Review-Exploratory-Data-Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>What are the main characteristics that have the most impact on the car price?</h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"import_data\">1. Import Data from Module 2</h2>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Setup</h4>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import libraries: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#install specific version of libraries used in lab\n",
    "#! mamba install pandas==1.3.3\n",
    "#! mamba install numpy=1.21.2\n",
    "#! mamba install scipy=1.7.1-y\n",
    "#!  mamba install seaborn=0.9.0-y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    }\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path='r\"C:\\Users\\Navneet\\Desktopr\"C:\\Users\\Navneet\\Desktop\\datamining\\automobileEDA.csv'\n",
    "df = pd.read_csv(path)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"pattern_visualization\">2. Analyzing Individual Feature Patterns Using Visualization</h2>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "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"
     ]
    }
   ],
   "source": [
    "# list the data types for each column\n",
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    .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>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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write your code below and press Shift+Enter to execute \n",
    "df[['bore', 'stroke', 'compression-ratio', 'horsepower']].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Positive Linear Relationship</h4>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 56229.71543126056)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Engine size as potential predictor variable of price\n",
    "sns.regplot(x=\"engine-size\", y=\"price\", data=df)\n",
    "plt.ylim(0,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>As the engine-size goes up, the price goes up: this indicates a positive direct correlation between these two variables. Engine size seems like a pretty good predictor of price since the regression line is almost a perfect diagonal line.</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "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>engine-size</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>engine-size</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.872335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>0.872335</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             engine-size     price\n",
       "engine-size     1.000000  0.872335\n",
       "price           0.872335  1.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"engine-size\", \"price\"]].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "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 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=\"highway-mpg\", y=\"price\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>As highway-mpg goes up, the price goes down: this indicates an inverse/negative relationship between these two variables. Highway mpg could potentially be a predictor of price.</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "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": "markdown",
   "metadata": {},
   "source": [
    "<h3>Weak Linear Relationship</h3>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "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 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=\"peak-rpm\", y=\"price\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Peak rpm does not seem like a good predictor of the price at all since the regression line is close to horizontal. Also, the data points are very scattered and far from the fitted line, showing lots of variability. Therefore, it's not a reliable variable.</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "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": {
    "tags": []
   },
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"stroke\",\"price\"]].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='stroke', ylabel='price'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=\"stroke\", y=\"price\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the relationship between \"body-style\" and \"price\".\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "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 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"body-style\", y=\"price\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>We see that the distributions of price between the different body-style categories have a significant overlap, so body-style would not be a good predictor of price. Let's examine engine \"engine-location\" and \"price\":</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "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 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"engine-location\", y=\"price\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Here we see that the distribution of price between these two engine-location categories, front and rear, are distinct enough to take engine-location as a potential good predictor of price.</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Let's examine \"drive-wheels\" and \"price\".\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "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 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# drive-wheels\n",
    "sns.boxplot(x=\"drive-wheels\", y=\"price\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Here we see that the distribution of price between the different drive-wheels categories differs. As such, drive-wheels could potentially be a predictor of price.</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"discriptive_statistics\">3. Descriptive Statistical Analysis and Grouping</h2>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "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": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</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": "markdown",
   "metadata": {},
   "source": [
    "<h3>Value Counts</h3>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fwd    118\n",
       "rwd     75\n",
       "4wd      8\n",
       "Name: drive-wheels, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['drive-wheels'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can convert the series to a dataframe as follows:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "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>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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['drive-wheels'].value_counts().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "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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       "        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>value_counts</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": [
       "     value_counts\n",
       "fwd           118\n",
       "rwd            75\n",
       "4wd             8"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drive_wheels_counts = df['drive-wheels'].value_counts().to_frame()\n",
    "drive_wheels_counts.rename(columns={'drive-wheels': 'value_counts'}, inplace=True)\n",
    "drive_wheels_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Renamimg the index to 'drive-wheels':\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "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",
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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>value_counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drive-wheels</th>\n",
       "      <th></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": [
       "              value_counts\n",
       "drive-wheels              \n",
       "fwd                    118\n",
       "rwd                     75\n",
       "4wd                      8"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drive_wheels_counts.index.name = 'drive-wheels'\n",
    "drive_wheels_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can repeat the above process for the variable 'engine-location'.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "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",
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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>value_counts</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": [
       "                 value_counts\n",
       "engine-location              \n",
       "front                     198\n",
       "rear                        3"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# engine-location as variable\n",
    "engine_loc_counts = df['engine-location'].value_counts().to_frame()\n",
    "engine_loc_counts.rename(columns={'engine-location': 'value_counts'}, inplace=True)\n",
    "engine_loc_counts.index.name = 'engine-location'\n",
    "engine_loc_counts.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>After examining the value counts of the engine location, we see that engine location would not be a good predictor variable for the price. This is because we only have three cars with a rear engine and 198 with an engine in the front, so this result is skewed. Thus, we are not able to draw any conclusions about the engine location.</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['rwd', 'fwd', '4wd'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['drive-wheels'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_group_one = df[['drive-wheels','body-style','price']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then calculate the average price for each of the different categories of data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping results\n",
    "df_group_one = df_group_one.groupby(['drive-wheels'],as_index=False).mean()\n",
    "df_group_one"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>From our data, it seems rear-wheel drive vehicles are, on average, the most expensive, while 4-wheel and front-wheel are approximately the same in price.</p>\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping results\n",
    "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": 57,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <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": 57,
     "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": 32,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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>0.0</td>\n",
       "      <td>0.000000</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                  0.0      0.000000   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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_pivot = grouped_pivot.fillna(0) #fill missing values with 0\n",
    "grouped_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": []
   },
   "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>body-style</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>convertible</td>\n",
       "      <td>21890.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hardtop</td>\n",
       "      <td>22208.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hatchback</td>\n",
       "      <td>9957.441176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sedan</td>\n",
       "      <td>14459.755319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>wagon</td>\n",
       "      <td>12371.960000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    body-style         price\n",
       "0  convertible  21890.500000\n",
       "1      hardtop  22208.500000\n",
       "2    hatchback   9957.441176\n",
       "3        sedan  14459.755319\n",
       "4        wagon  12371.960000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gptest2 = df[['body-style','price']]\n",
    "grouped_test_bodystyle = df_gptest2.groupby(['body-style'],as_index= False).mean()\n",
    "grouped_test_bodystyle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Variables: Drive Wheels and Body Style vs. Price</h4>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use a heat map to visualize the relationship between Body Style vs Price.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#use the grouped results\n",
    "plt.pcolor(grouped_pivot, cmap='RdBu')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>The heatmap plots the target variable (price) proportional to colour with respect to the variables 'drive-wheel' and 'body-style' on the vertical and horizontal axis, respectively. This allows us to visualize how the price is related to 'drive-wheel' and 'body-style'.</p>\n",
    "\n",
    "<p>The default labels convey no useful information to us. Let's change that:</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "im = ax.pcolor(grouped_pivot, cmap='RdBu')\n",
    "\n",
    "#label names\n",
    "row_labels = grouped_pivot.columns.levels[1]\n",
    "col_labels = grouped_pivot.index\n",
    "\n",
    "#move ticks and labels to the center\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",
    "#insert labels\n",
    "ax.set_xticklabels(row_labels, minor=False)\n",
    "ax.set_yticklabels(col_labels, minor=False)\n",
    "\n",
    "#rotate label if too long\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "fig.colorbar(im)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "<p>What are the main characteristics which have the most impact on the car price?.</p>\n",
    "\n",
    "<p>To get a better measure of the important characteristics, we look at the correlation of these variables with the car price. In other words: how is the car price dependent on this variable?</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"correlation_causation\">5. Correlation and Causation</h2>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><b>Pearson Correlation</b></p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": []
   },
   "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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Wheel-Base vs. Price</h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's calculate the  Pearson Correlation Coefficient and P-value of 'wheel-base' and 'price'. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.5846418222655085  with a P-value of P = 8.076488270732243e-20\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(df['wheel-base'], df['price'])\n",
    "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Conclusion:</h4>\n",
    "<p>Since the p-value is $<$ 0.001, the correlation between wheel-base and price is statistically significant, although the linear relationship isn't extremely strong (~0.585).</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Horsepower vs. Price</h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Let's calculate the  Pearson Correlation Coefficient and P-value of 'horsepower' and 'price'.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.8095745670036559  with a P-value of P =  6.369057428260101e-48\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(df['horsepower'], df['price'])\n",
    "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Conclusion:</h4>\n",
    "\n",
    "<p>Since the p-value is $<$ 0.001, the correlation between horsepower and price is statistically significant, and the linear relationship is quite strong (~0.809, close to 1).</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Length vs. Price</h3>\n",
    "\n",
    "Let's calculate the  Pearson Correlation Coefficient and P-value of 'length' and 'price'.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.6906283804483643  with a P-value of P =  8.01647746615853e-30\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(df['length'], df['price'])\n",
    "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Conclusion:</h4>\n",
    "<p>Since the p-value is $<$ 0.001, the correlation between length and price is statistically significant, and the linear relationship is moderately strong (~0.691).</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Width vs. Price</h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Let's calculate the Pearson Correlation Coefficient and P-value of 'width' and 'price':\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.7512653440522666  with a P-value of P = 9.200335510483739e-38\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(df['width'], df['price'])\n",
    "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value ) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion:\n",
    "\n",
    "Since the p-value is < 0.001, the correlation between width and price is statistically significant, and the linear relationship is quite strong (~0.751).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Curb-Weight vs. Price\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Let's calculate the Pearson Correlation Coefficient and P-value of 'curb-weight' and 'price':\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.8344145257702845  with a P-value of P =  2.189577238893816e-53\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(df['curb-weight'], df['price'])\n",
    "print( \"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Conclusion:</h4>\n",
    "<p>Since the p-value is $<$ 0.001, the correlation between curb-weight and price is statistically significant, and the linear relationship is quite strong (~0.834).</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Engine-Size vs. Price</h3>\n",
    "\n",
    "Let's calculate the Pearson Correlation Coefficient and P-value of 'engine-size' and 'price':\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.8723351674455188  with a P-value of P = 9.265491622196808e-64\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(df['engine-size'], df['price'])\n",
    "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Conclusion:</h4>\n",
    "\n",
    "<p>Since the p-value is $<$ 0.001, the correlation between engine-size and price is statistically significant, and the linear relationship is very strong (~0.872).</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Bore vs. Price</h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Let's calculate the  Pearson Correlation Coefficient and P-value of 'bore' and 'price':\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.54315538326266  with a P-value of P =   8.049189483935489e-17\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(df['bore'], df['price'])\n",
    "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =  \", p_value ) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Conclusion:</h4>\n",
    "<p>Since the p-value is $<$ 0.001, the correlation between bore and price is statistically significant, but the linear relationship is only moderate (~0.521).</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " We can relate the process for each 'city-mpg'  and 'highway-mpg':\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>City-mpg vs. Price</h3>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is -0.6865710067844684  with a P-value of P =  2.3211320655672453e-29\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(df['city-mpg'], df['price'])\n",
    "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Conclusion:</h4>\n",
    "<p>Since the p-value is $<$ 0.001, the correlation between city-mpg and price is statistically significant, and the coefficient of about -0.687 shows that the relationship is negative and moderately strong.</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Highway-mpg vs. Price</h3>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is -0.7046922650589534  with a P-value of P =  1.749547114447437e-31\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(df['highway-mpg'], df['price'])\n",
    "print( \"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value ) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion:\n",
    "Since the p-value is < 0.001, the correlation between highway-mpg and price is statistically significant, and the coefficient of about -0.705 shows that the relationship is negative and moderately strong.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"anova\">6. ANOVA</h2>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>ANOVA: Analysis of Variance</h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Drive Wheels</h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>To see if different types of 'drive-wheels' impact  'price', we group the data.</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\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>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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rwd</td>\n",
       "      <td>16500.0</td>\n",
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       "    <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": 48,
     "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": 49,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gptest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "tags": []
   },
   "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": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_test2.get_group('4wd')['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA results: F= 67.95406500780399 , P = 3.3945443577151245e-23\n"
     ]
    }
   ],
   "source": [
    "# ANOVA\n",
    "f_val, p_val = stats.f_oneway(grouped_test2.get_group('fwd')['price'], grouped_test2.get_group('rwd')['price'], grouped_test2.get_group('4wd')['price'])  \n",
    " \n",
    "print( \"ANOVA results: F=\", f_val, \", P =\", p_val)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a great result with a large F-test score showing a strong correlation and a P-value of almost 0 implying almost certain statistical significance. But does this mean all three tested groups are all this highly correlated? \n",
    "\n",
    "Let's examine them separately.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### fwd and rwd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "tags": []
   },
   "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",
    " \n",
    "print( \"ANOVA results: F=\", f_val, \", P =\", p_val )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Let's examine the other groups.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4wd and rwd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "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",
    "   \n",
    "print( \"ANOVA results: F=\", f_val, \", P =\", p_val)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>4wd and fwd</h4>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "tags": []
   },
   "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",
    " \n",
    "print(\"ANOVA results: F=\", f_val, \", P =\", p_val)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We notice that ANOVA for the categories `4wd` and `fwd` yields a high p-value > 0.1, so the calculated F-test score is not very statistically significant. This suggests we can't reject the assumption that the means of these two groups are the same, or, in other words, we can't conclude the difference in correlation to be significant.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Conclusion: Important Variables</h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>We now have a better idea of what our data looks like and which variables are important to take into account when predicting the car price. We have narrowed it down to the following variables:</p>\n",
    "\n",
    "Continuous numerical variables:\n",
    "<ul>\n",
    "    <li>Length</li>\n",
    "    <li>Width</li>\n",
    "    <li>Curb-weight</li>\n",
    "    <li>Engine-size</li>\n",
    "    <li>Horsepower</li>\n",
    "    <li>City-mpg</li>\n",
    "    <li>Highway-mpg</li>\n",
    "    <li>Wheel-base</li>\n",
    "    <li>Bore</li>\n",
    "</ul>\n",
    "    \n",
    "Categorical variables:\n",
    "<ul>\n",
    "    <li>Drive-wheels</li>\n",
    "</ul>\n",
    "\n"
   ]
  }
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