From e2272566ed1b88e4f6b9b2dd9a23b83727423d4b Mon Sep 17 00:00:00 2001
From: Ochai2018 <45337793+Ochai2018@users.noreply.github.com>
Date: Tue, 16 Aug 2022 16:58:03 -0700
Subject: [PATCH 01/13] Update README.md

---
 README.md | 106 +++++++++++++++++++++++++++++++++++++++++++++++++-----
 1 file changed, 98 insertions(+), 8 deletions(-)

diff --git a/README.md b/README.md
index 9145373..2c28007 100644
--- a/README.md
+++ b/README.md
@@ -1,25 +1,115 @@
 NB: This is a template to make documentation process easy. You can remove the `To-Do` notes in your final commit
 
 # Project Title Here
+Analyze Supermarket Data Across the Country - Company XYZ
+
+With an increase in Supermarket competition, Company XYZ seeks to understand sales trends and determine its growth. Data sets from from 3 branches (A:Lagos, B:Abuja, and C:Port Harcourt) of Company XYZ are to be analyzed. Each data file contain the following attributes: 
+  Invoice ID: Customer Identification number,
+  Branch: Supermarket Branch across the country (A, B, C)
+    A - Lagos Branch,
+    B - Abuja Branch.
+    C - Port Harcourt Branch,
+  City: Supermarket Location,
+  Customer Type: Type of customers, Members - Returning customer with membership card, Normal - Customer without membership (could be returning, first-time or walk-    in customer),
+  Gender: Customer Gender Information,
+Product line: Product categorization groups - Electronic accessories, Fashion accessories, Food and beverages, Health and beauty, Home and lifestyle, Sports and    travel,
+ Unit Price: Price of each product in Naira,
+Quantity: Number of products purchased by customer,
+Tax: 5% tax fee for customer buying,
+Total: Total price including tax,
+Date: Date of purchase (Supermarket Record available from January 2019 to March 2019),
+Time: Purchase time (Supermarket Hours - 10am to 9pm),
+Payment: Payment used by customer for purchase (3 methods are available – Cash, Card and Epay),
+COGS: Cost of goods sold,
+Gross margin percentage: Gross margin percentage,
+Gross income: Gross income,
+Rating: Customer Satisfaction rating on their overall shopping experience (On a scale of 1 to 10).
+The Data Scientist intends to analyze the data sets using popular data analytical packages such as python packages and visualization packages, to provide the company with the information they need to make decisions about their growth.
+
+
 
-To-Do - Write a short project description here.
 
 # Project Steps
+Step 1 - Loading Datasets
+
+Correct use of pathname pattern - glob 
+and to Combine all the files generated in a list and export to a CSV. 
+
+Step 2 - Data Exploration
+  Using the head() method to view first few rows of the dataset,
+  Checking the number of rows and columns present in the data using the shape attribute,
+  Generate the names of the columns using the columns attribute,
+  Using describe function to generate the statistical summary of the dataframe,
+  Using meaningful sentences to describe findings from the data statistical summary,
+  Using of correct method to check for Missing values,
+  Checking the information of the DataFrame using the info method.
+
+Step 3 -  Dealing with DateTime Features
+  Using to_datetime() to convert the date column to datetime,
+  Checking the datatype to confirm if it's in datetime,
+  Accurate conversion of the time column & prints appropriate data type,
+  Accurate extraction of the Day, Month, Year & Hour features,
+  Determinig the numbers of unique hours of sales in the supermarket are accurately,
+  Display result that shows an array that contains the unique sales hours.
+
+Step 4 - Unique Values in Columns
+  Using appropriate method to generate the unique values in the categorical columns (apart from the example - Branch column),
+  Generating the count figure of the categorical values using the value_counts() method,
+
+Step 5 -  Aggregation with GroupBy
+  Use of groupby object with the "City Column", and aggregation function of sum and mean,
+  Display a table that shows the gross income of each city, and determines the city with the highest total gross income,
+  Optional - Use of appropriate methods & descriptions to explore other columns such as Unit Price, Quantity.
+
+Step 6 - Data Visualization
+  Appropriate use of countplot to determine the branch with the highest sales record,
+  Optional - Appropriate use of countplot to determine the most used payment method & city with the most sales,
+  Appropriate use of countplot to determine the highest & lowest sold product line,
+Diplay result that shows the Payment channel used by most customers to pay for each product line with Chart showing the "product line" column on the Y-axis, and   the  "hue" parameter for the "Payment" column,
 
-To-Do - Explain in detail, the project steps and overview of different tasks completed here.
 
 # Insights
+The insights obtained from project's analysis are as follows:
 
-To-Do - Explain the insights you were able to uncover from the analysing the datasets.
+Data set is large with exactly 68,000 elements (4000 rows and 17 columns),
 
-# Future Work
+The elements for categorical raw data are complete,
 
-To-Do - Suggest tasks you might include in future work to make this project more robust.
+Python packages such as pandas analytical tools, Numpy, and Matplotlib as well as data illustrative tools such as Seaborn's statistical visualization package are very efficient and powerful in analysing huge data sets,
 
-# Standout Section
+Coding lines are not too restictive, i.e packages are user-friendly and flexible. Users creativity shows that usage can be interesting,
+
+Some 'take-home' facts about the company XYZ include:
+Sales of Electronic and fashion accessories are almost at par. Sales across cities are very competitive with close matching records.
 
-To-Do - Explain what you did differently in the project following the instructions in the notebook.
+Though quantities purchased for product lines generally are low, most customers prefer using Cash and Epay payment methods.
+
+# Future Work
+More information can be obtained from this project by including data cleaning and munging.
+
+# Standout Section
+Use of appropriate methods & descriptions to explore other columns such as Unit Price, Quantity in Aggregation with GroupBy.
 
 # Executive Summary.
+Summary
+
+Data set is large with exactly 68,000 elements (4000 rows and 17 columns).
+
+The elements for categorical raw data are complete.
+
+Approach:
+
+The approaches for data analyses for company XYZ were to combine seperate data set for each city, make inferential and descriptive statistical analyses and obtain key facts that show and compare the sales performances for Company's branches.
+
+Method:
+These were accomplished using some python packages such as pandas analytical tools, Numpy, and Matplotlib as well as data illustrative tools such as Seaborn's statistical visualization package.
+
+Insights:
+Python packages such as pandas analytical tools, Numpy, and Matplotlib as well as data illustrative tools such as Seaborn's statistical visualization package are very efficient and powerful in analysing huge data sets.
+
+Coding lines are not too restictive, i.e packages are user-friendly and flexible. Users creativity shows that usage can be interesting.
+
+Some 'take-home' facts about the company XYZ include:
+Sales of Electronic and fashion accessories are almost at par. Sales across cities are very competitive with close matching records.
 
-To-Do - Include your Executive Summary document in your repository.
+Though quantities purchased for product lines generally are low, most customers prefer using Cash and Epay payment methods.

From 3b6a3ecda260a5bcc34d1b79ad5233a5cab6723e Mon Sep 17 00:00:00 2001
From: Ochai2018 <45337793+Ochai2018@users.noreply.github.com>
Date: Tue, 16 Aug 2022 16:59:09 -0700
Subject: [PATCH 02/13] Update README.md

---
 README.md | 2 --
 1 file changed, 2 deletions(-)

diff --git a/README.md b/README.md
index 2c28007..1acfafc 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,3 @@
-NB: This is a template to make documentation process easy. You can remove the `To-Do` notes in your final commit
-
 # Project Title Here
 Analyze Supermarket Data Across the Country - Company XYZ
 

From 8fab8e6939a2efdef3e0ed931f96cef853115735 Mon Sep 17 00:00:00 2001
From: Ochai2018 <45337793+Ochai2018@users.noreply.github.com>
Date: Tue, 16 Aug 2022 18:38:10 -0700
Subject: [PATCH 03/13] Update README.md

---
 README.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 1acfafc..16cddb2 100644
--- a/README.md
+++ b/README.md
@@ -69,7 +69,7 @@ Diplay result that shows the Payment channel used by most customers to pay for e
 # Insights
 The insights obtained from project's analysis are as follows:
 
-Data set is large with exactly 68,000 elements (4000 rows and 17 columns),
+Data set is large with exactly 34,000 elements (2000 rows and 17 columns),
 
 The elements for categorical raw data are complete,
 
@@ -87,6 +87,7 @@ More information can be obtained from this project by including data cleaning an
 
 # Standout Section
 Use of appropriate methods & descriptions to explore other columns such as Unit Price, Quantity in Aggregation with GroupBy.
+A barplot was made to display the gross income for each city.
 
 # Executive Summary.
 Summary

From b3bdd755b6d0485e156a25bafd10d1e1b17c06f0 Mon Sep 17 00:00:00 2001
From: Ochai2018 <45337793+Ochai2018@users.noreply.github.com>
Date: Tue, 16 Aug 2022 18:45:40 -0700
Subject: [PATCH 04/13] Create Ochai Odeh_Panda_Analytics_Project2

---
 Ochai Odeh_Panda_Analytics_Project2 | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 Ochai Odeh_Panda_Analytics_Project2

diff --git a/Ochai Odeh_Panda_Analytics_Project2 b/Ochai Odeh_Panda_Analytics_Project2
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/Ochai Odeh_Panda_Analytics_Project2	
@@ -0,0 +1 @@
+

From 405326b379cbc333dc5df7a25a1229cde65cad5c Mon Sep 17 00:00:00 2001
From: Ochai2018 <45337793+Ochai2018@users.noreply.github.com>
Date: Tue, 16 Aug 2022 18:47:09 -0700
Subject: [PATCH 05/13] Create combined_csv.csv

---
 combined_csv.csv | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 combined_csv.csv

diff --git a/combined_csv.csv b/combined_csv.csv
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/combined_csv.csv
@@ -0,0 +1 @@
+

From 85c16ef14f6d19b21ac5eba70727664912cd5c41 Mon Sep 17 00:00:00 2001
From: Ochai2018 <45337793+Ochai2018@users.noreply.github.com>
Date: Thu, 25 Aug 2022 09:05:35 -0700
Subject: [PATCH 06/13] Add files via upload

---
 Ochai Pandas Analytics project.ipynb | 2825 ++++++++++++++++++++++++++
 1 file changed, 2825 insertions(+)
 create mode 100644 Ochai Pandas Analytics project.ipynb

diff --git a/Ochai Pandas Analytics project.ipynb b/Ochai Pandas Analytics project.ipynb
new file mode 100644
index 0000000..0406677
--- /dev/null
+++ b/Ochai Pandas Analytics project.ipynb	
@@ -0,0 +1,2825 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "b38ea413",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import glob\n",
+    "import pandas as pd\n",
+    "os.chdir('C:\\\\Users\\\\OCHAI ODEH\\\\Downloads\\\\Pandas')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "430a17ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Abuja_Branch.csv',\n",
+       " 'Lagos_Branch.csv',\n",
+       " 'Port_Harcourt_Branch.csv',\n",
+       " 'README.md',\n",
+       " 'Starter_notebook.ipynb']"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "os.listdir('C:\\\\Users\\\\OCHAI ODEH\\\\Downloads\\\\Pandas\\\\Pandas-Analytics-Project-main')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "575fe66a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import glob\n",
+    "\n",
+    "extension = 'csv'\n",
+    "glob.glob('C:/Users/OCHAI ODEH/Downloads/Pandas/Pandas-Analytics-Project-main extension')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "c53b13b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Abuja_Branch.csv', 'Lagos_Branch.csv', 'Port_Harcourt_Branch.csv']\n"
+     ]
+    }
+   ],
+   "source": [
+    "path = 'C:\\\\Users\\\\OCHAI ODEH\\\\Downloads\\\\Pandas\\\\Pandas-Analytics-Project-main'\n",
+    "extension = 'csv'\n",
+    "os.chdir(path)\n",
+    "files = glob.glob('*.{}'.format(extension))\n",
+    "print(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "53d6d3de",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Pandas-Analytics-Project-main\\\\Abuja_Branch.csv', 'Pandas-Analytics-Project-main\\\\Lagos_Branch.csv', 'Pandas-Analytics-Project-main\\\\Port_Harcourt_Branch.csv']\n"
+     ]
+    }
+   ],
+   "source": [
+    "extension ='csv'\n",
+    "os.chdir('C:/Users/OCHAI ODEH/Downloads/Pandas/')\n",
+    "result = glob.glob( '*/**.csv' )\n",
+    "print( result )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "83e37982",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = 'C:\\\\Users\\\\OCHAI ODEH\\\\Downloads\\\\Pandas\\\\Pandas-Analytics-Project-main'\n",
+    "extension = 'csv'\n",
+    "os.chdir(path)\n",
+    "#combine files generated in list\n",
+    "files = glob.glob('*.{}'.format(extension))\n",
+    "#Export to csv\n",
+    "combined_csv = pd.concat([pd.read_csv(f) for f in files ])\n",
+    "combined_csv.to_csv( \"combined_csv.csv\", index=False, encoding='utf-8-sig')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "9f934cf1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>692-92-5582</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Food and beverages</td>\n",
+       "      <td>19742.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2961.36</td>\n",
+       "      <td>62188.56</td>\n",
+       "      <td>2/20/2019</td>\n",
+       "      <td>13:27</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>59227.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>2961.36</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>351-62-0822</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Fashion accessories</td>\n",
+       "      <td>5212.8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1042.56</td>\n",
+       "      <td>21893.76</td>\n",
+       "      <td>2/6/2019</td>\n",
+       "      <td>18:07</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>20851.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1042.56</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>529-56-3974</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>9183.6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1836.72</td>\n",
+       "      <td>38571.12</td>\n",
+       "      <td>3/9/2019</td>\n",
+       "      <td>17:03</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>36734.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1836.72</td>\n",
+       "      <td>6.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>299-46-1805</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>33739.2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10121.76</td>\n",
+       "      <td>212556.96</td>\n",
+       "      <td>1/15/2019</td>\n",
+       "      <td>16:19</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>202435.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10121.76</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>319-50-3348</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>14508.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1450.80</td>\n",
+       "      <td>30466.80</td>\n",
+       "      <td>3/11/2019</td>\n",
+       "      <td>15:30</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>29016.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1450.80</td>\n",
+       "      <td>4.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1995</th>\n",
+       "      <td>148-41-7930</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>35985.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>264494.16</td>\n",
+       "      <td>1/23/2019</td>\n",
+       "      <td>10:33</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>251899.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>6.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1996</th>\n",
+       "      <td>189-40-5216</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>34693.2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>254995.02</td>\n",
+       "      <td>1/9/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>242852.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1997</th>\n",
+       "      <td>267-62-7380</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>29642.4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>311245.20</td>\n",
+       "      <td>3/29/2019</td>\n",
+       "      <td>19:12</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>296424.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>4.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998</th>\n",
+       "      <td>652-49-6720</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>23039.10</td>\n",
+       "      <td>2/18/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1999</th>\n",
+       "      <td>233-67-5758</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>15252.30</td>\n",
+       "      <td>1/29/2019</td>\n",
+       "      <td>13:46</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2000 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Invoice ID Branch           City Customer type  Gender  \\\n",
+       "0     692-92-5582      B          Abuja        Member  Female   \n",
+       "1     351-62-0822      B          Abuja        Member  Female   \n",
+       "2     529-56-3974      B          Abuja        Member    Male   \n",
+       "3     299-46-1805      B          Abuja        Member  Female   \n",
+       "4     319-50-3348      B          Abuja        Normal  Female   \n",
+       "...           ...    ...            ...           ...     ...   \n",
+       "1995  148-41-7930      C  Port Harcourt        Normal    Male   \n",
+       "1996  189-40-5216      C  Port Harcourt        Normal    Male   \n",
+       "1997  267-62-7380      C  Port Harcourt        Member    Male   \n",
+       "1998  652-49-6720      C  Port Harcourt        Member  Female   \n",
+       "1999  233-67-5758      C  Port Harcourt        Normal    Male   \n",
+       "\n",
+       "                Product line  Unit price  Quantity    Tax 5%      Total  \\\n",
+       "0         Food and beverages     19742.4         3   2961.36   62188.56   \n",
+       "1        Fashion accessories      5212.8         4   1042.56   21893.76   \n",
+       "2     Electronic accessories      9183.6         4   1836.72   38571.12   \n",
+       "3          Sports and travel     33739.2         6  10121.76  212556.96   \n",
+       "4         Home and lifestyle     14508.0         2   1450.80   30466.80   \n",
+       "...                      ...         ...       ...       ...        ...   \n",
+       "1995       Health and beauty     35985.6         7  12594.96  264494.16   \n",
+       "1996  Electronic accessories     34693.2         7  12142.62  254995.02   \n",
+       "1997  Electronic accessories     29642.4        10  14821.20  311245.20   \n",
+       "1998  Electronic accessories     21942.0         1   1097.10   23039.10   \n",
+       "1999       Health and beauty     14526.0         1    726.30   15252.30   \n",
+       "\n",
+       "           Date   Time Payment      cogs  gross margin percentage  \\\n",
+       "0     2/20/2019  13:27    Card   59227.2                 4.761905   \n",
+       "1      2/6/2019  18:07    Epay   20851.2                 4.761905   \n",
+       "2      3/9/2019  17:03    Cash   36734.4                 4.761905   \n",
+       "3     1/15/2019  16:19    Cash  202435.2                 4.761905   \n",
+       "4     3/11/2019  15:30    Epay   29016.0                 4.761905   \n",
+       "...         ...    ...     ...       ...                      ...   \n",
+       "1995  1/23/2019  10:33    Cash  251899.2                 4.761905   \n",
+       "1996   1/9/2019  11:40    Cash  242852.4                 4.761905   \n",
+       "1997  3/29/2019  19:12    Epay  296424.0                 4.761905   \n",
+       "1998  2/18/2019  11:40    Epay   21942.0                 4.761905   \n",
+       "1999  1/29/2019  13:46    Epay   14526.0                 4.761905   \n",
+       "\n",
+       "      gross income  Rating  \n",
+       "0          2961.36     5.9  \n",
+       "1          1042.56     4.5  \n",
+       "2          1836.72     6.8  \n",
+       "3         10121.76     4.5  \n",
+       "4          1450.80     4.4  \n",
+       "...            ...     ...  \n",
+       "1995      12594.96     6.1  \n",
+       "1996      12142.62     6.0  \n",
+       "1997      14821.20     4.3  \n",
+       "1998       1097.10     5.9  \n",
+       "1999        726.30     6.2  \n",
+       "\n",
+       "[2000 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Read the CSV file using pd.read_csv method\n",
+    "path = 'C:\\\\Users\\\\OCHAI ODEH\\\\Downloads\\\\Pandas\\\\Pandas-Analytics-Project-main'\n",
+    "extension = 'csv'\n",
+    "os.chdir(path)\n",
+    "#combine files generated in list\n",
+    "files = glob.glob('*.{}'.format(extension))\n",
+    "#Export to csv\n",
+    "combined_csv = pd.concat([pd.read_csv(f) for f in files ])\n",
+    "combined_csv.to_csv( \"combined_csv.csv\", index=False, encoding='utf-8-sig')\n",
+    "#Read the CSV file using pd.read_csv method\n",
+    "pd.read_csv(\"combined_csv.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da9651bb",
+   "metadata": {},
+   "source": [
+    "Data Exploration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "eefa7619",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "plt.style.use('fivethirtyeight')  \n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "167d6440",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = combined_csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "ee95da01",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>692-92-5582</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Food and beverages</td>\n",
+       "      <td>19742.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2961.36</td>\n",
+       "      <td>62188.56</td>\n",
+       "      <td>2/20/2019</td>\n",
+       "      <td>13:27</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>59227.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>2961.36</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>351-62-0822</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Fashion accessories</td>\n",
+       "      <td>5212.8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1042.56</td>\n",
+       "      <td>21893.76</td>\n",
+       "      <td>2/6/2019</td>\n",
+       "      <td>18:07</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>20851.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1042.56</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>529-56-3974</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>9183.6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1836.72</td>\n",
+       "      <td>38571.12</td>\n",
+       "      <td>3/9/2019</td>\n",
+       "      <td>17:03</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>36734.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1836.72</td>\n",
+       "      <td>6.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>299-46-1805</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>33739.2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10121.76</td>\n",
+       "      <td>212556.96</td>\n",
+       "      <td>1/15/2019</td>\n",
+       "      <td>16:19</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>202435.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10121.76</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>319-50-3348</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>14508.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1450.80</td>\n",
+       "      <td>30466.80</td>\n",
+       "      <td>3/11/2019</td>\n",
+       "      <td>15:30</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>29016.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1450.80</td>\n",
+       "      <td>4.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  692-92-5582      B  Abuja        Member  Female      Food and beverages   \n",
+       "1  351-62-0822      B  Abuja        Member  Female     Fashion accessories   \n",
+       "2  529-56-3974      B  Abuja        Member    Male  Electronic accessories   \n",
+       "3  299-46-1805      B  Abuja        Member  Female       Sports and travel   \n",
+       "4  319-50-3348      B  Abuja        Normal  Female      Home and lifestyle   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total       Date   Time Payment  \\\n",
+       "0     19742.4         3   2961.36   62188.56  2/20/2019  13:27    Card   \n",
+       "1      5212.8         4   1042.56   21893.76   2/6/2019  18:07    Epay   \n",
+       "2      9183.6         4   1836.72   38571.12   3/9/2019  17:03    Cash   \n",
+       "3     33739.2         6  10121.76  212556.96  1/15/2019  16:19    Cash   \n",
+       "4     14508.0         2   1450.80   30466.80  3/11/2019  15:30    Epay   \n",
+       "\n",
+       "       cogs  gross margin percentage  gross income  Rating  \n",
+       "0   59227.2                 4.761905       2961.36     5.9  \n",
+       "1   20851.2                 4.761905       1042.56     4.5  \n",
+       "2   36734.4                 4.761905       1836.72     6.8  \n",
+       "3  202435.2                 4.761905      10121.76     4.5  \n",
+       "4   29016.0                 4.761905       1450.80     4.4  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Use the head() method to view first few rows of the dataset\n",
+    "df = combined_csv\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "7693af83",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of rows is 2000\n",
+      "Number of columns is 17\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Check the number of rows and columns present in the data using the shape attribute\n",
+    "row_count = df.shape[0]  # Gives number of rows\n",
+    "col_count = df.shape[1] # Gives number of columns\n",
+    "print('Number of rows is', row_count)\n",
+    "print('Number of columns is', col_count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "f5a5e830",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Invoice ID', 'Branch', 'City', 'Customer type', 'Gender',\n",
+      "       'Product line', 'Unit price', 'Quantity', 'Tax 5%', 'Total', 'Date',\n",
+      "       'Time', 'Payment', 'cogs', 'gross margin percentage', 'gross income',\n",
+      "       'Rating'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Generate the names of the columns using the columns attribute.\n",
+    "\n",
+    "column_labels = df.columns   #return the column labels\n",
+    "print(column_labels)         # Print the result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "50a3a974",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.00000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2.000000e+03</td>\n",
+       "      <td>2000.00000</td>\n",
+       "      <td>2000.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>20041.966800</td>\n",
+       "      <td>5.510000</td>\n",
+       "      <td>5536.57284</td>\n",
+       "      <td>116268.029640</td>\n",
+       "      <td>110731.456800</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>5536.57284</td>\n",
+       "      <td>6.97270</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>9535.680197</td>\n",
+       "      <td>2.922699</td>\n",
+       "      <td>4214.12272</td>\n",
+       "      <td>88496.577116</td>\n",
+       "      <td>84282.454396</td>\n",
+       "      <td>5.419243e-14</td>\n",
+       "      <td>4214.12272</td>\n",
+       "      <td>1.71815</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>3628.800000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>183.06000</td>\n",
+       "      <td>3844.260000</td>\n",
+       "      <td>3661.200000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>183.06000</td>\n",
+       "      <td>4.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>11835.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2132.95500</td>\n",
+       "      <td>44792.055000</td>\n",
+       "      <td>42659.100000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>2132.95500</td>\n",
+       "      <td>5.50000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>19882.800000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>4351.68000</td>\n",
+       "      <td>91385.280000</td>\n",
+       "      <td>87033.600000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>4351.68000</td>\n",
+       "      <td>7.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>28056.600000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>8080.29000</td>\n",
+       "      <td>169686.090000</td>\n",
+       "      <td>161605.800000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>8080.29000</td>\n",
+       "      <td>8.50000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>35985.600000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>17874.00000</td>\n",
+       "      <td>375354.000000</td>\n",
+       "      <td>357480.000000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>17874.00000</td>\n",
+       "      <td>10.00000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Unit price     Quantity       Tax 5%          Total           cogs  \\\n",
+       "count   2000.000000  2000.000000   2000.00000    2000.000000    2000.000000   \n",
+       "mean   20041.966800     5.510000   5536.57284  116268.029640  110731.456800   \n",
+       "std     9535.680197     2.922699   4214.12272   88496.577116   84282.454396   \n",
+       "min     3628.800000     1.000000    183.06000    3844.260000    3661.200000   \n",
+       "25%    11835.000000     3.000000   2132.95500   44792.055000   42659.100000   \n",
+       "50%    19882.800000     5.000000   4351.68000   91385.280000   87033.600000   \n",
+       "75%    28056.600000     8.000000   8080.29000  169686.090000  161605.800000   \n",
+       "max    35985.600000    10.000000  17874.00000  375354.000000  357480.000000   \n",
+       "\n",
+       "       gross margin percentage  gross income      Rating  \n",
+       "count             2.000000e+03    2000.00000  2000.00000  \n",
+       "mean              4.761905e+00    5536.57284     6.97270  \n",
+       "std               5.419243e-14    4214.12272     1.71815  \n",
+       "min               4.761905e+00     183.06000     4.00000  \n",
+       "25%               4.761905e+00    2132.95500     5.50000  \n",
+       "50%               4.761905e+00    4351.68000     7.00000  \n",
+       "75%               4.761905e+00    8080.29000     8.50000  \n",
+       "max               4.761905e+00   17874.00000    10.00000  "
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Statistical summary\n",
+    "df.describe()\n",
+    "# Below this cell write in few sentences what you can derive from the data statistical summary\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "fd085f6e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "From the 2000 records, maximum sales of 10.000000 products and minimum sale of 1.000000 product was recorded.\n",
+      "Taxes of 5% on all products are equivalent to the gross incomes from sales.\n",
+      "The company's maximum gross income was 17874.0000 for 10 products has the highest rating.\n",
+      "Only one product with minimum unit price of 3628.800000 giving the lowest gross income of 183.06000 also has the lowest rating.\n",
+      "There are large differences in unit prices of products, Taxes of 5% and gross incomes, going by their respective value of standard deviations of 9534.487865 and 4213.59579 respectively.\n",
+      "Gross margin percentages for all variable are almost the same. There are no missing values in the table.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"From the 2000 records, maximum sales of 10.000000 products and minimum sale of 1.000000 product was recorded.\") \n",
+    "      \n",
+    "print(\"Taxes of 5% on all products are equivalent to the gross incomes from sales.\") \n",
+    "      \n",
+    "print(\"The company's maximum gross income was 17874.0000 for 10 products has the highest rating.\")\n",
+    "      \n",
+    "print(\"Only one product with minimum unit price of 3628.800000 giving the lowest gross income of 183.06000 also has the lowest rating.\")\n",
+    "      \n",
+    "print(\"There are large differences in unit prices of products, Taxes of 5% and gross incomes, going by their respective value of standard deviations of 9534.487865 and 4213.59579 respectively.\")      \n",
+    "\n",
+    "print(\"Gross margin percentages for all variable are almost the same. There are no missing values in the table.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "7746babf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2000 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Invoice ID  Branch   City  Customer type  Gender  Product line  \\\n",
+       "0         False   False  False          False   False         False   \n",
+       "1         False   False  False          False   False         False   \n",
+       "2         False   False  False          False   False         False   \n",
+       "3         False   False  False          False   False         False   \n",
+       "4         False   False  False          False   False         False   \n",
+       "..          ...     ...    ...            ...     ...           ...   \n",
+       "323       False   False  False          False   False         False   \n",
+       "324       False   False  False          False   False         False   \n",
+       "325       False   False  False          False   False         False   \n",
+       "326       False   False  False          False   False         False   \n",
+       "327       False   False  False          False   False         False   \n",
+       "\n",
+       "     Unit price  Quantity  Tax 5%  Total   Date   Time  Payment   cogs  \\\n",
+       "0         False     False   False  False  False  False    False  False   \n",
+       "1         False     False   False  False  False  False    False  False   \n",
+       "2         False     False   False  False  False  False    False  False   \n",
+       "3         False     False   False  False  False  False    False  False   \n",
+       "4         False     False   False  False  False  False    False  False   \n",
+       "..          ...       ...     ...    ...    ...    ...      ...    ...   \n",
+       "323       False     False   False  False  False  False    False  False   \n",
+       "324       False     False   False  False  False  False    False  False   \n",
+       "325       False     False   False  False  False  False    False  False   \n",
+       "326       False     False   False  False  False  False    False  False   \n",
+       "327       False     False   False  False  False  False    False  False   \n",
+       "\n",
+       "     gross margin percentage  gross income  Rating  \n",
+       "0                      False         False   False  \n",
+       "1                      False         False   False  \n",
+       "2                      False         False   False  \n",
+       "3                      False         False   False  \n",
+       "4                      False         False   False  \n",
+       "..                       ...           ...     ...  \n",
+       "323                    False         False   False  \n",
+       "324                    False         False   False  \n",
+       "325                    False         False   False  \n",
+       "326                    False         False   False  \n",
+       "327                    False         False   False  \n",
+       "\n",
+       "[2000 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Below this cell write in few sentences what you can derive from the data statistical summary\n",
+    "#missing values\n",
+    "df.isnull()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "aeaf6f79",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Invoice ID  Branch   City  Customer type  Gender  Product line  \\\n",
+      "0         False   False  False          False   False         False   \n",
+      "1         False   False  False          False   False         False   \n",
+      "2         False   False  False          False   False         False   \n",
+      "3         False   False  False          False   False         False   \n",
+      "4         False   False  False          False   False         False   \n",
+      "..          ...     ...    ...            ...     ...           ...   \n",
+      "323       False   False  False          False   False         False   \n",
+      "324       False   False  False          False   False         False   \n",
+      "325       False   False  False          False   False         False   \n",
+      "326       False   False  False          False   False         False   \n",
+      "327       False   False  False          False   False         False   \n",
+      "\n",
+      "     Unit price  Quantity  Tax 5%  Total   Date   Time  Payment   cogs  \\\n",
+      "0         False     False   False  False  False  False    False  False   \n",
+      "1         False     False   False  False  False  False    False  False   \n",
+      "2         False     False   False  False  False  False    False  False   \n",
+      "3         False     False   False  False  False  False    False  False   \n",
+      "4         False     False   False  False  False  False    False  False   \n",
+      "..          ...       ...     ...    ...    ...    ...      ...    ...   \n",
+      "323       False     False   False  False  False  False    False  False   \n",
+      "324       False     False   False  False  False  False    False  False   \n",
+      "325       False     False   False  False  False  False    False  False   \n",
+      "326       False     False   False  False  False  False    False  False   \n",
+      "327       False     False   False  False  False  False    False  False   \n",
+      "\n",
+      "     gross margin percentage  gross income  Rating  \n",
+      "0                      False         False   False  \n",
+      "1                      False         False   False  \n",
+      "2                      False         False   False  \n",
+      "3                      False         False   False  \n",
+      "4                      False         False   False  \n",
+      "..                       ...           ...     ...  \n",
+      "323                    False         False   False  \n",
+      "324                    False         False   False  \n",
+      "325                    False         False   False  \n",
+      "326                    False         False   False  \n",
+      "327                    False         False   False  \n",
+      "\n",
+      "[2000 rows x 17 columns]\n",
+      "     Invoice ID  Branch  City  Customer type  Gender  Product line  \\\n",
+      "0          True    True  True           True    True          True   \n",
+      "1          True    True  True           True    True          True   \n",
+      "2          True    True  True           True    True          True   \n",
+      "3          True    True  True           True    True          True   \n",
+      "4          True    True  True           True    True          True   \n",
+      "..          ...     ...   ...            ...     ...           ...   \n",
+      "323        True    True  True           True    True          True   \n",
+      "324        True    True  True           True    True          True   \n",
+      "325        True    True  True           True    True          True   \n",
+      "326        True    True  True           True    True          True   \n",
+      "327        True    True  True           True    True          True   \n",
+      "\n",
+      "     Unit price  Quantity  Tax 5%  Total  Date  Time  Payment  cogs  \\\n",
+      "0          True      True    True   True  True  True     True  True   \n",
+      "1          True      True    True   True  True  True     True  True   \n",
+      "2          True      True    True   True  True  True     True  True   \n",
+      "3          True      True    True   True  True  True     True  True   \n",
+      "4          True      True    True   True  True  True     True  True   \n",
+      "..          ...       ...     ...    ...   ...   ...      ...   ...   \n",
+      "323        True      True    True   True  True  True     True  True   \n",
+      "324        True      True    True   True  True  True     True  True   \n",
+      "325        True      True    True   True  True  True     True  True   \n",
+      "326        True      True    True   True  True  True     True  True   \n",
+      "327        True      True    True   True  True  True     True  True   \n",
+      "\n",
+      "     gross margin percentage  gross income  Rating  \n",
+      "0                       True          True    True  \n",
+      "1                       True          True    True  \n",
+      "2                       True          True    True  \n",
+      "3                       True          True    True  \n",
+      "4                       True          True    True  \n",
+      "..                       ...           ...     ...  \n",
+      "323                     True          True    True  \n",
+      "324                     True          True    True  \n",
+      "325                     True          True    True  \n",
+      "326                     True          True    True  \n",
+      "327                     True          True    True  \n",
+      "\n",
+      "[2000 rows x 17 columns]\n",
+      "if any missing values = False\n",
+      "Number of non-missing values in variables =\n",
+      "Invoice ID                 2000\n",
+      "Branch                     2000\n",
+      "City                       2000\n",
+      "Customer type              2000\n",
+      "Gender                     2000\n",
+      "Product line               2000\n",
+      "Unit price                 2000\n",
+      "Quantity                   2000\n",
+      "Tax 5%                     2000\n",
+      "Total                      2000\n",
+      "Date                       2000\n",
+      "Time                       2000\n",
+      "Payment                    2000\n",
+      "cogs                       2000\n",
+      "gross margin percentage    2000\n",
+      "gross income               2000\n",
+      "Rating                     2000\n",
+      "dtype: int64\n",
+      "Number of missing values in data= 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# missing values \n",
+    "missing_value_in_df = df.isnull()  # checking missing values\n",
+    "non_missing_valuein_df = df.notnull()  # checking non-missing values\n",
+    "any_missing_values_in_df = df.isnull().values.any()  # only want to know if there are any missing values\n",
+    "number_non_missing_values_in_variables = df.notnull().sum()  # knowling number of non-missing values for each variable\n",
+    "missing_values_in_data = df.isnull().sum().sum()  # knowing how many missing values in the data\n",
+    "print(missing_value_in_df)\n",
+    "print(non_missing_valuein_df)\n",
+    "print('if any missing values =',any_missing_values_in_df)\n",
+    "print('Number of non-missing values in variables =')\n",
+    "print(number_non_missing_values_in_variables)\n",
+    "print('Number of missing values in data=', missing_values_in_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "c3372d69",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 2000 entries, 0 to 327\n",
+      "Data columns (total 17 columns):\n",
+      " #   Column                   Non-Null Count  Dtype  \n",
+      "---  ------                   --------------  -----  \n",
+      " 0   Invoice ID               2000 non-null   object \n",
+      " 1   Branch                   2000 non-null   object \n",
+      " 2   City                     2000 non-null   object \n",
+      " 3   Customer type            2000 non-null   object \n",
+      " 4   Gender                   2000 non-null   object \n",
+      " 5   Product line             2000 non-null   object \n",
+      " 6   Unit price               2000 non-null   float64\n",
+      " 7   Quantity                 2000 non-null   int64  \n",
+      " 8   Tax 5%                   2000 non-null   float64\n",
+      " 9   Total                    2000 non-null   float64\n",
+      " 10  Date                     2000 non-null   object \n",
+      " 11  Time                     2000 non-null   object \n",
+      " 12  Payment                  2000 non-null   object \n",
+      " 13  cogs                     2000 non-null   float64\n",
+      " 14  gross margin percentage  2000 non-null   float64\n",
+      " 15  gross income             2000 non-null   float64\n",
+      " 16  Rating                   2000 non-null   float64\n",
+      "dtypes: float64(7), int64(1), object(9)\n",
+      "memory usage: 281.2+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Data Information\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14b98112",
+   "metadata": {},
+   "source": [
+    "Dealing with DateTime Features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "602132d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datetime as dt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "0d293afb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Use to_datetime() to convert the date column to datetime\n",
+    "df['Date'] =  pd.to_datetime(df['Date'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "65c46944",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 2000 entries, 0 to 327\n",
+      "Data columns (total 17 columns):\n",
+      " #   Column                   Non-Null Count  Dtype         \n",
+      "---  ------                   --------------  -----         \n",
+      " 0   Invoice ID               2000 non-null   object        \n",
+      " 1   Branch                   2000 non-null   object        \n",
+      " 2   City                     2000 non-null   object        \n",
+      " 3   Customer type            2000 non-null   object        \n",
+      " 4   Gender                   2000 non-null   object        \n",
+      " 5   Product line             2000 non-null   object        \n",
+      " 6   Unit price               2000 non-null   float64       \n",
+      " 7   Quantity                 2000 non-null   int64         \n",
+      " 8   Tax 5%                   2000 non-null   float64       \n",
+      " 9   Total                    2000 non-null   float64       \n",
+      " 10  Date                     2000 non-null   datetime64[ns]\n",
+      " 11  Time                     2000 non-null   object        \n",
+      " 12  Payment                  2000 non-null   object        \n",
+      " 13  cogs                     2000 non-null   float64       \n",
+      " 14  gross margin percentage  2000 non-null   float64       \n",
+      " 15  gross income             2000 non-null   float64       \n",
+      " 16  Rating                   2000 non-null   float64       \n",
+      "dtypes: datetime64[ns](1), float64(7), int64(1), object(8)\n",
+      "memory usage: 281.2+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Check the datatype to confirm if it's in datetime\n",
+    "\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "de57c399",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Repeat the two steps above to the time column\n",
+    "#Use to_datetime() to convert the Time column to datetime\n",
+    "df['Time'] =  pd.to_datetime(df['Time'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "5555c271",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 2000 entries, 0 to 327\n",
+      "Data columns (total 17 columns):\n",
+      " #   Column                   Non-Null Count  Dtype         \n",
+      "---  ------                   --------------  -----         \n",
+      " 0   Invoice ID               2000 non-null   object        \n",
+      " 1   Branch                   2000 non-null   object        \n",
+      " 2   City                     2000 non-null   object        \n",
+      " 3   Customer type            2000 non-null   object        \n",
+      " 4   Gender                   2000 non-null   object        \n",
+      " 5   Product line             2000 non-null   object        \n",
+      " 6   Unit price               2000 non-null   float64       \n",
+      " 7   Quantity                 2000 non-null   int64         \n",
+      " 8   Tax 5%                   2000 non-null   float64       \n",
+      " 9   Total                    2000 non-null   float64       \n",
+      " 10  Date                     2000 non-null   datetime64[ns]\n",
+      " 11  Time                     2000 non-null   datetime64[ns]\n",
+      " 12  Payment                  2000 non-null   object        \n",
+      " 13  cogs                     2000 non-null   float64       \n",
+      " 14  gross margin percentage  2000 non-null   float64       \n",
+      " 15  gross income             2000 non-null   float64       \n",
+      " 16  Rating                   2000 non-null   float64       \n",
+      "dtypes: datetime64[ns](2), float64(7), int64(1), object(7)\n",
+      "memory usage: 281.2+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Check the datatype to confirm if Time is in datetime format\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ba46f98",
+   "metadata": {},
+   "source": [
+    "Extract Features from date & time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "e9794b23",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Extract the Day feature from the Date column, and save to a new Day column\n",
+    "df['Day'] = df['Date'].dt.day"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "ac649948",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Extract the Month feature from the Date column, and save to a new Month column\n",
+    "df['Month'] = df['Date'].dt.month"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "eba64151",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Extract the Year feature from the Date column, and save to a new Year column\n",
+    "df['Year'] = df['Date'].dt.year"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "eb35f761",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Extract the Hour feature from the Time column and save to a new Hour column\n",
+    "df['Hour'] = df['Time'].dt.hour"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "d6e99530",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of unique hour of sales = 11\n"
+     ]
+    }
+   ],
+   "source": [
+    "#From the hours information, determine the numbers of unique hours of sales in the supermarket An array of the hours using the unique() method\n",
+    "Number_unique_of_sales = df['Hour'].nunique()\n",
+    "print('Number of unique hour of sales =', Number_unique_of_sales)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "908a3ec4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([13, 18, 17, 16, 15, 10, 12, 19, 14, 11, 20], dtype=int64)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# An array of unique hours using the unique() method\n",
+    "df['Hour'].unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bca7fc0c",
+   "metadata": {},
+   "source": [
+    "Unique Values in Columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "3953fff2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Invoice ID',\n",
+       " 'Branch',\n",
+       " 'City',\n",
+       " 'Customer type',\n",
+       " 'Gender',\n",
+       " 'Product line',\n",
+       " 'Payment']"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Uncomment the code and Run it\n",
+    "categorical_columns = [col for col in df.columns if df[col].dtype == \"object\" ]\n",
+    "categorical_columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "434e701c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Card', 'Epay', 'Cash']"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# generate the unique values in the categorical column\n",
+    "df['Invoice ID'].unique().tolist()\n",
+    "df['City'].unique().tolist()\n",
+    "df['Customer type'].unique().tolist()\n",
+    "df['Gender'].unique().tolist()\n",
+    "df['Product line'].unique().tolist()\n",
+    "df['Payment'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "ff10bc98",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Abuja', 'Lagos', 'Port Harcourt']"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['City'].unique().tolist()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "50292242",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Member', 'Normal']"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Customer type'].unique().tolist()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "d46e9bc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Female', 'Male']"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Gender'].unique().tolist()\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "6e4a7a92",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Food and beverages',\n",
+       " 'Fashion accessories',\n",
+       " 'Electronic accessories',\n",
+       " 'Sports and travel',\n",
+       " 'Home and lifestyle',\n",
+       " 'Health and beauty']"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Product line'].unique().tolist()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "4c05bb08",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Card', 'Epay', 'Cash']"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Payment'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "f6280742",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Number of unique values in the Invoice ID Column: 1000\n",
+      "Total Number of unique values in the City Column: 3\n",
+      "Total Number of unique values in the Customer type Column: 2\n",
+      "Total Number of unique values in the Gender Column: 2\n",
+      "Total Number of unique values in the Product line Column: 6\n",
+      "Total Number of unique values in the Payment Column: 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Number of unique vales in each categorical column\n",
+    "print(\"Total Number of unique values in the Invoice ID Column: {}\". format(len(df['Invoice ID'].unique().tolist())))\n",
+    "\n",
+    "print(\"Total Number of unique values in the City Column: {}\". format(len(df['City'].unique().tolist())))\n",
+    "\n",
+    "print(\"Total Number of unique values in the Customer type Column: {}\". format(len(df['Customer type'].unique().tolist())))\n",
+    "\n",
+    "print(\"Total Number of unique values in the Gender Column: {}\". format(len(df['Gender'].unique().tolist())))\n",
+    "\n",
+    "print(\"Total Number of unique values in the Product line Column: {}\". format(len(df['Product line'].unique().tolist())))\n",
+    "\n",
+    "print(\"Total Number of unique values in the Payment Column: {}\". format(len(df['Payment'].unique().tolist())))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "0b0708c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "A    680\n",
+       "B    664\n",
+       "C    656\n",
+       "Name: Branch, dtype: int64"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Branch'].value_counts()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "1ae0b514",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Lagos            680\n",
+       "Abuja            664\n",
+       "Port Harcourt    656\n",
+       "Name: City, dtype: int64"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['City'].value_counts()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "450792ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Member    1002\n",
+       "Normal     998\n",
+       "Name: Customer type, dtype: int64"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Customer type'].value_counts()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "3f814cc4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Female    1002\n",
+       "Male       998\n",
+       "Name: Gender, dtype: int64"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Gender'].value_counts()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "a8a01de7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fashion accessories       356\n",
+       "Food and beverages        348\n",
+       "Electronic accessories    340\n",
+       "Sports and travel         332\n",
+       "Home and lifestyle        320\n",
+       "Health and beauty         304\n",
+       "Name: Product line, dtype: int64"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Product line'].value_counts()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "530f5a71",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Epay    690\n",
+       "Cash    688\n",
+       "Card    622\n",
+       "Name: Payment, dtype: int64"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Payment'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30c29a6d",
+   "metadata": {},
+   "source": [
+    "Aggregration with GroupBy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "dce978f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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 tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Unit price</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Quantity</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Tax 5%</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Total</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">cogs</th>\n",
+       "      <th>...</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Rating</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Day</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Month</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Year</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Hour</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>sum</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sum</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sum</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sum</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sum</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>sum</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sum</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sum</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sum</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sum</th>\n",
+       "      <th>mean</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>City</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Abuja</th>\n",
+       "      <td>13304793.6</td>\n",
+       "      <td>20037.339759</td>\n",
+       "      <td>3640</td>\n",
+       "      <td>5.481928</td>\n",
+       "      <td>3641063.04</td>\n",
+       "      <td>5483.528675</td>\n",
+       "      <td>76462323.84</td>\n",
+       "      <td>115154.102169</td>\n",
+       "      <td>72821260.8</td>\n",
+       "      <td>109670.573494</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4527.2</td>\n",
+       "      <td>6.818072</td>\n",
+       "      <td>10136</td>\n",
+       "      <td>15.265060</td>\n",
+       "      <td>1330</td>\n",
+       "      <td>2.003012</td>\n",
+       "      <td>1340616</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>10058</td>\n",
+       "      <td>15.147590</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lagos</th>\n",
+       "      <td>13410352.8</td>\n",
+       "      <td>19721.107059</td>\n",
+       "      <td>3718</td>\n",
+       "      <td>5.467647</td>\n",
+       "      <td>3641155.56</td>\n",
+       "      <td>5354.640529</td>\n",
+       "      <td>76464266.76</td>\n",
+       "      <td>112447.451118</td>\n",
+       "      <td>72823111.2</td>\n",
+       "      <td>107092.810588</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4778.4</td>\n",
+       "      <td>7.027059</td>\n",
+       "      <td>10464</td>\n",
+       "      <td>15.388235</td>\n",
+       "      <td>1376</td>\n",
+       "      <td>2.023529</td>\n",
+       "      <td>1372920</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>9974</td>\n",
+       "      <td>14.667647</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Port Harcourt</th>\n",
+       "      <td>13368787.2</td>\n",
+       "      <td>20379.248780</td>\n",
+       "      <td>3662</td>\n",
+       "      <td>5.582317</td>\n",
+       "      <td>3790927.08</td>\n",
+       "      <td>5778.852256</td>\n",
+       "      <td>79609468.68</td>\n",
+       "      <td>121355.897378</td>\n",
+       "      <td>75818541.6</td>\n",
+       "      <td>115577.045122</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4639.8</td>\n",
+       "      <td>7.072866</td>\n",
+       "      <td>9912</td>\n",
+       "      <td>15.109756</td>\n",
+       "      <td>1280</td>\n",
+       "      <td>1.951220</td>\n",
+       "      <td>1324464</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>9788</td>\n",
+       "      <td>14.920732</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Unit price               Quantity                Tax 5%  \\\n",
+       "                      sum          mean      sum      mean         sum   \n",
+       "City                                                                     \n",
+       "Abuja          13304793.6  20037.339759     3640  5.481928  3641063.04   \n",
+       "Lagos          13410352.8  19721.107059     3718  5.467647  3641155.56   \n",
+       "Port Harcourt  13368787.2  20379.248780     3662  5.582317  3790927.08   \n",
+       "\n",
+       "                                  Total                       cogs  \\\n",
+       "                      mean          sum           mean         sum   \n",
+       "City                                                                 \n",
+       "Abuja          5483.528675  76462323.84  115154.102169  72821260.8   \n",
+       "Lagos          5354.640529  76464266.76  112447.451118  72823111.2   \n",
+       "Port Harcourt  5778.852256  79609468.68  121355.897378  75818541.6   \n",
+       "\n",
+       "                              ...  Rating              Day            Month  \\\n",
+       "                        mean  ...     sum      mean    sum       mean   sum   \n",
+       "City                          ...                                             \n",
+       "Abuja          109670.573494  ...  4527.2  6.818072  10136  15.265060  1330   \n",
+       "Lagos          107092.810588  ...  4778.4  7.027059  10464  15.388235  1376   \n",
+       "Port Harcourt  115577.045122  ...  4639.8  7.072866   9912  15.109756  1280   \n",
+       "\n",
+       "                            Year           Hour             \n",
+       "                   mean      sum    mean    sum       mean  \n",
+       "City                                                        \n",
+       "Abuja          2.003012  1340616  2019.0  10058  15.147590  \n",
+       "Lagos          2.023529  1372920  2019.0   9974  14.667647  \n",
+       "Port Harcourt  1.951220  1324464  2019.0   9788  14.920732  \n",
+       "\n",
+       "[3 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Your task here, will be to create a groupby object with the \"City Column\", and aggregation function of sum and mean\n",
+    "# Creating a groupby object to group the dataset according to City \n",
+    "city = df.groupby(\"City\")\n",
+    "\n",
+    "#To get the sum and mean of the numeric columns \n",
+    "city.agg([\"sum\", \"mean\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "20f29c5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Using the groupby object, display a table that shows the gross income of each city, and determine the city with the highest total gross income.\n",
+    "# Using the groupby object ot get the gross income of each city\n",
+    "\n",
+    "gross_income_city = city['gross income'].sum().reset_index()\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "b9da4b59",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>City</th>\n",
+       "      <th>gross income</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>3641063.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>3641155.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>3790927.08</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            City  gross income\n",
+       "0          Abuja    3641063.04\n",
+       "1          Lagos    3641155.56\n",
+       "2  Port Harcourt    3790927.08"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gross_income_city"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "c5aa1332",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The highest gross income is generated by Port Harcourt: 3790927.08\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"The highest gross income is generated by Port Harcourt:\", df.groupby('City')['gross income'].sum().max())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "0785c9c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City\n",
+       "Abuja            13304793.6\n",
+       "Lagos            13410352.8\n",
+       "Port Harcourt    13368787.2\n",
+       "Name: Unit price, dtype: float64"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('City')['Unit price'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "bf501f0c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The highest Unit price: 13410352.8\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"The highest Unit price:\", df.groupby('City')['Unit price'].sum().max())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "d37fcab8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City\n",
+       "Abuja            3640\n",
+       "Lagos            3718\n",
+       "Port Harcourt    3662\n",
+       "Name: Quantity, dtype: int64"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('City')['Quantity'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "1b6c4491",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The highest Quantity: 3718\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"The highest Quantity:\", df.groupby('City')['Quantity'].sum().max())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc1d928a",
+   "metadata": {},
+   "source": [
+    "Data Visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "36da6213",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Branch A has the highest sales record.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Using countplot, determine the branch with the highest sales record\n",
+    "sns.countplot(x= 'Branch', data = df).set_title('Sales record for Branches')\n",
+    "print('Branch A has the highest sales record.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "28931aa1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The most used payment method is Epay with a count of 690\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# most used payment method,\n",
+    "sns.countplot(x='Payment', data=df).set_title('Payment method')\n",
+    "print('The most used payment method is Epay with a count of 690')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "ac3365f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The City with the most sales is Lagos\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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YunXrsnTpUmrWrImXlxd2dnaEh4df9r10jo6OfPjhhwwePJj77ruP22+/nTZt2uDk5ERWVha7du1i7969fPPNN9SvX5/ff/+du+66C39/fwIDA2ncuDFnzpwhLS2NX3/9lf79++Pn51eu8yGWoaCTavXEE0/g7+9Peno6P/30E+np6fz111+4urqaVvr9+9//Nrvfceedd/LJJ58QFxfH//73P+zt7QkKCmLFihX89ttv5Q46OB8ivXr1IiUlhQ0bNnDy5Enq1auHp6cnI0eOZNiwYaayDz/8MK6urnz33Xds3bqVoqIiGjVqREREBGPGjCnX6stGjRqxatUqJk+ezObNm0lLS8Pf358ZM2bQrVu3Swadi4sLq1evJjk5mSVLlpg+1aNRo0b079+fTp06lXu8Pj4+rF27lri4OL744gs2b96Mk5MTnTt35sknnzS7HOzk5MSUKVNYv34927ZtY9WqVdSuXRsfHx9effVVIiMjTW87GDp0KEVFRWzZsoVly5bx119/YTAYuPvuuxk9ejRBQUHl6l9gYCDr168nPj6eNWvWkJSURO3atfH29mbMmDG4ubld8fh69eqxYMECYmNjWbp0qek9hh07drzim8ZbtWrFxo0bSUxMJDU1lY8//hij0YjBYKBly5am31M4Pyt/4YUXWL9+PRs3buT48ePUq1cPX19fxo4dy3333VeusYrl2OXm5hrLLiYiInJ90j06ERGxaQo6ERGxaQo6ERGxaQo6ERGxaQo6ERGxaRYLutatW5t9UeKFnwtvXjUajcTGxtKyZUs8PDwICwtjz549ZnUUFhYSHR2Nr68vnp6eREREkJWVZYnhiIiIlbJY0KWnp5OZmWn6WbduHXZ2dgwcOBCA+Ph4EhISmDZtGmlpabi5uTFo0CCzj0WKiYlhxYoVpKSkkJqaSn5+PuHh4RX6OCAREbFtVvM+uri4OGbOnMnPP/9M7dq1admyJY888gjPPvsscP5TG/z9/XnllVeIjIwkLy8PPz8/EhISTLPAI0eO0Lp1axYvXkzPnj0tORwREbESVnGPzmg0Mn/+fMLDw6lTpw4HDx4kOzvb9BUuALVr1yYkJMT0XVI7duygqKjIrIyXlxctWrQwlREREbGKoEtPT+fgwYOMGDEC+L8Phr3443/c3NxMXzmSk5ODg4NDqW/3/WcZERERq/isy7lz59KuXTvT14pccPF3bBmNxkt+71ZFy+zdu/fqOioiIlbnwueSXo7Fg+6PP/4gNTWVuLg407YL33yck5Nj+mR1OP91GRdmee7u7hQXF3PixAkaNmxoViYkJOSKbZb1pIiIiO2w+KXLBQsWUKtWLQYPHmza5uPjg8FgID093bStoKCAjIwMOnToAJz/1HNHR0ezMllZWWRmZprKiIiIWHRGZzQamTdvHoMHDzZ9szScv2QZFRXFjBkz8Pf3x8/Pj7i4OJycnBg6dChw/us5RowYwcSJE3Fzc8PV1ZUJEyYQEBBA9+7dLTQiERGxNhYNuvXr17N//37ef//9UvvGjh3L2bNniY6OJjc3l6CgIJYuXWoWiFOnTsXBwYHIyEgKCgro2rUrSUlJODg4VOcwRETEilnN++hERESqgsUXo4hYm/ynhlu6C/IPzm99bOkuyHXO4otRREREqpKCTkREbJqCTkREbJqCTkREbJoWo5QhYNZWS3dB/mH3qGBLd0FErjOa0YmIiE1T0ImIiE3TpUsRueEdSH3A0l2QizTrO6/S6tKMTkREbJqCTkREbJqCTkREbJqCTkREbJqCTkREbJqCTkREbJqCTkREbJqCTkREbJqCTkREbJqCTkREbJqCTkREbJqCTkREbJqCTkREbJpFg+7YsWM8/vjjNG/eHIPBQIcOHdiwYYNpv9FoJDY2lpYtW+Lh4UFYWBh79uwxq6OwsJDo6Gh8fX3x9PQkIiKCrKys6h6KiIhYKYsFXW5uLnfffTdGo5GFCxeyZcsWpk+fjpubm6lMfHw8CQkJTJs2jbS0NNzc3Bg0aBD5+fmmMjExMaxYsYKUlBRSU1PJz88nPDyc4uJiSwxLRESsjMW+j27mzJl4eHiQnJxs2ta0aVPTv41GI4mJiYwbN44BAwYAkJiYiL+/P4sXLyYyMpK8vDzmz59PQkICPXr0ACA5OZnWrVuzdu1aevbsWa1jEhER62OxGd3KlSsJCgoiMjISPz8/unTpwnvvvYfRaATg4MGDZGdnExoaajqmdu3ahISEsGXLFgB27NhBUVGRWRkvLy9atGhhKiMiIjc2i83ofvvtN1JSUhg1ahTjxo3jhx9+4Pnnnwfg0UcfJTs7G8DsUuaFx0ePHgUgJycHBwcHGjRoUKpMTk7OZdveu3dvZQ5FqlF1nDuPKm9BKqI6zrnFXgjlsipy3v39/a+432Lnt6SkhLZt2zJp0iQA2rRpw/79+5k9ezaPPvqoqZydnZ3ZcUajsdS2i5VVpqwnxcyXW8tfVqpchc7dVcovu4hUo+o45wf0t6/VqczzbrFLlwaDgRYtWphtu+WWWzhy5IhpP1BqZnb8+HHTLM/d3Z3i4mJOnDhx2TIiInJjs1jQdezYkX379plt27dvH02aNAHAx8cHg8FAenq6aX9BQQEZGRl06NABgMDAQBwdHc3KZGVlkZmZaSojIiI3Notduhw1ahS9evUiLi6OwYMHs2vXLt577z1eeukl4Pwly6ioKGbMmIG/vz9+fn7ExcXh5OTE0KFDAahXrx4jRoxg4sSJuLm54erqyoQJEwgICKB79+6WGpqIiFgRiwVdu3btWLBgAVOmTOGNN97Ay8uLF154gYcffthUZuzYsZw9e5bo6Ghyc3MJCgpi6dKlODs7m8pMnToVBwcHIiMjKSgooGvXriQlJeHg4GCJYYmIiJWxy83NNVq6E9YsYJYWo1iT3aOCq7yN/KeGV3kbUn7Ob31c5W0cSH2gytuQimnWd16l1aXPuhQREZumoBMREZumoBMREZumoBMREZumoBMREZumoBMREZumoBMREZumoBMREZumoBMREZumoBMREZumoBMREZumoBMREZumoBMREZumoBMREZumoBMREZumoBMREZumoBMREZumoBMREZumoBMREZumoBMREZumoBMREZumoBMREZtmsaCLjY3FxcXF7OeWW24x7TcajcTGxtKyZUs8PDwICwtjz549ZnUUFhYSHR2Nr68vnp6eREREkJWVVd1DERERK2bRGZ2/vz+ZmZmmn02bNpn2xcfHk5CQwLRp00hLS8PNzY1BgwaRn59vKhMTE8OKFStISUkhNTWV/Px8wsPDKS4utsRwRETEClk06GrUqIHBYDD9NGzYEDg/m0tMTGTcuHEMGDCAVq1akZiYyOnTp1m8eDEAeXl5zJ8/nylTptCjRw8CAwNJTk5m9+7drF271oKjEhERa2LRoPvtt9/417/+xW233cZDDz3Eb7/9BsDBgwfJzs4mNDTUVLZ27dqEhISwZcsWAHbs2EFRUZFZGS8vL1q0aGEqIyIiUsNSDd9+++3MmjULf39/jh8/zhtvvEGvXr3YvHkz2dnZALi5uZkd4+bmxtGjRwHIycnBwcGBBg0alCqTk5Nzxbb37t1biSOR6lQd586jyluQiqiOc26xF0K5rIqcd39//yvut9j5veuuu8we33777QQGBvLRRx/Rvn17AOzs7MzKGI3GUtsuVp4yZT0pZr7cWv6yUuUqdO6uUn7ZRaQaVcc5P6C/fa1OZZ53q3l7Qd26dWnZsiX79+/HYDAAlJqZHT9+3DTLc3d3p7i4mBMnTly2jIiIiNUEXUFBAXv37sVgMODj44PBYCA9Pd1sf0ZGBh06dAAgMDAQR0dHszJZWVlkZmaayoiIiFjs0uWLL75I79698fLyMt2j++uvvxg+fDh2dnZERUUxY8YM/P398fPzIy4uDicnJ4YOHQpAvXr1GDFiBBMnTsTNzQ1XV1cmTJhAQEAA3bt3t9SwRETEylgs6H7//XcefvhhTpw4QcOGDbn99tv58ssv8fb2BmDs2LGcPXuW6OhocnNzCQoKYunSpTg7O5vqmDp1Kg4ODkRGRlJQUEDXrl1JSkrCwcHBUsMSERErY5ebm2u0dCesWcAsLUaxJrtHBVd5G/lPDa/yNqT8nN/6uMrbOJD6QJW3IRXTrO+8SqvLau7RiYiIVAUFnYiI2DQFnYiI2DQFnYiI2DQFnYiI2DQFnYiI2DQFnYiI2DQFnYiI2DQFnYiI2DQFnYiI2DQFnYiI2DQFnYiI2DQFnYiI2DQFnYiI2DQFnYiI2DQFnYiI2DQFnYiI2DQFnYiI2LQKBV2bNm1ITU297P4vvviCNm3aXHOnREREKkuFgu7QoUOcOXPmsvvPnDnD4cOHr7lTIiIilaXCly7t7Owuu2/fvn04OztfU4dEREQqU5lB99FHH9G/f3/69+8PQFxcnOnxP3/uuOMOpk+fTufOna+qIzNmzMDFxYXo6GjTNqPRSGxsLC1btsTDw4OwsDD27NljdlxhYSHR0dH4+vri6elJREQEWVlZV9UHERGxPWUG3ZkzZ8jOziY7OxuAvLw80+MLPzk5OdSoUYMHH3yQt99+u8Kd2LZtG3PnziUgIMBse3x8PAkJCUybNo20tDTc3NwYNGgQ+fn5pjIxMTGsWLGClJQUUlNTyc/PJzw8nOLi4gr3Q0REbE+Nsgo88sgjPPLIIwDcdtttvP766/Tt27fSOpCXl8cjjzzCO++8w/Tp003bjUYjiYmJjBs3jgEDBgCQmJiIv78/ixcvJjIykry8PObPn09CQgI9evQAIDk5mdatW7N27Vp69uxZaf0UEZHrU4Xu0e3atatSQw4wBVm3bt3Mth88eJDs7GxCQ0NN22rXrk1ISAhbtmwBYMeOHRQVFZmV8fLyokWLFqYyIiJyYytzRncp+fn5HDlyhJMnT2I0GkvtL+99urlz57J//36Sk5NL7btwqdTNzc1su5ubG0ePHgUgJycHBwcHGjRoUKpMTk5OufogIiK2rUJBd/LkSZ5//nn+97//XfIemNFoxM7Ojj///LPMuvbu3cuUKVNYtWoVNWvWvGy5i1d5XmjjSsoqs3fv3jL7J9apOs6dR5W3IBVRHef8qv7ilypVkfPu7+9/xf0VOr9PPfUUn3/+OY888gidO3fGxcWlIoeb2bp1KydOnKBTp06mbcXFxWzatIk5c+awefNm4PyszcvLy1Tm+PHjplmeu7s7xcXFnDhxgoYNG5qVCQkJuWzbZT0pZr7cWv6yUuUqdO6uUn7ZRaQaVcc5P6C/fa1OZZ73CgXdV199xWOPPcZrr712zQ2HhYXRtm1bs22jR4+mefPmPP300/j5+WEwGEhPT6ddu3YAFBQUkJGRwZQpUwAIDAzE0dGR9PR0hg0bBkBWVhaZmZl06NDhmvsoIiLXvwoFXc2aNWnevHmlNOzi4lJqRlinTh1cXV1p1aoVAFFRUcyYMQN/f3/8/PyIi4vDycmJoUOHAlCvXj1GjBjBxIkTcXNzw9XVlQkTJhAQEED37t0rpZ8iInJ9q1DQDRgwgC+//JKHHnqoqvpjZuzYsZw9e5bo6Ghyc3MJCgpi6dKlZp++MnXqVBwcHIiMjKSgoICuXbuSlJSEg4NDtfRRRESsm11ubm7pZZOXsX//fkaOHEmLFi0YOXIkTZo0uWSgXLxS8noWMEv36KzJ7lHBVd5G/lPDq7wNKT/ntz6u8jYOpD5Q5W1IxTTrO6/S6qrQjC4oKAg7Ozt27NjBwoULL1uuPKsuRUREqkOFgu65554rc2m/iIiINalQ0MXExFRVP0RERKqEvmFcRERsWoVmdNOmTSuzjJ2dHc8999xVd0hERKQyVSjoXn/99cvus7OzM330loJORESsRYU/6/JiJSUlHDp0iOTkZLZs2cLixYsrrXMiIiLX6prv0dnb29O0aVNiY2Px8fFh/PjxldEvERGRSlGpi1HuuOMOVq9eXZlVioiIXJNKDbq9e/de8vvpRERELKVC9+g2btx4ye15eXmsX7+e999/n4EDB1ZGv0RERCpFhYKuX79+l/xkFKPRiIODA0OGDCnXWxBERESqS4WCbsWKFaW22dnZ4eLigre3t9m3CoiIiFiDCgVdly5dqqofIiIiVaJCQXdBfn4+GzZs4NChQwB4e3vTpUsXzehERMTqVDjokpOTefXVVzlz5ozZCksnJydeeuklHnvssUrtoIiIyLWoUNB98sknjB8/nqCgIKKiomjRogVGo5FffvmFpKQkYmJicHV15d57762q/oqIiFRIhYIuISGBDh068Pnnn1Ojxv8d2rp1awYMGEC/fv145513FHQiImI1KvSG8b179zJ48GCzkLugRo0aDB48mH379lVa50RERK5VhYLOycmJ7Ozsy+7Pzs6mTp0619wpERGRylKhoAsNDSU5OZn169eX2rdhwwbee+89evbsWWmdExERuVYVukc3adIkNm3axIABA7jtttu45ZZbAPjll1/YtWsXjRo1YtKkSVXSURERkatRoRmdl5cX69evZ9SoUfz1118sX76c5cuX89dffzF69GjWr19P48aNy1XX+++/T0hICE2aNKFJkybcddddZt98YDQaiY2NpWXLlnh4eBAWFsaePXvM6igsLCQ6OhpfX188PT2JiIggKyurIkMSEREbV6GgO3PmDGfOnOHVV19l69atHDt2jGPHjrF161ZeeeUVzpw5w19//VWuujw9PXn55ZdZt24d6enpdO3alfvvv58ff/wRgPj4eBISEpg2bRppaWm4ubkxaNAg8vPzTXXExMSwYsUKUlJSSE1NJT8/n/DwcIqLiysyLBERsWEVCroXXniB++6777L777//fl566aVy1RUWFsZdd92Fr68vfn5+vPTSS9StW5dt27ZhNBpJTExk3LhxDBgwgFatWpGYmMjp06dN32Cel5fH/PnzmTJlCj169CAwMJDk5GR2797N2rVrKzIsERGxYRUKuvT0dPr163fZ/f369ePrr7+ucCeKi4tZsmQJZ86cITg4mIMHD5KdnU1oaKipTO3atQkJCWHLli0A7Nixg6KiIrMyXl5etGjRwlRGRESkQotRsrOz8fDwuOx+g8HAsWPHyl3f7t276dWrFwUFBTg5OfHhhx8SEBBgCio3Nzez8m5ubhw9ehSAnJwcHBwcaNCgQakyOTk5V2x379695e6jWJfqOHeX/w0XS6iOc35VH/orVaoi593f3/+K+yt0fhs2bFhqQcg/7dmzh3r16pW7Pn9/f9avX09eXh7Lly8nKiqKzz//3LT/4u++MxqNl/w+vIqWKetJMfPl1vKXlSpXoXN3lfLLLiLVqDrO+QH97Wt1KvO8V+jS5V133cXcuXMveWlw27ZtzJ07l7vuuqvc9dWsWRNfX1/atm3LpEmTaN26NbNmzcJgMACUmpkdP37cNMtzd3enuLiYEydOXLaMiIhIhWZ0MTExfPnll/Tt25c777yTVq1aYWdnx+7du/nqq68wGAxMmDDhqjtTUlLCuXPn8PHxwWAwkJ6eTrt27QAoKCggIyODKVOmABAYGIijoyPp6ekMGzYMgKysLDIzM+nQocNV90FERGxLhYLuQvhMmjSJlStXsmbNGgCcnZ0JDw9n0qRJptlYWSZPnkyvXr1o3LixaTXlhg0bWLhwIXZ2dkRFRTFjxgz8/f3x8/MjLi4OJycnhg4dCkC9evUYMWIEEydOxM3NDVdXVyZMmEBAQADdu3ev2LMgIiI2q8L3YN3d3UlMTMRoNHL8+HGMRiNubm5l3he7WHZ2No8++ig5OTncfPPNBAQEsHjxYtNHiI0dO5azZ88SHR1Nbm4uQUFBLF261OzLXadOnYqDgwORkZEUFBTQtWtXkpKScHBwqOiwRETERtnl5uYayy524wqYpcUo1mT3qOAqbyP/qeFV3oaUn/NbH1d5GwdSH6jyNqRimvWdV2l1VWgxioiIyPVGQSciIjZNQSciIjZNQSciIjZNQSciIjZNQSciIjZNQSciIjZNQSciIjZNQSciIjZNQSciIjZNQSciIjZNQSciIjZNQSciIjZNQSciIjZNQSciIjZNQSciIjZNQSciIjZNQSciIjZNQSciIjZNQSciIjZNQSciIjZNQSciIjbNYkH35ptv0qNHD5o0aULz5s0JDw/np59+MitjNBqJjY2lZcuWeHh4EBYWxp49e8zKFBYWEh0dja+vL56enkRERJCVlVWdQxEREStmsaDbsGEDI0eOZPXq1SxfvpwaNWowcOBATp48aSoTHx9PQkIC06ZNIy0tDTc3NwYNGkR+fr6pTExMDCtWrCAlJYXU1FTy8/MJDw+nuLjYEsMSERErU8NSDS9dutTscXJyMt7e3mzevJk+ffpgNBpJTExk3LhxDBgwAIDExET8/f1ZvHgxkZGR5OXlMX/+fBISEujRo4epntatW7N27Vp69uxZ7eMSERHrYjX36E6fPk1JSQkuLi4AHDx4kOzsbEJDQ01lateuTUhICFu2bAFgx44dFBUVmZXx8vKiRYsWpjIiInJjs9iM7mLjx4+ndevWBAcHA5CdnQ2Am5ubWTk3NzeOHj0KQE5ODg4ODjRo0KBUmZycnMu2tXfv3srsulSj6jh3HlXeglREdZxzq3khFJOKnHd/f/8r7reK8/vCCy+wefNmvvjiCxwcHMz22dnZmT02Go2ltl2srDJlPSlmvtxa/rJS5Sp07q5SftlFpBpVxzk/oL99rU5lnneLX7qMiYlhyZIlLF++nKZNm5q2GwwGgFIzs+PHj5tmee7u7hQXF3PixInLlhERkRubRYPu+eefZ/HixSxfvpxbbrnFbJ+Pjw8Gg4H09HTTtoKCAjIyMujQoQMAgYGBODo6mpXJysoiMzPTVEZERG5sFrt0+eyzz/Lpp5/y4Ycf4uLiYron5+TkRN26dbGzsyMqKooZM2bg7++Pn58fcXFxODk5MXToUADq1avHiBEjmDhxIm5ubri6ujJhwgQCAgLo3r27pYYmIiJWxGJBN3v2bADTWwcueP7554mJiQFg7NixnD17lujoaHJzcwkKCmLp0qU4Ozubyk+dOhUHBwciIyMpKCiga9euJCUllbrXJyIiNya73Nxco6U7Yc0CZmkxijXZPSq4ytvIf2p4lbch5ef81sdV3saB1AeqvA2pmGZ951VaXRZfjCIiIlKVFHQiImLTFHQiImLTFHQiImLTFHQiImLTFHQiImLTFHQiImLTFHQiImLTFHQiImLTFHQiImLTFHQiImLTFHQiImLTFHQiImLTFHQiImLTFHQiImLTFHQiImLTFHQiImLTFHQiImLTFHQiImLTFHQiImLTFHQiImLTLBp0GzduJCIign/961+4uLiwYMECs/1Go5HY2FhatmyJh4cHYWFh7Nmzx6xMYWEh0dHR+Pr64unpSUREBFlZWdU5DBERsWIWDbozZ87QqlUrXn/9dWrXrl1qf3x8PAkJCUybNo20tDTc3NwYNGgQ+fn5pjIxMTGsWLGClJQUUlNTyc/PJzw8nOLi4uocioiIWCmLBl2vXr2YOHEiAwYMwN7evCtGo5HExETGjRvHgAEDaNWqFYmJiZw+fZrFixcDkJeXx/z585kyZQo9evQgMDCQ5ORkdu/ezdq1ay0wIhERsTZWe4/u4MGDZGdnExoaatpWu3ZtQkJC2LJlCwA7duygqKjIrIyXlxctWrQwlRERkRub1QZddnY2AG5ubmbb3dzcyMnJASAnJwcHBwcaNGhw2TIiInJjq2HpDpTFzs7O7LHRaCy17WJlldm7d2+l9E2qX3WcO48qb0EqojrOudW/EN6AKnLe/f39r7jfas+vwWAAzs/avLy8TNuPHz9umuW5u7tTXFzMiRMnaNiwoVmZkJCQy9Zd1pNi5sutFey5VKUKnburlF92EalG1XHOD+hvX6tTmefdai9d+vj4YDAYSE9PN20rKCggIyODDh06ABAYGIijo6NZmaysLDIzM01lRETkxmbRGd3p06fZv38/ACUlJRw5coRdu3bh6upKkyZNiIqKYsaMGfj7++Pn50dcXBxOTk4MHToUgHr16jFixAgmTpyIm5sbrq6uTJgwgYCAALp3727BkYmIiLWwaNB9//339O/f3/Q4NjaW2NhYhg8fTmJiImPHjuXs2bNER0eTm5tLUFAQS5cuxdnZ2XTM1KlTcXBwIDIykoKCArp27UpSUhIODg6WGJKIiFgZu9zcXKOlO2HNAmbpHp012T0quMrbyH9qeJW3IeXn/NbHVd7GgdQHqrwNqZhmfedVWl1We49ORESkMijoRETEpinoRETEpinoRETEpinoRETEpinoRETEpinoRETEpinoRETEpinoRETEpinoRETEpinoRETEpinoRETEpinoRETEpinoRETEpinoRETEpinoRETEpinoRETEpinoRETEpinoRETEpinoRETEpinoRETEpinoRETEptlM0M2ePZvbbrsNg8FAt27d2LRpk6W7JCIiVsAmgm7p0qWMHz+eZ555hm+++Ybg4GCGDRvG4cOHLd01ERGxMJsIuoSEBO677z4efPBBWrRowRtvvIHBYGDOnDmW7pqIiFiYXW5urtHSnbgW586do1GjRqSkpDBw4EDT9meffZaffvqJ1NRUy3VOREQs7rqf0Z04cYLi4mLc3NzMtru5uZGTk2OhXomIiLW47oPuAjs7O7PHRqOx1DYREbnxXPdB16BBAxwcHErN3o4fP15qliciIjee6z7oatasSWBgIOnp6Wbb09PT6dChg4V6JSIi1uK6DzqA0aNH89FHHzFv3jwyMzN5/vnnOXbsGJGRkZbuWpU6ePAgLi4ufP/991ZRj4iINbKJoBs8eDCxsbG88cYb3HHHHWzevJmFCxfi7e1t6a5Vip07d1K/fn3uvvvuKqnfy8uLzMxMWrduXSX1S/lFRUURHh5u6W4I58+Fi4sLLi4uNGzYkDZt2vDiiy9y5syZa6o3NjaWTp06lVluwYIFNG7c+JL7GjduzIIFC66pH5bm4uLCsmXLqqWtGtXSSjV4+OGHefjhhy3djSoxb948Ro4cyaeffkpmZiYtWrSo1PodHBwwGAyVWqeILejevTvJyckUFRWRkZHBk08+yV9//cWbb755VfUVFRVVcg+vTklJCUajEQcHh2pv+9y5c9SsWbNa27SJGZ0tO3v2LIsWLeLBBx/knnvuYf78+aXK7Nu3j969e2MwGGjfvj1paWmmfevXr8fFxYUTJ06Ytl18qfLix8XFxYwZM4bbbrsNDw8P2rVrR3x8PCUlJVU8WrmSd999l5CQEDw9PfnXv/7FE088QW5urlmZ+fPnc+utt9KoUSPCw8OZPXs2Li4uZmU++OAD2rZti5ubG23btmXu3Lml9gcFBWEwGGjevDmDBw/m77//ruLRWadatWphMBjw8vJi2LBhDBs2jJUrVwJQWFjI+PHj8ff3x2AwcOedd5KRkWE69sL/vTVr1hAaGoqbmxsffPAB06ZNY8+ePabZYmXMzMr63bgwO1yzZg2dOnXCzc2NzMxMzp07x5QpU7j11ltxd3enTZs2JCUlmY7buHEjPXv2xGAw4O/vT0xMDOfOnTPtDwsLIzo62qwvF1+VCAsL4+mnn+bFF1+kefPm3H333aarRw8++CAuLi5VfjXJZmZ0tmrZsmU0adKEW2+9lfDwcCIjI5k0aRKOjo6mMpMmTeK1114jICCA999/n/vuu4/t27fj6el5VW2WlJTQqFEj/vvf/9KgQQO2b9/O2LFjcXV15YEHHqisoUkF2dvbExsbS9OmTTl8+DDPPfcczz33HO+99x4AW7du5cknn2TSpEn069ePjRs3MmXKFLM6VqxYQXR0NFOnTiU0NJSvv/6aZ555Bnd3d/r06cP333/Ps88+S2JiIh07diQvL49vvvnGEsO1SjfddJNpVjZx4kQ+++wz3n33XZo2bUpCQgJDhw7lu+++w8PDw3TM5MmTefXVV/H19aVGjRocOnSI1atX8/nnnwNw8803X3O/yvrdACgoKCAuLo633nqLhg0bYjAYiIqKIiMjg9jYWNq0acOhQ4fIysoC4Pfff2fYsGGEh4cza9YsDhw4wJNPPom9vT2vvfZahfq3cOFCHnzwQVatWoXRaKRBgwb4+fkxc+ZM7r777iqfWSrorNy8efOIiIgAoEuXLtSuXZvU1FQGDBhgKvPQQw8xaNAgAKZNm0ZaWhpz5szhxRdfvKo2HR0dmTBhgumxj48PO3fuZMmSJQo6Cxo1apTp3z4+PkyZMoX77ruPpKQk7O3tSU5OJjQ0lHHjxgHg5+fH9u3bzWZs7777LuHh4Tz66KOmMjt27CA+Pp4+ffpw+PBhnJyc6NOnD87OzgC6d/v/fffddyxevJhu3bpx5swZ5syZY3qhBnjrrbf45ptvmD17ttn/veeff57Q0FDTYycnJ2rUqFGu2wVnzpy55H26i+8TlvW7Aeev1EyfPp3AwEAAfv31V5YsWcLixYu58847AWjatKmpnpSUFAwGAzNmzMDe3p4WLVowadIknnrqKSZMmECdOnXK7P8F3t7elwzHevXqVcttEwWdFdu/fz9btmwhJSUFOP+m+HvvvZf58+ebBV379u1N/7a3tycoKIiff/75mtqeM2cO8+bN4/DhwxQUFFBUVESTJk2uqU65NuvWreOtt97il19+4dSpUxQXF3Pu3Dmys7Np1KgRv/zyC7179zY7JigoyCzoMjMzuf/++83KdOrUiVWrVgHQo0cPvLy8aNOmDT179qRHjx7079/fFHo3mq+++orGjRvz999/U1RURN++fZk+fToHDhygqKiIjh07mso6ODgQHBxc6v9e27Ztr7r9OnXqsH79+lLbu3TpYva4rN8NgBo1apj90bJr1y7s7e254447Ltl2ZmYm7du3NwUlnP9dOXfuHPv37+fWW28t9zguhKulKOis2Lx58yguLjb7hTIaz3806ZEjR8pVx4Vf0gvHAWXeb1m6dCkxMTG88sorBAcHc/PNN/P++++bLrVI9Tt06BDh4eE88MADvPDCC9SvX5+dO3cycuRI0z2T8n4a0KXKXNjm7OzMN998w8aNG1m7di1vvfUWr7zyCmlpaaYXzBtJSEgI8fHx1KhRg0aNGpluGRw7dgy48nN5gZOT01W3b2dnh6+v7xXbKM/vBpy/3/jPS4T/fE24lCv9Pl3Ybm9vX6qeS72+XMtzUBm0GMVK/f3333z88cdMmjSJ9evXm342bNhAQECA2Q3sb7/91vRvo9HI9u3bTSszGzZsCPzff0yAH3744YptZ2RkEBQUxKOPPkpgYCC+vr4cOHCgMocnFfT9999z7tw5YmNjCQ4Oxs/Pj6NHj5qVadGiBdu3bzfbdvHjFi1asHnzZrNtGRkZtGzZ0vS4Ro0adOvWjUmTJrFx40bOnDnD6tWrK3lE14c6derg6+uLt7e32X1xX19fatasabb4pLi4mK1bt5a5KrpmzZoUFxdXWh/L87txKW3atKGkpOSSM0aAli1bsm3bNrNFaBkZGdSsWZNmzZoB519f/vnaAvDjjz+Wq9+Ojo6V+jxciWZ0Vmr16tWcOHGCBx98kPr165vtGzJkCCkpKaaVTXPmzMHPz49WrVoxe/ZsDh8+zEMPPQSc/w/p5eXF66+/zuTJkzl06BBvvPHGFdv28/Pj448/5ssvv8TX15clS5awadMm6tWrVzWDFTOnTp1i165dZtuaN29OSUkJs2bNon///nz77bdmq+MAHnvsMXr37s3MmTMJCwtj48aNpWbhTzzxBP/5z38IDAwkNDSUr776ikWLFplW837xxRccOHCAkJAQXF1dWb9+PadPn+aWW26p2kFfZ5ycnHjooYd4+eWXadCgAT4+PsyaNYs//vijzLc5eXt7c/jwYXbs2EGTJk2oW7cutWrVuuq+lOd343LHDRo0iCeffNK0GOX333/n0KFDREREMHLkSBITE3nmmWd4/PHH+e2333j55Zd55JFHTPfnunbtSkxMDKmpqfj7+/PBBx+QlZVVrvcwe3t7s27dOjp37kytWrVKrQ6uTJrRWan58+dzxx13lAo5gIEDB3L48GHWrl0LnF91mZCQQJcuXfj666/58MMPTTewHR0dSUlJ4bfffqNLly7ExsYyceLEK7YdGRnJwIEDefjhh+nRoweHDh1i9OjRlT5GubSMjAy6du1q9jN9+nRef/11Zs2aRceOHZk3bx6vvPKK2XHBwcHEx8eTnJxM586dWblyJWPHjuWmm24ylenXrx/Tp09n1qxZdOjQgaSkJGbMmEGfPn2A84sDVq5cycCBAwkODubdd99l5syZhISEVOtzcD14+eWXGThwIKNHj+aOO+5g9+7dLF682GzF5aXcc8893HXXXQwYMIDmzZuzePHia+rHrbfeWubvxuUkJSUxdOhQxo8fT3BwMKNGjeLUqVMAeHp6smjRInbt2sUdd9zBmDFjGDJkiNnrx7///W/+/e9/M2bMGO6++26cnJwICwsrV9uvvvoq69evJyAg4LL3CSvLdf99dHLt9u7dS/v27dm6dav+crcxMTExrFu3jk2bNlm6KyIWo0uXN7iTJ0+ybNkynJ2dtarSBsycOZPu3btTt25d1q5dywcffMBLL71k6W6JWJSC7gY3ZswYdu7cyZtvvknt2rUt3R25Rt9//z3vvPMOp06dwsfHh4kTJxIVFWXpbolYlC5dioiITdNiFBERsWkKOhERsWkKOhERsWkKOhEbEhUVpQ9hFrmIgk7kOvDHH38wefJkOnbsiKenJ40aNSIkJITJkyeX+gimi73xxhv6nFK5oWnVpYiV+/777xk2bBj5+fkMGTKEoKAg7O3t2b17N0uWLKF+/fp89913wPlvsC4pKTH7SCmDwcDgwYNJTEy01BBELErvoxOxYrm5udx///3Y2dmxdu1a/vWvf5ntf+mll3j77bdNj//5wcMicp4uXYpYsf/+97/8/vvvvPrqq6VCDs5/NuWkSZNMjy++R+fi4kJhYSEff/wxLi4uuLi4EBYWxq+//oqLiwsJCQml6vz5559xcXEx+3ZqkeuZgk7Eiq1atYqbbrrJ9A3yFZWcnIyjoyOdOnUiOTmZ5ORknn32WZo3b06HDh349NNPSx3z6aef4ujoyJAhQ661+yJWQUEnYsUyMzPx8/OjZs2aV3V8eHg49vb2NG3alPDwcMLDw+nRowcAw4cPZ9euXWbfiG00Glm0aBE9e/akQYMGlTIGEUtT0IlYsfz8fJydnauk7kGDBnHTTTeZzeo2bNjAkSNHiIiIqJI2RSxBQSdixZydncnPz6+SuuvVq0ffvn1ZtGgRRuP5xdcLFy7k5ptvpnfv3lXSpoglKOhErFiLFi3Yt28f586dq5L6hw8fzpEjR9i4cSOFhYUsW7aMgQMHmn1Zq8j1TkEnYsX69OlDQUEBn3322VXXYWdnd9l9oaGheHh48Omnn7Jq1SpOnTpFeHj4VbclYo0UdCJW7D//+Q+enp68+OKLZGZmltp/6tQppkyZcsU66tSpQ25u7iX3OTg4MGzYMJYtW8b8+fPx9vYmJCSkMrouYjUUdCJWzMXFhQULFlBSUkK3bt0YM2YMc+bM4b///S/R0dEEBgayfPnyK9bRtm1b1q1bxzvvvMOSJUtYt26d2f7hw4dz6tQpvv76a+69994rzgBFrkf6CDCR68Aff/zBu+++yxdffMGhQ4cwGo34+vrSp08fHnvsMdzd3YHzbxjfsGEDP/zwg+nYvXv38vTTT7N9+3bOnDlD586dWblypVn93bp1Y+fOnWzbtg1/f/9qHZtIVVPQiQh33XUXJSUlfP3115buikil06VLkRvcTz/9xLZt2xg+fLiluyJSJTSjE7lB/fTTT+zYsYOkpCSysrLYuXMndevWtXS3RCqdZnQiN6hly5YxevRo/vrrL1JSUhRyYrM0oxMREZumGZ2IiNg0BZ2IiNg0BZ2IiNg0BZ2IiNg0BZ2IiNg0BZ2IiNi0/weKHGALH/rOkwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#city with the most sales\n",
+    "sns.countplot(x = 'City', data = df).set_title('Sales across cities')\n",
+    "print('The City with the most sales is Lagos')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "caa9edd4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The highest product line sold is Fashion accessories\n",
+      "The lowest product line sold is Health and Beauty\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Determine the highest & lowest sold product line, using Countplot\n",
+    "sns.countplot(y = 'Product line', data = df).set_title('Sales for different product line')\n",
+    "print('The highest product line sold is Fashion accessories')\n",
+    "print('The lowest product line sold is Health and Beauty')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "b85abe93",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Food and Beverage: Payment channel most used is Card\n",
+      "Fashion accessories: Payment channel most used is Epay\n",
+      "Electronic accessories: Payment channel most used is Cash\n",
+      "Sports and travel: Payment channel most used is Cash\n",
+      "Home and lifestyle: Payment channel most used is Epay\n",
+      "Health and Beauty: Payment channel most used is Epay\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Determine the Payment channel used by most customer to pay for each product line\n",
+    "sns.countplot(y = 'Product line', data = df, hue = 'Payment').set_title('Payment channel for Product lines')\n",
+    "print('Food and Beverage: Payment channel most used is Card')\n",
+    "print('Fashion accessories: Payment channel most used is Epay')\n",
+    "print('Electronic accessories: Payment channel most used is Cash')\n",
+    "print('Sports and travel: Payment channel most used is Cash')\n",
+    "print('Home and lifestyle: Payment channel most used is Epay')\n",
+    "print('Health and Beauty: Payment channel most used is Epay')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "fbae3299",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The most used Payment channel for Branch A is Epay\n",
+      "The most used Payment channel for Branch B is Epay\n",
+      "The most used Payment channel for Branch C is Cash\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Determine the Payment channel for each branch.\n",
+    "sns.countplot(x = 'Payment', data = df, hue = 'Branch').set_title('Payment mode used by customers for each Branch')\n",
+    "plt.legend(loc='upper left', bbox_to_anchor=(1.25, 0.5), ncol=1)\n",
+    "print('The most used Payment channel for Branch A is Epay')\n",
+    "print('The most used Payment channel for Branch B is Epay')\n",
+    "print('The most used Payment channel for Branch C is Cash')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "5d03da15",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Branch with the lowest rating is B\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Determine the branch with the lowest rating using box plot\n",
+    "sns.boxplot(x = 'Branch', y = 'Rating', data = df).set_title(\"Rating for Branches\")\n",
+    "print('Branch with the lowest rating is B')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "cbef9fc0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ZZzp48KAWLVokd3f3WGN78OCBPD09tXz5cu3bt0/9+/fX0KFDtWPHDqvtVq1apQwZMmjLli2aMWOGAgMDrb48GzBggC5evKj169dr2bJlWr58eaznekmW6wEPDw9L/G3atLGs9/f3V+PGjfXLL7+oT58+8b431K9fX3ny5NH69estbURERGj16tWWL9Diy3+kHwn5PClFHtc5cuTQ9u3bNWTIEPn5+alLly4qVqyYfv75Z3Xs2FFvv/225Yvp+D6PxSa+62kpchDbzJkzFRAQoC1btigsLEy+vr6W9evWrdOkSZPk5+enHTt2yMPDI9rAmsQKCgqSh4eHBg4cqKCgIA0aNChBr3HYsGG6f/++Nm7cqL1792rq1KmWX6T9+OOPkiKLrEFBQVq6dKlq1aqll156yeoLk/DwcC1fvlzdunWLMTbyNf1ISA3izp076tChgzZt2qTt27erXLly8vb2jrU4HzW11Mcff6ygoCCrqaYuX76stWvXaunSpVq7dq2OHTumiRMnxhljtmzZFBAQoP379+vDDz/U2rVr9cEHH1htE1+7AQEB+vLLLzVr1ixt3bpVT548iVabic17772n8ePH66effpKbm5vat29vybeE5MK9e/fUv39//fjjj/r222+VK1cudezY0eqLzfikVO4CtsR0PTa0bds2FSxY0PK4Ro0aWr16tT777DOZzWZ9+OGHcnBwUIkSJTR27FgNHTpUo0ePVkREhBYtWqSPP/5YTZo0kSR99NFH2rlzpz799FONGTNGixYtkqurq6ZPny6TyaTixYvr3Llzmjx5cpwxPf1G5ebmppkzZ6pq1ar6448/rGIdPXq06tatK0kaMWKEmjZtqj///FMFCxbU/PnzVa9ePb3zzjuSJHd3dx06dCjeEU2S1KJFC/Xs2VOS9M4772jXrl0KDAzUJ598ojVr1igiIkLz5s2TyWSSJM2aNUvu7u7avHmzWrdurfbt22vu3LkaO3asTCaTrl69ahllJkmfffaZypYta3WBu2DBArm5uenw4cOWAkHmzJkVEBCgzJkzJ7hvFi1aJDc3N02ePFkmk0keHh46d+6c1Ynv448/VuvWrTVo0CDLsg8//FB169bVzZs35ejoqLCwMDVt2lQvvfSSJKl48eLx9huSL7Z8HDBggGVZkSJFNGHCBHXu3Fnz58+Xg4ODrly5opdfftly7MT0U98OHTpYPmCPHj1a8+fP1969e1W4cOFo265atUr37t3TggULlDNnTkmRx3mLFi104cIFFS1aVJKUM2dOzZw5U46OjipRooS8vLy0Y8cOqw9DT8uYMaNGjx5t9VqOHj2qNWvWqHv37pKkGTNmyMfHx+qCqXz58pKkn3/+WQcOHNC+fftUokQJSZF5ECW5x3Zc/fjzzz/r999/1+HDhy3LP/30U1WoUEE7duxQ/fr1JUV+EbdgwQI5OzvH2Af37t3T3LlztXbtWsvICzc3Nx08eFCffvqpmjRpoitXrshsNqthw4bKmDGjChUqpAoVKsTYHpLm2VyTpD59+kQrPEjSzp07dfz4cZ07d05Zs2aVJI0ZM0Y//PCDVqxYocGDB+vjjz9W2bJlNXv2bMvzoo5RKfLYz5YtW4xT6IwcOVINGzaUFHl8xHdujRLXOfBZ8cX3LHKVXDUKR0dHzZ07V4MHD9bixYv18ssvq1q1avLy8lLlypWttq1UqVK068J58+apZcuWyTpuYsrvK1euqFixYqpZs6ZMJpMKFSqkatWqxfo6ChQooLffftvy+I033tDOnTu1evVq1atXz7K8RIkSltx0d3fX4sWLtWPHDrVr107nzp3T1q1b9cMPP6h69eqSpMDAQEtexiRr1qzKnj27HB0dY3x/at26tSXno8T13pAhQwa1adNGq1atUq9evSRJ+/bt09WrV9WuXTtJ8ed/vnz5Yo0X9iOm8+izv/aK7/NktmzZJEklS5aUn5+fJGngwIGaNWuWHB0dLb8OGzlypGbPnq0DBw6oVatWCfo8FpP4rqelyC+ZP/jgA3l4eEiSBg0apLfeekvh4eFycHBQYGCgOnXqFO2z4oULF5Lcl1G/3M2ePbslD5csWRLva7xy5YpatmxpGT399Hk2arrPPHnyWOV29+7dtWTJEg0ePFhS5K9vbt68GeuNrslXY0hoPsZXg3j6fCNFDhLbsGGDtm3bFuMx8uKLL0qScufOHe0c8vjxY82bN8/yxdMbb7xh+aVYbJ7+0q1IkSLy9fXVnDlzrK5/42s3MDBQb7/9tuV9wN/f31I4j8/w4cPVqFEjSdLcuXNVunRprV69Wt27d09QLrRq1cqqvblz56pQoUI6ePBggu99k1K5C9gSRX4bqlmzptWH/qiRbEFBQapSpYrlgkeKLDg+fPjQchHz6NEjy4cIScqQIYOqVq2q06dPW9qoXLmy5cJEkqpWrRpvTEeOHJG/v7+OHz+u0NBQywiqq1evWp28ypQpY/m/i4uLpMifKRcsWDDGm4hWqVIlQUX+KlWqRHu8ZcsWSdLRo0d16dIlq1GYUuSIkosXL0qS2rVrp/fee0+//PKLatWqpdWrV8vNzc3y2o8ePapffvklxkLMxYsXLUWLUqVKWRX4E9I3Z86cUYUKFaz6/NkPuUePHtWFCxe0bt06y7Kodi5evKiqVauqc+fOatu2rerVq6e6devKy8sr2mtGyostH3fs2KGPPvpIZ86c0T///KMnT57o4cOHCgkJUf78+dW7d2/16NFDR48eVYMGDdS0aVPVrl3bqu2n88XR0VF58+bVzZs3Y4wjKChIZcqUsRT4pcgbeTo4OOj06dOWIn+JEiUs03tJkXn422+/xfkaFy1apC+//FJXrlzRgwcP9OjRIxUqVEhSZP7++eef0S4woxw7dkwuLi6xFieTe2zH1Y9BQUHKnz+/VTEx6ufRp0+fthSAChQoEGvRMKqdBw8eqF27dlZ5+ujRI8sXLl5eXpo/f748PT3VsGFDvfLKK2rWrFm09wMk3bO5JinW+diPHj1q+aXW0x48eGB53z927Jhef/31JMXydFH44sWL8Z5bo8R1DnxWUuIjV8lVo2jVqpWaNGmivXv36sCBA9q+fbsCAgL03nvvadiwYZbtYrq+27hxo6SUO26idO7cWa1bt1alSpXUsGFDvfrqq3r11Vetrquf9uTJE3300Udau3atrl27pocPH+rhw4dxnsulyNyPOpcHBQXJwcHB6tckhQsXVv78+eONNzYxfWkV13uDFDmoYP78+bp8+bIKFy6sVatWqXbt2pZf28SX/xQNjSGm8+jJkyfVtWtXy+P4Pk+WLVtWkvVxbTKZlC9fPqtlGTNmlJOTk+VYT8jnsZjEdz0tRQ6yiirwS5E59ujRI4WFhemFF15QUFBQtJGzVapUSVaRPyYJeY39+/eXr6+vtm/frnr16un111+P80s9SerUqZMmTpyo/fv3q1q1alq6dKmaN2+uPHnyxBoH+Wr/EpKPCalB3Lx5U5MnT9auXbt08+ZNPXnyRPfv39fVq1cTHVOhQoWsrq1dXFx069atOJ/zzTffKDAwUBcuXNC9e/f05MmTaFPtxNVuWFiYrl+/bnW+jzovPj39XmyerlXlyJFDZcqUsVx/JyQXLl68qMmTJ+u3337T7du3FR4ervDw8CT137MSm7uALVHkt6Fs2bJZCnZPi4iIsPpQ+zSTyWT5ZjimbaKWxTZtQFzu3buntm3bWm6KmC9fPt2+fVvNmjWL9jOnp29O+Ow+k7LvhAgPD1e5cuW0aNGiaOui5nPOly+f6tevr1WrVqlWrVpauXKlvL29rdpo3LixJk2aFK2Npy+UsmfPbrUuIX0T19/t6f13797dajRLlKgL3Hnz5snHx0fbt2/Xpk2bNGnSJC1btszyzTZSR0z5ePnyZXXo0EHdu3fXu+++qzx58ujo0aPq3bu35e/+6quv6vjx49q6dat27NihDh06qFWrVlY/H372Zp4mkynWPIkrf54+vhLTpiStXbtWfn5+mjhxoqpWrapcuXJp4cKF+vbbb+Pdb0LWJ/fYjqsf43tPjPJs3sYUoxR5z5CniyKSLF+YuLq66rffftOOHTv0888/a8yYMfL399e2bdvibR8JE9u5Lybh4eFydnbWpk2boq2L+iIsOeecp/+mUe3EdW6NEtc58FmJjY9cJVeNJkuWLGrQoIEaNGigkSNHatCgQZo2bZoGDRqUoJvFptRxE6V8+fI6duyYtm/frp07d8rHx0dly5bV+vXrYyz0z5kzRwEBAZo2bZpKly6tHDlyaMKECdG+jI/rvJsa177Pvt743hukyNdevHhxrV69WoMGDdL69es1YcIEy/qE5D/sX0zn0afv3SQlPK9iOq6fHkQStSzqfTkhn8eelZDraUkx7jdqn2kpIa+xe/fuatSokbZu3aqff/5ZjRs31tChQy2/iojJiy++qGbNmmnp0qXy8PDQpk2bot3v4Nk4yFf7l5B8TEgNwsfHRzdu3NCUKVNUuHBhZc6cWS1btkzUdDNRYsrruPLo119/Va9evTRy5EhNmTJFuXPn1vfff6/33nsvWe2mlITkQseOHZU/f37NmjVL+fPnl6Ojo6pVq2bpv6jz/9Pn66h7NsYnsbkL2BJFfjtUsmRJrVu3zvLTRCly/u1MmTLppZdeUkREhDJlyqS9e/dafhr45MkTHThwwPJz3JIlS2rDhg1WF3i//vprnPs9e/asbt++rffee8/SbtR8qYmN/9kRxfGNMH56u6dHaPz222+W0Yienp5avXq18uTJIycnp1jbaN++vUaMGKE33nhDJ0+etPoFgaenp9atW6dChQpFO0nFJSF9U6JECX3//fdWyw4ePGj12NPTU6dOnYq3wFWuXDmVK1dOQ4YMUbt27fT1119T5LeBw4cP6+HDh5o6daplPuoffvgh2nZ58+ZVx44d1bFjR7366qvq3bu3PvrooySNKC1ZsqSWLVumO3fuWIqY+/fvV3h4eJxTfMRn7969qlSpkvr162dZ9vSIK2dnZxUoUEA7duxQgwYNoj3f09NT169fV1BQUIxxpMSxHVs/lixZUn/++acuXbpkGekZHBysa9euqWTJkgnugxIlSihz5sy6cuVKrKOgpciCVZMmTdSkSRMNHTpUxYsX1/79+y3TuiDteHp66saNG3JwcLD6Kfyz2+zcuTPWNjJlypSgm34VLVo03nNrUsQX37PIVXLV6EqUKKHHjx/rwYMHliJ/TNeFUcdnco6b2PI7Z86c8vLykpeXlzp37qxXXnlFFy5ciHFu/r1796pp06bq2LGjpMgCwLlz52L9hVFsrzk8PFyHDh2yTA105cqVWG+wHV/8MYnvvSGKt7e35T4d//77r1q2bGlZl9D8h/HF93kyqRL6eexpCb2ejk+JEiVi/KyY0hL6GgsWLKg33nhDb7zxhmbNmqX58+fLz8/P8r4XU2736NFDPXr0kJubm5ydnS2/VIotDvI1fUhIDWLfvn2aNm2aZcrIGzduRLvZ67MyZsyY4HNIXPbt26f8+fNbTdlz5cqVRLWRO3duyy/Lo67dIiIidOjQoRinpHvWr7/+arn+vnfvnk6ePGk5L8eXC3/99ZeCgoI0Y8YMy3SaR44csbr/VdT0Rk/fZ+v48eNW7aRU7gK2xI137VDv3r11/fp1DRs2TEFBQdq8ebPGjx+vvn37Klu2bMqePbt69eql8ePHa8uWLQoKCpKvr69u3rypPn36SJJ69uypy5cva9SoUTp79qy++eYbff7553Hu19XVVZkzZ9bChQsVHByszZs3J/iGuU9788039fPPP2vmzJk6f/68Fi9ebDXKKC4bN27U4sWLdf78ec2cOVM7duywzAnp7e0tZ2dnde7cWbt371ZwcLD27Nmj0aNH6/z585Y2Xn/9dT1+/FgDBw5UpUqVVKxYMcu6Pn366J9//lHPnj3122+/KTg4WD///LMGDx6sO3fuJKtvevbsqYsXL2rMmDE6e/asNmzYYOnzqC9aBg8erEOHDmno0KGWn5398MMPGjJkiKTID7Xjxo3T/v37dfnyZe3cuVMnTpxIVnEXSVesWDGFh4dr3rx5Cg4O1urVq6Pd3Hby5Mn69ttvdf78eQUFBWnjxo1yc3NL8pQR3t7eypYtm/r3768TJ05oz549Gjp0qOXGnUnl7u6uY8eOaevWrTp//rymT5+uX375xWqbYcOGKTAwUHPnztW5c+d07NgxzZkzR5JUr149Va5cWd27d9f27dsVHBysn376yZLbyT224+rH+vXrq2zZsurXr5+OHDmiw4cPq2/fvvL09LRcyCVEzpw5NWjQIL333ntasmSJLly4oGPHjmnRokX64osvJEnLli3Tl19+qRMnTig4OFjLli1TxowZ+YCVgv777z+FhIRY/YvtJ8T169dX9erV1blzZ23dulXBwcE6cOCApkyZYjl+Bw0apGPHjmnw4ME6fvy4zp49a5nOQoqcMuPgwYO6dOmS5ee7MUnIuTUp4ovvWeQquWoUf/31l1q0aKEVK1bo999/V3BwsNavX6+PP/5Y9erVU65cuSzb/vbbb1bXhcuXL7eMyEvOcRNTfgcEBGj16tUKCgrShQsXtGrVKuXKlcsyZc2z3N3dtXPnTu3du1dnzpzR8OHD47xhbkw8PDz0yiuvaOjQoTpw4ICOHTumAQMGWO4lElf8V65c0ZEjR3T79m39999/sW6bkPcGKXKwy+nTpzV58mQ1a9bM6u8QX/4j/Yjv82RSJfTz2NMScj2dEP3799fXX39t9Vnx2QFVKSEhr3HkyJHatm2bgoODdezYMW3bts1ynsyXL5+yZs2q7du368aNG1ajuhs0aKAXXnhB/v7+6ty5c6zTiEnka3qSkBpEsWLFtHLlSp0+fVqHDh1Sr1694v01XOHChbVjxw6FhIQoNDQ0yfG5u7vr2rVrWrlypYKDg/XZZ59pzZo1iW6nf//+mj17tr755hudPXtWo0aNiveLiigffPCBfvrpJ506dUoDBw5UpkyZLINs4ssFJycn5c2bV19++aUuXLig3bt3y9fX1+rXQVmzZlWVKlU0e/ZsnTp1Svv377e634CUcrkL2BJHph0qUKCAVq1apWPHjqlOnToaOHCg2rZtq/fff9+yzfjx4+Xl5aW33npLderU0YkTJ7R69WrL3MCFChXSkiVLtH37dtWuXVvz5s3T2LFj49zviy++qMDAQH333XeqVq2a/P39471Rb0yqVKmiOXPmaNGiRapVq5Y2btyoUaNGJei5o0aN0oYNG1SrVi0tWrRIc+fOVcWKFSVF/hTu+++/l5ubm9544w1VrVpVPj4+Cg0NtRplkS1bNjVv3ly///672rdvb9V+/vz5tXnzZjk4OKht27aqXr263nnnHWXKlCnOomxC+qZw4cL68ssvtWnTJtWuXVuBgYEaOXKkpP/N7162bFl9//33unz5sl5//XXVrl1bEyZMsPxML1u2bDp37pzeeOMNVa5cWQMGDJC3tzcXczZStmxZTZs2TfPmzVP16tX15ZdfWt1IWYqcP3TSpEmqXbu2mjRport37ybr53vZsmXTmjVrdOfOHTVq1EidO3dWlSpVFBAQkKzX0rNnT3l5ealPnz5q0KCBLl++rLfeestqm969e2vGjBn68ssvVaNGDbVr184yF6KDg4NWrVqlatWqqV+/fqpWrZpGjRpl+Zljco/tuPrRZDJp2bJlyps3r15//XW1aNFCzs7OWrZsWbxTZD1r9OjRGjVqlAICAlS9enW1bt1aGzZssIwezZ07t5YsWaJmzZqpZs2a2rBhg5YsWRLrKHIk3s8//6wSJUpY/YutkGcymbRy5UrVqVNHgwcPVpUqVdSzZ0+dO3fO8vPcl19+WevXr9eZM2f06quvqlGjRlqzZo1lpFTUlCHVq1dXsWLF4hyZFN+5NSnii+9Z5GokctX+Zc+eXVWqVNH8+fPVvHlz1ahRQxMmTFC7du2iDSwZMGCATpw4obp162rSpEl69913LTfJS85xE1N+58yZUx9//LEaNWqkevXq6fjx41q1alWshc3hw4erYsWK8vb21muvvaZs2bJZTfWYUPPmzVPhwoXVsmVLderUSd7e3pZ7SMSmZcuWevXVV9WqVSsVK1ZMq1evjnXbhLw3SJHXo9WrV4/xOji+/Ef6kZDPk0mR0M9jT0vI9XRCtGnTRqNGjdLEiRNVt25dnTx5MsbpO5IrIa8xPDxcI0aMULVq1dS6dWs5OzsrMDBQUuS0Q/7+/lqyZIlKliypzp07W9o2mUzq0qWLHj16pC5dusQZB/mafiSkBhEQEKB79+6pfv366tWrl7p27RrvOWTSpEnatWuXypQpozp16iQ5vmbNmuntt9+Wn5+fatWqpZ9++knvvvtuotsZOHCgunTpokGDBqlRo0YKDw9P8Pl07NixGj16tOrVq6fz589rxYoVlmnr4ssFBwcHLVq0SCdOnFCNGjU0fPhwjR49Olp9J+rzdMOGDTV06NBoRf6Uyl3AlkyhoaGpM4E6AAUGBmrq1KkKDg7m214AAPDcKVeunPr166dBgwbZOhQAsDlfX19duHBB69evt3UoABKB3IURMCc/kIIWLlyoihUrKm/evPrtt980Y8YMderUiQI/AAAAADynwsLCdOTIES1fvjzeaXQB2A9yF0ZCkR9IQRcuXNDMmTP1119/qUCBAurVq5fVDWwAAAAAAM+Xzp0769ChQ+ratavl5qoA7B+5CyNhuh4AAAAAAAAAAAyKOUQAAAAAAAAAADAoivwAAAAAAAAAABgURX4AAAAAAAAAAAyKIj8AAAAQBx8fH5UrV87WYVjs2rVLTk5O2rVrl2XZ1KlT5eTkZLugAAAAANgMRX4AAADYrWXLlsnJycnyL2/evCpdurQGDhyo69ev2zq8FHHy5ElNnTpVly5dsnUoAAAAAAyIIj8AAADs3qhRo7RgwQJ99NFHql+/vr766is1a9ZM9+/ft3VoyXbq1Cn5+/vr8uXLSW5j+PDh6eZLDwAAAACJ42jrAAAAAID4NGrUSFWqVJEkde/eXS+88ILmzp2r77//Xm3bto3xOf/++6+yZcuWlmHajKOjoxwdubQHAAAAnkeM5AcAAIDh1K1bV5IUHBwsKXLefLPZrMuXL6tz584qXLiwvL29JUnh4eGaNWuWKlWqJGdnZ5UqVUrDhw9XWFhYtHaXLl2qSpUqyWw2q1atWtq0aVO0bWKaEz+Kk5OTpk6darXs+vXrGjJkiEqXLi1nZ2eVK1dOb7/9tu7cuaNly5apd+/ekqQWLVpYpiVatmxZovojpjn5y5Urp7Zt2+rgwYNq2rSpXFxcVKZMGc2bNy/a8x8+fKjp06ercuXKcnZ2VvHixTV06FCFhoYmKg4AAAAAaY/hPgAAADCcixcvSpLy5MljWRYeHq42bdqoYsWKGj9+vDJkyCBJGjZsmD7//HM1a9ZM/fv316lTp/TZZ5/p4MGD2rx5szJmzChJ+uqrrzRw4EBVrFhRffr00c2bN/Xmm2/K1dU1yXGGhISoUaNGunXrlrp3767SpUvr+vXr+vbbb/XXX3+pVq1a6tu3rxYuXKhhw4apePHikqRq1aoleZ9Pu3Tpkjp27KjOnTvL29tba9eu1bvvvquSJUuqYcOGkqSIiAh17dpVO3fuVLdu3VSmTBldvHhRCxcu1JEjR7RlyxZLHwEAAACwPxT5AQAAYPf++ecf3b59Ww8ePND+/fs1ffp0Zc2aVU2aNLFs8+jRIzVu3FhTpkyxLDt58qQ+//xztW/fXp988olluYeHh/z8/PT111+re/fuevz4scaNG6eSJUvq+++/V5YsWSRJtWvXVps2bVSoUKEkxT1u3Dj9+eef+u6771SzZk3Lcj8/P0VERMhkMql69epauHCh6tevrzp16iRpP7E5d+6c1q9fr/r160uSunbtqrJly2rx4sWWIv/q1au1detWffPNN5ZfSEhSrVq11L59e61Zs0YdO3Z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+      "text/plain": [
+       "<Figure size 1541.15x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# A catplot() generate visualization for the \"product line\" on x-axis, quantity on the y-axis, and hue showing that gender type often affects the kind of products being purchased at the supermarket.\n",
+    "sns.catplot(x = 'Product line', y = 'Quantity', hue = 'Gender', kind= 'boxen', data = df, aspect = 4);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "d468497d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x641ed1df40>"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# insight to explore is the interaction of Unit price on the Quantity of goods purchased\n",
+    "#Use the catplot() to plot Product line per unit price, \n",
+    "sns.catplot(y = 'Unit price', x = 'Product line', kind= 'point', data = df, aspect = 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "508ce359",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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mzRpFREQUKNhw1759+xQREaHx48dr3759mjBhQol+x8cee0xpaWn65ptvtGHDBs2YMcN5Fc1PP/0kKSfA27dvnz799FNdeumlatGiRb4w3m63a9GiRfrXv/7lcmwcrzVHSTKI5ORkDR8+XN99951Wr16tTp06adiwYYUGv7ntVt58803t27cvX/uVQ4cO6csvv9Snn36qL7/8Ujt27NALL7xQ5BgtFovmzJmjTZs26bXXXtOXX36pV199Nd86xe13zpw5+uSTT/T6669r1apVstlsBbKZwjzzzDOaPn26fv75Z4WFhenWW291Hm8lORZSU1N1//3366efftKKFSsUEBCgESNG5PvSrDhldezWJLSwqEJ+/PFHNWnSxPlz7969tXTpUn3wwQcKCQnRa6+9JqPRqDZt2mjatGmaOHGinn76aTkcDn344Yd68803dfXVV0uS/vOf/2jt2rV6//33NXXqVH344Ydq2rSpZs+eLYPBoNatWys6OlovvfRSkWPKexCEhYXp3//+t3r06KG///4731iffvpp9enTR5L05JNP6pprrtHRo0fVpEkTvfPOO+rbt68ef/xxSVJ4eLh+//33YisxJOn666/XXXfdJUl6/PHHtW7dOs2bN0///e9/9cUXX8jhcGju3LkyGAySpNdff13h4eFauXKlbrrpJt166616++23NW3aNBkMBh05csRZHSNJH3zwgTp27JjvzdO7776rsLAw/fHHH84Pnl5eXpozZ468vLxK/Nh8+OGHCgsL00svvSSDwaCIiAhFR0fne1F98803ddNNN2nChAnO21577TX16dNHJ0+elNlsVmJioq655hq1aNFCktS6detiHzeUjcKOyXHjxjlva968uZ5//nmNGjVK77zzjoxGow4fPqyLLrrI+fxxdQnc8OHDnR/gnn76ab3zzjvasGGDQkNDC6y7ZMkSpaam6t1335W/v7+knOf69ddfr5iYGLVs2VKS5O/vr3//+98ym81q06aNbrzxRq1Zsybfm+28PDw89PTTT+f7Xf7880998cUXuuOOOyRJr7zyih544IF8J+QuXbpIkn755Rdt3rxZGzduVJs2bSTlHAu5LvT5XdTj+Msvv+ivv/7SH3/84bz9/fffV9euXbVmzRr169dPUs4XPe+++66Cg4NdPgapqal6++239eWXXzq/NQ4LC9O2bdv0/vvv6+qrr9bhw4cVEhKiK664Qh4eHmrWrJm6du3qcn8onfOPNUm65557CnywlaS1a9dq586dio6Olo+PjyRp6tSp+v7777V48WI9/PDDevPNN9WxY0e98cYbzu1yn6NSznPfYrG4bCsxadIkXXHFFZJynh/FnV9zFXUePF9x4zsfxyrHanVgNpv19ttv6+GHH9bHH3+siy66SD179tSNN96oyMjIfOt269atwPvCuXPnasiQIRf0nHF1bB8+fFitWrXSJZdcIoPBoGbNmqlnz56F/h6NGzfWQw895Px59OjRWrt2rZYuXaq+ffs6b2/Tpo3zuAwPD9fHH3+sNWvWaOjQoYqOjtaqVav0/fffq1evXpKkefPmOY9JV3x8fOTr6yuz2ezytemmm25yHu+5inpdMJlMuvnmm7VkyRLdfffdkqSNGzfqyJEjGjp0qKTij/0GDRoUOl5UHa7OoedfoVLc50mLxSJJatu2raZMmSJJGj9+vF5//XWZzWbnFS2TJk3SG2+8oc2bN+uGG24o0ecxV4p7Ly3lfHn56quvKiIiQpI0YcIEPfjgg7Lb7TIajZo3b55GjhxZ4LNiTExMqR/L3KsNfX19ncfh/Pnzi/0dDx8+rCFDhjirPvOeY3Pb39WtWzffsX3HHXdo/vz5evjhhyXlXDVw8uTJQiet5HitHkp6PBaXQeQ930g5xUfLly/Xjz/+6PI5Ur9+fUlSYGBggXNIdna25s6d6/xSY/To0c4rXAqT9wud5s2b69FHH9Vbb72V771vcfudN2+eHnroIefrwKxZs5yhbHGeeOIJDRgwQJL09ttvq3379lq6dKnuuOOOEh0LN9xwQ779vf3222rWrJm2bdtW4rkGyurYrUkIkKuQSy65JN+Hydzqm3379ql79+7Ok6mUE2RlZmY6T5BZWVnON6iSZDKZ1KNHD+3du9e5j8jISOdJT5J69OhR7Ji2b9+uWbNmaefOnUpISHBWfhw5ciTfC2OHDh2c/27YsKGknEv3mjRp4nIysO7du5coQO7evXuBn3/44QdJ0p9//qmDBw/mqxyTcr4Jj42NlSQNHTpUzzzzjH777TddeumlWrp0qcLCwpy/+59//qnffvvN5Qf82NhY54fhdu3a5QuPS/LY7N+/X127ds33mJ//AerPP/9UTEyMvvrqK+dtufuJjY1Vjx49NGrUKN1yyy3q27ev+vTpoxtvvLHA74zyUdgxuWbNGv3nP//R/v37lZSUJJvNpszMTB0/flyNGjXSmDFjdOedd+rPP/9U//79dc011+iyyy7Lt++8x4zZbFa9evV08uRJl+PYt2+fOnTo4AyPpZxJuYxGo/bu3esMkNu0aeNseyPlHItbt24t8nf88MMP9cknn+jw4cNKT09XVlaWmjVrJinnGD569GiBNzC5duzYoYYNGxYafF3o87uox3Hfvn1q1KhRvqAq97LBvXv3OgOGxo0bFxpI5e4nPT1dQ4cOzXesZmVlOcP8G2+8Ue+88446d+6sK664QldeeaUGDRpU4DUBpXf+sSap0P63f/75p/Mqk7zS09Odr/07duzQddddV6qx5A0cY2Njiz2/5irqPHi+0oyPY5VjtTq44YYbdPXVV2vDhg3avHmzVq9erTlz5uiZZ57RY4895lzP1fu7b775RlLZPWdyjRo1SjfddJO6deumK664QgMHDtTAgQPzva/Oy2az6T//+Y++/PJLHTt2TJmZmcrMzCzyPC7lHPe55/F9+/bJaDTmq4APDQ1Vo0aNih1vYVx9GVLU64KU82X1O++8o0OHDik0NFRLlizRZZdd5rxCoLhjn0CqenB1Dt29e7duv/1258/FfZ7s2LGjpPzPa4PBoAYNGuS7zcPDQ0FBQc7nekk+j7lS3HtpKaeAJzc8lnKOsaysLCUmJqpOnTrat29fgYq/7t27X1CA7EpJfsf7779fjz76qFavXq2+ffvquuuuK/ILI0kaOXKkXnjhBW3atEk9e/bUp59+qsGDBxc64S7Ha/VQkuOxJBnEyZMn9dJLL2ndunU6efKkbDab0tLSdOTIEbfH1KxZs3zvqxs2bKhTp04Vuc3XX3+tefPmKSYmRqmpqbLZbAXaTxS138TERMXHx+c73+eeF/O2pCpM3qzKz89PHTp0cL73LsmxEBsbq5deeklbt27V6dOnZbfbZbfbS/X4nc/dY7cmIUCuQiwWizMIysvhcOT7sJSXwWBwfqPlap3c2wq7jLYoqampuuWWW5yTGzVo0ECnT5/WoEGDCpT+551k6Pz7LM19l4TdblenTp304YcfFliW2zuzQYMG6tevn5YsWaJLL71Un3/+uYYNG5ZvH1dddZVefPHFAvvIexL29fXNt6wkj01R/2957/+OO+7I9y18rtw3T3PnztUDDzyg1atX67vvvtOLL76oBQsWOL+RQ/lxdUweOnRIw4cP1x133KGnnnpKdevW1Z9//qkxY8Y4/+8HDhyonTt3atWqVVqzZo2GDx+uG264Id9ldedPzGUwGAo9Voo6hvI+x9zZpyR9+eWXmjJlil544QX16NFDAQEBeu+997RixYpi77ckyy/0+V3U41jc62Ku849dV2OUcvq05/3QLckZxjdt2lRbt27VmjVr9Msvv2jq1KmaNWuWfvzxx2L3j5Ip7Pznit1uV3BwsL777rsCy3K/ZLmQ807e/9Pc/RR1fs1V1HnwfO6Oj2OVY7U68fb2Vv/+/dW/f39NmjRJEyZM0MyZMzVhwoQSTfxWVs+ZXF26dNGOHTu0evVqrV27Vg888IA6duyoZcuWuQyR33rrLc2ZM0czZ85U+/bt5efnp+eff77Al7xFnXPL473v+b9vca8LUs7v3rp1ay1dulQTJkzQsmXL9PzzzzuXl+TYR9Xn6hyad54MqeTHlavndd7ihNzbcl+TS/J57HwleS8tyeX95t5nRSrJ73jHHXdowIABWrVqlX755RddddVVmjhxorOa25X69etr0KBB+vTTTxUREaHvvvuuQH/p88fB8Vr1leR4LEkG8cADD+jEiRN6+eWXFRoaKi8vLw0ZMsStFgy5XB3XRR1HW7Zs0d13361Jkybp5ZdfVmBgoL799ls988wzF7TfslKSY2HEiBFq1KiRXn/9dTVq1Ehms1k9e/Z0Pn655/+85+vcObKK4+6xW5MQIFcDbdu21VdffeW8XEfK6XXq6empFi1ayOFwyNPTUxs2bHBeLmOz2bR582bnJWpt27bV8uXL87152LJlS5H3GxUVpdOnT+uZZ55x7je3P5274z+/CrK4qsi86+X9Znnr1q3OCqrOnTtr6dKlqlu3roKCggrdx6233qonn3xSo0eP1u7du/NVPnfu3FlfffWVmjVrVuAFsCgleWzatGmjb7/9Nt9t27Zty/dz586dtWfPnmKDk06dOqlTp0565JFHNHToUC1cuJAAuZL88ccfyszM1IwZM5z9P7///vsC69WrV08jRozQiBEjNHDgQI0ZM0b/+c9/SlUJ17ZtWy1YsEDJycnOgGzTpk2y2+1FXvZenA0bNqhbt24aO3as87a81SLBwcFq3Lix1qxZo/79+xfYvnPnzoqPj9e+fftcjqMsnt+FPY5t27bV0aNHdfDgQWeVWlxcnI4dO6a2bduW+DFo06aNvLy8dPjw4UKrN6WcQOTqq6/W1VdfrYkTJ6p169batGmTs9UBKk7nzp114sQJGY3GfJeInr/O2rVrC92Hp6dniSbxaNmyZbHn19Iobnzn41jlWK3O2rRpo+zsbKWnpzsDZFfvC3OfmxfynCns2Pb399eNN96oG2+8UaNGjdKVV16pmJgYl72QN2zYoGuuuUYjRoyQlPPhMjo6utCrIgr7ne12u37//Xdnu4zDhw8XOlFmceN3pbjXhVzDhg1z9kS3Wq0aMmSIc1lJj31Uf8V9niytkn4ey6uk76WL06ZNG5efFctaSX/HJk2aaPTo0Ro9erRef/11vfPOO5oyZYrzdc/VsX3nnXfqzjvvVFhYmIKDg51XWBQ2Do7XmqEkGcTGjRs1c+ZMZwu1EydOFJi47XweHh4lPocUZePGjWrUqFG+NhaHDx92ax+BgYHOq2Fz37c5HA79/vvvLts0nW/Lli3O996pqanavXu387xc3LFw5swZ7du3T6+88oqzvdz27dvzzTWS2/Ij75wmO3fuzLefsjp2axIm0asGxowZo/j4eD322GPat2+fVq5cqenTp+vee++VxWKRr6+v7r77bk2fPl0//PCD9u3bp0cffVQnT57UPffcI0m66667dOjQIU2ePFlRUVH6+uuv9dFHHxV5v02bNpWXl5fee+89xcXFaeXKlSWe/C6v++67T7/88ov+/e9/68CBA/r444/zVUcU5ZtvvtHHH3+sAwcO6N///rfWrFnj7ME1bNgwBQcHa9SoUfr1118VFxen9evX6+mnn843K+51112n7OxsjR8/Xt26dVOrVq2cy+655x4lJSXprrvu0tatWxUXF6dffvlFDz/8sJKTky/osbnrrrsUGxurqVOnKioqSsuXL3c+5rkh/sMPP6zff/9dEydOdF6K8f333+uRRx6RlPOB6bnnntOmTZt06NAhrV27Vrt27bqg0BAXplWrVrLb7Zo7d67i4uK0dOnSAhPVvfTSS1qxYoUOHDigffv26ZtvvlFYWFipL6MeNmyYLBaL7r//fu3atUvr16/XxIkTnZNwlVZ4eLh27NihVatW6cCBA5o9e7Z+++23fOs89thjmjdvnt5++21FR0drx44deuuttyRJffv2VWRkpO644w6tXr1acXFx+vnnn53H94U+v4t6HPv166eOHTtq7Nix2r59u/744w/de++96ty5s/ONQkn4+/trwoQJeuaZZzR//nzFxMRox44d+vDDD/V///d/knJmAv/kk0+0a9cuxcXFacGCBfLw8OANfBnKyMjQ8ePH8/0p7NK6fv36qVevXho1apRWrVqluLg4bd68WS+//LLz+TthwgTt2LFDDz/8sHbu3KmoqCjnZd5SzqXk27Zt08GDB52XtblSkvNraRQ3vvNxrHKsVgdnzpzR9ddfr8WLF+uvv/5SXFycli1bpjfffFN9+/ZVQECAc92tW7fme1+4aNEiZyXRhTxnXB3bc+bM0dKlS7Vv3z7FxMRoyZIlCggIKDC5ba7w8HCtXbtWGzZs0P79+/XEE08UOfmdKxEREbryyis1ceJEbd68WTt27NC4ceOcfduLGv/hw4e1fft2nT59WhkZGYWuW5LXBSmnkGLv3r166aWXNGjQoHz/D8Ud+6g5ivs8WVol/TyWV0neS5fE/fffr4ULF+b7rHh+sU5ZKMnvOGnSJP3444+Ki4vTjh079OOPPzrPkQ0aNJCPj49Wr16tEydO5KtG7d+/v+rUqaNZs2Zp1KhRhbbWkThea5KSZBCtWrXS559/rr179+r333/X3XffXexVPKGhoVqzZo2OHz+uhISEUo8vPDxcx44d0+eff664uDh98MEH+uKLL9zez/3336833nhDX3/9taKiojR58uRiQ/Bcr776qn7++Wft2bNH48ePl6enp7N4o7hjISgoSPXq1dMnn3yimJgY/frrr3r00UfzXdXg4+Oj7t2764033tCePXu0adOmfP2dpbI7dmuS2vFbVnONGzfWkiVLtGPHDl1++eUaP368brnlFj377LPOdaZPn64bb7xRDz74oC6//HLt2rVLS5cudfZhbNasmebPn6/Vq1frsssu09y5czVt2rQi77d+/fqaN2+e/ve//6lnz56aNWtWsZPuudK9e3e99dZb+vDDD3XppZfqm2++0eTJk0u07eTJk7V8+XJdeuml+vDDD/X222/r4osvlpRzeci3336rsLAwjR49Wj169NADDzyghISEfN8OWywWDR48WH/99ZduvfXWfPtv1KiRVq5cKaPRqFtuuUW9evXS448/Lk9PzyLDvpI8NqGhofrkk0/03Xff6bLLLtO8efM0adIkSf/00u3YsaO+/fZbHTp0SNddd50uu+wyPf/8885LVywWi6KjozV69GhFRkZq3LhxGjZsGG8UKlHHjh01c+ZMzZ07V7169dInn3xSYBZbLy8vvfjii7rssst09dVXKyUl5YIua7FYLPriiy+UnJysAQMGaNSoUerevbvmzJlzQb/LXXfdpRtvvFH33HOP+vfvr0OHDunBBx/Mt86YMWP0yiuv6JNPPlHv3r01dOhQZ/8po9GoJUuWqGfPnho7dqx69uypyZMnOy//udDnd1GPo8Fg0IIFC1SvXj1dd911uv766xUcHKwFCxYU2zrmfE8//bQmT56sOXPmqFevXrrpppu0fPlyZ+VbYGCg5s+fr0GDBumSSy7R8uXLNX/+/EKrX+G+X375RW3atMn3p7CgyGAw6PPPP9fll1+uhx9+WN27d9ddd92l6Oho52VrF110kZYtW6b9+/dr4MCBGjBggL744gtnlUfupfS9evVSq1atiqyqKO78WhrFje98HKs5OFarNl9fX3Xv3l3vvPOOBg8erN69e+v555/X0KFDCxQtjBs3Trt27VKfPn304osv6qmnnnJOeHMhzxlXx7a/v7/efPNNDRgwQH379tXOnTu1ZMmSQkOzJ554QhdffLGGDRuma6+9VhaLJV/7s5KaO3euQkNDNWTIEI0cOVLDhg1zOVFuXkOGDNHAgQN1ww03qFWrVlq6dGmh65bkdUHKeT/aq1cvl++Dizv2UXOU5PNkaZT081heJXkvXRI333yzJk+erBdeeEF9+vTR7t27XV7SfqFK8jva7XY9+eST6tmzp2666SYFBwdr3rx5knJaccyaNUvz589X27ZtNWrUKOe+DQaDbrvtNmVlZem2224rchwcrzVHSTKIOXPmKDU1Vf369dPdd9+t22+/vdhzyIsvvqh169apQ4cOuvzyy0s9vkGDBumhhx7SlClTdOmll+rnn3/WU0895fZ+xo8fr9tuu00TJkzQgAEDZLfbS3w+nTZtmp5++mn17dtXBw4c0OLFi52tnIo7FoxGoz788EPt2rVLvXv31hNPPKGnn366QL6T+1n6iiuu0MSJEwsEyGV17NYkhoSEhPJpUAuggHnz5mnGjBmKi4urNd9SAQAA5OrUqZPGjh2bb/Z0AKitHn30UcXExGjZsmWVPRQAbqiNxy49kIFy9N577+niiy9WvXr1tHXrVr3yyisaOXIk4TEAAAAA1FKJiYnavn27Fi1aVGxrSQBVR20+dgmQgXIUExOjf//73zpz5owaN26su+++O18zegAAAABA7TJq1Cj9/vvvuv32250TpQGo+mrzsUsLCwAAAAAAAACAS1xHDwAAAAAAAABwiQAZAAAAAAAAAOASATIAAAAAAAAAwCUCZAAAAMANDzzwgDp16lTZw3Bat26dgoKCtG7dOudtM2bMUFBQUOUNCgAAADUGATIAAACqjQULFigoKMj5p169emrfvr3Gjx+v+Pj4yh5emdi9e7dmzJihgwcPVvZQAAAAAAJkAAAAVD+TJ0/Wu+++q//85z/q16+fPvvsMw0aNEhpaWmVPbQLtmfPHs2aNUuHDh0q9T6eeOKJGhOoAwAAoHKZK3sAAAAAgLsGDBig7t27S5LuuOMO1alTR2+//ba+/fZb3XLLLS63sVqtslgsFTnMSmM2m2U281YfAAAAF44KZAAAAFR7ffr0kSTFxcVJyulTHBISokOHDmnUqFEKDQ3VsGHDJEl2u12vv/66unXrpuDgYLVr105PPPGEEhMTC+z3008/Vbdu3RQSEqJLL71U3333XYF1XPUgzhUUFKQZM2bkuy0+Pl6PPPKI2rdvr+DgYHXq1EkPPfSQkpOTtWDBAo0ZM0aSdP311ztbdSxYsMCtx8NVD+ROnTrplltu0bZt23TNNdeoYcOG6tChg+bOnVtg+8zMTM2ePVuRkZEKDg5W69atNXHiRCUkJLg1DgAAAFR/lCUAAACg2ouNjZUk1a1b13mb3W7XzTffrIsvvljTp0+XyWSSJD322GP66KOPNGjQIN1///3as2ePPvjgA23btk0rV66Uh4eHJOmzzz7T+PHjdfHFF+uee+7RyZMndd9996lp06alHufx48c1YMAAnTp1SnfccYfat2+v+Ph4rVixQmfOnNGll16qe++9V++9954ee+wxtW7dWpLUs2fPUt9nXgcPHtSIESM0atQoDRs2TF9++aWeeuoptW3bVldccYUkyeFw6Pbbb9fatWv1r3/9Sx06dFBsbKzee+89bd++XT/88IPzMQIAAEDNR4AMAACAaicpKUmnT59Wenq6Nm3apNmzZ8vHx0dXX321c52srCxdddVVevnll5237d69Wx999JFuvfVW/fe//3XeHhERoSlTpmjhwoW64447lJ2dreeee05t/7+9+wmJag3jOP47p2wyYhBEstCZYiYcHSHSBP9kqISLWgQODQVJqG2tbYFkC4NciLNxIQqWiBDYP8WgJiKyFm2iFpVktpigxTCBIy1C1LmLmIPneupevaV2/X5AmPPOe16fmdXw4+E5gYDu37+v7du3S5IOHz6shoYG5efnr6ruK1eu6PPnzxofH1dlZaW1funSJaVSKRmGofLycvX19ammpkbV1dWr+j8/8uHDB929e1c1NTWSpDNnzqi4uFg3btywAuSRkRFFo1Hdu3fP6uyWpKqqKoXDYd26dUunTp36pXUBAABg42KEBQAAAP44oVBIPp9PwWBQzc3N2rVrl27evKk9e/bY9p07d852/eDBA0nS+fPnbevNzc1yu93W+y9fvlQ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+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Product line per Quantity. Set the kind parameter to point\n",
+    "sns.catplot(y = 'Quantity', x = 'Product line', kind= 'point', data = df, aspect = 4);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "cf79ab23",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='City', ylabel='gross income'>"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.barplot(x='City', y= 'gross income', data= df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9fc070bc",
+   "metadata": {},
+   "source": [
+    "Summary\n",
+    "\n",
+    "1. Data set is large with exactly 68,000 elements (4000 rows and 17 columns). \n",
+    "\n",
+    "2. The elements for categorical raw data are complete. \n",
+    "\n",
+    "    Approach:\n",
+    "    \n",
+    "The approaches for data analyses for company XYZ were to combine seperate data set for each city, make inferential and descriptive statistical analyses and obtain key facts that show and compare the sales performances for Company's branches.\n",
+    "\n",
+    "    Method:\n",
+    "    \n",
+    "These were accomplished using some python packages such as pandas analytical tools, Numpy, and Matplotlib as well as data illustrative tools such as Seaborn's statistical visualization package. \n",
+    "\n",
+    "    Insights:\n",
+    "    \n",
+    "Python packages such as pandas analytical tools, Numpy, and Matplotlib as well as data illustrative tools such as Seaborn's statistical visualization package are very efficient and powerful in analysing huge data sets.\n",
+    "\n",
+    "Coding lines are not too restictive, i.e packages are user-friendly and flexible. Users creativity shows that usage can be interesting.\n",
+    "\n",
+    "Some 'take-home' facts about the company XYZ include:  \n",
+    "Sales of Electronic and fashion accessories are almost at par. Sales across cities are very competitive with close matching records. \n",
+    "\n",
+    "Though quantities purchased for product lines generally are low, most customers prefer using Cash and Epay payment methods."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cadabbb4",
+   "metadata": {},
+   "source": [
+    "Documentation\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 0091e42cae15d01c490ad0bfbfb4613796e3ee4b Mon Sep 17 00:00:00 2001
From: Ochai2018 <45337793+Ochai2018@users.noreply.github.com>
Date: Thu, 25 Aug 2022 09:06:29 -0700
Subject: [PATCH 07/13] Delete Ochai Odeh_Panda_Analytics_Project2

---
 Ochai Odeh_Panda_Analytics_Project2 | 1 -
 1 file changed, 1 deletion(-)
 delete mode 100644 Ochai Odeh_Panda_Analytics_Project2

diff --git a/Ochai Odeh_Panda_Analytics_Project2 b/Ochai Odeh_Panda_Analytics_Project2
deleted file mode 100644
index 8b13789..0000000
--- a/Ochai Odeh_Panda_Analytics_Project2	
+++ /dev/null
@@ -1 +0,0 @@
-

From ff91edcff57cd49f19eeced90c01990282a5e655 Mon Sep 17 00:00:00 2001
From: Ochai2018 <45337793+Ochai2018@users.noreply.github.com>
Date: Sat, 27 Aug 2022 15:33:26 -0700
Subject: [PATCH 08/13] Update README.md

---
 README.md | 31 +++++++++++++++++++++++--------
 1 file changed, 23 insertions(+), 8 deletions(-)

diff --git a/README.md b/README.md
index 16cddb2..267c302 100644
--- a/README.md
+++ b/README.md
@@ -61,7 +61,7 @@ Step 5 -  Aggregation with GroupBy
 
 Step 6 - Data Visualization
   Appropriate use of countplot to determine the branch with the highest sales record,
-  Optional - Appropriate use of countplot to determine the most used payment method & city with the most sales,
+  Appropriate use of countplot to determine the most used payment method & city with the most sales,
   Appropriate use of countplot to determine the highest & lowest sold product line,
 Diplay result that shows the Payment channel used by most customers to pay for each product line with Chart showing the "product line" column on the Y-axis, and   the  "hue" parameter for the "Payment" column,
 
@@ -69,7 +69,7 @@ Diplay result that shows the Payment channel used by most customers to pay for e
 # Insights
 The insights obtained from project's analysis are as follows:
 
-Data set is large with exactly 34,000 elements (2000 rows and 17 columns),
+Data set is large with exactly 17,000 elements (1000 rows and 17 columns),
 
 The elements for categorical raw data are complete,
 
@@ -78,7 +78,20 @@ Python packages such as pandas analytical tools, Numpy, and Matplotlib as well a
 Coding lines are not too restictive, i.e packages are user-friendly and flexible. Users creativity shows that usage can be interesting,
 
 Some 'take-home' facts about the company XYZ include:
-Sales of Electronic and fashion accessories are almost at par. Sales across cities are very competitive with close matching records.
+Sales of Electronic and fashion accessories are almost at par. Sales across cities are very competitive with close matching records. However some details from analyses include:
+    -Branch A has the highest sales record.
+    -The most used payment method is Epay with a count of 345.
+    -The City with the most sales is Lagos.
+    -The highest product line sold is Fashion accessories.
+    -The lowest product line sold is Health and Beauty.
+    -Females purchased higher quantity of products. This might be explained by the higher tendency of women to always go shopping than men.
+    -Branch with the lowest rating is B
+    -Branch A had slightly higher normal customers than member customers.
+    -Branch B had almost equal number of Member and normal customers' patronage.
+    -Branch C had more member customers than normal customers.
+    -Though quantities purchased for product lines generally are low, most customers prefer using Cash and Epay payment methods.
+    -Home and lifestyle gave the highest overall income followed closely by Sports and travel.
+This is supported by the fact that home most home utilities are essentials used by a large population of people
 
 Though quantities purchased for product lines generally are low, most customers prefer using Cash and Epay payment methods.
 
@@ -87,7 +100,11 @@ More information can be obtained from this project by including data cleaning an
 
 # Standout Section
 Use of appropriate methods & descriptions to explore other columns such as Unit Price, Quantity in Aggregation with GroupBy.
-A barplot was made to display the gross income for each city.
+A barplot was made to:
+    Display and the gross income for each city.
+    Determine the highest customer type patronage;
+    Determine the Rating distribution.
+    Determine the gender with the highest product purchases.
 
 # Executive Summary.
 Summary
@@ -108,7 +125,5 @@ Python packages such as pandas analytical tools, Numpy, and Matplotlib as well a
 
 Coding lines are not too restictive, i.e packages are user-friendly and flexible. Users creativity shows that usage can be interesting.
 
-Some 'take-home' facts about the company XYZ include:
-Sales of Electronic and fashion accessories are almost at par. Sales across cities are very competitive with close matching records.
-
-Though quantities purchased for product lines generally are low, most customers prefer using Cash and Epay payment methods.
+A 'take-home' fact about the company XYZ include:
+Sales of Electronic and fashion accessories are almost at par. Sales across cities are very competitive with close matching records. 

From 124a87ec5e95e6fe63e625e50e0eb18125dd4565 Mon Sep 17 00:00:00 2001
From: Ochai2018 <45337793+Ochai2018@users.noreply.github.com>
Date: Sat, 27 Aug 2022 15:39:09 -0700
Subject: [PATCH 09/13] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 267c302..fcbfce8 100644
--- a/README.md
+++ b/README.md
@@ -109,7 +109,7 @@ A barplot was made to:
 # Executive Summary.
 Summary
 
-Data set is large with exactly 68,000 elements (4000 rows and 17 columns).
+Data set is large with exactly 17,000 elements (1000 rows and 17 columns).
 
 The elements for categorical raw data are complete.
 

From 3372defc76000291b17d5dcd2266a80887dd0a88 Mon Sep 17 00:00:00 2001
From: Ochai2018 <45337793+Ochai2018@users.noreply.github.com>
Date: Sat, 27 Aug 2022 23:43:22 +0100
Subject: [PATCH 10/13] Add files via upload

Updated Submission
---
 Ochai.Pandas.Analytics.Project.Updated.ipynb | 3810 ++++++++++++++++++
 1 file changed, 3810 insertions(+)
 create mode 100644 Ochai.Pandas.Analytics.Project.Updated.ipynb

diff --git a/Ochai.Pandas.Analytics.Project.Updated.ipynb b/Ochai.Pandas.Analytics.Project.Updated.ipynb
new file mode 100644
index 0000000..c1b1267
--- /dev/null
+++ b/Ochai.Pandas.Analytics.Project.Updated.ipynb
@@ -0,0 +1,3810 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d18893eb",
+   "metadata": {},
+   "source": [
+    "# Analyze Supermarket Data Across the Country - Company XYZ"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1490815d",
+   "metadata": {},
+   "source": [
+    "Company XYZ owns a supermarket chain across the country. Each major branch located in 3 cities across the country recorded sales information for 3 months, to help the company understand sales trends and determine its growth, as the rise of supermarkets competition is seen.\n",
+    "\n",
+    "You will apply learnings to analyse the dataset in the data folder, and the description of each feature can be found in this link"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "605a141a",
+   "metadata": {},
+   "source": [
+    "# Step 1 - Loading the Dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76cc9e60",
+   "metadata": {},
+   "source": [
+    "n this step, you will combine the dataset from each branch (3 branches) into one dataset for easy analysis. You expected to write the syntaxes that will read multiple files from your current working directory and export a CSV file after combining. The learning from this step is the ability to automate reading and combining multiple CSV files, because as a Data Professional, you will saddled with the responsibility of reading data from different sources, and this is one of the use case."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea55910f",
+   "metadata": {},
+   "source": [
+    "# To-Do - Import Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c799a707",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import glob\n",
+    "import pandas as pd\n",
+    "os.chdir('C:\\\\Users\\\\OCHAI ODEH\\\\Downloads\\\\Pandas')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5745c9be",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Abuja_Branch.csv',\n",
+       " 'Lagos_Branch.csv',\n",
+       " 'Port_Harcourt_Branch.csv',\n",
+       " 'README.md',\n",
+       " 'Starter_notebook.ipynb']"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "os.listdir('C:\\\\Users\\\\OCHAI ODEH\\\\Downloads\\\\Pandas\\\\Pandas-Analytics-Project-main')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2a7fe7e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "extension = 'csv'\n",
+    "glob.glob('C:/Users/OCHAI ODEH/Downloads/Pandas/Pandas-Analytics-Project-main extension')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ff23b0a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Abuja_Branch.csv', 'Lagos_Branch.csv', 'Port_Harcourt_Branch.csv']\n"
+     ]
+    }
+   ],
+   "source": [
+    "path = 'C:\\\\Users\\\\OCHAI ODEH\\\\Downloads\\\\Pandas\\\\Pandas-Analytics-Project-main'\n",
+    "extension = 'csv'\n",
+    "os.chdir(path)\n",
+    "#combine files generated in list\n",
+    "files = glob.glob('*.{}'.format(extension))\n",
+    "print(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f7f923d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Export to csv\n",
+    "combined_csv = pd.concat([pd.read_csv(f) for f in files ])\n",
+    "combined_csv.to_csv( \"combined_csv.csv\", index=False, encoding='utf-8-sig')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "52cd858f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>692-92-5582</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Food and beverages</td>\n",
+       "      <td>19742.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2961.36</td>\n",
+       "      <td>62188.56</td>\n",
+       "      <td>2/20/2019</td>\n",
+       "      <td>13:27</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>59227.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>2961.36</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>351-62-0822</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Fashion accessories</td>\n",
+       "      <td>5212.8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1042.56</td>\n",
+       "      <td>21893.76</td>\n",
+       "      <td>2/6/2019</td>\n",
+       "      <td>18:07</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>20851.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1042.56</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>529-56-3974</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>9183.6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1836.72</td>\n",
+       "      <td>38571.12</td>\n",
+       "      <td>3/9/2019</td>\n",
+       "      <td>17:03</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>36734.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1836.72</td>\n",
+       "      <td>6.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>299-46-1805</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>33739.2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10121.76</td>\n",
+       "      <td>212556.96</td>\n",
+       "      <td>1/15/2019</td>\n",
+       "      <td>16:19</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>202435.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10121.76</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>319-50-3348</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>14508.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1450.80</td>\n",
+       "      <td>30466.80</td>\n",
+       "      <td>3/11/2019</td>\n",
+       "      <td>15:30</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>29016.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1450.80</td>\n",
+       "      <td>4.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>148-41-7930</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>35985.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>264494.16</td>\n",
+       "      <td>1/23/2019</td>\n",
+       "      <td>10:33</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>251899.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12594.96</td>\n",
+       "      <td>6.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>189-40-5216</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>34693.2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>254995.02</td>\n",
+       "      <td>1/9/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>242852.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>12142.62</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>267-62-7380</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>29642.4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>311245.20</td>\n",
+       "      <td>3/29/2019</td>\n",
+       "      <td>19:12</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>296424.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>14821.20</td>\n",
+       "      <td>4.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>652-49-6720</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>23039.10</td>\n",
+       "      <td>2/18/2019</td>\n",
+       "      <td>11:40</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>21942.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1097.10</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>233-67-5758</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Health and beauty</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>15252.30</td>\n",
+       "      <td>1/29/2019</td>\n",
+       "      <td>13:46</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>14526.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>726.30</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Invoice ID Branch           City Customer type  Gender  \\\n",
+       "0    692-92-5582      B          Abuja        Member  Female   \n",
+       "1    351-62-0822      B          Abuja        Member  Female   \n",
+       "2    529-56-3974      B          Abuja        Member    Male   \n",
+       "3    299-46-1805      B          Abuja        Member  Female   \n",
+       "4    319-50-3348      B          Abuja        Normal  Female   \n",
+       "..           ...    ...            ...           ...     ...   \n",
+       "995  148-41-7930      C  Port Harcourt        Normal    Male   \n",
+       "996  189-40-5216      C  Port Harcourt        Normal    Male   \n",
+       "997  267-62-7380      C  Port Harcourt        Member    Male   \n",
+       "998  652-49-6720      C  Port Harcourt        Member  Female   \n",
+       "999  233-67-5758      C  Port Harcourt        Normal    Male   \n",
+       "\n",
+       "               Product line  Unit price  Quantity    Tax 5%      Total  \\\n",
+       "0        Food and beverages     19742.4         3   2961.36   62188.56   \n",
+       "1       Fashion accessories      5212.8         4   1042.56   21893.76   \n",
+       "2    Electronic accessories      9183.6         4   1836.72   38571.12   \n",
+       "3         Sports and travel     33739.2         6  10121.76  212556.96   \n",
+       "4        Home and lifestyle     14508.0         2   1450.80   30466.80   \n",
+       "..                      ...         ...       ...       ...        ...   \n",
+       "995       Health and beauty     35985.6         7  12594.96  264494.16   \n",
+       "996  Electronic accessories     34693.2         7  12142.62  254995.02   \n",
+       "997  Electronic accessories     29642.4        10  14821.20  311245.20   \n",
+       "998  Electronic accessories     21942.0         1   1097.10   23039.10   \n",
+       "999       Health and beauty     14526.0         1    726.30   15252.30   \n",
+       "\n",
+       "          Date   Time Payment      cogs  gross margin percentage  \\\n",
+       "0    2/20/2019  13:27    Card   59227.2                 4.761905   \n",
+       "1     2/6/2019  18:07    Epay   20851.2                 4.761905   \n",
+       "2     3/9/2019  17:03    Cash   36734.4                 4.761905   \n",
+       "3    1/15/2019  16:19    Cash  202435.2                 4.761905   \n",
+       "4    3/11/2019  15:30    Epay   29016.0                 4.761905   \n",
+       "..         ...    ...     ...       ...                      ...   \n",
+       "995  1/23/2019  10:33    Cash  251899.2                 4.761905   \n",
+       "996   1/9/2019  11:40    Cash  242852.4                 4.761905   \n",
+       "997  3/29/2019  19:12    Epay  296424.0                 4.761905   \n",
+       "998  2/18/2019  11:40    Epay   21942.0                 4.761905   \n",
+       "999  1/29/2019  13:46    Epay   14526.0                 4.761905   \n",
+       "\n",
+       "     gross income  Rating  \n",
+       "0         2961.36     5.9  \n",
+       "1         1042.56     4.5  \n",
+       "2         1836.72     6.8  \n",
+       "3        10121.76     4.5  \n",
+       "4         1450.80     4.4  \n",
+       "..            ...     ...  \n",
+       "995      12594.96     6.1  \n",
+       "996      12142.62     6.0  \n",
+       "997      14821.20     4.3  \n",
+       "998       1097.10     5.9  \n",
+       "999        726.30     6.2  \n",
+       "\n",
+       "[1000 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Read the CSV file using pd.read_csv method\n",
+    "pd.read_csv(\"combined_csv.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c700859a",
+   "metadata": {},
+   "source": [
+    "# Data Exploration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ec7a36a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "plt.style.use('fivethirtyeight')  \n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b0059b6f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = combined_csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "2fe466b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>692-92-5582</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Food and beverages</td>\n",
+       "      <td>19742.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2961.36</td>\n",
+       "      <td>62188.56</td>\n",
+       "      <td>2/20/2019</td>\n",
+       "      <td>13:27</td>\n",
+       "      <td>Card</td>\n",
+       "      <td>59227.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>2961.36</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>351-62-0822</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Fashion accessories</td>\n",
+       "      <td>5212.8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1042.56</td>\n",
+       "      <td>21893.76</td>\n",
+       "      <td>2/6/2019</td>\n",
+       "      <td>18:07</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>20851.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1042.56</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>529-56-3974</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Electronic accessories</td>\n",
+       "      <td>9183.6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1836.72</td>\n",
+       "      <td>38571.12</td>\n",
+       "      <td>3/9/2019</td>\n",
+       "      <td>17:03</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>36734.4</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1836.72</td>\n",
+       "      <td>6.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>299-46-1805</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Member</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Sports and travel</td>\n",
+       "      <td>33739.2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10121.76</td>\n",
+       "      <td>212556.96</td>\n",
+       "      <td>1/15/2019</td>\n",
+       "      <td>16:19</td>\n",
+       "      <td>Cash</td>\n",
+       "      <td>202435.2</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>10121.76</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>319-50-3348</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Home and lifestyle</td>\n",
+       "      <td>14508.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1450.80</td>\n",
+       "      <td>30466.80</td>\n",
+       "      <td>3/11/2019</td>\n",
+       "      <td>15:30</td>\n",
+       "      <td>Epay</td>\n",
+       "      <td>29016.0</td>\n",
+       "      <td>4.761905</td>\n",
+       "      <td>1450.80</td>\n",
+       "      <td>4.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
+       "0  692-92-5582      B  Abuja        Member  Female      Food and beverages   \n",
+       "1  351-62-0822      B  Abuja        Member  Female     Fashion accessories   \n",
+       "2  529-56-3974      B  Abuja        Member    Male  Electronic accessories   \n",
+       "3  299-46-1805      B  Abuja        Member  Female       Sports and travel   \n",
+       "4  319-50-3348      B  Abuja        Normal  Female      Home and lifestyle   \n",
+       "\n",
+       "   Unit price  Quantity    Tax 5%      Total       Date   Time Payment  \\\n",
+       "0     19742.4         3   2961.36   62188.56  2/20/2019  13:27    Card   \n",
+       "1      5212.8         4   1042.56   21893.76   2/6/2019  18:07    Epay   \n",
+       "2      9183.6         4   1836.72   38571.12   3/9/2019  17:03    Cash   \n",
+       "3     33739.2         6  10121.76  212556.96  1/15/2019  16:19    Cash   \n",
+       "4     14508.0         2   1450.80   30466.80  3/11/2019  15:30    Epay   \n",
+       "\n",
+       "       cogs  gross margin percentage  gross income  Rating  \n",
+       "0   59227.2                 4.761905       2961.36     5.9  \n",
+       "1   20851.2                 4.761905       1042.56     4.5  \n",
+       "2   36734.4                 4.761905       1836.72     6.8  \n",
+       "3  202435.2                 4.761905      10121.76     4.5  \n",
+       "4   29016.0                 4.761905       1450.80     4.4  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Use the head() method to view first few rows of the dataset\n",
+    "df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1794b307",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Check the number of rows and columns present in the data using the shape attribute\n",
+    "row_count = df.shape[0]  # Gives number of rows\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "7a486d35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "col_count = df.shape[1] # Gives number of columns\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "32eb2dc2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of rows is 1000\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Number of rows is', row_count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b96fb828",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of columns is 17\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Number of columns is', col_count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "00132d1c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The column titles of dataset include: Index(['Invoice ID', 'Branch', 'City', 'Customer type', 'Gender',\n",
+      "       'Product line', 'Unit price', 'Quantity', 'Tax 5%', 'Total', 'Date',\n",
+      "       'Time', 'Payment', 'cogs', 'gross margin percentage', 'gross income',\n",
+      "       'Rating', 'Day', 'Month', 'Year', 'Hour'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Generate the names of the columns using the columns attribute.\n",
+    "\n",
+    "column_labels = df.columns   #return the column labels\n",
+    "print('The column titles of dataset include:', column_labels)         "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "8ee060f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>Columns</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Invoice ID</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Branch</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>City</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Customer type</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Gender</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Product line</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Unit price</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Quantity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Tax 5%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Total</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Date</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Time</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Payment</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>cogs</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>gross margin percentage</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>gross income</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Rating</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Day</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Month</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Year</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Hour</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    Columns\n",
+       "0                Invoice ID\n",
+       "1                    Branch\n",
+       "2                      City\n",
+       "3             Customer type\n",
+       "4                    Gender\n",
+       "5              Product line\n",
+       "6                Unit price\n",
+       "7                  Quantity\n",
+       "8                    Tax 5%\n",
+       "9                     Total\n",
+       "10                     Date\n",
+       "11                     Time\n",
+       "12                  Payment\n",
+       "13                     cogs\n",
+       "14  gross margin percentage\n",
+       "15             gross income\n",
+       "16                   Rating\n",
+       "17                      Day\n",
+       "18                    Month\n",
+       "19                     Year\n",
+       "20                     Hour"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "column_labels = pd.DataFrame(df.columns,columns=['Columns'])\n",
+    "column_labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "0f458407",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1.000000e+03</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>20041.966800</td>\n",
+       "      <td>5.510000</td>\n",
+       "      <td>5536.572840</td>\n",
+       "      <td>116268.029640</td>\n",
+       "      <td>110731.456800</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>5536.572840</td>\n",
+       "      <td>6.97270</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>9538.066205</td>\n",
+       "      <td>2.923431</td>\n",
+       "      <td>4215.177173</td>\n",
+       "      <td>88518.720636</td>\n",
+       "      <td>84303.543463</td>\n",
+       "      <td>6.131498e-14</td>\n",
+       "      <td>4215.177173</td>\n",
+       "      <td>1.71858</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>3628.800000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>183.060000</td>\n",
+       "      <td>3844.260000</td>\n",
+       "      <td>3661.200000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>183.060000</td>\n",
+       "      <td>4.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>11835.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2132.955000</td>\n",
+       "      <td>44792.055000</td>\n",
+       "      <td>42659.100000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>2132.955000</td>\n",
+       "      <td>5.50000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>19882.800000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>4351.680000</td>\n",
+       "      <td>91385.280000</td>\n",
+       "      <td>87033.600000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>4351.680000</td>\n",
+       "      <td>7.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>28056.600000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>8080.290000</td>\n",
+       "      <td>169686.090000</td>\n",
+       "      <td>161605.800000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>8080.290000</td>\n",
+       "      <td>8.50000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>35985.600000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>17874.000000</td>\n",
+       "      <td>375354.000000</td>\n",
+       "      <td>357480.000000</td>\n",
+       "      <td>4.761905e+00</td>\n",
+       "      <td>17874.000000</td>\n",
+       "      <td>10.00000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Unit price     Quantity        Tax 5%          Total           cogs  \\\n",
+       "count   1000.000000  1000.000000   1000.000000    1000.000000    1000.000000   \n",
+       "mean   20041.966800     5.510000   5536.572840  116268.029640  110731.456800   \n",
+       "std     9538.066205     2.923431   4215.177173   88518.720636   84303.543463   \n",
+       "min     3628.800000     1.000000    183.060000    3844.260000    3661.200000   \n",
+       "25%    11835.000000     3.000000   2132.955000   44792.055000   42659.100000   \n",
+       "50%    19882.800000     5.000000   4351.680000   91385.280000   87033.600000   \n",
+       "75%    28056.600000     8.000000   8080.290000  169686.090000  161605.800000   \n",
+       "max    35985.600000    10.000000  17874.000000  375354.000000  357480.000000   \n",
+       "\n",
+       "       gross margin percentage  gross income      Rating  \n",
+       "count             1.000000e+03   1000.000000  1000.00000  \n",
+       "mean              4.761905e+00   5536.572840     6.97270  \n",
+       "std               6.131498e-14   4215.177173     1.71858  \n",
+       "min               4.761905e+00    183.060000     4.00000  \n",
+       "25%               4.761905e+00   2132.955000     5.50000  \n",
+       "50%               4.761905e+00   4351.680000     7.00000  \n",
+       "75%               4.761905e+00   8080.290000     8.50000  \n",
+       "max               4.761905e+00  17874.000000    10.00000  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Statistical summary\n",
+    "df.describe()\n",
+    "# Below this cell write in few sentences what you can derive from the data statistical summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "84e654bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "From the 1000 records, maximum sales of 10.000000 products and minimum sale of 1.000000 product was recorded.\n",
+      "Taxes of 5% on all products are equivalent to the gross incomes from sales.\n",
+      "The company's maximum gross income of 17874.0000 for 10 products has the highest rating.\n",
+      "Only one product with minimum unit price of 3628.800000 giving the lowest gross income of 183.06000 also has the lowest rating.\n",
+      "There are large differences in unit prices of products, Taxes of 5% and gross incomes, going by their respective value of standard deviations of 9534.487865 and 4213.59579 respectively.\n",
+      "Gross margin percentages for all variable are almost the same. There are no missing values in the table.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"From the 1000 records, maximum sales of 10.000000 products and minimum sale of 1.000000 product was recorded.\") \n",
+    "\n",
+    "      \n",
+    "print(\"Taxes of 5% on all products are equivalent to the gross incomes from sales.\") \n",
+    "\n",
+    "      \n",
+    "print(\"The company's maximum gross income of 17874.0000 for 10 products has the highest rating.\")\n",
+    "\n",
+    "      \n",
+    "print(\"Only one product with minimum unit price of 3628.800000 giving the lowest gross income of 183.06000 also has the lowest rating.\")\n",
+    "\n",
+    "      \n",
+    "print(\"There are large differences in unit prices of products, Taxes of 5% and gross incomes, going by their respective value of standard deviations of 9534.487865 and 4213.59579 respectively.\")   \n",
+    "\n",
+    "\n",
+    "print(\"Gross margin percentages for all variable are almost the same. There are no missing values in the table.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "da68d1a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>Invoice ID</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Customer type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Product line</th>\n",
+       "      <th>Unit price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Tax 5%</th>\n",
+       "      <th>Total</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Payment</th>\n",
+       "      <th>cogs</th>\n",
+       "      <th>gross margin percentage</th>\n",
+       "      <th>gross income</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Invoice ID  Branch   City  Customer type  Gender  Product line  \\\n",
+       "0         False   False  False          False   False         False   \n",
+       "1         False   False  False          False   False         False   \n",
+       "2         False   False  False          False   False         False   \n",
+       "3         False   False  False          False   False         False   \n",
+       "4         False   False  False          False   False         False   \n",
+       "..          ...     ...    ...            ...     ...           ...   \n",
+       "323       False   False  False          False   False         False   \n",
+       "324       False   False  False          False   False         False   \n",
+       "325       False   False  False          False   False         False   \n",
+       "326       False   False  False          False   False         False   \n",
+       "327       False   False  False          False   False         False   \n",
+       "\n",
+       "     Unit price  Quantity  Tax 5%  Total   Date   Time  Payment   cogs  \\\n",
+       "0         False     False   False  False  False  False    False  False   \n",
+       "1         False     False   False  False  False  False    False  False   \n",
+       "2         False     False   False  False  False  False    False  False   \n",
+       "3         False     False   False  False  False  False    False  False   \n",
+       "4         False     False   False  False  False  False    False  False   \n",
+       "..          ...       ...     ...    ...    ...    ...      ...    ...   \n",
+       "323       False     False   False  False  False  False    False  False   \n",
+       "324       False     False   False  False  False  False    False  False   \n",
+       "325       False     False   False  False  False  False    False  False   \n",
+       "326       False     False   False  False  False  False    False  False   \n",
+       "327       False     False   False  False  False  False    False  False   \n",
+       "\n",
+       "     gross margin percentage  gross income  Rating  \n",
+       "0                      False         False   False  \n",
+       "1                      False         False   False  \n",
+       "2                      False         False   False  \n",
+       "3                      False         False   False  \n",
+       "4                      False         False   False  \n",
+       "..                       ...           ...     ...  \n",
+       "323                    False         False   False  \n",
+       "324                    False         False   False  \n",
+       "325                    False         False   False  \n",
+       "326                    False         False   False  \n",
+       "327                    False         False   False  \n",
+       "\n",
+       "[1000 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#missing values\n",
+    "df.isnull()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "24cdee1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "missing_value_in_df = df.isnull()  # checking missing values\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "f993e123",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "non_missing_valuein_df = df.notnull()  # checking non-missing values\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "97101a24",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "any_missing_values_in_df = df.isnull().values.any()  # knowing if there are any missing values\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "83e042a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "number_non_missing_values_in_variables = df.notnull().sum()  # knowing number of non-missing values for each variable\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "96909ffa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "number_missing_values_in_data = df.isnull().sum().sum()  # knowing how many missing values in the data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "be76c0cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Invoice ID  Branch   City  Customer type  Gender  Product line  \\\n",
+      "0         False   False  False          False   False         False   \n",
+      "1         False   False  False          False   False         False   \n",
+      "2         False   False  False          False   False         False   \n",
+      "3         False   False  False          False   False         False   \n",
+      "4         False   False  False          False   False         False   \n",
+      "..          ...     ...    ...            ...     ...           ...   \n",
+      "323       False   False  False          False   False         False   \n",
+      "324       False   False  False          False   False         False   \n",
+      "325       False   False  False          False   False         False   \n",
+      "326       False   False  False          False   False         False   \n",
+      "327       False   False  False          False   False         False   \n",
+      "\n",
+      "     Unit price  Quantity  Tax 5%  Total   Date   Time  Payment   cogs  \\\n",
+      "0         False     False   False  False  False  False    False  False   \n",
+      "1         False     False   False  False  False  False    False  False   \n",
+      "2         False     False   False  False  False  False    False  False   \n",
+      "3         False     False   False  False  False  False    False  False   \n",
+      "4         False     False   False  False  False  False    False  False   \n",
+      "..          ...       ...     ...    ...    ...    ...      ...    ...   \n",
+      "323       False     False   False  False  False  False    False  False   \n",
+      "324       False     False   False  False  False  False    False  False   \n",
+      "325       False     False   False  False  False  False    False  False   \n",
+      "326       False     False   False  False  False  False    False  False   \n",
+      "327       False     False   False  False  False  False    False  False   \n",
+      "\n",
+      "     gross margin percentage  gross income  Rating  \n",
+      "0                      False         False   False  \n",
+      "1                      False         False   False  \n",
+      "2                      False         False   False  \n",
+      "3                      False         False   False  \n",
+      "4                      False         False   False  \n",
+      "..                       ...           ...     ...  \n",
+      "323                    False         False   False  \n",
+      "324                    False         False   False  \n",
+      "325                    False         False   False  \n",
+      "326                    False         False   False  \n",
+      "327                    False         False   False  \n",
+      "\n",
+      "[1000 rows x 17 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(missing_value_in_df)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "8ad475ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Invoice ID  Branch  City  Customer type  Gender  Product line  \\\n",
+      "0          True    True  True           True    True          True   \n",
+      "1          True    True  True           True    True          True   \n",
+      "2          True    True  True           True    True          True   \n",
+      "3          True    True  True           True    True          True   \n",
+      "4          True    True  True           True    True          True   \n",
+      "..          ...     ...   ...            ...     ...           ...   \n",
+      "323        True    True  True           True    True          True   \n",
+      "324        True    True  True           True    True          True   \n",
+      "325        True    True  True           True    True          True   \n",
+      "326        True    True  True           True    True          True   \n",
+      "327        True    True  True           True    True          True   \n",
+      "\n",
+      "     Unit price  Quantity  Tax 5%  Total  Date  Time  Payment  cogs  \\\n",
+      "0          True      True    True   True  True  True     True  True   \n",
+      "1          True      True    True   True  True  True     True  True   \n",
+      "2          True      True    True   True  True  True     True  True   \n",
+      "3          True      True    True   True  True  True     True  True   \n",
+      "4          True      True    True   True  True  True     True  True   \n",
+      "..          ...       ...     ...    ...   ...   ...      ...   ...   \n",
+      "323        True      True    True   True  True  True     True  True   \n",
+      "324        True      True    True   True  True  True     True  True   \n",
+      "325        True      True    True   True  True  True     True  True   \n",
+      "326        True      True    True   True  True  True     True  True   \n",
+      "327        True      True    True   True  True  True     True  True   \n",
+      "\n",
+      "     gross margin percentage  gross income  Rating  \n",
+      "0                       True          True    True  \n",
+      "1                       True          True    True  \n",
+      "2                       True          True    True  \n",
+      "3                       True          True    True  \n",
+      "4                       True          True    True  \n",
+      "..                       ...           ...     ...  \n",
+      "323                     True          True    True  \n",
+      "324                     True          True    True  \n",
+      "325                     True          True    True  \n",
+      "326                     True          True    True  \n",
+      "327                     True          True    True  \n",
+      "\n",
+      "[1000 rows x 17 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(non_missing_valuein_df)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "477a643c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "if any missing values = False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('if any missing values =',any_missing_values_in_df)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "751b8802",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of non-missing values in variables = Invoice ID                 1000\n",
+      "Branch                     1000\n",
+      "City                       1000\n",
+      "Customer type              1000\n",
+      "Gender                     1000\n",
+      "Product line               1000\n",
+      "Unit price                 1000\n",
+      "Quantity                   1000\n",
+      "Tax 5%                     1000\n",
+      "Total                      1000\n",
+      "Date                       1000\n",
+      "Time                       1000\n",
+      "Payment                    1000\n",
+      "cogs                       1000\n",
+      "gross margin percentage    1000\n",
+      "gross income               1000\n",
+      "Rating                     1000\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Number of non-missing values in variables =', number_non_missing_values_in_variables)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "70664681",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of missing values in data= 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Number of missing values in data=', number_missing_values_in_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "df688b08",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 1000 entries, 0 to 327\n",
+      "Data columns (total 17 columns):\n",
+      " #   Column                   Non-Null Count  Dtype  \n",
+      "---  ------                   --------------  -----  \n",
+      " 0   Invoice ID               1000 non-null   object \n",
+      " 1   Branch                   1000 non-null   object \n",
+      " 2   City                     1000 non-null   object \n",
+      " 3   Customer type            1000 non-null   object \n",
+      " 4   Gender                   1000 non-null   object \n",
+      " 5   Product line             1000 non-null   object \n",
+      " 6   Unit price               1000 non-null   float64\n",
+      " 7   Quantity                 1000 non-null   int64  \n",
+      " 8   Tax 5%                   1000 non-null   float64\n",
+      " 9   Total                    1000 non-null   float64\n",
+      " 10  Date                     1000 non-null   object \n",
+      " 11  Time                     1000 non-null   object \n",
+      " 12  Payment                  1000 non-null   object \n",
+      " 13  cogs                     1000 non-null   float64\n",
+      " 14  gross margin percentage  1000 non-null   float64\n",
+      " 15  gross income             1000 non-null   float64\n",
+      " 16  Rating                   1000 non-null   float64\n",
+      "dtypes: float64(7), int64(1), object(9)\n",
+      "memory usage: 140.6+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Viewing Data Information\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ffcf49ac",
+   "metadata": {},
+   "source": [
+    "# Dealing with DateTime Features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "f54cfe85",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datetime as dt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "2472e035",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Use to_datetime() to convert the date column to datetime\n",
+    "df['Date'] =  pd.to_datetime(df['Date'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "56ff21ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 1000 entries, 0 to 327\n",
+      "Data columns (total 17 columns):\n",
+      " #   Column                   Non-Null Count  Dtype         \n",
+      "---  ------                   --------------  -----         \n",
+      " 0   Invoice ID               1000 non-null   object        \n",
+      " 1   Branch                   1000 non-null   object        \n",
+      " 2   City                     1000 non-null   object        \n",
+      " 3   Customer type            1000 non-null   object        \n",
+      " 4   Gender                   1000 non-null   object        \n",
+      " 5   Product line             1000 non-null   object        \n",
+      " 6   Unit price               1000 non-null   float64       \n",
+      " 7   Quantity                 1000 non-null   int64         \n",
+      " 8   Tax 5%                   1000 non-null   float64       \n",
+      " 9   Total                    1000 non-null   float64       \n",
+      " 10  Date                     1000 non-null   datetime64[ns]\n",
+      " 11  Time                     1000 non-null   object        \n",
+      " 12  Payment                  1000 non-null   object        \n",
+      " 13  cogs                     1000 non-null   float64       \n",
+      " 14  gross margin percentage  1000 non-null   float64       \n",
+      " 15  gross income             1000 non-null   float64       \n",
+      " 16  Rating                   1000 non-null   float64       \n",
+      "dtypes: datetime64[ns](1), float64(7), int64(1), object(8)\n",
+      "memory usage: 140.6+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Check the datatype to confirm if it's in datetime\n",
+    "\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "74180d72",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Repeat the two steps above to the time column\n",
+    "#Use to_datetime() to convert the Time column to datetime\n",
+    "df['Time'] =  pd.to_datetime(df['Time'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "73f23dd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 1000 entries, 0 to 327\n",
+      "Data columns (total 17 columns):\n",
+      " #   Column                   Non-Null Count  Dtype         \n",
+      "---  ------                   --------------  -----         \n",
+      " 0   Invoice ID               1000 non-null   object        \n",
+      " 1   Branch                   1000 non-null   object        \n",
+      " 2   City                     1000 non-null   object        \n",
+      " 3   Customer type            1000 non-null   object        \n",
+      " 4   Gender                   1000 non-null   object        \n",
+      " 5   Product line             1000 non-null   object        \n",
+      " 6   Unit price               1000 non-null   float64       \n",
+      " 7   Quantity                 1000 non-null   int64         \n",
+      " 8   Tax 5%                   1000 non-null   float64       \n",
+      " 9   Total                    1000 non-null   float64       \n",
+      " 10  Date                     1000 non-null   datetime64[ns]\n",
+      " 11  Time                     1000 non-null   datetime64[ns]\n",
+      " 12  Payment                  1000 non-null   object        \n",
+      " 13  cogs                     1000 non-null   float64       \n",
+      " 14  gross margin percentage  1000 non-null   float64       \n",
+      " 15  gross income             1000 non-null   float64       \n",
+      " 16  Rating                   1000 non-null   float64       \n",
+      "dtypes: datetime64[ns](2), float64(7), int64(1), object(7)\n",
+      "memory usage: 140.6+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Check the datatype to confirm if Time is in datetime format\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5cca115a",
+   "metadata": {},
+   "source": [
+    "# Extract Features from date & time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "eb058523",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>Date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2019-02-20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2019-02-06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2019-03-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2019-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2019-03-11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>2019-01-23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>2019-01-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>2019-03-29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>2019-02-18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>2019-01-29</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Date\n",
+       "0   2019-02-20\n",
+       "1   2019-02-06\n",
+       "2   2019-03-09\n",
+       "3   2019-01-15\n",
+       "4   2019-03-11\n",
+       "..         ...\n",
+       "323 2019-01-23\n",
+       "324 2019-01-09\n",
+       "325 2019-03-29\n",
+       "326 2019-02-18\n",
+       "327 2019-01-29\n",
+       "\n",
+       "[1000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Extract the Day feature from the Date column, and save to a new Day column\n",
+    "df['Day'] = df['Date'].dt.day\n",
+    "Date = pd.DataFrame(df.Date)\n",
+    "Date"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "e4280b33",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>Month</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Month\n",
+       "0        2\n",
+       "1        2\n",
+       "2        3\n",
+       "3        1\n",
+       "4        3\n",
+       "..     ...\n",
+       "323      1\n",
+       "324      1\n",
+       "325      3\n",
+       "326      2\n",
+       "327      1\n",
+       "\n",
+       "[1000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Extract the Month feature from the Date column, and save to a new Month column\n",
+    "df['Month'] = df['Date'].dt.month\n",
+    "Month = pd.DataFrame(df.Month)\n",
+    "Month"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "6d096dac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>2019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>2019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>2019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>2019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>2019</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Year\n",
+       "0    2019\n",
+       "1    2019\n",
+       "2    2019\n",
+       "3    2019\n",
+       "4    2019\n",
+       "..    ...\n",
+       "323  2019\n",
+       "324  2019\n",
+       "325  2019\n",
+       "326  2019\n",
+       "327  2019\n",
+       "\n",
+       "[1000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Extract the Year feature from the Date column, and save to a new Year column\n",
+    "df['Year'] = df['Date'].dt.year\n",
+    "Year = pd.DataFrame(df.Year)\n",
+    "Year"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "93e13a5c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>Hour</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Hour\n",
+       "0      13\n",
+       "1      18\n",
+       "2      17\n",
+       "3      16\n",
+       "4      15\n",
+       "..    ...\n",
+       "323    10\n",
+       "324    11\n",
+       "325    19\n",
+       "326    11\n",
+       "327    13\n",
+       "\n",
+       "[1000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Extract the Hour feature from the Time column and save to a new Hour column\n",
+    "df['Hour'] = df['Time'].dt.hour\n",
+    "Hour = pd.DataFrame(df.Hour)\n",
+    "Hour"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "faab4d5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of unique hour of sales = 11\n"
+     ]
+    }
+   ],
+   "source": [
+    "#From the hours information, determine the numbers of unique hours of sales in the supermarket An array of the hours using the unique() method\n",
+    "Number_unique_of_sales = df['Hour'].nunique()\n",
+    "print('Number of unique hour of sales =', Number_unique_of_sales)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "2e457b89",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([13, 18, 17, 16, 15, 10, 12, 19, 14, 11, 20], dtype=int64)"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# An array of unique hours using the unique() method\n",
+    "df['Hour'].unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f95a45f",
+   "metadata": {},
+   "source": [
+    "# Unique Values in Columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "36c4add8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Uncomment the code and Run it\n",
+    "categorical_columns = [col for col in df.columns if df[col].dtype == \"object\" ]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "c4149f31",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Invoice ID',\n",
+       " 'Branch',\n",
+       " 'City',\n",
+       " 'Customer type',\n",
+       " 'Gender',\n",
+       " 'Product line',\n",
+       " 'Payment']"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "categorical_columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "fdb7540f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# generate the unique values in the categorical column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "71bd4fb0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Abuja', 'Lagos', 'Port Harcourt']"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['City'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "6727dc5a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Member', 'Normal']"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Customer type'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "1867fc06",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Female', 'Male']"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Gender'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "1ad1fcf7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Food and beverages',\n",
+       " 'Fashion accessories',\n",
+       " 'Electronic accessories',\n",
+       " 'Sports and travel',\n",
+       " 'Home and lifestyle',\n",
+       " 'Health and beauty']"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Product line'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "c8f29d43",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Card', 'Epay', 'Cash']"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Payment'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "8db86d54",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Number of unique values in the Invoice ID Column: 1000\n",
+      "Total Number of unique values in the City Column: 3\n",
+      "Total Number of unique values in the Customer type Column: 2\n",
+      "Total Number of unique values in the Gender Column: 2\n",
+      "Total Number of unique values in the Product line Column: 6\n",
+      "Total Number of unique values in the Payment Column: 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Number of unique vales in each categorical column\n",
+    "print(\"Total Number of unique values in the Invoice ID Column: {}\". format(len(df['Invoice ID'].unique().tolist())))\n",
+    "\n",
+    "print(\"Total Number of unique values in the City Column: {}\". format(len(df['City'].unique().tolist())))\n",
+    "\n",
+    "print(\"Total Number of unique values in the Customer type Column: {}\". format(len(df['Customer type'].unique().tolist())))\n",
+    "\n",
+    "print(\"Total Number of unique values in the Gender Column: {}\". format(len(df['Gender'].unique().tolist())))\n",
+    "\n",
+    "print(\"Total Number of unique values in the Product line Column: {}\". format(len(df['Product line'].unique().tolist())))\n",
+    "\n",
+    "print(\"Total Number of unique values in the Payment Column: {}\". format(len(df['Payment'].unique().tolist())))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "bb13f963",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "A    340\n",
+       "B    332\n",
+       "C    328\n",
+       "Name: Branch, dtype: int64"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Branch'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "19b63f57",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Lagos            340\n",
+       "Abuja            332\n",
+       "Port Harcourt    328\n",
+       "Name: City, dtype: int64"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['City'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "46119bd2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Member    501\n",
+       "Normal    499\n",
+       "Name: Customer type, dtype: int64"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Customer type'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "5f22d131",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Female    501\n",
+       "Male      499\n",
+       "Name: Gender, dtype: int64"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Gender'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "7c97f996",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fashion accessories       178\n",
+       "Food and beverages        174\n",
+       "Electronic accessories    170\n",
+       "Sports and travel         166\n",
+       "Home and lifestyle        160\n",
+       "Health and beauty         152\n",
+       "Name: Product line, dtype: int64"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Product line'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "b07baf6e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Epay    345\n",
+       "Cash    344\n",
+       "Card    311\n",
+       "Name: Payment, dtype: int64"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Payment'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5e35539",
+   "metadata": {},
+   "source": [
+    "# Aggregration with GroupBy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "930f4333",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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 tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Unit price</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Quantity</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Tax 5%</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Total</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">cogs</th>\n",
+       "      <th>...</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Rating</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Day</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Month</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Year</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Hour</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>sum</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sum</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sum</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sum</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sum</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>sum</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sum</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sum</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sum</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sum</th>\n",
+       "      <th>mean</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>City</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Abuja</th>\n",
+       "      <td>6652396.8</td>\n",
+       "      <td>20037.339759</td>\n",
+       "      <td>1820</td>\n",
+       "      <td>5.481928</td>\n",
+       "      <td>1820531.52</td>\n",
+       "      <td>5483.528675</td>\n",
+       "      <td>38231161.92</td>\n",
+       "      <td>115154.102169</td>\n",
+       "      <td>36410630.4</td>\n",
+       "      <td>109670.573494</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2263.6</td>\n",
+       "      <td>6.818072</td>\n",
+       "      <td>5068</td>\n",
+       "      <td>15.265060</td>\n",
+       "      <td>665</td>\n",
+       "      <td>2.003012</td>\n",
+       "      <td>670308</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>5029</td>\n",
+       "      <td>15.147590</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lagos</th>\n",
+       "      <td>6705176.4</td>\n",
+       "      <td>19721.107059</td>\n",
+       "      <td>1859</td>\n",
+       "      <td>5.467647</td>\n",
+       "      <td>1820577.78</td>\n",
+       "      <td>5354.640529</td>\n",
+       "      <td>38232133.38</td>\n",
+       "      <td>112447.451118</td>\n",
+       "      <td>36411555.6</td>\n",
+       "      <td>107092.810588</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2389.2</td>\n",
+       "      <td>7.027059</td>\n",
+       "      <td>5232</td>\n",
+       "      <td>15.388235</td>\n",
+       "      <td>688</td>\n",
+       "      <td>2.023529</td>\n",
+       "      <td>686460</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>4987</td>\n",
+       "      <td>14.667647</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Port Harcourt</th>\n",
+       "      <td>6684393.6</td>\n",
+       "      <td>20379.248780</td>\n",
+       "      <td>1831</td>\n",
+       "      <td>5.582317</td>\n",
+       "      <td>1895463.54</td>\n",
+       "      <td>5778.852256</td>\n",
+       "      <td>39804734.34</td>\n",
+       "      <td>121355.897378</td>\n",
+       "      <td>37909270.8</td>\n",
+       "      <td>115577.045122</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2319.9</td>\n",
+       "      <td>7.072866</td>\n",
+       "      <td>4956</td>\n",
+       "      <td>15.109756</td>\n",
+       "      <td>640</td>\n",
+       "      <td>1.951220</td>\n",
+       "      <td>662232</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>4894</td>\n",
+       "      <td>14.920732</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Unit price               Quantity                Tax 5%  \\\n",
+       "                     sum          mean      sum      mean         sum   \n",
+       "City                                                                    \n",
+       "Abuja          6652396.8  20037.339759     1820  5.481928  1820531.52   \n",
+       "Lagos          6705176.4  19721.107059     1859  5.467647  1820577.78   \n",
+       "Port Harcourt  6684393.6  20379.248780     1831  5.582317  1895463.54   \n",
+       "\n",
+       "                                  Total                       cogs  \\\n",
+       "                      mean          sum           mean         sum   \n",
+       "City                                                                 \n",
+       "Abuja          5483.528675  38231161.92  115154.102169  36410630.4   \n",
+       "Lagos          5354.640529  38232133.38  112447.451118  36411555.6   \n",
+       "Port Harcourt  5778.852256  39804734.34  121355.897378  37909270.8   \n",
+       "\n",
+       "                              ...  Rating             Day            Month  \\\n",
+       "                        mean  ...     sum      mean   sum       mean   sum   \n",
+       "City                          ...                                            \n",
+       "Abuja          109670.573494  ...  2263.6  6.818072  5068  15.265060   665   \n",
+       "Lagos          107092.810588  ...  2389.2  7.027059  5232  15.388235   688   \n",
+       "Port Harcourt  115577.045122  ...  2319.9  7.072866  4956  15.109756   640   \n",
+       "\n",
+       "                           Year          Hour             \n",
+       "                   mean     sum    mean   sum       mean  \n",
+       "City                                                      \n",
+       "Abuja          2.003012  670308  2019.0  5029  15.147590  \n",
+       "Lagos          2.023529  686460  2019.0  4987  14.667647  \n",
+       "Port Harcourt  1.951220  662232  2019.0  4894  14.920732  \n",
+       "\n",
+       "[3 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Your task here, will be to create a groupby object with the \"City Column\", and aggregation function of sum and mean\n",
+    "# Creating a groupby object to group the dataset according to City \n",
+    "city = df.groupby(\"City\")\n",
+    "\n",
+    "#To get the sum and mean of the numeric columns \n",
+    "city.agg([\"sum\", \"mean\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "a13123bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Using the groupby object, display a table that shows the gross income of each city, and determine the city with the highest total gross income.\n",
+    "# Using the groupby object to get the gross income of each city\n",
+    "\n",
+    "gross_income_city = city['gross income'].sum().reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "43a56adb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>City</th>\n",
+       "      <th>gross income</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Abuja</td>\n",
+       "      <td>1820531.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Lagos</td>\n",
+       "      <td>1820577.78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Port Harcourt</td>\n",
+       "      <td>1895463.54</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            City  gross income\n",
+       "0          Abuja    1820531.52\n",
+       "1          Lagos    1820577.78\n",
+       "2  Port Harcourt    1895463.54"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gross_income_city"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "4af13062",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The highest gross income is generated by Port Harcourt: 1895463.54\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"The highest gross income is generated by Port Harcourt:\", df.groupby('City')['gross income'].sum().max())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "9668f4c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City\n",
+       "Abuja            6652396.8\n",
+       "Lagos            6705176.4\n",
+       "Port Harcourt    6684393.6\n",
+       "Name: Unit price, dtype: float64"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('City')['Unit price'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "d693adbb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The highest Unit price is found in Lagos: 6705176.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"The highest Unit price is found in Lagos:\", df.groupby('City')['Unit price'].sum().max())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "ad438678",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City\n",
+       "Abuja            1820\n",
+       "Lagos            1859\n",
+       "Port Harcourt    1831\n",
+       "Name: Quantity, dtype: int64"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('City')['Quantity'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "376ff69e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The highest Quantity of products is in Lagos : 1859\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"The highest Quantity of products is in Lagos :\", df.groupby('City')['Quantity'].sum().max())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ee6ecd9",
+   "metadata": {},
+   "source": [
+    "# Data Visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "5a121b72",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Sales record for Branches')"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Using countplot, determine the branch with the highest sales record\n",
+    "sns.countplot(x= 'Branch', data = df).set_title('Sales record for Branches')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "9d38ae62",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Branch A has the highest sales record.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Branch A has the highest sales record.')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "4299c16e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The most used payment method is Epay with a count of 345\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# most used payment method,\n",
+    "sns.countplot(x='Payment', data=df).set_title('Payment method')\n",
+    "print('The most used payment method is Epay with a count of 345')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "d379bca8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The City with the most sales is Lagos\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#city with the most sales\n",
+    "sns.countplot(x = 'City', data = df).set_title('Sales across cities')\n",
+    "print('The City with the most sales is Lagos')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "d9655985",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The highest product line sold is Fashion accessories\n",
+      "The lowest product line sold is Health and Beauty\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Determine the highest & lowest sold product line, using Countplot\n",
+    "sns.countplot(y = 'Product line', data = df).set_title('Sales for different product line')\n",
+    "print('The highest product line sold is Fashion accessories')\n",
+    "print('The lowest product line sold is Health and Beauty')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "983e15b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Payment channel for Product lines')"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Determine the Payment channel used by most customer to pay for each product line\n",
+    "sns.countplot(y = 'Product line', data = df, hue = 'Payment').set_title('Payment channel for Product lines')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "063e64cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Food and Beverage: Payment channel most used is Card\n",
+      "Fashion accessories: Payment channel most used is Epay\n",
+      "Electronic accessories: Payment channel most used is Cash\n",
+      "Sports and travel: Payment channel most used is Cash\n",
+      "Home and lifestyle: Payment channel most used is Epay\n",
+      "Health and Beauty: Payment channel most used is Epay\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Food and Beverage: Payment channel most used is Card')\n",
+    "print('Fashion accessories: Payment channel most used is Epay')\n",
+    "print('Electronic accessories: Payment channel most used is Cash')\n",
+    "print('Sports and travel: Payment channel most used is Cash')\n",
+    "print('Home and lifestyle: Payment channel most used is Epay')\n",
+    "print('Health and Beauty: Payment channel most used is Epay')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "943c6cb4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0xa1cf623ee0>"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Determine the Payment channel for each branch.\n",
+    "sns.countplot(x = 'Payment', data = df, hue = 'Branch').set_title('Payment mode used by customers for each Branch')\n",
+    "plt.legend(loc='upper left', bbox_to_anchor=(1.25, 0.5), ncol=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "2209366d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The most used Payment channel for Branch A is Epay\n",
+      "The most used Payment channel for Branch B is Epay\n",
+      "The most used Payment channel for Branch C is Cash\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('The most used Payment channel for Branch A is Epay')\n",
+    "print('The most used Payment channel for Branch B is Epay')\n",
+    "print('The most used Payment channel for Branch C is Cash')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5fdf196",
+   "metadata": {},
+   "source": [
+    "The most used Payment channel for Branch A is Epay"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "5d08c40d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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cGOm1rKwsjZ+PKSgowOzZs/HkyRO8//77AiUjoorgSIz02pYtW7BgwQJ07twZderUwf3793Hs2DFcvXoV9evXR3R0NGrWrCl0TCIqBc+JkV5r1aoVvL29cfLkSWRmZqKoqAh169bFmDFjMHHiRBYYkchxJEZERJLFc2JERCRZLDEiIpIslhgREUkWS4yIiCSLJUZERJLFEiMiIsliiRERkWQJWmLx8fEIDg5Gs2bNIJfLER4errZcqVQiJCQETZs2hZ2dHQIDA3Hx4kWB0hIRkdgIWmKPHj2Cs7Mzli5dCjMzM43la9asQWhoKJYtW4bo6GhYW1ujd+/eqp+VJyIi/SaaO3bUqVMHy5cvx6BBgwA8H4U1bdoUI0aMwOTJkwEAeXl5UCgUWLhwIYYOHSpkXCIiEgHR3jsxNTUV6enpePfdd1XzzMzM0LFjR5w4cUIyJdarVy+hI2hFZGSk0BEkg/tcP3G/C0O0F3akp6cDAKytrdXmW1tbIyMjQ4hIREQkMqIdiRWTyWRq00qlUmPeixITE3UdSS/xfdU/3Of6SWz7XaFQlLlctCVma2sLAMjIyEDdunVV8+/evasxOntReS+Yqobvq/7hPtdPUtvvoi2xevXqwdbWFjExMXB3dwcAPHnyBAkJCViwYIHA6SpOl8eXXz4GL7Vj2W8q7nP9xP0uDEFL7OHDh0hKSgIAFBUV4ebNmzh79iysrKzg4OCAUaNGYeXKlVAoFGjcuDFWrFiB6tWrIygoSMjYREQkEoKW2N9//40ePXqopkNCQhASEoIPPvgA69atw7hx45CXl4cpU6YgOzsbrVu3RkREBCwsLARMTUREYiFoiXl7eyM7O7vU5TKZDDNmzMCMGTNeXygiIpIM0V5iT0REVB6WGBERSRZLjIiIJIslRkREksUSIyIiyWKJERGRZLHEiIhIslhiREQkWSwxIiKSLJYYERFJFkuMiIgkiyVGRESSxRIjIiLJYokREZFkscSIiEiyWGJERCRZLDEiIpIslhgREUkWS4yIiCSLJUZERJLFEiMiIsliiRERkWSxxIiISLJYYkREJFksMSIikizRl9iDBw8wffp0uLq6ws7ODr6+vvjrr7+EjkVERCIg+hIbO3YsoqOjsW7dOhw7dgxdunRBr169kJaWJnQ0IiISmKhLLC8vD7///jvmzp0Lb29vNGzYEDNmzECDBg2wadMmoeMREZHARF1iBQUFKCwshKmpqdp8MzMzJCQkCJSKiIjEwlDoAGWxsLBA27ZtsWLFCjRr1gy2trbYsWMHTp48iYYNG5b4mMTExNecUjz0+bXrK+5z/aRP+12hUJS5XNQlBgDr16/HmDFj4OzsDAMDA7Ro0QJBQUE4c+ZMieuX94LfZPr82vUV97l+4n7/H1EfTgSABg0aICoqCrdu3cKFCxcQHR2NZ8+eoV69ekJHIyIigYm+xIpVr14ddnZ2yM7OxqFDh/Dee+8JHYmIiAQm+sOJhw4dQlFRERQKBZKTkzFnzhwoFAoMGjRI6GhERCQw0ZdYbm4u5s+fj7S0NFhZWeH999/H7NmzYWRkJHQ0IiISmOhLrHfv3ujdu7fQMYiISIQkc06MiIjoZSwxIiKSLJYYERFJFkuMiIgkiyVGRESSxRIjIiLJYokREZFkscSIiEiyWGJERCRZor9jB5E29OrVS+gIOvGmvK7IyEidbPdNeX9e9qa8Lm3sd47EiIhIslhiREQkWSwxIiKSLJ4TI70U1sBM6Ah6bXByniDPu2rkW4I8Lz034ftcrW+TIzEiIpIslhgREUkWS4yIiCSLJUZERJLFEiMiIsnSy6sT35Rvu7/sTXldurp7AxG9eTgSIyIiyWKJERGRZLHEiIhIsvTynNjLEn1nCh1Bryn2LxE6AhFJlKhHYoWFhVi0aBGaN28OW1tbNG/eHIsWLUJBQYHQ0YiISAREPRJbvXo1NmzYgHXr1sHZ2RkXLlzAqFGjYGxsjKlTpwodj4iIBCbqEjt58iT8/f0REBAAAKhXrx4CAgJw+vRpgZMREZEYiPpwYvv27XH06FFcuXIFAHDp0iXExcWhW7duAicjIiIxEPVIbPz48Xj48CHatWsHAwMDFBQUYPLkyRg+fHipj0lMTHyNCUkXuA/1D/e5fqrIflcoFGUuF3WJRUREYNu2bdiwYQOaNm2Kc+fOYfr06XB0dMRHH31U4mPKe8EkftyH+of7XD9pY7+LusS++OIL/Oc//0Hfvn0BAC4uLrhx4wZWrVpVaokREZH+EPU5scePH8PAwEBtnoGBAYqKigRKREREYiLqkZi/vz9Wr16NevXqoWnTpjh79ixCQ0MRHBwsdDQiIhIBUZfY8uXLsXjxYkyaNAl3796Fra0thgwZwu+IERERAJGXmIWFBZYuXYqlS5cKHYWIiERI1OfEiIiIysISIyIiyWKJERGRZLHEiIhIslhiREQkWSwxIiKSrEpfYj9mzJgyl8tkMpiamsLe3h7vvPMO2rRpU+VwREREZal0iR05cgRPnjzB3bt3AQByuRxKpRI5OTkAgLfffhtFRUW4d+8eZDIZunXrhs2bN8PU1FS7yYmISO9V+nDirl27YGJigsmTJ+PatWtITk5GSkoKrl27hkmTJsHU1BT79+9HcnIyJk6ciP379/PLykREpBOVLrFp06bh3XffxaxZs1CzZk3V/Jo1a2L27Nno3Lkzpk6dCrlcjtmzZ6Nnz57YtWuXVkMTEREBVSix48ePo2XLlqUub9WqFU6cOKGa9vLywp07d6oUjoiIqCyVLjFzc3MkJCSUujw+Ph5mZmaq6by8PNSoUaNq6YiIiMpQ6RLr168fdu7ciSlTpuDKlSsoKChAQUEBrly5gsmTJ2PXrl3o16+fav3Dhw/DyclJq6GJiIiAKlydOHfuXGRkZGDDhg3YuHEjZDIZAECpVEKpVKJPnz6YN28eAODJkyfw8PBAu3bttBqaiIgIqEKJmZiYYMOGDRg7diwOHjyIGzduAAAcHBzg4+OD5s2bq9Y1NTXFtGnTtJeWiIjoBVX+PbHmzZurFRYREdHr9ko/ivno0SPcv38fSqVSY5mDg8OrbJqIiKhclS6xp0+fYvny5di8eTOysrJKXe/evXuvFIyIiKg8lS6xqVOnYsuWLQgICICnpyfkcrkOYhEREZWv0iUWGRmJgQMH4ptvvtFFHiIiogqr9PfEioqKeGd6IiIShUqXWNeuXXH8+HFdZCEiIqqUSpfY8uXLcf78eSxZsgTp6em6yERERFQhlT4n5ubmBqVSiRUrVmDFihUwMjJCtWrqXSiTyZCWlqa1kETaNjg5T+gIJIAJ3+cKHYG0rNIl1rt3b9WtpnTNzc1NdUeQF/n6+mL79u2vJQMREYlXpUts3bp1ushRopiYGBQWFqqm79y5g86dO6NXr16vLQMREYnXK92xQ9fefvtttemwsDBYWFiwxIiICEAFSiw+Ph4A4OnpqTZdnuL1tUWpVCIsLAwDBgyAubm5VrdN+iesgVn5K5HOCHVOctXItwR5XnpOF+ckyy2x7t27QyaT4c6dOzA2NlZNl0apVEImk2n9tlMxMTFITU3F4MGDtbpdIiKSrnJLbPfu3QAAY2NjtenXbfPmzXB3dy/3zvmJiYmvKRHpCveh/uE+108V2e8KhaLM5eWWmJeXV5nTr0NmZiaioqKwYsWKctct7wWT+HEf6h/uc/2kjf1e6S879+jRA4cPHy51+ZEjR9CjR49XCvWy8PBwmJiYoE+fPlrdLhERSVulS+zo0aPIyMgodfndu3crfPFHRSiVSmzZsgV9+vSBhYWF1rZLRETSV+kSK8+tW7dQvXp1rW0vLi4OSUlJGDJkiNa2SUREb4YKfU9sz549iIqKUk3/9NNPiI2N1VgvOzsbhw8fRuvWrbUW8J133kF2drbWtkdERG+OCpXYxYsXsXPnTgDP74t46tQpnD59Wm0dmUwGc3NztG/fHkuXLtV+UiIiopdUqMQmT56MyZMnAwCsrKwQGhqKfv366TQYERFReSp926n79+/rIoegFPuXCB2BiIiqQOsXdhAREb0uVboBcExMDNauXYt//vkHOTk5UCqVGuto+7ZTREREL6v0SGzv3r0ICgpCWloaevfujaKiIgQFBaFv374wNTWFq6srpk6dqousREREaio9Elu5ciXc3Nxw8OBB5OTkYNOmTRg0aBA6deqElJQU+Pj4oFGjRrrIqjOJvjOFjqDXeE6SiKqq0iOxCxcuoF+/fjA0NISBgQEAqH64sn79+hg2bBhWrVql3ZREREQlqHSJmZiYwMzs+W8xVa9eHTKZDJmZmarlderUQXJysvYSEhERlaLSJdagQQNcvnwZAGBkZAQnJye1n2eJioqCnZ2d9hISERGVotIl5uPjg127duHZs2cAgFGjRmHPnj1wd3eHu7s79u/fj2HDhmk9KBER0csqfWHH1KlTMWrUKBgaPn/oRx99BHNzc+zatQsGBgaYMmUK+vfvr/WgREREL6v0SMzIyAg1a9aETCZTzQsKCkJ4eDh++OEHPHjwAO7u7loNSUREVJIKj8Ty8/Oxb98+JCcnw8rKCv7+/rC1tQUAPH78GOvXr8e6deuQmZmJhg0b6iwwERFRsQqV2O3btxEYGIiUlBTV3TmqV6+Obdu2oVq1ahgxYgTS0tLQtm1brFy5Et27d9dpaCIiIqCCJbZw4UJcv34dY8eORceOHZGamorly5dj3LhxyMzMhLOzMzZu3Ij27dvrOi8REZFKhUosNjYWgwYNwrx581TzbGxs8PHHH8PX1xdbt25FtWq8lzAREb1eFWqejIwMtGnTRm2eh4cHAODDDz9kgRERkSAq1D6FhYUwNTVVm1c8/dZbb2k/FRERUQVU+OrElJQUnD59WjWdm5sLAEhMTESNGjU01m/durUW4hEREZWuwiUWEhKCkJAQjfkv/+yKUqmETCbj74kREZHOVajEQkNDdZ2DiIio0ipUYgMHDtR1DiIiokrjZYVERCRZLDEiIpIs0ZfYnTt38Nlnn6FRo0awtbVFu3btcPToUaFjERGRCFT6p1hep+zsbPj5+aF9+/bYvn07atWqhdTUVFhbWwsdjYiIREDUJfb111/Dzs4O69evV82rX7++cIGIiEhURH04cc+ePWjdujWGDh2Kxo0bw8vLC99//73qTvpERKTfRD0SS0lJwcaNGzF69GiMHz8e586dw7Rp0wAAI0eOLPExiYmJrzMi6QD3of7hPtdPFdnvCoWizOWiLrGioiK0atUKc+fOBQC0aNECSUlJ2LBhQ6klVt4LJvHjPtQ/3Of6SRv7XdSHE21tbeHk5KQ2r0mTJrh586ZAiYiISExEXWLt27fH1atX1eZdvXoVDg4OAiUiIiIxEXWJjR49GqdOncKKFSuQlJSEyMhIfP/99xg+fLjQ0YiISAREXWLu7u4IDw/Hrl270KFDByxcuBAzZ85kiREREQCRX9gBAH5+fvDz8xM6BhERiZCoR2JERERlYYkREZFkscSIiEiyWGJERCRZLDEiIpIs0V+dSKQLg5PzhI5AApjwfa7QEUjLOBIjIiLJYokREZFkscSIiEiyeE6M9EJkZKTQEbSiV69eatNvyuvSlTfl/eF+Lx1HYkREJFksMSIikiyWGBERSRZLjIiIJIslRkREksUSIyIiyeIl9gAU+5cIHYGIiKqAIzEiIpIslhgREUkWS4yIiCRLL8+JvSm3bOGtaIhI33EkRkREksUSIyIiyWKJERGRZIm6xEJCQiCXy9X+NWnSROhYREQkEqK/sEOhUOCPP/5QTRsYGAiYhoiIxET0JWZoaAhbW1uhYxARkQiJ+nAiAKSkpKBZs2Zo3rw5hg0bhpSUFKEjERGRSIh6JNamTRt8++23UCgUuHv3Lr788kv4+vri+PHjqFmzZomPSUxMfM0pxUOfX7u+4j7XT/q03xUKRZnLRV1i3bp1U5tu06YNWrZsiZ9//hn/+c9/SnxMeS/4TabPr11fcZ/rJ+73/xH94cQX1ahRA02bNkVSUpLQUYiISAQkVWJPnjxBYmIiL/QgIiIAIj+cOHv2bPj7+6Nu3bqqc2KPHz/GBx98IHQ0IiISAVGXWFpaGoYPH46srCy8/fbbaNOmDQ4cOABHR0ehoxERkQiIusQ2bdokdAQiIhIxSZ0TIyIiehFLjIiIJIslRkREksUSIyIiyWKJERGRZLHEiIhIslhiREQkWSwxIiKSLJYYERFJFkuMiIgkiyVGRESSxRIjIiLJYokREZFkscSIiEiyWGJERCRZLDEiIpIslhgREUkWS4yIiCSLJUZERJLFEiMiIsliiRERkWSxxIiISLJYYkREJFksMSIikixJldjKlSshl8sxZcoUoaMQEZEISKbETp06hc2bN8PFxUXoKEREJBKSKLGcnByMGDECa9euhVwuFzoOERGJhCw7O1spdIjyDB06FI6Ojpg/fz4CAwPh7OyML7/8UuhYFdKrVy+hI2hFZGSk0BEkg/tcP3G/C8NQ6ADl2bx5M5KSkrB+/XqhoxARkciIusQSExOxYMEC7N27F8bGxhV+DGkf31f9w32un8S23xUKRZnLRV1iJ0+eRFZWFjp06KCaV1hYiGPHjmHTpk1IS0uDiYmJ2mPKe8FUNXxf9Q/3uX6S2n4X9Tmx7OxspKWlqc0bM2YMGjVqhIkTJ6JZs2aQyWQCpSMiIqGJeiQml8s1rkY0NzeHlZUVnJ2dhQlFRESiIYlL7ImIiEoi6sOJREREZeFIjIiIJIslRkREksUSIyIiyWKJERGRZLHEiIhIslhiREQkWSwxIiKSLJYYERFJFkuMiIgkiyUmUaNGjVLdW1Iul6Nhw4YYMGAArly5InQ00rEzZ86gZs2a8PPzEzoKvSYZGRmYNm0aWrZsCRsbGzRr1gxBQUHYv3+/0NEExxKTsM6dO+Py5cu4fPkyIiIikJeXhw8//FDoWKRjW7ZswSeffIKLFy/i8uXLQschHUtNTUWnTp0QHR2NuXPnIj4+HpGRkfD19cXEiROFjic4Ud/FnspmYmICW1tbAICtrS1Gjx6N4OBg5OXlwczMTOB0pAt5eXn49ddfERUVhby8PISFhWHRokVCxyIdmjx5MpRKJWJiYlCjRg3VfCcnJ/Tv31/AZOLAkdgb4sGDB4iIiICzszML7A3222+/wcHBAa6urhgwYAC2bduGZ8+eCR2LdOT+/fs4ePAgRowYoVZgxV7+qSp9xJGYhB08eBB16tQBADx69Ah169bF9u3bBU5FurRlyxYEBwcDALy8vGBmZoaoqCj07NlT4GSkC0lJSVAqlWjSpInQUUSLIzEJ69ixI+Li4hAXF4dDhw7B29sbffr0wc2bN4WORjqQlJSEEydOICgoCAAgk8nQv39/hIWFCZyMdEWp5C9llYcjMQkzNzdHw4YNVdMtW7aEo6MjfvrpJ8yePVvAZKQLW7ZsQWFhIVxdXVXziv/I3bx5E3Xr1hUqGulIo0aNIJPJeNVxGTgSe4PIZDJUq1YNeXl5QkchLSsoKMDWrVsxd+5c1eg7Li4OR48ehYuLC8LDw4WOSDpgZWWFrl274ocffsDDhw81lmdnZ7/+UCLDEpOw/Px8pKenIz09HZcvX8bUqVPx8OFD+Pv7Cx2NtOzPP/9EVlYWhgwZAmdnZ7V/ffv2xX//+18UFRUJHZN0YMWKFVAqlejSpQsiIyORmJiIK1euYOPGjfDy8hI6nuBk2dnZPOgqQaNGjcLWrVtV0xYWFlAoFBg3bhxP8r+BgoODkZ+fj127dmksS0lJQcuWLREREYF3331XgHSka3fu3MHKlSuxf/9+3L59GzVr1oSrqys+/fRTdOvWTeh4gmKJERGRZPFwIhERSRZLjIiIJIslRkREksUSIyIiyWKJERGRZLHEiIhIslhiRAS5XI4JEyYIHYOo0lhiRFoQHh6u9kvbcrkcjRo1QkBAAH777Teh4xG9sXgDYCItmj59Oho0aAClUonMzEz88ssvGDJkCDZs2KC6+zwRaQ9LjEiLunbtCg8PD9X0xx9/DCcnJ+zYsaPUElMqlcjPz4epqenrikn0xuDhRCIdsrCwgLm5OYyMjFTzis8/RUZGomPHjrCxscHOnTsBPD8s2bNnTzRp0gQ2NjZo3bo1Vq9erXFz38DAQHh4eODatWvo27cv7O3toVAoMH/+fI11lUolfvjhB3h5ecHOzg4NGzZEr169cOzYMY28Bw4cgLe3N2xtbeHu7o4dO3bo4F0h0h6OxIi0KDc3F1lZWQCAzMxMbNq0CVlZWapfYy6WkJCA3377DSNGjICtra3ql3t/+OEHKBQK+Pj4wMzMDDExMZg3bx5yc3PxxRdfaDxXz5494e/vj8DAQBw8eBCrVq1CvXr18PHHH6vWGzduHLZs2YLOnTtj4MCBUCqVOHnyJBISEtCxY0fVeqdOncKePXswdOhQDB48GFu2bMHIkSPh5uYGJycnHb1jRK+GNwAm0oLw8HCMGTNGY76RkRG+/PJLtVKRy+WQyWQ4fPgwmjdvrrb+48ePYW5urjbv888/R0REBJKSkmBiYgLg+UgsPj4eX3/9NT766CPVul5eXjAyMkJMTAwAIC4uDj169MCQIUOwZs0ate0qlUrIZDJVJkNDQ8THx6sKKyMjQ3Wn9IULF1bxnSHSLY7EiLRo2bJlaiWwY8cOTJo0CVZWVmo/kdOuXTuNAgOgKrDCwkI8ePAAhYWF8PLyQlhYGBITE9V+1dnU1BSDBg1Se7ynpye2b9+umv79998BoMRf+i4usGLe3t5qIy4bGxsoFAqkpKRU9OUTvXYsMSItcnd3V7uwIygoCJ06dcLUqVMREBAAY2NjAED9+vVLfHxCQgIWLFiA06dP4+nTp2rLcnJy1Kbt7e1hYGCgNk8ul+P+/fuq6eTkZFhbW8Pa2rrc7A4ODhrzXt4ekdjwwg4iHapWrRq8vLyQnp6Oa9euqeabmZlprJuSkoLevXsjJycHISEh+OWXXxAZGYn58+cDgMYFGy8XWElePGRYntK2p1TyjAOJF0diRDpWUFAAAHj06FGZ60VFReHJkyfYtm0bHB0dVfNTU1Or/NwNGzbEoUOHkJmZWaHRGJHUcCRGpEPPnj1DTEwMjI2NVVcglqZ4JPTiyCc/Px/ff/99lZ///fffBwAsWbJEYxlHWPQm4EiMSIsOHTqEpKQkAM8vsY+IiMDVq1cxYcIEvPXWW2U+tmvXrjA2NkZwcDA+/vhjPH36FNu2bUO1alX/rOnt7Y2BAwfixx9/REpKCnx9fQE8v5zexcUFkyZNqvK2icSAJUakRUuXLlX9t6mpKRQKBb766isMHTq03Mc2btwY4eHhWLBgAebOnYtatWohODgYXl5e6N27d5UzffPNN3BxcUFYWBjmzp2LGjVqoEWLFvD09KzyNonEgt8TIyIiyeI5MSIikiyWGBERSRZLjIiIJIslRkREksUSIyIiyWKJERGRZLHEiIhIslhiREQkWSwxIiKSLJYYERFJ1v8DQDLmJexlfb0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Determine the branch with the lowest rating using box plot\n",
+    "sns.boxplot(x = 'Branch', y = 'Rating', data = df).set_title(\"Rating for Branches\");\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "1903d96a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Branch with the lowest rating is B\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Branch with the lowest rating is B')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "dc74d179",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0xa1cf683cd0>"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1541.15x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# A catplot() generate visualization for the \"product line\" on x-axis, quantity on the y-axis, and hue showing that gender type often affects the kind of products being purchased at the supermarket.\n",
+    "sns.catplot(x = 'Product line', y = 'Quantity', hue = 'Gender', kind= 'boxen', data = df, aspect = 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "6034c3ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# insight to explore is the interaction of Unit price on the Quantity of goods purchased\n",
+    "#Use the catplot() to plot Product line per unit price, \n",
+    "sns.catplot(y = 'Unit price', x = 'Product line', kind= 'point', data = df, aspect = 4);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "bc973060",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Product line per Quantity. Set the kind parameter to point\n",
+    "sns.catplot(y = 'Quantity', x = 'Product line', kind= 'point', data = df, aspect = 4);\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "6fdedec4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Gross Income for each City')"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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UrH06c+YMAK+++mqhtgMHDhRaVnCM9+zZo1OPriQVzJ8uWrToiT32R1328yBdeqYBAQFUr16dFStW8P3333Pt2jVef/11qlSponvizwkphgagQYMGhIaGcvfuXQYMGKDMJz3saW7bVK9ePbp160ZaWhrh4eFabadOnSI6OprKlSvz2muvAff/iBMSEsjPz9eK1Wg0XL16FUD5Qzxy5IjSs3hQwbLS/oMdMmQI1atXZ+XKlcTHxxdqf/CaMn9/f2rWrMmWLVsKfeiuXbuWY8eO8dJLL2mdKFOS3NzcaN++PXFxcaxcubLIOam//vrrma/V9PT0xMXFhSNHjhQ6cSUxMZGtW7diZWWldXbn06pVqxYBAQH89ttvhIWFKXOED0pLS9OaBy24/OPhYv3TTz8pd3B5kJubG+3ateOPP/4oNJcO94dgC4ZSS1r//v3x8fHhzJkzDBo0qMhpjby8PDZs2EBQUNAT11dwMtrjjnG1atUICAggJSWFjz/+GJVKZZBDpCBzhgYjJCQEjUbD7Nmz8fX1xc3NDXd3dywtLbl58ybnz58nISEBAC8vr2Kt+/PPP6dHjx7MnDmTvXv30qZNG+U6w+zsbMLDw5UzLbOzs+nTpw92dna0adOG+vXrc+/ePfbv389vv/1G69atlZMxYmJiWLZsGe3ataNRo0bUrFmTCxcuEBcXh5GREWPHji3R1+hJatasSXR0NMOGDaNfv3507dqVFi1akJWVxZ9//sm+ffuUE1PMzMxYvHgxw4YNo0+fPrz66qs0aNCA33//nV27dlGjRg0iIyOLPVRYHMuWLaN3796MGzeOJUuW0KZNGywtLbl06RL//e9/SU5OZs2aNc90raZKpSIyMpI+ffrwzjvv8N133ynXGX7//feYmpoSFRVV5BnBT2Pu3LmcOXOGOXPmsH79ery8vLC1teXKlSv89ddfHD58mJkzZyp3ZHnrrbdYu3YtgYGBvPrqq9SpU4dTp06xe/du+vbty6ZNmwptY8mSJcqlJ3FxcXTq1AljY2NSU1PZs2cP69at08s9bo2MjIiOjmb06NF8//33tGzZkk6dOuHi4qLcjm3fvn38888/Og1jdu3alSNHjjB06FB8fHyoUqUK9evXZ9CgQVpxI0aMYOnSpVy+fJkuXbrQqFGjEt+3ikCKoQH58MMP6d+/P9HR0ezdu5eYmBiysrKoVq0aDRs25M033+S1117D3d29WOt1cHAgISGB+fPns2PHDn755RfMzMxo3749Y8eO1frgMDMzY/r06ezbt4/Dhw+zfft2qlatioODAzNmzCAwMFC55GLAgAHcu3ePQ4cOsWXLFv79919sbW3x9fVlzJgxxc6zJHTv3p2EhAS++OILEhMT2bdvH+bm5jg6OvLRRx9pxfbo0YNdu3bx+eefk5iYyJYtW7C2tmbw4MF8+OGHNGjQQK+51qlTh/j4eJYuXcqWLVvYuHEj9+7dw8bGBicnJ2bPnk2HDh2eeTutWrUiISGBefPmkZCQwE8//USNGjXw9/fngw8+eOx1jMVlbm7ODz/8wOrVq4mJieGHH34gJycHa2tr7O3tmTJlilahaNq0KVu3bmXGjBns2rWLvLw8mjZtyurVq6lRo0aRxdDe3p7ExES++uorfvjhB6KjozExMaFevXoMHTq00CUUJalatWqsWrWKxMRE5UbdBw4cUG7U3bp1awYMGKB1ec2jfPDBB9y6dYu4uDjCw8PJzc2lffv2hYqhi4sLbdq04fDhwwbbKwRQqdXq0j+nVwghRLmQlZVFkyZNqFq1Kr///vsj70D1vJM5QyGEMGDR0dHcvHlTa1TGEEnPUAghDMzNmzf5+uuvuXz5MmvWrKFGjRocPny4yGtuDYUUQyGEMDCpqam0aNGCypUr06JFC+bMmUPLli3LOq0yJcVQCCGEwZM5QyGEEAZPiqEQQgiDJ8VQCCGEwZNiKIQQwuBJMRRCCGHwpBgKIYQweFIMhRBCGDwphkIIIQyeFEMhhBAGT4qhEEIIgyfFUAghhMGTYiiEEMLgGe6XVwkhhAHIzs5my5Ytj2zv3bs3VatWLcWMyicphkII8RzLzs5mw4YNj2z38fGRYogMkwohhBBSDIUQQogyLYaXL1/mnXfeoVGjRtja2uLp6cn+/fuVdo1GQ1hYGI0bN6Z27dr4+/tz6tQprXXcuXOHkJAQHB0dqVu3LoMGDSItLU0rRq1WExQUhL29Pfb29gQFBaFWq0tjF4UQQlQAZVYM1Wo1vr6+aDQaNmzYwKFDh5g7dy7W1tZKTHh4OBEREcyZM4c9e/ZgbW1N3759yczMVGImTZrE1q1bWb58OXFxcWRmZhIQEEBeXp4SM2LECJKTk4mJiSE2Npbk5GRGjhxZqvsrhBCi/CqzE2i+/PJLateuzZIlS5RlDRo0UH7XaDRERkYyfvx4evfuDUBkZCTOzs7ExsYSGBjIzZs3Wb16NREREXTt2hWAJUuW0KxZMxISEujWrRspKSns3r2bHTt24OnpCcDChQvx8/Pj9OnTODs7l95OCyGEKJfKrGe4bds23N3dCQwMxMnJiQ4dOvD111+j0WgASE1N5cqVK3h7eyvPqVq1Kl5eXhw6dAiA48ePc+/ePa0YOzs7XFxclJikpCSqVaumFEKAtm3bYmZmpsQIIYQwbGVWDM+dO8fy5ctp0KABGzdu5J133uHTTz9l6dKlAFy5cgVAa9i04HF6ejoA6enpGBsbY2Vl9dgYKysrVCqV0q5SqahVq5YSI4QQwrCV2TBpfn4+LVu2ZOrUqQC0aNGCM2fOsGzZMoKCgpS4B4sY3B8+fXjZwx6OKSr+Ses5ffq0TvshhBDl2a1btx7bfvbsWTIyMkopm7LzpCmxMiuGtra2uLi4aC178cUXuXjxotIO93t2dnZ2Ssy1a9eU3qKNjQ15eXlkZGRQq1YtrRgvLy8l5tq1a1rFT6PRkJGRUajX+SCZSxRCPA+uX7/+2PaGDRtSs2bNUsqm/CqzYti2bVv++usvrWV//fUX9evXB8DBwQFbW1vi4+Np1aoVADk5ORw8eJDp06cD4ObmhomJCfHx8QwcOBCAtLQ0UlJSlDlCDw8Pbt++TVJSkrIsKSmJrKwsrXlEQyS3aRJCiPvKrBiOHj0aHx8f5s+fT79+/UhOTubrr7/mk08+Ae4PbY4aNYoFCxbg7OyMk5MT8+fPx8zMjAEDBgBQo0YNhg4dypQpU7C2tsbS0pLJkyfj6upKly5dAHBxcaF79+4EBwcTHh6ORqMhODgYX19fg+/9yW2ahBDivjIrhq1atWLt2rVMnz6defPmYWdnx0cffcSIESOUmHHjxpGdnU1ISAhqtRp3d3c2bdqEubm5EjNr1iyMjY0JDAwkJyeHTp06ERUVhbGxsRKzdOlSQkND6devHwB+fn7MnTu39HZWCCFEuaZSq9Wask5ClI3r169r/fPxsGXLlslcghAVnPyd60buTSqEEMLgyVc4CWFA5KQpIYomxVAIAyInTT29s3HDyjqFp3IzK/+x7ed/GstNs4o5SNiw56oSW1fFfAWEEEKIEiTFUAghhMGTYiiEEMLgSTEUQghh8KQYCiGEMHhSDIUQQhg8KYZCCCEMnhRDIYQQBk+KoRBCCIMnd6ApAa6Lk8o6hadifCcTp8e0d1l5jLzK5o+JKJ9OjvYo6xSEEBWM9AyFEEIYPCmGQgghDJ4UQyGEEAZPiqEQQgiDJyfQCPEUMoMHl3UKT+V2rubx7VNHY1JJVUrZlBzzhevKOgVRwUkxFEKI51hlExU+LU0e2y6kGAohxHOtiqkKX/fKZZ1GuSdzhkIIIQyeFEMhhBAGT4qhEEIIgyfFUAghhMGTYiiEEMLglVkxDAsLw8LCQuvnxRdfVNo1Gg1hYWE0btyY2rVr4+/vz6lTp7TWcefOHUJCQnB0dKRu3boMGjSItLQ0rRi1Wk1QUBD29vbY29sTFBSEWq0ujV0UQghRQZRpz9DZ2ZmUlBTl5+eff1bawsPDiYiIYM6cOezZswdra2v69u1LZmamEjNp0iS2bt3K8uXLiYuLIzMzk4CAAPLy8pSYESNGkJycTExMDLGxsSQnJzNy5MhS3U8hhBDlW5leZ1ipUiVsbW0LLddoNERGRjJ+/Hh69+4NQGRkJM7OzsTGxhIYGMjNmzdZvXo1ERERdO3aFYAlS5bQrFkzEhIS6NatGykpKezevZsdO3bg6ekJwMKFC/Hz8+P06dM4OzuX3s4KIYQot8q0Z3ju3DleeuklmjdvzvDhwzl37hwAqampXLlyBW9vbyW2atWqeHl5cejQIQCOHz/OvXv3tGLs7OxwcXFRYpKSkqhWrZpSCAHatm2LmZmZEmPI8o1Nudaw/SN/8o1NyzpFIYQoFWXWM2zdujWLFy/G2dmZa9euMW/ePHx8fPjll1+4cuUKANbW1lrPsba25p9//gEgPT0dY2NjrKysCsWkp6crMVZWVqhU/7vdkEqlolatWkrMo5w+ffqZ97G801SqTEajjmWdRokrjWNXW+9bEMVRGsdcbtdV/hTnuD9pJLDMju/LL7+s9bh169a4ubnx7bff0qZNGwCtIgb3h08fXvawh2OKitdlPcUaQv2xYn7T/fOqNIa/M58cIkpRaRzzs8///8cVTkke93JzaUW1atVo3LgxZ86cUeYRH+69Xbt2Tekt2tjYkJeXR0ZGxmNjrl27hkbzvzv1azQaMjIyCvU6hRBCGK5yUwxzcnI4ffo0tra2ODg4YGtrS3x8vFb7wYMHlfk/Nzc3TExMtGLS0tJISUlRYjw8PLh9+zZJSf/ruSUlJZGVlaU1jyiEoahiBH1qGD/yp0q5+UQQonSV2TDpxx9/TI8ePbCzs1PmDP/9918GDx6MSqVi1KhRLFiwAGdnZ5ycnJg/fz5mZmYMGDAAgBo1ajB06FCmTJmCtbU1lpaWTJ48GVdXV7p06QKAi4sL3bt3Jzg4mPDwcDQaDcHBwfj6+sqZpMIgVTVS0ddCZr+EeFiZ/VVcunSJESNGkJGRQa1atWjdujU//vgj9vb2AIwbN47s7GxCQkJQq9W4u7uzadMmzM3NlXXMmjULY2NjAgMDycnJoVOnTkRFRWFsbKzELF26lNDQUPr16weAn58fc+fOLd2dFUIIUa6p1Gr147/6WjyR62I5gaY8OTnaQ+/bqKjfdP+8Ko1vuj8bN0zv2xDF07DnqhJbl8wQCCGEMHjFKobnz59n7NixuLm5Ub9+ffbv3w9ARkYGH3zwAcePH9dHjkIIIYRe6TxnmJKSQo8ePcjPz6d169acP39euQeolZUVhw8f5s6dO3z11Vd6S1YIIYTQB52L4dSpUzE3N2f37t0YGxvj5OSk1e7j48PmzZtLOj8hhBBC73QeJv35558ZMWIENjY2Rd69pX79+sqt0oQQQoiKROdimJubi5mZ2SPbb9y4oXVJgxBCCFFR6FwMmzRpwr59+4ps02g0bN26FTc3t5LKSwghhCg1OhfDUaNGsWXLFubOncv169cByM/P588//2T48OEcO3aM9957T2+JCiGEEPqi8wk0/fv358KFC8ycOZPZs2crywCMjY2ZMWNGoW+iEEIIISqCYt2Obfz48QwYMIDvv/+eM2fOkJ+fT8OGDXn11VdxcHDQV45CCCGEXhX73qR2dnaMHj1aH7kIIYQQZeKpb9R97949re8JLGBqavpMCQkhhBClTedimJ+fz9dff83q1as5d+4c2dnZhWJUKlWhL9sVQgghyjudi2FoaCjLly/nxRdfpE+fPlSvXl2feQkhhBClRudiuH79enr37s0333yjz3yEEEKIUqfzdYYmJiZ06tRJn7kIIYQQZULnYtirVy8SExP1mYsQQghRJnQuhrNmzeLff/9l1KhRHDhwgHPnznHhwoVCP0IIIURFo/OcYaVKlWjQoAHLli1j/fr1j4wruFWbEEIIUVHoXAzff/99vv32Wzp06EDr1q3lbFIhhBDPDZ2L4ffff8/rr7/OokWL9JmPEEIIUep0njOsXLmyfEWTEEKI55LOxXDgwIHExcXpMxchhBCiTOg8TOrv78/+/fvp378/r7/+OnZ2dkV+s727u3uJJiiEEELom87F8JVXXlF+j4+PL9Su0WhQqVRyNqkQQogKR+diGBERoc88WLBgAZ999hlvv/028+bNA+4X2NmzZ7Ny5UrUajXu7u7Mnz+fl156SXnenTt3+Pjjj9m4cSM5OTl06tSJBQsWUK9ePSVGrVbz4YcfsmPHDgB69OjB3LlzsbCw0Os+CSGEqBh0LoZDhgzRWxKHDx9m5cqVuLq6ai0PDw8nIiKCiIgInJ2dmTt3Ln379uXw4cOYm5sDMGnSJOLi4li+fDmWlpZMnjyZgIAAEhMTlWHcESNGcPHiRWJiYlCpVIwdO5aRI0c+9npJIYQQhkPnE2gedPPmTU6ePMnJkye5efPmMyVw8+ZN3n77bRYtWqTVU9NoNERGRjJ+/Hh69+5NkyZNiIyM5Pbt28TGxirPXb16NdOnT6dr1664ubmxZMkSTp48SUJCAgApKSns3r2bL774Ak9PTzw8PFi4cCE7d+7k9OnTz5S7EEKI50OxiuHRo0fx8/PD0dGRjh070rFjRxwdHenZsydHjx59qgQKil3nzp21lqempnLlyhW8vb2VZVWrVsXLy4tDhw4BcPz4ce7du6cVY2dnh4uLixKTlJREtWrV8PT0VGLatm2LmZmZEiOEEMKw6TxMeuTIEfz9/TExMWHYsGG4uLig0Wj4888/iY2Nxd/fn23bttGqVSudN75y5UrOnDnDkiVLCrVduXIFAGtra63l1tbW/PPPPwCkp6djbGyMlZVVoZj09HQlxsrKCpVKpbSrVCpq1aqlxBRFeo0VV2kcu9p634IojtI45jp/WIpSU5zj7uzs/Nh2nY/vjBkzsLa2ZteuXdSpU0er7cMPP8THx4cZM2awadMmndZ3+vRppk+fzvbt2zE1NX1k3INFDP531urjPBxTVPyT1vOkF07Lj0m6xwq9K9axe0qZet+CKI7SOOZn5f/jcqckj7vOw6S//vorw4cPL1QIAerUqcPw4cM5fPiwzhtOSkoiIyODdu3aYWVlhZWVFQcOHGDZsmVYWVlRs2ZNgEK9t2vXrim9RRsbG/Ly8sjIyHhszLVr19BoNEq7RqMhIyOjUK9TCCGEYdK5GGo0miIvsldWZGSkVXCexN/fn59//pl9+/YpPy1btqR///7s27cPJycnbG1tta5pzMnJ4eDBg8r8n5ubGyYmJloxaWlppKSkKDEeHh7cvn2bpKT/9d6SkpLIysrSmkcUQghhuHQeJm3ZsiUrVqxg6NChWFpaarXduHGDlStXFmu+0MLCotB1fi+88AKWlpY0adIEgFGjRrFgwQKcnZ1xcnJi/vz5mJmZMWDAAABq1KjB0KFDmTJlCtbW1sqlFa6urnTp0gUAFxcXunfvTnBwMOHh4Wg0GoKDg/H19S2VoRUhhBDln87F8KOPPqJPnz60bt2aIUOGKIXkzz//5D//+Q+ZmZksXry4RJMbN24c2dnZhISEKBfdb9q0SbnGEO5/6bCxsTGBgYHKRfdRUVFavdilS5cSGhpKv379APDz82Pu3LklmqsQQoiKS6VWq3Ue2zxw4ACTJ0/mxIkTWsvd3NyYOXMmXl5eJZ5gReC6WE6gKU9OjvbQ+zYygwfrfRtCd+YL1+l9G2fjhul9G6J4GvZcVWLrKtbZwu3btychIYH09HTOnz8PgL29PTY2NiWWkBBCCFHanurSGRsbGymAQgghnhs6n026cOFCfH19H9nu5+fHokWLSiQpIYQQojTpXAxjYmJo06bNI9vbtGnDf/7znxJJSgghhChNOhfDc+fOPfZShEaNGpGamloiSQkhhBClSediWLlyZeWeoEW5dOkSRkZP9SUYQgghRJnSuXp5eHiwevVqbty4Uajtxo0brF27Vu7oIoQQokLS+WzSiRMn4ufnR/v27Rk1ahRNmjRBpVJx8uRJoqKiuHbtGitWrNBjqkIIIYR+FOt2bOvXr2fcuHFMmTJF+cYHjUZDgwYNWL9+Pa1bt9ZbokIIIYS+FOs6w86dO3Ps2DFOnDjB2bNn0Wg0ODo60qJFiyd+rZIQQghRXhX7onuVSoWbmxtubm56SEcIIYQofcUuhikpKZw7d44bN24U+ZVNgwfLPRuFEEJULDoXw9TUVEaOHElSUtIjv7dQpVJJMRRCCFHh6FwMg4ODSU5OZubMmbRv377QdxEKIYQQFZXOxfDgwYOMHTuWUaNG6TMfIYQQotTpfNF9jRo1sLKy0mcuQgghRJnQuRgOGTKEzZs36zEVIYQQomzoPEz68ssvEx8fT69evQgMDMTOzg5jY+NCce7u7iWaoBBCCKFvOhfDnj17Kr8fOHCgULtGo0GlUnH9+vWSyUwIIYQoJToXw4iICH3mIYQQQpQZnYvhkCFD9JmHEEIIUWbkCwiFEEIYvEf2DOfMmYNKpWLChAkYGRkxZ86cJ65MpVLx4YcflmiCQgghhL49shjOnj0blUrF+PHjMTU1Zfbs2U9cmRRDIYQQFdEjh0lv3LjB9evXMTU1VR4/6ac4Z5IuXboULy8v6tevT/369Xn55ZfZuXOn0q7RaAgLC6Nx48bUrl0bf39/Tp06pbWOO3fuEBISgqOjI3Xr1mXQoEGkpaVpxajVaoKCgrC3t8fe3p6goCDUarXOeQohhHj+ldmcYd26dfn0009JTEwkPj6eTp068frrr/P7778DEB4eTkREBHPmzGHPnj1YW1vTt29fMjMzlXVMmjSJrVu3snz5cuLi4sjMzCQgIIC8vDwlZsSIESQnJxMTE0NsbCzJycmMHDmy1PdXCCFE+VVmxdDf35+XX34ZR0dHnJyc+OSTT6hWrRqHDx9Go9EQGRnJ+PHj6d27N02aNCEyMpLbt28TGxsLwM2bN1m9ejXTp0+na9euuLm5sWTJEk6ePElCQgJw/+umdu/ezRdffIGnpyceHh4sXLiQnTt3cvr06bLadSGEEOVMuTibNC8vj40bN5KVlYWHhwepqalcuXIFb29vJaZq1ap4eXlx6NAhAI4fP869e/e0Yuzs7HBxcVFikpKSqFatGp6enkpM27ZtMTMzU2KEEEKIYn+5b0k6efIkPj4+5OTkYGZmxpo1a3B1dVUKlbW1tVa8tbU1//zzDwDp6ekYGxsXunm4tbU16enpSoyVlRUqlUppV6lU1KpVS4l5FOk5Vlylcexq630LojhK45iX6YelKFJxjruzs/Nj28v0+Do7O7Nv3z5u3rzJ999/z6hRo/jhhx+U9geLGPzvlm+P83BMUfG6rOdJL5yWH5N0jxV6V6xj95QynxwiSlFpHPOz8v9xuVOSx71Mh0lNTU1xdHSkZcuWTJ06lWbNmrF48WJsbW0BCvXerl27pvQWbWxsyMvLIyMj47Ex165dQ6PRKO0ajYaMjIxCvU4hhBCG65mL4eXLl/nvf/9bErmQn5/P3bt3cXBwwNbWlvj4eKUtJyeHgwcPKvN/bm5umJiYaMWkpaWRkpKixHh4eHD79m2Skv7Xc0tKSiIrK0trHlEIIYRh03mY9JtvvuGXX35hyZIlyrIPPviAb775BgBXV1c2b96s8xcAT5s2DR8fH+rVq6ecJbp//342bNiASqVi1KhRLFiwAGdnZ5ycnJg/fz5mZmYMGDAAuP9lw0OHDmXKlClYW1tjaWnJ5MmTcXV1pUuXLgC4uLjQvXt3goODCQ8PR6PREBwcjK+vb6kMqwghhKgYdC6GK1eupHXr1srjvXv3Eh0dzcCBA2nSpAnz589n/vz5hIWF6bS+K1euEBQURHp6OtWrV8fV1ZXY2Fi6desGwLhx48jOziYkJAS1Wo27uzubNm3C3NxcWcesWbMwNjYmMDCQnJwcOnXqRFRUlNb3LC5dupTQ0FD69esHgJ+fH3PnztV1t4UQQhgAlVqt1jw5DBo2bMjkyZMZMWIEAO+//z67du0iOTkZIyMjPv30U7777juOHz+uz3zLJdfFcgJNeXJytIfet5EZPFjv2xC6M1+4Tu/bOBs3TO/bEMXTsOeqEluXznOGd+/excTERHkcHx9P9+7dMTK6vwpHR0cuX75cYokJIYQQpUXnYujg4KDc2eXo0aOcO3dO64L39PR0rSFMIYQQoqLQec5w+PDhhISEkJKSwqVLl6hXrx4vv/yy0v7LL7/QuHFjvSQphBBC6JPOxXDEiBGYmpqya9cuWrRowfjx46latSpw/xstrl69yvDhw/WWqBBCCKEvxboDzbBhwxg2rPAksqWlpTKEKoQQQlQ0z3Q7tjt37rB161bUajV+fn7Uq1evpPISQgghSo3OJ9BMmDCBDh06KI9zc3Px9fUlKCiIkJAQ2rZty8mTJ/WSpBBCCKFPOhfDxMREfH19lcffffcdJ06cYP78+fz4449YWVkxb948vSQphBBC6JPOw6T//PMPDg4OyuO4uDiaNm2qnDQzfPhwoqKiSj5DIYQQQs907hlWqlSJ7Oxs4P43P+zdu1e5dRqAhYUF169fL/kMhRBCCD3TuRg2adKEDRs2oFarWbNmDTdu3KB79+5K+/nz56lVq5ZekhRCCCH0Sedh0tDQUAICAnB0dATA09NT64SanTt30qpVq5LPUAghhNAznYth586dSUxMJD4+HnNzc/r376+03bhxgw4dOuDv76+XJIUQQgh9KtZ1hi4uLri4uBRabmlpqfNXNwkhhBDlTbEvuj979iy7du3i/PnzANjb2+Pj40PDhg1LPDkhhBCiNBSrGE6ePJmoqCjy8/O1ln/00Ue88847zJw5s0STE0IIIUqDzmeTRkREsHjxYnr27MmuXbtITU0lNTWVXbt24e/vT2RkJIsXL9ZnrkIIIYRe6FwMV61ahY+PD6tXr6ZNmzZUr16d6tWr06ZNG1atWkX37t1ZsWKFHlMVQggh9EPnYnju3Dl8fHwe2e7j40NqamqJJCWEEEKUJp2LoaWlJadPn35k+19//YWlpWWJJCWEEEKUJp2LYc+ePVm+fDlr165Fo9EoyzUaDd9++y3R0dFynaEQQogKSeezSadMmUJSUhLvvfce06ZNo1GjRgCcOXOGq1ev0rRpUz755BO9JSqEEELoi87F0MLCgj179rBixQqt6wybN2+Or68vw4YNo3LlynpLVAghhNAXnYphTk4O4eHhtGnThqCgIIKCgvSdlxBCCFFqdJozrFKlCgsXLuTixYv6zkcIIYQodTqfQNOsWTPOnDlTYhv+/PPP6dq1K/Xr16dRo0YEBATwxx9/aMVoNBrCwsJo3LgxtWvXxt/fn1OnTmnF3Llzh5CQEBwdHalbty6DBg0iLS1NK0atVhMUFIS9vT329vYEBQWhVqtLbF+EEEJUbDoXwylTprBq1Sp27txZIhvev38/b731Fjt37uT777+nUqVK9OnThxs3bigx4eHhREREMGfOHPbs2YO1tTV9+/YlMzNTiZk0aRJbt25l+fLlxMXFkZmZSUBAAHl5eUrMiBEjSE5OJiYmhtjYWJKTkxk5cmSJ7IcQQoiKT6VWqzVPDoOBAwfy999/c+7cOerWrUuDBg2oWrWq9spUKjZs2PBUidy+fRt7e3vWrl2Ln58fGo2Gxo0b8/bbbzNhwgQAsrOzcXZ25rPPPiMwMJCbN2/i5OREREQEr732GgAXL16kWbNmxMbG0q1bN1JSUvD09GTHjh20bdsWgIMHD+Ln58fhw4dxdnZ+qnwf5Lo46ZnXIUrOydEeet9GZvBgvW9D6M584Tq9b+Ns3DC9b0MUT8Oeq0psXTr3DP/73/+Sm5uLnZ0dRkZGnD9/npSUlEI/T+v27dvk5+djYWEBQGpqKleuXMHb21uJqVq1Kl5eXhw6dAiA48ePc+/ePa0YOzs7XFxclJikpCSqVauGp6enEtO2bVvMzMyUGCGEEIZN50srfvvtN33mwcSJE2nWrBkeHvf/q79y5QoA1tbWWnHW1tb8888/AKSnp2NsbIyVlVWhmPT0dCXGysoKlUqltKtUKmrVqqXEFOVxd9sR5VtpHLvaet+CKI7SOObF/r47oXfFOe5PGgUsF8f3o48+4pdffmHHjh0YGxtrtT1YxOD+STUPL3vYwzFFxT9pPcUaPv1RhknLk5IY+n6SzCeHiFJUGsf8rPx/XO6U5HHXuRheuHDhse0qlYoqVaoU6oU9yaRJk9i0aRNbt26lQYMGynJbW1vgfs/Ozs5OWX7t2jWlt2hjY0NeXh4ZGRnUqlVLK8bLy0uJuXbtmlbx02g0ZGRkFOp1CiGEMEw6F8PmzZvrVOSqVKlC+/bt+fDDD2nTps1jY0NDQ9m0aRM//PADL774olabg4MDtra2xMfH06pVK+D+xf8HDx5k+vTpALi5uWFiYkJ8fDwDBw4EIC0tTTlpBsDDw4Pbt2+TlJSkLEtKSiIrK0trHlEIIYTh0rkYLlq0iK+//poLFy4wYMAAnJyc0Gg0/P3338TGxuLg4MCQIUP4+++/2bBhA6+88gqbN2+mXbt2Ra5vwoQJrF+/njVr1mBhYaHMEZqZmVGtWjVUKhWjRo1iwYIFODs74+TkxPz58zEzM2PAgAEA1KhRg6FDhzJlyhSsra2xtLRk8uTJuLq60qVLFwBcXFzo3r07wcHBhIeHo9FoCA4OxtfXt1SGVoQQQpR/OhfD69evk52dzdGjRwt9VdPEiRPx9fXlzp07zJkzh5CQEDp37szs2bPZsmVLketbtmwZAL1799ZaHhoayqRJkwAYN24c2dnZhISEoFarcXd3Z9OmTZibmyvxs2bNwtjYmMDAQHJycujUqRNRUVFac49Lly4lNDSUfv36AeDn58fcuXN13XUhhBDPOZ2vM2zWrBlBQUG89957RbZ/+eWXLFu2jOTkZADCwsKIjIxUbuj9PJPrDMsXuc7Q8Mh1hoapTK4zvHr1Krm5uY9sz83N1bpUoW7duo+NF0IIIcoLnYuhq6sry5cvL3TfT7h/15fo6GiaNm2qLDt9+jQ2NjYlk6UQQgihRzrPGc6YMYP+/fvTunVr/Pz8cHR0BO5/ue/27dvRaDQsXboUuH/W54YNG/D19dVP1kIIIUQJ0rkYtmvXjp07dzJr1ix27NhBdnY2cP8WaV27dmXSpEk0a9YMuH95xZ9//qmfjIUQQogSVqw70DRr1ox169aRn5/P1atXgfu3PjMy0nm0VQghhCh3nup2bEZGRsodYoQQQoiKTrp0QgghDJ4UQyGEEAZPiqEQQgiDJ8VQCCGEwZNiKIQQwuBJMRRCCGHwpBgKIYQweFIMhRBCGDwphkIIIQyeFEMhhBAGT4qhEEIIgyfFUAghhMGTYiiEEMLgSTEUQghh8KQYCiGEMHhSDIUQQhg8KYZCCCEMnhRDIYQQBq9Mi+GBAwcYNGgQL730EhYWFqxdu1arXaPREBYWRuPGjalduzb+/v6cOnVKK+bOnTuEhITg6OhI3bp1GTRoEGlpaVoxarWaoKAg7O3tsbe3JygoCLVare/dE0IIUUGUaTHMysqiSZMmzJ49m6pVqxZqDw8PJyIigjlz5rBnzx6sra3p27cvmZmZSsykSZPYunUry5cvJy4ujszMTAICAsjLy1NiRowYQXJyMjExMcTGxpKcnMzIkSNLZR+FEEKUf5XKcuM+Pj74+PgAMHr0aK02jUZDZGQk48ePp3fv3gBERkbi7OxMbGwsgYGB3Lx5k9WrVxMREUHXrl0BWLJkCc2aNSMhIYFu3bqRkpLC7t272bFjB56engAsXLgQPz8/Tp8+jbOzcynusRBCiPKo3M4ZpqamcuXKFby9vZVlVatWxcvLi0OHDgFw/Phx7t27pxVjZ2eHi4uLEpOUlES1atWUQgjQtm1bzMzMlBghhBCGrdwWwytXrgBgbW2ttdza2pr09HQA0tPTMTY2xsrK6rExVlZWqFQqpV2lUlGrVi0lRgghhGEr02FSXTxYxOD+8OnDyx72cExR8U9az+nTp4uZqSgvSuPY1db7FkRxlMYxL/cflgaoOMf9SVNi5fb42traAvd7dnZ2dsrya9euKb1FGxsb8vLyyMjIoFatWloxXl5eSsy1a9e0ip9GoyEjI6NQr/NBxZpL/DFJ91ihd6UxD5z55BBRikrjmJ+V/4/LnZI87uV2mNTBwQFbW1vi4+OVZTk5ORw8eFCZ/3Nzc8PExEQrJi0tjZSUFCXGw8OD27dvk5T0v4KVlJREVlaW1jyiEEIIw1WmPcPbt29z5swZAPLz87l48SLJyclYWlpSv359Ro0axYIFC3B2dsbJyYn58+djZmbGgAEDAKhRowZDhw5lypQpWFtbY2lpyeTJk3F1daVLly4AuLi40L17d4KDgwkPD0ej0RAcHIyvr6+cSSqEEAIo42J47NgxevXqpTwOCwsjLCyMwYMHExkZybhx48jOziYkJAS1Wo27uzubNm3C3Nxcec6sWbMwNjYmMDCQnJwcOnXqRFRUFMbGxkrM0qVLCQ0NpV+/fgD4+fkxd+7c0ttRIYQQ5ZpKrVZryjqJis51scwZlicnR3vofRuZwYP1vg2hO/OF6/S+jbNxw/S+DVE8DXuuKrF1lds5QyGEEKK0SDEUQghh8KQYCiGEMHhSDIUQQhg8KYZCCCEMnhRDIYQQBk+KoRBCCIMnxVAIIYTBk2IohBDC4EkxFEIIYfCkGAohhDB4UgyFEEIYPCmGQgghDJ4UQyGEEAZPiqEQQgiDJ8VQCCGEwZNiKIQQwuBJMRRCCGHwpBgKIYQweFIMhRBCGDwphkIIIQyeFEMhhBAGT4qhEEIIgyfFUAghhMGTYiiEEMLgGVQxXLZsGc2bN8fW1pbOnTvz888/l3VKQgghygGDKYabNm1i4sSJfPDBB+zduxcPDw8GDhzIhQsXyjo1IYQQZcxgimFERARDhgzhjTfewMXFhXnz5mFra0t0dHRZpyaEEKKMqdRqtaask9C3u3fvUqdOHZYvX06fPn2U5RMmTOCPP/4gLi6u7JITQghR5gyiZ5iRkUFeXh7W1tZay62trUlPTy+jrIQQQpQXBlEMC6hUKq3HGo2m0DIhhBCGxyCKoZWVFcbGxoV6gdeuXSvUWxRCCGF4DKIYmpqa4ubmRnx8vNby+Ph4PD09yygrIYQQ5YVBFEOAMWPG8O2337Jq1SpSUlIIDQ3l8uXLBAYGlnVqepWamoqFhQXHjh0rF+sRQojyyGCKYb9+/QgLC2PevHl07NiRX375hQ0bNmBvb1/WqZWIEydOULNmTXx9ffWyfjs7O1JSUmjWrJle1i90N2rUKAICAso6DcH9Y2FhYYGFhQW1atWiRYsWfPzxx2RlZT3TesPCwmjXrt0T49auXUu9evWKbKtXrx5r1659pjzKmoWFBVu2bCmVbVUqla2UEyNGjGDEiBFlnYZerFq1irfeeov169eTkpKCi4tLia7f2NgYW1vbEl2nEM+DLl26sGTJEu7du8fBgwcZO3Ys//77L59//vlTre/evXslnOHTyc/PR6PRYGxsXOrbvnv3LqampqW6TYPpGT7PsrOziYmJ4Y033uDVV19l9erVhWL++usvevToga2tLW3atGHPnj1K2759+7CwsCAjI0NZ9vCw6MOP8/LyePfdd2nevDm1a9emVatWhIeHk5+fr+e9FY/z1Vdf4eXlRd26dXnppZd47733UKvVWjGrV6+madOm1KlTh4CAAJYtW4aFhYVWzDfffEPLli2xtramZcuWrFy5slC7u7s7tra2NGrUiH79+pGbm6vnvSufKleujK2tLXZ2dgwcOJCBAweybds2AO7cucPEiRNxdnbG1taW7t27c/DgQeW5BX97u3btwtvbG2tra7755hvmzJnDqVOnlF5nSfTwnvTeKOhl7tq1i3bt2mFtbU1KSgp3795l+vTpNG3aFBsbG1q0aEFUVJTyvAMHDtCtWzdsbW1xdnZm0qRJ3L17V2n39/cnJCREK5eHRzf8/f15//33+fjjj2nUqBG+vr7KKNQbb7yBhYWF3kelDKpn+LzasmUL9evXp2nTpgQEBBAYGMjUqVMxMTFRYqZOncrMmTNxdXVl6dKlDBkyhKNHj1K3bt2n2mZ+fj516tRhxYoVWFlZcfToUcaNG4elpSXDhg0rqV0TxWRkZERYWBgNGjTgwoULfPjhh3z44Yd8/fXXACQlJTF27FimTp3KK6+8woEDB5g+fbrWOrZu3UpISAizZs3C29ubn376iQ8++AAbGxv8/Pw4duwYEyZMIDIykrZt23Lz5k327t1bFrtbLlWpUkXp3U2ZMoXNmzfz1Vdf0aBBAyIiIhgwYABHjhyhdu3aynOmTZvGjBkzcHR0pFKlSpw/f56dO3fyww8/AFC9evVnzutJ7w2AnJwc5s+fz8KFC6lVqxa2traMGjWKgwcPEhYWRosWLTh//jxpaWkAXLp0iYEDBxIQEMDixYs5e/YsY8eOxcjIiJkzZxYrvw0bNvDGG2+wfft2NBoNVlZWODk58eWXX+Lr66v3HqoUw+fAqlWrGDRoEAAdOnSgatWqxMXF0bt3byVm+PDh9O3bF4A5c+awZ88eoqOj+fjjj59qmyYmJkyePFl57ODgwIkTJ9i4caMUwzI0evRo5XcHBwemT5/OkCFDiIqKwsjIiCVLluDt7c348eMBcHJy4ujRo1o9v6+++oqAgACCgoKUmOPHjxMeHo6fnx8XLlzAzMwMPz8/zM3NAWQu+f87cuQIsbGxdO7cmaysLKKjo5UPc4CFCxeyd+9eli1bpvW3Fxoaire3t/LYzMyMSpUq6TQ1kZWVVeS84cPzlk96b8D9EZ+5c+fi5uYGwN9//83GjRuJjY2le/fuADRo0EBZz/Lly7G1tWXBggUYGRnh4uLC1KlTCQ4OZvLkybzwwgtPzL+Avb19kQW0Ro0apTJFI8Wwgjtz5gyHDh1i+fLlwP0bC7z22musXr1aqxi2adNG+d3IyAh3d3f++9//PtO2o6OjWbVqFRcuXCAnJ4d79+5Rv379Z1qneDaJiYksXLiQP//8k1u3bpGXl8fdu3e5cuUKderU4c8//6RHjx5az3F3d9cqhikpKbz++utaMe3atWP79u0AdO3aFTs7O1q0aEG3bt3o2rUrvXr1Ugqjodm9ezf16tUjNzeXe/fu0bNnT+bOncvZs2e5d+8ebdu2VWKNjY3x8PAo9LfXsmXLp97+Cy+8wL59+wot79Chg9bjJ703ACpVqqT1j01ycjJGRkZ07NixyG2npKTQpk0bpZjC/ffK3bt3OXPmDE2bNtV5PwoKcFmRYljBrVq1iry8PK03nUZz/3azFy9e1GkdBW/kgucBT5z/2bRpE5MmTeKzzz7Dw8OD6tWrs3TpUmVYR5S+8+fPExAQwLBhw/joo4+oWbMmJ06c4K233lLmcHS961JRMQXLzM3N2bt3LwcOHCAhIYGFCxfy2WefsWfPHuVD1ZB4eXkRHh5OpUqVqFOnjjI9cfnyZeDxr2UBMzOzp96+SqXC0dHxsdvQ5b0B9+c/HxyOfPAzoSiPez8VLDcyMiq0nqI+X57lNSgJcgJNBZabm8u6deuYOnUq+/btU37279+Pq6ur1qT7r7/+qvyu0Wg4evSocsZprVq1gP/98QL89ttvj932wYMHcXd3JygoCDc3NxwdHTl79mxJ7p4opmPHjnH37l3CwsLw8PDAycmJf/75RyvGxcWFo0ePai17+LGLiwu//PKL1rKDBw/SuHFj5XGlSpXo3LkzU6dO5cCBA2RlZbFz584S3qOK4YUXXsDR0RF7e3uteXpHR0dMTU21TpjJy8sjKSnpiWd7m5qakpeXV2I56vLeKEqLFi3Iz88vsucJ0LhxYw4fPqx14tzBgwcxNTWlYcOGwP3Plwc/WwB+//13nfI2MTEp0dfhcaRnWIHt3LmTjIwM3njjDWrWrKnV1r9/f5YvX66csRUdHY2TkxNNmjRh2bJlXLhwgeHDhwP3/2jt7OyYPXs206ZN4/z588ybN++x23ZycmLdunX8+OOPODo6snHjRn7++Wdq1Kihn50VWm7dukVycrLWskaNGpGfn8/ixYvp1asXv/76q9ZZfwAjR46kR48efPnll/j7+3PgwIFCvfn33nuPN998Ezc3N7y9vdm9ezcxMTHKWco7duzg7NmzeHl5YWlpyb59+7h9+zYvvviifne6gjEzM2P48OF8+umnWFlZ4eDgwOLFi7l69eoTL/Gyt7fnwoULHD9+nPr161OtWjUqV6781Lno8t541PP69u3L2LFjlRNoLl26xPnz5xk0aBBvvfUWkZGRfPDBB7zzzjucO3eOTz/9lLfffluZL+zUqROTJk0iLi4OZ2dnvvnmG9LS0nS6xtve3p7ExETat29P5cqVC531XJKkZ1iBrV69mo4dOxYqhAB9+vThwoULJCQkAPfPJo2IiKBDhw789NNPrFmzRpl0NzExYfny5Zw7d44OHToQFhbGlClTHrvtwMBA+vTpw4gRI+jatSvnz59nzJgxJb6PomgHDx6kU6dOWj9z585l9uzZLF68mLZt27Jq1So+++wzred5eHgQHh7OkiVLaN++Pdu2bWPcuHFUqVJFiXnllVeYO3cuixcvxtPTk6ioKBYsWICfnx9w/4SGbdu20adPHzw8PPjqq6/48ssv8fLyKtXXoCL49NNP6dOnD2PGjKFjx46cPHmS2NhYrTNJi/Lqq6/y8ssv07t3bxo1akRsbOwz5dG0adMnvjceJSoqigEDBjBx4kQ8PDwYPXo0t27dAqBu3brExMSQnJxMx44deffdd+nfv7/W58f//d//8X//93+8++67+Pr6YmZmhr+/v07bnjFjBvv27cPV1fWR85YlxSC+z1A8u9OnT9OmTRuSkpKkB/CcmTRpEomJifz8889lnYoQZUaGScUT3bhxgy1btmBubi5niz4HvvzyS7p06UK1atVISEjgm2++4ZNPPinrtIQoU1IMxRO9++67nDhxgs8//5yqVauWdTriGR07doxFixZx69YtHBwcmDJlCqNGjSrrtIQoUzJMKoQQwuDJCTRCCCEMnhRDIYQQBk+KoRBCCIMnxVAIAzNq1Ci5sbYQD5FiKMRz4urVq0ybNo22bdtSt25d6tSpg5eXF9OmTSt0O6yHzZs3T+4rKwyanE0qxHPg2LFjDBw4kMzMTPr374+7uztGRkacPHmSjRs3UrNmTY4cOQLc/yb1/Px8rdt72dra0q9fPyIjI8tqF4QoU3KdoRAVnFqt5vXXX0elUpGQkMBLL72k1f7JJ5/wxRdfKI8fvJm0EOI+GSYVooJbsWIFly5dYsaMGYUKIdy/l+jUqVOVxw/PGVpYWHDnzh3WrVuHhYUFFhYW+Pv78/fff2NhYUFEREShdf73v//FwsJC61vShajIpBgKUcFt376dKlWq0Ldv36d6/pIlSzAxMaFdu3YsWbKEJUuWMGHCBBo1aoSnpyfr168v9Jz169djYmJC//79nzV9IcoFKYZCVHApKSk4OTlhamr6VM8PCAjAyMiIBg0aEBAQQEBAAF27dgVg8ODBJCcna30zu0ajISYmhm7dumFlZVUi+yBEWZNiKEQFl5mZibm5uV7W3bdvX6pUqaLVO9y/fz8XL15k0KBBetmmEGVBiqEQFZy5uTmZmZl6WXeNGjXo2bMnMTExaDT3TzzfsGED1atXp0ePHnrZphBlQYqhEBWci4sLf/31F3fv3tXL+gcPHszFixc5cOAAd+7cYcuWLfTp00frC4GFqOikGApRwfn5+ZGTk8PmzZufeh0qleqRbd7e3tSuXZv169ezfft2bt26RUBAwFNvS4jySIqhEBXcm2++Sd26dfn4449JSUkp1H7r1i2mT5/+2HW88MILqNXqItuMjY0ZOHAgW7ZsYfXq1djb2+Pl5VUSqQtRbkgxFKKCs7CwYO3ateTn59O5c2feffddoqOjWbFiBSEhIbi5ufH9998/dh0tW7YkMTGRRYsWsXHjRhITE7XaBw8ezK1bt/jpp5947bXXHtuTFKIiktuxCfGcuHr1Kl999RU7duzg/PnzaDQaHB0d8fPzY+TIkdjY2AD3L7rfv38/v/32m/Lc06dP8/7773P06FGysrJo374927Zt01p/586dOXHiBIcPH8bZ2blU900IfZNiKITQycsvv0x+fj4//fRTWaciRImTYVIhxBP98ccfHD58mMGDB5d1KkLohfQMhRCP9Mcff3D8+HGioqJIS0vjxIkTVKtWrazTEqLESc9QCPFIW7ZsYcyYMfz7778sX75cCqF4bknPUAghhMGTnqEQQgiDJ8VQCCGEwZNiKIQQwuBJMRRCCGHwpBgKIYQweFIMhRBCGLz/B63+8OyO3zkuAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.barplot(x='City', y= 'gross income', data= df).set_title('Gross Income for each City')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff30da02",
+   "metadata": {},
+   "source": [
+    "Prot Harcourt grossed in the highest income"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "756b41f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'gross income for different product line')"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.barplot(y = 'Product line', x = 'gross income', data = df).set_title('gross income for different product line')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0453b137",
+   "metadata": {},
+   "source": [
+    "Home and lifestyle gave the highest overall income followed closely by Sports and travel.\n",
+    "This is supported by the fact that home most home utilities are essentials used by a large population of people"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "d44084a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Customer type patronage for each Branch')"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x = 'Customer type', data = df, hue = 'Branch').set_title('Customer type patronage for each Branch')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f37c5643",
+   "metadata": {},
+   "source": [
+    "Branch A had slightly higher normal customers than member customers.\n",
+    "Branch B had almost equal number of Member and normal customers' patronage.\n",
+    "Branch C had more member customers than normal customers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "fcd1197b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Rating Distribution')"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(df['Rating']).set_title('Rating Distribution')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "298d0231",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "The rating for sales is between 4 and 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "f2f96c6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Quantity of purchases by Gender')"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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XfqP9ZziJRKIC38/85PetW/r+P3v2rFQugl61ahXevHmDVatWKRzG2rNnj9KRfNbW1tiwYQNyc3Nx584dREdHY8OGDZg7dy7y8vIwefJkAJ/eg5ycHJVG3xW176JITk5WOJKQlZWF9PR0VKtWTW66jo4OBg8ejJCQEBw7dgzNmjVDVFQUGjZsiJYtWxZpfa6urgA+jZBLS0tTeRuUtv/7778V6isp0nVIt93PKfu7ki6zbds2dOvWTaX1qXJ0qTSU63NQgwYNko0OunPnTr7tTp48iWvXrqFChQpyx32lHzjK9sD+97//Kd0LSEhIgI2NjUI45eXlyU7SFteX7lUVZTkfHx9UrVoVW7ZswaFDh/Dq1SsMHDhQ7nxJQaQjmOLi4pReiS8d8qrskMeXUhb68fHxSE5ORt26deUCSiwW57tH/c9v0EXl4uIC4NN5gNIgPTyj7IOnsC87mpqaaNy4McaNG4c9e/YAACIjI2XzXVxckJ6ejtu3b6tcV2F9F4Wy+i9dugSJRKJ0JNzw4cOhoaGBzZs3Y/v27cjJySny3hMAWFpaok2bNsjMzPyia5yk7/WlS5dUXraopF+Qlb02+X2GSOu6fPlyqdVVWsp1QFlYWCAoKAg5OTno168f/vjjD4U2Fy9ehL+/PwDg+++/lzup3bRpU2hoaCAiIkLu+qB3794pHU0jXWdCQoLcYQSJRIKFCxfi3r17JfK8pIMwinLoUtlyBX1jrlKlCnx8fPDXX39hxowZEIlEKn0I1KpVCx06dMDTp08REhIiN+/u3bvYtGkTKlasiL59+6pUe0HWrl0r95xyc3Px008/QSKRKOx1uLi4IDExUSFQtm7d+kV3DRgxYgQqVKiApUuXKv2gL+6gGwsLCwBQuDbr9OnT2LZtm0L733//Xen1S9Jp//yiIR2JN2HCBKV1ZmVlyX3oqdJ3USxevFju/EtmZibmzp0LAEoHPdSuXRvffPMNzpw5g7Vr16JSpUrw8fFRaZ3BwcGoWrUqli1bhuXLlyv9EpWXl6f0Gjp/f39oa2tjxowZSs/n5ObmKrxPqmrevDmsra0RFxenMKhh48aNSq+16ty5M+rWrYvNmzfne+3WzZs3ZYcChaRcH+IDPl1DkpWVhWXLlqFNmzZo27YtbG1tkZeXh+vXr8u+qfTq1QvTp0+XW9bIyAgDBw7E9u3b4ebmBg8PD2RlZeH06dOwsLBQOhpozJgxCAgIQJs2bdCtWzdoaWkhLi4Of/31F7799lscO3as2M/J2toa5ubmuHz5MkaOHAkrKytoamqiU6dOBQ5kaNeuHX7//XcMHjwYHh4e0NHRgbm5Ofr16yfXzs/PD+vXr8eLFy/Qtm1bWFlZqVTfsmXL8O2332LevHmIjo6Gi4uL7DqozMxMhISE5Du67Uu0aNECbm5uctdB/fnnn2jatCnGjRsn1/a7777DqVOnMGjQIHh7e8PAwAA3btzAjRs34OnpKbvlUlHZ2Nhg2bJlmDBhAtq1awdPT0/Y2NggNTUVd+7cwbNnzxRO+KtixIgR2LFjB3x9fdGtWzeYmJjg7t27OHXqFHr06IF9+/bJtd+9ezc2bNgAV1dXWFlZoXr16rLr8DQ0NOSu/3F3d8ecOXPw008/wcnJCd988w0sLS2RlZWFJ0+e4NKlS7CwsJBdXK1K30V97VxdXWV/J1FRUXj48CE6d+4sG2L+OT8/Pxw/fhzJyckYMmSIysPFGzRogL1798LX1xezZs3C6tWr4e7uDjMzM+Tm5iIpKQkxMTF4+vQpzMzMZF8QgE9/d6tXr8bYsWPh6uqKjh07wsrKCrm5uXj69Cni4uKQnZ2Nx48fq1TTP4lEIqxYsQI9evSAr6+v3HVQZ8+eRceOHXHq1Cm5ZSpUqIDQ0FD07NkTAwYMgLOzMxwcHFC5cmU8ffoUt27dQnx8PKKjo1G9evUvrq00lPuAEolEmDlzJrp164b169fj4sWLuHTpkmwklJ6eHn755Zd8/yCWLVsGQ0NDhIeHY9OmTTAyMkKfPn0wefJkNGvWTKG9r68vtLW1sWbNGoSFhUFHRweurq5YtWoVDh06VCIBpaGhgR07duCnn37CiRMnkJaWBolEAlNT0wIDatKkSUhLS8ORI0cQEhKCjx8/olWrVgoBZWNjAxcXF1y9elWlvSep2rVr49y5c1iyZAmOHTuG2NhYVK5cGa1atcJ3330nu5arpMyfPx+HDx/G1q1b8fjxY9SsWRNjxozB1KlTFS7Sbd26NcLDw7Fw4UIcOnQI2traaNmyJU6ePImDBw+qHFAAMHjwYDRq1AgrVqzApUuXcOLECVSrVg3W1tbFvgbLzs4Ohw8fxty5c3HixAnk5ubCzs4O27dvh76+vkJA9e7dGzk5ObJv4O/fv4eRkRE8PT0xduxYODk5ybUfP348WrRogbVr1+Ly5cs4duwYqlSpAhMTE/Tp00d2fc6X9F2YzZs3Y9GiRYiIiEBSUhJMTEwwdepUBAQE5HtupGPHjjAxMcHz588xfPhwldYn5eLigitXrmDHjh04evQooqOj8fbtW2hpacHIyAjOzs746aef0K1bN4W9wt69e8POzg6rVq3C+fPncfbsWejo6MDY2BgdO3ZUGBr+JVq0aIGjR49izpw5OH36NE6fPg0nJydERkbi9OnTCgEFAI0aNUJMTAzWrFmDI0eOICwsDBKJBEZGRmjQoAHGjx+v9DytuolSUlKU3x+knMvKypLdPuSXX375og/isurdu3do1KgRdHV18ccffyi9ol4IvLy8EBMTg5s3b6J27drqLoe+gsTERDg4OMDBwQFnzpxRdzlUTOX6HFRBdHR0EBYWhrp162LixIll5vdVSsKmTZuQmpoKX19fwYYTlU8hISHIzc3FyJEj1V0KlQB+uhSgevXq2LNnD8LDw/H06VOlw8bLi9TUVPz222948eIFQkNDYWhoiFGjRqm7LCI8fvwY4eHhSEhIwK5du9CwYcMi3ROShI8BVYi6det+0f3fyhrpPdYqVqwIBwcHBAcHl9uwJmF59OgR5s2bh0qVKqFt27ZYunQp9+zLCJ6DIiIiQeI5KCIiEiQGFBERCRIDioiIBIkBRUREgsSAIiIiQWJAERGRIDGgiIhIkNQaUC9evMCoUaNgZWUFIyMjNG/eXHZnZCIiKt/Udrl1SkoKPD090aJFC0RERKBGjRp49OhRsX9SnYiIyga1BdSvv/4KY2NjrFu3TjbN0tJSXeUQEZHAqO0QX1RUFJycnODr64t69eqhdevW+O233yCR8M5LRESkxoB6+PAhNm7cCEtLS+zduxejRo3C7NmzsX79enWVREREAqK2m8UaGBigSZMmOHHihGzazz//jMjISFy5ciXf5eLj479GeeVWdnY2oqOj853v7u6u8Cu0RERforBf8VXbOSgjIyPY2NjITatfvz4SExMLXE6IP0tclrx580bpT0ZL9evXD9WrV/+KFRFReaW2Q3wtWrTAgwcP5KY9ePAA5ubmaqqIiIiERG0BNWbMGFy9ehVLlixBQkICDhw4gN9++w1+fn7qKomIiAREbQHVtGlT7NixA/v374erqyvmzJmDadOmMaCIiAiAmn/y3dPTE56enuosgYiIBIr34iMiIkFiQBERkSAxoIiISJAYUEREJEhqHSShDrar879LBQGa2emoV8D8tluvI7ei3ler59/kzphm6i6BqEzhHhQREQkSA4qIiASJAUVERILEgCIiIkFiQBERkSAxoIiISJDK3TBzIiIAyMzMxMGDB/Od3717d+jq6n7FiuhzDCgiKpcyMzMRERGR73wPDw8GlJoxoEhOnqY2XtVpVeB8IqKvgQFFciRaFfHayk3dZRARcZAEEREJEwOKiIgEiQFFRESCxIAiIiJBYkAREZEgcRQfURmWHtBf3SUIVsZHScHzfxqDClqir1TNv4/eL2Glvg7uQRERkSAxoIiISJAYUEREJEgMKCIiEiQGFBERCZLaAmrBggUQi8Vy/+rXr6+ucoiISGDUOszc2toakZGRsseamppqrIaIiIRErQGlpaUFIyMjdZZAREQCpdaAevjwIRo2bIgKFSrA2dkZP/74IywtLdVZEhGVEzoagLd+/kdtdHiGXu3UFlDOzs5YvXo1rK2t8erVKyxevBgeHh6IjY1F9erV1VUWEZUTuhoi9BDzZjpCprZ355tvvpF77OzsDEdHR+zcuRPjxo3Ld7n4+PjSLo3oiwhx2zRWdwFUZpXE9m5tbV3gfMF8fahSpQoaNGiAhISEAtsV9oQKdfJK8ZYnykext81SkK7uAqjM+hrbu2COsmZlZSE+Pp6DJoiICIAa96BmzJiBb7/9FmZmZrJzUO/fv0f//rz7MhERqTGgnj17Bj8/P7x+/Ro1a9aEs7MzTp48CQsLC3WVREREAqK2gNq0aZO6Vk1ERP8CgjkHRURE9E8MKCIiEiQGFBERCRIDioiIBIkBRUREgsSAIiIiQWJAERGRIDGgiIhIkBhQREQkSAwoIiISJAYUEREJEgOKiIgEiQFFRESCxIAiIiJBYkAREZEgMaCIiEiQGFBERCRIDCgiIhIkBhQREQkSA4qIiASJAUVERILEgCIiIkFiQBERkSAxoIiISJAYUEREJEgMKCIiEiQGFBERCZJgAmrp0qUQi8UIDAxUdylERCQAKgXUvXv3SqWIq1evYuvWrbC1tS2V/omI6N9HpYBydXVF27ZtsWbNGiQnJ5dIAampqRg5ciRWrFgBsVhcIn0SEdG/n0oBtWjRImhra2PatGmwtbVFnz59sHfvXmRlZX1xARMmTED37t3Rpk2bL+6DiIjKHi1VGo8cORIjR47Ew4cPERYWhj179sDPzw96enro2rUrfHx84O7uXuT+tm7dioSEBKxbt67Iy8THx6tSMtFXI8Rt01jdBVCZVRLbu7W1dYHzVQooKUtLS0ydOhVTp07F1atXERERgf379yMsLAympqbo27cv+vXrh/r16+fbR3x8PH7++WccPXoU2traRV53YU+oUCevFG95onwUe9ssBenqLoDKrK+xvRd7FJ+dnR2aNWsGW1tbSCQSvHz5EqtXr0aLFi3g4+ODxMREpctduXIFr1+/hqurK2rUqIEaNWogJiYGGzZsQI0aNZCdnV3c0oiI6F/si/agJBIJzp07h/DwcERFReHdu3dwdHTEokWL0Lt3b2hqamLXrl0IDg7GqFGjEBkZqdCHl5cXmjRpIjdt7NixsLKywsSJE1XaqyIiorJHpYC6ffs2wsPDsXfvXiQlJcHY2BgjRoxA//79YWNjI9fW398fIpEIM2bMUNqXWCxWGLVXqVIlVKtWDY0aNVLtWRARUZmjUkC5u7tDV1cXXl5e6N+/P9q2bQsNjfyPEtrY2MDFxaXYRRIRUfmjUkCFhISgR48e0NPTK1J7d3d3lUb1RUVFqVIOERGVYSoNknj+/DmePHmS7/y7d+8iODi42EURERGpFFDBwcG4c+dOvvMZUEREVFJUCiiJRFLg/IyMDFSoUKFYBREREQFFOAf1xx9/4Pbt27LHly9fxsePHxXapaSkYNOmTahXr17JVkhEROVSoQEVGRkpO2wnEomwefNmbN68WWlbPT09rF+/vmQrJCKicqnQgBoyZAg6duwIiUQCDw8PTJkyBR06dJBrIxKJUKlSJVhZWfECWyIiKhGFBpSpqSlMTU0BAIcPH4aNjQ0MDAxKvTAiIirfVLoOqnXr1qVVBxERkZwCA2rs2LEQiUQICQmBpqYmxo4dW2iHIpEIK1euLLECiYiofCowoKKjo6GhoYG8vDxoamoiOjoaIpGowA4Lm09ERFQUBQbUP4eXK3tMRERUWlS6UPfJkyfIzMzMd35mZmaBt0IiIiIqKpUCysHBQelvO0kdPXoUDg4OxS6KiIioRG919PHjR56DIiKiEqHyT77nF0Cpqak4deoUr5EiIqISUeh1UAsXLsSiRYsAfAonf39/+Pv759v+P//5T8lVR0RE5VahAdWkSRMMGzYMEokEW7Zsgbu7O6ysrOTaSG911KRJE3h7e5dWrUREVI4UGlCenp7w9PQEAGRnZ2P48OFwdnYu9cKIiKh8U+lWR6tXry6tOoiIiOSoFFAAkJubizNnzuDhw4d4+/atwsg+kUiEyZMnl1iBRERUPqkUULdu3cKgQYOQmJiY75BzBhQREZUElQLqhx9+QEZGBrZv345WrVpBLBaXUllERFTeqbwHNXXqVHh5eZVWPURERABUvFDX0NAQWloqn7YiIiJSmUoB5e/vj127diEnJ6e06iEiIgKg4iE+U1NTaGlpwdXVFYMGDYKZmRk0NTUV2vXo0aPECiQiovJJpYAaMWKE7P+zZ89W2kYkEhUpoNavX4/NmzfLfp6jQYMG+OGHH2QXBRMRUfmmUkAdPny4xFZsamqK2bNnw8rKCnl5eQgLC8PAgQNx7tw52NnZldh6iIjo30mlgGrdunWJrfjzkYAzZ87Exo0bcfXqVQYUERGpfieJ0pCbm4sDBw7g3bt3aNasmbrLISIiAVA5oF6+fInt27fjxo0bSE1NRV5entx8kUiEQ4cOFamvO3fuwMPDA1lZWahcuTJCQ0Nha2tb4DLx8fGqlkz0VQhx2zRWdwFUZpXE9m5tbV3gfJUC6t69e/Dy8sK7d+9gZWWFu3fvokGDBkhJScHz589Rp04d1KpVS6XiLly4gNTUVBw6dAijR49GZGQkGjVqVOAyxXLySvGWJ8pHsbfNUpCu7gKozPoa27tK10HNmjULFSpUQGxsLA4dOgSJRIIFCxbgzz//xPr165GSkoI5c+YUuT9tbW3UrVsXTZo0wU8//QR7e3veMZ2IiACoGFCXL1+Gr68vLC0toaHxaVHpTWN79+6Nnj17YubMmV9cTF5eHj58+PDFyxMRUdmh0iG+nJwcmJiYAAB0dHQAAKmpqbL59vb22LVrV5H6mjVrFjw8PFCrVi1kZGRgz549uHjxIiIiIlQpiYiIyiiVAsrMzAyPHz8GAOjq6sLY2BhXrlxB9+7dAQB//vknKleuXKS+kpKS4O/vj+TkZFStWhW2trbYs2cPOnTooOJTICKiskilgHJzc8ORI0cwY8YMAECfPn2wevVqpKWlIS8vD+Hh4Rg8eHCR+lqzZo3q1RIRUbmhUkBNmDAB7u7uyMrKgo6ODqZPn460tDTs378fmpqa8PHxUWmQBBERUX5UCihzc3OYm5vLHlesWBHLly/H8uXLS7ouIiIq51QaxUdERPS1qLQHFRwcXGgbkUiEyZMnf3FBREREgIoBtXDhwnzniUQiSCQSBhQREZUIlQLq7du3CtPy8vLw+PFjrFu3DnFxcdizZ0+JFUdEROVXsc9BaWhowNLSEgsWLEDt2rUxZcqUkqiLiIjKuRIdJOHm5objx4+XZJdERFROlWhAxcfHy+7NR0REVBwqnYOKiYlROj01NRUXLlzA+vXr4e3tXRJ1ERFROadSQHXp0gUikUhhukQigaamJnr16lWkoehERESFUSmgDh8+rDBNJBJBLBbDwsICenp6JVYYERGVbyoFVOvWrUurDiIiIjkqBRQAPHr0CPfu3UNGRgZq1qyJ2rVrw9LSshRKIyKi8qzIARUZGYn58+fj3r17CvMcHR0xadIkeHl5yaZ9/PgRWloq5x8RERGAIgbU/PnzsWTJEujp6cHHxwf29vaoUqUKMjIycPv2bRw9ehSDBw/GlClTMHnyZKSnp2PQoEE4ePBgaddPRERlVKEBdebMGSxevBjdunXDr7/+Cn19fYU2aWlp+P777xEcHAwrKyuEhITgr7/+KpWCiYiofCg0oNauXQtbW1ts3rwZGhrKr+utWrUqNm7cCHd3d4wcORKVK1fGrl27SrxYIiIqPwq9k8Tvv/+Ovn375htOso40NNCnTx8An4ajt2vXrmQqJCKicqnQgHr37h2qV69epM6qV6+OChUqwNHRsbh1ERFROVdoQJmYmODPP/8sUmd//vknTExMil0UERFRoQHVsWNHbN++HQkJCQW2S0hIQGhoKDw8PEqsOCIiKr8KDaiJEydCU1MT3377LXbt2oUPHz7Izf/w4QN27dqFzp07Q0tLCwEBAaVWLBERlR+FjuIzMTHB7t27MWTIEIwZMwYBAQGoV68e9PT0kJ6ejgcPHiA7OxtGRkbYvXs3D/EREVGJKNKFui4uLoiNjcXmzZtx7Ngx2a2OqlSpAgcHB3Tq1AnDhg1Teo0UERHRlyjyvYj09fUxYcIETJgwoRTLISIi+qREf1FXFcuWLUO7du1gbm4OKysr+Pj4FHm0IBERlX1qC6iLFy9ixIgROH78OA4dOgQtLS14e3vj7du36iqJiIgERG23G9+3b5/c43Xr1sHCwgKxsbHo1KmTmqoiIiKhUNse1OcyMjKQl5cHsVis7lKIiEgABPODTVOmTIG9vT2aNWtWYLv4+PivVBGRaoS4bRqruwAqs0pie7e2ti5wviACatq0aYiNjcWxY8egqalZYNvCnlChTl4p3vJE+Sj2tlkK0tVdAJVZX2N7V3tATZ06Ffv27cPhw4f50/FERCSj1oAKCgrCvn37EBkZifr166uzFCIiEhi1BdQPP/yA8PBwhIaGQiwWIykpCQBQuXJlVKlSRV1lERGRQKhtFN+GDRuQnp6O7t27w8bGRvZvxYoV6iqJiIgERG17UCkpKepaNRER/QsI5jooIiKif2JAERGRIDGgiIhIkBhQREQkSAwoIiISJAYUEREJEgOKiIgEiQFFRESCxIAiIiJBYkAREZEgMaCIiEiQGFBERCRIDCgiIhIkBhQREQkSA4qIiASJAUVERILEgCIiIkFiQBERkSAxoIiISJAYUEREJEgMKCIiEiQGFBERCRIDioiIBIkBRUREgsSAIiIiQWJAERGRIKk1oGJiYtCvXz80bNgQYrEYO3bsUGc5REQkIGoNqHfv3qFRo0ZYuHAhdHV11VkKEREJjJY6V+7h4QEPDw8AwJgxY9RZChERCQzPQRERkSCpdQ/qS8THx6u7BCKlhLhtGqu7ACqzSmJ7t7a2LnD+vy6gCntChTp5pWQKIfpMsbfNUpCu7gKozPoa2zsP8RERkSAxoIiISJDUeogvIyMDCQkJAIC8vDwkJibi1q1bqFatGszNzdVZGhERqZla96CuX78Od3d3uLu7IzMzEwsWLIC7uzvmz5+vzrKIiEgA1LoH5ebmhpSUFHWWQEREAsVzUEREJEgMKCIiEiQGFBERCRIDioiIBIkBRUREgsSAIiIiQWJAERGRIDGgiIhIkBhQREQkSAwoIiISJAYUEREJEgOKiIgEiQFFRESCxIAiIiJBYkAREZEgMaCIiEiQGFBERCRIDCgiIhIkBhQREQkSA4qIiASJAUVERILEgCIiIkFiQBERkSAxoIiISJAYUEREJEgMKCIiEiS1B9SGDRvQuHFjGBkZoU2bNrh06ZK6SyIiIgFQa0Dt27cPU6ZMwaRJkxAdHY1mzZqhT58+ePLkiTrLIiIiAVBrQK1atQoDBgzA0KFDYWNjg8WLF8PIyAibNm1SZ1lERCQAWupa8YcPH3Djxg2MHz9ebnr79u0RFxdXauu9M6ZZqfVNJDR6v4SpuwSiL6a2PajXr18jNzcXBgYGctMNDAyQnJyspqqIiEgo1D5IQiQSyT2WSCQK04iIqPxRW0DVqFEDmpqaCntLr169UtirIiKi8kdtAaWtrQ1HR0ecPXtWbvrZs2fRvHlzNVVFRERCodZDfGPHjsXOnTuxbds2/PXXXwgKCsKLFy/g6+urzrKomBYsWABXV1d1l0FUbPb29lixYoW6yyi31BpQPXv2xIIFC7B48WK4ubkhNjYWERERsLCwUGdZ/yqjR4+GWCxW+Hfr1i11l0b01Un/Hj4fHQwAP/74I8RiMXx8fNRQGX0JtQ0zl/Lz84Ofn5+6y/hXa9u2LdatWyc3rUaNGmqqhki9zMzMsH//fixcuBCVK1cGAHz8+BHh4eEwMzNTc3WkCrWP4qPiq1ixIoyMjOT+aWlp4ejRo2jTpg2MjIzQuHFjzJkzBx8+fJAtZ29vj+DgYIwePRpmZmawtbXFvn37kJKSguHDh6NWrVpo2rQpzpw5I1smNzcX48aNQ+PGjWFsbIymTZsiJCQEeXl5BdYYGhqK5s2bw8jICE5OTli1alWhyxB9CVtbW9StWxf79++XTTt+/DgqVqyI1q1by6Zdu3YNPXr0QN26dWFubo5vv/0WV65cKbDv1NRUfP/996hXrx7MzMzQuXNnXL9+vdSeS3nHgCqjTp8+DX9/f4wcORKxsbFYuXIlDh48iJ9//lmu3Zo1a+Dk5ITz58/D29sbo0ePxsiRI/HNN9/gwoULaNmyJfz9/ZGVlQUAyMvLg4mJCbZs2YK4uDjMnDkTS5cuRWhoaL61bN26FXPmzMG0adMQFxeHuXPnIiQkBBs2bCjV14DKr8GDB2PHjh2yx6GhoRg4cKDcJSzp6enw8fHB0aNHcfr0adjb26NPnz54/fq10j4lEgl8fHzw/PlzhIeHIzo6Gi1btkS3bt3w4sWLUn9O5REDqgw4deoUatWqJfvXu3dvLFmyBOPHj8egQYNQp04duLu7Y9asWdi8eTMkEols2Q4dOsDPzw9WVlaYOnUqsrOzUadOHfTv3x9169ZFYGAgXr16hbt37wIAKlSogOnTp6Np06aoXbs2evTogeHDh2Pv3r351rd48WLMnj0b3bt3h6WlJTp16oQJEyZg48aNpf7aUPnUp08fXL9+Hf/73/+QlJSE06dPY8CAAXJt2rRpg379+sHGxgb169fHokWLoKOjg1OnTintMzo6Grdv38bWrVvh5OSEunXrYsaMGahduzbCw8O/xtMqd9R+DoqKr2XLlggJCZE91tHRgbOzM65duyY3PS8vD5mZmUhKSoKxsTGAT4dDpKpUqYJKlSrJTTM0NAQAvHz5UjZt06ZN2LZtG548eYKsrCzk5OTA3NxcaW2vXr1CYmIiAgICMGnSJNn0jx8/ygUlUUkSi8Xo0qULQkNDoa+vj9atWytsoy9fvsS8efNw4cIFvHz5Erm5ucjMzERiYqLSPm/evIn379+jXr16ctOzsrLw999/l9pzKc8YUGVApUqVULduXblpeXl5CAoKgre3t0L7mjVryv5foUIFuXkikQhaWlpyj6X9AZ/uQD916lTMmTMHzZo1Q9WqVbF+/XpERkYqrU263LJly3h9G31VgwYNwujRo1G5cmVMmzZNYf7o0aORnJyM+fPnw8LCAhUrVkS3bt3kztP+U15eHgwNDXH06FGFeXp6eiVePzGgyiwHBwfcv39fIbiK6/Lly3BycoK/v79sWkHfHg0NDWFqaoq///4b/fv3L9FaiArSpk0bVKhQAa9fv4aXl5fC/NjYWCxcuBCenp4AgOTkZCQlJeXbn4ODA5KTk6GhoQFLS8vSKpv+gQFVRk2ePBk+Pj4wNzdHjx49oKWlhbt37+L3339XGCihinr16iEsLAwnT55E3bp1sXfvXly6dAn6+vr5LjNlyhRMnjwZ+vr68PDwQE5ODm7evInnz59j4sSJX1wLUUFEIhFiYmIgkUhQsWJFhflWVlaIiIiAs7Mz3r9/jx9//BHa2tr59te2bVu0aNECAwYMwOzZs2FtbY3k5GScOnUKbdu2RcuWLUvz6ZRLHCRRRnXo0AERERG4ePEiOnTogA4dOuCXX34p9nUgvr6+8Pb2hp+fH9q1a4fHjx9j7NixBS4zZMgQrFy5EuHh4WjdujU6deqErVu3onbt2sWqhagwenp6qFq1qtJ5K1euxLt379C2bVsMHz4cgwYNKvAmASKRCBEREXBzc8P3338PFxcX+Pr64sGDBzAxMSmtp1CuiVJSUnimmoiIBId7UEREJEgMKCIiEiQGFBERCRIDioiIBIkBRUREgsSAIiIiQWJAEf1LXbhwAWKxGBcuXFB3KUSlgneSICrE27dvsXr1ahw7dgwPHz5EdnY2DA0N4eTkhN69e6NLly5yP+NARCWDAUVUgJs3b6Jv3754+/YtvL29MXjwYOjq6uLp06c4efIkBg8ejCVLlvBXoYlKAQOKKB+pqakYMGAAJBIJzp07h0aNGsnNnzJlCqKjo5GamqqmCkvW+/fvUalSJXWXQSTDc1BE+diyZQuePn2KefPmKYSTlLu7O7p27So3LS0tDTNmzIC9vT0MDQ1hZ2eHWbNmITs7W66dWCxGQEAATp48CTc3NxgZGaFp06bYs2ePwnri4+PRs2dPmJiYoH79+pg5cyZycnKU1nT9+nX4+PjAwsICxsbGaN++PY4dOybXZseOHRCLxYiOjsaUKVNQv359mJqaqvLyEJU67kER5ePo0aPQ1dVF9+7di7xMZmYmunTpgkePHmHYsGGoU6cObt++jZUrV+L+/fvYuXOnXPurV68iKioKvr6+GDx4MLZt2wZ/f3/Y29vDxsYGwKcf1vPy8kJmZibGjh2L6tWrY9euXTh79qzC+i9evIhevXqhUaNGCAwMhLa2Nvbv34/+/ftj69at6Natm1z7oKAg6OvrY+LEiUhLS/uCV4mo9DCgiPJx7949WFlZKfwEw7t375CVlSV7rKWlJfu5kdWrVyM+Ph7nzp2TBQwANGzYED/88AMuXbok97MM9+7dQ0xMjKytt7c37OzsEBoaijlz5gAAli9fjuTkZBw5ckS27LBhw9C6dWu5uiQSCQICAtCsWTMcPHgQGhqfDpCMHDkSnp6e+PHHHxUCqlKlSoiMjJT7kUoioeAhPqJ8pKenK/2l1FmzZsHKykr2b8CAAbJ5+/fvR/PmzVGzZk28fv1a9q9t27YAgOjoaLm+3Nzc5ILM0NAQ1tbWePjwoWza8ePH4eDgIBdslSpVwrBhw+T6un37NuLj42WDOqTrfvv2LTp27IiHDx/i8ePHcssMHTqU4USCxS2TKB96enpIT09XmO7v7y/7hdZx48bJzfvf//6HP/74A1ZWVkr7fPXqldxjc3NzhTZisRhv376VPX7y5InCeS7g049Hfr5uABg/fjzGjx+f7/r/+ZtH/GVYEjIGFFE+GjRogBs3buDDhw9yh/msra1hbW0NANDV1ZVbJi8vD+7u7vn+UvDnAxE0NTWVtpNI5H+mTdl1Vp+3ycvLA/BpD8/R0VFpv5+H2uf1EwkJA4ooH506dUJsbCwOHDiAvn37FmmZOnXqICMjQ3ZIrySYm5vj/v37CtOle0z/XDcAVKlSpUTXT6QuPAdFlI9hw4ahVq1amD59Ou7evau0zed7MT179sS1a9dw5MgRhbaZmZnIyMhQuQ4PDw/cvHkTly5dkk17//49tmzZItfO0dERVlZWWLFihdJrsz4/vEgkdNyDIsqHvr4+duzYAR8fH7Rp0wbe3t5wdnaGrq4uXrx4gePHj+PBgwdwdnaWLTN+/HicOHECgwcPRt++feHk5ITs7Gw8ePAA+/fvx+7du+Hi4qJSHRMmTEBERAT69esHf39/2TDzzy+q1dDQwMqVK9GrVy+0aNECAwcOhIWFBV68eIGrV6/iyZMniI2NLZHXhuhrYEARFcDR0RGXLl3CmjVrcPToUURFRSEnJweGhoZwdnbGxIkT0alTJ1l7XV1dHDp0CCEhIdi3bx/27t2LypUrw9LSEqNHj5adu1KFoaEhoqKiEBQUhJUrV0JPTw8+Pj5o3749evbsKdfW1dUVp0+fxqJFi7BlyxakpaXBwMAAdnZ2mDp1arFfD6KvSZSSkiIpvBkREdHXxXNQREQkSAwoIiISJAYUEREJEgOKiIgEiQFFRESCxIAiIiJBYkAREZEgMaCIiEiQGFBERCRIDCgiIhKk/wdJ2w7yCX/+rgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.barplot(y='Quantity',x ='Gender', data = df).set_title('Quantity of purchases by Gender')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b394839f",
+   "metadata": {},
+   "source": [
+    "Females purchased higher quantity of products. This might be explained by the higher tendency of women to always go shopping than men."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "86579fcc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Gross Income for Branches')"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x= 'Branch', y='gross income', data=df).set_title('Gross Income for Branches')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee13c68a",
+   "metadata": {},
+   "source": [
+    "# Summary\n",
+    "\n",
+    "Data set is large with exactly 17,000 elements (1000 rows and 17 columns).\n",
+    "\n",
+    "The elements for categorical raw data are complete.\n",
+    "\n",
+    "Approach:\n",
+    "\n",
+    "The approaches for data analyses for company XYZ were to combine seperate data set for each city, make inferential and descriptive statistical analyses and obtain key facts that show and compare the sales performances for Company's branches.\n",
+    "\n",
+    "Method:\n",
+    "\n",
+    "These were accomplished using some python packages such as pandas analytical tools, Numpy, and Matplotlib as well as data illustrative tools such as Seaborn's statistical visualization package.\n",
+    "\n",
+    "Insights:\n",
+    "\n",
+    "Python packages such as pandas analytical tools, Numpy, and Matplotlib as well as data illustrative tools such as Seaborn's statistical visualization package are very efficient and powerful in analysing huge data sets.\n",
+    "\n",
+    "Coding lines are not too restictive, i.e packages are user-friendly and flexible. Users creativity shows that usage can be interesting.\n",
+    "\n",
+    "Some 'take-home' facts about the company XYZ include:\n",
+    "    \n",
+    "    Sales of Electronic and fashion accessories are almost at par. Sales across cities are very competitive with close matching records. However the followin details can be inferred from the comparative analyses:\n",
+    "    \n",
+    "    \n",
+    "    Branch A has the highest sales record.\n",
+    "    \n",
+    "    The most used payment method is Epay with a count of 345.\n",
+    "    \n",
+    "    The City with the most sales is Lagos.\n",
+    "    \n",
+    "    The highest product line sold is Fashion accessories.\n",
+    "    \n",
+    "    The lowest product line sold is Health and Beauty.\n",
+    "    \n",
+    "    Females purchased higher quantity of products. This might be explained by the higher tendency of women to always go shopping than men.\n",
+    "    \n",
+    "    Branch with the lowest rating is B\n",
+    "    \n",
+    "    Branch A had slightly higher normal customers than member customers.\n",
+    "    \n",
+    "    Branch B had almost equal number of Member and normal customers' patronage.\n",
+    "    \n",
+    "    Branch C had more member customers than normal customers.\n",
+    "    \n",
+    "    Though quantities purchased for product lines generally are low, most customers prefer using Cash and Epay payment methods.\n",
+    "    \n",
+    "    Home and lifestyle gave the highest overall income followed closely by Sports and travel.\n",
+    "    \n",
+    "This is supported by the fact that home most home utilities are essentials used by a large population of people.\n",
+    "Documentation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "974dc7e2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From ba6c9c651d68cb8c8e31a83da28f0f22343ce228 Mon Sep 17 00:00:00 2001
From: Ochai2018 <45337793+Ochai2018@users.noreply.github.com>
Date: Sat, 27 Aug 2022 15:45:52 -0700
Subject: [PATCH 11/13] Delete Ochai Pandas Analytics project.ipynb

---
 Ochai Pandas Analytics project.ipynb | 2825 --------------------------
 1 file changed, 2825 deletions(-)
 delete mode 100644 Ochai Pandas Analytics project.ipynb

diff --git a/Ochai Pandas Analytics project.ipynb b/Ochai Pandas Analytics project.ipynb
deleted file mode 100644
index 0406677..0000000
--- a/Ochai Pandas Analytics project.ipynb	
+++ /dev/null
@@ -1,2825 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "b38ea413",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import glob\n",
-    "import pandas as pd\n",
-    "os.chdir('C:\\\\Users\\\\OCHAI ODEH\\\\Downloads\\\\Pandas')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "430a17ad",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Abuja_Branch.csv',\n",
-       " 'Lagos_Branch.csv',\n",
-       " 'Port_Harcourt_Branch.csv',\n",
-       " 'README.md',\n",
-       " 'Starter_notebook.ipynb']"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "os.listdir('C:\\\\Users\\\\OCHAI ODEH\\\\Downloads\\\\Pandas\\\\Pandas-Analytics-Project-main')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "575fe66a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[]"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import glob\n",
-    "\n",
-    "extension = 'csv'\n",
-    "glob.glob('C:/Users/OCHAI ODEH/Downloads/Pandas/Pandas-Analytics-Project-main extension')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "c53b13b3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Abuja_Branch.csv', 'Lagos_Branch.csv', 'Port_Harcourt_Branch.csv']\n"
-     ]
-    }
-   ],
-   "source": [
-    "path = 'C:\\\\Users\\\\OCHAI ODEH\\\\Downloads\\\\Pandas\\\\Pandas-Analytics-Project-main'\n",
-    "extension = 'csv'\n",
-    "os.chdir(path)\n",
-    "files = glob.glob('*.{}'.format(extension))\n",
-    "print(files)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "53d6d3de",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Pandas-Analytics-Project-main\\\\Abuja_Branch.csv', 'Pandas-Analytics-Project-main\\\\Lagos_Branch.csv', 'Pandas-Analytics-Project-main\\\\Port_Harcourt_Branch.csv']\n"
-     ]
-    }
-   ],
-   "source": [
-    "extension ='csv'\n",
-    "os.chdir('C:/Users/OCHAI ODEH/Downloads/Pandas/')\n",
-    "result = glob.glob( '*/**.csv' )\n",
-    "print( result )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "83e37982",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "path = 'C:\\\\Users\\\\OCHAI ODEH\\\\Downloads\\\\Pandas\\\\Pandas-Analytics-Project-main'\n",
-    "extension = 'csv'\n",
-    "os.chdir(path)\n",
-    "#combine files generated in list\n",
-    "files = glob.glob('*.{}'.format(extension))\n",
-    "#Export to csv\n",
-    "combined_csv = pd.concat([pd.read_csv(f) for f in files ])\n",
-    "combined_csv.to_csv( \"combined_csv.csv\", index=False, encoding='utf-8-sig')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "9f934cf1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "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>Invoice ID</th>\n",
-       "      <th>Branch</th>\n",
-       "      <th>City</th>\n",
-       "      <th>Customer type</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Product line</th>\n",
-       "      <th>Unit price</th>\n",
-       "      <th>Quantity</th>\n",
-       "      <th>Tax 5%</th>\n",
-       "      <th>Total</th>\n",
-       "      <th>Date</th>\n",
-       "      <th>Time</th>\n",
-       "      <th>Payment</th>\n",
-       "      <th>cogs</th>\n",
-       "      <th>gross margin percentage</th>\n",
-       "      <th>gross income</th>\n",
-       "      <th>Rating</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>692-92-5582</td>\n",
-       "      <td>B</td>\n",
-       "      <td>Abuja</td>\n",
-       "      <td>Member</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>Food and beverages</td>\n",
-       "      <td>19742.4</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2961.36</td>\n",
-       "      <td>62188.56</td>\n",
-       "      <td>2/20/2019</td>\n",
-       "      <td>13:27</td>\n",
-       "      <td>Card</td>\n",
-       "      <td>59227.2</td>\n",
-       "      <td>4.761905</td>\n",
-       "      <td>2961.36</td>\n",
-       "      <td>5.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>351-62-0822</td>\n",
-       "      <td>B</td>\n",
-       "      <td>Abuja</td>\n",
-       "      <td>Member</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>Fashion accessories</td>\n",
-       "      <td>5212.8</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1042.56</td>\n",
-       "      <td>21893.76</td>\n",
-       "      <td>2/6/2019</td>\n",
-       "      <td>18:07</td>\n",
-       "      <td>Epay</td>\n",
-       "      <td>20851.2</td>\n",
-       "      <td>4.761905</td>\n",
-       "      <td>1042.56</td>\n",
-       "      <td>4.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>529-56-3974</td>\n",
-       "      <td>B</td>\n",
-       "      <td>Abuja</td>\n",
-       "      <td>Member</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>Electronic accessories</td>\n",
-       "      <td>9183.6</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1836.72</td>\n",
-       "      <td>38571.12</td>\n",
-       "      <td>3/9/2019</td>\n",
-       "      <td>17:03</td>\n",
-       "      <td>Cash</td>\n",
-       "      <td>36734.4</td>\n",
-       "      <td>4.761905</td>\n",
-       "      <td>1836.72</td>\n",
-       "      <td>6.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>299-46-1805</td>\n",
-       "      <td>B</td>\n",
-       "      <td>Abuja</td>\n",
-       "      <td>Member</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>Sports and travel</td>\n",
-       "      <td>33739.2</td>\n",
-       "      <td>6</td>\n",
-       "      <td>10121.76</td>\n",
-       "      <td>212556.96</td>\n",
-       "      <td>1/15/2019</td>\n",
-       "      <td>16:19</td>\n",
-       "      <td>Cash</td>\n",
-       "      <td>202435.2</td>\n",
-       "      <td>4.761905</td>\n",
-       "      <td>10121.76</td>\n",
-       "      <td>4.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>319-50-3348</td>\n",
-       "      <td>B</td>\n",
-       "      <td>Abuja</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>Home and lifestyle</td>\n",
-       "      <td>14508.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1450.80</td>\n",
-       "      <td>30466.80</td>\n",
-       "      <td>3/11/2019</td>\n",
-       "      <td>15:30</td>\n",
-       "      <td>Epay</td>\n",
-       "      <td>29016.0</td>\n",
-       "      <td>4.761905</td>\n",
-       "      <td>1450.80</td>\n",
-       "      <td>4.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1995</th>\n",
-       "      <td>148-41-7930</td>\n",
-       "      <td>C</td>\n",
-       "      <td>Port Harcourt</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>Health and beauty</td>\n",
-       "      <td>35985.6</td>\n",
-       "      <td>7</td>\n",
-       "      <td>12594.96</td>\n",
-       "      <td>264494.16</td>\n",
-       "      <td>1/23/2019</td>\n",
-       "      <td>10:33</td>\n",
-       "      <td>Cash</td>\n",
-       "      <td>251899.2</td>\n",
-       "      <td>4.761905</td>\n",
-       "      <td>12594.96</td>\n",
-       "      <td>6.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1996</th>\n",
-       "      <td>189-40-5216</td>\n",
-       "      <td>C</td>\n",
-       "      <td>Port Harcourt</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>Electronic accessories</td>\n",
-       "      <td>34693.2</td>\n",
-       "      <td>7</td>\n",
-       "      <td>12142.62</td>\n",
-       "      <td>254995.02</td>\n",
-       "      <td>1/9/2019</td>\n",
-       "      <td>11:40</td>\n",
-       "      <td>Cash</td>\n",
-       "      <td>242852.4</td>\n",
-       "      <td>4.761905</td>\n",
-       "      <td>12142.62</td>\n",
-       "      <td>6.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1997</th>\n",
-       "      <td>267-62-7380</td>\n",
-       "      <td>C</td>\n",
-       "      <td>Port Harcourt</td>\n",
-       "      <td>Member</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>Electronic accessories</td>\n",
-       "      <td>29642.4</td>\n",
-       "      <td>10</td>\n",
-       "      <td>14821.20</td>\n",
-       "      <td>311245.20</td>\n",
-       "      <td>3/29/2019</td>\n",
-       "      <td>19:12</td>\n",
-       "      <td>Epay</td>\n",
-       "      <td>296424.0</td>\n",
-       "      <td>4.761905</td>\n",
-       "      <td>14821.20</td>\n",
-       "      <td>4.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1998</th>\n",
-       "      <td>652-49-6720</td>\n",
-       "      <td>C</td>\n",
-       "      <td>Port Harcourt</td>\n",
-       "      <td>Member</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>Electronic accessories</td>\n",
-       "      <td>21942.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1097.10</td>\n",
-       "      <td>23039.10</td>\n",
-       "      <td>2/18/2019</td>\n",
-       "      <td>11:40</td>\n",
-       "      <td>Epay</td>\n",
-       "      <td>21942.0</td>\n",
-       "      <td>4.761905</td>\n",
-       "      <td>1097.10</td>\n",
-       "      <td>5.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999</th>\n",
-       "      <td>233-67-5758</td>\n",
-       "      <td>C</td>\n",
-       "      <td>Port Harcourt</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>Health and beauty</td>\n",
-       "      <td>14526.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>726.30</td>\n",
-       "      <td>15252.30</td>\n",
-       "      <td>1/29/2019</td>\n",
-       "      <td>13:46</td>\n",
-       "      <td>Epay</td>\n",
-       "      <td>14526.0</td>\n",
-       "      <td>4.761905</td>\n",
-       "      <td>726.30</td>\n",
-       "      <td>6.2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2000 rows × 17 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       Invoice ID Branch           City Customer type  Gender  \\\n",
-       "0     692-92-5582      B          Abuja        Member  Female   \n",
-       "1     351-62-0822      B          Abuja        Member  Female   \n",
-       "2     529-56-3974      B          Abuja        Member    Male   \n",
-       "3     299-46-1805      B          Abuja        Member  Female   \n",
-       "4     319-50-3348      B          Abuja        Normal  Female   \n",
-       "...           ...    ...            ...           ...     ...   \n",
-       "1995  148-41-7930      C  Port Harcourt        Normal    Male   \n",
-       "1996  189-40-5216      C  Port Harcourt        Normal    Male   \n",
-       "1997  267-62-7380      C  Port Harcourt        Member    Male   \n",
-       "1998  652-49-6720      C  Port Harcourt        Member  Female   \n",
-       "1999  233-67-5758      C  Port Harcourt        Normal    Male   \n",
-       "\n",
-       "                Product line  Unit price  Quantity    Tax 5%      Total  \\\n",
-       "0         Food and beverages     19742.4         3   2961.36   62188.56   \n",
-       "1        Fashion accessories      5212.8         4   1042.56   21893.76   \n",
-       "2     Electronic accessories      9183.6         4   1836.72   38571.12   \n",
-       "3          Sports and travel     33739.2         6  10121.76  212556.96   \n",
-       "4         Home and lifestyle     14508.0         2   1450.80   30466.80   \n",
-       "...                      ...         ...       ...       ...        ...   \n",
-       "1995       Health and beauty     35985.6         7  12594.96  264494.16   \n",
-       "1996  Electronic accessories     34693.2         7  12142.62  254995.02   \n",
-       "1997  Electronic accessories     29642.4        10  14821.20  311245.20   \n",
-       "1998  Electronic accessories     21942.0         1   1097.10   23039.10   \n",
-       "1999       Health and beauty     14526.0         1    726.30   15252.30   \n",
-       "\n",
-       "           Date   Time Payment      cogs  gross margin percentage  \\\n",
-       "0     2/20/2019  13:27    Card   59227.2                 4.761905   \n",
-       "1      2/6/2019  18:07    Epay   20851.2                 4.761905   \n",
-       "2      3/9/2019  17:03    Cash   36734.4                 4.761905   \n",
-       "3     1/15/2019  16:19    Cash  202435.2                 4.761905   \n",
-       "4     3/11/2019  15:30    Epay   29016.0                 4.761905   \n",
-       "...         ...    ...     ...       ...                      ...   \n",
-       "1995  1/23/2019  10:33    Cash  251899.2                 4.761905   \n",
-       "1996   1/9/2019  11:40    Cash  242852.4                 4.761905   \n",
-       "1997  3/29/2019  19:12    Epay  296424.0                 4.761905   \n",
-       "1998  2/18/2019  11:40    Epay   21942.0                 4.761905   \n",
-       "1999  1/29/2019  13:46    Epay   14526.0                 4.761905   \n",
-       "\n",
-       "      gross income  Rating  \n",
-       "0          2961.36     5.9  \n",
-       "1          1042.56     4.5  \n",
-       "2          1836.72     6.8  \n",
-       "3         10121.76     4.5  \n",
-       "4          1450.80     4.4  \n",
-       "...            ...     ...  \n",
-       "1995      12594.96     6.1  \n",
-       "1996      12142.62     6.0  \n",
-       "1997      14821.20     4.3  \n",
-       "1998       1097.10     5.9  \n",
-       "1999        726.30     6.2  \n",
-       "\n",
-       "[2000 rows x 17 columns]"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Read the CSV file using pd.read_csv method\n",
-    "path = 'C:\\\\Users\\\\OCHAI ODEH\\\\Downloads\\\\Pandas\\\\Pandas-Analytics-Project-main'\n",
-    "extension = 'csv'\n",
-    "os.chdir(path)\n",
-    "#combine files generated in list\n",
-    "files = glob.glob('*.{}'.format(extension))\n",
-    "#Export to csv\n",
-    "combined_csv = pd.concat([pd.read_csv(f) for f in files ])\n",
-    "combined_csv.to_csv( \"combined_csv.csv\", index=False, encoding='utf-8-sig')\n",
-    "#Read the CSV file using pd.read_csv method\n",
-    "pd.read_csv(\"combined_csv.csv\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "da9651bb",
-   "metadata": {},
-   "source": [
-    "Data Exploration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "eefa7619",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "plt.style.use('fivethirtyeight')  \n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "167d6440",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = combined_csv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "ee95da01",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "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",
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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>Invoice ID</th>\n",
-       "      <th>Branch</th>\n",
-       "      <th>City</th>\n",
-       "      <th>Customer type</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Product line</th>\n",
-       "      <th>Unit price</th>\n",
-       "      <th>Quantity</th>\n",
-       "      <th>Tax 5%</th>\n",
-       "      <th>Total</th>\n",
-       "      <th>Date</th>\n",
-       "      <th>Time</th>\n",
-       "      <th>Payment</th>\n",
-       "      <th>cogs</th>\n",
-       "      <th>gross margin percentage</th>\n",
-       "      <th>gross income</th>\n",
-       "      <th>Rating</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>692-92-5582</td>\n",
-       "      <td>B</td>\n",
-       "      <td>Abuja</td>\n",
-       "      <td>Member</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>Food and beverages</td>\n",
-       "      <td>19742.4</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2961.36</td>\n",
-       "      <td>62188.56</td>\n",
-       "      <td>2/20/2019</td>\n",
-       "      <td>13:27</td>\n",
-       "      <td>Card</td>\n",
-       "      <td>59227.2</td>\n",
-       "      <td>4.761905</td>\n",
-       "      <td>2961.36</td>\n",
-       "      <td>5.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>351-62-0822</td>\n",
-       "      <td>B</td>\n",
-       "      <td>Abuja</td>\n",
-       "      <td>Member</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>Fashion accessories</td>\n",
-       "      <td>5212.8</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1042.56</td>\n",
-       "      <td>21893.76</td>\n",
-       "      <td>2/6/2019</td>\n",
-       "      <td>18:07</td>\n",
-       "      <td>Epay</td>\n",
-       "      <td>20851.2</td>\n",
-       "      <td>4.761905</td>\n",
-       "      <td>1042.56</td>\n",
-       "      <td>4.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>529-56-3974</td>\n",
-       "      <td>B</td>\n",
-       "      <td>Abuja</td>\n",
-       "      <td>Member</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>Electronic accessories</td>\n",
-       "      <td>9183.6</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1836.72</td>\n",
-       "      <td>38571.12</td>\n",
-       "      <td>3/9/2019</td>\n",
-       "      <td>17:03</td>\n",
-       "      <td>Cash</td>\n",
-       "      <td>36734.4</td>\n",
-       "      <td>4.761905</td>\n",
-       "      <td>1836.72</td>\n",
-       "      <td>6.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>299-46-1805</td>\n",
-       "      <td>B</td>\n",
-       "      <td>Abuja</td>\n",
-       "      <td>Member</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>Sports and travel</td>\n",
-       "      <td>33739.2</td>\n",
-       "      <td>6</td>\n",
-       "      <td>10121.76</td>\n",
-       "      <td>212556.96</td>\n",
-       "      <td>1/15/2019</td>\n",
-       "      <td>16:19</td>\n",
-       "      <td>Cash</td>\n",
-       "      <td>202435.2</td>\n",
-       "      <td>4.761905</td>\n",
-       "      <td>10121.76</td>\n",
-       "      <td>4.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>319-50-3348</td>\n",
-       "      <td>B</td>\n",
-       "      <td>Abuja</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>Home and lifestyle</td>\n",
-       "      <td>14508.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1450.80</td>\n",
-       "      <td>30466.80</td>\n",
-       "      <td>3/11/2019</td>\n",
-       "      <td>15:30</td>\n",
-       "      <td>Epay</td>\n",
-       "      <td>29016.0</td>\n",
-       "      <td>4.761905</td>\n",
-       "      <td>1450.80</td>\n",
-       "      <td>4.4</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Invoice ID Branch   City Customer type  Gender            Product line  \\\n",
-       "0  692-92-5582      B  Abuja        Member  Female      Food and beverages   \n",
-       "1  351-62-0822      B  Abuja        Member  Female     Fashion accessories   \n",
-       "2  529-56-3974      B  Abuja        Member    Male  Electronic accessories   \n",
-       "3  299-46-1805      B  Abuja        Member  Female       Sports and travel   \n",
-       "4  319-50-3348      B  Abuja        Normal  Female      Home and lifestyle   \n",
-       "\n",
-       "   Unit price  Quantity    Tax 5%      Total       Date   Time Payment  \\\n",
-       "0     19742.4         3   2961.36   62188.56  2/20/2019  13:27    Card   \n",
-       "1      5212.8         4   1042.56   21893.76   2/6/2019  18:07    Epay   \n",
-       "2      9183.6         4   1836.72   38571.12   3/9/2019  17:03    Cash   \n",
-       "3     33739.2         6  10121.76  212556.96  1/15/2019  16:19    Cash   \n",
-       "4     14508.0         2   1450.80   30466.80  3/11/2019  15:30    Epay   \n",
-       "\n",
-       "       cogs  gross margin percentage  gross income  Rating  \n",
-       "0   59227.2                 4.761905       2961.36     5.9  \n",
-       "1   20851.2                 4.761905       1042.56     4.5  \n",
-       "2   36734.4                 4.761905       1836.72     6.8  \n",
-       "3  202435.2                 4.761905      10121.76     4.5  \n",
-       "4   29016.0                 4.761905       1450.80     4.4  "
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Use the head() method to view first few rows of the dataset\n",
-    "df = combined_csv\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "7693af83",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of rows is 2000\n",
-      "Number of columns is 17\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Check the number of rows and columns present in the data using the shape attribute\n",
-    "row_count = df.shape[0]  # Gives number of rows\n",
-    "col_count = df.shape[1] # Gives number of columns\n",
-    "print('Number of rows is', row_count)\n",
-    "print('Number of columns is', col_count)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "f5a5e830",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Index(['Invoice ID', 'Branch', 'City', 'Customer type', 'Gender',\n",
-      "       'Product line', 'Unit price', 'Quantity', 'Tax 5%', 'Total', 'Date',\n",
-      "       'Time', 'Payment', 'cogs', 'gross margin percentage', 'gross income',\n",
-      "       'Rating'],\n",
-      "      dtype='object')\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Generate the names of the columns using the columns attribute.\n",
-    "\n",
-    "column_labels = df.columns   #return the column labels\n",
-    "print(column_labels)         # Print the result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "50a3a974",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "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>Unit price</th>\n",
-       "      <th>Quantity</th>\n",
-       "      <th>Tax 5%</th>\n",
-       "      <th>Total</th>\n",
-       "      <th>cogs</th>\n",
-       "      <th>gross margin percentage</th>\n",
-       "      <th>gross income</th>\n",
-       "      <th>Rating</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.00000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2.000000e+03</td>\n",
-       "      <td>2000.00000</td>\n",
-       "      <td>2000.00000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>20041.966800</td>\n",
-       "      <td>5.510000</td>\n",
-       "      <td>5536.57284</td>\n",
-       "      <td>116268.029640</td>\n",
-       "      <td>110731.456800</td>\n",
-       "      <td>4.761905e+00</td>\n",
-       "      <td>5536.57284</td>\n",
-       "      <td>6.97270</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>9535.680197</td>\n",
-       "      <td>2.922699</td>\n",
-       "      <td>4214.12272</td>\n",
-       "      <td>88496.577116</td>\n",
-       "      <td>84282.454396</td>\n",
-       "      <td>5.419243e-14</td>\n",
-       "      <td>4214.12272</td>\n",
-       "      <td>1.71815</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>3628.800000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>183.06000</td>\n",
-       "      <td>3844.260000</td>\n",
-       "      <td>3661.200000</td>\n",
-       "      <td>4.761905e+00</td>\n",
-       "      <td>183.06000</td>\n",
-       "      <td>4.00000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>11835.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>2132.95500</td>\n",
-       "      <td>44792.055000</td>\n",
-       "      <td>42659.100000</td>\n",
-       "      <td>4.761905e+00</td>\n",
-       "      <td>2132.95500</td>\n",
-       "      <td>5.50000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>19882.800000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>4351.68000</td>\n",
-       "      <td>91385.280000</td>\n",
-       "      <td>87033.600000</td>\n",
-       "      <td>4.761905e+00</td>\n",
-       "      <td>4351.68000</td>\n",
-       "      <td>7.00000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>28056.600000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>8080.29000</td>\n",
-       "      <td>169686.090000</td>\n",
-       "      <td>161605.800000</td>\n",
-       "      <td>4.761905e+00</td>\n",
-       "      <td>8080.29000</td>\n",
-       "      <td>8.50000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>35985.600000</td>\n",
-       "      <td>10.000000</td>\n",
-       "      <td>17874.00000</td>\n",
-       "      <td>375354.000000</td>\n",
-       "      <td>357480.000000</td>\n",
-       "      <td>4.761905e+00</td>\n",
-       "      <td>17874.00000</td>\n",
-       "      <td>10.00000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Unit price     Quantity       Tax 5%          Total           cogs  \\\n",
-       "count   2000.000000  2000.000000   2000.00000    2000.000000    2000.000000   \n",
-       "mean   20041.966800     5.510000   5536.57284  116268.029640  110731.456800   \n",
-       "std     9535.680197     2.922699   4214.12272   88496.577116   84282.454396   \n",
-       "min     3628.800000     1.000000    183.06000    3844.260000    3661.200000   \n",
-       "25%    11835.000000     3.000000   2132.95500   44792.055000   42659.100000   \n",
-       "50%    19882.800000     5.000000   4351.68000   91385.280000   87033.600000   \n",
-       "75%    28056.600000     8.000000   8080.29000  169686.090000  161605.800000   \n",
-       "max    35985.600000    10.000000  17874.00000  375354.000000  357480.000000   \n",
-       "\n",
-       "       gross margin percentage  gross income      Rating  \n",
-       "count             2.000000e+03    2000.00000  2000.00000  \n",
-       "mean              4.761905e+00    5536.57284     6.97270  \n",
-       "std               5.419243e-14    4214.12272     1.71815  \n",
-       "min               4.761905e+00     183.06000     4.00000  \n",
-       "25%               4.761905e+00    2132.95500     5.50000  \n",
-       "50%               4.761905e+00    4351.68000     7.00000  \n",
-       "75%               4.761905e+00    8080.29000     8.50000  \n",
-       "max               4.761905e+00   17874.00000    10.00000  "
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Statistical summary\n",
-    "df.describe()\n",
-    "# Below this cell write in few sentences what you can derive from the data statistical summary\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "fd085f6e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "From the 2000 records, maximum sales of 10.000000 products and minimum sale of 1.000000 product was recorded.\n",
-      "Taxes of 5% on all products are equivalent to the gross incomes from sales.\n",
-      "The company's maximum gross income was 17874.0000 for 10 products has the highest rating.\n",
-      "Only one product with minimum unit price of 3628.800000 giving the lowest gross income of 183.06000 also has the lowest rating.\n",
-      "There are large differences in unit prices of products, Taxes of 5% and gross incomes, going by their respective value of standard deviations of 9534.487865 and 4213.59579 respectively.\n",
-      "Gross margin percentages for all variable are almost the same. There are no missing values in the table.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"From the 2000 records, maximum sales of 10.000000 products and minimum sale of 1.000000 product was recorded.\") \n",
-    "      \n",
-    "print(\"Taxes of 5% on all products are equivalent to the gross incomes from sales.\") \n",
-    "      \n",
-    "print(\"The company's maximum gross income was 17874.0000 for 10 products has the highest rating.\")\n",
-    "      \n",
-    "print(\"Only one product with minimum unit price of 3628.800000 giving the lowest gross income of 183.06000 also has the lowest rating.\")\n",
-    "      \n",
-    "print(\"There are large differences in unit prices of products, Taxes of 5% and gross incomes, going by their respective value of standard deviations of 9534.487865 and 4213.59579 respectively.\")      \n",
-    "\n",
-    "print(\"Gross margin percentages for all variable are almost the same. There are no missing values in the table.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "7746babf",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "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>Invoice ID</th>\n",
-       "      <th>Branch</th>\n",
-       "      <th>City</th>\n",
-       "      <th>Customer type</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Product line</th>\n",
-       "      <th>Unit price</th>\n",
-       "      <th>Quantity</th>\n",
-       "      <th>Tax 5%</th>\n",
-       "      <th>Total</th>\n",
-       "      <th>Date</th>\n",
-       "      <th>Time</th>\n",
-       "      <th>Payment</th>\n",
-       "      <th>cogs</th>\n",
-       "      <th>gross margin percentage</th>\n",
-       "      <th>gross income</th>\n",
-       "      <th>Rating</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>323</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>324</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>325</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>326</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>327</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2000 rows × 17 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     Invoice ID  Branch   City  Customer type  Gender  Product line  \\\n",
-       "0         False   False  False          False   False         False   \n",
-       "1         False   False  False          False   False         False   \n",
-       "2         False   False  False          False   False         False   \n",
-       "3         False   False  False          False   False         False   \n",
-       "4         False   False  False          False   False         False   \n",
-       "..          ...     ...    ...            ...     ...           ...   \n",
-       "323       False   False  False          False   False         False   \n",
-       "324       False   False  False          False   False         False   \n",
-       "325       False   False  False          False   False         False   \n",
-       "326       False   False  False          False   False         False   \n",
-       "327       False   False  False          False   False         False   \n",
-       "\n",
-       "     Unit price  Quantity  Tax 5%  Total   Date   Time  Payment   cogs  \\\n",
-       "0         False     False   False  False  False  False    False  False   \n",
-       "1         False     False   False  False  False  False    False  False   \n",
-       "2         False     False   False  False  False  False    False  False   \n",
-       "3         False     False   False  False  False  False    False  False   \n",
-       "4         False     False   False  False  False  False    False  False   \n",
-       "..          ...       ...     ...    ...    ...    ...      ...    ...   \n",
-       "323       False     False   False  False  False  False    False  False   \n",
-       "324       False     False   False  False  False  False    False  False   \n",
-       "325       False     False   False  False  False  False    False  False   \n",
-       "326       False     False   False  False  False  False    False  False   \n",
-       "327       False     False   False  False  False  False    False  False   \n",
-       "\n",
-       "     gross margin percentage  gross income  Rating  \n",
-       "0                      False         False   False  \n",
-       "1                      False         False   False  \n",
-       "2                      False         False   False  \n",
-       "3                      False         False   False  \n",
-       "4                      False         False   False  \n",
-       "..                       ...           ...     ...  \n",
-       "323                    False         False   False  \n",
-       "324                    False         False   False  \n",
-       "325                    False         False   False  \n",
-       "326                    False         False   False  \n",
-       "327                    False         False   False  \n",
-       "\n",
-       "[2000 rows x 17 columns]"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Below this cell write in few sentences what you can derive from the data statistical summary\n",
-    "#missing values\n",
-    "df.isnull()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "aeaf6f79",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     Invoice ID  Branch   City  Customer type  Gender  Product line  \\\n",
-      "0         False   False  False          False   False         False   \n",
-      "1         False   False  False          False   False         False   \n",
-      "2         False   False  False          False   False         False   \n",
-      "3         False   False  False          False   False         False   \n",
-      "4         False   False  False          False   False         False   \n",
-      "..          ...     ...    ...            ...     ...           ...   \n",
-      "323       False   False  False          False   False         False   \n",
-      "324       False   False  False          False   False         False   \n",
-      "325       False   False  False          False   False         False   \n",
-      "326       False   False  False          False   False         False   \n",
-      "327       False   False  False          False   False         False   \n",
-      "\n",
-      "     Unit price  Quantity  Tax 5%  Total   Date   Time  Payment   cogs  \\\n",
-      "0         False     False   False  False  False  False    False  False   \n",
-      "1         False     False   False  False  False  False    False  False   \n",
-      "2         False     False   False  False  False  False    False  False   \n",
-      "3         False     False   False  False  False  False    False  False   \n",
-      "4         False     False   False  False  False  False    False  False   \n",
-      "..          ...       ...     ...    ...    ...    ...      ...    ...   \n",
-      "323       False     False   False  False  False  False    False  False   \n",
-      "324       False     False   False  False  False  False    False  False   \n",
-      "325       False     False   False  False  False  False    False  False   \n",
-      "326       False     False   False  False  False  False    False  False   \n",
-      "327       False     False   False  False  False  False    False  False   \n",
-      "\n",
-      "     gross margin percentage  gross income  Rating  \n",
-      "0                      False         False   False  \n",
-      "1                      False         False   False  \n",
-      "2                      False         False   False  \n",
-      "3                      False         False   False  \n",
-      "4                      False         False   False  \n",
-      "..                       ...           ...     ...  \n",
-      "323                    False         False   False  \n",
-      "324                    False         False   False  \n",
-      "325                    False         False   False  \n",
-      "326                    False         False   False  \n",
-      "327                    False         False   False  \n",
-      "\n",
-      "[2000 rows x 17 columns]\n",
-      "     Invoice ID  Branch  City  Customer type  Gender  Product line  \\\n",
-      "0          True    True  True           True    True          True   \n",
-      "1          True    True  True           True    True          True   \n",
-      "2          True    True  True           True    True          True   \n",
-      "3          True    True  True           True    True          True   \n",
-      "4          True    True  True           True    True          True   \n",
-      "..          ...     ...   ...            ...     ...           ...   \n",
-      "323        True    True  True           True    True          True   \n",
-      "324        True    True  True           True    True          True   \n",
-      "325        True    True  True           True    True          True   \n",
-      "326        True    True  True           True    True          True   \n",
-      "327        True    True  True           True    True          True   \n",
-      "\n",
-      "     Unit price  Quantity  Tax 5%  Total  Date  Time  Payment  cogs  \\\n",
-      "0          True      True    True   True  True  True     True  True   \n",
-      "1          True      True    True   True  True  True     True  True   \n",
-      "2          True      True    True   True  True  True     True  True   \n",
-      "3          True      True    True   True  True  True     True  True   \n",
-      "4          True      True    True   True  True  True     True  True   \n",
-      "..          ...       ...     ...    ...   ...   ...      ...   ...   \n",
-      "323        True      True    True   True  True  True     True  True   \n",
-      "324        True      True    True   True  True  True     True  True   \n",
-      "325        True      True    True   True  True  True     True  True   \n",
-      "326        True      True    True   True  True  True     True  True   \n",
-      "327        True      True    True   True  True  True     True  True   \n",
-      "\n",
-      "     gross margin percentage  gross income  Rating  \n",
-      "0                       True          True    True  \n",
-      "1                       True          True    True  \n",
-      "2                       True          True    True  \n",
-      "3                       True          True    True  \n",
-      "4                       True          True    True  \n",
-      "..                       ...           ...     ...  \n",
-      "323                     True          True    True  \n",
-      "324                     True          True    True  \n",
-      "325                     True          True    True  \n",
-      "326                     True          True    True  \n",
-      "327                     True          True    True  \n",
-      "\n",
-      "[2000 rows x 17 columns]\n",
-      "if any missing values = False\n",
-      "Number of non-missing values in variables =\n",
-      "Invoice ID                 2000\n",
-      "Branch                     2000\n",
-      "City                       2000\n",
-      "Customer type              2000\n",
-      "Gender                     2000\n",
-      "Product line               2000\n",
-      "Unit price                 2000\n",
-      "Quantity                   2000\n",
-      "Tax 5%                     2000\n",
-      "Total                      2000\n",
-      "Date                       2000\n",
-      "Time                       2000\n",
-      "Payment                    2000\n",
-      "cogs                       2000\n",
-      "gross margin percentage    2000\n",
-      "gross income               2000\n",
-      "Rating                     2000\n",
-      "dtype: int64\n",
-      "Number of missing values in data= 0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# missing values \n",
-    "missing_value_in_df = df.isnull()  # checking missing values\n",
-    "non_missing_valuein_df = df.notnull()  # checking non-missing values\n",
-    "any_missing_values_in_df = df.isnull().values.any()  # only want to know if there are any missing values\n",
-    "number_non_missing_values_in_variables = df.notnull().sum()  # knowling number of non-missing values for each variable\n",
-    "missing_values_in_data = df.isnull().sum().sum()  # knowing how many missing values in the data\n",
-    "print(missing_value_in_df)\n",
-    "print(non_missing_valuein_df)\n",
-    "print('if any missing values =',any_missing_values_in_df)\n",
-    "print('Number of non-missing values in variables =')\n",
-    "print(number_non_missing_values_in_variables)\n",
-    "print('Number of missing values in data=', missing_values_in_data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "c3372d69",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "Int64Index: 2000 entries, 0 to 327\n",
-      "Data columns (total 17 columns):\n",
-      " #   Column                   Non-Null Count  Dtype  \n",
-      "---  ------                   --------------  -----  \n",
-      " 0   Invoice ID               2000 non-null   object \n",
-      " 1   Branch                   2000 non-null   object \n",
-      " 2   City                     2000 non-null   object \n",
-      " 3   Customer type            2000 non-null   object \n",
-      " 4   Gender                   2000 non-null   object \n",
-      " 5   Product line             2000 non-null   object \n",
-      " 6   Unit price               2000 non-null   float64\n",
-      " 7   Quantity                 2000 non-null   int64  \n",
-      " 8   Tax 5%                   2000 non-null   float64\n",
-      " 9   Total                    2000 non-null   float64\n",
-      " 10  Date                     2000 non-null   object \n",
-      " 11  Time                     2000 non-null   object \n",
-      " 12  Payment                  2000 non-null   object \n",
-      " 13  cogs                     2000 non-null   float64\n",
-      " 14  gross margin percentage  2000 non-null   float64\n",
-      " 15  gross income             2000 non-null   float64\n",
-      " 16  Rating                   2000 non-null   float64\n",
-      "dtypes: float64(7), int64(1), object(9)\n",
-      "memory usage: 281.2+ KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Data Information\n",
-    "df.info()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "14b98112",
-   "metadata": {},
-   "source": [
-    "Dealing with DateTime Features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "602132d9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import datetime as dt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "0d293afb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Use to_datetime() to convert the date column to datetime\n",
-    "df['Date'] =  pd.to_datetime(df['Date'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "65c46944",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "Int64Index: 2000 entries, 0 to 327\n",
-      "Data columns (total 17 columns):\n",
-      " #   Column                   Non-Null Count  Dtype         \n",
-      "---  ------                   --------------  -----         \n",
-      " 0   Invoice ID               2000 non-null   object        \n",
-      " 1   Branch                   2000 non-null   object        \n",
-      " 2   City                     2000 non-null   object        \n",
-      " 3   Customer type            2000 non-null   object        \n",
-      " 4   Gender                   2000 non-null   object        \n",
-      " 5   Product line             2000 non-null   object        \n",
-      " 6   Unit price               2000 non-null   float64       \n",
-      " 7   Quantity                 2000 non-null   int64         \n",
-      " 8   Tax 5%                   2000 non-null   float64       \n",
-      " 9   Total                    2000 non-null   float64       \n",
-      " 10  Date                     2000 non-null   datetime64[ns]\n",
-      " 11  Time                     2000 non-null   object        \n",
-      " 12  Payment                  2000 non-null   object        \n",
-      " 13  cogs                     2000 non-null   float64       \n",
-      " 14  gross margin percentage  2000 non-null   float64       \n",
-      " 15  gross income             2000 non-null   float64       \n",
-      " 16  Rating                   2000 non-null   float64       \n",
-      "dtypes: datetime64[ns](1), float64(7), int64(1), object(8)\n",
-      "memory usage: 281.2+ KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Check the datatype to confirm if it's in datetime\n",
-    "\n",
-    "df.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "de57c399",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Repeat the two steps above to the time column\n",
-    "#Use to_datetime() to convert the Time column to datetime\n",
-    "df['Time'] =  pd.to_datetime(df['Time'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "5555c271",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "Int64Index: 2000 entries, 0 to 327\n",
-      "Data columns (total 17 columns):\n",
-      " #   Column                   Non-Null Count  Dtype         \n",
-      "---  ------                   --------------  -----         \n",
-      " 0   Invoice ID               2000 non-null   object        \n",
-      " 1   Branch                   2000 non-null   object        \n",
-      " 2   City                     2000 non-null   object        \n",
-      " 3   Customer type            2000 non-null   object        \n",
-      " 4   Gender                   2000 non-null   object        \n",
-      " 5   Product line             2000 non-null   object        \n",
-      " 6   Unit price               2000 non-null   float64       \n",
-      " 7   Quantity                 2000 non-null   int64         \n",
-      " 8   Tax 5%                   2000 non-null   float64       \n",
-      " 9   Total                    2000 non-null   float64       \n",
-      " 10  Date                     2000 non-null   datetime64[ns]\n",
-      " 11  Time                     2000 non-null   datetime64[ns]\n",
-      " 12  Payment                  2000 non-null   object        \n",
-      " 13  cogs                     2000 non-null   float64       \n",
-      " 14  gross margin percentage  2000 non-null   float64       \n",
-      " 15  gross income             2000 non-null   float64       \n",
-      " 16  Rating                   2000 non-null   float64       \n",
-      "dtypes: datetime64[ns](2), float64(7), int64(1), object(7)\n",
-      "memory usage: 281.2+ KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Check the datatype to confirm if Time is in datetime format\n",
-    "df.info()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9ba46f98",
-   "metadata": {},
-   "source": [
-    "Extract Features from date & time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "e9794b23",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Extract the Day feature from the Date column, and save to a new Day column\n",
-    "df['Day'] = df['Date'].dt.day"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "ac649948",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Extract the Month feature from the Date column, and save to a new Month column\n",
-    "df['Month'] = df['Date'].dt.month"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "eba64151",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Extract the Year feature from the Date column, and save to a new Year column\n",
-    "df['Year'] = df['Date'].dt.year"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "eb35f761",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Extract the Hour feature from the Time column and save to a new Hour column\n",
-    "df['Hour'] = df['Time'].dt.hour"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "d6e99530",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of unique hour of sales = 11\n"
-     ]
-    }
-   ],
-   "source": [
-    "#From the hours information, determine the numbers of unique hours of sales in the supermarket An array of the hours using the unique() method\n",
-    "Number_unique_of_sales = df['Hour'].nunique()\n",
-    "print('Number of unique hour of sales =', Number_unique_of_sales)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "908a3ec4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([13, 18, 17, 16, 15, 10, 12, 19, 14, 11, 20], dtype=int64)"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# An array of unique hours using the unique() method\n",
-    "df['Hour'].unique()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bca7fc0c",
-   "metadata": {},
-   "source": [
-    "Unique Values in Columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "3953fff2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Invoice ID',\n",
-       " 'Branch',\n",
-       " 'City',\n",
-       " 'Customer type',\n",
-       " 'Gender',\n",
-       " 'Product line',\n",
-       " 'Payment']"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Uncomment the code and Run it\n",
-    "categorical_columns = [col for col in df.columns if df[col].dtype == \"object\" ]\n",
-    "categorical_columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "434e701c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Card', 'Epay', 'Cash']"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# generate the unique values in the categorical column\n",
-    "df['Invoice ID'].unique().tolist()\n",
-    "df['City'].unique().tolist()\n",
-    "df['Customer type'].unique().tolist()\n",
-    "df['Gender'].unique().tolist()\n",
-    "df['Product line'].unique().tolist()\n",
-    "df['Payment'].unique().tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "ff10bc98",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Abuja', 'Lagos', 'Port Harcourt']"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['City'].unique().tolist()\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "50292242",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Member', 'Normal']"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Customer type'].unique().tolist()\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "d46e9bc9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Female', 'Male']"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Gender'].unique().tolist()\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "6e4a7a92",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Food and beverages',\n",
-       " 'Fashion accessories',\n",
-       " 'Electronic accessories',\n",
-       " 'Sports and travel',\n",
-       " 'Home and lifestyle',\n",
-       " 'Health and beauty']"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Product line'].unique().tolist()\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "4c05bb08",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Card', 'Epay', 'Cash']"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Payment'].unique().tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "f6280742",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total Number of unique values in the Invoice ID Column: 1000\n",
-      "Total Number of unique values in the City Column: 3\n",
-      "Total Number of unique values in the Customer type Column: 2\n",
-      "Total Number of unique values in the Gender Column: 2\n",
-      "Total Number of unique values in the Product line Column: 6\n",
-      "Total Number of unique values in the Payment Column: 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Number of unique vales in each categorical column\n",
-    "print(\"Total Number of unique values in the Invoice ID Column: {}\". format(len(df['Invoice ID'].unique().tolist())))\n",
-    "\n",
-    "print(\"Total Number of unique values in the City Column: {}\". format(len(df['City'].unique().tolist())))\n",
-    "\n",
-    "print(\"Total Number of unique values in the Customer type Column: {}\". format(len(df['Customer type'].unique().tolist())))\n",
-    "\n",
-    "print(\"Total Number of unique values in the Gender Column: {}\". format(len(df['Gender'].unique().tolist())))\n",
-    "\n",
-    "print(\"Total Number of unique values in the Product line Column: {}\". format(len(df['Product line'].unique().tolist())))\n",
-    "\n",
-    "print(\"Total Number of unique values in the Payment Column: {}\". format(len(df['Payment'].unique().tolist())))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "0b0708c1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "A    680\n",
-       "B    664\n",
-       "C    656\n",
-       "Name: Branch, dtype: int64"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Branch'].value_counts()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "1ae0b514",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Lagos            680\n",
-       "Abuja            664\n",
-       "Port Harcourt    656\n",
-       "Name: City, dtype: int64"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['City'].value_counts()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "450792ae",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Member    1002\n",
-       "Normal     998\n",
-       "Name: Customer type, dtype: int64"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Customer type'].value_counts()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "3f814cc4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Female    1002\n",
-       "Male       998\n",
-       "Name: Gender, dtype: int64"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Gender'].value_counts()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "a8a01de7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Fashion accessories       356\n",
-       "Food and beverages        348\n",
-       "Electronic accessories    340\n",
-       "Sports and travel         332\n",
-       "Home and lifestyle        320\n",
-       "Health and beauty         304\n",
-       "Name: Product line, dtype: int64"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Product line'].value_counts()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "530f5a71",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Epay    690\n",
-       "Cash    688\n",
-       "Card    622\n",
-       "Name: Payment, dtype: int64"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Payment'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "30c29a6d",
-   "metadata": {},
-   "source": [
-    "Aggregration with GroupBy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "dce978f6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "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 tr th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead tr:last-of-type th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th colspan=\"2\" halign=\"left\">Unit price</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">Quantity</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">Tax 5%</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">Total</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">cogs</th>\n",
-       "      <th>...</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">Rating</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">Day</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">Month</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">Year</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">Hour</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th>sum</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>sum</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>sum</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>sum</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>sum</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>...</th>\n",
-       "      <th>sum</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>sum</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>sum</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>sum</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>sum</th>\n",
-       "      <th>mean</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>City</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Abuja</th>\n",
-       "      <td>13304793.6</td>\n",
-       "      <td>20037.339759</td>\n",
-       "      <td>3640</td>\n",
-       "      <td>5.481928</td>\n",
-       "      <td>3641063.04</td>\n",
-       "      <td>5483.528675</td>\n",
-       "      <td>76462323.84</td>\n",
-       "      <td>115154.102169</td>\n",
-       "      <td>72821260.8</td>\n",
-       "      <td>109670.573494</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4527.2</td>\n",
-       "      <td>6.818072</td>\n",
-       "      <td>10136</td>\n",
-       "      <td>15.265060</td>\n",
-       "      <td>1330</td>\n",
-       "      <td>2.003012</td>\n",
-       "      <td>1340616</td>\n",
-       "      <td>2019.0</td>\n",
-       "      <td>10058</td>\n",
-       "      <td>15.147590</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Lagos</th>\n",
-       "      <td>13410352.8</td>\n",
-       "      <td>19721.107059</td>\n",
-       "      <td>3718</td>\n",
-       "      <td>5.467647</td>\n",
-       "      <td>3641155.56</td>\n",
-       "      <td>5354.640529</td>\n",
-       "      <td>76464266.76</td>\n",
-       "      <td>112447.451118</td>\n",
-       "      <td>72823111.2</td>\n",
-       "      <td>107092.810588</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4778.4</td>\n",
-       "      <td>7.027059</td>\n",
-       "      <td>10464</td>\n",
-       "      <td>15.388235</td>\n",
-       "      <td>1376</td>\n",
-       "      <td>2.023529</td>\n",
-       "      <td>1372920</td>\n",
-       "      <td>2019.0</td>\n",
-       "      <td>9974</td>\n",
-       "      <td>14.667647</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Port Harcourt</th>\n",
-       "      <td>13368787.2</td>\n",
-       "      <td>20379.248780</td>\n",
-       "      <td>3662</td>\n",
-       "      <td>5.582317</td>\n",
-       "      <td>3790927.08</td>\n",
-       "      <td>5778.852256</td>\n",
-       "      <td>79609468.68</td>\n",
-       "      <td>121355.897378</td>\n",
-       "      <td>75818541.6</td>\n",
-       "      <td>115577.045122</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4639.8</td>\n",
-       "      <td>7.072866</td>\n",
-       "      <td>9912</td>\n",
-       "      <td>15.109756</td>\n",
-       "      <td>1280</td>\n",
-       "      <td>1.951220</td>\n",
-       "      <td>1324464</td>\n",
-       "      <td>2019.0</td>\n",
-       "      <td>9788</td>\n",
-       "      <td>14.920732</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>3 rows × 24 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               Unit price               Quantity                Tax 5%  \\\n",
-       "                      sum          mean      sum      mean         sum   \n",
-       "City                                                                     \n",
-       "Abuja          13304793.6  20037.339759     3640  5.481928  3641063.04   \n",
-       "Lagos          13410352.8  19721.107059     3718  5.467647  3641155.56   \n",
-       "Port Harcourt  13368787.2  20379.248780     3662  5.582317  3790927.08   \n",
-       "\n",
-       "                                  Total                       cogs  \\\n",
-       "                      mean          sum           mean         sum   \n",
-       "City                                                                 \n",
-       "Abuja          5483.528675  76462323.84  115154.102169  72821260.8   \n",
-       "Lagos          5354.640529  76464266.76  112447.451118  72823111.2   \n",
-       "Port Harcourt  5778.852256  79609468.68  121355.897378  75818541.6   \n",
-       "\n",
-       "                              ...  Rating              Day            Month  \\\n",
-       "                        mean  ...     sum      mean    sum       mean   sum   \n",
-       "City                          ...                                             \n",
-       "Abuja          109670.573494  ...  4527.2  6.818072  10136  15.265060  1330   \n",
-       "Lagos          107092.810588  ...  4778.4  7.027059  10464  15.388235  1376   \n",
-       "Port Harcourt  115577.045122  ...  4639.8  7.072866   9912  15.109756  1280   \n",
-       "\n",
-       "                            Year           Hour             \n",
-       "                   mean      sum    mean    sum       mean  \n",
-       "City                                                        \n",
-       "Abuja          2.003012  1340616  2019.0  10058  15.147590  \n",
-       "Lagos          2.023529  1372920  2019.0   9974  14.667647  \n",
-       "Port Harcourt  1.951220  1324464  2019.0   9788  14.920732  \n",
-       "\n",
-       "[3 rows x 24 columns]"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Your task here, will be to create a groupby object with the \"City Column\", and aggregation function of sum and mean\n",
-    "# Creating a groupby object to group the dataset according to City \n",
-    "city = df.groupby(\"City\")\n",
-    "\n",
-    "#To get the sum and mean of the numeric columns \n",
-    "city.agg([\"sum\", \"mean\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "20f29c5e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Using the groupby object, display a table that shows the gross income of each city, and determine the city with the highest total gross income.\n",
-    "# Using the groupby object ot get the gross income of each city\n",
-    "\n",
-    "gross_income_city = city['gross income'].sum().reset_index()\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "b9da4b59",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "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>City</th>\n",
-       "      <th>gross income</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Abuja</td>\n",
-       "      <td>3641063.04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Lagos</td>\n",
-       "      <td>3641155.56</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Port Harcourt</td>\n",
-       "      <td>3790927.08</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            City  gross income\n",
-       "0          Abuja    3641063.04\n",
-       "1          Lagos    3641155.56\n",
-       "2  Port Harcourt    3790927.08"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gross_income_city"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "c5aa1332",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The highest gross income is generated by Port Harcourt: 3790927.08\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"The highest gross income is generated by Port Harcourt:\", df.groupby('City')['gross income'].sum().max())\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "0785c9c2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "City\n",
-       "Abuja            13304793.6\n",
-       "Lagos            13410352.8\n",
-       "Port Harcourt    13368787.2\n",
-       "Name: Unit price, dtype: float64"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.groupby('City')['Unit price'].sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "bf501f0c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The highest Unit price: 13410352.8\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"The highest Unit price:\", df.groupby('City')['Unit price'].sum().max())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "d37fcab8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "City\n",
-       "Abuja            3640\n",
-       "Lagos            3718\n",
-       "Port Harcourt    3662\n",
-       "Name: Quantity, dtype: int64"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.groupby('City')['Quantity'].sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "1b6c4491",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The highest Quantity: 3718\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"The highest Quantity:\", df.groupby('City')['Quantity'].sum().max())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dc1d928a",
-   "metadata": {},
-   "source": [
-    "Data Visualization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "36da6213",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Branch A has the highest sales record.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Using countplot, determine the branch with the highest sales record\n",
-    "sns.countplot(x= 'Branch', data = df).set_title('Sales record for Branches')\n",
-    "print('Branch A has the highest sales record.')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "28931aa1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The most used payment method is Epay with a count of 690\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# most used payment method,\n",
-    "sns.countplot(x='Payment', data=df).set_title('Payment method')\n",
-    "print('The most used payment method is Epay with a count of 690')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "ac3365f1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The City with the most sales is Lagos\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#city with the most sales\n",
-    "sns.countplot(x = 'City', data = df).set_title('Sales across cities')\n",
-    "print('The City with the most sales is Lagos')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "caa9edd4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The highest product line sold is Fashion accessories\n",
-      "The lowest product line sold is Health and Beauty\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Determine the highest & lowest sold product line, using Countplot\n",
-    "sns.countplot(y = 'Product line', data = df).set_title('Sales for different product line')\n",
-    "print('The highest product line sold is Fashion accessories')\n",
-    "print('The lowest product line sold is Health and Beauty')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "b85abe93",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Food and Beverage: Payment channel most used is Card\n",
-      "Fashion accessories: Payment channel most used is Epay\n",
-      "Electronic accessories: Payment channel most used is Cash\n",
-      "Sports and travel: Payment channel most used is Cash\n",
-      "Home and lifestyle: Payment channel most used is Epay\n",
-      "Health and Beauty: Payment channel most used is Epay\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Determine the Payment channel used by most customer to pay for each product line\n",
-    "sns.countplot(y = 'Product line', data = df, hue = 'Payment').set_title('Payment channel for Product lines')\n",
-    "print('Food and Beverage: Payment channel most used is Card')\n",
-    "print('Fashion accessories: Payment channel most used is Epay')\n",
-    "print('Electronic accessories: Payment channel most used is Cash')\n",
-    "print('Sports and travel: Payment channel most used is Cash')\n",
-    "print('Home and lifestyle: Payment channel most used is Epay')\n",
-    "print('Health and Beauty: Payment channel most used is Epay')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "fbae3299",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The most used Payment channel for Branch A is Epay\n",
-      "The most used Payment channel for Branch B is Epay\n",
-      "The most used Payment channel for Branch C is Cash\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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uZs+erbTfXF1d0aVLF1y6dKlYL1m8GZcgCNDX11cZW40aNcS/Dx48iBs3bmDkyJFij4WcmZkZvvjiC+Tk5GD37t3i9PXr10MikYg9BnLW1tb47LPPPihmbW1tBAUFKbRXt25djBo1Ci9evMCWLVvE6YMHD4aBgQH++OMPhTYSExMRFRWFJk2aoEWLFu9d5oYNG/DkyRMMGzYMnp6eSuWWlpYftC5y8nVR1VtTvXr1Qrdz//59HDlyBBYWFkrHmqOjI0aMGIHc3Fxs3rxZad46deqIvZVy8mMmPz8f33//vcLx5OPjA0Dx/JCfn49ly5bBxMQEwcHBCp8pLS0tzJgxAxKJROXyAWDmzJlK2+Bd20ZbW7tQvaXv8jHbzNTUVLwBrTA+dPvUrVtXqS0tLS1xGMCb3xGPHj3C9u3bUbNmTfz8889KPZsGBgYqe8/69esHV1dXhWnDhw8HgCKfn588eYK5c+eKr9DQUJw5cwYNGzbEgAEDULVqVZXzdenSReXxBajeBvr6+hg9ejRevXpV4GX7SZMmoVatWuJ7HR0dDB48WGm99u3bJ/Y6Dxs2TKmdGjVqwMDA4IPbJ/Uq0qVS+VgtiUSCatWqoWnTpujfv794QACvL8etWbMGO3bswNWrV5GVlYX8/Hyx/O2xUQ0aNEC7du1w5MgR3LlzR0wQ5IncyJEjVcai6lKbmZkZAKi81CAve/DggTjtzJkzAF6PWwoODlaaRz524fr160pljRs3Vpkc1KpVC3/99ZfKmD9UvXr1lLq05esjlUoVLu0Ar78ATExMFNY1KysLt27dgqmpKRo2bKi0DPml3YsXL4rT5H/Lk/I31alTB7Vq1cLdu3cVpsu36cmTJxXaknv06BGA19tUVRwfo1q1aujatat4Cal79+5o27YtnJyclE6u8jgTExNV7nv5WDz5vpdvPzMzM9jZ2SnVV7WNCsPS0lIhKX6zvV9++UVhjFH16tXRt29fbNiwAcePHxcTzrCwMOTl5RV4rLztn3/+AQB06tTpg2J+nwEDBmDPnj3w8PBAnz594OrqCicnJ4UxPoUhX/c2bdoo/KMh5+bmhpCQEJWfs0aNGikkw8D/jhkbGxulxEnV+eHGjRtIS0tD3bp1lcb9yVWqVEnlIyH09fVVnodcXFxgaWmJX3/9FefPn0enTp3QunVrNG3aVCkp+RAfu8309fULvawP3T6PHz/G4sWLcfDgQSQkJCiNtXrzO+LcuXPIz89H27ZtCxyaoUqzZs2UpskTkowijteysrJSSOgzMzNx6dIlBAYGYtiwYZgzZw7Gjh2rNF/Lli0LbPPevXtYtGgRYmJicP/+fTx//lyhvKAxxIVdrw89xotzu1HJKdKZojA7zs/PD3v37kWdOnXQrVs3yGQy8QQSGhqK3NxcpXlGjx6N48ePIywsDEFBQUhPT8fu3bthY2MDNzc3lcuRj5N7kzyRelfZy5cvxWmPHz8GABw9erTAwegAVA7iLOgkoq2trZCoFgdV6yM/yasqk8fx6tUr8f2TJ08AFHwXWuXKlWFoaCjWe3MeExMTlfOYmpoqJW7ybfrbb7+pnEeuoIGxH2v16tVYsmQJtm7dip9//hkAoKurCy8vL8yaNUtMHuRx7tmzR+VNC2/HWZht8SEKmk++nDf3B/D6WNmwYQPWrFmDdu3a4dWrVwgPD4ehoaFSr3FBMjMzAUBlT3Zx6N69O7Zv344lS5Zg48aNYi+ug4MDJk+ejF69ehWqnfd9ZmUymUK9NxX1/CA/nlSdH27fvi3+01pYJiYmCj16b8Z16NAhzJ07F5GRkYiJiQEAcYzjtGnT3juu7F0+ZpsV9TP8IdsnIyMDHTt2REJCAlq0aAFfX18YGxtDW1sbmZmZWLZsmcJ3xId+VlWdn+X7+GOfO2pkZARXV1esXbsWzZs3x+zZszF06FCl/VbQ9rxz5w7c3d2RkZGBtm3bwt3dHYaGhtDW1sbdu3exceNGld+TBa2X/HP95noV53ZT1T6pV7HenHD+/Hns3bsXHTp0wLZt2xQu3+Xn52Px4sUq5+vWrRssLCwQHh6OwMBAbNiwATk5ORg+fLjKk19xkX9IZ82ahc8//7zEllMWyNc1JSVFZfmzZ8/w5MkThUtZ8nnkvWRvU9WWfJ7bt2/D2Nj4o2KW95gUdMLIzMxUusHDwMAAAQEBCAgIQFJSEk6dOoUtW7YgIiIC165dw8mTJ6GrqyvGuXbtWoUbFgryIduiMAqaT76ct0+k//d//4cWLVogIiICqampOHXqFJKSkjBq1CilXtmCyLdZUlISmjZt+t76EonknftAFQ8PD3h4eOD58+c4e/YsoqKisGrVKgwfPhwRERFKl6dVed9nVj7Yuyg9MUUhb9fLywubNm0q0rzvOm+Zm5vj119/xcKFC3H9+nWcOHECq1evRkhICDIzM9/7T09hYv6QbVbUc+2HbJ9169YhISFB5U1ff/31l9LNUW9+VssaGxsbGBsbIz09HTdu3FC6ClTQ9gwJCcHjx48REhKCTz/9VKFs27ZtBd5ZWhRlebvRxyvWn7ySX17q2rWrQtIGvH5cyNvdwXI6OjoYNmwYHj16hL179yIsLAz6+vpKH+ri1qpVKwDAqVOnSnQ5ZeE/lmrVqsHGxgYpKSm4du2aUrl8TMWbXeXyL3VVd0zeuXMH9+/fV5ouH3d48uTJj45ZPt4nMTFRqezmzZsqew3eZG5ujr59+2LTpk1o1aoV4uPjERcXpxBnYfe9fPslJyfjxo0bSuUfeldpYmKiysfCyNtTNSRAPv4tPDxcHFLg5+dX6GXK1/3gwYOFqi+VSlXuA+D1P2vvUqlSJbRr1w4//PADZs6cCUEQFO5Okx8bqnqp5et+5swZlY+VkPeSq7q8Uxzq168PIyMjnD17VuXyP5ZEIoG9vT1GjBiBffv2QV9fH3v37v2oNktzm33I9pF/R6j6Z0nVMdSiRQtoaWnh1KlTyMrK+riAi9mrV6+QnZ0NAEV6HFBRt8GHKOoxTpqlWBM3+e38bz8r6NGjR5g4ceI75x0+fDh0dXUxdepUXL9+Hb169VIYTF4SmjVrBhcXF0RGRiIsLEzlwXfjxg3cu3fvo5YjX4+CvvxKi/zRFt99953CJaEnT55gxowZAKDwmID+/ftDV1cXK1aswJ07d8Tp+fn5+PHHH1UmomPGjIGenh6+++47lWMD8/LyCv3zRs2bN4eWlha2bNkiniCB15cvVT2uIjU1FX///bfS9NzcXLFnSD4gt2vXrrCxscGaNWsKvM394sWL4uUgAPj0008hCAKmT5+ukGjcvXsXy5cvL9Q6vS0vLw8//PCDQnu3b9/GypUroaurK95U86a+ffuievXqCA0NRXR0NNq0aQNHR8dCL3PQoEEwNDREWFgYoqOjlcrfTsidnJyQmJio9CUQFhYmjhV8U0xMDJ49e6Y0Xd7b8+agaPmxoeoYq1WrFjw8PHD//n0sWrRIoezq1atYvXo19PX1xZsOipuOjg7Gjh0rnr9UrVNaWlqhnnUmd+XKFYVjSe7x48d4+fKlygHjRVGa2+xDto/8O+Ltc8DFixdV/gJEzZo10a9fPzx69AhTpkxROufk5uYiNTX1o9flQ/z+++94+fIlqlevDgcHh0LPV9A2OHz4MNauXVsssXXp0gW1a9dGVFQU1q1bp1T++PFj5OTkFMuyqPQV66XS5s2bo02bNoiIiECnTp3Qpk0bpKSkICoqCnZ2djA3Ny9wXplMhu7du2Pnzp0AgBEjRhRnaAVauXIlevXqhS+//BLLly+Hk5MTjI2N8eDBA1y7dg2XLl1CeHi40g0ARdGhQwdoaWlh2bJlSE9PF8c+jBkzptDPcisO48ePR1RUFKKiouDs7IzOnTvj5cuXiIiIwIMHD+Dr6yvemQsAtWvXRlBQEL777ju0b98effr0gbGxMQ4fPoyMjAw4Ojri8uXLCsuws7PD0qVLMX78eLRt2xaenp6oV68e8vLycP/+fZw5cwa5ublKY+NUkclk+PTTT7Fu3Tq4urqiU6dOyMnJweHDh2Ftba30eXrw4AE++eQT2NnZoVmzZqhVqxaePn2KI0eO4ObNm+jRowdsbW0BvB73Fh4ejr59+2LQoEFo2bIlmjZtiipVquD+/fu4dOkS4uPjcezYMfHy8eeff44///wTkZGRcHV1haenJ548eYKdO3eibdu22LdvX5H3iaOjI86ePQs3Nze4u7uLzwN88uQJfvrpJ5UD+vX19TFkyBDxi7mox0r16tWxevVqDB06FH379kXHjh3RtGlTPH36FNevX0dsbKzCQ0UnTJiAqKgoDB48GL1794aJiQkuXLiACxcuoHPnzjhw4IBC+9999x3u3r0LFxcXWFtbw8DAAJcvX8bhw4dRvXp1hbvc3N3dsWjRIsyYMQNXr14Ve1nlifkvv/wCLy8v/PTTTzh27BicnJzEZ5I9f/4cixYt+ui7YN8lICAAV65cwdq1a3Hw4EG0b98etWrVQmpqKm7fvo3Tp09j1KhRhX4uYUxMDKZNmwYnJyfUr18fpqamSE5ORmRkJPLz8/HVV199dMyluc2Kun18fX2xePFiTJ06FcePH0e9evVw8+ZNHDhwAD169FB68DQA/Pzzz7h27RrWr1+PU6dOwcPDA1WqVEFiYiKOHDmCmTNnlujVmczMTIUbmLKysnDx4kWcOHECWlpaWLBggdIVpncZOXIk1q9fDz8/P/Ts2RPm5ua4evUqoqKi0KdPH5XboKh0dXURFhaGvn374osvvsD69evRqlUrvHr1Cjdv3kRMTAz++uuvIt8wRGVDsSZu2tra2LhxI2bNmoWDBw9i+fLlMDc3x9ChQzFx4kS0bt36nfMPHjwYO3fuhIODA9q0aVOcoRXI3Nwc0dHRWLFiBXbv3o3t27fj5cuX4tPe58yZU6jxOO9ia2uLVatWYdGiRQgPDxcvGQ8YMKBUEzc9PT3s2LEDoaGh2LJlC1auXAktLS00bNgQU6ZMUXjYrNznn38OMzMzLF68GJs2bULVqlXh4eGBH3/8UXyUy9v69euHRo0aISQkBEePHkV0dDQMDAxgZmYGT0/PQg9OB15/CZmammLz5s1YvXo1ZDIZ+vfvj0mTJomXuuWsra0xdepUxMbG4sSJE0hNTYWRkRFsbGzw5Zdfig9DlnNwcMCJEycQGhqKyMhIbNy4EYIgQCaToUGDBvjiiy8U7iDV19fHrl27MGfOHOzcuRPLli2DtbU1vv32W/To0eODEjepVIpt27YhKCgI69atQ3Z2Nho0aIAJEybA29u7wPkGDx6MRYsWoUaNGkXannKenp6IiYnBr7/+iqNHjyI2Nla8HDx16lSFuu3atcPmzZsxZ84c7NmzB3p6enB2dsahQ4ewe/dupcTt22+/xZ9//onz58+LvQoWFhbw9/fHuHHjFJKGDh064Oeff8aaNWuwcuVKcVC2PHGrXbs2YmJiMH/+fOzfvx+nT59GlSpV4OLiggkTJig98qG46ejoYO3atdi+fTvWr1+PQ4cOITs7G9WrV4eVlRW+/vpr+Pr6Fro9Dw8PJCYm4tSpU9i/fz+ePHkCU1NTtGrVCmPHjkXHjh0/OubS3GZF3T7m5ubYt28ffvjhB5w+fRpHjhyBnZ0dFixYgA4dOqhMWqRSKQ4cOIDly5eLy5G31aNHD7Rt27bY1kcV+eNA5HR1dWFqagpvb2+MHz++wAfwFqRRo0aIiIgQvyfz8vLQqFEjrFu3DkZGRsWSuAGvryjFxsZi0aJFOHjwIJYtW4ZKlSrB2toan3/+eYE3WlHZJ8nIyPjw3+opZgsWLMDMmTMxf/78ApMCInr9jL3Ro0djwoQJ4mVuIiIq/8pM4padnY2WLVvi2bNnuHz5coGPuSCq6PLy8uDm5oYrV67g7NmzKp8DR0RE5VOx/1ZpUe3btw/nz5/HoUOH8PDhQwQFBTFpI1LhxIkTiI2NxcmTJ/Hvv//Cz8+PSRsRUQWj9sRtz5492LhxI0xNTfHVV19hwoQJ6g6JqEw6duwY5s6dC2NjYwwdOhQ//fSTukMiIqJSVmYulRIRERHRuxXrc9yIiIiIqOQwcSMiIiLSEEzciIiIiDQEEzciIiIiDcHEjYiIiEhDMHEjIiIi0hBM3IiIiIg0BBM3IiIiIg3BxI2IiIhIQzBxIyIiItIQ5SJx++WXX9CxY0dYWVmhXr168PHxwZUrVxTq+Pv7QyqVKrw8PT0V6uTm5iIgIAA2NjawsLCAr68v7t+/X5qrQkRERFSgcpG4HT9+HCNHjsSBAwewZ88e6OjooHfv3khPT1eo5+bmhri4OPG1detWhfLAwEBERERg1apViIyMRFZWFnx8fJCXl1eaq0NERESkUrn8kfns7GxYW1tj/fr16NKlC4DXPW6PHz/G5s2bVc6TmZkJW1tbhISEYMCAAQCAxMRENG7cGNu2bYOHh0epxU9U3LK+HqjuEFBt4UZ1h0BEpPHKRY/b27Kzs5Gfnw+pVKow/dSpU7C1tUWLFi0wYcIEPHr0SCy7cOECXr58CXd3d3GapaUl7O3tcebMmdIKnYiIiKhAOuoOoCRMmTIFjRs3RqtWrcRpnp6e6NGjB2rXro27d+9i1qxZ6NmzJ2JiYqCvr4+UlBRoa2ujRo0aCm2ZmJggJSWlwGXFx8eX2HoQFRczdQcAHiukGezs7NQdAtE7lbvEberUqTh9+jT2798PbW1tcbq3t7f4t6OjI5o1a4bGjRvjwIED6NmzZ4HtCYIAiURSYDkPctIEWeoOADxWiIiKQ7lK3AIDA7Fjxw5ERESgTp0676xrbm4OCwsL3Lp1CwBgamqKvLw8pKWloWbNmmK91NRUODs7l2TYRETl0u3IoeoOAQBQt+tadYdAVGzKzRi3yZMnY9u2bdizZw/q16//3vppaWlISkqCTCYDADRr1gy6urqIjo4W69y/fx9xcXFo3bp1icVNREREVFjlosdt4sSJ2Lx5M8LDwyGVSpGcnAwAqFKlCqpWrYrs7GzMmTMHPXv2hEwmw927dzFjxgyYmJige/fuAAAjIyMMGTIE06dPh4mJCYyNjTFt2jQ4OjrCzc1NjWtHRERE9Fq5SNxWrlwJAOjVq5fC9MmTJyMwMBDa2tq4cuUKNm3ahMzMTMhkMri6umLNmjWoVq2aWH/27NnQ1taGn58fcnJy0L59eyxbtkxhrBwRERGRupTL57gRkSI+x43UgWPciIpfuRnjRkRERFTeMXEjIiIi0hBM3IiIiIg0BBM3IiIiIg3BxI2IiIhIQzBxIyIiItIQTNyIiIiINAQTNyIiIiINwcSNiIiISEMwcSMiIiLSEEzciIiIiDQEEzciIiIiDcHEjYiIiEhDMHEjIiIi0hA66g6AqDxzXPqXukMAAJxWdwBERFQs2ONGREREpCGYuBERERFpCCZuRERERBqCiRsRERGRhuDNCURExajM3JASv1DdIQCf6Ko7AqJyhz1uRERERBqCPW6lqCz8J355XCt1h0BEREQfiD1uRERERBqCiRsRERGRhmDiRkRERKQhmLgRERERaQjenEBEpeJ25FB1h4C6XdeqOwQioo/CHjciIiIiDcHEjYiIiEhDMHEjIiIi0hAc41bBZH09UN0hAACqLdyo7hCIiIg0DnvciIiIiDQEEzciIiIiDVEuErdffvkFHTt2hJWVFerVqwcfHx9cuXJFoY4gCAgODkaDBg1gZmaGbt264erVqwp1cnNzERAQABsbG1hYWMDX1xf3798vzVUhIiIiKlC5SNyOHz+OkSNH4sCBA9izZw90dHTQu3dvpKeni3UWLVqEkJAQzJ07F0eOHIGJiQn69OmDrKwssU5gYCAiIiKwatUqREZGIisrCz4+PsjLy1PHahEREREpKBc3J+zYsUPh/fLly2FtbY3Tp0+jS5cuEAQBoaGh+Oqrr9CrVy8AQGhoKOzs7LBt2zb4+fkhMzMT69atQ0hICDp27Ci207hxY8TExMDDw6PU14uIiIjoTeWix+1t2dnZyM/Ph1QqBQAkJCQgOTkZ7u7uYp1KlSrB2dkZZ86cAQBcuHABL1++VKhjaWkJe3t7sQ4RERGROpWLHre3TZkyBY0bN0arVq0AAMnJyQAAExMThXomJiZISkoCAKSkpEBbWxs1atRQqpOSklLgsuLj44sz9AqD243UgZ+7iqko+93Ozq4EIyH6eOUucZs6dSpOnz6N/fv3Q1tbW6FMIpEovBcEQWna295Xp0gH+aG/Cl+3nKswJ0fu8zKlVD533OdlToU531CFUK4ulQYGBmL79u3Ys2cP6tSpI06XyWQAoNRzlpqaKvbCmZqaIi8vD2lpaQXWISIiIlKncpO4TZ48Gdu2bcOePXtQv359hbLatWtDJpMhOjpanJaTk4NTp06hdevWAIBmzZpBV1dXoc79+/cRFxcn1iEiIiJSp3JxqXTixInYvHkzwsPDIZVKxTFtVapUQdWqVSGRSODv748FCxbAzs4Otra2mD9/PqpUqYJ+/foBAIyMjDBkyBBMnz4dJiYmMDY2xrRp0+Do6Ag3Nzc1rl35dDtyqLpDQN2ua9UdAhERUZGUi8Rt5cqVACA+6kNu8uTJCAwMBAB8+eWXeP78OQICApCRkYEWLVpgx44dqFatmlh/9uzZ0NbWhp+fH3JyctC+fXssW7ZMaawcERERkTqUi8QtIyPjvXUkEgkCAwPFRE4VAwMDzJs3D/PmzSvG6IiIiIiKR7kZ40ZERERU3jFxIyIiItIQTNyIiIiINAQTNyIiIiINwcSNiIiISEMwcSMiIiLSEEzciIiIiDQEEzciIiIiDcHEjYiIiEhDMHEjIiIi0hBM3IiIiIg0BBM3IiIiIg3BxI2IiIhIQzBxIyIiItIQTNyIiIiINAQTNyIiIiINwcSNiIiISEMwcSMiIiLSEEzciIiIiDQEEzciIiIiDcHEjYiIiEhDMHEjIiIi0hBM3IiIiIg0BBM3IiIiIg3BxI2IiIhIQzBxIyIiItIQTNyIiIiINAQTNyIiIiINwcSNiIiISEMwcSMiIiLSEEzciIiIiDSE2hK3pk2bIjIyssDy/fv3o2nTpqUYEREREVHZprbE7e7du3j69GmB5U+fPsW9e/dKMSIiIiKisk2tl0olEkmBZTdu3EC1atUK3daJEyfg6+uLhg0bQiqVYv369Qrl/v7+kEqlCi9PT0+FOrm5uQgICICNjQ0sLCzg6+uL+/fvF22liIiIiEqITmkubMOGDdi4caP4fv78+QgLC1Oql5GRgStXrqBz586Fbvvp06dwcHDAwIEDMXbsWJV13NzcsHz5cvG9np6eQnlgYCAiIyOxatUqGBsbY9q0afDx8cHRo0ehra1d6FiIiIiISkKpJm5Pnz5FcnKy+D4zMxP5+fkKdSQSCSpXroxhw4ZhypQphW67U6dO6NSpEwBg3LhxKuvo6+tDJpOpLMvMzMS6desQEhKCjh07AgCWL1+Oxo0bIyYmBh4eHoWOhYiIiKgklGriNnr0aIwePRoA0KRJE8yZMwddu3YtteWfOnUKtra2MDIygouLC77//nuYmJgAAC5cuICXL1/C3d1drG9paQl7e3ucOXOGiRsRERGpXakmbm+6dOlSqS7P09MTPXr0QO3atXH37l3MmjULPXv2RExMDPT19ZGSkgJtbW3UqFFDYT4TExOkpKQU2G58fHxJh04lhPuu4uE+r5iKst/t7OxKMBKij6e2xE0uKysLiYmJSE9PhyAISuUuLi7Fshxvb2/xb0dHRzRr1gyNGzfGgQMH0LNnzwLnEwThnTdRFOkgP/RX4etSiSuVEzT3eZnCfV4xMRmj8kRtiVt6ejomT56MnTt3Ii8vT6lcnjA9fvy4RJZvbm4OCwsL3Lp1CwBgamqKvLw8pKWloWbNmmK91NRUODs7l0gMREREREWhtsTt66+/xt69ezF69Gi4uLhAKpWW6vLT0tKQlJQk3qzQrFkz6OrqIjo6Gv379wcA3L9/H3FxcWjdunWpxkZERESkitoSt6ioKHz22Wf46aefiqW97OxssfcsPz8fiYmJuHTpEoyNjWFsbIw5c+agZ8+ekMlkuHv3LmbMmAETExN0794dAGBkZIQhQ4Zg+vTpMDExER8H4ujoCDc3t2KJkYiIiOhjqC1x09PTQ7169YqtvfPnz6NHjx7i++DgYAQHB2PgwIH45ZdfcOXKFWzatAmZmZmQyWRwdXXFmjVrFB7yO3v2bGhra8PPzw85OTlo3749li1bxme4ERERUZmgtsStV69eOHToEEaMGFEs7bm6uiIjI6PA8h07dry3DQMDA8ybNw/z5s0rlpiIiIiIipPafvLqiy++wMOHDzF27Fj8/fffePjwIR49eqT0IiIiIqLX1Nbj1qJFC0gkEly4cAFbtmwpsF5J3VVKREREpGnUlrhNmjTpnc9HIyIiIiJFakvcAgMD1bVoIiIiIo2ktjFuRERERFQ0autxmzt37nvrSCQSTJo0qRSiISIiIir71Ja4zZkzp8AyiUQi/uQVEzciIiKi19T6W6Vvy8/Px927d7F8+XKcOXMG27ZtU0NkRERERGVTmRrjpqWlhTp16iA4OBi1a9fGlClT1B0SERERUZlRphK3N7m6uuLAgQPqDoOIiIiozCiziVt8fDwEQVB3GERERERlhtrGuJ04cULl9MzMTMTGxmLFihXo3bt36QZFREREVIapLXHr3r27yl9OEAQB2tra8Pb2LtQjQ4iIiIgqCrUlbhEREUrTJBIJpFIprK2tUa1aNTVERURERFR2qS1xa9eunboWTURERKSR1Ja4yWVlZeH48eO4e/cuAMDa2hrt2rVjjxsRERHRW9SauC1fvhyzZs3C06dPFe4grVKlCr7//nt89tlnaoyOiIiIyorc3Fzk5OSoO4wSp6WlhapVq6q8DwBQY+K2adMmTJkyBS1atIC/vz/s7e0hCAKuX7+OZcuWITAwEMbGxhgwYIC6QiQiIqIy4OnTpwAAQ0PDAhOa8uLFixfIzs4u8Mqj2hK3kJAQtG7dGnv37oWOzv/CaNy4MXr16oXu3btjyZIlTNyIiIgquFevXsHIyEjdYZQKPT09PH/+vMBytT2ANz4+Hn379lVI2uR0dHTQt29f3LhxQw2REREREZVNakvcqlSpguTk5ALLk5OTUbly5VKMiIiIiKhsU1vi5u7ujuXLlyM2Nlap7Pjx4/j999/h4eGhhsiIiIiIyia1jXELCgrCyZMn0atXLzRp0gT169cHAFy/fh2XLl2Cubk5goKC1BUeERERlXGOS/8q1eVdHteqSPX9/f2xceNG8X316tXh5OSEmTNninlPUamtx83S0hKxsbEYN24cnj17hj179mDPnj149uwZxo8fj9jYWNSqVUtd4RERERF9NDc3N8TFxSEuLg47duzA8+fPMXjw4A9uT209bk+fPsXTp08xa9YszJo1S6n83r17MDAw4Dg3IiIi0lj6+vqQyWQAAJlMhnHjxsHX1xfPnz9HpUqVitye2nrcpk6dikGDBhVY/umnn+L7778vxYiIiIiISk5WVhZ27NgBBweHD0raADX2uEVHR+PTTz8tsLx79+7YsGFDKUZEREREVLyioqLEoV9Pnz6FpaUltmzZ8sHtqa3HLTk5GWZmZgWWy2QyPHz4sBQjIiIiIipezs7OiI2NRWxsLA4fPgxXV1f07dsXiYmJH9Se2nrcatasiatXrxZYfvXq1QrzlGQiIiIqnypXrgwbGxvxfbNmzWBtbY0//vgD3333XZHbU1uP2yeffIKwsDCcOXNGqezvv/9GWFgYPvnkEzVERkRERFQyJBIJtLS03vmzVu+ith63wMBAHDp0CF27doWnpyccHBwgkUhw+fJlREVFQSaTYdq0aeoKj4iIiOij5ebmir8UlZGRgRUrViA7OxteXl4f1J7aEjeZTIbo6GgEBQXhzz//xMGDBwEA1apVg4+PD4KCgsTbZ4mIiIg0UUxMDOzt7QG8znHs7Ozwxx9/wNXV9YPaU1viBgCmpqYIDQ2FIAhITU2FIAgwMTGBRCJRZ1hERESkAYr6SwalLTQ0FKGhocXaptrGuL1JIpHAxMQEpqamH5y0nThxAr6+vmjYsCGkUinWr1+vUC4IAoKDg9GgQQOYmZmhW7duSjdH5ObmIiAgADY2NrCwsICvry/u37//wetFREREVJzKROJWHJ4+fQoHBwfMmTNH5UPtFi1ahJCQEMydOxdHjhyBiYkJ+vTpg6ysLLFOYGAgIiIisGrVKkRGRiIrKws+Pj7Iy8srzVUhIiIiUqncJG6dOnXC9OnT0atXL2hpKa6WIAgIDQ3FV199hV69esHBwQGhoaHIzs7Gtm3bAACZmZlYt24dZsyYgY4dO6JZs2ZYvnw5Ll++jJiYGDWsEREREZGicpO4vUtCQgKSk5Ph7u4uTqtUqRKcnZ3Fx5FcuHABL1++VKhjaWkJe3t7lY8sISIiIiptar05obTIb8M1MTFRmG5iYoKkpCQAQEpKCrS1tVGjRg2lOikpKQW2HR8fX8zRUmnhvqt4uM8rpqLsdzs7uxKMhOjjVYjETe7tGx8EQXjvzRDvq1Okg/zQX4WvSyWuVE7Q3OdlCvd5xcRkjMqTCnGpVP48uLd7zlJTU8VeOFNTU+Tl5SEtLa3AOkRERETqVCESt9q1a4sP/JXLycnBqVOn0Lp1awCvfztMV1dXoc79+/cRFxcn1iEiIiJSp3JzqTQ7Oxu3bt0CAOTn5yMxMRGXLl2CsbExrKys4O/vjwULFsDOzg62traYP38+qlSpgn79+gEAjIyMMGTIEEyfPh0mJiYwNjbGtGnT4OjoCDc3NzWuGREREdFr5SZxO3/+PHr06CG+Dw4ORnBwMAYOHIjQ0FB8+eWXeP78OQICApCRkYEWLVpgx44dqFatmjjP7Nmzoa2tDT8/P+Tk5KB9+/ZYtmwZtLW11bFKRERERArKTeLm6uqKjIyMAsslEgkCAwMRGBhYYB0DAwPMmzcP8+bNK4EIiYiIqDhlfT2wVJdXbeHGD5rv4sWL6NixI5ycnHDgwIGPiqFCjHEjIiIiUpe1a9di5MiRuHr1KuLi4j6qLSZuRERERCXk+fPn2Lp1K4YNG4aePXti3bp1H9UeEzciIiKiErJ7925YWVmhUaNG8PHxwaZNm/Dy5csPbo+JGxEREVEJWbt2LXx9fQEA7dq1Q6VKlRAZGfnB7TFxIyIiIioBt27dwpkzZ8RHj0kkEgwYMOCjLpeWm7tKiYiIiMqStWvXIi8vD40aNRKnCYIAAEhMTISlpWWR22SPGxEREVExe/XqFTZu3IigoCDExsaKr+PHj8PR0RHr16//oHbZ40ZERERUzA4cOIC0tDQMGzYM1atXVyjz9vbGqlWrEBAQAC2tovWhsceNiIiIqJitW7cOrq6uSkkbAPTu3Rv37t1DTExMkdtljxsRERFppA/9JYPSsGnTpgLL6tSp885fe3oX9rgRERERaQgmbkREREQagokbERERkYZg4kZERESkIZi4EREREWkIJm5EREREGoKJGxEREZGGYOJGREREpCGYuBERERFpCCZuRERERBqCP3lFREREGul25NBSXV7drms/aL6UlBQsWLAABw4cwIMHD1CjRg04OjpizJgx6NSpU5HaYuJGREREVEISEhLg5eWFqlWrIigoCI0aNUJ+fj6OHj2Kb775Bv/991+R2mPiRkRERFRCJk6cCEEQEB0djapVq4rT7e3tMWDAgCK3xzFuRERERCUgPT0dUVFRGD16tELSJieVSovcJhM3IiIiohJw69YtCIKA+vXrF1ubTNyIiIiISoAgCMXeJhM3IiIiohJQr149SCQSXL9+vdjaZOJGREREVAKMjY3h4eGBFStWIDs7W6k8IyOjyG0ycSMiIiIqIfPnz4cgCOjYsSN27dqF+Ph4XL9+HatWrUK7du2K3B4fB0JERERUQurUqYOjR49iwYIFCAoKQlJSEqpXr45GjRph4cKFRW6PiRsRERFppA/9JYPSZmZmhnnz5mHevHkf3RYvlRIRERFpCCZuRERERBqiwiRuwcHBkEqlCq83H4gnCAKCg4PRoEEDmJmZoVu3brh69aoaIyYiIiJSVGESNwCws7NDXFyc+Dp58qRYtmjRIoSEhGDu3Lk4cuQITExM0KdPH2RlZakxYiIiIqL/qVCJm46ODmQymfiqWbMmgNe9baGhofjqq6/Qq1cvODg4IDQ0FNnZ2di2bZuaoyYiIiJ6rUIlbnfu3EHDhg3RpEkTjBgxAnfu3AEAJCQkIDk5Ge7u7mLdSpUqwdnZGWfOnFFTtERERESKKszjQFq2bImlS5fCzs4OqampmDdvHjp16oTTp08jOTkZAGBiYqIwj4mJCZKSkt7Zbnx8fInFTCWL+67i4T6vmIqy3+3s7EowEvoYgiBAIpGoO4wS977fN60widsnn3yi8L5ly5Zo1qwZNmzYACcnJwBQ+kAU5kNSpIP80F+Fr0slrlRO0NznZQr3ecXEZEzzValSBRkZGZBKpeU+eXv27BkMDAwKLK8widvbqlatigYNGuDWrVvo3r07ACAlJQWWlpZindTUVKVeOCIiIipdOjo6qFatGp48eaLuUEqcjo4O9PX1Cy4vxVjKlJycHMTHx8PV1RW1a9eGTCZDdHQ0mjdvLpafOnUKM2bMUHOkREREpKOjAyMjI3WHoXYVJnH77rvv4OXlBUtLS3GM27NnzzBw4EBIJBL4+/tjwYIFsLOzg62tLebPn48qVaqgX79+6g6diIiICEAFStwePHiAUaNGIS0tDTVr1kTLli1x6NAhWFtbAwC+/PJLPH/+HAEBAcjIyECLFi2wY8cOVKtWTc2RExEREb1WYRK31atXv7NcIpEgMDAQgYGBpRQRERERUdFUqOe4EREREWkyJm5EREREGoKJGxEREZGGYOJGREREpCGYuBERERFpCCZuRERERBqCiRsRERGRhmDiRkRERKQhmLgRERERaQgmbkREREQagokbERERkYZg4kZERESkIZi4EREREWkIJm5EREREGoKJGxEREZGGYOJGREREpCGYuBERERFpCCZuRERERBqCiRsRERGRhmDiRkRERKQhmLgRERERaQgmbkREREQagokbERERkYZg4kZERESkIZi4EREREWkIJm5EREREGoKJGxEREZGGYOJGREREpCGYuBERERFpCCZuRERERBqCiRsRERGRhmDiRkRERKQhmLgRERERaQgmbiqsXLkSTZo0gUwmQ4cOHXDy5El1h0RERETExO1tO3bswJQpU/Dtt9/i2LFjaNWqFfr374979+6pOzQiIiKq4Ji4vSUkJASDBg3CsGHDYG9vj3nz5kEmk2H16tXqDo2IiIgqOElGRoag7iDKihcvXsDc3ByrVq1C7969xekTJ07ElStXEBkZqb7giIiIqMJjj9sb0tLSkJeXBxMTE4XpJiYmSElJUVNURERERK8xcVNBIpEovBcEQWkaERERUWlj4vaGGjVqQFtbW6l3LTU1VakXjoiIiKi0MXF7g56eHpo1a4bo6GiF6dHR0WjdurWaoiIiIiJ6jYnbW8aPH48NGzZg7dq1iIuLw+TJk/Hw4UP4+fmpOzSN1rZtWwQHB6s7DCIqZlKpFLt371Z3GEQVBhO3t/Tt2xfBwcGYN28eXF1dcfr0aWzZsgXW1tbqDq3YpaSkYPLkyWjWrBlMTU3RsGFD9OvXDwcPHlR3aFTM/P39IZVKlV6enp7qDo1KGI9zovJFR90BlEWjRo3CqFGj1B1GiUpISICXlxeqVq2KoKAgNGrUCPn5+Th69Ci++eYb/Pfff0VuMz8/H4IgQFtbuwQipo/l5uaG5cuXK0zT09NTUzRUGkriOCci9WKPWwU1ceJECIKA6Oho9OnTB3Z2drC3t8eYMWNw/PhxAMBvv/0GZ2dnWFhYoGHDhvjiiy+QkZEhtrF+/XrUqlULBw8eRNu2bWFiYoK4uDg8evQIAwcOhJmZGRo1aoR169apaS3pTfr6+pDJZAovY2NjAK8vd/3+++8YMGAAzM3N0ahRI2zevFlh/h9++AEtW7aEmZkZGjdujOnTpyMnJwfA6wShevXqOH/+vMI8YWFhsLGxwYsXL0pnJUlBcRznmZmZGDNmDGxtbSGTydC0aVMsXbpUYTnp6ekYNmwYLCws0LRpU6XPDhEVHyZuFVB6ejqioqIwevRoVK1aValcKpUCALS0tBAcHIxTp05hxYoVOHv2LCZNmqRQNycnB/Pnz8fChQtx5swZWFlZYdy4cbh9+zZ27dqF9evXY9OmTbh7925prBp9hODgYHTp0gWxsbEYPnw4xo4dq5CIVa5cGb/99hvOnDmDBQsWYMeOHZg/fz4AoHbt2nBzc0N4eLhCm+Hh4fDx8WHPnhoU13E+a9YsXLlyBZs3b8Zff/2F3377DRYWFgpt/fzzz+jatSuOHz+Ovn374vPPP+cxT1RC+MsJFdDZs2fh4eGBdevWoUePHoWeLyoqCoMGDcLDhw+hpaWF9evXY/z48YiJiUGzZs0AADdu3EDLli2xf/9+tGnTBgBw9+5dNGvWDAEBAQgMDCyJVaL38Pf3x5YtW2BgYKAwfdSoUfjxxx8hlUoxdOhQLF68WCzr1asXZDIZfv/9d5Vtrl69GkuWLBGTu927d2PChAmIi4uDgYEB4uLi0Lp1a5w8eRIODg4lt3KkUnEd576+vqhevbpSL5ucVCrF119/jaCgIADAq1evYGVlhV9//RU+Pj7Fsi5E9D8c41YBCULhcvWjR49i4cKFuH79Op48eYK8vDy8ePECycnJMDc3BwDo6OigcePG4jxxcXHQ0tJCixYtxGnW1tZifVIfZ2dnLFq0SGGakZGR+LeTk5NCmZOTk8IA9t27dyM0NBS3bt3C06dPkZeXh7y8PLG8a9eumDhxIiIiItC/f3+Eh4ejRYsWTNrUpLiO85EjR2LYsGG4ePEiOnbsCC8vL7Rr106hDUdHR/FvHR0d1KhRA48ePSrW9SGi13iptAKqV68eJBIJrl+/XmCdu3fvwsfHB/Xr18cff/yBmJgY/PbbbwCgMF5JX19f4WaEwn5ZUOmrXLkybGxsFF41atQo1Lx///03RowYAXd3d2zatAnHjh3DtGnT8PLlS7GOrq4ufH19ER4ejlevXmHz5s0YMmRISa0OvUdxHeeffPIJ/v33X3zxxRdIS0uDj48Pxo0bp9COrq6uwnuJRMJzAVEJYeJWARkbG8PDwwMrVqxAdna2UnlGRgbOnz+PFy9eIDg4GK1atYKtrS2SkpLe27a9vT3y8/Nx7tw5cdq9e/cKNS+p1z///KP03t7eHgBw+vRpmJubY9KkSWjevDnq1auHe/fuKbUxbNgwxMbGYuXKlcjOzkbfvn1LJXZSVpzHeY0aNeDr64vQ0FAsWbIEGzduRG5ubmmsBhG9hYlbBTV//nwIgoCOHTti165diI+Px/Xr17Fq1Sq0a9cO9erVQ35+PpYuXYo7d+5g27ZtWLZs2XvbtbOzg6enJ77++mv89ddfuHTpEsaNG4dKlSqVwlrRu+Tm5iI5OVnhlZqaKpZHREQgLCwMN2/exC+//IKjR4/C398fAMQv9C1btuDOnTtYtWoVtm/frrQMW1tbtGnTBtOnT0fPnj1haGhYautHyorjOP/pp5+wd+9e3Lx5E3FxcYiIiECdOnWgr6+vprUiqtiYuFVQderUwdGjR+Hm5oagoCC4uLigZ8+e2LdvHxYuXIhGjRphzpw5WLp0Kdq0aYO1a9di5syZhWp76dKlsLa2Rs+ePTFw4ED079+/XD7AWNPExMTA3t5e4dW+fXuxfMqUKdizZw9cXFywevVqhISEoHnz5gCALl26YMKECQgMDISLiwuio6MxdepUlcsZMmQIXrx4wcukZUBxHOf6+vqYNWsW2rVrh86dOyM7OxubNm1S0xoREe8qJSJIpVKEhYWhV69eH93Wr7/+inXr1uHs2bPFEBkREb2Jd5USUbHIzs5GfHw8li1bhm+//Vbd4RARlUu8VEpExSIgIACdO3dG69at4efnp+5wiIjKJV4qJSIiItIQ7HEjIiIi0hBM3IiIiIg0BBM3IiIiIg3BxI2IiIhIQzBxIyqj1q9fD6lUKr5q1KgBBwcHfP7553j48KG6wys1p06dQnBwMDIyMtQdChGR2vE5bkRl3JQpU1C3bl3k5ubi9OnT2LBhA06cOIGTJ09WiJ8SO336NObOnYtBgwZBKpWqOxwiIrVi4kZUxnl4eMDJyQkAMHToUBgbGyMkJASRkZHw9vZWc3RERFSaeKmUSMPIf1/0zp07WLJkCTp37gwbGxvIZDI4Oztj7dq1CvVHjx4NGxsbvHz5UqmtYcOGoX79+nj16hUAoHHjxvD29sapU6fg4eEBMzMztGnTBtHR0QCAqKgotG/fXlzWmTNnlNp8+PAhvvzySzRo0ACmpqZo3rw5Fi1aBEH43yMjExISIJVKsXDhQmzcuBFOTk4wNTWFs7MzYmJixHrBwcH48ccfAQBNmzYVLxvHxsZ+3EYkItJQ7HEj0jC3b98GAFSvXh0///wzPD090bt3b0gkEuzduxcTJkxAfn4+hg8fDgAYNGgQtm7dikOHDqFr165iO5mZmThw4AD8/Pygo/O/U0FCQgL8/PwwZMgQ9OvXD0uXLsXAgQOxdOlSfPfddxgxYgT09fWxaNEiDBkyBP/++y/09fUBAI8ePYKnpydevXqFYcOGwczMDKdOnUJQUBCSkpIwZ84chXXZvXs30tLS4OfnBwMDA4SGhmLw4MH4999/YWxsjB49eiA+Ph47duzA7NmzUaNGDQCAvb19SW5iIqIyi4kbURn35MkTpKWlIScnB2fOnMHPP/+MSpUqoXPnzvDx8UHlypXFumPHjkXv3r2xePFiMXHr0KEDatWqhS1btigkbrt370ZOTg58fHwUlnfjxg38+eefcHFxAQA0adIE3bp1w2effYaTJ0/Czs4OAGBpaQk/Pz/s379f/HH6WbNmITc3FydOnICpqSkAwM/PD2ZmZvjtt9/g7++P2rVri8u6ffs2zp49i5o1awIA2rVrh/bt22Pbtm0YPXo0GjVqhMaNG2PHjh3o1q2bwrxERBURL5USlXHe3t6oV68eHB0dMWLECMhkMmzevBkWFhZi0vby5Uukp6cjLS0N7du3x61bt5CZmQkA0NLSwoABA7B//35xGgBs3rwZDRo0QLNmzRSWZ2trKyZtANCyZUsAQKtWrcSkDQBatGgB4PUlWwAQBAG7d+9G586doa2tjbS0NPHl4eGB/Px8nDhxQmFZvXv3FpM24HWSaGhoKLZJRESK2ONGVMbNnTsX9vb20NfXh6WlJSwtLSGRSAAAf/75J+bNm4d///0XeXl5CvM9efIERkZGAICBAwdi4cKF2L17N4YOHYrExEScPHkS06dPV1qepaWlwnt9fX3o6+ujVq1aCtMNDQ0BQHxMR2pqKjIyMhAeHo7w8HCV65Kamqrw3srKSqmOkZER0tPTC9ocREQVGhM3ojKuefPm4l2lbzp9+jQGDx6MNm3aYOHChTAzM4Oenh4OHjyIpUuXIj8/X6xbv359tGjRAlu2bMHQoUOxdetWAED//v2V2tXW1lYZR0HT5TcdyJfXr18/DB48WGVdGxubIrVJRESKmLgRaahdu3bBwMAAO3fuhIGBgTi9oDsuBw4ciICAACQmJmLLli1o166dUu/ax6hZsyYMDQ3x6tUruLm5FVu78t5FIiLiGDcijaWtrQ2JRKLQsya/VKmKt7c3dHV18f333+Pq1avw9fUt9nh69uyJvXv34sKFC0rlmZmZKh9J8j7ycXz85QQiIva4EWksLy8vhISEoE+fPvDx8UF6ejrCwsJgamqK5ORkpfrGxsbw8vLCzp07UalSJfTs2bPYY/rhhx9w4sQJeHl5YciQIXBwcEBWVhauXLmCiIgInDt3DjKZrEht/t///R8AYObMmfD29oaenh7at28PExOTYo+fiKisY48bkYZydXVFaGgo0tPTERgYiPDwcIwZMwafffZZgfMMHDgQANCtWzdUq1at2GOqWbMmDh8+DD8/Pxw4cACTJk3C4sWLcefOHUyZMgXGxsZFbtPJyQnfffcdrl27hvHjx2PkyJG4du1ascdORKQJJBkZGRwFTFRBHD58GN7e3ti6dSs++eQTdYdDRERFxB43ogokLCwMZmZmcHd3V3coRET0ATjGjagC2L59O65du4aIiAgEBQUV+BgOIiIq23iplKgCkEqlqFKlCnr06IHFixdDT09P3SEREdEHYI8bUQXAR2kQEZUPHONGREREpCGYuBERERFpCCZuRERERBqCiRsRERGRhmDiRkRERKQhmLgRERERaYj/Bzu2UByDyqC4AAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Determine the Payment channel for each branch.\n",
-    "sns.countplot(x = 'Payment', data = df, hue = 'Branch').set_title('Payment mode used by customers for each Branch')\n",
-    "plt.legend(loc='upper left', bbox_to_anchor=(1.25, 0.5), ncol=1)\n",
-    "print('The most used Payment channel for Branch A is Epay')\n",
-    "print('The most used Payment channel for Branch B is Epay')\n",
-    "print('The most used Payment channel for Branch C is Cash')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "5d03da15",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Branch with the lowest rating is B\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Determine the branch with the lowest rating using box plot\n",
-    "sns.boxplot(x = 'Branch', y = 'Rating', data = df).set_title(\"Rating for Branches\")\n",
-    "print('Branch with the lowest rating is B')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "cbef9fc0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1541.15x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# A catplot() generate visualization for the \"product line\" on x-axis, quantity on the y-axis, and hue showing that gender type often affects the kind of products being purchased at the supermarket.\n",
-    "sns.catplot(x = 'Product line', y = 'Quantity', hue = 'Gender', kind= 'boxen', data = df, aspect = 4);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "d468497d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x641ed1df40>"
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# insight to explore is the interaction of Unit price on the Quantity of goods purchased\n",
-    "#Use the catplot() to plot Product line per unit price, \n",
-    "sns.catplot(y = 'Unit price', x = 'Product line', kind= 'point', data = df, aspect = 4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "508ce359",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1440x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Product line per Quantity. Set the kind parameter to point\n",
-    "sns.catplot(y = 'Quantity', x = 'Product line', kind= 'point', data = df, aspect = 4);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "cf79ab23",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:xlabel='City', ylabel='gross income'>"
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.barplot(x='City', y= 'gross income', data= df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9fc070bc",
-   "metadata": {},
-   "source": [
-    "Summary\n",
-    "\n",
-    "1. Data set is large with exactly 68,000 elements (4000 rows and 17 columns). \n",
-    "\n",
-    "2. The elements for categorical raw data are complete. \n",
-    "\n",
-    "    Approach:\n",
-    "    \n",
-    "The approaches for data analyses for company XYZ were to combine seperate data set for each city, make inferential and descriptive statistical analyses and obtain key facts that show and compare the sales performances for Company's branches.\n",
-    "\n",
-    "    Method:\n",
-    "    \n",
-    "These were accomplished using some python packages such as pandas analytical tools, Numpy, and Matplotlib as well as data illustrative tools such as Seaborn's statistical visualization package. \n",
-    "\n",
-    "    Insights:\n",
-    "    \n",
-    "Python packages such as pandas analytical tools, Numpy, and Matplotlib as well as data illustrative tools such as Seaborn's statistical visualization package are very efficient and powerful in analysing huge data sets.\n",
-    "\n",
-    "Coding lines are not too restictive, i.e packages are user-friendly and flexible. Users creativity shows that usage can be interesting.\n",
-    "\n",
-    "Some 'take-home' facts about the company XYZ include:  \n",
-    "Sales of Electronic and fashion accessories are almost at par. Sales across cities are very competitive with close matching records. \n",
-    "\n",
-    "Though quantities purchased for product lines generally are low, most customers prefer using Cash and Epay payment methods."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cadabbb4",
-   "metadata": {},
-   "source": [
-    "Documentation\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From 1734219aef99b3962dc3b0bcfa1dbfbda93e9b07 Mon Sep 17 00:00:00 2001
From: Ochai2018 <45337793+Ochai2018@users.noreply.github.com>
Date: Sat, 27 Aug 2022 15:48:08 -0700
Subject: [PATCH 12/13] Delete combined_csv.csv

---
 combined_csv.csv | 1 -
 1 file changed, 1 deletion(-)
 delete mode 100644 combined_csv.csv

diff --git a/combined_csv.csv b/combined_csv.csv
deleted file mode 100644
index 8b13789..0000000
--- a/combined_csv.csv
+++ /dev/null
@@ -1 +0,0 @@
-

From ac21c19848cc53eb9cdffae27884c300c1837529 Mon Sep 17 00:00:00 2001
From: Ochai2018 <45337793+Ochai2018@users.noreply.github.com>
Date: Sat, 27 Aug 2022 23:49:10 +0100
Subject: [PATCH 13/13] Add files via upload

---
 combined_csv.csv | 1001 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 1001 insertions(+)
 create mode 100644 combined_csv.csv

diff --git a/combined_csv.csv b/combined_csv.csv
new file mode 100644
index 0000000..8a3c52e
--- /dev/null
+++ b/combined_csv.csv
@@ -0,0 +1,1001 @@
+Invoice ID,Branch,City,Customer type,Gender,Product line,Unit price,Quantity,Tax 5%,Total,Date,Time,Payment,cogs,gross margin percentage,gross income,Rating
+692-92-5582,B,Abuja,Member,Female,Food and beverages,19742.4,3,2961.36,62188.55999999999,2/20/2019,13:27,Card,59227.2,4.761904762,2961.36,5.9
+351-62-0822,B,Abuja,Member,Female,Fashion accessories,5212.8,4,1042.5600000000002,21893.76,2/6/2019,18:07,Epay,20851.2,4.761904762,1042.5600000000002,4.5
+529-56-3974,B,Abuja,Member,Male,Electronic accessories,9183.6,4,1836.72,38571.12,3/9/2019,17:03,Cash,36734.4,4.761904762,1836.72,6.8
+299-46-1805,B,Abuja,Member,Female,Sports and travel,33739.2,6,10121.76,212556.96,1/15/2019,16:19,Cash,202435.2,4.761904762,10121.76,4.5
+319-50-3348,B,Abuja,Normal,Female,Home and lifestyle,14507.999999999998,2,1450.8000000000002,30466.8,3/11/2019,15:30,Epay,29016.0,4.761904762,1450.8000000000002,4.4
+371-85-5789,B,Abuja,Normal,Male,Health and beauty,31672.800000000003,3,4750.92,99769.32,3/5/2019,10:40,Epay,95018.4,4.761904762,4750.92,5.1
+273-16-6619,B,Abuja,Normal,Male,Home and lifestyle,11952.000000000002,2,1195.2,25099.2,3/15/2019,12:20,Card,23904.000000000004,4.761904762,1195.2,4.4
+649-29-6775,B,Abuja,Normal,Male,Fashion accessories,12067.2,1,603.36,12670.56,2/8/2019,15:31,Cash,12067.2,4.761904762,603.36,6.7
+145-94-9061,B,Abuja,Normal,Female,Food and beverages,31809.6,5,7952.4,167000.4,1/25/2019,19:48,Cash,159048.0,4.761904762,7952.4,9.6
+871-79-8483,B,Abuja,Normal,Male,Fashion accessories,33886.8,5,8471.699999999999,177905.7,2/25/2019,19:39,Card,169434.0,4.761904762,8471.699999999999,4.8
+149-71-6266,B,Abuja,Member,Male,Sports and travel,28105.2,9,12647.34,265594.14,1/28/2019,12:43,Cash,252946.8,4.761904762,12647.34,4.5
+640-49-2076,B,Abuja,Normal,Male,Sports and travel,30160.8,8,12064.32,253350.72,1/10/2019,14:49,Cash,241286.4,4.761904762,12064.32,5.1
+777-82-7220,B,Abuja,Member,Male,Home and lifestyle,10843.2,8,4337.28,91082.88,3/3/2019,13:01,Cash,86745.6,4.761904762,4337.28,7.7
+280-35-5823,B,Abuja,Member,Female,Home and lifestyle,31219.2,1,1560.96,32780.16,1/17/2019,18:45,Epay,31219.2,4.761904762,1560.96,7.9
+354-25-5821,B,Abuja,Member,Female,Sports and travel,24883.2,6,7464.96,156764.15999999997,2/8/2019,13:03,Cash,149299.2,4.761904762,7464.96,5.6
+132-32-9879,B,Abuja,Member,Female,Electronic accessories,33825.6,4,6765.120000000001,142067.52,3/9/2019,18:00,Cash,135302.4,4.761904762,6765.120000000001,9.5
+370-41-7321,B,Abuja,Member,Male,Health and beauty,20408.4,9,9183.78,192859.38,2/27/2019,17:24,Card,183675.6,4.761904762,9183.78,8.4
+727-46-3608,B,Abuja,Member,Female,Food and beverages,7203.6,9,3241.62,68074.02,2/6/2019,15:47,Epay,64832.4,4.761904762,3241.62,4.1
+669-54-1719,B,Abuja,Member,Male,Electronic accessories,6814.8,6,2044.44,42933.24,2/10/2019,12:45,Card,40888.8,4.761904762,2044.44,8.1
+616-24-2851,B,Abuja,Member,Female,Fashion accessories,6433.200000000001,4,1286.64,27019.44,3/22/2019,14:42,Epay,25732.800000000003,4.761904762,1286.64,6.5
+242-55-6721,B,Abuja,Normal,Male,Home and lifestyle,5817.6,2,581.76,12216.96,3/7/2019,11:49,Epay,11635.2,4.761904762,581.76,6.5
+347-34-2234,B,Abuja,Member,Female,Sports and travel,19825.2,9,8921.34,187348.14,2/3/2019,13:40,Epay,178426.8,4.761904762,8921.34,10.0
+853-23-2453,B,Abuja,Member,Male,Health and beauty,27266.4,4,5453.28,114518.88,2/14/2019,14:35,Cash,109065.6,4.761904762,5453.28,7.6
+109-28-2512,B,Abuja,Member,Female,Fashion accessories,35139.6,6,10541.88,221379.48,1/7/2019,15:01,Epay,210837.6,4.761904762,10541.88,9.9
+510-95-6347,B,Abuja,Member,Female,Food and beverages,17467.2,3,2620.08,55021.68,3/5/2019,18:17,Epay,52401.6,4.761904762,2620.08,4.0
+847-38-7188,B,Abuja,Normal,Female,Food and beverages,34804.8,3,5220.72,109635.12,1/26/2019,19:56,Epay,104414.4,4.761904762,5220.72,6.4
+318-68-5053,B,Abuja,Normal,Female,Health and beauty,27716.4,6,8314.92,174613.31999999998,2/27/2019,17:55,Cash,166298.4,4.761904762,8314.92,6.1
+152-08-9985,B,Abuja,Member,Male,Health and beauty,23169.6,9,10426.32,218952.72,3/12/2019,12:09,Card,208526.4,4.761904762,10426.32,8.6
+766-85-7061,B,Abuja,Normal,Male,Health and beauty,31633.2,10,15816.6,332148.6,3/29/2019,10:25,Epay,316332.0,4.761904762,15816.6,5.1
+733-01-9107,B,Abuja,Normal,Male,Home and lifestyle,29772.0,6,8931.6,187563.6,3/5/2019,18:14,Cash,178632.0,4.761904762,8931.6,7.4
+716-39-1409,B,Abuja,Normal,Male,Health and beauty,10926.0,7,3824.1,80306.09999999999,3/19/2019,18:19,Cash,76482.0,4.761904762,3824.1,8.0
+479-26-8945,B,Abuja,Member,Female,Sports and travel,5936.4,2,593.64,12466.44,2/5/2019,11:32,Epay,11872.8,4.761904762,593.64,4.6
+227-78-1148,B,Abuja,Normal,Female,Fashion accessories,26222.4,7,9177.84,192734.64,2/15/2019,12:44,Cash,183556.8,4.761904762,9177.84,8.4
+291-32-1427,B,Abuja,Member,Male,Fashion accessories,7898.400000000001,5,1974.6,41466.6,3/5/2019,12:29,Epay,39492.0,4.761904762,1974.6,5.3
+659-65-8956,B,Abuja,Member,Male,Fashion accessories,18489.6,1,924.48,19414.08,1/16/2019,15:26,Epay,18489.6,4.761904762,924.48,5.2
+378-24-2715,B,Abuja,Normal,Male,Home and lifestyle,19238.4,2,1923.84,40400.64,1/20/2019,20:38,Epay,38476.8,4.761904762,1923.84,4.1
+219-22-9386,B,Abuja,Member,Male,Sports and travel,35985.6,9,16193.52,340063.92,3/9/2019,17:26,Card,323870.4,4.761904762,16193.52,4.2
+268-27-6179,B,Abuja,Member,Female,Fashion accessories,20329.2,8,8131.68,170765.28,3/9/2019,14:57,Epay,162633.6,4.761904762,8131.68,7.3
+549-84-7482,B,Abuja,Normal,Female,Sports and travel,32500.8,9,14625.36,307132.56,2/8/2019,11:15,Epay,292507.2,4.761904762,14625.36,7.2
+191-10-6171,B,Abuja,Normal,Female,Fashion accessories,14263.2,7,4992.12,104834.52,1/25/2019,13:18,Cash,99842.4,4.761904762,4992.12,7.5
+695-51-0018,B,Abuja,Normal,Female,Sports and travel,12542.4,4,2508.48,52678.08,2/10/2019,18:36,Cash,50169.600000000006,4.761904762,2508.48,7.4
+590-83-4591,B,Abuja,Member,Male,Electronic accessories,31482.0,6,9444.6,198336.6,2/17/2019,14:40,Card,188892.00000000003,4.761904762,9444.6,8.8
+241-72-9525,B,Abuja,Normal,Male,Sports and travel,18687.6,10,9343.8,196219.8,2/16/2019,12:21,Cash,186876.0,4.761904762,9343.8,8.2
+262-47-2794,B,Abuja,Member,Male,Home and lifestyle,25869.6,8,10347.84,217304.64,3/6/2019,15:07,Card,206956.8,4.761904762,10347.84,6.2
+608-96-3517,B,Abuja,Member,Female,Fashion accessories,32954.4,4,6590.88,138408.48,3/23/2019,19:20,Card,131817.6,4.761904762,6590.88,4.8
+279-74-2924,B,Abuja,Member,Male,Electronic accessories,25981.2,1,1299.06,27280.26,1/4/2019,19:40,Cash,25981.2,4.761904762,1299.06,6.1
+307-85-2293,B,Abuja,Normal,Male,Home and lifestyle,18100.8,5,4525.2,95029.2,3/7/2019,13:58,Epay,90504.0,4.761904762,4525.2,9.7
+743-04-1105,B,Abuja,Member,Male,Health and beauty,34999.2,9,15749.639999999998,330742.44,3/30/2019,14:43,Epay,314992.8,4.761904762,15749.639999999998,6.0
+423-57-2993,B,Abuja,Normal,Male,Sports and travel,33620.4,6,10086.12,211808.52,3/27/2019,19:18,Epay,201722.4,4.761904762,10086.12,10.0
+548-46-9322,B,Abuja,Normal,Male,Food and beverages,14364.0,10,7182.0,150822.0,2/20/2019,15:24,Card,143640.0,4.761904762,7182.0,5.9
+505-02-0892,B,Abuja,Member,Male,Health and beauty,15325.2,8,6130.08,128731.68,2/25/2019,14:12,Epay,122601.6,4.761904762,6130.08,5.6
+316-55-4634,B,Abuja,Member,Male,Food and beverages,28818.0,5,7204.5,151294.5,1/26/2019,12:45,Card,144090.0,4.761904762,7204.5,9.4
+608-27-6295,B,Abuja,Member,Male,Electronic accessories,19040.4,6,5712.12,119954.52,1/19/2019,17:34,Card,114242.4,4.761904762,5712.12,9.8
+414-12-7047,B,Abuja,Normal,Male,Food and beverages,7124.4,8,2849.76,59844.96,1/18/2019,12:04,Epay,56995.2,4.761904762,2849.76,8.7
+895-66-0685,B,Abuja,Member,Male,Food and beverages,6508.799999999999,3,976.32,20502.72,3/5/2019,19:46,Epay,19526.4,4.761904762,976.32,8.0
+305-14-0245,B,Abuja,Member,Female,Home and lifestyle,34016.4,8,13606.56,285737.76,3/3/2019,19:00,Epay,272131.2,4.761904762,13606.56,7.5
+732-04-5373,B,Abuja,Member,Male,Home and lifestyle,16729.2,4,3345.84,70262.64,2/8/2019,10:53,Cash,66916.8,4.761904762,3345.84,7.0
+284-34-9626,B,Abuja,Normal,Female,Home and lifestyle,27734.4,3,4160.16,87363.36,2/11/2019,10:39,Card,83203.2,4.761904762,4160.16,7.2
+437-58-8131,B,Abuja,Normal,Female,Fashion accessories,26467.2,2,2646.7200000000003,55581.12,1/15/2019,13:41,Epay,52934.4,4.761904762,2646.7200000000003,4.6
+641-43-2399,B,Abuja,Normal,Male,Home and lifestyle,9198.0,4,1839.6,38631.6,1/26/2019,20:23,Epay,36792.0,4.761904762,1839.6,5.7
+542-41-0513,B,Abuja,Member,Female,Electronic accessories,20696.4,4,4139.28,86924.88,3/15/2019,11:57,Cash,82785.6,4.761904762,4139.28,6.6
+875-46-5808,B,Abuja,Member,Male,Health and beauty,9324.0,10,4662.0,97902.0,2/6/2019,14:51,Epay,93240.0,4.761904762,4662.0,8.7
+394-43-4238,B,Abuja,Member,Male,Home and lifestyle,6397.2,5,1599.3,33585.3,2/15/2019,12:42,Card,31986.0,4.761904762,1599.3,5.4
+573-58-9734,B,Abuja,Normal,Female,Fashion accessories,10933.2,3,1639.98,34439.579999999994,3/28/2019,13:41,Epay,32799.6,4.761904762,1639.98,5.1
+817-69-8206,B,Abuja,Normal,Female,Electronic accessories,35902.8,9,16156.26,339281.46,3/2/2019,19:42,Card,323125.2,4.761904762,16156.26,6.5
+142-63-6033,B,Abuja,Normal,Male,Home and lifestyle,33249.6,5,8312.4,174560.4,3/20/2019,19:17,Epay,166248.0,4.761904762,8312.4,4.9
+656-16-1063,B,Abuja,Normal,Male,Sports and travel,16711.2,3,2506.68,52640.27999999999,1/4/2019,13:24,Card,50133.6,4.761904762,2506.68,4.4
+891-58-8335,B,Abuja,Member,Female,Sports and travel,10659.6,7,3730.86,78348.06,3/11/2019,15:53,Cash,74617.2,4.761904762,3730.86,6.5
+560-30-5617,B,Abuja,Normal,Female,Sports and travel,8917.2,5,2229.3,46815.3,3/24/2019,18:27,Cash,44586.0,4.761904762,2229.3,8.5
+549-03-9315,B,Abuja,Normal,Male,Fashion accessories,34153.200000000004,8,13661.28,286886.88,2/12/2019,12:58,Epay,273225.60000000003,4.761904762,13661.28,8.7
+790-29-1172,B,Abuja,Normal,Female,Food and beverages,20642.4,3,3096.36,65023.55999999999,3/10/2019,18:59,Card,61927.2,4.761904762,3096.36,7.9
+239-36-3640,B,Abuja,Normal,Male,Electronic accessories,16326.0,6,4897.8,102853.8,1/31/2019,13:44,Epay,97956.0,4.761904762,4897.8,6.1
+468-01-2051,B,Abuja,Normal,Male,Food and beverages,22348.8,7,7822.080000000001,164263.68,3/6/2019,13:46,Epay,156441.6,4.761904762,7822.080000000001,5.4
+836-82-5858,B,Abuja,Member,Male,Health and beauty,24973.2,9,11237.94,235996.74,1/26/2019,19:14,Epay,224758.8,4.761904762,11237.94,4.0
+466-61-5506,B,Abuja,Member,Female,Electronic accessories,32652.0,6,9795.6,205707.6,2/26/2019,10:52,Cash,195912.00000000003,4.761904762,9795.6,5.3
+289-65-5721,B,Abuja,Normal,Female,Fashion accessories,29293.2,2,2929.32,61515.72,1/26/2019,19:28,Cash,58586.4,4.761904762,2929.32,6.5
+545-46-3100,B,Abuja,Member,Female,Electronic accessories,3812.4,3,571.86,12009.06,3/12/2019,13:52,Card,11437.2,4.761904762,571.86,8.7
+418-02-5978,B,Abuja,Normal,Female,Health and beauty,30272.4,9,13622.58,286074.18,2/11/2019,10:54,Cash,272451.6,4.761904762,13622.58,8.0
+269-04-5750,B,Abuja,Member,Male,Fashion accessories,26575.2,4,5315.04,111615.84,2/21/2019,18:31,Cash,106300.8,4.761904762,5315.04,6.7
+346-84-3103,B,Abuja,Member,Female,Electronic accessories,4759.2,5,1189.8,24985.8,3/2/2019,19:26,Cash,23796.0,4.761904762,1189.8,4.3
+376-02-8238,B,Abuja,Normal,Male,Home and lifestyle,33793.200000000004,8,13517.28,283862.88,2/2/2019,18:42,Card,270345.60000000003,4.761904762,13517.28,8.3
+866-05-7563,B,Abuja,Member,Female,Electronic accessories,29304.000000000004,3,4395.6,92307.6,2/9/2019,19:43,Cash,87912.0,4.761904762,4395.6,4.8
+785-13-7708,B,Abuja,Normal,Male,Food and beverages,26301.6,7,9205.56,193316.76,1/14/2019,19:06,Card,184111.2,4.761904762,9205.56,4.2
+845-51-0542,B,Abuja,Member,Male,Food and beverages,16758.0,9,7541.1,158363.1,2/2/2019,15:34,Epay,150822.0,4.761904762,7541.1,6.4
+110-48-7033,B,Abuja,Member,Male,Fashion accessories,11743.2,4,2348.64,49321.44,1/29/2019,14:12,Cash,46972.8,4.761904762,2348.64,9.0
+655-85-5130,B,Abuja,Member,Female,Fashion accessories,13787.999999999998,4,2757.6,57909.600000000006,3/13/2019,19:22,Cash,55151.99999999999,4.761904762,2757.6,5.7
+154-74-7179,B,Abuja,Normal,Male,Sports and travel,19602.0,1,980.1,20582.1,2/26/2019,19:24,Epay,19602.0,4.761904762,980.1,7.9
+571-94-0759,B,Abuja,Member,Female,Food and beverages,26856.0,10,13427.999999999998,281988.0,1/8/2019,20:55,Cash,268560.0,4.761904762,13427.999999999998,9.5
+783-09-1637,B,Abuja,Normal,Female,Sports and travel,24274.800000000003,5,6068.700000000001,127442.7,3/6/2019,18:13,Epay,121374.0,4.761904762,6068.700000000001,6.3
+477-24-6490,B,Abuja,Normal,Female,Health and beauty,35895.6,6,10768.68,226142.28,2/26/2019,16:52,Epay,215373.6,4.761904762,10768.68,7.9
+566-19-5475,B,Abuja,Normal,Male,Fashion accessories,17269.2,7,6044.22,126928.62,1/7/2019,20:52,Cash,120884.4,4.761904762,6044.22,6.2
+498-41-1961,B,Abuja,Normal,Male,Health and beauty,24004.800000000003,5,6001.200000000001,126025.2,2/20/2019,18:01,Cash,120024.0,4.761904762,6001.200000000001,7.6
+283-79-9594,B,Abuja,Normal,Female,Food and beverages,17463.6,7,6112.26,128357.46,1/25/2019,13:30,Card,122245.2,4.761904762,6112.26,5.2
+139-20-0155,B,Abuja,Member,Male,Electronic accessories,14507.999999999998,10,7253.999999999999,152334.0,1/24/2019,17:37,Card,145080.0,4.761904762,7253.999999999999,7.0
+585-03-5943,B,Abuja,Normal,Male,Health and beauty,6519.599999999999,10,3259.8,68455.8,3/13/2019,11:46,Epay,65196.0,4.761904762,3259.8,5.9
+573-10-3877,B,Abuja,Member,Male,Health and beauty,14043.6,1,702.18,14745.78,3/12/2019,16:46,Card,14043.6,4.761904762,702.18,4.7
+396-90-2219,B,Abuja,Normal,Female,Electronic accessories,5385.6,8,2154.24,45239.04,2/23/2019,12:29,Cash,43084.8,4.761904762,2154.24,8.6
+532-59-7201,B,Abuja,Member,Male,Sports and travel,28774.800000000003,6,8632.44,181281.24,1/31/2019,14:04,Cash,172648.8,4.761904762,8632.44,5.5
+276-54-0879,B,Abuja,Normal,Male,Sports and travel,35186.4,4,7037.28,147782.88,3/12/2019,19:53,Epay,140745.6,4.761904762,7037.28,6.4
+730-61-8757,B,Abuja,Member,Male,Health and beauty,18406.8,4,3681.36,77308.56,1/25/2019,10:11,Card,73627.2,4.761904762,3681.36,4.0
+868-81-1752,B,Abuja,Normal,Male,Home and lifestyle,7927.2,9,3567.24,74912.04000000001,2/7/2019,18:48,Cash,71344.8,4.761904762,3567.24,6.8
+692-27-8933,B,Abuja,Normal,Female,Sports and travel,20862.0,6,6258.6,131430.6,2/24/2019,13:02,Cash,125172.0,4.761904762,6258.6,5.2
+374-17-3652,B,Abuja,Member,Female,Food and beverages,15415.2,9,6936.840000000001,145673.64,2/5/2019,15:26,Card,138736.8,4.761904762,6936.840000000001,8.9
+378-07-7001,B,Abuja,Member,Male,Electronic accessories,17312.4,3,2596.86,54534.06,2/10/2019,18:23,Card,51937.2,4.761904762,2596.86,7.8
+433-75-6987,B,Abuja,Member,Female,Health and beauty,20149.2,7,7052.22,148096.62,3/5/2019,19:06,Epay,141044.4,4.761904762,7052.22,8.9
+873-95-4984,B,Abuja,Member,Female,Health and beauty,27684.000000000004,7,9689.4,203477.4,2/15/2019,20:21,Cash,193788.0,4.761904762,9689.4,7.7
+400-45-1220,B,Abuja,Normal,Female,Health and beauty,4860.0,10,2430.0,51030.0,2/27/2019,11:06,Card,48600.0,4.761904762,2430.0,4.8
+115-99-4379,B,Abuja,Member,Female,Fashion accessories,19702.8,7,6895.98,144815.58,3/14/2019,19:02,Card,137919.6,4.761904762,6895.98,8.5
+565-67-6697,B,Abuja,Member,Male,Home and lifestyle,9720.0,9,4374.0,91854.0,3/2/2019,14:16,Cash,87480.0,4.761904762,4374.0,4.8
+889-04-9723,B,Abuja,Member,Female,Food and beverages,32090.4,4,6418.08,134779.68000000002,1/7/2019,12:20,Card,128361.6,4.761904762,6418.08,7.8
+453-63-6187,B,Abuja,Normal,Male,Electronic accessories,9900.0,3,1485.0,31185.0,3/1/2019,15:40,Epay,29700.0,4.761904762,1485.0,6.5
+578-80-7669,B,Abuja,Normal,Male,Sports and travel,26989.2,1,1349.46,28338.660000000003,3/16/2019,16:58,Cash,26989.2,4.761904762,1349.46,5.6
+201-86-2184,B,Abuja,Member,Female,Electronic accessories,9453.6,7,3308.76,69483.95999999999,2/2/2019,19:40,Cash,66175.2,4.761904762,3308.76,9.9
+261-12-8671,B,Abuja,Normal,Female,Fashion accessories,21945.6,2,2194.56,46085.76,1/25/2019,19:39,Card,43891.2,4.761904762,2194.56,4.9
+843-01-4703,B,Abuja,Member,Female,Home and lifestyle,12736.8,9,5731.56,120362.76,1/5/2019,19:50,Card,114631.2,4.761904762,5731.56,9.6
+182-69-8360,B,Abuja,Normal,Female,Electronic accessories,8514.0,4,1702.8000000000002,35758.8,1/30/2019,13:32,Card,34056.0,4.761904762,1702.8000000000002,4.0
+868-52-7573,B,Abuja,Normal,Female,Food and beverages,35888.4,5,8972.1,188414.1,1/14/2019,12:09,Cash,179442.0,4.761904762,8972.1,9.9
+525-88-7307,B,Abuja,Member,Male,Sports and travel,27295.2,1,1364.7600000000002,28659.960000000003,1/31/2019,13:19,Cash,27295.2,4.761904762,1364.7600000000002,5.8
+596-42-3999,B,Abuja,Normal,Male,Food and beverages,6559.2,7,2295.7200000000003,48210.12,3/10/2019,14:04,Card,45914.4,4.761904762,2295.7200000000003,6.6
+173-82-9529,B,Abuja,Normal,Female,Fashion accessories,13662.000000000002,10,6831.000000000001,143451.0,1/26/2019,14:51,Cash,136620.0,4.761904762,6831.000000000001,9.7
+760-54-1821,B,Abuja,Normal,Male,Home and lifestyle,4892.4,9,2201.58,46233.18,3/15/2019,10:26,Cash,44031.6,4.761904762,2201.58,5.8
+793-10-3222,B,Abuja,Member,Female,Health and beauty,14781.6,6,4434.48,93124.08,3/5/2019,13:30,Card,88689.6,4.761904762,4434.48,8.3
+346-12-3257,B,Abuja,Member,Male,Electronic accessories,6926.4,9,3116.88,65454.48,3/4/2019,16:28,Cash,62337.6,4.761904762,3116.88,8.0
+831-64-0259,B,Abuja,Normal,Female,Fashion accessories,14310.0,5,3577.5,75127.5,2/22/2019,10:43,Epay,71550.0,4.761904762,3577.5,9.6
+725-32-9708,B,Abuja,Member,Female,Fashion accessories,24735.6,4,4947.12,103889.52,1/4/2019,19:01,Cash,98942.4,4.761904762,4947.12,4.1
+244-08-0162,B,Abuja,Normal,Female,Health and beauty,12315.6,10,6157.8,129313.8,1/2/2019,13:00,Cash,123156.0,4.761904762,6157.8,5.1
+569-71-4390,B,Abuja,Normal,Male,Sports and travel,7873.200000000001,2,787.3199999999999,16533.72,1/25/2019,14:29,Epay,15746.4,4.761904762,787.3199999999999,6.9
+268-03-6164,B,Abuja,Normal,Male,Health and beauty,34599.6,1,1729.98,36329.58,1/25/2019,16:28,Epay,34599.6,4.761904762,1729.98,7.8
+848-07-1692,B,Abuja,Normal,Female,Health and beauty,20599.2,2,2059.92,43258.32,1/12/2019,17:13,Epay,41198.4,4.761904762,2059.92,8.3
+301-81-8610,B,Abuja,Member,Male,Fashion accessories,9151.2,8,3660.48,76870.08,3/19/2019,19:42,Card,73209.6,4.761904762,3660.48,6.7
+198-84-7132,B,Abuja,Member,Male,Fashion accessories,14619.6,9,6578.82,138155.22,1/2/2019,13:40,Cash,131576.4,4.761904762,6578.82,7.0
+650-98-6268,B,Abuja,Member,Female,Food and beverages,7513.200000000001,3,1126.98,23666.58,3/20/2019,13:53,Card,22539.6,4.761904762,1126.98,8.0
+741-73-3559,B,Abuja,Normal,Male,Sports and travel,24217.2,5,6054.299999999999,127140.3,2/27/2019,17:27,Cash,121086.0,4.761904762,6054.299999999999,6.9
+286-75-7818,B,Abuja,Normal,Male,Fashion accessories,24868.8,2,2486.88,52224.48,1/31/2019,19:48,Card,49737.6,4.761904762,2486.88,6.9
+616-87-0016,B,Abuja,Normal,Male,Fashion accessories,34394.4,7,12038.04,252798.84,3/9/2019,14:36,Card,240760.8,4.761904762,12038.04,9.6
+837-55-7229,B,Abuja,Normal,Female,Fashion accessories,17078.399999999998,1,853.92,17932.32,2/22/2019,18:19,Card,17078.399999999998,4.761904762,853.92,6.8
+394-30-3170,B,Abuja,Member,Female,Sports and travel,31834.800000000003,8,12733.92,267412.32,3/22/2019,19:35,Card,254678.4,4.761904762,12733.92,4.3
+390-80-5128,B,Abuja,Member,Female,Health and beauty,6893.999999999999,1,344.7,7238.700000000001,1/28/2019,17:58,Card,6893.999999999999,4.761904762,344.7,9.5
+296-11-7041,B,Abuja,Member,Female,Health and beauty,9745.2,1,487.26,10232.46,1/12/2019,20:07,Card,9745.2,4.761904762,487.26,5.3
+449-27-2918,B,Abuja,Member,Female,Sports and travel,14083.2,1,704.16,14787.36,3/26/2019,11:02,Card,14083.2,4.761904762,704.16,9.6
+891-01-7034,B,Abuja,Normal,Female,Electronic accessories,26895.6,6,8068.68,169442.28,1/1/2019,19:07,Cash,161373.6,4.761904762,8068.68,6.7
+744-09-5786,B,Abuja,Normal,Male,Electronic accessories,7923.6,6,2377.08,49918.67999999999,1/2/2019,18:50,Cash,47541.6,4.761904762,2377.08,7.6
+767-54-1907,B,Abuja,Member,Female,Fashion accessories,10641.6,5,2660.4,55868.4,2/13/2019,16:59,Cash,53208.00000000001,4.761904762,2660.4,6.9
+710-46-4433,B,Abuja,Member,Female,Food and beverages,27864.000000000004,9,12538.8,263314.8,2/15/2019,14:15,Card,250776.0,4.761904762,12538.8,4.5
+533-33-5337,B,Abuja,Normal,Male,Electronic accessories,28580.4,10,14290.2,300094.2,2/7/2019,20:24,Cash,285804.0,4.761904762,14290.2,6.2
+234-03-4040,B,Abuja,Member,Female,Food and beverages,26298.0,10,13149.0,276129.0,3/3/2019,12:25,Card,262980.0,4.761904762,13149.0,8.7
+554-53-3790,B,Abuja,Normal,Male,Sports and travel,13327.2,6,3998.16,83961.36,3/22/2019,18:33,Cash,79963.2,4.761904762,3998.16,4.5
+731-59-7531,B,Abuja,Member,Male,Health and beauty,26125.2,8,10450.08,219451.68,3/30/2019,17:58,Cash,209001.6,4.761904762,10450.08,4.6
+701-69-8742,B,Abuja,Normal,Male,Sports and travel,12373.2,10,6186.599999999999,129918.6,3/16/2019,10:11,Epay,123732.0,4.761904762,6186.599999999999,6.7
+305-18-3552,B,Abuja,Member,Male,Home and lifestyle,21736.8,10,10868.4,228236.4,2/12/2019,16:19,Cash,217368.0,4.761904762,10868.4,6.0
+438-01-4015,B,Abuja,Member,Female,Sports and travel,17816.4,4,3563.28,74828.88,3/21/2019,15:25,Epay,71265.6,4.761904762,3563.28,6.6
+709-58-4068,B,Abuja,Normal,Female,Fashion accessories,14792.4,10,7396.200000000001,155320.2,2/28/2019,14:42,Cash,147924.0,4.761904762,7396.200000000001,7.3
+627-95-3243,B,Abuja,Member,Female,Home and lifestyle,27964.800000000003,9,12584.159999999998,264267.36,2/4/2019,13:21,Epay,251683.2,4.761904762,12584.159999999998,9.8
+686-41-0932,B,Abuja,Normal,Female,Fashion accessories,12492.000000000002,2,1249.2,26233.2,3/13/2019,19:48,Epay,24984.000000000004,4.761904762,1249.2,8.2
+608-04-3797,B,Abuja,Member,Female,Health and beauty,9115.2,8,3646.08,76567.68,3/5/2019,20:24,Epay,72921.6,4.761904762,3646.08,8.7
+437-53-3084,B,Abuja,Normal,Male,Fashion accessories,35960.4,2,3596.0399999999995,75516.84,2/26/2019,11:48,Epay,71920.8,4.761904762,3596.0399999999995,7.1
+632-32-4574,B,Abuja,Normal,Male,Sports and travel,27331.2,8,10932.48,229582.08,3/20/2019,14:14,Cash,218649.6,4.761904762,10932.48,5.5
+401-18-8016,B,Abuja,Member,Female,Sports and travel,35326.8,1,1766.34,37093.14,1/21/2019,17:36,Cash,35326.8,4.761904762,1766.34,8.9
+277-63-2961,B,Abuja,Member,Male,Sports and travel,26629.2,1,1331.46,27960.66,2/3/2019,15:53,Card,26629.2,4.761904762,1331.46,5.4
+282-35-2475,B,Abuja,Normal,Female,Sports and travel,33591.6,2,3359.16,70542.36,3/25/2019,17:53,Cash,67183.2,4.761904762,3359.16,6.3
+511-54-3087,B,Abuja,Normal,Male,Sports and travel,31842.0,1,1592.1,33434.1,2/25/2019,16:36,Card,31842.0,4.761904762,1592.1,9.5
+387-49-4215,B,Abuja,Member,Female,Sports and travel,17460.0,3,2619.0,54999.0,1/8/2019,12:50,Cash,52380.0,4.761904762,2619.0,6.7
+862-17-9201,B,Abuja,Normal,Female,Food and beverages,30258.0,6,9077.4,190625.4,1/29/2019,10:48,Card,181548.0,4.761904762,9077.4,7.7
+291-21-5991,B,Abuja,Member,Male,Health and beauty,22064.4,5,5516.1,115838.1,3/29/2019,14:28,Cash,110322.0,4.761904762,5516.1,7.0
+347-72-6115,B,Abuja,Member,Female,Sports and travel,32666.4,7,11433.24,240098.04,1/16/2019,18:03,Card,228664.8,4.761904762,11433.24,6.2
+425-85-2085,B,Abuja,Member,Male,Health and beauty,19749.6,5,4937.4,103685.4,3/29/2019,16:48,Epay,98748.0,4.761904762,4937.4,9.8
+734-91-1155,B,Abuja,Normal,Female,Electronic accessories,16455.6,3,2468.34,51835.14,3/26/2019,10:34,Card,49366.8,4.761904762,2468.34,7.7
+286-62-6248,B,Abuja,Normal,Male,Fashion accessories,14115.6,4,2823.1200000000003,59285.52,1/16/2019,20:03,Card,56462.4,4.761904762,2823.1200000000003,9.0
+339-38-9982,B,Abuja,Member,Male,Fashion accessories,21549.6,2,2154.96,45254.16,1/13/2019,14:55,Epay,43099.2,4.761904762,2154.96,6.7
+827-44-5872,B,Abuja,Member,Female,Food and beverages,19569.6,10,9784.8,205480.8,2/7/2019,11:28,Card,195696.0,4.761904762,9784.8,6.1
+857-67-9057,B,Abuja,Normal,Male,Electronic accessories,8262.0,10,4131.0,86751.0,2/6/2019,19:20,Epay,82620.0,4.761904762,4131.0,8.2
+339-12-4827,B,Abuja,Member,Female,Fashion accessories,26625.6,1,1331.28,27956.88,1/5/2019,11:32,Card,26625.6,4.761904762,1331.28,5.0
+173-50-1108,B,Abuja,Member,Female,Sports and travel,7264.8,4,1452.9600000000005,30512.16,2/13/2019,12:14,Card,29059.2,4.761904762,1452.9600000000005,5.0
+841-18-8232,B,Abuja,Normal,Female,Food and beverages,25632.0,1,1281.6,26913.6,1/5/2019,20:40,Card,25632.0,4.761904762,1281.6,9.2
+701-23-5550,B,Abuja,Member,Male,Home and lifestyle,13971.6,4,2794.32,58680.72,3/19/2019,13:40,Epay,55886.4,4.761904762,2794.32,4.9
+539-21-7227,B,Abuja,Normal,Female,Sports and travel,18554.4,5,4638.6,97410.6,1/26/2019,17:45,Cash,92772.0,4.761904762,4638.6,4.2
+747-58-7183,B,Abuja,Normal,Male,Fashion accessories,20617.2,3,3092.5800000000004,64944.18,2/9/2019,20:31,Epay,61851.6,4.761904762,3092.5800000000004,6.5
+582-52-8065,B,Abuja,Normal,Female,Fashion accessories,19551.6,9,8798.22,184762.62,2/22/2019,10:49,Cash,175964.4,4.761904762,8798.22,8.9
+210-57-1719,B,Abuja,Normal,Female,Health and beauty,20966.4,9,9434.88,198132.48,2/5/2019,12:34,Cash,188697.6,4.761904762,9434.88,9.7
+356-44-8813,B,Abuja,Normal,Male,Home and lifestyle,13492.8,3,2023.9200000000003,42502.32000000001,1/20/2019,13:45,Card,40478.4,4.761904762,2023.9200000000003,7.7
+198-66-9832,B,Abuja,Member,Female,Fashion accessories,25934.4,2,2593.44,54462.24,2/4/2019,19:38,Cash,51868.8,4.761904762,2593.44,9.5
+174-75-0888,B,Abuja,Normal,Male,Electronic accessories,7768.799999999999,9,3495.96,73415.15999999999,3/14/2019,12:32,Cash,69919.2,4.761904762,3495.96,7.3
+134-54-4720,B,Abuja,Normal,Female,Electronic accessories,15271.2,8,6108.48,128278.08000000002,1/30/2019,13:58,Epay,122169.6,4.761904762,6108.48,5.7
+514-37-2845,B,Abuja,Normal,Male,Fashion accessories,35730.0,2,3573.0000000000005,75033.0,3/20/2019,13:02,Cash,71460.0,4.761904762,3573.0000000000005,9.0
+241-11-2261,B,Abuja,Normal,Female,Fashion accessories,28749.6,7,10062.36,211309.56,1/10/2019,10:33,Card,201247.2,4.761904762,10062.36,5.5
+834-83-1826,B,Abuja,Member,Female,Home and lifestyle,29534.4,5,7383.6,155055.6,2/25/2019,17:16,Card,147672.0,4.761904762,7383.6,7.6
+343-61-3544,B,Abuja,Member,Male,Sports and travel,9601.2,10,4800.6,100812.6,1/29/2019,11:48,Cash,96012.0,4.761904762,4800.6,8.6
+355-34-6244,B,Abuja,Normal,Male,Food and beverages,26060.4,2,2606.04,54726.84,1/13/2019,19:55,Card,52120.8,4.761904762,2606.04,8.1
+339-96-8318,B,Abuja,Member,Male,Fashion accessories,29271.6,7,10245.06,215146.26,3/1/2019,19:49,Epay,204901.2,4.761904762,10245.06,6.3
+458-61-0011,B,Abuja,Normal,Male,Food and beverages,21708.0,4,4341.6,91173.6,2/20/2019,18:43,Cash,86832.0,4.761904762,4341.6,5.8
+207-73-1363,B,Abuja,Normal,Male,Health and beauty,25023.6,2,2502.36,52549.56,3/1/2019,12:15,Epay,50047.2,4.761904762,2502.36,8.1
+359-90-3665,B,Abuja,Member,Female,Fashion accessories,6508.799999999999,4,1301.76,27336.960000000003,1/14/2019,18:03,Card,26035.2,4.761904762,1301.76,9.5
+375-72-3056,B,Abuja,Normal,Male,Sports and travel,22701.6,3,3405.24,71510.04000000001,1/19/2019,15:58,Epay,68104.8,4.761904762,3405.24,7.0
+585-11-6748,B,Abuja,Member,Male,Sports and travel,34848.0,3,5227.2,109771.2,3/15/2019,13:05,Cash,104544.0,4.761904762,5227.2,5.3
+470-31-3286,B,Abuja,Normal,Male,Health and beauty,5335.2,3,800.2800000000001,16805.88,3/1/2019,11:30,Card,16005.6,4.761904762,800.2800000000001,8.7
+642-30-6693,B,Abuja,Normal,Female,Sports and travel,19623.6,6,5887.079999999999,123628.68,3/17/2019,13:54,Epay,117741.6,4.761904762,5887.079999999999,7.8
+830-58-2383,B,Abuja,Normal,Male,Home and lifestyle,11430.0,4,2286.0,48006.0,2/8/2019,15:26,Cash,45720.0,4.761904762,2286.0,8.6
+349-97-8902,B,Abuja,Member,Male,Food and beverages,20840.4,2,2084.04,43764.84,1/17/2019,10:37,Epay,41680.8,4.761904762,2084.04,8.9
+789-23-8625,B,Abuja,Member,Male,Fashion accessories,33559.2,3,5033.879999999999,105711.48,1/24/2019,11:45,Cash,100677.6,4.761904762,5033.879999999999,7.2
+327-40-9673,B,Abuja,Member,Male,Sports and travel,26136.0,6,7840.8,164656.8,1/13/2019,19:51,Cash,156816.0,4.761904762,7840.8,6.9
+670-79-6321,B,Abuja,Member,Female,Home and lifestyle,34052.4,7,11918.339999999998,250285.14,1/17/2019,15:27,Card,238366.8,4.761904762,11918.339999999998,4.9
+852-62-7105,B,Abuja,Normal,Female,Fashion accessories,29970.0,10,14985.0,314685.0,1/12/2019,11:25,Card,299700.0,4.761904762,14985.0,4.4
+598-06-7312,B,Abuja,Member,Male,Fashion accessories,32886.0,1,1644.3,34530.3,2/16/2019,15:42,Cash,32886.0,4.761904762,1644.3,6.8
+135-13-8269,B,Abuja,Member,Female,Food and beverages,28396.8,2,2839.68,59633.28,1/26/2019,16:04,Cash,56793.6,4.761904762,2839.68,9.1
+628-90-8624,B,Abuja,Member,Male,Health and beauty,29728.8,10,14864.4,312152.4,3/14/2019,14:41,Cash,297288.0,4.761904762,14864.4,5.0
+420-04-7590,B,Abuja,Normal,Male,Home and lifestyle,35892.0,3,5383.8,113059.8,3/18/2019,11:29,Epay,107676.0,4.761904762,5383.8,4.7
+182-88-2763,B,Abuja,Member,Male,Food and beverages,28767.6,3,4315.139999999999,90617.94,3/20/2019,19:28,Card,86302.8,4.761904762,4315.139999999999,5.0
+188-55-0967,B,Abuja,Member,Male,Health and beauty,23929.2,10,11964.6,251256.6,1/15/2019,15:01,Card,239292.00000000003,4.761904762,11964.6,5.0
+364-33-8584,B,Abuja,Member,Female,Food and beverages,6346.799999999999,5,1586.6999999999998,33320.700000000004,3/8/2019,15:27,Cash,31734.000000000004,4.761904762,1586.6999999999998,8.5
+665-63-9737,B,Abuja,Normal,Male,Fashion accessories,18871.2,3,2830.68,59444.28,2/27/2019,17:36,Epay,56613.6,4.761904762,2830.68,7.5
+695-09-5146,B,Abuja,Member,Female,Food and beverages,35564.4,3,5334.66,112027.86,2/23/2019,20:00,Epay,106693.2,4.761904762,5334.66,6.4
+794-32-2436,B,Abuja,Member,Male,Electronic accessories,20041.2,2,2004.12,42086.52,3/27/2019,15:08,Epay,40082.4,4.761904762,2004.12,6.0
+574-31-8277,B,Abuja,Member,Male,Fashion accessories,12106.8,1,605.34,12712.14,3/20/2019,19:55,Cash,12106.8,4.761904762,605.34,5.6
+369-82-2676,B,Abuja,Normal,Male,Electronic accessories,27237.6,5,6809.4,142997.4,1/15/2019,18:22,Epay,136188.0,4.761904762,6809.4,7.8
+563-47-4072,B,Abuja,Normal,Female,Health and beauty,20091.6,6,6027.48,126577.08,1/22/2019,11:52,Cash,120549.6,4.761904762,6027.48,9.9
+149-15-7606,B,Abuja,Member,Male,Sports and travel,13435.2,9,6045.84,126962.64,3/6/2019,15:31,Epay,120916.8,4.761904762,6045.84,5.1
+133-77-3154,B,Abuja,Member,Male,Fashion accessories,21664.8,4,4332.96,90992.16,2/16/2019,18:04,Card,86659.2,4.761904762,4332.96,9.4
+322-02-2271,B,Abuja,Normal,Female,Sports and travel,15469.2,3,2320.38,48727.98,2/3/2019,11:46,Cash,46407.6,4.761904762,2320.38,9.3
+725-67-2480,B,Abuja,Member,Female,Fashion accessories,21150.0,6,6345.0,133245.0,3/24/2019,18:14,Card,126900.0,4.761904762,6345.0,5.9
+779-42-2410,B,Abuja,Member,Male,Food and beverages,20786.4,3,3117.96,65477.15999999999,2/20/2019,13:06,Epay,62359.2,4.761904762,3117.96,7.7
+190-14-3147,B,Abuja,Normal,Female,Health and beauty,6469.2,4,1293.84,27170.64,2/23/2019,20:43,Epay,25876.8,4.761904762,1293.84,6.4
+679-22-6530,B,Abuja,Normal,Female,Sports and travel,14623.2,2,1462.3200000000002,30708.72,1/17/2019,10:01,Card,29246.4,4.761904762,1462.3200000000002,4.1
+642-61-4706,B,Abuja,Member,Male,Food and beverages,33624.0,2,3362.4,70610.4,3/30/2019,16:34,Cash,67248.0,4.761904762,3362.4,5.5
+576-31-4774,B,Abuja,Normal,Female,Health and beauty,26427.6,3,3964.14,83246.94,3/2/2019,13:10,Epay,79282.8,4.761904762,3964.14,4.0
+242-11-3142,B,Abuja,Member,Male,Fashion accessories,30157.2,2,3015.7200000000003,63330.12,2/24/2019,19:57,Cash,60314.4,4.761904762,3015.7200000000003,4.6
+752-23-3760,B,Abuja,Member,Female,Sports and travel,23068.8,7,8074.08,169555.68000000002,2/19/2019,19:29,Card,161481.6,4.761904762,8074.08,7.3
+528-87-5606,B,Abuja,Member,Female,Electronic accessories,14212.8,1,710.64,14923.439999999997,2/12/2019,19:43,Cash,14212.8,4.761904762,710.64,6.5
+320-85-2052,B,Abuja,Normal,Female,Sports and travel,12531.6,1,626.5799999999999,13158.18,1/14/2019,10:11,Card,12531.6,4.761904762,626.5799999999999,7.0
+510-79-0415,B,Abuja,Member,Female,Sports and travel,8308.8,6,2492.64,52345.44,1/24/2019,19:20,Epay,49852.8,4.761904762,2492.64,4.9
+241-96-5076,B,Abuja,Member,Female,Home and lifestyle,17676.0,2,1767.6,37119.6,1/8/2019,12:58,Card,35352.0,4.761904762,1767.6,6.4
+767-97-4650,B,Abuja,Member,Female,Sports and travel,23338.8,2,2333.88,49011.48,1/8/2019,11:59,Card,46677.6,4.761904762,2333.88,8.0
+826-58-8051,B,Abuja,Normal,Male,Home and lifestyle,22388.4,4,4477.679999999999,94031.28,1/6/2019,19:46,Epay,89553.59999999999,4.761904762,4477.679999999999,4.3
+176-64-7711,B,Abuja,Normal,Male,Food and beverages,11635.2,3,1745.28,36650.88,3/27/2019,19:11,Card,34905.6,4.761904762,1745.28,4.3
+191-29-0321,B,Abuja,Member,Female,Fashion accessories,7117.2,10,3558.6,74730.6,2/27/2019,18:57,Card,71172.0,4.761904762,3558.6,5.0
+729-06-2010,B,Abuja,Member,Male,Health and beauty,28969.2,9,13036.14,273758.94,1/6/2019,11:18,Cash,260722.8,4.761904762,13036.14,9.2
+640-48-5028,B,Abuja,Member,Female,Home and lifestyle,31820.4,9,14319.18,300702.78,3/2/2019,12:40,Cash,286383.6,4.761904762,14319.18,6.3
+186-79-9562,B,Abuja,Normal,Male,Health and beauty,25837.2,7,9043.02,189903.42,3/29/2019,14:06,Cash,180860.4,4.761904762,9043.02,8.9
+834-45-5519,B,Abuja,Normal,Female,Electronic accessories,15480.0,4,3096.0,65016.0,1/31/2019,20:48,Epay,61920.0,4.761904762,3096.0,7.6
+821-14-9046,B,Abuja,Member,Female,Fashion accessories,6292.8,6,1887.84,39644.64,1/18/2019,15:04,Card,37756.8,4.761904762,1887.84,6.1
+418-05-0656,B,Abuja,Normal,Female,Fashion accessories,9201.6,7,3220.56,67631.76,2/2/2019,20:42,Cash,64411.2,4.761904762,3220.56,7.1
+776-68-1096,B,Abuja,Normal,Male,Home and lifestyle,15883.2,3,2382.48,50032.08,3/18/2019,13:45,Card,47649.600000000006,4.761904762,2382.48,7.9
+434-35-9162,B,Abuja,Member,Male,Food and beverages,8402.4,4,1680.48,35290.079999999994,2/4/2019,18:53,Epay,33609.6,4.761904762,1680.48,7.4
+174-64-0215,B,Abuja,Normal,Male,Sports and travel,25106.4,10,12553.2,263617.2,3/5/2019,17:49,Card,251064.0,4.761904762,12553.2,8.9
+299-29-0180,B,Abuja,Member,Female,Home and lifestyle,18784.8,7,6574.679999999999,138068.28,3/9/2019,10:54,Cash,131493.6,4.761904762,6574.679999999999,9.3
+438-23-1242,B,Abuja,Normal,Male,Electronic accessories,27316.8,7,9560.88,200778.48000000004,1/24/2019,10:38,Epay,191217.6,4.761904762,9560.88,8.9
+238-45-6950,B,Abuja,Member,Male,Food and beverages,19339.2,1,966.9600000000002,20306.160000000003,3/1/2019,20:03,Epay,19339.2,4.761904762,966.9600000000002,6.4
+197-77-7132,B,Abuja,Member,Male,Electronic accessories,32961.6,8,13184.64,276877.44,1/12/2019,18:22,Epay,263692.8,4.761904762,13184.64,6.0
+457-13-1708,B,Abuja,Member,Male,Fashion accessories,23482.800000000003,10,11741.4,246569.4,1/8/2019,19:07,Card,234828.0,4.761904762,11741.4,5.2
+487-79-6868,B,Abuja,Member,Female,Home and lifestyle,4424.4,9,1990.98,41810.58,3/26/2019,19:28,Card,39819.6,4.761904762,1990.98,8.0
+210-30-7976,B,Abuja,Member,Female,Fashion accessories,8035.2,4,1607.0399999999995,33747.84,3/14/2019,11:16,Epay,32140.8,4.761904762,1607.0399999999995,4.1
+775-72-1988,B,Abuja,Normal,Male,Home and lifestyle,26380.8,5,6595.2,138499.2,1/24/2019,15:05,Epay,131904.0,4.761904762,6595.2,8.4
+785-96-0615,B,Abuja,Member,Female,Electronic accessories,12866.4,8,5146.56,108077.76,2/17/2019,15:28,Epay,102931.2,4.761904762,5146.56,4.9
+842-40-8179,B,Abuja,Member,Female,Food and beverages,27792.0,10,13896.0,291816.0,2/11/2019,10:38,Card,277920.0,4.761904762,13896.0,5.6
+525-09-8450,B,Abuja,Normal,Male,Electronic accessories,25966.8,10,12983.4,272651.4,1/31/2019,15:12,Card,259668.0,4.761904762,12983.4,4.2
+593-14-4239,B,Abuja,Normal,Female,Home and lifestyle,34365.6,8,13746.24,288671.04,3/5/2019,19:40,Epay,274924.8,4.761904762,13746.24,4.7
+388-76-2555,B,Abuja,Normal,Male,Sports and travel,4928.4,6,1478.52,31048.92000000001,2/13/2019,13:59,Cash,29570.4,4.761904762,1478.52,6.3
+711-31-1234,B,Abuja,Normal,Female,Electronic accessories,34430.4,4,6886.08,144607.68000000002,3/16/2019,18:51,Cash,137721.6,4.761904762,6886.08,7.9
+707-32-7409,B,Abuja,Member,Female,Sports and travel,34394.4,4,6878.88,144456.48,2/26/2019,11:58,Epay,137577.6,4.761904762,6878.88,4.5
+120-54-2248,B,Abuja,Normal,Female,Food and beverages,10389.6,5,2597.4,54545.4,1/22/2019,18:08,Card,51948.00000000001,4.761904762,2597.4,8.0
+875-31-8302,B,Abuja,Normal,Male,Sports and travel,33616.8,1,1680.84,35297.64,1/3/2019,13:07,Cash,33616.8,4.761904762,1680.84,9.6
+457-94-0464,B,Abuja,Member,Male,Electronic accessories,31633.2,9,14234.94,298933.74,1/31/2019,20:32,Epay,284698.8,4.761904762,14234.94,5.6
+744-16-7898,B,Abuja,Normal,Female,Home and lifestyle,35053.200000000004,10,17526.600000000002,368058.6,1/15/2019,13:48,Card,350532.0,4.761904762,17526.600000000002,4.9
+605-83-1050,B,Abuja,Normal,Male,Fashion accessories,9784.8,2,978.48,20548.08,3/15/2019,16:26,Epay,19569.6,4.761904762,978.48,4.3
+359-94-5395,B,Abuja,Normal,Male,Health and beauty,33400.8,1,1670.0399999999995,35070.84,3/15/2019,10:50,Card,33400.8,4.761904762,1670.0399999999995,9.8
+751-15-6198,B,Abuja,Normal,Male,Sports and travel,8283.6,6,2485.08,52186.68,1/12/2019,16:45,Epay,49701.6,4.761904762,2485.08,7.9
+831-81-6575,B,Abuja,Member,Female,Electronic accessories,27212.4,9,12245.580000000002,257157.18,2/23/2019,11:12,Cash,244911.6,4.761904762,12245.580000000002,8.0
+559-61-5987,B,Abuja,Normal,Female,Health and beauty,6390.0,1,319.5,6709.5,1/14/2019,10:38,Cash,6390.0,4.761904762,319.5,8.6
+565-91-4567,B,Abuja,Normal,Male,Health and beauty,3870.0,8,1548.0,32508.0,3/15/2019,14:38,Epay,30960.0,4.761904762,1548.0,6.2
+499-27-7781,B,Abuja,Normal,Female,Food and beverages,19155.6,8,7662.240000000001,160907.04,3/14/2019,16:45,Epay,153244.8,4.761904762,7662.240000000001,5.0
+869-11-3082,B,Abuja,Member,Male,Health and beauty,34617.6,4,6923.52,145393.91999999998,1/27/2019,20:03,Card,138470.4,4.761904762,6923.52,8.4
+190-59-3964,B,Abuja,Member,Male,Food and beverages,16977.6,5,4244.4,89132.4,2/3/2019,14:35,Card,84888.0,4.761904762,4244.4,6.0
+366-43-6862,B,Abuja,Normal,Male,Electronic accessories,19040.4,4,3808.08,79969.68000000001,3/25/2019,16:32,Epay,76161.6,4.761904762,3808.08,6.7
+109-86-4363,B,Abuja,Member,Female,Sports and travel,21628.8,7,7570.080000000001,158971.68000000002,2/14/2019,11:36,Card,151401.6,4.761904762,7570.080000000001,4.5
+222-42-0244,B,Abuja,Member,Female,Health and beauty,25959.6,9,11681.82,245318.22,1/28/2019,13:53,Card,233636.4,4.761904762,11681.82,7.7
+636-17-0325,B,Abuja,Normal,Male,Health and beauty,22525.2,4,4505.04,94605.84,2/25/2019,18:37,Cash,90100.8,4.761904762,4505.04,9.5
+343-75-9322,B,Abuja,Member,Female,Sports and travel,4266.0,8,1706.4,35834.4,1/9/2019,16:34,Cash,34128.0,4.761904762,1706.4,4.1
+427-45-9297,B,Abuja,Member,Female,Home and lifestyle,14662.8,7,5131.98,107771.58,3/12/2019,11:01,Epay,102639.6,4.761904762,5131.98,5.4
+670-71-7306,B,Abuja,Normal,Male,Sports and travel,16066.8,6,4820.04,101220.84,1/2/2019,20:08,Card,96400.8,4.761904762,4820.04,5.1
+291-59-1384,B,Abuja,Normal,Male,Electronic accessories,21708.0,1,1085.4,22793.4,2/28/2019,17:38,Cash,21708.0,4.761904762,1085.4,6.0
+866-70-2814,B,Abuja,Normal,Female,Electronic accessories,19004.4,10,9502.2,199546.2,2/25/2019,11:58,Epay,190044.0,4.761904762,9502.2,10.0
+895-03-6665,B,Abuja,Normal,Female,Fashion accessories,13143.6,9,5914.62,124207.02,2/16/2019,10:52,Cash,118292.4,4.761904762,5914.62,4.2
+770-42-8960,B,Abuja,Normal,Male,Food and beverages,7603.200000000001,8,3041.28,63866.88,1/1/2019,19:31,Cash,60825.600000000006,4.761904762,3041.28,6.3
+234-36-2483,B,Abuja,Normal,Male,Health and beauty,20732.4,6,6219.72,130614.11999999998,2/15/2019,13:51,Cash,124394.4,4.761904762,6219.72,5.1
+152-03-4217,B,Abuja,Normal,Female,Home and lifestyle,4060.8,9,1827.36,38374.56,3/17/2019,11:55,Card,36547.2,4.761904762,1827.36,4.3
+533-66-5566,B,Abuja,Normal,Female,Home and lifestyle,18385.2,7,6434.820000000001,135131.22,1/12/2019,11:42,Cash,128696.4,4.761904762,6434.820000000001,7.0
+361-59-0574,B,Abuja,Member,Male,Sports and travel,32590.8,8,13036.319999999998,273762.72,3/15/2019,14:48,Card,260726.4,4.761904762,13036.319999999998,6.5
+544-55-9589,B,Abuja,Member,Female,Electronic accessories,7714.8,10,3857.4,81005.4,1/28/2019,11:51,Cash,77148.0,4.761904762,3857.4,6.2
+608-05-3804,B,Abuja,Member,Male,Electronic accessories,14310.0,1,715.5,15025.499999999998,2/25/2019,20:19,Cash,14310.0,4.761904762,715.5,6.1
+761-49-0439,B,Abuja,Member,Female,Electronic accessories,4356.0,8,1742.4,36590.4,1/19/2019,10:17,Epay,34848.0,4.761904762,1742.4,8.6
+490-95-0021,B,Abuja,Member,Female,Food and beverages,11955.6,10,5977.8,125533.8,1/8/2019,14:25,Epay,119556.0,4.761904762,5977.8,6.0
+311-13-6971,B,Abuja,Member,Male,Sports and travel,11516.4,10,5758.2,120922.2,2/20/2019,15:18,Card,115164.0,4.761904762,5758.2,9.9
+114-35-5271,B,Abuja,Normal,Female,Electronic accessories,20847.6,8,8339.04,175119.84,2/7/2019,15:06,Cash,166780.8,4.761904762,8339.04,8.1
+715-20-1673,B,Abuja,Normal,Male,Electronic accessories,10216.8,5,2554.2,53638.2,3/6/2019,20:57,Cash,51084.0,4.761904762,2554.2,9.4
+811-35-1094,B,Abuja,Member,Male,Electronic accessories,18162.0,6,5448.6,114420.6,2/6/2019,15:16,Card,108972.0,4.761904762,5448.6,8.9
+699-88-1972,B,Abuja,Normal,Male,Health and beauty,35697.6,8,14279.04,299859.84,1/28/2019,17:47,Card,285580.8,4.761904762,14279.04,4.2
+509-10-0516,B,Abuja,Normal,Male,Home and lifestyle,16549.2,4,3309.84,69506.64,2/9/2019,12:02,Epay,66196.8,4.761904762,3309.84,5.1
+851-98-3555,B,Abuja,Normal,Female,Health and beauty,29836.8,5,7459.2,156643.2,3/24/2019,14:08,Card,149184.0,4.761904762,7459.2,6.6
+624-01-8356,B,Abuja,Normal,Female,Home and lifestyle,17643.6,10,8821.8,185257.8,1/27/2019,10:44,Card,176436.0,4.761904762,8821.8,4.2
+313-66-9943,B,Abuja,Member,Female,Food and beverages,10494.0,3,1574.1,33056.1,3/27/2019,20:29,Card,31482.0,4.761904762,1574.1,7.3
+777-67-2495,B,Abuja,Normal,Male,Home and lifestyle,24829.2,3,3724.38,78211.98000000001,2/22/2019,11:26,Epay,74487.6,4.761904762,3724.38,8.7
+636-98-3364,B,Abuja,Member,Female,Electronic accessories,9453.6,3,1418.04,29778.84,3/2/2019,12:36,Epay,28360.8,4.761904762,1418.04,6.3
+181-82-6255,B,Abuja,Normal,Female,Home and lifestyle,5893.200000000001,6,1767.9600000000005,37127.16,2/8/2019,10:58,Cash,35359.2,4.761904762,1767.9600000000005,7.0
+226-34-0034,B,Abuja,Normal,Female,Electronic accessories,4960.8,4,992.16,20835.36,1/10/2019,11:10,Epay,19843.2,4.761904762,992.16,9.0
+321-49-7382,B,Abuja,Member,Male,Sports and travel,31791.6,1,1589.58,33381.18,2/15/2019,17:38,Card,31791.6,4.761904762,1589.58,5.2
+431-66-2305,B,Abuja,Normal,Female,Electronic accessories,31770.0,9,14296.5,300226.5,2/15/2019,20:51,Card,285930.0,4.761904762,14296.5,7.6
+825-94-5922,B,Abuja,Normal,Male,Sports and travel,9111.6,2,911.16,19134.36,3/2/2019,19:26,Epay,18223.2,4.761904762,911.16,7.2
+641-62-7288,B,Abuja,Normal,Male,Home and lifestyle,35971.2,6,10791.36,226618.56,3/24/2019,13:33,Epay,215827.2,4.761904762,10791.36,7.1
+501-61-1753,B,Abuja,Normal,Female,Home and lifestyle,22734.0,6,6820.2,143224.2,1/3/2019,20:24,Epay,136404.0,4.761904762,6820.2,9.8
+676-10-2200,B,Abuja,Member,Male,Fashion accessories,19360.8,1,968.0399999999998,20328.84,2/3/2019,20:13,Epay,19360.8,4.761904762,968.0399999999998,4.7
+365-16-4334,B,Abuja,Normal,Female,Food and beverages,9514.8,8,3805.920000000001,79924.32,2/24/2019,14:26,Epay,76118.4,4.761904762,3805.920000000001,8.9
+503-21-4385,B,Abuja,Member,Male,Health and beauty,14367.6,3,2155.1400000000003,45257.94,2/21/2019,12:40,Epay,43102.8,4.761904762,2155.1400000000003,9.3
+305-89-2768,B,Abuja,Member,Female,Home and lifestyle,7883.999999999999,3,1182.6,24834.6,1/9/2019,18:43,Epay,23652.0,4.761904762,1182.6,4.7
+574-80-1489,B,Abuja,Member,Female,Food and beverages,22626.0,4,4525.2,95029.2,2/25/2019,13:22,Epay,90504.0,4.761904762,4525.2,8.7
+200-40-6154,B,Abuja,Member,Male,Home and lifestyle,23727.6,6,7118.28,149483.88,2/9/2019,11:45,Cash,142365.6,4.761904762,7118.28,5.7
+430-02-3888,B,Abuja,Normal,Male,Electronic accessories,16567.2,6,4970.16,104373.36,2/7/2019,15:55,Cash,99403.2,4.761904762,4970.16,7.1
+794-42-3736,B,Abuja,Normal,Male,Food and beverages,11998.8,2,1199.88,25197.48,1/26/2019,14:41,Card,23997.6,4.761904762,1199.88,6.4
+172-42-8274,B,Abuja,Normal,Female,Electronic accessories,13777.2,2,1377.72,28932.120000000003,3/2/2019,18:18,Card,27554.4,4.761904762,1377.72,5.8
+214-30-2776,B,Abuja,Member,Female,Electronic accessories,12416.4,5,3104.1000000000004,65186.1,3/11/2019,19:44,Card,62081.99999999999,4.761904762,3104.1000000000004,9.0
+746-04-1077,B,Abuja,Member,Female,Food and beverages,30466.8,10,15233.4,319901.4,1/1/2019,11:36,Card,304668.0,4.761904762,15233.4,9.0
+448-34-8700,B,Abuja,Member,Male,Home and lifestyle,13287.6,7,4650.66,97663.86,2/10/2019,13:51,Epay,93013.2,4.761904762,4650.66,6.7
+452-04-8808,B,Abuja,Normal,Male,Electronic accessories,31348.8,7,10972.08,230413.68,1/26/2019,15:17,Cash,219441.6,4.761904762,10972.08,5.5
+883-69-1285,B,Abuja,Member,Male,Fashion accessories,17971.2,2,1797.12,37739.52,3/6/2019,11:55,Card,35942.4,4.761904762,1797.12,7.0
+518-71-6847,B,Abuja,Member,Male,Food and beverages,9576.0,6,2872.8,60328.8,2/26/2019,15:10,Epay,57456.0,4.761904762,2872.8,4.9
+156-20-0370,B,Abuja,Normal,Female,Electronic accessories,9162.0,1,458.1,9620.1,3/10/2019,18:10,Card,9162.0,4.761904762,458.1,5.1
+151-33-7434,B,Abuja,Normal,Female,Food and beverages,24397.2,1,1219.86,25617.06,2/4/2019,20:43,Card,24397.2,4.761904762,1219.86,6.5
+374-38-5555,B,Abuja,Normal,Female,Fashion accessories,22935.6,5,5733.9,120411.9,2/7/2019,19:30,Epay,114678.0,4.761904762,5733.9,8.5
+764-44-8999,B,Abuja,Normal,Female,Health and beauty,5313.6,2,531.36,11158.56,2/18/2019,14:42,Epay,10627.2,4.761904762,531.36,4.3
+552-44-5977,B,Abuja,Member,Male,Health and beauty,22320.0,8,8928.0,187488.0,1/3/2019,19:08,Card,178560.0,4.761904762,8928.0,6.2
+430-53-4718,B,Abuja,Member,Male,Health and beauty,27133.2,8,10853.28,227918.88000000003,1/28/2019,15:46,Card,217065.6,4.761904762,10853.28,8.4
+602-16-6955,B,Abuja,Normal,Female,Sports and travel,27576.0,10,13787.999999999998,289548.0,1/24/2019,18:10,Epay,275760.0,4.761904762,13787.999999999998,6.0
+690-01-6631,B,Abuja,Normal,Male,Fashion accessories,6296.4,10,3148.2,66112.2,2/22/2019,18:35,Epay,62964.0,4.761904762,3148.2,6.6
+303-96-2227,B,Abuja,Normal,Female,Home and lifestyle,35056.8,10,17528.399999999998,368096.4,3/2/2019,17:16,Epay,350568.0,4.761904762,17528.399999999998,4.4
+750-67-8428,A,Lagos,Member,Female,Health and beauty,26888.4,7,9410.94,197629.74,1/5/2019,13:08,Epay,188218.8,4.761904762,9410.94,9.1
+631-41-3108,A,Lagos,Normal,Male,Home and lifestyle,16678.8,7,5837.58,122589.18,3/3/2019,13:23,Card,116751.6,4.761904762,5837.58,7.4
+123-19-1176,A,Lagos,Member,Male,Health and beauty,20959.2,8,8383.68,176057.28,1/27/2019,20:33,Epay,167673.6,4.761904762,8383.68,8.4
+373-73-7910,A,Lagos,Normal,Male,Sports and travel,31071.6,7,10875.06,228376.26,2/8/2019,10:37,Epay,217501.2,4.761904762,10875.06,5.3
+355-53-5943,A,Lagos,Member,Female,Electronic accessories,24782.4,6,7434.719999999999,156129.12,2/25/2019,14:36,Epay,148694.4,4.761904762,7434.719999999999,5.8
+665-32-9167,A,Lagos,Member,Female,Health and beauty,13053.6,2,1305.36,27412.56,1/10/2019,17:15,Card,26107.2,4.761904762,1305.36,7.2
+365-64-0515,A,Lagos,Normal,Female,Electronic accessories,16902.0,5,4225.5,88735.5,2/12/2019,10:25,Epay,84510.0,4.761904762,4225.5,7.1
+252-56-2699,A,Lagos,Normal,Male,Food and beverages,15548.4,10,7774.2,163258.2,2/7/2019,16:48,Epay,155484.0,4.761904762,7774.2,8.2
+829-34-3910,A,Lagos,Normal,Female,Health and beauty,25696.8,10,12848.4,269816.4,3/29/2019,19:21,Cash,256968.0,4.761904762,12848.4,5.7
+656-95-9349,A,Lagos,Member,Female,Health and beauty,24814.800000000003,7,8685.18,182388.78,3/11/2019,11:03,Card,173703.6,4.761904762,8685.18,4.6
+765-26-6951,A,Lagos,Normal,Male,Sports and travel,26139.6,6,7841.88,164679.48,1/1/2019,10:39,Card,156837.6,4.761904762,7841.88,6.9
+329-62-1586,A,Lagos,Normal,Male,Food and beverages,19681.2,3,2952.18,61995.78,1/21/2019,18:00,Card,59043.6,4.761904762,2952.18,8.6
+636-48-8204,A,Lagos,Normal,Male,Electronic accessories,12441.6,5,3110.4,65318.4,2/17/2019,11:15,Epay,62208.00000000001,4.761904762,3110.4,9.9
+549-59-1358,A,Lagos,Member,Male,Sports and travel,31906.8,3,4786.02,100506.42,3/2/2019,17:36,Epay,95720.4,4.761904762,4786.02,6.0
+227-03-5010,A,Lagos,Member,Female,Home and lifestyle,18932.4,8,7572.959999999999,159032.16,3/22/2019,19:20,Card,151459.2,4.761904762,7572.959999999999,8.5
+189-17-4241,A,Lagos,Normal,Female,Fashion accessories,31561.2,2,3156.1200000000003,66278.52,3/10/2019,12:17,Card,63122.4,4.761904762,3156.1200000000003,7.7
+848-62-7243,A,Lagos,Normal,Male,Health and beauty,8960.4,9,4032.18,84675.78,3/15/2019,15:36,Cash,80643.59999999999,4.761904762,4032.18,7.4
+595-11-5460,A,Lagos,Normal,Male,Health and beauty,34768.8,2,3476.88,73014.48,3/15/2019,10:12,Card,69537.6,4.761904762,3476.88,5.1
+129-29-8530,A,Lagos,Member,Male,Sports and travel,22543.2,5,5635.8,118351.8,3/10/2019,19:15,Epay,112716.0,4.761904762,5635.8,7.0
+272-65-1806,A,Lagos,Normal,Female,Electronic accessories,21916.8,9,9862.56,207113.76,1/15/2019,17:17,Epay,197251.2,4.761904762,9862.56,4.7
+162-48-8011,A,Lagos,Member,Female,Food and beverages,16052.4,5,4013.1,84275.1,2/10/2019,15:10,Cash,80262.0,4.761904762,4013.1,8.5
+106-35-6779,A,Lagos,Member,Male,Home and lifestyle,15962.4,2,1596.24,33521.03999999999,3/27/2019,11:26,Cash,31924.800000000003,4.761904762,1596.24,5.8
+635-40-6220,A,Lagos,Normal,Male,Health and beauty,32256.0,8,12902.4,270950.4,2/7/2019,11:28,Epay,258048.0,4.761904762,12902.4,6.6
+817-48-8732,A,Lagos,Member,Female,Home and lifestyle,26046.0,10,13022.999999999998,273483.0,1/20/2019,15:55,Cash,260460.0,4.761904762,13022.999999999998,5.4
+199-75-8169,A,Lagos,Member,Male,Sports and travel,5691.6,10,2845.8,59761.8,3/6/2019,12:27,Card,56916.0,4.761904762,2845.8,8.6
+877-22-3308,A,Lagos,Member,Male,Health and beauty,5713.2,10,2856.6,59988.6,3/13/2019,16:40,Cash,57131.99999999999,4.761904762,2856.6,5.8
+232-11-3025,A,Lagos,Normal,Male,Sports and travel,28357.2,10,14178.6,297750.60000000003,1/24/2019,10:04,Cash,283572.0,4.761904762,14178.6,6.4
+382-03-4532,A,Lagos,Member,Female,Health and beauty,6598.799999999999,1,329.94,6928.740000000001,2/2/2019,18:50,Cash,6598.799999999999,4.761904762,329.94,4.3
+287-21-9091,A,Lagos,Normal,Male,Home and lifestyle,26881.2,9,12096.54,254027.34,1/22/2019,10:55,Epay,241930.8,4.761904762,12096.54,9.4
+381-20-0914,A,Lagos,Member,Female,Fashion accessories,7203.6,9,3241.62,68074.02,1/12/2019,15:48,Card,64832.4,4.761904762,3241.62,5.7
+633-44-8566,A,Lagos,Member,Male,Food and beverages,17776.8,7,6221.88,130659.48,3/27/2019,20:35,Card,124437.6,4.761904762,6221.88,7.3
+504-35-8843,A,Lagos,Normal,Male,Sports and travel,15289.2,1,764.46,16053.66,1/2/2019,16:57,Cash,15289.2,4.761904762,764.46,5.7
+873-51-0671,A,Lagos,Member,Female,Sports and travel,7912.8,7,2769.48,58159.08,1/10/2019,16:42,Epay,55389.600000000006,4.761904762,2769.48,5.1
+594-34-4444,A,Lagos,Normal,Male,Electronic accessories,34977.6,1,1748.88,36726.48,3/8/2019,20:38,Epay,34977.6,4.761904762,1748.88,7.2
+865-92-6136,A,Lagos,Normal,Male,Food and beverages,18990.0,3,2848.5,59818.5,3/23/2019,10:16,Epay,56970.0,4.761904762,2848.5,9.3
+212-62-1842,A,Lagos,Normal,Male,Food and beverages,20973.6,6,6292.079999999999,132133.68,3/28/2019,16:44,Cash,125841.6,4.761904762,6292.079999999999,9.9
+704-48-3927,A,Lagos,Member,Male,Electronic accessories,31921.2,10,15960.6,335172.6,1/12/2019,14:50,Epay,319212.0,4.761904762,15960.6,7.3
+630-74-5166,A,Lagos,Normal,Male,Sports and travel,22366.8,6,6710.04,140910.84000000003,3/22/2019,20:19,Cash,134200.8,4.761904762,6710.04,7.4
+645-44-1170,A,Lagos,Member,Male,Home and lifestyle,20905.2,9,9407.34,197554.14,1/19/2019,20:07,Epay,188146.8,4.761904762,9407.34,4.3
+642-32-2990,A,Lagos,Normal,Female,Food and beverages,3945.6,10,1972.8,41428.8,2/2/2019,20:48,Epay,39456.0,4.761904762,1972.8,6.0
+638-60-7125,A,Lagos,Normal,Female,Electronic accessories,35841.6,8,14336.64,301069.44,2/14/2019,17:03,Card,286732.8,4.761904762,14336.64,5.2
+668-90-8900,A,Lagos,Normal,Female,Home and lifestyle,33728.4,7,11804.94,247903.74,3/10/2019,18:44,Card,236098.8,4.761904762,11804.94,4.5
+870-54-3162,A,Lagos,Normal,Female,Sports and travel,11610.0,5,2902.5,60952.5,1/27/2019,13:26,Cash,58050.0,4.761904762,2902.5,9.0
+802-70-5316,A,Lagos,Member,Female,Sports and travel,33166.8,6,9950.04,208950.84,3/6/2019,20:34,Cash,199000.8,4.761904762,9950.04,8.3
+700-81-1757,A,Lagos,Normal,Female,Electronic accessories,9471.6,5,2367.9,49725.9,1/18/2019,20:59,Card,47358.00000000001,4.761904762,2367.9,8.8
+354-39-5160,A,Lagos,Member,Female,Home and lifestyle,12391.2,6,3717.36,78064.56,2/18/2019,15:39,Cash,74347.2,4.761904762,3717.36,9.8
+575-30-8091,A,Lagos,Normal,Male,Sports and travel,26100.0,8,10440.0,219240.0,3/16/2019,19:25,Epay,208800.0,4.761904762,10440.0,9.2
+239-10-7476,A,Lagos,Normal,Female,Home and lifestyle,28062.0,6,8418.6,176790.6,1/21/2019,16:37,Epay,168372.0,4.761904762,8418.6,8.0
+685-64-1609,A,Lagos,Member,Female,Fashion accessories,10850.4,10,5425.2,113929.2,2/10/2019,12:28,Epay,108504.0,4.761904762,5425.2,9.2
+238-49-0436,A,Lagos,Normal,Male,Health and beauty,11685.6,8,4674.240000000001,98159.04,3/27/2019,13:48,Card,93484.8,4.761904762,4674.240000000001,4.9
+746-94-0204,A,Lagos,Normal,Male,Fashion accessories,29966.4,9,13484.88,283182.48000000004,1/29/2019,11:56,Card,269697.6,4.761904762,13484.88,7.4
+782-95-9291,A,Lagos,Member,Male,Food and beverages,33224.4,5,8306.1,174428.1,2/20/2019,15:55,Card,166122.0,4.761904762,8306.1,9.0
+275-28-0149,A,Lagos,Normal,Male,Sports and travel,22928.4,1,1146.42,24074.82,2/25/2019,16:21,Cash,22928.4,4.761904762,1146.42,6.0
+101-17-6199,A,Lagos,Normal,Male,Food and beverages,16484.4,7,5769.539999999999,121160.34,3/13/2019,19:44,Card,115390.8,4.761904762,5769.539999999999,7.0
+687-47-8271,A,Lagos,Normal,Male,Fashion accessories,35632.8,10,17816.4,374144.4,2/8/2019,16:20,Card,356328.0,4.761904762,17816.4,8.7
+796-32-9050,A,Lagos,Normal,Male,Food and beverages,18460.8,6,5538.24,116303.04,1/19/2019,16:31,Cash,110764.8,4.761904762,5538.24,6.5
+105-31-1824,A,Lagos,Member,Male,Sports and travel,25027.2,7,8759.519999999999,183949.92,2/1/2019,15:10,Card,175190.4,4.761904762,8759.519999999999,8.5
+249-42-3782,A,Lagos,Normal,Male,Health and beauty,25203.6,5,6300.900000000001,132318.9,1/3/2019,11:36,Epay,126018.0,4.761904762,6300.900000000001,5.5
+827-26-2100,A,Lagos,Member,Male,Home and lifestyle,12182.4,9,5482.08,115123.68,3/21/2019,16:21,Epay,109641.6,4.761904762,5482.08,8.8
+175-54-2529,A,Lagos,Member,Male,Food and beverages,7981.200000000001,8,3192.48,67042.07999999999,3/3/2019,17:01,Card,63849.600000000006,4.761904762,3192.48,9.6
+407-63-8975,A,Lagos,Normal,Male,Food and beverages,26596.8,6,7979.040000000001,167559.84,3/23/2019,19:16,Epay,159580.8,4.761904762,7979.040000000001,4.4
+851-28-6367,A,Lagos,Member,Male,Sports and travel,5580.0,10,2790.0,58590.0,3/23/2019,10:55,Epay,55800.0,4.761904762,2790.0,8.0
+586-25-0848,A,Lagos,Normal,Female,Sports and travel,4442.4,7,1554.84,32651.64,3/4/2019,11:19,Card,31096.8,4.761904762,1554.84,6.7
+400-60-7251,A,Lagos,Normal,Male,Home and lifestyle,26665.2,1,1333.26,27998.46,2/10/2019,12:50,Epay,26665.2,4.761904762,1333.26,9.9
+831-07-6050,A,Lagos,Normal,Male,Electronic accessories,11775.6,5,2943.9,61821.9,3/19/2019,11:30,Card,58878.00000000001,4.761904762,2943.9,9.9
+856-22-8149,A,Lagos,Normal,Female,Home and lifestyle,9104.4,1,455.22,9559.62,3/23/2019,10:13,Epay,9104.4,4.761904762,455.22,6.1
+749-24-1565,A,Lagos,Normal,Female,Health and beauty,8290.800000000001,9,3730.86,78348.06,1/3/2019,12:02,Epay,74617.2,4.761904762,3730.86,7.9
+888-02-0338,A,Lagos,Normal,Male,Electronic accessories,9442.8,9,4249.26,89234.46,1/25/2019,20:24,Epay,84985.2,4.761904762,4249.26,5.9
+802-43-8934,A,Lagos,Normal,Male,Home and lifestyle,6580.8,1,329.04,6909.840000000001,3/22/2019,15:05,Card,6580.8,4.761904762,329.04,8.3
+319-74-2561,A,Lagos,Member,Female,Electronic accessories,34070.4,3,5110.56,107321.76,2/21/2019,16:55,Cash,102211.2,4.761904762,5110.56,5.5
+213-72-6612,A,Lagos,Normal,Male,Food and beverages,15570.0,2,1557.0,32697.0,3/20/2019,15:56,Cash,31140.0,4.761904762,1557.0,6.2
+721-86-6247,A,Lagos,Normal,Female,Home and lifestyle,22831.2,8,9132.48,191782.08,3/11/2019,12:55,Epay,182649.6,4.761904762,9132.48,7.4
+157-13-5295,A,Lagos,Member,Male,Health and beauty,18698.4,10,9349.2,196333.2,3/9/2019,18:24,Epay,186984.0,4.761904762,9349.2,6.5
+645-78-8093,A,Lagos,Normal,Female,Sports and travel,33530.4,2,3353.04,70413.84000000001,1/20/2019,18:09,Epay,67060.8,4.761904762,3353.04,4.1
+478-06-7835,A,Lagos,Normal,Male,Fashion accessories,32288.4,1,1614.4199999999998,33902.82,1/11/2019,11:20,Epay,32288.4,4.761904762,1614.4199999999998,4.9
+540-11-4336,A,Lagos,Normal,Male,Food and beverages,8978.4,9,4040.28,84845.88,1/11/2019,16:49,Card,80805.6,4.761904762,4040.28,5.6
+448-81-5016,A,Lagos,Normal,Male,Health and beauty,21517.2,2,2151.7200000000003,45186.12,3/11/2019,12:01,Card,43034.4,4.761904762,2151.7200000000003,5.8
+217-58-1179,A,Lagos,Member,Male,Home and lifestyle,22554.0,4,4510.8,94726.8,1/5/2019,11:25,Cash,90216.0,4.761904762,4510.8,4.2
+530-90-9855,A,Lagos,Member,Male,Home and lifestyle,17132.4,8,6852.959999999999,143912.16,1/1/2019,14:47,Cash,137059.2,4.761904762,6852.959999999999,5.7
+604-70-6476,A,Lagos,Member,Male,Fashion accessories,6458.400000000001,5,1614.6,33906.6,1/23/2019,14:04,Epay,32292.0,4.761904762,1614.6,6.8
+799-71-1548,A,Lagos,Member,Male,Electronic accessories,27979.2,4,5595.84,117512.64,1/7/2019,16:11,Card,111916.8,4.761904762,5595.84,8.8
+290-68-2984,A,Lagos,Normal,Male,Home and lifestyle,8550.0,4,1710.0,35910.0,3/16/2019,11:22,Cash,34200.0,4.761904762,1710.0,5.2
+704-11-6354,A,Lagos,Member,Male,Home and lifestyle,21204.0,8,8481.6,178113.6,1/6/2019,11:23,Cash,169632.0,4.761904762,8481.6,8.9
+366-93-0948,A,Lagos,Member,Male,Electronic accessories,23886.0,1,1194.3,25080.300000000003,1/31/2019,10:46,Card,23886.0,4.761904762,1194.3,9.7
+729-09-9681,A,Lagos,Member,Male,Home and lifestyle,9327.6,6,2798.28,58763.880000000005,2/5/2019,10:16,Epay,55965.600000000006,4.761904762,2798.28,8.7
+151-16-1484,A,Lagos,Member,Male,Electronic accessories,11610.0,4,2322.0,48761.99999999999,2/13/2019,12:38,Epay,46440.0,4.761904762,2322.0,6.5
+850-41-9669,A,Lagos,Normal,Female,Electronic accessories,27021.6,9,12159.72,255354.12,3/19/2019,13:25,Epay,243194.4,4.761904762,12159.72,6.2
+447-15-7839,A,Lagos,Member,Female,Sports and travel,8006.4,10,4003.2,84067.2,2/9/2019,11:00,Cash,80064.0,4.761904762,4003.2,4.2
+253-12-6086,A,Lagos,Member,Female,Sports and travel,35424.0,7,12398.4,260366.4,3/12/2019,12:43,Card,247968.0,4.761904762,12398.4,8.7
+144-51-6085,A,Lagos,Member,Male,Home and lifestyle,25466.4,4,5093.28,106958.88,1/5/2019,16:05,Card,101865.6,4.761904762,5093.28,4.4
+731-14-2199,A,Lagos,Member,Female,Home and lifestyle,12794.4,10,6397.2,134341.2,1/4/2019,13:34,Epay,127944.0,4.761904762,6397.2,7.0
+126-54-1082,A,Lagos,Member,Female,Home and lifestyle,7754.4,9,3489.48,73279.08,1/7/2019,11:44,Card,69789.6,4.761904762,3489.48,8.8
+633-91-1052,A,Lagos,Normal,Female,Home and lifestyle,4330.8,2,433.08,9094.68,1/27/2019,15:51,Cash,8661.6,4.761904762,433.08,5.1
+828-61-5674,A,Lagos,Member,Male,Sports and travel,15847.2,10,7923.6,166395.6,3/20/2019,19:57,Card,158472.0,4.761904762,7923.6,9.6
+136-08-6195,A,Lagos,Normal,Female,Home and lifestyle,25185.6,8,10074.24,211559.04,2/15/2019,17:01,Card,201484.8,4.761904762,10074.24,6.4
+490-29-1201,A,Lagos,Normal,Female,Sports and travel,5522.4,1,276.12,5798.52,1/6/2019,11:09,Cash,5522.4,4.761904762,276.12,6.5
+667-92-0055,A,Lagos,Member,Male,Health and beauty,35938.8,6,10781.64,226414.44,3/4/2019,15:02,Epay,215632.8,4.761904762,10781.64,8.5
+565-17-3836,A,Lagos,Member,Female,Health and beauty,17161.2,4,3432.24,72077.04000000001,3/12/2019,14:21,Cash,68644.8,4.761904762,3432.24,9.1
+430-60-3493,A,Lagos,Member,Female,Home and lifestyle,34156.8,7,11954.88,251052.48,2/3/2019,14:38,Cash,239097.6,4.761904762,11954.88,4.2
+278-97-7759,A,Lagos,Member,Female,Electronic accessories,22492.8,1,1124.64,23617.44,2/18/2019,20:29,Cash,22492.8,4.761904762,1124.64,4.7
+316-68-6352,A,Lagos,Member,Female,Food and beverages,13089.6,2,1308.96,27488.160000000003,1/21/2019,10:00,Cash,26179.2,4.761904762,1308.96,7.1
+744-02-5987,A,Lagos,Member,Male,Home and lifestyle,28216.8,6,8465.039999999999,177765.84000000003,1/10/2019,14:16,Epay,169300.8,4.761904762,8465.039999999999,5.8
+307-83-9164,A,Lagos,Member,Male,Home and lifestyle,21603.6,4,4320.72,90735.12,1/25/2019,15:54,Cash,86414.4,4.761904762,4320.72,4.5
+439-54-7422,A,Lagos,Normal,Female,Electronic accessories,18428.4,4,3685.68,77399.28,3/18/2019,17:15,Card,73713.59999999999,4.761904762,3685.68,4.7
+411-77-0180,A,Lagos,Member,Male,Electronic accessories,25992.0,7,9097.2,191041.2,3/26/2019,20:14,Epay,181944.0,4.761904762,9097.2,4.3
+286-01-5402,A,Lagos,Normal,Female,Sports and travel,14482.8,7,5068.9800000000005,106448.58,3/30/2019,13:22,Cash,101379.6,4.761904762,5068.9800000000005,9.6
+803-17-8013,A,Lagos,Member,Female,Home and lifestyle,31964.4,8,12785.76,268500.96,2/17/2019,17:09,Cash,255715.2,4.761904762,12785.76,4.1
+512-98-1403,A,Lagos,Member,Female,Electronic accessories,9532.8,3,1429.92,30028.320000000003,3/21/2019,10:40,Epay,28598.4,4.761904762,1429.92,4.7
+848-42-2560,A,Lagos,Normal,Female,Fashion accessories,29487.6,2,2948.76,61923.96,3/5/2019,17:43,Cash,58975.2,4.761904762,2948.76,7.8
+870-76-1733,A,Lagos,Member,Female,Food and beverages,5122.8,5,1280.7,26894.7,2/1/2019,10:08,Card,25614.000000000004,4.761904762,1280.7,4.4
+423-64-4619,A,Lagos,Member,Female,Health and beauty,5598.0,9,2519.1,52901.1,3/7/2019,13:12,Cash,50381.99999999999,4.761904762,2519.1,5.0
+372-94-8041,A,Lagos,Normal,Male,Health and beauty,5493.6,6,1648.0800000000002,34609.68,2/15/2019,18:03,Epay,32961.6,4.761904762,1648.0800000000002,9.8
+563-91-7120,A,Lagos,Normal,Female,Fashion accessories,22237.2,5,5559.3,116745.3,3/8/2019,13:21,Cash,111186.0,4.761904762,5559.3,6.7
+746-54-5508,A,Lagos,Normal,Male,Home and lifestyle,7747.2,6,2324.1600000000003,48807.36,1/17/2019,12:48,Card,46483.2,4.761904762,2324.1600000000003,9.4
+815-11-1168,A,Lagos,Member,Male,Food and beverages,35920.8,5,8980.2,188584.2,3/9/2019,19:09,Cash,179604.0,4.761904762,8980.2,5.4
+340-66-0321,A,Lagos,Member,Male,Electronic accessories,13089.6,4,2617.92,54976.32000000001,3/25/2019,13:07,Cash,52358.4,4.761904762,2617.92,7.6
+634-97-8956,A,Lagos,Normal,Male,Food and beverages,11844.0,3,1776.6,37308.6,2/17/2019,17:27,Card,35532.0,4.761904762,1776.6,9.1
+566-71-1091,A,Lagos,Normal,Male,Fashion accessories,27727.2,5,6931.799999999999,145567.80000000002,2/3/2019,15:59,Cash,138636.0,4.761904762,6931.799999999999,5.5
+442-48-3607,A,Lagos,Member,Male,Food and beverages,8452.8,2,845.2800000000001,17750.88,3/14/2019,11:21,Card,16905.6,4.761904762,845.2800000000001,7.9
+527-09-6272,A,Lagos,Member,Female,Electronic accessories,10242.0,5,2560.5,53770.50000000001,3/21/2019,10:17,Card,51210.0,4.761904762,2560.5,9.1
+898-04-2717,A,Lagos,Normal,Male,Fashion accessories,27504.000000000004,9,12376.8,259912.8,3/19/2019,15:49,Epay,247536.0,4.761904762,12376.8,7.5
+150-89-8043,A,Lagos,Normal,Male,Sports and travel,16074.0,3,2411.1,50633.100000000006,2/14/2019,15:04,Cash,48221.99999999999,4.761904762,2411.1,6.2
+135-84-8019,A,Lagos,Normal,Female,Fashion accessories,28054.800000000003,9,12624.66,265117.86,2/27/2019,16:10,Epay,252493.2,4.761904762,12624.66,7.6
+441-94-7118,A,Lagos,Member,Male,Electronic accessories,25902.0,1,1295.1,27197.1,2/4/2019,12:14,Cash,25902.0,4.761904762,1295.1,7.3
+531-80-1784,A,Lagos,Normal,Male,Electronic accessories,9367.2,7,3278.5200000000004,68848.92,3/28/2019,17:38,Cash,65570.4,4.761904762,3278.5200000000004,5.1
+834-61-8124,A,Lagos,Normal,Male,Electronic accessories,18608.4,7,6512.94,136771.74,1/26/2019,18:22,Cash,130258.8,4.761904762,6512.94,5.5
+612-36-5536,A,Lagos,Member,Male,Food and beverages,29145.6,8,11658.24,244823.04,2/17/2019,11:12,Card,233164.8,4.761904762,11658.24,7.4
+462-67-9126,A,Lagos,Normal,Male,Home and lifestyle,26359.2,6,7907.759999999999,166062.96,1/21/2019,17:44,Cash,158155.2,4.761904762,7907.759999999999,7.2
+468-88-0009,A,Lagos,Member,Male,Home and lifestyle,23738.4,4,4747.679999999999,99701.28,3/24/2019,10:29,Cash,94953.6,4.761904762,4747.679999999999,6.0
+254-31-0042,A,Lagos,Member,Male,Electronic accessories,7740.0,9,3483.0000000000005,73143.0,3/6/2019,12:46,Card,69660.0,4.761904762,3483.0000000000005,7.8
+422-29-8786,A,Lagos,Normal,Female,Home and lifestyle,24152.4,5,6038.1,126800.1,1/3/2019,16:47,Card,120762.0,4.761904762,6038.1,9.1
+667-23-5919,A,Lagos,Member,Female,Fashion accessories,34812.0,5,8703.0,182763.0,1/14/2019,12:52,Epay,174060.0,4.761904762,8703.0,7.0
+289-15-7034,A,Lagos,Member,Male,Sports and travel,29638.8,4,5927.759999999999,124482.96,1/11/2019,10:37,Card,118555.2,4.761904762,5927.759999999999,7.5
+662-72-2873,A,Lagos,Normal,Female,Food and beverages,14738.4,5,3684.6,77376.6,1/6/2019,13:58,Epay,73692.0,4.761904762,3684.6,9.9
+725-56-0833,A,Lagos,Normal,Female,Health and beauty,11635.2,10,5817.6,122169.6,2/20/2019,16:49,Card,116352.0,4.761904762,5817.6,10.0
+563-36-9814,A,Lagos,Member,Male,Electronic accessories,27655.2,1,1382.76,29037.96,2/13/2019,18:27,Epay,27655.2,4.761904762,1382.76,7.2
+308-47-4913,A,Lagos,Member,Female,Sports and travel,18813.6,10,9406.8,197542.8,3/9/2019,12:45,Card,188136.0,4.761904762,9406.8,6.2
+885-17-6250,A,Lagos,Normal,Female,Health and beauty,28706.4,1,1435.32,30141.72,3/6/2019,10:36,Epay,28706.4,4.761904762,1435.32,7.3
+726-27-2396,A,Lagos,Normal,Female,Health and beauty,27900.0,5,6975.0,146475.0,1/24/2019,20:36,Epay,139500.0,4.761904762,6975.0,4.3
+316-01-3952,A,Lagos,Normal,Female,Food and beverages,19537.2,5,4884.3,102570.3,3/13/2019,14:16,Epay,97686.0,4.761904762,4884.3,4.6
+882-40-4577,A,Lagos,Member,Male,Sports and travel,24213.6,4,4842.72,101697.12,1/19/2019,15:28,Card,96854.4,4.761904762,4842.72,8.0
+732-67-5346,A,Lagos,Normal,Male,Food and beverages,4964.4,5,1241.1,26063.1,1/11/2019,19:07,Card,24822.0,4.761904762,1241.1,7.8
+256-08-8343,A,Lagos,Normal,Female,Home and lifestyle,20350.8,4,4070.16,85473.36,3/4/2019,19:48,Epay,81403.2,4.761904762,4070.16,5.5
+132-23-6451,A,Lagos,Member,Male,Health and beauty,7549.2,5,1887.3,39633.3,1/4/2019,13:21,Cash,37746.0,4.761904762,1887.3,7.8
+696-90-2548,A,Lagos,Normal,Male,Sports and travel,9302.4,3,1395.36,29302.56,3/10/2019,18:55,Epay,27907.2,4.761904762,1395.36,6.6
+472-15-9636,A,Lagos,Normal,Male,Home and lifestyle,18334.8,8,7333.92,154012.31999999998,3/22/2019,19:36,Epay,146678.4,4.761904762,7333.92,9.2
+745-71-3520,A,Lagos,Member,Female,Electronic accessories,9079.2,7,3177.72,66732.12000000001,2/4/2019,10:23,Cash,63554.4,4.761904762,3177.72,8.2
+269-10-8440,A,Lagos,Member,Male,Health and beauty,19141.2,7,6699.42,140687.82,1/21/2019,18:01,Cash,133988.4,4.761904762,6699.42,8.9
+325-77-6186,A,Lagos,Member,Female,Home and lifestyle,32634.000000000004,10,16317.000000000002,342657.0,3/8/2019,10:53,Epay,326340.0,4.761904762,16317.000000000002,7.3
+459-50-7686,A,Lagos,Normal,Female,Electronic accessories,8445.6,6,2533.68,53207.28,1/13/2019,19:14,Epay,50673.6,4.761904762,2533.68,6.4
+345-08-4992,A,Lagos,Normal,Male,Home and lifestyle,12236.4,6,3670.920000000001,77089.32,3/8/2019,15:37,Card,73418.4,4.761904762,3670.920000000001,7.7
+138-17-5109,A,Lagos,Member,Female,Home and lifestyle,32115.6,9,14452.02,303492.42,1/15/2019,15:42,Card,289040.4,4.761904762,14452.02,6.5
+301-11-9629,A,Lagos,Normal,Female,Sports and travel,6876.000000000001,7,2406.6,50538.6,1/15/2019,10:43,Cash,48131.99999999999,4.761904762,2406.6,9.7
+727-17-0390,A,Lagos,Normal,Female,Food and beverages,22899.6,5,5724.9,120222.9,3/16/2019,12:43,Epay,114498.0,4.761904762,5724.9,4.8
+568-88-3448,A,Lagos,Normal,Male,Health and beauty,9000.0,1,450.0,9450.0,3/3/2019,15:09,Epay,9000.0,4.761904762,450.0,5.5
+187-83-5490,A,Lagos,Member,Male,Electronic accessories,7477.2,4,1495.44,31404.24,1/31/2019,13:47,Cash,29908.8,4.761904762,1495.44,4.7
+470-32-9057,A,Lagos,Member,Male,Food and beverages,18482.4,5,4620.6,97032.6,3/28/2019,15:31,Card,92412.0,4.761904762,4620.6,9.1
+340-21-9136,A,Lagos,Member,Female,Sports and travel,14417.999999999998,4,2883.6,60555.600000000006,1/25/2019,11:40,Cash,57671.99999999999,4.761904762,2883.6,9.7
+405-31-3305,A,Lagos,Member,Male,Fashion accessories,15526.8,10,7763.400000000001,163031.4,2/2/2019,18:31,Card,155268.0,4.761904762,7763.400000000001,5.5
+676-39-6028,A,Lagos,Member,Female,Electronic accessories,23198.4,5,5799.599999999999,121791.6,3/30/2019,17:04,Cash,115992.0,4.761904762,5799.599999999999,6.6
+502-05-1910,A,Lagos,Normal,Male,Health and beauty,23464.800000000003,3,3519.7200000000003,73914.12,2/25/2019,20:35,Card,70394.4,4.761904762,3519.7200000000003,6.3
+485-30-8700,A,Lagos,Normal,Female,Sports and travel,11973.6,5,2993.4,62861.4,3/18/2019,16:10,Card,59868.00000000001,4.761904762,2993.4,4.2
+575-67-1508,A,Lagos,Normal,Male,Electronic accessories,13896.0,1,694.8,14590.8,1/29/2019,11:26,Epay,13896.0,4.761904762,694.8,6.7
+674-15-9296,A,Lagos,Normal,Male,Sports and travel,13370.4,5,3342.6,70194.6,1/8/2019,13:05,Epay,66852.0,4.761904762,3342.6,5.0
+795-49-7276,A,Lagos,Normal,Male,Fashion accessories,13374.0,4,2674.8,56170.8,3/23/2019,18:59,Epay,53496.0,4.761904762,2674.8,8.3
+510-09-5628,A,Lagos,Member,Female,Fashion accessories,7077.6,10,3538.8,74314.8,3/15/2019,18:20,Card,70776.0,4.761904762,3538.8,7.2
+420-18-8989,A,Lagos,Member,Female,Sports and travel,18547.2,8,7418.88,155796.48,2/2/2019,15:47,Cash,148377.6,4.761904762,7418.88,9.6
+726-29-6793,A,Lagos,Member,Male,Electronic accessories,8704.8,8,3481.92,73120.31999999999,1/28/2019,20:54,Epay,69638.4,4.761904762,3481.92,9.8
+209-61-0206,A,Lagos,Normal,Female,Home and lifestyle,15447.6,5,3861.9,81099.9,1/5/2019,17:29,Epay,77238.0,4.761904762,3861.9,6.1
+595-27-4851,A,Lagos,Normal,Female,Fashion accessories,19540.8,7,6839.280000000001,143624.88,1/27/2019,18:05,Epay,136785.6,4.761904762,6839.280000000001,9.3
+189-52-0236,A,Lagos,Normal,Male,Electronic accessories,35838.0,7,12543.3,263409.3,3/14/2019,12:07,Cash,250866.0,4.761904762,12543.3,7.6
+220-28-1851,A,Lagos,Normal,Male,Home and lifestyle,12502.8,2,1250.2800000000002,26255.88,3/1/2019,18:14,Epay,25005.6,4.761904762,1250.2800000000002,9.7
+609-81-8548,A,Lagos,Member,Female,Home and lifestyle,13478.4,6,4043.52,84913.92,2/6/2019,13:55,Card,80870.4,4.761904762,4043.52,5.9
+534-01-4457,A,Lagos,Normal,Male,Food and beverages,29415.6,6,8824.68,185318.28,1/27/2019,14:36,Card,176493.6,4.761904762,8824.68,8.0
+719-89-8991,A,Lagos,Member,Female,Sports and travel,32907.6,5,8226.9,172764.9,2/25/2019,16:03,Epay,164538.0,4.761904762,8226.9,7.1
+827-77-7633,A,Lagos,Normal,Male,Sports and travel,35312.4,9,15890.580000000002,333702.18,2/17/2019,19:41,Cash,317811.6,4.761904762,15890.580000000002,9.3
+287-83-1405,A,Lagos,Normal,Male,Health and beauty,9154.8,6,2746.44,57675.24,2/12/2019,19:01,Epay,54928.8,4.761904762,2746.44,7.0
+435-13-4908,A,Lagos,Member,Male,Fashion accessories,31204.800000000003,8,12481.919999999998,262120.32,1/24/2019,18:04,Card,249638.4,4.761904762,12481.919999999998,7.2
+892-05-6689,A,Lagos,Normal,Female,Home and lifestyle,10195.2,5,2548.8,53524.8,3/11/2019,13:28,Epay,50976.0,4.761904762,2548.8,6.2
+643-38-7867,A,Lagos,Normal,Male,Home and lifestyle,35258.4,1,1762.92,37021.32,3/7/2019,11:44,Epay,35258.4,4.761904762,1762.92,6.9
+308-81-0538,A,Lagos,Normal,Female,Fashion accessories,26298.0,4,5259.599999999999,110451.6,2/25/2019,17:16,Card,105192.0,4.761904762,5259.599999999999,4.9
+460-35-4390,A,Lagos,Normal,Male,Home and lifestyle,11044.8,3,1656.72,34791.12,1/22/2019,11:00,Epay,33134.4,4.761904762,1656.72,9.1
+647-50-1224,A,Lagos,Normal,Female,Fashion accessories,10591.2,10,5295.6,111207.6,1/12/2019,16:23,Epay,105912.0,4.761904762,5295.6,8.9
+541-48-8554,A,Lagos,Normal,Male,Sports and travel,21942.0,9,9873.9,207351.9,1/7/2019,12:08,Card,197478.0,4.761904762,9873.9,6.0
+213-32-1216,A,Lagos,Normal,Female,Electronic accessories,23781.6,6,7134.48,149824.08000000002,1/23/2019,10:28,Cash,142689.6,4.761904762,7134.48,7.3
+134-75-2619,A,Lagos,Member,Male,Electronic accessories,6955.2,7,2434.32,51120.72,3/25/2019,18:51,Cash,48686.4,4.761904762,2434.32,6.9
+712-39-0363,A,Lagos,Member,Male,Food and beverages,14997.6,6,4499.28,94484.88,1/2/2019,15:24,Epay,89985.6,4.761904762,4499.28,5.6
+218-59-9410,A,Lagos,Member,Female,Home and lifestyle,26071.2,3,3910.68,82124.28,3/29/2019,16:54,Epay,78213.59999999999,4.761904762,3910.68,8.2
+760-90-2357,A,Lagos,Member,Male,Electronic accessories,26823.6,6,8047.080000000001,168988.68,3/20/2019,15:08,Epay,160941.6,4.761904762,8047.080000000001,5.0
+698-98-5964,A,Lagos,Normal,Female,Food and beverages,29235.6,10,14617.8,306973.8,1/17/2019,13:01,Card,292356.0,4.761904762,14617.8,6.3
+651-88-7328,A,Lagos,Normal,Female,Fashion accessories,23666.4,9,10649.88,223647.48,1/1/2019,13:55,Cash,212997.6,4.761904762,10649.88,7.7
+239-48-4278,A,Lagos,Member,Male,Food and beverages,3646.8,7,1276.38,26803.98,3/10/2019,19:35,Epay,25527.6,4.761904762,1276.38,8.3
+550-84-8664,A,Lagos,Normal,Male,Sports and travel,30927.6,5,7731.9,162369.9,3/22/2019,14:33,Card,154638.0,4.761904762,7731.9,8.6
+797-88-0493,A,Lagos,Normal,Female,Health and beauty,23137.2,4,4627.4400000000005,97176.24,3/26/2019,13:54,Cash,92548.8,4.761904762,4627.4400000000005,7.7
+443-82-0585,A,Lagos,Member,Female,Health and beauty,27964.800000000003,4,5592.96,117452.16,2/1/2019,19:54,Cash,111859.2,4.761904762,5592.96,8.4
+127-47-6963,A,Lagos,Normal,Male,Health and beauty,18615.6,4,3723.12,78185.52,3/9/2019,13:53,Card,74462.4,4.761904762,3723.12,9.8
+278-86-2735,A,Lagos,Normal,Female,Food and beverages,18842.4,3,2826.36,59353.55999999999,3/27/2019,14:03,Cash,56527.2,4.761904762,2826.36,9.2
+695-28-6250,A,Lagos,Normal,Female,Sports and travel,15501.6,5,3875.4,81383.4,2/4/2019,16:38,Epay,77508.0,4.761904762,3875.4,7.7
+227-50-3718,A,Lagos,Normal,Male,Health and beauty,5263.2,5,1315.8,27631.8,3/4/2019,12:23,Cash,26316.0,4.761904762,1315.8,4.4
+560-49-6611,A,Lagos,Member,Female,Sports and travel,16408.8,1,820.4399999999999,17229.239999999998,2/7/2019,14:13,Cash,16408.8,4.761904762,820.4399999999999,9.8
+880-35-0356,A,Lagos,Member,Female,Sports and travel,27072.0,3,4060.8,85276.8,2/5/2019,11:51,Epay,81216.0,4.761904762,4060.8,4.8
+152-68-2907,A,Lagos,Normal,Male,Food and beverages,18792.0,3,2818.8,59194.8,2/15/2019,13:30,Card,56376.0,4.761904762,2818.8,9.5
+334-64-2006,A,Lagos,Member,Female,Home and lifestyle,25315.2,2,2531.52,53161.92,3/24/2019,14:22,Epay,50630.4,4.761904762,2531.52,9.6
+559-98-9873,A,Lagos,Member,Female,Fashion accessories,19314.0,7,6759.9,141957.9,2/10/2019,12:56,Epay,135198.0,4.761904762,6759.9,5.2
+318-12-0304,A,Lagos,Normal,Male,Fashion accessories,11019.6,1,550.98,11570.580000000002,1/23/2019,12:20,Epay,11019.6,4.761904762,550.98,5.2
+421-95-9805,A,Lagos,Normal,Female,Electronic accessories,10425.6,1,521.2800000000001,10946.88,2/7/2019,10:18,Card,10425.6,4.761904762,521.2800000000001,6.2
+443-59-0061,A,Lagos,Member,Male,Food and beverages,24282.0,10,12141.0,254961.0,2/3/2019,11:25,Epay,242820.0,4.761904762,12141.0,4.2
+509-29-3912,A,Lagos,Member,Female,Sports and travel,13939.2,9,6272.639999999999,131725.44,3/20/2019,12:24,Epay,125452.8,4.761904762,6272.639999999999,4.2
+828-46-6863,A,Lagos,Member,Male,Food and beverages,35470.8,6,10641.24,223466.04,1/23/2019,11:22,Card,212824.8,4.761904762,10641.24,4.0
+420-97-3340,A,Lagos,Normal,Female,Food and beverages,25804.800000000003,3,3870.72,81285.12,3/28/2019,15:30,Card,77414.4,4.761904762,3870.72,9.2
+436-54-4512,A,Lagos,Member,Female,Food and beverages,32979.6,1,1648.98,34628.58,3/20/2019,19:44,Cash,32979.6,4.761904762,1648.98,9.8
+816-57-2053,A,Lagos,Normal,Male,Sports and travel,21913.2,2,2191.32,46017.72,3/9/2019,12:37,Epay,43826.4,4.761904762,2191.32,8.7
+856-66-2701,A,Lagos,Member,Male,Home and lifestyle,19188.0,3,2878.2,60442.2,1/25/2019,14:19,Epay,57564.0,4.761904762,2878.2,7.5
+308-39-1707,A,Lagos,Normal,Female,Fashion accessories,4352.4,1,217.62,4570.02,1/26/2019,18:19,Card,4352.4,4.761904762,217.62,8.2
+149-61-1929,A,Lagos,Normal,Male,Sports and travel,23108.4,10,11554.2,242638.2,1/19/2019,14:08,Card,231084.0,4.761904762,11554.2,6.7
+655-07-2265,A,Lagos,Normal,Male,Electronic accessories,28191.6,3,4228.74,88803.54000000001,3/5/2019,16:38,Epay,84574.8,4.761904762,4228.74,5.4
+589-02-8023,A,Lagos,Member,Male,Food and beverages,30157.2,2,3015.7200000000003,63330.12,1/15/2019,10:54,Card,60314.4,4.761904762,3015.7200000000003,7.0
+610-46-4100,A,Lagos,Normal,Male,Health and beauty,10422.0,7,3647.7,76601.7,3/3/2019,20:31,Card,72954.0,4.761904762,3647.7,6.0
+706-36-6154,A,Lagos,Member,Male,Home and lifestyle,6969.599999999999,9,3136.32,65862.72,1/18/2019,18:43,Epay,62726.4,4.761904762,3136.32,8.7
+742-04-5161,A,Lagos,Member,Male,Home and lifestyle,26200.8,10,13100.4,275108.4,2/3/2019,17:24,Cash,262008.0,4.761904762,13100.4,7.3
+169-52-4504,A,Lagos,Normal,Female,Electronic accessories,5648.4,3,847.26,17792.46,3/14/2019,14:13,Card,16945.2,4.761904762,847.26,5.8
+562-12-5430,A,Lagos,Member,Female,Fashion accessories,31734.000000000004,3,4760.1,99962.1,1/18/2019,10:11,Epay,95202.0,4.761904762,4760.1,7.9
+816-72-8853,A,Lagos,Member,Female,Sports and travel,10054.8,5,2513.7,52787.7,1/29/2019,15:48,Cash,50274.0,4.761904762,2513.7,5.9
+491-38-3499,A,Lagos,Member,Male,Fashion accessories,19962.0,1,998.1,20960.1,2/26/2019,17:46,Card,19962.0,4.761904762,998.1,4.9
+518-17-2983,A,Lagos,Normal,Female,Fashion accessories,17506.8,4,3501.36,73528.56,2/4/2019,15:44,Epay,70027.2,4.761904762,3501.36,7.6
+588-47-8641,A,Lagos,Member,Male,Fashion accessories,20174.4,10,10087.2,211831.2,1/14/2019,19:30,Epay,201744.0,4.761904762,10087.2,4.4
+811-03-8790,A,Lagos,Normal,Female,Electronic accessories,16372.8,10,8186.4,171914.4,3/1/2019,10:22,Card,163728.0,4.761904762,8186.4,4.8
+274-05-5470,A,Lagos,Member,Female,Food and beverages,26449.2,4,5289.84,111086.64,2/23/2019,18:30,Cash,105796.8,4.761904762,5289.84,6.0
+130-67-4723,A,Lagos,Member,Male,Food and beverages,17460.0,6,5238.0,109998.0,1/11/2019,13:57,Epay,104760.0,4.761904762,5238.0,9.4
+105-10-6182,A,Lagos,Member,Male,Fashion accessories,7732.8,2,773.2800000000001,16238.88,2/27/2019,12:22,Epay,15465.6,4.761904762,773.2800000000001,6.6
+648-83-1321,A,Lagos,Member,Male,Home and lifestyle,22881.6,10,11440.8,240256.8,1/16/2019,17:59,Cash,228816.0,4.761904762,11440.8,4.3
+305-03-2383,A,Lagos,Normal,Female,Food and beverages,24156.0,3,3623.4,76091.40000000001,2/15/2019,10:36,Cash,72468.0,4.761904762,3623.4,7.5
+689-05-1884,A,Lagos,Member,Male,Health and beauty,17506.8,10,8753.4,183821.4,3/4/2019,12:44,Cash,175068.0,4.761904762,8753.4,8.8
+800-09-8606,A,Lagos,Member,Female,Home and lifestyle,31453.2,5,7863.3,165129.3,1/29/2019,19:45,Cash,157266.0,4.761904762,7863.3,6.6
+182-52-7000,A,Lagos,Member,Female,Sports and travel,9734.4,4,1946.88,40884.48,1/1/2019,20:26,Epay,38937.6,4.761904762,1946.88,6.9
+868-06-0466,A,Lagos,Member,Male,Electronic accessories,25048.8,9,11271.96,236711.16000000003,2/19/2019,19:38,Card,225439.2,4.761904762,11271.96,7.8
+445-30-9252,A,Lagos,Normal,Male,Sports and travel,9252.0,3,1387.8,29143.8,1/17/2019,17:59,Epay,27756.0,4.761904762,1387.8,6.1
+786-94-2700,A,Lagos,Member,Male,Food and beverages,29023.2,6,8706.96,182846.16,2/28/2019,20:18,Cash,174139.2,4.761904762,8706.96,9.1
+258-92-7466,A,Lagos,Normal,Female,Health and beauty,12844.8,5,3211.2,67435.2,2/6/2019,18:33,Card,64224.0,4.761904762,3211.2,6.6
+857-16-3520,A,Lagos,Member,Female,Fashion accessories,25725.6,7,9003.96,189083.16,3/28/2019,16:06,Epay,180079.2,4.761904762,9003.96,4.5
+482-17-1179,A,Lagos,Member,Male,Electronic accessories,4298.4,3,644.76,13539.96,1/19/2019,12:47,Card,12895.2,4.761904762,644.76,8.1
+788-21-5741,A,Lagos,Normal,Male,Fashion accessories,16336.8,3,2450.52,51460.92,2/17/2019,13:34,Card,49010.4,4.761904762,2450.52,7.2
+247-11-2470,A,Lagos,Member,Female,Fashion accessories,8035.2,4,1607.0399999999995,33747.84,3/1/2019,16:23,Card,32140.8,4.761904762,1607.0399999999995,4.4
+635-28-5728,A,Lagos,Normal,Male,Health and beauty,20160.0,3,3024.0,63504.0,2/28/2019,19:33,Epay,60480.0,4.761904762,3024.0,4.8
+756-49-0168,A,Lagos,Member,Male,Fashion accessories,7092.0,1,354.6,7446.599999999999,2/8/2019,11:39,Epay,7092.0,4.761904762,354.6,9.5
+805-86-0265,A,Lagos,Normal,Male,Home and lifestyle,33825.6,9,15221.52,319651.92,3/20/2019,11:32,Cash,304430.4,4.761904762,15221.52,9.8
+373-14-0504,A,Lagos,Member,Female,Sports and travel,25786.8,2,2578.68,54152.28,2/12/2019,14:33,Epay,51573.6,4.761904762,2578.68,8.8
+546-80-2899,A,Lagos,Member,Male,Home and lifestyle,13568.4,2,1356.84,28493.64,2/20/2019,15:29,Epay,27136.8,4.761904762,1356.84,9.5
+585-86-8361,A,Lagos,Normal,Female,Food and beverages,9820.8,5,2455.2000000000003,51559.2,2/3/2019,10:31,Card,49104.0,4.761904762,2455.2000000000003,8.6
+807-14-7833,A,Lagos,Member,Female,Electronic accessories,6271.200000000001,10,3135.6000000000004,65847.6,2/22/2019,12:30,Epay,62711.99999999999,4.761904762,3135.6000000000004,7.0
+652-43-6591,A,Lagos,Normal,Female,Fashion accessories,35024.4,8,14009.759999999998,294204.96,3/9/2019,13:18,Card,280195.2,4.761904762,14009.759999999998,6.2
+406-46-7107,A,Lagos,Normal,Female,Home and lifestyle,34747.2,6,10424.16,218907.36,1/11/2019,11:52,Cash,208483.2,4.761904762,10424.16,4.5
+250-17-5703,A,Lagos,Member,Male,Food and beverages,6786.000000000001,10,3393.0000000000005,71253.0,2/27/2019,18:24,Epay,67860.0,4.761904762,3393.0000000000005,5.6
+156-95-3964,A,Lagos,Normal,Female,Food and beverages,19940.4,4,3988.08,83749.68000000001,3/25/2019,15:19,Epay,79761.6,4.761904762,3988.08,8.0
+410-67-1709,A,Lagos,Member,Female,Fashion accessories,22996.8,8,9198.72,193173.12,1/20/2019,17:48,Epay,183974.4,4.761904762,9198.72,9.9
+587-73-4862,A,Lagos,Member,Female,Health and beauty,3848.4,5,962.1,20204.1,3/26/2019,11:07,Epay,19242.0,4.761904762,962.1,7.6
+787-87-2010,A,Lagos,Member,Male,Health and beauty,19980.0,4,3996.0,83916.0,1/20/2019,15:48,Card,79920.0,4.761904762,3996.0,6.6
+886-54-6089,A,Lagos,Normal,Female,Home and lifestyle,4114.8,6,1234.4399999999998,25923.24,1/15/2019,17:24,Cash,24688.8,4.761904762,1234.4399999999998,7.7
+534-53-3526,A,Lagos,Normal,Female,Sports and travel,34113.6,4,6822.72,143277.12,2/11/2019,16:06,Epay,136454.4,4.761904762,6822.72,7.8
+307-04-2070,A,Lagos,Member,Female,Fashion accessories,11023.2,1,551.16,11574.36,2/5/2019,14:14,Card,11023.2,4.761904762,551.16,4.1
+404-91-5964,A,Lagos,Normal,Male,Electronic accessories,26848.8,7,9397.08,197338.68,2/4/2019,16:09,Card,187941.6,4.761904762,9397.08,9.0
+497-37-6538,A,Lagos,Normal,Male,Sports and travel,21207.6,7,7422.660000000001,155875.86,1/17/2019,15:15,Epay,148453.2,4.761904762,7422.660000000001,9.7
+651-96-5970,A,Lagos,Normal,Male,Fashion accessories,16707.6,1,835.38,17542.98,3/3/2019,20:06,Card,16707.6,4.761904762,835.38,4.0
+263-12-5321,A,Lagos,Member,Male,Electronic accessories,33336.0,7,11667.6,245019.6,2/27/2019,12:52,Card,233352.00000000003,4.761904762,11667.6,9.3
+702-72-0487,A,Lagos,Normal,Female,Electronic accessories,16779.6,2,1677.9600000000005,35237.16,2/26/2019,12:28,Card,33559.2,4.761904762,1677.9600000000005,6.6
+864-24-7918,A,Lagos,Member,Female,Sports and travel,8816.4,10,4408.2,92572.2,2/22/2019,15:15,Cash,88164.0,4.761904762,4408.2,8.1
+759-29-9521,A,Lagos,Member,Female,Fashion accessories,17625.6,9,7931.519999999999,166561.91999999998,3/4/2019,11:27,Cash,158630.4,4.761904762,7931.519999999999,8.0
+220-68-6701,A,Lagos,Normal,Female,Home and lifestyle,27889.2,4,5577.84,117134.64,3/17/2019,16:36,Cash,111556.8,4.761904762,5577.84,4.2
+618-34-8551,A,Lagos,Normal,Female,Sports and travel,33544.8,2,3354.48,70444.08,1/16/2019,18:41,Card,67089.6,4.761904762,3354.48,8.5
+257-60-7754,A,Lagos,Normal,Female,Electronic accessories,18082.8,4,3616.56,75947.76,1/8/2019,17:12,Cash,72331.2,4.761904762,3616.56,9.0
+380-60-5336,A,Lagos,Normal,Female,Electronic accessories,14493.6,10,7246.799999999999,152182.80000000002,2/24/2019,18:06,Card,144936.0,4.761904762,7246.799999999999,5.0
+674-56-6360,A,Lagos,Normal,Male,Electronic accessories,34254.0,1,1712.7,35966.7,3/22/2019,14:00,Cash,34254.0,4.761904762,1712.7,6.0
+778-34-2523,A,Lagos,Member,Female,Electronic accessories,17503.2,8,7001.28,147026.88,1/24/2019,10:57,Cash,140025.6,4.761904762,7001.28,5.0
+832-51-6761,A,Lagos,Normal,Male,Food and beverages,12196.8,8,4878.72,102453.12,1/19/2019,20:29,Epay,97574.4,4.761904762,4878.72,9.6
+186-43-8965,A,Lagos,Member,Female,Home and lifestyle,17164.8,2,1716.48,36046.08,2/24/2019,10:10,Card,34329.6,4.761904762,1716.48,4.1
+276-75-6884,A,Lagos,Normal,Female,Health and beauty,24735.6,3,3710.34,77917.14,3/4/2019,10:05,Cash,74206.8,4.761904762,3710.34,8.7
+569-76-2760,A,Lagos,Member,Female,Sports and travel,7923.6,4,1584.72,33279.12,1/29/2019,18:15,Card,31694.4,4.761904762,1584.72,6.6
+760-53-9233,A,Lagos,Member,Male,Fashion accessories,14860.8,3,2229.12,46811.52,3/26/2019,18:37,Card,44582.4,4.761904762,2229.12,8.5
+416-17-9926,A,Lagos,Member,Female,Electronic accessories,26719.2,10,13359.6,280551.6,1/1/2019,14:42,Card,267192.0,4.761904762,13359.6,4.3
+237-44-6163,A,Lagos,Normal,Male,Electronic accessories,3801.6,8,1520.64,31933.44,1/24/2019,17:43,Cash,30412.800000000003,4.761904762,1520.64,7.6
+528-14-9470,A,Lagos,Member,Male,Health and beauty,32868.0,1,1643.4,34511.4,2/14/2019,14:42,Epay,32868.0,4.761904762,1643.4,9.2
+807-34-3742,A,Lagos,Normal,Male,Fashion accessories,18856.8,1,942.84,19799.64,3/26/2019,19:44,Cash,18856.8,4.761904762,942.84,5.8
+288-62-1085,A,Lagos,Member,Male,Fashion accessories,13874.4,5,3468.6,72840.6,1/9/2019,13:34,Epay,69372.0,4.761904762,3468.6,5.6
+497-36-0989,A,Lagos,Normal,Male,Fashion accessories,18698.4,3,2804.76,58899.96,2/15/2019,15:21,Cash,56095.2,4.761904762,2804.76,7.9
+860-73-6466,A,Lagos,Member,Female,Sports and travel,14209.2,2,1420.92,29839.32,3/2/2019,16:16,Card,28418.4,4.761904762,1420.92,5.0
+896-34-0956,A,Lagos,Normal,Male,Fashion accessories,7675.2,1,383.75999999999993,8058.96,1/26/2019,12:43,Cash,7675.2,4.761904762,383.75999999999993,5.9
+804-38-3935,A,Lagos,Member,Male,Electronic accessories,33760.8,3,5064.12,106346.52,1/30/2019,11:32,Card,101282.4,4.761904762,5064.12,5.9
+585-90-0249,A,Lagos,Member,Male,Electronic accessories,26373.6,1,1318.68,27692.28,1/27/2019,18:08,Epay,26373.6,4.761904762,1318.68,9.7
+125-45-2293,A,Lagos,Normal,Female,Fashion accessories,35676.0,6,10702.8,224758.8,1/19/2019,13:11,Cash,214056.0,4.761904762,10702.8,4.2
+843-73-4724,A,Lagos,Normal,Male,Fashion accessories,26676.0,1,1333.8,28009.800000000003,1/25/2019,11:05,Cash,26676.0,4.761904762,1333.8,9.2
+409-33-9708,A,Lagos,Normal,Female,Fashion accessories,35452.8,2,3545.28,74450.88,2/19/2019,10:12,Epay,70905.6,4.761904762,3545.28,9.2
+160-22-2687,A,Lagos,Member,Female,Health and beauty,34542.0,5,8635.5,181345.5,1/23/2019,14:21,Epay,172710.0,4.761904762,8635.5,8.8
+748-45-2862,A,Lagos,Member,Female,Home and lifestyle,10191.6,4,2038.3200000000004,42804.72,3/7/2019,18:35,Cash,40766.4,4.761904762,2038.3200000000004,8.2
+316-66-3011,A,Lagos,Member,Female,Food and beverages,17146.8,9,7716.059999999999,162037.26,1/23/2019,12:35,Cash,154321.2,4.761904762,7716.059999999999,5.0
+840-76-5966,A,Lagos,Member,Male,Sports and travel,4593.6,2,459.36,9646.56,1/8/2019,18:06,Epay,9187.2,4.761904762,459.36,7.8
+124-31-1458,A,Lagos,Member,Female,Electronic accessories,28652.4,3,4297.86,90255.06,1/8/2019,14:30,Cash,85957.2,4.761904762,4297.86,6.6
+852-82-2749,A,Lagos,Normal,Male,Sports and travel,23252.4,4,4650.48,97660.08,1/6/2019,13:35,Epay,93009.6,4.761904762,4650.48,9.3
+873-14-6353,A,Lagos,Member,Male,Food and beverages,8935.2,7,3127.3200000000006,65673.72,2/16/2019,10:33,Card,62546.4,4.761904762,3127.3200000000006,7.1
+166-19-2553,A,Lagos,Member,Male,Sports and travel,32061.6,6,9618.48,201988.08,1/18/2019,17:26,Cash,192369.6,4.761904762,9618.48,9.9
+737-88-5876,A,Lagos,Member,Male,Home and lifestyle,8384.4,4,1676.88,35214.48,3/19/2019,11:52,Card,33537.6,4.761904762,1676.88,5.9
+448-61-3783,A,Lagos,Normal,Female,Electronic accessories,32407.2,8,12962.88,272220.48,3/21/2019,16:08,Card,259257.6,4.761904762,12962.88,4.5
+291-55-6563,A,Lagos,Member,Female,Home and lifestyle,12391.2,6,3717.36,78064.56,3/30/2019,12:45,Epay,74347.2,4.761904762,3717.36,7.5
+548-48-3156,A,Lagos,Member,Female,Food and beverages,30002.4,2,3000.24,63005.04,3/19/2019,13:37,Cash,60004.8,4.761904762,3000.24,7.6
+460-93-5834,A,Lagos,Normal,Male,Sports and travel,16408.8,7,5743.08,120604.68,1/13/2019,10:03,Cash,114861.6,4.761904762,5743.08,5.0
+325-89-4209,A,Lagos,Member,Male,Food and beverages,31644.000000000004,1,1582.1999999999998,33226.2,2/5/2019,19:42,Epay,31644.000000000004,4.761904762,1582.1999999999998,6.7
+884-80-6021,A,Lagos,Member,Female,Electronic accessories,26449.2,10,13224.6,277716.6,3/23/2019,13:14,Epay,264492.0,4.761904762,13224.6,9.5
+880-46-5796,A,Lagos,Member,Male,Sports and travel,27691.2,10,13845.6,290757.6,3/17/2019,19:53,Epay,276912.0,4.761904762,13845.6,5.6
+146-09-5432,A,Lagos,Member,Male,Food and beverages,12614.4,9,5676.48,119206.08000000002,2/9/2019,19:17,Epay,113529.6,4.761904762,5676.48,4.6
+595-94-9924,A,Lagos,Member,Female,Health and beauty,9982.8,5,2495.7,52409.7,3/26/2019,20:21,Card,49914.0,4.761904762,2495.7,4.2
+865-41-9075,A,Lagos,Normal,Male,Food and beverages,4150.8,7,1452.78,30508.38,1/28/2019,17:35,Cash,29055.6,4.761904762,1452.78,8.1
+186-71-5196,A,Lagos,Member,Female,Food and beverages,28634.4,2,2863.44,60132.240000000005,3/27/2019,16:30,Epay,57268.8,4.761904762,2863.44,6.2
+453-33-6436,A,Lagos,Normal,Female,Home and lifestyle,33523.200000000004,8,13409.280000000002,281594.88,2/7/2019,10:09,Cash,268185.60000000003,4.761904762,13409.280000000002,6.8
+522-57-8364,A,Lagos,Member,Male,Fashion accessories,18482.4,8,7392.959999999999,155252.16,1/31/2019,10:00,Epay,147859.2,4.761904762,7392.959999999999,7.6
+459-45-2396,A,Lagos,Member,Female,Food and beverages,35856.0,3,5378.4,112946.4,2/25/2019,18:45,Cash,107568.0,4.761904762,5378.4,5.8
+749-81-8133,A,Lagos,Normal,Female,Fashion accessories,34081.2,4,6816.240000000001,143141.04,3/11/2019,12:04,Cash,136324.8,4.761904762,6816.240000000001,6.8
+397-25-8725,A,Lagos,Member,Female,Health and beauty,14263.2,9,6418.4400000000005,134787.24,1/13/2019,17:54,Card,128368.8,4.761904762,6418.4400000000005,6.8
+243-55-8457,A,Lagos,Normal,Female,Food and beverages,26798.4,10,13399.2,281383.2,2/27/2019,11:40,Epay,267984.0,4.761904762,13399.2,5.1
+361-85-2571,A,Lagos,Normal,Female,Sports and travel,32212.800000000003,5,8053.200000000001,169117.19999999998,3/30/2019,10:18,Cash,161064.0,4.761904762,8053.200000000001,7.4
+131-70-8179,A,Lagos,Member,Female,Health and beauty,33152.4,3,4972.86,104430.06,2/17/2019,16:27,Cash,99457.2,4.761904762,4972.86,4.2
+720-72-2436,A,Lagos,Normal,Male,Food and beverages,23947.2,4,4789.4400000000005,100578.24,3/2/2019,18:14,Epay,95788.8,4.761904762,4789.4400000000005,6.9
+809-69-9497,A,Lagos,Normal,Female,Home and lifestyle,16444.8,10,8222.4,172670.4,1/19/2019,19:30,Epay,164448.0,4.761904762,8222.4,5.7
+449-16-6770,A,Lagos,Normal,Male,Health and beauty,18284.4,5,4571.1,95993.1,2/19/2019,14:53,Card,91422.0,4.761904762,4571.1,5.3
+333-23-2632,A,Lagos,Member,Male,Health and beauty,3628.8,7,1270.08,26671.680000000004,3/28/2019,20:14,Cash,25401.6,4.761904762,1270.08,4.2
+489-82-1237,A,Lagos,Normal,Female,Electronic accessories,33796.8,7,11828.88,248406.48,1/5/2019,11:51,Card,236577.6,4.761904762,11828.88,7.3
+846-10-0341,A,Lagos,Normal,Female,Fashion accessories,15325.2,7,5363.82,112640.22,1/6/2019,11:51,Cash,107276.4,4.761904762,5363.82,6.8
+384-59-6655,A,Lagos,Member,Female,Food and beverages,35517.6,9,15982.92,335641.32,2/19/2019,15:07,Cash,319658.4,4.761904762,15982.92,8.4
+324-92-3863,A,Lagos,Member,Male,Electronic accessories,7520.400000000001,2,752.04,15792.84,2/5/2019,18:45,Cash,15040.8,4.761904762,752.04,9.8
+593-08-5916,A,Lagos,Normal,Female,Fashion accessories,5580.0,1,279.0,5858.999999999999,3/19/2019,15:23,Card,5580.0,4.761904762,279.0,7.4
+558-60-5016,A,Lagos,Normal,Female,Home and lifestyle,11987.999999999998,9,5394.599999999999,113286.6,3/4/2019,15:27,Epay,107892.0,4.761904762,5394.599999999999,7.2
+195-06-0432,A,Lagos,Member,Male,Home and lifestyle,29163.6,3,4374.54,91865.34,1/13/2019,12:55,Card,87490.8,4.761904762,4374.54,9.3
+605-03-2706,A,Lagos,Normal,Female,Health and beauty,5688.0,3,853.2,17917.2,3/25/2019,18:02,Cash,17064.0,4.761904762,853.2,9.5
+531-56-4728,A,Lagos,Normal,Male,Home and lifestyle,28828.8,3,4324.32,90810.72,2/11/2019,15:29,Cash,86486.40000000001,4.761904762,4324.32,5.4
+221-25-5073,A,Lagos,Normal,Female,Food and beverages,26877.6,4,5375.52,112885.92,3/4/2019,10:39,Cash,107510.4,4.761904762,5375.52,8.5
+809-46-1866,A,Lagos,Normal,Male,Health and beauty,20934.0,4,4186.8,87922.8,1/23/2019,17:44,Cash,83736.0,4.761904762,4186.8,8.4
+139-32-4183,A,Lagos,Member,Female,Sports and travel,35092.8,9,15791.760000000002,331626.96,3/14/2019,14:19,Epay,315835.2,4.761904762,15791.760000000002,7.4
+886-18-2897,A,Lagos,Normal,Female,Food and beverages,20361.6,5,5090.400000000001,106898.4,3/22/2019,19:06,Card,101808.0,4.761904762,5090.400000000001,4.5
+745-74-0715,A,Lagos,Normal,Male,Electronic accessories,20890.8,2,2089.08,43870.68,3/10/2019,20:46,Epay,41781.6,4.761904762,2089.08,8.8
+727-02-1313,A,Lagos,Member,Male,Food and beverages,11462.4,1,573.12,12035.52,2/9/2019,13:22,Cash,11462.4,4.761904762,573.12,7.7
+347-56-2442,A,Lagos,Normal,Male,Home and lifestyle,23695.2,1,1184.7600000000002,24879.960000000003,2/22/2019,15:33,Cash,23695.2,4.761904762,1184.7600000000002,4.1
+849-09-3807,A,Lagos,Member,Female,Fashion accessories,31802.4,7,11130.84,233747.64,2/18/2019,13:28,Cash,222616.8,4.761904762,11130.84,6.6
+226-31-3081,C,Port Harcourt,Normal,Female,Electronic accessories,5500.8,5,1375.2,28879.2,3/8/2019,10:29,Cash,27504.000000000004,4.761904762,1375.2,9.6
+699-14-3026,C,Port Harcourt,Normal,Male,Electronic accessories,30740.4,7,10759.14,225941.94,3/25/2019,18:30,Epay,215182.8,4.761904762,10759.14,4.1
+315-22-5665,C,Port Harcourt,Normal,Female,Home and lifestyle,26481.6,10,13240.8,278056.8,2/24/2019,11:38,Epay,264816.0,4.761904762,13240.8,8.0
+300-71-4605,C,Port Harcourt,Member,Male,Electronic accessories,30974.4,5,7743.6,162615.6,2/25/2019,11:24,Epay,154872.0,4.761904762,7743.6,4.8
+183-56-6882,C,Port Harcourt,Member,Female,Food and beverages,35791.2,4,7158.24,150323.04,2/6/2019,10:42,Epay,143164.8,4.761904762,7158.24,7.5
+232-16-2483,C,Port Harcourt,Member,Female,Sports and travel,24523.2,1,1226.16,25749.360000000004,1/7/2019,12:28,Epay,24523.2,4.761904762,1226.16,6.8
+333-73-7901,C,Port Harcourt,Normal,Female,Health and beauty,19771.2,8,7908.480000000001,166078.08000000002,3/23/2019,13:24,Epay,158169.6,4.761904762,7908.480000000001,7.6
+554-53-8700,C,Port Harcourt,Member,Male,Home and lifestyle,20199.6,2,2019.9600000000005,42419.16,2/2/2019,10:11,Cash,40399.2,4.761904762,2019.9600000000005,6.3
+228-96-1411,C,Port Harcourt,Member,Female,Food and beverages,35532.0,8,14212.8,298468.8,3/4/2019,20:39,Cash,284256.0,4.761904762,14212.8,7.6
+617-15-4209,C,Port Harcourt,Member,Male,Health and beauty,5533.2,2,553.3199999999999,11619.72,3/16/2019,19:47,Cash,11066.4,4.761904762,553.3199999999999,7.2
+574-22-5561,C,Port Harcourt,Member,Female,Fashion accessories,29746.8,10,14873.4,312341.4,3/19/2019,17:08,Epay,297468.0,4.761904762,14873.4,7.9
+326-78-5178,C,Port Harcourt,Member,Male,Food and beverages,32904.0,7,11516.4,241844.4,2/3/2019,10:19,Cash,230328.0,4.761904762,11516.4,9.5
+778-71-5554,C,Port Harcourt,Member,Male,Fashion accessories,5554.8,1,277.74,5832.54,1/25/2019,15:46,Card,5554.8,4.761904762,277.74,6.1
+399-46-5918,C,Port Harcourt,Normal,Female,Electronic accessories,30952.800000000003,8,12381.12,260003.52,2/28/2019,19:01,Cash,247622.4,4.761904762,12381.12,8.2
+120-06-4233,C,Port Harcourt,Normal,Male,Electronic accessories,11019.6,6,3305.88,69423.48,3/12/2019,20:36,Cash,66117.6,4.761904762,3305.88,9.3
+285-68-5083,C,Port Harcourt,Member,Female,Sports and travel,8906.4,3,1335.96,28055.16,2/15/2019,17:47,Card,26719.2,4.761904762,1335.96,10.0
+803-83-5989,C,Port Harcourt,Normal,Male,Home and lifestyle,20062.8,6,6018.84,126395.64,2/24/2019,10:55,Epay,120376.8,4.761904762,6018.84,7.0
+838-78-4295,C,Port Harcourt,Normal,Female,Health and beauty,12049.2,2,1204.92,25303.32,2/10/2019,15:43,Epay,24098.4,4.761904762,1204.92,6.7
+393-65-2792,C,Port Harcourt,Normal,Male,Food and beverages,32212.800000000003,10,16106.4,338234.4,1/6/2019,12:46,Card,322128.0,4.761904762,16106.4,9.6
+796-12-2025,C,Port Harcourt,Normal,Male,Fashion accessories,22363.2,10,11181.6,234813.6,2/11/2019,16:19,Cash,223632.00000000003,4.761904762,11181.6,5.9
+841-35-6630,C,Port Harcourt,Normal,Female,Electronic accessories,27327.6,6,8198.28,172163.88,3/9/2019,18:21,Cash,163965.6,4.761904762,8198.28,8.7
+732-94-0499,C,Port Harcourt,Normal,Female,Electronic accessories,14994.0,10,7497.0,157437.0,1/13/2019,17:04,Card,149940.0,4.761904762,7497.0,5.4
+263-10-3913,C,Port Harcourt,Member,Male,Fashion accessories,17654.4,9,7944.48,166834.08000000002,1/9/2019,14:20,Card,158889.6,4.761904762,7944.48,8.6
+829-49-1914,C,Port Harcourt,Member,Female,Food and beverages,28191.6,10,14095.8,296011.8,3/5/2019,16:24,Epay,281916.0,4.761904762,14095.8,6.6
+756-01-7507,C,Port Harcourt,Normal,Female,Health and beauty,7336.799999999999,5,1834.2,38518.2,1/22/2019,18:56,Cash,36684.0,4.761904762,1834.2,6.0
+870-72-4431,C,Port Harcourt,Normal,Female,Health and beauty,35708.4,6,10712.52,224962.92,1/21/2019,14:42,Card,214250.4,4.761904762,10712.52,5.5
+480-63-2856,C,Port Harcourt,Normal,Male,Food and beverages,6930.0,8,2772.0,58211.99999999999,1/23/2019,18:37,Epay,55440.0,4.761904762,2772.0,6.6
+787-56-0757,C,Port Harcourt,Member,Female,Food and beverages,28929.6,4,5785.92,121504.31999999998,2/23/2019,18:45,Card,115718.4,4.761904762,5785.92,8.3
+360-39-5055,C,Port Harcourt,Member,Male,Sports and travel,17607.6,5,4401.9,92439.9,3/9/2019,10:17,Cash,88038.0,4.761904762,4401.9,6.6
+730-50-9884,C,Port Harcourt,Normal,Female,Sports and travel,29901.6,7,10465.56,219776.76,3/5/2019,14:31,Epay,209311.2,4.761904762,10465.56,4.0
+362-58-8315,C,Port Harcourt,Normal,Male,Fashion accessories,27547.2,5,6886.799999999999,144622.80000000002,3/25/2019,10:23,Cash,137736.0,4.761904762,6886.799999999999,9.9
+565-80-5980,C,Port Harcourt,Member,Female,Home and lifestyle,17056.8,4,3411.36,71638.56,1/23/2019,10:25,Cash,68227.2,4.761904762,3411.36,7.1
+225-32-0908,C,Port Harcourt,Normal,Female,Sports and travel,16149.6,10,8074.8,169570.8,1/26/2019,19:54,Epay,161496.0,4.761904762,8074.8,8.2
+512-91-0811,C,Port Harcourt,Normal,Male,Health and beauty,32310.0,1,1615.5,33925.5,2/6/2019,20:05,Card,32310.0,4.761904762,1615.5,6.6
+871-39-9221,C,Port Harcourt,Normal,Female,Electronic accessories,4482.0,6,1344.6,28236.6,2/9/2019,13:11,Cash,26892.0,4.761904762,1344.6,4.1
+163-56-7055,C,Port Harcourt,Member,Male,Fashion accessories,17535.6,1,876.7800000000001,18412.38,3/26/2019,19:20,Cash,17535.6,4.761904762,876.7800000000001,4.1
+189-98-2939,C,Port Harcourt,Normal,Male,Fashion accessories,28278.0,9,12725.1,267227.1,3/1/2019,13:22,Cash,254502.00000000003,4.761904762,12725.1,7.2
+551-21-3069,C,Port Harcourt,Normal,Female,Electronic accessories,8305.2,9,3737.34,78484.14,2/1/2019,11:27,Cash,74746.8,4.761904762,3737.34,4.9
+628-34-3388,C,Port Harcourt,Normal,Male,Fashion accessories,9856.8,6,2957.04,62097.84000000001,1/5/2019,20:54,Card,59140.8,4.761904762,2957.04,7.9
+588-01-7461,C,Port Harcourt,Normal,Female,Food and beverages,12232.8,9,5504.759999999999,115599.96,3/24/2019,10:43,Cash,110095.2,4.761904762,5504.759999999999,4.2
+861-77-0145,C,Port Harcourt,Member,Male,Electronic accessories,29509.2,10,14754.6,309846.6,3/3/2019,14:30,Cash,295092.0,4.761904762,14754.6,9.2
+210-67-5886,C,Port Harcourt,Member,Female,Health and beauty,35355.6,3,5303.34,111370.13999999998,2/5/2019,10:41,Card,106066.8,4.761904762,5303.34,7.8
+237-01-6122,C,Port Harcourt,Member,Female,Home and lifestyle,29084.4,9,13087.98,274847.58,2/1/2019,20:31,Card,261759.6,4.761904762,13087.98,9.5
+225-98-1496,C,Port Harcourt,Normal,Female,Fashion accessories,9727.2,3,1459.08,30640.68,3/2/2019,13:01,Card,29181.6,4.761904762,1459.08,7.1
+659-36-1684,C,Port Harcourt,Member,Male,Sports and travel,20563.2,7,7197.12,151139.52,1/12/2019,12:02,Card,143942.4,4.761904762,7197.12,6.5
+336-78-2147,C,Port Harcourt,Member,Male,Home and lifestyle,23007.6,8,9203.04,193263.84000000003,3/13/2019,19:52,Card,184060.8,4.761904762,9203.04,4.6
+189-08-9157,C,Port Harcourt,Normal,Female,Fashion accessories,11422.8,9,5140.259999999999,107945.46,1/8/2019,16:17,Card,102805.2,4.761904762,5140.259999999999,5.9
+663-86-9076,C,Port Harcourt,Member,Female,Food and beverages,24674.4,8,9869.76,207264.96,1/8/2019,15:57,Epay,197395.2,4.761904762,9869.76,8.5
+483-71-1164,C,Port Harcourt,Normal,Female,Health and beauty,29268.0,6,8780.4,184388.4,3/8/2019,16:43,Epay,175608.0,4.761904762,8780.4,5.3
+597-78-7908,C,Port Harcourt,Normal,Male,Fashion accessories,32479.2,3,4871.88,102309.48,2/18/2019,19:39,Cash,97437.6,4.761904762,4871.88,6.2
+731-81-9469,C,Port Harcourt,Member,Female,Sports and travel,32328.0,10,16164.0,339444.0,1/23/2019,13:00,Card,323280.0,4.761904762,16164.0,5.4
+280-17-4359,C,Port Harcourt,Member,Male,Health and beauty,32580.0,10,16290.0,342090.0,1/25/2019,13:48,Cash,325800.0,4.761904762,16290.0,8.1
+338-65-2210,C,Port Harcourt,Member,Female,Health and beauty,24696.0,10,12347.999999999998,259308.0,2/5/2019,19:57,Cash,246960.0,4.761904762,12347.999999999998,9.1
+488-25-4221,C,Port Harcourt,Member,Female,Food and beverages,10947.6,1,547.38,11494.98,2/22/2019,10:36,Card,10947.6,4.761904762,547.38,8.4
+458-41-1477,C,Port Harcourt,Normal,Female,Health and beauty,16653.6,6,4996.08,104917.68,3/8/2019,17:11,Card,99921.6,4.761904762,4996.08,9.5
+568-90-5112,C,Port Harcourt,Normal,Male,Health and beauty,23810.4,4,4762.08,100003.68,3/19/2019,12:46,Card,95241.6,4.761904762,4762.08,5.6
+584-86-7256,C,Port Harcourt,Member,Male,Sports and travel,12441.6,7,4354.56,91445.76,3/11/2019,16:07,Card,87091.2,4.761904762,4354.56,7.3
+214-17-6927,C,Port Harcourt,Normal,Female,Food and beverages,5932.8,6,1779.84,37376.64,2/7/2019,18:23,Epay,35596.8,4.761904762,1779.84,9.9
+400-89-4171,C,Port Harcourt,Normal,Female,Sports and travel,29149.2,8,11659.680000000002,244853.28,1/28/2019,13:05,Cash,233193.6,4.761904762,11659.680000000002,9.3
+894-41-5205,C,Port Harcourt,Normal,Female,Food and beverages,15544.8,8,6217.920000000001,130576.32,1/19/2019,19:39,Card,124358.4,4.761904762,6217.920000000001,8.3
+423-80-0988,C,Port Harcourt,Normal,Male,Sports and travel,27504.000000000004,2,2750.4,57758.4,1/30/2019,19:42,Epay,55008.00000000001,4.761904762,2750.4,6.5
+234-65-2137,C,Port Harcourt,Normal,Male,Home and lifestyle,34408.8,10,17204.4,361292.4,1/16/2019,13:32,Cash,344088.0,4.761904762,17204.4,4.8
+733-33-4967,C,Port Harcourt,Normal,Male,Electronic accessories,7506.000000000001,8,3002.4,63050.4,3/3/2019,19:17,Cash,60048.00000000001,4.761904762,3002.4,6.3
+139-52-2867,C,Port Harcourt,Normal,Female,Fashion accessories,8103.6,7,2836.26,59561.46,2/13/2019,10:50,Card,56725.2,4.761904762,2836.26,4.8
+342-65-4817,C,Port Harcourt,Member,Male,Health and beauty,31248.0,3,4687.2,98431.2,1/28/2019,16:47,Epay,93744.0,4.761904762,4687.2,9.9
+130-98-8941,C,Port Harcourt,Normal,Male,Fashion accessories,23133.6,7,8096.76,170031.96,2/9/2019,10:00,Cash,161935.2,4.761904762,8096.76,5.7
+434-83-9547,C,Port Harcourt,Member,Male,Food and beverages,13849.2,8,5539.68,116333.27999999998,1/23/2019,11:51,Cash,110793.6,4.761904762,5539.68,7.7
+824-88-3614,C,Port Harcourt,Normal,Male,Health and beauty,12351.6,8,4940.64,103753.44,1/25/2019,15:00,Epay,98812.8,4.761904762,4940.64,5.7
+593-65-1552,C,Port Harcourt,Normal,Female,Home and lifestyle,25131.6,4,5026.32,105552.72,1/28/2019,20:50,Card,100526.4,4.761904762,5026.32,5.9
+286-43-6208,C,Port Harcourt,Normal,Female,Food and beverages,31608.0,9,14223.6,298695.60000000003,3/16/2019,19:08,Cash,284472.0,4.761904762,14223.6,9.2
+556-86-3144,C,Port Harcourt,Member,Female,Fashion accessories,26744.4,1,1337.22,28081.62,1/13/2019,19:30,Cash,26744.4,4.761904762,1337.22,5.0
+848-24-9445,C,Port Harcourt,Member,Male,Health and beauty,15732.000000000002,2,1573.2,33037.2,3/26/2019,18:03,Cash,31464.000000000004,4.761904762,1573.2,4.9
+699-01-4164,C,Port Harcourt,Normal,Male,Health and beauty,14940.0,4,2988.0000000000005,62748.00000000001,3/12/2019,19:58,Card,59760.0,4.761904762,2988.0000000000005,8.2
+420-11-4919,C,Port Harcourt,Member,Female,Food and beverages,25700.4,5,6425.1,134927.1,2/17/2019,19:57,Card,128502.0,4.761904762,6425.1,5.5
+606-80-4905,C,Port Harcourt,Member,Female,Sports and travel,6893.999999999999,6,2068.2,43432.2,1/29/2019,10:01,Card,41364.0,4.761904762,2068.2,6.8
+426-39-2418,C,Port Harcourt,Normal,Male,Electronic accessories,22107.6,7,7737.660000000001,162490.86,1/14/2019,10:02,Cash,154753.2,4.761904762,7737.660000000001,9.8
+672-51-8681,C,Port Harcourt,Member,Female,Electronic accessories,23994.000000000004,9,10797.3,226743.3,1/4/2019,18:19,Card,215946.0,4.761904762,10797.3,9.7
+263-87-5680,C,Port Harcourt,Member,Female,Home and lifestyle,10270.8,10,5135.400000000001,107843.4,3/18/2019,17:38,Epay,102708.0,4.761904762,5135.400000000001,7.8
+677-11-0152,C,Port Harcourt,Normal,Female,Food and beverages,33573.6,9,15108.12,317270.52,1/16/2019,18:08,Cash,302162.4,4.761904762,15108.12,8.8
+389-25-3394,C,Port Harcourt,Normal,Male,Electronic accessories,4251.6,5,1062.9,22320.9,2/17/2019,18:06,Cash,21258.0,4.761904762,1062.9,9.4
+279-62-1445,C,Port Harcourt,Member,Female,Fashion accessories,4514.4,1,225.72,4740.12,2/21/2019,12:38,Cash,4514.4,4.761904762,225.72,8.2
+746-68-6593,C,Port Harcourt,Member,Female,Sports and travel,31377.6,2,3137.76,65892.95999999999,1/11/2019,14:29,Card,62755.2,4.761904762,3137.76,9.7
+583-72-1480,C,Port Harcourt,Member,Male,Electronic accessories,13341.6,4,2668.32,56034.72000000001,1/31/2019,16:24,Epay,53366.4,4.761904762,2668.32,9.7
+211-30-9270,C,Port Harcourt,Normal,Male,Health and beauty,6267.6,5,1566.9,32904.9,1/28/2019,15:16,Card,31338.0,4.761904762,1566.9,4.9
+755-12-3214,C,Port Harcourt,Member,Female,Fashion accessories,15919.2,5,3979.8,83575.8,3/5/2019,17:07,Card,79596.0,4.761904762,3979.8,8.6
+142-72-4741,C,Port Harcourt,Member,Male,Fashion accessories,33552.0,2,3355.2000000000003,70459.2,2/28/2019,18:37,Card,67104.0,4.761904762,3355.2000000000003,6.0
+662-47-5456,C,Port Harcourt,Member,Male,Fashion accessories,12668.4,10,6334.2,133018.2,3/17/2019,19:06,Card,126684.0,4.761904762,6334.2,8.4
+883-17-4236,C,Port Harcourt,Normal,Female,Sports and travel,5180.400000000001,2,518.04,10878.84,3/2/2019,19:44,Card,10360.8,4.761904762,518.04,7.2
+380-94-4661,C,Port Harcourt,Member,Male,Electronic accessories,23738.4,4,4747.679999999999,99701.28,2/7/2019,13:05,Card,94953.6,4.761904762,4747.679999999999,6.9
+821-07-3596,C,Port Harcourt,Normal,Female,Fashion accessories,5922.0,4,1184.4,24872.4,3/7/2019,14:53,Epay,23688.0,4.761904762,1184.4,5.6
+808-65-0703,C,Port Harcourt,Normal,Male,Home and lifestyle,12769.2,4,2553.84,53630.64,3/14/2019,17:22,Card,51076.8,4.761904762,2553.84,6.9
+687-15-1097,C,Port Harcourt,Member,Female,Health and beauty,7603.200000000001,2,760.32,15966.72,1/3/2019,19:17,Cash,15206.4,4.761904762,760.32,9.7
+526-86-8552,C,Port Harcourt,Member,Female,Home and lifestyle,7855.2,10,3927.6,82479.6,1/7/2019,17:36,Cash,78552.0,4.761904762,3927.6,7.1
+376-56-3573,C,Port Harcourt,Normal,Female,Fashion accessories,34351.2,4,6870.24,144275.04,2/2/2019,13:23,Epay,137404.8,4.761904762,6870.24,6.4
+537-72-0426,C,Port Harcourt,Member,Male,Fashion accessories,25556.4,10,12778.2,268342.2,3/20/2019,16:28,Cash,255564.0,4.761904762,12778.2,5.7
+523-38-0215,C,Port Harcourt,Normal,Male,Home and lifestyle,13320.0,1,666.0,13986.0,3/6/2019,13:29,Card,13320.0,4.761904762,666.0,7.9
+593-95-4461,C,Port Harcourt,Member,Male,Home and lifestyle,26949.6,1,1347.48,28297.08,3/24/2019,14:49,Cash,26949.6,4.761904762,1347.48,6.9
+226-71-3580,C,Port Harcourt,Normal,Female,Sports and travel,8550.0,9,3847.5,80797.5,1/31/2019,12:02,Cash,76950.0,4.761904762,3847.5,9.5
+558-80-4082,C,Port Harcourt,Normal,Male,Electronic accessories,10026.0,7,3509.1000000000004,73691.09999999999,3/14/2019,17:20,Epay,70182.0,4.761904762,3509.1000000000004,6.0
+211-05-0490,C,Port Harcourt,Member,Female,Electronic accessories,18691.2,5,4672.8,98128.8,3/3/2019,13:42,Cash,93456.0,4.761904762,4672.8,7.5
+727-75-6477,C,Port Harcourt,Normal,Male,Electronic accessories,10382.4,4,2076.48,43606.08,3/29/2019,14:44,Cash,41529.6,4.761904762,2076.48,6.4
+779-06-0012,C,Port Harcourt,Member,Female,Home and lifestyle,31899.6,1,1594.98,33494.579999999994,1/19/2019,10:21,Cash,31899.6,4.761904762,1594.98,7.7
+446-47-6729,C,Port Harcourt,Normal,Male,Fashion accessories,35935.2,2,3593.5200000000004,75463.92,1/2/2019,18:09,Card,71870.4,4.761904762,3593.5200000000004,6.7
+735-06-4124,C,Port Harcourt,Normal,Male,Food and beverages,17499.6,1,874.9799999999999,18374.58,2/25/2019,15:31,Cash,17499.6,4.761904762,874.9799999999999,4.4
+181-94-6432,C,Port Harcourt,Member,Male,Fashion accessories,24958.8,2,2495.88,52413.48,2/5/2019,19:05,Epay,49917.6,4.761904762,2495.88,9.7
+227-07-4446,C,Port Harcourt,Member,Female,Electronic accessories,28126.8,10,14063.4,295331.4,2/10/2019,20:51,Cash,281268.0,4.761904762,14063.4,4.4
+174-36-3675,C,Port Harcourt,Member,Male,Food and beverages,35773.200000000004,2,3577.3200000000006,75123.72,2/14/2019,17:29,Cash,71546.40000000001,4.761904762,3577.3200000000006,5.2
+428-83-5800,C,Port Harcourt,Member,Female,Food and beverages,7588.799999999999,3,1138.32,23904.72,2/9/2019,10:25,Cash,22766.4,4.761904762,1138.32,7.3
+603-07-0961,C,Port Harcourt,Member,Male,Electronic accessories,26924.4,5,6731.1,141353.1,1/10/2019,11:34,Cash,134622.0,4.761904762,6731.1,4.9
+704-20-4138,C,Port Harcourt,Member,Female,Health and beauty,10681.2,7,3738.42,78506.82,3/11/2019,18:58,Card,74768.4,4.761904762,3738.42,8.1
+787-15-1757,C,Port Harcourt,Member,Male,Health and beauty,15865.2,4,3173.04,66633.84000000001,2/18/2019,16:28,Epay,63460.8,4.761904762,3173.04,8.4
+649-11-3678,C,Port Harcourt,Normal,Female,Food and beverages,8254.8,9,3714.66,78007.86,2/26/2019,20:26,Cash,74293.2,4.761904762,3714.66,5.5
+622-20-1945,C,Port Harcourt,Normal,Female,Health and beauty,14191.2,1,709.56,14900.760000000002,1/18/2019,15:08,Cash,14191.2,4.761904762,709.56,8.4
+719-76-3868,C,Port Harcourt,Member,Male,Food and beverages,33933.6,4,6786.72,142521.12,3/12/2019,16:30,Cash,135734.4,4.761904762,6786.72,8.6
+835-16-0096,C,Port Harcourt,Member,Male,Sports and travel,5292.0,5,1323.0,27783.0,3/24/2019,13:48,Epay,26460.0,4.761904762,1323.0,8.5
+633-09-3463,C,Port Harcourt,Normal,Female,Electronic accessories,17154.0,3,2573.1,54035.1,3/28/2019,12:58,Card,51461.99999999999,4.761904762,2573.1,9.5
+416-13-5917,C,Port Harcourt,Normal,Female,Food and beverages,34930.8,5,8732.7,183386.7,1/30/2019,16:24,Epay,174654.0,4.761904762,8732.7,9.3
+725-96-3778,C,Port Harcourt,Member,Female,Home and lifestyle,32130.0,8,12852.000000000002,269892.0,1/20/2019,10:13,Cash,257040.0,4.761904762,12852.000000000002,4.7
+860-79-0874,C,Port Harcourt,Member,Female,Fashion accessories,35748.0,10,17874.0,375354.00000000006,2/15/2019,14:53,Card,357480.0,4.761904762,17874.0,6.6
+320-49-6392,C,Port Harcourt,Normal,Female,Electronic accessories,10886.4,1,544.3199999999999,11430.72,3/4/2019,15:44,Cash,10886.4,4.761904762,544.3199999999999,8.4
+632-90-0281,C,Port Harcourt,Normal,Female,Fashion accessories,13517.999999999998,10,6758.999999999999,141939.0,3/8/2019,20:01,Card,135180.0,4.761904762,6758.999999999999,9.3
+554-42-2417,C,Port Harcourt,Normal,Female,Sports and travel,34358.4,10,17179.2,360763.2,1/9/2019,13:45,Cash,343584.0,4.761904762,17179.2,5.2
+605-72-4132,C,Port Harcourt,Normal,Female,Food and beverages,34009.2,8,13603.680000000002,285677.28,2/27/2019,15:12,Cash,272073.6,4.761904762,13603.680000000002,9.1
+471-41-2823,C,Port Harcourt,Normal,Male,Food and beverages,35924.4,2,3592.44,75441.24,3/7/2019,20:37,Epay,71848.8,4.761904762,3592.44,8.0
+272-27-9238,C,Port Harcourt,Normal,Female,Food and beverages,14846.4,4,2969.28,62354.88,2/19/2019,16:23,Cash,59385.600000000006,4.761904762,2969.28,7.1
+834-25-9262,C,Port Harcourt,Normal,Female,Fashion accessories,29404.800000000003,4,5880.959999999999,123500.16,1/6/2019,12:12,Cash,117619.2,4.761904762,5880.959999999999,9.1
+122-61-9553,C,Port Harcourt,Normal,Female,Electronic accessories,18475.2,9,8313.84,174590.63999999998,3/14/2019,19:33,Cash,166276.8,4.761904762,8313.84,5.6
+613-59-9758,C,Port Harcourt,Normal,Female,Sports and travel,5169.599999999999,10,2584.8,54280.8,1/27/2019,14:28,Cash,51696.0,4.761904762,2584.8,5.4
+730-70-9830,C,Port Harcourt,Normal,Female,Home and lifestyle,25239.6,6,7571.88,159009.48,3/14/2019,17:54,Epay,151437.6,4.761904762,7571.88,5.2
+382-25-8917,C,Port Harcourt,Normal,Male,Fashion accessories,15148.8,6,4544.64,95437.44000000002,1/29/2019,12:25,Cash,90892.8,4.761904762,4544.64,8.9
+743-88-1662,C,Port Harcourt,Normal,Male,Sports and travel,34376.4,7,12031.74,252666.54,2/22/2019,18:17,Epay,240634.8,4.761904762,12031.74,8.7
+595-86-2894,C,Port Harcourt,Member,Male,Fashion accessories,34912.8,4,6982.56,146633.75999999998,2/6/2019,17:20,Epay,139651.2,4.761904762,6982.56,9.4
+462-78-5240,C,Port Harcourt,Normal,Female,Electronic accessories,9579.6,2,957.96,20117.16,3/19/2019,14:35,Cash,19159.2,4.761904762,957.96,4.2
+153-58-4872,C,Port Harcourt,Member,Female,Food and beverages,26960.4,4,5392.08,113233.68,3/1/2019,15:32,Epay,107841.6,4.761904762,5392.08,4.2
+689-16-9784,C,Port Harcourt,Normal,Male,Food and beverages,16837.2,6,5051.16,106074.36,3/11/2019,13:37,Cash,101023.2,4.761904762,5051.16,6.0
+394-41-0748,C,Port Harcourt,Member,Female,Fashion accessories,19465.2,9,8759.34,183946.14,1/27/2019,14:55,Epay,175186.8,4.761904762,8759.34,9.5
+541-89-9860,C,Port Harcourt,Member,Female,Fashion accessories,28972.800000000003,3,4345.92,91264.32,2/15/2019,12:31,Cash,86918.4,4.761904762,4345.92,8.1
+110-05-6330,C,Port Harcourt,Normal,Female,Food and beverages,14194.8,6,4258.4400000000005,89427.24,3/25/2019,20:18,Card,85168.8,4.761904762,4258.4400000000005,9.4
+651-61-0874,C,Port Harcourt,Normal,Male,Home and lifestyle,16639.2,4,3327.84,69884.64000000001,3/12/2019,20:04,Card,66556.8,4.761904762,3327.84,6.2
+236-86-3015,C,Port Harcourt,Member,Male,Home and lifestyle,5032.8,1,251.64,5284.4400000000005,2/4/2019,13:38,Epay,5032.8,4.761904762,251.64,9.8
+587-03-7455,C,Port Harcourt,Member,Female,Fashion accessories,35204.4,7,12321.54,258752.34,2/16/2019,17:30,Epay,246430.8,4.761904762,12321.54,4.9
+372-26-1506,C,Port Harcourt,Normal,Female,Fashion accessories,8575.2,5,2143.8,45019.8,1/28/2019,19:24,Epay,42876.0,4.761904762,2143.8,5.4
+750-57-9686,C,Port Harcourt,Normal,Female,Home and lifestyle,16336.8,4,3267.36,68614.56,1/8/2019,13:48,Card,65347.2,4.761904762,3267.36,8.7
+186-09-3669,C,Port Harcourt,Member,Female,Health and beauty,29343.6,1,1467.18,30810.78,1/22/2019,10:57,Epay,29343.6,4.761904762,1467.18,9.2
+266-76-6436,C,Port Harcourt,Member,Female,Food and beverages,13896.0,3,2084.4,43772.4,3/28/2019,13:57,Epay,41688.0,4.761904762,2084.4,7.5
+740-22-2500,C,Port Harcourt,Normal,Female,Electronic accessories,30258.0,3,4538.7,95312.7,1/23/2019,13:29,Cash,90774.0,4.761904762,4538.7,9.8
+271-88-8734,C,Port Harcourt,Member,Female,Fashion accessories,34995.6,10,17497.8,367453.8,2/8/2019,13:00,Card,349956.0,4.761904762,17497.8,8.7
+489-64-4354,C,Port Harcourt,Normal,Male,Fashion accessories,5860.8,1,293.04,6153.84,3/9/2019,15:36,Cash,5860.8,4.761904762,293.04,5.0
+574-57-9721,C,Port Harcourt,Normal,Male,Food and beverages,15577.2,2,1557.72,32712.120000000003,3/8/2019,16:53,Epay,31154.4,4.761904762,1557.72,5.7
+751-69-0068,C,Port Harcourt,Normal,Male,Sports and travel,35726.4,9,16076.88,337614.48,3/19/2019,19:09,Epay,321537.6,4.761904762,16076.88,9.0
+257-73-1380,C,Port Harcourt,Member,Male,Sports and travel,29854.800000000003,4,5970.959999999999,125390.16,1/20/2019,16:51,Epay,119419.2,4.761904762,5970.959999999999,9.6
+549-96-4200,C,Port Harcourt,Member,Male,Food and beverages,6134.4,4,1226.88,25764.48,3/8/2019,20:15,Epay,24537.6,4.761904762,1226.88,7.0
+810-60-6344,C,Port Harcourt,Normal,Female,Electronic accessories,14709.6,8,5883.84,123560.64,2/7/2019,14:38,Card,117676.8,4.761904762,5883.84,6.5
+450-28-2866,C,Port Harcourt,Member,Male,Food and beverages,6278.400000000001,5,1569.6,32961.6,1/15/2019,19:25,Cash,31392.0,4.761904762,1569.6,8.1
+192-98-7397,C,Port Harcourt,Normal,Male,Fashion accessories,4600.8,1,230.04,4830.84,1/8/2019,14:11,Epay,4600.8,4.761904762,230.04,9.5
+235-46-8343,C,Port Harcourt,Member,Male,Food and beverages,9957.6,10,4978.8,104554.8,2/14/2019,11:26,Card,99576.0,4.761904762,4978.8,8.9
+453-12-7053,C,Port Harcourt,Normal,Male,Fashion accessories,16466.4,3,2469.96,51869.16,3/10/2019,17:38,Card,49399.2,4.761904762,2469.96,6.5
+325-90-8763,C,Port Harcourt,Member,Female,Electronic accessories,16765.2,10,8382.6,176034.6,1/27/2019,13:58,Cash,167652.0,4.761904762,8382.6,7.6
+729-46-7422,C,Port Harcourt,Normal,Male,Food and beverages,12920.4,1,646.02,13566.42,2/23/2019,16:52,Card,12920.4,4.761904762,646.02,7.9
+639-76-1242,C,Port Harcourt,Normal,Male,Food and beverages,14587.2,5,3646.8,76582.8,2/3/2019,15:19,Cash,72936.0,4.761904762,3646.8,4.5
+326-71-2155,C,Port Harcourt,Normal,Female,Sports and travel,26622.0,4,5324.4,111812.4,2/3/2019,10:02,Cash,106488.0,4.761904762,5324.4,6.1
+320-32-8842,C,Port Harcourt,Member,Female,Food and beverages,8143.200000000001,1,407.16,8550.359999999999,3/17/2019,18:58,Cash,8143.200000000001,4.761904762,407.16,6.4
+878-30-2331,C,Port Harcourt,Member,Female,Sports and travel,19638.0,10,9819.0,206199.0,3/2/2019,11:22,Card,196380.0,4.761904762,9819.0,7.1
+440-59-5691,C,Port Harcourt,Member,Female,Health and beauty,13374.0,7,4680.9,98298.9,2/8/2019,13:12,Card,93618.0,4.761904762,4680.9,7.7
+746-19-0921,C,Port Harcourt,Normal,Male,Food and beverages,7768.799999999999,1,388.44,8157.240000000001,2/9/2019,10:02,Epay,7768.799999999999,4.761904762,388.44,7.2
+233-34-0817,C,Port Harcourt,Member,Female,Electronic accessories,35582.4,1,1779.12,37361.52,2/15/2019,11:21,Cash,35582.4,4.761904762,1779.12,8.4
+767-05-1286,C,Port Harcourt,Member,Female,Home and lifestyle,30157.2,6,9047.16,189990.36,1/23/2019,12:10,Epay,180943.2,4.761904762,9047.16,5.4
+598-47-9715,C,Port Harcourt,Normal,Male,Electronic accessories,30265.2,4,6053.04,127113.84,3/7/2019,16:54,Epay,121060.8,4.761904762,6053.04,4.4
+541-08-3113,C,Port Harcourt,Normal,Male,Food and beverages,23749.2,8,9499.68,199493.28,2/2/2019,20:29,Cash,189993.6,4.761904762,9499.68,8.4
+246-11-3901,C,Port Harcourt,Normal,Female,Electronic accessories,11807.999999999998,10,5903.999999999999,123984.0,2/15/2019,12:12,Cash,118080.0,4.761904762,5903.999999999999,6.2
+493-65-6248,C,Port Harcourt,Member,Female,Sports and travel,13312.8,10,6656.4,139784.4,1/1/2019,19:48,Card,133128.0,4.761904762,6656.4,7.0
+556-72-8512,C,Port Harcourt,Normal,Male,Home and lifestyle,8265.6,1,413.28,8678.880000000001,1/30/2019,20:47,Cash,8265.6,4.761904762,413.28,4.3
+148-82-2527,C,Port Harcourt,Member,Female,Home and lifestyle,4363.2,10,2181.6,45813.6,3/5/2019,13:44,Card,43632.0,4.761904762,2181.6,8.4
+556-97-7101,C,Port Harcourt,Normal,Female,Electronic accessories,22759.2,2,2275.92,47794.32,1/1/2019,15:51,Cash,45518.4,4.761904762,2275.92,8.5
+862-59-8517,C,Port Harcourt,Normal,Female,Food and beverages,32486.4,6,9745.92,204664.32,1/27/2019,11:17,Cash,194918.4,4.761904762,9745.92,6.2
+573-98-8548,C,Port Harcourt,Member,Female,Fashion accessories,11484.0,1,574.2,12058.2,1/5/2019,12:40,Epay,11484.0,4.761904762,574.2,9.1
+620-02-2046,C,Port Harcourt,Normal,Male,Home and lifestyle,24984.000000000004,2,2498.4,52466.4,1/27/2019,19:48,Epay,49968.00000000001,4.761904762,2498.4,9.0
+602-80-9671,C,Port Harcourt,Member,Female,Home and lifestyle,5742.0,6,1722.6,36174.6,2/9/2019,17:15,Card,34452.0,4.761904762,1722.6,5.1
+503-07-0930,C,Port Harcourt,Member,Male,Sports and travel,21020.4,7,7357.139999999999,154499.94,2/23/2019,19:49,Card,147142.80000000002,4.761904762,7357.139999999999,8.2
+413-20-6708,C,Port Harcourt,Member,Female,Fashion accessories,18529.2,1,926.46,19455.66,3/18/2019,15:52,Epay,18529.2,4.761904762,926.46,8.5
+521-18-7827,C,Port Harcourt,Member,Male,Home and lifestyle,14180.4,5,3545.1,74447.1,1/22/2019,20:46,Card,70902.0,4.761904762,3545.1,8.7
+600-38-9738,C,Port Harcourt,Member,Male,Sports and travel,25891.2,5,6472.8,135928.8,1/17/2019,15:05,Card,129456.0,4.761904762,6472.8,4.3
+451-28-5717,C,Port Harcourt,Member,Female,Home and lifestyle,29941.2,6,8982.36,188629.56,3/20/2019,11:23,Cash,179647.19999999998,4.761904762,8982.36,7.3
+133-14-7229,C,Port Harcourt,Normal,Male,Health and beauty,22633.2,2,2263.32,47529.72000000001,1/1/2019,11:43,Cash,45266.4,4.761904762,2263.32,5.0
+236-27-1144,C,Port Harcourt,Normal,Female,Food and beverages,5871.599999999999,9,2642.2200000000003,55486.62,3/26/2019,10:31,Epay,52844.4,4.761904762,2642.2200000000003,8.4
+583-41-4548,C,Port Harcourt,Normal,Male,Home and lifestyle,6001.200000000001,7,2100.42,44108.82,2/7/2019,11:36,Epay,42008.4,4.761904762,2100.42,7.4
+358-88-9262,C,Port Harcourt,Member,Female,Food and beverages,31492.800000000003,6,9447.84,198404.64,2/1/2019,18:43,Epay,188956.8,4.761904762,9447.84,5.1
+343-87-0864,C,Port Harcourt,Member,Male,Health and beauty,27316.8,1,1365.84,28682.64,1/3/2019,10:30,Card,27316.8,4.761904762,1365.84,7.1
+243-47-2663,C,Port Harcourt,Member,Male,Electronic accessories,6757.2,6,2027.16,42570.36,1/28/2019,16:43,Card,40543.2,4.761904762,2027.16,5.5
+399-69-4630,C,Port Harcourt,Normal,Male,Electronic accessories,7995.6,6,2398.68,50372.28,3/7/2019,10:23,Card,47973.6,4.761904762,2398.68,8.6
+283-26-5248,C,Port Harcourt,Member,Female,Food and beverages,35467.2,10,17733.6,372405.6,1/30/2019,20:23,Epay,354672.0,4.761904762,17733.6,4.5
+866-99-7614,C,Port Harcourt,Normal,Male,Food and beverages,32112.0,10,16056.0,337176.0,2/11/2019,15:42,Card,321120.0,4.761904762,16056.0,4.4
+718-57-9773,C,Port Harcourt,Normal,Female,Sports and travel,17758.8,10,8879.4,186467.4,2/3/2019,16:40,Card,177588.0,4.761904762,8879.4,9.4
+408-26-9866,C,Port Harcourt,Normal,Female,Sports and travel,26632.800000000003,7,9321.48,195751.08,3/2/2019,16:42,Epay,186429.6,4.761904762,9321.48,4.1
+592-34-6155,C,Port Harcourt,Normal,Male,Food and beverages,11437.2,4,2287.44,48036.24,1/14/2019,14:43,Epay,45748.8,4.761904762,2287.44,6.2
+390-31-6381,C,Port Harcourt,Normal,Male,Food and beverages,9799.2,3,1469.88,30867.48,1/7/2019,12:37,Cash,29397.6,4.761904762,1469.88,7.3
+339-18-7061,C,Port Harcourt,Member,Female,Fashion accessories,33472.8,2,3347.28,70292.87999999999,2/13/2019,15:06,Card,66945.6,4.761904762,3347.28,8.0
+379-17-6588,C,Port Harcourt,Normal,Male,Fashion accessories,21459.6,10,10729.8,225325.8,3/14/2019,11:07,Cash,214596.0,4.761904762,10729.8,5.3
+302-15-2162,C,Port Harcourt,Member,Male,Health and beauty,16750.8,6,5025.240000000001,105530.04,3/3/2019,10:54,Card,100504.8,4.761904762,5025.240000000001,4.3
+788-07-8452,C,Port Harcourt,Member,Female,Home and lifestyle,8726.4,7,3054.24,64139.04000000001,1/27/2019,17:38,Epay,61084.8,4.761904762,3054.24,9.4
+123-35-4896,C,Port Harcourt,Normal,Female,Sports and travel,16797.6,9,7558.92,158737.31999999998,2/17/2019,19:11,Epay,151178.4,4.761904762,7558.92,5.3
+258-69-7810,C,Port Harcourt,Normal,Female,Fashion accessories,13266.0,5,3316.5,69646.5,1/26/2019,18:53,Cash,66330.0,4.761904762,3316.5,9.2
+219-61-4139,C,Port Harcourt,Normal,Male,Electronic accessories,29908.8,1,1495.44,31404.24,1/23/2019,17:16,Epay,29908.8,4.761904762,1495.44,6.4
+881-41-7302,C,Port Harcourt,Normal,Female,Fashion accessories,23396.4,1,1169.82,24566.22,1/26/2019,10:06,Card,23396.4,4.761904762,1169.82,4.5
+373-09-4567,C,Port Harcourt,Normal,Male,Food and beverages,27921.6,10,13960.8,293176.8,3/14/2019,20:35,Epay,279216.0,4.761904762,13960.8,6.9
+484-22-8230,C,Port Harcourt,Member,Female,Fashion accessories,18680.4,7,6538.14,137300.94,1/8/2019,20:08,Cash,130762.8,4.761904762,6538.14,4.5
+544-32-5024,C,Port Harcourt,Member,Female,Food and beverages,17924.4,4,3584.88,75282.48,3/28/2019,19:16,Card,71697.6,4.761904762,3584.88,6.4
+277-35-5865,C,Port Harcourt,Member,Female,Food and beverages,35629.2,9,16033.14,336695.94,3/9/2019,11:23,Cash,320662.8,4.761904762,16033.14,6.7
+284-54-4231,C,Port Harcourt,Member,Male,Sports and travel,29134.800000000003,1,1456.74,30591.54,1/19/2019,16:08,Card,29134.800000000003,4.761904762,1456.74,9.0
+840-19-2096,C,Port Harcourt,Member,Male,Electronic accessories,31647.6,5,7911.9,166149.9,3/14/2019,18:10,Epay,158238.0,4.761904762,7911.9,4.4
+641-96-3695,C,Port Harcourt,Member,Female,Fashion accessories,15645.6,6,4693.68,98567.28,2/7/2019,17:55,Epay,93873.6,4.761904762,4693.68,8.5
+318-81-2368,C,Port Harcourt,Normal,Female,Electronic accessories,16632.0,1,831.6,17463.6,3/19/2019,12:16,Cash,16632.0,4.761904762,831.6,6.3
+155-45-3814,C,Port Harcourt,Member,Female,Electronic accessories,31878.0,8,12751.2,267775.2,3/19/2019,15:29,Epay,255024.0,4.761904762,12751.2,4.7
+131-15-8856,C,Port Harcourt,Member,Female,Food and beverages,26107.2,8,10442.88,219300.48,3/30/2019,19:26,Card,208857.6,4.761904762,10442.88,4.0
+273-84-2164,C,Port Harcourt,Member,Male,Electronic accessories,4338.0,5,1084.5,22774.5,2/16/2019,15:53,Epay,21690.0,4.761904762,1084.5,5.5
+778-89-7974,C,Port Harcourt,Normal,Male,Health and beauty,25275.6,6,7582.680000000001,159236.28,3/30/2019,14:58,Cash,151653.6,4.761904762,7582.680000000001,7.4
+859-71-0933,C,Port Harcourt,Member,Female,Sports and travel,5576.4,2,557.6400000000001,11710.44,1/16/2019,15:10,Cash,11152.8,4.761904762,557.6400000000001,6.3
+740-11-5257,C,Port Harcourt,Normal,Male,Electronic accessories,8906.4,10,4453.2,93517.2,2/24/2019,16:44,Cash,89064.0,4.761904762,4453.2,7.1
+250-81-7186,C,Port Harcourt,Normal,Female,Electronic accessories,35888.4,1,1794.4199999999998,37682.82,2/27/2019,10:23,Card,35888.4,4.761904762,1794.4199999999998,8.0
+842-29-4695,C,Port Harcourt,Member,Male,Sports and travel,6170.400000000001,7,2159.64,45352.44,1/16/2019,12:07,Card,43192.8,4.761904762,2159.64,7.9
+641-51-2661,C,Port Harcourt,Member,Female,Food and beverages,31356.0,10,15677.999999999998,329238.0,2/12/2019,14:45,Card,313560.0,4.761904762,15677.999999999998,9.9
+714-02-3114,C,Port Harcourt,Normal,Female,Sports and travel,35568.0,2,3556.8,74692.8,2/21/2019,11:39,Cash,71136.0,4.761904762,3556.8,7.7
+408-66-6712,C,Port Harcourt,Member,Female,Health and beauty,17175.6,6,5152.679999999999,108206.28,2/16/2019,14:19,Epay,103053.6,4.761904762,5152.679999999999,4.4
+556-41-6224,C,Port Harcourt,Normal,Male,Health and beauty,12110.4,8,4844.16,101727.36,2/15/2019,17:10,Card,96883.2,4.761904762,4844.16,9.3
+648-94-3045,C,Port Harcourt,Normal,Male,Health and beauty,21222.0,10,10611.0,222831.0,2/7/2019,14:27,Epay,212220.0,4.761904762,10611.0,8.1
+370-96-0655,C,Port Harcourt,Normal,Female,Fashion accessories,17755.2,6,5326.56,111857.76,1/9/2019,13:46,Epay,106531.2,4.761904762,5326.56,7.1
+173-57-2300,C,Port Harcourt,Member,Male,Sports and travel,26236.8,2,2623.68,55097.28,3/13/2019,12:51,Cash,52473.6,4.761904762,2623.68,6.1
+394-55-6384,C,Port Harcourt,Member,Female,Sports and travel,25268.4,9,11370.78,238786.38,1/25/2019,13:38,Cash,227415.6,4.761904762,11370.78,6.7
+266-20-6657,C,Port Harcourt,Member,Male,Food and beverages,19814.4,7,6935.04,145635.84000000003,3/12/2019,19:39,Epay,138700.8,4.761904762,6935.04,5.2
+196-01-2849,C,Port Harcourt,Member,Female,Fashion accessories,26416.8,7,9245.88,194163.48000000004,2/10/2019,13:56,Cash,184917.6,4.761904762,9245.88,9.5
+372-62-5264,C,Port Harcourt,Normal,Female,Food and beverages,18936.0,9,8521.2,178945.2,1/16/2019,14:42,Cash,170424.0,4.761904762,8521.2,7.6
+751-41-9720,C,Port Harcourt,Normal,Male,Home and lifestyle,35100.0,10,17550.0,368550.0,1/12/2019,16:18,Epay,351000.0,4.761904762,17550.0,8.0
+626-43-7888,C,Port Harcourt,Normal,Female,Fashion accessories,21747.6,8,8699.04,182679.84,2/7/2019,12:23,Epay,173980.8,4.761904762,8699.04,9.6
+162-65-8559,C,Port Harcourt,Member,Male,Food and beverages,24832.800000000003,1,1241.64,26074.44,1/21/2019,20:13,Cash,24832.800000000003,4.761904762,1241.64,4.8
+760-27-5490,C,Port Harcourt,Normal,Male,Fashion accessories,5623.2,8,2249.28,47234.88,1/20/2019,20:37,Epay,44985.6,4.761904762,2249.28,9.1
+728-88-7867,C,Port Harcourt,Member,Female,Home and lifestyle,27190.8,4,5438.16,114201.36,3/19/2019,15:52,Epay,108763.2,4.761904762,5438.16,8.3
+183-21-3799,C,Port Harcourt,Normal,Female,Electronic accessories,27946.8,9,12576.06,264097.26,2/19/2019,15:14,Epay,251521.2,4.761904762,12576.06,7.2
+268-20-3585,C,Port Harcourt,Normal,Female,Health and beauty,4986.0,9,2243.7,47117.7,2/4/2019,12:50,Epay,44874.0,4.761904762,2243.7,6.0
+735-32-9839,C,Port Harcourt,Member,Male,Fashion accessories,35532.0,8,14212.8,298468.8,1/31/2019,10:36,Epay,284256.0,4.761904762,14212.8,8.5
+678-79-0726,C,Port Harcourt,Member,Female,Sports and travel,32626.8,9,14682.06,308323.26,1/18/2019,15:28,Cash,293641.2,4.761904762,14682.06,5.1
+592-46-1692,C,Port Harcourt,Member,Female,Food and beverages,13237.2,7,4633.02,97293.42,1/11/2019,20:10,Cash,92660.4,4.761904762,4633.02,7.4
+149-14-0304,C,Port Harcourt,Member,Female,Health and beauty,10260.0,8,4104.0,86184.0,2/6/2019,14:24,Cash,82080.0,4.761904762,4104.0,6.6
+442-44-6497,C,Port Harcourt,Member,Male,Home and lifestyle,20005.2,3,3000.78,63016.380000000005,1/8/2019,11:42,Card,60015.600000000006,4.761904762,3000.78,5.9
+210-74-9613,C,Port Harcourt,Normal,Male,Fashion accessories,35013.6,4,7002.72,147057.12,3/16/2019,15:33,Epay,140054.4,4.761904762,7002.72,6.8
+607-65-2441,C,Port Harcourt,Member,Male,Health and beauty,29502.0,10,14751.0,309771.0,3/10/2019,12:39,Card,295020.0,4.761904762,14751.0,6.0
+386-27-7606,C,Port Harcourt,Member,Female,Home and lifestyle,29232.0,7,10231.2,214855.2,3/23/2019,15:59,Card,204624.0,4.761904762,10231.2,8.1
+137-63-5492,C,Port Harcourt,Normal,Male,Electronic accessories,21153.6,10,10576.8,222112.8,1/29/2019,14:26,Epay,211536.0,4.761904762,10576.8,9.0
+733-29-1227,C,Port Harcourt,Normal,Male,Home and lifestyle,20019.6,7,7006.86,147144.06,3/23/2019,12:41,Cash,140137.19999999998,4.761904762,7006.86,8.5
+451-73-2711,C,Port Harcourt,Normal,Male,Food and beverages,30538.8,1,1526.94,32065.74,1/14/2019,15:20,Epay,30538.8,4.761904762,1526.94,8.8
+345-68-9016,C,Port Harcourt,Member,Female,Sports and travel,11401.2,8,4560.48,95770.08,1/2/2019,16:19,Card,91209.6,4.761904762,4560.48,5.6
+390-17-5806,C,Port Harcourt,Member,Female,Food and beverages,13831.2,1,691.5600000000001,14522.76,2/2/2019,16:33,Cash,13831.2,4.761904762,691.5600000000001,8.6
+664-14-2882,C,Port Harcourt,Member,Female,Home and lifestyle,3790.8,5,947.7,19901.7,1/30/2019,14:43,Card,18954.0,4.761904762,947.7,5.8
+314-23-4520,C,Port Harcourt,Member,Male,Health and beauty,29242.800000000003,7,10234.98,214934.58,1/15/2019,20:44,Cash,204699.6,4.761904762,10234.98,9.0
+288-38-3758,C,Port Harcourt,Member,Female,Fashion accessories,30553.2,3,4582.98,96242.58,1/25/2019,18:30,Epay,91659.6,4.761904762,4582.98,7.4
+801-88-0346,C,Port Harcourt,Normal,Female,Fashion accessories,27381.6,3,4107.240000000001,86252.04000000001,1/5/2019,20:30,Card,82144.8,4.761904762,4107.240000000001,9.8
+759-98-4285,C,Port Harcourt,Member,Female,Health and beauty,30913.2,7,10819.62,227212.02,2/27/2019,19:01,Card,216392.4,4.761904762,10819.62,8.0
+201-63-8275,C,Port Harcourt,Member,Female,Sports and travel,24476.4,7,8566.74,179901.54,2/17/2019,16:50,Epay,171334.8,4.761904762,8566.74,5.7
+471-06-8611,C,Port Harcourt,Normal,Female,Food and beverages,18871.2,1,943.5600000000002,19814.76,2/6/2019,10:22,Card,18871.2,4.761904762,943.5600000000002,6.3
+200-16-5952,C,Port Harcourt,Member,Male,Food and beverages,23634.000000000004,2,2363.4,49631.4,1/17/2019,16:46,Cash,47268.00000000001,4.761904762,2363.4,6.0
+102-77-2261,C,Port Harcourt,Member,Male,Health and beauty,23511.6,7,8229.06,172810.26,3/5/2019,18:02,Card,164581.2,4.761904762,8229.06,4.2
+102-06-2002,C,Port Harcourt,Member,Male,Sports and travel,9090.0,5,2272.5,47722.5,3/20/2019,17:52,Cash,45450.0,4.761904762,2272.5,6.1
+629-42-4133,C,Port Harcourt,Normal,Male,Health and beauty,7848.0,8,3139.2000000000003,65923.2,2/19/2019,19:24,Cash,62784.0,4.761904762,3139.2000000000003,8.3
+468-99-7231,C,Port Harcourt,Normal,Female,Home and lifestyle,15843.6,8,6337.44,133086.24000000002,3/3/2019,17:36,Cash,126748.8,4.761904762,6337.44,8.8
+516-77-6464,C,Port Harcourt,Member,Female,Health and beauty,3657.6,5,914.4,19202.4,2/24/2019,13:08,Epay,18288.0,4.761904762,914.4,4.1
+886-77-9084,C,Port Harcourt,Normal,Male,Electronic accessories,25880.4,8,10352.16,217395.36,2/19/2019,11:33,Epay,207043.2,4.761904762,10352.16,5.5
+790-38-4466,C,Port Harcourt,Normal,Female,Health and beauty,3956.4,5,989.1,20771.1,1/23/2019,10:18,Card,19782.0,4.761904762,989.1,9.3
+704-10-4056,C,Port Harcourt,Member,Male,Health and beauty,21769.2,3,3265.38,68572.98,1/14/2019,10:55,Card,65307.6,4.761904762,3265.38,5.6
+400-80-4065,C,Port Harcourt,Member,Male,Health and beauty,24678.0,4,4935.6,103647.6,2/15/2019,20:21,Card,98712.0,4.761904762,4935.6,9.2
+443-60-9639,C,Port Harcourt,Member,Female,Home and lifestyle,21913.2,1,1095.66,23008.86,1/24/2019,13:24,Cash,21913.2,4.761904762,1095.66,5.5
+401-09-4232,C,Port Harcourt,Member,Male,Home and lifestyle,31208.4,5,7802.099999999999,163844.1,2/11/2019,18:38,Epay,156042.0,4.761904762,7802.099999999999,9.4
+324-41-6833,C,Port Harcourt,Member,Female,Electronic accessories,10872.0,8,4348.8,91324.8,3/3/2019,19:30,Epay,86976.0,4.761904762,4348.8,5.1
+474-33-8305,C,Port Harcourt,Member,Male,Fashion accessories,24260.4,7,8491.14,178313.94,3/23/2019,13:23,Epay,169822.80000000002,4.761904762,8491.14,6.9
+189-55-2313,C,Port Harcourt,Normal,Female,Fashion accessories,22384.8,10,11192.4,235040.4,1/31/2019,10:33,Epay,223848.0,4.761904762,11192.4,6.0
+815-04-6282,C,Port Harcourt,Member,Female,Sports and travel,23389.2,5,5847.3,122793.3,2/8/2019,12:52,Card,116946.0,4.761904762,5847.3,6.5
+477-59-2456,C,Port Harcourt,Normal,Female,Fashion accessories,16358.4,7,5725.4400000000005,120234.24000000002,1/23/2019,11:15,Cash,114508.8,4.761904762,5725.4400000000005,9.2
+784-21-9238,C,Port Harcourt,Member,Male,Sports and travel,3661.2,1,183.06,3844.26,2/7/2019,14:15,Cash,3661.2,4.761904762,183.06,5.9
+538-22-0304,C,Port Harcourt,Normal,Male,Electronic accessories,23382.0,10,11691.0,245511.0,3/24/2019,18:27,Cash,233820.0,4.761904762,11691.0,5.2
+660-29-7083,C,Port Harcourt,Normal,Male,Electronic accessories,20113.2,10,10056.6,211188.6,1/15/2019,15:01,Cash,201132.00000000003,4.761904762,10056.6,5.8
+271-77-8740,C,Port Harcourt,Member,Female,Sports and travel,10519.2,6,3155.76,66270.95999999999,1/1/2019,11:40,Epay,63115.2,4.761904762,3155.76,5.0
+549-23-9016,C,Port Harcourt,Member,Female,Food and beverages,5353.2,2,535.3199999999999,11241.72,2/13/2019,18:15,Card,10706.4,4.761904762,535.3199999999999,8.9
+862-29-5914,C,Port Harcourt,Normal,Female,Sports and travel,8056.799999999999,1,402.84,8459.640000000001,1/30/2019,17:08,Card,8056.799999999999,4.761904762,402.84,8.6
+845-94-6841,C,Port Harcourt,Member,Female,Food and beverages,26236.8,9,11806.56,247937.76,1/8/2019,19:38,Cash,236131.2,4.761904762,11806.56,4.0
+658-66-3967,C,Port Harcourt,Normal,Male,Health and beauty,19148.4,7,6701.94,140740.74,1/14/2019,15:42,Epay,134038.8,4.761904762,6701.94,5.0
+848-95-6252,C,Port Harcourt,Member,Female,Home and lifestyle,31057.2,1,1552.86,32610.06,2/20/2019,13:24,Epay,31057.2,4.761904762,1552.86,7.0
+176-78-1170,C,Port Harcourt,Member,Male,Health and beauty,12171.6,3,1825.74,38340.54,1/26/2019,15:11,Epay,36514.8,4.761904762,1825.74,7.3
+101-81-4070,C,Port Harcourt,Member,Female,Health and beauty,22615.2,2,2261.52,47491.92,1/17/2019,12:36,Epay,45230.4,4.761904762,2261.52,4.9
+631-34-1880,C,Port Harcourt,Member,Male,Food and beverages,8751.6,3,1312.74,27567.54,1/8/2019,19:09,Card,26254.800000000003,4.761904762,1312.74,4.3
+584-66-4073,C,Port Harcourt,Normal,Male,Fashion accessories,20340.0,1,1017.0,21357.0,3/13/2019,15:45,Epay,20340.0,4.761904762,1017.0,9.6
+154-87-7367,C,Port Harcourt,Normal,Male,Home and lifestyle,23493.6,8,9397.44,197346.24,3/15/2019,14:04,Epay,187948.8,4.761904762,9397.44,6.3
+885-56-0389,C,Port Harcourt,Member,Male,Fashion accessories,18846.0,1,942.3,19788.3,2/12/2019,17:49,Cash,18846.0,4.761904762,942.3,4.0
+115-38-7388,C,Port Harcourt,Member,Female,Fashion accessories,3664.8,8,1465.92,30784.32,3/30/2019,12:51,Card,29318.4,4.761904762,1465.92,9.5
+137-74-8729,C,Port Harcourt,Normal,Female,Fashion accessories,4388.4,8,1755.36,36862.56,3/13/2019,12:47,Epay,35107.2,4.761904762,1755.36,6.8
+389-70-2397,C,Port Harcourt,Normal,Female,Health and beauty,30117.6,5,7529.4,158117.4,2/21/2019,10:26,Cash,150588.0,4.761904762,7529.4,7.2
+607-76-6216,C,Port Harcourt,Member,Female,Fashion accessories,33296.4,5,8324.1,174806.1,3/2/2019,16:35,Card,166482.0,4.761904762,8324.1,8.6
+781-84-8059,C,Port Harcourt,Normal,Male,Fashion accessories,21866.4,7,7653.24,160718.04,1/18/2019,16:23,Epay,153064.8,4.761904762,7653.24,5.0
+409-49-6995,C,Port Harcourt,Member,Female,Food and beverages,17017.2,6,5105.16,107208.36,2/5/2019,10:17,Cash,102103.2,4.761904762,5105.16,8.8
+725-54-0677,C,Port Harcourt,Member,Male,Health and beauty,30816.0,7,10785.6,226497.6,3/2/2019,13:50,Cash,215712.00000000003,4.761904762,10785.6,5.3
+377-79-7592,C,Port Harcourt,Member,Female,Electronic accessories,16142.4,9,7264.08,152545.68000000002,1/14/2019,14:00,Card,145281.6,4.761904762,7264.08,7.5
+545-07-8534,C,Port Harcourt,Normal,Female,Health and beauty,20995.2,2,2099.5200000000004,44089.920000000006,2/14/2019,12:42,Epay,41990.4,4.761904762,2099.5200000000004,6.0
+118-62-1812,C,Port Harcourt,Member,Female,Home and lifestyle,28216.8,4,5643.36,118510.55999999998,3/24/2019,17:56,Cash,112867.2,4.761904762,5643.36,7.9
+450-42-3339,C,Port Harcourt,Normal,Male,Health and beauty,30459.6,10,15229.8,319825.8,2/9/2019,18:58,Card,304596.0,4.761904762,15229.8,8.8
+151-27-8496,C,Port Harcourt,Normal,Female,Electronic accessories,20206.8,4,4041.36,84868.56,1/19/2019,11:43,Epay,80827.2,4.761904762,4041.36,8.6
+717-96-4189,C,Port Harcourt,Normal,Female,Electronic accessories,12776.4,6,3832.92,80491.32,2/2/2019,12:40,Cash,76658.4,4.761904762,3832.92,4.1
+722-13-2115,C,Port Harcourt,Member,Male,Sports and travel,15426.0,1,771.3000000000001,16197.3,3/14/2019,15:36,Card,15426.0,4.761904762,771.3000000000001,9.3
+246-55-6923,C,Port Harcourt,Member,Female,Home and lifestyle,12884.4,9,5797.98,121757.58,3/10/2019,15:06,Card,115959.6,4.761904762,5797.98,5.1
+838-02-1821,C,Port Harcourt,Member,Female,Home and lifestyle,4582.8,2,458.28,9623.88,2/22/2019,12:10,Card,9165.6,4.761904762,458.28,5.2
+887-42-0517,C,Port Harcourt,Normal,Female,Sports and travel,29930.4,7,10475.64,219988.44000000003,1/10/2019,10:31,Card,209512.8,4.761904762,10475.64,6.6
+457-12-0244,C,Port Harcourt,Member,Female,Sports and travel,12679.2,6,3803.76,79878.95999999999,3/14/2019,13:49,Epay,76075.2,4.761904762,3803.76,6.5
+756-93-1854,C,Port Harcourt,Member,Female,Fashion accessories,30006.0,2,3000.6000000000004,63012.6,2/2/2019,14:05,Card,60011.99999999999,4.761904762,3000.6000000000004,9.5
+458-10-8612,C,Port Harcourt,Normal,Male,Health and beauty,23068.8,7,8074.08,169555.68000000002,1/20/2019,12:27,Epay,161481.6,4.761904762,8074.08,7.6
+235-06-8510,C,Port Harcourt,Member,Male,Home and lifestyle,30859.2,3,4628.879999999999,97206.48,1/24/2019,20:59,Epay,92577.6,4.761904762,4628.879999999999,5.1
+433-08-7822,C,Port Harcourt,Normal,Female,Health and beauty,28400.4,7,9940.14,208742.94,1/5/2019,19:48,Epay,198802.8,4.761904762,9940.14,7.5
+500-02-2261,C,Port Harcourt,Normal,Female,Food and beverages,20624.4,6,6187.320000000001,129933.72,3/21/2019,17:04,Epay,123746.4,4.761904762,6187.320000000001,5.9
+702-83-5291,C,Port Harcourt,Member,Male,Fashion accessories,35935.2,9,16170.839999999998,339587.64,3/27/2019,10:43,Cash,323416.8,4.761904762,16170.839999999998,6.6
+859-97-6048,C,Port Harcourt,Member,Male,Electronic accessories,30330.0,2,3033.0000000000005,63693.00000000001,3/26/2019,14:13,Card,60660.0,4.761904762,3033.0000000000005,5.3
+373-88-1424,C,Port Harcourt,Member,Male,Home and lifestyle,12891.6,5,3222.9,67680.9,2/6/2019,18:44,Epay,64458.00000000001,4.761904762,3222.9,7.9
+784-08-0310,C,Port Harcourt,Member,Female,Food and beverages,7574.4,4,1514.88,31812.48,1/13/2019,13:58,Cash,30297.6,4.761904762,1514.88,7.6
+577-34-7579,C,Port Harcourt,Member,Male,Food and beverages,18176.4,9,8179.38,171766.97999999998,1/10/2019,17:16,Cash,163587.6,4.761904762,8179.38,5.4
+867-47-1948,C,Port Harcourt,Normal,Female,Home and lifestyle,5688.0,10,2844.0,59724.0,1/9/2019,12:07,Cash,56880.0,4.761904762,2844.0,7.8
+256-58-3609,C,Port Harcourt,Member,Male,Fashion accessories,33112.8,1,1655.64,34768.439999999995,3/18/2019,15:29,Cash,33112.8,4.761904762,1655.64,9.8
+364-34-2972,C,Port Harcourt,Member,Male,Electronic accessories,34855.2,3,5228.28,109793.88,3/30/2019,20:37,Cash,104565.6,4.761904762,5228.28,6.7
+744-82-9138,C,Port Harcourt,Normal,Male,Fashion accessories,31006.8,2,3100.68,65114.28,2/7/2019,17:59,Cash,62013.6,4.761904762,3100.68,8.2
+728-47-9078,C,Port Harcourt,Member,Male,Food and beverages,21452.4,4,4290.48,90100.08,1/19/2019,12:46,Cash,85809.6,4.761904762,4290.48,9.8
+148-41-7930,C,Port Harcourt,Normal,Male,Health and beauty,35985.6,7,12594.96,264494.16000000003,1/23/2019,10:33,Cash,251899.2,4.761904762,12594.96,6.1
+189-40-5216,C,Port Harcourt,Normal,Male,Electronic accessories,34693.200000000004,7,12142.62,254995.02,1/9/2019,11:40,Cash,242852.4,4.761904762,12142.62,6.0
+267-62-7380,C,Port Harcourt,Member,Male,Electronic accessories,29642.4,10,14821.2,311245.2,3/29/2019,19:12,Epay,296424.0,4.761904762,14821.2,4.3
+652-49-6720,C,Port Harcourt,Member,Female,Electronic accessories,21942.0,1,1097.1,23039.1,2/18/2019,11:40,Epay,21942.0,4.761904762,1097.1,5.9
+233-67-5758,C,Port Harcourt,Normal,Male,Health and beauty,14526.0,1,726.3000000000001,15252.3,1/29/2019,13:46,Epay,14526.0,4.761904762,726.3000000000001,6.2