Code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('insurance.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>expenses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>27.9</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>16884.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>33.8</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1725.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4449.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>22.7</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>21984.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>28.9</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3866.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>1333</th>\n",
       "      <td>50</td>\n",
       "      <td>male</td>\n",
       "      <td>31.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>10600.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1334</th>\n",
       "      <td>18</td>\n",
       "      <td>female</td>\n",
       "      <td>31.9</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>2205.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>18</td>\n",
       "      <td>female</td>\n",
       "      <td>36.9</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1629.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>21</td>\n",
       "      <td>female</td>\n",
       "      <td>25.8</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>2007.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>61</td>\n",
       "      <td>female</td>\n",
       "      <td>29.1</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>northwest</td>\n",
       "      <td>29141.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1338 rows × 7 columns</p>\n",
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      ],
      "text/plain": [
       "      age     sex   bmi  children smoker     region  expenses\n",
       "0      19  female  27.9         0    yes  southwest  16884.92\n",
       "1      18    male  33.8         1     no  southeast   1725.55\n",
       "2      28    male  33.0         3     no  southeast   4449.46\n",
       "3      33    male  22.7         0     no  northwest  21984.47\n",
       "4      32    male  28.9         0     no  northwest   3866.86\n",
       "...   ...     ...   ...       ...    ...        ...       ...\n",
       "1333   50    male  31.0         3     no  northwest  10600.55\n",
       "1334   18  female  31.9         0     no  northeast   2205.98\n",
       "1335   18  female  36.9         0     no  southeast   1629.83\n",
       "1336   21  female  25.8         0     no  southwest   2007.95\n",
       "1337   61  female  29.1         0    yes  northwest  29141.36\n",
       "\n",
       "[1338 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1338 entries, 0 to 1337\n",
      "Data columns (total 7 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       1338 non-null   int64  \n",
      " 1   sex       1338 non-null   object \n",
      " 2   bmi       1338 non-null   float64\n",
      " 3   children  1338 non-null   int64  \n",
      " 4   smoker    1338 non-null   object \n",
      " 5   region    1338 non-null   object \n",
      " 6   expenses  1338 non-null   float64\n",
      "dtypes: float64(2), int64(2), object(3)\n",
      "memory usage: 73.3+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1338.000000</td>\n",
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       "      <td>1338.000000</td>\n",
       "      <td>1338.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>39.207025</td>\n",
       "      <td>30.665471</td>\n",
       "      <td>1.094918</td>\n",
       "      <td>13270.422414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.049960</td>\n",
       "      <td>6.098382</td>\n",
       "      <td>1.205493</td>\n",
       "      <td>12110.011240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1121.870000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>26.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4740.287500</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
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       "      <td>30.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9382.030000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>34.700000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>16639.915000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>64.000000</td>\n",
       "      <td>53.100000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>63770.430000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "               age          bmi     children      expenses\n",
       "count  1338.000000  1338.000000  1338.000000   1338.000000\n",
       "mean     39.207025    30.665471     1.094918  13270.422414\n",
       "std      14.049960     6.098382     1.205493  12110.011240\n",
       "min      18.000000    16.000000     0.000000   1121.870000\n",
       "25%      27.000000    26.300000     0.000000   4740.287500\n",
       "50%      39.000000    30.400000     1.000000   9382.030000\n",
       "75%      51.000000    34.700000     2.000000  16639.915000\n",
       "max      64.000000    53.100000     5.000000  63770.430000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>count</th>\n",
       "      <td>1338</td>\n",
       "      <td>1338</td>\n",
       "      <td>1338</td>\n",
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       "      <th>unique</th>\n",
       "      <td>2</td>\n",
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       "      <td>4</td>\n",
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       "      <th>top</th>\n",
       "      <td>male</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>676</td>\n",
       "      <td>1064</td>\n",
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      "text/plain": [
       "         sex smoker     region\n",
       "count   1338   1338       1338\n",
       "unique     2      2          4\n",
       "top     male     no  southeast\n",
       "freq     676   1064        364"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include=\"O\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "bmi         0\n",
       "children    0\n",
       "smoker      0\n",
       "region      0\n",
       "expenses    0\n",
       "dtype: int64"
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     "metadata": {},
     "output_type": "execute_result"
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    "data.isnull().sum()"
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  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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     "execution_count": 12,
     "metadata": {},
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   "source": [
    "data.duplicated().sum()"
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   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
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   "source": [
    "fig = px.histogram(data, \n",
    "                   x='age', \n",
    "                   marginal='box', \n",
    "                   nbins=47, \n",
    "                   title='Distribution of Age')\n",
    "fig.update_layout(bargap=0.1)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
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          },
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            "gridwidth": 2,
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            "showbackground": true,
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          },
          "shapedefaults": {
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           "bgcolor": "#E5ECF6",
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          },
          "title": {
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          "xaxis": {
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          "text": "region"
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          "text": "count"
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "px.histogram(data,x=\"region\",color=\"smoker\",title=\"Region\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby(\"region\")[\"expenses\"].median().plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby(\"age\")['expenses'].median().plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "domain": {
          "x": [
           0,
           1
          ],
          "y": [
           0,
           1
          ]
         },
         "hovertemplate": "sex=%{label}<br>expenses=%{value}<extra></extra>",
         "labels": [
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           "ticks": "",
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        "title": {
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        },
        "width": 800
       }
      }
     },
     "metadata": {},
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    }
   ],
   "source": [
    "fig = px.pie(data_frame=data, names='sex', values='expenses', width=800, height=500, title='Expenses by Gender')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.29900819228508263"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.expenses.corr(data.age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop(['expenses'], axis=1)\n",
    "y = data['expenses']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.to_csv('clean_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ColumnTransformer(transformers=[('num',\n",
      "                                 Pipeline(steps=[('scaler', StandardScaler())]),\n",
      "                                 ['age', 'bmi', 'children']),\n",
      "                                ('cat', OneHotEncoder(handle_unknown='ignore'),\n",
      "                                 ['sex', 'smoker', 'region'])])\n"
     ]
    }
   ],
   "source": [
    "numeric_features = ['age', 'bmi', 'children']\n",
    "numeric_transformer = Pipeline(steps=[('scaler', StandardScaler())])\n",
    "categorical_features = ['sex', 'smoker','region']\n",
    "categorical_transformer = OneHotEncoder(handle_unknown='ignore')\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numeric_transformer, numeric_features),\n",
    "        ('cat', categorical_transformer, categorical_features)])\n",
    "print(preprocessor)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error,r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Mean Squared Error using Linear Regression :31823064.323450267\n",
      "The Root Mean Squared Error using Linear Regression :5641.193519411497\n",
      "The r2_score using Linear Regression :0.8000184017333828\n"
     ]
    }
   ],
   "source": [
    "lr = Pipeline(steps=[('prepocessor', preprocessor), ('linear_regressor', LinearRegression())])\n",
    "lr.fit(x_train, y_train)\n",
    "y_pred = lr.predict(x_test)\n",
    "\n",
    "lr_mse = mean_squared_error(y_test, y_pred)\n",
    "lr_rmse = mean_squared_error(y_test, y_pred, squared=False)\n",
    "lr_r2_score = r2_score(y_test, y_pred)\n",
    "\n",
    "print(\"The Mean Squared Error using Linear Regression :{}\".format(lr_mse))\n",
    "print(('The Root Mean Squared Error using Linear Regression :{}'.format(lr_rmse)))\n",
    "print(('The r2_score using Linear Regression :{}'.format(lr_r2_score)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Mean Squared Error using Decision Tree Regressor : 31823064.323450267\n",
      "The Root Mean Squared Error using Decision Tree Regressor : 4365.623719926021\n",
      "The r2_score using Decision Tree Regressor : 0.8802320436057114\n"
     ]
    }
   ],
   "source": [
    "dt = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                        ('decision_tree_regressor', DecisionTreeRegressor(max_depth=4, min_samples_split=4,random_state=42 ))])\n",
    "dt.fit(x_train, y_train)\n",
    "\n",
    "y_pred1 = dt.predict(x_test)\n",
    "\n",
    "dt_mse = mean_squared_error(y_test, y_pred)\n",
    "dt_rmse = mean_squared_error(y_test, y_pred1, squared=False)\n",
    "dt_r2_score = r2_score(y_test, y_pred1)\n",
    "\n",
    "print(\"The Mean Squared Error using Decision Tree Regressor : {}\".format(dt_mse))\n",
    "print(\"The Root Mean Squared Error using Decision Tree Regressor : {}\".format(dt_rmse))\n",
    "print(\"The r2_score using Decision Tree Regressor : {}\".format(dt_r2_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Mean Squared Error using Random Forest Regressor : 16230839.460296616\n",
      "The Root Mean Squared Error using Random Forest Regressor : 4028.7516007190884\n",
      "The r2_score using Random Forest Regressor : 0.8980026189970923\n"
     ]
    }
   ],
   "source": [
    "rf = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                                ('random_forest_regressor', RandomForestRegressor(n_estimators=100, max_depth=4, random_state=42))])\n",
    "rf.fit(x_train,y_train)\n",
    "\n",
    "y_pred2=rf.predict(x_test)\n",
    "\n",
    "rf_mse = mean_squared_error(y_test, y_pred2)\n",
    "rf_rmse = mean_squared_error(y_test, y_pred2, squared=False)\n",
    "rf_r2_score = r2_score(y_test, y_pred2)\n",
    "\n",
    "print(\"The Mean Squared Error using Random Forest Regressor : {}\".format(rf_mse))\n",
    "print(\"The Root Mean Squared Error using Random Forest Regressor : {}\".format(rf_rmse))\n",
    "print(\"The r2_score using Random Forest Regressor : {}\".format(rf_r2_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Mean Squared Error using Gradient Boosting Regressor : 16501454.648546465\n",
      "The Root Mean Squared Error using Gradient Boosting Regressor : 4062.198253230197\n",
      "The r2_sccore using Gradient Boosting Regressor : 0.8963020267061875\n"
     ]
    }
   ],
   "source": [
    "gb = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                        ('gradient_boosting' , GradientBoostingRegressor(n_estimators=40, max_depth=4, min_samples_split=4))])\n",
    "gb.fit(x_train,y_train)\n",
    "\n",
    "y_pred3=gb.predict(x_test)\n",
    "\n",
    "gb_mse = mean_squared_error(y_test, y_pred3)\n",
    "gb_rmse = mean_squared_error(y_test, y_pred3, squared=False)\n",
    "gb_r2_score = r2_score(y_test, y_pred3)\n",
    "\n",
    "print(\"The Mean Squared Error using Gradient Boosting Regressor : {}\".format(gb_mse))\n",
    "print(\"The Root Mean Squared Error using Gradient Boosting Regressor : {}\".format(gb_rmse))\n",
    "print(\"The r2_sccore using Gradient Boosting Regressor : {}\".format(gb_r2_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean squared error using KNN is 30453656.910110194\n",
      "The root mean squared error using KNN is 5518.483207377748\n",
      "The r2_score using KNN is 0.8086239929616322\n"
     ]
    }
   ],
   "source": [
    "knn = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                ('knn', KNeighborsRegressor(n_neighbors=5))])\n",
    "\n",
    "knn.fit(x_train, y_train)\n",
    "\n",
    "y_pred4 = knn.predict(x_test)\n",
    "\n",
    "knn_mse = mean_squared_error(y_test, y_pred4)\n",
    "knn_rmse = mean_squared_error(y_test, y_pred4, squared=False)\n",
    "knn_r2_score = r2_score(y_test, y_pred4)\n",
    "\n",
    "print(\"The mean squared error using KNN is {}\".format(knn_mse))\n",
    "print(\"The root mean squared error using KNN is {}\".format(knn_rmse))\n",
    "print(\"The r2_score using KNN is {}\".format(knn_r2_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>Model</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>r2_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>4028.751601</td>\n",
       "      <td>0.898003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>4062.198253</td>\n",
       "      <td>0.896302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>4365.623720</td>\n",
       "      <td>0.880232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KNN</td>\n",
       "      <td>5518.483207</td>\n",
       "      <td>0.808624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>5641.193519</td>\n",
       "      <td>0.800018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Model         RMSE  r2_score\n",
       "2      Random Forest  4028.751601  0.898003\n",
       "3  Gradient Boosting  4062.198253  0.896302\n",
       "1      Decision Tree  4365.623720  0.880232\n",
       "4                KNN  5518.483207  0.808624\n",
       "0  Linear Regression  5641.193519  0.800018"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Model' : ['Linear Regression', 'Decision Tree', 'Random Forest','Gradient Boosting', 'KNN'],\n",
    "    'RMSE' : [lr_rmse, dt_rmse, rf_rmse,gb_rmse, knn_rmse],\n",
    "    'r2_score' : [lr_r2_score, dt_r2_score, rf_r2_score, gb_r2_score, knn_r2_score]\n",
    "})\n",
    "\n",
    "models.sort_values(by='RMSE', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(rf, open('random_forest_model.pkl', 'wb'))"
   ]
  }
 ],
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