diff --git a/homeprices Multi Variable.ipynb b/homeprices Multi Variable.ipynb
new file mode 100644
index 0000000..18509a2
--- /dev/null
+++ b/homeprices Multi Variable.ipynb	
@@ -0,0 +1,161 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.8.8 64-bit"
+  },
+  "interpreter": {
+   "hash": "2db524e06e9f5f4ffedc911c917cb75e12dbc923643829bf417064a77eb14d37"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n",
+    "import numpy as np \n",
+    "import matplotlib.pyplot as plt \n",
+    "from sklearn import linear_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('homeprices.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "medaian_bed = df.bedrooms.median()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0  Unnamed: 0.1  area  bedrooms  age   price  \\\n",
+       "0           0             0  2600       3.0   20  550000   \n",
+       "1           1             1  3000       4.0   15  565000   \n",
+       "2           2             2  3200       4.0   18  610000   \n",
+       "3           3             3  3600       3.0   30  595000   \n",
+       "4           4             4  4000       5.0    8  760000   \n",
+       "5           5             5  4100       6.0    8  810000   \n",
+       "\n",
+       "   Predicted Per Capita Price  \n",
+       "0               518217.632976  \n",
+       "1               602590.079374  \n",
+       "2               615307.414037  \n",
+       "3               597962.895832  \n",
+       "4               760663.426755  \n",
+       "5               795258.551027  "
+      ],
+      "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>Unnamed: 0</th>\n      <th>Unnamed: 0.1</th>\n      <th>area</th>\n      <th>bedrooms</th>\n      <th>age</th>\n      <th>price</th>\n      <th>Predicted Per Capita Price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>2600</td>\n      <td>3.0</td>\n      <td>20</td>\n      <td>550000</td>\n      <td>518217.632976</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>3000</td>\n      <td>4.0</td>\n      <td>15</td>\n      <td>565000</td>\n      <td>602590.079374</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>2</td>\n      <td>3200</td>\n      <td>4.0</td>\n      <td>18</td>\n      <td>610000</td>\n      <td>615307.414037</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>3</td>\n      <td>3600</td>\n      <td>3.0</td>\n      <td>30</td>\n      <td>595000</td>\n      <td>597962.895832</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>4</td>\n      <td>4000</td>\n      <td>5.0</td>\n      <td>8</td>\n      <td>760000</td>\n      <td>760663.426755</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>5</td>\n      <td>5</td>\n      <td>4100</td>\n      <td>6.0</td>\n      <td>8</td>\n      <td>810000</td>\n      <td>795258.551027</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "source": [
+    "df.bedrooms=df.bedrooms.fillna(medaian_bed)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "source": [
+    "reg = linear_model.LinearRegression()\n",
+    "reg.fit(df[['area' , 'bedrooms' , 'age']], df.price)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([498408.25158031])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "source": [
+    "reg.predict([[3000, 3, 40]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    " p = reg.predict(df[['area' , 'bedrooms' , 'age']])\n",
+    "df['Predicted Per Capita Price'] = p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv('homeprices.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ]
+}
\ No newline at end of file
diff --git a/random forest algorithm.ipynb b/random forest algorithm.ipynb
new file mode 100644
index 0000000..fdab020
--- /dev/null
+++ b/random forest algorithm.ipynb	
@@ -0,0 +1,263 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.8.8 64-bit"
+  },
+  "interpreter": {
+   "hash": "2db524e06e9f5f4ffedc911c917cb75e12dbc923643829bf417064a77eb14d37"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n",
+    "from sklearn.datasets import load_digits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = load_digits()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['DESCR', 'data', 'feature_names', 'frame', 'images', 'target', 'target_names']"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    }
+   ],
+   "source": [
+    "dir(digits)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 0 Axes>"
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt \n",
+    "plt.gray()\n",
+    "for i in range(5):\n",
+    "    plt.matshow(digits.images[i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(digits.data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Target'] = digits.target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "     0    1    2     3     4     5    6    7    8    9  ...   55   56   57  \\\n",
+       "0  0.0  0.0  5.0  13.0   9.0   1.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
+       "1  0.0  0.0  0.0  12.0  13.0   5.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
+       "2  0.0  0.0  0.0   4.0  15.0  12.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
+       "3  0.0  0.0  7.0  15.0  13.0   1.0  0.0  0.0  0.0  8.0  ...  0.0  0.0  0.0   \n",
+       "4  0.0  0.0  0.0   1.0  11.0   0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
+       "\n",
+       "    58    59    60    61   62   63  Target  \n",
+       "0  6.0  13.0  10.0   0.0  0.0  0.0       0  \n",
+       "1  0.0  11.0  16.0  10.0  0.0  0.0       1  \n",
+       "2  0.0   3.0  11.0  16.0  9.0  0.0       2  \n",
+       "3  7.0  13.0  13.0   9.0  0.0  0.0       3  \n",
+       "4  0.0   2.0  16.0   4.0  0.0  0.0       4  \n",
+       "\n",
+       "[5 rows x 65 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>55</th>\n      <th>56</th>\n      <th>57</th>\n      <th>58</th>\n      <th>59</th>\n      <th>60</th>\n      <th>61</th>\n      <th>62</th>\n      <th>63</th>\n      <th>Target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>13.0</td>\n      <td>9.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>13.0</td>\n      <td>10.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>12.0</td>\n      <td>13.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>11.0</td>\n      <td>16.0</td>\n      <td>10.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>15.0</td>\n      <td>12.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>11.0</td>\n      <td>16.0</td>\n      <td>9.0</td>\n      <td>0.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>15.0</td>\n      <td>13.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>8.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>13.0</td>\n      <td>13.0</td>\n      <td>9.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>11.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>16.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 65 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 24
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target = df.Target\n",
+    "df = df.drop(['Target'], axis='columns')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(df,target,test_size=0.2)\n"
+   ]
+  },
+  {
+   "source": [
+    "model.fit(X_train,y_train)"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 49,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "RandomForestClassifier()"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 49
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0.9805555555555555"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 50
+    }
+   ],
+   "source": [
+    "model.score(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ]
+}
\ No newline at end of file