car price prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "47c4e1d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "97be5e9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Car details v3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "215ae76c",
   "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>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",
       "  </tbody>\n",
       "</table>\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",
       "  seller_type transmission         owner     mileage   engine   max_power  \\\n",
       "0  Individual       Manual   First Owner   23.4 kmpl  1248 CC      74 bhp   \n",
       "1  Individual       Manual  Second Owner  21.14 kmpl  1498 CC  103.52 bhp   \n",
       "2  Individual       Manual   Third Owner   17.7 kmpl  1497 CC      78 bhp   \n",
       "3  Individual       Manual   First Owner   23.0 kmpl  1396 CC      90 bhp   \n",
       "4  Individual       Manual   First Owner   16.1 kmpl  1298 CC    88.2 bhp   \n",
       "\n",
       "                     torque  seats  \n",
       "0            190Nm@ 2000rpm    5.0  \n",
       "1       250Nm@ 1500-2500rpm    5.0  \n",
       "2     12.7@ 2,700(kgm@ rpm)    5.0  \n",
       "3  22.4 kgm at 1750-2750rpm    5.0  \n",
       "4     11.5@ 4,500(kgm@ rpm)    5.0  "
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "3722fcc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8128 entries, 0 to 8127\n",
      "Data columns (total 13 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   name           8128 non-null   object \n",
      " 1   year           8128 non-null   int64  \n",
      " 2   selling_price  8128 non-null   int64  \n",
      " 3   km_driven      8128 non-null   int64  \n",
      " 4   fuel           8128 non-null   object \n",
      " 5   seller_type    8128 non-null   object \n",
      " 6   transmission   8128 non-null   object \n",
      " 7   owner          8128 non-null   object \n",
      " 8   mileage        7907 non-null   object \n",
      " 9   engine         7907 non-null   object \n",
      " 10  max_power      7913 non-null   object \n",
      " 11  torque         7906 non-null   object \n",
      " 12  seats          7907 non-null   float64\n",
      "dtypes: float64(1), int64(3), object(9)\n",
      "memory usage: 825.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "698ea1e5",
   "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>year</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>km_driven</th>\n",
       "      <th>seats</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8128.000000</td>\n",
       "      <td>8.128000e+03</td>\n",
       "      <td>8.128000e+03</td>\n",
       "      <td>7907.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2013.804011</td>\n",
       "      <td>6.382718e+05</td>\n",
       "      <td>6.981951e+04</td>\n",
       "      <td>5.416719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.044249</td>\n",
       "      <td>8.062534e+05</td>\n",
       "      <td>5.655055e+04</td>\n",
       "      <td>0.959588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1983.000000</td>\n",
       "      <td>2.999900e+04</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2011.000000</td>\n",
       "      <td>2.549990e+05</td>\n",
       "      <td>3.500000e+04</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>4.500000e+05</td>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>6.750000e+05</td>\n",
       "      <td>9.800000e+04</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>2.360457e+06</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              year  selling_price     km_driven        seats\n",
       "count  8128.000000   8.128000e+03  8.128000e+03  7907.000000\n",
       "mean   2013.804011   6.382718e+05  6.981951e+04     5.416719\n",
       "std       4.044249   8.062534e+05  5.655055e+04     0.959588\n",
       "min    1983.000000   2.999900e+04  1.000000e+00     2.000000\n",
       "25%    2011.000000   2.549990e+05  3.500000e+04     5.000000\n",
       "50%    2015.000000   4.500000e+05  6.000000e+04     5.000000\n",
       "75%    2017.000000   6.750000e+05  9.800000e+04     5.000000\n",
       "max    2020.000000   1.000000e+07  2.360457e+06    14.000000"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "a6913b70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=8128, step=1)"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "fa3658fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'year', 'selling_price', 'km_driven', 'fuel', 'seller_type',\n",
       "       'transmission', 'owner', 'mileage', 'engine', 'max_power', 'torque',\n",
       "       'seats'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "b7d80be1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name               0\n",
       "year               0\n",
       "selling_price      0\n",
       "km_driven          0\n",
       "fuel               0\n",
       "seller_type        0\n",
       "transmission       0\n",
       "owner              0\n",
       "mileage          221\n",
       "engine           221\n",
       "max_power        215\n",
       "torque           222\n",
       "seats            221\n",
       "dtype: int64"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "5ad0c739",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdn = df['seats'].median()\n",
    "mdn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "f1766622",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "221"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['seats'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "a081679c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['seats'].fillna(mdn, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "ae37bfb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['seats'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "a13de4b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.405388779527559"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['seats'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "6a09ce47",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "2811172b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name             0\n",
       "year             0\n",
       "selling_price    0\n",
       "km_driven        0\n",
       "fuel             0\n",
       "seller_type      0\n",
       "transmission     0\n",
       "owner            0\n",
       "mileage          0\n",
       "engine           0\n",
       "max_power        0\n",
       "torque           0\n",
       "seats            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "8f9106e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7906, 13)"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "f8a226c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x212bc459af0>"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "04dd0da0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x212bb9727f0>"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['#000099', '#ffff00'] # specify the colours - yellow is missing. blue is not missing.\n",
    "sns.heatmap(df.isnull(), cmap=sns.color_palette(colors))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "900ddc31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name - 0.0%\n",
      "year - 0.0%\n",
      "selling_price - 0.0%\n",
      "km_driven - 0.0%\n",
      "fuel - 0.0%\n",
      "seller_type - 0.0%\n",
      "transmission - 0.0%\n",
      "owner - 0.0%\n",
      "mileage - 0.0%\n",
      "engine - 0.0%\n",
      "max_power - 0.0%\n",
      "torque - 0.0%\n",
      "seats - 0.0%\n"
     ]
    }
   ],
   "source": [
    "for col in df.columns:\n",
    "    cnt_missing = np.mean(df[col].isnull())\n",
    "    print('{} - {}%'.format(col, round(cnt_missing*100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "529fd91e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mileage.isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "5dc08e23",
   "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",
       "  </tbody>\n",
       "</table>\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",
       "  seller_type transmission         owner     mileage   engine   max_power  \\\n",
       "0  Individual       Manual   First Owner   23.4 kmpl  1248 CC      74 bhp   \n",
       "1  Individual       Manual  Second Owner  21.14 kmpl  1498 CC  103.52 bhp   \n",
       "2  Individual       Manual   Third Owner   17.7 kmpl  1497 CC      78 bhp   \n",
       "3  Individual       Manual   First Owner   23.0 kmpl  1396 CC      90 bhp   \n",
       "4  Individual       Manual   First Owner   16.1 kmpl  1298 CC    88.2 bhp   \n",
       "\n",
       "                     torque  seats  \n",
       "0            190Nm@ 2000rpm    5.0  \n",
       "1       250Nm@ 1500-2500rpm    5.0  \n",
       "2     12.7@ 2,700(kgm@ rpm)    5.0  \n",
       "3  22.4 kgm at 1750-2750rpm    5.0  \n",
       "4     11.5@ 4,500(kgm@ rpm)    5.0  "
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "bc67e411",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x212c2a61f10>"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "00bacbcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Diesel    4299\n",
       "Petrol    3520\n",
       "CNG         52\n",
       "LPG         35\n",
       "Name: fuel, dtype: int64"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fuel'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "f4399144",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Individual', 'Dealer', 'Trustmark Dealer'], dtype=object)"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['seller_type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "0550b5cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Manual', 'Automatic'], dtype=object)"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['transmission'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "8aa3385b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['First Owner', 'Second Owner', 'Third Owner',\n",
       "       'Fourth & Above Owner', 'Test Drive Car'], dtype=object)"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['owner'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "f53cebc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "fuel_d = pd.get_dummies(df.fuel, drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "5d803807",
   "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>Diesel</th>\n",
       "      <th>LPG</th>\n",
       "      <th>Petrol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8123</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8124</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8125</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8126</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8127</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7906 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Diesel  LPG  Petrol\n",
       "0          1    0       0\n",
       "1          1    0       0\n",
       "2          0    0       1\n",
       "3          1    0       0\n",
       "4          0    0       1\n",
       "...      ...  ...     ...\n",
       "8123       0    0       1\n",
       "8124       1    0       0\n",
       "8125       1    0       0\n",
       "8126       1    0       0\n",
       "8127       1    0       0\n",
       "\n",
       "[7906 rows x 3 columns]"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuel_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "92d19566",
   "metadata": {},
   "outputs": [],
   "source": [
    "transmission_d = pd.get_dummies(df.transmission, drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "85b17905",
   "metadata": {},
   "outputs": [],
   "source": [
    "seller_type_d = pd.get_dummies(df.seller_type, drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "21c6365c",
   "metadata": {},
   "outputs": [],
   "source": [
    "owner_d = pd.get_dummies(df.owner, drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "27e3d979",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      Fourth & Above Owner  Second Owner  Test Drive Car  Third Owner\n",
       "0                        0             0               0            0\n",
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       "...                    ...           ...             ...          ...\n",
       "8123                     0             0               0            0\n",
       "8124                     1             0               0            0\n",
       "8125                     0             0               0            0\n",
       "8126                     0             0               0            0\n",
       "8127                     0             0               0            0\n",
       "\n",
       "[7906 rows x 4 columns]"
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  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "ab1823c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.concat([df, fuel_d, transmission_d, seller_type_d, owner_d], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "c05dbc88",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "  </thead>\n",
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       "      <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",
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       "      <td>0</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <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>...</td>\n",
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       "      <td>Individual</td>\n",
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       "      <td>1197 CC</td>\n",
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       "      <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>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </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>...</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>8126</th>\n",
       "      <td>Tata Indigo CR4</td>\n",
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       "      <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>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Tata Indigo CR4</td>\n",
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       "      <td>25000</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>7906 rows × 23 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",
       "     Diesel LPG  Petrol  Manual  Individual  Trustmark Dealer  \\\n",
       "0         1   0       0       1           1                 0   \n",
       "1         1   0       0       1           1                 0   \n",
       "2         0   0       1       1           1                 0   \n",
       "3         1   0       0       1           1                 0   \n",
       "4         0   0       1       1           1                 0   \n",
       "...     ...  ..     ...     ...         ...               ...   \n",
       "8123      0   0       1       1           1                 0   \n",
       "8124      1   0       0       1           1                 0   \n",
       "8125      1   0       0       1           1                 0   \n",
       "8126      1   0       0       1           1                 0   \n",
       "8127      1   0       0       1           1                 0   \n",
       "\n",
       "      Fourth & Above Owner  Second Owner  Test Drive Car  Third Owner  \n",
       "0                        0             0               0            0  \n",
       "1                        0             1               0            0  \n",
       "2                        0             0               0            1  \n",
       "3                        0             0               0            0  \n",
       "4                        0             0               0            0  \n",
       "...                    ...           ...             ...          ...  \n",
       "8123                     0             0               0            0  \n",
       "8124                     1             0               0            0  \n",
       "8125                     0             0               0            0  \n",
       "8126                     0             0               0            0  \n",
       "8127                     0             0               0            0  \n",
       "\n",
       "[7906 rows x 23 columns]"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "21522bae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x212c2e50d30>"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(), annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "ee255965",
   "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>...</th>\n",
       "      <th>Diesel</th>\n",
       "      <th>LPG</th>\n",
       "      <th>Petrol</th>\n",
       "      <th>Manual</th>\n",
       "      <th>Individual</th>\n",
       "      <th>Trustmark Dealer</th>\n",
       "      <th>Fourth &amp; Above Owner</th>\n",
       "      <th>Second Owner</th>\n",
       "      <th>Test Drive Car</th>\n",
       "      <th>Third Owner</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>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 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",
       "  seller_type transmission         owner     mileage   engine  ... Diesel LPG  \\\n",
       "0  Individual       Manual   First Owner   23.4 kmpl  1248 CC  ...      1   0   \n",
       "1  Individual       Manual  Second Owner  21.14 kmpl  1498 CC  ...      1   0   \n",
       "2  Individual       Manual   Third Owner   17.7 kmpl  1497 CC  ...      0   0   \n",
       "3  Individual       Manual   First Owner   23.0 kmpl  1396 CC  ...      1   0   \n",
       "4  Individual       Manual   First Owner   16.1 kmpl  1298 CC  ...      0   0   \n",
       "\n",
       "   Petrol  Manual  Individual  Trustmark Dealer  Fourth & Above Owner  \\\n",
       "0       0       1           1                 0                     0   \n",
       "1       0       1           1                 0                     0   \n",
       "2       1       1           1                 0                     0   \n",
       "3       0       1           1                 0                     0   \n",
       "4       1       1           1                 0                     0   \n",
       "\n",
       "   Second Owner  Test Drive Car  Third Owner  \n",
       "0             0               0            0  \n",
       "1             1               0            0  \n",
       "2             0               0            1  \n",
       "3             0               0            0  \n",
       "4             0               0            0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "ea12a42f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x212c2ed2df0>"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,12))\n",
    "sns.heatmap(df1.corr(), annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "5920deef",
   "metadata": {
    "scrolled": true
   },
   "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>...</th>\n",
       "      <th>Diesel</th>\n",
       "      <th>LPG</th>\n",
       "      <th>Petrol</th>\n",
       "      <th>Manual</th>\n",
       "      <th>Individual</th>\n",
       "      <th>Trustmark Dealer</th>\n",
       "      <th>Fourth &amp; Above Owner</th>\n",
       "      <th>Second Owner</th>\n",
       "      <th>Test Drive Car</th>\n",
       "      <th>Third Owner</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>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 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",
       "  seller_type transmission         owner     mileage   engine  ... Diesel LPG  \\\n",
       "0  Individual       Manual   First Owner   23.4 kmpl  1248 CC  ...      1   0   \n",
       "1  Individual       Manual  Second Owner  21.14 kmpl  1498 CC  ...      1   0   \n",
       "2  Individual       Manual   Third Owner   17.7 kmpl  1497 CC  ...      0   0   \n",
       "3  Individual       Manual   First Owner   23.0 kmpl  1396 CC  ...      1   0   \n",
       "4  Individual       Manual   First Owner   16.1 kmpl  1298 CC  ...      0   0   \n",
       "\n",
       "   Petrol  Manual  Individual  Trustmark Dealer  Fourth & Above Owner  \\\n",
       "0       0       1           1                 0                     0   \n",
       "1       0       1           1                 0                     0   \n",
       "2       1       1           1                 0                     0   \n",
       "3       0       1           1                 0                     0   \n",
       "4       1       1           1                 0                     0   \n",
       "\n",
       "   Second Owner  Test Drive Car  Third Owner  \n",
       "0             0               0            0  \n",
       "1             1               0            0  \n",
       "2             0               0            1  \n",
       "3             0               0            0  \n",
       "4             0               0            0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "e55319c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>km_driven</th>\n",
       "      <th>seats</th>\n",
       "      <th>Diesel</th>\n",
       "      <th>LPG</th>\n",
       "      <th>Petrol</th>\n",
       "      <th>Manual</th>\n",
       "      <th>Individual</th>\n",
       "      <th>Trustmark Dealer</th>\n",
       "      <th>Fourth &amp; Above Owner</th>\n",
       "      <th>Second Owner</th>\n",
       "      <th>Test Drive Car</th>\n",
       "      <th>Third Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7906.000000</td>\n",
       "      <td>7.906000e+03</td>\n",
       "      <td>7.906000e+03</td>\n",
       "      <td>7906.000000</td>\n",
       "      <td>7906.000000</td>\n",
       "      <td>7906.000000</td>\n",
       "      <td>7906.000000</td>\n",
       "      <td>7906.000000</td>\n",
       "      <td>7906.000000</td>\n",
       "      <td>7906.000000</td>\n",
       "      <td>7906.000000</td>\n",
       "      <td>7906.000000</td>\n",
       "      <td>7906.000000</td>\n",
       "      <td>7906.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2013.983936</td>\n",
       "      <td>6.498137e+05</td>\n",
       "      <td>6.918866e+04</td>\n",
       "      <td>5.416393</td>\n",
       "      <td>0.543764</td>\n",
       "      <td>0.004427</td>\n",
       "      <td>0.445231</td>\n",
       "      <td>0.868328</td>\n",
       "      <td>0.830129</td>\n",
       "      <td>0.029851</td>\n",
       "      <td>0.020238</td>\n",
       "      <td>0.254996</td>\n",
       "      <td>0.000632</td>\n",
       "      <td>0.064508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.863695</td>\n",
       "      <td>8.135827e+05</td>\n",
       "      <td>5.679230e+04</td>\n",
       "      <td>0.959208</td>\n",
       "      <td>0.498113</td>\n",
       "      <td>0.066393</td>\n",
       "      <td>0.497023</td>\n",
       "      <td>0.338155</td>\n",
       "      <td>0.375543</td>\n",
       "      <td>0.170186</td>\n",
       "      <td>0.140822</td>\n",
       "      <td>0.435887</td>\n",
       "      <td>0.025142</td>\n",
       "      <td>0.245671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1994.000000</td>\n",
       "      <td>2.999900e+04</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>2.700000e+05</td>\n",
       "      <td>3.500000e+04</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>4.500000e+05</td>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>6.900000e+05</td>\n",
       "      <td>9.542500e+04</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>2.360457e+06</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              year  selling_price     km_driven        seats       Diesel  \\\n",
       "count  7906.000000   7.906000e+03  7.906000e+03  7906.000000  7906.000000   \n",
       "mean   2013.983936   6.498137e+05  6.918866e+04     5.416393     0.543764   \n",
       "std       3.863695   8.135827e+05  5.679230e+04     0.959208     0.498113   \n",
       "min    1994.000000   2.999900e+04  1.000000e+00     2.000000     0.000000   \n",
       "25%    2012.000000   2.700000e+05  3.500000e+04     5.000000     0.000000   \n",
       "50%    2015.000000   4.500000e+05  6.000000e+04     5.000000     1.000000   \n",
       "75%    2017.000000   6.900000e+05  9.542500e+04     5.000000     1.000000   \n",
       "max    2020.000000   1.000000e+07  2.360457e+06    14.000000     1.000000   \n",
       "\n",
       "               LPG       Petrol       Manual   Individual  Trustmark Dealer  \\\n",
       "count  7906.000000  7906.000000  7906.000000  7906.000000       7906.000000   \n",
       "mean      0.004427     0.445231     0.868328     0.830129          0.029851   \n",
       "std       0.066393     0.497023     0.338155     0.375543          0.170186   \n",
       "min       0.000000     0.000000     0.000000     0.000000          0.000000   \n",
       "25%       0.000000     0.000000     1.000000     1.000000          0.000000   \n",
       "50%       0.000000     0.000000     1.000000     1.000000          0.000000   \n",
       "75%       0.000000     1.000000     1.000000     1.000000          0.000000   \n",
       "max       1.000000     1.000000     1.000000     1.000000          1.000000   \n",
       "\n",
       "       Fourth & Above Owner  Second Owner  Test Drive Car  Third Owner  \n",
       "count           7906.000000   7906.000000     7906.000000  7906.000000  \n",
       "mean               0.020238      0.254996        0.000632     0.064508  \n",
       "std                0.140822      0.435887        0.025142     0.245671  \n",
       "min                0.000000      0.000000        0.000000     0.000000  \n",
       "25%                0.000000      0.000000        0.000000     0.000000  \n",
       "50%                0.000000      0.000000        0.000000     0.000000  \n",
       "75%                0.000000      1.000000        0.000000     0.000000  \n",
       "max                1.000000      1.000000        1.000000     1.000000  "
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "0f440011",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'selling_price', 'km_driven', 'seats'], dtype='object')"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df._get_numeric_data().columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "d99e0eb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['selling_price'] = df['selling_price'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "071a88ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "649813.720844928"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.selling_price.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "e4fdee52",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>km_driven</th>\n",
       "      <th>seats</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7906.000000</td>\n",
       "      <td>7.906000e+03</td>\n",
       "      <td>7.906000e+03</td>\n",
       "      <td>7906.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2013.983936</td>\n",
       "      <td>6.498137e+05</td>\n",
       "      <td>6.918866e+04</td>\n",
       "      <td>5.416393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.863695</td>\n",
       "      <td>8.135827e+05</td>\n",
       "      <td>5.679230e+04</td>\n",
       "      <td>0.959208</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1994.000000</td>\n",
       "      <td>2.999900e+04</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>2.700000e+05</td>\n",
       "      <td>3.500000e+04</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>4.500000e+05</td>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>6.900000e+05</td>\n",
       "      <td>9.542500e+04</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>2.360457e+06</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              year  selling_price     km_driven        seats\n",
       "count  7906.000000   7.906000e+03  7.906000e+03  7906.000000\n",
       "mean   2013.983936   6.498137e+05  6.918866e+04     5.416393\n",
       "std       3.863695   8.135827e+05  5.679230e+04     0.959208\n",
       "min    1994.000000   2.999900e+04  1.000000e+00     2.000000\n",
       "25%    2012.000000   2.700000e+05  3.500000e+04     5.000000\n",
       "50%    2015.000000   4.500000e+05  6.000000e+04     5.000000\n",
       "75%    2017.000000   6.900000e+05  9.542500e+04     5.000000\n",
       "max    2020.000000   1.000000e+07  2.360457e+06    14.000000"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "0a5d3c25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'year', 'selling_price', 'km_driven', 'fuel', 'seller_type',\n",
       "       'transmission', 'owner', 'mileage', 'engine', 'max_power', 'torque',\n",
       "       'seats', 'Diesel', 'LPG', 'Petrol', 'Manual', 'Individual',\n",
       "       'Trustmark Dealer', 'Fourth & Above Owner', 'Second Owner',\n",
       "       'Test Drive Car', 'Third Owner'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cfa1bf2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "8cce84a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>name</th>\n",
       "      <th>year</th>\n",
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       "      <th>engine</th>\n",
       "      <th>max_power</th>\n",
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       "    </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>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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <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>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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <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>17.7 kmpl</td>\n",
       "      <td>1497 CC</td>\n",
       "      <td>78 bhp</td>\n",
       "      <td>12.7@ 2,700(kgm@ rpm)</td>\n",
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       "      <td>1</td>\n",
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       "    <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>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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "      <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>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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7906 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              name  year  selling_price  km_driven  \\\n",
       "0           Maruti Swift Dzire VDI  2014         450000     145500   \n",
       "1     Skoda Rapid 1.5 TDI Ambition  2014         370000     120000   \n",
       "2         Honda City 2017-2020 EXi  2006         158000     140000   \n",
       "3        Hyundai i20 Sportz Diesel  2010         225000     127000   \n",
       "4           Maruti Swift VXI BSIII  2007         130000     120000   \n",
       "...                            ...   ...            ...        ...   \n",
       "8123             Hyundai i20 Magna  2013         320000     110000   \n",
       "8124         Hyundai Verna CRDi SX  2007         135000     119000   \n",
       "8125        Maruti Swift Dzire ZDi  2009         382000     120000   \n",
       "8126               Tata Indigo CR4  2013         290000      25000   \n",
       "8127               Tata Indigo CR4  2013         290000      25000   \n",
       "\n",
       "         mileage   engine   max_power                     torque  seats  \\\n",
       "0      23.4 kmpl  1248 CC      74 bhp             190Nm@ 2000rpm    5.0   \n",
       "1     21.14 kmpl  1498 CC  103.52 bhp        250Nm@ 1500-2500rpm    5.0   \n",
       "2      17.7 kmpl  1497 CC      78 bhp      12.7@ 2,700(kgm@ rpm)    5.0   \n",
       "3      23.0 kmpl  1396 CC      90 bhp   22.4 kgm at 1750-2750rpm    5.0   \n",
       "4      16.1 kmpl  1298 CC    88.2 bhp      11.5@ 4,500(kgm@ rpm)    5.0   \n",
       "...          ...      ...         ...                        ...    ...   \n",
       "8123   18.5 kmpl  1197 CC   82.85 bhp           113.7Nm@ 4000rpm    5.0   \n",
       "8124   16.8 kmpl  1493 CC     110 bhp  24@ 1,900-2,750(kgm@ rpm)    5.0   \n",
       "8125   19.3 kmpl  1248 CC    73.9 bhp             190Nm@ 2000rpm    5.0   \n",
       "8126  23.57 kmpl  1396 CC      70 bhp        140Nm@ 1800-3000rpm    5.0   \n",
       "8127  23.57 kmpl  1396 CC      70 bhp        140Nm@ 1800-3000rpm    5.0   \n",
       "\n",
       "      Diesel  LPG  Petrol  Manual  Individual  Trustmark Dealer  \\\n",
       "0          1    0       0       1           1                 0   \n",
       "1          1    0       0       1           1                 0   \n",
       "2          0    0       1       1           1                 0   \n",
       "3          1    0       0       1           1                 0   \n",
       "4          0    0       1       1           1                 0   \n",
       "...      ...  ...     ...     ...         ...               ...   \n",
       "8123       0    0       1       1           1                 0   \n",
       "8124       1    0       0       1           1                 0   \n",
       "8125       1    0       0       1           1                 0   \n",
       "8126       1    0       0       1           1                 0   \n",
       "8127       1    0       0       1           1                 0   \n",
       "\n",
       "      Fourth & Above Owner  Second Owner  Test Drive Car  Third Owner  \n",
       "0                        0             0               0            0  \n",
       "1                        0             1               0            0  \n",
       "2                        0             0               0            1  \n",
       "3                        0             0               0            0  \n",
       "4                        0             0               0            0  \n",
       "...                    ...           ...             ...          ...  \n",
       "8123                     0             0               0            0  \n",
       "8124                     1             0               0            0  \n",
       "8125                     0             0               0            0  \n",
       "8126                     0             0               0            0  \n",
       "8127                     0             0               0            0  \n",
       "\n",
       "[7906 rows x 19 columns]"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.drop(['fuel', 'seller_type', 'transmission', 'owner'],axis =1 , inplace = True)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "b30cdee6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.rename(columns = {'Manual':'Transmission'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "ef981836",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'mileage'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mc:\\users\\arshdeep singh\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3360\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3361\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3362\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\arshdeep singh\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mc:\\users\\arshdeep singh\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'mileage'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\ARSHDE~1\\AppData\\Local\\Temp/ipykernel_10340/3385965424.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmileage_alter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'mileage'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexpand\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mmileage_new\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmileage_alter\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mdf1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Mileage'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmileage_new\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mdf1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'mileage'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mdf1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\arshdeep singh\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3453\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3454\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3455\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3456\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3457\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\arshdeep singh\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3361\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3362\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3363\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3364\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3365\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'mileage'"
     ]
    }
   ],
   "source": [
    "mileage_alter = df1['mileage'].str.split(expand=True)\n",
    "mileage_new = mileage_alter[0]\n",
    "df1['Mileage'] = mileage_new\n",
    "df1 = df.drop(['mileage'],axis=1)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "23ae7d93",
   "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>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>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>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>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>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>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",
       "  </tbody>\n",
       "</table>\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",
       "  seller_type transmission         owner   engine   max_power  \\\n",
       "0  Individual       Manual   First Owner  1248 CC      74 bhp   \n",
       "1  Individual       Manual  Second Owner  1498 CC  103.52 bhp   \n",
       "2  Individual       Manual   Third Owner  1497 CC      78 bhp   \n",
       "3  Individual       Manual   First Owner  1396 CC      90 bhp   \n",
       "4  Individual       Manual   First Owner  1298 CC    88.2 bhp   \n",
       "\n",
       "                     torque  seats  \n",
       "0            190Nm@ 2000rpm    5.0  \n",
       "1       250Nm@ 1500-2500rpm    5.0  \n",
       "2     12.7@ 2,700(kgm@ rpm)    5.0  \n",
       "3  22.4 kgm at 1750-2750rpm    5.0  \n",
       "4     11.5@ 4,500(kgm@ rpm)    5.0  "
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "4f8acf21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x212ca437040>"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,12))\n",
    "sns.heatmap(df1.corr(), annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "35abe70d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7906 entries, 0 to 8127\n",
      "Data columns (total 19 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   name                  7906 non-null   object \n",
      " 1   year                  7906 non-null   int64  \n",
      " 2   selling_price         7906 non-null   int64  \n",
      " 3   km_driven             7906 non-null   int64  \n",
      " 4   mileage               7906 non-null   object \n",
      " 5   engine                7906 non-null   object \n",
      " 6   max_power             7906 non-null   object \n",
      " 7   torque                7906 non-null   object \n",
      " 8   seats                 7906 non-null   float64\n",
      " 9   Diesel                7906 non-null   uint8  \n",
      " 10  LPG                   7906 non-null   uint8  \n",
      " 11  Petrol                7906 non-null   uint8  \n",
      " 12  Transmission          7906 non-null   uint8  \n",
      " 13  Individual            7906 non-null   uint8  \n",
      " 14  Trustmark Dealer      7906 non-null   uint8  \n",
      " 15  Fourth & Above Owner  7906 non-null   uint8  \n",
      " 16  Second Owner          7906 non-null   uint8  \n",
      " 17  Test Drive Car        7906 non-null   uint8  \n",
      " 18  Third Owner           7906 non-null   uint8  \n",
      "dtypes: float64(1), int64(3), object(5), uint8(10)\n",
      "memory usage: 694.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a2b1bdc",
   "metadata": {},
   "source": [
    "# SelectKBest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5d911e90",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6bfdde50",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df1.drop(['name', 'mileage', 'engine', 'max_power', 'torque', 'selling_price'], axis = 1)\n",
    "y =  df1.selling_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "baa3e5b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_features = SelectKBest(score_func = chi2, k=10)\n",
    "fit = best_features.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3e341321",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   Specs         Score\n",
      "1              km_driven  5.497862e+07\n",
      "11        Test Drive Car  4.886681e+03\n",
      "8       Trustmark Dealer  2.908887e+03\n",
      "4                    LPG  1.149598e+03\n",
      "5                 Petrol  1.047291e+03\n",
      "12           Third Owner  9.787980e+02\n",
      "9   Fourth & Above Owner  9.300784e+02\n",
      "10          Second Owner  9.174338e+02\n",
      "3                 Diesel  8.876942e+02\n",
      "6           Transmission  5.486337e+02\n"
     ]
    }
   ],
   "source": [
    "df_scores = pd.DataFrame(fit.scores_)\n",
    "df_columns = pd.DataFrame(x.columns)\n",
    "##concat two dataframes for better visualization \n",
    "features_scores = pd.concat([df_columns, df_scores], axis = 1)\n",
    "features_scores.columns = ['Specs', 'Score'] #naming columns\n",
    "print(features_scores.nlargest(10,'Score')) #print 10 best features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e116306f",
   "metadata": {},
   "source": [
    "# ExtraTreesClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "fc53259a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "286e0dca",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ExtraTreesClassifier()\n",
    "#model.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "332cb16e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(model.feature_importances_) #use inbuilt class feature_importances of tree based classifiers\n",
    "#feat_importances = pd.Series(model.feature_importances_, index = df.columns)\n",
    "#feat_importances.nlargest(18).plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b5ecffc",
   "metadata": {},
   "source": [
    "# k nearlest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e9fdd22a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "f4394f2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y, test_size = 0.3, random_state= 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0324a3c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 19)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "005b28fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=19)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "0a5fa188",
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = knn.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ed7ccee3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([350000, 300000, 750000, ..., 300000, 170000, 484999], dtype=int64)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "289320cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2ed65867",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       30000       0.00      0.00      0.00         1\n",
      "       31504       0.00      0.00      0.00         1\n",
      "       40000       0.00      0.00      0.00         6\n",
      "       45000       0.00      0.00      0.00         3\n",
      "       45957       0.00      0.00      0.00         1\n",
      "       50000       0.00      0.00      0.00         6\n",
      "       55000       0.00      0.00      0.00         6\n",
      "       55599       1.00      1.00      1.00         1\n",
      "       60000       0.00      0.00      0.00         3\n",
      "       65000       0.00      0.00      0.00         5\n",
      "       70000       0.00      0.00      0.00         8\n",
      "       75000       0.00      0.00      0.00         4\n",
      "       78000       0.00      0.00      0.00         1\n",
      "       80000       0.00      0.00      0.00         8\n",
      "       81000       0.00      0.00      0.00         2\n",
      "       85000       0.00      0.00      0.00         8\n",
      "       89000       0.00      0.00      0.00         1\n",
      "       90000       0.00      0.00      0.00         7\n",
      "       93000       0.00      0.00      0.00         1\n",
      "       95000       0.00      0.00      0.00         4\n",
      "       96000       0.00      0.00      0.00         1\n",
      "      100000       0.09      0.06      0.07        16\n",
      "      101000       0.00      0.00      0.00         1\n",
      "      105000       0.00      0.00      0.00         3\n",
      "      108000       0.00      0.00      0.00         2\n",
      "      110000       0.00      0.00      0.00         9\n",
      "      114999       0.00      0.00      0.00         5\n",
      "      115999       0.00      0.00      0.00         1\n",
      "      119000       0.00      0.00      0.00         1\n",
      "      120000       0.00      0.00      0.00        23\n",
      "      125000       0.00      0.00      0.00        10\n",
      "      128000       0.00      0.00      0.00         1\n",
      "      129000       0.00      0.00      0.00         1\n",
      "      130000       0.06      0.08      0.07        13\n",
      "      135000       0.00      0.00      0.00        13\n",
      "      140000       0.12      0.09      0.11        11\n",
      "      141000       0.00      0.00      0.00         1\n",
      "      142000       0.00      0.00      0.00         0\n",
      "      145000       0.00      0.00      0.00         6\n",
      "      148000       0.00      0.00      0.00         2\n",
      "      150000       0.09      0.17      0.12        42\n",
      "      152000       0.00      0.00      0.00         0\n",
      "      155000       0.00      0.00      0.00         5\n",
      "      158000       0.00      0.00      0.00         1\n",
      "      160000       0.00      0.00      0.00        15\n",
      "      165000       0.00      0.00      0.00         7\n",
      "      170000       0.00      0.00      0.00        13\n",
      "      175000       0.00      0.00      0.00         9\n",
      "      180000       0.02      0.06      0.03        16\n",
      "      185000       0.00      0.00      0.00        10\n",
      "      187000       0.00      0.00      0.00         1\n",
      "      190000       0.00      0.00      0.00        13\n",
      "      191000       0.00      0.00      0.00         1\n",
      "      195000       0.00      0.00      0.00         8\n",
      "      196000       0.00      0.00      0.00         2\n",
      "      198000       0.00      0.00      0.00         2\n",
      "      200000       0.05      0.15      0.08        33\n",
      "      204000       0.00      0.00      0.00         1\n",
      "      204999       0.00      0.00      0.00         6\n",
      "      210000       0.09      0.05      0.07        19\n",
      "      211000       0.00      0.00      0.00         1\n",
      "      215000       0.00      0.00      0.00         8\n",
      "      217000       0.00      0.00      0.00         0\n",
      "      219000       0.00      0.00      0.00         1\n",
      "      220000       0.00      0.00      0.00        18\n",
      "      221000       0.00      0.00      0.00         2\n",
      "      225000       0.29      0.35      0.32        26\n",
      "      227999       0.00      0.00      0.00         0\n",
      "      229999       0.00      0.00      0.00        11\n",
      "      231000       0.00      0.00      0.00         1\n",
      "      235000       0.00      0.00      0.00         9\n",
      "      236000       0.00      0.00      0.00         1\n",
      "      240000       0.00      0.00      0.00         9\n",
      "      242000       0.00      0.00      0.00         1\n",
      "      245000       0.00      0.00      0.00         7\n",
      "      246000       0.00      0.00      0.00         2\n",
      "      250000       0.04      0.10      0.06        50\n",
      "      250999       0.00      0.00      0.00         2\n",
      "      254999       0.00      0.00      0.00         3\n",
      "      259000       0.00      0.00      0.00         1\n",
      "      260000       0.00      0.00      0.00        13\n",
      "      262000       0.00      0.00      0.00         2\n",
      "      265000       0.10      0.11      0.11         9\n",
      "      267000       0.00      0.00      0.00         0\n",
      "      270000       0.00      0.00      0.00        19\n",
      "      275000       0.00      0.00      0.00        11\n",
      "      277000       0.00      0.00      0.00         1\n",
      "      279000       0.00      0.00      0.00         1\n",
      "      280000       0.00      0.00      0.00        24\n",
      "      282000       0.00      0.00      0.00         2\n",
      "      285000       0.00      0.00      0.00         4\n",
      "      287000       0.00      0.00      0.00         1\n",
      "      290000       0.00      0.00      0.00        14\n",
      "      295000       0.00      0.00      0.00         2\n",
      "      297000       0.00      0.00      0.00         1\n",
      "      299000       0.00      0.00      0.00         1\n",
      "      300000       0.06      0.15      0.08        61\n",
      "      305000       0.00      0.00      0.00         3\n",
      "      308000       0.00      0.00      0.00         1\n",
      "      310000       0.00      0.00      0.00        23\n",
      "      312000       0.00      0.00      0.00         1\n",
      "      313000       0.00      0.00      0.00         1\n",
      "      315000       0.00      0.00      0.00         2\n",
      "      316000       0.00      0.00      0.00         1\n",
      "      320000       0.00      0.00      0.00        28\n",
      "      321000       0.00      0.00      0.00         1\n",
      "      325000       0.00      0.00      0.00        22\n",
      "      327000       0.00      0.00      0.00         1\n",
      "      328000       0.00      0.00      0.00         2\n",
      "      329000       0.00      0.00      0.00         2\n",
      "      330000       0.00      0.00      0.00        10\n",
      "      335000       0.14      0.29      0.19         7\n",
      "      339000       0.00      0.00      0.00         1\n",
      "      340000       0.00      0.00      0.00        13\n",
      "      341000       0.00      0.00      0.00         1\n",
      "      345000       0.00      0.00      0.00         2\n",
      "      346000       0.00      0.00      0.00         1\n",
      "      347000       0.00      0.00      0.00         1\n",
      "      350000       0.01      0.02      0.01        58\n",
      "      351000       0.00      0.00      0.00         3\n",
      "      352000       0.00      0.00      0.00         1\n",
      "      355000       0.00      0.00      0.00         4\n",
      "      356000       0.00      0.00      0.00         1\n",
      "      360000       0.00      0.00      0.00        10\n",
      "      365000       0.00      0.00      0.00         1\n",
      "      366000       0.00      0.00      0.00         1\n",
      "      370000       0.08      0.08      0.08        13\n",
      "      371000       0.00      0.00      0.00         3\n",
      "      374000       0.00      0.00      0.00         1\n",
      "      375000       0.00      0.00      0.00        18\n",
      "      380000       0.00      0.00      0.00        15\n",
      "      382000       0.00      0.00      0.00         2\n",
      "      385000       0.00      0.00      0.00         3\n",
      "      388000       0.00      0.00      0.00         1\n",
      "      390000       0.00      0.00      0.00         7\n",
      "      391000       0.00      0.00      0.00         2\n",
      "      395000       0.00      0.00      0.00         4\n",
      "      400000       0.08      0.09      0.08        57\n",
      "      408000       0.00      0.00      0.00         1\n",
      "      409000       0.00      0.00      0.00         1\n",
      "      409999       0.00      0.00      0.00         8\n",
      "      411000       0.00      0.00      0.00         3\n",
      "      415000       0.00      0.00      0.00         5\n",
      "      416000       0.00      0.00      0.00         1\n",
      "      420000       0.00      0.00      0.00         6\n",
      "      421000       0.00      0.00      0.00         1\n",
      "      423000       0.00      0.00      0.00         1\n",
      "      425000       0.00      0.00      0.00        12\n",
      "      428000       0.00      0.00      0.00         1\n",
      "      430000       0.00      0.00      0.00        13\n",
      "      434999       0.00      0.00      0.00         6\n",
      "      440000       0.00      0.00      0.00         7\n",
      "      441000       0.00      0.00      0.00         1\n",
      "      445000       0.00      0.00      0.00         4\n",
      "      448000       0.00      0.00      0.00         1\n",
      "      450000       0.13      0.19      0.15        64\n",
      "      451000       0.00      0.00      0.00         1\n",
      "      451999       0.00      0.00      0.00         1\n",
      "      453000       0.00      0.00      0.00         1\n",
      "      454000       0.00      0.00      0.00         1\n",
      "      455000       0.00      0.00      0.00         3\n",
      "      455999       0.00      0.00      0.00         1\n",
      "      459999       0.00      0.00      0.00        11\n",
      "      465000       0.00      0.00      0.00         5\n",
      "      466000       0.00      0.00      0.00         2\n",
      "      467000       0.00      0.00      0.00         1\n",
      "      470000       0.12      0.11      0.12         9\n",
      "      472000       0.00      0.00      0.00         2\n",
      "      475000       0.11      0.06      0.07        18\n",
      "      476999       0.00      0.00      0.00         1\n",
      "      480000       0.00      0.00      0.00        13\n",
      "      483000       0.00      0.00      0.00         1\n",
      "      484999       0.00      0.00      0.00         8\n",
      "      488999       0.00      0.00      0.00         1\n",
      "      490000       0.00      0.00      0.00         4\n",
      "      495000       0.00      0.00      0.00         2\n",
      "      499000       0.00      0.00      0.00         2\n",
      "      500000       0.09      0.13      0.11        52\n",
      "      503000       0.33      1.00      0.50         6\n",
      "      509999       0.00      0.00      0.00         9\n",
      "      511000       0.00      0.00      0.00         2\n",
      "      515000       0.00      0.00      0.00         7\n",
      "      520000       0.12      0.22      0.16         9\n",
      "      521000       0.00      0.00      0.00         1\n",
      "      522000       0.00      0.00      0.00         1\n",
      "      524000       0.00      0.00      0.00         1\n",
      "      525000       0.00      0.00      0.00        29\n",
      "      530000       0.00      0.00      0.00        12\n",
      "      535000       0.00      0.00      0.00         7\n",
      "      540000       0.00      0.00      0.00         4\n",
      "      541000       0.00      0.00      0.00         1\n",
      "      545000       0.00      0.00      0.00         2\n",
      "      550000       0.13      0.19      0.16        58\n",
      "      551000       0.00      0.00      0.00         2\n",
      "      555000       0.00      0.00      0.00         1\n",
      "      556000       0.00      0.00      0.00         1\n",
      "      560000       0.00      0.00      0.00        10\n",
      "      562000       0.00      0.00      0.00         1\n",
      "      565000       0.00      0.00      0.00         3\n",
      "      570000       0.00      0.00      0.00         8\n",
      "      571000       0.00      0.00      0.00         1\n",
      "      573000       0.00      0.00      0.00         1\n",
      "      574000       0.00      0.00      0.00         1\n",
      "      575000       0.00      0.00      0.00         8\n",
      "      576000       0.00      0.00      0.00         1\n",
      "      580000       0.00      0.00      0.00        10\n",
      "      585000       0.00      0.00      0.00         3\n",
      "      590000       0.00      0.00      0.00         6\n",
      "      595000       0.00      0.00      0.00         3\n",
      "      597000       0.00      0.00      0.00         1\n",
      "      599000       0.00      0.00      0.00         1\n",
      "      600000       0.12      0.19      0.15        63\n",
      "      604000       0.00      0.00      0.00         1\n",
      "      605000       0.00      0.00      0.00         2\n",
      "      610000       0.00      0.00      0.00        13\n",
      "      611000       0.00      0.00      0.00         2\n",
      "      615000       0.00      0.00      0.00         4\n",
      "      620000       0.00      0.00      0.00        14\n",
      "      625000       0.19      0.29      0.23        14\n",
      "      626000       0.00      0.00      0.00         2\n",
      "      630000       0.00      0.00      0.00         7\n",
      "      631000       0.00      0.00      0.00         1\n",
      "      634000       0.00      0.00      0.00         1\n",
      "      635000       0.00      0.00      0.00         4\n",
      "      640000       0.00      0.00      0.00         5\n",
      "      643000       0.00      0.00      0.00         1\n",
      "      645000       0.70      1.00      0.82         7\n",
      "      650000       0.27      0.29      0.28        62\n",
      "      651000       0.00      0.00      0.00         1\n",
      "      655000       0.00      0.00      0.00         2\n",
      "      660000       0.00      0.00      0.00         8\n",
      "      665000       0.00      0.00      0.00         2\n",
      "      666000       0.00      0.00      0.00         1\n",
      "      667000       0.00      0.00      0.00         1\n",
      "      669000       0.00      0.00      0.00         1\n",
      "      670000       0.00      0.00      0.00         8\n",
      "      671000       0.00      0.00      0.00         1\n",
      "      675000       0.00      0.00      0.00        16\n",
      "      680000       0.00      0.00      0.00         5\n",
      "      681000       0.00      0.00      0.00         2\n",
      "      690000       0.08      0.20      0.12         5\n",
      "      693000       0.00      0.00      0.00         2\n",
      "      695000       0.00      0.00      0.00         2\n",
      "      700000       0.09      0.19      0.13        42\n",
      "      710000       0.00      0.00      0.00         2\n",
      "      711000       0.00      0.00      0.00         1\n",
      "      715000       0.00      0.00      0.00         1\n",
      "      720000       0.00      0.00      0.00         0\n",
      "      721000       0.00      0.00      0.00         1\n",
      "      725000       0.00      0.00      0.00         5\n",
      "      730000       0.00      0.00      0.00         4\n",
      "      735000       0.00      0.00      0.00         3\n",
      "      740000       0.36      0.77      0.49        13\n",
      "      741000       0.00      0.00      0.00         1\n",
      "      745000       0.00      0.00      0.00         3\n",
      "      749000       0.00      0.00      0.00         1\n",
      "      750000       0.19      0.22      0.20        46\n",
      "      754000       0.00      0.00      0.00         1\n",
      "      755000       0.00      0.00      0.00         1\n",
      "      764000       0.00      0.00      0.00         1\n",
      "      770000       0.00      0.00      0.00         2\n",
      "      775000       0.00      0.00      0.00         5\n",
      "      778000       0.00      0.00      0.00         1\n",
      "      779000       0.62      1.00      0.77        10\n",
      "      780000       0.00      0.00      0.00         4\n",
      "      790000       0.00      0.00      0.00         2\n",
      "      795000       0.00      0.00      0.00         2\n",
      "      797000       0.00      0.00      0.00         1\n",
      "      800000       0.06      0.06      0.06        32\n",
      "      803999       0.00      0.00      0.00         1\n",
      "      805000       0.00      0.00      0.00         0\n",
      "      810000       0.00      0.00      0.00         2\n",
      "      811000       0.00      0.00      0.00         1\n",
      "      819999       0.00      0.00      0.00         5\n",
      "      822000       0.00      0.00      0.00         1\n",
      "      825000       0.00      0.00      0.00         4\n",
      "      830000       0.00      0.00      0.00         2\n",
      "      833000       0.00      0.00      0.00         1\n",
      "      840000       0.00      0.00      0.00         1\n",
      "      850000       0.00      0.00      0.00        19\n",
      "      860000       0.00      0.00      0.00         4\n",
      "      866000       0.00      0.00      0.00         1\n",
      "      869999       0.00      0.00      0.00         4\n",
      "      875000       0.00      0.00      0.00         6\n",
      "      880000       0.00      0.00      0.00         2\n",
      "      885000       0.00      0.00      0.00         1\n",
      "      889000       0.00      0.00      0.00         2\n",
      "      890000       0.00      0.00      0.00         4\n",
      "      894999       0.00      0.00      0.00         1\n",
      "      899000       0.00      0.00      0.00         1\n",
      "      900000       0.10      0.04      0.06        25\n",
      "      910000       0.00      0.00      0.00         4\n",
      "      911000       0.00      0.00      0.00         1\n",
      "      925000       0.00      0.00      0.00         7\n",
      "      934000       0.00      0.00      0.00         1\n",
      "      950000       0.04      0.06      0.05        16\n",
      "      955000       0.00      0.00      0.00         1\n",
      "      965000       0.00      0.00      0.00         1\n",
      "      969999       0.00      0.00      0.00         1\n",
      "      975000       0.00      0.00      0.00         3\n",
      "      980000       0.00      0.00      0.00         1\n",
      "      990000       0.00      0.00      0.00         2\n",
      "      999000       0.00      0.00      0.00         1\n",
      "     1000000       0.50      0.06      0.11        17\n",
      "     1019999       0.00      0.00      0.00         1\n",
      "     1025000       0.00      0.00      0.00         4\n",
      "     1031000       0.00      0.00      0.00         1\n",
      "     1035000       0.00      0.00      0.00         1\n",
      "     1050000       0.00      0.00      0.00        11\n",
      "     1075000       0.00      0.00      0.00         1\n",
      "     1085000       0.00      0.00      0.00         1\n",
      "     1090000       0.00      0.00      0.00         2\n",
      "     1100000       0.00      0.00      0.00        13\n",
      "     1110000       0.00      0.00      0.00         1\n",
      "     1125000       0.00      0.00      0.00         3\n",
      "     1132000       0.00      0.00      0.00         1\n",
      "     1140000       0.00      0.00      0.00         1\n",
      "     1145000       0.00      0.00      0.00         1\n",
      "     1147000       0.00      0.00      0.00         1\n",
      "     1150000       0.00      0.00      0.00         4\n",
      "     1151000       0.00      0.00      0.00         1\n",
      "     1160000       0.00      0.00      0.00         1\n",
      "     1200000       0.00      0.00      0.00        10\n",
      "     1220000       0.00      0.00      0.00         1\n",
      "     1225000       0.00      0.00      0.00         1\n",
      "     1237000       0.00      0.00      0.00         1\n",
      "     1250000       0.00      0.00      0.00         3\n",
      "     1265000       0.00      0.00      0.00         1\n",
      "     1275000       0.00      0.00      0.00         1\n",
      "     1290000       0.00      0.00      0.00         2\n",
      "     1295000       0.00      0.00      0.00         1\n",
      "     1300000       0.06      0.25      0.10         4\n",
      "     1350000       0.00      0.00      0.00         4\n",
      "     1380000       0.00      0.00      0.00         2\n",
      "     1390000       0.00      0.00      0.00         1\n",
      "     1400000       0.00      0.00      0.00         1\n",
      "     1405000       0.00      0.00      0.00         2\n",
      "     1445000       0.00      0.00      0.00         1\n",
      "     1450000       0.00      0.00      0.00         1\n",
      "     1460000       0.00      0.00      0.00         2\n",
      "     1465000       0.00      0.00      0.00         1\n",
      "     1500000       0.00      0.00      0.00         2\n",
      "     1525000       0.00      0.00      0.00         1\n",
      "     1550000       0.00      0.00      0.00         1\n",
      "     1575000       0.00      0.00      0.00         3\n",
      "     1576000       0.00      0.00      0.00         1\n",
      "     1600000       0.00      0.00      0.00         2\n",
      "     1630000       0.00      0.00      0.00         1\n",
      "     1650000       0.00      0.00      0.00         2\n",
      "     1689999       0.00      0.00      0.00         2\n",
      "     1700000       0.00      0.00      0.00         6\n",
      "     1750000       0.00      0.00      0.00         5\n",
      "     1757000       0.00      0.00      0.00         1\n",
      "     1800000       0.00      0.00      0.00         3\n",
      "     1825000       0.00      0.00      0.00         1\n",
      "     1850000       0.00      0.00      0.00         5\n",
      "     1864999       0.00      0.00      0.00         1\n",
      "     1920000       0.00      0.00      0.00         1\n",
      "     1925000       0.00      0.00      0.00         2\n",
      "     1938000       0.00      0.00      0.00         1\n",
      "     1950000       0.00      0.00      0.00         2\n",
      "     2000000       0.35      0.69      0.46        13\n",
      "     2064000       0.00      0.00      0.00         1\n",
      "     2100000       0.00      0.00      0.00         1\n",
      "     2125000       0.00      0.00      0.00         2\n",
      "     2150000       0.00      0.00      0.00         1\n",
      "     2450000       0.00      0.00      0.00         1\n",
      "     2475000       0.62      1.00      0.77         5\n",
      "     2500000       0.00      0.00      0.00         1\n",
      "     2600000       0.00      0.00      0.00         2\n",
      "     2625000       0.00      0.00      0.00         4\n",
      "     2700000       0.00      0.00      0.00         2\n",
      "     2711000       0.47      1.00      0.64         8\n",
      "     2800000       0.00      0.00      0.00         1\n",
      "     2825000       0.00      0.00      0.00         1\n",
      "     2900000       0.00      0.00      0.00         2\n",
      "     2940000       0.00      0.00      0.00         1\n",
      "     2950000       0.00      0.00      0.00         3\n",
      "     3000000       0.00      0.00      0.00         2\n",
      "     3100000       0.00      0.00      0.00         1\n",
      "     3200000       0.40      1.00      0.57         4\n",
      "     3250000       0.00      0.00      0.00         1\n",
      "     3300000       0.00      0.00      0.00         1\n",
      "     3350000       0.00      0.00      0.00         1\n",
      "     3400000       0.00      0.00      0.00         2\n",
      "     3500000       0.00      0.00      0.00         1\n",
      "     3750000       0.00      0.00      0.00         1\n",
      "     3790000       0.00      0.00      0.00         1\n",
      "     3800000       0.46      1.00      0.63         6\n",
      "     3900000       0.00      0.00      0.00         1\n",
      "     3975000       0.50      1.00      0.67         2\n",
      "     4100000       0.00      0.00      0.00         0\n",
      "     4500000       0.00      0.00      0.00         1\n",
      "     4600000       0.00      0.00      0.00         1\n",
      "     5150000       0.83      1.00      0.91        15\n",
      "     5400000       0.71      1.00      0.83        10\n",
      "     5500000       1.00      0.91      0.95        11\n",
      "     5800000       0.00      0.00      0.00         1\n",
      "     6000000       0.00      0.00      0.00         2\n",
      "     7200000       0.00      0.00      0.00         1\n",
      "\n",
      "    accuracy                           0.10      2372\n",
      "   macro avg       0.03      0.04      0.03      2372\n",
      "weighted avg       0.07      0.10      0.08      2372\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\arshdeep singh\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\users\\arshdeep singh\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\users\\arshdeep singh\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\users\\arshdeep singh\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\users\\arshdeep singh\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\users\\arshdeep singh\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "06d5bdda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.10370994940978077\n"
     ]
    }
   ],
   "source": [
    "print(knn.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f928e0fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'year', 'selling_price', 'km_driven', 'mileage', 'engine',\n",
       "       'max_power', 'torque', 'seats', 'Diesel', 'LPG', 'Petrol',\n",
       "       'Transmission', 'Individual', 'Trustmark Dealer',\n",
       "       'Fourth & Above Owner', 'Second Owner', 'Test Drive Car',\n",
       "       'Third Owner'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "ac654392",
   "metadata": {},
   "outputs": [],
   "source": [
    "#x = df1[['year', 'Diesel', 'Test Drive Car', 'seats', 'Trustmark Dealer']]  # positive heatmap\n",
    "#x = df1[['Transmission', 'Individual', 'km_driven', 'Petrol', 'Second Owner', 'Third Owner']] # negative cor relation\n",
    "x = df1[['km_driven', 'Test Drive Car', 'Trustmark Dealer', 'LPG', 'Petrol', 'Third Owner', 'Fourth & Above Owner']]\n",
    "y = df1.selling_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "12bce43a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y, test_size = 0.2, random_state= 101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "afcb7167",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 150000,  500000,  150000, ...,  503000,  135000, 5150000],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 7)\n",
    "knn.fit(x_train, y_train)\n",
    "predict = knn.predict(x_test)\n",
    "predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "8d2da16a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1011378002528445\n"
     ]
    }
   ],
   "source": [
    "print(knn.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "acadb96d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'year', 'selling_price', 'km_driven', 'mileage', 'engine',\n",
       "       'max_power', 'torque', 'seats', 'Diesel', 'LPG', 'Petrol',\n",
       "       'Transmission', 'Individual', 'Trustmark Dealer',\n",
       "       'Fourth & Above Owner', 'Second Owner', 'Test Drive Car',\n",
       "       'Third Owner'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "587e7143",
   "metadata": {},
   "source": [
    "# linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "c7e180f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#x = df1[['year', 'Diesel', 'Test Drive Car', 'seats', 'Trustmark Dealer']]  # positive heatmap\n",
    "x = df1[['Transmission', 'Individual', 'km_driven', 'Petrol', 'Second Owner', 'Third Owner']] # negative cor relation\n",
    "#x = df1[['km_driven', 'Test Drive Car', 'Trustmark Dealer', 'LPG', 'Petrol']]\n",
    "y = df1['selling_price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "ea9b55fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "afd7dd4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y, test_size = 0.2, random_state= 101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "3c4d0d9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "ed02b998",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "69be9cef",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "594640c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 143912.08176283,  353238.64092511,   82091.86337995, ...,\n",
       "       1794782.28458017,  241822.95418911,  344668.20348388])"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "2718257a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4672301270192729\n"
     ]
    }
   ],
   "source": [
    "print(lr.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cd22f55",
   "metadata": {},
   "source": [
    "# logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "427edcd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "d901ea06",
   "metadata": {},
   "outputs": [],
   "source": [
    "#x = df1[['year', 'Diesel', 'Test Drive Car', 'seats', 'Trustmark Dealer']]  # positive heatmap\n",
    "x = df1[['Transmission', 'Individual', 'km_driven', 'Petrol', 'Second Owner', 'Third Owner']] # negative cor relation\n",
    "#x = df1[['km_driven', 'Test Drive Car', 'Trustmark Dealer', 'LPG', 'Petrol']]\n",
    "y = df1['selling_price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "6c7ffbe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "370e3c2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y, test_size = 0.3, random_state= 101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "a183877c",
   "metadata": {},
   "outputs": [],
   "source": [
    "lg = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "361560bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\arshdeep singh\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "8608687e",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = lg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "ea37f9ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([550000, 550000, 550000, ..., 550000, 550000, 550000], dtype=int64)"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "b2839439",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.026138279932546374\n"
     ]
    }
   ],
   "source": [
    "print(lg.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ba81a05",
   "metadata": {},
   "source": [
    "# svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "f0cd7fff",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df1[['year', 'Diesel', 'Test Drive Car', 'seats', 'Trustmark Dealer']]  # positive heatmap\n",
    "#x = df1[['Transmission', 'Individual', 'km_driven', 'Petrol', 'Second Owner', 'Third Owner']] # negative cor relation\n",
    "#x = df1[['km_driven', 'Test Drive Car', 'Trustmark Dealer', 'LPG', 'Petrol']]\n",
    "y = df1['selling_price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "c3263099",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "7b7339a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y, test_size = 0.3, random_state= 101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "fd2e8c42",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "5d74ced8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "84d00784",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03035413153456998"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc5d9dc0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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