Car Price Prediction (2nd trial).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "6ee152fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "de8dd590",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_df = pd.read_csv('archive.zip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "faf63461",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_df.columns = car_df.columns.str.lower().str.replace(' ', '_')\n",
    "string_columns = list(car_df.dtypes[car_df.dtypes == 'object'].index)\n",
    "for col in string_columns:\n",
    "    car_df[col] = car_df[col].str.lower().str.replace(' ', '_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3dc289e4",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>2010.384338</td>\n",
       "      <td>7.579740</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>2016.00</td>\n",
       "      <td>2017.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine_hp</th>\n",
       "      <td>11845.0</td>\n",
       "      <td>249.386070</td>\n",
       "      <td>109.191870</td>\n",
       "      <td>55.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>300.00</td>\n",
       "      <td>1001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine_cylinders</th>\n",
       "      <td>11884.0</td>\n",
       "      <td>5.628829</td>\n",
       "      <td>1.780559</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_of_doors</th>\n",
       "      <td>11908.0</td>\n",
       "      <td>3.436093</td>\n",
       "      <td>0.881315</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>highway_mpg</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>26.637485</td>\n",
       "      <td>8.863001</td>\n",
       "      <td>12.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>354.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city_mpg</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>19.733255</td>\n",
       "      <td>8.987798</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.00</td>\n",
       "      <td>137.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>popularity</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>1554.911197</td>\n",
       "      <td>1441.855347</td>\n",
       "      <td>2.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>1385.0</td>\n",
       "      <td>2009.00</td>\n",
       "      <td>5657.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>msrp</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>40594.737032</td>\n",
       "      <td>60109.103604</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>21000.0</td>\n",
       "      <td>29995.0</td>\n",
       "      <td>42231.25</td>\n",
       "      <td>2065902.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count          mean           std     min      25%  \\\n",
       "year              11914.0   2010.384338      7.579740  1990.0   2007.0   \n",
       "engine_hp         11845.0    249.386070    109.191870    55.0    170.0   \n",
       "engine_cylinders  11884.0      5.628829      1.780559     0.0      4.0   \n",
       "number_of_doors   11908.0      3.436093      0.881315     2.0      2.0   \n",
       "highway_mpg       11914.0     26.637485      8.863001    12.0     22.0   \n",
       "city_mpg          11914.0     19.733255      8.987798     7.0     16.0   \n",
       "popularity        11914.0   1554.911197   1441.855347     2.0    549.0   \n",
       "msrp              11914.0  40594.737032  60109.103604  2000.0  21000.0   \n",
       "\n",
       "                      50%       75%        max  \n",
       "year               2015.0   2016.00     2017.0  \n",
       "engine_hp           227.0    300.00     1001.0  \n",
       "engine_cylinders      6.0      6.00       16.0  \n",
       "number_of_doors       4.0      4.00        4.0  \n",
       "highway_mpg          26.0     30.00      354.0  \n",
       "city_mpg             18.0     22.00      137.0  \n",
       "popularity         1385.0   2009.00     5657.0  \n",
       "msrp              29995.0  42231.25  2065902.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "350f35c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['make', 'model', 'year', 'engine_fuel_type', 'engine_hp',\n",
       "       'engine_cylinders', 'transmission_type', 'driven_wheels',\n",
       "       'number_of_doors', 'market_category', 'vehicle_size', 'vehicle_style',\n",
       "       'highway_mpg', 'city_mpg', 'popularity', 'msrp'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2e1a6aee",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_vars = ['object']\n",
    "cat_df = car_df.select_dtypes(include=cat_vars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dfe7dfbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "make :\n",
      "chevrolet     1123\n",
      "ford           881\n",
      "volkswagen     809\n",
      "toyota         746\n",
      "dodge          626\n",
      "Name: make, dtype: int64\n",
      "--------------------------------------------------\n",
      "model :\n",
      "silverado_1500        156\n",
      "tundra                140\n",
      "f-150                 126\n",
      "sierra_1500            90\n",
      "beetle_convertible     89\n",
      "Name: model, dtype: int64\n",
      "--------------------------------------------------\n",
      "engine_fuel_type :\n",
      "regular_unleaded                  7172\n",
      "premium_unleaded_(required)       2009\n",
      "premium_unleaded_(recommended)    1523\n",
      "flex-fuel_(unleaded/e85)           899\n",
      "diesel                             154\n",
      "Name: engine_fuel_type, dtype: int64\n",
      "--------------------------------------------------\n",
      "transmission_type :\n",
      "automatic           8266\n",
      "manual              2935\n",
      "automated_manual     626\n",
      "direct_drive          68\n",
      "unknown               19\n",
      "Name: transmission_type, dtype: int64\n",
      "--------------------------------------------------\n",
      "driven_wheels :\n",
      "front_wheel_drive    4787\n",
      "rear_wheel_drive     3371\n",
      "all_wheel_drive      2353\n",
      "four_wheel_drive     1403\n",
      "Name: driven_wheels, dtype: int64\n",
      "--------------------------------------------------\n",
      "market_category :\n",
      "crossover             1110\n",
      "flex_fuel              872\n",
      "luxury                 855\n",
      "luxury,performance     673\n",
      "hatchback              641\n",
      "Name: market_category, dtype: int64\n",
      "--------------------------------------------------\n",
      "vehicle_size :\n",
      "compact    4764\n",
      "midsize    4373\n",
      "large      2777\n",
      "Name: vehicle_size, dtype: int64\n",
      "--------------------------------------------------\n",
      "vehicle_style :\n",
      "sedan            3048\n",
      "4dr_suv          2488\n",
      "coupe            1211\n",
      "convertible       793\n",
      "4dr_hatchback     702\n",
      "Name: vehicle_style, dtype: int64\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#Most Frequent variables\n",
    "for col in cat_df.columns:\n",
    "    print(col, ':')\n",
    "    print(car_df[col].value_counts().sort_values(ascending=False).head(5))\n",
    "    print('-'*50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "0b741724",
   "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>year</th>\n",
       "      <th>engine_hp</th>\n",
       "      <th>engine_cylinders</th>\n",
       "      <th>number_of_doors</th>\n",
       "      <th>highway_mpg</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>popularity</th>\n",
       "      <th>msrp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>335.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>3916</td>\n",
       "      <td>46135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28</td>\n",
       "      <td>19</td>\n",
       "      <td>3916</td>\n",
       "      <td>40650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28</td>\n",
       "      <td>20</td>\n",
       "      <td>3916</td>\n",
       "      <td>36350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>230.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>3916</td>\n",
       "      <td>29450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011</td>\n",
       "      <td>230.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>3916</td>\n",
       "      <td>34500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  engine_hp  engine_cylinders  number_of_doors  highway_mpg  city_mpg  \\\n",
       "0  2011      335.0               6.0              2.0           26        19   \n",
       "1  2011      300.0               6.0              2.0           28        19   \n",
       "2  2011      300.0               6.0              2.0           28        20   \n",
       "3  2011      230.0               6.0              2.0           28        18   \n",
       "4  2011      230.0               6.0              2.0           28        18   \n",
       "\n",
       "   popularity   msrp  \n",
       "0        3916  46135  \n",
       "1        3916  40650  \n",
       "2        3916  36350  \n",
       "3        3916  29450  \n",
       "4        3916  34500  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerics = ['int64', 'float64']\n",
    "num_df = car_df.select_dtypes(include=numerics)\n",
    "num_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "efa3fa20",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_df = num_df.drop(['msrp'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "78d2a822",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'engine_hp', 'engine_cylinders', 'number_of_doors',\n",
       "       'highway_mpg', 'city_mpg', 'popularity'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = num_df.columns\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "008e8ebf",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_df['year'] = 2017 - num_df['year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "aa0f0a1b",
   "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>year</th>\n",
       "      <th>engine_hp</th>\n",
       "      <th>engine_cylinders</th>\n",
       "      <th>number_of_doors</th>\n",
       "      <th>highway_mpg</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>335.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>3916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28</td>\n",
       "      <td>19</td>\n",
       "      <td>3916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28</td>\n",
       "      <td>20</td>\n",
       "      <td>3916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>230.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>3916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>230.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>3916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  engine_hp  engine_cylinders  number_of_doors  highway_mpg  city_mpg  \\\n",
       "0     6      335.0               6.0              2.0           26        19   \n",
       "1     6      300.0               6.0              2.0           28        19   \n",
       "2     6      300.0               6.0              2.0           28        20   \n",
       "3     6      230.0               6.0              2.0           28        18   \n",
       "4     6      230.0               6.0              2.0           28        18   \n",
       "\n",
       "   popularity  \n",
       "0        3916  \n",
       "1        3916  \n",
       "2        3916  \n",
       "3        3916  \n",
       "4        3916  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "7f8e901d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year                 0\n",
       "engine_hp           69\n",
       "engine_cylinders    30\n",
       "number_of_doors      6\n",
       "highway_mpg          0\n",
       "city_mpg             0\n",
       "popularity           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "fc0a0116",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>6.615662</td>\n",
       "      <td>7.579740</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine_hp</th>\n",
       "      <td>11845.0</td>\n",
       "      <td>249.386070</td>\n",
       "      <td>109.191870</td>\n",
       "      <td>55.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine_cylinders</th>\n",
       "      <td>11884.0</td>\n",
       "      <td>5.628829</td>\n",
       "      <td>1.780559</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_of_doors</th>\n",
       "      <td>11908.0</td>\n",
       "      <td>3.436093</td>\n",
       "      <td>0.881315</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>highway_mpg</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>26.637485</td>\n",
       "      <td>8.863001</td>\n",
       "      <td>12.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>354.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city_mpg</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>19.733255</td>\n",
       "      <td>8.987798</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>137.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>popularity</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>1554.911197</td>\n",
       "      <td>1441.855347</td>\n",
       "      <td>2.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>1385.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>5657.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count         mean          std   min    25%     50%  \\\n",
       "year              11914.0     6.615662     7.579740   0.0    1.0     2.0   \n",
       "engine_hp         11845.0   249.386070   109.191870  55.0  170.0   227.0   \n",
       "engine_cylinders  11884.0     5.628829     1.780559   0.0    4.0     6.0   \n",
       "number_of_doors   11908.0     3.436093     0.881315   2.0    2.0     4.0   \n",
       "highway_mpg       11914.0    26.637485     8.863001  12.0   22.0    26.0   \n",
       "city_mpg          11914.0    19.733255     8.987798   7.0   16.0    18.0   \n",
       "popularity        11914.0  1554.911197  1441.855347   2.0  549.0  1385.0   \n",
       "\n",
       "                     75%     max  \n",
       "year                10.0    27.0  \n",
       "engine_hp          300.0  1001.0  \n",
       "engine_cylinders     6.0    16.0  \n",
       "number_of_doors      4.0     4.0  \n",
       "highway_mpg         30.0   354.0  \n",
       "city_mpg            22.0   137.0  \n",
       "popularity        2009.0  5657.0  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "6cd6a397",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_df.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "2038e6e4",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>6.615662</td>\n",
       "      <td>7.579740</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine_hp</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>247.941749</td>\n",
       "      <td>110.507669</td>\n",
       "      <td>0.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine_cylinders</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>5.614655</td>\n",
       "      <td>1.800554</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_of_doors</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>3.434363</td>\n",
       "      <td>0.884460</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>highway_mpg</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>26.637485</td>\n",
       "      <td>8.863001</td>\n",
       "      <td>12.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>354.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city_mpg</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>19.733255</td>\n",
       "      <td>8.987798</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>137.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>popularity</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>1554.911197</td>\n",
       "      <td>1441.855347</td>\n",
       "      <td>2.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>1385.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>5657.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count         mean          std   min    25%     50%  \\\n",
       "year              11914.0     6.615662     7.579740   0.0    1.0     2.0   \n",
       "engine_hp         11914.0   247.941749   110.507669   0.0  170.0   225.0   \n",
       "engine_cylinders  11914.0     5.614655     1.800554   0.0    4.0     6.0   \n",
       "number_of_doors   11914.0     3.434363     0.884460   0.0    2.0     4.0   \n",
       "highway_mpg       11914.0    26.637485     8.863001  12.0   22.0    26.0   \n",
       "city_mpg          11914.0    19.733255     8.987798   7.0   16.0    18.0   \n",
       "popularity        11914.0  1554.911197  1441.855347   2.0  549.0  1385.0   \n",
       "\n",
       "                     75%     max  \n",
       "year                10.0    27.0  \n",
       "engine_hp          300.0  1001.0  \n",
       "engine_cylinders     6.0    16.0  \n",
       "number_of_doors      4.0     4.0  \n",
       "highway_mpg         30.0   354.0  \n",
       "city_mpg            22.0   137.0  \n",
       "popularity        2009.0  5657.0  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "fbad2b5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "num_df = scaler.fit_transform(num_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "28014820",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_df = pd.DataFrame(num_df, columns=cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "bdf4b6e7",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>0.245025</td>\n",
       "      <td>0.280731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.370370</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine_hp</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>0.247694</td>\n",
       "      <td>0.110397</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.169830</td>\n",
       "      <td>0.224775</td>\n",
       "      <td>0.299700</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine_cylinders</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>0.350916</td>\n",
       "      <td>0.112535</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_of_doors</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>0.858591</td>\n",
       "      <td>0.221115</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>highway_mpg</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>0.042800</td>\n",
       "      <td>0.025915</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.029240</td>\n",
       "      <td>0.040936</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city_mpg</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>0.097948</td>\n",
       "      <td>0.069137</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.069231</td>\n",
       "      <td>0.084615</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>popularity</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>0.274609</td>\n",
       "      <td>0.254970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.096729</td>\n",
       "      <td>0.244562</td>\n",
       "      <td>0.354907</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count      mean       std  min       25%       50%  \\\n",
       "year              11914.0  0.245025  0.280731  0.0  0.037037  0.074074   \n",
       "engine_hp         11914.0  0.247694  0.110397  0.0  0.169830  0.224775   \n",
       "engine_cylinders  11914.0  0.350916  0.112535  0.0  0.250000  0.375000   \n",
       "number_of_doors   11914.0  0.858591  0.221115  0.0  0.500000  1.000000   \n",
       "highway_mpg       11914.0  0.042800  0.025915  0.0  0.029240  0.040936   \n",
       "city_mpg          11914.0  0.097948  0.069137  0.0  0.069231  0.084615   \n",
       "popularity        11914.0  0.274609  0.254970  0.0  0.096729  0.244562   \n",
       "\n",
       "                       75%  max  \n",
       "year              0.370370  1.0  \n",
       "engine_hp         0.299700  1.0  \n",
       "engine_cylinders  0.375000  1.0  \n",
       "number_of_doors   1.000000  1.0  \n",
       "highway_mpg       0.052632  1.0  \n",
       "city_mpg          0.115385  1.0  \n",
       "popularity        0.354907  1.0  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "be18ff17",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = num_df\n",
    "y = np.log1p(car_df['msrp'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "adbd9c93",
   "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>year</th>\n",
       "      <th>engine_hp</th>\n",
       "      <th>engine_cylinders</th>\n",
       "      <th>number_of_doors</th>\n",
       "      <th>highway_mpg</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.334665</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.040936</td>\n",
       "      <td>0.092308</td>\n",
       "      <td>0.692131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.299700</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.046784</td>\n",
       "      <td>0.092308</td>\n",
       "      <td>0.692131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.299700</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.046784</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.692131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.229770</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.046784</td>\n",
       "      <td>0.084615</td>\n",
       "      <td>0.692131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.229770</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.046784</td>\n",
       "      <td>0.084615</td>\n",
       "      <td>0.692131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  engine_hp  engine_cylinders  number_of_doors  highway_mpg  \\\n",
       "0  0.222222   0.334665             0.375              0.5     0.040936   \n",
       "1  0.222222   0.299700             0.375              0.5     0.046784   \n",
       "2  0.222222   0.299700             0.375              0.5     0.046784   \n",
       "3  0.222222   0.229770             0.375              0.5     0.046784   \n",
       "4  0.222222   0.229770             0.375              0.5     0.046784   \n",
       "\n",
       "   city_mpg  popularity  \n",
       "0  0.092308    0.692131  \n",
       "1  0.092308    0.692131  \n",
       "2  0.100000    0.692131  \n",
       "3  0.084615    0.692131  \n",
       "4  0.084615    0.692131  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "47f320dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10.73934884, 10.61277871, 10.50097699, 10.29048314, 10.44874359])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "92412ede",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split #split train and test dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "c7b33132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train: (9531, 7)\n",
      "Shape of y_train: (9531,)\n",
      "-------------------------\n",
      "Shape of X_test: (2383, 7)\n",
      "Shape of y_test: (2383,)\n"
     ]
    }
   ],
   "source": [
    "print(f\"Shape of X_train: {X_train.shape}\")\n",
    "print(f\"Shape of y_train: {y_train.shape}\")\n",
    "print('-'*25)\n",
    "print(f\"Shape of X_test: {X_test.shape}\")\n",
    "print(f\"Shape of y_test: {y_test.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "fc7f2f97",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(X_train, y_train)\n",
    "y_pred = lin_reg.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "2b7dfd84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#let see the how good was the prediction\n",
    "plt.figure(figsize=(5,4))\n",
    "\n",
    "sns.histplot(y_train, label='target', color='black',bins=30)\n",
    "sns.histplot(y_pred, label='prediction',color='red', bins=30)\n",
    "plt.legend()\n",
    "plt.xlabel('log(1+price)')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Predictions vs Actual Distribution')\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "ffd16c8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5164583338020942"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#RMSE\n",
    "lin_mse = mean_squared_error(y_train, y_pred)\n",
    "lin_rmse = np.sqrt(lin_mse)\n",
    "lin_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "73340c30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5180420905424017"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CV\n",
    "lin_reg_cv_scores = cross_val_score(lin_reg, X_train, y_train, scoring='neg_mean_squared_error', cv = 10)\n",
    "lin_reg_rmse_scores = np.sqrt(-lin_reg_cv_scores)\n",
    "lin_reg_rmse_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "7f4cbc0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06908834901196552"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor()\n",
    "tree_reg.fit(X_train, y_train)\n",
    "price_predictions = tree_reg.predict(X_train)\n",
    "tree_mse = mean_squared_error(y_train, price_predictions)\n",
    "tree_rmse = np.sqrt(tree_mse)\n",
    "tree_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "c925ab28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1422709854959153"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_reg_cv_scores = cross_val_score(tree_reg, X_train, y_train, scoring='neg_mean_squared_error', cv = 10)\n",
    "tree_reg_rmse_scores = np.sqrt(-tree_reg_cv_scores)\n",
    "tree_reg_rmse_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "d0b1bb05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06908834901196552"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = RandomForestRegressor()\n",
    "forest_reg.fit(X_train, y_train)\n",
    "price_predictions = forest_reg.predict(X_train)\n",
    "forest_mse = mean_squared_error(y_train, price_predictions)\n",
    "forest_rmse = np.sqrt(tree_mse)\n",
    "forest_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "ecc53a2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12235134999527256"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_reg_cv_scores = cross_val_score(forest_reg, X_train, y_train, scoring='neg_mean_squared_error', cv = 10)\n",
    "forest_reg_rmse_scores = np.sqrt(-forest_reg_cv_scores)\n",
    "forest_reg_rmse_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "cfa0bceb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=RandomForestRegressor(),\n",
       "             param_grid=[{'max_features': [2, 4, 6, 8],\n",
       "                          'n_estimators': [3, 10, 30]},\n",
       "                         {'bootstrap': [False], 'max_features': [2, 3, 4],\n",
       "                          'n_estimators': [3, 10]}],\n",
       "             return_train_score=True, scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hyper tuning of RF\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [\n",
    "    {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},\n",
    "    {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},\n",
    "  ]\n",
    "\n",
    "forest_reg = RandomForestRegressor()\n",
    "\n",
    "grid_search = GridSearchCV(forest_reg, param_grid,\n",
    "                           scoring='neg_mean_squared_error',\n",
    "                           return_train_score=True,\n",
    "                           cv=10,\n",
    "                          )\n",
    "\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "79a8b699",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.7349515 , 0.19459454, 0.03513006, 0.00279718, 0.00889349,\n",
       "       0.01014302, 0.01349021])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = grid_search.best_estimator_.feature_importances_\n",
    "feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "87e6b796",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11838455149087972"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model = grid_search.best_estimator_\n",
    "final_predictions = final_model.predict(X_test)\n",
    "final_mse = mean_squared_error(y_test, final_predictions)\n",
    "final_rmse = np.sqrt(final_mse)\n",
    "final_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a97562d",
   "metadata": {},
   "source": [
    "# Final Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "6e94a605",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cat_preparing_X(df):\n",
    "    \"\"\"\n",
    "    Trying some more simple feature engineering.\n",
    "    \"\"\"\n",
    "    df = df.copy()\n",
    "    features = []\n",
    "    \n",
    "    for index in ['chevrolet', 'ford', 'volkswagen', 'toyota']:\n",
    "        feature = 'is_make_%s' % index \n",
    "        df[feature] = (df['make'] == index).astype(int)\n",
    "        features.append(feature)\n",
    "        \n",
    "    for index in ['regular_unleaded', 'premium_unleaded_(required)',\n",
    "                  'premium_unleaded_(recommended)', 'flex-fuel_(unleaded/e85)']:\n",
    "        feature = 'is_type_%s' % index\n",
    "        df[feature] = (df['engine_fuel_type'] == index).astype(int)\n",
    "        features.append(feature)\n",
    "    \n",
    "    for index in ['automatic', 'manual', 'automated_manual', 'direct_drive']:\n",
    "        feature = 'is_tranmission_%s' % index\n",
    "        df[feature] = (df['transmission_type'] == index).astype(int)\n",
    "        features.append(feature)\n",
    "    \n",
    "    for index in ['crossover', 'flex_fuel', 'luxury', 'luxury,performance']:\n",
    "        feature = 'is_market_category_%s' % index\n",
    "        df[feature] = (df['market_category'] == index).astype(int)\n",
    "        features.append(feature)\n",
    "\n",
    "    df = df[features]\n",
    "    df = df.fillna(0)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "62f8354a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "make                    0\n",
       "model                   0\n",
       "engine_fuel_type        3\n",
       "transmission_type       0\n",
       "driven_wheels           0\n",
       "market_category      3742\n",
       "vehicle_size            0\n",
       "vehicle_style           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "e4ec06b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_df = cat_preparing_X(cat_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "2c72ea4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11914 entries, 0 to 11913\n",
      "Data columns (total 16 columns):\n",
      " #   Column                                  Non-Null Count  Dtype\n",
      "---  ------                                  --------------  -----\n",
      " 0   is_make_chevrolet                       11914 non-null  int32\n",
      " 1   is_make_ford                            11914 non-null  int32\n",
      " 2   is_make_volkswagen                      11914 non-null  int32\n",
      " 3   is_make_toyota                          11914 non-null  int32\n",
      " 4   is_type_regular_unleaded                11914 non-null  int32\n",
      " 5   is_type_premium_unleaded_(required)     11914 non-null  int32\n",
      " 6   is_type_premium_unleaded_(recommended)  11914 non-null  int32\n",
      " 7   is_type_flex-fuel_(unleaded/e85)        11914 non-null  int32\n",
      " 8   is_tranmission_automatic                11914 non-null  int32\n",
      " 9   is_tranmission_manual                   11914 non-null  int32\n",
      " 10  is_tranmission_automated_manual         11914 non-null  int32\n",
      " 11  is_tranmission_direct_drive             11914 non-null  int32\n",
      " 12  is_market_category_crossover            11914 non-null  int32\n",
      " 13  is_market_category_flex_fuel            11914 non-null  int32\n",
      " 14  is_market_category_luxury               11914 non-null  int32\n",
      " 15  is_market_category_luxury,performance   11914 non-null  int32\n",
      "dtypes: int32(16)\n",
      "memory usage: 744.8 KB\n"
     ]
    }
   ],
   "source": [
    "cat_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "ec7d3ec3",
   "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>year</th>\n",
       "      <th>engine_hp</th>\n",
       "      <th>engine_cylinders</th>\n",
       "      <th>number_of_doors</th>\n",
       "      <th>highway_mpg</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>335.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>3916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28</td>\n",
       "      <td>19</td>\n",
       "      <td>3916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28</td>\n",
       "      <td>20</td>\n",
       "      <td>3916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>230.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>3916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>230.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>3916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  engine_hp  engine_cylinders  number_of_doors  highway_mpg  city_mpg  \\\n",
       "0     6      335.0               6.0              2.0           26        19   \n",
       "1     6      300.0               6.0              2.0           28        19   \n",
       "2     6      300.0               6.0              2.0           28        20   \n",
       "3     6      230.0               6.0              2.0           28        18   \n",
       "4     6      230.0               6.0              2.0           28        18   \n",
       "\n",
       "   popularity  \n",
       "0        3916  \n",
       "1        3916  \n",
       "2        3916  \n",
       "3        3916  \n",
       "4        3916  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerics = ['int64', 'float64']\n",
    "num_df = car_df.select_dtypes(include=numerics)\n",
    "num_df = num_df.drop(['msrp'], axis=1)\n",
    "cols = num_df.columns\n",
    "num_df['year'] = 2017 - num_df['year']\n",
    "num_df.fillna(0, inplace=True)\n",
    "num_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "7b20625b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11914 entries, 0 to 11913\n",
      "Data columns (total 7 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   year              11914 non-null  int64  \n",
      " 1   engine_hp         11914 non-null  float64\n",
      " 2   engine_cylinders  11914 non-null  float64\n",
      " 3   number_of_doors   11914 non-null  float64\n",
      " 4   highway_mpg       11914 non-null  int64  \n",
      " 5   city_mpg          11914 non-null  int64  \n",
      " 6   popularity        11914 non-null  int64  \n",
      "dtypes: float64(3), int64(4)\n",
      "memory usage: 651.7 KB\n"
     ]
    }
   ],
   "source": [
    "num_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "c4031292",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>6.615662</td>\n",
       "      <td>7.579740</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine_hp</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>247.941749</td>\n",
       "      <td>110.507669</td>\n",
       "      <td>0.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine_cylinders</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>5.614655</td>\n",
       "      <td>1.800554</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_of_doors</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>3.434363</td>\n",
       "      <td>0.884460</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>highway_mpg</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>26.637485</td>\n",
       "      <td>8.863001</td>\n",
       "      <td>12.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>354.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city_mpg</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>19.733255</td>\n",
       "      <td>8.987798</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>137.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>popularity</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>1554.911197</td>\n",
       "      <td>1441.855347</td>\n",
       "      <td>2.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>1385.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>5657.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count         mean          std   min    25%     50%  \\\n",
       "year              11914.0     6.615662     7.579740   0.0    1.0     2.0   \n",
       "engine_hp         11914.0   247.941749   110.507669   0.0  170.0   225.0   \n",
       "engine_cylinders  11914.0     5.614655     1.800554   0.0    4.0     6.0   \n",
       "number_of_doors   11914.0     3.434363     0.884460   0.0    2.0     4.0   \n",
       "highway_mpg       11914.0    26.637485     8.863001  12.0   22.0    26.0   \n",
       "city_mpg          11914.0    19.733255     8.987798   7.0   16.0    18.0   \n",
       "popularity        11914.0  1554.911197  1441.855347   2.0  549.0  1385.0   \n",
       "\n",
       "                     75%     max  \n",
       "year                10.0    27.0  \n",
       "engine_hp          300.0  1001.0  \n",
       "engine_cylinders     6.0    16.0  \n",
       "number_of_doors      4.0     4.0  \n",
       "highway_mpg         30.0   354.0  \n",
       "city_mpg            22.0   137.0  \n",
       "popularity        2009.0  5657.0  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "3cebdc9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "num_df = scaler.fit_transform(num_df)\n",
    "num_df = pd.DataFrame(num_df, columns=cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "2596451d",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>0.245025</td>\n",
       "      <td>0.280731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.370370</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine_hp</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>0.247694</td>\n",
       "      <td>0.110397</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.169830</td>\n",
       "      <td>0.224775</td>\n",
       "      <td>0.299700</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine_cylinders</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>0.350916</td>\n",
       "      <td>0.112535</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_of_doors</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>0.858591</td>\n",
       "      <td>0.221115</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>highway_mpg</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>0.042800</td>\n",
       "      <td>0.025915</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.029240</td>\n",
       "      <td>0.040936</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city_mpg</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>0.097948</td>\n",
       "      <td>0.069137</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.069231</td>\n",
       "      <td>0.084615</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>popularity</th>\n",
       "      <td>11914.0</td>\n",
       "      <td>0.274609</td>\n",
       "      <td>0.254970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.096729</td>\n",
       "      <td>0.244562</td>\n",
       "      <td>0.354907</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count      mean       std  min       25%       50%  \\\n",
       "year              11914.0  0.245025  0.280731  0.0  0.037037  0.074074   \n",
       "engine_hp         11914.0  0.247694  0.110397  0.0  0.169830  0.224775   \n",
       "engine_cylinders  11914.0  0.350916  0.112535  0.0  0.250000  0.375000   \n",
       "number_of_doors   11914.0  0.858591  0.221115  0.0  0.500000  1.000000   \n",
       "highway_mpg       11914.0  0.042800  0.025915  0.0  0.029240  0.040936   \n",
       "city_mpg          11914.0  0.097948  0.069137  0.0  0.069231  0.084615   \n",
       "popularity        11914.0  0.274609  0.254970  0.0  0.096729  0.244562   \n",
       "\n",
       "                       75%  max  \n",
       "year              0.370370  1.0  \n",
       "engine_hp         0.299700  1.0  \n",
       "engine_cylinders  0.375000  1.0  \n",
       "number_of_doors   1.000000  1.0  \n",
       "highway_mpg       0.052632  1.0  \n",
       "city_mpg          0.115385  1.0  \n",
       "popularity        0.354907  1.0  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "5364cc86",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.concat([num_df, cat_df], axis=1)\n",
    "y = np.log1p(car_df['msrp'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "bee015c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "58aa96cc",
   "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>year</th>\n",
       "      <th>engine_hp</th>\n",
       "      <th>engine_cylinders</th>\n",
       "      <th>number_of_doors</th>\n",
       "      <th>highway_mpg</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>popularity</th>\n",
       "      <th>is_make_chevrolet</th>\n",
       "      <th>is_make_ford</th>\n",
       "      <th>is_make_volkswagen</th>\n",
       "      <th>...</th>\n",
       "      <th>is_type_premium_unleaded_(recommended)</th>\n",
       "      <th>is_type_flex-fuel_(unleaded/e85)</th>\n",
       "      <th>is_tranmission_automatic</th>\n",
       "      <th>is_tranmission_manual</th>\n",
       "      <th>is_tranmission_automated_manual</th>\n",
       "      <th>is_tranmission_direct_drive</th>\n",
       "      <th>is_market_category_crossover</th>\n",
       "      <th>is_market_category_flex_fuel</th>\n",
       "      <th>is_market_category_luxury</th>\n",
       "      <th>is_market_category_luxury,performance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.334665</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.040936</td>\n",
       "      <td>0.092308</td>\n",
       "      <td>0.692131</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.299700</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.046784</td>\n",
       "      <td>0.092308</td>\n",
       "      <td>0.692131</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.299700</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.046784</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.692131</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.229770</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.046784</td>\n",
       "      <td>0.084615</td>\n",
       "      <td>0.692131</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.229770</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.046784</td>\n",
       "      <td>0.084615</td>\n",
       "      <td>0.692131</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  engine_hp  engine_cylinders  number_of_doors  highway_mpg  \\\n",
       "0  0.222222   0.334665             0.375              0.5     0.040936   \n",
       "1  0.222222   0.299700             0.375              0.5     0.046784   \n",
       "2  0.222222   0.299700             0.375              0.5     0.046784   \n",
       "3  0.222222   0.229770             0.375              0.5     0.046784   \n",
       "4  0.222222   0.229770             0.375              0.5     0.046784   \n",
       "\n",
       "   city_mpg  popularity  is_make_chevrolet  is_make_ford  is_make_volkswagen  \\\n",
       "0  0.092308    0.692131                  0             0                   0   \n",
       "1  0.092308    0.692131                  0             0                   0   \n",
       "2  0.100000    0.692131                  0             0                   0   \n",
       "3  0.084615    0.692131                  0             0                   0   \n",
       "4  0.084615    0.692131                  0             0                   0   \n",
       "\n",
       "   ...  is_type_premium_unleaded_(recommended)  \\\n",
       "0  ...                                       0   \n",
       "1  ...                                       0   \n",
       "2  ...                                       0   \n",
       "3  ...                                       0   \n",
       "4  ...                                       0   \n",
       "\n",
       "   is_type_flex-fuel_(unleaded/e85)  is_tranmission_automatic  \\\n",
       "0                                 0                         0   \n",
       "1                                 0                         0   \n",
       "2                                 0                         0   \n",
       "3                                 0                         0   \n",
       "4                                 0                         0   \n",
       "\n",
       "   is_tranmission_manual  is_tranmission_automated_manual  \\\n",
       "0                      1                                0   \n",
       "1                      1                                0   \n",
       "2                      1                                0   \n",
       "3                      1                                0   \n",
       "4                      1                                0   \n",
       "\n",
       "   is_tranmission_direct_drive  is_market_category_crossover  \\\n",
       "0                            0                             0   \n",
       "1                            0                             0   \n",
       "2                            0                             0   \n",
       "3                            0                             0   \n",
       "4                            0                             0   \n",
       "\n",
       "   is_market_category_flex_fuel  is_market_category_luxury  \\\n",
       "0                             0                          0   \n",
       "1                             0                          0   \n",
       "2                             0                          0   \n",
       "3                             0                          0   \n",
       "4                             0                          1   \n",
       "\n",
       "   is_market_category_luxury,performance  \n",
       "0                                      0  \n",
       "1                                      1  \n",
       "2                                      0  \n",
       "3                                      1  \n",
       "4                                      0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "a88ca7d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year                                      0\n",
       "engine_hp                                 0\n",
       "engine_cylinders                          0\n",
       "number_of_doors                           0\n",
       "highway_mpg                               0\n",
       "city_mpg                                  0\n",
       "popularity                                0\n",
       "is_make_chevrolet                         0\n",
       "is_make_ford                              0\n",
       "is_make_volkswagen                        0\n",
       "is_make_toyota                            0\n",
       "is_type_regular_unleaded                  0\n",
       "is_type_premium_unleaded_(required)       0\n",
       "is_type_premium_unleaded_(recommended)    0\n",
       "is_type_flex-fuel_(unleaded/e85)          0\n",
       "is_tranmission_automatic                  0\n",
       "is_tranmission_manual                     0\n",
       "is_tranmission_automated_manual           0\n",
       "is_tranmission_direct_drive               0\n",
       "is_market_category_crossover              0\n",
       "is_market_category_flex_fuel              0\n",
       "is_market_category_luxury                 0\n",
       "is_market_category_luxury,performance     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "3f0fd2c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split #split train and test dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "6d5f7a97",
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(X_train, y_train)\n",
    "y_pred = lin_reg.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "83bfedfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#let see the how good was the prediction\n",
    "plt.figure(figsize=(5,4))\n",
    "\n",
    "sns.histplot(y_train, label='target', color='black',bins=30)\n",
    "sns.histplot(y_pred, label='prediction',color='red', bins=30)\n",
    "plt.legend()\n",
    "plt.xlabel('log(1+price)')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Predictions vs Actual Distribution')\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "bc23ab00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4698198592595307"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_mse = mean_squared_error(y_train, y_pred)\n",
    "lin_rmse = np.sqrt(lin_mse)\n",
    "lin_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "bc8fb8ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4741214391143681"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg_cv_scores = cross_val_score(lin_reg, X_train, y_train, scoring='neg_mean_squared_error', cv = 10)\n",
    "lin_reg_rmse_scores = np.sqrt(-lin_reg_cv_scores)\n",
    "lin_reg_rmse_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "e0d19668",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06479186054514156"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_reg = DecisionTreeRegressor()\n",
    "tree_reg.fit(X_train, y_train)\n",
    "y_pred = tree_reg.predict(X_train)\n",
    "tree_mse = mean_squared_error(y_train, price_predictions)\n",
    "tree_rmse = np.sqrt(tree_mse)\n",
    "tree_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "6077df20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#let see the how good was the prediction\n",
    "plt.figure(figsize=(5,4))\n",
    "\n",
    "sns.histplot(y_train, label='target', color='black',bins=30)\n",
    "sns.histplot(y_pred, label='prediction',color='red', bins=30)\n",
    "plt.legend()\n",
    "plt.xlabel('log(1+price)')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Predictions vs Actual Distribution')\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "0baa5e1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12913530047109556"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_reg_cv_scores = cross_val_score(tree_reg, X_train, y_train, scoring='neg_mean_squared_error', cv = 10)\n",
    "tree_reg_rmse_scores = np.sqrt(-tree_reg_cv_scores)\n",
    "tree_reg_rmse_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "37699435",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07055448055173379"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_reg = RandomForestRegressor()\n",
    "forest_reg.fit(X_train, y_train)\n",
    "y_pred = forest_reg.predict(X_train)\n",
    "forest_mse = mean_squared_error(y_train, price_predictions)\n",
    "forest_rmse = np.sqrt(forest_mse)\n",
    "forest_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "9db46ba3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#let see the how good was the prediction\n",
    "plt.figure(figsize=(5,4))\n",
    "\n",
    "sns.histplot(y_train, label='target', color='black',bins=30)\n",
    "sns.histplot(y_pred, label='prediction',color='red', bins=30)\n",
    "plt.legend()\n",
    "plt.xlabel('log(1+price)')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Predictions vs Actual Distribution')\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "b483d8eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11608487450326206"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_reg_cv_scores = cross_val_score(forest_reg, X_train, y_train, scoring='neg_mean_squared_error', cv = 10)\n",
    "forest_reg_rmse_scores = np.sqrt(-forest_reg_cv_scores)\n",
    "forest_reg_rmse_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "206c28d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=RandomForestRegressor(),\n",
       "             param_grid=[{'max_features': [2, 4, 6, 8],\n",
       "                          'n_estimators': [3, 10, 30]},\n",
       "                         {'bootstrap': [False], 'max_features': [2, 3, 4],\n",
       "                          'n_estimators': [3, 10]}],\n",
       "             return_train_score=True, scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hyper tuning of RF\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [\n",
    "    {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},\n",
    "    {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},\n",
    "  ]\n",
    "\n",
    "forest_reg = RandomForestRegressor()\n",
    "\n",
    "grid_search = GridSearchCV(forest_reg, param_grid, scoring='neg_mean_squared_error', return_train_score=True, cv=10,)\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "97c8141d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.06917725e-01, 1.91350060e-01, 3.23844578e-02, 8.70641228e-03,\n",
       "       3.02720495e-02, 3.18410250e-02, 1.32149858e-02, 7.80214627e-04,\n",
       "       3.92953985e-04, 3.48320159e-04, 5.18421912e-04, 6.25743870e-02,\n",
       "       6.08740516e-02, 5.20277870e-03, 1.21956641e-03, 7.15030612e-03,\n",
       "       3.73911656e-02, 6.66338316e-04, 5.01588797e-05, 1.84877557e-03,\n",
       "       1.40956792e-03, 3.29207467e-03, 1.59420271e-03])"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = grid_search.best_estimator_.feature_importances_\n",
    "feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "fc466286",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11098764417254152"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model = grid_search.best_estimator_\n",
    "final_predictions = final_model.predict(X_test)\n",
    "final_mse = mean_squared_error(y_test, final_predictions)\n",
    "final_rmse = np.sqrt(final_mse)\n",
    "final_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "eb18c26f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,4))\n",
    "\n",
    "sns.histplot(y_test, label='target', color='black',bins=30)\n",
    "sns.histplot(final_predictions, label='prediction',color='red', bins=30)\n",
    "plt.legend()\n",
    "plt.xlabel('log(1+price)')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Predictions vs Actual Distribution of Test data')\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "bf5c6544",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, final_predictions);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7eb7ff5a",
   "metadata": {},
   "source": [
    "# Save the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "f399f17e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle \n",
    "file = open('model.pkl', 'wb') # open a file, where you ant to store the data\n",
    "pickle.dump(final_model, file) #dump information to that file"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}