CarPricePrediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "Multiple models that predict the car selling prices in rupee"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "cd5bf51cf7bc0c76"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0355ef70",
   "metadata": {
    "is_executing": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "da3b1dfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#reading dataset\n",
    "dfml = pd.read_csv('cardetailsv3.csv', sep=',')\n",
    "dfshow = pd.read_csv('cardetailsv3.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8e66b412",
   "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>name</th>\n",
       "      <th>year</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>km_driven</th>\n",
       "      <th>fuel</th>\n",
       "      <th>seller_type</th>\n",
       "      <th>transmission</th>\n",
       "      <th>owner</th>\n",
       "      <th>mileage</th>\n",
       "      <th>engine</th>\n",
       "      <th>max_power</th>\n",
       "      <th>torque</th>\n",
       "      <th>seats</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maruti Swift Dzire VDI</td>\n",
       "      <td>2014</td>\n",
       "      <td>450000</td>\n",
       "      <td>145500</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>23.4 kmpl</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>74 bhp</td>\n",
       "      <td>190Nm@ 2000rpm</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Skoda Rapid 1.5 TDI Ambition</td>\n",
       "      <td>2014</td>\n",
       "      <td>370000</td>\n",
       "      <td>120000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Second Owner</td>\n",
       "      <td>21.14 kmpl</td>\n",
       "      <td>1498 CC</td>\n",
       "      <td>103.52 bhp</td>\n",
       "      <td>250Nm@ 1500-2500rpm</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda City 2017-2020 EXi</td>\n",
       "      <td>2006</td>\n",
       "      <td>158000</td>\n",
       "      <td>140000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Third Owner</td>\n",
       "      <td>17.7 kmpl</td>\n",
       "      <td>1497 CC</td>\n",
       "      <td>78 bhp</td>\n",
       "      <td>12.7@ 2,700(kgm@ rpm)</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hyundai i20 Sportz Diesel</td>\n",
       "      <td>2010</td>\n",
       "      <td>225000</td>\n",
       "      <td>127000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>23.0 kmpl</td>\n",
       "      <td>1396 CC</td>\n",
       "      <td>90 bhp</td>\n",
       "      <td>22.4 kgm at 1750-2750rpm</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Maruti Swift VXI BSIII</td>\n",
       "      <td>2007</td>\n",
       "      <td>130000</td>\n",
       "      <td>120000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>16.1 kmpl</td>\n",
       "      <td>1298 CC</td>\n",
       "      <td>88.2 bhp</td>\n",
       "      <td>11.5@ 4,500(kgm@ rpm)</td>\n",
       "      <td>5.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8123</th>\n",
       "      <td>Hyundai i20 Magna</td>\n",
       "      <td>2013</td>\n",
       "      <td>320000</td>\n",
       "      <td>110000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>18.5 kmpl</td>\n",
       "      <td>1197 CC</td>\n",
       "      <td>82.85 bhp</td>\n",
       "      <td>113.7Nm@ 4000rpm</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8124</th>\n",
       "      <td>Hyundai Verna CRDi SX</td>\n",
       "      <td>2007</td>\n",
       "      <td>135000</td>\n",
       "      <td>119000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Fourth &amp; Above Owner</td>\n",
       "      <td>16.8 kmpl</td>\n",
       "      <td>1493 CC</td>\n",
       "      <td>110 bhp</td>\n",
       "      <td>24@ 1,900-2,750(kgm@ rpm)</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8125</th>\n",
       "      <td>Maruti Swift Dzire ZDi</td>\n",
       "      <td>2009</td>\n",
       "      <td>382000</td>\n",
       "      <td>120000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>19.3 kmpl</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>73.9 bhp</td>\n",
       "      <td>190Nm@ 2000rpm</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8126</th>\n",
       "      <td>Tata Indigo CR4</td>\n",
       "      <td>2013</td>\n",
       "      <td>290000</td>\n",
       "      <td>25000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>23.57 kmpl</td>\n",
       "      <td>1396 CC</td>\n",
       "      <td>70 bhp</td>\n",
       "      <td>140Nm@ 1800-3000rpm</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8127</th>\n",
       "      <td>Tata Indigo CR4</td>\n",
       "      <td>2013</td>\n",
       "      <td>290000</td>\n",
       "      <td>25000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>23.57 kmpl</td>\n",
       "      <td>1396 CC</td>\n",
       "      <td>70 bhp</td>\n",
       "      <td>140Nm@ 1800-3000rpm</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8128 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              name  year  selling_price  km_driven    fuel  \\\n",
       "0           Maruti Swift Dzire VDI  2014         450000     145500  Diesel   \n",
       "1     Skoda Rapid 1.5 TDI Ambition  2014         370000     120000  Diesel   \n",
       "2         Honda City 2017-2020 EXi  2006         158000     140000  Petrol   \n",
       "3        Hyundai i20 Sportz Diesel  2010         225000     127000  Diesel   \n",
       "4           Maruti Swift VXI BSIII  2007         130000     120000  Petrol   \n",
       "...                            ...   ...            ...        ...     ...   \n",
       "8123             Hyundai i20 Magna  2013         320000     110000  Petrol   \n",
       "8124         Hyundai Verna CRDi SX  2007         135000     119000  Diesel   \n",
       "8125        Maruti Swift Dzire ZDi  2009         382000     120000  Diesel   \n",
       "8126               Tata Indigo CR4  2013         290000      25000  Diesel   \n",
       "8127               Tata Indigo CR4  2013         290000      25000  Diesel   \n",
       "\n",
       "     seller_type transmission                 owner     mileage   engine  \\\n",
       "0     Individual       Manual           First Owner   23.4 kmpl  1248 CC   \n",
       "1     Individual       Manual          Second Owner  21.14 kmpl  1498 CC   \n",
       "2     Individual       Manual           Third Owner   17.7 kmpl  1497 CC   \n",
       "3     Individual       Manual           First Owner   23.0 kmpl  1396 CC   \n",
       "4     Individual       Manual           First Owner   16.1 kmpl  1298 CC   \n",
       "...          ...          ...                   ...         ...      ...   \n",
       "8123  Individual       Manual           First Owner   18.5 kmpl  1197 CC   \n",
       "8124  Individual       Manual  Fourth & Above Owner   16.8 kmpl  1493 CC   \n",
       "8125  Individual       Manual           First Owner   19.3 kmpl  1248 CC   \n",
       "8126  Individual       Manual           First Owner  23.57 kmpl  1396 CC   \n",
       "8127  Individual       Manual           First Owner  23.57 kmpl  1396 CC   \n",
       "\n",
       "       max_power                     torque  seats  \n",
       "0         74 bhp             190Nm@ 2000rpm    5.0  \n",
       "1     103.52 bhp        250Nm@ 1500-2500rpm    5.0  \n",
       "2         78 bhp      12.7@ 2,700(kgm@ rpm)    5.0  \n",
       "3         90 bhp   22.4 kgm at 1750-2750rpm    5.0  \n",
       "4       88.2 bhp      11.5@ 4,500(kgm@ rpm)    5.0  \n",
       "...          ...                        ...    ...  \n",
       "8123   82.85 bhp           113.7Nm@ 4000rpm    5.0  \n",
       "8124     110 bhp  24@ 1,900-2,750(kgm@ rpm)    5.0  \n",
       "8125    73.9 bhp             190Nm@ 2000rpm    5.0  \n",
       "8126      70 bhp        140Nm@ 1800-3000rpm    5.0  \n",
       "8127      70 bhp        140Nm@ 1800-3000rpm    5.0  \n",
       "\n",
       "[8128 rows x 13 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "87bb711a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f5b482c1f0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5),dpi=100)\n",
    "plt.ticklabel_format(style = 'plain')\n",
    "\n",
    "fig = sns.barplot(x='fuel', y='selling_price', hue = dfshow[dfshow.owner != \"Test Drive Car\"][\"owner\"], data= dfshow[dfshow.owner != \"Test Drive Car\"])\n",
    "fig.set_title(\"Selling prices of cars by fuel type and owner type\")\n",
    "fig.set_ylabel(\"Selling price (in Rupees)\")\n",
    "fig.set_xlabel(\"Fuel type\")\n",
    "fig.legend(title=\"Owner Type\", loc=(1.02,0.05), fontsize=9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "016bc204",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "sns.heatmap(dfml.corr(),annot=True,cmap='viridis',linewidths=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b6c0f326",
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting name column into two columns named brand and model\n",
    "pattern = r'([A-Z0-9._%+-]+) ([A-Z0-9._%+-]+)'\n",
    "\n",
    "name = dfml['name'].str.findall(pattern, flags = re.IGNORECASE).str[0]\n",
    "\n",
    "brand = name.str.get(0)\n",
    "model = name.str.get(1)\n",
    "\n",
    "dfml.insert(1, 'model', model)\n",
    "dfml.insert(1, 'brand', brand)\n",
    "\n",
    "dfml = dfml.drop(['name'], axis=1)\n",
    "dfml = dfml.drop(['torque'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "85131c9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting engine column to float\n",
    "pattern2 = r'([A-Z0-9._%+-]+) ([A-Z0-9._%+-]+)'\n",
    "\n",
    "engineCc = dfml['engine'].str.findall(pattern, flags = re.IGNORECASE).str[0]\n",
    "\n",
    "engineInCc = engineCc.str.get(0)\n",
    "\n",
    "dfml['engine'] = engineInCc.astype(float)\n",
    "dfml.rename(columns = {'engine':'engine_cc'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cd0bcb2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting max_power column to float\n",
    "\n",
    "engineCc = dfml['max_power'].str.findall(pattern, flags = re.IGNORECASE).str[0]\n",
    "\n",
    "engineInCc = engineCc.str.get(0)\n",
    "\n",
    "dfml['max_power'] = engineInCc.astype(float)\n",
    "dfml.rename(columns = {'max_power':'max_power_bhp'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4439ba36",
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting mileage column to float\n",
    "\n",
    "engineCc = dfml['mileage'].str.findall(pattern, flags = re.IGNORECASE).str[0]\n",
    "\n",
    "engineInCc = engineCc.str.get(0)\n",
    "\n",
    "dfml['mileage'] = engineInCc.astype(float)\n",
    "dfml.rename(columns = {'mileage':'mileage_kmpl'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c4e2425b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = dfml['selling_price']\n",
    "X = dfml.drop(['selling_price'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>brand</th>\n",
       "      <th>model</th>\n",
       "      <th>year</th>\n",
       "      <th>km_driven</th>\n",
       "      <th>fuel</th>\n",
       "      <th>seller_type</th>\n",
       "      <th>transmission</th>\n",
       "      <th>owner</th>\n",
       "      <th>mileage_kmpl</th>\n",
       "      <th>engine_cc</th>\n",
       "      <th>max_power_bhp</th>\n",
       "      <th>seats</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maruti</td>\n",
       "      <td>Swift</td>\n",
       "      <td>2014</td>\n",
       "      <td>145500</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>23.40</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>74.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Skoda</td>\n",
       "      <td>Rapid</td>\n",
       "      <td>2014</td>\n",
       "      <td>120000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Second Owner</td>\n",
       "      <td>21.14</td>\n",
       "      <td>1498.0</td>\n",
       "      <td>103.52</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>City</td>\n",
       "      <td>2006</td>\n",
       "      <td>140000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Third Owner</td>\n",
       "      <td>17.70</td>\n",
       "      <td>1497.0</td>\n",
       "      <td>78.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hyundai</td>\n",
       "      <td>i20</td>\n",
       "      <td>2010</td>\n",
       "      <td>127000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>23.00</td>\n",
       "      <td>1396.0</td>\n",
       "      <td>90.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Maruti</td>\n",
       "      <td>Swift</td>\n",
       "      <td>2007</td>\n",
       "      <td>120000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>16.10</td>\n",
       "      <td>1298.0</td>\n",
       "      <td>88.20</td>\n",
       "      <td>5.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8123</th>\n",
       "      <td>Hyundai</td>\n",
       "      <td>i20</td>\n",
       "      <td>2013</td>\n",
       "      <td>110000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>18.50</td>\n",
       "      <td>1197.0</td>\n",
       "      <td>82.85</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8124</th>\n",
       "      <td>Hyundai</td>\n",
       "      <td>Verna</td>\n",
       "      <td>2007</td>\n",
       "      <td>119000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Fourth &amp; Above Owner</td>\n",
       "      <td>16.80</td>\n",
       "      <td>1493.0</td>\n",
       "      <td>110.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8125</th>\n",
       "      <td>Maruti</td>\n",
       "      <td>Swift</td>\n",
       "      <td>2009</td>\n",
       "      <td>120000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>19.30</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>73.90</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8126</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Indigo</td>\n",
       "      <td>2013</td>\n",
       "      <td>25000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>23.57</td>\n",
       "      <td>1396.0</td>\n",
       "      <td>70.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8127</th>\n",
       "      <td>Tata</td>\n",
       "      <td>Indigo</td>\n",
       "      <td>2013</td>\n",
       "      <td>25000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>23.57</td>\n",
       "      <td>1396.0</td>\n",
       "      <td>70.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8128 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        brand   model  year  km_driven    fuel seller_type transmission  \\\n",
       "0      Maruti   Swift  2014     145500  Diesel  Individual       Manual   \n",
       "1       Skoda   Rapid  2014     120000  Diesel  Individual       Manual   \n",
       "2       Honda    City  2006     140000  Petrol  Individual       Manual   \n",
       "3     Hyundai     i20  2010     127000  Diesel  Individual       Manual   \n",
       "4      Maruti   Swift  2007     120000  Petrol  Individual       Manual   \n",
       "...       ...     ...   ...        ...     ...         ...          ...   \n",
       "8123  Hyundai     i20  2013     110000  Petrol  Individual       Manual   \n",
       "8124  Hyundai   Verna  2007     119000  Diesel  Individual       Manual   \n",
       "8125   Maruti   Swift  2009     120000  Diesel  Individual       Manual   \n",
       "8126     Tata  Indigo  2013      25000  Diesel  Individual       Manual   \n",
       "8127     Tata  Indigo  2013      25000  Diesel  Individual       Manual   \n",
       "\n",
       "                     owner  mileage_kmpl  engine_cc  max_power_bhp  seats  \n",
       "0              First Owner         23.40     1248.0          74.00    5.0  \n",
       "1             Second Owner         21.14     1498.0         103.52    5.0  \n",
       "2              Third Owner         17.70     1497.0          78.00    5.0  \n",
       "3              First Owner         23.00     1396.0          90.00    5.0  \n",
       "4              First Owner         16.10     1298.0          88.20    5.0  \n",
       "...                    ...           ...        ...            ...    ...  \n",
       "8123           First Owner         18.50     1197.0          82.85    5.0  \n",
       "8124  Fourth & Above Owner         16.80     1493.0         110.00    5.0  \n",
       "8125           First Owner         19.30     1248.0          73.90    5.0  \n",
       "8126           First Owner         23.57     1396.0          70.00    5.0  \n",
       "8127           First Owner         23.57     1396.0          70.00    5.0  \n",
       "\n",
       "[8128 rows x 12 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "d096ab4c"
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "342a7c66",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cols = ['year', 'km_driven', 'mileage_kmpl', 'engine_cc', 'max_power_bhp', 'seats']\n",
    "\n",
    "cat_cols = ['brand', 'model', 'fuel', 'seller_type', 'transmission', 'owner']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "46330e19",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_pipeline = Pipeline([\n",
    "    ('imputer', SimpleImputer(strategy='mean')),\n",
    "    ('scaler', StandardScaler())\n",
    "])\n",
    "\n",
    "cat_pipeline = Pipeline([\n",
    "    ('cat_encoder', OneHotEncoder()),\n",
    "    ('imputer', SimpleImputer(strategy='most_frequent'))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "978f0191",
   "metadata": {},
   "outputs": [],
   "source": [
    "col_tr = ColumnTransformer([\n",
    "    ('num', num_pipeline, num_cols),\n",
    "    ('cat', cat_pipeline, cat_cols)\n",
    "])\n",
    "\n",
    "X = col_tr.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c7a1b85c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Primitive Linear Regression"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "55d6e3ad79dc9ead"
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "92d80df1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 46.9 ms\n",
      "Wall time: 57.8 ms\n"
     ]
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "%time model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f0f6124a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9320162395298985\n"
     ]
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "bfe15985",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9546735035678198"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## RandomForestRegressor"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "2c76f9900d07d984"
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "853374a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "model2 = RandomForestRegressor(n_estimators=300, random_state=42, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "53c896ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 1min 25s\n",
      "Wall time: 11.9 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(n_estimators=300, n_jobs=-1, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(n_estimators=300, n_jobs=-1, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(n_estimators=300, n_jobs=-1, random_state=42)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time model2.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "98944802",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9128cca0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9720824749373221"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c5d4bc09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9954245258804845"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## GradientBoostingRegressor"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "a7ff2bd72ec14572"
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a971f3db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 812 ms\n",
      "Wall time: 802 ms\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingRegressor(random_state=42)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#GradientBoostingRegressor\n",
    "model3 = GradientBoostingRegressor(random_state=42)\n",
    "%time model3.fit(X_train, y_train)\n",
    "GradientBoostingRegressor(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "546b7fd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9599165469893418"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "821933b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9754764873988954"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## RandomForestRegressor with Randomized Search"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "513adfe978c5e726"
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0e64ef8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "random_grid = {\n",
    "    'min_samples_leaf': [1, 2, 4],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "}\n",
    "\n",
    "model4 = RandomizedSearchCV(RandomForestRegressor(), param_distributions = random_grid, n_iter=100, cv=3,\n",
    "verbose=2, random_state=42, n_jobs=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92433e8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "%time model4.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4197fe9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9737852080886394"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model4.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a9fc8221",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9947040584089404"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model4.score(X_train, y_train)"
   ]
  }
 ],
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