Voting Regressor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"../Data/housing.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RM</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.575</td>\n",
       "      <td>4.98</td>\n",
       "      <td>15.3</td>\n",
       "      <td>504000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.421</td>\n",
       "      <td>9.14</td>\n",
       "      <td>17.8</td>\n",
       "      <td>453600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.185</td>\n",
       "      <td>4.03</td>\n",
       "      <td>17.8</td>\n",
       "      <td>728700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.998</td>\n",
       "      <td>2.94</td>\n",
       "      <td>18.7</td>\n",
       "      <td>701400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.147</td>\n",
       "      <td>5.33</td>\n",
       "      <td>18.7</td>\n",
       "      <td>760200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "      RM  LSTAT  PTRATIO      MEDV\n",
       "0  6.575   4.98     15.3  504000.0\n",
       "1  6.421   9.14     17.8  453600.0\n",
       "2  7.185   4.03     17.8  728700.0\n",
       "3  6.998   2.94     18.7  701400.0\n",
       "4  7.147   5.33     18.7  760200.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.drop(columns=[\"MEDV\"])\n",
    "y=df[\"MEDV\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "xtrain,xtest,ytrain,ytest=train_test_split(x,y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression,Ridge,ElasticNet,Lasso\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.svm import SVR\n",
    "models=[\n",
    "    ('lr',LinearRegression()),\n",
    "    ('ridge',Ridge()),\n",
    "    ('en',ElasticNet()),\n",
    "    ('Lasso',Lasso()),\n",
    "    ('GBR',GradientBoostingRegressor(n_estimators=50)),\n",
    "    ('ABR',AdaBoostRegressor(n_estimators=50)),\n",
    "    ('RFR',RandomForestRegressor(n_estimators=50)),\n",
    "    ('DTR',DecisionTreeRegressor()),\n",
    "    ('svr',SVR())\n",
    "    \n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score  0.8022603631938814\n",
      "MSE: 5615475460.100301\n",
      "MAE: 56389.072217906265\n",
      "RMSE : 74936.476165485\n"
     ]
    }
   ],
   "source": [
    "# voting Regressor\n",
    "from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error\n",
    "from sklearn.ensemble import VotingRegressor\n",
    "model=VotingRegressor(estimators =models)\n",
    "model.fit(xtrain,ytrain)\n",
    "ypred=model.predict(xtest)\n",
    "#Evaluation of Model\n",
    "print(\"R2 Score \" ,r2_score(ytest,ypred))\n",
    "mse=mean_squared_error(ytest,ypred)\n",
    "mae=mean_absolute_error(ytest,ypred)\n",
    "print(\"MSE:\",mse)\n",
    "print(\"MAE:\",mae)\n",
    "print(\"RMSE :\",np.sqrt(mse))"
   ]
  }
 ],
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