+6rS7mIiIiIiIiKaKiW1LONq77kJYOExERERERSQkV3ZJytnHdx0Ih5rq5WURERERERDIBFd2ScrlLgG8BiI+Bo2vNTiMiIiIiIpLhqeiWlLNYoPgts5iLiIiIiIjIXanoltQppqJbREREREQkpVR0S+oENwSLE5zbB5EnzE4jIiIiIiKSoanoltTxCoCgqtbtw8vNzSIiIiIiIpLBqeiW1Cv+79Jhh7V0mIiIiIiIyN2o6JbUs43rDlsBCfGmRhEREREREcnIVHRL6j1UDdx94cYlOLXd7DQiIiIiIiIZlopuST1nVwhuYN3WLOYiIiIiIiJ3pKJb7o+WDhMREREREbknFd1yf2yTqZ3YCDejzM0iIiIiIiKSQanolvuTswgEFIWEODiy2uw0IiIiIiIiGZKKbrl/xf692n1IS4eJiIiIiIgkR0W33D+N6xYREREREbkrFd1y/4rUAycXuBQOF8PMTiMiIiIiIpLhqOiW++fhCwVrWbd1tVtERERERCQJFd3yYIo9bL09vNzcHCIiIiIiIhmQim55MLbJ1MJWQnysuVlEREREREQyGBXd8mDyVwLPAIi5Aic2m51GREREREQkQ1HRLQ/GyfmWLuZaOkxERERERORWKrrlwWnpMBERERERkWSp6JYHV/TfK90nt8L1i+ZmERERERERyUBUdMuD83sI8pQBDAhbYXYaERERERGRDENFtziGupiLiIiIiIgkoaJbHKP4LUW3YZibRUREREREJINQ0S2OUagOOLtD1Ek4f8DsNCIiIiIiIhmCim5xDDcvKFzHun1IS4eJiIiIiIiAim5xJI3rFhERERERSURFtziOreg+sgbios3NIiIiIiIikgGo6BbHyVsOfPJC3A04Fmp2GhEREREREdOp6BbHsVjUxVxEREREROQWKrrFsYo1sd4eUtEtIiIiIiKiolscq2gj6+3ZnXDlrKlRREREREREzKaiWxzLJw/kq2jdDltubhYRERERERGTqegWxyv+bxdzjesWEREREZFsTkW3OJ59MrXlkJBgbhYRERERERETqegWxytYC1y94VoEnN1ldhoRERERERHTqOgWx3NxhyL1rNvqYi4iIiIiItmY6UX3yZMn6dq1K7ly5cLT05MKFSqwefNm+/2GYTB06FDy58+Pp6cnTZs25eDBg4mOcfHiRbp06YKvry/+/v706tWLq1evJmqzY8cO6tevj4eHBwULFmTMmDFJsvz000+ULl0aDw8PKlSowF9//ZU2Tzo7sI/rXmpuDhEREREREROZWnRfunSJunXr4urqyt9//82ePXsYO3YsOXPmtLcZM2YM48ePZ8qUKWzYsAFvb29atGjBzZs37W26dOnC7t27Wbx4MfPmzWPVqlX07dvXfn9UVBTNmzencOHCbNmyhY8++oh3332XadOm2dusW7eOp556il69erFt2zbat29P+/bt2bVL3aPvi21c97H1EHPN3CwiIiIiIiImsRiGYZh18jfffJO1a9eyevXqZO83DIOgoCBeeeUVXn31VQAiIyPJmzcvM2bMoHPnzuzdu5eyZcuyadMmqlevDsCCBQt45JFHOHHiBEFBQUyePJm33nqLM2fO4ObmZj/33Llz2bdvHwBPPvkk165dY968efbz165dm8qVKzNlypR7PpeoqCj8/PyIjIzE19f3gV6XLMEwYFwFiDwOXX6GEs3MTiQiIiIiIuIwKa0BTb3S/ccff1C9enUef/xxAgMDqVKlCl988YX9/vDwcM6cOUPTpk3t+/z8/KhVqxahoaEAhIaG4u/vby+4AZo2bYqTkxMbNmywt2nQoIG94AZo0aIF+/fv59KlS/Y2t57H1sZ2nttFR0cTFRWV6EtuYbH8d7X7kLqYi4iIiIhI9mRq0R0WFsbkyZMpUaIECxcu5Pnnn2fgwIHMnDkTgDNnzgCQN2/eRI/Lmzev/b4zZ84QGBiY6H4XFxcCAgIStUnuGLee405tbPffbuTIkfj5+dm/ChYsmOrnn+XZlw7TZGoiIiIiIpI9mVp0JyQkULVqVUaMGEGVKlXo27cvffr0SVF3brMNGTKEyMhI+9fx48fNjpTxFG0IFic4vx8iT5idRkREREREJN2ZWnTnz5+fsmXLJtpXpkwZjh07BkC+fPkAOHv2bKI2Z8+etd+XL18+IiIiEt0fFxfHxYsXE7VJ7hi3nuNObWz3387d3R1fX99EX3Ibz5zwUDXrtq52i4iIiIhINmRq0V23bl3279+faN+BAwcoXLgwAMHBweTLl4+lS/8bExwVFcWGDRsICQkBICQkhMuXL7NlyxZ7m2XLlpGQkECtWrXsbVatWkVsbKy9zeLFiylVqpR9pvSQkJBE57G1sZ1H7lOxf5cO07huERERERHJhkwtul9++WXWr1/PiBEjOHToEHPmzGHatGn0798fAIvFwqBBg/jggw/4448/2LlzJ927dycoKIj27dsD1ivjLVu2pE+fPmzcuJG1a9cyYMAAOnfuTFBQEABPP/00bm5u9OrVi927d/PDDz/w2WefMXjwYHuWl156iQULFjB27Fj27dvHu+++y+bNmxkwYEC6vy5Zim1cd9gKSIg3NYqIiIiIiEh6M3XJMIB58+YxZMgQDh48SHBwMIMHD6ZPnz72+w3DYNiwYUybNo3Lly9Tr149Jk2aRMmSJe1tLl68yIABA/jzzz9xcnKiY8eOjB8/Hh8fH3ubHTt20L9/fzZt2kTu3Ll58cUXeeONNxJl+emnn3j77bc5cuQIJUqUYMyYMTzyyCMpeh5aMuwO4uNgTFGIjoTey6BANbMTiYiIiIiIPLCU1oCmF91ZhYruu/ihK+z9Ex5+Cxq+bnYaERERERGRB5Yp1umWbEJLh4mIiIiISDalolvSnq3oPr4Rbkaam0VERERERCQdqeiWtJezCAQUAyMewlebnUZERERERCTdqOiW9FH836XDDmvpMBERERERyT5UdEv60LhuERERERHJhlJddM+cOZP58+fbv3/99dfx9/enTp06HD161KHhJAspUg+cXODSEbgYZnYaERERERGRdJHqonvEiBF4enoCEBoaysSJExkzZgy5c+fm5ZdfdnhAySLcc0DB2tbtQ+piLiIiIiIi2UOqi+7jx49TvHhxAObOnUvHjh3p27cvI0eOZPVqTZIld1HsYevt4eXm5hAREREREUknqS66fXx8uHDhAgCLFi2iWbNmAHh4eHDjxg3HppOsxTaZWvgqiI81N4uIiIiIiEg6cEntA5o1a0bv3r2pUqUKBw4c4JFHHgFg9+7dFClSxNH5JCvJVwm8csH1C3BiExSuY3YiERERERGRNJXqK90TJ06kTp06nDt3jl9++YVcuXIBsGXLFp566imHB5QsxMkJitq6mGsWcxERERERyfoshmEYKW0cFxfHiBEj6NmzJwUKFEjLXJlOVFQUfn5+REZG4uvra3acjGvbbPj9BQiqCn01tltERERERDKnlNaAqbrS7eLiwpgxY4iLi3vggJJN2SZTO7UNrl80N4uIiIiIiEgaS3X38iZNmrBy5cq0yCLZgW8QBJYFDAjTlW4REREREcnaUj2RWqtWrXjzzTfZuXMn1apVw9vbO9H9jz76qMPCSRZVrDFE7LGO6y7f0ew0IiIiIiIiaSZVY7oBnJzufHHcYrEQHx//wKEyI43pToVDS+HbDpAjCAbvAYvF7EQiIiIiIiKpkiZjugESEhLu+JVdC25JpcJ1wMUDrpyCc/vNTiMiIiIiIpJmUl103+rmzZuOyiHZiavnf2t0a+kwERERERHJwlJddMfHx/P+++/z0EMP4ePjQ1hYGADvvPMOX331lcMDShZVrLH19vBSc3OIiIiIiIikoVQX3R9++CEzZsxgzJgxuLm52feXL1+eL7/80qHhJAuzFd1H1kKsekyIiIiIiEjWlOqie9asWUybNo0uXbrg7Oxs31+pUiX27dvn0HCShQWWBZ98EHcDjoWanUZERERERCRNpLroPnnyJMWLF0+yPyEhgdjYWIeEkmzAYrmli7nGdYuIiIiISNaU6qK7bNmyrF69Osn+n3/+mSpVqjgklGQTxZtYb1V0i4iIiIhIFuWS2gcMHTqUZ555hpMnT5KQkMCvv/7K/v37mTVrFvPmzUuLjJJVFW0EWODsLrhyBnLkMzuRiIiIiIiIQ6X6Sne7du34888/WbJkCd7e3gwdOpS9e/fy559/0qxZs7TIKFmVd27IX8m6HbbC1CgiIiIiIiJpIdVXugHq16/P4sWLHZ1FsqNijeH0dji0FCp1NjuNiIiIiIiIQ6X6Svfx48c5ceKE/fuNGzcyaNAgpk2b5tBgkk3YJlMLWw4JCeZmERERERERcbBUF91PP/00y5cvB+DMmTM0bdqUjRs38tZbbzF8+HCHB5QsrmAtcPWGa+fg7E6z04iIiIiIiDhUqovuXbt2UbNmTQB+/PFHKlSowLp165g9ezYzZsxwdD7J6lzcILi+dVuzmIuIiIiISBaT6qI7NjYWd3d3AJYsWcKjjz4KQOnSpTl9+rRj00n2UExLh4mIiIiISNaU6qK7XLlyTJkyhdWrV7N48WJatmwJwKlTp8iVK5fDA0o2YBvXfWw9xFwzN4uIiIiIiIgDpbroHj16NFOnTqVRo0Y89dRTVKpkXfLpjz/+sHc7F0mVXMXArxDEx8CRtWanERERERERcZhULxnWqFEjzp8/T1RUFDlz5rTv79u3L15eXg4NJ9mExQLFG8OWGXB4KZRsbnYiERERERERh0j1lW4AZ2fnRAU3QJEiRQgMDHRIKMmGbF3MNa5bRERERESykBRf6c6ZMycWiyXJfj8/P0qWLMmrr75Ks2bNHBpOspHghmBxgvMH4PJx8C9odiIREREREZEHluKie9y4ccnuv3z5Mlu2bKFNmzb8/PPPtG3b1lHZJDvx9IeHqsOJjdar3dWeMTuRiIiIiIjIA0tx0f3MM3cvgipXrszIkSNVdMv9K95ERbeIiIiIiGQp9zWmOzlt2rRh3759jjqcZEe2cd1hKyAh3tQoIiIiIiIijuCwojs6Oho3NzdHHU6yo6Cq4O4HNy/DqW1mpxEREREREXlgDiu6v/rqKypXruyow0l25OwCRRtatw8tNTeLiIiIiIiIA6R4TPfgwYOT3R8ZGcnWrVs5cOAAq1atclgwyaaKNYa9f1jHdTd6w+w0IiIiIiIiDyTFRfe2bcl39/X19aVZs2b8+uuvBAcHOyyYZFO2cd0nNsHNSPDwMzePiIiIiIjIA0hx0b18+fK0zCFilbMw5CoOFw5B+Cooo9nwRUREREQk83LYmG4RhynWxHp7eJm5OURERERERB6Qim7JeGxdzPfOg5hr5mYRERERERF5ACq6JeMp9jD4F4ZrEbBugtlpRERERERE7puKbsl4XNyh6TDr9trP4MoZc/OIiIiIiIjcpxQV3VWrVuXSpUsADB8+nOvXr6dpKBHKdYCHqkPsNVj+odlpRERERERE7kuKiu69e/dy7Zp1bO17773H1atX0zSUCBYLtBhh3d72LZzdbW4eERERERGR+5CiJcMqV67Ms88+S7169TAMg48//hgfH59k2w4dOtShASUbK1QLyraDPb/Doneg269mJxIREREREUkVi2EYxr0a7d+/n2HDhnH48GG2bt1K2bJlcXFJWq9bLBa2bt2aJkEzuqioKPz8/IiMjMTX19fsOFnHxTCYUBMSYqHrL1C8qdmJREREREREUlwDpqjovpWTkxNnzpwhMDDwgUNmJSq609CC/8H6iRBYFp5bA07OZicSEREREZFsLqU1YKpnL09ISFDBLemrwavg4Q8Re6zju0VERERERDKJ+1oy7PDhw7z44os0bdqUpk2bMnDgQA4fPuzobCJWXgHQ8HXr9vIPIVoT+YmIiIiISOaQ6qJ74cKFlC1blo0bN1KxYkUqVqzIhg0bKFeuHIsXL06LjCJQow/kDIarZ2HdeLPTiIiIiIiIpEiqx3RXqVKFFi1aMGrUqET733zzTRYtWqSJ1DSmO+3sngs/PQMunjBwK/gGmZ1IRERERESyqTQb071371569eqVZH/Pnj3Zs2dPag8nknJl20HBWhB3A5Z9aHYaERERERGRe0p10Z0nTx62b9+eZP/27ds1wZqkLYsFmv9bbG+fDWd2mptHRERERETkHpIutn0Pffr0oW/fvoSFhVGnTh0A1q5dy+jRoxk8eLDDA4okUrAGlOsAu3+FRW9Dt7nWYlxERERERCQDSvWYbsMwGDduHGPHjuXUqVMABAUF8dprrzFw4EAs2bQA0pjudHTpCEyoAfEx8PRPULK52YlERERERCSbSWkNmOqi+1ZXrlwBIEeOHPd7iCxDRXc6W/Q2rPsc8pSG59aCc6o7bYiIiIiIiNy3NJtI7VY5cuRQwS3mqP8qeOaEc/tg2yyz04iIiIiIiCTrgYpuEdN4+kPDN63by0dA9BVT44iIiIiIiCRHRbdkXtV7QkAxuHYO1owzO42IiIiIiEgSKrol83Jxg2bvWbdDJ0DkSXPziIiIiIiI3CZVRXdsbCxNmjTh4MGDaZVHJHVKt4FCdSDuJix73+w0IiIiIiIiiaSq6HZ1dWXHjh1plUUk9SwWaPGBdfuf7+HUdlPjiIiIiIiI3CrV3cu7du3KV199lRZZRO7PQ9WgwuOAYV1K7P5XwRMREREREXGoVC9uHBcXx9dff82SJUuoVq0a3t7eie7/5JNPHBZOJMWaDIU9f8CR1XBgIZRqaXYiERERERGR1F/p3rVrF1WrViVHjhwcOHCAbdu22b+2b99+30FGjRqFxWJh0KBB9n03b96kf//+5MqVCx8fHzp27MjZs2cTPe7YsWO0bt0aLy8vAgMDee2114iLi0vUZsWKFVStWhV3d3eKFy/OjBkzkpx/4sSJFClSBA8PD2rVqsXGjRvv+7mICfwLQe3nrduL34H4WHPziIiIiIiIcB9XupcvX+7wEJs2bWLq1KlUrFgx0f6XX36Z+fPn89NPP+Hn58eAAQPo0KEDa9euBSA+Pp7WrVuTL18+1q1bx+nTp+nevTuurq6MGDECgPDwcFq3bs1zzz3H7NmzWbp0Kb179yZ//vy0aNECgB9++IHBgwczZcoUatWqxbhx42jRogX79+8nMDDQ4c9X0kj9wbDtGzh/ALbMgJp9zE4kIiIiIiLZnMUw7m8A7KFDhzh8+DANGjTA09MTwzCwWCypPs7Vq1epWrUqkyZN4oMPPqBy5cqMGzeOyMhI8uTJw5w5c+jUqRMA+/bto0yZMoSGhlK7dm3+/vtv2rRpw6lTp8ibNy8AU6ZM4Y033uDcuXO4ubnxxhtvMH/+fHbt2mU/Z+fOnbl8+TILFiwAoFatWtSoUYMJEyYAkJCQQMGCBXnxxRd58803U/Q8oqKi8PPzIzIyEl9f31S/DuIgG7+Av14Fr1wwcBt4+JmdSEREREREsqCU1oCp7l5+4cIFmjRpQsmSJXnkkUc4ffo0AL169eKVV15JddD+/fvTunVrmjZtmmj/li1biI2NTbS/dOnSFCpUiNDQUABCQ0OpUKGCveAGaNGiBVFRUezevdve5vZjt2jRwn6MmJgYtmzZkqiNk5MTTZs2tbeRTKRaD8hVAq5fgDWfmp1GRERERLK6+Dg4f8jsFJKBpbrofvnll3F1deXYsWN4eXnZ9z/55JP2K8cp9f3337N161ZGjhyZ5L4zZ87g5uaGv79/ov158+blzJkz9ja3Fty2+2333a1NVFQUN27c4Pz588THxyfbxnaM5ERHRxMVFZXoSzIAZ1doNty6HToJLh83N4+IiIiIZG0rRsKEarDrF7OTSAaV6qJ70aJFjB49mgIFCiTaX6JECY4ePZri4xw/fpyXXnqJ2bNn4+HhkdoYphs5ciR+fn72r4IFC5odSWxKtYIi9SE+GpYONzuNiIiIiGRlu3623u5U0S3JS3XRfe3atURXuG0uXryIu7t7io+zZcsWIiIiqFq1Ki4uLri4uLBy5UrGjx+Pi4sLefPmJSYmhsuXLyd63NmzZ8mXLx8A+fLlSzKbue37e7Xx9fXF09OT3Llz4+zsnGwb2zGSM2TIECIjI+1fx4/rimqGYbFA8/et2zt/hJNbzc0jIiIiIlnTxXC4dMS6Hb5KK+hIslJddNevX59Zs2bZv7dYLCQkJDBmzBgefvjhFB+nSZMm7Ny5k+3bt9u/qlevTpcuXezbrq6uLF261P6Y/fv3c+zYMUJCQgAICQlh586dRERE2NssXrwYX19fypYta29z6zFsbWzHcHNzo1q1aonaJCQksHTpUnub5Li7u+Pr65voSzKQoCpQsbN1e9HbcH/zBYqIiIiI3FnYLSs7xVyBE5vNyyIZVqqXDBszZgxNmjRh8+bNxMTE8Prrr7N7924uXrxoX8orJXLkyEH58uUT7fP29iZXrlz2/b169WLw4MEEBATg6+vLiy++SEhICLVr1wagefPmlC1blm7dujFmzBjOnDnD22+/Tf/+/e1X3Z977jkmTJjA66+/Ts+ePVm2bBk//vgj8+fPt5938ODBPPPMM1SvXp2aNWsybtw4rl27xrPPPpval0cykibvwJ65cHQt7P8LSrc2O5GIiIiIZCWH/y26LU5gJMDhZVD4zhfuJHtK9ZXu8uXLc+DAAerVq0e7du24du0aHTp0YNu2bRQrVsyh4T799FPatGlDx44dadCgAfny5ePXX3+13+/s7My8efNwdnYmJCSErl270r17d4YP/28cb3BwMPPnz2fx4sVUqlSJsWPH8uWXX9rX6AbrJHAff/wxQ4cOpXLlymzfvp0FCxYkmVxNMhm/AhDS37q9eKi6+4iIiIiI4yTEW7uUA1TpZr09vMy8PJJh3fc63ZKY1unOoKKvwPgqcO0ctPoIavU1O5GIiIiIZAUntsCXjcHdD55bDZ9VtF7xfu0weAWYnU7SQZqt0w1w6dIlPv74Y3r16kWvXr0YO3YsFy9evO+wImnGPQc0GmLdXjESblw2NY6IiIiIZBFh/17VDq4POQtD7lLWLua2q98i/0p10b1q1SqKFCnC+PHjuXTpEpcuXWL8+PEEBwezapXeYJIBVX3G+kvwxkVYPdbsNCIiIiKSFRxeYb0t9u9k0sUa/7tfXcwlsVQX3f379+fJJ58kPDycX3/9lV9//ZWwsDA6d+5M//790yKjyINxdvlvCbENU+BSyteTFxERERFJIvoqHN9g3S56e9G9XCvnSCKpLroPHTrEK6+8grOzs32fs7MzgwcP5tChQw4NJ+IwJZpDcEOIj4Gl75mdRkREREQys6NrISEW/AtBQFHrviJ1wckVIo/BhcPm5pMMJdVFd9WqVdm7d2+S/Xv37qVSpUoOCSXicBYLNP8AsMCuX7SGooiIiIjcP9tSYUUftv4/E8DNGwrV/vd+dTGX/6Rone4dO3bYtwcOHMhLL73EoUOH7Otlr1+/nokTJzJq1Ki0SSniCPkrQuWnYftsWPgW9Fzw3y9JEREREZGUCvu36LaN57Yp1hiOrLYW3Vo1R/6VoiXDnJycsFgs3KupxWIhPj7eYeEyEy0ZlklEnYLxVSHuBjzxDZR91OxEIiIiIpKZRJ2CT8oAFng9LPHyYKe2w7SG4OYDr4eDi5tZKSUdpLQGTNGV7vDwcIcFEzGVbxDUeRFWjYElw6BkS/0yFBEREZGUC1thvQ2qknQ97nwVwSsXXL8AJzZZx3lLtpeiortw4cJpnUMk/dQdCFtmwMUw2PwV1H7e7EQiIiIiklkcvkPXcgAnJ+s4710/W7uYq+gWUlh03+7UqVOsWbOGiIgIEhISEt03cOBAhwQTSTPuOaDxW/DnS7ByNFTqDJ45zU4lIiIiIhmdYfx3pbtoMkU3WMd124ruJu+kWzTJuFJddM+YMYN+/frh5uZGrly5sNwyEZXFYlHRLZlDlW6wYSpE7IFVH0OLD81OJCIiIiIZ3dndcC0CXL2gYM3k29iugJ/aBtcvJu2CLtlOqpcMe+eddxg6dCiRkZEcOXKE8PBw+1dYWFhaZBRxPCdnaPa+dXvjNLioeQtERERE5B5ss5YXrgsu7sm38Q2CPGWAW66KS7aW6qL7+vXrdO7cGSenVD9UJGMp0dTa/Sc+Bpa8a3YaEREREcno7jae+1bFGv/bXut1y30U3b169eKnn35Kiywi6a/Z+4AF9syF4xvNTiMiIiIiGVXsTTi6zrp9p/HcNvaie7l1HLhka6ke0z1y5EjatGnDggULqFChAq6uronu/+STTxwWTiTN5SsPVbrCtm9g4f+g12K4ZZ4CEREREREAjm+AuBvgkw8Cy9y9beE64OwOUSfg/EHIUzJ9MkqGdF9F98KFCylVqhRAkonURDKdh9+CXb9Y11LcMxfKPWZ2IhERERHJaGzjuYs2uvdFGjcvKBxiHdN9eJmK7mwu1UX32LFj+frrr+nRo0caxBExgW9+qPsSrBgJi4dBqUfuPDGGiIiIiGRPKR3PbVOs8X9Fd+3n0iyWZHypHtPt7u5O3bpa5F2ymDovWrsKXT4KG78wO42IiIiIZCTXLsDpf6zbRRul7DG2cd1HVkNcdJrEkswh1UX3Sy+9xOeff54WWUTM4+YNjd+2bq8aY11TUUREREQEIHwFYEBgWciRL2WPCSwH3oEQe10T9mZzqe5evnHjRpYtW8a8efMoV65ckonUfv31V4eFE0lXlZ+GDVPg7C5Y9RG0HGl2IhERERHJCGxdy+81a/mtnJysXdF3/GDtYh5cP22ySYaX6ivd/v7+dOjQgYYNG5I7d278/PwSfYlkWk7O0Px96/bGL+DCYXPziIiIiIj5DMM6NhtSPp7bRut1C/dxpXv69OlpkUMkYyjWGIo3g0OLYcm78OQ3ZicSERERETNdOAyRx8HZzboUWGrYxn+f/geunQfv3A6PJxlfqq90i2R5zd8HixPs/QOOhpqdRkRERETMZFsqrGAt6zxAqZEjH+QtD9xytVyynVRf6Q4ODr7retxhYWEPFEjEdIFloGp32DIDFr0FvZZYx+SIiIiISPaT2qXCblfsYeucQYeXQ4VOjsslmUaqi+5BgwYl+j42NpZt27axYMECXnvtNUflEjFXo//Bzp/h5BbY/at+QYqIiIhkR/Fx1iW/IHWTqN2qWGNY97l1XLdhwF0uYErWlOqi+6WXXkp2/8SJE9m8efMDBxLJEHLkhbqDYPkHsOQ9KN0GXD3MTiUiIiIi6enkFoiOAs+ckL/S/R2jUAi4eMCVU3BuPwSWdmxGyfAc1me2VatW/PLLL446nIj5QvpDjiCIPAYbp5qdRkRERETSm208d3BD60o398PV878J2DSLebbksKL7559/JiAgwFGHEzGfmxc0ece6vWosXLtgbh4RERERSV8POp7bRkuHZWup7l5epUqVRBOpGYbBmTNnOHfuHJMmTXJoOBHTVewM6yfBmZ2wcjQ8MsbsRCIiIiKSHm5GwYlN1u37Hc9tYyu6j6yBuGhwcX+w40mmkuqiu3379om+d3JyIk+ePDRq1IjSpTU+QbIYJydo/iHMehQ2fwU1+0Lu4manEhEREZG0dmQNGPEQUBRyFn6wYwWWBZ+8cPUsHFsPRRs6JqNkCqkuuocNG5YWOUQyrqINoWRLOLAAlgyDzrPNTiQiIiIiac02nvtBr3KDdcbyYo3hn++sXcxVdGcrWnxYJCWaDQeLM+ybZ/2rp4iIiIhkbY4az22jcd3ZVoqLbicnJ5ydne/65eKS6gvnIplDnlJQrYd1e9HbkJBgahwRERERSUOXj8OFg2BxgiL1HXPMoo2st2d2wNVzjjmmZAoprpJ/++23O94XGhrK+PHjSVAhIllZoyGw40c4tQ12/QwVnzA7kYiIiIikBVvX8oeqgae/Y47pEwj5Klgn6A1bARUfd8xxJcNLcdHdrl27JPv279/Pm2++yZ9//kmXLl0YPny4Q8OJZCg+eaD+y7B0OCx5D8q0ta67KCIiIiJZy2EHjue+VbHG1qL78DIV3dnIfY3pPnXqFH369KFChQrExcWxfft2Zs6cSeHCDzirn0hGV/sF8C0AUSdg/WSz04iIiIiIoyUkQPhK67ajxnPb3Dqu2zAce2zJsFJVdEdGRvLGG29QvHhxdu/ezdKlS/nzzz8pX758WuUTyVhcPaHJUOv26k80HkdEREQkqzmzA65fADcfKFDDsccuWBtcPOHqGYjY69hjS4aV4qJ7zJgxFC1alHnz5vHdd9+xbt066td30KQCIplJhcchf2WIuQIrR5mdRkREREQcyTaeu0g9cHZ17LFdPaBIXeu2ZjHPNiyGkbJ+DU5OTnh6etK0aVOcnZ3v2O7XX391WLjMJCoqCj8/PyIjI/H19TU7jqS1I2tgRmvrMmIvhFpnNxcRERGRzG/mo9bu5a3GQK1+jj9+6ERY+D8o1gS6Zc/aKatIaQ2Y4onUunfvjsVicUg4kUyvSD0o1Rr2z4fFw+Dp781OJCIiIiIPKvYGHFtv3Xb0JGo2tnHdR9dC7E3r1W/J0lJcdM+YMSMNY4hkQs3eg4ML4cDfEL4KghuYnUhEREREHsTRdRAfDb4PQe4SaXOOPKUhR364chqOhTp+sjbJcO5r9nIRwfqLuHpP6/aS9zQDpYiIiEhmF3bLUmFp1cvXYrllFvOlaXMOyVBUdIs8iAavgbM7nNwMxzeYnUZEREREHsThFdbbtL76bC+6l6fteSRDUNEt8iB8AqHSk9btdZ+bm0VERERE7t/VCDi707od3DBtz1W0kfX27C64ciZtzyWmU9Et8qBCBlhv982HC4fNzSIiIiIi9ydspfU2XwXwyZO25/LODfkr/XveFWl7LjGdim6RB5WnFJRoDhiwfrLZaURERETkftw6njs92LuYa73urE5Ft4gj2K52b58N1y+am0VEREREUscw/htfnV6zid86rjshIX3OKaZQ0S3iCMENIG8FiL0OW6abnUZEREREUuP8AbhyyjpBbqGQ9DlnwVrg6gXXIiBid/qcU0yholvEESwWqPPv1e4N0yAuxtw8IiIiIpJyti7ehUPA1TN9zuniDkXqJT6/ZEkqukUcpVwHyJEfrp6BXT+bnUZEREREUupwOo/nttG47mxBRbeIo7i4Qa1+1u11E6xjg0REREQkY4uLgSNrrNvpNZ7bxlZ0Hw2FmOvpe25JNyq6RRypWg9w9baOy9HyDyIiIiIZ34lNEHsNvHJb5+hJT7lLgu9DEB8Nx9al77kl3ajoFnEkz5xQpat1O3SCuVlERERE5N7sS4U1BKd0Lo8slv+urtu6uEuWo6JbxNFqPw8WJzi0BCL2mp1GRERERO7GrPHcNhrXneWp6BZxtIBgKN3Guq2r3SIiIiIZ141LcGqrdTu9x3PbBDcCLBCxB6JOm5NB0pSKbpG0UOdF6+2OH+HKWXOziIiIiEjywleDkWAdW+1XwJwM3rkgqLJ1O0xdzO02fgGLh2aJP0So6BZJCwVrQoEaEB8Dm740O42IiIiIJCfM5K7lNupinlhcDKweC2s/yxKviYpukbQSMsB6u+lLLQEhIiIikhHZxnOb1bXcxl50L4eEBHOzZAS7foYrpyFHfqjwuNlpHpiKbpG0UqYt+BeGGxfhn+/MTiMiIiIit7p0BC6Fg5MLFKlnbpYCNa3Lzl4/D2d3mpvFbIYB6z63btd6DlzczM3jACq6RdKKkzPUfsG6vX6S/mopIiIikpHYrnIXqAHuOczN4uIGwfWt21mgO/UDObTEOqmcmw9U62F2GodQ0S2Slqp0BXc/uHAIDiwwO42IiIiI2GSU8dw2GtdttW689bZaD/D0NzOJw6joFklL7j5QvYd1O3SiqVFERERE5F8J8RC20rpt9nhuG1vRfWw9xFwzN4tZTm2H8FVgcbZ2Lc8iVHSLpLWa/axjhY6ugVPbzE4jIiIiIqe3w83L1h6JQVXNTmOVqzj4FbKufnN0ndlpzGG7yl2+I/gXNDeLA6noFklrfg9Zf3EArJtgbhYRERER+a8Ld3B9cHYxN4uNxfLfVffs2MX80lHYPde6XedFU6M4mopukfRgWz5s929w+bi5WURERESyu8MrrLdFG5mZIqnsPK57/WQw4q0/k/wVzU7jUCq6RdJD/ooQ3MD6i2TDFLPTiIiIiGRf0Vfh+Abrtq3IzSiCG4DFCc7tg8iTZqdJPzcuwdZZ1u06A83NkgZUdIukF9vV7q2z4GaUuVlEREREsquj6yAh1jp+OqCo2WkS8wr4b4y5bXb17GDz1xB7DfKWz3h/CHEAFd0i6aV4M8hdEqKjYNs3ZqcRERERyZ5sxWyxRtZx1BlNdutiHhcNG6Zat+u8mDF/Jg9IRbdIenFygpD+1u31UyA+ztw8IiIiItnR4Qy2Pvft7EX3ckhIMDdLetjxI1w9C763TD6cxajoFklPFZ8Er9wQeQz2/m52GhEREZHsJeo0nNsLWDLeJGo2BaqDWw64cRHO/GN2mrSVkADrPrdu134enF3NzZNGTC26R44cSY0aNciRIweBgYG0b9+e/fv3J2pz8+ZN+vfvT65cufDx8aFjx46cPXs2UZtjx47RunVrvLy8CAwM5LXXXiMuLvFVxBUrVlC1alXc3d0pXrw4M2bMSJJn4sSJFClSBA8PD2rVqsXGjRsd/pwlm3P1hBq9rdvrJoBhmJtHREREJDsJW2G9DapsHT+dETm7WidUg6zfxfzgIji/H9x9oeozZqdJM6YW3StXrqR///6sX7+exYsXExsbS/Pmzbl27Zq9zcsvv8yff/7JTz/9xMqVKzl16hQdOnSw3x8fH0/r1q2JiYlh3bp1zJw5kxkzZjB06FB7m/DwcFq3bs3DDz/M9u3bGTRoEL1792bhwoX2Nj/88AODBw9m2LBhbN26lUqVKtGiRQsiIiLS58WQ7KNGb3B2h1Nb4dh6s9OIiIiIZB9hGbxruY19ve4sPpma7Sp3tR7g4WtqlLRkMYyMc6nt3LlzBAYGsnLlSho0aEBkZCR58uRhzpw5dOrUCYB9+/ZRpkwZQkNDqV27Nn///Tdt2rTh1KlT5M2bF4ApU6bwxhtvcO7cOdzc3HjjjTeYP38+u3btsp+rc+fOXL58mQULFgBQq1YtatSowYQJEwBISEigYMGCvPjii7z55pv3zB4VFYWfnx+RkZH4+mbdN4w4yB8DYetMKN0GOs82O42IiIhI1mcYMLaUdfzwM3/+dzU5I7pwGD6vCk6u8MYRcPcxO5HjndwCXzQGJxcYtBN8g8xOlGoprQEz1JjuyMhIAAICrF09tmzZQmxsLE2bNrW3KV26NIUKFSI0NBSA0NBQKlSoYC+4AVq0aEFUVBS7d++2t7n1GLY2tmPExMSwZcuWRG2cnJxo2rSpvY2IQ9mWD9s33/pLVURERETSVsQea8Ht6gUFa5md5u4CioJ/YevSZkfXmp0mbawdb72t8HimLLhTI8MU3QkJCQwaNIi6detSvnx5AM6cOYObmxv+/v6J2ubNm5czZ87Y29xacNvut913tzZRUVHcuHGD8+fPEx8fn2wb2zFuFx0dTVRUVKIvyX6OXrhGv28289u2E6l7YJ6SUKIFYMD6SWmSTURERERuYeuqXbgOuLibm+VeLJasvXTYxXDY+4d1u86L5mZJBxmm6O7fvz+7du3i+++/NztKiowcORI/Pz/7V8GCBc2OJOnsyPlrdJ62noW7z/L6zzsIO3c1dQewLR+2bTZcv+j4gCIiIiLyn8wyntsmKxfd6yeBkQDFm0LecmanSXMZougeMGAA8+bNY/ny5RQoUMC+P1++fMTExHD58uVE7c+ePUu+fPnsbW6fzdz2/b3a+Pr64unpSe7cuXF2dk62je0YtxsyZAiRkZH2r+PHj6f+iUumFXbuKk9OC+V05E0sFoiNN3jvzz2kaoqE4AaQrwLE3YDNX6ddWBEREZHsLi4ajvzbTbtYJim6gxuAxQnOH4DLWajWuH4Rtn1r3c4GV7nB5KLbMAwGDBjAb7/9xrJlywgODk50f7Vq1XB1dWXp0qX2ffv37+fYsWOEhIQAEBISws6dOxPNMr548WJ8fX0pW7asvc2tx7C1sR3Dzc2NatWqJWqTkJDA0qVL7W1u5+7ujq+vb6IvyR4ORVyl87T1nI2KpmReH37sF4KbsxMrD5xj8Z6z9z6AjcUCIf/+otk4zfqPgYiIiIg43vEN1gsdPnkhsKzZaVLG0x8eqm7dDstCs5hv+gpir0O+ihDc0Ow06cLUort///58++23zJkzhxw5cnDmzBnOnDnDjRs3APDz86NXr14MHjyY5cuXs2XLFp599llCQkKoXbs2AM2bN6ds2bJ069aNf/75h4ULF/L222/Tv39/3N2tYzWee+45wsLCeP3119m3bx+TJk3ixx9/5OWXX7ZnGTx4MF988QUzZ85k7969PP/881y7do1nn302/V8YybAOnr1C52nribgSTel8OZjTpzY1igTQu771D0bD5+3hZmx8yg9YvgPkCLJO6rHz5zRKLSIiIpLN2bpoF21kvfCRWWS1LuaxN2HjVOt2nYGZ62fxAEwtuidPnkxkZCSNGjUif/789q8ffvjB3ubTTz+lTZs2dOzYkQYNGpAvXz5+/fVX+/3Ozs7MmzcPZ2dnQkJC6Nq1K927d2f48OH2NsHBwcyfP5/FixdTqVIlxo4dy5dffkmLFi3sbZ588kk+/vhjhg4dSuXKldm+fTsLFixIMrmaZF/7z1zhqS/Wc/7qfwV3bh/rH3YGNC5Ofj8PTly6weQVqZiN3NkVavWzbodOtC5lISIiIiKOdTiTjee2sRXdYSsgIRUXdjKqf76Da+fAryCUa292mnSTodbpzsy0TnfWtu9MFE9/sYGL12Iom9+X2b1rkdPbLVGb+TtO03/OVtxcnFjyckMK5fJK2cFvXIJPykHsNej223+/XEVERETkwV2/CGOKAgYM3ge++c1OlHLxcTAmGKKjoM8yeKia2YnuX0ICTKwBFw5Bi5EQ8oLZiR5YplynWyQj2nMqiqemrefitRjKP+TLnD5JC26ARyrko27xXMTEJfD+/D0pP4FnTqjazbq9boKDUouIiIgIYL1KjAF5ymSughvA2cU6oRpk/i7mB/62Ftzufv/93zebUNEtche7Tkby9JfruXQ9lkoF/Jjdqzb+XkkLbgCLxcK7bcvh4mRh8Z6zLN8fkWy7ZNV+3jo75eGlcDYVBbuIiIiI3J1tErLMMmv57ezjujP5ZGprx1tva/QE9xzmZklnKrpF7mDniUi6fLmBy9djqVzQn1m9auHn5XrXx5TIm4Nn6xYB4L0/dhMdl8KxNzmLQJm21u3QifcfWkRERET+YxhweIV1O7ON57axFd3HN0D0FXOz3K/jG+H4enByhVrPmZ0m3anoFknGP8cv0+XL9UTeiKVKIX9m9aqJn+fdC26bgU1KEJjDnSMXrvPl6vCUnzRkgPV2549wJRVLj4mIiIhI8i6GQeQxa7FXpK7Zae5PQDDkDIaEODiyxuw092fdv1e5Kz4JOfKZm8UEKrpFbrPt2CW6frmBqJtxVCuck1k9a+LrkbKCGyCHhyv/e6QMAJ8vO8jJyzdS9sCCNaFATYiPgU1f3E90EREREbmVbRx0wVrg5m1ulgeRmZcOu3AY9s6zbtd50dwsJlHRLXKLLUcv0e2rjVyJjqNGkZzM7FmTHKkouG3aVQ6iZpEAbsYmMGL+3pQ/sM6/V7s3fQUx11N9XhERERG5RdgK621mHc9tk5mL7tCJgAElWkBgabPTmEJFt8i/Nh+5SPevNnA1Oo5awQHMeLYmPu4u93Usi8XCe+3K4WSB+TtPs+bg+ZQ9sHQb6/juGxfhnzn3dW4RERERwbrcVvgq63ZmL7qD64PF2Tr796WjZqdJuWvnYfts63Y2vcoNKrpFANgYfpHuX2/kWkw8IUVzMf3ZGnjfZ8FtUya/L91DigAw7I9dxMQl3PtBTs5Q+981C0MnWdczFBEREZHUO7XVur61hz/kr2x2mgfj4QcFali3wzLRLOYbv4C4mxBUBYrUMzuNaVR0S7a3PuwCPaZv5HpMPPWK5+brHjXwcnuwgtvm5WYlyeXtxuFz15ixLoWTqlXuYv3FevEwHFjgkBwiIiIi2Y5tia2iDa0XNjK7zNbFPOb6f/MU1RkIFou5eUykoluytXWHztsL7volcvPlM9XxdHPcL2U/T1feaGUdu/LZkoOcjbp57we5+0C1Z63boRMclkVEREQkW7FdEc6sS4XdzlZ0h62AhBQuS2umf+bA9QvgXwjKPGp2GlOp6JZsa83B8zw7YxM3YxNoWDIPX3Svjoer4/8K2qlqASoX9OdaTDwj/0rhpGq1+oGTCxxdCye3OjyTiIiISJYWfQVObLJuZ/bx3DZBVay9IW9GwqltZqe5u4R4WPfvxaOQAeDsmF6kmZWKbsmWVh04R6+Zm4iOS+DhUnmY2q1amhTcAE5OFt5vVx6LBeZuP8WGsAv3fpBvEJTvZN3W1W4RERGR1Dmyxrqudc5g6yS1WYGzCwQ3tG5n9C7m++bDpXDrePoqXc1OYzoV3ZLtrNgfQe9Zm4mOS6BpmUCmpGHBbVOhgB9P1SwEwLA/dhMXn4IJ0kL6W293z4XLx9MunIiIiEhWYytKs8pVbpvMMK7bMGDdeOt2jd6Ze310B1HRLdnKsn1n6TtrCzFxCTQvm5dJXarh7pI+E2u81rwU/l6u7DtzhW/Wp2Cph/wVIbgBGPGwYUraBxQRERHJKg5nsfHcNrY/IhzfCDejzM1yJ8c3WLv2O7tZh0yKim7JPpbsOUu/b7YQE59Ay3L5mNilKm4u6fcRyOntxmstSgHwyaIDnLsSfe8Hhfy7nuHWWRn3F6uIiIhIRhJ5Ai4cBIuT9QJGVpKzCAQUs16UObLa7DTJW/vvVe5KncEn0NwsGYSKbskWFu0+w/OztxAbb9C6Qn4+f7oKrs7p//bvXKMQ5R/y5Up0HGMW7Lv3A4o3hdylrGtMbp2V9gFFREREMjvbVe6gquDpb2qUNJGRu5ifPwj7/7Ju2y4eiYru7ORQxBUSEgyzY6S7BbtO88LsrcTGG7SpmJ/POlc2peAGcHayMLxdeQB+2nKCLUcv3f0BTk7/je3eMAXi49I4oYiIiEgmZ1sqLKuN57bJyEV36ATAgFKPQJ6SZqfJMFR0ZxNXbsbSfuI6Go9dwfS14Vy5GWt2pHQxf8dp+s/ZRlyCQbvKQYx7sjIuJhXcNlUL5eTxagUAGPbHLuLv9YeQik+CV26IPA575qZ9QBEREZHMKiHBuo41ZL3x3DZF6lmXlr0YBhfDzU7zn6sRsP0763YdXeW+lYrubGLfmStYLHDkwnXe+3MPISOX8d6fuzl64ZrZ0dLMn/+cYuD324hPMOhQ5SE+ecL8gtvmjValyeHhwq6TUXy38djdG7t6QM0+1u3QCdYZIUVEREQkqbM74foFcPWGAjXMTpM2PHyhQE3rtu2qfkawcRrER8ND1aFQiNlpMpSMUYFImqtRJID1Q5rwfrtyFM3jzdXoOKavPUKjj1fQa8Ym1hw8j5GFirnft5/kpX8L7k7VCvDR45VwdrKYHcsut487rzSzdrn5eNF+Ll2LufsDavQGFw84tQ2OhaZDQhEREZFMyDaeu0g9cHEzN0tasnUxP7TU3Bw2Mddg05fW7boDwZJx/t+dEajozka83V3oFlKEJS83ZGbPmjQqlQfDgKX7Iuj61QZajFvFnA3HuBETb3bUB/LbthO8/MN2Egx4onoBxnSsmKEKbpuutQtTOl8OLl+P5aNF++/e2Du3dQZIgHUT0j6ciIiISGaU1cdz29iK7vBVGWPOn22z4cYlyBkMpduYnSbDUdGdDTk5WWhYMg8znq3J0lca0j2kMF5uzhw4e5X//baTkFFLGfX3Pk5dvmF21FT7ecsJBv/4DwkGPFWzIKM6VMQpAxbcAC7OTvZJ1b7beIwdJy7f/QG1/51Qbf9fcOFw2oYTERERyWxib8DRf3sE2orSrCqoMnj4W1e4ObnF3Czxcf9OoIZ1AmAnZ3PzZEAqurO5Ynl8GN6uPKFDmvB26zIUyOnJ5euxTFl5mPpjltN/9lY2H7mYKbqe/7jpOK/9/A+GAV1qFeLD9hUybMFtUzM4gPaVgzAMGPr77rvPLp+nJJRoARgQOjHdMoqIiIhkCsdCrWOKcwRB7iw+c7aTMxRtZN02exbzfX/C5aPgGQCVu5ibJYNS0S0A+Hm60rt+UVa+9jBTu1WjdtEA4hMM5u88TacpoTw6YS2/bDlBdFzG7Hr+3cZjvP7LDgwDutUuzAfty2f4gtvmf4+UwdvNme3HL/PzlhN3b1xngPV2+xy4fjHtw4mIiIhkFodv6VqeHcYUZ4SlwwwD1o63btfsA25e5mXJwFR0SyLOThZalMvH931D+Pul+jxZvSBuLk7sPBnJKz/9Q91Ry/l08QEirtw0O6rdt+uPMuTXnQD0qFOE4e3KYclEv2gDfT0Y1NT619jRC/YRef0uy7kVqQ/5KkLcDdj8VTolFBEREckEbOO5s+pSYbezjVs/uRluXDYnw9G1cGqrdcLfmn3NyZAJqOiWOyqT35fRnSqyfkgTXmtRiry+7py/Gs1nSw9Sd9QyBv+wnZ0nIk3NOCv0CG/P3QVAz7rBDGtbNlMV3DY96haheKAPF67F8OmSA3duaLH8t+7hxi8gLjp9AoqIiIhkZFfPwRnrRRh7t+uszr8Q5CoBRoJ1QjUzrPvcelv5aevEv5IsFd1yTwHebvR/uDhr3mjM+KeqUKWQP7HxBr9uO0nbCWvoNHkd83ecJi4+IV1zzVgbztDfdwPQt0FR3mlTJlMW3ACuzk6892g5wPqHhD2nou7cuNxj1rFKV8/Czp/SKaGIiIhIBha+0nqbtwL45DE3S3oys4t5xD44sACwQMiA9D9/JqKiW1LM1dmJRysF8dsLdZnbvy7tKgfh4mRh89FL9J+zlfpjljNpxaF7rzntAF+tCefdP/cA8FzDYgxpVTrTFtw2dYvnpnWF/CQYMOyPXXeevM7ZFWr1s26HTrSOpRERERHJzmxFZ7FGpsZId/aie2n6/58w9N+r3KVbQ65i6XvuTEZFt9yXygX9+axzFda+2ZiBjYuTy9uN05E3GbNgPyGjljLk1x3sP3MlTc79xaow3p9nLbj7P1yMN1qWyvQFt81brcvg6erMpiOXmLv95J0bVusBbj4Qscf8GStFREREzGQY/02ill3Gc9sUqQdOrnD5GFwMS7/zXjkDO360btcZmH7nzaRUdMsDyevrweDmpVj7ZmM+6lSRsvl9uRmbwHcbj9Ni3Cq6fLmeJXvO3n0prFSYsvIwH/61F4CBjYvzavOsU3ADBPl7MqBxcQBG/LWPKzfvMKmapz9U6Wbdtq2LKCIiIpIdnT8AV06BszsUrmN2mvTl7gMFa1m30/NCzIapEB9jPXehWul33kxKRbc4hIerM49XL8j8gfX4oW9tWpbLh5MF1h66QO9Zm3l47Aq+XhN+5yIyBSYuP8Sov/cBMKhpCQZnsYLbpnf9YIJze3PuSjTjlx68c8Paz4HFyfoL9uzu9Asomdby/RG88uM/HD531ewoIiIijmO7yl2oNrh6mpvFDMVtXcyXp8/5oq/8t4qOrnKniIpucSiLxUKtormY0q0aK197mL4NiuLr4cLRC9cZPm8PtUcs5d0/dhN+/lqqjjt+6UE+WrgfgMHNStqX2MqK3F2cGda2LADT1x7h4Nk7dNPPWQTKPGrdDp2YPuEk0/p6TTg9Z2zil60neHxKKDtOXDY7koiIiGOE3bI+d3ZkG9cdvgri7/8CV4pt+xZuRkJAMSjVKu3PlwWo6JY0UzDAi/89Uob1/2vCB+3LUzzQh2sx8cxYd4TGY1fQc8YmVh88d+cJw/41bskBPllsXUbrtRalGNikRHrEN1WjUoE0K5uXuASDYX/svvNrZJspcseP1rE1IreJTzB494/dDJ+3B8OA3D5uXLwWw1PT1rPu0Hmz44mIiDyY+Fg4ssa6nd3Gc9vkqwSeARBzBU5sTttzxcdB6CTrdp0B4OSctufLIlR0S5rzcnOha+3CLH65AbN61uThUnkwDFi2L4JuX22k+aermL3hKDdi4hM9zjAMPlm0n3FLrF2s32xVmv4PFzfjKZhiaJuyuLs4se7wBebvPJ18o4I1rGNpEmKt63aL3OJadBx9Z21mxrojALzdPJhVzwRSp2gA12Li6TF9Ewt23eG9JSIikhmc2AQxV8ErF+SraHYaczg5/XeVP63Hde+ZC5HHwCs3VHoqbc+VhajolnRjsVhoUDIP05+tyfJXG9GjThG83Zw5GHGVt37bRe2RSxn5915OXr6BYRh8vGg/45cdAuCtR8rwXMPstRRBwQAvnm9kfc4fzt/Ltei45BvarnZv/gpiUtdtX7Kus1E3eWJqKEv3ReDu4sTsVq703t0Nr68a8I3fZB4t40dMfAIvzN7KD5uOmR1XRETk/tjGMQc3tBaf2VV6rNdtGLBuvHW7Zt/sOX7+PmXjd6aYKTi3N+8+Wo7Q/zXhnTZlKRTgReSNWKauDKP+6GW0n7SOicsPA/B26zL0aVDU5MTmeK5hMQoGeHI68iYTlx9KvlHp1tbx3TcuwfY56ZpPMqY9p6JoP3Etu09FkdfLiRXVQ6m7ojNcsL6HnPf+zmfXX+e5ii4kGPDGLzuZsvKwyalFRETuQ3Yfz21j61p/aitcv5g25whfBaf/ARdPqNE7bc6RRanoFlP5erjSq14wy19txBfdq1OnWC4SDPjn+GUAhrUtS+/62bPgBuus8EPblAPgi9VhhCU367STM9R+wbq9fhIkxCdtI9nGiv0RPD5lHacjb9Io4BKrc48k//bPwIiH8h3h6R/BOw+Ws7t54/hzjKhyGYBRf+9j5F977znHgoiISIZx4zKc3GLdzq7juW38HoI8pcFIsBbHacF2lbtKV/DOlTbnyKJUdEuG4OxkoVnZvMzpU5sFg+rTu14w45+qwrN1g82OZrqmZQJpVCoPsfEG7/65J/miqHIX8PCDi2FwYEH6h5QMYfaGo/SauZnrMbG8G7ia6TGv4BbxD3j4Q8evoNPXULIF9F0B+StjuXGRp/e9yLcVtgMGU1eF8eYvO4mLTzD3iYiIiKTEkdXWIjNXCfAvaHYa86VlF/Oze+DQEutytSEvOP74WZyKbslwSufz5e02ZXm0UpDZUTIEi8XCsLblcHN2YtWBcyzaczZpI3cfqN7Tur1uQvoGFNMlJBiM+Gsvb/22i8CE8yzM9Sk9oiZjibtp/Qf4hVCo0Om/B/gVgJ4LoOKTYMRT7+AYlhX/GQ9LLD9sPk7/OVu5GaseEyIiksEdVtfyRIrdsl63o3uurfvcelumLQRk316o90tFt0gmEJzbmz4NrFf9h/+5J/mCqGY/cHKFY+v+62olWd6NmHhemL2VaasO085pDSu8h1Dy2hbreKtHPoauv4JvMn/AcvWEx6ZC8w/A4kTRE7+xPv+nPOQcycLdZ3l2+iau3EyHtT5FRETul208d3bvWm5TuA44u1lnF7/gwLlaok7Bzp+s23UGOu642YiKbpFMov/DxQny8+Dk5RtMXpHML1Lf/P9dzdTV7mzh3JVoOn+xnvW7DzLJ7XM+c5uEe/xVeKgaPLcGavYBi+XOB7BYoM6L0OVn8PDD/+J2lvm+S4h7OKFhF3j6iw1cuBqdfk9IREQkpS4dtQ6rszhDkXpmp8kY3LyhUG3rtiO7mG+YYl2etlAdKFDdccfNRlR0i2QSXm4uvN2mLACTVx7m2IXrSRuF9Lfe7vkdLmsZqKzs4NkrPDZpLTlPrmCxxxs84rQenFzg4beg5yLInYo17Ys3gT7LIU9p3G+cZbbzezzjuZadJyN5fGooJy/fSLsnIiIicj9sV7kL1AAPX3OzZCSOHtd9Mwo2T7du19VV7vulolskE2lVPh91i+ciJi6B4fP2JG2Qr4J1nUojHjZMTf+Aki7WHjpPl8nLeP7KBGa4jSEPlyF3Sei9BBq+Ds4uqT9ormLWx5dug1NCDO8ZExnjPYej56LoNHkdhyKuOPx5iIiI3DeN506ereg+shriYh78eFtnQXSU9f8ZJVo8+PGyKRXdIpmIxWLhvUfL4eJkYcnesyzfF5G0UZ0XrbdbZsLNyPQNKGnux83HGTf9W35MeI0uLkutO2u/AP1WQVCVBzu4ew544hto+CYAT8TP4yfvj7gReY7Hp4Sy/d+l/EREREyVEA9hK6zbGs+dWN4K4JUbYq7CiU0Pdqz4WFg/2bodMgCcVDreL71yIplM8cAc9KxnnVTt3T93J51UrXhT6zqNMVesf52ULMEwDD75excRc9/ie5d3KeJ0FsP3Iej+B7QcaZ0YzRGcnODhIfDkt+DqTdX4HSzwGkreG4d5+ov1rDl43jHnERERuV+nt8PNy+Dua53HRP7j5PTf1f8H7WK+61eIOgHegdYVT+S+qegWyYQGNilBYA53jl64zldrwhPfabH8N7Z7/RTrXyklU7sZG8/Imb/RPLQLA1x+x9liYFR8Esvz66Bow7Q5aZm21u7mOYuQL+Escz2G0TBuHT1nbOLvnafT5pwiIiIpYetaXqT+/Q2pyuocMa7bMP5bJqxWX3D1ePBc2ZiKbpFMyMfdhbdalwHg82UHk050VeEJ8M5j/evknt9NSCiOcvHqTX4Y/wavhPelvNMRol394YlZWDpMA0//tD153rLWCdaKPoyHEc1kt88YYPmeAXM2891GTdQnIiImsXUt13ju5Nm63J/aBtcv3t8xwpbD2Z3g6g3VezkuWzalolskk3q0UhA1iwRwMzaBD+ffNqmaqwfU6GPdDp1g/WulZDrHw/Zz7JPGPHPlC9wtsVwMaoT7wA1Qtl36hfAKsC4pFjIAgIEuc5ni8gkf/rqBSSsOYei9JSIi6SnmGhxbb93WeO7k+eaHwLKA8d8fKFJr7XjrbdVu1v8LyANR0S2SSVksFt5rVw5nJwt/7TyTdKxtjV7g4mH9K+fRdeaElPtjGIQtnkbOWQ2pnLCb63hwtuEoAvrMhRz50j+Pswu0+BDaT8FwdqeZ81Z+cxvGjwtXMOKvvSq8RUQk/RxdZ10z2q+gdeUNSd6DdDE/s9N6pdviBLWfd2yubEpFt0gmVia/L91qFwZg2B+7iIlL+O9O79xQ6SnrdugEE9LJfbl2nlPTOlF07Wv4cIO9LmW40WsleR9+3jpe30yVn8LS82/IEUQJp5P87vYOB9bO5bWfdxAXn3Dvx4t5DMM6v0PsTeusvyIimZVtPHfRRub/u5iR2SdTW576Ho+2sdxl20POIo5MlW1p5gGRTO7lZiWZt+MUh89dY8a6cPo2uOWvvrVfgC3TYf/f/2fvvMOayNoofpLQOyoCKtgBxYK6inXtXXftgooFXVfWXsGGa69r72sA27LYsHz2igWxAyoKWEGlSA+dkPv9ERgJJBALmQl7f8+Th2TuiMeY3Jlz71uAhFdAlXrsCaWUCXl5DlnHJ6NaXhJyiQBnK49Dr4mroaujxba0L1RvAUy8ARxxgXH0PXhprsPa4Ci4ZY7HthHNoaMpYFuh6vkUDHx8BEjEUkMrEcs+J3KOlfhZ9Lzi5xd9XXhesWMSSbHX+bJ/LymyKKJlANTvATToB9TrDugYsfbWUSgUylfzhvbnVgrrtoBAW1rfJyESMLNR7s+lfgCeHZc+L2xDS/luqOmmUNQcY11NuPeyw9xjodhyJRK/OlSHuVFBhUkzG8CmFxBxAQjaCfTbyK5YinxyRMg/Px+C4IPQA/BSYoXbjVfCdfAv4PM5uIpvaA6MOQOcmwPB4wNYoOmLU6/e4zfhPOwc2x6GOppsKyx/8rKBsJPA/b+Bjw/ZVvN15KYDz09IHwIt6W6RXT/Atg9gYMa2OgqFQlGMKBaIDwPAA2p3YlkMx9HSA2q2keZ0v76mvOkO2iVdrK3VAajevFwl/pfgEZqM90NIS0uDsbExUlNTYWREdw0oqkUiIRi8OxBPolLwq0M1bHFq9mXw7S1gfz9AQxeYFUaLYXCN94GQnJgEfup7SAgP+/L7QL/3nxjZTsmLI5sQAjzYB8l5D/CJGM8ktfBXJU+sn9APVQy02VZXPiS/Bx56AU8OApmJ0mN8Talx1TYA+BoFD4H0J09Q8ljxn6WeU/BcmXNkflcpvychAnhxBnj5PyDx1Zd/G48PWLWW7oDb9aUhhRWFvCxpfubHx9KIjNhQwKga0GyUdLFFo4J+VykVk5B/Af/fAcumwO832VbDfe5sAS57AvV7AiOPlH1+diqw0R7IFQEjjgI2PcpfowLi07JxKSwOV1/EYatzM84u6CvrAanp/kFQ001hm6cfUvHLjtsgBPh3Ymu0rlNZOkAIsLcjEBMCdF4EdJzLrlCKFHEOcH0lyJ2t4IHgA6mChWQyxo4chc62VdlW93W8uw3xvy7QyE5CAjHCMj0PzJs4DjVM9dhW9mOQSIA314D7+6RRIyi4bBrVAH4aBzQfDRio2f8ZIJ0bPocDL88AL/4HxATLjps3LjDg/QBze5o7qQ5I8qWLKh8ffXnEPZfuWslD1xRo4iStTmxur1qtFMq3cOJ3IPRfoP1MoNufbKvhPrFPgd3tAU09wP1d2YtstzcDV5YAZnbAH0Eqn/fffE7HpbA4XHweiydRKczx7SOaoV+TairVoizUdKsYaropXGCh/1McvhcFW3NDnJ3WHhqCglqJoUeBExMA/arAjKfSlmJfCyFAfq70IS74mZ8jLc4kzvky9qPGiQSoUh+o8ZM0j9ioesW56Y99Jl2pj3sGADgi7ojdur9h+7iOaFhNTeePlCjkHHKCdsJz5BEBNmpMwKCJi1Hf3JBtZd9OVjLw5DDwUAgkvflyvE4naUs+m17Syu4VhZRo4OVZ6Q74+zuyeeCmtaW73w36AzVaAXxah5V1CJHmXjIG+7F04SQ3veS5+mbSebR6C8CiifT84MNA2scv51RvATRzARoNpnn+FG5CCPCXHZAeC4w+DdTpyLYi7iORAH/ZAhnxwJj/AbU7KD5XnAtsaQKIYoBfd0ijYcoZQgiefkzFxeexuPQ8DpHxsvOXg5UJetpboH9TS84u5FPTrWKo6aZwgeSMXHT+6wZSMvOwpH9DjGtXWzqQnwdsaSq9warTCdAxLmJ8C41uTummWJLH6r8NBuZfbhqrtwCqNQN0TdjV9LVI8oHArcC1lYAkD4nECB55E/DJoguEY1rCwvgbFkO4RG4mso5Ngm7EKQDAMXRDvTE74VDbnGVhX0lMiDRX++kxQJwlPaZtBDiMAFpOkC4GVXQyEoGI89Id8NfXpHNBIfpVAbs+gF1/oPbPgAaHCv1VZDKTgE+PgY9PvhjtjPiS52nqS+fH6s2/zJfGNUouWkrypf+3jw8A4ee+7IZr6gH2A6URHFaOFWexk6L+xIUBu9pI0+U83tPUCGU5MREI9QPazwK6LVF8XvA/wEk3wMACmBFabu9vXr4E998m4dLzWFwKi0NMajYzpsHnoU3dyuhhb4EeDc2/1CjiMNR0qxhquilc4Z97UVjg/xSG2hq4NqcTzAwLJs07W4HLi3/cX8TXkBZhKnxoaAMCTWmlTIFmwesi4/KOlfZnSL50R7gwPJLIaXNUuchOePXm0nBYrhqApLfSi1nUXQDApfwWmJ83AU3t6mObczPoa1eQHVNCkHn9L+jcXAE+CB4RW+QN3o/WTRqwrax0xDnA85PAg33Ah/tfjps3khrtJsMALX3W5LFKTjrw6op0BzziIpCT9mVM20i2Erq2AXs6KxJMHnaRMPGi0RaF8DWkYeHMYmRzwMxWmr//NaR/BkJ8pbUKEiK+HK9cX2q+mzrTInsU9rm7A7i4AKjbFXA5wbYa9YHJg3cAfg+Qfw4hwK620iJ13f6Uhu//QDJzxbgZkYBLz2Nx9WU8UrO+bOToaQnQydYMPe0t0Mm2Kox1uZm7rQhqulUMNd0UrpAvIRiw4w6efkzFkBY1sGFo04KBPGk4YW6mEgZYS2peSzPIX3tT9z3kZhbcgD78cgOa/K7keQItaehk4Q1ojZ+ASnXY3akhRLqTdHEBkJuObL4eFueMwtH8jhjdphY8+zX8kgZQgch+fh75x8ZDn2QghlTC6y570L4jewVZFJISLS2M9vgAkJkgPcbXBBr+Ig0ht25Nd/qKIs4F3t2U7oCHnwPS476MCbSlLXwKK6HrV2ZPpzohyZfm1hfObZ8eK87DrlT3yyJj9RaARWNAU/fHaSEEiL4v/T48PwHkZUqP8zUA295As9FAva6qnf8plEIODQFeXQZ6rKCtrL4GUaw0xBw8YO4rQL9KyXMirwCHB0tbSs58/kMiCZMzcnHlRRwuhcXhVuRnZOd9SVmqrK+Fbg3M0cPeHO3qVVHrdqPUdKsYaropXOJxVDIG7QwEABx3a4sWNU1ZVqSYfAlBVFImIuJEiIwTISIuHRFxInxIzkKtKnpwsDKBg5UpHKyMUaeKwZcWWhmJsrtAHx8BWUkl/wIdE9kwy+otVFf0Kj0eOD1NGqYL4KV2Y0xIm4CPMMPivg0xrl0t8CqwocuNC0eScAgscqOQTTTxqOlStBs0mW1Z0hy3tzcKCqOd/5K7bFQdaFFQGM1QzULi2UAiAT48+FKILfntlzEeH6jZTmrA7foCJlbs6eQShACp0V9ysD8+Bj49AfIySp6rX7XIvNVcGjKuyu4T2WlS4/34oGxbPKPq0lSLZqNohXuK6hDnAGtqSlN+Jt0BLBqxrYghJTMXtyITEB4rgnUlPdhYGMLG3AB6WhyKYNvVTlpHZrAQaDyk5Pj+/sDbm0DryUCvVd/813xIzsTlgkJoD94lI1/yxWbWMNVFT3sL9LS3QIuaphBwsSXqN0BNt4qhppvCNeYdC8GRhx9gX80Ip6e0Z31yk0gIPqZkITxWhIh4ESILzPWr+HTkiCVl/wIAhjoaaFrDpMCIm8DB2uRLaypCpDf9hW1xPj6S5uaKs0v+ImPrL0a8xk/S1iM/OnT4xRngzHQgMxFEoIV9miOxOqUrtDU1scXJAT3sLX7s38dR8jNTELHbGQ3SpItAT2qMgsO4zeAJWAgfy0qR5qw9FMq2yqr9s3RX27ZPxSqMpkoIkYYlvvif1ITHPpUdt3QoqITeXxr+XIEXm2Rg8rCLzEsZn0uep2XwJQ+7WnPFedhsEfdcar5D/5UWGCykdkfpIpVdv28r0EmhKMvbm1JjqF8VmBPB6ndDIpEW/7oR/hkBEfEIjk6BpJib4vEgNeDmhrCzMGR+1qqiD002otsuLQICtwEOo4ABO2THPgVLu9zwBMD0kK9aJCWEICIuXVoILSwWzz6myYw3sDRCT3tz9GhogQaWhhVyo4GabhVDTTeFaySk56DLhhtIyxZjxYBGGNW6pkr+XkIIPqVmMzvX4bHpiIyXmuvMXDl52QC0NfioV9UANuaGqG9uAJuqhrCqpIdX8ekIjk5GcHQKnn5MlQlNKqSGqS5jwptZm8C+mvGXMKX8POnNIrOr9FAaxoniV0c+ULVhgREvyBE3s/s2A5adCpz3AEL+AQBkVWoI17QJuJtuATNDbQjH/IQmNUy+/veqMUSSj3tes9H6gzcA4LWRI2r/7ge+vooiMGKfFhRGO/olXFbbSJqn2nK81ARSfizJ76SV0F/8r6COQZHvXOV60t1vu/7S71pFqYSelwXEhMpG3xTd/S+EryGtFVA0AqeKjXqEbItzpP+vjw8Ab26A+X/VMQGaOkmrn3NoB5JSgbiyFLi9EWg8DBj8t8r/+oT0HNyK/IyA8M+4GZmApIxcmXEbcwM0szLFh5RMhMeKkJCeK/f3aAn4qGOmD1sLQ9hafDHk1U10y9eQvr4GHBwIGFYDZoXJLlocGw88OwY0HgoM3lfmr5JICJ5EJ+Pi8zhceh6Ld4mZzBiPB7SsWQk9Coy2dWVuVhz/kVDTrWKo6aZwkf2B77Dk9HMY62ri+pxOqKT/44qMEUIQl5aDiDhRgcFOR3jBznV6jvyesIUXGxtzaehVfXPpxca6kl6ZO/F5+RKEx4oQHJ3CPF7Fl2yNo8HnoYGlkcxueO3K+l/C0rPTpG11Cm+KPzwCRJ9K/oWaetLduRpFwtKNrUpfXX97S1osLTUa4PHx1vY3/Pq8A9Ly+LAxN4DX2JacbXmhCq4e34M2oYuhx8tBgmZ1GLsehaZlOfUGFucCYaekhdGig74cr9qwoDDacFr0S1Wkf5bmf7/8n9So5Re5GTW0lEYYNOgH1OogrRfBFvliIFcE5Iik80SOSFo0rvAnc6zI8ezUL6+T38sv+MjkYbcokoddAXaFk99L64Q8OQykffhyvFpzad/vRkNo6zHKj2NvJ2kqxoBd0vSGckacL0HIh5SC3ezPePoxFUUdk6G2BtrVq4JOtmb42cYM1UxkayskpOcgIlaE8DgRwgt+RsSKkKFg88FAWwM25gawtTCCbcFPOwtDmP6o+7a8LGBtLWn03x/3gKp20uMpUcAWB+nc9ftNaeSfHHLE+Qh8nYhLz+NwOSwOCelfOlpoafDRoV4V9LA3R9cG5l8iEP8jUNOtYqjppnARcb4E/bbdxstYEZxbWWP1oMZf/TsIIficnsOEg0fEpRfkXouQli3fXGvweahdRZ/ZubY1N0R9c0PUqqz3Q4uGpWXnITQ6ldkND45Okbu6bKSjgaZWJmhWYMIdrExlFyDSPhUJ/3wobcmTKyr5FxbPsazeHNA1BfKygavLgKCCkC3TWjhXbwmm3NaGhAAd6lfBjpHNYaSjXhU5y4Mr167ANmASrHifkc3TBX/w39Bq1P/H/QWpH4CH3sDj/V/CePkaQINfpGa7ZlvuhOz+F8lOkxZCevE/IPKy7PdMx1ja+9yun7RYl7IpHxKJtDd1ThFTnJ1W8Lr4MZHs8aJGWl5e9deiX7Wgo0LzIq0NVVtTQ5wvQXJmHpIycos8cpCYkYvkjFwkZebBRFcTdpaGaGBpBFtzw+/rniDJB15fB54cAF6e+9JeUlMPaDhAGn5OCxJSvofMJGBdHQAEmPUCMKpWLn9NfFo2bkRITfbtyASZCtsA0NDSCJ1szdDRxgzNa5p+dZh40TQ7xozHivD6czrExePTCzAz1GZ2w20tDGFbsFmhq/UNkTEHB0p3vHuuBtr8IT123gO4t0uaKjLmtMzpouw83Aj/jIvPY3Ej/LPMhoqhjga62FVFT3sL/GxjBoOK0oHlG6CmW8VQ003hKvffJmHYnrvg8YBTk9uVGtqclJHL7FwXNdjJmfJ7dPN5QK0q+rCp+mXn2tbCELUq60NLQ/Uho4QQfEjOktkNf/YxVW7OuHUlPZnd8IaWRl/C0iUSIDGyYCe8oGJ63DP51YQr15PedBaEkUqaj8Wa/FHYe0/aP9eppRWWD2jETg4XRwl48gLa/uPRmv8cAJDd3h06XTy+PcyYEOkO6oN90h3VwsJohpbSwmgtxgCG/40cerVCnAO8CZDmgL8896V6PCDtw1u3i3RXuNBQy91pTpO/QPY9aOhI0w+0DaUPHaMir42KHCt8XfDcxEpaZOwHm8vsvHzGPCcWGOikjLyCn7lITC8w1pnSn6lZefiaOzseD6hZSQ92FkZoYGmEBgVmvIbpN4S7ZiRI2xM9PgAkhH85Xrm+dPe7qbPqCllSKg7P/YGjY6VpX5Pv/bBfm5cvwaP3ycxu9osY2XxkY11NdKhfBR1tpEa7ajn1jM4VS/A2IaPAiKcxpjw6KUvu+YX54raF+eIFYeq1KuuXvrERuE2a212vOzDqmLQ+w0Z76YLjqONAvW74LMrB5bA4XAqLReCrROTmf7l/qmqozYSNt65TmZX7PC5CTbeKoaabwmVm+gXD/8lHNLUygb9bW4iyxYiILzDWsQXmOl5xDlLRgiA25gW511UNUcdMn/NtHvLyJXgZI0JwdDKeFBjxN59L7mhpCnhoWBiWXrAbXquy3pebzrzskm3LivbN1a+K7D5bMOWhGa68kBpu9152mNSxToUsHPK93H8Vi4iDMzCKJ63snlOvD7SH7pWaF2XJTgWCfaVmOzHyy/FaHaS72nZ92Q1XpiiPJF/aqurl/6RFCFPef/3v4GvKN8M6RQw0c8y45LHCnxo/Lg2nOIQQiHLESEovMMnppZjpAqOtqBZGafB4gImuJirpa6GyvjZM9TVRSV8blfW1YKKnic/pOXgRI8LLmDTEi3Lk/g4DbQ3YWUgN+FfvihMirWz/eD/wzP9LFAFfQxrN0Hy0tNcyLVxIUYbT06SfJUc3oPea7/pVH1OyEFBQAO3Oq0SZ3VseD2hS3Vhqsm2romkNY1ZbeqbniAtq48jujCdmKM4Xr1vVQCY83cbCENWMdaT3IXHPpb24NXQBj/fA3e3A1WXIrdwAPk0O4WJYPB5HJcss2tWpoo8e9hboaW+OpjVMvqTqURio6VYx1HRTuEx8Wja6/BWA9BwxTPU0Fe5cA9LCZDYF4UuFBruumcG3hTJxlNTMPIR8SJHZES9eFAUATPQ0v1RLtzaBQw0T2fyqzCRpWLooBvHVu8D1yBs8+5gGLQ0+Ng1zQN8mlir8V6kfzz+l4ti+NfDI3wttnhh5lWyhOepfaW/10oh7Li2MFnrky828lkFBYbQJX3LVKOoJIdLIkpdnpf1l5RlpmR3oQrOsrbIQZomEIFucj4ycfGTl5iMzT4yUEiHdJc10ckaezM6RsmgKeDDV05KaaAMtxkCb6mmhkoEWKusXjOlrwbTguLIdKxLTc/AyVoQXMWkIi0nDyxhpbQ55Or9pVzxHBDw7ATw5KDXihRhW+9J6rFLtr35PKP8RCAG2NJHmHo84Atj0/Ko/niPOx4O3yQiIiMeN8M+ILFYLprK+Fn4u2MnuUL8KKqtBPnJCeg5jwJl88TiRwgU6Q20N2FgYwtbcAIteDoRebgJe9/CB+fXZMMhLxKzcSTgh+Zk5v2kNY8Zo16v6FQvh/1Go6VYx1HRTuM6+W2+w4uwL5nU1Y52CQmYGjMmuV9Xg+3L71BRCCKKTsvCkSG74809pyJUTll6zcpGwdCsTNKxmhLcJGXD1foBPqdmopK+Fv0f/xOne6FzibUIG1vx9AMuy18Ccl4J8bWMIhu0H6naWPVGcC7w4Ld3Vjrr75biZndRoN3X6ul1yNYAQgtx8CbLzJMjOyy94fHmeVfA6R/xlLKvYeTliqSnMzpMgu+A8CZF2DNDRFEgfzHM+tDWkP3U0BdCWGRMU+TMFPwvO1S4Y09bgcy6qIy9fgszcfGTmipGZK30vMnLEyMzLR2bOl+PSMTEyCp7LnJ8rlvlZeP73oKclYExypQKjLH2uXfKYgRYMtTVU+t7m5Uvw5nMGXsSk4UVs2o/bFY9/IW09FuILZCV9OV77Z6D5GNp6jFKSxNfAtubSSBb3d0oVwIxKzMSNiHgEhH9G4OtEZOV9+b7yeUAza1N0tDFDJ1szNKpmXCF2byUSaXodE6Iel47w2DS8+Zwhky/+l+YuDBbcwntJVdTkxyOGVELnvC1oUUean929oTksjXVL+ZsoxaGmW8VQ003hOoQQ3IpMgL62BuqbG9CiXmWQK5bgRUyazG7424SSYelaAj54PCBHLEEdM314j22JmpV/cM/vCk5cWjZm/H0e81JXoBn/FQiPD16PFUDrP6RF7h75SEML0+Okf4AnABr0B1r9BtRsp/ICTRIJQVae1LxlFJq43HwZM5xTxOQWN8M5efnILmGGpcdlTLM4/6tyc9mGxyti5ouadw2pMS9u8GWNvOzzwgUAADLGOJMxzAXHij7PLW6i879pV/lr0dMSQE9LACMdaTj3l91o6Y5z0Z3pwnGup+Uo4oftiufnSusvPD4gLcJWtPVYk+HS/G+Lry/8SVGOvHwJNPg8zi2SyeXBPuDsbOlcP+6c3FOy8/Jx901iQdj45xLXajNDbcZkt69XBSZ65ZdCwjVyxRK8SUhndsVNXp3ExITVzPjThnNg3c8Dxnr0nvBboaZbxVDTTaFUfFIycxEcnYKQIhXTC0P1HWtXwh6XFv+pi/mPJCUzF79738GQmI0YqnFTetCiiTSUvLANk4EF0GKs9GGkXOh+4W5xRs4Xc5yeI0ZmrlhqmnOkO5gZBbue6TliZObkIz1XjMwi48yfKzB9qr5y8nmALmNIBdDW5Bd5zS8wuQLZnegi5xTdtebzeAW74wW75kVMf4642K66WHbnPLdwvMh5CorucgoNPq/AHGtIf2oLoKepIf1Z5LiulgD6hecwP6XPdbUE0C/253Q0BBVil+x7+NpdcUNtDdgW2RVvapgG20+nofnUV9pusRBLB2nud60O0mrVtMWf0mTl5uNjSiaik7PwITkLH5Iz8SGp4GdyFhIzciHg82CoowEjHU0Y6WrAUFv600hHE4Y6RZ9rwEhXk3luXPDcQEdD6RSG7+LfkdJaD50XAR3nApDO628SMhAQ/hk3Ij7j3ptEmYKpGnwemtc0ZSqNN7Q0Uo8FBlWQHg9sqC99rmUIzHourXFB+Wao6VYx1HRTKP89CCGISspEQnoumtQwphXKv5PMXDHcDj5C7TeHsEjjEDR40puoVHNHvK/jjHdmnZGexy8wzIVmWSyz45xRsBta9JiiVizfC48HGGgVGjCNYgZYanB1Cw1vkddFw7NlzXPxP/flPE0BN3elCCHIyyeMOc9hwt0lMmY9W1w05L3gZzHzXnw8u+AmWl9LUMwEFzXIBa+1i58jez6tsqt6vnZXvLapNgYYv0KvvMuolxgAPilWe0TbSNqRwMhSWiW+8LlhNakpN6oG6FX59i4IakR2Xv4XM11grKMLnn9MzkRmehqq8pJRFSmoyvvyMOOlwAwpqMxLgxgCZEML2UQLWdBGVsHzbEhfS49Ln+dAE1lEm3ldeB5PSw+a2nrQ1NGHlq4+dHT0YaSnJWvmdTSLPZeaeEMdDSaaRSH5YmmrsJxUZI25hDtZtaRh4xGfS1T2tjTWYUx223pVaDRfaexuLy0M23Yq0GMF22rUHmq6v5EdO3Zg/fr1iI2NRdOmTbFt2za0atWqzD9HTTeFQqF8P7liCWYfDUFM6DX8LAjF//LbIIJY/ZDfraPJl+5iakt3M/W1Cx5aAuannrYGDLSlZk16TAP62tLnelqCgjHpOTqa3MthplC4jLK74pWQhoGC2xiscQc1ebHQh/zWScUhfE3wDC2kBtzQUvZn4XNDS87njWfn5eNjSpaMsY5OzEBqUjxyU2KgmRWPqkiWa6ir8lJgwMtmTXtWgVnPhhayiHYRE68p/VnE6OfxtUE0dEA0dAFNPfC19SDQ0oVAWx+aOvqoShLRJWI50nkG+Cl3L7KLlFLQFPDQqnalgrDxqqhf1YDOx8ryPhB4egzo6gnomrCtRu2hpvsb8PPzw+jRo7F79244Ojpi8+bNOHr0KMLDw1G1aul9JanpplAolB9DvoRg3cWXOP7oA7QEfOgVMcZSw1vMHDMGWsCY6kJzzBhmTQGrrV8oFIpiytoV10cWLHhJ0geSYc5LggUvGZa8JOZ5FaSCz1PuljZPuxLyDSzAM6oGDZPqEBhXL7ZrbinNLy8nE5edl49PBab6Y6IISZ8/ICPxE/JSPgHpcdDNTpDuVBcz1Fq8ryjgp6kPGJpL03IKfxpUBQwtAH0zgEiAvEwgL0v2IS58niltlVl4jvjLc5KXBZKbCZKXBZ44C3yJ4o4oP4Jz+a3wR94MWFXSRSebquhoY4Y2dSv/Jwu/UrgHNd3fgKOjI1q2bInt27cDACQSCaysrDB16lR4eHiU+mep6aZQKBQKhUL5MeTlS/A2IQOfRTlIzsxFcmYeUjKk/c1TMvOYY8kZuUjOzEVWdjaqIgUWvCSY85K/mPSC5+aQmnRtnnIGMZenjQztqsjWNYdY3wLEsBo0jKtBq1IN6FWxgm7lGuAZWMjtNZ4jzkdMQjI+f3qPlPgPyEj8gLzUWPDS46CV9RlG4sQCM52MyhApvVgAAGJtE/AMLcA3spD+/SWMtbn0uSq7OeSLi5j1Iqa9iFEvauAleVnIzUpHbnYGxNkZEOdkIj8384uRz8sCX5wFfn4OJODhUeMlaNC6J2pX0ae72RTOoawHpEtEBeTm5uLRo0eYP38+c4zP56Nbt264e/duifNzcnKQk/MlHCotLU0lOikUCoVCoVAqOpoCPtPOUhnE+RKkZBWacKkpT8nMxaeMPIRlSo15ckYu8tIToZkRC53sOBjmxqMqpIa8qEE35aVDi+RAKzsayI4GkuX/nfngI5lnghSNKkjXrAJBXgYM8hJQmSSjFi8TtRSJLZbKnA8BsrUqQaxXFTxDC2ibVoOWiYU0VN7AQro7bWAOGFSFhgYH+0gLNACBodJGnw9Ap+ChDH2/VReFwiGo6S4gISEB+fn5MDc3lzlubm6Oly9fljh/9erVWLp0qarkUSgUCoVCoVAUoCHgo4qBNqoYKG9KJRICUba4YNc8F7GZeQjLyEWaKA3i1Bgg7RME6THQzoqDXnY8jPI+o5IkAVWRhKpIgSYvH1VIEqrkJQF5EV9+ccFmbA60kKpRCdnaZhDrVQXfSGqoDavUgH7l6uAZmAOGFhDoVYY+Xz3byFEoFOWgpvsbmT9/PmbNmsW8TktLg5XVjyn2Q6FQKBQKhUIpX/h8Hoz1NGGsp4la0C822lDunyGEICsvH/EZORAlfEJWYjTykj9CIoqDtr4RDKvUQGULa5hUrQFtHWNUpeHQFAoF1HQzVKlSBQKBAHFxcTLH4+LiYGFhUeJ8bW1taGtzMMSHQqFQKBQKhVIu8Hi8gnZ0GoBpfaB+fbYlUSgUNYCWci1AS0sLLVq0wNWrV5ljEokEV69eRZs2bVhURqFQKBQKhUKhUCgUdYXudBdh1qxZGDNmDH766Se0atUKmzdvRkZGBsaNG8e2NAqFQqFQKBQKhUKhqCHUdBdh+PDh+Pz5Mzw9PREbGwsHBwdcuHChRHE1CoVCoVAoFAqFQqFQlIH26f5B0D7dFAqFQqFQKBQKhfLfQVkPSHO6KRQKhUKhUCgUCoVCKSeo6aZQKBQKhUKhUCgUCqWcoKabQqFQKBQKhUKhUCiUcoKabgqFQqFQKBQKhUKhUMoJaropFAqFQqFQKBQKhUIpJ6jpplAoFAqFQqFQKBQKpZygpptCoVAoFAqFQqFQKJRygppuCoVCoVAoFAqFQqFQyglquikUCoVCoVAoFAqFQiknqOmmUCgUCoVCoVAoFAqlnNBgW0BFgRACAEhLS2NZCYVCoVAoFAqFQqFQyptC71foBRVBTfcPQiQSAQCsrKxYVkKhUCgUCoVCoVAoFFUhEolgbGyscJxHyrLlFKWQSCT49OkTDA0NwePx2JYjl7S0NFhZWSE6OhpGRkZsyykVqrV8oFp/POqiE6BaywuqtXygWssHqrV8oFrLB3XRqi46Aar1R0MIgUgkQrVq1cDnK87cpjvdPwg+n48aNWqwLUMpjIyMOPvBLQ7VWj5QrT8eddEJUK3lBdVaPlCt5QPVWj5QreWDumhVF50A1fojKW2HuxBaSI1CoVAoFAqFQqFQKJRygppuCoVCoVAoFAqFQqFQyglquv9DaGtrY8mSJdDW1mZbSplQreUD1frjURedANVaXlCt5QPVWj5QreUD1Vo+qItWddEJUK1sQQupUSgUCoVCoVAoFAqFUk7QnW4KhUKhUCgUCoVCoVDKCWq6KRQKhUKhUCgUCoVCKSeo6aZQKBQKhUKhUCgUCqWcoKa7AhIcHMy2hB/Gmzdv0KNHD7ZlKM2zZ8/YlqAU8fHxWLVqFdsyAADnzp1jW4JS7Ny5k20JPwRCCM6fP48hQ4awLYXCMklJSWxLqJDk5eWxLaFM1G0ekEgkiIqKYltGmajb+5qSkoLt27ezLUMpuKQ1LS0NEomkxPH8/HykpaWxoKjiI5FI8L///Y9tGd8FNd0VEEdHR6xatUruhKBuiEQiXL16lW0ZpSISibB37160atUKTZs2ZVuOUsTExGDx4sVsywAADBo0CBMnTkR6ejrbUkpl0aJF6NmzJz59+sS2lG/i7du3WLx4MaytrTFw4EBkZ2ezLQkAULduXWzatEnheFxcHAQCgQoVKWbx4sUQi8UKx6OiotC9e3cVKvo2Ll26hGHDhqF69epsS2EQi8VYv349mjdvDgMDAxgYGKB58+bYsGEDp0ysi4tLqTfVDx8+RLNmzVSo6Ovg6jwgEAgwffp0hfctnz9/Ru3atVWsSnm4+r4q4urVqxgxYgQsLS2xZMkStuWUCte0+vv746effpL7f5ydnY2WLVvizJkzLCgriaura5mP8ePHsy2zVF69eoUFCxagRo0aGDhwINtyvgtquisg/v7+2LlzJ9q2bYvIyEi25VRYbt68iTFjxsDS0hIbNmxAly5dEBQUxLYstePevXt48OABmjRpgps3b7ItRyHPnj2DhoYGGjVqhEOHDrEtRylycnJw+PBhdOnSBba2tli1ahVmzZqF+Ph4zqwYv337Fu7u7hg7dixyc3PlnsOVJhv79+9Hy5Yt5Ua07NmzB40aNYKGhgYLysrm/fv3WLJkCWrVqoWhQ4eCz+fjwIEDbMsCAGRlZaFTp07w8PCAmZkZJkyYgAkTJsDMzAzu7u7o2rUrZ0xMWFgYGjZsiIsXL8ocz8vLw/z589G2bVu0b9+eJXXyUYd5gBACb29v9OjRQ2EEBlfmgULU4X0tSnR0NJYtW4batWujR48e4PF48Pf3R2xsLNvSSsBlrbt27cK8efOgp6dXYkxfXx/u7u6c2ZFPTk5W+EhISMC///4LHx8ftmWWICsrCwcOHMDPP/8MW1tbBAYGwtPTEx8+fGBb2vdBKBWSlJQUMmbMGKKvr0+2bt3KtpxvJjg4mPD5fLZlMMTExJDVq1eTevXqkapVq5IpU6YQDQ0N8vz5c7alfRVce1/z8vLIkiVLiLa2Npk1axZJTEwkqampMg+u4O3tTUxNTcmgQYPIo0ePSEhIiMyDCzx8+JC4ubkRExMT8tNPP5EtW7aQ2NhYTn5WeTwe+d///kesrKyIo6Mj+fTpk8x4bGwsZz6rqampxMXFhWhra5NVq1aR/Px88v79e9K1a1diZGRE9uzZw7ZEGXJycoivry/p2rUr0dHRIf369SMCgYCEhoayLU0GT09PYm1tLff7ExwcTKytrcmSJUtUL0wOeXl5ZPHixURTU5NMnDiRiEQi8uDBA9KwYUNibW1NLl68yLZEBnWaB/h8PgkKCiJNmzYlderUIU+fPpUZ59I8oE7va25uLjly5Ajp0aMH0dXVJQMHDiRHjx6lWr8DS0tLEhkZqXA8MjKSWFpaqlDR13Py5EnSsGFDYmJiQlavXs22HIb79++TiRMnEiMjI9KsWTOyYcMGIhAIOPX//z1Q013BOXr0KBEIBMTIyIiYmprKPNQBLpnDfv36ESMjI+Ls7Ez+97//EbFYTAghnLsgKAOX3teiXLx4kQgEAsLn85kHj8fjnNbLly8zOgv1cUmnQCAgM2bMIC9fvpQ5zsXPKo/HI3FxcSQ2Npa0a9eOVKtWjQQFBTHjXLrZLuTkyZPE3NycNG3alBgZGZFu3bqRd+/esS1LhilTppDKlSuT1q1bk+3bt5OEhARCCDc/AzY2NuTYsWMKx48cOULq16+vQkVl8+DBA2Jvb08sLS2JpqYmcXV15dTiICHqOQ9kZmaSYcOGEUNDQ3L8+HFmnEvzgDq9r2ZmZqRDhw5kz549JCkpiTlOtX47Ojo65MWLFwrHw8LCiI6OjgoVKc/t27dJ+/btiZ6eHpk3b57M+8w2jRs3JjVr1iTz588nz549Y45z7f//e+BmHBzlh/DgwQMsXrwY9evXx5w5czgZ9tisWTPweDyF45mZmSpUUzrnz5/HtGnT4Obmhvr167Mtp1RmzZpV6vjnz59VpER5Tpw4ATc3N/z8889YuHAhJz+vALBx40YsXrwYo0aNwuLFizmps2vXrhAKhYiPj4eLiwt69uxZ6veMC5ibm+PGjRuYPHkyOnXqhJ07d2LcuHFsy5JL69at0bhxY1y9ehX6+vpYtGgRatasybYsGXbt2gV3d3d4eHjA0NCQbTml8v79e7Rq1UrheOvWrTlXREtHRweamppITU2FlpYWOnfuDCMjI7ZlyaCO84Curi78/PywevVqDB8+HAsWLMDSpUvZliWDOr2vYrEYPB4PPB6PM7UxFKEuWmvVqoWHDx/Czs5O7vjDhw85dz0ICwuDu7s7Lly4gNGjR8PX1xc1atRgW5YM4eHhGD58ODp37oyGDRuyLadc4N7dIuW7EYvFWLJkCTZs2IDJkydj1apV0NHRYVuWXAYMGMC2BKW5ffs2hEIhWrRogQYNGsDFxQVOTk5sy5LLkydPyjzn559/VoGSsklJScEff/yBU6dOYdWqVZg+fTrbkuTy5s0bjBkzBpGRkfjnn3/w66+/si1JIRcvXkR0dDS8vb3h5uaGrKwsDB8+HAA4e3MIABoaGtizZw+aNWuGSZMmITg4GPPmzWNblgy+vr6YMmUKHBwc8OLFCwiFQvTo0QN//PEHVq9ezZm59uDBg/Dy8oKlpSX69u0LFxcX9O7dm21ZcjEyMkJ8fDysrKzkjsfGxnJm4YAQgjVr1mDp0qVwdnbG9evX8c8//8DNzQ0nTpzAnj17YGZmxrZMAOo7DwDA/Pnz0bRpU4wcORKhoaHYsGED25IY1Ol9/fTpE44fPw6hUIjp06ejd+/eGDVqFOd0AuqjddCgQVi4cCG6d+8Oc3NzmbHY2FgsWrQIo0aNYkmdLNHR0fD09MShQ4fQr18/hIaGokGDBmzLksubN2/g4+PDfKecnZ0xcuRIzv3/fxdsb7VTfjyNGzcmtWvXJtevX2dbSoUkPT2dCIVC0q5dO6KpqUn4fD7ZvHkzSUtLY1uaWmJpaUkcHR1LhOpxDX19fTJ48GDy+fNntqV8NZcuXSLOzs5ER0eH1K9fn8yfP588evSIbVmEEGkuZ1xcXInjt27dIubm5qRFixacCSsdNGiQ3DoZd+7cITY2NsTGxoYEBgaypE4+b968YXKmq1SpQvh8Pjl69CjbsmQYNmwYGTRokMLxQYMGkaFDh6pQkWJatWpFLC0tyenTp2WOv379mnTo0IFUqVKF/PvvvyypKx11nAfCw8NJgwYNSK1atTgzDxSHy+9rUV69ekUWLlxIatSoQXg8HhkxYgS5dOkSkyrHJV69ekUWLFjASa1paWnE3t6eGBoaEjc3N7J582ayefNmMmnSJGJoaEgaNmzImftBXV1dJpT81KlTCh9c4+rVq2TkyJFEV1eX8Hg8MnfuXBIeHs62rO+Gmu4KyPjx4znzha/ovHz5ksydO5dYWFgQHR0d0r9/f7YlEUIIyc/PZ1uC0ixfvpwTF9KyWL58OcnOzmZbxneRlJREtm7dShwcHDhzA1uYyymPqKgoTpnutm3bkoiICLljmZmZZNq0aURTU1PFqpRDIpGQCxcukKFDhxJtbW1SvXp1MnXqVLZlEUIIef78OTEwMCCOjo7Ez8+PhISEkODgYOLr60tatWpFDAwMZHL82GT48OEkMTFR7phEIiEbN24k+vr6Klb1dajbPJCWlkb69etHeDyeilV9HVx8X+WRn59Pzp07RwYPHky0tLRIpUqV2JakkOJaK1euzLYkQoi0WLGbmxupVKkS4fF4hMfjEVNTU+Lm5sapPOlCbaU9uPxZTUlJITt27CAtWrQgPB6PNG7cmG1J3wWPEI71YKD8pygrp7uQx48fq0DNt5Ofn48zZ87Ay8sLp0+fZlsOBAIBYmJiULVqVQDA3LlzMX/+fFSqVIllZSW5f/8+WrRooTCHKycnB6dOncKwYcNUrEyW4u+puvP48WM0b96cbRl4//49rK2tFc4DOTk5uHfvHifSIQghZc5XN2/e5ITW0khKSsKBAwfg7e2NkJAQtuUAAIKCgjB+/Hi8ePGCeY8JIbCzs4NQKESbNm1YViglKioKVlZWpX4OIiMjOVH3Y8yYMejatSs6deoEa2truedwZR5QhqioKIX/Dq6hLu9rQkICDhw4UGYdGFURFBSEM2fOIDc3F127dkWvXr2Ysc+fP+PgwYOc0QpI56iEhAQQQmBmZlaxQqE5RnBwMLy8vLB161a2pXwz1HRXQLp06VLmOTweD1evXlWBmtJRtkDKkiVLyllJ2eTn5+P58+eoX78+dHV1ZcaysrIQGRkJe3t7ThQA4fP5iI2NZQyikZERgoODUadOHZaVlaS4mS2uNS4uDtWqVUN+fj6bMku8p1wmMjISnp6e2LNnT4niTqmpqXBzc8OKFSs4+XkoSlpaGg4fPgyhUIiHDx+yLafCLbxwkeDgYERERAAAbGxs4ODgwK6gYqjTZ6BTp064d+8ecnNzUatWLXTu3BmdO3dGly5dYGlpybY8pYmIiIBQKMSBAwcQExPDthy1ml+Tk5Nx6NAhjBkzRq7WAwcOyB1jg2PHjmH48OHQ1dWFpqYm0tLSsHbtWsyZM4dtaQpJSEjAu3fvwOPxUKtWLVSuXJltSTK4urpiy5YtnKmJ8V+HFlKrgDRt2lThmEgkwj///IOcnBwVKlIMF8y0shw8eBDbt2/HvXv3SoxpamrC1dUVM2bM4EwBjaJweW2tuDZ5WrmiX11WsdevXw8rKyu5N1LGxsawsrLC+vXrsWvXLhbUlc3169fh5eWFEydOwNjYGAMHDmRbEgDufA6VISUlBb6+vnBzcwMAjBw5EllZWcy4hoYG9u7dCxMTE5YUypKWlgYDAwM4ODjIGG2JRIL09HROmAJAvT4DN27cQE5ODgIDA3Hjxg3cuHEDhw4dQl5eHurXr88Y8KFDh7IttQSZmZnw8/ODl5cX7t69i59++okzO5zqNL9u374doaGhmDp1aokxY2Nj3Lp1CyKRCAsWLGBBnSyrV6/Gb7/9hh07dkAgEGD16tVYtWoVJ0338+fP4ebmhjt37sgc79ixI3bt2gVbW1uWlMmyf/9+rFmzRi1MtzptGH4rdKf7P4JYLMaOHTuwcuVKGBsbY/ny5ZytvM1VOnTogMmTJyt8344cOYLt27fj5s2bKlZWkuK7soaGhggJCeHEyntxytLKpZ3uRo0aldkijAupELa2tjh06BBatmwpd/zRo0cYMWIEwsPDVaxMMR8/foSPjw+8vb2RkpKC5ORk/PPPPxg2bBhnFjvUKdph/fr1CA4OxuHDhwFIv1c9e/Zkbr7u3r0LJycn/PnnnyyqlOLv7w93d3cEBwdDT09PZiwjIwPNmzfHhg0b0L9/f5YUfoHP5yMuLo4zFcq/luzsbAQGBuL8+fPYu3cv0tPTWZ9bixIUFIR9+/bh6NGjsLa2xosXL3D9+nV06NCBbWkM6jS/Ojg44K+//kLXrl3ljl+9ehVz5sxRquNJeWNgYIDg4GDUq1cPAJCbmwt9fX18/PiRU3NubGwsGjVqBDMzM0yaNAl2dnYghCAsLAx///03EhMT8ezZM05oVqdrFp/PR82aNdG3b19oamoqPG/Tpk0qVPVjoTvd/wEOHz4MT09PZGVl4c8//8TEiRM52VuY64SHh6N169YKx1u2bIkXL16oUFHpeHp6Mjewubm5zIJLUTZu3MiGNLWlZ8+eMDAwYFtGmURFRZV6ka1SpQqio6NVqEgxhS1ibt68id69e+Ovv/5C7969oa+vj8aNG3PGcBeyb9++Mj8D06ZNU5EaxRw7dgwrV66UObZu3TpmMcvf3x/Lli3jhOnetWsX5s2bV8JwA4C+vj7c3d2xfft2TphuAFi8eLFcrUXh2tyam5uLu3fv4saNG7h+/Tru3buHatWqYfDgwWxLAwD89ddf8PLyQmpqKpydnXHz5k00bdoUmpqanAvZVaf59fXr16XWF6hfvz5ev36tQkWKyczMlIke0NLSgo6ODtLT0zllGjdt2oSaNWvizp07Mi0ie/XqBTc3N7Rv3x6bNm3C6tWrWVT5BZFIVGYrSy5EEq1duxbe3t44evQoRo4cCVdXVzRq1IhtWT8U6rwqMBcuXICHhwfevn2LOXPmYNasWdDX12dbltqSkZGBtLQ0heMikQiZmZkqVKSYn3/+WWaVvW3btnjz5o3MOVwyM2FhYYiNjQUgDd98+fIl0tPTAUhzprjC3LlzOXXxV4SxsTFev36NmjVryh1/9eoVJy6yADB8+HC4u7vDz89PLULgdu/eXWrdBh6PxwnT/ebNG5kQR1tbW2hpaTGvmzZtisjISDakleDZs2fYuXOnwvGff/4ZixYtUqGi0nn69KnMe1kcrsytN2/elDHZ1tbW6NixIyZOnIhDhw6hRo0abEtkcHd3h7u7O5YtW8aJuiiloU7zq0AgwKdPnxQWoPv06RP4fL6KVSmm+KKmWCyGj48PqlSpwhxje369fPkyPDw85BpZXV1dzJ07F+vWreOM6baxsVE4VlgclAvRLnPnzsXcuXNx9+5deHl5oV27drC1tYWrqytGjBjBme/U90DDyysg9+/fh7u7O4KCgjBp0iQsXLhQZsKifBsODg6YNGkSJk2aJHd8586d2Lt3L4KDg1UrTM3h8/ng8XhycyULj3PhoqBOBZSGDRuGvLw8+Pv7yx3/9ddfoaWlhaNHj6pYWUl+//13+Pn5wd7eHi4uLhg+fDhMTU2hqamJkJAQNGzYkG2JDOoUqqenp4f79+8r3Cl4+vQpHB0dObFQqKuriydPnsDOzk7u+IsXL9C8eXOZnHS2UKfPAJ/Ph7W1Ndzd3TFo0CCYm5uzLUkhq1evhre3N7Kzs+Hs7AwXFxc0atSIk/OAOs2vnTt3hqOjI9asWSN33N3dHffv38f169dVrKwktWrVKnPBisfjldhAUDUmJiZ4+PAhEwZfnFevXuGnn35CSkqKaoXJgc/n4/jx42V2runYsaOKFClPZmYmjh49ih07diAsLAyfPn1Se+NNd7orIK1bt4auri4mTZqE2rVr459//pF7HturherGiBEjsGjRIrRt2xZNmjSRGQsJCYGnpyfmzZvHkjr15e3bt2xLUAp1Wp+cP38+2rRpgyFDhmDevHnMjufLly+xbt06XLx4EYGBgSyrlLJnzx5s3rwZR44cgZeXF2bMmIGePXuCEAKJRMK2PBm4soOpDHXq1MHjx48Vmu6HDx+idu3aKlYln1q1auHhw4cKTffDhw8V7iqqGnX6DMybNw83btzAjBkzsGvXLnTs2BGdOnVCx44dObcQP3/+fMyfPx8BAQHw8vKCo6Mj6tWrB0IIkpOT2ZYngzrNr1OmTIGTkxNq1KgBNzc3JoogPz8fO3fuxKZNmxTeI6qad+/esS1BKUQiUanmz9DQkInU4wLt2rVTi0XC4jx+/BgBAQF48eIFswCn9qiqIThFddSsWZPUqlWr1Eft2rXZlsmQl5dH1q1bR5o1a0b09fWJvr4+adasGVm/fj3Jzc1lWx5Dbm4u6dSpE9HQ0CC9evUiM2bMIDNmzCC9evUiGhoapGPHjpzSW1FITk4mhw8fZlsGeffuHZFIJGzLUJozZ84QMzMzwufzZR5mZmbk1KlTbMtTSEREBJk/fz6pVq0aMTIyIs7OzuT48eNsyyKEEMLj8UhcXBzbMpRi0aJFxMrKisTGxpYYi4mJIVZWVmThwoUsKCvJggULiLW1tUKt1tbWZMGCBSwoK4k6fQYKEYlE5Ny5c2TevHmkVatWRFNTk9jb25M//viDHD16lG15cklLSyO7d+8mrVq1IgKBgLRp04b89ddfbMtiUKf5dcGCBYTH4xEjIyPi4OBAHBwciJGREeHz+cTd3Z1teWoHn88nr169IqmpqXIfERERhM/nsy2TEKJ+89XHjx/JypUrSf369Ym5uTmZPXs2ef78Oduyfhg0vJzCKllZWejevTvu3r2Lbt26oUGDBgCk4YRXrlxBu3btcOnSpTKLQKiKvLw8ZmU4MjIShBDY2NhgxIgRmDFjRql5fpRvIyQkBM2bN2c9vHzZsmVKnefp6VnOSpQnKysLFy5cwKtXr5jPao8ePcosAsUFJBIJzp49C6FQiPPnz3OizeHSpUsxd+5ctXj/RCIRHB0d8eHDB7i4uDB5feHh4Th06BCqV6+O+/fvcyKPXiQSoU2bNoiKisKoUaNkdg4PHz4MKysrBAUFcULr/v374eTkBG1tbbalfDNJSUnYuHEjtm3bxrnq5fJ4+vQphEIh/vnnH8THx7Mth0Gd5tf79+/j8OHDMlpHjBiBVq1asS1N7ShMiVME4UhKHADUrl0bDx8+5FwxQnn06dMH169fR48ePeDq6oq+fftWuKLP1HRTWGXJkiXw8fHBmTNn5IZs//LLLxg3bhwnKuxS2IErprtZs2YKx3g8HsLDw5Gdnc26zopIfHy8WobHsU1ycjLmz5+PI0eOMPmFJiYmGDZsGFatWlVmnp8qSU1Nxfz58+Hn58eEE5uYmMDJyQkrV66EqakpywrVF4lEggcPHjC9uu/cuYP09HRYW1ujc+fO8Pb2ZluiUuTl5VWMEFOKWhMQEKDUeVzMk+YyfD4flpaWqFq1aqmLGlxoy/qtUNNNYRVbW1usWrVKYduSo0ePYuHChYiIiFCxMgpX4IrpVkRwcDA8PDxw7do1uLq6Yvfu3WxLolBkIITg8+fPAAAzMzNO5yUTQpCQkABCCOe1cp1169YxJlskEqF69ero1KkTOnfujM6dO3Mmp59CoVCWLl2q1HlLliwpZyXlBzXdFFbR0dFBZGQkrKys5I5HR0ejfv36yM7OVrEy9SY0NFSp84pHF3ARrprut2/fYvHixfDz88OgQYOwYsWKUvuhUigUiiqpVq2ajMlWVG2ZQqFQKOVPxQqWp6gdRkZGiI+PV2i6Y2NjOZHHp244ODioRRsuANi6dWup4x8/flSREuVISEjA0qVLsXfvXrRv3x6BgYFo2bIl27IoFApFhk+fPrEtgUJRmrS0NKXOU/e2UZT/LnSnuwIiEonKNKoBAQGcyDcZPnw4xGIxjh8/Lnd88ODBEAgEOHLkiIqVqTfv379X6jwutOFRNsSR7dZiGRkZ2LBhAzZu3Ih69eph9erV6NGjB6uaKNwgICAAGRkZaNOmDc09plAolG9AnQqUUSjfAjXdFZBOnTrh4sWLCqurBgQEoF+/fhCJRCpWVpKwsDA4OjrC3t4es2bNgp2dHQghePHiBTZt2oSwsDAEBQXB3t6ebakKEYvFyM7OhoGBAdtSKOWIhYUFRCIRpk6dCmdnZ4U3B1wK2f/48SOOHz/O1ESwtbXFoEGDUL16dZaVfeHKlSvo1q2bwnGJRIJVq1Zh0aJFKlQln7Vr1yI9PR3Lly8HIL0J7N27Ny5dugQAqFq1Kq5evcrp+YryfdSuXbvMPHMej4fXr1+rSBGFLdRhfi1KaGgoo9XGxoZT1ypAtkAZIQR9+vTBvn37SryfXNgwUhf69OkDX19fGBsbAwDWrFmDSZMmwcTEBACQmJiIDh06ICwsjEWV/x2o6a6ANG7cGHXq1IG/vz/4fL7M2M2bN9GnTx+MGzcO27ZtY0mhLEFBQRg/fjxevHjB3MwQQmBnZwehUIg2bdqwrFDKmTNnkJiYiLFjxzLHVq5cieXLl0MsFqNLly7w8/PjxE5XVFSUUudZW1uXs5Lv58OHD1i2bBn27t3Lqo6i36XioftcC9kHgJ07d2LWrFnIzc1lwvHS0tKgpaWFjRs34o8//mBZoRQtLS1MnDgR69atK9Fq59mzZxgzZgxiY2M5kWbQvHlzuLu7Y/jw4QCkhR7HjBmDy5cvo0GDBhg9ejT09PTUIjInJSUFhw4dwpQpU9iWohQfP37khJnZsmWLwrF3795hz549yMnJ4cw8oC6UtcsJSOdZsVisIkWloy7zKyBtFzZ+/HiEhYUx1y0ejwd7e3sIhULOpkcZGhoiJCQEderUYVuK2iIQCBATE8N0/zAyMkJwcDDznsbFxaFatWp0vlIR1HRXQD59+oQOHTqgXbt2OHDgAHP81q1b6Nu3L1xcXLBjxw4WFconODhYZhXWwcGBXUHF6Ny5M4YMGYLJkycDAAIDA9GhQwcsW7YMDRo0wMKFC9G7d29s3LiRZaXSibaQohfZose4ZBBLgyuF1NQpZP/s2bP49ddfMWPGDMyePRuWlpYAgJiYGKxfvx7btm3DqVOn0KdPH5aVAvfu3cPYsWMhFovh4+ODdu3aMbvby5cvx+DBg7Fjxw5OLGaZmpoiMDAQDRo0AACMGzcO+fn5zDwbFBSEoUOHIjo6mk2ZpXL16lUIhUL4+/tDT08PiYmJbEsqldjYWKxcuRJCoRCZmZlsy5FLUlISli9fjl27dsHR0RFr165F69at2ZalVpw6dUrh2N27d7F161ZIJBJOFFVVp/m1MJqwQYMGmDlzJjN3hYWFYdOmTQgPD0dQUBAaNmzIstKScNV0u7q6lnkOj8eDUChUgZrS4fP5iI2NZUx38feUmm7VQk13BeX169fo0KEDhg4dii1btuD27dvo3bs3Ro4cSVsafSNVq1bFxYsXmX7Ns2bNQlhYGC5cuAAAOHfuHKZPn47IyEg2ZQIANDQ0UKNGDYwdOxb9+/eHhob8molNmzZVsbKvhyumW53o1KkT2rdvjxUrVsgdX7RoEW7fvo0bN26oVpgCsrOz4eHhgZ07d2LixIkICgpCdHQ0du3ahUGDBrEtj6H4DYudnR1mzJiBSZMmAZBGmNja2iIrK4tNmSWIjo6Gt7c3vL29ERUVBScnJ7i4uKBr166c6HucnJyMP/74A5cvX4aWlhY8PDwwZcoU/Pnnn9iwYQOaNGmCmTNnMhEGXCErKwsbN27Ehg0bULNmTaxatYoTRquQsvrbi8ViPH78GK1atVKhKuUJDw+Hh4cHzpw5g5EjR2LZsmWcWNRUp/l12LBhTN2c4pEEhBAMGjQImpqanIzO4arpHjhwoMKx/Px8XLlyhTPRLupkuotuEpbG6NGjy1lJOUIoFZaQkBBiampKxowZQ4yMjMhvv/3GtqQSJCcnk507dzKvR4wYQQYOHMg8hgwZQpKTk9kTWAQdHR3y/v175nXLli3JunXrmNfv3r0jenp6bEgrQUxMDFmzZg2xtbUl5ubmZPbs2SQsLIxtWd9EcHAw4fP5bMsgp06dkvu4ceMG+fTpE9vyZDA0NCQvX75UOP7y5UtiaGioQkVlI5FIiLOzM+HxeMTAwKBU/WzRtGlT4u3tTQgh5P3794TH45Hnz58z43fu3CHVq1dnSZ0subm55MiRI6RHjx5EV1eXDBw4kBw9epRoaGjIaOYCEydOJNbW1mT27NmkUaNGhM/nk969e5O+ffuSu3fvsi2vBGKxmOzatYtYWFiQWrVqkQMHDhCJRMK2rBLw+XwSFxfHvG7UqBGJiopiXsfGxnJibi3Ox48fyYQJE4impibp168fefr0KduSZFCn+bVKlSrkwYMHCsfv379PqlSpokJFymNgYEDevHnDtgylOXnyJGnYsCExMTEhq1evZlsOIUQ6B8THxzOvi7+nXJoDTExMFD5MTU2JlpYWZ7R+K7RlWAWksO1CrVq1cPjwYQwcOBADBgzA+vXrZVoycKHtwt9//43g4GC4ubkBAE6fPo2ePXsy1dfv3r2LzZs3488//2RRpZTq1avjxYsXsLa2Rnp6OkJCQrBp0yZmPDExsUROKltYWFjA3d0d7u7uuH37Nry9veHo6IiGDRti/PjxGD9+fIl8f0rpDBgwQOEYj8eDk5MT/v77b058BvLz80vdwdTU1OTEynYhr1+/xtixYxEZGYndu3fD29sbnTp1wu7du/Hrr7+yLY9h8uTJmDJlCm7duoWgoCC0adNGJizz2rVrTCQM21SvXh12dnYYNWoU/v33XyY839nZmWVlJTl//jx8fHzQpUsXTJkyBXXq1IGDgwNWrVrFtrQSHDlyBIsWLUJKSgoWLlwINzc3aGlpsS1LLqRYIOO7d++Ql5dX6jlskpqailWrVmHbtm1wcHDA1atX0aFDB7ZllUCd5leRSARzc3OF44UFQrlA8aim7OxsTJo0Cfr6+jLHT5w4oUpZZXLnzh14eHjg8ePHmDJlCjw8PDiRDgVIv99jx45lCisXf09zcnLYlCdDcnKy3OMxMTFYunQpvLy80L17dxWr+rFQ010BMTExKZG/e+TIERw9epR5zZV83mPHjmHlypUyx9atW8eEvvj7+2PZsmWcMN1Dhw7FjBkzsGDBApw7dw4WFhYyeXsPHz6Era0tiwrl0759e7Rv3x6rVq2Cs7MzJk2ahMGDB6NSpUpsSwNQ8kJbnJSUFNUIKQOJRCL3eGpqKh49eoTJkydjxYoVnDAK9vb2OHXqFGbOnCl3/OTJk5ypsL19+3Z4eHigZ8+eOHHiBMzMzDBhwgSsX78eTk5OGDJkCLZt28ZUW2WT3377DQKBAGfOnMHPP/+MJUuWyIx/+vQJ48aNY0mdLGKxGDweDzweT6bGAxf59OkTk2taq1Yt6OjoYNSoUSyrko+TkxN0dXXh7OyM9+/fw8PDQ+55XKjtoQxlFS9TFevWrcPatWthYWEBX19fTi22FUed5teaNWvi/v37sLKykjt+7949ToTsA2AqbBfC1TmgkLCwMLi7u+PChQsYPXo0fH19UaNGDbZlyTBmzBiZ1/LeU66Ga4tEIqxduxZbtmyBvb09Ll68iM6dO7Mt67ugprsCcv36dbYlKM2bN29kjKqtra3MrkHTpk05kSMNAJ6envj48SOmTZsGCwsLHDp0SOZm1tfXF/3792dRoXwCAwPh5eWFo0ePwtbWFjt27OCEgSmk+IVW3jhXLwqAVF+XLl2wadMmzJgxgxOme/LkyXBzc4O2tjYmTpzI5PSLxWLs2bMHixYtws6dO1lWKcXT0xN79uzByJEjmWN8Ph/u7u7o168fxowZA3t7e05ULwekRXQUFdLZuXMnkpKSVKxIPp8+fcLx48chFAoxffp09O7dG6NGjeKMySoKIUSm7oRAIICuri6LihTz888/l9kSjIvvMdfx8PCArq4u6tWrh/3792P//v1yz+PCLqc6za9OTk6YNWsWbG1t0ahRI5mxp0+fYs6cOZy5vnp7e7MtQSmio6Ph6emJQ4cOoV+/fggNDWUWDbmGurynRcnLy8O2bduwatUqVK5cGd7e3hgyZAjbsn4ItJAahVX09PRw//79EheDQp4+fQpHR0fOVq3lKjExMThw4AC8vb2RnJyMkSNHwtXVVeH7TPl+3r17h0aNGiE9PZ1tKQCAOXPmYOPGjTA0NETdunVBCMGbN2+Qnp6OadOmyaRGsElMTAxT/Vce+fn5WLVqFRYvXqxCVV/PpUuXIBQKcfr0ac4VUnv9+jW8vb2xf/9+fPz4Ec7Ozhg7diy6dOnCiV1wPp+PRo0aMeYlNDQUdnZ2JcK2Hz9+zIY8tUUgECAiIgJmZmYghMDKygq3b99GrVq1AEiLKNnZ2XEi6m3s2LFKLVZwxUSoy/yanZ2Nrl274t69e+jevTsaNGgAQghevHiBK1euoFWrVrh27Rp0dHTYlqo26OnpgcfjYcqUKWjXrp3C83755RcVqvo2Xr58iV9++YXpHMQmhBAcOHAAnp6eEIvFWLJkCcaPH8+Ja9SPgpru/yCPHz+Gp6cn/ve//7EtBY0aNcK8efMUrrR6e3tjw4YNeP78uYqVlaSsSrD5+fl49OgRJyrBampqonr16hgzZgx++eUXhflnTZo0UbGyr4MQggsXLkAoFOLYsWNsyymVa9euYdKkSZy4eBUSFBQEX19fJlrExsYGTk5OtJ3RD+L9+/fw8vLC/v37kZycjN69e2Pw4MEYOnQo29Jw4MABDB8+nMnlA6QpEhcvXoRQKMSZM2dgaGiIhIQEFlVKWbp0qVLnFQ/np5RO8d7XhallxV9zwXSrI+oyv+bm5mLTpk3w9fWVacvq5OSEmTNnyswRbBITE4Pt27czKYft27eX2XARCAQ4efIkqlevzpZEAFCqHo66fK+41B2mcePGePPmDaZOnYoZM2YorI/DhXpU3wo13RWUixcvMu1XJkyYgDp16uDly5dM+42ePXvi3LlzbMvE4sWLsX//fjx48KBEsY/Y2Fi0atUKo0ePVtiaQ5UIBALExMQwxrtx48Y4d+4ckyvFpdYLRS8KhTdZxb/qXL4ovH37Fl5eXvDx8cHnz5/RrVs3TiwSKSI4OBiurq7o2LEjZ3Y41IWtW7eWeQ6Px8PUqVNVoKZscnNzceLECezbtw937txBt27dcP78eTx58gSNGzdmWx5D8fmqOJ8/f8bBgwcxa9YsFStTb5R9v7iQ0x0QEKDUeR07dixnJV9HSkoKXr16BQCoV68ep9Kh1I2bN2+ibdu2CtuGconFixcjMTGRCc03NDSEq6srU3/m/PnzaN++PTZs2MCmzAoFl0y3vPvWolSERUJquisgQqEQv/32GypVqoTk5GRUrlwZGzduxNSpUzF8+HBMnz6dM/knIpEIjo6O+PDhA1xcXGBjYwNA2p/z0KFDqF69Ou7fv89UM2cTZfodWlpaKiy4pUrev3+v1HlcKaACSKtoHjt2DEKhELdv30Z+fj42bNiA8ePHc2Jl09TUVO6FICMjA2KxGN27d8eRI0c4oRWQdjEo1HLu3DmIxWJmTCAQoG/fvmxJk6F27dplnsPj8fDmzRsVqCmdqVOnwtfXF/Xr18eoUaPg5OSEypUrQ1NTEyEhITKVzNmm+HzFZdSpn3SnTp3KDIPm8Xi4du2aihR9O5mZmQgODkbbtm3ZlgJAmqIzefJkXLx4kVkk5vF46NWrF7Zv386ExbNNZGQkU4ui+HyfmpoKNzc3rFixghP9pctafOMSzZo1w9atW5mK9cXvsS5evIhZs2ZxIvKxosAl062ui4RfA/eXvihfzZYtW7B27VrMnTsXx48fx9ChQ7Fz5048ffqUc5UVDQ0NcefOHcyfPx++vr5MpWoTExOMGDECq1at4oThVhauFNBRxkw/e/ZMBUrK5tGjRxAKhfD19UW9evXg4uLCVAHt2bMnZ0zs5s2b5R43MjKCra0tpwzX//73PyxevBhPnjwBAAwfPhwZGRnMOI/Hg5+fHyeKk7x9+5ZtCUqza9cuuLu7w8PDQy3mJa7MR2VhaWlZahRRYmIi2rRpw4kbwxs3brAt4YcRGRmJDh06cOJ9jY6ORuvWraGpqYnly5czGwNhYWHYtWsX2rRpgwcPHnDiHmb9+vWwsrKSe20yNjaGlZUV1q9fj127drGgThZ12ld79+6dzCJs9+7dZdqF2dracup6cfTo0RIh+yNGjODEdVUdUWczrSzUdFdAXr9+zeQUDho0CBoaGli/fj0nLlbyMDU1xe7du7Fr1y58/vwZAGBmZsbcMCYlJXGmvZW6IxKJ4Ovri3379uHRo0ecuNlydHTE1KlTERQUxMmWa4UUb73BZfbu3VsiHPvVq1fMjsG6devg5eVFbw6+koMHD8LLywuWlpbo27cvXFxc0Lt3b7ZlKaRr165lhpVyoTiZOvWTrlOnDh48eIDKlSuzLaVC8eeff8LW1hYXL16UKeo1YMAAzJw5E7169cKff/6Jffv2sahSSkBAAA4dOqRwfNiwYRgxYoQKFZWOuiy+5eXl4fPnz8y9avFK9cnJyUrlU5c3EokEzs7OOHr0KGxsbGBnZwcAeP78OYYPH46hQ4fC19eXE++7ogi9QopGwHGFjx8/4vjx48xihq2tLQYNGsR6Lv+PgJruCkhWVhZTgIDH40FbW7vU6sBcgcfjyYRAXbp0Cfv27cOZM2c4UQ2Yx+NBJBJBR0eHyS1JT09HWloaADA/ucjNmzchFApx/PhxVKtWDYMGDcKOHTvYlgVAagyEQiHi4+Ph4uKCnj17cuJipQh1uCA8ffoU69evVzjeu3dvzuTFHThwQKnzuNDWxtnZGc7Oznj79i18fHwwefJkZGZmQiKRICwsjFPRDgDQs2dPGBgYsC3jh8CVOeHdu3ecWKysaFy4cAF+fn5yq2jr6upi+fLlcHJyYkFZSaKiokoN165SpQqio6NVqKh0xo4dW2axNC60YrO1tUVgYCCaNWsmd/zWrVtMCiKbbNmyBVeuXMHp06fRr18/mbHTp09j3Lhx2LJlC2bMmMGOwCIoitDjKjt37sSsWbOQm5vLRJKkpaVh7ty52LhxI/744w+WFX4f1HRXUPbt28fcbInFYvj4+KBKlSoy50ybNo0NaaUirxqwsjfl5Q0hRGbCJ4TIXByKV4Zlm9jYWPj4+EAoFCItLQ3Dhg1DTk4OTp48ySlzcPHiRURHR8Pb2xtubm7IysrC8OHDAXDnRrsQdbkgxMTEyNxkXb9+nQnVBQADAwOkpqayIa0E06dPVzjG4/GYnHkumO5CateujaVLl+LPP/9kWoWNGjUKM2bMwKBBg5QqDqcK5s6dqxa5nBRKQkJCqTnbderUQVJSkuoElYKxsTFev36tMI3r1atXnEmLAqRpfFzte18UJycneHp6okOHDiU6q4SEhGDZsmVwd3dnSd0XvL29sX79+hKGG5C2CVu3bh1nTHdZEXpisRjx8fEqUlM6Z8+exbRp0zBjxgzMnj2b2SyMiYnB+vXrMX36dNSqVQt9+vRhWem3QwupVUBq1aqlVKEXLhQmAtSnGrA6FXno378/bt68ib59+2LkyJHo1asXBAIBJws+Fefy5cvw9vaGv78/rKysMGTIEAwZMgTNmzdnVdfZs2fx66+/KrwgbNu2DadOneLEBaFatWo4cOAAunXrJnf80qVLGDNmDGJiYlSsTHliYmKwdOlSeHl5oUuXLrhw4QLbkkolKSkJBw4cgI+PD4KDg9mWo1YFlNSpnzSfz8f+/fthbGxc6nlc6NF7+vTpUsffvn2LWbNmceJ9rVWrFvbu3YsePXrIHb9w4QImTZqEd+/eqVaYHIYNG4a8vDz4+/vLHf/111+hpaWFo0ePqlhZSdSpoGJeXh66deuGwMBAdO/enUk3Cw8Px+XLl9GmTRtcvXpVYQtUVaGrq4vw8HBYW1vLHX///j3s7Ow4EaFZFlwqpNapUye0b99eYbeiRYsW4fbt2+pdV4NQKCwyZcoUUrlyZdK6dWuyfft2kpCQQAghRENDgzx//pxldV9HRkYGuXPnDtsyCCGECAQCMnPmTBIRESFznIvva2JiotzjSUlJZOvWrcTBwYHw+XwVqypJx44dycKFCxWOL1y4kHTs2FF1gkph+PDhpH///grH+/btS4YNG6ZCRcqTlpZGFi5cSAwMDIijoyO5du0a25KUJjk5mRw+fJhtGYQQQng8HomLi2NbhlLweDzC5/OZh6LXXIDH45X5oFq/nunTp5PGjRuT+Pj4EmNxcXGkSZMmZPr06aoXJofHjx8TbW1tMnjwYHLv3j2SkpJCUlJSSFBQEBk0aBDR1tYmjx49YlsmIYQQPp+vNvMAIYTk5OSQ1atXk6ZNmxJdXV2iq6tLmjRpQlavXk2ys7PZlkcIIcTU1JSEhIQoHA8NDSUmJiYqVPTtBAcHc2YOMDQ0JC9fvlQ4/vLlS2JoaKhCRT8eutNNYRUNDQ251YDVYUe2OFxaMQwKCoJQKISfnx8aNGgAFxcXODk5wdLSknPvq46ODgYMGIDx48eje/fucs95/Pgx6zvdRkZGePDggcJib+Hh4WjZsiUncvufPHmCNm3aoH///pg3b55MK761a9fi7NmzCAwMZP09LUpeXh62bduGVatWoXLlyli5cqXaFXrj0hzw/v17WFtbcy5FQx7qFEWkTjuH6kRycjIcHR0RGxuLUaNGwc7ODoQQvHjxAv/88w8sLCwQFBTEmaKq//vf/+Dq6orExESZ45UrV8a+ffs4EekA0M9redC3b19YW1srrE4/adIkREVF4dy5cypW9vVw6Zqlr6+Pp0+fKmy19+bNGzRu3FimE4u6QXO6KyhisRibNm2S285g+vTprIfnFKJu1YDVhdatW6N169bYvHkz/Pz84OXlhVmzZkEikeDy5cuwsrLiTMujv//+Gz4+PujVqxesrKwwduxYjB07Via/jwvmMD8/v9TvjaamJicuXIC036mfnx8mTJhQokCOqakp/v33X068p4C0FsKBAwfg6ekJsViMVatWYfz48RAIBGxLU2v279+v1Hmenp7lrKRsGjduzBkzVRbqsIhRiKurK7Zs2cKZub40TE1Nce/ePSxYsAD//vuv3PahXPqM9OvXD+/fv8eFCxfw6tUrpuZLjx49mEK2XOD69eucet++h5iYGKxcuRLbt29nVcfChQvRqVMnJCYmYs6cOTILRH/99RdOnTqF69evs6pRHbG3t8epU6cwc+ZMueMnT56Evb29ilX9WOhOdwUkKysL3bt3x927d9GtWzem3+WLFy9w5coVtGvXDpcuXZJbJZQtCqsB+/j4IDMzE0lJSZzpI6wsXFoxlEd4eDiEQiEOHjyIlJQUdO/evcycP1VS+Bk4cOAAoqOj0blzZ0yYMAEDBw6ElpYW2/LQqlUrODs7K7wgbNy4Ef/++y/u37+vYmWKyczMxMWLFxEZGQkAqF+/Pnr06CHT+5RtGjdujDdv3mDq1KmYMWOGwhtWLhUmUgSX5gBFFYABqXEMDw9HdnY2J7QqE+3CFdRp51Cd8vqLQgiR2z6U8vWoU3cIQNp26/r169DS0sKwYcNgYmKChIQErFixAnv27EGdOnXw/PlztmXC398fEydOLFHcz9TUFHv27MHgwYNZUiZLaGhoqeMvX76Es7MzJ64D+/fvh5ubGzZs2ICJEycy7S7FYjH27NmDuXPnYufOnRg7diy7Qr8H1gLbKeWGp6cnsba2lptzEhwcTKytrcmSJUtUL0wJJBIJuXDhAhk6dCjR1tYm1atXJ1OnTmVbllJwKTemNMRiMfH39y8155dtLl++TEaMGEH09PRIpUqVOPEZ8PHxIbq6umTHjh0kLy+POZ6Xl0e2b99OdHV1ibe3N3sC1ZTi+aXFH1zKOy0LdZgDnjx5Qnr27Ek0NTXJ77//zrYcQgghBw4cIF26dCF8Pp/UrFmTLFmyhLx9+5ZtWXIZO3YsSUtLY1uGUqhTXr8ibty4Qc6ePUuSkpLYlsIQGBhIzpw5I3Ns//79pFatWsTMzIz89ttvnMk/NjExUfgwNTUlWlpanJmzTp06RTQ1NZnrQd26dcm1a9dIlSpVSM+ePcn58+fZlihDRkYGOXHiBFm7di1Zu3Yt8ff3JxkZGWzLkqHw+qmongPXrq+zZ88mPB6PGBkZkWbNmhEHBwdiZGRE+Hw+mTFjBtvyvhu6010BsbW1xapVqxSutB09ehQLFy5kws65SmE1YG9vb4SEhLAtR60qwVYUjh8/jokTJyIlJYUT7+ucOXOwceNGGBoaom7duiCE4M2bN0hPT8e0adOwadMmtiUCAPr06QNfX1+mwvKaNWswadIkmJiYAAASExPRoUMHhIWFsahSijrl85bVCuzjx4/YsGEDJz6rxXn79i0WL14MPz8/DBo0CCtWrED9+vXZliUD16NdAGntAYlEItOSLy4uDrt370ZGRgZ++eUXtG/fnkWFX+Dz+YiMjISZmVmp53EhimTt2rVIT0/H8uXLAUh3u3v37o1Lly4BAKpWrYqrV69yIry0d+/e6NSpE9O+6unTp2jevDnGjh2LBg0aYP369fj999/x559/siu0FLjYHaJVq1Zo164dli9fjn379mHWrFmwt7eHl5cXWrZsybY8hi5duuDEiRPM9ZTLvH//XqnzFLW/Y4OgoCD4+voyEXo2NjZwcnJC69atWVb2/VDTXQHR0dFBZGSkTF/eokRHR6N+/frIzs5WsTL5pKWlwcDAAHw+X+a4RCJBeno6J24IAJTQJw8ej8eJG+5BgwaVeQ6Px8Px48dVoObreP/+Pby9vbF//37mxnv8+PFwcnJiWxoA9bggFA8rNTIyQnBwMFOgJC4uDtWqVePEZ1WdqF27tlLnvX37tpyVKE9CQgKWLl2KvXv3on379lizZg2nbmAVceXKFXh7e+PkyZPQ0dHByJEjOdH/fNy4cdDS0sKePXsAACKRCPb29sjOzoalpSXCwsI40zqQz+eXGppNCOHMNat58+Zwd3fH8OHDAUg3B8aMGYPLly+jQYMGGD16NPT09HDkyBGWlQKWlpY4c+YMfvrpJwDSHN+AgADcvn0bgFT7kiVLOLGoWRyRSIS1a9diy5YtsLe3x+rVq9G5c2e2ZQGQ9j9/9OgR6tWrh/z8fGhra+PChQsKW1+yhTqlmKgTy5Ytw5w5czhVE+FHQwupVUCMjIwQHx+v0HTHxsZyprCKv78/3N3dERwcXOKLlpWVhZYtW2LDhg3o378/Swq/IJFI2JagNGX1kOUaOTk5OH78OLy8vHDjxg1Ur14dY8eOxbhx42QKqnGBwiJ1XKb4WiqX11aPHDmCAQMGMDuZHz58QLVq1ZhFrszMTGzfvh3z5s1jUyYAbpnpssjIyMCGDRuwceNG1KtXD2fOnFHYA5mLdOvWDd26dWOiXXbs2MEJ033nzh2ZQk4HDhxAfn4+IiMjYWxsDHd3d6xfv54TphsAjh07phaFtN6+fYsmTZowr8+dO4chQ4agXbt2AKQ9eocOHcqWPBmSk5Nhbm7OvA4ICJApANuyZUtER0ezIU0hxbtDeHt7c65mjkgkYjZZBAIBdHV1FVaypihHWTndhRT97rHF0qVLMWnSJGq6KepF586dsWrVKoW7mGvWrOHMyuauXbswb948uV8yfX19uLu7Y/v27Zww3eqEt7c32xKU5o8//sC///6LzMxM/Prrrzh37hy6d+/OueI5kZGR8PT0xJ49e0pEX6SmpsLNzQ0rVqygNwlfibOzs8yufMOGDWV25UUiEebPn88J010WHz58wLJly7B37162paBu3boQiUSYOnUqnJ2dwePx5N6AceFmqziKol24wMePH2XC8q9evYrBgwczC51jxozh1Pzbrl07tdiRE4vFMiH7d+/exYwZM5jX1apVQ0JCAgvKSmJubo63b9/CysoKubm5ePz4MZYuXcqMi0QiznSIIWrWHeLixYvMd0kikeDq1at49uyZzDlcaMcWFhaG2NjYUs/hwtzq4OAAHo9X6sI7V6JduLw58KOgprsC4unpyezGzZo1S6adwaZNmxAWFoagoCC2ZQIAnj17hp07dyoc//nnn7Fo0SIVKlLMH3/8gXXr1sHAwAAA4Ovri19++YWpBJ2SkoIRI0aoRW9GLnH79m0sWbIEo0aNQuXKldmWo5D169fDyspKbrqDsbExrKyssH79eoW9O1UJj8crsWjBtUWMQtRpV74sEhMTIRQKOWG64+PjAQDr1q3D+vXrZd7XwpswrtxsAeoT7aKjo4OsrCzmdVBQENavXy8znp6ezoY0taZu3bq4efMm6tSpg6ioKERERODnn39mxj98+MCZ60OfPn3g4eGBtWvX4uTJk9DT00OHDh2Y8dDQUNStW5dFhV9o0qRJie4Q8voccyWNb8yYMTKvf//9d5nXXJmzunbtKvdaxbW5VZ2iswDu3qf8KKjproDY29vj8uXLTB5s4YeYEAI7OztcunSJE8VIAGmYllgsVjiel5eH5ORkFSpSzJ49e/Dnn38ypvv333+Ho6MjsyOXk5ODixcvsimRwdXVVanzvLy8yllJ2Sgb/sQ2AQEBOHTokMLxYcOGYcSIESpUpBhCCMaOHcvsHGVnZ2PSpEnMAlFOTg6b8igqQJ1uttQl2gWQ7hwdPHgQq1evxq1btxAXF4cuXbow469fv0a1atVYVPiFmjVrIi8vj20ZSjF58mRMmTIFt27dQlBQENq0aYOGDRsy49euXSu1DZ4qWb58OQYNGoSOHTvCwMAA+/fvlyn05+XlxZlUjsL2WoWLb8XhkkFUpxS+e/fulVmgkAuIRCI0atSIbRlKY2NjU+a8X7xNmzpBTXcF5Ny5c+jTpw+eP3+O4OBgpkq5jY0NHBwc2BVXjFq1auHhw4ews7OTO/7w4UPOVFVUpx05Hx8f1KxZE82aNeO0TnUiKiqq1DDNKlWqcCaPr/huwahRo0qcw5W+rJTyQZl5s3jYJluoS7QLII0k6927N44cOYKYmBiMHTsWlpaWzLi/vz+Th8w2UVFRnAlzLovffvsNAoEAZ86cwc8//4wlS5bIjH/69EnpxeTypkqVKrh58yZSU1NhYGBQIlT76NGjzOI821y/fp1tCRUSa2trtUjbaNKkCVq2bIkJEybAycmJM/WcFLF06VK1q0n0NVDTXQEZNGgQRo8ejU2bNsHBwYFzRrsogwYNwsKFC9G9e3eZwiSAtODbokWL5BoGSum4ubnB19cXb9++xbhx4zBq1Ci1KKbDZYyNjfH69WuFZubVq1ecCdHjUk6pMpSWx5eSksKisoqHSCSCr68v9u3bh0ePHnFih0tdol0Aaeu6hw8f4vLly7CwsChR3MvBwQGtWrViSZ0s6rbg6urqqtBYl5aGxhaKzAGXrrVcaLWoLIoKJRobG8PGxgZt2rRRsSL1JyAgAN7e3pg9ezZmzpyJwYMHY8KECTLpEFzCyclJLRYzvhXaMqwCEhISgrFjxyI1NRU+Pj4yeVFcQyQSoU2bNoiKisKoUaNga2sLAHj58iUOHz4MKysrBAUFcWJ1rnibCENDQ4SEhHC2DVNOTg5OnDgBLy8vBAYGom/fvhg/fjx69OjBybBNrjNs2DDk5eXB399f7vivv/4KLS0tHD16VMXK1JuK1IovJSUFAQEBnNBanJs3b0IoFOL48eOoVq0aBg0ahMGDB3OifdiyZcuUOs/T07OclVQs+Hw+4uLi1CIM9tOnT9i4cSM8PT3lFqpcsWIF5syZU2Jxng2UackJACdOnChnJWWjTt0hFLVkTElJQWpqKtq2bYvTp0+zvqjRuXNn+Pv7q0Wf7kIyMjJw5MgR+Pj44NatW6hXrx7Gjx+PMWPGwMLCgm15AEq2Oq2IUNNdQRGLxVixYgXWrFmDyZMnY+HChdDQkA1s4MquXGpqKubPnw8/Pz8mf9vExAROTk5YuXIlTE1NWVYohc/nY+LEiUyl9R07dmDUqFHMandmZib+/vtvTt5wv3//Hj4+Pjhw4ADEYjGeP3/OmfA3deHJkydo06YN+vXrh3nz5sksEK1btw5nz55FYGAgmjdvzrJS9bopVCfGjRun1HlciTSIjY2Fj48PhEIh0tLSMGzYMOzevRshISEy+bJsU1quLo/HQ3h4OLKzszkxt6rTbhyfz0ejRo1KXPuL8/jxYxUpUsycOXOQlpamsAjhpEmTYGxsjLVr16pYWUnUaR4obmSMjIxkukNwbbNAEW/evMGoUaPg4ODAetRDVlYWrl69in79+gEA5s+fL1MnRSAQYPny5dDR0WFLYqm8evUK3t7eOHjwIGJjY9GrVy+cPn2abVn/if7n1HRXcC5duoQ+ffrIhJlxqXBGUQghSEhIACEEZmZmnNuN7dSpk1KauJhDFR0dDW9vb/j4+CA3NxcvX77kjOmOiopS6jxra+tyVlI2//vf/+Dq6orExESZ45UrV8a+ffs40coEUK+bQkr50L9/f9y8eRN9+/bFyJEj0atXLwgEAmhqanLOdCsiODgYHh4euHbtGlxdXbF79262JanNbhwgvYmdPXt2mXN98fxpNmjUqBF2796N9u3byx0PDAzEb7/9xhQGoyiHukXolcbNmzfh6uqKV69esapj9+7dOHv2LM6cOQNA+p7a29tDV1cXgHQhft68eZg5cyabMkslIyMDhw8fxvz585GSksLp///3798jIyMDdnZ2SkXGcRpCqbAcP36cVK1alXTu3JlcuXKF3LhxQ+ZBqdhkZ2eTf/75h3Tr1o3o6OiQIUOGkLNnz5L8/Hy2pcnA4/EIn88v8Sh6XCAQsC2TITMzk5w4cYKsW7eOrF27lvj7+5OMjAy2Zak9R44cIQMHDiT29vbE3t6eDBw4kBw9epRtWUojkUjIuXPnyODBg9mWQgghRCAQkJkzZ5KIiAiZ4xoaGuT58+csqVKON2/ekJEjRxINDQ0ybNiwEv8GrvL69WvSpk0b4ubmxrYUQoh0bo2Li2NbhlLo6emR9+/fKxx///490dPTU6EixYjFYhISEkIyMzNLjGVkZJCQkBDOXGeLfwYMDAzI69evmdexsbGEz+ezIe2refv2LdHX12dbBmnXrh05ffo087r4e3rw4EHSunVrNqSVSUBAABkzZgwxMDAgRkZGZMKECeTu3btsyyKEECIUCslff/0lc+y3335j7gMbNGhAoqKiWFL3Y6CF1CogKSkp+OOPP3Dq1CmsWrUK06dPZ1uSQpo1a6bU7jEXwt/UicIWPFZWVnB1dYWvry+qVKnCtiy5PHnyRO5xQgj+/fdfbN26lTO78gCgq6uLgQMHsi1DaQghSExMBI/H42RlaIlEAmdnZxw9ehQ2NjZMJ4Pnz59j+PDhGDp0KHx9fTkX+VLI27dv4eXlBR8fH3z+/BndunVjWxIAaUVwoVCIFi1aoEGDBnBxcYGTkxPbskolISEBS5cuxd69e9G+fXsEBgZyIudcWerUqYM1a9Zwpso2V78z8tDV1cW7d+8URjS9e/eO2Ulkm4MHD2L79u24d+9eiTEtLS24urpixowZtAjsD+bp06ec6Gbz+vVrNG7cmHmto6MjswPbqlUrTJ48mQ1pcvn06RN8fHzg4+ODV69eoW3btti6dSuGDRvGtBHlAnv37pXpy37hwgV4e3vjwIEDaNCgAaZMmYKlS5di3759LKr8PqjproA0bNgQ1tbWePz4MZN3ylUGDBjAtgSlSUlJga+vL9zc3AAAI0eORFZWFjMuEAjw999/c6K4xu7du2FtbY06deogICAAAQEBcs/jQk5v06ZNSxy7cuUKPDw8EBERgXnz5mH27NksKFNvYmNjMW/ePJw+fRoikQiANJ9v4MCBWL16NScKEgHAli1bcOXKFZw+fZrJkSvk9OnTGDduHLZs2YIZM2awI1AOOTk5OHbsGIRCIW7fvo38/Hxs2LAB48eP50ytjNatW6N169bYvHkz/Pz84OXlhVmzZkEikeDy5cuwsrLiRIFKQBrquGHDBmzcuBH16tXDmTNnONPn+GuxtrZGbGws2zIAlF69nBCCCxcuQCgU4tixYypUJR9HR0ccPHhQYeHXAwcOcKYqvFAoxJw5c0q0CgMADQ0NzJs3D9u3b+eM6VaX7hBpaWlyj6empuLRo0eYPXt2iXaYbJCSkiKTw/3582eZcYlEIjPOJr1798aVK1dQpUoVjB49Gq6urpz1BZGRkfjpp5+Y16dOncKvv/6KkSNHAgBWrVqldOocV6GmuwLyxx9/YP78+XIvCFyDC7lkyvL3338jODiYMd2nT59Gz549mRvXu3fvYvPmzfjzzz9ZVCll9OjRarXLUcjjx4/h7u6OW7duYcKECTh37lyFLqpRXqSlpaFt27ZIT0/HuHHjYGdnB0IIwsLC4Ovri9u3b+Px48eciCDw9vbG+vXrSxhuAPjll1+wbt06zpjuR48eQSgUwtfXF/Xq1YOLiwt8fX1Ro0YN9OzZkzOGuyj6+vpMK6bw8HAIhUKsWbMGHh4e6N69OycK6NStWxcikQhTp06Fs7MzeDye3DZiTZo0YUHd18GV3ThAGoVRPMKJq5EZc+bMQffu3WFsbIy5c+cyi4JxcXFYt24dfHx8cOnSJZZVSgkPD0fr1q0Vjrds2RIvXrxQoaLSKW5Ui+4mAtyJiDAxMVGohcfjYcKECfDw8FCxqpLUqFEDz549U2heQ0NDUaNGDRWrko+mpiaOHTuGfv36cd4TZGVlyVxDAwMDMX78eOZ1nTp1OLOg+c2wGtxOoagRrVq1IpcvX2ZeF8/jOXHiBHFwcGBDmtrz6tUrMmzYMCIQCIizs7PM+0r5epYtW0bq1atH4uPjS4zFxcWRevXqkZUrV7KgrCQ6Ojql5nK+e/eO6OjoqFCRYgQCAZkxYwZ5+fKlzHF1yJMuilgsJv7+/qR///5sSyGESPNOCx+F9RyKv+ZK3mlqaqrcR1RUFPH39yd16tQhS5cuZVumDNnZ2eTQoUOkc+fORFNTk/D5fLJx40aSmprKtjQZdu/eTbS1tQmfzycmJibE1NSU8Pl8oq2tTXbu3Mm2PAY9PT0SEhKicDwkJIQz+efqRPG6Q4WPx48fE5FIxLY8hmnTppGGDRuSrKysEmOZmZmkYcOGZNq0aSwoU2/s7OzI8ePHCSGEfP78mQgEAvLw4UNm/N69e8Tc3JwteT8EutNNYRV1yul+8+aNzMqmra0t0/sSkIZJR0ZGsiFNrfnjjz8gFArRuXNnPHz4EA4ODmxLUnvOnj2LBQsWyO3PW7VqVcyfPx9///03FixYwII6WXR1dZGSkqIwlzMtLY0zrVe6du0KoVCI+Ph4uLi4oGfPnpzZJfoaBAIB2rZti7CwMLalAJDuvqoL6rIbB6hfZMbvv/+Ofv364ciRI3j16hUIIbCxscGQIUM4s3MIAPXr10dgYKDCyIvbt2+jfv36Klal/nTs2JFtCUqxYMECHDlyBLa2tpgyZQpsbGwASCMgtm/fDrFYzIlrK6Be7UPHjBmDyZMn4/nz57h27Rrs7OzQokULZjwwMBCNGjViUeH3Q003hVWK5nQTQrB69WpMmjSJE+1WipORkYHU1FRYWVkBAB4+fFhiXCKRsCFNrdm9ezd0dHQQHx9fagEithdejhw5ggEDBjALLR8+fEC1atWYAiqZmZnYvn075s2bx6ZMAEBERATatm2rcLxt27aYM2eOChUppk2bNti1axd27dold3zHjh2c6X188eJFpv2em5sbsrKyMHz4cADcCdFUlpiYGCxevJgTN4dcCcdWBkUtIY2MjFC/fn1OpGwU4ujoiKlTpyIoKIizeZzFqV69OqdbLQHAiBEjsGjRIrRt27aE8Q4JCYGnpycnrgOAtM2WMijKpWebjIwM+Pn5ISsrCz169ODEYoa5uTkCAwPh5uYGDw8PpnYCj8dD9+7dsXPnTs7UTCnM5S/kn3/+Qf/+/TlTz6Mo8+bNQ2ZmJk6cOAELCwscPXpUZvzOnTtwdnZmSd2PgfbppnCK4j0kuUSjRo0wb948jB49Wu64t7c3NmzYQPuIfiVLly5V6jy28/8FAgFiYmKYHHMjIyMEBwdzst+phoYGPn78qPDCHxsbixo1akAsFqtYWUkCAwPRqVMnDBgwAHPmzGHyz1+8eIG//voLp06dwvXr19GuXTu2pZbg8uXL8Pb2hr+/P6ysrDBkyBAMGTIEzZs3Z1tamYSEhKB58+ac+LyuW7cOU6dOZapT37lzBz/99BO0tbUBACKRCO7u7ti5cyebMpXm2bNnnNiR6dmzJ+7evYv+/fvLRGZwsVf71q1b5R43NjaGjY0NZxbeACAvLw89evTA7du30a1bN6bjwsuXL3HlyhW0a9cOly9fhqamJstKpX26CxcEFd3u83g8TswDUVFRcHFxwePHj9G6dWsIhUJ0796diSDU1dXF+fPnObVAkJSUxPQNr1evHic3jIrC5Xvs/wLUdFdACm9Qi4Y+qwtcnhAWL16M/fv348GDByXMTGxsLFq1aoXRo0djxYoVLCmklCd8Ph+xsbGM6S7+WeWS6RYIBIiNjZUbXg5wSysA+Pv7Y+LEiUhKSpI5bmpqij179mDw4MEsKVOO5ORkHDp0CF5eXggNDeXM+1oaXDLd6rSgpQiRSARfX1/s27cPjx494ozWwsgMb29vJjJj586dCA0NRYMGDdiWx1C7dm25x1NSUpCamoq2bdvi9OnTnDE1eXl52LRpE/755x9ERkYyofAjRozAjBkzOHP/VblyZRgaGmLs2LFwcXFR2Dq0+I4oGwwbNgzR0dGYMmUKjhw5goiICNStWxdCoRB8Ph9ubm5ISkrCtWvX2JaqtnD5HruQrKwsXL58GREREQAAGxsbdO/enTMtA78HarorIHw+Hzo6OmjdujU6d+6Mzp07o3Xr1tDQ4H42AZcnBJFIBEdHR3z48AEuLi4yeTyHDh1C9erVcf/+fU6G7agLoaGhMhMtl6oVq5Pp5vP5MDY2VhjyTAhBWloaJ7QWkpmZiYsXLzK7GjY2NujRowf09PRYVvZ1PH78mO50fyXq9N0qzs2bNyEUCnH8+HFUq1YNgwYNwuDBgznZX1xdIzPevHmDUaNGwcHBQW2iHbhCbm4u/P394eXlhVu3bqFPnz4YP348evXqxbmUGAsLC5w+fRqtWrVCUlISqlSpgjt37jBRDiEhIejatSsSEhJY1VlaGlwhPB4PQqFQBWq+Di7fYwPSrkATJkwo8X9cpUoVCIVC9O/fnyVlPwbuuzDKV/P27Vtcu3YNAQEBEAqFWLJkCfT09NCuXTvGhLds2ZLJRaUoh6GhIe7cuYP58+fD19eX6W9pYmKCESNGYNWqVdRwfyP379/H+PHjERYWJpMfZW9vD6FQyMkbWC7j7e3NtoSvRk9PDwMHDmRbRplIJBL4+PjgxIkTePfuHXg8HmrXro0hQ4bAxcWFMwZm1qxZpY4X7y1LUZ7Y2Fj4+PhAKBQiLS0Nw4YNQ05ODk6ePMmpkO3idO/eHd27d5eJzFi7di0nFzOKUqdOHaxZs0Yps0ORRUtLC8OHD8fw4cMRFRUFHx8fTJkyBTk5ORgzZgyWLl3KmQ2Z+Ph4pr5DpUqVoKenJxNVaGFhgeTkZLbkMZSmIT8/H1euXEFOTg4nTTeXCQwMxJAhQ/DLL79g9uzZTCROWFgY/vrrLwwZMgQBAQGltuvjOnSn+z/AmzdvcOPGDdy4cQMBAQH48OEDDA0NGdPIJsXzuNzd3TF37twSIVDTpk1TpawyIYQwN61mZmacWzFWJ8LCwuDo6IgGDRpg5syZMhPtpk2bEB4ejqCgINZvZvl8Pvbv38+E4Tk7O2Pz5s3MTUFKSgrGjRvH+RtYrqEol7M4XJgDCCHo378/zp07h6ZNm8rknz99+hS//PILTp48ybZMAECnTp2UmpcUFQZTJeq0092/f3/cvHkTffv2xciRI9GrVy8IBAJO5kkrg7pEZrx79w6NGjVCeno621JQu3btMr9bPB4Pr1+/VpGir+Pt27cYP348AgIC8PnzZ86E7KvTPCCPU6dOYcGCBfj06RPc3d050cXg9OnTMq+L37cU8ssvv6hSllz69OkDKysr7NmzR+7477//jujoaJw7d07Fyn4c1HT/R3j//j2uXbuGGzdu4OTJk8jLy0NmZibbshTmcRWFx+PhzZs3KlBDYYNhw4ZBLBbj+PHjJW5kCCEYNGgQNDU1ceTIEZYUSlEmMoQrBWnUieJzQHR0NCwtLWV2X7gyB3h7e2P69Ok4deoUOnfuLDN27do1DBgwANu3b1dYbJEiHz6fjxUrVjCVv4svvopEInh6enLiu6WhoYFp06bBzc1NppIyF013RSpQd+bMGXh4eHCiUOmWLVsUjr179w579uxBTk4OJz6vheTk5OD48ePw8vLC3bt30bdvX7i6uqJXr15sS2Pg8/mYOHEik1K0Y8cOjBo1ilnozszMxN9//82p9xWQfq88PDzw+PFjTJkyBR4eHjA1NWVbFgD1um+pVKkSAgIC0LhxY7njoaGh6NixIyeiHb4VarorKFFRUbhx4wauX7+OGzduICEhAW3btkWHDh3QsWNHODo6cqbQh7rQuXNnpVa3r169qiJFFQMzMzOcP38eP/30k9zxBw8eoE+fPjQc9j8Cl3POevTogS5duijcwVi1ahUCAgJw8eJFFSsrSZ06dfDgwQNUrlyZbSllUqtWLaV25bnQzzsoKAhCoRB+fn5o0KABXFxc4OTkBEtLS86ZbnUqUJeWlib3eGpqKh49eoTZs2djzJgx8PT0VLEy5UhKSsLy5cuxa9cuODo6Yu3atZwIg71//z68vb3x77//olatWhg3bhxGjRrFmd3toqhTdA4gjcZzd3fHhQsXMHr0aCxdupRT/eTVDV1dXbx8+VJhC8n379/Dzs4OWVlZKlb24+BGIgflh1KnTh0kJyejXbt2+Pnnn/H777/jp59+4kzejrri4OCgcEwkEuGff/5BTk6O6gRVEEQiUak9LS0sLCASiVSoiEKRT2hoKNatW6dwvHfv3kqHy5c3796944SZUoZ3796xLUFpWrdujdatW2Pz5s3w8/ODl5cXZs2aBYlEgsuXL8PKyooztT2K76lweY/FxMREoeHi8XiYMGECJ8J1i5OVlYWNGzdiw4YNqFmzJk6cOIE+ffqwLYuhdevWsLa2xrRp09CiRQsAwO3bt0ucx4Xw4hs3brAtQSmio6Ph6emJQ4cOoV+/fpzrBKCu1K9fH9euXcO4cePkjl+9epUTfdq/B+rCKiCFq0B8Ph8aGhrQ1NSEQCBgWZV8rl27hilTpiAoKAhGRkYyY4VtQnbt2sWJvoybNm0qcUwsFmPHjh1YuXIlqlevjuXLl7OgTL2pWbMm7t+/DysrK7nj9+7dU7jyqUryauxmqAAAKsRJREFU8/MRFhbGhD7t3r0bubm5zLhAIICbmxstUFiBSUpKKnWByNzcXK1D3yjKo6+vD1dXV7i6uiI8PBxCoRBr1qyBh4cHunfvXiKXklI6inYvjYyMUL9+fSb1gCvk5+fj77//xtKlS6Gjo4OtW7di1KhRnKzvEhUVVeq9CVfCixUhFouRnZ3Nmc+Ara0teDweZs2ahXbt2iEyMpLpulEULixkREREICUlBa1atWKOXb16FStWrEBGRgYGDBiABQsWsKjwC+PGjcOcOXNgbm5eYuHq7NmzmDdvHme0fivUdFdAYmJi8PLlSya8fN26dcjOzkb79u3RqVMndOzYES1atOCEOdi8eTN+++23EoYbkPaN/P3337Fp0yZOmO7iHD58GJ6ensjKysKff/6JiRMn0miCb8DJyQmzZs2Cra0tGjVqJDP29OlTzJkzhxM5sn5+fti9ezdu3rwJAJg7dy5MTEyY//OEhATo6Ohg/PjxbMqklCP5+fmlfscFAgHEYrEKFZXOxYsXy+y/y4UbQ6DsqvBcNDOF2NraYt26dVi9ejXOnDkDLy8vtiWpHQEBAZgzZ45atAg8cuQIFi1ahJSUFCxcuBBubm6cTdeTSCRsS1CaM2fOIDExEWPHjmWOrVy5EsuXL4dYLEaXLl3g5+fHer50dnY2AGD9+vVYv3693HO4spDh7u6Oxo0bM6b77du36N+/Pzp06IAmTZpg9erV0NPTw4wZM9gVCmD69OkIDAxEv379YGtriwYNGjCFSiMjIzFgwABO6PweaE73f4QXL14w+d2XLl0CAE5UL69ZsyYuXLigMDTn5cuX6NGjB6KiolSsTDEXLlyAh4cH3r59izlz5mDWrFnQ19dnW5bakp2dja5du+LevXvo3r27zER75coVtGrVCteuXYOOjg6rOrt3744JEyZg+PDhAErmHu/evRt+fn6cyDdbtmyZUudxIT+yeC5njRo1cPv2bdSqVUvmuLyFOVXD5/PRu3dvphBVcXJycnDhwgVO3GypUwEddaoKr06oU4G64vnnXIbP50NXVxfOzs6lzksbN25UoapvQyKR4Ny5c+jXrx/bUtC5c2cMGTIEkydPBiBtIdWhQwcsW7YMDRo0wMKFC9G7d2+1eF+5gpWVFY4cOcL0Ol+xYgWOHTuG4OBgAIBQKMS2bduY11zAz88Pvr6+iIiIAADY2NjAyckJTk5OLCv7fqjp/g8QFxfH7Hpfv34dkZGR0NbW5kQxAh0dHTx79gz16tWTO/7q1Ss0btyYE1rv378Pd3d3BAUFYdKkSVi4cGGJ1maUbyM3NxebNm2SO9HOnDlToclRJVZWVrhx4wbq1q0LoKTpfvHiBdq1a4ekpCQ2ZQIAmjVrpnCMx+MhPDwc2dnZnLjZ5vP5MruYhBC5r7mgVVGuWXG40Ce9ePsdLkOrwpcP6lSgTp0+r+pW8Eser169gpeXF3x8fPD582fk5eWxLQlVq1bFxYsXmevXrFmzEBYWhgsXLgAAzp07h+nTp8sN5abIR1dXFxEREUz6XteuXdG2bVsm3eD169do0aIFJzbh/gvQWNgKSHx8PNOX+/r164iIiICmpiZatWoFJycndO7cmVn1Ypvq1auXarpDQ0NhaWmpYlXyad26NXR1dTFp0iTUrl0b//zzj9zzuNBPWN3Q0tKCu7s73N3d2ZaikOLV09+8eSNTGVpTUxMZGRmqliWXJ0+eyD0eHBwMDw8PPHv2DL/99puKVcmHyzemxeGCmVYWLodjF8fX1xcLFiwoYbgBMNXiDx8+TE33V6JOBeoA9fnMqkvBr+JkZWXh6NGj2LdvH+7cuYMOHTrA09MTAwcOZFsaAGnkRdFr6u3btzF06FDmtb29PT59+sSGNBmUrdfAhdSdSpUqISYmBlZWVpBIJHj48CFmzZrFjOfm5nKuwGJiYiLzOYiOjsbff/+NrKws9O/fn5Oppl8FoVQ4eDwe0dLSIm3btiULFy4kV65cIZmZmWzLksuUKVNIo0aNSFZWVomxzMxM0qhRIzJ16lQWlJWkZs2apFatWqU+ateuzbZMtSMpKYls3bqVpKamlhhLSUlROKZqrK2tydmzZxWOnz59mlhbW6tQkfK8efOGjBw5kmhoaJBhw4aRiIgItiVRyhkej0fi4uJKPefp06cqUlM65ubm5MmTJwrHHz9+TMzNzVUniKJyeDweMTExIaampqU+1IGwsDAye/ZstmUw3L9/n0ycOJEYGRmRZs2akQ0bNhCBQECeP3/OtjQZ6tatSy5cuEAIIUQkEhEtLS1y+/ZtZvzRo0ekSpUqbMlj4PF4ZT74fD7bMgkhhIwYMYL069ePREVFkb/++osYGBiQ9PR0ZvzYsWOkSZMmLCr8QmhoKKlZsybh8/nE1taWPHnyhJibmxMDAwNiZGREBAIB8ff3Z1vmd0F3uisg58+fR/v27dUiz3jRokU4ceIEbGxsMGXKFNja2gKQ5nLv2LED+fn5WLhwIcsqpajbroG6sH37doSGhmLq1KklxoyNjXHr1i2kpaWx/jno2rUrVq5cKbcdDCEEq1evRteuXVlQppiEhAQsXboUe/fuRfv27REYGIiWLVuyLUsGiUSC9evX4/Tp08jNzUXXrl2xZMkS6Orqsi1NrRkzZozc91AkEsHX1xf79u3Do0ePOBG2T6vClw93795FYmKiTL7ugQMHsGTJEqZy8bZt2ziRvgMAS5cuLbPwH1fJyMjAv//+C6FQiKCgIDRs2BAbNmxgWxaaNGmCtLQ0jBgxAoGBgbC3twcATrZfGzp0KGbMmIEFCxbg3LlzsLCwkOl1/vDhQ+YekU3UqTjdypUr0b17d9SsWRMCgQBbt26V8QYHDx5Ely5dWFT4hXnz5qFx48Y4fPgwDh48iH79+qFv3774+++/AQBTp07FmjVrMGDAAHaFfg9su37Kj6es3Y28vDxy7949Fakpm3fv3pHevXsTPp8vs0rYu3dv8ubNG7blKU1ycjLZtm0b2zLUjqZNm5IrV64oHL9y5QpxcHBQoSL5vHr1ihgZGZFWrVqRI0eOkODgYBIcHEz8/PxIy5YtiZGREYmMjGRbJiGEkPT0dPLnn38SIyMj0rx5c3Lx4kW2JSlk2bJlhM/nkx49epBff/2V6OjokHHjxrEtq8IREBBARo8eTfT19Un9+vWJu7s7uX//PtuyCCGE8Pl8Eh8fr3A8NjaWMztH6kSvXr3ImjVrmNehoaFEQ0ODTJgwgfz111/EwsKCLFmyhD2BRVAmMoOL3L59m4wbN47o6+sTPp9PZs+eTV68eMG2LAYtLS3i4uJCLl26RCQSCXNcQ0ODczvdmZmZxMXFhZiYmBA7Ozty8+ZNmfFOnTrJfJ4pypGXl0eCg4PJx48fS4wFBweTxMREFlSVpHLlyiQkJIQQIo104PF45OHDh8z4ixcviLGxMUvqfgy0kFoFpHgV0MaNG+PcuXNMIYW4uDhUq1aNEzscRUlOTsarV69ACEH9+vVZbwuhLFevXoVQKIS/vz/09PSQmJjItiS1wtDQEM+fP4e1tbXc8aioKDRq1KhElWs2uH//PsaOHYuXL18y+YeEENjZ2cHb2xuOjo4sK5RiYWEBkUiEqVOnwtnZWWGuZJMmTVSsrCT169fHnDlz8PvvvwMArly5gr59+yIrK4sTbQ3VmdjYWPj4+EAoFCItLQ3Dhg3D7t27ERISgoYNG7Itj0GdqsKrE5aWljhz5gx++uknAMDChQsREBCA27dvAwCOHj2KJUuWICwsjE2ZANSrenl8fDx8fHzg5eWF1NRUODs7Y8SIEWjTpg3nvlsfP36Ej48PvL29kZWVBWdnZ4wcORKOjo4IDg7mlNaoqCjUqFGD8/O+OuV0l8WLFy8gFAo5EZVRvJhi8WK1XPUuXwM13RUQZT64lpaWahUiwzWio6Ph7e0Nb29vREVFwcnJCS4uLujatSs0NTXZlqdWmJiY4MKFCzJhZEUJCgpCr169OFVd88mTJ0wF1fr165daLZwNit608Hg8mUIpha+5UhFcW1sbr169YhYFAWlXg1evXqFGjRosKlNv+vfvj5s3b6Jv374YOXIkevXqBYFAAE1NTc4ZA3WqCq9O6OjoIDIykvlutW/fHr1792ZSdd69e4fGjRtDJBKxKROAelUv19XVxZAhQzBq1Ch0796dmW+5+N0qyrVr1+Dl5YUTJ04gOzsbc+bMwYQJE2BjY8O2NADqs/BSfFGg+DW28BgXrq/ykJcK8ezZM7Zlgc/nIy4uDmZmZgCk3iU0NBS1a9cGUDFMN83p/o+iLlVCuUReXh5OnjyJffv24datW+jVqxfWr18PZ2dnLFy4kLMXWq7TrFkznDx5UqHp9vf355ypbdasGec0FYULLYCURSwWl+jBrqmpyYkWNurM+fPnMW3aNLi5uaF+/fpsyykVaqbLB3Nzc7x9+xZWVlbIzc3F48ePsXTpUmZcJBJxZpFYnTYBatasidu3b8Pa2ho1a9aEnZ0d25KUokuXLujSpQtSU1Nx+PBheHl5YcOGDWjUqBFCQ0PZlse5KtqKKP5ZLb6xxVXu3LkDoVCII0eOICsrCzNnzoSXlxenPr9jx45lIp6ys7MxadIkJgc9JyeHTWk/BGq6KRQlqV69Ouzs7DBq1Cj8+++/TPi7s7Mzy8rUmylTpsDJyQk1atSAm5sbBAIBACA/Px87d+7Epk2bFLZnUyXLli1T6jxPT89yVlI2NWvWLPMcLqxsA9IbraIXWqDkxRYATpw4wYY8teX27dsQCoVo0aIFGjRoABcXFzg5ObEti6JC+vTpAw8PD6xduxYnT56Enp4eOnTowIyHhoaibt26LCpUT16+fMkYmJYtW8LGxgajRo0CoB4bGsbGxvjjjz/wxx9/IDg4GLt27WJbEoM6vH/qhLxUiBs3bqBNmzZwdXXllOEeM2aMzOvC71RR1L1tJA0vr4AIBAJERETAzMwMhBBYWVnh9u3bqFWrFgBpiIadnZ1ah2iwQaVKldC4cWOMGjUKw4cPh5GREQDuh5SpAwsXLsTq1athaGjIrBa/efMG6enpmDt3LtasWcOyQpS6s83j8RAeHo7s7GxOf6+4WLmahhaXLxkZGfDz84OXlxfu37+P/Px8bNy4Ea6urjA0NGRbHqUcSUhIwKBBg3D79m0YGBhg//79Mj2Zu3btitatW2PlypUsqlRv0tPT4evrC29vbwQFBaFjx44YMWIEBgwYwITJcpWcnBzs2LED69atQ2xsLNtywOfzMXHiROjp6ZV63saNG1WkSDm4vNOtrqkQFRVquisgfD5fZrWwMH+z+Gsu3HCrE9nZ2Th+/DiTB9O7d2/GgHOtIIk6cv/+fRw+fJgppmdjY4MRI0agVatWbEsrleDgYHh4eODatWtwdXXF7t272ZZUgps3b0IoFOL48eOoVq0aBg0ahMGDB3OufRilfAkPD4dQKMTBgweRkpKC7t27K10UiKK+pKamwsDAgIkiKiQpKQkGBgbQ0tJiSVnForAo1cGDB5GUlMSJFJmcnBz8+eefuHz5MrS0tDBv3jwMGDAA3t7eWLhwIQQCAaZMmQJ3d3e2pYLP56NNmzalfh55PB6uXbumQlVlw2XTbWdnh5ycHIwYMQIuLi7MzjY13exATXcFJCAgQKnzOnbsWM5KKi6vX7+Gt7c39u/fj48fP8LZ2Rljx45Fly5dStzYUComb9++xeLFi+Hn54dBgwZhxYoVnMqdVZfK1RTVk5+fjzNnzsDLy4uabgrlByMWi7Fx40bMmzePbSlwd3fHnj170K1bNwQGBuLz588YN24cgoKCsGDBAgwdOpQz9yzqVEyvKEZGRggJCWEKfnGNwlSIo0ePMqkQ8+bNQ2hoKBo0aMC2vP8U1HRTKN+BRCLBxYsXIRQKcebMGRgaGiIhIYFtWWqFsgVcuNDeCpCGbC5duhR79+5F+/btsWbNGs7tGKtT5WoKhUJRR9LT0yEQCKCrq8scCw4OhqenJ86ePcuJaMI6depg8+bN+OWXX/Ds2TM0adIEY8eOhVAo5Fz+tLqYblNTU5n3LiUlBUZGRiWqmiclJalaWqmocypERYGabgrlB/H582ccPHgQs2bNYluKWlGYDlHaVMSFdIiMjAxs2LABGzduRL169bB69Wr06NGDVU2K0NDQkFu5mppuCoVC+T6io6MxbNgw3L9/nwnPXrFiBSZNmgQ/Pz8MHDgQM2fOhKOjI9tSoaWlhbdv36J69eoApDm+9+/fR+PGjVlWVhJ1Md379+9X6rzihcG4BBdTIf4LUNNdASme0y0PHo8HsVisIkUUimLev3+v1HnKVOQuTywsLCASiTB16lQ4Ozsr/I5xYUc+KCgIQqEQfn5+MpWrLS0tqemmUCiU78DJyQnh4eEYP348Tpw4gYCAADRv3hyOjo7w8PBAjRo12JbIIBAIEBsbq7D3MZeoXbs2bt++zSwQUMqfvLw8nDlzBoMGDWJbyn8CarorIKdOnVI4dvfuXWzduhUSiQTZ2dkqVEWhfBspKSk4d+4cRowYwaqOoqFjxXfmC19zYUe+KLRyNYVCofxYqlWrhhMnTqB169aIj4+HhYUFNm7ciBkzZrAtrQR8Ph+9e/dmWjKeOXMGXbp0kWnHCHCjJaNAIEBMTAznd7rlkZ2dDT8/P2RkZKB79+6cqu9C4Q7UdP9HCA8Ph4eHB86cOYORI0di2bJlrO8cUijKEBISgubNm7NuZtVlR14RtHI1hUKhfD8CgQCfPn2Cubk5AMDAwACPHj2Cra0ty8pKMnbsWKVyt7nQklFdwstnzZqFvLw8bNu2DQCQm5sLR0dHPH/+HHp6ehCLxbh8+TLatGnDslIa+co1NNgWQClfPn36hCVLlmD//v3o2bMngoOD0ahRI7ZlUShqhzJm+tmzZypQ8m3Y2tpi3bp1WL16NVO5mkKhUChfT9HIJz6fz9m2az4+PmxL+CouXrwIY2PjUs/55ZdfVKRGPpcuXcKqVauY14cPH8b79+//3969B0V1nn8A/64IKK6KGOWSrka5KM1l2ipKapDLCgqRiqShJCaEQi8mjsbGjqVSXVkhSsygEbRCXRRptNSaUDZeUq/ABoVRRiCACOnSaBLbcnWzKSyu/P5w3F82gK7R3XOWfD8zzHjOuzpf/hGec573fdDc3IwpU6YgKSkJ6enpOHLkiIApb3v//feHXPt65yvZBt90D1Pd3d148803kZ2djR/84AfIzMxEUFCQ0LGGpZs3b6KnpwdSqVToKMOSWN50D0Wn0+HgwYPYs2cPLl68KNqcRET04EaMGIHx48eb3iCK+fRqo9GI+vp6+Pr6mp2yDgBfffUVWlpa8MQTTwzILgRLMohhC9e4ceNQXV0NHx8fAMALL7yAsWPHIi8vD8DtE+yjoqLw+eefCxlzSOx8FQ7fdA9Db731FjIzM+Hh4YGDBw9iyZIlQkcaFtRqNdrb25GYmGi6l5GRgU2bNuHmzZsICwtDUVERJkyYIFxIspmysjKoVCocPnwYXl5eiI2Nxc6dO4WOBeD2E+z29nYsXrzYdG///v1QKBTQ6/WIiYlBdna2aZ8fERFZRgyt2JYqLCxETk4OKisrB6w5OTkhKSkJq1evxksvvSRAuoHsob18xIgRZme6nD9/HuvXrzddu7q6orOzU4hod8XOV+Gx6B6GUlJSMHr0aPj4+KCgoGDI8QZiODjDnmRlZeGnP/2p6bqiogIbNmyAUqmEv78/UlNTsWnTJmRlZQmY0v7s2LHjruufffaZjZLc2/Xr17Fv3z6oVCrcuHEDcXFx6O3tRXFxsahOBFcqlQgJCTEV3XV1dUhOTkZiYiL8/f2xdetWeHl5YePGjcIGJSKyM2IeBfVNKpUKv/3tb+Hg4DBgbeTIkVi7di1ycnJEUXSLbW74UPz9/aFWq/HGG2+gvr4en376KUJDQ03r//rXv0z7/cXgm52vp06dYuerQFh0D0MJCQl285+XPamvrzcrqP/2t78hPDwcqampAIBRo0bh9ddfZ9F9n7Zt23bPz0yZMsUGSe4uOjoaZWVlePbZZ7F9+3YsWrQIDg4O2L17t9DRBrh06RI2bdpkuv7LX/6CuXPn4k9/+hMAQCaTQaFQsOgmIrpP+fn5WLZsmV10CjU1NSEwMHDI9YCAADQ2Ntow0dDsZbfr2rVrER8fjyNHjqC+vh5RUVFmI9iOHj2KOXPmCJjw/7HzVVxYdA9D9nZwhr3Q6XSYOHGi6Vqj0eD55583XT/++OOi3cMjZlqtVugIFjl27BhWrVqFV199VfTjQDo7O82etJeWliIyMtJ0HRAQgKtXrwoRjYjIrv3yl7/E4sWLTW3QXl5eqKiowGOPPSZssEHo9XrcuHFjyHWdToevvvrKhomG9sorrwzYdy5GS5cuxdGjR/HBBx8gIiICK1euNFt3cXHBa6+9JlA6c+x8FRcW3UQWevTRR9HY2IgpU6bgyy+/RE1Njdlb2vb2dri4uAiYkKxJo9FApVJh1qxZ8Pf3x8svv4z4+HihYw3K3d0dWq0WMpkMBoMB1dXVSEtLM63rdDo4OjoKmJCIyD59842sTqcT7QnQvr6+qKiowFNPPTXoukajEc1D5HfeeQf9/f13fUgA3D7ITGhyuRxyuXzQNYVCIZpJJux8FRcW3UQWev7557F69WqsW7cOR48ehYeHh1nb1oULF0Q5p1Ps7rWn+45Vq1ZZOcndBQYGIjAwENu3b0dRURHy8/Pxxhtv4NatWzhx4gRkMhnGjh0raMY7oqKikJKSgszMTBQXF8PFxcVsD1dtbS28vb0FTEhERNb24osv4g9/+AN+/OMfDyi8a2pqsGHDBqxdu1agdOZcXV3vWiD29/eL4vTyodyZZKJSqXDhwgVR5GTnq7hwZBiRhf73v//h17/+NdRqNTw8PJCXl2dWyISGhmLRokX43e9+J2BK+/P1vVBDkUgk+Oc//2mDNPenqakJKpUKhYWF6OrqQnh4OEpKSoSOhba2NsTGxkKj0UAqlaKgoABLly41rcvlcgQGBiIjI0PAlERE9sfBwQHXr1/HpEmTANx+81pTU2PRzzJb6+vrQ0REBDQaDRYsWICZM2cCAC5fvoyTJ09i3rx5OHHihCg6n0pLSy36XHBwsJWT3J/BJpk899xzCAgIEDoaiQyLbiKiB2Q0GqFWq7F37178/e9/FzqOSXd3N6RS6YCTazs6OiCVSuHk5CRQMiIi+2RPc7qB24X3tm3bcODAATQ3N6O/vx9+fn548cUXsXr1av4c+BYGm2Sye/du1NTUiGqSCYkL28uJvoXa2lpcuXIFAODn5zfkfin6bnBwcIDRaBTdoXDjx48f9L6bm5uNkxARDQ/2NKcbABwdHbF27doh28g//vhjUcxrvnXrFrZu3YqSkhIYDAbI5XIoFArRHa5mT5NMSFxYdBPdh6qqKiQnJ6OhocF0mIpEIsHjjz8OlUrFdqJv4dy5c2hvbzfNlAaA/fv3Q6FQQK/XIyYmBtnZ2aIYz5Kbm4sTJ07AyckJr7/+OubOnYvTp09jzZo1uHLlil3NbyUiovs3HP6fv7P/eM+ePbh48aIo9h9nZGRg48aNWLBgAUaPHo133nkH//nPf5Cfny90NDP2NMmExGXEvT9CRADQ0NAAuVyO0aNH489//jOqq6tRXV2NwsJCODs7Qy6Xo6GhQeiYdkepVKK+vt50XVdXh+TkZCxYsAApKSlQq9XYvHmzgAlv27JlC1auXInW1laUlJQgLCwMb775JpYtW4af/exnuHbtGnbt2iV0TCIiokGVlZUhISEBnp6eePvttxEWFobz588LHQvA7Yftu3btwocffoji4mKo1Wq8++67ojsZXqPRQKfTYdasWZg7dy5ycnLQ1tYmdCyyA9zTTWShuLg43Lx5E4cPHx5wwmZ/fz9iY2Ph6OiIv/71rwIltE+enp5Qq9WYPXs2ACA1NRWlpaXQaDQAgEOHDkGhUAj+QGPGjBlYt24dXnnlFZSXlyM4OBhRUVEoKirCmDFjBM1GREQ0GHvZf+zs7IyWlhbIZDLTvVGjRqGlpQXf+973BEw2OL1eb5pkUlVVBaPRiKysLCQlJYlmkgmJC990E1nozJkzWLdu3aAjLSQSCdatW4czZ84IkMy+dXZ2wt3d3XRdWlqKyMhI03VAQACuXr0qRDQzn376KcLCwgAAQUFBcHR0RFpaGgtuIiISpejoaMyYMQO1tbXYvn07Pv/8c2RnZwsda1A3b97EqFGjzO45Ojqir69PoER3N2bMGCQlJUGj0aCurg5r1qzBli1bMHnyZPzkJz8ROh6JEPd0E1lIp9OZFYff5OHhAZ1OZ8NEw4O7uzu0Wi1kMhkMBgOqq6uRlpZmWtfpdKIYZ9Lb22v2C4GTkxMPJCMiItGyp/3H/f39SExMNDu/paenB8uXLzd7uP3ee+8JEc9Eq9UOGA83Y8YMvPXWW9i8eTPUarXo9qGTOLDoJrLQ1KlTUVVVZdb69HWVlZWYOnWqjVPZv6ioKKSkpCAzMxPFxcVwcXExm39eW1sLb29vARP+v/Xr18PFxQUAYDAYkJ6ePuCE8KysLCGiERERmdFoNFCpVJg1axb8/f3x8ssvIz4+XuhYgxrsgLqXXnpJgCR35+3tjalTpyI0NNT0daf93cHBATExMYiJiRE2JIkS93QTWUihUGDfvn04cuTIgPEadXV1iI6ORkJCApRKpUAJ7VNbWxtiY2Oh0WgglUpRUFCApUuXmtblcjkCAwORkZEhYEogJCRk0K0FXyeRSHD69GkbJSIiIluz9Gf8hg0brJzEctx//PCcPXvW9FVZWQmDwYDp06cjLCzMVITfrSuSvrtYdBNZqKenB3K5HJWVlQgPD4e/vz/6+/vR2NiIkydPYs6cOTh9+vSAPUlkme7ubkilUjg4OJjd7+jogFQqhZOTk0DJiIiIbvvhD3845JpEIkFTUxN6enpEMYZrME1NTVCpVCgsLERXVxfCw8NRUlIidCy71NPTg4qKClMRXlVVhb6+PsycOdNsKgsRwKKb6L4YDAZs27YNBw8exJUrVwAAfn5+iI+Px29+8xtRzJIm27gzIuSRRx4ROAkREQnt0qVLSElJwenTp5GUlITdu3cLHemujEajaf8xi+4HYzAY8NFHH+HYsWPIzc3Fl19+KdqHLiQcFt1ED8m1a9egVCqRl5cndBSykq6uLqSmpqKoqAidnZ0AgAkTJiA+Ph7p6elwdXUVNiAREdmUVqvF+vXrUVRUhNjYWKSnp4v+0DJ6MAaDAefPn8eZM2dMbeYymQzz58/H/PnzERwcjClTpggdk0SGRTfRQ1JTU4Mf/ehHfLo5THV0dODpp5/GZ599hmXLlsHf3x8A0NDQgAMHDkAmk6GiogITJkwQOCkREVlbW1sb0tLSkJeXh2eeeQZbtmxBQECA0LHMnDt3Du3t7Vi8eLHp3v79+6FQKKDX6xETE4Ps7Gx26d2HsLAwVFZWYtq0aQgODkZQUBCCg4Ph6ekpdDQSOZ5eTkRkAaVSCScnJ3zyyScDDklRKpWIiIiAUqnEtm3bBEpIRETWptfr8fbbbyMrKws+Pj5Qq9WIiIgQOtaglEolQkJCTEV3XV0dkpOTkZiYCH9/f2zduhVeXl7YuHGjsEHtSHl5OTw9PREWFoaQkBAEBwdj4sSJQsciO8A33UQPCd90D2+PPfYYcnNzsXDhwkHXjx8/juXLl6O1tdW2wYiIyGY8PDyg0+mwcuVKvPDCC0NOtXjqqadsnGwgT09PqNVqzJ49GwCQmpqK0tJSaDQaAMChQ4egUCjQ0NAgZEy7otfrUV5ejrNnz+LMmTO4dOkS/Pz8EBwcbCrCJ02aJHRMEiEW3UQPCYvu4c3Z2RmffPKJaR7nN127dg0+Pj7o6emxcTIiIrKVESNGmP4skUjw9V+j71xLJBJR/C4watQoNDc3QyaTAQCeeeYZREZGIjU1FQDQ2tqKJ598EjqdTsiYdk2n00Gj0Zj2d9fU1MDX1xcff/yx0NFIZNheTmSh2NjYu653dXXZJggJ4pFHHkFra+uQRbdWq4Wbm5uNUxERkS1ptVqhI1jM3d0dWq0WMpkMBoMB1dXVSEtLM63rdDo4OjoKmND+jRkzBm5ubnBzc8OECRMwcuRINDY2Ch2LRIhFN5GFxo8ff8/1hIQEG6UhW1u4cCFSU1Nx4sSJATPDe3t7sX79eixatEigdEREZAtTp06952fE8pYzKioKKSkpyMzMRHFxMVxcXBAUFGRar62thbe3t4AJ7c+tW7dw4cIFU3v5Rx99BL1ej0cffRShoaHYuXMnQkNDhY5JIsT2ciIiC1y7dg2zZ8+Gs7MzVqxYgZkzZ6K/vx+NjY3YtWsXent7ceHCBVMbHxERfXfodDocPHgQe/bswcWLF0XRXt7W1obY2FhoNBpIpVIUFBRg6dKlpnW5XI7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-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df3 = pd.read_csv('Transaction_ID.csv')\n",
-        "df3"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 424
-        },
-        "id": "02ORKDVwZI9H",
-        "outputId": "3597cfb2-8880-4357-bac4-1e00390f96d9"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "        Transaction ID  Customer ID Payment_Mode\n",
-              "0             10000011      29290.0         Card\n",
-              "1             10000012      27703.0         Card\n",
-              "2             10000013      28712.0         Cash\n",
-              "3             10000014      28020.0         Cash\n",
-              "4             10000015      27182.0         Card\n",
-              "...                ...          ...          ...\n",
-              "307683        10307694      42426.0         Cash\n",
-              "307684        10307695      42819.0         Cash\n",
-              "307685        10307696      44212.0         Card\n",
-              "307686        10307697      44827.0         Card\n",
-              "307687             103          NaN          NaN\n",
-              "\n",
-              "[307688 rows x 3 columns]"
-            ],
-            "text/html": [
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-              "      <td>42819.0</td>\n",
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-              "        document.querySelector('#df-eb77c9c0-5dc0-428e-8314-d40899ad4ed5 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-eb77c9c0-5dc0-428e-8314-d40899ad4ed5');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-20fa4755-2c55-41d8-95d1-5d6cce0d3b97\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-20fa4755-2c55-41d8-95d1-5d6cce0d3b97')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-20fa4755-2c55-41d8-95d1-5d6cce0d3b97 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "  <div id=\"id_28a52d13-3169-4eeb-9213-8e435274f7de\">\n",
-              "    <style>\n",
-              "      .colab-df-generate {\n",
-              "        background-color: #E8F0FE;\n",
-              "        border: none;\n",
-              "        border-radius: 50%;\n",
-              "        cursor: pointer;\n",
-              "        display: none;\n",
-              "        fill: #1967D2;\n",
-              "        height: 32px;\n",
-              "        padding: 0 0 0 0;\n",
-              "        width: 32px;\n",
-              "      }\n",
-              "\n",
-              "      .colab-df-generate:hover {\n",
-              "        background-color: #E2EBFA;\n",
-              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "        fill: #174EA6;\n",
-              "      }\n",
-              "\n",
-              "      [theme=dark] .colab-df-generate {\n",
-              "        background-color: #3B4455;\n",
-              "        fill: #D2E3FC;\n",
-              "      }\n",
-              "\n",
-              "      [theme=dark] .colab-df-generate:hover {\n",
-              "        background-color: #434B5C;\n",
-              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "        fill: #FFFFFF;\n",
-              "      }\n",
-              "    </style>\n",
-              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df3')\"\n",
-              "            title=\"Generate code using this dataframe.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "       width=\"24px\">\n",
-              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "    <script>\n",
-              "      (() => {\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#id_28a52d13-3169-4eeb-9213-8e435274f7de button.colab-df-generate');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      buttonEl.onclick = () => {\n",
-              "        google.colab.notebook.generateWithVariable('df3');\n",
-              "      }\n",
-              "      })();\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df3"
-            }
-          },
-          "metadata": {},
-          "execution_count": 4
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "\n",
-        "df3 = df3.fillna(0)\n",
-        "df3.isnull().sum()\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 178
-        },
-        "id": "bbOEMXM0kUdS",
-        "outputId": "653cdb57-f3e8-4173-d6b1-d21ff300a7a0"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "Transaction ID    0\n",
-              "Customer ID       0\n",
-              "Payment_Mode      0\n",
-              "dtype: int64"
-            ],
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>0</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>Transaction ID</th>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>Customer ID</th>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>Payment_Mode</th>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div><br><label><b>dtype:</b> int64</label>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 9
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df4 = pd.read_csv('Customer_ID.csv')\n",
-        "df4.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "64BsXf3bkToQ",
-        "outputId": "2fe40639-bd6f-4bf6-db74-ee6cf4a24f4e"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "   Customer ID Gender  Age  Income (USD/Month)\n",
-              "0        29290   Male   28               10813\n",
-              "1        27703   Male   27                9237\n",
-              "2        28712   Male   53               11242\n",
-              "3        28020   Male   23               23327\n",
-              "4        27182   Male   33                8536"
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-de4edf0e-d992-4c95-a735-95045f372109\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
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-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Customer ID</th>\n",
-              "      <th>Gender</th>\n",
-              "      <th>Age</th>\n",
-              "      <th>Income (USD/Month)</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>29290</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>28</td>\n",
-              "      <td>10813</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>27703</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>27</td>\n",
-              "      <td>9237</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>28712</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>53</td>\n",
-              "      <td>11242</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>28020</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>23</td>\n",
-              "      <td>23327</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>27182</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>33</td>\n",
-              "      <td>8536</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-de4edf0e-d992-4c95-a735-95045f372109')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-de4edf0e-d992-4c95-a735-95045f372109 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-de4edf0e-d992-4c95-a735-95045f372109');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-0060794c-844e-40e1-b957-d2d55815f585\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0060794c-844e-40e1-b957-d2d55815f585')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-0060794c-844e-40e1-b957-d2d55815f585 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df4",
-              "summary": "{\n  \"name\": \"df4\",\n  \"rows\": 49171,\n  \"fields\": [\n    {\n      \"column\": \"Customer ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17714,\n        \"min\": 1,\n        \"max\": 60000,\n        \"num_unique_values\": 49171,\n        \"samples\": [\n          32451,\n          524,\n          19349\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 18,\n        \"max\": 65,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          32,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income (USD/Month)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8002,\n        \"min\": 2000,\n        \"max\": 35000,\n        \"num_unique_values\": 23341,\n        \"samples\": [\n          12932,\n          16207\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 5
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "merged_df1 = pd.merge(df3, df4, on='Customer ID')\n",
-        "merged_df2 = merged_df1.merge(df1, on='Transaction ID')\n",
-        "merged_df2.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 348
-        },
-        "id": "v8U48YCOFasv",
-        "outputId": "f9516c30-e25a-4ef3-8a41-ddb6cdf4a0a3"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "   Transaction ID  Customer ID Payment_Mode Gender  Age  Income (USD/Month)  \\\n",
-              "0        10000011      29290.0         Card   Male   28               10813   \n",
-              "1        10000012      27703.0         Card   Male   27                9237   \n",
-              "2        10000013      28712.0         Cash   Male   53               11242   \n",
-              "3        10000014      28020.0         Cash   Male   23               23327   \n",
-              "4        10000015      27182.0         Card   Male   33                8536   \n",
-              "\n",
-              "   Date of Travel   Company        City  KM Travelled  Price Charged  \\\n",
-              "0         42377.0  Pink Cab  ATLANTA GA         30.45         370.95   \n",
-              "1         42375.0  Pink Cab  ATLANTA GA         28.62         358.52   \n",
-              "2         42371.0  Pink Cab  ATLANTA GA          9.04         125.20   \n",
-              "3         42376.0  Pink Cab  ATLANTA GA         33.17         377.40   \n",
-              "4         42372.0  Pink Cab  ATLANTA GA          8.73         114.62   \n",
-              "\n",
-              "   Cost of Trip  profit  \n",
-              "0       313.635  57.315  \n",
-              "1       334.854  23.666  \n",
-              "2        97.632  27.568  \n",
-              "3       351.602  25.798  \n",
-              "4        97.776  16.844  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-977a3625-8670-4406-b627-b1b07f9ce9cf\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Transaction ID</th>\n",
-              "      <th>Customer ID</th>\n",
-              "      <th>Payment_Mode</th>\n",
-              "      <th>Gender</th>\n",
-              "      <th>Age</th>\n",
-              "      <th>Income (USD/Month)</th>\n",
-              "      <th>Date of Travel</th>\n",
-              "      <th>Company</th>\n",
-              "      <th>City</th>\n",
-              "      <th>KM Travelled</th>\n",
-              "      <th>Price Charged</th>\n",
-              "      <th>Cost of Trip</th>\n",
-              "      <th>profit</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>10000011</td>\n",
-              "      <td>29290.0</td>\n",
-              "      <td>Card</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>28</td>\n",
-              "      <td>10813</td>\n",
-              "      <td>42377.0</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>30.45</td>\n",
-              "      <td>370.95</td>\n",
-              "      <td>313.635</td>\n",
-              "      <td>57.315</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>10000012</td>\n",
-              "      <td>27703.0</td>\n",
-              "      <td>Card</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>27</td>\n",
-              "      <td>9237</td>\n",
-              "      <td>42375.0</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>28.62</td>\n",
-              "      <td>358.52</td>\n",
-              "      <td>334.854</td>\n",
-              "      <td>23.666</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>10000013</td>\n",
-              "      <td>28712.0</td>\n",
-              "      <td>Cash</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>53</td>\n",
-              "      <td>11242</td>\n",
-              "      <td>42371.0</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>9.04</td>\n",
-              "      <td>125.20</td>\n",
-              "      <td>97.632</td>\n",
-              "      <td>27.568</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>10000014</td>\n",
-              "      <td>28020.0</td>\n",
-              "      <td>Cash</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>23</td>\n",
-              "      <td>23327</td>\n",
-              "      <td>42376.0</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>33.17</td>\n",
-              "      <td>377.40</td>\n",
-              "      <td>351.602</td>\n",
-              "      <td>25.798</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>10000015</td>\n",
-              "      <td>27182.0</td>\n",
-              "      <td>Card</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>33</td>\n",
-              "      <td>8536</td>\n",
-              "      <td>42372.0</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>8.73</td>\n",
-              "      <td>114.62</td>\n",
-              "      <td>97.776</td>\n",
-              "      <td>16.844</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-977a3625-8670-4406-b627-b1b07f9ce9cf')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-977a3625-8670-4406-b627-b1b07f9ce9cf button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-977a3625-8670-4406-b627-b1b07f9ce9cf');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-d68e6d85-bbb6-425a-bc1f-ab3e6605fcd0\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d68e6d85-bbb6-425a-bc1f-ab3e6605fcd0')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-d68e6d85-bbb6-425a-bc1f-ab3e6605fcd0 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "merged_df2",
-              "summary": "{\n  \"name\": \"merged_df2\",\n  \"rows\": 71151,\n  \"fields\": [\n    {\n      \"column\": \"Transaction ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25592,\n        \"min\": 10000011,\n        \"max\": 10088433,\n        \"num_unique_values\": 71151,\n        \"samples\": [\n          10047350,\n          10009057,\n          10065991\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21212.115589102177,\n        \"min\": 1.0,\n        \"max\": 60000.0,\n        \"num_unique_values\": 26259,\n        \"samples\": [\n          59647.0,\n          53174.0,\n          24110.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Payment_Mode\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Cash\",\n          \"Card\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 18,\n        \"max\": 65,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          32,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income (USD/Month)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7994,\n        \"min\": 2000,\n        \"max\": 34996,\n        \"num_unique_values\": 16961,\n        \"samples\": [\n          5974,\n          2136\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date of Travel\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 80.38125009707976,\n        \"min\": 42371.0,\n        \"max\": 42661.0,\n        \"num_unique_values\": 291,\n        \"samples\": [\n          42458.0,\n          42631.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Company\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yellow Cab\",\n          \"Pink Cab\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"ATLANTA GA\",\n          \"DENVER CO\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"KM Travelled\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.213689673845838,\n        \"min\": 1.9,\n        \"max\": 48.0,\n        \"num_unique_values\": 874,\n        \"samples\": [\n          4.0,\n          29.43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price Charged\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 288.486993996938,\n        \"min\": 16.38,\n        \"max\": 1978.79,\n        \"num_unique_values\": 49504,\n        \"samples\": [\n          464.18,\n          626.96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cost of Trip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 157.89328330950855,\n        \"min\": 19.2,\n        \"max\": 691.2,\n        \"num_unique_values\": 14297,\n        \"samples\": [\n          434.5704,\n          380.4408\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"profit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 176.18102319112268,\n        \"min\": -198.69800000000004,\n        \"max\": 1336.8700000000001,\n        \"num_unique_values\": 68226,\n        \"samples\": [\n          730.5763999999999,\n          109.31200000000001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 12
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "\n",
-        "income_company_counts = merged_df2.groupby(['Income (USD/Month)', 'Company'])['Company'].count().unstack()\n",
-        "income_company_counts.dropna(inplace = True)\n",
-        "\n",
-        "income_company_counts = income_company_counts.reset_index()\n",
-        "\n",
-        "\n",
-        "plt.figure(figsize=(10, 6))\n",
-        "\n",
-        "\n",
-        "for company in income_company_counts.columns[1:]:\n",
-        "    plt.bar(income_company_counts['Income (USD/Month)'], income_company_counts[company], label=company)\n",
-        "\n",
-        "plt.xlabel('Income (USD)')\n",
-        "plt.ylabel('Number of Users')\n",
-        "plt.title('Number of Users per Income for Pink and Yellow Cabs')\n",
-        "plt.xticks(rotation=90)\n",
-        "plt.legend()\n",
-        "plt.tight_layout()\n",
-        "plt.show()\n",
-        "plt.savefig('bar_chart_income_new.png')\n",
-        "\n",
-        "# Hypothesis Result for Bar Chart of Number of Users per Income\n",
-        "\n",
-        "# The bar chart above depicts the relationship between customer income and the usage of Pink and Yellow Cabs.\n",
-        "# It appears that across various income levels, Yellow Cabs tend to have a larger user base compared to Pink Cabs.\n",
-        "# This observation reinforces the potential market share dominance of Yellow Cabs.\n",
-        "# It is noticeable that as income levels increase, the user count for both Pink and Yellow Cabs also tends to increase, though the difference in user counts between the two companies seems consistent across different income brackets.\n",
-        "# If this trend continues, it suggests a strong preference for Yellow Cabs among customers regardless of their income level, potentially translating into greater profitability for the Yellow Cab company.\n",
-        "# This information can assist XYZ firm in making investment decisions, potentially favoring the Yellow Cab company due to its extensive user base across different income segments.\n",
-        "\n",
-        "# EDA Recommendations:\n",
-        "# Further examination of the customer demographic, such as locations."
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 624
-        },
-        "id": "hXTgM55hGHoS",
-        "outputId": "69875007-f601-4bd4-f238-fc819a904d2b"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1000x600 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 0 Axes>"
-            ]
-          },
-          "metadata": {}
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file

From dbe9a7f18edcf2130be4789b7c3a8c0675d7af62 Mon Sep 17 00:00:00 2001
From: An204o <166038499+An204o@users.noreply.github.com>
Date: Tue, 22 Oct 2024 16:45:26 -0400
Subject: [PATCH 07/22] Delete
 Copy_of_Copy_of_Data_Analysis_Week_2_Project.ipynb

---
 ...Copy_of_Data_Analysis_Week_2_Project.ipynb | 2925 -----------------
 1 file changed, 2925 deletions(-)
 delete mode 100644 Copy_of_Copy_of_Data_Analysis_Week_2_Project.ipynb

diff --git a/Copy_of_Copy_of_Data_Analysis_Week_2_Project.ipynb b/Copy_of_Copy_of_Data_Analysis_Week_2_Project.ipynb
deleted file mode 100644
index 8864177c..00000000
--- a/Copy_of_Copy_of_Data_Analysis_Week_2_Project.ipynb
+++ /dev/null
@@ -1,2925 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "source": [
-        "Loading the csv file for the Cab_Data.csv and checking for its outliers. The boxplot seems to not have any outliers as it is not above the maximum value or below the minimum value."
-      ],
-      "metadata": {
-        "id": "nxz9GK3qZWv2"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "\n",
-        "\n",
-        "import pandas as pd\n",
-        "import matplotlib.pyplot as plt\n",
-        "import seaborn as sns\n",
-        "from sklearn.preprocessing import StandardScaler\n",
-        "#This code loads the dataset by using pandas library\n",
-        "df1 = pd.read_csv('Cab_Data.csv', on_bad_lines='skip')\n",
-        "\n",
-        "#Creating a new column called profit\n",
-        "df1['profit'] = df1['Price Charged'] - df1['Cost of Trip']\n",
-        "\n",
-        "#Using seaborn and drawing boxplot by using the profit column\n",
-        "sns.boxplot(x=df1['profit'])\n",
-        "\n",
-        "#saving the graph in a png form by using savefig()\n",
-        "plt.savefig('boxplot_new.png')\n",
-        "\n",
-        "#Hypothesis:\n",
-        "#In the code above, I loaded the first dataset, which is Cab_Data.csv, and verified the whether the outliers are presentor not by using the boxplot chart. In the boxplot below,\n",
-        "# it seems that there are no outliers as the minimum is -150.38, and maximum is 1327.6219999999998, so there are no values which are less than minimum or above the maximum.\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 449
-        },
-        "id": "zeCKWNYVOkql",
-        "outputId": "99558f26-5f10-4b9e-caf4-2559e332999f"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#This shows the first 5 rows of all the columns row values\n",
-        "df1.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "_9QBzdB0dcb-",
-        "outputId": "015153a3-7370-4fc6-cd23-cd0d9654cb2d"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "   Transaction ID  Date of Travel   Company        City  KM Travelled  \\\n",
-              "0        10000011           42377  Pink Cab  ATLANTA GA         30.45   \n",
-              "1        10000012           42375  Pink Cab  ATLANTA GA         28.62   \n",
-              "2        10000013           42371  Pink Cab  ATLANTA GA          9.04   \n",
-              "3        10000014           42376  Pink Cab  ATLANTA GA         33.17   \n",
-              "4        10000015           42372  Pink Cab  ATLANTA GA          8.73   \n",
-              "\n",
-              "   Price Charged  Cost of Trip  profit  \n",
-              "0         370.95       313.635  57.315  \n",
-              "1         358.52       334.854  23.666  \n",
-              "2         125.20        97.632  27.568  \n",
-              "3         377.40       351.602  25.798  \n",
-              "4         114.62        97.776  16.844  "
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-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df1"
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-          "metadata": {},
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-    {
-      "cell_type": "markdown",
-      "source": [
-        "Bar chart for comparing the average profits of both Pink and Yellow Cab Companies"
-      ],
-      "metadata": {
-        "id": "ocbjQ1Svoc4J"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#This code provides the categories for Company column values, which are pink and yellow cabs, and these categories corresponds to the average profits of these two cabs.\n",
-        "average_profit = df1.groupby('Company')['profit'].mean()\n",
-        "\n",
-        "#I used the above series for average profits for both yellow and pink cab, and this code is used to plot the bar chart for average profit comparisons\n",
-        "average_profit.plot(kind='bar', color=['blue', 'red'])\n",
-        "\n",
-        "#x label for the graph\n",
-        "plt.xlabel('Company')\n",
-        "#y label for the graph\n",
-        "plt.ylabel('Profit')\n",
-        "#the title of the graph above\n",
-        "plt.title('Pink and Yellow Cab Companies vs Average Profits')\n",
-        "#saving the image as png\n",
-        "plt.savefig('barplot_comparison_profits.png')\n",
-        "\n",
-        "#EDA Recommendation\n",
-        "# Based on the exploratory data analysis, it is recommended to further investigate the factors contributing to the higher average profits of Yellow Cabs compared to Pink Cabs.\n",
-        "# This includes analyzing variables such as trip duration, fare rates, customer satisfaction, and the operational strategies of both cab companies.\n",
-        "\n",
-        "# Hypothesis Results\n",
-        "# In the bar chart below, I am comparing the average profits for two cab companies, Yellow and Pink Cabs.\n",
-        "# As you can see, the data indicates that the Yellow Cab company has more average profits, with a maximum profit of approximately $158,\n",
-        "# while the Pink Cab has an average profit of about $63. If this hypothesis holds true, it suggests that XYZ firm should consider investing in the Yellow Cab company\n",
-        "# due to its stronger profitability. This analysis supports the notion that Yellow Cabs may provide a more lucrative investment opportunity."
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 532
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-        "id": "A-hBShAXUwUw",
-        "outputId": "1c313882-44c2-4ba3-fa30-a0130a283d17"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
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-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#This loads the dataset for City.csv\n",
-        "df2 = pd.read_csv('City.csv')\n",
-        "df2.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "9HFXuXAzWAro",
-        "outputId": "79e2dd6a-56cc-4256-e5cd-1461fb68f420"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "             City   Population      Users\n",
-              "0     NEW YORK NY   8,405,837    302,149 \n",
-              "1      CHICAGO IL   1,955,130    164,468 \n",
-              "2  LOS ANGELES CA   1,595,037    144,132 \n",
-              "3        MIAMI FL   1,339,155     17,675 \n",
-              "4  SILICON VALLEY   1,177,609     27,247 "
-            ],
-            "text/html": [
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-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
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-              "\n",
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-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>City</th>\n",
-              "      <th>Population</th>\n",
-              "      <th>Users</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>NEW YORK NY</td>\n",
-              "      <td>8,405,837</td>\n",
-              "      <td>302,149</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>CHICAGO IL</td>\n",
-              "      <td>1,955,130</td>\n",
-              "      <td>164,468</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>LOS ANGELES CA</td>\n",
-              "      <td>1,595,037</td>\n",
-              "      <td>144,132</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>MIAMI FL</td>\n",
-              "      <td>1,339,155</td>\n",
-              "      <td>17,675</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>SILICON VALLEY</td>\n",
-              "      <td>1,177,609</td>\n",
-              "      <td>27,247</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
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-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ba8c576-5618-4e5e-a93f-5a15ef622129')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
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-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
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-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
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-              "      display: none;\n",
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-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
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-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
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-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
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-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-4ba8c576-5618-4e5e-a93f-5a15ef622129 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-4ba8c576-5618-4e5e-a93f-5a15ef622129');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
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-              "\n",
-              "\n",
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-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-407a8eb6-3504-4fb7-869e-56f573ef9ac5')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-407a8eb6-3504-4fb7-869e-56f573ef9ac5 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df2",
-              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"NEW YORK NY\",\n          \"WASHINGTON DC\",\n          \"SACRAMENTO CA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 8,405,837 \",\n          \" 418,859 \",\n          \" 545,776 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Users\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 302,149 \",\n          \" 127,001 \",\n          \" 7,044 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 8
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "Stacked bar chart with the number of trips in different cities for both yellow and pink cabs"
-      ],
-      "metadata": {
-        "id": "yDvVWeIVu2RW"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "\n",
-        "import pandas as pd\n",
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "#The series type that provides the categories for both cities and company values and counts the number of companies appears in each city. The unstack() converts it\n",
-        "#into the dataframe when having multi index.\n",
-        "city_company_counts = df1.groupby(['City', 'Company'])['Company'].count().unstack()\n",
-        "\n",
-        "#Then I use the series to plot the bar chart for showing number of trips.\n",
-        "city_company_counts.plot(kind='bar', stacked=True)\n",
-        "\n",
-        "#x label\n",
-        "plt.xlabel('City')\n",
-        "#y label\n",
-        "plt.ylabel('Number of Trips')\n",
-        "#title for the bar chart graph\n",
-        "plt.title('Most Used Cabs (Pink and Yellow) by City')\n",
-        "\n",
-        "#provides the particular color representation for both pink and yellow cab companies\n",
-        "plt.legend(title='Company')\n",
-        "\n",
-        "#saving the image as png\n",
-        "plt.savefig('stacked_bar_city_company.png')\n",
-        "#providing the output of the graph image\n",
-        "plt.show()\n",
-        "\n",
-        "#Hypothesis\n",
-        "# As you can see in the stacked bar chart below, the most used cabs are yellow cabs.\n",
-        "# In most of the countries, the most used cab is yellow. For instance, in the city\n",
-        "# New York, the number of trips for pink cabs is approximately 1,600, while the yellow cab\n",
-        "# has 980,000 trips.\n",
-        "\n",
-        "# Based on the stacked bar chart analysis, it appears that yellow cabs are predominantly used\n",
-        "# over pink cabs in New York, as evidenced by the trip counts.\n",
-        "# This indicates a strong preference for yellow cabs among consumers in that area.\n",
-        "# If these trends hold true across other locations, this may reflect a broader market trend\n",
-        "# that XYZ firm should consider when deciding on cab investments.\n",
-        "\n",
-        "#Recommendations:\n",
-        "#Data Visualizations: I would have made a graph such as linear or pie chart to better show the distribution of the values for this particular goal."
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 579
-        },
-        "id": "Tc89YuH-Zcg4",
-        "outputId": "1cd1a65c-62e5-4426-aeaa-1574afd6a9e4"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df1.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "Mue7qS3FdC55",
-        "outputId": "4f3a1c8e-4a82-4afa-bf0b-9877e6909c9d"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "   Transaction ID  Date of Travel   Company        City  KM Travelled  \\\n",
-              "0        10000011           42377  Pink Cab  ATLANTA GA         30.45   \n",
-              "1        10000012           42375  Pink Cab  ATLANTA GA         28.62   \n",
-              "2        10000013           42371  Pink Cab  ATLANTA GA          9.04   \n",
-              "3        10000014           42376  Pink Cab  ATLANTA GA         33.17   \n",
-              "4        10000015           42372  Pink Cab  ATLANTA GA          8.73   \n",
-              "\n",
-              "   Price Charged  Cost of Trip  profit  \n",
-              "0         370.95       313.635  57.315  \n",
-              "1         358.52       334.854  23.666  \n",
-              "2         125.20        97.632  27.568  \n",
-              "3         377.40       351.602  25.798  \n",
-              "4         114.62        97.776  16.844  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-0f61529b-5bcc-456c-a8f1-b095116fa7aa\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
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-              "\n",
-              "    .dataframe thead th {\n",
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-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Transaction ID</th>\n",
-              "      <th>Date of Travel</th>\n",
-              "      <th>Company</th>\n",
-              "      <th>City</th>\n",
-              "      <th>KM Travelled</th>\n",
-              "      <th>Price Charged</th>\n",
-              "      <th>Cost of Trip</th>\n",
-              "      <th>profit</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>10000011</td>\n",
-              "      <td>42377</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>30.45</td>\n",
-              "      <td>370.95</td>\n",
-              "      <td>313.635</td>\n",
-              "      <td>57.315</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>10000012</td>\n",
-              "      <td>42375</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>28.62</td>\n",
-              "      <td>358.52</td>\n",
-              "      <td>334.854</td>\n",
-              "      <td>23.666</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>10000013</td>\n",
-              "      <td>42371</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>9.04</td>\n",
-              "      <td>125.20</td>\n",
-              "      <td>97.632</td>\n",
-              "      <td>27.568</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>10000014</td>\n",
-              "      <td>42376</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>33.17</td>\n",
-              "      <td>377.40</td>\n",
-              "      <td>351.602</td>\n",
-              "      <td>25.798</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>10000015</td>\n",
-              "      <td>42372</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>8.73</td>\n",
-              "      <td>114.62</td>\n",
-              "      <td>97.776</td>\n",
-              "      <td>16.844</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
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-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f61529b-5bcc-456c-a8f1-b095116fa7aa')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
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-              "type": "dataframe",
-              "variable_name": "df1"
-            }
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-          "metadata": {},
-          "execution_count": 9
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-    {
-      "cell_type": "markdown",
-      "source": [
-        "The bar chart that represents the most number of trips for both yellow and pink cabs in the year of 1970"
-      ],
-      "metadata": {
-        "id": "I3xr1iPUyaUy"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "\n",
-        "import pandas as pd\n",
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "#converting the Date of Travel column in the DataFrame df1 to a datetime format using the pd.to_datetime() function from pandas.\n",
-        "df1['Date of Travel'] = pd.to_datetime(df1['Date of Travel'])\n",
-        "\n",
-        "#choosing the only the values from the dat of travel with the values of 1970 from df1 dataset\n",
-        "df1_1970 = df1[df1['Date of Travel'].dt.year == 1970]\n",
-        "\n",
-        "#The series type that provides the categories for company values and counts the number of companies appears\n",
-        "company_counts_1970 = df1_1970.groupby('Company')['Company'].count()\n",
-        "\n",
-        "#plotting the bar chart by using teh previous series and making each of the column values from the Company color pink and yellow\n",
-        "company_counts_1970.plot(kind='bar', color = ['pink', 'yellow'])\n",
-        "\n",
-        "\n",
-        "#x label\n",
-        "plt.xlabel('Company')\n",
-        "#y label\n",
-        "plt.ylabel('Number of Trips in 1970')\n",
-        "#title for the bar chart graph\n",
-        "plt.title('Most Used Cabs (Pink and Yellow) in 1970')\n",
-        "\n",
-        "#saving the image as png\n",
-        "plt.savefig('bar_chart_1970.png')\n",
-        "\n",
-        "#showing the plot\n",
-        "plt.show()\n",
-        "\n",
-        "# Hypothesis Result for Bar Chart of Most Used Cabs in 1970\n",
-        "\n",
-        "# The bar chart above showcases the number of trips undertaken by Yellow and Pink Cab companies in the year 1970.\n",
-        "# As depicted, Yellow Cabs had significantly more trips compared to Pink Cabs in that year.\n",
-        "# This suggests a higher demand or preference for Yellow Cabs in the market during 1970.\n",
-        "# If this trend holds for other years, it indicates that Yellow Cabs potentially have a stronger market share and possibly higher profitability.\n",
-        "# This information can be useful for XYZ firm in making informed decisions about which cab company to invest in, potentially favoring the Yellow Cab company given their historical dominance.\n",
-        "\n",
-        "#EDA Recommendations:\n",
-        "#We should collect and analyze data on customer demographics (age, gender, income level) to better understand who is using each cab company.\n",
-        "#This could provide insights into targeted marketing strategies."
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 532
-        },
-        "id": "kQaKMrE7eRqw",
-        "outputId": "ceedad65-74bd-4261-f1a3-871c62d5c9f6"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df2 = pd.read_csv('City.csv')\n",
-        "df2.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "JPYgpO2sfGkf",
-        "outputId": "b7854f08-5674-4a57-a750-5d236960514f"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "             City   Population      Users\n",
-              "0     NEW YORK NY   8,405,837    302,149 \n",
-              "1      CHICAGO IL   1,955,130    164,468 \n",
-              "2  LOS ANGELES CA   1,595,037    144,132 \n",
-              "3        MIAMI FL   1,339,155     17,675 \n",
-              "4  SILICON VALLEY   1,177,609     27,247 "
-            ],
-            "text/html": [
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-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-8b7768ec-b9a8-4fe4-9a62-0cbd5ac9b727 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df2",
-              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"NEW YORK NY\",\n          \"WASHINGTON DC\",\n          \"SACRAMENTO CA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 8,405,837 \",\n          \" 418,859 \",\n          \" 545,776 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Users\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 302,149 \",\n          \" 127,001 \",\n          \" 7,044 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 11
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "\n",
-        "#The series type that provides the categories for both cities and company values and counts the number of companies appears in each city. The unstack() converts it\n",
-        "#into the dataframe when having multi index.\n",
-        "city_company_counts = df1.groupby(['City', 'Company'])['Company'].count().unstack()\n",
-        "\n",
-        "#replaces the previous index into a default index values like 0, 1, 2, 3, ...\n",
-        "city_company_counts = city_company_counts.reset_index()\n",
-        "\n",
-        "#Renaming the prevous columns\n",
-        "city_company_counts = city_company_counts.rename(\n",
-        "    columns={'Pink Cab': 'Pink Cab Users', 'Yellow Cab': 'Yellow Cab Users'}\n",
-        ")\n",
-        "\n",
-        "#Merging or combining the series values with the df2 dataset\n",
-        "merged_df = pd.merge(df2, city_company_counts, on='City', how='left')\n",
-        "\n",
-        "#replacing the NaN values with 0\n",
-        "merged_df = merged_df.fillna(0)\n",
-        "\n",
-        "#size of the plot\n",
-        "plt.figure(figsize=(10, 6))\n",
-        "#drawing only the line graph for pink cab users\n",
-        "plt.plot(merged_df['City'], merged_df['Pink Cab Users'], label='Pink Cab Users')\n",
-        "#drawing only the line graph for yellow cab users\n",
-        "plt.plot(merged_df['City'], merged_df['Yellow Cab Users'], label='Yellow Cab Users')\n",
-        "#x label\n",
-        "plt.xlabel('City')\n",
-        "#y label\n",
-        "plt.ylabel('Number of Users')\n",
-        "#title of the graph\n",
-        "plt.title('Number of Users per City for Pink and Yellow Cabs')\n",
-        "#rotating the x label values into 90 degrees\n",
-        "plt.xticks(rotation=90)\n",
-        "#provides the particular color representation for both pink and yellow cab companies\n",
-        "plt.legend()\n",
-        "#adjust the spacing between subplots to minimize overlap and ensure that all elements are neatly contained within the figure area.\n",
-        "plt.tight_layout()\n",
-        "#plotting line graph\n",
-        "plt.show()\n",
-        "\n",
-        "# Hypothesis Result for Line Graph of Number of Users per City\n",
-        "\n",
-        "# The line graph above visually depicts the number of users for Pink and Yellow Cabs in different cities.\n",
-        "# It seems that the Yellow Cabs consistently have a larger user base across most cities compared to Pink Cabs.\n",
-        "# This indicates a potential market share dominance for Yellow Cabs.\n",
-        "# Notably, in certain cities, the difference in user numbers is significant, which may reflect a strong preference for Yellow Cabs in those areas.\n",
-        "# If these trends persist, it implies a greater demand for Yellow Cabs, potentially contributing to higher profitability for the company.\n",
-        "# This information can guide XYZ firm's investment decisions, possibly favouring the Yellow Cab company due to its extensive user base.\n",
-        "\n",
-        "#EDA Recommendations:\n",
-        "#I can analyze trends and patterns, and observe the overall trends in user counts for both companies across different cities."
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 607
-        },
-        "id": "dSmc_aL3VSKR",
-        "outputId": "7bb7ca04-299e-48d7-da61-442d8144dd6f"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1000x600 with 1 Axes>"
-            ],
-            "image/png": 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-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#loading dataset called Transaction_ID.csv\n",
-        "df3 = pd.read_csv('Transaction_ID.csv')\n",
-        "df3"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 424
-        },
-        "id": "02ORKDVwZI9H",
-        "outputId": "3597cfb2-8880-4357-bac4-1e00390f96d9"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "        Transaction ID  Customer ID Payment_Mode\n",
-              "0             10000011      29290.0         Card\n",
-              "1             10000012      27703.0         Card\n",
-              "2             10000013      28712.0         Cash\n",
-              "3             10000014      28020.0         Cash\n",
-              "4             10000015      27182.0         Card\n",
-              "...                ...          ...          ...\n",
-              "307683        10307694      42426.0         Cash\n",
-              "307684        10307695      42819.0         Cash\n",
-              "307685        10307696      44212.0         Card\n",
-              "307686        10307697      44827.0         Card\n",
-              "307687             103          NaN          NaN\n",
-              "\n",
-              "[307688 rows x 3 columns]"
-            ],
-            "text/html": [
-              "\n",
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-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
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-              "\n",
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-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>10000013</td>\n",
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-              "    <tr>\n",
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-              "      <td>28020.0</td>\n",
-              "      <td>Cash</td>\n",
-              "    </tr>\n",
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-              "      <td>10000015</td>\n",
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-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>307684</th>\n",
-              "      <td>10307695</td>\n",
-              "      <td>42819.0</td>\n",
-              "      <td>Cash</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>307685</th>\n",
-              "      <td>10307696</td>\n",
-              "      <td>44212.0</td>\n",
-              "      <td>Card</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>307686</th>\n",
-              "      <td>10307697</td>\n",
-              "      <td>44827.0</td>\n",
-              "      <td>Card</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>307687</th>\n",
-              "      <td>103</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
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-              "<p>307688 rows × 3 columns</p>\n",
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-              "        document.querySelector('#df-eb77c9c0-5dc0-428e-8314-d40899ad4ed5 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-eb77c9c0-5dc0-428e-8314-d40899ad4ed5');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-20fa4755-2c55-41d8-95d1-5d6cce0d3b97\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-20fa4755-2c55-41d8-95d1-5d6cce0d3b97')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-20fa4755-2c55-41d8-95d1-5d6cce0d3b97 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "  <div id=\"id_28a52d13-3169-4eeb-9213-8e435274f7de\">\n",
-              "    <style>\n",
-              "      .colab-df-generate {\n",
-              "        background-color: #E8F0FE;\n",
-              "        border: none;\n",
-              "        border-radius: 50%;\n",
-              "        cursor: pointer;\n",
-              "        display: none;\n",
-              "        fill: #1967D2;\n",
-              "        height: 32px;\n",
-              "        padding: 0 0 0 0;\n",
-              "        width: 32px;\n",
-              "      }\n",
-              "\n",
-              "      .colab-df-generate:hover {\n",
-              "        background-color: #E2EBFA;\n",
-              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "        fill: #174EA6;\n",
-              "      }\n",
-              "\n",
-              "      [theme=dark] .colab-df-generate {\n",
-              "        background-color: #3B4455;\n",
-              "        fill: #D2E3FC;\n",
-              "      }\n",
-              "\n",
-              "      [theme=dark] .colab-df-generate:hover {\n",
-              "        background-color: #434B5C;\n",
-              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "        fill: #FFFFFF;\n",
-              "      }\n",
-              "    </style>\n",
-              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df3')\"\n",
-              "            title=\"Generate code using this dataframe.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "       width=\"24px\">\n",
-              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "    <script>\n",
-              "      (() => {\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#id_28a52d13-3169-4eeb-9213-8e435274f7de button.colab-df-generate');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      buttonEl.onclick = () => {\n",
-              "        google.colab.notebook.generateWithVariable('df3');\n",
-              "      }\n",
-              "      })();\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df3"
-            }
-          },
-          "metadata": {},
-          "execution_count": 4
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Replacing NaN values with 0\n",
-        "df3 = df3.fillna(0)\n",
-        "#Checking the sum of the null values whether there are null values left or not\n",
-        "df3.isnull().sum()\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 178
-        },
-        "id": "bbOEMXM0kUdS",
-        "outputId": "653cdb57-f3e8-4173-d6b1-d21ff300a7a0"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "Transaction ID    0\n",
-              "Customer ID       0\n",
-              "Payment_Mode      0\n",
-              "dtype: int64"
-            ],
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>0</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>Transaction ID</th>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>Customer ID</th>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>Payment_Mode</th>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div><br><label><b>dtype:</b> int64</label>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 9
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Loadingt the dataset called Customer_ID.csv\n",
-        "df4 = pd.read_csv('Customer_ID.csv')\n",
-        "df4.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "64BsXf3bkToQ",
-        "outputId": "2fe40639-bd6f-4bf6-db74-ee6cf4a24f4e"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "   Customer ID Gender  Age  Income (USD/Month)\n",
-              "0        29290   Male   28               10813\n",
-              "1        27703   Male   27                9237\n",
-              "2        28712   Male   53               11242\n",
-              "3        28020   Male   23               23327\n",
-              "4        27182   Male   33                8536"
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-de4edf0e-d992-4c95-a735-95045f372109\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
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-              "</style>\n",
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-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Customer ID</th>\n",
-              "      <th>Gender</th>\n",
-              "      <th>Age</th>\n",
-              "      <th>Income (USD/Month)</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>29290</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>28</td>\n",
-              "      <td>10813</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>27703</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>27</td>\n",
-              "      <td>9237</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>28712</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>53</td>\n",
-              "      <td>11242</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>28020</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>23</td>\n",
-              "      <td>23327</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>27182</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>33</td>\n",
-              "      <td>8536</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-de4edf0e-d992-4c95-a735-95045f372109')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
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-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-de4edf0e-d992-4c95-a735-95045f372109 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-de4edf0e-d992-4c95-a735-95045f372109');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
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-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-0060794c-844e-40e1-b957-d2d55815f585\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0060794c-844e-40e1-b957-d2d55815f585')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-0060794c-844e-40e1-b957-d2d55815f585 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df4",
-              "summary": "{\n  \"name\": \"df4\",\n  \"rows\": 49171,\n  \"fields\": [\n    {\n      \"column\": \"Customer ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17714,\n        \"min\": 1,\n        \"max\": 60000,\n        \"num_unique_values\": 49171,\n        \"samples\": [\n          32451,\n          524,\n          19349\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 18,\n        \"max\": 65,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          32,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income (USD/Month)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8002,\n        \"min\": 2000,\n        \"max\": 35000,\n        \"num_unique_values\": 23341,\n        \"samples\": [\n          12932,\n          16207\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 5
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Merging the dataset fro both df3 and df4 by Customer ID feature\n",
-        "merged_df1 = pd.merge(df3, df4, on='Customer ID')\n",
-        "#Merging the df1 to the merged_df1 with df3 and df4 merged datasets with Transaction ID\n",
-        "merged_df2 = merged_df1.merge(df1, on='Transaction ID')\n",
-        "merged_df2.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 348
-        },
-        "id": "v8U48YCOFasv",
-        "outputId": "f9516c30-e25a-4ef3-8a41-ddb6cdf4a0a3"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "   Transaction ID  Customer ID Payment_Mode Gender  Age  Income (USD/Month)  \\\n",
-              "0        10000011      29290.0         Card   Male   28               10813   \n",
-              "1        10000012      27703.0         Card   Male   27                9237   \n",
-              "2        10000013      28712.0         Cash   Male   53               11242   \n",
-              "3        10000014      28020.0         Cash   Male   23               23327   \n",
-              "4        10000015      27182.0         Card   Male   33                8536   \n",
-              "\n",
-              "   Date of Travel   Company        City  KM Travelled  Price Charged  \\\n",
-              "0         42377.0  Pink Cab  ATLANTA GA         30.45         370.95   \n",
-              "1         42375.0  Pink Cab  ATLANTA GA         28.62         358.52   \n",
-              "2         42371.0  Pink Cab  ATLANTA GA          9.04         125.20   \n",
-              "3         42376.0  Pink Cab  ATLANTA GA         33.17         377.40   \n",
-              "4         42372.0  Pink Cab  ATLANTA GA          8.73         114.62   \n",
-              "\n",
-              "   Cost of Trip  profit  \n",
-              "0       313.635  57.315  \n",
-              "1       334.854  23.666  \n",
-              "2        97.632  27.568  \n",
-              "3       351.602  25.798  \n",
-              "4        97.776  16.844  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-977a3625-8670-4406-b627-b1b07f9ce9cf\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Transaction ID</th>\n",
-              "      <th>Customer ID</th>\n",
-              "      <th>Payment_Mode</th>\n",
-              "      <th>Gender</th>\n",
-              "      <th>Age</th>\n",
-              "      <th>Income (USD/Month)</th>\n",
-              "      <th>Date of Travel</th>\n",
-              "      <th>Company</th>\n",
-              "      <th>City</th>\n",
-              "      <th>KM Travelled</th>\n",
-              "      <th>Price Charged</th>\n",
-              "      <th>Cost of Trip</th>\n",
-              "      <th>profit</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>10000011</td>\n",
-              "      <td>29290.0</td>\n",
-              "      <td>Card</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>28</td>\n",
-              "      <td>10813</td>\n",
-              "      <td>42377.0</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>30.45</td>\n",
-              "      <td>370.95</td>\n",
-              "      <td>313.635</td>\n",
-              "      <td>57.315</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>10000012</td>\n",
-              "      <td>27703.0</td>\n",
-              "      <td>Card</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>27</td>\n",
-              "      <td>9237</td>\n",
-              "      <td>42375.0</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>28.62</td>\n",
-              "      <td>358.52</td>\n",
-              "      <td>334.854</td>\n",
-              "      <td>23.666</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>10000013</td>\n",
-              "      <td>28712.0</td>\n",
-              "      <td>Cash</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>53</td>\n",
-              "      <td>11242</td>\n",
-              "      <td>42371.0</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>9.04</td>\n",
-              "      <td>125.20</td>\n",
-              "      <td>97.632</td>\n",
-              "      <td>27.568</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>10000014</td>\n",
-              "      <td>28020.0</td>\n",
-              "      <td>Cash</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>23</td>\n",
-              "      <td>23327</td>\n",
-              "      <td>42376.0</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>33.17</td>\n",
-              "      <td>377.40</td>\n",
-              "      <td>351.602</td>\n",
-              "      <td>25.798</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>10000015</td>\n",
-              "      <td>27182.0</td>\n",
-              "      <td>Card</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>33</td>\n",
-              "      <td>8536</td>\n",
-              "      <td>42372.0</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>8.73</td>\n",
-              "      <td>114.62</td>\n",
-              "      <td>97.776</td>\n",
-              "      <td>16.844</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-977a3625-8670-4406-b627-b1b07f9ce9cf')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-977a3625-8670-4406-b627-b1b07f9ce9cf button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-977a3625-8670-4406-b627-b1b07f9ce9cf');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-d68e6d85-bbb6-425a-bc1f-ab3e6605fcd0\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d68e6d85-bbb6-425a-bc1f-ab3e6605fcd0')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-d68e6d85-bbb6-425a-bc1f-ab3e6605fcd0 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "merged_df2",
-              "summary": "{\n  \"name\": \"merged_df2\",\n  \"rows\": 71151,\n  \"fields\": [\n    {\n      \"column\": \"Transaction ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25592,\n        \"min\": 10000011,\n        \"max\": 10088433,\n        \"num_unique_values\": 71151,\n        \"samples\": [\n          10047350,\n          10009057,\n          10065991\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21212.115589102177,\n        \"min\": 1.0,\n        \"max\": 60000.0,\n        \"num_unique_values\": 26259,\n        \"samples\": [\n          59647.0,\n          53174.0,\n          24110.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Payment_Mode\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Cash\",\n          \"Card\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 18,\n        \"max\": 65,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          32,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income (USD/Month)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7994,\n        \"min\": 2000,\n        \"max\": 34996,\n        \"num_unique_values\": 16961,\n        \"samples\": [\n          5974,\n          2136\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date of Travel\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 80.38125009707976,\n        \"min\": 42371.0,\n        \"max\": 42661.0,\n        \"num_unique_values\": 291,\n        \"samples\": [\n          42458.0,\n          42631.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Company\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yellow Cab\",\n          \"Pink Cab\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"ATLANTA GA\",\n          \"DENVER CO\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"KM Travelled\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.213689673845838,\n        \"min\": 1.9,\n        \"max\": 48.0,\n        \"num_unique_values\": 874,\n        \"samples\": [\n          4.0,\n          29.43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price Charged\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 288.486993996938,\n        \"min\": 16.38,\n        \"max\": 1978.79,\n        \"num_unique_values\": 49504,\n        \"samples\": [\n          464.18,\n          626.96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cost of Trip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 157.89328330950855,\n        \"min\": 19.2,\n        \"max\": 691.2,\n        \"num_unique_values\": 14297,\n        \"samples\": [\n          434.5704,\n          380.4408\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"profit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 176.18102319112268,\n        \"min\": -198.69800000000004,\n        \"max\": 1336.8700000000001,\n        \"num_unique_values\": 68226,\n        \"samples\": [\n          730.5763999999999,\n          109.31200000000001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 12
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#This groups the DataFrame merged_df2 by two columns: Income (USD/Month) and Company. This means that the data will be organized into groups where each unique combination of income level and company is represented.\n",
-        "\n",
-        "income_company_counts = merged_df2.groupby(['Income (USD/Month)', 'Company'])['Company'].count().unstack()\n",
-        "#removes the NaN values\n",
-        "income_company_counts.dropna(inplace = True)\n",
-        "\n",
-        "#Resets index values into original default values like 0, 1, 2\n",
-        "income_company_counts = income_company_counts.reset_index()\n",
-        "\n",
-        "#The figure size of the graph\n",
-        "plt.figure(figsize=(10, 6))\n",
-        "\n",
-        "#This line iterates through all the columns of the income_company_counts DataFrame, starting from the second column (index 1) onward.\n",
-        "#The loop will generate a series of bar charts, one for each company, where each bar represents the count of that company at each income level.\n",
-        "for company in income_company_counts.columns[1:]:\n",
-        "    plt.bar(income_company_counts['Income (USD/Month)'], income_company_counts[company], label=company)\n",
-        "\n",
-        "#x label\n",
-        "plt.xlabel('Income (USD)')\n",
-        "#y label\n",
-        "plt.ylabel('Number of Users')\n",
-        "#Title\n",
-        "plt.title('Number of Users per Income for Pink and Yellow Cabs')\n",
-        "#Rotating the x values to 90 degrees\n",
-        "plt.xticks(rotation=90)\n",
-        "#Providingt he particular colors for values\n",
-        "plt.legend()\n",
-        "#adjust the spacing between subplots to minimize overlap and ensure that all elements are neatly contained within the figure area.\n",
-        "plt.tight_layout()\n",
-        "#output the graph\n",
-        "plt.show()\n",
-        "#downloading the graph image into png\n",
-        "plt.savefig('bar_chart_income_new.png')\n",
-        "\n",
-        "# Hypothesis Result for Bar Chart of Number of Users per Income\n",
-        "\n",
-        "# The bar chart above depicts the relationship between customer income and the usage of Pink and Yellow Cabs.\n",
-        "# It appears that across various income levels, Yellow Cabs tend to have a larger user base compared to Pink Cabs.\n",
-        "# This observation reinforces the potential market share dominance of Yellow Cabs.\n",
-        "# It is noticeable that as income levels increase, the user count for both Pink and Yellow Cabs also tends to increase, though the difference in user counts between the two companies seems consistent across different income brackets.\n",
-        "# If this trend continues, it suggests a strong preference for Yellow Cabs among customers regardless of their income level, potentially translating into greater profitability for the Yellow Cab company.\n",
-        "# This information can assist XYZ firm in making investment decisions, potentially favoring the Yellow Cab company due to its extensive user base across different income segments.\n",
-        "\n",
-        "# EDA Recommendations:\n",
-        "# Further examination of the customer demographic, such as locations."
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 624
-        },
-        "id": "hXTgM55hGHoS",
-        "outputId": "69875007-f601-4bd4-f238-fc819a904d2b"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1000x600 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 0 Axes>"
-            ]
-          },
-          "metadata": {}
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file

From e5e0df6f5db1705945de17705b3f96acad7e868b Mon Sep 17 00:00:00 2001
From: An204o <166038499+An204o@users.noreply.github.com>
Date: Tue, 22 Oct 2024 16:45:41 -0400
Subject: [PATCH 08/22] Add files via upload

---
 ...Copy_of_Data_Analysis_Week_2_Project.ipynb | 2925 +++++++++++++++++
 1 file changed, 2925 insertions(+)
 create mode 100644 Copy_of_Copy_of_Data_Analysis_Week_2_Project.ipynb

diff --git a/Copy_of_Copy_of_Data_Analysis_Week_2_Project.ipynb b/Copy_of_Copy_of_Data_Analysis_Week_2_Project.ipynb
new file mode 100644
index 00000000..8864177c
--- /dev/null
+++ b/Copy_of_Copy_of_Data_Analysis_Week_2_Project.ipynb
@@ -0,0 +1,2925 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Loading the csv file for the Cab_Data.csv and checking for its outliers. The boxplot seems to not have any outliers as it is not above the maximum value or below the minimum value."
+      ],
+      "metadata": {
+        "id": "nxz9GK3qZWv2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "#This code loads the dataset by using pandas library\n",
+        "df1 = pd.read_csv('Cab_Data.csv', on_bad_lines='skip')\n",
+        "\n",
+        "#Creating a new column called profit\n",
+        "df1['profit'] = df1['Price Charged'] - df1['Cost of Trip']\n",
+        "\n",
+        "#Using seaborn and drawing boxplot by using the profit column\n",
+        "sns.boxplot(x=df1['profit'])\n",
+        "\n",
+        "#saving the graph in a png form by using savefig()\n",
+        "plt.savefig('boxplot_new.png')\n",
+        "\n",
+        "#Hypothesis:\n",
+        "#In the code above, I loaded the first dataset, which is Cab_Data.csv, and verified the whether the outliers are presentor not by using the boxplot chart. In the boxplot below,\n",
+        "# it seems that there are no outliers as the minimum is -150.38, and maximum is 1327.6219999999998, so there are no values which are less than minimum or above the maximum.\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "id": "zeCKWNYVOkql",
+        "outputId": "99558f26-5f10-4b9e-caf4-2559e332999f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#This shows the first 5 rows of all the columns row values\n",
+        "df1.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "_9QBzdB0dcb-",
+        "outputId": "015153a3-7370-4fc6-cd23-cd0d9654cb2d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Transaction ID  Date of Travel   Company        City  KM Travelled  \\\n",
+              "0        10000011           42377  Pink Cab  ATLANTA GA         30.45   \n",
+              "1        10000012           42375  Pink Cab  ATLANTA GA         28.62   \n",
+              "2        10000013           42371  Pink Cab  ATLANTA GA          9.04   \n",
+              "3        10000014           42376  Pink Cab  ATLANTA GA         33.17   \n",
+              "4        10000015           42372  Pink Cab  ATLANTA GA          8.73   \n",
+              "\n",
+              "   Price Charged  Cost of Trip  profit  \n",
+              "0         370.95       313.635  57.315  \n",
+              "1         358.52       334.854  23.666  \n",
+              "2         125.20        97.632  27.568  \n",
+              "3         377.40       351.602  25.798  \n",
+              "4         114.62        97.776  16.844  "
+            ],
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Transaction ID</th>\n",
+              "      <th>Date of Travel</th>\n",
+              "      <th>Company</th>\n",
+              "      <th>City</th>\n",
+              "      <th>KM Travelled</th>\n",
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+              "      <th>profit</th>\n",
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+              "      <td>10000011</td>\n",
+              "      <td>42377</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>30.45</td>\n",
+              "      <td>370.95</td>\n",
+              "      <td>313.635</td>\n",
+              "      <td>57.315</td>\n",
+              "    </tr>\n",
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+              "      <th>1</th>\n",
+              "      <td>10000012</td>\n",
+              "      <td>42375</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>28.62</td>\n",
+              "      <td>358.52</td>\n",
+              "      <td>334.854</td>\n",
+              "      <td>23.666</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>10000013</td>\n",
+              "      <td>42371</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>9.04</td>\n",
+              "      <td>125.20</td>\n",
+              "      <td>97.632</td>\n",
+              "      <td>27.568</td>\n",
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+              "      <th>3</th>\n",
+              "      <td>10000014</td>\n",
+              "      <td>42376</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>33.17</td>\n",
+              "      <td>377.40</td>\n",
+              "      <td>351.602</td>\n",
+              "      <td>25.798</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>10000015</td>\n",
+              "      <td>42372</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>8.73</td>\n",
+              "      <td>114.62</td>\n",
+              "      <td>97.776</td>\n",
+              "      <td>16.844</td>\n",
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+              "                                                    [key], {});\n",
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+              "\n",
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+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
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+              "\n",
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+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df1"
+            }
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Bar chart for comparing the average profits of both Pink and Yellow Cab Companies"
+      ],
+      "metadata": {
+        "id": "ocbjQ1Svoc4J"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#This code provides the categories for Company column values, which are pink and yellow cabs, and these categories corresponds to the average profits of these two cabs.\n",
+        "average_profit = df1.groupby('Company')['profit'].mean()\n",
+        "\n",
+        "#I used the above series for average profits for both yellow and pink cab, and this code is used to plot the bar chart for average profit comparisons\n",
+        "average_profit.plot(kind='bar', color=['blue', 'red'])\n",
+        "\n",
+        "#x label for the graph\n",
+        "plt.xlabel('Company')\n",
+        "#y label for the graph\n",
+        "plt.ylabel('Profit')\n",
+        "#the title of the graph above\n",
+        "plt.title('Pink and Yellow Cab Companies vs Average Profits')\n",
+        "#saving the image as png\n",
+        "plt.savefig('barplot_comparison_profits.png')\n",
+        "\n",
+        "#EDA Recommendation\n",
+        "# Based on the exploratory data analysis, it is recommended to further investigate the factors contributing to the higher average profits of Yellow Cabs compared to Pink Cabs.\n",
+        "# This includes analyzing variables such as trip duration, fare rates, customer satisfaction, and the operational strategies of both cab companies.\n",
+        "\n",
+        "# Hypothesis Results\n",
+        "# In the bar chart below, I am comparing the average profits for two cab companies, Yellow and Pink Cabs.\n",
+        "# As you can see, the data indicates that the Yellow Cab company has more average profits, with a maximum profit of approximately $158,\n",
+        "# while the Pink Cab has an average profit of about $63. If this hypothesis holds true, it suggests that XYZ firm should consider investing in the Yellow Cab company\n",
+        "# due to its stronger profitability. This analysis supports the notion that Yellow Cabs may provide a more lucrative investment opportunity."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 532
+        },
+        "id": "A-hBShAXUwUw",
+        "outputId": "1c313882-44c2-4ba3-fa30-a0130a283d17"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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Z7XbnGBUvLy/Vq1fPue5du3bp77//1vPPPy9jjDZu3ChJWrdunSpXrpzh/Pvl0l+n/PnzZ6tGf39/Z22pqan6+++/lS9fPpUrVy7T9+eRRx5xWff999+vsLAw5/uSEzWlr+vyU3jSpQHmkjKM7alYsaLq1q3rvF+nTh1Jl04LFC9ePEN7Zvt2v379nP9OPw114cIFLV++3Nl++b78zz//KDExUQ0bNsz0dYqOjlbp0qWd96tWrSq73Z7pttN9//33SkhIUNeuXV32PW9vb9WpU0erVq1y1uHn56fVq1c7TztlV+fOnbVlyxYdOHDA2bZgwQL5+/urXbt2kiSHwyHp0imlf59Czo45c+aoVatWzvc7KipKNWvWdDmV1blzZ6WkpOiLL75wtn333XdKSEhQ586dJV0aaP/555+rTZs2Msa4vCYxMTFKTEzM8Np3797d5X2Ssr+PL126VEWLFlXbtm2dbQEBAerTp4/L+rZv3659+/bpwQcf1N9//+2s6cyZM2rWrJnWrl2brVPeI0aMUOHChRUaGqomTZrowIEDGjNmjDp06ODSr0+fPi5ju7L7vZITbnRfyA0IO7nMpEmT9P3332vVqlXauXOncwzC1Vz+YyDJGXz+/SWanJysVq1aqUaNGvr000+zPcAtPj5eAwcOVEhIiAIDA1W4cGFFRkZKUqbnfK9Wz6FDh+Tl5eXyQyJJ5cqVy1Y9l0s/T50+sFu6dH566dKlKly4sMstOjpa0v8G/GVH+tiIf9fm5+enUqVKuYydaNiwobZs2aJz585p3bp1CgsL0x133KFq1app3bp1kqT169c7w2hW7Ha7pEuDrrMjLS1N77zzjqKiouTv769ChQqpcOHC+vXXXzN9f6Kiolzu22w2lSlT5opjRa61pvT3+PLZI5IUGhqqoKCgDGNO/r3PpH9BR0REZNr+733by8tLpUqVcmkrW7asJLk8ryVLluiuu+5SQECAgoODVbhwYU2ZMiVb+7F0aV++UjjZt2+fpEsh7d/733fffefc9/z9/TVmzBh9++23CgkJUaNGjTR27FjFxsZmue50DzzwgLy8vLRgwQJJlwLFwoUL1aJFC+f7FBkZqSFDhuiDDz5QoUKFFBMTo0mTJmVrjMauXbu0bds21a9fX/v373femjRpoiVLljiDb7Vq1VS+fHlnHdKl0FWoUCHn2JWTJ08qISFB06ZNy/B69OzZU1LGz2P6d8vlsruPHzp0SKVLl84wzu3f+2H6+9S9e/cMdX3wwQc6f/58tl6rxx57TN9//71WrFihLVu26MSJE3r22Wcz9Pv3c7qW75UbdSP7Qm7BbKxc5s4773T50c6urGaDmH8NzvT391fLli315ZdfaunSpWrdunW21t+pUydt2LBBzzzzjKpXr658+fIpLS1NzZs3z/R/P9mt52ZJS0vTPffck+mXjvS/H8Gc1qBBA6WkpGjjxo1at26dM9Q0bNhQ69at0+7du3Xy5Mmrhp3y5ctLkn777Te1b9/+qtt9/fXXNXz4cPXq1UuvvPKKgoOD5eXlpUGDBuXYpQrsdrvCw8P1+++/X9PjsjtVOat9Jif3pXXr1qlt27Zq1KiRJk+erLCwMPn6+mrmzJmaO3dujmw7/fWePXu2QkNDMyy/fAbdoEGD1KZNGy1evFjLli3T8OHDNXr0aK1cuVI1atTIchvh4eFq2LChPv30U73wwgv68ccfdfjwYY0ZM8al39tvv60ePXroyy+/1HfffacBAwZo9OjR+vHHH10Gyf7bJ598IkkaPHiwBg8enGH5559/7gwqnTt31muvvaa//vpL+fPn11dffaWuXbs6n2f66/HQQw+pe/fumW6vatWqLvf/fVRHyvl9PP0xb775ZpbTtLNzEdaoqCjnf6KuJLPndCtd776QWxB24MJms2nOnDlq166dHnjgAX377bdXvQLnP//8oxUrVmjkyJF66aWXnO3p/zO6HiVKlFBaWpoOHDjg8j+bPXv2XPc6L1e6dGmdPn06W19CV1OiRAlJl2q7/MjBhQsXdPDgQZdt3HnnnfLz89O6deu0bt06PfPMM5KkRo0aafr06VqxYoXz/pU0aNBABQoU0Lx58/TCCy9cdWrzZ599pqZNm2rGjBku7QkJCSpUqFCG/v9+74wx2r9/f4YfnX9r3bq1pk2bpo0bN7qccspM+nu8b98+VahQwdkeFxenhIQE5+uaU9LS0vTHH3+4BNm9e/dKknMG4+eff66AgAAtW7bM5fTtzJkzc6yO9KOVRYoUydb+V7p0aQ0dOlRDhw7Vvn37VL16db399tvOwJGVzp0766mnntKePXu0YMEC5cmTR23atMnQr0qVKqpSpYpefPFFbdiwQfXr19fUqVP16quvZrpeY4zmzp2rpk2b6qmnnsqw/JVXXtGcOXNcws7IkSP1+eefKyQkRElJSerSpYuzf/psy9TU1Bv6PGZ3Hy9RooR27twpY4xL0N6/f7/L49LfJ7vdniPfE9fqWr5Xsutq/7G41n0hN+E0FjLw8/PTF198odq1a6tNmzbavHnzFfun/9D++3+z48ePv+4aWrRoIenSdO2cWuflOnXqpI0bN2Z6leWEhARdvHgx2+uKjo6Wn5+fJkyY4PIazJgxQ4mJiWrVqpWzLSAgQLVr19a8efN0+PBhlyM7586d04QJE1S6dOlMp21fLk+ePHruuee0a9cuPffcc5keSfjkk0+c7523t3eGPgsXLtSxY8cyXf/HH3/scjrqs88+03//+1/n+5KVZ599Vnnz5tWjjz6a6ZW9Dxw4oHfffVfSpStZSxnf03HjxkmSy+uWUyZOnOj8tzFGEydOlK+vr5o1aybp0utks9mUmprq7Pfnn39q8eLFOVZDTEyM7Ha7Xn/9daWkpGRYfvLkSUnS2bNnlZyc7LKsdOnSyp8/f4ap+Znp2LGjvL29NW/ePC1cuFCtW7d2uXZLUlJShv28SpUq8vLyuuL6f/jhB/3555/q2bOn7r///gy3zp07a9WqVc4rlFeoUEFVqlTRggULtGDBAoWFhbmEeW9vb3Xs2FGff/55pkcF01+Pq8nuPh4TE6Njx47pq6++crYlJydr+vTpLv1q1qyp0qVL66233sr0shvZret6Xcv3Snalv/+XT12Xrn9fyE04soNMBQYGasmSJbr77rvVokULrVmzRpUrV860r91ud44nSElJUdGiRfXdd985ryNxPapXr66uXbtq8uTJSkxMVL169bRixYoM//u6Xs8884y++uortW7dWj169FDNmjV15swZ/fbbb/rss8/0559/ZnrEIzOFCxfWsGHDNHLkSDVv3lxt27bVnj17NHnyZNWuXdvlgmLSpWDzxhtvyOFwqEqVKpIu/S+/XLly2rNnj3r06JHt57Bjxw69/fbbWrVqle6//36FhoYqNjZWixcv1ubNm7VhwwZJl464jBo1Sj179lS9evX022+/ac6cORnGsKQLDg5WgwYN1LNnT8XFxWn8+PEqU6ZMhkGc/1a6dGnNnTtXnTt3VoUKFVyuoLxhwwYtXLjQ+fyqVaum7t27a9q0aUpISFDjxo21efNmffTRR2rfvr2aNm2ardchuwICArR06VJ1795dderU0bfffqtvvvlGL7zwggoXLizpUsAaN26cmjdvrgcffFAnTpzQpEmTVKZMGf366685UofdbteUKVP08MMP64477lCXLl1UuHBhHT58WN98843q16+viRMnau/evWrWrJk6deqkihUrysfHR4sWLVJcXJzLkZGsFClSRE2bNtW4ceN06tQp54DgdCtXrlS/fv30wAMPqGzZsrp48aJmz57tDB9ZmTNnjry9vbP8sW3btq3+85//aP78+c7B5507d9ZLL72kgIAA9e7dO8MFAN944w2tWrVKderUUZ8+fVSxYkXFx8dr69atWr58ueLj46/6fLO7jz/++OOaOHGiunbtqoEDByosLExz5sxRQECApP8d/fDy8tIHH3ygFi1aqFKlSurZs6eKFi2qY8eOadWqVbLb7fr666+vWtf1utbvlewoXbq0goKCNHXqVOXPn1958+ZVnTp19Msvv1zXvpCr3OrpX7g+WV1n59+ymnqe2fRV/WtK9b+vs2OMMX/99ZepWLGiCQ0NNfv27ctyu0ePHjX33XefCQoKMg6HwzzwwAPm+PHjGbaR1RTJzK5Fcu7cOTNgwABTsGBBkzdvXtOmTRtz5MiRa5p6fqVtnjp1ygwbNsyUKVPG+Pn5mUKFCpl69eqZt956y+X6Q//eXma1GnNpSmj58uWNr6+vCQkJMU8++WSm08e/+eYbI8m0aNHCpT39ekMzZszI9nMz5tL0+nvvvdcEBwcbHx8fExYWZjp37mxWr17t7JOcnGyGDh1qwsLCTGBgoKlfv77ZuHFjhqmp6VN7582bZ4YNG2aKFCliAgMDTatWrVymd1/N3r17TZ8+fUzJkiWNn5+fyZ8/v6lfv7557733XC5lkJKSYkaOHGkiIyONr6+viYiIMMOGDctwuYMSJUo4rw9zOUmmb9++Lm2Z7fPp+/aBAwfMvffea/LkyWNCQkLMiBEjMkxXnjFjhomKijL+/v6mfPnyZubMmc596GrbTq+1e/fuzvtZ7S+rVq0yMTExxuFwmICAAFO6dGnTo0cP8/PPPxtjLn32+vbta8qXL2/y5s1rHA6HqVOnjvn0008zecUzN336dCPJ5M+f35w7d85l2R9//GF69eplSpcubQICAkxwcLBp2rSpWb58eZbru3DhgilYsKBp2LDhFbcbGRlpatSo4by/b98+I8lIMuvXr8/0MXFxcaZv374mIiLC+Pr6mtDQUNOsWTMzbdo0Z5/0/TOzKdLZ3cfTn3urVq1MYGCgKVy4sBk6dKj5/PPPjSTz448/uvTdtm2b6dChgylYsKDx9/c3JUqUMJ06dTIrVqy44mtwpe/ey13tuz073yvZnXpuzKXLjlSsWNH4+Pg4p6Ffz76Q29iMuUUjQgHATXr06KHPPvvsilcBx+1t/PjxGjx4sI4ePXrVS3kg92HMDgDgtnLu3DmX+8nJyXr//fcVFRVF0LEoxuwAAG4rHTp0UPHixVW9enUlJibqk08+0e7du10uiAhrIewAAG4rMTEx+uCDDzRnzhylpqaqYsWKmj9/foZB3LAOxuwAAABLY8wOAACwNMIOAACwNMbs6NJl5I8fP678+fNn++/0AAAA9zLG6NSpUwoPD89wscrLEXYkHT9+PMNfTgYAALnDkSNHrvgHSwk7kvLnzy/p0otlt9vdXA0AAMiOpKQkRUREOH/Hs0LY0f/+FordbifsAACQy1xtCAoDlAEAgKURdgAAgKURdgAAgKURdgAAgKURdgAAgKURdgAAgKURdgAAgKURdgAAgKURdgAAgKURdgAAgKURdgAAgKW5NeysXbtWbdq0UXh4uGw2mxYvXpyhz65du9S2bVs5HA7lzZtXtWvX1uHDh53Lk5OT1bdvXxUsWFD58uVTx44dFRcXdwufBQAA8GRuDTtnzpxRtWrVNGnSpEyXHzhwQA0aNFD58uW1evVq/frrrxo+fLgCAgKcfQYPHqyvv/5aCxcu1Jo1a3T8+HF16NDhVj0FAADg4WzGGOPuIqRLf7F00aJFat++vbOtS5cu8vX11ezZszN9TGJiogoXLqy5c+fq/vvvlyTt3r1bFSpU0MaNG3XXXXdla9tJSUlyOBxKTEzkr54DAJBLZPf322PH7KSlpembb75R2bJlFRMToyJFiqhOnToup7q2bNmilJQURUdHO9vKly+v4sWLa+PGjVmu+/z580pKSnK5AYDl2GzcbqcbsuSxYefEiRM6ffq03njjDTVv3lzfffed7rvvPnXo0EFr1qyRJMXGxsrPz09BQUEujw0JCVFsbGyW6x49erQcDofzFhERcTOfCgAAcCOPDTtpaWmSpHbt2mnw4MGqXr26nn/+ebVu3VpTp069oXUPGzZMiYmJztuRI0dyomQAAOCBfNxdQFYKFSokHx8fVaxY0aW9QoUKWr9+vSQpNDRUFy5cUEJCgsvRnbi4OIWGhma5bn9/f/n7+9+UugEAgGfx2CM7fn5+ql27tvbs2ePSvnfvXpUoUUKSVLNmTfn6+mrFihXO5Xv27NHhw4dVt27dW1ovAADwTG49snP69Gnt37/fef/gwYPavn27goODVbx4cT3zzDPq3LmzGjVqpKZNm2rp0qX6+uuvtXr1akmSw+FQ7969NWTIEAUHB8tut6t///6qW7dutmdiAQAAa3Pr1PPVq1eradOmGdq7d++uWbNmSZI+/PBDjR49WkePHlW5cuU0cuRItWvXztk3OTlZQ4cO1bx583T+/HnFxMRo8uTJVzyN9W9MPQdgSczQub14xpVkbqns/n57zHV23ImwA8CSCDu3l9vw5zzXX2cHAAAgJxB2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApbk17Kxdu1Zt2rRReHi4bDabFi9enGXfJ554QjabTePHj3dpj4+PV7du3WS32xUUFKTevXvr9OnTN7dwAACQa7g17Jw5c0bVqlXTpEmTrthv0aJF+vHHHxUeHp5hWbdu3bRjxw59//33WrJkidauXavHHnvsZpUMAAByGR93brxFixZq0aLFFfscO3ZM/fv317Jly9SqVSuXZbt27dLSpUv1008/qVatWpKk9957Ty1bttRbb72VaTgCAAC3F48es5OWlqaHH35YzzzzjCpVqpRh+caNGxUUFOQMOpIUHR0tLy8vbdq0Kcv1nj9/XklJSS43AABgTR4ddsaMGSMfHx8NGDAg0+WxsbEqUqSIS5uPj4+Cg4MVGxub5XpHjx4th8PhvEVERORo3QAAwHN4bNjZsmWL3n33Xc2aNUs2my1H1z1s2DAlJiY6b0eOHMnR9QMAAM/hsWFn3bp1OnHihIoXLy4fHx/5+Pjo0KFDGjp0qEqWLClJCg0N1YkTJ1wed/HiRcXHxys0NDTLdfv7+8tut7vcAACANbl1gPKVPPzww4qOjnZpi4mJ0cMPP6yePXtKkurWrauEhARt2bJFNWvWlCStXLlSaWlpqlOnzi2vGQAAeB63hp3Tp09r//79zvsHDx7U9u3bFRwcrOLFi6tgwYIu/X19fRUaGqpy5cpJkipUqKDmzZurT58+mjp1qlJSUtSvXz916dKFmVgAAECSm09j/fzzz6pRo4Zq1KghSRoyZIhq1Kihl156KdvrmDNnjsqXL69mzZqpZcuWatCggaZNm3azSgYAALmMzRhj3F2EuyUlJcnhcCgxMZHxOwCsI4cnd8DD3YY/59n9/fbYAcoAAAA5gbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsza1hZ+3atWrTpo3Cw8Nls9m0ePFi57KUlBQ999xzqlKlivLmzavw8HA98sgjOn78uMs64uPj1a1bN9ntdgUFBal37946ffr0LX4mAADAU7k17Jw5c0bVqlXTpEmTMiw7e/astm7dquHDh2vr1q364osvtGfPHrVt29alX7du3bRjxw59//33WrJkidauXavHHnvsVj0FAADg4WzGGOPuIiTJZrNp0aJFat++fZZ9fvrpJ9155506dOiQihcvrl27dqlixYr66aefVKtWLUnS0qVL1bJlSx09elTh4eHZ2nZSUpIcDocSExNlt9tz4ukAgPvZbO6uALeSZ/yc31LZ/f3OVWN2EhMTZbPZFBQUJEnauHGjgoKCnEFHkqKjo+Xl5aVNmzZluZ7z588rKSnJ5QYAAKwp14Sd5ORkPffcc+rataszvcXGxqpIkSIu/Xx8fBQcHKzY2Ngs1zV69Gg5HA7nLSIi4qbWDgAA3CdXhJ2UlBR16tRJxhhNmTLlhtc3bNgwJSYmOm9HjhzJgSoBAIAn8nF3AVeTHnQOHTqklStXupyTCw0N1YkTJ1z6X7x4UfHx8QoNDc1ynf7+/vL3979pNQMAAM/h0Ud20oPOvn37tHz5chUsWNBled26dZWQkKAtW7Y421auXKm0tDTVqVPnVpcLAAA8kFuP7Jw+fVr79+933j948KC2b9+u4OBghYWF6f7779fWrVu1ZMkSpaamOsfhBAcHy8/PTxUqVFDz5s3Vp08fTZ06VSkpKerXr5+6dOmS7ZlYAADA2tw69Xz16tVq2rRphvbu3bvr5ZdfVmRkZKaPW7VqlZo0aSLp0kUF+/Xrp6+//lpeXl7q2LGjJkyYoHz58mW7DqaeA7Akpp7fXph6nmU/j7nOjjsRdgBYEmHn9nIb/pxb8jo7AAAA14qwAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALM2tYWft2rVq06aNwsPDZbPZtHjxYpflxhi99NJLCgsLU2BgoKKjo7Vv3z6XPvHx8erWrZvsdruCgoLUu3dvnT59+hY+CwAA4MncGnbOnDmjatWqadKkSZkuHzt2rCZMmKCpU6dq06ZNyps3r2JiYpScnOzs061bN+3YsUPff/+9lixZorVr1+qxxx67VU8BAAB4OJsxxri7CEmy2WxatGiR2rdvL+nSUZ3w8HANHTpUTz/9tCQpMTFRISEhmjVrlrp06aJdu3apYsWK+umnn1SrVi1J0tKlS9WyZUsdPXpU4eHh2dp2UlKSHA6HEhMTZbfbb8rzA4BbzmZzdwW4lTzj5/yWyu7vt8eO2Tl48KBiY2MVHR3tbHM4HKpTp442btwoSdq4caOCgoKcQUeSoqOj5eXlpU2bNmW57vPnzyspKcnlBgAArMljw05sbKwkKSQkxKU9JCTEuSw2NlZFihRxWe7j46Pg4GBnn8yMHj1aDofDeYuIiMjh6gEAgKfw2LBzMw0bNkyJiYnO25EjR9xdEgAAuEk8NuyEhoZKkuLi4lza4+LinMtCQ0N14sQJl+UXL15UfHy8s09m/P39ZbfbXW4AAMCaPDbsREZGKjQ0VCtWrHC2JSUladOmTapbt64kqW7dukpISNCWLVucfVauXKm0tDTVqVPnltcMAAA8j487N3769Gnt37/fef/gwYPavn27goODVbx4cQ0aNEivvvqqoqKiFBkZqeHDhys8PNw5Y6tChQpq3ry5+vTpo6lTpyolJUX9+vVTly5dsj0TCwAAWJtbw87PP/+spk2bOu8PGTJEktS9e3fNmjVLzz77rM6cOaPHHntMCQkJatCggZYuXaqAgADnY+bMmaN+/fqpWbNm8vLyUseOHTVhwoRb/lwAAIBn8pjr7LgT19kBYElcZ+f2chv+nOf66+wAAADkBMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwtOsKO4cPH1Zmfz/UGKPDhw/fcFEAAAA55brCTmRkpE6ePJmhPT4+XpGRkTdcFAAAQE65rrBjjJHNZsvQfvr0aQUEBNxwUQAAADnF51o6DxkyRJJks9k0fPhw5cmTx7ksNTVVmzZtUvXq1XO0QAAAgBtxTWFn27Ztki4d2fntt9/k5+fnXObn56dq1arp6aefztkKAQAAbsA1hZ1Vq1ZJknr27Kl3331Xdrv9phQFAACQU64p7KSbOXNmTtcBAABwU2Q77HTo0EGzZs2S3W5Xhw4drtj3iy++uOHCAAAAckK2w47D4XDOwLLb7ZnOxgIAAPA02Q479913n3Na+axZs25WPQAAADkq29fZue+++5SQkCBJ8vb21okTJ25WTQAAADkm22GncOHC+vHHHyVlfVFBAAAAT5Pt01hPPPGE2rVrJ5vNJpvNptDQ0Cz7pqam5khxAAAANyrbYefll19Wly5dtH//frVt21YzZ85UUFDQTSwNAADgxl3TdXbKly+v8uXLa8SIEXrggQdc/lwEAACAJ7IZY8z1PvjkyZPas2ePJKlcuXIqXLhwjhV2KyUlJcnhcCgxMZGrQgOwDsZW3l6u/+c818ru7/d1/dXzs2fPqlevXgoPD1ejRo3UqFEjhYeHq3fv3jp79ux1Fw0AAJDTrivsDB48WGvWrNFXX32lhIQEJSQk6Msvv9SaNWs0dOjQnK4RAADgul3XaaxChQrps88+U5MmTVzaV61apU6dOunkyZM5Vd8twWksAJbEaazbC6exsux33aexQkJCMrQXKVKE01gAAMCjXFfYqVu3rkaMGKHk5GRn27lz5zRy5EjVrVs3x4oDAAC4UdcVdsaPH68ffvhBxYoVU7NmzdSsWTNFRERow4YNevfdd3OsuNTUVA0fPlyRkZEKDAxU6dKl9corr+jyM2/GGL300ksKCwtTYGCgoqOjtW/fvhyrAQAA5G7XdJ2ddFWqVNG+ffs0Z84c7d69W5LUtWtXdevWTYGBgTlW3JgxYzRlyhR99NFHqlSpkn7++Wf17NlTDodDAwYMkCSNHTtWEyZM0EcffaTIyEgNHz5cMTEx2rlzp/MPlwIAgNvXNQ9QTklJUfny5bVkyRJVqFDhZtUlSWrdurVCQkI0Y8YMZ1vHjh0VGBioTz75RMYYhYeHa+jQoXr66aclSYmJiQoJCdGsWbPUpUuXbG2HAcoALIkByrcXBihn2e+aT2P5+vq6jNW5merVq6cVK1Zo7969kqRffvlF69evV4sWLSRJBw8eVGxsrKKjo52PcTgcqlOnjjZu3Jjles+fP6+kpCSXGwAAsKbrGrPTt29fjRkzRhcvXszpelw8//zz6tKli8qXLy9fX1/VqFFDgwYNUrdu3SRJsbGxkpRhZlhISIhzWWZGjx4th8PhvEVERNy8JwEAANzqusbs/PTTT1qxYoW+++47ValSRXnz5nVZ/sUXX+RIcZ9++qnmzJmjuXPnqlKlStq+fbsGDRqk8PBwde/e/brXO2zYMA0ZMsR5PykpicADAIBFXVfYCQoKUseOHXO6lgyeeeYZ59Ed6dLA6EOHDmn06NHq3r27QkNDJUlxcXEKCwtzPi4uLk7Vq1fPcr3+/v7y9/e/qbUDAADPcE1hJy0tTW+++ab27t2rCxcu6O6779bLL7+cozOwLnf27Fl5ebmeafP29lZaWpokKTIyUqGhoVqxYoUz3CQlJWnTpk168sknb0pNAAAgd7mmsPPaa6/p5ZdfVnR0tAIDAzVhwgSdPHlSH3744U0prk2bNnrttddUvHhxVapUSdu2bdO4cePUq1cvSZLNZtOgQYP06quvKioqyjn1PDw8XO3bt78pNQEAgNzlmqaeR0VF6emnn9bjjz8uSVq+fLlatWqlc+fOZTgCkxNOnTql4cOHa9GiRTpx4oTCw8PVtWtXvfTSS/Lz85N06aKCI0aM0LRp05SQkKAGDRpo8uTJKlu2bLa3w9RzAJbE1PPbC1PPs+x3TWHH399f+/fvdxnMGxAQoP3796tYsWI3VrEbEXYAWBJh5/ZC2Mmy3zUdjrl48WKGqxL7+voqJSXl+qoEAAC4ya5pzI4xRj169HCZyZScnKwnnnjCZfp5Tk09BwAAuFHXFHYyu7bNQw89lGPFAAAA5LRrCjszZ868WXUAAADcFDk/hQoAAMCDEHYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAICl+bi7ALiXzebuCnArGePuCgDg1uPIDgAAsDSPDzvHjh3TQw89pIIFCyowMFBVqlTRzz//7FxujNFLL72ksLAwBQYGKjo6Wvv27XNjxQAAwJN4dNj5559/VL9+ffn6+urbb7/Vzp079fbbb6tAgQLOPmPHjtWECRM0depUbdq0SXnz5lVMTIySk5PdWDkAAPAUNmM89yz+888/rx9++EHr1q3LdLkxRuHh4Ro6dKiefvppSVJiYqJCQkI0a9YsdenSJVvbSUpKksPhUGJioux2e47VnxswZuf24rmfdtwUfMBvL7fhBzy7v98efWTnq6++Uq1atfTAAw+oSJEiqlGjhqZPn+5cfvDgQcXGxio6OtrZ5nA4VKdOHW3cuDHL9Z4/f15JSUkuNwAAYE0eHXb++OMPTZkyRVFRUVq2bJmefPJJDRgwQB999JEkKTY2VpIUEhLi8riQkBDnssyMHj1aDofDeYuIiLh5TwIAALiVR4edtLQ03XHHHXr99ddVo0YNPfbYY+rTp4+mTp16Q+sdNmyYEhMTnbcjR47kUMUAAMDTeHTYCQsLU8WKFV3aKlSooMOHD0uSQkNDJUlxcXEufeLi4pzLMuPv7y+73e5yAwAA1uTRYad+/fras2ePS9vevXtVokQJSVJkZKRCQ0O1YsUK5/KkpCRt2rRJdevWvaW1AgAAz+TRV1AePHiw6tWrp9dff12dOnXS5s2bNW3aNE2bNk2SZLPZNGjQIL366quKiopSZGSkhg8frvDwcLVv3969xQMAAI/g0WGndu3aWrRokYYNG6ZRo0YpMjJS48ePV7du3Zx9nn32WZ05c0aPPfaYEhIS1KBBAy1dulQBAQFurBwAAHgKj77Ozq3CdXZwu+DTfpvhA357uQ0/4Ja4zg4AAMCNIuwAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLy1Vh54033pDNZtOgQYOcbcnJyerbt68KFiyofPnyqWPHjoqLi3NfkQAAwKPkmrDz008/6f3331fVqlVd2gcPHqyvv/5aCxcu1Jo1a3T8+HF16NDBTVUCAABPkyvCzunTp9WtWzdNnz5dBQoUcLYnJiZqxowZGjdunO6++27VrFlTM2fO1IYNG/Tjjz+6sWIAAOApckXY6du3r1q1aqXo6GiX9i1btiglJcWlvXz58ipevLg2btyY5frOnz+vpKQklxsAALAmH3cXcDXz58/X1q1b9dNPP2VYFhsbKz8/PwUFBbm0h4SEKDY2Nst1jh49WiNHjszpUgEAgAfy6CM7R44c0cCBAzVnzhwFBATk2HqHDRumxMRE5+3IkSM5tm4AAOBZPDrsbNmyRSdOnNAdd9whHx8f+fj4aM2aNZowYYJ8fHwUEhKiCxcuKCEhweVxcXFxCg0NzXK9/v7+stvtLjcAAGBNHn0aq1mzZvrtt99c2nr27Kny5cvrueeeU0REhHx9fbVixQp17NhRkrRnzx4dPnxYdevWdUfJAADAw3h02MmfP78qV67s0pY3b14VLFjQ2d67d28NGTJEwcHBstvt6t+/v+rWrau77rrLHSUDAAAP49FhJzveeecdeXl5qWPHjjp//rxiYmI0efJkd5cFAAA8hM0YY9xdhLslJSXJ4XAoMTHxthu/Y7O5uwLcSnzabzN8wG8vt+EHPLu/3x49QBkAAOBGEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAICleXzYGT16tGrXrq38+fOrSJEiat++vfbs2ePSJzk5WX379lXBggWVL18+dezYUXFxcW6qGAAAeBKPDztr1qxR37599eOPP+r7779XSkqK7r33Xp05c8bZZ/Dgwfr666+1cOFCrVmzRsePH1eHDh3cWDUAAPAUNmOMcXcR1+LkyZMqUqSI1qxZo0aNGikxMVGFCxfW3Llzdf/990uSdu/erQoVKmjjxo266667rrrOpKQkORwOJSYmym633+yn4FFsNndXgFspd33accP4gN9ebsMPeHZ/vz3+yM6/JSYmSpKCg4MlSVu2bFFKSoqio6OdfcqXL6/ixYtr48aNma7j/PnzSkpKcrkBAABrylVhJy0tTYMGDVL9+vVVuXJlSVJsbKz8/PwUFBTk0jckJESxsbGZrmf06NFyOBzOW0RExM0uHQAAuEmuCjt9+/bV77//rvnz59/QeoYNG6bExETn7ciRIzlUIQAA8DQ+7i4gu/r166clS5Zo7dq1KlasmLM9NDRUFy5cUEJCgsvRnbi4OIWGhma6Ln9/f/n7+9/skgEAgAfw+CM7xhj169dPixYt0sqVKxUZGemyvGbNmvL19dWKFSucbXv27NHhw4dVt27dW10uAADwMB5/ZKdv376aO3euvvzyS+XPn985DsfhcCgwMFAOh0O9e/fWkCFDFBwcLLvdrv79+6tu3brZmokFAACszeOnntuymDo5c+ZM9ejRQ9KliwoOHTpU8+bN0/nz5xUTE6PJkydneRrr35h6jtuFZ3/akeP4gN9ebsMPeHZ/vz0+7NwKhB3cLvi032b4gN9ebsMPuGWvswMAAHAtCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSLBN2Jk2apJIlSyogIEB16tTR5s2b3V0SAADwAJYIOwsWLNCQIUM0YsQIbd26VdWqVVNMTIxOnDjh7tIAAICbWSLsjBs3Tn369FHPnj1VsWJFTZ06VXny5NGHH37o7tIAAICb+bi7gBt14cIFbdmyRcOGDXO2eXl5KTo6Whs3bsz0MefPn9f58+ed9xMTEyVJSUlJN7dYwM3YxQELuw0/4Om/28aYK/bL9WHnr7/+UmpqqkJCQlzaQ0JCtHv37kwfM3r0aI0cOTJDe0RExE2pEfAUDoe7KwBw09zGH/BTp07JcYXnn+vDzvUYNmyYhgwZ4ryflpam+Ph4FSxYUDabzY2V4VZISkpSRESEjhw5Irvd7u5yAOQgPt+3F2OMTp06pfDw8Cv2y/Vhp1ChQvL29lZcXJxLe1xcnEJDQzN9jL+/v/z9/V3agoKCblaJ8FB2u50vQ8Ci+HzfPq50RCddrh+g7Ofnp5o1a2rFihXOtrS0NK1YsUJ169Z1Y2UAAMAT5PojO5I0ZMgQde/eXbVq1dKdd96p8ePH68yZM+rZs6e7SwMAAG5mibDTuXNnnTx5Ui+99JJiY2NVvXp1LV26NMOgZUC6dBpzxIgRGU5lAsj9+HwjMzZztflaAAAAuViuH7MDAABwJYQdAABgaYQdAABgaYQdAABgaYQdAABgaZaYeg5czT///KMZM2Zo165dkqQKFSqoV69eCg4OdnNlAHLCzz//7PL5rlWrlpsrgidh6jksb+3atWrbtq3sdrvzC3DLli1KSEjQ119/rUaNGrm5QgDX6+jRo+ratat++OEH55/9SUhIUL169TR//nwVK1bMvQXCIxB2YHlVqlRR3bp1NWXKFHl7e0uSUlNT9dRTT2nDhg367bff3FwhgOvVvHlzJSQk6KOPPlK5cuUkSXv27FHPnj1lt9u1dOlSN1cIT0DYgeUFBgZq+/btzi/CdHv27FH16tV17tw5N1UG4EYFBgZqw4YNqlGjhkv7li1b1LBhQ509e9ZNlcGTMEAZlnfHHXc4z+VfbteuXapWrZobKgKQUyIiIpSSkpKhPTU1VeHh4W6oCJ6IAcqwpF9//dX57wEDBmjgwIHav3+/7rrrLknSjz/+qEmTJumNN95wV4kAcsCbb76p/v37a9KkSc4xeT///LMGDhyot956y83VwVNwGguW5OXlJZvNpqvt3jabTampqbeoKgA5oUCBArLZbM77Z86c0cWLF+Xjc+n/7+n/zps3r+Lj491VJjwIR3ZgSQcPHnR3CQBukvHjx7u7BOQyHNkBAACWxpEd3DZ27typw4cP68KFCy7tbdu2dVNFAHJScnJyhs+33W53UzXwJIQdWN4ff/yh++67T7/99pvLOJ70c/6M2QFyrzNnzui5557Tp59+qr///jvDcj7fkJh6jtvAwIEDFRkZqRMnTihPnjzasWOH1q5dq1q1amn16tXuLg/ADXj22We1cuVKTZkyRf7+/vrggw80cuRIhYeH6+OPP3Z3efAQjNmB5RUqVEgrV65U1apV5XA4tHnzZpUrV04rV67U0KFDtW3bNneXCOA6FS9eXB9//LGaNGkiu92urVu3qkyZMpo9e7bmzZun//u//3N3ifAAHNmB5aWmpip//vySLgWf48ePS5JKlCihPXv2uLM0ADcoPj5epUqVknRpfE76VPMGDRpo7dq17iwNHoSwA8urXLmyfvnlF0lSnTp1NHbsWP3www8aNWqU80sSQO5UqlQp56Umypcvr08//VSS9PXXXzv/MCjAaSxY3rJly3TmzBl16NBB+/fvV+vWrbV3714VLFhQCxYs0N133+3uEgFcp3feeUfe3t4aMGCAli9frjZt2sgYo5SUFI0bN04DBw50d4nwAIQd3Jbi4+MzXIUVQO536NAhbdmyRWXKlFHVqlXdXQ48BGEHlpWamqodO3YoKipKgYGBLsvOnTunffv2qXLlyvLy4mwuAFgZ3/KwrNmzZ6tXr17y8/PLsMzX11e9evXS3Llz3VAZgBu1cuVKVaxYUUlJSRmWJSYmqlKlSlq3bp0bKoMnIuzAsmbMmKGnn35a3t7eGZb5+Pjo2Wef1bRp09xQGYAbNX78ePXp0yfTKyQ7HA49/vjjGjdunBsqgyci7MCy9uzZo7vuuivL5bVr19auXbtuYUUAcsovv/yi5s2bZ7n83nvv1ZYtW25hRfBkhB1Y1pkzZzI9xJ3u1KlTOnv27C2sCEBOiYuLk6+vb5bLfXx8dPLkyVtYETwZYQeWFRUVpQ0bNmS5fP369YqKirqFFQHIKUWLFtXvv/+e5fJff/1VYWFht7AieDLCDizrwQcf1Isvvqhff/01w7JffvlFL730kh588EE3VAbgRrVs2VLDhw9XcnJyhmXnzp3TiBEj1Lp1azdUBk/E1HNYVkpKiu69916tX79e0dHRKl++vCRp9+7dWr58uerXr6/vv//+iofCAXimuLg43XHHHfL29la/fv1Urlw5SZc+35MmTVJqaqq2bt2qkJAQN1cKT0DYgaWlpKTonXfe0dy5c7Vv3z4ZY1S2bFk9+OCDGjRoUKbT0gHkDocOHdKTTz6pZcuWKf2nzGazKSYmRpMmTVJkZKSbK4SnIOwAAHK1f/75R/v375cxRlFRUSpQoIC7S4KHIewAAABLY4AyAACwNMIOAACwNMIOAACwNMIOLO9KFx5bvHjxrSsEAOAWhB1YXkxMjA4ePJih/fPPP1e3bt3cUBGAnFK8eHE98sgjmjFjhg4cOODucuChCDuwvEcffVTR0dGKjY11ti1YsECPPPKIZs2a5b7CANyw119/XQEBARozZoyioqIUERGhhx56SNOnT9e+ffvcXR48BFPPcVvo37+/Vq1apbVr12rp0qV69NFHNXv2bHXs2NHdpQHIIf/973+1Zs0aLVmyRAsWLFBaWppSU1PdXRY8gI+7CwBuhffee0/dunXTXXfdpWPHjmnevHlq166du8sCkAPOnj2r9evXa/Xq1Vq1apW2bdumypUrq0mTJu4uDR6CIzuwpK+++ipDW0pKigYPHqx7771Xbdu2dbZf/m8AuUu9evW0bds2VahQQU2aNFHjxo3VqFEjrqIMF4QdWJKXV/aGo9lsNg5zA7lYcHCwvLy8dO+996pJkyZq0qSJypYt6+6y4GEIOwCAXMsYo99++02rV6/WmjVrtHbtWvn5+alx48Zq2rSp+vTp4+4S4QEIOwAASzDGaMuWLZo4caLmzJnDAGU4MUAZt4UVK1ZoxYoVOnHihNLS0lyWffjhh26qCsCN2rp1q1avXq3Vq1dr/fr1OnXqlKpUqaL+/furcePG7i4PHoIjO7C8kSNHatSoUapVq5bCwsJks9lcli9atMhNlQG4UT4+PqpRo4YaN27sHJzscDjcXRY8DGEHlhcWFqaxY8fq4YcfdncpAHJYUlKS7Ha7u8uAh+M0FizvwoULqlevnrvLAHATpAedLVu2aNeuXZKkihUr6o477nBnWfAwHNmB5T333HPKly+fhg8f7u5SAOSwEydOqHPnzlqzZo2CgoIkSQkJCWratKnmz5+vwoULu7dAeASO7MDykpOTNW3aNC1fvlxVq1aVr6+vy/Jx48a5qTIAN6p///46ffq0duzYoQoVKkiSdu7cqe7du2vAgAGaN2+emyuEJ+DIDiyvadOmWS6z2WxauXLlLawGQE5yOBxavny5ateu7dK+efNm3XvvvUpISHBPYfAoHNmB5a1atcrdJQC4SdLS0jIcrZUkX1/fDJeZwO0re9fUBwDAA919990aOHCgjh8/7mw7duyYBg8erGbNmrmxMngSTmPBkjp06KBZs2bJbrerQ4cOV+z7xRdf3KKqAOS0I0eOqG3bttqxY4ciIiKcbZUrV9ZXX32lYsWKublCeAJOY8GSHA6H8+KBdrs9w4UEAVhDRESEtm7dquXLl2v37t2SpAoVKig6OtrNlcGTcGQHAABYGkd2YFlpaWl688039dVXX+nChQtq1qyZRowYocDAQHeXBuAGTJgwIdt9BwwYcBMrQW7BkR1Y1iuvvKKXX35Z0dHRCgwM1LJly9S1a1f+8CeQy0VGRmarn81m0x9//HGTq0FuQNiBZUVFRenpp5/W448/Lklavny5WrVqpXPnzsnLi4mIAHC7IOzAsvz9/bV//37nDA1JCggI0P79+5mhAQC3EcbswLIuXryogIAAlzZfX1+lpKS4qSIAOWHIkCHZ7sufg4FE2IGFGWPUo0cP+fv7O9uSk5P1xBNPKG/evM42rrMD5C7btm3LVj8uOYF0nMaCZfXs2TNb/WbOnHmTKwEAuBNhBwCQ6+3fv18HDhxQo0aNFBgYKGMMR3bgxJQUAECu9ffff6tZs2YqW7asWrZsqf/+97+SpN69e2vo0KFurg6egrADAMi1Bg8eLF9fXx0+fFh58uRxtnfu3FlLly51Y2XwJAxQBgDkWt99952WLVuW4XISUVFROnTokJuqgqfhyA4AINc6c+aMyxGddPHx8S4zMXF7I+wAAHKd48ePS5IaNmyojz/+2Nlus9mUlpamsWPHqmnTpu4qDx6G2VgAgFynQIECmjRpkqpVq6a7775bd9xxh1auXKm2bdtqx44dio+P1w8//KDSpUu7u1R4AMbsAABynddee02PP/64mjdvrp07d2rq1KnKnz+/Tp8+rQ4dOqhv374KCwtzd5nwEBzZAQDkSgcPHlTv3r21c+dOTZs2TW3btnV3SfBQhB0AQK42ceJEDR48WBUqVJCPj+sJi61bt7qpKngSTmMBAHKtQ4cO6YsvvlCBAgXUrl27DGEHkAg7AIBcavr06Ro6dKiio6O1Y8cOFS5c2N0lwUMRdgAAuU7z5s21efNmTZw4UY888oi7y4GHI+wAAHKd1NRU/frrrxmunAxkhgHKAADA0riCMgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDoBbIjY2Vv3791epUqXk7++viIgItWnTRitWrHB3aQAsjuvsALjp/vzzT9WvX19BQUF68803VaVKFaWkpGjZsmXq27evdu/e7e4SAVgYR3YA3HRPPfWUbDabNm/erI4dO6ps2bKqVKmShgwZoh9//FGSdPjwYbVr10758uWT3W5Xp06dFBcX51zHyy+/rOrVq+vDDz9U8eLFlS9fPj311FNKTU3V2LFjFRoaqiJFiui1115z2bbNZtOUKVPUokULBQYGqlSpUvrss89c+jz33HMqW7as8uTJo1KlSmn48OFKSUnJsO3Zs2erZMmScjgc6tKli06dOiVJ+vjjj1WwYEGdP3/eZb3t27fXww8/nKOvJYBrR9gBcFPFx8dr6dKl6tu3r/LmzZtheVBQkNLS0tSuXTvFx8drzZo1+v777/XHH3+oc+fOLn0PHDigb7/9VkuXLtW8efM0Y8YMtWrVSkePHtWaNWs0ZswYvfjii9q0aZPL44YPH66OHTvql19+Ubdu3dSlSxft2rXLuTx//vyaNWuWdu7cqXfffVfTp0/XO++8k2Hbixcv1pIlS7RkyRKtWbNGb7zxhiTpgQceUGpqqr766itn/xMnTuibb75Rr169bvg1BHCDDADcRJs2bTKSzBdffJFln++++854e3ubw4cPO9t27NhhJJnNmzcbY4wZMWKEyZMnj0lKSnL2iYmJMSVLljSpqanOtnLlypnRo0c770syTzzxhMv26tSpY5588sks63nzzTdNzZo1nfcz2/Yzzzxj6tSp47z/5JNPmhYtWjjvv/3226ZUqVImLS0ty+0AuDUYswPgpjLZ+Is0u3btUkREhCIiIpxtFStWVFBQkHbt2qXatWtLkkqWLKn8+fM7+4SEhMjb21teXl4ubSdOnHBZf926dTPc3759u/P+ggULNGHCBB04cECnT5/WxYsXZbfbXR7z722HhYW5bKdPnz6qXbu2jh07pqJFi2rWrFnq0aOHbDbbVZ8/gJuL01gAbqqoqCjZbLYcGYTs6+vrct9ms2XalpaWlu11bty4Ud26dVPLli21ZMkSbdu2Tf/5z3904cKFq2778u3UqFFD1apV08cff6wtW7Zox44d6tGjR7brAHDzEHYA3FTBwcGKiYnRpEmTdObMmQzLExISVKFCBR05ckRHjhxxtu/cuVMJCQmqWLHiDdeQPgj68vsVKlSQJG3YsEElSpTQf/7zH9WqVUtRUVE6dOjQdW3n0Ucf1axZszRz5kxFR0e7HKkC4D6EHQA33aRJk5Samqo777xTn3/+ufbt26ddu3ZpwoQJqlu3rqKjo1WlShV169ZNW7du1ebNm/XII4+ocePGqlWr1g1vf+HChfrwww+1d+9ejRgxQps3b1a/fv0kXTrydPjwYc2fP18HDhzQhAkTtGjRouvazoMPPqijR49q+vTpDEwGPAhhB8BNV6pUKW3dulVNmzbV0KFDVblyZd1zzz1asWKFpkyZIpvNpi+//FIFChRQo0aNFB0drVKlSmnBggU5sv2RI0dq/vz5qlq1qj7++GPNmzfPecSobdu2Gjx4sPr166fq1atrw4YNGj58+HVtx+FwqGPHjsqXL5/at2+fI7UDuHE2k53RgwCQS9lsNi1atOiWhY9mzZqpUqVKmjBhwi3ZHoCrYzYWAOSAf/75R6tXr9bq1as1efJkd5cD4DKEHQDIATVq1NA///yjMWPGqFy5cu4uB8BlOI0FAAAsjQHKAADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0v4fnigg0hPNzqcAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#This loads the dataset for City.csv\n",
+        "df2 = pd.read_csv('City.csv')\n",
+        "df2.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "9HFXuXAzWAro",
+        "outputId": "79e2dd6a-56cc-4256-e5cd-1461fb68f420"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "             City   Population      Users\n",
+              "0     NEW YORK NY   8,405,837    302,149 \n",
+              "1      CHICAGO IL   1,955,130    164,468 \n",
+              "2  LOS ANGELES CA   1,595,037    144,132 \n",
+              "3        MIAMI FL   1,339,155     17,675 \n",
+              "4  SILICON VALLEY   1,177,609     27,247 "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-4ba8c576-5618-4e5e-a93f-5a15ef622129\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Users</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>NEW YORK NY</td>\n",
+              "      <td>8,405,837</td>\n",
+              "      <td>302,149</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>CHICAGO IL</td>\n",
+              "      <td>1,955,130</td>\n",
+              "      <td>164,468</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>LOS ANGELES CA</td>\n",
+              "      <td>1,595,037</td>\n",
+              "      <td>144,132</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>MIAMI FL</td>\n",
+              "      <td>1,339,155</td>\n",
+              "      <td>17,675</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>SILICON VALLEY</td>\n",
+              "      <td>1,177,609</td>\n",
+              "      <td>27,247</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ba8c576-5618-4e5e-a93f-5a15ef622129')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-4ba8c576-5618-4e5e-a93f-5a15ef622129 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-4ba8c576-5618-4e5e-a93f-5a15ef622129');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-407a8eb6-3504-4fb7-869e-56f573ef9ac5\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-407a8eb6-3504-4fb7-869e-56f573ef9ac5')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-407a8eb6-3504-4fb7-869e-56f573ef9ac5 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df2",
+              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"NEW YORK NY\",\n          \"WASHINGTON DC\",\n          \"SACRAMENTO CA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 8,405,837 \",\n          \" 418,859 \",\n          \" 545,776 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Users\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 302,149 \",\n          \" 127,001 \",\n          \" 7,044 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Stacked bar chart with the number of trips in different cities for both yellow and pink cabs"
+      ],
+      "metadata": {
+        "id": "yDvVWeIVu2RW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "#The series type that provides the categories for both cities and company values and counts the number of companies appears in each city. The unstack() converts it\n",
+        "#into the dataframe when having multi index.\n",
+        "city_company_counts = df1.groupby(['City', 'Company'])['Company'].count().unstack()\n",
+        "\n",
+        "#Then I use the series to plot the bar chart for showing number of trips.\n",
+        "city_company_counts.plot(kind='bar', stacked=True)\n",
+        "\n",
+        "#x label\n",
+        "plt.xlabel('City')\n",
+        "#y label\n",
+        "plt.ylabel('Number of Trips')\n",
+        "#title for the bar chart graph\n",
+        "plt.title('Most Used Cabs (Pink and Yellow) by City')\n",
+        "\n",
+        "#provides the particular color representation for both pink and yellow cab companies\n",
+        "plt.legend(title='Company')\n",
+        "\n",
+        "#saving the image as png\n",
+        "plt.savefig('stacked_bar_city_company.png')\n",
+        "#providing the output of the graph image\n",
+        "plt.show()\n",
+        "\n",
+        "#Hypothesis\n",
+        "# As you can see in the stacked bar chart below, the most used cabs are yellow cabs.\n",
+        "# In most of the countries, the most used cab is yellow. For instance, in the city\n",
+        "# New York, the number of trips for pink cabs is approximately 1,600, while the yellow cab\n",
+        "# has 980,000 trips.\n",
+        "\n",
+        "# Based on the stacked bar chart analysis, it appears that yellow cabs are predominantly used\n",
+        "# over pink cabs in New York, as evidenced by the trip counts.\n",
+        "# This indicates a strong preference for yellow cabs among consumers in that area.\n",
+        "# If these trends hold true across other locations, this may reflect a broader market trend\n",
+        "# that XYZ firm should consider when deciding on cab investments.\n",
+        "\n",
+        "#Recommendations:\n",
+        "#Data Visualizations: I would have made a graph such as linear or pie chart to better show the distribution of the values for this particular goal."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 579
+        },
+        "id": "Tc89YuH-Zcg4",
+        "outputId": "1cd1a65c-62e5-4426-aeaa-1574afd6a9e4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df1.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "Mue7qS3FdC55",
+        "outputId": "4f3a1c8e-4a82-4afa-bf0b-9877e6909c9d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Transaction ID  Date of Travel   Company        City  KM Travelled  \\\n",
+              "0        10000011           42377  Pink Cab  ATLANTA GA         30.45   \n",
+              "1        10000012           42375  Pink Cab  ATLANTA GA         28.62   \n",
+              "2        10000013           42371  Pink Cab  ATLANTA GA          9.04   \n",
+              "3        10000014           42376  Pink Cab  ATLANTA GA         33.17   \n",
+              "4        10000015           42372  Pink Cab  ATLANTA GA          8.73   \n",
+              "\n",
+              "   Price Charged  Cost of Trip  profit  \n",
+              "0         370.95       313.635  57.315  \n",
+              "1         358.52       334.854  23.666  \n",
+              "2         125.20        97.632  27.568  \n",
+              "3         377.40       351.602  25.798  \n",
+              "4         114.62        97.776  16.844  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-0f61529b-5bcc-456c-a8f1-b095116fa7aa\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Transaction ID</th>\n",
+              "      <th>Date of Travel</th>\n",
+              "      <th>Company</th>\n",
+              "      <th>City</th>\n",
+              "      <th>KM Travelled</th>\n",
+              "      <th>Price Charged</th>\n",
+              "      <th>Cost of Trip</th>\n",
+              "      <th>profit</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>10000011</td>\n",
+              "      <td>42377</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>30.45</td>\n",
+              "      <td>370.95</td>\n",
+              "      <td>313.635</td>\n",
+              "      <td>57.315</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>10000012</td>\n",
+              "      <td>42375</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>28.62</td>\n",
+              "      <td>358.52</td>\n",
+              "      <td>334.854</td>\n",
+              "      <td>23.666</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>10000013</td>\n",
+              "      <td>42371</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>9.04</td>\n",
+              "      <td>125.20</td>\n",
+              "      <td>97.632</td>\n",
+              "      <td>27.568</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>10000014</td>\n",
+              "      <td>42376</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>33.17</td>\n",
+              "      <td>377.40</td>\n",
+              "      <td>351.602</td>\n",
+              "      <td>25.798</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>10000015</td>\n",
+              "      <td>42372</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>8.73</td>\n",
+              "      <td>114.62</td>\n",
+              "      <td>97.776</td>\n",
+              "      <td>16.844</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f61529b-5bcc-456c-a8f1-b095116fa7aa')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "\n",
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+              "      background-color: #3B4455;\n",
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+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
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+              "        document.querySelector('#df-0f61529b-5bcc-456c-a8f1-b095116fa7aa button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-0f61529b-5bcc-456c-a8f1-b095116fa7aa');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
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+              "</svg>\n",
+              "  </button>\n",
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+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
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+              "      --disabled-fill-color: #AAA;\n",
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+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
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+              "    border-radius: 50%;\n",
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+              "    height: 32px;\n",
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+              "\n",
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+              "  .colab-df-spinner {\n",
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+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-0ad292d2-411a-4734-ae75-ba6c58245641 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df1"
+            }
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The bar chart that represents the most number of trips for both yellow and pink cabs in the year of 1970"
+      ],
+      "metadata": {
+        "id": "I3xr1iPUyaUy"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "#converting the Date of Travel column in the DataFrame df1 to a datetime format using the pd.to_datetime() function from pandas.\n",
+        "df1['Date of Travel'] = pd.to_datetime(df1['Date of Travel'])\n",
+        "\n",
+        "#choosing the only the values from the dat of travel with the values of 1970 from df1 dataset\n",
+        "df1_1970 = df1[df1['Date of Travel'].dt.year == 1970]\n",
+        "\n",
+        "#The series type that provides the categories for company values and counts the number of companies appears\n",
+        "company_counts_1970 = df1_1970.groupby('Company')['Company'].count()\n",
+        "\n",
+        "#plotting the bar chart by using teh previous series and making each of the column values from the Company color pink and yellow\n",
+        "company_counts_1970.plot(kind='bar', color = ['pink', 'yellow'])\n",
+        "\n",
+        "\n",
+        "#x label\n",
+        "plt.xlabel('Company')\n",
+        "#y label\n",
+        "plt.ylabel('Number of Trips in 1970')\n",
+        "#title for the bar chart graph\n",
+        "plt.title('Most Used Cabs (Pink and Yellow) in 1970')\n",
+        "\n",
+        "#saving the image as png\n",
+        "plt.savefig('bar_chart_1970.png')\n",
+        "\n",
+        "#showing the plot\n",
+        "plt.show()\n",
+        "\n",
+        "# Hypothesis Result for Bar Chart of Most Used Cabs in 1970\n",
+        "\n",
+        "# The bar chart above showcases the number of trips undertaken by Yellow and Pink Cab companies in the year 1970.\n",
+        "# As depicted, Yellow Cabs had significantly more trips compared to Pink Cabs in that year.\n",
+        "# This suggests a higher demand or preference for Yellow Cabs in the market during 1970.\n",
+        "# If this trend holds for other years, it indicates that Yellow Cabs potentially have a stronger market share and possibly higher profitability.\n",
+        "# This information can be useful for XYZ firm in making informed decisions about which cab company to invest in, potentially favoring the Yellow Cab company given their historical dominance.\n",
+        "\n",
+        "#EDA Recommendations:\n",
+        "#We should collect and analyze data on customer demographics (age, gender, income level) to better understand who is using each cab company.\n",
+        "#This could provide insights into targeted marketing strategies."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 532
+        },
+        "id": "kQaKMrE7eRqw",
+        "outputId": "ceedad65-74bd-4261-f1a3-871c62d5c9f6"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df2 = pd.read_csv('City.csv')\n",
+        "df2.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "JPYgpO2sfGkf",
+        "outputId": "b7854f08-5674-4a57-a750-5d236960514f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "             City   Population      Users\n",
+              "0     NEW YORK NY   8,405,837    302,149 \n",
+              "1      CHICAGO IL   1,955,130    164,468 \n",
+              "2  LOS ANGELES CA   1,595,037    144,132 \n",
+              "3        MIAMI FL   1,339,155     17,675 \n",
+              "4  SILICON VALLEY   1,177,609     27,247 "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-7e8d8437-3bda-49c9-9b22-0c73cde27d3b\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Users</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>NEW YORK NY</td>\n",
+              "      <td>8,405,837</td>\n",
+              "      <td>302,149</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>CHICAGO IL</td>\n",
+              "      <td>1,955,130</td>\n",
+              "      <td>164,468</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>LOS ANGELES CA</td>\n",
+              "      <td>1,595,037</td>\n",
+              "      <td>144,132</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>MIAMI FL</td>\n",
+              "      <td>1,339,155</td>\n",
+              "      <td>17,675</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>SILICON VALLEY</td>\n",
+              "      <td>1,177,609</td>\n",
+              "      <td>27,247</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7e8d8437-3bda-49c9-9b22-0c73cde27d3b')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
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+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-7e8d8437-3bda-49c9-9b22-0c73cde27d3b button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-7e8d8437-3bda-49c9-9b22-0c73cde27d3b');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
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+              "            title=\"Suggest charts\"\n",
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+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-8b7768ec-b9a8-4fe4-9a62-0cbd5ac9b727 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df2",
+              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"NEW YORK NY\",\n          \"WASHINGTON DC\",\n          \"SACRAMENTO CA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 8,405,837 \",\n          \" 418,859 \",\n          \" 545,776 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Users\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 302,149 \",\n          \" 127,001 \",\n          \" 7,044 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "#The series type that provides the categories for both cities and company values and counts the number of companies appears in each city. The unstack() converts it\n",
+        "#into the dataframe when having multi index.\n",
+        "city_company_counts = df1.groupby(['City', 'Company'])['Company'].count().unstack()\n",
+        "\n",
+        "#replaces the previous index into a default index values like 0, 1, 2, 3, ...\n",
+        "city_company_counts = city_company_counts.reset_index()\n",
+        "\n",
+        "#Renaming the prevous columns\n",
+        "city_company_counts = city_company_counts.rename(\n",
+        "    columns={'Pink Cab': 'Pink Cab Users', 'Yellow Cab': 'Yellow Cab Users'}\n",
+        ")\n",
+        "\n",
+        "#Merging or combining the series values with the df2 dataset\n",
+        "merged_df = pd.merge(df2, city_company_counts, on='City', how='left')\n",
+        "\n",
+        "#replacing the NaN values with 0\n",
+        "merged_df = merged_df.fillna(0)\n",
+        "\n",
+        "#size of the plot\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "#drawing only the line graph for pink cab users\n",
+        "plt.plot(merged_df['City'], merged_df['Pink Cab Users'], label='Pink Cab Users')\n",
+        "#drawing only the line graph for yellow cab users\n",
+        "plt.plot(merged_df['City'], merged_df['Yellow Cab Users'], label='Yellow Cab Users')\n",
+        "#x label\n",
+        "plt.xlabel('City')\n",
+        "#y label\n",
+        "plt.ylabel('Number of Users')\n",
+        "#title of the graph\n",
+        "plt.title('Number of Users per City for Pink and Yellow Cabs')\n",
+        "#rotating the x label values into 90 degrees\n",
+        "plt.xticks(rotation=90)\n",
+        "#provides the particular color representation for both pink and yellow cab companies\n",
+        "plt.legend()\n",
+        "#adjust the spacing between subplots to minimize overlap and ensure that all elements are neatly contained within the figure area.\n",
+        "plt.tight_layout()\n",
+        "#plotting line graph\n",
+        "plt.show()\n",
+        "\n",
+        "# Hypothesis Result for Line Graph of Number of Users per City\n",
+        "\n",
+        "# The line graph above visually depicts the number of users for Pink and Yellow Cabs in different cities.\n",
+        "# It seems that the Yellow Cabs consistently have a larger user base across most cities compared to Pink Cabs.\n",
+        "# This indicates a potential market share dominance for Yellow Cabs.\n",
+        "# Notably, in certain cities, the difference in user numbers is significant, which may reflect a strong preference for Yellow Cabs in those areas.\n",
+        "# If these trends persist, it implies a greater demand for Yellow Cabs, potentially contributing to higher profitability for the company.\n",
+        "# This information can guide XYZ firm's investment decisions, possibly favouring the Yellow Cab company due to its extensive user base.\n",
+        "\n",
+        "#EDA Recommendations:\n",
+        "#I can analyze trends and patterns, and observe the overall trends in user counts for both companies across different cities."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 607
+        },
+        "id": "dSmc_aL3VSKR",
+        "outputId": "7bb7ca04-299e-48d7-da61-442d8144dd6f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#loading dataset called Transaction_ID.csv\n",
+        "df3 = pd.read_csv('Transaction_ID.csv')\n",
+        "df3"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "02ORKDVwZI9H",
+        "outputId": "3597cfb2-8880-4357-bac4-1e00390f96d9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "        Transaction ID  Customer ID Payment_Mode\n",
+              "0             10000011      29290.0         Card\n",
+              "1             10000012      27703.0         Card\n",
+              "2             10000013      28712.0         Cash\n",
+              "3             10000014      28020.0         Cash\n",
+              "4             10000015      27182.0         Card\n",
+              "...                ...          ...          ...\n",
+              "307683        10307694      42426.0         Cash\n",
+              "307684        10307695      42819.0         Cash\n",
+              "307685        10307696      44212.0         Card\n",
+              "307686        10307697      44827.0         Card\n",
+              "307687             103          NaN          NaN\n",
+              "\n",
+              "[307688 rows x 3 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-eb77c9c0-5dc0-428e-8314-d40899ad4ed5\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Transaction ID</th>\n",
+              "      <th>Customer ID</th>\n",
+              "      <th>Payment_Mode</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>10000011</td>\n",
+              "      <td>29290.0</td>\n",
+              "      <td>Card</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>10000012</td>\n",
+              "      <td>27703.0</td>\n",
+              "      <td>Card</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>10000013</td>\n",
+              "      <td>28712.0</td>\n",
+              "      <td>Cash</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>10000014</td>\n",
+              "      <td>28020.0</td>\n",
+              "      <td>Cash</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>10000015</td>\n",
+              "      <td>27182.0</td>\n",
+              "      <td>Card</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>307683</th>\n",
+              "      <td>10307694</td>\n",
+              "      <td>42426.0</td>\n",
+              "      <td>Cash</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>307684</th>\n",
+              "      <td>10307695</td>\n",
+              "      <td>42819.0</td>\n",
+              "      <td>Cash</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>307685</th>\n",
+              "      <td>10307696</td>\n",
+              "      <td>44212.0</td>\n",
+              "      <td>Card</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>307686</th>\n",
+              "      <td>10307697</td>\n",
+              "      <td>44827.0</td>\n",
+              "      <td>Card</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>307687</th>\n",
+              "      <td>103</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>307688 rows × 3 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb77c9c0-5dc0-428e-8314-d40899ad4ed5')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
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+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-eb77c9c0-5dc0-428e-8314-d40899ad4ed5 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-eb77c9c0-5dc0-428e-8314-d40899ad4ed5');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-20fa4755-2c55-41d8-95d1-5d6cce0d3b97\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-20fa4755-2c55-41d8-95d1-5d6cce0d3b97')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-20fa4755-2c55-41d8-95d1-5d6cce0d3b97 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_28a52d13-3169-4eeb-9213-8e435274f7de\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df3')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_28a52d13-3169-4eeb-9213-8e435274f7de button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df3');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df3"
+            }
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Replacing NaN values with 0\n",
+        "df3 = df3.fillna(0)\n",
+        "#Checking the sum of the null values whether there are null values left or not\n",
+        "df3.isnull().sum()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 178
+        },
+        "id": "bbOEMXM0kUdS",
+        "outputId": "653cdb57-f3e8-4173-d6b1-d21ff300a7a0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Transaction ID    0\n",
+              "Customer ID       0\n",
+              "Payment_Mode      0\n",
+              "dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Transaction ID</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Customer ID</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Payment_Mode</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Loadingt the dataset called Customer_ID.csv\n",
+        "df4 = pd.read_csv('Customer_ID.csv')\n",
+        "df4.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "64BsXf3bkToQ",
+        "outputId": "2fe40639-bd6f-4bf6-db74-ee6cf4a24f4e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Customer ID Gender  Age  Income (USD/Month)\n",
+              "0        29290   Male   28               10813\n",
+              "1        27703   Male   27                9237\n",
+              "2        28712   Male   53               11242\n",
+              "3        28020   Male   23               23327\n",
+              "4        27182   Male   33                8536"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-de4edf0e-d992-4c95-a735-95045f372109\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Customer ID</th>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Income (USD/Month)</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>29290</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>28</td>\n",
+              "      <td>10813</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>27703</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>27</td>\n",
+              "      <td>9237</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>28712</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>53</td>\n",
+              "      <td>11242</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>28020</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>23</td>\n",
+              "      <td>23327</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>27182</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>33</td>\n",
+              "      <td>8536</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-de4edf0e-d992-4c95-a735-95045f372109')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-de4edf0e-d992-4c95-a735-95045f372109 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-de4edf0e-d992-4c95-a735-95045f372109');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-0060794c-844e-40e1-b957-d2d55815f585\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0060794c-844e-40e1-b957-d2d55815f585')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-0060794c-844e-40e1-b957-d2d55815f585 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df4",
+              "summary": "{\n  \"name\": \"df4\",\n  \"rows\": 49171,\n  \"fields\": [\n    {\n      \"column\": \"Customer ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17714,\n        \"min\": 1,\n        \"max\": 60000,\n        \"num_unique_values\": 49171,\n        \"samples\": [\n          32451,\n          524,\n          19349\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 18,\n        \"max\": 65,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          32,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income (USD/Month)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8002,\n        \"min\": 2000,\n        \"max\": 35000,\n        \"num_unique_values\": 23341,\n        \"samples\": [\n          12932,\n          16207\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Merging the dataset fro both df3 and df4 by Customer ID feature\n",
+        "merged_df1 = pd.merge(df3, df4, on='Customer ID')\n",
+        "#Merging the df1 to the merged_df1 with df3 and df4 merged datasets with Transaction ID\n",
+        "merged_df2 = merged_df1.merge(df1, on='Transaction ID')\n",
+        "merged_df2.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 348
+        },
+        "id": "v8U48YCOFasv",
+        "outputId": "f9516c30-e25a-4ef3-8a41-ddb6cdf4a0a3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Transaction ID  Customer ID Payment_Mode Gender  Age  Income (USD/Month)  \\\n",
+              "0        10000011      29290.0         Card   Male   28               10813   \n",
+              "1        10000012      27703.0         Card   Male   27                9237   \n",
+              "2        10000013      28712.0         Cash   Male   53               11242   \n",
+              "3        10000014      28020.0         Cash   Male   23               23327   \n",
+              "4        10000015      27182.0         Card   Male   33                8536   \n",
+              "\n",
+              "   Date of Travel   Company        City  KM Travelled  Price Charged  \\\n",
+              "0         42377.0  Pink Cab  ATLANTA GA         30.45         370.95   \n",
+              "1         42375.0  Pink Cab  ATLANTA GA         28.62         358.52   \n",
+              "2         42371.0  Pink Cab  ATLANTA GA          9.04         125.20   \n",
+              "3         42376.0  Pink Cab  ATLANTA GA         33.17         377.40   \n",
+              "4         42372.0  Pink Cab  ATLANTA GA          8.73         114.62   \n",
+              "\n",
+              "   Cost of Trip  profit  \n",
+              "0       313.635  57.315  \n",
+              "1       334.854  23.666  \n",
+              "2        97.632  27.568  \n",
+              "3       351.602  25.798  \n",
+              "4        97.776  16.844  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-977a3625-8670-4406-b627-b1b07f9ce9cf\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Transaction ID</th>\n",
+              "      <th>Customer ID</th>\n",
+              "      <th>Payment_Mode</th>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Income (USD/Month)</th>\n",
+              "      <th>Date of Travel</th>\n",
+              "      <th>Company</th>\n",
+              "      <th>City</th>\n",
+              "      <th>KM Travelled</th>\n",
+              "      <th>Price Charged</th>\n",
+              "      <th>Cost of Trip</th>\n",
+              "      <th>profit</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>10000011</td>\n",
+              "      <td>29290.0</td>\n",
+              "      <td>Card</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>28</td>\n",
+              "      <td>10813</td>\n",
+              "      <td>42377.0</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>30.45</td>\n",
+              "      <td>370.95</td>\n",
+              "      <td>313.635</td>\n",
+              "      <td>57.315</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>10000012</td>\n",
+              "      <td>27703.0</td>\n",
+              "      <td>Card</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>27</td>\n",
+              "      <td>9237</td>\n",
+              "      <td>42375.0</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>28.62</td>\n",
+              "      <td>358.52</td>\n",
+              "      <td>334.854</td>\n",
+              "      <td>23.666</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>10000013</td>\n",
+              "      <td>28712.0</td>\n",
+              "      <td>Cash</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>53</td>\n",
+              "      <td>11242</td>\n",
+              "      <td>42371.0</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>9.04</td>\n",
+              "      <td>125.20</td>\n",
+              "      <td>97.632</td>\n",
+              "      <td>27.568</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>10000014</td>\n",
+              "      <td>28020.0</td>\n",
+              "      <td>Cash</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>23</td>\n",
+              "      <td>23327</td>\n",
+              "      <td>42376.0</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>33.17</td>\n",
+              "      <td>377.40</td>\n",
+              "      <td>351.602</td>\n",
+              "      <td>25.798</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>10000015</td>\n",
+              "      <td>27182.0</td>\n",
+              "      <td>Card</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>33</td>\n",
+              "      <td>8536</td>\n",
+              "      <td>42372.0</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>8.73</td>\n",
+              "      <td>114.62</td>\n",
+              "      <td>97.776</td>\n",
+              "      <td>16.844</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-977a3625-8670-4406-b627-b1b07f9ce9cf')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-977a3625-8670-4406-b627-b1b07f9ce9cf button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-977a3625-8670-4406-b627-b1b07f9ce9cf');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-d68e6d85-bbb6-425a-bc1f-ab3e6605fcd0\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d68e6d85-bbb6-425a-bc1f-ab3e6605fcd0')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-d68e6d85-bbb6-425a-bc1f-ab3e6605fcd0 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "merged_df2",
+              "summary": "{\n  \"name\": \"merged_df2\",\n  \"rows\": 71151,\n  \"fields\": [\n    {\n      \"column\": \"Transaction ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25592,\n        \"min\": 10000011,\n        \"max\": 10088433,\n        \"num_unique_values\": 71151,\n        \"samples\": [\n          10047350,\n          10009057,\n          10065991\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21212.115589102177,\n        \"min\": 1.0,\n        \"max\": 60000.0,\n        \"num_unique_values\": 26259,\n        \"samples\": [\n          59647.0,\n          53174.0,\n          24110.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Payment_Mode\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Cash\",\n          \"Card\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 18,\n        \"max\": 65,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          32,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income (USD/Month)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7994,\n        \"min\": 2000,\n        \"max\": 34996,\n        \"num_unique_values\": 16961,\n        \"samples\": [\n          5974,\n          2136\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date of Travel\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 80.38125009707976,\n        \"min\": 42371.0,\n        \"max\": 42661.0,\n        \"num_unique_values\": 291,\n        \"samples\": [\n          42458.0,\n          42631.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Company\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yellow Cab\",\n          \"Pink Cab\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"ATLANTA GA\",\n          \"DENVER CO\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"KM Travelled\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.213689673845838,\n        \"min\": 1.9,\n        \"max\": 48.0,\n        \"num_unique_values\": 874,\n        \"samples\": [\n          4.0,\n          29.43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price Charged\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 288.486993996938,\n        \"min\": 16.38,\n        \"max\": 1978.79,\n        \"num_unique_values\": 49504,\n        \"samples\": [\n          464.18,\n          626.96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cost of Trip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 157.89328330950855,\n        \"min\": 19.2,\n        \"max\": 691.2,\n        \"num_unique_values\": 14297,\n        \"samples\": [\n          434.5704,\n          380.4408\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"profit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 176.18102319112268,\n        \"min\": -198.69800000000004,\n        \"max\": 1336.8700000000001,\n        \"num_unique_values\": 68226,\n        \"samples\": [\n          730.5763999999999,\n          109.31200000000001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#This groups the DataFrame merged_df2 by two columns: Income (USD/Month) and Company. This means that the data will be organized into groups where each unique combination of income level and company is represented.\n",
+        "\n",
+        "income_company_counts = merged_df2.groupby(['Income (USD/Month)', 'Company'])['Company'].count().unstack()\n",
+        "#removes the NaN values\n",
+        "income_company_counts.dropna(inplace = True)\n",
+        "\n",
+        "#Resets index values into original default values like 0, 1, 2\n",
+        "income_company_counts = income_company_counts.reset_index()\n",
+        "\n",
+        "#The figure size of the graph\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "\n",
+        "#This line iterates through all the columns of the income_company_counts DataFrame, starting from the second column (index 1) onward.\n",
+        "#The loop will generate a series of bar charts, one for each company, where each bar represents the count of that company at each income level.\n",
+        "for company in income_company_counts.columns[1:]:\n",
+        "    plt.bar(income_company_counts['Income (USD/Month)'], income_company_counts[company], label=company)\n",
+        "\n",
+        "#x label\n",
+        "plt.xlabel('Income (USD)')\n",
+        "#y label\n",
+        "plt.ylabel('Number of Users')\n",
+        "#Title\n",
+        "plt.title('Number of Users per Income for Pink and Yellow Cabs')\n",
+        "#Rotating the x values to 90 degrees\n",
+        "plt.xticks(rotation=90)\n",
+        "#Providingt he particular colors for values\n",
+        "plt.legend()\n",
+        "#adjust the spacing between subplots to minimize overlap and ensure that all elements are neatly contained within the figure area.\n",
+        "plt.tight_layout()\n",
+        "#output the graph\n",
+        "plt.show()\n",
+        "#downloading the graph image into png\n",
+        "plt.savefig('bar_chart_income_new.png')\n",
+        "\n",
+        "# Hypothesis Result for Bar Chart of Number of Users per Income\n",
+        "\n",
+        "# The bar chart above depicts the relationship between customer income and the usage of Pink and Yellow Cabs.\n",
+        "# It appears that across various income levels, Yellow Cabs tend to have a larger user base compared to Pink Cabs.\n",
+        "# This observation reinforces the potential market share dominance of Yellow Cabs.\n",
+        "# It is noticeable that as income levels increase, the user count for both Pink and Yellow Cabs also tends to increase, though the difference in user counts between the two companies seems consistent across different income brackets.\n",
+        "# If this trend continues, it suggests a strong preference for Yellow Cabs among customers regardless of their income level, potentially translating into greater profitability for the Yellow Cab company.\n",
+        "# This information can assist XYZ firm in making investment decisions, potentially favoring the Yellow Cab company due to its extensive user base across different income segments.\n",
+        "\n",
+        "# EDA Recommendations:\n",
+        "# Further examination of the customer demographic, such as locations."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 624
+        },
+        "id": "hXTgM55hGHoS",
+        "outputId": "69875007-f601-4bd4-f238-fc819a904d2b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 0 Axes>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

From eee1914f7577e9d3e57af1bb6fdea12632f5be89 Mon Sep 17 00:00:00 2001
From: An204o <166038499+An204o@users.noreply.github.com>
Date: Tue, 22 Oct 2024 16:46:25 -0400
Subject: [PATCH 09/22] Delete
 Copy_of_Copy_of_Data_Analysis_Week_2_Project.ipynb

---
 ...Copy_of_Data_Analysis_Week_2_Project.ipynb | 2925 -----------------
 1 file changed, 2925 deletions(-)
 delete mode 100644 Copy_of_Copy_of_Data_Analysis_Week_2_Project.ipynb

diff --git a/Copy_of_Copy_of_Data_Analysis_Week_2_Project.ipynb b/Copy_of_Copy_of_Data_Analysis_Week_2_Project.ipynb
deleted file mode 100644
index 8864177c..00000000
--- a/Copy_of_Copy_of_Data_Analysis_Week_2_Project.ipynb
+++ /dev/null
@@ -1,2925 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "source": [
-        "Loading the csv file for the Cab_Data.csv and checking for its outliers. The boxplot seems to not have any outliers as it is not above the maximum value or below the minimum value."
-      ],
-      "metadata": {
-        "id": "nxz9GK3qZWv2"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "\n",
-        "\n",
-        "import pandas as pd\n",
-        "import matplotlib.pyplot as plt\n",
-        "import seaborn as sns\n",
-        "from sklearn.preprocessing import StandardScaler\n",
-        "#This code loads the dataset by using pandas library\n",
-        "df1 = pd.read_csv('Cab_Data.csv', on_bad_lines='skip')\n",
-        "\n",
-        "#Creating a new column called profit\n",
-        "df1['profit'] = df1['Price Charged'] - df1['Cost of Trip']\n",
-        "\n",
-        "#Using seaborn and drawing boxplot by using the profit column\n",
-        "sns.boxplot(x=df1['profit'])\n",
-        "\n",
-        "#saving the graph in a png form by using savefig()\n",
-        "plt.savefig('boxplot_new.png')\n",
-        "\n",
-        "#Hypothesis:\n",
-        "#In the code above, I loaded the first dataset, which is Cab_Data.csv, and verified the whether the outliers are presentor not by using the boxplot chart. In the boxplot below,\n",
-        "# it seems that there are no outliers as the minimum is -150.38, and maximum is 1327.6219999999998, so there are no values which are less than minimum or above the maximum.\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 449
-        },
-        "id": "zeCKWNYVOkql",
-        "outputId": "99558f26-5f10-4b9e-caf4-2559e332999f"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#This shows the first 5 rows of all the columns row values\n",
-        "df1.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "_9QBzdB0dcb-",
-        "outputId": "015153a3-7370-4fc6-cd23-cd0d9654cb2d"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "   Transaction ID  Date of Travel   Company        City  KM Travelled  \\\n",
-              "0        10000011           42377  Pink Cab  ATLANTA GA         30.45   \n",
-              "1        10000012           42375  Pink Cab  ATLANTA GA         28.62   \n",
-              "2        10000013           42371  Pink Cab  ATLANTA GA          9.04   \n",
-              "3        10000014           42376  Pink Cab  ATLANTA GA         33.17   \n",
-              "4        10000015           42372  Pink Cab  ATLANTA GA          8.73   \n",
-              "\n",
-              "   Price Charged  Cost of Trip  profit  \n",
-              "0         370.95       313.635  57.315  \n",
-              "1         358.52       334.854  23.666  \n",
-              "2         125.20        97.632  27.568  \n",
-              "3         377.40       351.602  25.798  \n",
-              "4         114.62        97.776  16.844  "
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-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df1"
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-          "metadata": {},
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-    {
-      "cell_type": "markdown",
-      "source": [
-        "Bar chart for comparing the average profits of both Pink and Yellow Cab Companies"
-      ],
-      "metadata": {
-        "id": "ocbjQ1Svoc4J"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#This code provides the categories for Company column values, which are pink and yellow cabs, and these categories corresponds to the average profits of these two cabs.\n",
-        "average_profit = df1.groupby('Company')['profit'].mean()\n",
-        "\n",
-        "#I used the above series for average profits for both yellow and pink cab, and this code is used to plot the bar chart for average profit comparisons\n",
-        "average_profit.plot(kind='bar', color=['blue', 'red'])\n",
-        "\n",
-        "#x label for the graph\n",
-        "plt.xlabel('Company')\n",
-        "#y label for the graph\n",
-        "plt.ylabel('Profit')\n",
-        "#the title of the graph above\n",
-        "plt.title('Pink and Yellow Cab Companies vs Average Profits')\n",
-        "#saving the image as png\n",
-        "plt.savefig('barplot_comparison_profits.png')\n",
-        "\n",
-        "#EDA Recommendation\n",
-        "# Based on the exploratory data analysis, it is recommended to further investigate the factors contributing to the higher average profits of Yellow Cabs compared to Pink Cabs.\n",
-        "# This includes analyzing variables such as trip duration, fare rates, customer satisfaction, and the operational strategies of both cab companies.\n",
-        "\n",
-        "# Hypothesis Results\n",
-        "# In the bar chart below, I am comparing the average profits for two cab companies, Yellow and Pink Cabs.\n",
-        "# As you can see, the data indicates that the Yellow Cab company has more average profits, with a maximum profit of approximately $158,\n",
-        "# while the Pink Cab has an average profit of about $63. If this hypothesis holds true, it suggests that XYZ firm should consider investing in the Yellow Cab company\n",
-        "# due to its stronger profitability. This analysis supports the notion that Yellow Cabs may provide a more lucrative investment opportunity."
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 532
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-        "id": "A-hBShAXUwUw",
-        "outputId": "1c313882-44c2-4ba3-fa30-a0130a283d17"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
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-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#This loads the dataset for City.csv\n",
-        "df2 = pd.read_csv('City.csv')\n",
-        "df2.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "9HFXuXAzWAro",
-        "outputId": "79e2dd6a-56cc-4256-e5cd-1461fb68f420"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "             City   Population      Users\n",
-              "0     NEW YORK NY   8,405,837    302,149 \n",
-              "1      CHICAGO IL   1,955,130    164,468 \n",
-              "2  LOS ANGELES CA   1,595,037    144,132 \n",
-              "3        MIAMI FL   1,339,155     17,675 \n",
-              "4  SILICON VALLEY   1,177,609     27,247 "
-            ],
-            "text/html": [
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-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
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-              "\n",
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-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>City</th>\n",
-              "      <th>Population</th>\n",
-              "      <th>Users</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>NEW YORK NY</td>\n",
-              "      <td>8,405,837</td>\n",
-              "      <td>302,149</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>CHICAGO IL</td>\n",
-              "      <td>1,955,130</td>\n",
-              "      <td>164,468</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>LOS ANGELES CA</td>\n",
-              "      <td>1,595,037</td>\n",
-              "      <td>144,132</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>MIAMI FL</td>\n",
-              "      <td>1,339,155</td>\n",
-              "      <td>17,675</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>SILICON VALLEY</td>\n",
-              "      <td>1,177,609</td>\n",
-              "      <td>27,247</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
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-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ba8c576-5618-4e5e-a93f-5a15ef622129')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
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-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
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-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
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-              "      display: none;\n",
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-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
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-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
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-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
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-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-4ba8c576-5618-4e5e-a93f-5a15ef622129 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-4ba8c576-5618-4e5e-a93f-5a15ef622129');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
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-              "\n",
-              "\n",
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-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-407a8eb6-3504-4fb7-869e-56f573ef9ac5')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-407a8eb6-3504-4fb7-869e-56f573ef9ac5 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df2",
-              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"NEW YORK NY\",\n          \"WASHINGTON DC\",\n          \"SACRAMENTO CA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 8,405,837 \",\n          \" 418,859 \",\n          \" 545,776 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Users\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 302,149 \",\n          \" 127,001 \",\n          \" 7,044 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 8
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "Stacked bar chart with the number of trips in different cities for both yellow and pink cabs"
-      ],
-      "metadata": {
-        "id": "yDvVWeIVu2RW"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "\n",
-        "import pandas as pd\n",
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "#The series type that provides the categories for both cities and company values and counts the number of companies appears in each city. The unstack() converts it\n",
-        "#into the dataframe when having multi index.\n",
-        "city_company_counts = df1.groupby(['City', 'Company'])['Company'].count().unstack()\n",
-        "\n",
-        "#Then I use the series to plot the bar chart for showing number of trips.\n",
-        "city_company_counts.plot(kind='bar', stacked=True)\n",
-        "\n",
-        "#x label\n",
-        "plt.xlabel('City')\n",
-        "#y label\n",
-        "plt.ylabel('Number of Trips')\n",
-        "#title for the bar chart graph\n",
-        "plt.title('Most Used Cabs (Pink and Yellow) by City')\n",
-        "\n",
-        "#provides the particular color representation for both pink and yellow cab companies\n",
-        "plt.legend(title='Company')\n",
-        "\n",
-        "#saving the image as png\n",
-        "plt.savefig('stacked_bar_city_company.png')\n",
-        "#providing the output of the graph image\n",
-        "plt.show()\n",
-        "\n",
-        "#Hypothesis\n",
-        "# As you can see in the stacked bar chart below, the most used cabs are yellow cabs.\n",
-        "# In most of the countries, the most used cab is yellow. For instance, in the city\n",
-        "# New York, the number of trips for pink cabs is approximately 1,600, while the yellow cab\n",
-        "# has 980,000 trips.\n",
-        "\n",
-        "# Based on the stacked bar chart analysis, it appears that yellow cabs are predominantly used\n",
-        "# over pink cabs in New York, as evidenced by the trip counts.\n",
-        "# This indicates a strong preference for yellow cabs among consumers in that area.\n",
-        "# If these trends hold true across other locations, this may reflect a broader market trend\n",
-        "# that XYZ firm should consider when deciding on cab investments.\n",
-        "\n",
-        "#Recommendations:\n",
-        "#Data Visualizations: I would have made a graph such as linear or pie chart to better show the distribution of the values for this particular goal."
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 579
-        },
-        "id": "Tc89YuH-Zcg4",
-        "outputId": "1cd1a65c-62e5-4426-aeaa-1574afd6a9e4"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df1.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "Mue7qS3FdC55",
-        "outputId": "4f3a1c8e-4a82-4afa-bf0b-9877e6909c9d"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "   Transaction ID  Date of Travel   Company        City  KM Travelled  \\\n",
-              "0        10000011           42377  Pink Cab  ATLANTA GA         30.45   \n",
-              "1        10000012           42375  Pink Cab  ATLANTA GA         28.62   \n",
-              "2        10000013           42371  Pink Cab  ATLANTA GA          9.04   \n",
-              "3        10000014           42376  Pink Cab  ATLANTA GA         33.17   \n",
-              "4        10000015           42372  Pink Cab  ATLANTA GA          8.73   \n",
-              "\n",
-              "   Price Charged  Cost of Trip  profit  \n",
-              "0         370.95       313.635  57.315  \n",
-              "1         358.52       334.854  23.666  \n",
-              "2         125.20        97.632  27.568  \n",
-              "3         377.40       351.602  25.798  \n",
-              "4         114.62        97.776  16.844  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-0f61529b-5bcc-456c-a8f1-b095116fa7aa\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
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-              "\n",
-              "    .dataframe thead th {\n",
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-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Transaction ID</th>\n",
-              "      <th>Date of Travel</th>\n",
-              "      <th>Company</th>\n",
-              "      <th>City</th>\n",
-              "      <th>KM Travelled</th>\n",
-              "      <th>Price Charged</th>\n",
-              "      <th>Cost of Trip</th>\n",
-              "      <th>profit</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>10000011</td>\n",
-              "      <td>42377</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>30.45</td>\n",
-              "      <td>370.95</td>\n",
-              "      <td>313.635</td>\n",
-              "      <td>57.315</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>10000012</td>\n",
-              "      <td>42375</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>28.62</td>\n",
-              "      <td>358.52</td>\n",
-              "      <td>334.854</td>\n",
-              "      <td>23.666</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>10000013</td>\n",
-              "      <td>42371</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>9.04</td>\n",
-              "      <td>125.20</td>\n",
-              "      <td>97.632</td>\n",
-              "      <td>27.568</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>10000014</td>\n",
-              "      <td>42376</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>33.17</td>\n",
-              "      <td>377.40</td>\n",
-              "      <td>351.602</td>\n",
-              "      <td>25.798</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>10000015</td>\n",
-              "      <td>42372</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>8.73</td>\n",
-              "      <td>114.62</td>\n",
-              "      <td>97.776</td>\n",
-              "      <td>16.844</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
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-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f61529b-5bcc-456c-a8f1-b095116fa7aa')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
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-              "type": "dataframe",
-              "variable_name": "df1"
-            }
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-          "metadata": {},
-          "execution_count": 9
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-    {
-      "cell_type": "markdown",
-      "source": [
-        "The bar chart that represents the most number of trips for both yellow and pink cabs in the year of 1970"
-      ],
-      "metadata": {
-        "id": "I3xr1iPUyaUy"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "\n",
-        "import pandas as pd\n",
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "#converting the Date of Travel column in the DataFrame df1 to a datetime format using the pd.to_datetime() function from pandas.\n",
-        "df1['Date of Travel'] = pd.to_datetime(df1['Date of Travel'])\n",
-        "\n",
-        "#choosing the only the values from the dat of travel with the values of 1970 from df1 dataset\n",
-        "df1_1970 = df1[df1['Date of Travel'].dt.year == 1970]\n",
-        "\n",
-        "#The series type that provides the categories for company values and counts the number of companies appears\n",
-        "company_counts_1970 = df1_1970.groupby('Company')['Company'].count()\n",
-        "\n",
-        "#plotting the bar chart by using teh previous series and making each of the column values from the Company color pink and yellow\n",
-        "company_counts_1970.plot(kind='bar', color = ['pink', 'yellow'])\n",
-        "\n",
-        "\n",
-        "#x label\n",
-        "plt.xlabel('Company')\n",
-        "#y label\n",
-        "plt.ylabel('Number of Trips in 1970')\n",
-        "#title for the bar chart graph\n",
-        "plt.title('Most Used Cabs (Pink and Yellow) in 1970')\n",
-        "\n",
-        "#saving the image as png\n",
-        "plt.savefig('bar_chart_1970.png')\n",
-        "\n",
-        "#showing the plot\n",
-        "plt.show()\n",
-        "\n",
-        "# Hypothesis Result for Bar Chart of Most Used Cabs in 1970\n",
-        "\n",
-        "# The bar chart above showcases the number of trips undertaken by Yellow and Pink Cab companies in the year 1970.\n",
-        "# As depicted, Yellow Cabs had significantly more trips compared to Pink Cabs in that year.\n",
-        "# This suggests a higher demand or preference for Yellow Cabs in the market during 1970.\n",
-        "# If this trend holds for other years, it indicates that Yellow Cabs potentially have a stronger market share and possibly higher profitability.\n",
-        "# This information can be useful for XYZ firm in making informed decisions about which cab company to invest in, potentially favoring the Yellow Cab company given their historical dominance.\n",
-        "\n",
-        "#EDA Recommendations:\n",
-        "#We should collect and analyze data on customer demographics (age, gender, income level) to better understand who is using each cab company.\n",
-        "#This could provide insights into targeted marketing strategies."
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 532
-        },
-        "id": "kQaKMrE7eRqw",
-        "outputId": "ceedad65-74bd-4261-f1a3-871c62d5c9f6"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df2 = pd.read_csv('City.csv')\n",
-        "df2.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "JPYgpO2sfGkf",
-        "outputId": "b7854f08-5674-4a57-a750-5d236960514f"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "             City   Population      Users\n",
-              "0     NEW YORK NY   8,405,837    302,149 \n",
-              "1      CHICAGO IL   1,955,130    164,468 \n",
-              "2  LOS ANGELES CA   1,595,037    144,132 \n",
-              "3        MIAMI FL   1,339,155     17,675 \n",
-              "4  SILICON VALLEY   1,177,609     27,247 "
-            ],
-            "text/html": [
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-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-8b7768ec-b9a8-4fe4-9a62-0cbd5ac9b727 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df2",
-              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"NEW YORK NY\",\n          \"WASHINGTON DC\",\n          \"SACRAMENTO CA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 8,405,837 \",\n          \" 418,859 \",\n          \" 545,776 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Users\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 302,149 \",\n          \" 127,001 \",\n          \" 7,044 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 11
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "\n",
-        "#The series type that provides the categories for both cities and company values and counts the number of companies appears in each city. The unstack() converts it\n",
-        "#into the dataframe when having multi index.\n",
-        "city_company_counts = df1.groupby(['City', 'Company'])['Company'].count().unstack()\n",
-        "\n",
-        "#replaces the previous index into a default index values like 0, 1, 2, 3, ...\n",
-        "city_company_counts = city_company_counts.reset_index()\n",
-        "\n",
-        "#Renaming the prevous columns\n",
-        "city_company_counts = city_company_counts.rename(\n",
-        "    columns={'Pink Cab': 'Pink Cab Users', 'Yellow Cab': 'Yellow Cab Users'}\n",
-        ")\n",
-        "\n",
-        "#Merging or combining the series values with the df2 dataset\n",
-        "merged_df = pd.merge(df2, city_company_counts, on='City', how='left')\n",
-        "\n",
-        "#replacing the NaN values with 0\n",
-        "merged_df = merged_df.fillna(0)\n",
-        "\n",
-        "#size of the plot\n",
-        "plt.figure(figsize=(10, 6))\n",
-        "#drawing only the line graph for pink cab users\n",
-        "plt.plot(merged_df['City'], merged_df['Pink Cab Users'], label='Pink Cab Users')\n",
-        "#drawing only the line graph for yellow cab users\n",
-        "plt.plot(merged_df['City'], merged_df['Yellow Cab Users'], label='Yellow Cab Users')\n",
-        "#x label\n",
-        "plt.xlabel('City')\n",
-        "#y label\n",
-        "plt.ylabel('Number of Users')\n",
-        "#title of the graph\n",
-        "plt.title('Number of Users per City for Pink and Yellow Cabs')\n",
-        "#rotating the x label values into 90 degrees\n",
-        "plt.xticks(rotation=90)\n",
-        "#provides the particular color representation for both pink and yellow cab companies\n",
-        "plt.legend()\n",
-        "#adjust the spacing between subplots to minimize overlap and ensure that all elements are neatly contained within the figure area.\n",
-        "plt.tight_layout()\n",
-        "#plotting line graph\n",
-        "plt.show()\n",
-        "\n",
-        "# Hypothesis Result for Line Graph of Number of Users per City\n",
-        "\n",
-        "# The line graph above visually depicts the number of users for Pink and Yellow Cabs in different cities.\n",
-        "# It seems that the Yellow Cabs consistently have a larger user base across most cities compared to Pink Cabs.\n",
-        "# This indicates a potential market share dominance for Yellow Cabs.\n",
-        "# Notably, in certain cities, the difference in user numbers is significant, which may reflect a strong preference for Yellow Cabs in those areas.\n",
-        "# If these trends persist, it implies a greater demand for Yellow Cabs, potentially contributing to higher profitability for the company.\n",
-        "# This information can guide XYZ firm's investment decisions, possibly favouring the Yellow Cab company due to its extensive user base.\n",
-        "\n",
-        "#EDA Recommendations:\n",
-        "#I can analyze trends and patterns, and observe the overall trends in user counts for both companies across different cities."
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 607
-        },
-        "id": "dSmc_aL3VSKR",
-        "outputId": "7bb7ca04-299e-48d7-da61-442d8144dd6f"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1000x600 with 1 Axes>"
-            ],
-            "image/png": 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-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#loading dataset called Transaction_ID.csv\n",
-        "df3 = pd.read_csv('Transaction_ID.csv')\n",
-        "df3"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 424
-        },
-        "id": "02ORKDVwZI9H",
-        "outputId": "3597cfb2-8880-4357-bac4-1e00390f96d9"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "        Transaction ID  Customer ID Payment_Mode\n",
-              "0             10000011      29290.0         Card\n",
-              "1             10000012      27703.0         Card\n",
-              "2             10000013      28712.0         Cash\n",
-              "3             10000014      28020.0         Cash\n",
-              "4             10000015      27182.0         Card\n",
-              "...                ...          ...          ...\n",
-              "307683        10307694      42426.0         Cash\n",
-              "307684        10307695      42819.0         Cash\n",
-              "307685        10307696      44212.0         Card\n",
-              "307686        10307697      44827.0         Card\n",
-              "307687             103          NaN          NaN\n",
-              "\n",
-              "[307688 rows x 3 columns]"
-            ],
-            "text/html": [
-              "\n",
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-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
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-              "\n",
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-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>10000013</td>\n",
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-              "    <tr>\n",
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-              "      <td>28020.0</td>\n",
-              "      <td>Cash</td>\n",
-              "    </tr>\n",
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-              "      <td>10000015</td>\n",
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-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>307684</th>\n",
-              "      <td>10307695</td>\n",
-              "      <td>42819.0</td>\n",
-              "      <td>Cash</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>307685</th>\n",
-              "      <td>10307696</td>\n",
-              "      <td>44212.0</td>\n",
-              "      <td>Card</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>307686</th>\n",
-              "      <td>10307697</td>\n",
-              "      <td>44827.0</td>\n",
-              "      <td>Card</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>307687</th>\n",
-              "      <td>103</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
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-              "<p>307688 rows × 3 columns</p>\n",
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-              "        document.querySelector('#df-eb77c9c0-5dc0-428e-8314-d40899ad4ed5 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-eb77c9c0-5dc0-428e-8314-d40899ad4ed5');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-20fa4755-2c55-41d8-95d1-5d6cce0d3b97\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-20fa4755-2c55-41d8-95d1-5d6cce0d3b97')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-20fa4755-2c55-41d8-95d1-5d6cce0d3b97 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "  <div id=\"id_28a52d13-3169-4eeb-9213-8e435274f7de\">\n",
-              "    <style>\n",
-              "      .colab-df-generate {\n",
-              "        background-color: #E8F0FE;\n",
-              "        border: none;\n",
-              "        border-radius: 50%;\n",
-              "        cursor: pointer;\n",
-              "        display: none;\n",
-              "        fill: #1967D2;\n",
-              "        height: 32px;\n",
-              "        padding: 0 0 0 0;\n",
-              "        width: 32px;\n",
-              "      }\n",
-              "\n",
-              "      .colab-df-generate:hover {\n",
-              "        background-color: #E2EBFA;\n",
-              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "        fill: #174EA6;\n",
-              "      }\n",
-              "\n",
-              "      [theme=dark] .colab-df-generate {\n",
-              "        background-color: #3B4455;\n",
-              "        fill: #D2E3FC;\n",
-              "      }\n",
-              "\n",
-              "      [theme=dark] .colab-df-generate:hover {\n",
-              "        background-color: #434B5C;\n",
-              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "        fill: #FFFFFF;\n",
-              "      }\n",
-              "    </style>\n",
-              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df3')\"\n",
-              "            title=\"Generate code using this dataframe.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "       width=\"24px\">\n",
-              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "    <script>\n",
-              "      (() => {\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#id_28a52d13-3169-4eeb-9213-8e435274f7de button.colab-df-generate');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      buttonEl.onclick = () => {\n",
-              "        google.colab.notebook.generateWithVariable('df3');\n",
-              "      }\n",
-              "      })();\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df3"
-            }
-          },
-          "metadata": {},
-          "execution_count": 4
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Replacing NaN values with 0\n",
-        "df3 = df3.fillna(0)\n",
-        "#Checking the sum of the null values whether there are null values left or not\n",
-        "df3.isnull().sum()\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 178
-        },
-        "id": "bbOEMXM0kUdS",
-        "outputId": "653cdb57-f3e8-4173-d6b1-d21ff300a7a0"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "Transaction ID    0\n",
-              "Customer ID       0\n",
-              "Payment_Mode      0\n",
-              "dtype: int64"
-            ],
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>0</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>Transaction ID</th>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>Customer ID</th>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>Payment_Mode</th>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div><br><label><b>dtype:</b> int64</label>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 9
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Loadingt the dataset called Customer_ID.csv\n",
-        "df4 = pd.read_csv('Customer_ID.csv')\n",
-        "df4.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "64BsXf3bkToQ",
-        "outputId": "2fe40639-bd6f-4bf6-db74-ee6cf4a24f4e"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "   Customer ID Gender  Age  Income (USD/Month)\n",
-              "0        29290   Male   28               10813\n",
-              "1        27703   Male   27                9237\n",
-              "2        28712   Male   53               11242\n",
-              "3        28020   Male   23               23327\n",
-              "4        27182   Male   33                8536"
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-de4edf0e-d992-4c95-a735-95045f372109\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
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-              "</style>\n",
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-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Customer ID</th>\n",
-              "      <th>Gender</th>\n",
-              "      <th>Age</th>\n",
-              "      <th>Income (USD/Month)</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>29290</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>28</td>\n",
-              "      <td>10813</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>27703</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>27</td>\n",
-              "      <td>9237</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>28712</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>53</td>\n",
-              "      <td>11242</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>28020</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>23</td>\n",
-              "      <td>23327</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>27182</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>33</td>\n",
-              "      <td>8536</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-de4edf0e-d992-4c95-a735-95045f372109')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
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-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-de4edf0e-d992-4c95-a735-95045f372109 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-de4edf0e-d992-4c95-a735-95045f372109');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
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-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-0060794c-844e-40e1-b957-d2d55815f585\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0060794c-844e-40e1-b957-d2d55815f585')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-0060794c-844e-40e1-b957-d2d55815f585 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df4",
-              "summary": "{\n  \"name\": \"df4\",\n  \"rows\": 49171,\n  \"fields\": [\n    {\n      \"column\": \"Customer ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17714,\n        \"min\": 1,\n        \"max\": 60000,\n        \"num_unique_values\": 49171,\n        \"samples\": [\n          32451,\n          524,\n          19349\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 18,\n        \"max\": 65,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          32,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income (USD/Month)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8002,\n        \"min\": 2000,\n        \"max\": 35000,\n        \"num_unique_values\": 23341,\n        \"samples\": [\n          12932,\n          16207\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 5
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Merging the dataset fro both df3 and df4 by Customer ID feature\n",
-        "merged_df1 = pd.merge(df3, df4, on='Customer ID')\n",
-        "#Merging the df1 to the merged_df1 with df3 and df4 merged datasets with Transaction ID\n",
-        "merged_df2 = merged_df1.merge(df1, on='Transaction ID')\n",
-        "merged_df2.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 348
-        },
-        "id": "v8U48YCOFasv",
-        "outputId": "f9516c30-e25a-4ef3-8a41-ddb6cdf4a0a3"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "   Transaction ID  Customer ID Payment_Mode Gender  Age  Income (USD/Month)  \\\n",
-              "0        10000011      29290.0         Card   Male   28               10813   \n",
-              "1        10000012      27703.0         Card   Male   27                9237   \n",
-              "2        10000013      28712.0         Cash   Male   53               11242   \n",
-              "3        10000014      28020.0         Cash   Male   23               23327   \n",
-              "4        10000015      27182.0         Card   Male   33                8536   \n",
-              "\n",
-              "   Date of Travel   Company        City  KM Travelled  Price Charged  \\\n",
-              "0         42377.0  Pink Cab  ATLANTA GA         30.45         370.95   \n",
-              "1         42375.0  Pink Cab  ATLANTA GA         28.62         358.52   \n",
-              "2         42371.0  Pink Cab  ATLANTA GA          9.04         125.20   \n",
-              "3         42376.0  Pink Cab  ATLANTA GA         33.17         377.40   \n",
-              "4         42372.0  Pink Cab  ATLANTA GA          8.73         114.62   \n",
-              "\n",
-              "   Cost of Trip  profit  \n",
-              "0       313.635  57.315  \n",
-              "1       334.854  23.666  \n",
-              "2        97.632  27.568  \n",
-              "3       351.602  25.798  \n",
-              "4        97.776  16.844  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-977a3625-8670-4406-b627-b1b07f9ce9cf\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Transaction ID</th>\n",
-              "      <th>Customer ID</th>\n",
-              "      <th>Payment_Mode</th>\n",
-              "      <th>Gender</th>\n",
-              "      <th>Age</th>\n",
-              "      <th>Income (USD/Month)</th>\n",
-              "      <th>Date of Travel</th>\n",
-              "      <th>Company</th>\n",
-              "      <th>City</th>\n",
-              "      <th>KM Travelled</th>\n",
-              "      <th>Price Charged</th>\n",
-              "      <th>Cost of Trip</th>\n",
-              "      <th>profit</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>10000011</td>\n",
-              "      <td>29290.0</td>\n",
-              "      <td>Card</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>28</td>\n",
-              "      <td>10813</td>\n",
-              "      <td>42377.0</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>30.45</td>\n",
-              "      <td>370.95</td>\n",
-              "      <td>313.635</td>\n",
-              "      <td>57.315</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>10000012</td>\n",
-              "      <td>27703.0</td>\n",
-              "      <td>Card</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>27</td>\n",
-              "      <td>9237</td>\n",
-              "      <td>42375.0</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>28.62</td>\n",
-              "      <td>358.52</td>\n",
-              "      <td>334.854</td>\n",
-              "      <td>23.666</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>10000013</td>\n",
-              "      <td>28712.0</td>\n",
-              "      <td>Cash</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>53</td>\n",
-              "      <td>11242</td>\n",
-              "      <td>42371.0</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>9.04</td>\n",
-              "      <td>125.20</td>\n",
-              "      <td>97.632</td>\n",
-              "      <td>27.568</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>10000014</td>\n",
-              "      <td>28020.0</td>\n",
-              "      <td>Cash</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>23</td>\n",
-              "      <td>23327</td>\n",
-              "      <td>42376.0</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>33.17</td>\n",
-              "      <td>377.40</td>\n",
-              "      <td>351.602</td>\n",
-              "      <td>25.798</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>10000015</td>\n",
-              "      <td>27182.0</td>\n",
-              "      <td>Card</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>33</td>\n",
-              "      <td>8536</td>\n",
-              "      <td>42372.0</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>8.73</td>\n",
-              "      <td>114.62</td>\n",
-              "      <td>97.776</td>\n",
-              "      <td>16.844</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-977a3625-8670-4406-b627-b1b07f9ce9cf')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-977a3625-8670-4406-b627-b1b07f9ce9cf button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-977a3625-8670-4406-b627-b1b07f9ce9cf');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-d68e6d85-bbb6-425a-bc1f-ab3e6605fcd0\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d68e6d85-bbb6-425a-bc1f-ab3e6605fcd0')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-d68e6d85-bbb6-425a-bc1f-ab3e6605fcd0 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "merged_df2",
-              "summary": "{\n  \"name\": \"merged_df2\",\n  \"rows\": 71151,\n  \"fields\": [\n    {\n      \"column\": \"Transaction ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25592,\n        \"min\": 10000011,\n        \"max\": 10088433,\n        \"num_unique_values\": 71151,\n        \"samples\": [\n          10047350,\n          10009057,\n          10065991\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21212.115589102177,\n        \"min\": 1.0,\n        \"max\": 60000.0,\n        \"num_unique_values\": 26259,\n        \"samples\": [\n          59647.0,\n          53174.0,\n          24110.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Payment_Mode\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Cash\",\n          \"Card\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 18,\n        \"max\": 65,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          32,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income (USD/Month)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7994,\n        \"min\": 2000,\n        \"max\": 34996,\n        \"num_unique_values\": 16961,\n        \"samples\": [\n          5974,\n          2136\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date of Travel\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 80.38125009707976,\n        \"min\": 42371.0,\n        \"max\": 42661.0,\n        \"num_unique_values\": 291,\n        \"samples\": [\n          42458.0,\n          42631.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Company\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yellow Cab\",\n          \"Pink Cab\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"ATLANTA GA\",\n          \"DENVER CO\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"KM Travelled\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.213689673845838,\n        \"min\": 1.9,\n        \"max\": 48.0,\n        \"num_unique_values\": 874,\n        \"samples\": [\n          4.0,\n          29.43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price Charged\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 288.486993996938,\n        \"min\": 16.38,\n        \"max\": 1978.79,\n        \"num_unique_values\": 49504,\n        \"samples\": [\n          464.18,\n          626.96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cost of Trip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 157.89328330950855,\n        \"min\": 19.2,\n        \"max\": 691.2,\n        \"num_unique_values\": 14297,\n        \"samples\": [\n          434.5704,\n          380.4408\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"profit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 176.18102319112268,\n        \"min\": -198.69800000000004,\n        \"max\": 1336.8700000000001,\n        \"num_unique_values\": 68226,\n        \"samples\": [\n          730.5763999999999,\n          109.31200000000001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 12
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#This groups the DataFrame merged_df2 by two columns: Income (USD/Month) and Company. This means that the data will be organized into groups where each unique combination of income level and company is represented.\n",
-        "\n",
-        "income_company_counts = merged_df2.groupby(['Income (USD/Month)', 'Company'])['Company'].count().unstack()\n",
-        "#removes the NaN values\n",
-        "income_company_counts.dropna(inplace = True)\n",
-        "\n",
-        "#Resets index values into original default values like 0, 1, 2\n",
-        "income_company_counts = income_company_counts.reset_index()\n",
-        "\n",
-        "#The figure size of the graph\n",
-        "plt.figure(figsize=(10, 6))\n",
-        "\n",
-        "#This line iterates through all the columns of the income_company_counts DataFrame, starting from the second column (index 1) onward.\n",
-        "#The loop will generate a series of bar charts, one for each company, where each bar represents the count of that company at each income level.\n",
-        "for company in income_company_counts.columns[1:]:\n",
-        "    plt.bar(income_company_counts['Income (USD/Month)'], income_company_counts[company], label=company)\n",
-        "\n",
-        "#x label\n",
-        "plt.xlabel('Income (USD)')\n",
-        "#y label\n",
-        "plt.ylabel('Number of Users')\n",
-        "#Title\n",
-        "plt.title('Number of Users per Income for Pink and Yellow Cabs')\n",
-        "#Rotating the x values to 90 degrees\n",
-        "plt.xticks(rotation=90)\n",
-        "#Providingt he particular colors for values\n",
-        "plt.legend()\n",
-        "#adjust the spacing between subplots to minimize overlap and ensure that all elements are neatly contained within the figure area.\n",
-        "plt.tight_layout()\n",
-        "#output the graph\n",
-        "plt.show()\n",
-        "#downloading the graph image into png\n",
-        "plt.savefig('bar_chart_income_new.png')\n",
-        "\n",
-        "# Hypothesis Result for Bar Chart of Number of Users per Income\n",
-        "\n",
-        "# The bar chart above depicts the relationship between customer income and the usage of Pink and Yellow Cabs.\n",
-        "# It appears that across various income levels, Yellow Cabs tend to have a larger user base compared to Pink Cabs.\n",
-        "# This observation reinforces the potential market share dominance of Yellow Cabs.\n",
-        "# It is noticeable that as income levels increase, the user count for both Pink and Yellow Cabs also tends to increase, though the difference in user counts between the two companies seems consistent across different income brackets.\n",
-        "# If this trend continues, it suggests a strong preference for Yellow Cabs among customers regardless of their income level, potentially translating into greater profitability for the Yellow Cab company.\n",
-        "# This information can assist XYZ firm in making investment decisions, potentially favoring the Yellow Cab company due to its extensive user base across different income segments.\n",
-        "\n",
-        "# EDA Recommendations:\n",
-        "# Further examination of the customer demographic, such as locations."
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 624
-        },
-        "id": "hXTgM55hGHoS",
-        "outputId": "69875007-f601-4bd4-f238-fc819a904d2b"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1000x600 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 0 Axes>"
-            ]
-          },
-          "metadata": {}
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file

From 1c189e94e98dcafb7d034cd66584eeeb0d7dbc7e Mon Sep 17 00:00:00 2001
From: An204o <166038499+An204o@users.noreply.github.com>
Date: Tue, 22 Oct 2024 16:46:49 -0400
Subject: [PATCH 10/22] Add files via upload

---
 ..._of_Data_Analysis_Week_2_Project (1).ipynb | 2924 +++++++++++++++++
 1 file changed, 2924 insertions(+)
 create mode 100644 Copy_of_Copy_of_Data_Analysis_Week_2_Project (1).ipynb

diff --git a/Copy_of_Copy_of_Data_Analysis_Week_2_Project (1).ipynb b/Copy_of_Copy_of_Data_Analysis_Week_2_Project (1).ipynb
new file mode 100644
index 00000000..59bf5268
--- /dev/null
+++ b/Copy_of_Copy_of_Data_Analysis_Week_2_Project (1).ipynb	
@@ -0,0 +1,2924 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Loading the csv file for the Cab_Data.csv and checking for its outliers. The boxplot seems to not have any outliers as it is not above the maximum value or below the minimum value."
+      ],
+      "metadata": {
+        "id": "nxz9GK3qZWv2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#This code contains a mix of my own and AI generated\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "#This code loads the dataset by using pandas library\n",
+        "df1 = pd.read_csv('Cab_Data.csv', on_bad_lines='skip')\n",
+        "\n",
+        "#Creating a new column called profit\n",
+        "df1['profit'] = df1['Price Charged'] - df1['Cost of Trip']\n",
+        "\n",
+        "#Using seaborn and drawing boxplot by using the profit column\n",
+        "sns.boxplot(x=df1['profit'])\n",
+        "\n",
+        "#saving the graph in a png form by using savefig()\n",
+        "plt.savefig('boxplot_new.png')\n",
+        "\n",
+        "#Hypothesis:\n",
+        "#In the code above, I loaded the first dataset, which is Cab_Data.csv, and verified the whether the outliers are presentor not by using the boxplot chart. In the boxplot below,\n",
+        "# it seems that there are no outliers as the minimum is -150.38, and maximum is 1327.6219999999998, so there are no values which are less than minimum or above the maximum.\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "id": "zeCKWNYVOkql",
+        "outputId": "99558f26-5f10-4b9e-caf4-2559e332999f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#This shows the first 5 rows of all the columns row values\n",
+        "df1.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "_9QBzdB0dcb-",
+        "outputId": "015153a3-7370-4fc6-cd23-cd0d9654cb2d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Transaction ID  Date of Travel   Company        City  KM Travelled  \\\n",
+              "0        10000011           42377  Pink Cab  ATLANTA GA         30.45   \n",
+              "1        10000012           42375  Pink Cab  ATLANTA GA         28.62   \n",
+              "2        10000013           42371  Pink Cab  ATLANTA GA          9.04   \n",
+              "3        10000014           42376  Pink Cab  ATLANTA GA         33.17   \n",
+              "4        10000015           42372  Pink Cab  ATLANTA GA          8.73   \n",
+              "\n",
+              "   Price Charged  Cost of Trip  profit  \n",
+              "0         370.95       313.635  57.315  \n",
+              "1         358.52       334.854  23.666  \n",
+              "2         125.20        97.632  27.568  \n",
+              "3         377.40       351.602  25.798  \n",
+              "4         114.62        97.776  16.844  "
+            ],
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Transaction ID</th>\n",
+              "      <th>Date of Travel</th>\n",
+              "      <th>Company</th>\n",
+              "      <th>City</th>\n",
+              "      <th>KM Travelled</th>\n",
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+              "      <th>profit</th>\n",
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+              "      <td>10000011</td>\n",
+              "      <td>42377</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>30.45</td>\n",
+              "      <td>370.95</td>\n",
+              "      <td>313.635</td>\n",
+              "      <td>57.315</td>\n",
+              "    </tr>\n",
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+              "      <th>1</th>\n",
+              "      <td>10000012</td>\n",
+              "      <td>42375</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>28.62</td>\n",
+              "      <td>358.52</td>\n",
+              "      <td>334.854</td>\n",
+              "      <td>23.666</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>10000013</td>\n",
+              "      <td>42371</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>9.04</td>\n",
+              "      <td>125.20</td>\n",
+              "      <td>97.632</td>\n",
+              "      <td>27.568</td>\n",
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+              "      <th>3</th>\n",
+              "      <td>10000014</td>\n",
+              "      <td>42376</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>33.17</td>\n",
+              "      <td>377.40</td>\n",
+              "      <td>351.602</td>\n",
+              "      <td>25.798</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>10000015</td>\n",
+              "      <td>42372</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>8.73</td>\n",
+              "      <td>114.62</td>\n",
+              "      <td>97.776</td>\n",
+              "      <td>16.844</td>\n",
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+              "                                                    [key], {});\n",
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+              "\n",
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+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
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+              "\n",
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+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df1"
+            }
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Bar chart for comparing the average profits of both Pink and Yellow Cab Companies"
+      ],
+      "metadata": {
+        "id": "ocbjQ1Svoc4J"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#This code provides the categories for Company column values, which are pink and yellow cabs, and these categories corresponds to the average profits of these two cabs.\n",
+        "average_profit = df1.groupby('Company')['profit'].mean()\n",
+        "\n",
+        "#I used the above series for average profits for both yellow and pink cab, and this code is used to plot the bar chart for average profit comparisons\n",
+        "average_profit.plot(kind='bar', color=['blue', 'red'])\n",
+        "\n",
+        "#x label for the graph\n",
+        "plt.xlabel('Company')\n",
+        "#y label for the graph\n",
+        "plt.ylabel('Profit')\n",
+        "#the title of the graph above\n",
+        "plt.title('Pink and Yellow Cab Companies vs Average Profits')\n",
+        "#saving the image as png\n",
+        "plt.savefig('barplot_comparison_profits.png')\n",
+        "\n",
+        "#EDA Recommendation\n",
+        "# Based on the exploratory data analysis, it is recommended to further investigate the factors contributing to the higher average profits of Yellow Cabs compared to Pink Cabs.\n",
+        "# This includes analyzing variables such as trip duration, fare rates, customer satisfaction, and the operational strategies of both cab companies.\n",
+        "\n",
+        "# Hypothesis Results\n",
+        "# In the bar chart below, I am comparing the average profits for two cab companies, Yellow and Pink Cabs.\n",
+        "# As you can see, the data indicates that the Yellow Cab company has more average profits, with a maximum profit of approximately $158,\n",
+        "# while the Pink Cab has an average profit of about $63. If this hypothesis holds true, it suggests that XYZ firm should consider investing in the Yellow Cab company\n",
+        "# due to its stronger profitability. This analysis supports the notion that Yellow Cabs may provide a more lucrative investment opportunity."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 532
+        },
+        "id": "A-hBShAXUwUw",
+        "outputId": "1c313882-44c2-4ba3-fa30-a0130a283d17"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#This loads the dataset for City.csv\n",
+        "df2 = pd.read_csv('City.csv')\n",
+        "df2.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "9HFXuXAzWAro",
+        "outputId": "79e2dd6a-56cc-4256-e5cd-1461fb68f420"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "             City   Population      Users\n",
+              "0     NEW YORK NY   8,405,837    302,149 \n",
+              "1      CHICAGO IL   1,955,130    164,468 \n",
+              "2  LOS ANGELES CA   1,595,037    144,132 \n",
+              "3        MIAMI FL   1,339,155     17,675 \n",
+              "4  SILICON VALLEY   1,177,609     27,247 "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-4ba8c576-5618-4e5e-a93f-5a15ef622129\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Users</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>NEW YORK NY</td>\n",
+              "      <td>8,405,837</td>\n",
+              "      <td>302,149</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>CHICAGO IL</td>\n",
+              "      <td>1,955,130</td>\n",
+              "      <td>164,468</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>LOS ANGELES CA</td>\n",
+              "      <td>1,595,037</td>\n",
+              "      <td>144,132</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>MIAMI FL</td>\n",
+              "      <td>1,339,155</td>\n",
+              "      <td>17,675</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>SILICON VALLEY</td>\n",
+              "      <td>1,177,609</td>\n",
+              "      <td>27,247</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ba8c576-5618-4e5e-a93f-5a15ef622129')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-4ba8c576-5618-4e5e-a93f-5a15ef622129 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-4ba8c576-5618-4e5e-a93f-5a15ef622129');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-407a8eb6-3504-4fb7-869e-56f573ef9ac5\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-407a8eb6-3504-4fb7-869e-56f573ef9ac5')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-407a8eb6-3504-4fb7-869e-56f573ef9ac5 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df2",
+              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"NEW YORK NY\",\n          \"WASHINGTON DC\",\n          \"SACRAMENTO CA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 8,405,837 \",\n          \" 418,859 \",\n          \" 545,776 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Users\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 302,149 \",\n          \" 127,001 \",\n          \" 7,044 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Stacked bar chart with the number of trips in different cities for both yellow and pink cabs"
+      ],
+      "metadata": {
+        "id": "yDvVWeIVu2RW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "#The series type that provides the categories for both cities and company values and counts the number of companies appears in each city. The unstack() converts it\n",
+        "#into the dataframe when having multi index.\n",
+        "city_company_counts = df1.groupby(['City', 'Company'])['Company'].count().unstack()\n",
+        "\n",
+        "#Then I use the series to plot the bar chart for showing number of trips.\n",
+        "city_company_counts.plot(kind='bar', stacked=True)\n",
+        "\n",
+        "#x label\n",
+        "plt.xlabel('City')\n",
+        "#y label\n",
+        "plt.ylabel('Number of Trips')\n",
+        "#title for the bar chart graph\n",
+        "plt.title('Most Used Cabs (Pink and Yellow) by City')\n",
+        "\n",
+        "#provides the particular color representation for both pink and yellow cab companies\n",
+        "plt.legend(title='Company')\n",
+        "\n",
+        "#saving the image as png\n",
+        "plt.savefig('stacked_bar_city_company.png')\n",
+        "#providing the output of the graph image\n",
+        "plt.show()\n",
+        "\n",
+        "#Hypothesis\n",
+        "# As you can see in the stacked bar chart below, the most used cabs are yellow cabs.\n",
+        "# In most of the countries, the most used cab is yellow. For instance, in the city\n",
+        "# New York, the number of trips for pink cabs is approximately 1,600, while the yellow cab\n",
+        "# has 980,000 trips.\n",
+        "\n",
+        "# Based on the stacked bar chart analysis, it appears that yellow cabs are predominantly used\n",
+        "# over pink cabs in New York, as evidenced by the trip counts.\n",
+        "# This indicates a strong preference for yellow cabs among consumers in that area.\n",
+        "# If these trends hold true across other locations, this may reflect a broader market trend\n",
+        "# that XYZ firm should consider when deciding on cab investments.\n",
+        "\n",
+        "#Recommendations:\n",
+        "#Data Visualizations: I would have made a graph such as linear or pie chart to better show the distribution of the values for this particular goal."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 579
+        },
+        "id": "Tc89YuH-Zcg4",
+        "outputId": "1cd1a65c-62e5-4426-aeaa-1574afd6a9e4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df1.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "Mue7qS3FdC55",
+        "outputId": "4f3a1c8e-4a82-4afa-bf0b-9877e6909c9d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Transaction ID  Date of Travel   Company        City  KM Travelled  \\\n",
+              "0        10000011           42377  Pink Cab  ATLANTA GA         30.45   \n",
+              "1        10000012           42375  Pink Cab  ATLANTA GA         28.62   \n",
+              "2        10000013           42371  Pink Cab  ATLANTA GA          9.04   \n",
+              "3        10000014           42376  Pink Cab  ATLANTA GA         33.17   \n",
+              "4        10000015           42372  Pink Cab  ATLANTA GA          8.73   \n",
+              "\n",
+              "   Price Charged  Cost of Trip  profit  \n",
+              "0         370.95       313.635  57.315  \n",
+              "1         358.52       334.854  23.666  \n",
+              "2         125.20        97.632  27.568  \n",
+              "3         377.40       351.602  25.798  \n",
+              "4         114.62        97.776  16.844  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-0f61529b-5bcc-456c-a8f1-b095116fa7aa\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Transaction ID</th>\n",
+              "      <th>Date of Travel</th>\n",
+              "      <th>Company</th>\n",
+              "      <th>City</th>\n",
+              "      <th>KM Travelled</th>\n",
+              "      <th>Price Charged</th>\n",
+              "      <th>Cost of Trip</th>\n",
+              "      <th>profit</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>10000011</td>\n",
+              "      <td>42377</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>30.45</td>\n",
+              "      <td>370.95</td>\n",
+              "      <td>313.635</td>\n",
+              "      <td>57.315</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>10000012</td>\n",
+              "      <td>42375</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>28.62</td>\n",
+              "      <td>358.52</td>\n",
+              "      <td>334.854</td>\n",
+              "      <td>23.666</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>10000013</td>\n",
+              "      <td>42371</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>9.04</td>\n",
+              "      <td>125.20</td>\n",
+              "      <td>97.632</td>\n",
+              "      <td>27.568</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>10000014</td>\n",
+              "      <td>42376</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>33.17</td>\n",
+              "      <td>377.40</td>\n",
+              "      <td>351.602</td>\n",
+              "      <td>25.798</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>10000015</td>\n",
+              "      <td>42372</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>8.73</td>\n",
+              "      <td>114.62</td>\n",
+              "      <td>97.776</td>\n",
+              "      <td>16.844</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f61529b-5bcc-456c-a8f1-b095116fa7aa')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "\n",
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+              "      background-color: #3B4455;\n",
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+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
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+              "        document.querySelector('#df-0f61529b-5bcc-456c-a8f1-b095116fa7aa button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-0f61529b-5bcc-456c-a8f1-b095116fa7aa');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
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+              "</svg>\n",
+              "  </button>\n",
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+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
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+              "      --disabled-fill-color: #AAA;\n",
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+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
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+              "    border-radius: 50%;\n",
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+              "    height: 32px;\n",
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+              "\n",
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+              "  .colab-df-spinner {\n",
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+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-0ad292d2-411a-4734-ae75-ba6c58245641 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df1"
+            }
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The bar chart that represents the most number of trips for both yellow and pink cabs in the year of 1970"
+      ],
+      "metadata": {
+        "id": "I3xr1iPUyaUy"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "#converting the Date of Travel column in the DataFrame df1 to a datetime format using the pd.to_datetime() function from pandas.\n",
+        "df1['Date of Travel'] = pd.to_datetime(df1['Date of Travel'])\n",
+        "\n",
+        "#choosing the only the values from the dat of travel with the values of 1970 from df1 dataset\n",
+        "df1_1970 = df1[df1['Date of Travel'].dt.year == 1970]\n",
+        "\n",
+        "#The series type that provides the categories for company values and counts the number of companies appears\n",
+        "company_counts_1970 = df1_1970.groupby('Company')['Company'].count()\n",
+        "\n",
+        "#plotting the bar chart by using teh previous series and making each of the column values from the Company color pink and yellow\n",
+        "company_counts_1970.plot(kind='bar', color = ['pink', 'yellow'])\n",
+        "\n",
+        "\n",
+        "#x label\n",
+        "plt.xlabel('Company')\n",
+        "#y label\n",
+        "plt.ylabel('Number of Trips in 1970')\n",
+        "#title for the bar chart graph\n",
+        "plt.title('Most Used Cabs (Pink and Yellow) in 1970')\n",
+        "\n",
+        "#saving the image as png\n",
+        "plt.savefig('bar_chart_1970.png')\n",
+        "\n",
+        "#showing the plot\n",
+        "plt.show()\n",
+        "\n",
+        "# Hypothesis Result for Bar Chart of Most Used Cabs in 1970\n",
+        "\n",
+        "# The bar chart above showcases the number of trips undertaken by Yellow and Pink Cab companies in the year 1970.\n",
+        "# As depicted, Yellow Cabs had significantly more trips compared to Pink Cabs in that year.\n",
+        "# This suggests a higher demand or preference for Yellow Cabs in the market during 1970.\n",
+        "# If this trend holds for other years, it indicates that Yellow Cabs potentially have a stronger market share and possibly higher profitability.\n",
+        "# This information can be useful for XYZ firm in making informed decisions about which cab company to invest in, potentially favoring the Yellow Cab company given their historical dominance.\n",
+        "\n",
+        "#EDA Recommendations:\n",
+        "#We should collect and analyze data on customer demographics (age, gender, income level) to better understand who is using each cab company.\n",
+        "#This could provide insights into targeted marketing strategies."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 532
+        },
+        "id": "kQaKMrE7eRqw",
+        "outputId": "ceedad65-74bd-4261-f1a3-871c62d5c9f6"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df2 = pd.read_csv('City.csv')\n",
+        "df2.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "JPYgpO2sfGkf",
+        "outputId": "b7854f08-5674-4a57-a750-5d236960514f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "             City   Population      Users\n",
+              "0     NEW YORK NY   8,405,837    302,149 \n",
+              "1      CHICAGO IL   1,955,130    164,468 \n",
+              "2  LOS ANGELES CA   1,595,037    144,132 \n",
+              "3        MIAMI FL   1,339,155     17,675 \n",
+              "4  SILICON VALLEY   1,177,609     27,247 "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-7e8d8437-3bda-49c9-9b22-0c73cde27d3b\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Users</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>NEW YORK NY</td>\n",
+              "      <td>8,405,837</td>\n",
+              "      <td>302,149</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>CHICAGO IL</td>\n",
+              "      <td>1,955,130</td>\n",
+              "      <td>164,468</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>LOS ANGELES CA</td>\n",
+              "      <td>1,595,037</td>\n",
+              "      <td>144,132</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>MIAMI FL</td>\n",
+              "      <td>1,339,155</td>\n",
+              "      <td>17,675</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>SILICON VALLEY</td>\n",
+              "      <td>1,177,609</td>\n",
+              "      <td>27,247</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7e8d8437-3bda-49c9-9b22-0c73cde27d3b')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
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+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-7e8d8437-3bda-49c9-9b22-0c73cde27d3b button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-7e8d8437-3bda-49c9-9b22-0c73cde27d3b');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
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+              "            title=\"Suggest charts\"\n",
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+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-8b7768ec-b9a8-4fe4-9a62-0cbd5ac9b727 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df2",
+              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"NEW YORK NY\",\n          \"WASHINGTON DC\",\n          \"SACRAMENTO CA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 8,405,837 \",\n          \" 418,859 \",\n          \" 545,776 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Users\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 302,149 \",\n          \" 127,001 \",\n          \" 7,044 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "#The series type that provides the categories for both cities and company values and counts the number of companies appears in each city. The unstack() converts it\n",
+        "#into the dataframe when having multi index.\n",
+        "city_company_counts = df1.groupby(['City', 'Company'])['Company'].count().unstack()\n",
+        "\n",
+        "#replaces the previous index into a default index values like 0, 1, 2, 3, ...\n",
+        "city_company_counts = city_company_counts.reset_index()\n",
+        "\n",
+        "#Renaming the prevous columns\n",
+        "city_company_counts = city_company_counts.rename(\n",
+        "    columns={'Pink Cab': 'Pink Cab Users', 'Yellow Cab': 'Yellow Cab Users'}\n",
+        ")\n",
+        "\n",
+        "#Merging or combining the series values with the df2 dataset\n",
+        "merged_df = pd.merge(df2, city_company_counts, on='City', how='left')\n",
+        "\n",
+        "#replacing the NaN values with 0\n",
+        "merged_df = merged_df.fillna(0)\n",
+        "\n",
+        "#size of the plot\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "#drawing only the line graph for pink cab users\n",
+        "plt.plot(merged_df['City'], merged_df['Pink Cab Users'], label='Pink Cab Users')\n",
+        "#drawing only the line graph for yellow cab users\n",
+        "plt.plot(merged_df['City'], merged_df['Yellow Cab Users'], label='Yellow Cab Users')\n",
+        "#x label\n",
+        "plt.xlabel('City')\n",
+        "#y label\n",
+        "plt.ylabel('Number of Users')\n",
+        "#title of the graph\n",
+        "plt.title('Number of Users per City for Pink and Yellow Cabs')\n",
+        "#rotating the x label values into 90 degrees\n",
+        "plt.xticks(rotation=90)\n",
+        "#provides the particular color representation for both pink and yellow cab companies\n",
+        "plt.legend()\n",
+        "#adjust the spacing between subplots to minimize overlap and ensure that all elements are neatly contained within the figure area.\n",
+        "plt.tight_layout()\n",
+        "#plotting line graph\n",
+        "plt.show()\n",
+        "\n",
+        "# Hypothesis Result for Line Graph of Number of Users per City\n",
+        "\n",
+        "# The line graph above visually depicts the number of users for Pink and Yellow Cabs in different cities.\n",
+        "# It seems that the Yellow Cabs consistently have a larger user base across most cities compared to Pink Cabs.\n",
+        "# This indicates a potential market share dominance for Yellow Cabs.\n",
+        "# Notably, in certain cities, the difference in user numbers is significant, which may reflect a strong preference for Yellow Cabs in those areas.\n",
+        "# If these trends persist, it implies a greater demand for Yellow Cabs, potentially contributing to higher profitability for the company.\n",
+        "# This information can guide XYZ firm's investment decisions, possibly favouring the Yellow Cab company due to its extensive user base.\n",
+        "\n",
+        "#EDA Recommendations:\n",
+        "#I can analyze trends and patterns, and observe the overall trends in user counts for both companies across different cities."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 607
+        },
+        "id": "dSmc_aL3VSKR",
+        "outputId": "7bb7ca04-299e-48d7-da61-442d8144dd6f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#loading dataset called Transaction_ID.csv\n",
+        "df3 = pd.read_csv('Transaction_ID.csv')\n",
+        "df3"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "02ORKDVwZI9H",
+        "outputId": "3597cfb2-8880-4357-bac4-1e00390f96d9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "        Transaction ID  Customer ID Payment_Mode\n",
+              "0             10000011      29290.0         Card\n",
+              "1             10000012      27703.0         Card\n",
+              "2             10000013      28712.0         Cash\n",
+              "3             10000014      28020.0         Cash\n",
+              "4             10000015      27182.0         Card\n",
+              "...                ...          ...          ...\n",
+              "307683        10307694      42426.0         Cash\n",
+              "307684        10307695      42819.0         Cash\n",
+              "307685        10307696      44212.0         Card\n",
+              "307686        10307697      44827.0         Card\n",
+              "307687             103          NaN          NaN\n",
+              "\n",
+              "[307688 rows x 3 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-eb77c9c0-5dc0-428e-8314-d40899ad4ed5\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Transaction ID</th>\n",
+              "      <th>Customer ID</th>\n",
+              "      <th>Payment_Mode</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>10000011</td>\n",
+              "      <td>29290.0</td>\n",
+              "      <td>Card</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>10000012</td>\n",
+              "      <td>27703.0</td>\n",
+              "      <td>Card</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>10000013</td>\n",
+              "      <td>28712.0</td>\n",
+              "      <td>Cash</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>10000014</td>\n",
+              "      <td>28020.0</td>\n",
+              "      <td>Cash</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>10000015</td>\n",
+              "      <td>27182.0</td>\n",
+              "      <td>Card</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>307683</th>\n",
+              "      <td>10307694</td>\n",
+              "      <td>42426.0</td>\n",
+              "      <td>Cash</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>307684</th>\n",
+              "      <td>10307695</td>\n",
+              "      <td>42819.0</td>\n",
+              "      <td>Cash</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>307685</th>\n",
+              "      <td>10307696</td>\n",
+              "      <td>44212.0</td>\n",
+              "      <td>Card</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>307686</th>\n",
+              "      <td>10307697</td>\n",
+              "      <td>44827.0</td>\n",
+              "      <td>Card</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>307687</th>\n",
+              "      <td>103</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>307688 rows × 3 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb77c9c0-5dc0-428e-8314-d40899ad4ed5')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
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+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-eb77c9c0-5dc0-428e-8314-d40899ad4ed5 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-eb77c9c0-5dc0-428e-8314-d40899ad4ed5');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-20fa4755-2c55-41d8-95d1-5d6cce0d3b97\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-20fa4755-2c55-41d8-95d1-5d6cce0d3b97')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-20fa4755-2c55-41d8-95d1-5d6cce0d3b97 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_28a52d13-3169-4eeb-9213-8e435274f7de\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df3')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_28a52d13-3169-4eeb-9213-8e435274f7de button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df3');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df3"
+            }
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Replacing NaN values with 0\n",
+        "df3 = df3.fillna(0)\n",
+        "#Checking the sum of the null values whether there are null values left or not\n",
+        "df3.isnull().sum()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 178
+        },
+        "id": "bbOEMXM0kUdS",
+        "outputId": "653cdb57-f3e8-4173-d6b1-d21ff300a7a0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Transaction ID    0\n",
+              "Customer ID       0\n",
+              "Payment_Mode      0\n",
+              "dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Transaction ID</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Customer ID</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Payment_Mode</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Loadingt the dataset called Customer_ID.csv\n",
+        "df4 = pd.read_csv('Customer_ID.csv')\n",
+        "df4.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "64BsXf3bkToQ",
+        "outputId": "2fe40639-bd6f-4bf6-db74-ee6cf4a24f4e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Customer ID Gender  Age  Income (USD/Month)\n",
+              "0        29290   Male   28               10813\n",
+              "1        27703   Male   27                9237\n",
+              "2        28712   Male   53               11242\n",
+              "3        28020   Male   23               23327\n",
+              "4        27182   Male   33                8536"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-de4edf0e-d992-4c95-a735-95045f372109\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Customer ID</th>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Income (USD/Month)</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>29290</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>28</td>\n",
+              "      <td>10813</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>27703</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>27</td>\n",
+              "      <td>9237</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>28712</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>53</td>\n",
+              "      <td>11242</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>28020</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>23</td>\n",
+              "      <td>23327</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>27182</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>33</td>\n",
+              "      <td>8536</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-de4edf0e-d992-4c95-a735-95045f372109')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-de4edf0e-d992-4c95-a735-95045f372109 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-de4edf0e-d992-4c95-a735-95045f372109');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-0060794c-844e-40e1-b957-d2d55815f585\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0060794c-844e-40e1-b957-d2d55815f585')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-0060794c-844e-40e1-b957-d2d55815f585 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df4",
+              "summary": "{\n  \"name\": \"df4\",\n  \"rows\": 49171,\n  \"fields\": [\n    {\n      \"column\": \"Customer ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17714,\n        \"min\": 1,\n        \"max\": 60000,\n        \"num_unique_values\": 49171,\n        \"samples\": [\n          32451,\n          524,\n          19349\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 18,\n        \"max\": 65,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          32,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income (USD/Month)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8002,\n        \"min\": 2000,\n        \"max\": 35000,\n        \"num_unique_values\": 23341,\n        \"samples\": [\n          12932,\n          16207\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Merging the dataset fro both df3 and df4 by Customer ID feature\n",
+        "merged_df1 = pd.merge(df3, df4, on='Customer ID')\n",
+        "#Merging the df1 to the merged_df1 with df3 and df4 merged datasets with Transaction ID\n",
+        "merged_df2 = merged_df1.merge(df1, on='Transaction ID')\n",
+        "merged_df2.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 348
+        },
+        "id": "v8U48YCOFasv",
+        "outputId": "f9516c30-e25a-4ef3-8a41-ddb6cdf4a0a3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Transaction ID  Customer ID Payment_Mode Gender  Age  Income (USD/Month)  \\\n",
+              "0        10000011      29290.0         Card   Male   28               10813   \n",
+              "1        10000012      27703.0         Card   Male   27                9237   \n",
+              "2        10000013      28712.0         Cash   Male   53               11242   \n",
+              "3        10000014      28020.0         Cash   Male   23               23327   \n",
+              "4        10000015      27182.0         Card   Male   33                8536   \n",
+              "\n",
+              "   Date of Travel   Company        City  KM Travelled  Price Charged  \\\n",
+              "0         42377.0  Pink Cab  ATLANTA GA         30.45         370.95   \n",
+              "1         42375.0  Pink Cab  ATLANTA GA         28.62         358.52   \n",
+              "2         42371.0  Pink Cab  ATLANTA GA          9.04         125.20   \n",
+              "3         42376.0  Pink Cab  ATLANTA GA         33.17         377.40   \n",
+              "4         42372.0  Pink Cab  ATLANTA GA          8.73         114.62   \n",
+              "\n",
+              "   Cost of Trip  profit  \n",
+              "0       313.635  57.315  \n",
+              "1       334.854  23.666  \n",
+              "2        97.632  27.568  \n",
+              "3       351.602  25.798  \n",
+              "4        97.776  16.844  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-977a3625-8670-4406-b627-b1b07f9ce9cf\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Transaction ID</th>\n",
+              "      <th>Customer ID</th>\n",
+              "      <th>Payment_Mode</th>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Income (USD/Month)</th>\n",
+              "      <th>Date of Travel</th>\n",
+              "      <th>Company</th>\n",
+              "      <th>City</th>\n",
+              "      <th>KM Travelled</th>\n",
+              "      <th>Price Charged</th>\n",
+              "      <th>Cost of Trip</th>\n",
+              "      <th>profit</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>10000011</td>\n",
+              "      <td>29290.0</td>\n",
+              "      <td>Card</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>28</td>\n",
+              "      <td>10813</td>\n",
+              "      <td>42377.0</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>30.45</td>\n",
+              "      <td>370.95</td>\n",
+              "      <td>313.635</td>\n",
+              "      <td>57.315</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>10000012</td>\n",
+              "      <td>27703.0</td>\n",
+              "      <td>Card</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>27</td>\n",
+              "      <td>9237</td>\n",
+              "      <td>42375.0</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>28.62</td>\n",
+              "      <td>358.52</td>\n",
+              "      <td>334.854</td>\n",
+              "      <td>23.666</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>10000013</td>\n",
+              "      <td>28712.0</td>\n",
+              "      <td>Cash</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>53</td>\n",
+              "      <td>11242</td>\n",
+              "      <td>42371.0</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>9.04</td>\n",
+              "      <td>125.20</td>\n",
+              "      <td>97.632</td>\n",
+              "      <td>27.568</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>10000014</td>\n",
+              "      <td>28020.0</td>\n",
+              "      <td>Cash</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>23</td>\n",
+              "      <td>23327</td>\n",
+              "      <td>42376.0</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>33.17</td>\n",
+              "      <td>377.40</td>\n",
+              "      <td>351.602</td>\n",
+              "      <td>25.798</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>10000015</td>\n",
+              "      <td>27182.0</td>\n",
+              "      <td>Card</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>33</td>\n",
+              "      <td>8536</td>\n",
+              "      <td>42372.0</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>8.73</td>\n",
+              "      <td>114.62</td>\n",
+              "      <td>97.776</td>\n",
+              "      <td>16.844</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-977a3625-8670-4406-b627-b1b07f9ce9cf')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-977a3625-8670-4406-b627-b1b07f9ce9cf button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-977a3625-8670-4406-b627-b1b07f9ce9cf');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-d68e6d85-bbb6-425a-bc1f-ab3e6605fcd0\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d68e6d85-bbb6-425a-bc1f-ab3e6605fcd0')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-d68e6d85-bbb6-425a-bc1f-ab3e6605fcd0 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "merged_df2",
+              "summary": "{\n  \"name\": \"merged_df2\",\n  \"rows\": 71151,\n  \"fields\": [\n    {\n      \"column\": \"Transaction ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25592,\n        \"min\": 10000011,\n        \"max\": 10088433,\n        \"num_unique_values\": 71151,\n        \"samples\": [\n          10047350,\n          10009057,\n          10065991\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21212.115589102177,\n        \"min\": 1.0,\n        \"max\": 60000.0,\n        \"num_unique_values\": 26259,\n        \"samples\": [\n          59647.0,\n          53174.0,\n          24110.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Payment_Mode\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Cash\",\n          \"Card\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 18,\n        \"max\": 65,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          32,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income (USD/Month)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7994,\n        \"min\": 2000,\n        \"max\": 34996,\n        \"num_unique_values\": 16961,\n        \"samples\": [\n          5974,\n          2136\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date of Travel\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 80.38125009707976,\n        \"min\": 42371.0,\n        \"max\": 42661.0,\n        \"num_unique_values\": 291,\n        \"samples\": [\n          42458.0,\n          42631.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Company\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yellow Cab\",\n          \"Pink Cab\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"ATLANTA GA\",\n          \"DENVER CO\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"KM Travelled\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.213689673845838,\n        \"min\": 1.9,\n        \"max\": 48.0,\n        \"num_unique_values\": 874,\n        \"samples\": [\n          4.0,\n          29.43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price Charged\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 288.486993996938,\n        \"min\": 16.38,\n        \"max\": 1978.79,\n        \"num_unique_values\": 49504,\n        \"samples\": [\n          464.18,\n          626.96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cost of Trip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 157.89328330950855,\n        \"min\": 19.2,\n        \"max\": 691.2,\n        \"num_unique_values\": 14297,\n        \"samples\": [\n          434.5704,\n          380.4408\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"profit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 176.18102319112268,\n        \"min\": -198.69800000000004,\n        \"max\": 1336.8700000000001,\n        \"num_unique_values\": 68226,\n        \"samples\": [\n          730.5763999999999,\n          109.31200000000001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#This groups the DataFrame merged_df2 by two columns: Income (USD/Month) and Company. This means that the data will be organized into groups where each unique combination of income level and company is represented.\n",
+        "\n",
+        "income_company_counts = merged_df2.groupby(['Income (USD/Month)', 'Company'])['Company'].count().unstack()\n",
+        "#removes the NaN values\n",
+        "income_company_counts.dropna(inplace = True)\n",
+        "\n",
+        "#Resets index values into original default values like 0, 1, 2\n",
+        "income_company_counts = income_company_counts.reset_index()\n",
+        "\n",
+        "#The figure size of the graph\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "\n",
+        "#This line iterates through all the columns of the income_company_counts DataFrame, starting from the second column (index 1) onward.\n",
+        "#The loop will generate a series of bar charts, one for each company, where each bar represents the count of that company at each income level.\n",
+        "for company in income_company_counts.columns[1:]:\n",
+        "    plt.bar(income_company_counts['Income (USD/Month)'], income_company_counts[company], label=company)\n",
+        "\n",
+        "#x label\n",
+        "plt.xlabel('Income (USD)')\n",
+        "#y label\n",
+        "plt.ylabel('Number of Users')\n",
+        "#Title\n",
+        "plt.title('Number of Users per Income for Pink and Yellow Cabs')\n",
+        "#Rotating the x values to 90 degrees\n",
+        "plt.xticks(rotation=90)\n",
+        "#Providingt he particular colors for values\n",
+        "plt.legend()\n",
+        "#adjust the spacing between subplots to minimize overlap and ensure that all elements are neatly contained within the figure area.\n",
+        "plt.tight_layout()\n",
+        "#output the graph\n",
+        "plt.show()\n",
+        "#downloading the graph image into png\n",
+        "plt.savefig('bar_chart_income_new.png')\n",
+        "\n",
+        "# Hypothesis Result for Bar Chart of Number of Users per Income\n",
+        "\n",
+        "# The bar chart above depicts the relationship between customer income and the usage of Pink and Yellow Cabs.\n",
+        "# It appears that across various income levels, Yellow Cabs tend to have a larger user base compared to Pink Cabs.\n",
+        "# This observation reinforces the potential market share dominance of Yellow Cabs.\n",
+        "# It is noticeable that as income levels increase, the user count for both Pink and Yellow Cabs also tends to increase, though the difference in user counts between the two companies seems consistent across different income brackets.\n",
+        "# If this trend continues, it suggests a strong preference for Yellow Cabs among customers regardless of their income level, potentially translating into greater profitability for the Yellow Cab company.\n",
+        "# This information can assist XYZ firm in making investment decisions, potentially favoring the Yellow Cab company due to its extensive user base across different income segments.\n",
+        "\n",
+        "# EDA Recommendations:\n",
+        "# Further examination of the customer demographic, such as locations."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 624
+        },
+        "id": "hXTgM55hGHoS",
+        "outputId": "69875007-f601-4bd4-f238-fc819a904d2b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 0 Axes>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

From be65e37c2b6b1cc55a4fdf278f9d163ad745b0fb Mon Sep 17 00:00:00 2001
From: An204o <166038499+An204o@users.noreply.github.com>
Date: Tue, 22 Oct 2024 17:37:47 -0400
Subject: [PATCH 11/22] Delete Copy_of_Copy_of_Data_Analysis_Week_2_Project
 (1).ipynb

---
 ..._of_Data_Analysis_Week_2_Project (1).ipynb | 2924 -----------------
 1 file changed, 2924 deletions(-)
 delete mode 100644 Copy_of_Copy_of_Data_Analysis_Week_2_Project (1).ipynb

diff --git a/Copy_of_Copy_of_Data_Analysis_Week_2_Project (1).ipynb b/Copy_of_Copy_of_Data_Analysis_Week_2_Project (1).ipynb
deleted file mode 100644
index 59bf5268..00000000
--- a/Copy_of_Copy_of_Data_Analysis_Week_2_Project (1).ipynb	
+++ /dev/null
@@ -1,2924 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "source": [
-        "Loading the csv file for the Cab_Data.csv and checking for its outliers. The boxplot seems to not have any outliers as it is not above the maximum value or below the minimum value."
-      ],
-      "metadata": {
-        "id": "nxz9GK3qZWv2"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#This code contains a mix of my own and AI generated\n",
-        "import pandas as pd\n",
-        "import matplotlib.pyplot as plt\n",
-        "import seaborn as sns\n",
-        "from sklearn.preprocessing import StandardScaler\n",
-        "#This code loads the dataset by using pandas library\n",
-        "df1 = pd.read_csv('Cab_Data.csv', on_bad_lines='skip')\n",
-        "\n",
-        "#Creating a new column called profit\n",
-        "df1['profit'] = df1['Price Charged'] - df1['Cost of Trip']\n",
-        "\n",
-        "#Using seaborn and drawing boxplot by using the profit column\n",
-        "sns.boxplot(x=df1['profit'])\n",
-        "\n",
-        "#saving the graph in a png form by using savefig()\n",
-        "plt.savefig('boxplot_new.png')\n",
-        "\n",
-        "#Hypothesis:\n",
-        "#In the code above, I loaded the first dataset, which is Cab_Data.csv, and verified the whether the outliers are presentor not by using the boxplot chart. In the boxplot below,\n",
-        "# it seems that there are no outliers as the minimum is -150.38, and maximum is 1327.6219999999998, so there are no values which are less than minimum or above the maximum.\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 449
-        },
-        "id": "zeCKWNYVOkql",
-        "outputId": "99558f26-5f10-4b9e-caf4-2559e332999f"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#This shows the first 5 rows of all the columns row values\n",
-        "df1.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "_9QBzdB0dcb-",
-        "outputId": "015153a3-7370-4fc6-cd23-cd0d9654cb2d"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "   Transaction ID  Date of Travel   Company        City  KM Travelled  \\\n",
-              "0        10000011           42377  Pink Cab  ATLANTA GA         30.45   \n",
-              "1        10000012           42375  Pink Cab  ATLANTA GA         28.62   \n",
-              "2        10000013           42371  Pink Cab  ATLANTA GA          9.04   \n",
-              "3        10000014           42376  Pink Cab  ATLANTA GA         33.17   \n",
-              "4        10000015           42372  Pink Cab  ATLANTA GA          8.73   \n",
-              "\n",
-              "   Price Charged  Cost of Trip  profit  \n",
-              "0         370.95       313.635  57.315  \n",
-              "1         358.52       334.854  23.666  \n",
-              "2         125.20        97.632  27.568  \n",
-              "3         377.40       351.602  25.798  \n",
-              "4         114.62        97.776  16.844  "
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-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df1"
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-          "metadata": {},
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-    {
-      "cell_type": "markdown",
-      "source": [
-        "Bar chart for comparing the average profits of both Pink and Yellow Cab Companies"
-      ],
-      "metadata": {
-        "id": "ocbjQ1Svoc4J"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#This code provides the categories for Company column values, which are pink and yellow cabs, and these categories corresponds to the average profits of these two cabs.\n",
-        "average_profit = df1.groupby('Company')['profit'].mean()\n",
-        "\n",
-        "#I used the above series for average profits for both yellow and pink cab, and this code is used to plot the bar chart for average profit comparisons\n",
-        "average_profit.plot(kind='bar', color=['blue', 'red'])\n",
-        "\n",
-        "#x label for the graph\n",
-        "plt.xlabel('Company')\n",
-        "#y label for the graph\n",
-        "plt.ylabel('Profit')\n",
-        "#the title of the graph above\n",
-        "plt.title('Pink and Yellow Cab Companies vs Average Profits')\n",
-        "#saving the image as png\n",
-        "plt.savefig('barplot_comparison_profits.png')\n",
-        "\n",
-        "#EDA Recommendation\n",
-        "# Based on the exploratory data analysis, it is recommended to further investigate the factors contributing to the higher average profits of Yellow Cabs compared to Pink Cabs.\n",
-        "# This includes analyzing variables such as trip duration, fare rates, customer satisfaction, and the operational strategies of both cab companies.\n",
-        "\n",
-        "# Hypothesis Results\n",
-        "# In the bar chart below, I am comparing the average profits for two cab companies, Yellow and Pink Cabs.\n",
-        "# As you can see, the data indicates that the Yellow Cab company has more average profits, with a maximum profit of approximately $158,\n",
-        "# while the Pink Cab has an average profit of about $63. If this hypothesis holds true, it suggests that XYZ firm should consider investing in the Yellow Cab company\n",
-        "# due to its stronger profitability. This analysis supports the notion that Yellow Cabs may provide a more lucrative investment opportunity."
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 532
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-        "id": "A-hBShAXUwUw",
-        "outputId": "1c313882-44c2-4ba3-fa30-a0130a283d17"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
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-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#This loads the dataset for City.csv\n",
-        "df2 = pd.read_csv('City.csv')\n",
-        "df2.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "9HFXuXAzWAro",
-        "outputId": "79e2dd6a-56cc-4256-e5cd-1461fb68f420"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "             City   Population      Users\n",
-              "0     NEW YORK NY   8,405,837    302,149 \n",
-              "1      CHICAGO IL   1,955,130    164,468 \n",
-              "2  LOS ANGELES CA   1,595,037    144,132 \n",
-              "3        MIAMI FL   1,339,155     17,675 \n",
-              "4  SILICON VALLEY   1,177,609     27,247 "
-            ],
-            "text/html": [
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-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
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-              "\n",
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-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>City</th>\n",
-              "      <th>Population</th>\n",
-              "      <th>Users</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>NEW YORK NY</td>\n",
-              "      <td>8,405,837</td>\n",
-              "      <td>302,149</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>CHICAGO IL</td>\n",
-              "      <td>1,955,130</td>\n",
-              "      <td>164,468</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>LOS ANGELES CA</td>\n",
-              "      <td>1,595,037</td>\n",
-              "      <td>144,132</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>MIAMI FL</td>\n",
-              "      <td>1,339,155</td>\n",
-              "      <td>17,675</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>SILICON VALLEY</td>\n",
-              "      <td>1,177,609</td>\n",
-              "      <td>27,247</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
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-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ba8c576-5618-4e5e-a93f-5a15ef622129')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
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-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
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-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
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-              "      display: none;\n",
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-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
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-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
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-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
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-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-4ba8c576-5618-4e5e-a93f-5a15ef622129 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-4ba8c576-5618-4e5e-a93f-5a15ef622129');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
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-              "\n",
-              "\n",
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-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-407a8eb6-3504-4fb7-869e-56f573ef9ac5')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-407a8eb6-3504-4fb7-869e-56f573ef9ac5 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df2",
-              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"NEW YORK NY\",\n          \"WASHINGTON DC\",\n          \"SACRAMENTO CA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 8,405,837 \",\n          \" 418,859 \",\n          \" 545,776 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Users\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 302,149 \",\n          \" 127,001 \",\n          \" 7,044 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 8
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "Stacked bar chart with the number of trips in different cities for both yellow and pink cabs"
-      ],
-      "metadata": {
-        "id": "yDvVWeIVu2RW"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "\n",
-        "import pandas as pd\n",
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "#The series type that provides the categories for both cities and company values and counts the number of companies appears in each city. The unstack() converts it\n",
-        "#into the dataframe when having multi index.\n",
-        "city_company_counts = df1.groupby(['City', 'Company'])['Company'].count().unstack()\n",
-        "\n",
-        "#Then I use the series to plot the bar chart for showing number of trips.\n",
-        "city_company_counts.plot(kind='bar', stacked=True)\n",
-        "\n",
-        "#x label\n",
-        "plt.xlabel('City')\n",
-        "#y label\n",
-        "plt.ylabel('Number of Trips')\n",
-        "#title for the bar chart graph\n",
-        "plt.title('Most Used Cabs (Pink and Yellow) by City')\n",
-        "\n",
-        "#provides the particular color representation for both pink and yellow cab companies\n",
-        "plt.legend(title='Company')\n",
-        "\n",
-        "#saving the image as png\n",
-        "plt.savefig('stacked_bar_city_company.png')\n",
-        "#providing the output of the graph image\n",
-        "plt.show()\n",
-        "\n",
-        "#Hypothesis\n",
-        "# As you can see in the stacked bar chart below, the most used cabs are yellow cabs.\n",
-        "# In most of the countries, the most used cab is yellow. For instance, in the city\n",
-        "# New York, the number of trips for pink cabs is approximately 1,600, while the yellow cab\n",
-        "# has 980,000 trips.\n",
-        "\n",
-        "# Based on the stacked bar chart analysis, it appears that yellow cabs are predominantly used\n",
-        "# over pink cabs in New York, as evidenced by the trip counts.\n",
-        "# This indicates a strong preference for yellow cabs among consumers in that area.\n",
-        "# If these trends hold true across other locations, this may reflect a broader market trend\n",
-        "# that XYZ firm should consider when deciding on cab investments.\n",
-        "\n",
-        "#Recommendations:\n",
-        "#Data Visualizations: I would have made a graph such as linear or pie chart to better show the distribution of the values for this particular goal."
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 579
-        },
-        "id": "Tc89YuH-Zcg4",
-        "outputId": "1cd1a65c-62e5-4426-aeaa-1574afd6a9e4"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df1.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "Mue7qS3FdC55",
-        "outputId": "4f3a1c8e-4a82-4afa-bf0b-9877e6909c9d"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "   Transaction ID  Date of Travel   Company        City  KM Travelled  \\\n",
-              "0        10000011           42377  Pink Cab  ATLANTA GA         30.45   \n",
-              "1        10000012           42375  Pink Cab  ATLANTA GA         28.62   \n",
-              "2        10000013           42371  Pink Cab  ATLANTA GA          9.04   \n",
-              "3        10000014           42376  Pink Cab  ATLANTA GA         33.17   \n",
-              "4        10000015           42372  Pink Cab  ATLANTA GA          8.73   \n",
-              "\n",
-              "   Price Charged  Cost of Trip  profit  \n",
-              "0         370.95       313.635  57.315  \n",
-              "1         358.52       334.854  23.666  \n",
-              "2         125.20        97.632  27.568  \n",
-              "3         377.40       351.602  25.798  \n",
-              "4         114.62        97.776  16.844  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-0f61529b-5bcc-456c-a8f1-b095116fa7aa\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
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-              "\n",
-              "    .dataframe thead th {\n",
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-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Transaction ID</th>\n",
-              "      <th>Date of Travel</th>\n",
-              "      <th>Company</th>\n",
-              "      <th>City</th>\n",
-              "      <th>KM Travelled</th>\n",
-              "      <th>Price Charged</th>\n",
-              "      <th>Cost of Trip</th>\n",
-              "      <th>profit</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>10000011</td>\n",
-              "      <td>42377</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>30.45</td>\n",
-              "      <td>370.95</td>\n",
-              "      <td>313.635</td>\n",
-              "      <td>57.315</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>10000012</td>\n",
-              "      <td>42375</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>28.62</td>\n",
-              "      <td>358.52</td>\n",
-              "      <td>334.854</td>\n",
-              "      <td>23.666</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>10000013</td>\n",
-              "      <td>42371</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>9.04</td>\n",
-              "      <td>125.20</td>\n",
-              "      <td>97.632</td>\n",
-              "      <td>27.568</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>10000014</td>\n",
-              "      <td>42376</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>33.17</td>\n",
-              "      <td>377.40</td>\n",
-              "      <td>351.602</td>\n",
-              "      <td>25.798</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>10000015</td>\n",
-              "      <td>42372</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>8.73</td>\n",
-              "      <td>114.62</td>\n",
-              "      <td>97.776</td>\n",
-              "      <td>16.844</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
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-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f61529b-5bcc-456c-a8f1-b095116fa7aa')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
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-              "type": "dataframe",
-              "variable_name": "df1"
-            }
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-          "metadata": {},
-          "execution_count": 9
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-    {
-      "cell_type": "markdown",
-      "source": [
-        "The bar chart that represents the most number of trips for both yellow and pink cabs in the year of 1970"
-      ],
-      "metadata": {
-        "id": "I3xr1iPUyaUy"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "\n",
-        "import pandas as pd\n",
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "#converting the Date of Travel column in the DataFrame df1 to a datetime format using the pd.to_datetime() function from pandas.\n",
-        "df1['Date of Travel'] = pd.to_datetime(df1['Date of Travel'])\n",
-        "\n",
-        "#choosing the only the values from the dat of travel with the values of 1970 from df1 dataset\n",
-        "df1_1970 = df1[df1['Date of Travel'].dt.year == 1970]\n",
-        "\n",
-        "#The series type that provides the categories for company values and counts the number of companies appears\n",
-        "company_counts_1970 = df1_1970.groupby('Company')['Company'].count()\n",
-        "\n",
-        "#plotting the bar chart by using teh previous series and making each of the column values from the Company color pink and yellow\n",
-        "company_counts_1970.plot(kind='bar', color = ['pink', 'yellow'])\n",
-        "\n",
-        "\n",
-        "#x label\n",
-        "plt.xlabel('Company')\n",
-        "#y label\n",
-        "plt.ylabel('Number of Trips in 1970')\n",
-        "#title for the bar chart graph\n",
-        "plt.title('Most Used Cabs (Pink and Yellow) in 1970')\n",
-        "\n",
-        "#saving the image as png\n",
-        "plt.savefig('bar_chart_1970.png')\n",
-        "\n",
-        "#showing the plot\n",
-        "plt.show()\n",
-        "\n",
-        "# Hypothesis Result for Bar Chart of Most Used Cabs in 1970\n",
-        "\n",
-        "# The bar chart above showcases the number of trips undertaken by Yellow and Pink Cab companies in the year 1970.\n",
-        "# As depicted, Yellow Cabs had significantly more trips compared to Pink Cabs in that year.\n",
-        "# This suggests a higher demand or preference for Yellow Cabs in the market during 1970.\n",
-        "# If this trend holds for other years, it indicates that Yellow Cabs potentially have a stronger market share and possibly higher profitability.\n",
-        "# This information can be useful for XYZ firm in making informed decisions about which cab company to invest in, potentially favoring the Yellow Cab company given their historical dominance.\n",
-        "\n",
-        "#EDA Recommendations:\n",
-        "#We should collect and analyze data on customer demographics (age, gender, income level) to better understand who is using each cab company.\n",
-        "#This could provide insights into targeted marketing strategies."
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 532
-        },
-        "id": "kQaKMrE7eRqw",
-        "outputId": "ceedad65-74bd-4261-f1a3-871c62d5c9f6"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df2 = pd.read_csv('City.csv')\n",
-        "df2.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "JPYgpO2sfGkf",
-        "outputId": "b7854f08-5674-4a57-a750-5d236960514f"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "             City   Population      Users\n",
-              "0     NEW YORK NY   8,405,837    302,149 \n",
-              "1      CHICAGO IL   1,955,130    164,468 \n",
-              "2  LOS ANGELES CA   1,595,037    144,132 \n",
-              "3        MIAMI FL   1,339,155     17,675 \n",
-              "4  SILICON VALLEY   1,177,609     27,247 "
-            ],
-            "text/html": [
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-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-8b7768ec-b9a8-4fe4-9a62-0cbd5ac9b727 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df2",
-              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"NEW YORK NY\",\n          \"WASHINGTON DC\",\n          \"SACRAMENTO CA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 8,405,837 \",\n          \" 418,859 \",\n          \" 545,776 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Users\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 302,149 \",\n          \" 127,001 \",\n          \" 7,044 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 11
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "\n",
-        "#The series type that provides the categories for both cities and company values and counts the number of companies appears in each city. The unstack() converts it\n",
-        "#into the dataframe when having multi index.\n",
-        "city_company_counts = df1.groupby(['City', 'Company'])['Company'].count().unstack()\n",
-        "\n",
-        "#replaces the previous index into a default index values like 0, 1, 2, 3, ...\n",
-        "city_company_counts = city_company_counts.reset_index()\n",
-        "\n",
-        "#Renaming the prevous columns\n",
-        "city_company_counts = city_company_counts.rename(\n",
-        "    columns={'Pink Cab': 'Pink Cab Users', 'Yellow Cab': 'Yellow Cab Users'}\n",
-        ")\n",
-        "\n",
-        "#Merging or combining the series values with the df2 dataset\n",
-        "merged_df = pd.merge(df2, city_company_counts, on='City', how='left')\n",
-        "\n",
-        "#replacing the NaN values with 0\n",
-        "merged_df = merged_df.fillna(0)\n",
-        "\n",
-        "#size of the plot\n",
-        "plt.figure(figsize=(10, 6))\n",
-        "#drawing only the line graph for pink cab users\n",
-        "plt.plot(merged_df['City'], merged_df['Pink Cab Users'], label='Pink Cab Users')\n",
-        "#drawing only the line graph for yellow cab users\n",
-        "plt.plot(merged_df['City'], merged_df['Yellow Cab Users'], label='Yellow Cab Users')\n",
-        "#x label\n",
-        "plt.xlabel('City')\n",
-        "#y label\n",
-        "plt.ylabel('Number of Users')\n",
-        "#title of the graph\n",
-        "plt.title('Number of Users per City for Pink and Yellow Cabs')\n",
-        "#rotating the x label values into 90 degrees\n",
-        "plt.xticks(rotation=90)\n",
-        "#provides the particular color representation for both pink and yellow cab companies\n",
-        "plt.legend()\n",
-        "#adjust the spacing between subplots to minimize overlap and ensure that all elements are neatly contained within the figure area.\n",
-        "plt.tight_layout()\n",
-        "#plotting line graph\n",
-        "plt.show()\n",
-        "\n",
-        "# Hypothesis Result for Line Graph of Number of Users per City\n",
-        "\n",
-        "# The line graph above visually depicts the number of users for Pink and Yellow Cabs in different cities.\n",
-        "# It seems that the Yellow Cabs consistently have a larger user base across most cities compared to Pink Cabs.\n",
-        "# This indicates a potential market share dominance for Yellow Cabs.\n",
-        "# Notably, in certain cities, the difference in user numbers is significant, which may reflect a strong preference for Yellow Cabs in those areas.\n",
-        "# If these trends persist, it implies a greater demand for Yellow Cabs, potentially contributing to higher profitability for the company.\n",
-        "# This information can guide XYZ firm's investment decisions, possibly favouring the Yellow Cab company due to its extensive user base.\n",
-        "\n",
-        "#EDA Recommendations:\n",
-        "#I can analyze trends and patterns, and observe the overall trends in user counts for both companies across different cities."
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 607
-        },
-        "id": "dSmc_aL3VSKR",
-        "outputId": "7bb7ca04-299e-48d7-da61-442d8144dd6f"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1000x600 with 1 Axes>"
-            ],
-            "image/png": 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-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#loading dataset called Transaction_ID.csv\n",
-        "df3 = pd.read_csv('Transaction_ID.csv')\n",
-        "df3"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 424
-        },
-        "id": "02ORKDVwZI9H",
-        "outputId": "3597cfb2-8880-4357-bac4-1e00390f96d9"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "        Transaction ID  Customer ID Payment_Mode\n",
-              "0             10000011      29290.0         Card\n",
-              "1             10000012      27703.0         Card\n",
-              "2             10000013      28712.0         Cash\n",
-              "3             10000014      28020.0         Cash\n",
-              "4             10000015      27182.0         Card\n",
-              "...                ...          ...          ...\n",
-              "307683        10307694      42426.0         Cash\n",
-              "307684        10307695      42819.0         Cash\n",
-              "307685        10307696      44212.0         Card\n",
-              "307686        10307697      44827.0         Card\n",
-              "307687             103          NaN          NaN\n",
-              "\n",
-              "[307688 rows x 3 columns]"
-            ],
-            "text/html": [
-              "\n",
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-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
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-              "\n",
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-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>10000013</td>\n",
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-              "    <tr>\n",
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-              "      <td>28020.0</td>\n",
-              "      <td>Cash</td>\n",
-              "    </tr>\n",
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-              "      <td>10000015</td>\n",
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-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>307684</th>\n",
-              "      <td>10307695</td>\n",
-              "      <td>42819.0</td>\n",
-              "      <td>Cash</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>307685</th>\n",
-              "      <td>10307696</td>\n",
-              "      <td>44212.0</td>\n",
-              "      <td>Card</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>307686</th>\n",
-              "      <td>10307697</td>\n",
-              "      <td>44827.0</td>\n",
-              "      <td>Card</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>307687</th>\n",
-              "      <td>103</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
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-              "<p>307688 rows × 3 columns</p>\n",
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-              "        document.querySelector('#df-eb77c9c0-5dc0-428e-8314-d40899ad4ed5 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-eb77c9c0-5dc0-428e-8314-d40899ad4ed5');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-20fa4755-2c55-41d8-95d1-5d6cce0d3b97\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-20fa4755-2c55-41d8-95d1-5d6cce0d3b97')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-20fa4755-2c55-41d8-95d1-5d6cce0d3b97 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "  <div id=\"id_28a52d13-3169-4eeb-9213-8e435274f7de\">\n",
-              "    <style>\n",
-              "      .colab-df-generate {\n",
-              "        background-color: #E8F0FE;\n",
-              "        border: none;\n",
-              "        border-radius: 50%;\n",
-              "        cursor: pointer;\n",
-              "        display: none;\n",
-              "        fill: #1967D2;\n",
-              "        height: 32px;\n",
-              "        padding: 0 0 0 0;\n",
-              "        width: 32px;\n",
-              "      }\n",
-              "\n",
-              "      .colab-df-generate:hover {\n",
-              "        background-color: #E2EBFA;\n",
-              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "        fill: #174EA6;\n",
-              "      }\n",
-              "\n",
-              "      [theme=dark] .colab-df-generate {\n",
-              "        background-color: #3B4455;\n",
-              "        fill: #D2E3FC;\n",
-              "      }\n",
-              "\n",
-              "      [theme=dark] .colab-df-generate:hover {\n",
-              "        background-color: #434B5C;\n",
-              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "        fill: #FFFFFF;\n",
-              "      }\n",
-              "    </style>\n",
-              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df3')\"\n",
-              "            title=\"Generate code using this dataframe.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "       width=\"24px\">\n",
-              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "    <script>\n",
-              "      (() => {\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#id_28a52d13-3169-4eeb-9213-8e435274f7de button.colab-df-generate');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      buttonEl.onclick = () => {\n",
-              "        google.colab.notebook.generateWithVariable('df3');\n",
-              "      }\n",
-              "      })();\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df3"
-            }
-          },
-          "metadata": {},
-          "execution_count": 4
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Replacing NaN values with 0\n",
-        "df3 = df3.fillna(0)\n",
-        "#Checking the sum of the null values whether there are null values left or not\n",
-        "df3.isnull().sum()\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 178
-        },
-        "id": "bbOEMXM0kUdS",
-        "outputId": "653cdb57-f3e8-4173-d6b1-d21ff300a7a0"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "Transaction ID    0\n",
-              "Customer ID       0\n",
-              "Payment_Mode      0\n",
-              "dtype: int64"
-            ],
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>0</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>Transaction ID</th>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>Customer ID</th>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>Payment_Mode</th>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div><br><label><b>dtype:</b> int64</label>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 9
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Loadingt the dataset called Customer_ID.csv\n",
-        "df4 = pd.read_csv('Customer_ID.csv')\n",
-        "df4.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "64BsXf3bkToQ",
-        "outputId": "2fe40639-bd6f-4bf6-db74-ee6cf4a24f4e"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "   Customer ID Gender  Age  Income (USD/Month)\n",
-              "0        29290   Male   28               10813\n",
-              "1        27703   Male   27                9237\n",
-              "2        28712   Male   53               11242\n",
-              "3        28020   Male   23               23327\n",
-              "4        27182   Male   33                8536"
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-de4edf0e-d992-4c95-a735-95045f372109\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
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-              "</style>\n",
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-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Customer ID</th>\n",
-              "      <th>Gender</th>\n",
-              "      <th>Age</th>\n",
-              "      <th>Income (USD/Month)</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>29290</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>28</td>\n",
-              "      <td>10813</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>27703</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>27</td>\n",
-              "      <td>9237</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>28712</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>53</td>\n",
-              "      <td>11242</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>28020</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>23</td>\n",
-              "      <td>23327</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>27182</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>33</td>\n",
-              "      <td>8536</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-de4edf0e-d992-4c95-a735-95045f372109')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
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-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-de4edf0e-d992-4c95-a735-95045f372109 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-de4edf0e-d992-4c95-a735-95045f372109');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
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-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-0060794c-844e-40e1-b957-d2d55815f585\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0060794c-844e-40e1-b957-d2d55815f585')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-0060794c-844e-40e1-b957-d2d55815f585 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df4",
-              "summary": "{\n  \"name\": \"df4\",\n  \"rows\": 49171,\n  \"fields\": [\n    {\n      \"column\": \"Customer ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17714,\n        \"min\": 1,\n        \"max\": 60000,\n        \"num_unique_values\": 49171,\n        \"samples\": [\n          32451,\n          524,\n          19349\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 18,\n        \"max\": 65,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          32,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income (USD/Month)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8002,\n        \"min\": 2000,\n        \"max\": 35000,\n        \"num_unique_values\": 23341,\n        \"samples\": [\n          12932,\n          16207\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 5
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Merging the dataset fro both df3 and df4 by Customer ID feature\n",
-        "merged_df1 = pd.merge(df3, df4, on='Customer ID')\n",
-        "#Merging the df1 to the merged_df1 with df3 and df4 merged datasets with Transaction ID\n",
-        "merged_df2 = merged_df1.merge(df1, on='Transaction ID')\n",
-        "merged_df2.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 348
-        },
-        "id": "v8U48YCOFasv",
-        "outputId": "f9516c30-e25a-4ef3-8a41-ddb6cdf4a0a3"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "   Transaction ID  Customer ID Payment_Mode Gender  Age  Income (USD/Month)  \\\n",
-              "0        10000011      29290.0         Card   Male   28               10813   \n",
-              "1        10000012      27703.0         Card   Male   27                9237   \n",
-              "2        10000013      28712.0         Cash   Male   53               11242   \n",
-              "3        10000014      28020.0         Cash   Male   23               23327   \n",
-              "4        10000015      27182.0         Card   Male   33                8536   \n",
-              "\n",
-              "   Date of Travel   Company        City  KM Travelled  Price Charged  \\\n",
-              "0         42377.0  Pink Cab  ATLANTA GA         30.45         370.95   \n",
-              "1         42375.0  Pink Cab  ATLANTA GA         28.62         358.52   \n",
-              "2         42371.0  Pink Cab  ATLANTA GA          9.04         125.20   \n",
-              "3         42376.0  Pink Cab  ATLANTA GA         33.17         377.40   \n",
-              "4         42372.0  Pink Cab  ATLANTA GA          8.73         114.62   \n",
-              "\n",
-              "   Cost of Trip  profit  \n",
-              "0       313.635  57.315  \n",
-              "1       334.854  23.666  \n",
-              "2        97.632  27.568  \n",
-              "3       351.602  25.798  \n",
-              "4        97.776  16.844  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-977a3625-8670-4406-b627-b1b07f9ce9cf\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Transaction ID</th>\n",
-              "      <th>Customer ID</th>\n",
-              "      <th>Payment_Mode</th>\n",
-              "      <th>Gender</th>\n",
-              "      <th>Age</th>\n",
-              "      <th>Income (USD/Month)</th>\n",
-              "      <th>Date of Travel</th>\n",
-              "      <th>Company</th>\n",
-              "      <th>City</th>\n",
-              "      <th>KM Travelled</th>\n",
-              "      <th>Price Charged</th>\n",
-              "      <th>Cost of Trip</th>\n",
-              "      <th>profit</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>10000011</td>\n",
-              "      <td>29290.0</td>\n",
-              "      <td>Card</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>28</td>\n",
-              "      <td>10813</td>\n",
-              "      <td>42377.0</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>30.45</td>\n",
-              "      <td>370.95</td>\n",
-              "      <td>313.635</td>\n",
-              "      <td>57.315</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>10000012</td>\n",
-              "      <td>27703.0</td>\n",
-              "      <td>Card</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>27</td>\n",
-              "      <td>9237</td>\n",
-              "      <td>42375.0</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>28.62</td>\n",
-              "      <td>358.52</td>\n",
-              "      <td>334.854</td>\n",
-              "      <td>23.666</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>10000013</td>\n",
-              "      <td>28712.0</td>\n",
-              "      <td>Cash</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>53</td>\n",
-              "      <td>11242</td>\n",
-              "      <td>42371.0</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>9.04</td>\n",
-              "      <td>125.20</td>\n",
-              "      <td>97.632</td>\n",
-              "      <td>27.568</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>10000014</td>\n",
-              "      <td>28020.0</td>\n",
-              "      <td>Cash</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>23</td>\n",
-              "      <td>23327</td>\n",
-              "      <td>42376.0</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>33.17</td>\n",
-              "      <td>377.40</td>\n",
-              "      <td>351.602</td>\n",
-              "      <td>25.798</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>10000015</td>\n",
-              "      <td>27182.0</td>\n",
-              "      <td>Card</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>33</td>\n",
-              "      <td>8536</td>\n",
-              "      <td>42372.0</td>\n",
-              "      <td>Pink Cab</td>\n",
-              "      <td>ATLANTA GA</td>\n",
-              "      <td>8.73</td>\n",
-              "      <td>114.62</td>\n",
-              "      <td>97.776</td>\n",
-              "      <td>16.844</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-977a3625-8670-4406-b627-b1b07f9ce9cf')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-977a3625-8670-4406-b627-b1b07f9ce9cf button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-977a3625-8670-4406-b627-b1b07f9ce9cf');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-d68e6d85-bbb6-425a-bc1f-ab3e6605fcd0\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d68e6d85-bbb6-425a-bc1f-ab3e6605fcd0')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-d68e6d85-bbb6-425a-bc1f-ab3e6605fcd0 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "merged_df2",
-              "summary": "{\n  \"name\": \"merged_df2\",\n  \"rows\": 71151,\n  \"fields\": [\n    {\n      \"column\": \"Transaction ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25592,\n        \"min\": 10000011,\n        \"max\": 10088433,\n        \"num_unique_values\": 71151,\n        \"samples\": [\n          10047350,\n          10009057,\n          10065991\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21212.115589102177,\n        \"min\": 1.0,\n        \"max\": 60000.0,\n        \"num_unique_values\": 26259,\n        \"samples\": [\n          59647.0,\n          53174.0,\n          24110.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Payment_Mode\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Cash\",\n          \"Card\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 18,\n        \"max\": 65,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          32,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income (USD/Month)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7994,\n        \"min\": 2000,\n        \"max\": 34996,\n        \"num_unique_values\": 16961,\n        \"samples\": [\n          5974,\n          2136\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date of Travel\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 80.38125009707976,\n        \"min\": 42371.0,\n        \"max\": 42661.0,\n        \"num_unique_values\": 291,\n        \"samples\": [\n          42458.0,\n          42631.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Company\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yellow Cab\",\n          \"Pink Cab\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"ATLANTA GA\",\n          \"DENVER CO\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"KM Travelled\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.213689673845838,\n        \"min\": 1.9,\n        \"max\": 48.0,\n        \"num_unique_values\": 874,\n        \"samples\": [\n          4.0,\n          29.43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price Charged\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 288.486993996938,\n        \"min\": 16.38,\n        \"max\": 1978.79,\n        \"num_unique_values\": 49504,\n        \"samples\": [\n          464.18,\n          626.96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cost of Trip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 157.89328330950855,\n        \"min\": 19.2,\n        \"max\": 691.2,\n        \"num_unique_values\": 14297,\n        \"samples\": [\n          434.5704,\n          380.4408\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"profit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 176.18102319112268,\n        \"min\": -198.69800000000004,\n        \"max\": 1336.8700000000001,\n        \"num_unique_values\": 68226,\n        \"samples\": [\n          730.5763999999999,\n          109.31200000000001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 12
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#This groups the DataFrame merged_df2 by two columns: Income (USD/Month) and Company. This means that the data will be organized into groups where each unique combination of income level and company is represented.\n",
-        "\n",
-        "income_company_counts = merged_df2.groupby(['Income (USD/Month)', 'Company'])['Company'].count().unstack()\n",
-        "#removes the NaN values\n",
-        "income_company_counts.dropna(inplace = True)\n",
-        "\n",
-        "#Resets index values into original default values like 0, 1, 2\n",
-        "income_company_counts = income_company_counts.reset_index()\n",
-        "\n",
-        "#The figure size of the graph\n",
-        "plt.figure(figsize=(10, 6))\n",
-        "\n",
-        "#This line iterates through all the columns of the income_company_counts DataFrame, starting from the second column (index 1) onward.\n",
-        "#The loop will generate a series of bar charts, one for each company, where each bar represents the count of that company at each income level.\n",
-        "for company in income_company_counts.columns[1:]:\n",
-        "    plt.bar(income_company_counts['Income (USD/Month)'], income_company_counts[company], label=company)\n",
-        "\n",
-        "#x label\n",
-        "plt.xlabel('Income (USD)')\n",
-        "#y label\n",
-        "plt.ylabel('Number of Users')\n",
-        "#Title\n",
-        "plt.title('Number of Users per Income for Pink and Yellow Cabs')\n",
-        "#Rotating the x values to 90 degrees\n",
-        "plt.xticks(rotation=90)\n",
-        "#Providingt he particular colors for values\n",
-        "plt.legend()\n",
-        "#adjust the spacing between subplots to minimize overlap and ensure that all elements are neatly contained within the figure area.\n",
-        "plt.tight_layout()\n",
-        "#output the graph\n",
-        "plt.show()\n",
-        "#downloading the graph image into png\n",
-        "plt.savefig('bar_chart_income_new.png')\n",
-        "\n",
-        "# Hypothesis Result for Bar Chart of Number of Users per Income\n",
-        "\n",
-        "# The bar chart above depicts the relationship between customer income and the usage of Pink and Yellow Cabs.\n",
-        "# It appears that across various income levels, Yellow Cabs tend to have a larger user base compared to Pink Cabs.\n",
-        "# This observation reinforces the potential market share dominance of Yellow Cabs.\n",
-        "# It is noticeable that as income levels increase, the user count for both Pink and Yellow Cabs also tends to increase, though the difference in user counts between the two companies seems consistent across different income brackets.\n",
-        "# If this trend continues, it suggests a strong preference for Yellow Cabs among customers regardless of their income level, potentially translating into greater profitability for the Yellow Cab company.\n",
-        "# This information can assist XYZ firm in making investment decisions, potentially favoring the Yellow Cab company due to its extensive user base across different income segments.\n",
-        "\n",
-        "# EDA Recommendations:\n",
-        "# Further examination of the customer demographic, such as locations."
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 624
-        },
-        "id": "hXTgM55hGHoS",
-        "outputId": "69875007-f601-4bd4-f238-fc819a904d2b"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1000x600 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 0 Axes>"
-            ]
-          },
-          "metadata": {}
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file

From cb54bb4671f24d286858ab68d0a1bcb9296da199 Mon Sep 17 00:00:00 2001
From: An204o <166038499+An204o@users.noreply.github.com>
Date: Tue, 22 Oct 2024 17:38:01 -0400
Subject: [PATCH 12/22] Add files via upload

---
 ..._of_Data_Analysis_Week_2_Project (2).ipynb | 2924 +++++++++++++++++
 1 file changed, 2924 insertions(+)
 create mode 100644 Copy_of_Copy_of_Data_Analysis_Week_2_Project (2).ipynb

diff --git a/Copy_of_Copy_of_Data_Analysis_Week_2_Project (2).ipynb b/Copy_of_Copy_of_Data_Analysis_Week_2_Project (2).ipynb
new file mode 100644
index 00000000..fde047a9
--- /dev/null
+++ b/Copy_of_Copy_of_Data_Analysis_Week_2_Project (2).ipynb	
@@ -0,0 +1,2924 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Loading the csv file for the Cab_Data.csv and checking for its outliers. The boxplot seems to not have any outliers as it is not above the maximum value or below the minimum value."
+      ],
+      "metadata": {
+        "id": "nxz9GK3qZWv2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "#This code loads the dataset by using pandas library\n",
+        "df1 = pd.read_csv('Cab_Data.csv', on_bad_lines='skip')\n",
+        "\n",
+        "#Creating a new column called profit\n",
+        "df1['profit'] = df1['Price Charged'] - df1['Cost of Trip']\n",
+        "\n",
+        "#Using seaborn and drawing boxplot by using the profit column\n",
+        "sns.boxplot(x=df1['profit'])\n",
+        "\n",
+        "#saving the graph in a png form by using savefig()\n",
+        "plt.savefig('boxplot_new.png')\n",
+        "\n",
+        "#Hypothesis:\n",
+        "#In the code above, I loaded the first dataset, which is Cab_Data.csv, and verified the whether the outliers are presentor not by using the boxplot chart. In the boxplot below,\n",
+        "# it seems that there are no outliers as the minimum is -150.38, and maximum is 1327.6219999999998, so there are no values which are less than minimum or above the maximum.\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "id": "zeCKWNYVOkql",
+        "outputId": "99558f26-5f10-4b9e-caf4-2559e332999f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#This shows the first 5 rows of all the columns row values\n",
+        "df1.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "_9QBzdB0dcb-",
+        "outputId": "015153a3-7370-4fc6-cd23-cd0d9654cb2d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Transaction ID  Date of Travel   Company        City  KM Travelled  \\\n",
+              "0        10000011           42377  Pink Cab  ATLANTA GA         30.45   \n",
+              "1        10000012           42375  Pink Cab  ATLANTA GA         28.62   \n",
+              "2        10000013           42371  Pink Cab  ATLANTA GA          9.04   \n",
+              "3        10000014           42376  Pink Cab  ATLANTA GA         33.17   \n",
+              "4        10000015           42372  Pink Cab  ATLANTA GA          8.73   \n",
+              "\n",
+              "   Price Charged  Cost of Trip  profit  \n",
+              "0         370.95       313.635  57.315  \n",
+              "1         358.52       334.854  23.666  \n",
+              "2         125.20        97.632  27.568  \n",
+              "3         377.40       351.602  25.798  \n",
+              "4         114.62        97.776  16.844  "
+            ],
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Transaction ID</th>\n",
+              "      <th>Date of Travel</th>\n",
+              "      <th>Company</th>\n",
+              "      <th>City</th>\n",
+              "      <th>KM Travelled</th>\n",
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+              "      <th>profit</th>\n",
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+              "      <td>10000011</td>\n",
+              "      <td>42377</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>30.45</td>\n",
+              "      <td>370.95</td>\n",
+              "      <td>313.635</td>\n",
+              "      <td>57.315</td>\n",
+              "    </tr>\n",
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+              "      <th>1</th>\n",
+              "      <td>10000012</td>\n",
+              "      <td>42375</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>28.62</td>\n",
+              "      <td>358.52</td>\n",
+              "      <td>334.854</td>\n",
+              "      <td>23.666</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>10000013</td>\n",
+              "      <td>42371</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>9.04</td>\n",
+              "      <td>125.20</td>\n",
+              "      <td>97.632</td>\n",
+              "      <td>27.568</td>\n",
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+              "      <th>3</th>\n",
+              "      <td>10000014</td>\n",
+              "      <td>42376</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>33.17</td>\n",
+              "      <td>377.40</td>\n",
+              "      <td>351.602</td>\n",
+              "      <td>25.798</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>10000015</td>\n",
+              "      <td>42372</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>8.73</td>\n",
+              "      <td>114.62</td>\n",
+              "      <td>97.776</td>\n",
+              "      <td>16.844</td>\n",
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+              "                                                    [key], {});\n",
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+              "\n",
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+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
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+              "\n",
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+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df1"
+            }
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Bar chart for comparing the average profits of both Pink and Yellow Cab Companies"
+      ],
+      "metadata": {
+        "id": "ocbjQ1Svoc4J"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#This code provides the categories for Company column values, which are pink and yellow cabs, and these categories corresponds to the average profits of these two cabs.\n",
+        "average_profit = df1.groupby('Company')['profit'].mean()\n",
+        "\n",
+        "#I used the above series for average profits for both yellow and pink cab, and this code is used to plot the bar chart for average profit comparisons\n",
+        "average_profit.plot(kind='bar', color=['blue', 'red'])\n",
+        "\n",
+        "#x label for the graph\n",
+        "plt.xlabel('Company')\n",
+        "#y label for the graph\n",
+        "plt.ylabel('Profit')\n",
+        "#the title of the graph above\n",
+        "plt.title('Pink and Yellow Cab Companies vs Average Profits')\n",
+        "#saving the image as png\n",
+        "plt.savefig('barplot_comparison_profits.png')\n",
+        "\n",
+        "#EDA Recommendation\n",
+        "# Based on the exploratory data analysis, it is recommended to further investigate the factors contributing to the higher average profits of Yellow Cabs compared to Pink Cabs.\n",
+        "# This includes analyzing variables such as trip duration, fare rates, customer satisfaction, and the operational strategies of both cab companies.\n",
+        "\n",
+        "# Hypothesis Results\n",
+        "# In the bar chart below, I am comparing the average profits for two cab companies, Yellow and Pink Cabs.\n",
+        "# As you can see, the data indicates that the Yellow Cab company has more average profits, with a maximum profit of approximately $158,\n",
+        "# while the Pink Cab has an average profit of about $63. If this hypothesis holds true, it suggests that XYZ firm should consider investing in the Yellow Cab company\n",
+        "# due to its stronger profitability. This analysis supports the notion that Yellow Cabs may provide a more lucrative investment opportunity."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 532
+        },
+        "id": "A-hBShAXUwUw",
+        "outputId": "1c313882-44c2-4ba3-fa30-a0130a283d17"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#This loads the dataset for City.csv\n",
+        "df2 = pd.read_csv('City.csv')\n",
+        "df2.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "9HFXuXAzWAro",
+        "outputId": "79e2dd6a-56cc-4256-e5cd-1461fb68f420"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "             City   Population      Users\n",
+              "0     NEW YORK NY   8,405,837    302,149 \n",
+              "1      CHICAGO IL   1,955,130    164,468 \n",
+              "2  LOS ANGELES CA   1,595,037    144,132 \n",
+              "3        MIAMI FL   1,339,155     17,675 \n",
+              "4  SILICON VALLEY   1,177,609     27,247 "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-4ba8c576-5618-4e5e-a93f-5a15ef622129\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Users</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>NEW YORK NY</td>\n",
+              "      <td>8,405,837</td>\n",
+              "      <td>302,149</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>CHICAGO IL</td>\n",
+              "      <td>1,955,130</td>\n",
+              "      <td>164,468</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>LOS ANGELES CA</td>\n",
+              "      <td>1,595,037</td>\n",
+              "      <td>144,132</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>MIAMI FL</td>\n",
+              "      <td>1,339,155</td>\n",
+              "      <td>17,675</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>SILICON VALLEY</td>\n",
+              "      <td>1,177,609</td>\n",
+              "      <td>27,247</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ba8c576-5618-4e5e-a93f-5a15ef622129')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-4ba8c576-5618-4e5e-a93f-5a15ef622129 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-4ba8c576-5618-4e5e-a93f-5a15ef622129');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-407a8eb6-3504-4fb7-869e-56f573ef9ac5\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-407a8eb6-3504-4fb7-869e-56f573ef9ac5')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-407a8eb6-3504-4fb7-869e-56f573ef9ac5 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df2",
+              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"NEW YORK NY\",\n          \"WASHINGTON DC\",\n          \"SACRAMENTO CA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 8,405,837 \",\n          \" 418,859 \",\n          \" 545,776 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Users\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 302,149 \",\n          \" 127,001 \",\n          \" 7,044 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Stacked bar chart with the number of trips in different cities for both yellow and pink cabs"
+      ],
+      "metadata": {
+        "id": "yDvVWeIVu2RW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "#The series type that provides the categories for both cities and company values and counts the number of companies appears in each city. The unstack() converts it\n",
+        "#into the dataframe when having multi index.\n",
+        "city_company_counts = df1.groupby(['City', 'Company'])['Company'].count().unstack()\n",
+        "\n",
+        "#Then I use the series to plot the bar chart for showing number of trips.\n",
+        "city_company_counts.plot(kind='bar', stacked=True)\n",
+        "\n",
+        "#x label\n",
+        "plt.xlabel('City')\n",
+        "#y label\n",
+        "plt.ylabel('Number of Trips')\n",
+        "#title for the bar chart graph\n",
+        "plt.title('Most Used Cabs (Pink and Yellow) by City')\n",
+        "\n",
+        "#provides the particular color representation for both pink and yellow cab companies\n",
+        "plt.legend(title='Company')\n",
+        "\n",
+        "#saving the image as png\n",
+        "plt.savefig('stacked_bar_city_company.png')\n",
+        "#providing the output of the graph image\n",
+        "plt.show()\n",
+        "\n",
+        "#Hypothesis\n",
+        "# As you can see in the stacked bar chart below, the most used cabs are yellow cabs.\n",
+        "# In most of the countries, the most used cab is yellow. For instance, in the city\n",
+        "# New York, the number of trips for pink cabs is approximately 1,600, while the yellow cab\n",
+        "# has 980,000 trips.\n",
+        "\n",
+        "# Based on the stacked bar chart analysis, it appears that yellow cabs are predominantly used\n",
+        "# over pink cabs in New York, as evidenced by the trip counts.\n",
+        "# This indicates a strong preference for yellow cabs among consumers in that area.\n",
+        "# If these trends hold true across other locations, this may reflect a broader market trend\n",
+        "# that XYZ firm should consider when deciding on cab investments.\n",
+        "\n",
+        "#Recommendations:\n",
+        "#Data Visualizations: I would have made a graph such as linear or pie chart to better show the distribution of the values for this particular goal."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 579
+        },
+        "id": "Tc89YuH-Zcg4",
+        "outputId": "1cd1a65c-62e5-4426-aeaa-1574afd6a9e4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df1.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "Mue7qS3FdC55",
+        "outputId": "4f3a1c8e-4a82-4afa-bf0b-9877e6909c9d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Transaction ID  Date of Travel   Company        City  KM Travelled  \\\n",
+              "0        10000011           42377  Pink Cab  ATLANTA GA         30.45   \n",
+              "1        10000012           42375  Pink Cab  ATLANTA GA         28.62   \n",
+              "2        10000013           42371  Pink Cab  ATLANTA GA          9.04   \n",
+              "3        10000014           42376  Pink Cab  ATLANTA GA         33.17   \n",
+              "4        10000015           42372  Pink Cab  ATLANTA GA          8.73   \n",
+              "\n",
+              "   Price Charged  Cost of Trip  profit  \n",
+              "0         370.95       313.635  57.315  \n",
+              "1         358.52       334.854  23.666  \n",
+              "2         125.20        97.632  27.568  \n",
+              "3         377.40       351.602  25.798  \n",
+              "4         114.62        97.776  16.844  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-0f61529b-5bcc-456c-a8f1-b095116fa7aa\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Transaction ID</th>\n",
+              "      <th>Date of Travel</th>\n",
+              "      <th>Company</th>\n",
+              "      <th>City</th>\n",
+              "      <th>KM Travelled</th>\n",
+              "      <th>Price Charged</th>\n",
+              "      <th>Cost of Trip</th>\n",
+              "      <th>profit</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>10000011</td>\n",
+              "      <td>42377</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>30.45</td>\n",
+              "      <td>370.95</td>\n",
+              "      <td>313.635</td>\n",
+              "      <td>57.315</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>10000012</td>\n",
+              "      <td>42375</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>28.62</td>\n",
+              "      <td>358.52</td>\n",
+              "      <td>334.854</td>\n",
+              "      <td>23.666</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>10000013</td>\n",
+              "      <td>42371</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>9.04</td>\n",
+              "      <td>125.20</td>\n",
+              "      <td>97.632</td>\n",
+              "      <td>27.568</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>10000014</td>\n",
+              "      <td>42376</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>33.17</td>\n",
+              "      <td>377.40</td>\n",
+              "      <td>351.602</td>\n",
+              "      <td>25.798</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>10000015</td>\n",
+              "      <td>42372</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>8.73</td>\n",
+              "      <td>114.62</td>\n",
+              "      <td>97.776</td>\n",
+              "      <td>16.844</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f61529b-5bcc-456c-a8f1-b095116fa7aa')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "\n",
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+              "      background-color: #3B4455;\n",
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+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
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+              "        document.querySelector('#df-0f61529b-5bcc-456c-a8f1-b095116fa7aa button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-0f61529b-5bcc-456c-a8f1-b095116fa7aa');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
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+              "</svg>\n",
+              "  </button>\n",
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+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
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+              "      --disabled-fill-color: #AAA;\n",
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+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
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+              "    border-radius: 50%;\n",
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+              "    height: 32px;\n",
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+              "\n",
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+              "  .colab-df-spinner {\n",
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+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-0ad292d2-411a-4734-ae75-ba6c58245641 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df1"
+            }
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The bar chart that represents the most number of trips for both yellow and pink cabs in the year of 1970"
+      ],
+      "metadata": {
+        "id": "I3xr1iPUyaUy"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "#converting the Date of Travel column in the DataFrame df1 to a datetime format using the pd.to_datetime() function from pandas.\n",
+        "df1['Date of Travel'] = pd.to_datetime(df1['Date of Travel'])\n",
+        "\n",
+        "#choosing the only the values from the dat of travel with the values of 1970 from df1 dataset\n",
+        "df1_1970 = df1[df1['Date of Travel'].dt.year == 1970]\n",
+        "\n",
+        "#The series type that provides the categories for company values and counts the number of companies appears\n",
+        "company_counts_1970 = df1_1970.groupby('Company')['Company'].count()\n",
+        "\n",
+        "#plotting the bar chart by using teh previous series and making each of the column values from the Company color pink and yellow\n",
+        "company_counts_1970.plot(kind='bar', color = ['pink', 'yellow'])\n",
+        "\n",
+        "\n",
+        "#x label\n",
+        "plt.xlabel('Company')\n",
+        "#y label\n",
+        "plt.ylabel('Number of Trips in 1970')\n",
+        "#title for the bar chart graph\n",
+        "plt.title('Most Used Cabs (Pink and Yellow) in 1970')\n",
+        "\n",
+        "#saving the image as png\n",
+        "plt.savefig('bar_chart_1970.png')\n",
+        "\n",
+        "#showing the plot\n",
+        "plt.show()\n",
+        "\n",
+        "# Hypothesis Result for Bar Chart of Most Used Cabs in 1970\n",
+        "\n",
+        "# The bar chart above showcases the number of trips undertaken by Yellow and Pink Cab companies in the year 1970.\n",
+        "# As depicted, Yellow Cabs had significantly more trips compared to Pink Cabs in that year.\n",
+        "# This suggests a higher demand or preference for Yellow Cabs in the market during 1970.\n",
+        "# If this trend holds for other years, it indicates that Yellow Cabs potentially have a stronger market share and possibly higher profitability.\n",
+        "# This information can be useful for XYZ firm in making informed decisions about which cab company to invest in, potentially favoring the Yellow Cab company given their historical dominance.\n",
+        "\n",
+        "#EDA Recommendations:\n",
+        "#We should collect and analyze data on customer demographics (age, gender, income level) to better understand who is using each cab company.\n",
+        "#This could provide insights into targeted marketing strategies."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 532
+        },
+        "id": "kQaKMrE7eRqw",
+        "outputId": "ceedad65-74bd-4261-f1a3-871c62d5c9f6"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df2 = pd.read_csv('City.csv')\n",
+        "df2.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "JPYgpO2sfGkf",
+        "outputId": "b7854f08-5674-4a57-a750-5d236960514f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "             City   Population      Users\n",
+              "0     NEW YORK NY   8,405,837    302,149 \n",
+              "1      CHICAGO IL   1,955,130    164,468 \n",
+              "2  LOS ANGELES CA   1,595,037    144,132 \n",
+              "3        MIAMI FL   1,339,155     17,675 \n",
+              "4  SILICON VALLEY   1,177,609     27,247 "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-7e8d8437-3bda-49c9-9b22-0c73cde27d3b\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Users</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>NEW YORK NY</td>\n",
+              "      <td>8,405,837</td>\n",
+              "      <td>302,149</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>CHICAGO IL</td>\n",
+              "      <td>1,955,130</td>\n",
+              "      <td>164,468</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>LOS ANGELES CA</td>\n",
+              "      <td>1,595,037</td>\n",
+              "      <td>144,132</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>MIAMI FL</td>\n",
+              "      <td>1,339,155</td>\n",
+              "      <td>17,675</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>SILICON VALLEY</td>\n",
+              "      <td>1,177,609</td>\n",
+              "      <td>27,247</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7e8d8437-3bda-49c9-9b22-0c73cde27d3b')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
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+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-7e8d8437-3bda-49c9-9b22-0c73cde27d3b button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-7e8d8437-3bda-49c9-9b22-0c73cde27d3b');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
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+              "            title=\"Suggest charts\"\n",
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+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-8b7768ec-b9a8-4fe4-9a62-0cbd5ac9b727 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df2",
+              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"NEW YORK NY\",\n          \"WASHINGTON DC\",\n          \"SACRAMENTO CA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 8,405,837 \",\n          \" 418,859 \",\n          \" 545,776 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Users\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \" 302,149 \",\n          \" 127,001 \",\n          \" 7,044 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "#The series type that provides the categories for both cities and company values and counts the number of companies appears in each city. The unstack() converts it\n",
+        "#into the dataframe when having multi index.\n",
+        "city_company_counts = df1.groupby(['City', 'Company'])['Company'].count().unstack()\n",
+        "\n",
+        "#replaces the previous index into a default index values like 0, 1, 2, 3, ...\n",
+        "city_company_counts = city_company_counts.reset_index()\n",
+        "\n",
+        "#Renaming the prevous columns\n",
+        "city_company_counts = city_company_counts.rename(\n",
+        "    columns={'Pink Cab': 'Pink Cab Users', 'Yellow Cab': 'Yellow Cab Users'}\n",
+        ")\n",
+        "\n",
+        "#Merging or combining the series values with the df2 dataset\n",
+        "merged_df = pd.merge(df2, city_company_counts, on='City', how='left')\n",
+        "\n",
+        "#replacing the NaN values with 0\n",
+        "merged_df = merged_df.fillna(0)\n",
+        "\n",
+        "#size of the plot\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "#drawing only the line graph for pink cab users\n",
+        "plt.plot(merged_df['City'], merged_df['Pink Cab Users'], label='Pink Cab Users')\n",
+        "#drawing only the line graph for yellow cab users\n",
+        "plt.plot(merged_df['City'], merged_df['Yellow Cab Users'], label='Yellow Cab Users')\n",
+        "#x label\n",
+        "plt.xlabel('City')\n",
+        "#y label\n",
+        "plt.ylabel('Number of Users')\n",
+        "#title of the graph\n",
+        "plt.title('Number of Users per City for Pink and Yellow Cabs')\n",
+        "#rotating the x label values into 90 degrees\n",
+        "plt.xticks(rotation=90)\n",
+        "#provides the particular color representation for both pink and yellow cab companies\n",
+        "plt.legend()\n",
+        "#adjust the spacing between subplots to minimize overlap and ensure that all elements are neatly contained within the figure area.\n",
+        "plt.tight_layout()\n",
+        "#plotting line graph\n",
+        "plt.show()\n",
+        "\n",
+        "# Hypothesis Result for Line Graph of Number of Users per City\n",
+        "\n",
+        "# The line graph above visually depicts the number of users for Pink and Yellow Cabs in different cities.\n",
+        "# It seems that the Yellow Cabs consistently have a larger user base across most cities compared to Pink Cabs.\n",
+        "# This indicates a potential market share dominance for Yellow Cabs.\n",
+        "# Notably, in certain cities, the difference in user numbers is significant, which may reflect a strong preference for Yellow Cabs in those areas.\n",
+        "# If these trends persist, it implies a greater demand for Yellow Cabs, potentially contributing to higher profitability for the company.\n",
+        "# This information can guide XYZ firm's investment decisions, possibly favouring the Yellow Cab company due to its extensive user base.\n",
+        "\n",
+        "#EDA Recommendations:\n",
+        "#I can analyze trends and patterns, and observe the overall trends in user counts for both companies across different cities."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 607
+        },
+        "id": "dSmc_aL3VSKR",
+        "outputId": "7bb7ca04-299e-48d7-da61-442d8144dd6f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#loading dataset called Transaction_ID.csv\n",
+        "df3 = pd.read_csv('Transaction_ID.csv')\n",
+        "df3"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "02ORKDVwZI9H",
+        "outputId": "3597cfb2-8880-4357-bac4-1e00390f96d9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "        Transaction ID  Customer ID Payment_Mode\n",
+              "0             10000011      29290.0         Card\n",
+              "1             10000012      27703.0         Card\n",
+              "2             10000013      28712.0         Cash\n",
+              "3             10000014      28020.0         Cash\n",
+              "4             10000015      27182.0         Card\n",
+              "...                ...          ...          ...\n",
+              "307683        10307694      42426.0         Cash\n",
+              "307684        10307695      42819.0         Cash\n",
+              "307685        10307696      44212.0         Card\n",
+              "307686        10307697      44827.0         Card\n",
+              "307687             103          NaN          NaN\n",
+              "\n",
+              "[307688 rows x 3 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-eb77c9c0-5dc0-428e-8314-d40899ad4ed5\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Transaction ID</th>\n",
+              "      <th>Customer ID</th>\n",
+              "      <th>Payment_Mode</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>10000011</td>\n",
+              "      <td>29290.0</td>\n",
+              "      <td>Card</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>10000012</td>\n",
+              "      <td>27703.0</td>\n",
+              "      <td>Card</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>10000013</td>\n",
+              "      <td>28712.0</td>\n",
+              "      <td>Cash</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>10000014</td>\n",
+              "      <td>28020.0</td>\n",
+              "      <td>Cash</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>10000015</td>\n",
+              "      <td>27182.0</td>\n",
+              "      <td>Card</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>307683</th>\n",
+              "      <td>10307694</td>\n",
+              "      <td>42426.0</td>\n",
+              "      <td>Cash</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>307684</th>\n",
+              "      <td>10307695</td>\n",
+              "      <td>42819.0</td>\n",
+              "      <td>Cash</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>307685</th>\n",
+              "      <td>10307696</td>\n",
+              "      <td>44212.0</td>\n",
+              "      <td>Card</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>307686</th>\n",
+              "      <td>10307697</td>\n",
+              "      <td>44827.0</td>\n",
+              "      <td>Card</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>307687</th>\n",
+              "      <td>103</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>307688 rows × 3 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb77c9c0-5dc0-428e-8314-d40899ad4ed5')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
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+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-eb77c9c0-5dc0-428e-8314-d40899ad4ed5 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-eb77c9c0-5dc0-428e-8314-d40899ad4ed5');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-20fa4755-2c55-41d8-95d1-5d6cce0d3b97\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-20fa4755-2c55-41d8-95d1-5d6cce0d3b97')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-20fa4755-2c55-41d8-95d1-5d6cce0d3b97 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_28a52d13-3169-4eeb-9213-8e435274f7de\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df3')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_28a52d13-3169-4eeb-9213-8e435274f7de button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df3');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df3"
+            }
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Replacing NaN values with 0\n",
+        "df3 = df3.fillna(0)\n",
+        "#Checking the sum of the null values whether there are null values left or not\n",
+        "df3.isnull().sum()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 178
+        },
+        "id": "bbOEMXM0kUdS",
+        "outputId": "653cdb57-f3e8-4173-d6b1-d21ff300a7a0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Transaction ID    0\n",
+              "Customer ID       0\n",
+              "Payment_Mode      0\n",
+              "dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Transaction ID</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Customer ID</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Payment_Mode</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Loadingt the dataset called Customer_ID.csv\n",
+        "df4 = pd.read_csv('Customer_ID.csv')\n",
+        "df4.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "64BsXf3bkToQ",
+        "outputId": "2fe40639-bd6f-4bf6-db74-ee6cf4a24f4e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Customer ID Gender  Age  Income (USD/Month)\n",
+              "0        29290   Male   28               10813\n",
+              "1        27703   Male   27                9237\n",
+              "2        28712   Male   53               11242\n",
+              "3        28020   Male   23               23327\n",
+              "4        27182   Male   33                8536"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-de4edf0e-d992-4c95-a735-95045f372109\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Customer ID</th>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Income (USD/Month)</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>29290</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>28</td>\n",
+              "      <td>10813</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>27703</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>27</td>\n",
+              "      <td>9237</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>28712</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>53</td>\n",
+              "      <td>11242</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>28020</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>23</td>\n",
+              "      <td>23327</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>27182</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>33</td>\n",
+              "      <td>8536</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-de4edf0e-d992-4c95-a735-95045f372109')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-de4edf0e-d992-4c95-a735-95045f372109 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-de4edf0e-d992-4c95-a735-95045f372109');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-0060794c-844e-40e1-b957-d2d55815f585\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0060794c-844e-40e1-b957-d2d55815f585')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-0060794c-844e-40e1-b957-d2d55815f585 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df4",
+              "summary": "{\n  \"name\": \"df4\",\n  \"rows\": 49171,\n  \"fields\": [\n    {\n      \"column\": \"Customer ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17714,\n        \"min\": 1,\n        \"max\": 60000,\n        \"num_unique_values\": 49171,\n        \"samples\": [\n          32451,\n          524,\n          19349\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 18,\n        \"max\": 65,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          32,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income (USD/Month)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8002,\n        \"min\": 2000,\n        \"max\": 35000,\n        \"num_unique_values\": 23341,\n        \"samples\": [\n          12932,\n          16207\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Merging the dataset fro both df3 and df4 by Customer ID feature\n",
+        "merged_df1 = pd.merge(df3, df4, on='Customer ID')\n",
+        "#Merging the df1 to the merged_df1 with df3 and df4 merged datasets with Transaction ID\n",
+        "merged_df2 = merged_df1.merge(df1, on='Transaction ID')\n",
+        "merged_df2.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 348
+        },
+        "id": "v8U48YCOFasv",
+        "outputId": "f9516c30-e25a-4ef3-8a41-ddb6cdf4a0a3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Transaction ID  Customer ID Payment_Mode Gender  Age  Income (USD/Month)  \\\n",
+              "0        10000011      29290.0         Card   Male   28               10813   \n",
+              "1        10000012      27703.0         Card   Male   27                9237   \n",
+              "2        10000013      28712.0         Cash   Male   53               11242   \n",
+              "3        10000014      28020.0         Cash   Male   23               23327   \n",
+              "4        10000015      27182.0         Card   Male   33                8536   \n",
+              "\n",
+              "   Date of Travel   Company        City  KM Travelled  Price Charged  \\\n",
+              "0         42377.0  Pink Cab  ATLANTA GA         30.45         370.95   \n",
+              "1         42375.0  Pink Cab  ATLANTA GA         28.62         358.52   \n",
+              "2         42371.0  Pink Cab  ATLANTA GA          9.04         125.20   \n",
+              "3         42376.0  Pink Cab  ATLANTA GA         33.17         377.40   \n",
+              "4         42372.0  Pink Cab  ATLANTA GA          8.73         114.62   \n",
+              "\n",
+              "   Cost of Trip  profit  \n",
+              "0       313.635  57.315  \n",
+              "1       334.854  23.666  \n",
+              "2        97.632  27.568  \n",
+              "3       351.602  25.798  \n",
+              "4        97.776  16.844  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-977a3625-8670-4406-b627-b1b07f9ce9cf\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Transaction ID</th>\n",
+              "      <th>Customer ID</th>\n",
+              "      <th>Payment_Mode</th>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Income (USD/Month)</th>\n",
+              "      <th>Date of Travel</th>\n",
+              "      <th>Company</th>\n",
+              "      <th>City</th>\n",
+              "      <th>KM Travelled</th>\n",
+              "      <th>Price Charged</th>\n",
+              "      <th>Cost of Trip</th>\n",
+              "      <th>profit</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>10000011</td>\n",
+              "      <td>29290.0</td>\n",
+              "      <td>Card</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>28</td>\n",
+              "      <td>10813</td>\n",
+              "      <td>42377.0</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>30.45</td>\n",
+              "      <td>370.95</td>\n",
+              "      <td>313.635</td>\n",
+              "      <td>57.315</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>10000012</td>\n",
+              "      <td>27703.0</td>\n",
+              "      <td>Card</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>27</td>\n",
+              "      <td>9237</td>\n",
+              "      <td>42375.0</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>28.62</td>\n",
+              "      <td>358.52</td>\n",
+              "      <td>334.854</td>\n",
+              "      <td>23.666</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>10000013</td>\n",
+              "      <td>28712.0</td>\n",
+              "      <td>Cash</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>53</td>\n",
+              "      <td>11242</td>\n",
+              "      <td>42371.0</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>9.04</td>\n",
+              "      <td>125.20</td>\n",
+              "      <td>97.632</td>\n",
+              "      <td>27.568</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>10000014</td>\n",
+              "      <td>28020.0</td>\n",
+              "      <td>Cash</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>23</td>\n",
+              "      <td>23327</td>\n",
+              "      <td>42376.0</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>33.17</td>\n",
+              "      <td>377.40</td>\n",
+              "      <td>351.602</td>\n",
+              "      <td>25.798</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>10000015</td>\n",
+              "      <td>27182.0</td>\n",
+              "      <td>Card</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>33</td>\n",
+              "      <td>8536</td>\n",
+              "      <td>42372.0</td>\n",
+              "      <td>Pink Cab</td>\n",
+              "      <td>ATLANTA GA</td>\n",
+              "      <td>8.73</td>\n",
+              "      <td>114.62</td>\n",
+              "      <td>97.776</td>\n",
+              "      <td>16.844</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-977a3625-8670-4406-b627-b1b07f9ce9cf')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-977a3625-8670-4406-b627-b1b07f9ce9cf button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-977a3625-8670-4406-b627-b1b07f9ce9cf');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-d68e6d85-bbb6-425a-bc1f-ab3e6605fcd0\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d68e6d85-bbb6-425a-bc1f-ab3e6605fcd0')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-d68e6d85-bbb6-425a-bc1f-ab3e6605fcd0 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "merged_df2",
+              "summary": "{\n  \"name\": \"merged_df2\",\n  \"rows\": 71151,\n  \"fields\": [\n    {\n      \"column\": \"Transaction ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25592,\n        \"min\": 10000011,\n        \"max\": 10088433,\n        \"num_unique_values\": 71151,\n        \"samples\": [\n          10047350,\n          10009057,\n          10065991\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21212.115589102177,\n        \"min\": 1.0,\n        \"max\": 60000.0,\n        \"num_unique_values\": 26259,\n        \"samples\": [\n          59647.0,\n          53174.0,\n          24110.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Payment_Mode\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Cash\",\n          \"Card\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 18,\n        \"max\": 65,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          32,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income (USD/Month)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7994,\n        \"min\": 2000,\n        \"max\": 34996,\n        \"num_unique_values\": 16961,\n        \"samples\": [\n          5974,\n          2136\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date of Travel\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 80.38125009707976,\n        \"min\": 42371.0,\n        \"max\": 42661.0,\n        \"num_unique_values\": 291,\n        \"samples\": [\n          42458.0,\n          42631.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Company\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yellow Cab\",\n          \"Pink Cab\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"ATLANTA GA\",\n          \"DENVER CO\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"KM Travelled\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.213689673845838,\n        \"min\": 1.9,\n        \"max\": 48.0,\n        \"num_unique_values\": 874,\n        \"samples\": [\n          4.0,\n          29.43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price Charged\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 288.486993996938,\n        \"min\": 16.38,\n        \"max\": 1978.79,\n        \"num_unique_values\": 49504,\n        \"samples\": [\n          464.18,\n          626.96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cost of Trip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 157.89328330950855,\n        \"min\": 19.2,\n        \"max\": 691.2,\n        \"num_unique_values\": 14297,\n        \"samples\": [\n          434.5704,\n          380.4408\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"profit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 176.18102319112268,\n        \"min\": -198.69800000000004,\n        \"max\": 1336.8700000000001,\n        \"num_unique_values\": 68226,\n        \"samples\": [\n          730.5763999999999,\n          109.31200000000001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#This groups the DataFrame merged_df2 by two columns: Income (USD/Month) and Company. This means that the data will be organized into groups where each unique combination of income level and company is represented.\n",
+        "\n",
+        "income_company_counts = merged_df2.groupby(['Income (USD/Month)', 'Company'])['Company'].count().unstack()\n",
+        "#removes the NaN values\n",
+        "income_company_counts.dropna(inplace = True)\n",
+        "\n",
+        "#Resets index values into original default values like 0, 1, 2\n",
+        "income_company_counts = income_company_counts.reset_index()\n",
+        "\n",
+        "#The figure size of the graph\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "\n",
+        "#This line iterates through all the columns of the income_company_counts DataFrame, starting from the second column (index 1) onward.\n",
+        "#The loop will generate a series of bar charts, one for each company, where each bar represents the count of that company at each income level.\n",
+        "for company in income_company_counts.columns[1:]:\n",
+        "    plt.bar(income_company_counts['Income (USD/Month)'], income_company_counts[company], label=company)\n",
+        "\n",
+        "#x label\n",
+        "plt.xlabel('Income (USD)')\n",
+        "#y label\n",
+        "plt.ylabel('Number of Users')\n",
+        "#Title\n",
+        "plt.title('Number of Users per Income for Pink and Yellow Cabs')\n",
+        "#Rotating the x values to 90 degrees\n",
+        "plt.xticks(rotation=90)\n",
+        "#Providingt he particular colors for values\n",
+        "plt.legend()\n",
+        "#adjust the spacing between subplots to minimize overlap and ensure that all elements are neatly contained within the figure area.\n",
+        "plt.tight_layout()\n",
+        "#output the graph\n",
+        "plt.show()\n",
+        "#downloading the graph image into png\n",
+        "plt.savefig('bar_chart_income_new.png')\n",
+        "\n",
+        "# Hypothesis Result for Bar Chart of Number of Users per Income\n",
+        "\n",
+        "# The bar chart above depicts the relationship between customer income and the usage of Pink and Yellow Cabs.\n",
+        "# It appears that across various income levels, Yellow Cabs tend to have a larger user base compared to Pink Cabs.\n",
+        "# This observation reinforces the potential market share dominance of Yellow Cabs.\n",
+        "# It is noticeable that as income levels increase, the user count for both Pink and Yellow Cabs also tends to increase, though the difference in user counts between the two companies seems consistent across different income brackets.\n",
+        "# If this trend continues, it suggests a strong preference for Yellow Cabs among customers regardless of their income level, potentially translating into greater profitability for the Yellow Cab company.\n",
+        "# This information can assist XYZ firm in making investment decisions, potentially favoring the Yellow Cab company due to its extensive user base across different income segments.\n",
+        "\n",
+        "# EDA Recommendations:\n",
+        "# Further examination of the customer demographic, such as locations."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 624
+        },
+        "id": "hXTgM55hGHoS",
+        "outputId": "69875007-f601-4bd4-f238-fc819a904d2b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 0 Axes>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

From 9c770490dab1ba3d5e0dedec4db1087f1311b38c Mon Sep 17 00:00:00 2001
From: An204o <166038499+An204o@users.noreply.github.com>
Date: Sun, 27 Oct 2024 23:01:02 -0400
Subject: [PATCH 13/22] Add files via upload

---
 Copy_of_Deployment_on_Flask_Part_1.ipynb | 877 +++++++++++++++++++++++
 1 file changed, 877 insertions(+)
 create mode 100644 Copy_of_Deployment_on_Flask_Part_1.ipynb

diff --git a/Copy_of_Deployment_on_Flask_Part_1.ipynb b/Copy_of_Deployment_on_Flask_Part_1.ipynb
new file mode 100644
index 00000000..741db2cb
--- /dev/null
+++ b/Copy_of_Deployment_on_Flask_Part_1.ipynb
@@ -0,0 +1,877 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "5yU3oebRmxaj",
+        "outputId": "540d1148-f7d3-4ed7-e82a-576c261fd16d"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
+              "0            6      148             72             35        0  33.6   \n",
+              "1            1       85             66             29        0  26.6   \n",
+              "2            8      183             64              0        0  23.3   \n",
+              "3            1       89             66             23       94  28.1   \n",
+              "4            0      137             40             35      168  43.1   \n",
+              "\n",
+              "   DiabetesPedigreeFunction  Age  Outcome  \n",
+              "0                     0.627   50        1  \n",
+              "1                     0.351   31        0  \n",
+              "2                     0.672   32        1  \n",
+              "3                     0.167   21        0  \n",
+              "4                     2.288   33        1  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-4ebeda0c-77ad-4ab6-b913-578feb0da6b9\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Pregnancies</th>\n",
+              "      <th>Glucose</th>\n",
+              "      <th>BloodPressure</th>\n",
+              "      <th>SkinThickness</th>\n",
+              "      <th>Insulin</th>\n",
+              "      <th>BMI</th>\n",
+              "      <th>DiabetesPedigreeFunction</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Outcome</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>6</td>\n",
+              "      <td>148</td>\n",
+              "      <td>72</td>\n",
+              "      <td>35</td>\n",
+              "      <td>0</td>\n",
+              "      <td>33.6</td>\n",
+              "      <td>0.627</td>\n",
+              "      <td>50</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>85</td>\n",
+              "      <td>66</td>\n",
+              "      <td>29</td>\n",
+              "      <td>0</td>\n",
+              "      <td>26.6</td>\n",
+              "      <td>0.351</td>\n",
+              "      <td>31</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>8</td>\n",
+              "      <td>183</td>\n",
+              "      <td>64</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>23.3</td>\n",
+              "      <td>0.672</td>\n",
+              "      <td>32</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1</td>\n",
+              "      <td>89</td>\n",
+              "      <td>66</td>\n",
+              "      <td>23</td>\n",
+              "      <td>94</td>\n",
+              "      <td>28.1</td>\n",
+              "      <td>0.167</td>\n",
+              "      <td>21</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0</td>\n",
+              "      <td>137</td>\n",
+              "      <td>40</td>\n",
+              "      <td>35</td>\n",
+              "      <td>168</td>\n",
+              "      <td>43.1</td>\n",
+              "      <td>2.288</td>\n",
+              "      <td>33</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
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+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ebeda0c-77ad-4ab6-b913-578feb0da6b9')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-4ebeda0c-77ad-4ab6-b913-578feb0da6b9 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-4ebeda0c-77ad-4ab6-b913-578feb0da6b9');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-8444480f-ec01-420e-a811-3a35762773e9\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8444480f-ec01-420e-a811-3a35762773e9')\"\n",
+              "            title=\"Suggest charts\"\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-8444480f-ec01-420e-a811-3a35762773e9 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 768,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          6,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 0,\n        \"max\": 199,\n        \"num_unique_values\": 136,\n        \"samples\": [\n          151,\n          101,\n          112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 122,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          86,\n          46,\n          85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 0,\n        \"max\": 99,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          7,\n          12,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115,\n        \"min\": 0,\n        \"max\": 846,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          52,\n          41,\n          183\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.8841603203754405,\n        \"min\": 0.0,\n        \"max\": 67.1,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          19.9,\n          31.0,\n          38.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.33132859501277484,\n        \"min\": 0.078,\n        \"max\": 2.42,\n        \"num_unique_values\": 517,\n        \"samples\": [\n          1.731,\n          0.426,\n          0.138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 21,\n        \"max\": 81,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          60,\n          47,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 1
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "from sklearn.metrics import accuracy_score\n",
+        "import pickle\n",
+        "\n",
+        "#Load our dataset\n",
+        "\n",
+        "df = pd.read_csv('diabete.csv')\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Using y variable as Outcome class labels with 0 and 1,a dn X is our input feature which will drop only Outcome column\n",
+        "X = df.drop('Outcome', axis=1)\n",
+        "y = df['Outcome']"
+      ],
+      "metadata": {
+        "id": "zxSgNcnYsM7S"
+      },
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Splitting the data X and Y into train and test set\n",
+        "\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
+      ],
+      "metadata": {
+        "id": "bUWTZ2OSstAQ"
+      },
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Reducing the std deviation\n",
+        "\n",
+        "scaler = StandardScaler()\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)"
+      ],
+      "metadata": {
+        "id": "ClmXmmcD2zd1"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Training 100 models using Random Classifier\n",
+        "\n",
+        "clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
+        "clf.fit(X_train_scaled, y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "id": "ca1Y27RI3QrZ",
+        "outputId": "dee93547-1e2c-4461-be5a-182487b97551"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "RandomForestClassifier(random_state=42)"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-1 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-1 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-1 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-1 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Making pickle file of our model\n",
+        "pickle.dump(clf, open('model.pkl', 'wb'))"
+      ],
+      "metadata": {
+        "id": "1y0OtxOm9O3v"
+      },
+      "execution_count": 6,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "s1nyFZThAA4_"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file

From 6be7416b547c42c4e579e0d6724236567e39c1c7 Mon Sep 17 00:00:00 2001
From: An204o <166038499+An204o@users.noreply.github.com>
Date: Sun, 27 Oct 2024 23:01:45 -0400
Subject: [PATCH 14/22] Add files via upload

---
 ...Deployment_on_Flask_(Part_2)_Web_App.ipynb | 132 ++++++++++++++++++
 1 file changed, 132 insertions(+)
 create mode 100644 Copy_of_Deployment_on_Flask_(Part_2)_Web_App.ipynb

diff --git a/Copy_of_Deployment_on_Flask_(Part_2)_Web_App.ipynb b/Copy_of_Deployment_on_Flask_(Part_2)_Web_App.ipynb
new file mode 100644
index 00000000..2524628c
--- /dev/null
+++ b/Copy_of_Deployment_on_Flask_(Part_2)_Web_App.ipynb
@@ -0,0 +1,132 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "UQGW_KMH_UkC"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "from flask import Flask, request, jsonify, render_template\n",
+        "import pickle\n",
+        "import joblib"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Loading the model.pkl from the file for reading the file's content\n",
+        "app = Flask(__name__)\n",
+        "model = joblib.load(open('model.pkl', 'rb'))\n"
+      ],
+      "metadata": {
+        "id": "EPnfAz1H_rty"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Creating the home route\n",
+        "@app.route('/')\n",
+        "def home():\n",
+        "    return render_template('index.html')"
+      ],
+      "metadata": {
+        "id": "SFRDm-mv_36R"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "source": [
+        "#Loading the model.pkl from the file for reading the file's content\n",
+        "app = Flask(__name__)\n",
+        "model = joblib.load(open('model.pkl', 'rb'))\n",
+        "\n",
+        "#Creating the home route\n",
+        "@app.route('/')\n",
+        "def home():\n",
+        "    return render_template('index.html')\n",
+        "\n",
+        "#Creating the predict route\n",
+        "@app.route('/predict', methods=['POST'])\n",
+        "def predict():\n",
+        "    int_features = [int(x) for x in request.form.values()]\n",
+        "    final_features = [np.array(int_features)]\n",
+        "    prediction = model.predict(final_features)\n",
+        "\n",
+        "    output = round(prediction[0], 2)\n",
+        "\n",
+        "    return render_template('index.html', prediction_text='Employee Salary should be $ {}'.format(output))"
+      ],
+      "cell_type": "code",
+      "metadata": {
+        "id": "tuRz-qy0BmIK"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "if __name__ == \"__main__\":\n",
+        "    app.run(debug=True)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kJ0rgZWsAjFn",
+        "outputId": "6507f9a1-bbaf-4a3e-fd60-2303daf73b88"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " * Serving Flask app '__main__'\n",
+            " * Debug mode: on\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "INFO:werkzeug:\u001b[31m\u001b[1mWARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.\u001b[0m\n",
+            " * Running on http://127.0.0.1:5000\n",
+            "INFO:werkzeug:\u001b[33mPress CTRL+C to quit\u001b[0m\n",
+            "INFO:werkzeug: * Restarting with stat\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "rcY1QTy1BRjL"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file

From 994b201e56589045732220c6439f2ad8b5c26e45 Mon Sep 17 00:00:00 2001
From: An204o <166038499+An204o@users.noreply.github.com>
Date: Sun, 27 Oct 2024 23:07:35 -0400
Subject: [PATCH 15/22] Create Deployment on Flask Part 1

---
 Deployment Flask Folder/Deployment on Flask Part 1 | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 Deployment Flask Folder/Deployment on Flask Part 1

diff --git a/Deployment Flask Folder/Deployment on Flask Part 1 b/Deployment Flask Folder/Deployment on Flask Part 1
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/Deployment Flask Folder/Deployment on Flask Part 1	
@@ -0,0 +1 @@
+

From dda046ab62368bd489f22d9cbe86aa394c822c5a Mon Sep 17 00:00:00 2001
From: An204o <166038499+An204o@users.noreply.github.com>
Date: Sun, 27 Oct 2024 23:08:50 -0400
Subject: [PATCH 16/22] Delete Deployment Flask Folder/Deployment on Flask Part
 1

---
 Deployment Flask Folder/Deployment on Flask Part 1 | 1 -
 1 file changed, 1 deletion(-)
 delete mode 100644 Deployment Flask Folder/Deployment on Flask Part 1

diff --git a/Deployment Flask Folder/Deployment on Flask Part 1 b/Deployment Flask Folder/Deployment on Flask Part 1
deleted file mode 100644
index 8b137891..00000000
--- a/Deployment Flask Folder/Deployment on Flask Part 1	
+++ /dev/null
@@ -1 +0,0 @@
-

From 84ce873c54b7e82e0619c3cffc4c5910d2013c78 Mon Sep 17 00:00:00 2001
From: An204o <166038499+An204o@users.noreply.github.com>
Date: Sun, 27 Oct 2024 23:11:31 -0400
Subject: [PATCH 17/22] Delete Copy_of_Deployment_on_Flask_Part_1.ipynb

---
 Copy_of_Deployment_on_Flask_Part_1.ipynb | 877 -----------------------
 1 file changed, 877 deletions(-)
 delete mode 100644 Copy_of_Deployment_on_Flask_Part_1.ipynb

diff --git a/Copy_of_Deployment_on_Flask_Part_1.ipynb b/Copy_of_Deployment_on_Flask_Part_1.ipynb
deleted file mode 100644
index 741db2cb..00000000
--- a/Copy_of_Deployment_on_Flask_Part_1.ipynb
+++ /dev/null
@@ -1,877 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "5yU3oebRmxaj",
-        "outputId": "540d1148-f7d3-4ed7-e82a-576c261fd16d"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
-              "0            6      148             72             35        0  33.6   \n",
-              "1            1       85             66             29        0  26.6   \n",
-              "2            8      183             64              0        0  23.3   \n",
-              "3            1       89             66             23       94  28.1   \n",
-              "4            0      137             40             35      168  43.1   \n",
-              "\n",
-              "   DiabetesPedigreeFunction  Age  Outcome  \n",
-              "0                     0.627   50        1  \n",
-              "1                     0.351   31        0  \n",
-              "2                     0.672   32        1  \n",
-              "3                     0.167   21        0  \n",
-              "4                     2.288   33        1  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-4ebeda0c-77ad-4ab6-b913-578feb0da6b9\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Pregnancies</th>\n",
-              "      <th>Glucose</th>\n",
-              "      <th>BloodPressure</th>\n",
-              "      <th>SkinThickness</th>\n",
-              "      <th>Insulin</th>\n",
-              "      <th>BMI</th>\n",
-              "      <th>DiabetesPedigreeFunction</th>\n",
-              "      <th>Age</th>\n",
-              "      <th>Outcome</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>6</td>\n",
-              "      <td>148</td>\n",
-              "      <td>72</td>\n",
-              "      <td>35</td>\n",
-              "      <td>0</td>\n",
-              "      <td>33.6</td>\n",
-              "      <td>0.627</td>\n",
-              "      <td>50</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>1</td>\n",
-              "      <td>85</td>\n",
-              "      <td>66</td>\n",
-              "      <td>29</td>\n",
-              "      <td>0</td>\n",
-              "      <td>26.6</td>\n",
-              "      <td>0.351</td>\n",
-              "      <td>31</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>8</td>\n",
-              "      <td>183</td>\n",
-              "      <td>64</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>23.3</td>\n",
-              "      <td>0.672</td>\n",
-              "      <td>32</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>1</td>\n",
-              "      <td>89</td>\n",
-              "      <td>66</td>\n",
-              "      <td>23</td>\n",
-              "      <td>94</td>\n",
-              "      <td>28.1</td>\n",
-              "      <td>0.167</td>\n",
-              "      <td>21</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>0</td>\n",
-              "      <td>137</td>\n",
-              "      <td>40</td>\n",
-              "      <td>35</td>\n",
-              "      <td>168</td>\n",
-              "      <td>43.1</td>\n",
-              "      <td>2.288</td>\n",
-              "      <td>33</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ebeda0c-77ad-4ab6-b913-578feb0da6b9')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-4ebeda0c-77ad-4ab6-b913-578feb0da6b9 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-4ebeda0c-77ad-4ab6-b913-578feb0da6b9');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-8444480f-ec01-420e-a811-3a35762773e9\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8444480f-ec01-420e-a811-3a35762773e9')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-8444480f-ec01-420e-a811-3a35762773e9 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df",
-              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 768,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          6,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 0,\n        \"max\": 199,\n        \"num_unique_values\": 136,\n        \"samples\": [\n          151,\n          101,\n          112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 122,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          86,\n          46,\n          85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 0,\n        \"max\": 99,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          7,\n          12,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115,\n        \"min\": 0,\n        \"max\": 846,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          52,\n          41,\n          183\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.8841603203754405,\n        \"min\": 0.0,\n        \"max\": 67.1,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          19.9,\n          31.0,\n          38.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.33132859501277484,\n        \"min\": 0.078,\n        \"max\": 2.42,\n        \"num_unique_values\": 517,\n        \"samples\": [\n          1.731,\n          0.426,\n          0.138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 21,\n        \"max\": 81,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          60,\n          47,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 1
-        }
-      ],
-      "source": [
-        "import pandas as pd\n",
-        "from sklearn.preprocessing import StandardScaler\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "from sklearn.ensemble import RandomForestClassifier\n",
-        "from sklearn.metrics import accuracy_score\n",
-        "import pickle\n",
-        "\n",
-        "#Load our dataset\n",
-        "\n",
-        "df = pd.read_csv('diabete.csv')\n",
-        "df.head()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Using y variable as Outcome class labels with 0 and 1,a dn X is our input feature which will drop only Outcome column\n",
-        "X = df.drop('Outcome', axis=1)\n",
-        "y = df['Outcome']"
-      ],
-      "metadata": {
-        "id": "zxSgNcnYsM7S"
-      },
-      "execution_count": 2,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Splitting the data X and Y into train and test set\n",
-        "\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
-      ],
-      "metadata": {
-        "id": "bUWTZ2OSstAQ"
-      },
-      "execution_count": 3,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Reducing the std deviation\n",
-        "\n",
-        "scaler = StandardScaler()\n",
-        "X_train_scaled = scaler.fit_transform(X_train)\n",
-        "X_test_scaled = scaler.transform(X_test)"
-      ],
-      "metadata": {
-        "id": "ClmXmmcD2zd1"
-      },
-      "execution_count": 4,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Training 100 models using Random Classifier\n",
-        "\n",
-        "clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
-        "clf.fit(X_train_scaled, y_train)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 80
-        },
-        "id": "ca1Y27RI3QrZ",
-        "outputId": "dee93547-1e2c-4461-be5a-182487b97551"
-      },
-      "execution_count": 5,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "RandomForestClassifier(random_state=42)"
-            ],
-            "text/html": [
-              "<style>#sk-container-id-1 {\n",
-              "  /* Definition of color scheme common for light and dark mode */\n",
-              "  --sklearn-color-text: black;\n",
-              "  --sklearn-color-line: gray;\n",
-              "  /* Definition of color scheme for unfitted estimators */\n",
-              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-              "  --sklearn-color-unfitted-level-3: chocolate;\n",
-              "  /* Definition of color scheme for fitted estimators */\n",
-              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-              "\n",
-              "  /* Specific color for light theme */\n",
-              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-              "  --sklearn-color-icon: #696969;\n",
-              "\n",
-              "  @media (prefers-color-scheme: dark) {\n",
-              "    /* Redefinition of color scheme for dark theme */\n",
-              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-              "    --sklearn-color-icon: #878787;\n",
-              "  }\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 {\n",
-              "  color: var(--sklearn-color-text);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 pre {\n",
-              "  padding: 0;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 input.sk-hidden--visually {\n",
-              "  border: 0;\n",
-              "  clip: rect(1px 1px 1px 1px);\n",
-              "  clip: rect(1px, 1px, 1px, 1px);\n",
-              "  height: 1px;\n",
-              "  margin: -1px;\n",
-              "  overflow: hidden;\n",
-              "  padding: 0;\n",
-              "  position: absolute;\n",
-              "  width: 1px;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
-              "  border: 1px dashed var(--sklearn-color-line);\n",
-              "  margin: 0 0.4em 0.5em 0.4em;\n",
-              "  box-sizing: border-box;\n",
-              "  padding-bottom: 0.4em;\n",
-              "  background-color: var(--sklearn-color-background);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-container {\n",
-              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-              "     so we also need the `!important` here to be able to override the\n",
-              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-              "  display: inline-block !important;\n",
-              "  position: relative;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
-              "  display: none;\n",
-              "}\n",
-              "\n",
-              "div.sk-parallel-item,\n",
-              "div.sk-serial,\n",
-              "div.sk-item {\n",
-              "  /* draw centered vertical line to link estimators */\n",
-              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-              "  background-size: 2px 100%;\n",
-              "  background-repeat: no-repeat;\n",
-              "  background-position: center center;\n",
-              "}\n",
-              "\n",
-              "/* Parallel-specific style estimator block */\n",
-              "\n",
-              "#sk-container-id-1 div.sk-parallel-item::after {\n",
-              "  content: \"\";\n",
-              "  width: 100%;\n",
-              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-              "  flex-grow: 1;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-parallel {\n",
-              "  display: flex;\n",
-              "  align-items: stretch;\n",
-              "  justify-content: center;\n",
-              "  background-color: var(--sklearn-color-background);\n",
-              "  position: relative;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-parallel-item {\n",
-              "  display: flex;\n",
-              "  flex-direction: column;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
-              "  align-self: flex-end;\n",
-              "  width: 50%;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
-              "  align-self: flex-start;\n",
-              "  width: 50%;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
-              "  width: 0;\n",
-              "}\n",
-              "\n",
-              "/* Serial-specific style estimator block */\n",
-              "\n",
-              "#sk-container-id-1 div.sk-serial {\n",
-              "  display: flex;\n",
-              "  flex-direction: column;\n",
-              "  align-items: center;\n",
-              "  background-color: var(--sklearn-color-background);\n",
-              "  padding-right: 1em;\n",
-              "  padding-left: 1em;\n",
-              "}\n",
-              "\n",
-              "\n",
-              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-              "clickable and can be expanded/collapsed.\n",
-              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-              "*/\n",
-              "\n",
-              "/* Pipeline and ColumnTransformer style (default) */\n",
-              "\n",
-              "#sk-container-id-1 div.sk-toggleable {\n",
-              "  /* Default theme specific background. It is overwritten whether we have a\n",
-              "  specific estimator or a Pipeline/ColumnTransformer */\n",
-              "  background-color: var(--sklearn-color-background);\n",
-              "}\n",
-              "\n",
-              "/* Toggleable label */\n",
-              "#sk-container-id-1 label.sk-toggleable__label {\n",
-              "  cursor: pointer;\n",
-              "  display: block;\n",
-              "  width: 100%;\n",
-              "  margin-bottom: 0;\n",
-              "  padding: 0.5em;\n",
-              "  box-sizing: border-box;\n",
-              "  text-align: center;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
-              "  /* Arrow on the left of the label */\n",
-              "  content: \"▸\";\n",
-              "  float: left;\n",
-              "  margin-right: 0.25em;\n",
-              "  color: var(--sklearn-color-icon);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
-              "  color: var(--sklearn-color-text);\n",
-              "}\n",
-              "\n",
-              "/* Toggleable content - dropdown */\n",
-              "\n",
-              "#sk-container-id-1 div.sk-toggleable__content {\n",
-              "  max-height: 0;\n",
-              "  max-width: 0;\n",
-              "  overflow: hidden;\n",
-              "  text-align: left;\n",
-              "  /* unfitted */\n",
-              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
-              "  /* fitted */\n",
-              "  background-color: var(--sklearn-color-fitted-level-0);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
-              "  margin: 0.2em;\n",
-              "  border-radius: 0.25em;\n",
-              "  color: var(--sklearn-color-text);\n",
-              "  /* unfitted */\n",
-              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
-              "  /* unfitted */\n",
-              "  background-color: var(--sklearn-color-fitted-level-0);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-              "  /* Expand drop-down */\n",
-              "  max-height: 200px;\n",
-              "  max-width: 100%;\n",
-              "  overflow: auto;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-              "  content: \"▾\";\n",
-              "}\n",
-              "\n",
-              "/* Pipeline/ColumnTransformer-specific style */\n",
-              "\n",
-              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-              "  color: var(--sklearn-color-text);\n",
-              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-              "  background-color: var(--sklearn-color-fitted-level-2);\n",
-              "}\n",
-              "\n",
-              "/* Estimator-specific style */\n",
-              "\n",
-              "/* Colorize estimator box */\n",
-              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-              "  /* unfitted */\n",
-              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-              "  /* fitted */\n",
-              "  background-color: var(--sklearn-color-fitted-level-2);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-              "#sk-container-id-1 div.sk-label label {\n",
-              "  /* The background is the default theme color */\n",
-              "  color: var(--sklearn-color-text-on-default-background);\n",
-              "}\n",
-              "\n",
-              "/* On hover, darken the color of the background */\n",
-              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
-              "  color: var(--sklearn-color-text);\n",
-              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-              "}\n",
-              "\n",
-              "/* Label box, darken color on hover, fitted */\n",
-              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-              "  color: var(--sklearn-color-text);\n",
-              "  background-color: var(--sklearn-color-fitted-level-2);\n",
-              "}\n",
-              "\n",
-              "/* Estimator label */\n",
-              "\n",
-              "#sk-container-id-1 div.sk-label label {\n",
-              "  font-family: monospace;\n",
-              "  font-weight: bold;\n",
-              "  display: inline-block;\n",
-              "  line-height: 1.2em;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-label-container {\n",
-              "  text-align: center;\n",
-              "}\n",
-              "\n",
-              "/* Estimator-specific */\n",
-              "#sk-container-id-1 div.sk-estimator {\n",
-              "  font-family: monospace;\n",
-              "  border: 1px dotted var(--sklearn-color-border-box);\n",
-              "  border-radius: 0.25em;\n",
-              "  box-sizing: border-box;\n",
-              "  margin-bottom: 0.5em;\n",
-              "  /* unfitted */\n",
-              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-estimator.fitted {\n",
-              "  /* fitted */\n",
-              "  background-color: var(--sklearn-color-fitted-level-0);\n",
-              "}\n",
-              "\n",
-              "/* on hover */\n",
-              "#sk-container-id-1 div.sk-estimator:hover {\n",
-              "  /* unfitted */\n",
-              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
-              "  /* fitted */\n",
-              "  background-color: var(--sklearn-color-fitted-level-2);\n",
-              "}\n",
-              "\n",
-              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-              "\n",
-              "/* Common style for \"i\" and \"?\" */\n",
-              "\n",
-              ".sk-estimator-doc-link,\n",
-              "a:link.sk-estimator-doc-link,\n",
-              "a:visited.sk-estimator-doc-link {\n",
-              "  float: right;\n",
-              "  font-size: smaller;\n",
-              "  line-height: 1em;\n",
-              "  font-family: monospace;\n",
-              "  background-color: var(--sklearn-color-background);\n",
-              "  border-radius: 1em;\n",
-              "  height: 1em;\n",
-              "  width: 1em;\n",
-              "  text-decoration: none !important;\n",
-              "  margin-left: 1ex;\n",
-              "  /* unfitted */\n",
-              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-              "  color: var(--sklearn-color-unfitted-level-1);\n",
-              "}\n",
-              "\n",
-              ".sk-estimator-doc-link.fitted,\n",
-              "a:link.sk-estimator-doc-link.fitted,\n",
-              "a:visited.sk-estimator-doc-link.fitted {\n",
-              "  /* fitted */\n",
-              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-              "  color: var(--sklearn-color-fitted-level-1);\n",
-              "}\n",
-              "\n",
-              "/* On hover */\n",
-              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-              ".sk-estimator-doc-link:hover,\n",
-              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-              ".sk-estimator-doc-link:hover {\n",
-              "  /* unfitted */\n",
-              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-              "  color: var(--sklearn-color-background);\n",
-              "  text-decoration: none;\n",
-              "}\n",
-              "\n",
-              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-              ".sk-estimator-doc-link.fitted:hover,\n",
-              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-              ".sk-estimator-doc-link.fitted:hover {\n",
-              "  /* fitted */\n",
-              "  background-color: var(--sklearn-color-fitted-level-3);\n",
-              "  color: var(--sklearn-color-background);\n",
-              "  text-decoration: none;\n",
-              "}\n",
-              "\n",
-              "/* Span, style for the box shown on hovering the info icon */\n",
-              ".sk-estimator-doc-link span {\n",
-              "  display: none;\n",
-              "  z-index: 9999;\n",
-              "  position: relative;\n",
-              "  font-weight: normal;\n",
-              "  right: .2ex;\n",
-              "  padding: .5ex;\n",
-              "  margin: .5ex;\n",
-              "  width: min-content;\n",
-              "  min-width: 20ex;\n",
-              "  max-width: 50ex;\n",
-              "  color: var(--sklearn-color-text);\n",
-              "  box-shadow: 2pt 2pt 4pt #999;\n",
-              "  /* unfitted */\n",
-              "  background: var(--sklearn-color-unfitted-level-0);\n",
-              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-              "}\n",
-              "\n",
-              ".sk-estimator-doc-link.fitted span {\n",
-              "  /* fitted */\n",
-              "  background: var(--sklearn-color-fitted-level-0);\n",
-              "  border: var(--sklearn-color-fitted-level-3);\n",
-              "}\n",
-              "\n",
-              ".sk-estimator-doc-link:hover span {\n",
-              "  display: block;\n",
-              "}\n",
-              "\n",
-              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-              "\n",
-              "#sk-container-id-1 a.estimator_doc_link {\n",
-              "  float: right;\n",
-              "  font-size: 1rem;\n",
-              "  line-height: 1em;\n",
-              "  font-family: monospace;\n",
-              "  background-color: var(--sklearn-color-background);\n",
-              "  border-radius: 1rem;\n",
-              "  height: 1rem;\n",
-              "  width: 1rem;\n",
-              "  text-decoration: none;\n",
-              "  /* unfitted */\n",
-              "  color: var(--sklearn-color-unfitted-level-1);\n",
-              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
-              "  /* fitted */\n",
-              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-              "  color: var(--sklearn-color-fitted-level-1);\n",
-              "}\n",
-              "\n",
-              "/* On hover */\n",
-              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
-              "  /* unfitted */\n",
-              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-              "  color: var(--sklearn-color-background);\n",
-              "  text-decoration: none;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
-              "  /* fitted */\n",
-              "  background-color: var(--sklearn-color-fitted-level-3);\n",
-              "}\n",
-              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 5
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Making pickle file of our model\n",
-        "pickle.dump(clf, open('model.pkl', 'wb'))"
-      ],
-      "metadata": {
-        "id": "1y0OtxOm9O3v"
-      },
-      "execution_count": 6,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [],
-      "metadata": {
-        "id": "s1nyFZThAA4_"
-      },
-      "execution_count": null,
-      "outputs": []
-    }
-  ]
-}
\ No newline at end of file

From cc577199925a6f12362b64adc9e49b0b99d6fb90 Mon Sep 17 00:00:00 2001
From: An204o <166038499+An204o@users.noreply.github.com>
Date: Sun, 27 Oct 2024 23:11:43 -0400
Subject: [PATCH 18/22] Delete
 Copy_of_Deployment_on_Flask_(Part_2)_Web_App.ipynb

---
 ...Deployment_on_Flask_(Part_2)_Web_App.ipynb | 132 ------------------
 1 file changed, 132 deletions(-)
 delete mode 100644 Copy_of_Deployment_on_Flask_(Part_2)_Web_App.ipynb

diff --git a/Copy_of_Deployment_on_Flask_(Part_2)_Web_App.ipynb b/Copy_of_Deployment_on_Flask_(Part_2)_Web_App.ipynb
deleted file mode 100644
index 2524628c..00000000
--- a/Copy_of_Deployment_on_Flask_(Part_2)_Web_App.ipynb
+++ /dev/null
@@ -1,132 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "UQGW_KMH_UkC"
-      },
-      "outputs": [],
-      "source": [
-        "import numpy as np\n",
-        "from flask import Flask, request, jsonify, render_template\n",
-        "import pickle\n",
-        "import joblib"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Loading the model.pkl from the file for reading the file's content\n",
-        "app = Flask(__name__)\n",
-        "model = joblib.load(open('model.pkl', 'rb'))\n"
-      ],
-      "metadata": {
-        "id": "EPnfAz1H_rty"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Creating the home route\n",
-        "@app.route('/')\n",
-        "def home():\n",
-        "    return render_template('index.html')"
-      ],
-      "metadata": {
-        "id": "SFRDm-mv_36R"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "source": [
-        "#Loading the model.pkl from the file for reading the file's content\n",
-        "app = Flask(__name__)\n",
-        "model = joblib.load(open('model.pkl', 'rb'))\n",
-        "\n",
-        "#Creating the home route\n",
-        "@app.route('/')\n",
-        "def home():\n",
-        "    return render_template('index.html')\n",
-        "\n",
-        "#Creating the predict route\n",
-        "@app.route('/predict', methods=['POST'])\n",
-        "def predict():\n",
-        "    int_features = [int(x) for x in request.form.values()]\n",
-        "    final_features = [np.array(int_features)]\n",
-        "    prediction = model.predict(final_features)\n",
-        "\n",
-        "    output = round(prediction[0], 2)\n",
-        "\n",
-        "    return render_template('index.html', prediction_text='Employee Salary should be $ {}'.format(output))"
-      ],
-      "cell_type": "code",
-      "metadata": {
-        "id": "tuRz-qy0BmIK"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "if __name__ == \"__main__\":\n",
-        "    app.run(debug=True)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "kJ0rgZWsAjFn",
-        "outputId": "6507f9a1-bbaf-4a3e-fd60-2303daf73b88"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            " * Serving Flask app '__main__'\n",
-            " * Debug mode: on\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "INFO:werkzeug:\u001b[31m\u001b[1mWARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.\u001b[0m\n",
-            " * Running on http://127.0.0.1:5000\n",
-            "INFO:werkzeug:\u001b[33mPress CTRL+C to quit\u001b[0m\n",
-            "INFO:werkzeug: * Restarting with stat\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [],
-      "metadata": {
-        "id": "rcY1QTy1BRjL"
-      },
-      "execution_count": null,
-      "outputs": []
-    }
-  ]
-}
\ No newline at end of file

From a6285d0e6435df06fd403bcf75a0c208191f1f77 Mon Sep 17 00:00:00 2001
From: An204o <166038499+An204o@users.noreply.github.com>
Date: Sun, 27 Oct 2024 23:13:07 -0400
Subject: [PATCH 19/22] Add files via upload

---
 ...Deployment_on_Flask_(Part_2)_Web_App.ipynb | 132 +++
 Copy_of_Deployment_on_Flask_Part_1.ipynb      | 877 ++++++++++++++++++
 diabete.csv                                   | 769 +++++++++++++++
 model.pkl                                     | Bin 0 -> 1701241 bytes
 4 files changed, 1778 insertions(+)
 create mode 100644 Copy_of_Deployment_on_Flask_(Part_2)_Web_App.ipynb
 create mode 100644 Copy_of_Deployment_on_Flask_Part_1.ipynb
 create mode 100644 diabete.csv
 create mode 100644 model.pkl

diff --git a/Copy_of_Deployment_on_Flask_(Part_2)_Web_App.ipynb b/Copy_of_Deployment_on_Flask_(Part_2)_Web_App.ipynb
new file mode 100644
index 00000000..2524628c
--- /dev/null
+++ b/Copy_of_Deployment_on_Flask_(Part_2)_Web_App.ipynb
@@ -0,0 +1,132 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "UQGW_KMH_UkC"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "from flask import Flask, request, jsonify, render_template\n",
+        "import pickle\n",
+        "import joblib"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Loading the model.pkl from the file for reading the file's content\n",
+        "app = Flask(__name__)\n",
+        "model = joblib.load(open('model.pkl', 'rb'))\n"
+      ],
+      "metadata": {
+        "id": "EPnfAz1H_rty"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Creating the home route\n",
+        "@app.route('/')\n",
+        "def home():\n",
+        "    return render_template('index.html')"
+      ],
+      "metadata": {
+        "id": "SFRDm-mv_36R"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "source": [
+        "#Loading the model.pkl from the file for reading the file's content\n",
+        "app = Flask(__name__)\n",
+        "model = joblib.load(open('model.pkl', 'rb'))\n",
+        "\n",
+        "#Creating the home route\n",
+        "@app.route('/')\n",
+        "def home():\n",
+        "    return render_template('index.html')\n",
+        "\n",
+        "#Creating the predict route\n",
+        "@app.route('/predict', methods=['POST'])\n",
+        "def predict():\n",
+        "    int_features = [int(x) for x in request.form.values()]\n",
+        "    final_features = [np.array(int_features)]\n",
+        "    prediction = model.predict(final_features)\n",
+        "\n",
+        "    output = round(prediction[0], 2)\n",
+        "\n",
+        "    return render_template('index.html', prediction_text='Employee Salary should be $ {}'.format(output))"
+      ],
+      "cell_type": "code",
+      "metadata": {
+        "id": "tuRz-qy0BmIK"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "if __name__ == \"__main__\":\n",
+        "    app.run(debug=True)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kJ0rgZWsAjFn",
+        "outputId": "6507f9a1-bbaf-4a3e-fd60-2303daf73b88"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " * Serving Flask app '__main__'\n",
+            " * Debug mode: on\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "INFO:werkzeug:\u001b[31m\u001b[1mWARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.\u001b[0m\n",
+            " * Running on http://127.0.0.1:5000\n",
+            "INFO:werkzeug:\u001b[33mPress CTRL+C to quit\u001b[0m\n",
+            "INFO:werkzeug: * Restarting with stat\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "rcY1QTy1BRjL"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Copy_of_Deployment_on_Flask_Part_1.ipynb b/Copy_of_Deployment_on_Flask_Part_1.ipynb
new file mode 100644
index 00000000..741db2cb
--- /dev/null
+++ b/Copy_of_Deployment_on_Flask_Part_1.ipynb
@@ -0,0 +1,877 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "5yU3oebRmxaj",
+        "outputId": "540d1148-f7d3-4ed7-e82a-576c261fd16d"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
+              "0            6      148             72             35        0  33.6   \n",
+              "1            1       85             66             29        0  26.6   \n",
+              "2            8      183             64              0        0  23.3   \n",
+              "3            1       89             66             23       94  28.1   \n",
+              "4            0      137             40             35      168  43.1   \n",
+              "\n",
+              "   DiabetesPedigreeFunction  Age  Outcome  \n",
+              "0                     0.627   50        1  \n",
+              "1                     0.351   31        0  \n",
+              "2                     0.672   32        1  \n",
+              "3                     0.167   21        0  \n",
+              "4                     2.288   33        1  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-4ebeda0c-77ad-4ab6-b913-578feb0da6b9\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Pregnancies</th>\n",
+              "      <th>Glucose</th>\n",
+              "      <th>BloodPressure</th>\n",
+              "      <th>SkinThickness</th>\n",
+              "      <th>Insulin</th>\n",
+              "      <th>BMI</th>\n",
+              "      <th>DiabetesPedigreeFunction</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Outcome</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>6</td>\n",
+              "      <td>148</td>\n",
+              "      <td>72</td>\n",
+              "      <td>35</td>\n",
+              "      <td>0</td>\n",
+              "      <td>33.6</td>\n",
+              "      <td>0.627</td>\n",
+              "      <td>50</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>85</td>\n",
+              "      <td>66</td>\n",
+              "      <td>29</td>\n",
+              "      <td>0</td>\n",
+              "      <td>26.6</td>\n",
+              "      <td>0.351</td>\n",
+              "      <td>31</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>8</td>\n",
+              "      <td>183</td>\n",
+              "      <td>64</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>23.3</td>\n",
+              "      <td>0.672</td>\n",
+              "      <td>32</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1</td>\n",
+              "      <td>89</td>\n",
+              "      <td>66</td>\n",
+              "      <td>23</td>\n",
+              "      <td>94</td>\n",
+              "      <td>28.1</td>\n",
+              "      <td>0.167</td>\n",
+              "      <td>21</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0</td>\n",
+              "      <td>137</td>\n",
+              "      <td>40</td>\n",
+              "      <td>35</td>\n",
+              "      <td>168</td>\n",
+              "      <td>43.1</td>\n",
+              "      <td>2.288</td>\n",
+              "      <td>33</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ebeda0c-77ad-4ab6-b913-578feb0da6b9')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-4ebeda0c-77ad-4ab6-b913-578feb0da6b9 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-4ebeda0c-77ad-4ab6-b913-578feb0da6b9');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-8444480f-ec01-420e-a811-3a35762773e9\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8444480f-ec01-420e-a811-3a35762773e9')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-8444480f-ec01-420e-a811-3a35762773e9 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 768,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          6,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 0,\n        \"max\": 199,\n        \"num_unique_values\": 136,\n        \"samples\": [\n          151,\n          101,\n          112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 122,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          86,\n          46,\n          85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 0,\n        \"max\": 99,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          7,\n          12,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115,\n        \"min\": 0,\n        \"max\": 846,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          52,\n          41,\n          183\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.8841603203754405,\n        \"min\": 0.0,\n        \"max\": 67.1,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          19.9,\n          31.0,\n          38.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.33132859501277484,\n        \"min\": 0.078,\n        \"max\": 2.42,\n        \"num_unique_values\": 517,\n        \"samples\": [\n          1.731,\n          0.426,\n          0.138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 21,\n        \"max\": 81,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          60,\n          47,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 1
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "from sklearn.metrics import accuracy_score\n",
+        "import pickle\n",
+        "\n",
+        "#Load our dataset\n",
+        "\n",
+        "df = pd.read_csv('diabete.csv')\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Using y variable as Outcome class labels with 0 and 1,a dn X is our input feature which will drop only Outcome column\n",
+        "X = df.drop('Outcome', axis=1)\n",
+        "y = df['Outcome']"
+      ],
+      "metadata": {
+        "id": "zxSgNcnYsM7S"
+      },
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Splitting the data X and Y into train and test set\n",
+        "\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
+      ],
+      "metadata": {
+        "id": "bUWTZ2OSstAQ"
+      },
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Reducing the std deviation\n",
+        "\n",
+        "scaler = StandardScaler()\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)"
+      ],
+      "metadata": {
+        "id": "ClmXmmcD2zd1"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Training 100 models using Random Classifier\n",
+        "\n",
+        "clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
+        "clf.fit(X_train_scaled, y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "id": "ca1Y27RI3QrZ",
+        "outputId": "dee93547-1e2c-4461-be5a-182487b97551"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "RandomForestClassifier(random_state=42)"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-1 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-1 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-1 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-1 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Making pickle file of our model\n",
+        "pickle.dump(clf, open('model.pkl', 'wb'))"
+      ],
+      "metadata": {
+        "id": "1y0OtxOm9O3v"
+      },
+      "execution_count": 6,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "s1nyFZThAA4_"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/diabete.csv b/diabete.csv
new file mode 100644
index 00000000..db6f3176
--- /dev/null
+++ b/diabete.csv
@@ -0,0 +1,769 @@
+Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome
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diff --git a/model.pkl b/model.pkl
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From 200b642babda413067326de7955b31dbf77fe5f9 Mon Sep 17 00:00:00 2001
From: An204o <166038499+An204o@users.noreply.github.com>
Date: Sun, 27 Oct 2024 23:16:56 -0400
Subject: [PATCH 20/22] Delete
 Copy_of_Deployment_on_Flask_(Part_2)_Web_App.ipynb

---
 ...Deployment_on_Flask_(Part_2)_Web_App.ipynb | 132 ------------------
 1 file changed, 132 deletions(-)
 delete mode 100644 Copy_of_Deployment_on_Flask_(Part_2)_Web_App.ipynb

diff --git a/Copy_of_Deployment_on_Flask_(Part_2)_Web_App.ipynb b/Copy_of_Deployment_on_Flask_(Part_2)_Web_App.ipynb
deleted file mode 100644
index 2524628c..00000000
--- a/Copy_of_Deployment_on_Flask_(Part_2)_Web_App.ipynb
+++ /dev/null
@@ -1,132 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "UQGW_KMH_UkC"
-      },
-      "outputs": [],
-      "source": [
-        "import numpy as np\n",
-        "from flask import Flask, request, jsonify, render_template\n",
-        "import pickle\n",
-        "import joblib"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Loading the model.pkl from the file for reading the file's content\n",
-        "app = Flask(__name__)\n",
-        "model = joblib.load(open('model.pkl', 'rb'))\n"
-      ],
-      "metadata": {
-        "id": "EPnfAz1H_rty"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Creating the home route\n",
-        "@app.route('/')\n",
-        "def home():\n",
-        "    return render_template('index.html')"
-      ],
-      "metadata": {
-        "id": "SFRDm-mv_36R"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "source": [
-        "#Loading the model.pkl from the file for reading the file's content\n",
-        "app = Flask(__name__)\n",
-        "model = joblib.load(open('model.pkl', 'rb'))\n",
-        "\n",
-        "#Creating the home route\n",
-        "@app.route('/')\n",
-        "def home():\n",
-        "    return render_template('index.html')\n",
-        "\n",
-        "#Creating the predict route\n",
-        "@app.route('/predict', methods=['POST'])\n",
-        "def predict():\n",
-        "    int_features = [int(x) for x in request.form.values()]\n",
-        "    final_features = [np.array(int_features)]\n",
-        "    prediction = model.predict(final_features)\n",
-        "\n",
-        "    output = round(prediction[0], 2)\n",
-        "\n",
-        "    return render_template('index.html', prediction_text='Employee Salary should be $ {}'.format(output))"
-      ],
-      "cell_type": "code",
-      "metadata": {
-        "id": "tuRz-qy0BmIK"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "if __name__ == \"__main__\":\n",
-        "    app.run(debug=True)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "kJ0rgZWsAjFn",
-        "outputId": "6507f9a1-bbaf-4a3e-fd60-2303daf73b88"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            " * Serving Flask app '__main__'\n",
-            " * Debug mode: on\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "INFO:werkzeug:\u001b[31m\u001b[1mWARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.\u001b[0m\n",
-            " * Running on http://127.0.0.1:5000\n",
-            "INFO:werkzeug:\u001b[33mPress CTRL+C to quit\u001b[0m\n",
-            "INFO:werkzeug: * Restarting with stat\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [],
-      "metadata": {
-        "id": "rcY1QTy1BRjL"
-      },
-      "execution_count": null,
-      "outputs": []
-    }
-  ]
-}
\ No newline at end of file

From eafca038772dd90a93469018c3730fd36cedbca0 Mon Sep 17 00:00:00 2001
From: An204o <166038499+An204o@users.noreply.github.com>
Date: Sun, 27 Oct 2024 23:17:08 -0400
Subject: [PATCH 21/22] Delete Copy_of_Deployment_on_Flask_Part_1.ipynb

---
 Copy_of_Deployment_on_Flask_Part_1.ipynb | 877 -----------------------
 1 file changed, 877 deletions(-)
 delete mode 100644 Copy_of_Deployment_on_Flask_Part_1.ipynb

diff --git a/Copy_of_Deployment_on_Flask_Part_1.ipynb b/Copy_of_Deployment_on_Flask_Part_1.ipynb
deleted file mode 100644
index 741db2cb..00000000
--- a/Copy_of_Deployment_on_Flask_Part_1.ipynb
+++ /dev/null
@@ -1,877 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "5yU3oebRmxaj",
-        "outputId": "540d1148-f7d3-4ed7-e82a-576c261fd16d"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
-              "0            6      148             72             35        0  33.6   \n",
-              "1            1       85             66             29        0  26.6   \n",
-              "2            8      183             64              0        0  23.3   \n",
-              "3            1       89             66             23       94  28.1   \n",
-              "4            0      137             40             35      168  43.1   \n",
-              "\n",
-              "   DiabetesPedigreeFunction  Age  Outcome  \n",
-              "0                     0.627   50        1  \n",
-              "1                     0.351   31        0  \n",
-              "2                     0.672   32        1  \n",
-              "3                     0.167   21        0  \n",
-              "4                     2.288   33        1  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-4ebeda0c-77ad-4ab6-b913-578feb0da6b9\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Pregnancies</th>\n",
-              "      <th>Glucose</th>\n",
-              "      <th>BloodPressure</th>\n",
-              "      <th>SkinThickness</th>\n",
-              "      <th>Insulin</th>\n",
-              "      <th>BMI</th>\n",
-              "      <th>DiabetesPedigreeFunction</th>\n",
-              "      <th>Age</th>\n",
-              "      <th>Outcome</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>6</td>\n",
-              "      <td>148</td>\n",
-              "      <td>72</td>\n",
-              "      <td>35</td>\n",
-              "      <td>0</td>\n",
-              "      <td>33.6</td>\n",
-              "      <td>0.627</td>\n",
-              "      <td>50</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>1</td>\n",
-              "      <td>85</td>\n",
-              "      <td>66</td>\n",
-              "      <td>29</td>\n",
-              "      <td>0</td>\n",
-              "      <td>26.6</td>\n",
-              "      <td>0.351</td>\n",
-              "      <td>31</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>8</td>\n",
-              "      <td>183</td>\n",
-              "      <td>64</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>23.3</td>\n",
-              "      <td>0.672</td>\n",
-              "      <td>32</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>1</td>\n",
-              "      <td>89</td>\n",
-              "      <td>66</td>\n",
-              "      <td>23</td>\n",
-              "      <td>94</td>\n",
-              "      <td>28.1</td>\n",
-              "      <td>0.167</td>\n",
-              "      <td>21</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>0</td>\n",
-              "      <td>137</td>\n",
-              "      <td>40</td>\n",
-              "      <td>35</td>\n",
-              "      <td>168</td>\n",
-              "      <td>43.1</td>\n",
-              "      <td>2.288</td>\n",
-              "      <td>33</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ebeda0c-77ad-4ab6-b913-578feb0da6b9')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-4ebeda0c-77ad-4ab6-b913-578feb0da6b9 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-4ebeda0c-77ad-4ab6-b913-578feb0da6b9');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-8444480f-ec01-420e-a811-3a35762773e9\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8444480f-ec01-420e-a811-3a35762773e9')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
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-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-8444480f-ec01-420e-a811-3a35762773e9 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df",
-              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 768,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          6,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 0,\n        \"max\": 199,\n        \"num_unique_values\": 136,\n        \"samples\": [\n          151,\n          101,\n          112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 122,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          86,\n          46,\n          85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 0,\n        \"max\": 99,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          7,\n          12,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115,\n        \"min\": 0,\n        \"max\": 846,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          52,\n          41,\n          183\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.8841603203754405,\n        \"min\": 0.0,\n        \"max\": 67.1,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          19.9,\n          31.0,\n          38.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.33132859501277484,\n        \"min\": 0.078,\n        \"max\": 2.42,\n        \"num_unique_values\": 517,\n        \"samples\": [\n          1.731,\n          0.426,\n          0.138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 21,\n        \"max\": 81,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          60,\n          47,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 1
-        }
-      ],
-      "source": [
-        "import pandas as pd\n",
-        "from sklearn.preprocessing import StandardScaler\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "from sklearn.ensemble import RandomForestClassifier\n",
-        "from sklearn.metrics import accuracy_score\n",
-        "import pickle\n",
-        "\n",
-        "#Load our dataset\n",
-        "\n",
-        "df = pd.read_csv('diabete.csv')\n",
-        "df.head()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Using y variable as Outcome class labels with 0 and 1,a dn X is our input feature which will drop only Outcome column\n",
-        "X = df.drop('Outcome', axis=1)\n",
-        "y = df['Outcome']"
-      ],
-      "metadata": {
-        "id": "zxSgNcnYsM7S"
-      },
-      "execution_count": 2,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Splitting the data X and Y into train and test set\n",
-        "\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
-      ],
-      "metadata": {
-        "id": "bUWTZ2OSstAQ"
-      },
-      "execution_count": 3,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Reducing the std deviation\n",
-        "\n",
-        "scaler = StandardScaler()\n",
-        "X_train_scaled = scaler.fit_transform(X_train)\n",
-        "X_test_scaled = scaler.transform(X_test)"
-      ],
-      "metadata": {
-        "id": "ClmXmmcD2zd1"
-      },
-      "execution_count": 4,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Training 100 models using Random Classifier\n",
-        "\n",
-        "clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
-        "clf.fit(X_train_scaled, y_train)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 80
-        },
-        "id": "ca1Y27RI3QrZ",
-        "outputId": "dee93547-1e2c-4461-be5a-182487b97551"
-      },
-      "execution_count": 5,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "RandomForestClassifier(random_state=42)"
-            ],
-            "text/html": [
-              "<style>#sk-container-id-1 {\n",
-              "  /* Definition of color scheme common for light and dark mode */\n",
-              "  --sklearn-color-text: black;\n",
-              "  --sklearn-color-line: gray;\n",
-              "  /* Definition of color scheme for unfitted estimators */\n",
-              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-              "  --sklearn-color-unfitted-level-3: chocolate;\n",
-              "  /* Definition of color scheme for fitted estimators */\n",
-              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-              "\n",
-              "  /* Specific color for light theme */\n",
-              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-              "  --sklearn-color-icon: #696969;\n",
-              "\n",
-              "  @media (prefers-color-scheme: dark) {\n",
-              "    /* Redefinition of color scheme for dark theme */\n",
-              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-              "    --sklearn-color-icon: #878787;\n",
-              "  }\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 {\n",
-              "  color: var(--sklearn-color-text);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 pre {\n",
-              "  padding: 0;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 input.sk-hidden--visually {\n",
-              "  border: 0;\n",
-              "  clip: rect(1px 1px 1px 1px);\n",
-              "  clip: rect(1px, 1px, 1px, 1px);\n",
-              "  height: 1px;\n",
-              "  margin: -1px;\n",
-              "  overflow: hidden;\n",
-              "  padding: 0;\n",
-              "  position: absolute;\n",
-              "  width: 1px;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
-              "  border: 1px dashed var(--sklearn-color-line);\n",
-              "  margin: 0 0.4em 0.5em 0.4em;\n",
-              "  box-sizing: border-box;\n",
-              "  padding-bottom: 0.4em;\n",
-              "  background-color: var(--sklearn-color-background);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-container {\n",
-              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-              "     so we also need the `!important` here to be able to override the\n",
-              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-              "  display: inline-block !important;\n",
-              "  position: relative;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
-              "  display: none;\n",
-              "}\n",
-              "\n",
-              "div.sk-parallel-item,\n",
-              "div.sk-serial,\n",
-              "div.sk-item {\n",
-              "  /* draw centered vertical line to link estimators */\n",
-              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-              "  background-size: 2px 100%;\n",
-              "  background-repeat: no-repeat;\n",
-              "  background-position: center center;\n",
-              "}\n",
-              "\n",
-              "/* Parallel-specific style estimator block */\n",
-              "\n",
-              "#sk-container-id-1 div.sk-parallel-item::after {\n",
-              "  content: \"\";\n",
-              "  width: 100%;\n",
-              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-              "  flex-grow: 1;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-parallel {\n",
-              "  display: flex;\n",
-              "  align-items: stretch;\n",
-              "  justify-content: center;\n",
-              "  background-color: var(--sklearn-color-background);\n",
-              "  position: relative;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-parallel-item {\n",
-              "  display: flex;\n",
-              "  flex-direction: column;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
-              "  align-self: flex-end;\n",
-              "  width: 50%;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
-              "  align-self: flex-start;\n",
-              "  width: 50%;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
-              "  width: 0;\n",
-              "}\n",
-              "\n",
-              "/* Serial-specific style estimator block */\n",
-              "\n",
-              "#sk-container-id-1 div.sk-serial {\n",
-              "  display: flex;\n",
-              "  flex-direction: column;\n",
-              "  align-items: center;\n",
-              "  background-color: var(--sklearn-color-background);\n",
-              "  padding-right: 1em;\n",
-              "  padding-left: 1em;\n",
-              "}\n",
-              "\n",
-              "\n",
-              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-              "clickable and can be expanded/collapsed.\n",
-              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-              "*/\n",
-              "\n",
-              "/* Pipeline and ColumnTransformer style (default) */\n",
-              "\n",
-              "#sk-container-id-1 div.sk-toggleable {\n",
-              "  /* Default theme specific background. It is overwritten whether we have a\n",
-              "  specific estimator or a Pipeline/ColumnTransformer */\n",
-              "  background-color: var(--sklearn-color-background);\n",
-              "}\n",
-              "\n",
-              "/* Toggleable label */\n",
-              "#sk-container-id-1 label.sk-toggleable__label {\n",
-              "  cursor: pointer;\n",
-              "  display: block;\n",
-              "  width: 100%;\n",
-              "  margin-bottom: 0;\n",
-              "  padding: 0.5em;\n",
-              "  box-sizing: border-box;\n",
-              "  text-align: center;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
-              "  /* Arrow on the left of the label */\n",
-              "  content: \"▸\";\n",
-              "  float: left;\n",
-              "  margin-right: 0.25em;\n",
-              "  color: var(--sklearn-color-icon);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
-              "  color: var(--sklearn-color-text);\n",
-              "}\n",
-              "\n",
-              "/* Toggleable content - dropdown */\n",
-              "\n",
-              "#sk-container-id-1 div.sk-toggleable__content {\n",
-              "  max-height: 0;\n",
-              "  max-width: 0;\n",
-              "  overflow: hidden;\n",
-              "  text-align: left;\n",
-              "  /* unfitted */\n",
-              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
-              "  /* fitted */\n",
-              "  background-color: var(--sklearn-color-fitted-level-0);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
-              "  margin: 0.2em;\n",
-              "  border-radius: 0.25em;\n",
-              "  color: var(--sklearn-color-text);\n",
-              "  /* unfitted */\n",
-              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
-              "  /* unfitted */\n",
-              "  background-color: var(--sklearn-color-fitted-level-0);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-              "  /* Expand drop-down */\n",
-              "  max-height: 200px;\n",
-              "  max-width: 100%;\n",
-              "  overflow: auto;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-              "  content: \"▾\";\n",
-              "}\n",
-              "\n",
-              "/* Pipeline/ColumnTransformer-specific style */\n",
-              "\n",
-              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-              "  color: var(--sklearn-color-text);\n",
-              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-              "  background-color: var(--sklearn-color-fitted-level-2);\n",
-              "}\n",
-              "\n",
-              "/* Estimator-specific style */\n",
-              "\n",
-              "/* Colorize estimator box */\n",
-              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-              "  /* unfitted */\n",
-              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-              "  /* fitted */\n",
-              "  background-color: var(--sklearn-color-fitted-level-2);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-              "#sk-container-id-1 div.sk-label label {\n",
-              "  /* The background is the default theme color */\n",
-              "  color: var(--sklearn-color-text-on-default-background);\n",
-              "}\n",
-              "\n",
-              "/* On hover, darken the color of the background */\n",
-              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
-              "  color: var(--sklearn-color-text);\n",
-              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-              "}\n",
-              "\n",
-              "/* Label box, darken color on hover, fitted */\n",
-              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-              "  color: var(--sklearn-color-text);\n",
-              "  background-color: var(--sklearn-color-fitted-level-2);\n",
-              "}\n",
-              "\n",
-              "/* Estimator label */\n",
-              "\n",
-              "#sk-container-id-1 div.sk-label label {\n",
-              "  font-family: monospace;\n",
-              "  font-weight: bold;\n",
-              "  display: inline-block;\n",
-              "  line-height: 1.2em;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-label-container {\n",
-              "  text-align: center;\n",
-              "}\n",
-              "\n",
-              "/* Estimator-specific */\n",
-              "#sk-container-id-1 div.sk-estimator {\n",
-              "  font-family: monospace;\n",
-              "  border: 1px dotted var(--sklearn-color-border-box);\n",
-              "  border-radius: 0.25em;\n",
-              "  box-sizing: border-box;\n",
-              "  margin-bottom: 0.5em;\n",
-              "  /* unfitted */\n",
-              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-estimator.fitted {\n",
-              "  /* fitted */\n",
-              "  background-color: var(--sklearn-color-fitted-level-0);\n",
-              "}\n",
-              "\n",
-              "/* on hover */\n",
-              "#sk-container-id-1 div.sk-estimator:hover {\n",
-              "  /* unfitted */\n",
-              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
-              "  /* fitted */\n",
-              "  background-color: var(--sklearn-color-fitted-level-2);\n",
-              "}\n",
-              "\n",
-              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-              "\n",
-              "/* Common style for \"i\" and \"?\" */\n",
-              "\n",
-              ".sk-estimator-doc-link,\n",
-              "a:link.sk-estimator-doc-link,\n",
-              "a:visited.sk-estimator-doc-link {\n",
-              "  float: right;\n",
-              "  font-size: smaller;\n",
-              "  line-height: 1em;\n",
-              "  font-family: monospace;\n",
-              "  background-color: var(--sklearn-color-background);\n",
-              "  border-radius: 1em;\n",
-              "  height: 1em;\n",
-              "  width: 1em;\n",
-              "  text-decoration: none !important;\n",
-              "  margin-left: 1ex;\n",
-              "  /* unfitted */\n",
-              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-              "  color: var(--sklearn-color-unfitted-level-1);\n",
-              "}\n",
-              "\n",
-              ".sk-estimator-doc-link.fitted,\n",
-              "a:link.sk-estimator-doc-link.fitted,\n",
-              "a:visited.sk-estimator-doc-link.fitted {\n",
-              "  /* fitted */\n",
-              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-              "  color: var(--sklearn-color-fitted-level-1);\n",
-              "}\n",
-              "\n",
-              "/* On hover */\n",
-              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-              ".sk-estimator-doc-link:hover,\n",
-              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-              ".sk-estimator-doc-link:hover {\n",
-              "  /* unfitted */\n",
-              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-              "  color: var(--sklearn-color-background);\n",
-              "  text-decoration: none;\n",
-              "}\n",
-              "\n",
-              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-              ".sk-estimator-doc-link.fitted:hover,\n",
-              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-              ".sk-estimator-doc-link.fitted:hover {\n",
-              "  /* fitted */\n",
-              "  background-color: var(--sklearn-color-fitted-level-3);\n",
-              "  color: var(--sklearn-color-background);\n",
-              "  text-decoration: none;\n",
-              "}\n",
-              "\n",
-              "/* Span, style for the box shown on hovering the info icon */\n",
-              ".sk-estimator-doc-link span {\n",
-              "  display: none;\n",
-              "  z-index: 9999;\n",
-              "  position: relative;\n",
-              "  font-weight: normal;\n",
-              "  right: .2ex;\n",
-              "  padding: .5ex;\n",
-              "  margin: .5ex;\n",
-              "  width: min-content;\n",
-              "  min-width: 20ex;\n",
-              "  max-width: 50ex;\n",
-              "  color: var(--sklearn-color-text);\n",
-              "  box-shadow: 2pt 2pt 4pt #999;\n",
-              "  /* unfitted */\n",
-              "  background: var(--sklearn-color-unfitted-level-0);\n",
-              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-              "}\n",
-              "\n",
-              ".sk-estimator-doc-link.fitted span {\n",
-              "  /* fitted */\n",
-              "  background: var(--sklearn-color-fitted-level-0);\n",
-              "  border: var(--sklearn-color-fitted-level-3);\n",
-              "}\n",
-              "\n",
-              ".sk-estimator-doc-link:hover span {\n",
-              "  display: block;\n",
-              "}\n",
-              "\n",
-              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-              "\n",
-              "#sk-container-id-1 a.estimator_doc_link {\n",
-              "  float: right;\n",
-              "  font-size: 1rem;\n",
-              "  line-height: 1em;\n",
-              "  font-family: monospace;\n",
-              "  background-color: var(--sklearn-color-background);\n",
-              "  border-radius: 1rem;\n",
-              "  height: 1rem;\n",
-              "  width: 1rem;\n",
-              "  text-decoration: none;\n",
-              "  /* unfitted */\n",
-              "  color: var(--sklearn-color-unfitted-level-1);\n",
-              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
-              "  /* fitted */\n",
-              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-              "  color: var(--sklearn-color-fitted-level-1);\n",
-              "}\n",
-              "\n",
-              "/* On hover */\n",
-              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
-              "  /* unfitted */\n",
-              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-              "  color: var(--sklearn-color-background);\n",
-              "  text-decoration: none;\n",
-              "}\n",
-              "\n",
-              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
-              "  /* fitted */\n",
-              "  background-color: var(--sklearn-color-fitted-level-3);\n",
-              "}\n",
-              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 5
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Making pickle file of our model\n",
-        "pickle.dump(clf, open('model.pkl', 'wb'))"
-      ],
-      "metadata": {
-        "id": "1y0OtxOm9O3v"
-      },
-      "execution_count": 6,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [],
-      "metadata": {
-        "id": "s1nyFZThAA4_"
-      },
-      "execution_count": null,
-      "outputs": []
-    }
-  ]
-}
\ No newline at end of file

From d6754fb0be6f9d871d9a3a4e8e5876f48cc941eb Mon Sep 17 00:00:00 2001
From: An204o <166038499+An204o@users.noreply.github.com>
Date: Sun, 27 Oct 2024 23:23:21 -0400
Subject: [PATCH 22/22] Add files via upload

---
 Deployment on Flask.pdf | Bin 0 -> 515014 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 Deployment on Flask.pdf

diff --git a/Deployment on Flask.pdf b/Deployment on Flask.pdf
new file mode 100644
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