ml_03_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First analisys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from sklearn import preprocessing as pr\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>...</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "      <th>city-L/100km</th>\n",
       "      <th>horsepower-binned</th>\n",
       "      <th>diesel</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>0.822681</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>0.848630</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>9.791667</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>0.848630</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>13.055556</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  normalized-losses         make aspiration num-of-doors  \\\n",
       "0          3                122  alfa-romero        std          two   \n",
       "1          3                122  alfa-romero        std          two   \n",
       "2          1                122  alfa-romero        std          two   \n",
       "3          2                164         audi        std         four   \n",
       "4          2                164         audi        std         four   \n",
       "\n",
       "    body-style drive-wheels engine-location  wheel-base    length  ...  \\\n",
       "0  convertible          rwd           front        88.6  0.811148  ...   \n",
       "1  convertible          rwd           front        88.6  0.811148  ...   \n",
       "2    hatchback          rwd           front        94.5  0.822681  ...   \n",
       "3        sedan          fwd           front        99.8  0.848630  ...   \n",
       "4        sedan          4wd           front        99.4  0.848630  ...   \n",
       "\n",
       "   compression-ratio  horsepower  peak-rpm city-mpg highway-mpg    price  \\\n",
       "0                9.0       111.0    5000.0       21          27  13495.0   \n",
       "1                9.0       111.0    5000.0       21          27  16500.0   \n",
       "2                9.0       154.0    5000.0       19          26  16500.0   \n",
       "3               10.0       102.0    5500.0       24          30  13950.0   \n",
       "4                8.0       115.0    5500.0       18          22  17450.0   \n",
       "\n",
       "  city-L/100km  horsepower-binned  diesel  gas  \n",
       "0    11.190476             Medium       0    1  \n",
       "1    11.190476             Medium       0    1  \n",
       "2    12.368421             Medium       0    1  \n",
       "3     9.791667             Medium       0    1  \n",
       "4    13.055556             Medium       0    1  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('datasets/auto_clean.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "symboling              int64\n",
      "normalized-losses      int64\n",
      "make                  object\n",
      "aspiration            object\n",
      "num-of-doors          object\n",
      "body-style            object\n",
      "drive-wheels          object\n",
      "engine-location       object\n",
      "wheel-base           float64\n",
      "length               float64\n",
      "width                float64\n",
      "height               float64\n",
      "curb-weight            int64\n",
      "engine-type           object\n",
      "num-of-cylinders      object\n",
      "engine-size            int64\n",
      "fuel-system           object\n",
      "bore                 float64\n",
      "stroke               float64\n",
      "compression-ratio    float64\n",
      "horsepower           float64\n",
      "peak-rpm             float64\n",
      "city-mpg               int64\n",
      "highway-mpg            int64\n",
      "price                float64\n",
      "city-L/100km         float64\n",
      "horsepower-binned     object\n",
      "diesel                 int64\n",
      "gas                    int64\n",
      "dtype: object\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 201 entries, 0 to 200\n",
      "Data columns (total 29 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   symboling          201 non-null    int64  \n",
      " 1   normalized-losses  201 non-null    int64  \n",
      " 2   make               201 non-null    object \n",
      " 3   aspiration         201 non-null    object \n",
      " 4   num-of-doors       201 non-null    object \n",
      " 5   body-style         201 non-null    object \n",
      " 6   drive-wheels       201 non-null    object \n",
      " 7   engine-location    201 non-null    object \n",
      " 8   wheel-base         201 non-null    float64\n",
      " 9   length             201 non-null    float64\n",
      " 10  width              201 non-null    float64\n",
      " 11  height             201 non-null    float64\n",
      " 12  curb-weight        201 non-null    int64  \n",
      " 13  engine-type        201 non-null    object \n",
      " 14  num-of-cylinders   201 non-null    object \n",
      " 15  engine-size        201 non-null    int64  \n",
      " 16  fuel-system        201 non-null    object \n",
      " 17  bore               201 non-null    float64\n",
      " 18  stroke             197 non-null    float64\n",
      " 19  compression-ratio  201 non-null    float64\n",
      " 20  horsepower         201 non-null    float64\n",
      " 21  peak-rpm           201 non-null    float64\n",
      " 22  city-mpg           201 non-null    int64  \n",
      " 23  highway-mpg        201 non-null    int64  \n",
      " 24  price              201 non-null    float64\n",
      " 25  city-L/100km       201 non-null    float64\n",
      " 26  horsepower-binned  200 non-null    object \n",
      " 27  diesel             201 non-null    int64  \n",
      " 28  gas                201 non-null    int64  \n",
      "dtypes: float64(11), int64(8), object(10)\n",
      "memory usage: 45.7+ KB\n"
     ]
    }
   ],
   "source": [
    "print(df.dtypes)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "      <th>city-L/100km</th>\n",
       "      <th>diesel</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.00000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.840796</td>\n",
       "      <td>122.00000</td>\n",
       "      <td>98.797015</td>\n",
       "      <td>0.837102</td>\n",
       "      <td>0.915126</td>\n",
       "      <td>53.766667</td>\n",
       "      <td>2555.666667</td>\n",
       "      <td>126.875622</td>\n",
       "      <td>3.330692</td>\n",
       "      <td>3.256904</td>\n",
       "      <td>10.164279</td>\n",
       "      <td>103.405534</td>\n",
       "      <td>5117.665368</td>\n",
       "      <td>25.179104</td>\n",
       "      <td>30.686567</td>\n",
       "      <td>13207.129353</td>\n",
       "      <td>9.944145</td>\n",
       "      <td>0.099502</td>\n",
       "      <td>0.900498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.254802</td>\n",
       "      <td>31.99625</td>\n",
       "      <td>6.066366</td>\n",
       "      <td>0.059213</td>\n",
       "      <td>0.029187</td>\n",
       "      <td>2.447822</td>\n",
       "      <td>517.296727</td>\n",
       "      <td>41.546834</td>\n",
       "      <td>0.268072</td>\n",
       "      <td>0.319256</td>\n",
       "      <td>4.004965</td>\n",
       "      <td>37.365700</td>\n",
       "      <td>478.113805</td>\n",
       "      <td>6.423220</td>\n",
       "      <td>6.815150</td>\n",
       "      <td>7947.066342</td>\n",
       "      <td>2.534599</td>\n",
       "      <td>0.300083</td>\n",
       "      <td>0.300083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.000000</td>\n",
       "      <td>65.00000</td>\n",
       "      <td>86.600000</td>\n",
       "      <td>0.678039</td>\n",
       "      <td>0.837500</td>\n",
       "      <td>47.800000</td>\n",
       "      <td>1488.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>2.540000</td>\n",
       "      <td>2.070000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>4150.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>5118.000000</td>\n",
       "      <td>4.795918</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>101.00000</td>\n",
       "      <td>94.500000</td>\n",
       "      <td>0.801538</td>\n",
       "      <td>0.890278</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>2169.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>3.150000</td>\n",
       "      <td>3.110000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>4800.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>7775.000000</td>\n",
       "      <td>7.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>122.00000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>0.832292</td>\n",
       "      <td>0.909722</td>\n",
       "      <td>54.100000</td>\n",
       "      <td>2414.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>3.310000</td>\n",
       "      <td>3.290000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>5125.369458</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>10295.000000</td>\n",
       "      <td>9.791667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>137.00000</td>\n",
       "      <td>102.400000</td>\n",
       "      <td>0.881788</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>55.500000</td>\n",
       "      <td>2926.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>3.580000</td>\n",
       "      <td>3.410000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>16500.000000</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>256.00000</td>\n",
       "      <td>120.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>59.800000</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>3.940000</td>\n",
       "      <td>4.170000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>6600.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>45400.000000</td>\n",
       "      <td>18.076923</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        symboling  normalized-losses  wheel-base      length       width  \\\n",
       "count  201.000000          201.00000  201.000000  201.000000  201.000000   \n",
       "mean     0.840796          122.00000   98.797015    0.837102    0.915126   \n",
       "std      1.254802           31.99625    6.066366    0.059213    0.029187   \n",
       "min     -2.000000           65.00000   86.600000    0.678039    0.837500   \n",
       "25%      0.000000          101.00000   94.500000    0.801538    0.890278   \n",
       "50%      1.000000          122.00000   97.000000    0.832292    0.909722   \n",
       "75%      2.000000          137.00000  102.400000    0.881788    0.925000   \n",
       "max      3.000000          256.00000  120.900000    1.000000    1.000000   \n",
       "\n",
       "           height  curb-weight  engine-size        bore      stroke  \\\n",
       "count  201.000000   201.000000   201.000000  201.000000  197.000000   \n",
       "mean    53.766667  2555.666667   126.875622    3.330692    3.256904   \n",
       "std      2.447822   517.296727    41.546834    0.268072    0.319256   \n",
       "min     47.800000  1488.000000    61.000000    2.540000    2.070000   \n",
       "25%     52.000000  2169.000000    98.000000    3.150000    3.110000   \n",
       "50%     54.100000  2414.000000   120.000000    3.310000    3.290000   \n",
       "75%     55.500000  2926.000000   141.000000    3.580000    3.410000   \n",
       "max     59.800000  4066.000000   326.000000    3.940000    4.170000   \n",
       "\n",
       "       compression-ratio  horsepower     peak-rpm    city-mpg  highway-mpg  \\\n",
       "count         201.000000  201.000000   201.000000  201.000000   201.000000   \n",
       "mean           10.164279  103.405534  5117.665368   25.179104    30.686567   \n",
       "std             4.004965   37.365700   478.113805    6.423220     6.815150   \n",
       "min             7.000000   48.000000  4150.000000   13.000000    16.000000   \n",
       "25%             8.600000   70.000000  4800.000000   19.000000    25.000000   \n",
       "50%             9.000000   95.000000  5125.369458   24.000000    30.000000   \n",
       "75%             9.400000  116.000000  5500.000000   30.000000    34.000000   \n",
       "max            23.000000  262.000000  6600.000000   49.000000    54.000000   \n",
       "\n",
       "              price  city-L/100km      diesel         gas  \n",
       "count    201.000000    201.000000  201.000000  201.000000  \n",
       "mean   13207.129353      9.944145    0.099502    0.900498  \n",
       "std     7947.066342      2.534599    0.300083    0.300083  \n",
       "min     5118.000000      4.795918    0.000000    0.000000  \n",
       "25%     7775.000000      7.833333    0.000000    1.000000  \n",
       "50%    10295.000000      9.791667    0.000000    1.000000  \n",
       "75%    16500.000000     12.368421    0.000000    1.000000  \n",
       "max    45400.000000     18.076923    1.000000    1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "      <th>city-L/100km</th>\n",
       "      <th>diesel</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>symboling</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.466264</td>\n",
       "      <td>-0.535987</td>\n",
       "      <td>-0.365404</td>\n",
       "      <td>-0.242423</td>\n",
       "      <td>-0.550160</td>\n",
       "      <td>-0.233118</td>\n",
       "      <td>-0.110581</td>\n",
       "      <td>-0.140019</td>\n",
       "      <td>-0.008245</td>\n",
       "      <td>-0.182196</td>\n",
       "      <td>0.075819</td>\n",
       "      <td>0.279740</td>\n",
       "      <td>-0.035527</td>\n",
       "      <td>0.036233</td>\n",
       "      <td>-0.082391</td>\n",
       "      <td>0.066171</td>\n",
       "      <td>-0.196735</td>\n",
       "      <td>0.196735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>normalized-losses</th>\n",
       "      <td>0.466264</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.056661</td>\n",
       "      <td>0.019424</td>\n",
       "      <td>0.086802</td>\n",
       "      <td>-0.373737</td>\n",
       "      <td>0.099404</td>\n",
       "      <td>0.112360</td>\n",
       "      <td>-0.029862</td>\n",
       "      <td>0.055563</td>\n",
       "      <td>-0.114713</td>\n",
       "      <td>0.217299</td>\n",
       "      <td>0.239543</td>\n",
       "      <td>-0.225016</td>\n",
       "      <td>-0.181877</td>\n",
       "      <td>0.133999</td>\n",
       "      <td>0.238567</td>\n",
       "      <td>-0.101546</td>\n",
       "      <td>0.101546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wheel-base</th>\n",
       "      <td>-0.535987</td>\n",
       "      <td>-0.056661</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.876024</td>\n",
       "      <td>0.814507</td>\n",
       "      <td>0.590742</td>\n",
       "      <td>0.782097</td>\n",
       "      <td>0.572027</td>\n",
       "      <td>0.493244</td>\n",
       "      <td>0.158502</td>\n",
       "      <td>0.250313</td>\n",
       "      <td>0.371147</td>\n",
       "      <td>-0.360305</td>\n",
       "      <td>-0.470606</td>\n",
       "      <td>-0.543304</td>\n",
       "      <td>0.584642</td>\n",
       "      <td>0.476153</td>\n",
       "      <td>0.307237</td>\n",
       "      <td>-0.307237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>length</th>\n",
       "      <td>-0.365404</td>\n",
       "      <td>0.019424</td>\n",
       "      <td>0.876024</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.857170</td>\n",
       "      <td>0.492063</td>\n",
       "      <td>0.880665</td>\n",
       "      <td>0.685025</td>\n",
       "      <td>0.608971</td>\n",
       "      <td>0.124139</td>\n",
       "      <td>0.159733</td>\n",
       "      <td>0.579821</td>\n",
       "      <td>-0.285970</td>\n",
       "      <td>-0.665192</td>\n",
       "      <td>-0.698142</td>\n",
       "      <td>0.690628</td>\n",
       "      <td>0.657373</td>\n",
       "      <td>0.211187</td>\n",
       "      <td>-0.211187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>width</th>\n",
       "      <td>-0.242423</td>\n",
       "      <td>0.086802</td>\n",
       "      <td>0.814507</td>\n",
       "      <td>0.857170</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.306002</td>\n",
       "      <td>0.866201</td>\n",
       "      <td>0.729436</td>\n",
       "      <td>0.544885</td>\n",
       "      <td>0.188829</td>\n",
       "      <td>0.189867</td>\n",
       "      <td>0.615077</td>\n",
       "      <td>-0.245800</td>\n",
       "      <td>-0.633531</td>\n",
       "      <td>-0.680635</td>\n",
       "      <td>0.751265</td>\n",
       "      <td>0.673363</td>\n",
       "      <td>0.244356</td>\n",
       "      <td>-0.244356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>height</th>\n",
       "      <td>-0.550160</td>\n",
       "      <td>-0.373737</td>\n",
       "      <td>0.590742</td>\n",
       "      <td>0.492063</td>\n",
       "      <td>0.306002</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.307581</td>\n",
       "      <td>0.074694</td>\n",
       "      <td>0.180449</td>\n",
       "      <td>-0.062704</td>\n",
       "      <td>0.259737</td>\n",
       "      <td>-0.087027</td>\n",
       "      <td>-0.309974</td>\n",
       "      <td>-0.049800</td>\n",
       "      <td>-0.104812</td>\n",
       "      <td>0.135486</td>\n",
       "      <td>0.003811</td>\n",
       "      <td>0.281578</td>\n",
       "      <td>-0.281578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>curb-weight</th>\n",
       "      <td>-0.233118</td>\n",
       "      <td>0.099404</td>\n",
       "      <td>0.782097</td>\n",
       "      <td>0.880665</td>\n",
       "      <td>0.866201</td>\n",
       "      <td>0.307581</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.849072</td>\n",
       "      <td>0.644060</td>\n",
       "      <td>0.167562</td>\n",
       "      <td>0.156433</td>\n",
       "      <td>0.757976</td>\n",
       "      <td>-0.279361</td>\n",
       "      <td>-0.749543</td>\n",
       "      <td>-0.794889</td>\n",
       "      <td>0.834415</td>\n",
       "      <td>0.785353</td>\n",
       "      <td>0.221046</td>\n",
       "      <td>-0.221046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine-size</th>\n",
       "      <td>-0.110581</td>\n",
       "      <td>0.112360</td>\n",
       "      <td>0.572027</td>\n",
       "      <td>0.685025</td>\n",
       "      <td>0.729436</td>\n",
       "      <td>0.074694</td>\n",
       "      <td>0.849072</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.572609</td>\n",
       "      <td>0.209523</td>\n",
       "      <td>0.028889</td>\n",
       "      <td>0.822676</td>\n",
       "      <td>-0.256733</td>\n",
       "      <td>-0.650546</td>\n",
       "      <td>-0.679571</td>\n",
       "      <td>0.872335</td>\n",
       "      <td>0.745059</td>\n",
       "      <td>0.070779</td>\n",
       "      <td>-0.070779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bore</th>\n",
       "      <td>-0.140019</td>\n",
       "      <td>-0.029862</td>\n",
       "      <td>0.493244</td>\n",
       "      <td>0.608971</td>\n",
       "      <td>0.544885</td>\n",
       "      <td>0.180449</td>\n",
       "      <td>0.644060</td>\n",
       "      <td>0.572609</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.055390</td>\n",
       "      <td>0.001263</td>\n",
       "      <td>0.566936</td>\n",
       "      <td>-0.267392</td>\n",
       "      <td>-0.582027</td>\n",
       "      <td>-0.591309</td>\n",
       "      <td>0.543155</td>\n",
       "      <td>0.554610</td>\n",
       "      <td>0.054458</td>\n",
       "      <td>-0.054458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stroke</th>\n",
       "      <td>-0.008245</td>\n",
       "      <td>0.055563</td>\n",
       "      <td>0.158502</td>\n",
       "      <td>0.124139</td>\n",
       "      <td>0.188829</td>\n",
       "      <td>-0.062704</td>\n",
       "      <td>0.167562</td>\n",
       "      <td>0.209523</td>\n",
       "      <td>-0.055390</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.187923</td>\n",
       "      <td>0.098462</td>\n",
       "      <td>-0.065713</td>\n",
       "      <td>-0.034696</td>\n",
       "      <td>-0.035201</td>\n",
       "      <td>0.082310</td>\n",
       "      <td>0.037300</td>\n",
       "      <td>0.241303</td>\n",
       "      <td>-0.241303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compression-ratio</th>\n",
       "      <td>-0.182196</td>\n",
       "      <td>-0.114713</td>\n",
       "      <td>0.250313</td>\n",
       "      <td>0.159733</td>\n",
       "      <td>0.189867</td>\n",
       "      <td>0.259737</td>\n",
       "      <td>0.156433</td>\n",
       "      <td>0.028889</td>\n",
       "      <td>0.001263</td>\n",
       "      <td>0.187923</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.214514</td>\n",
       "      <td>-0.435780</td>\n",
       "      <td>0.331425</td>\n",
       "      <td>0.268465</td>\n",
       "      <td>0.071107</td>\n",
       "      <td>-0.299372</td>\n",
       "      <td>0.985231</td>\n",
       "      <td>-0.985231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>horsepower</th>\n",
       "      <td>0.075819</td>\n",
       "      <td>0.217299</td>\n",
       "      <td>0.371147</td>\n",
       "      <td>0.579821</td>\n",
       "      <td>0.615077</td>\n",
       "      <td>-0.087027</td>\n",
       "      <td>0.757976</td>\n",
       "      <td>0.822676</td>\n",
       "      <td>0.566936</td>\n",
       "      <td>0.098462</td>\n",
       "      <td>-0.214514</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.107885</td>\n",
       "      <td>-0.822214</td>\n",
       "      <td>-0.804575</td>\n",
       "      <td>0.809575</td>\n",
       "      <td>0.889488</td>\n",
       "      <td>-0.169053</td>\n",
       "      <td>0.169053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peak-rpm</th>\n",
       "      <td>0.279740</td>\n",
       "      <td>0.239543</td>\n",
       "      <td>-0.360305</td>\n",
       "      <td>-0.285970</td>\n",
       "      <td>-0.245800</td>\n",
       "      <td>-0.309974</td>\n",
       "      <td>-0.279361</td>\n",
       "      <td>-0.256733</td>\n",
       "      <td>-0.267392</td>\n",
       "      <td>-0.065713</td>\n",
       "      <td>-0.435780</td>\n",
       "      <td>0.107885</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.115413</td>\n",
       "      <td>-0.058598</td>\n",
       "      <td>-0.101616</td>\n",
       "      <td>0.115830</td>\n",
       "      <td>-0.475812</td>\n",
       "      <td>0.475812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city-mpg</th>\n",
       "      <td>-0.035527</td>\n",
       "      <td>-0.225016</td>\n",
       "      <td>-0.470606</td>\n",
       "      <td>-0.665192</td>\n",
       "      <td>-0.633531</td>\n",
       "      <td>-0.049800</td>\n",
       "      <td>-0.749543</td>\n",
       "      <td>-0.650546</td>\n",
       "      <td>-0.582027</td>\n",
       "      <td>-0.034696</td>\n",
       "      <td>0.331425</td>\n",
       "      <td>-0.822214</td>\n",
       "      <td>-0.115413</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.972044</td>\n",
       "      <td>-0.686571</td>\n",
       "      <td>-0.949713</td>\n",
       "      <td>0.265676</td>\n",
       "      <td>-0.265676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>highway-mpg</th>\n",
       "      <td>0.036233</td>\n",
       "      <td>-0.181877</td>\n",
       "      <td>-0.543304</td>\n",
       "      <td>-0.698142</td>\n",
       "      <td>-0.680635</td>\n",
       "      <td>-0.104812</td>\n",
       "      <td>-0.794889</td>\n",
       "      <td>-0.679571</td>\n",
       "      <td>-0.591309</td>\n",
       "      <td>-0.035201</td>\n",
       "      <td>0.268465</td>\n",
       "      <td>-0.804575</td>\n",
       "      <td>-0.058598</td>\n",
       "      <td>0.972044</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.704692</td>\n",
       "      <td>-0.930028</td>\n",
       "      <td>0.198690</td>\n",
       "      <td>-0.198690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>-0.082391</td>\n",
       "      <td>0.133999</td>\n",
       "      <td>0.584642</td>\n",
       "      <td>0.690628</td>\n",
       "      <td>0.751265</td>\n",
       "      <td>0.135486</td>\n",
       "      <td>0.834415</td>\n",
       "      <td>0.872335</td>\n",
       "      <td>0.543155</td>\n",
       "      <td>0.082310</td>\n",
       "      <td>0.071107</td>\n",
       "      <td>0.809575</td>\n",
       "      <td>-0.101616</td>\n",
       "      <td>-0.686571</td>\n",
       "      <td>-0.704692</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.789898</td>\n",
       "      <td>0.110326</td>\n",
       "      <td>-0.110326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city-L/100km</th>\n",
       "      <td>0.066171</td>\n",
       "      <td>0.238567</td>\n",
       "      <td>0.476153</td>\n",
       "      <td>0.657373</td>\n",
       "      <td>0.673363</td>\n",
       "      <td>0.003811</td>\n",
       "      <td>0.785353</td>\n",
       "      <td>0.745059</td>\n",
       "      <td>0.554610</td>\n",
       "      <td>0.037300</td>\n",
       "      <td>-0.299372</td>\n",
       "      <td>0.889488</td>\n",
       "      <td>0.115830</td>\n",
       "      <td>-0.949713</td>\n",
       "      <td>-0.930028</td>\n",
       "      <td>0.789898</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.241282</td>\n",
       "      <td>0.241282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diesel</th>\n",
       "      <td>-0.196735</td>\n",
       "      <td>-0.101546</td>\n",
       "      <td>0.307237</td>\n",
       "      <td>0.211187</td>\n",
       "      <td>0.244356</td>\n",
       "      <td>0.281578</td>\n",
       "      <td>0.221046</td>\n",
       "      <td>0.070779</td>\n",
       "      <td>0.054458</td>\n",
       "      <td>0.241303</td>\n",
       "      <td>0.985231</td>\n",
       "      <td>-0.169053</td>\n",
       "      <td>-0.475812</td>\n",
       "      <td>0.265676</td>\n",
       "      <td>0.198690</td>\n",
       "      <td>0.110326</td>\n",
       "      <td>-0.241282</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gas</th>\n",
       "      <td>0.196735</td>\n",
       "      <td>0.101546</td>\n",
       "      <td>-0.307237</td>\n",
       "      <td>-0.211187</td>\n",
       "      <td>-0.244356</td>\n",
       "      <td>-0.281578</td>\n",
       "      <td>-0.221046</td>\n",
       "      <td>-0.070779</td>\n",
       "      <td>-0.054458</td>\n",
       "      <td>-0.241303</td>\n",
       "      <td>-0.985231</td>\n",
       "      <td>0.169053</td>\n",
       "      <td>0.475812</td>\n",
       "      <td>-0.265676</td>\n",
       "      <td>-0.198690</td>\n",
       "      <td>-0.110326</td>\n",
       "      <td>0.241282</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   symboling  normalized-losses  wheel-base    length  \\\n",
       "symboling           1.000000           0.466264   -0.535987 -0.365404   \n",
       "normalized-losses   0.466264           1.000000   -0.056661  0.019424   \n",
       "wheel-base         -0.535987          -0.056661    1.000000  0.876024   \n",
       "length             -0.365404           0.019424    0.876024  1.000000   \n",
       "width              -0.242423           0.086802    0.814507  0.857170   \n",
       "height             -0.550160          -0.373737    0.590742  0.492063   \n",
       "curb-weight        -0.233118           0.099404    0.782097  0.880665   \n",
       "engine-size        -0.110581           0.112360    0.572027  0.685025   \n",
       "bore               -0.140019          -0.029862    0.493244  0.608971   \n",
       "stroke             -0.008245           0.055563    0.158502  0.124139   \n",
       "compression-ratio  -0.182196          -0.114713    0.250313  0.159733   \n",
       "horsepower          0.075819           0.217299    0.371147  0.579821   \n",
       "peak-rpm            0.279740           0.239543   -0.360305 -0.285970   \n",
       "city-mpg           -0.035527          -0.225016   -0.470606 -0.665192   \n",
       "highway-mpg         0.036233          -0.181877   -0.543304 -0.698142   \n",
       "price              -0.082391           0.133999    0.584642  0.690628   \n",
       "city-L/100km        0.066171           0.238567    0.476153  0.657373   \n",
       "diesel             -0.196735          -0.101546    0.307237  0.211187   \n",
       "gas                 0.196735           0.101546   -0.307237 -0.211187   \n",
       "\n",
       "                      width    height  curb-weight  engine-size      bore  \\\n",
       "symboling         -0.242423 -0.550160    -0.233118    -0.110581 -0.140019   \n",
       "normalized-losses  0.086802 -0.373737     0.099404     0.112360 -0.029862   \n",
       "wheel-base         0.814507  0.590742     0.782097     0.572027  0.493244   \n",
       "length             0.857170  0.492063     0.880665     0.685025  0.608971   \n",
       "width              1.000000  0.306002     0.866201     0.729436  0.544885   \n",
       "height             0.306002  1.000000     0.307581     0.074694  0.180449   \n",
       "curb-weight        0.866201  0.307581     1.000000     0.849072  0.644060   \n",
       "engine-size        0.729436  0.074694     0.849072     1.000000  0.572609   \n",
       "bore               0.544885  0.180449     0.644060     0.572609  1.000000   \n",
       "stroke             0.188829 -0.062704     0.167562     0.209523 -0.055390   \n",
       "compression-ratio  0.189867  0.259737     0.156433     0.028889  0.001263   \n",
       "horsepower         0.615077 -0.087027     0.757976     0.822676  0.566936   \n",
       "peak-rpm          -0.245800 -0.309974    -0.279361    -0.256733 -0.267392   \n",
       "city-mpg          -0.633531 -0.049800    -0.749543    -0.650546 -0.582027   \n",
       "highway-mpg       -0.680635 -0.104812    -0.794889    -0.679571 -0.591309   \n",
       "price              0.751265  0.135486     0.834415     0.872335  0.543155   \n",
       "city-L/100km       0.673363  0.003811     0.785353     0.745059  0.554610   \n",
       "diesel             0.244356  0.281578     0.221046     0.070779  0.054458   \n",
       "gas               -0.244356 -0.281578    -0.221046    -0.070779 -0.054458   \n",
       "\n",
       "                     stroke  compression-ratio  horsepower  peak-rpm  \\\n",
       "symboling         -0.008245          -0.182196    0.075819  0.279740   \n",
       "normalized-losses  0.055563          -0.114713    0.217299  0.239543   \n",
       "wheel-base         0.158502           0.250313    0.371147 -0.360305   \n",
       "length             0.124139           0.159733    0.579821 -0.285970   \n",
       "width              0.188829           0.189867    0.615077 -0.245800   \n",
       "height            -0.062704           0.259737   -0.087027 -0.309974   \n",
       "curb-weight        0.167562           0.156433    0.757976 -0.279361   \n",
       "engine-size        0.209523           0.028889    0.822676 -0.256733   \n",
       "bore              -0.055390           0.001263    0.566936 -0.267392   \n",
       "stroke             1.000000           0.187923    0.098462 -0.065713   \n",
       "compression-ratio  0.187923           1.000000   -0.214514 -0.435780   \n",
       "horsepower         0.098462          -0.214514    1.000000  0.107885   \n",
       "peak-rpm          -0.065713          -0.435780    0.107885  1.000000   \n",
       "city-mpg          -0.034696           0.331425   -0.822214 -0.115413   \n",
       "highway-mpg       -0.035201           0.268465   -0.804575 -0.058598   \n",
       "price              0.082310           0.071107    0.809575 -0.101616   \n",
       "city-L/100km       0.037300          -0.299372    0.889488  0.115830   \n",
       "diesel             0.241303           0.985231   -0.169053 -0.475812   \n",
       "gas               -0.241303          -0.985231    0.169053  0.475812   \n",
       "\n",
       "                   city-mpg  highway-mpg     price  city-L/100km    diesel  \\\n",
       "symboling         -0.035527     0.036233 -0.082391      0.066171 -0.196735   \n",
       "normalized-losses -0.225016    -0.181877  0.133999      0.238567 -0.101546   \n",
       "wheel-base        -0.470606    -0.543304  0.584642      0.476153  0.307237   \n",
       "length            -0.665192    -0.698142  0.690628      0.657373  0.211187   \n",
       "width             -0.633531    -0.680635  0.751265      0.673363  0.244356   \n",
       "height            -0.049800    -0.104812  0.135486      0.003811  0.281578   \n",
       "curb-weight       -0.749543    -0.794889  0.834415      0.785353  0.221046   \n",
       "engine-size       -0.650546    -0.679571  0.872335      0.745059  0.070779   \n",
       "bore              -0.582027    -0.591309  0.543155      0.554610  0.054458   \n",
       "stroke            -0.034696    -0.035201  0.082310      0.037300  0.241303   \n",
       "compression-ratio  0.331425     0.268465  0.071107     -0.299372  0.985231   \n",
       "horsepower        -0.822214    -0.804575  0.809575      0.889488 -0.169053   \n",
       "peak-rpm          -0.115413    -0.058598 -0.101616      0.115830 -0.475812   \n",
       "city-mpg           1.000000     0.972044 -0.686571     -0.949713  0.265676   \n",
       "highway-mpg        0.972044     1.000000 -0.704692     -0.930028  0.198690   \n",
       "price             -0.686571    -0.704692  1.000000      0.789898  0.110326   \n",
       "city-L/100km      -0.949713    -0.930028  0.789898      1.000000 -0.241282   \n",
       "diesel             0.265676     0.198690  0.110326     -0.241282  1.000000   \n",
       "gas               -0.265676    -0.198690 -0.110326      0.241282 -1.000000   \n",
       "\n",
       "                        gas  \n",
       "symboling          0.196735  \n",
       "normalized-losses  0.101546  \n",
       "wheel-base        -0.307237  \n",
       "length            -0.211187  \n",
       "width             -0.244356  \n",
       "height            -0.281578  \n",
       "curb-weight       -0.221046  \n",
       "engine-size       -0.070779  \n",
       "bore              -0.054458  \n",
       "stroke            -0.241303  \n",
       "compression-ratio -0.985231  \n",
       "horsepower         0.169053  \n",
       "peak-rpm           0.475812  \n",
       "city-mpg          -0.265676  \n",
       "highway-mpg       -0.198690  \n",
       "price             -0.110326  \n",
       "city-L/100km       0.241282  \n",
       "diesel            -1.000000  \n",
       "gas                1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_ = df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "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(corr_, annot=True, fmt='.2f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wheel-base length 0.8760238919618005\n",
      "wheel-base width 0.8145066522681035\n",
      "wheel-base curb-weight 0.782097244415664\n",
      "engine-size horsepower 0.8226756460813216\n",
      "engine-size price 0.8723351674455182\n",
      "engine-size city-L/100km 0.7450588874770459\n",
      "compression-ratio diesel 0.9852311290083917\n",
      "city-mpg highway-mpg 0.9720437058960102\n"
     ]
    }
   ],
   "source": [
    "CorField = []\n",
    "for i in corr_:\n",
    "    for j in corr_.index[corr_[i] > 0.7]:\n",
    "        if i != j and j not in CorField and i not in CorField:\n",
    "            CorField.append(j)\n",
    "            print (i,j, corr_[i][corr_.index==j].values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bore</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.055390</td>\n",
       "      <td>0.001263</td>\n",
       "      <td>0.566936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stroke</th>\n",
       "      <td>-0.055390</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.187923</td>\n",
       "      <td>0.098462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compression-ratio</th>\n",
       "      <td>0.001263</td>\n",
       "      <td>0.187923</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.214514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>horsepower</th>\n",
       "      <td>0.566936</td>\n",
       "      <td>0.098462</td>\n",
       "      <td>-0.214514</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       bore    stroke  compression-ratio  horsepower\n",
       "bore               1.000000 -0.055390           0.001263    0.566936\n",
       "stroke            -0.055390  1.000000           0.187923    0.098462\n",
       "compression-ratio  0.001263  0.187923           1.000000   -0.214514\n",
       "horsepower         0.566936  0.098462          -0.214514    1.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_ = df[['bore', 'stroke', 'compression-ratio', 'horsepower']].corr()\n",
    "corr_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 53088.41900446395)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.regplot(x='engine-size', y='price', data=df)\n",
    "plt.ylim(0,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='highway-mpg', ylabel='price'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.regplot(x='highway-mpg', y='price', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>highway-mpg</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.704692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>-0.704692</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             highway-mpg     price\n",
       "highway-mpg     1.000000 -0.704692\n",
       "price          -0.704692  1.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['highway-mpg', 'price']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='peak-rpm', ylabel='price'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.regplot(x='peak-rpm', y='price', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>peak-rpm</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>peak-rpm</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>-0.101616</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          peak-rpm     price\n",
       "peak-rpm  1.000000 -0.101616\n",
       "price    -0.101616  1.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['peak-rpm', 'price']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='stroke', ylabel='price'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.regplot(x='stroke', y='price', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>stroke</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>stroke</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.08231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>0.08231</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         stroke    price\n",
       "stroke  1.00000  0.08231\n",
       "price   0.08231  1.00000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['stroke', 'price']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='body-style', ylabel='price'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.boxplot(x='body-style', y='price', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='engine-location', ylabel='price'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.boxplot(x='engine-location', y='price', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='drive-wheels', ylabel='price'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.boxplot(x='drive-wheels', y='price', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "      <th>city-L/100km</th>\n",
       "      <th>diesel</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.00000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.840796</td>\n",
       "      <td>122.00000</td>\n",
       "      <td>98.797015</td>\n",
       "      <td>0.837102</td>\n",
       "      <td>0.915126</td>\n",
       "      <td>53.766667</td>\n",
       "      <td>2555.666667</td>\n",
       "      <td>126.875622</td>\n",
       "      <td>3.330692</td>\n",
       "      <td>3.256904</td>\n",
       "      <td>10.164279</td>\n",
       "      <td>103.405534</td>\n",
       "      <td>5117.665368</td>\n",
       "      <td>25.179104</td>\n",
       "      <td>30.686567</td>\n",
       "      <td>13207.129353</td>\n",
       "      <td>9.944145</td>\n",
       "      <td>0.099502</td>\n",
       "      <td>0.900498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.254802</td>\n",
       "      <td>31.99625</td>\n",
       "      <td>6.066366</td>\n",
       "      <td>0.059213</td>\n",
       "      <td>0.029187</td>\n",
       "      <td>2.447822</td>\n",
       "      <td>517.296727</td>\n",
       "      <td>41.546834</td>\n",
       "      <td>0.268072</td>\n",
       "      <td>0.319256</td>\n",
       "      <td>4.004965</td>\n",
       "      <td>37.365700</td>\n",
       "      <td>478.113805</td>\n",
       "      <td>6.423220</td>\n",
       "      <td>6.815150</td>\n",
       "      <td>7947.066342</td>\n",
       "      <td>2.534599</td>\n",
       "      <td>0.300083</td>\n",
       "      <td>0.300083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.000000</td>\n",
       "      <td>65.00000</td>\n",
       "      <td>86.600000</td>\n",
       "      <td>0.678039</td>\n",
       "      <td>0.837500</td>\n",
       "      <td>47.800000</td>\n",
       "      <td>1488.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>2.540000</td>\n",
       "      <td>2.070000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>4150.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>5118.000000</td>\n",
       "      <td>4.795918</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>101.00000</td>\n",
       "      <td>94.500000</td>\n",
       "      <td>0.801538</td>\n",
       "      <td>0.890278</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>2169.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>3.150000</td>\n",
       "      <td>3.110000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>4800.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>7775.000000</td>\n",
       "      <td>7.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>122.00000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>0.832292</td>\n",
       "      <td>0.909722</td>\n",
       "      <td>54.100000</td>\n",
       "      <td>2414.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>3.310000</td>\n",
       "      <td>3.290000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>5125.369458</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>10295.000000</td>\n",
       "      <td>9.791667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>137.00000</td>\n",
       "      <td>102.400000</td>\n",
       "      <td>0.881788</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>55.500000</td>\n",
       "      <td>2926.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>3.580000</td>\n",
       "      <td>3.410000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>16500.000000</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>256.00000</td>\n",
       "      <td>120.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>59.800000</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>3.940000</td>\n",
       "      <td>4.170000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>6600.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>45400.000000</td>\n",
       "      <td>18.076923</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        symboling  normalized-losses  wheel-base      length       width  \\\n",
       "count  201.000000          201.00000  201.000000  201.000000  201.000000   \n",
       "mean     0.840796          122.00000   98.797015    0.837102    0.915126   \n",
       "std      1.254802           31.99625    6.066366    0.059213    0.029187   \n",
       "min     -2.000000           65.00000   86.600000    0.678039    0.837500   \n",
       "25%      0.000000          101.00000   94.500000    0.801538    0.890278   \n",
       "50%      1.000000          122.00000   97.000000    0.832292    0.909722   \n",
       "75%      2.000000          137.00000  102.400000    0.881788    0.925000   \n",
       "max      3.000000          256.00000  120.900000    1.000000    1.000000   \n",
       "\n",
       "           height  curb-weight  engine-size        bore      stroke  \\\n",
       "count  201.000000   201.000000   201.000000  201.000000  197.000000   \n",
       "mean    53.766667  2555.666667   126.875622    3.330692    3.256904   \n",
       "std      2.447822   517.296727    41.546834    0.268072    0.319256   \n",
       "min     47.800000  1488.000000    61.000000    2.540000    2.070000   \n",
       "25%     52.000000  2169.000000    98.000000    3.150000    3.110000   \n",
       "50%     54.100000  2414.000000   120.000000    3.310000    3.290000   \n",
       "75%     55.500000  2926.000000   141.000000    3.580000    3.410000   \n",
       "max     59.800000  4066.000000   326.000000    3.940000    4.170000   \n",
       "\n",
       "       compression-ratio  horsepower     peak-rpm    city-mpg  highway-mpg  \\\n",
       "count         201.000000  201.000000   201.000000  201.000000   201.000000   \n",
       "mean           10.164279  103.405534  5117.665368   25.179104    30.686567   \n",
       "std             4.004965   37.365700   478.113805    6.423220     6.815150   \n",
       "min             7.000000   48.000000  4150.000000   13.000000    16.000000   \n",
       "25%             8.600000   70.000000  4800.000000   19.000000    25.000000   \n",
       "50%             9.000000   95.000000  5125.369458   24.000000    30.000000   \n",
       "75%             9.400000  116.000000  5500.000000   30.000000    34.000000   \n",
       "max            23.000000  262.000000  6600.000000   49.000000    54.000000   \n",
       "\n",
       "              price  city-L/100km      diesel         gas  \n",
       "count    201.000000    201.000000  201.000000  201.000000  \n",
       "mean   13207.129353      9.944145    0.099502    0.900498  \n",
       "std     7947.066342      2.534599    0.300083    0.300083  \n",
       "min     5118.000000      4.795918    0.000000    0.000000  \n",
       "25%     7775.000000      7.833333    0.000000    1.000000  \n",
       "50%    10295.000000      9.791667    0.000000    1.000000  \n",
       "75%    16500.000000     12.368421    0.000000    1.000000  \n",
       "max    45400.000000     18.076923    1.000000    1.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>make</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>engine-type</th>\n",
       "      <th>num-of-cylinders</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>horsepower-binned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>toyota</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>32</td>\n",
       "      <td>165</td>\n",
       "      <td>115</td>\n",
       "      <td>94</td>\n",
       "      <td>118</td>\n",
       "      <td>198</td>\n",
       "      <td>145</td>\n",
       "      <td>157</td>\n",
       "      <td>92</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          make aspiration num-of-doors body-style drive-wheels  \\\n",
       "count      201        201          201        201          201   \n",
       "unique      22          2            2          5            3   \n",
       "top     toyota        std         four      sedan          fwd   \n",
       "freq        32        165          115         94          118   \n",
       "\n",
       "       engine-location engine-type num-of-cylinders fuel-system  \\\n",
       "count              201         201              201         201   \n",
       "unique               2           6                7           8   \n",
       "top              front         ohc             four        mpfi   \n",
       "freq               198         145              157          92   \n",
       "\n",
       "       horsepower-binned  \n",
       "count                200  \n",
       "unique                 3  \n",
       "top                  Low  \n",
       "freq                 115  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>drive-wheels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fwd</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rwd</th>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4wd</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     drive-wheels\n",
       "fwd           118\n",
       "rwd            75\n",
       "4wd             8"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['drive-wheels'].value_counts().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "drive_wheel_counts = df['drive-wheels'].value_counts().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\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>drive-wheels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fwd</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rwd</th>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4wd</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     drive-wheels\n",
       "fwd           118\n",
       "rwd            75\n",
       "4wd             8"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drive_wheel_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\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>engine-location</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine-location</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>front</th>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rear</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 engine-location\n",
       "engine-location                 \n",
       "front                        198\n",
       "rear                           3"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "engine_loc_counts = df['engine-location'].value_counts().to_frame()\n",
    "engine_loc_counts.rename(columns={'engine-localtion': 'value_counts'}, inplace=True)\n",
    "engine_loc_counts.index.name = 'engine-location'\n",
    "engine_loc_counts.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['rwd', 'fwd', '4wd'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['drive-wheels'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_group_one = df[['drive-wheels', 'body-style', 'price']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>drive-wheels</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4wd</td>\n",
       "      <td>10241.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fwd</td>\n",
       "      <td>9244.779661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rwd</td>\n",
       "      <td>19757.613333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  drive-wheels         price\n",
       "0          4wd  10241.000000\n",
       "1          fwd   9244.779661\n",
       "2          rwd  19757.613333"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group_one = df_group_one.groupby(['drive-wheels'], as_index=False).mean()\n",
    "df_group_one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\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>drive-wheels</th>\n",
       "      <th>body-style</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4wd</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>7603.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4wd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>12647.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4wd</td>\n",
       "      <td>wagon</td>\n",
       "      <td>9095.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fwd</td>\n",
       "      <td>convertible</td>\n",
       "      <td>11595.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fwd</td>\n",
       "      <td>hardtop</td>\n",
       "      <td>8249.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fwd</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>8396.387755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>fwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>9811.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>fwd</td>\n",
       "      <td>wagon</td>\n",
       "      <td>9997.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>rwd</td>\n",
       "      <td>convertible</td>\n",
       "      <td>23949.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>rwd</td>\n",
       "      <td>hardtop</td>\n",
       "      <td>24202.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>rwd</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>14337.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>rwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>21711.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>rwd</td>\n",
       "      <td>wagon</td>\n",
       "      <td>16994.222222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   drive-wheels   body-style         price\n",
       "0           4wd    hatchback   7603.000000\n",
       "1           4wd        sedan  12647.333333\n",
       "2           4wd        wagon   9095.750000\n",
       "3           fwd  convertible  11595.000000\n",
       "4           fwd      hardtop   8249.000000\n",
       "5           fwd    hatchback   8396.387755\n",
       "6           fwd        sedan   9811.800000\n",
       "7           fwd        wagon   9997.333333\n",
       "8           rwd  convertible  23949.600000\n",
       "9           rwd      hardtop  24202.714286\n",
       "10          rwd    hatchback  14337.777778\n",
       "11          rwd        sedan  21711.833333\n",
       "12          rwd        wagon  16994.222222"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gptest = df[['drive-wheels', 'body-style', 'price']]\n",
    "grouped_test1 = df_gptest.groupby(['drive-wheels', 'body-style'], as_index=False).mean()\n",
    "grouped_test1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>body-style</th>\n",
       "      <th>convertible</th>\n",
       "      <th>hardtop</th>\n",
       "      <th>hatchback</th>\n",
       "      <th>sedan</th>\n",
       "      <th>wagon</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drive-wheels</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4wd</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7603.000000</td>\n",
       "      <td>12647.333333</td>\n",
       "      <td>9095.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fwd</th>\n",
       "      <td>11595.0</td>\n",
       "      <td>8249.000000</td>\n",
       "      <td>8396.387755</td>\n",
       "      <td>9811.800000</td>\n",
       "      <td>9997.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rwd</th>\n",
       "      <td>23949.6</td>\n",
       "      <td>24202.714286</td>\n",
       "      <td>14337.777778</td>\n",
       "      <td>21711.833333</td>\n",
       "      <td>16994.222222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   price                                            \\\n",
       "body-style   convertible       hardtop     hatchback         sedan   \n",
       "drive-wheels                                                         \n",
       "4wd                  NaN           NaN   7603.000000  12647.333333   \n",
       "fwd              11595.0   8249.000000   8396.387755   9811.800000   \n",
       "rwd              23949.6  24202.714286  14337.777778  21711.833333   \n",
       "\n",
       "                            \n",
       "body-style           wagon  \n",
       "drive-wheels                \n",
       "4wd            9095.750000  \n",
       "fwd            9997.333333  \n",
       "rwd           16994.222222  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_pivot = grouped_test1.pivot(index='drive-wheels', columns='body-style')\n",
    "grouped_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.pcolor(grouped_pivot, cmap='RdBu')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig, ax = plt.subplots()\n",
    "im = ax.pcolor(grouped_pivot, cmap='RdBu')\n",
    "\n",
    "row_labels = grouped_pivot.columns.levels[1]\n",
    "col_labels = grouped_pivot.index\n",
    "\n",
    "ax.set_xticks(np.arange(grouped_pivot.shape[1]) + 0.5, minor=False)\n",
    "ax.set_yticks(np.arange(grouped_pivot.shape[0]) + 0.5, minor=False)\n",
    "\n",
    "ax.set_xticklabels(row_labels, minor=False)\n",
    "ax.set_yticklabels(col_labels, minor=False)\n",
    "\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "fig.colorbar(im)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='None-body-style', ylabel='drive-wheels'>"
      ]
     },
     "execution_count": 33,
     "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(grouped_pivot, annot=True, fmt='.2f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "      <th>city-L/100km</th>\n",
       "      <th>diesel</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>symboling</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.466264</td>\n",
       "      <td>-0.535987</td>\n",
       "      <td>-0.365404</td>\n",
       "      <td>-0.242423</td>\n",
       "      <td>-0.550160</td>\n",
       "      <td>-0.233118</td>\n",
       "      <td>-0.110581</td>\n",
       "      <td>-0.140019</td>\n",
       "      <td>-0.008245</td>\n",
       "      <td>-0.182196</td>\n",
       "      <td>0.075819</td>\n",
       "      <td>0.279740</td>\n",
       "      <td>-0.035527</td>\n",
       "      <td>0.036233</td>\n",
       "      <td>-0.082391</td>\n",
       "      <td>0.066171</td>\n",
       "      <td>-0.196735</td>\n",
       "      <td>0.196735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>normalized-losses</th>\n",
       "      <td>0.466264</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.056661</td>\n",
       "      <td>0.019424</td>\n",
       "      <td>0.086802</td>\n",
       "      <td>-0.373737</td>\n",
       "      <td>0.099404</td>\n",
       "      <td>0.112360</td>\n",
       "      <td>-0.029862</td>\n",
       "      <td>0.055563</td>\n",
       "      <td>-0.114713</td>\n",
       "      <td>0.217299</td>\n",
       "      <td>0.239543</td>\n",
       "      <td>-0.225016</td>\n",
       "      <td>-0.181877</td>\n",
       "      <td>0.133999</td>\n",
       "      <td>0.238567</td>\n",
       "      <td>-0.101546</td>\n",
       "      <td>0.101546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wheel-base</th>\n",
       "      <td>-0.535987</td>\n",
       "      <td>-0.056661</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.876024</td>\n",
       "      <td>0.814507</td>\n",
       "      <td>0.590742</td>\n",
       "      <td>0.782097</td>\n",
       "      <td>0.572027</td>\n",
       "      <td>0.493244</td>\n",
       "      <td>0.158502</td>\n",
       "      <td>0.250313</td>\n",
       "      <td>0.371147</td>\n",
       "      <td>-0.360305</td>\n",
       "      <td>-0.470606</td>\n",
       "      <td>-0.543304</td>\n",
       "      <td>0.584642</td>\n",
       "      <td>0.476153</td>\n",
       "      <td>0.307237</td>\n",
       "      <td>-0.307237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>length</th>\n",
       "      <td>-0.365404</td>\n",
       "      <td>0.019424</td>\n",
       "      <td>0.876024</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.857170</td>\n",
       "      <td>0.492063</td>\n",
       "      <td>0.880665</td>\n",
       "      <td>0.685025</td>\n",
       "      <td>0.608971</td>\n",
       "      <td>0.124139</td>\n",
       "      <td>0.159733</td>\n",
       "      <td>0.579821</td>\n",
       "      <td>-0.285970</td>\n",
       "      <td>-0.665192</td>\n",
       "      <td>-0.698142</td>\n",
       "      <td>0.690628</td>\n",
       "      <td>0.657373</td>\n",
       "      <td>0.211187</td>\n",
       "      <td>-0.211187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>width</th>\n",
       "      <td>-0.242423</td>\n",
       "      <td>0.086802</td>\n",
       "      <td>0.814507</td>\n",
       "      <td>0.857170</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.306002</td>\n",
       "      <td>0.866201</td>\n",
       "      <td>0.729436</td>\n",
       "      <td>0.544885</td>\n",
       "      <td>0.188829</td>\n",
       "      <td>0.189867</td>\n",
       "      <td>0.615077</td>\n",
       "      <td>-0.245800</td>\n",
       "      <td>-0.633531</td>\n",
       "      <td>-0.680635</td>\n",
       "      <td>0.751265</td>\n",
       "      <td>0.673363</td>\n",
       "      <td>0.244356</td>\n",
       "      <td>-0.244356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>height</th>\n",
       "      <td>-0.550160</td>\n",
       "      <td>-0.373737</td>\n",
       "      <td>0.590742</td>\n",
       "      <td>0.492063</td>\n",
       "      <td>0.306002</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.307581</td>\n",
       "      <td>0.074694</td>\n",
       "      <td>0.180449</td>\n",
       "      <td>-0.062704</td>\n",
       "      <td>0.259737</td>\n",
       "      <td>-0.087027</td>\n",
       "      <td>-0.309974</td>\n",
       "      <td>-0.049800</td>\n",
       "      <td>-0.104812</td>\n",
       "      <td>0.135486</td>\n",
       "      <td>0.003811</td>\n",
       "      <td>0.281578</td>\n",
       "      <td>-0.281578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>curb-weight</th>\n",
       "      <td>-0.233118</td>\n",
       "      <td>0.099404</td>\n",
       "      <td>0.782097</td>\n",
       "      <td>0.880665</td>\n",
       "      <td>0.866201</td>\n",
       "      <td>0.307581</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.849072</td>\n",
       "      <td>0.644060</td>\n",
       "      <td>0.167562</td>\n",
       "      <td>0.156433</td>\n",
       "      <td>0.757976</td>\n",
       "      <td>-0.279361</td>\n",
       "      <td>-0.749543</td>\n",
       "      <td>-0.794889</td>\n",
       "      <td>0.834415</td>\n",
       "      <td>0.785353</td>\n",
       "      <td>0.221046</td>\n",
       "      <td>-0.221046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine-size</th>\n",
       "      <td>-0.110581</td>\n",
       "      <td>0.112360</td>\n",
       "      <td>0.572027</td>\n",
       "      <td>0.685025</td>\n",
       "      <td>0.729436</td>\n",
       "      <td>0.074694</td>\n",
       "      <td>0.849072</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.572609</td>\n",
       "      <td>0.209523</td>\n",
       "      <td>0.028889</td>\n",
       "      <td>0.822676</td>\n",
       "      <td>-0.256733</td>\n",
       "      <td>-0.650546</td>\n",
       "      <td>-0.679571</td>\n",
       "      <td>0.872335</td>\n",
       "      <td>0.745059</td>\n",
       "      <td>0.070779</td>\n",
       "      <td>-0.070779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bore</th>\n",
       "      <td>-0.140019</td>\n",
       "      <td>-0.029862</td>\n",
       "      <td>0.493244</td>\n",
       "      <td>0.608971</td>\n",
       "      <td>0.544885</td>\n",
       "      <td>0.180449</td>\n",
       "      <td>0.644060</td>\n",
       "      <td>0.572609</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.055390</td>\n",
       "      <td>0.001263</td>\n",
       "      <td>0.566936</td>\n",
       "      <td>-0.267392</td>\n",
       "      <td>-0.582027</td>\n",
       "      <td>-0.591309</td>\n",
       "      <td>0.543155</td>\n",
       "      <td>0.554610</td>\n",
       "      <td>0.054458</td>\n",
       "      <td>-0.054458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stroke</th>\n",
       "      <td>-0.008245</td>\n",
       "      <td>0.055563</td>\n",
       "      <td>0.158502</td>\n",
       "      <td>0.124139</td>\n",
       "      <td>0.188829</td>\n",
       "      <td>-0.062704</td>\n",
       "      <td>0.167562</td>\n",
       "      <td>0.209523</td>\n",
       "      <td>-0.055390</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.187923</td>\n",
       "      <td>0.098462</td>\n",
       "      <td>-0.065713</td>\n",
       "      <td>-0.034696</td>\n",
       "      <td>-0.035201</td>\n",
       "      <td>0.082310</td>\n",
       "      <td>0.037300</td>\n",
       "      <td>0.241303</td>\n",
       "      <td>-0.241303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compression-ratio</th>\n",
       "      <td>-0.182196</td>\n",
       "      <td>-0.114713</td>\n",
       "      <td>0.250313</td>\n",
       "      <td>0.159733</td>\n",
       "      <td>0.189867</td>\n",
       "      <td>0.259737</td>\n",
       "      <td>0.156433</td>\n",
       "      <td>0.028889</td>\n",
       "      <td>0.001263</td>\n",
       "      <td>0.187923</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.214514</td>\n",
       "      <td>-0.435780</td>\n",
       "      <td>0.331425</td>\n",
       "      <td>0.268465</td>\n",
       "      <td>0.071107</td>\n",
       "      <td>-0.299372</td>\n",
       "      <td>0.985231</td>\n",
       "      <td>-0.985231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>horsepower</th>\n",
       "      <td>0.075819</td>\n",
       "      <td>0.217299</td>\n",
       "      <td>0.371147</td>\n",
       "      <td>0.579821</td>\n",
       "      <td>0.615077</td>\n",
       "      <td>-0.087027</td>\n",
       "      <td>0.757976</td>\n",
       "      <td>0.822676</td>\n",
       "      <td>0.566936</td>\n",
       "      <td>0.098462</td>\n",
       "      <td>-0.214514</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.107885</td>\n",
       "      <td>-0.822214</td>\n",
       "      <td>-0.804575</td>\n",
       "      <td>0.809575</td>\n",
       "      <td>0.889488</td>\n",
       "      <td>-0.169053</td>\n",
       "      <td>0.169053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peak-rpm</th>\n",
       "      <td>0.279740</td>\n",
       "      <td>0.239543</td>\n",
       "      <td>-0.360305</td>\n",
       "      <td>-0.285970</td>\n",
       "      <td>-0.245800</td>\n",
       "      <td>-0.309974</td>\n",
       "      <td>-0.279361</td>\n",
       "      <td>-0.256733</td>\n",
       "      <td>-0.267392</td>\n",
       "      <td>-0.065713</td>\n",
       "      <td>-0.435780</td>\n",
       "      <td>0.107885</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.115413</td>\n",
       "      <td>-0.058598</td>\n",
       "      <td>-0.101616</td>\n",
       "      <td>0.115830</td>\n",
       "      <td>-0.475812</td>\n",
       "      <td>0.475812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city-mpg</th>\n",
       "      <td>-0.035527</td>\n",
       "      <td>-0.225016</td>\n",
       "      <td>-0.470606</td>\n",
       "      <td>-0.665192</td>\n",
       "      <td>-0.633531</td>\n",
       "      <td>-0.049800</td>\n",
       "      <td>-0.749543</td>\n",
       "      <td>-0.650546</td>\n",
       "      <td>-0.582027</td>\n",
       "      <td>-0.034696</td>\n",
       "      <td>0.331425</td>\n",
       "      <td>-0.822214</td>\n",
       "      <td>-0.115413</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.972044</td>\n",
       "      <td>-0.686571</td>\n",
       "      <td>-0.949713</td>\n",
       "      <td>0.265676</td>\n",
       "      <td>-0.265676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>highway-mpg</th>\n",
       "      <td>0.036233</td>\n",
       "      <td>-0.181877</td>\n",
       "      <td>-0.543304</td>\n",
       "      <td>-0.698142</td>\n",
       "      <td>-0.680635</td>\n",
       "      <td>-0.104812</td>\n",
       "      <td>-0.794889</td>\n",
       "      <td>-0.679571</td>\n",
       "      <td>-0.591309</td>\n",
       "      <td>-0.035201</td>\n",
       "      <td>0.268465</td>\n",
       "      <td>-0.804575</td>\n",
       "      <td>-0.058598</td>\n",
       "      <td>0.972044</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.704692</td>\n",
       "      <td>-0.930028</td>\n",
       "      <td>0.198690</td>\n",
       "      <td>-0.198690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>-0.082391</td>\n",
       "      <td>0.133999</td>\n",
       "      <td>0.584642</td>\n",
       "      <td>0.690628</td>\n",
       "      <td>0.751265</td>\n",
       "      <td>0.135486</td>\n",
       "      <td>0.834415</td>\n",
       "      <td>0.872335</td>\n",
       "      <td>0.543155</td>\n",
       "      <td>0.082310</td>\n",
       "      <td>0.071107</td>\n",
       "      <td>0.809575</td>\n",
       "      <td>-0.101616</td>\n",
       "      <td>-0.686571</td>\n",
       "      <td>-0.704692</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.789898</td>\n",
       "      <td>0.110326</td>\n",
       "      <td>-0.110326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city-L/100km</th>\n",
       "      <td>0.066171</td>\n",
       "      <td>0.238567</td>\n",
       "      <td>0.476153</td>\n",
       "      <td>0.657373</td>\n",
       "      <td>0.673363</td>\n",
       "      <td>0.003811</td>\n",
       "      <td>0.785353</td>\n",
       "      <td>0.745059</td>\n",
       "      <td>0.554610</td>\n",
       "      <td>0.037300</td>\n",
       "      <td>-0.299372</td>\n",
       "      <td>0.889488</td>\n",
       "      <td>0.115830</td>\n",
       "      <td>-0.949713</td>\n",
       "      <td>-0.930028</td>\n",
       "      <td>0.789898</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.241282</td>\n",
       "      <td>0.241282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diesel</th>\n",
       "      <td>-0.196735</td>\n",
       "      <td>-0.101546</td>\n",
       "      <td>0.307237</td>\n",
       "      <td>0.211187</td>\n",
       "      <td>0.244356</td>\n",
       "      <td>0.281578</td>\n",
       "      <td>0.221046</td>\n",
       "      <td>0.070779</td>\n",
       "      <td>0.054458</td>\n",
       "      <td>0.241303</td>\n",
       "      <td>0.985231</td>\n",
       "      <td>-0.169053</td>\n",
       "      <td>-0.475812</td>\n",
       "      <td>0.265676</td>\n",
       "      <td>0.198690</td>\n",
       "      <td>0.110326</td>\n",
       "      <td>-0.241282</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gas</th>\n",
       "      <td>0.196735</td>\n",
       "      <td>0.101546</td>\n",
       "      <td>-0.307237</td>\n",
       "      <td>-0.211187</td>\n",
       "      <td>-0.244356</td>\n",
       "      <td>-0.281578</td>\n",
       "      <td>-0.221046</td>\n",
       "      <td>-0.070779</td>\n",
       "      <td>-0.054458</td>\n",
       "      <td>-0.241303</td>\n",
       "      <td>-0.985231</td>\n",
       "      <td>0.169053</td>\n",
       "      <td>0.475812</td>\n",
       "      <td>-0.265676</td>\n",
       "      <td>-0.198690</td>\n",
       "      <td>-0.110326</td>\n",
       "      <td>0.241282</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   symboling  normalized-losses  wheel-base    length  \\\n",
       "symboling           1.000000           0.466264   -0.535987 -0.365404   \n",
       "normalized-losses   0.466264           1.000000   -0.056661  0.019424   \n",
       "wheel-base         -0.535987          -0.056661    1.000000  0.876024   \n",
       "length             -0.365404           0.019424    0.876024  1.000000   \n",
       "width              -0.242423           0.086802    0.814507  0.857170   \n",
       "height             -0.550160          -0.373737    0.590742  0.492063   \n",
       "curb-weight        -0.233118           0.099404    0.782097  0.880665   \n",
       "engine-size        -0.110581           0.112360    0.572027  0.685025   \n",
       "bore               -0.140019          -0.029862    0.493244  0.608971   \n",
       "stroke             -0.008245           0.055563    0.158502  0.124139   \n",
       "compression-ratio  -0.182196          -0.114713    0.250313  0.159733   \n",
       "horsepower          0.075819           0.217299    0.371147  0.579821   \n",
       "peak-rpm            0.279740           0.239543   -0.360305 -0.285970   \n",
       "city-mpg           -0.035527          -0.225016   -0.470606 -0.665192   \n",
       "highway-mpg         0.036233          -0.181877   -0.543304 -0.698142   \n",
       "price              -0.082391           0.133999    0.584642  0.690628   \n",
       "city-L/100km        0.066171           0.238567    0.476153  0.657373   \n",
       "diesel             -0.196735          -0.101546    0.307237  0.211187   \n",
       "gas                 0.196735           0.101546   -0.307237 -0.211187   \n",
       "\n",
       "                      width    height  curb-weight  engine-size      bore  \\\n",
       "symboling         -0.242423 -0.550160    -0.233118    -0.110581 -0.140019   \n",
       "normalized-losses  0.086802 -0.373737     0.099404     0.112360 -0.029862   \n",
       "wheel-base         0.814507  0.590742     0.782097     0.572027  0.493244   \n",
       "length             0.857170  0.492063     0.880665     0.685025  0.608971   \n",
       "width              1.000000  0.306002     0.866201     0.729436  0.544885   \n",
       "height             0.306002  1.000000     0.307581     0.074694  0.180449   \n",
       "curb-weight        0.866201  0.307581     1.000000     0.849072  0.644060   \n",
       "engine-size        0.729436  0.074694     0.849072     1.000000  0.572609   \n",
       "bore               0.544885  0.180449     0.644060     0.572609  1.000000   \n",
       "stroke             0.188829 -0.062704     0.167562     0.209523 -0.055390   \n",
       "compression-ratio  0.189867  0.259737     0.156433     0.028889  0.001263   \n",
       "horsepower         0.615077 -0.087027     0.757976     0.822676  0.566936   \n",
       "peak-rpm          -0.245800 -0.309974    -0.279361    -0.256733 -0.267392   \n",
       "city-mpg          -0.633531 -0.049800    -0.749543    -0.650546 -0.582027   \n",
       "highway-mpg       -0.680635 -0.104812    -0.794889    -0.679571 -0.591309   \n",
       "price              0.751265  0.135486     0.834415     0.872335  0.543155   \n",
       "city-L/100km       0.673363  0.003811     0.785353     0.745059  0.554610   \n",
       "diesel             0.244356  0.281578     0.221046     0.070779  0.054458   \n",
       "gas               -0.244356 -0.281578    -0.221046    -0.070779 -0.054458   \n",
       "\n",
       "                     stroke  compression-ratio  horsepower  peak-rpm  \\\n",
       "symboling         -0.008245          -0.182196    0.075819  0.279740   \n",
       "normalized-losses  0.055563          -0.114713    0.217299  0.239543   \n",
       "wheel-base         0.158502           0.250313    0.371147 -0.360305   \n",
       "length             0.124139           0.159733    0.579821 -0.285970   \n",
       "width              0.188829           0.189867    0.615077 -0.245800   \n",
       "height            -0.062704           0.259737   -0.087027 -0.309974   \n",
       "curb-weight        0.167562           0.156433    0.757976 -0.279361   \n",
       "engine-size        0.209523           0.028889    0.822676 -0.256733   \n",
       "bore              -0.055390           0.001263    0.566936 -0.267392   \n",
       "stroke             1.000000           0.187923    0.098462 -0.065713   \n",
       "compression-ratio  0.187923           1.000000   -0.214514 -0.435780   \n",
       "horsepower         0.098462          -0.214514    1.000000  0.107885   \n",
       "peak-rpm          -0.065713          -0.435780    0.107885  1.000000   \n",
       "city-mpg          -0.034696           0.331425   -0.822214 -0.115413   \n",
       "highway-mpg       -0.035201           0.268465   -0.804575 -0.058598   \n",
       "price              0.082310           0.071107    0.809575 -0.101616   \n",
       "city-L/100km       0.037300          -0.299372    0.889488  0.115830   \n",
       "diesel             0.241303           0.985231   -0.169053 -0.475812   \n",
       "gas               -0.241303          -0.985231    0.169053  0.475812   \n",
       "\n",
       "                   city-mpg  highway-mpg     price  city-L/100km    diesel  \\\n",
       "symboling         -0.035527     0.036233 -0.082391      0.066171 -0.196735   \n",
       "normalized-losses -0.225016    -0.181877  0.133999      0.238567 -0.101546   \n",
       "wheel-base        -0.470606    -0.543304  0.584642      0.476153  0.307237   \n",
       "length            -0.665192    -0.698142  0.690628      0.657373  0.211187   \n",
       "width             -0.633531    -0.680635  0.751265      0.673363  0.244356   \n",
       "height            -0.049800    -0.104812  0.135486      0.003811  0.281578   \n",
       "curb-weight       -0.749543    -0.794889  0.834415      0.785353  0.221046   \n",
       "engine-size       -0.650546    -0.679571  0.872335      0.745059  0.070779   \n",
       "bore              -0.582027    -0.591309  0.543155      0.554610  0.054458   \n",
       "stroke            -0.034696    -0.035201  0.082310      0.037300  0.241303   \n",
       "compression-ratio  0.331425     0.268465  0.071107     -0.299372  0.985231   \n",
       "horsepower        -0.822214    -0.804575  0.809575      0.889488 -0.169053   \n",
       "peak-rpm          -0.115413    -0.058598 -0.101616      0.115830 -0.475812   \n",
       "city-mpg           1.000000     0.972044 -0.686571     -0.949713  0.265676   \n",
       "highway-mpg        0.972044     1.000000 -0.704692     -0.930028  0.198690   \n",
       "price             -0.686571    -0.704692  1.000000      0.789898  0.110326   \n",
       "city-L/100km      -0.949713    -0.930028  0.789898      1.000000 -0.241282   \n",
       "diesel             0.265676     0.198690  0.110326     -0.241282  1.000000   \n",
       "gas               -0.265676    -0.198690 -0.110326      0.241282 -1.000000   \n",
       "\n",
       "                        gas  \n",
       "symboling          0.196735  \n",
       "normalized-losses  0.101546  \n",
       "wheel-base        -0.307237  \n",
       "length            -0.211187  \n",
       "width             -0.244356  \n",
       "height            -0.281578  \n",
       "curb-weight       -0.221046  \n",
       "engine-size       -0.070779  \n",
       "bore              -0.054458  \n",
       "stroke            -0.241303  \n",
       "compression-ratio -0.985231  \n",
       "horsepower         0.169053  \n",
       "peak-rpm           0.475812  \n",
       "city-mpg          -0.265676  \n",
       "highway-mpg       -0.198690  \n",
       "price             -0.110326  \n",
       "city-L/100km       0.241282  \n",
       "diesel            -1.000000  \n",
       "gas                1.000000  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PearsonR между wheel-base и price 0.5846418222655081 c Р-значением Р=8.076488270732989e-20\n",
      "Корреляция между wheel-base и price статистически значимая\n",
      "Коэффициент ~0.5846418222655081 показывает положительную среднюю сильную связь\n",
      "\n",
      "PearsonR между horsepower и price 0.809574567003656 c Р-значением Р=6.369057428259557e-48\n",
      "Корреляция между horsepower и price статистически значимая\n",
      "Коэффициент ~0.809574567003656 показывает положительную достаточно сильную связь\n",
      "\n",
      "PearsonR между length и price 0.690628380448364 c Р-значением Р=8.016477466158986e-30\n",
      "Корреляция между length и price статистически значимая\n",
      "Коэффициент ~0.690628380448364 показывает положительную относительно сильную связь\n",
      "\n",
      "PearsonR между width и price 0.7512653440522672 c Р-значением Р=9.20033551048217e-38\n",
      "Корреляция между width и price статистически значимая\n",
      "Коэффициент ~0.7512653440522672 показывает положительную достаточно сильную связь\n",
      "\n",
      "PearsonR между curb-weight и price 0.8344145257702843 c Р-значением Р=2.189577238894065e-53\n",
      "Корреляция между curb-weight и price статистически значимая\n",
      "Коэффициент ~0.8344145257702843 показывает положительную достаточно сильную связь\n",
      "\n",
      "PearsonR между engine-size и price 0.8723351674455182 c Р-значением Р=9.265491622200232e-64\n",
      "Корреляция между engine-size и price статистически значимая\n",
      "Коэффициент ~0.8723351674455182 показывает положительную очень сильную связь\n",
      "\n",
      "PearsonR между bore и price 0.5431553832626606 c Р-значением Р=8.049189483935032e-17\n",
      "Корреляция между bore и price статистически значимая\n",
      "Коэффициент ~0.5431553832626606 показывает положительную среднюю сильную связь\n",
      "\n",
      "PearsonR между city-mpg и price -0.6865710067844676 c Р-значением Р=2.321132065567707e-29\n",
      "Корреляция между city-mpg и price статистически значимая\n",
      "Коэффициент ~-0.6865710067844676 показывает негативную относительно сильную связь\n",
      "\n",
      "PearsonR между highway-mpg и price -0.7046922650589529 c Р-значением Р=1.7495471144477352e-31\n",
      "Корреляция между highway-mpg и price статистически значимая\n",
      "Коэффициент ~-0.7046922650589529 показывает негативную относительно сильную связь\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cols = ['wheel-base', 'horsepower', 'length', 'width', 'curb-weight', 'engine-size', 'bore', 'city-mpg', 'highway-mpg']\n",
    "for col in cols:\n",
    "    pearson_coef, p_value = stats.pearsonr(df[col], df['price'])\n",
    "    print(f'PearsonR между {col} и price {pearson_coef} c Р-значением Р={p_value}')\n",
    "    \n",
    "    if p_value < 0.001:\n",
    "        print(f'Корреляция между {col} и price статистически значимая')\n",
    "    elif p_value < 0.05:\n",
    "        print(f'Корреляция между {col} и price средняя')\n",
    "    elif p_value < 0.1:\n",
    "        print(f'Корреляция между {col} и price слабая')\n",
    "    \n",
    "    if pearson_coef > 0:\n",
    "        if pearson_coef > 0.85:\n",
    "            print(f'Коэффициент ~{pearson_coef} показывает положительную очень сильную связь\\n')\n",
    "        elif pearson_coef > 0.75:\n",
    "            print(f'Коэффициент ~{pearson_coef} показывает положительную достаточно сильную связь\\n')\n",
    "        elif pearson_coef > 0.60:\n",
    "            print(f'Коэффициент ~{pearson_coef} показывает положительную относительно сильную связь\\n')\n",
    "        elif pearson_coef > 0.50:\n",
    "            print(f'Коэффициент ~{pearson_coef} показывает положительную среднюю сильную связь\\n')\n",
    "        else:\n",
    "            print(f'Коэффициент ~{pearson_coef} показывает положительную слабую связь')\n",
    "    else:\n",
    "        if abs(pearson_coef) > 0.85:\n",
    "            print(f'Коэффициент ~{pearson_coef} показывает негативную очень сильную связь\\n')\n",
    "        elif abs(pearson_coef) > 0.75:\n",
    "            print(f'Коэффициент ~{pearson_coef} показывает негативную достаточно сильную связь\\n')\n",
    "        elif abs(pearson_coef) > 0.60:\n",
    "            print(f'Коэффициент ~{pearson_coef} показывает негативную относительно сильную связь\\n')\n",
    "        elif abs(pearson_coef) > 0.50:\n",
    "            print(f'Коэффициент ~{pearson_coef} показывает положительную среднюю сильную связь\\n')\n",
    "        else:\n",
    "            print(f'Коэффициент ~{pearson_coef} показывает положительную слабую связь')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>drive-wheels</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rwd</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rwd</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fwd</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4wd</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fwd</td>\n",
       "      <td>15250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>4wd</td>\n",
       "      <td>7603.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    drive-wheels    price\n",
       "0            rwd  13495.0\n",
       "1            rwd  16500.0\n",
       "3            fwd  13950.0\n",
       "4            4wd  17450.0\n",
       "5            fwd  15250.0\n",
       "136          4wd   7603.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_test2 = df_gptest[['drive-wheels', 'price']].groupby(['drive-wheels'])\n",
    "grouped_test2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>drive-wheels</th>\n",
       "      <th>body-style</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rwd</td>\n",
       "      <td>convertible</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rwd</td>\n",
       "      <td>convertible</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rwd</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4wd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>rwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>16845.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>rwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>19045.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>rwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>21485.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>rwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>22470.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>rwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>22625.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    drive-wheels   body-style    price\n",
       "0            rwd  convertible  13495.0\n",
       "1            rwd  convertible  16500.0\n",
       "2            rwd    hatchback  16500.0\n",
       "3            fwd        sedan  13950.0\n",
       "4            4wd        sedan  17450.0\n",
       "..           ...          ...      ...\n",
       "196          rwd        sedan  16845.0\n",
       "197          rwd        sedan  19045.0\n",
       "198          rwd        sedan  21485.0\n",
       "199          rwd        sedan  22470.0\n",
       "200          rwd        sedan  22625.0\n",
       "\n",
       "[201 rows x 3 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gptest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4      17450.0\n",
       "136     7603.0\n",
       "140     9233.0\n",
       "141    11259.0\n",
       "144     8013.0\n",
       "145    11694.0\n",
       "150     7898.0\n",
       "151     8778.0\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_test2.get_group('4wd')['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA results: F= 67.95406500780399, P= 3.3945443577151245e-23\n"
     ]
    }
   ],
   "source": [
    "f_val, p_val = stats.f_oneway(grouped_test2.get_group('fwd')['price'],\n",
    "                              grouped_test2.get_group('rwd')['price'],\n",
    "                              grouped_test2.get_group('4wd')['price'])\n",
    "print(f'ANOVA results: F= {f_val}, P= {p_val}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA results: F= 130.5533160959111, P= 2.2355306355677845e-23\n"
     ]
    }
   ],
   "source": [
    "f_val, p_val = stats.f_oneway(grouped_test2.get_group('fwd')['price'], grouped_test2.get_group('rwd')['price'])\n",
    "print(f'ANOVA results: F= {f_val}, P= {p_val}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA results: F= 8.580681368924756, P= 0.004411492211225333\n"
     ]
    }
   ],
   "source": [
    "f_val, p_val = stats.f_oneway(grouped_test2.get_group('4wd')['price'], grouped_test2.get_group('rwd')['price'])\n",
    "print(f'ANOVA results: F= {f_val}, P= {p_val}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA results: F= 0.665465750252303, P= 0.41620116697845666\n"
     ]
    }
   ],
   "source": [
    "f_val, p_val = stats.f_oneway(grouped_test2.get_group('4wd')['price'], grouped_test2.get_group('fwd')['price'])\n",
    "print(f'ANOVA results: F= {f_val}, P= {p_val}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing by sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MultiColumnLabelEncoder:\n",
    "    def __init__(self, columns=None):\n",
    "        self.columns = columns\n",
    "            \n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X):\n",
    "        output = X.copy()\n",
    "        if self.columns is not None:\n",
    "            for col in self.columns:\n",
    "                output[col] = pr.LabelEncoder().fit_transform(output[col].astype(str))\n",
    "        else:\n",
    "            for colname, col in output.iteritems():\n",
    "                output[colname] = pr.LabelEncoder().fit_transform(col.astype(str))\n",
    "        return output\n",
    "    \n",
    "    def fit_transform(self, X, y=None):\n",
    "        return self.fit(X, y).transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 201 entries, 0 to 200\n",
      "Data columns (total 29 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   symboling          201 non-null    int64  \n",
      " 1   normalized-losses  201 non-null    int64  \n",
      " 2   make               201 non-null    object \n",
      " 3   aspiration         201 non-null    object \n",
      " 4   num-of-doors       201 non-null    object \n",
      " 5   body-style         201 non-null    object \n",
      " 6   drive-wheels       201 non-null    object \n",
      " 7   engine-location    201 non-null    object \n",
      " 8   wheel-base         201 non-null    float64\n",
      " 9   length             201 non-null    float64\n",
      " 10  width              201 non-null    float64\n",
      " 11  height             201 non-null    float64\n",
      " 12  curb-weight        201 non-null    int64  \n",
      " 13  engine-type        201 non-null    object \n",
      " 14  num-of-cylinders   201 non-null    object \n",
      " 15  engine-size        201 non-null    int64  \n",
      " 16  fuel-system        201 non-null    object \n",
      " 17  bore               201 non-null    float64\n",
      " 18  stroke             197 non-null    float64\n",
      " 19  compression-ratio  201 non-null    float64\n",
      " 20  horsepower         201 non-null    float64\n",
      " 21  peak-rpm           201 non-null    float64\n",
      " 22  city-mpg           201 non-null    int64  \n",
      " 23  highway-mpg        201 non-null    int64  \n",
      " 24  price              201 non-null    float64\n",
      " 25  city-L/100km       201 non-null    float64\n",
      " 26  horsepower-binned  200 non-null    object \n",
      " 27  diesel             201 non-null    int64  \n",
      " 28  gas                201 non-null    int64  \n",
      "dtypes: float64(11), int64(8), object(10)\n",
      "memory usage: 45.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>...</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "      <th>city-L/100km</th>\n",
       "      <th>horsepower-binned</th>\n",
       "      <th>diesel</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>88.6</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>88.6</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>94.5</td>\n",
       "      <td>0.822681</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>99.8</td>\n",
       "      <td>0.848630</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>9.791667</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.4</td>\n",
       "      <td>0.848630</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>13.055556</td>\n",
       "      <td>2</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",
       "      <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>196</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>109.1</td>\n",
       "      <td>0.907256</td>\n",
       "      <td>...</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>16845.0</td>\n",
       "      <td>10.217391</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>109.1</td>\n",
       "      <td>0.907256</td>\n",
       "      <td>...</td>\n",
       "      <td>8.7</td>\n",
       "      <td>160.0</td>\n",
       "      <td>5300.0</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>19045.0</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>109.1</td>\n",
       "      <td>0.907256</td>\n",
       "      <td>...</td>\n",
       "      <td>8.8</td>\n",
       "      <td>134.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>21485.0</td>\n",
       "      <td>13.055556</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>109.1</td>\n",
       "      <td>0.907256</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>22470.0</td>\n",
       "      <td>9.038462</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>109.1</td>\n",
       "      <td>0.907256</td>\n",
       "      <td>...</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>22625.0</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     symboling  normalized-losses  make  aspiration  num-of-doors  body-style  \\\n",
       "0            3                122     0           0             1           0   \n",
       "1            3                122     0           0             1           0   \n",
       "2            1                122     0           0             1           2   \n",
       "3            2                164     1           0             0           3   \n",
       "4            2                164     1           0             0           3   \n",
       "..         ...                ...   ...         ...           ...         ...   \n",
       "196         -1                 95    21           0             0           3   \n",
       "197         -1                 95    21           1             0           3   \n",
       "198         -1                 95    21           0             0           3   \n",
       "199         -1                 95    21           1             0           3   \n",
       "200         -1                 95    21           1             0           3   \n",
       "\n",
       "     drive-wheels  engine-location  wheel-base    length  ...  \\\n",
       "0               2                0        88.6  0.811148  ...   \n",
       "1               2                0        88.6  0.811148  ...   \n",
       "2               2                0        94.5  0.822681  ...   \n",
       "3               1                0        99.8  0.848630  ...   \n",
       "4               0                0        99.4  0.848630  ...   \n",
       "..            ...              ...         ...       ...  ...   \n",
       "196             2                0       109.1  0.907256  ...   \n",
       "197             2                0       109.1  0.907256  ...   \n",
       "198             2                0       109.1  0.907256  ...   \n",
       "199             2                0       109.1  0.907256  ...   \n",
       "200             2                0       109.1  0.907256  ...   \n",
       "\n",
       "     compression-ratio  horsepower  peak-rpm  city-mpg  highway-mpg    price  \\\n",
       "0                  9.0       111.0    5000.0        21           27  13495.0   \n",
       "1                  9.0       111.0    5000.0        21           27  16500.0   \n",
       "2                  9.0       154.0    5000.0        19           26  16500.0   \n",
       "3                 10.0       102.0    5500.0        24           30  13950.0   \n",
       "4                  8.0       115.0    5500.0        18           22  17450.0   \n",
       "..                 ...         ...       ...       ...          ...      ...   \n",
       "196                9.5       114.0    5400.0        23           28  16845.0   \n",
       "197                8.7       160.0    5300.0        19           25  19045.0   \n",
       "198                8.8       134.0    5500.0        18           23  21485.0   \n",
       "199               23.0       106.0    4800.0        26           27  22470.0   \n",
       "200                9.5       114.0    5400.0        19           25  22625.0   \n",
       "\n",
       "     city-L/100km  horsepower-binned  diesel  gas  \n",
       "0       11.190476                  2       0    1  \n",
       "1       11.190476                  2       0    1  \n",
       "2       12.368421                  2       0    1  \n",
       "3        9.791667                  2       0    1  \n",
       "4       13.055556                  2       0    1  \n",
       "..            ...                ...     ...  ...  \n",
       "196     10.217391                  2       0    1  \n",
       "197     12.368421                  0       0    1  \n",
       "198     13.055556                  2       0    1  \n",
       "199      9.038462                  2       1    0  \n",
       "200     12.368421                  2       0    1  \n",
       "\n",
       "[201 rows x 29 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = MultiColumnLabelEncoder(columns=['make', 'aspiration', 'num-of-doors', 'body-style', 'drive-wheels', 'engine-location', 'engine-type', 'num-of-cylinders', 'fuel-system', 'horsepower-binned']).fit_transform(df)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('price', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=2, random_state=0)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr = RandomForestRegressor(max_depth=2, random_state=0, n_estimators=100)\n",
    "regr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00000000e+00 0.00000000e+00 4.19445982e-04 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 1.59187667e-03 3.29384960e-03 4.92277560e-03 0.00000000e+00\n",
      " 4.28988715e-01 1.22252081e-03 2.32488689e-03 3.83127806e-01\n",
      " 0.00000000e+00 2.08281324e-03 4.45625958e-04 1.53848273e-03\n",
      " 5.89497477e-02 3.74527089e-04 1.37245544e-02 5.84475101e-02\n",
      " 3.85448619e-02 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n"
     ]
    }
   ],
   "source": [
    "print(regr.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>...</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>city-L/100km</th>\n",
       "      <th>horsepower-binned</th>\n",
       "      <th>diesel</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>88.6</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>...</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>88.6</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>...</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>94.5</td>\n",
       "      <td>0.822681</td>\n",
       "      <td>...</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>99.8</td>\n",
       "      <td>0.848630</td>\n",
       "      <td>...</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>9.791667</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.4</td>\n",
       "      <td>0.848630</td>\n",
       "      <td>...</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>13.055556</td>\n",
       "      <td>2</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",
       "      <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>196</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>109.1</td>\n",
       "      <td>0.907256</td>\n",
       "      <td>...</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>10.217391</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>109.1</td>\n",
       "      <td>0.907256</td>\n",
       "      <td>...</td>\n",
       "      <td>3.15</td>\n",
       "      <td>8.7</td>\n",
       "      <td>160.0</td>\n",
       "      <td>5300.0</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>109.1</td>\n",
       "      <td>0.907256</td>\n",
       "      <td>...</td>\n",
       "      <td>2.87</td>\n",
       "      <td>8.8</td>\n",
       "      <td>134.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>13.055556</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>109.1</td>\n",
       "      <td>0.907256</td>\n",
       "      <td>...</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>9.038462</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>109.1</td>\n",
       "      <td>0.907256</td>\n",
       "      <td>...</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     symboling  normalized-losses  make  aspiration  num-of-doors  body-style  \\\n",
       "0            3                122     0           0             1           0   \n",
       "1            3                122     0           0             1           0   \n",
       "2            1                122     0           0             1           2   \n",
       "3            2                164     1           0             0           3   \n",
       "4            2                164     1           0             0           3   \n",
       "..         ...                ...   ...         ...           ...         ...   \n",
       "196         -1                 95    21           0             0           3   \n",
       "197         -1                 95    21           1             0           3   \n",
       "198         -1                 95    21           0             0           3   \n",
       "199         -1                 95    21           1             0           3   \n",
       "200         -1                 95    21           1             0           3   \n",
       "\n",
       "     drive-wheels  engine-location  wheel-base    length  ...  stroke  \\\n",
       "0               2                0        88.6  0.811148  ...    2.68   \n",
       "1               2                0        88.6  0.811148  ...    2.68   \n",
       "2               2                0        94.5  0.822681  ...    3.47   \n",
       "3               1                0        99.8  0.848630  ...    3.40   \n",
       "4               0                0        99.4  0.848630  ...    3.40   \n",
       "..            ...              ...         ...       ...  ...     ...   \n",
       "196             2                0       109.1  0.907256  ...    3.15   \n",
       "197             2                0       109.1  0.907256  ...    3.15   \n",
       "198             2                0       109.1  0.907256  ...    2.87   \n",
       "199             2                0       109.1  0.907256  ...    3.40   \n",
       "200             2                0       109.1  0.907256  ...    3.15   \n",
       "\n",
       "     compression-ratio  horsepower  peak-rpm  city-mpg  highway-mpg  \\\n",
       "0                  9.0       111.0    5000.0        21           27   \n",
       "1                  9.0       111.0    5000.0        21           27   \n",
       "2                  9.0       154.0    5000.0        19           26   \n",
       "3                 10.0       102.0    5500.0        24           30   \n",
       "4                  8.0       115.0    5500.0        18           22   \n",
       "..                 ...         ...       ...       ...          ...   \n",
       "196                9.5       114.0    5400.0        23           28   \n",
       "197                8.7       160.0    5300.0        19           25   \n",
       "198                8.8       134.0    5500.0        18           23   \n",
       "199               23.0       106.0    4800.0        26           27   \n",
       "200                9.5       114.0    5400.0        19           25   \n",
       "\n",
       "     city-L/100km  horsepower-binned  diesel  gas  \n",
       "0       11.190476                  2       0    1  \n",
       "1       11.190476                  2       0    1  \n",
       "2       12.368421                  2       0    1  \n",
       "3        9.791667                  2       0    1  \n",
       "4       13.055556                  2       0    1  \n",
       "..            ...                ...     ...  ...  \n",
       "196     10.217391                  2       0    1  \n",
       "197     12.368421                  0       0    1  \n",
       "198     13.055556                  2       0    1  \n",
       "199      9.038462                  2       1    0  \n",
       "200     12.368421                  2       0    1  \n",
       "\n",
       "[201 rows x 28 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('price', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "importances = list(zip(regr.feature_importances_, df.columns))\n",
    "importances.sort(reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.42898871516993176, 'curb-weight'),\n",
       " (0.3831278061693488, 'engine-size'),\n",
       " (0.05894974774754659, 'horsepower'),\n",
       " (0.058447510065482176, 'highway-mpg'),\n",
       " (0.03854486191196443, 'price'),\n",
       " (0.013724554362176653, 'city-mpg'),\n",
       " (0.004922775600059438, 'width'),\n",
       " (0.0032938496040047552, 'length'),\n",
       " (0.0023248868919002207, 'num-of-cylinders'),\n",
       " (0.0020828132435733717, 'bore'),\n",
       " (0.001591876668266976, 'wheel-base'),\n",
       " (0.001538482728906754, 'compression-ratio'),\n",
       " (0.0012225208084215952, 'engine-type'),\n",
       " (0.00044562595775643455, 'stroke'),\n",
       " (0.0004194459821507219, 'make'),\n",
       " (0.00037452708850942016, 'peak-rpm'),\n",
       " (0.0, 'symboling'),\n",
       " (0.0, 'num-of-doors'),\n",
       " (0.0, 'normalized-losses'),\n",
       " (0.0, 'horsepower-binned'),\n",
       " (0.0, 'height'),\n",
       " (0.0, 'fuel-system'),\n",
       " (0.0, 'engine-location'),\n",
       " (0.0, 'drive-wheels'),\n",
       " (0.0, 'diesel'),\n",
       " (0.0, 'city-L/100km'),\n",
       " (0.0, 'body-style'),\n",
       " (0.0, 'aspiration')]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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