Automobile_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import pandas library\n",
    "import pandas as pd\n",
    "\n",
    "# Read the online file by the URL provides above, and assign it to variable \"df\"\n",
    "other_path = \"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DA0101EN/auto.csv\"\n",
    "df = pd.read_csv(other_path, header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first 5 rows of the dataframe\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</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>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</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>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</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>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</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>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</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>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   0    1            2    3    4     5            6    7      8     9   ...  \\\n",
       "0   3    ?  alfa-romero  gas  std   two  convertible  rwd  front  88.6  ...   \n",
       "1   3    ?  alfa-romero  gas  std   two  convertible  rwd  front  88.6  ...   \n",
       "2   1    ?  alfa-romero  gas  std   two    hatchback  rwd  front  94.5  ...   \n",
       "3   2  164         audi  gas  std  four        sedan  fwd  front  99.8  ...   \n",
       "4   2  164         audi  gas  std  four        sedan  4wd  front  99.4  ...   \n",
       "\n",
       "    16    17    18    19    20   21    22  23  24     25  \n",
       "0  130  mpfi  3.47  2.68   9.0  111  5000  21  27  13495  \n",
       "1  130  mpfi  3.47  2.68   9.0  111  5000  21  27  16500  \n",
       "2  152  mpfi  2.68  3.47   9.0  154  5000  19  26  16500  \n",
       "3  109  mpfi  3.19  3.40  10.0  102  5500  24  30  13950  \n",
       "4  136  mpfi  3.19  3.40   8.0  115  5500  18  22  17450  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the first 5 rows using dataframe.head() method\n",
    "print(\"The first 5 rows of the dataframe\") \n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>-1</td>\n",
       "      <td>74</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>wagon</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>104.3</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>13415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>-2</td>\n",
       "      <td>103</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>104.3</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>15985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>-1</td>\n",
       "      <td>74</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>wagon</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>104.3</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>16515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>-2</td>\n",
       "      <td>103</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>104.3</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.15</td>\n",
       "      <td>7.5</td>\n",
       "      <td>162</td>\n",
       "      <td>5100</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>18420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>-1</td>\n",
       "      <td>74</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>wagon</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>104.3</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.15</td>\n",
       "      <td>7.5</td>\n",
       "      <td>162</td>\n",
       "      <td>5100</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>18950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>16845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>8.7</td>\n",
       "      <td>160</td>\n",
       "      <td>5300</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>19045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>173</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.58</td>\n",
       "      <td>2.87</td>\n",
       "      <td>8.8</td>\n",
       "      <td>134</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>21485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>145</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.0</td>\n",
       "      <td>106</td>\n",
       "      <td>4800</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>22470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>22625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1      2       3      4     5      6    7      8      9   ...   16  \\\n",
       "195  -1   74  volvo     gas    std  four  wagon  rwd  front  104.3  ...  141   \n",
       "196  -2  103  volvo     gas    std  four  sedan  rwd  front  104.3  ...  141   \n",
       "197  -1   74  volvo     gas    std  four  wagon  rwd  front  104.3  ...  141   \n",
       "198  -2  103  volvo     gas  turbo  four  sedan  rwd  front  104.3  ...  130   \n",
       "199  -1   74  volvo     gas  turbo  four  wagon  rwd  front  104.3  ...  130   \n",
       "200  -1   95  volvo     gas    std  four  sedan  rwd  front  109.1  ...  141   \n",
       "201  -1   95  volvo     gas  turbo  four  sedan  rwd  front  109.1  ...  141   \n",
       "202  -1   95  volvo     gas    std  four  sedan  rwd  front  109.1  ...  173   \n",
       "203  -1   95  volvo  diesel  turbo  four  sedan  rwd  front  109.1  ...  145   \n",
       "204  -1   95  volvo     gas  turbo  four  sedan  rwd  front  109.1  ...  141   \n",
       "\n",
       "       17    18    19    20   21    22  23  24     25  \n",
       "195  mpfi  3.78  3.15   9.5  114  5400  23  28  13415  \n",
       "196  mpfi  3.78  3.15   9.5  114  5400  24  28  15985  \n",
       "197  mpfi  3.78  3.15   9.5  114  5400  24  28  16515  \n",
       "198  mpfi  3.62  3.15   7.5  162  5100  17  22  18420  \n",
       "199  mpfi  3.62  3.15   7.5  162  5100  17  22  18950  \n",
       "200  mpfi  3.78  3.15   9.5  114  5400  23  28  16845  \n",
       "201  mpfi  3.78  3.15   8.7  160  5300  19  25  19045  \n",
       "202  mpfi  3.58  2.87   8.8  134  5500  18  23  21485  \n",
       "203   idi  3.01  3.40  23.0  106  4800  26  27  22470  \n",
       "204  mpfi  3.78  3.15   9.5  114  5400  19  25  22625  \n",
       "\n",
       "[10 rows x 26 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the bottom 10 rows of data frame \"df\".\n",
    "df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "headers\n",
      " ['symboling', 'normalized-losses', 'make', 'fuel-type', 'aspiration', 'num-of-doors', 'body-style', 'drive-wheels', 'engine-location', 'wheel-base', 'length', 'width', 'height', 'curb-weight', 'engine-type', 'num-of-cylinders', 'engine-size', 'fuel-system', 'bore', 'stroke', 'compression-ratio', 'horsepower', 'peak-rpm', 'city-mpg', 'highway-mpg', 'price']\n"
     ]
    }
   ],
   "source": [
    "# create headers list\n",
    "headers = [\"symboling\",\"normalized-losses\",\"make\",\"fuel-type\",\"aspiration\", \"num-of-doors\",\"body-style\",\n",
    "         \"drive-wheels\",\"engine-location\",\"wheel-base\", \"length\",\"width\",\"height\",\"curb-weight\",\"engine-type\",\n",
    "         \"num-of-cylinders\", \"engine-size\",\"fuel-system\",\"bore\",\"stroke\",\"compression-ratio\",\"horsepower\",\n",
    "         \"peak-rpm\",\"city-mpg\",\"highway-mpg\",\"price\"]\n",
    "print(\"headers\\n\", headers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</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>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</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>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</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>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</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>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</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>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</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>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>?</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.5</td>\n",
       "      <td>110</td>\n",
       "      <td>5500</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>15250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>158</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>105.8</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.5</td>\n",
       "      <td>110</td>\n",
       "      <td>5500</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>17710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>wagon</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>105.8</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.5</td>\n",
       "      <td>110</td>\n",
       "      <td>5500</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>18920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>158</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>105.8</td>\n",
       "      <td>...</td>\n",
       "      <td>131</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.13</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.3</td>\n",
       "      <td>140</td>\n",
       "      <td>5500</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>23875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.5</td>\n",
       "      <td>...</td>\n",
       "      <td>131</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.13</td>\n",
       "      <td>3.40</td>\n",
       "      <td>7.0</td>\n",
       "      <td>160</td>\n",
       "      <td>5500</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling normalized-losses         make fuel-type aspiration num-of-doors  \\\n",
       "0          3                 ?  alfa-romero       gas        std          two   \n",
       "1          3                 ?  alfa-romero       gas        std          two   \n",
       "2          1                 ?  alfa-romero       gas        std          two   \n",
       "3          2               164         audi       gas        std         four   \n",
       "4          2               164         audi       gas        std         four   \n",
       "5          2                 ?         audi       gas        std          two   \n",
       "6          1               158         audi       gas        std         four   \n",
       "7          1                 ?         audi       gas        std         four   \n",
       "8          1               158         audi       gas      turbo         four   \n",
       "9          0                 ?         audi       gas      turbo          two   \n",
       "\n",
       "    body-style drive-wheels engine-location  wheel-base  ...  engine-size  \\\n",
       "0  convertible          rwd           front        88.6  ...          130   \n",
       "1  convertible          rwd           front        88.6  ...          130   \n",
       "2    hatchback          rwd           front        94.5  ...          152   \n",
       "3        sedan          fwd           front        99.8  ...          109   \n",
       "4        sedan          4wd           front        99.4  ...          136   \n",
       "5        sedan          fwd           front        99.8  ...          136   \n",
       "6        sedan          fwd           front       105.8  ...          136   \n",
       "7        wagon          fwd           front       105.8  ...          136   \n",
       "8        sedan          fwd           front       105.8  ...          131   \n",
       "9    hatchback          4wd           front        99.5  ...          131   \n",
       "\n",
       "   fuel-system  bore  stroke compression-ratio horsepower  peak-rpm city-mpg  \\\n",
       "0         mpfi  3.47    2.68               9.0        111      5000       21   \n",
       "1         mpfi  3.47    2.68               9.0        111      5000       21   \n",
       "2         mpfi  2.68    3.47               9.0        154      5000       19   \n",
       "3         mpfi  3.19    3.40              10.0        102      5500       24   \n",
       "4         mpfi  3.19    3.40               8.0        115      5500       18   \n",
       "5         mpfi  3.19    3.40               8.5        110      5500       19   \n",
       "6         mpfi  3.19    3.40               8.5        110      5500       19   \n",
       "7         mpfi  3.19    3.40               8.5        110      5500       19   \n",
       "8         mpfi  3.13    3.40               8.3        140      5500       17   \n",
       "9         mpfi  3.13    3.40               7.0        160      5500       16   \n",
       "\n",
       "  highway-mpg  price  \n",
       "0          27  13495  \n",
       "1          27  16500  \n",
       "2          26  16500  \n",
       "3          30  13950  \n",
       "4          22  17450  \n",
       "5          25  15250  \n",
       "6          25  17710  \n",
       "7          25  18920  \n",
       "8          20  23875  \n",
       "9          22      ?  \n",
       "\n",
       "[10 rows x 26 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We replace headers and recheck our data frame\n",
    "df.columns = headers\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</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>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
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       "      <td>std</td>\n",
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       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
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       "      <td>13495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</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>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.00</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</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>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.00</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</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>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.00</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</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>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.00</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>?</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.50</td>\n",
       "      <td>110</td>\n",
       "      <td>5500</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>15250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>158</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>105.8</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.50</td>\n",
       "      <td>110</td>\n",
       "      <td>5500</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>17710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>wagon</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>105.8</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.50</td>\n",
       "      <td>110</td>\n",
       "      <td>5500</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>18920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>158</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>105.8</td>\n",
       "      <td>...</td>\n",
       "      <td>131</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.13</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.30</td>\n",
       "      <td>140</td>\n",
       "      <td>5500</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>23875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.5</td>\n",
       "      <td>...</td>\n",
       "      <td>131</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.13</td>\n",
       "      <td>3.40</td>\n",
       "      <td>7.00</td>\n",
       "      <td>160</td>\n",
       "      <td>5500</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>192</td>\n",
       "      <td>bmw</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>101.2</td>\n",
       "      <td>...</td>\n",
       "      <td>108</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.80</td>\n",
       "      <td>8.80</td>\n",
       "      <td>101</td>\n",
       "      <td>5800</td>\n",
       "      <td>23</td>\n",
       "      <td>29</td>\n",
       "      <td>16430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>192</td>\n",
       "      <td>bmw</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>101.2</td>\n",
       "      <td>...</td>\n",
       "      <td>108</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.80</td>\n",
       "      <td>8.80</td>\n",
       "      <td>101</td>\n",
       "      <td>5800</td>\n",
       "      <td>23</td>\n",
       "      <td>29</td>\n",
       "      <td>16925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>188</td>\n",
       "      <td>bmw</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>101.2</td>\n",
       "      <td>...</td>\n",
       "      <td>164</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.19</td>\n",
       "      <td>9.00</td>\n",
       "      <td>121</td>\n",
       "      <td>4250</td>\n",
       "      <td>21</td>\n",
       "      <td>28</td>\n",
       "      <td>20970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>188</td>\n",
       "      <td>bmw</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>101.2</td>\n",
       "      <td>...</td>\n",
       "      <td>164</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.19</td>\n",
       "      <td>9.00</td>\n",
       "      <td>121</td>\n",
       "      <td>4250</td>\n",
       "      <td>21</td>\n",
       "      <td>28</td>\n",
       "      <td>21105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>bmw</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>103.5</td>\n",
       "      <td>...</td>\n",
       "      <td>164</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.19</td>\n",
       "      <td>9.00</td>\n",
       "      <td>121</td>\n",
       "      <td>4250</td>\n",
       "      <td>20</td>\n",
       "      <td>25</td>\n",
       "      <td>24565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>bmw</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>103.5</td>\n",
       "      <td>...</td>\n",
       "      <td>209</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.39</td>\n",
       "      <td>8.00</td>\n",
       "      <td>182</td>\n",
       "      <td>5400</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>30760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>bmw</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>103.5</td>\n",
       "      <td>...</td>\n",
       "      <td>209</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.39</td>\n",
       "      <td>8.00</td>\n",
       "      <td>182</td>\n",
       "      <td>5400</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>41315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>bmw</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>110.0</td>\n",
       "      <td>...</td>\n",
       "      <td>209</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.39</td>\n",
       "      <td>8.00</td>\n",
       "      <td>182</td>\n",
       "      <td>5400</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>36880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>121</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.4</td>\n",
       "      <td>...</td>\n",
       "      <td>61</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>2.91</td>\n",
       "      <td>3.03</td>\n",
       "      <td>9.50</td>\n",
       "      <td>48</td>\n",
       "      <td>5100</td>\n",
       "      <td>47</td>\n",
       "      <td>53</td>\n",
       "      <td>5151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>98</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>90</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.11</td>\n",
       "      <td>9.60</td>\n",
       "      <td>70</td>\n",
       "      <td>5400</td>\n",
       "      <td>38</td>\n",
       "      <td>43</td>\n",
       "      <td>6295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>81</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>90</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.11</td>\n",
       "      <td>9.60</td>\n",
       "      <td>70</td>\n",
       "      <td>5400</td>\n",
       "      <td>38</td>\n",
       "      <td>43</td>\n",
       "      <td>6575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>118</td>\n",
       "      <td>dodge</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>93.7</td>\n",
       "      <td>...</td>\n",
       "      <td>90</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>2.97</td>\n",
       "      <td>3.23</td>\n",
       "      <td>9.41</td>\n",
       "      <td>68</td>\n",
       "      <td>5500</td>\n",
       "      <td>37</td>\n",
       "      <td>41</td>\n",
       "      <td>5572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>118</td>\n",
       "      <td>dodge</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>93.7</td>\n",
       "      <td>...</td>\n",
       "      <td>90</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>2.97</td>\n",
       "      <td>3.23</td>\n",
       "      <td>9.40</td>\n",
       "      <td>68</td>\n",
       "      <td>5500</td>\n",
       "      <td>31</td>\n",
       "      <td>38</td>\n",
       "      <td>6377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>118</td>\n",
       "      <td>dodge</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>93.7</td>\n",
       "      <td>...</td>\n",
       "      <td>98</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.39</td>\n",
       "      <td>7.60</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>7957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>148</td>\n",
       "      <td>dodge</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>93.7</td>\n",
       "      <td>...</td>\n",
       "      <td>90</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>2.97</td>\n",
       "      <td>3.23</td>\n",
       "      <td>9.40</td>\n",
       "      <td>68</td>\n",
       "      <td>5500</td>\n",
       "      <td>31</td>\n",
       "      <td>38</td>\n",
       "      <td>6229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>148</td>\n",
       "      <td>dodge</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>93.7</td>\n",
       "      <td>...</td>\n",
       "      <td>90</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>2.97</td>\n",
       "      <td>3.23</td>\n",
       "      <td>9.40</td>\n",
       "      <td>68</td>\n",
       "      <td>5500</td>\n",
       "      <td>31</td>\n",
       "      <td>38</td>\n",
       "      <td>6692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>148</td>\n",
       "      <td>dodge</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>93.7</td>\n",
       "      <td>...</td>\n",
       "      <td>90</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>2.97</td>\n",
       "      <td>3.23</td>\n",
       "      <td>9.40</td>\n",
       "      <td>68</td>\n",
       "      <td>5500</td>\n",
       "      <td>31</td>\n",
       "      <td>38</td>\n",
       "      <td>7609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>148</td>\n",
       "      <td>dodge</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>?</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>93.7</td>\n",
       "      <td>...</td>\n",
       "      <td>98</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.39</td>\n",
       "      <td>7.60</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>8558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-1</td>\n",
       "      <td>110</td>\n",
       "      <td>dodge</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>wagon</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>103.3</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>3.34</td>\n",
       "      <td>3.46</td>\n",
       "      <td>8.50</td>\n",
       "      <td>88</td>\n",
       "      <td>5000</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>8921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>dodge</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>95.9</td>\n",
       "      <td>...</td>\n",
       "      <td>156</td>\n",
       "      <td>mfi</td>\n",
       "      <td>3.60</td>\n",
       "      <td>3.90</td>\n",
       "      <td>7.00</td>\n",
       "      <td>145</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>12964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>-1</td>\n",
       "      <td>65</td>\n",
       "      <td>toyota</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>102.4</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.54</td>\n",
       "      <td>8.70</td>\n",
       "      <td>92</td>\n",
       "      <td>4200</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>9988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>-1</td>\n",
       "      <td>65</td>\n",
       "      <td>toyota</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>102.4</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.54</td>\n",
       "      <td>8.70</td>\n",
       "      <td>92</td>\n",
       "      <td>4200</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>10898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>-1</td>\n",
       "      <td>65</td>\n",
       "      <td>toyota</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>102.4</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.54</td>\n",
       "      <td>8.70</td>\n",
       "      <td>92</td>\n",
       "      <td>4200</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>11248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>3</td>\n",
       "      <td>197</td>\n",
       "      <td>toyota</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>102.9</td>\n",
       "      <td>...</td>\n",
       "      <td>171</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.35</td>\n",
       "      <td>9.30</td>\n",
       "      <td>161</td>\n",
       "      <td>5200</td>\n",
       "      <td>20</td>\n",
       "      <td>24</td>\n",
       "      <td>16558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>3</td>\n",
       "      <td>197</td>\n",
       "      <td>toyota</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>102.9</td>\n",
       "      <td>...</td>\n",
       "      <td>171</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.35</td>\n",
       "      <td>9.30</td>\n",
       "      <td>161</td>\n",
       "      <td>5200</td>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>15998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>-1</td>\n",
       "      <td>90</td>\n",
       "      <td>toyota</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>104.5</td>\n",
       "      <td>...</td>\n",
       "      <td>171</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.35</td>\n",
       "      <td>9.20</td>\n",
       "      <td>156</td>\n",
       "      <td>5200</td>\n",
       "      <td>20</td>\n",
       "      <td>24</td>\n",
       "      <td>15690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>-1</td>\n",
       "      <td>?</td>\n",
       "      <td>toyota</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>wagon</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>104.5</td>\n",
       "      <td>...</td>\n",
       "      <td>161</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.35</td>\n",
       "      <td>9.20</td>\n",
       "      <td>156</td>\n",
       "      <td>5200</td>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>15750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>diesel</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>97.3</td>\n",
       "      <td>...</td>\n",
       "      <td>97</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.00</td>\n",
       "      <td>52</td>\n",
       "      <td>4800</td>\n",
       "      <td>37</td>\n",
       "      <td>46</td>\n",
       "      <td>7775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>97.3</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>9.00</td>\n",
       "      <td>85</td>\n",
       "      <td>5250</td>\n",
       "      <td>27</td>\n",
       "      <td>34</td>\n",
       "      <td>7975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>diesel</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>97.3</td>\n",
       "      <td>...</td>\n",
       "      <td>97</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.00</td>\n",
       "      <td>52</td>\n",
       "      <td>4800</td>\n",
       "      <td>37</td>\n",
       "      <td>46</td>\n",
       "      <td>7995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>97.3</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>9.00</td>\n",
       "      <td>85</td>\n",
       "      <td>5250</td>\n",
       "      <td>27</td>\n",
       "      <td>34</td>\n",
       "      <td>8195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>97.3</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>9.00</td>\n",
       "      <td>85</td>\n",
       "      <td>5250</td>\n",
       "      <td>27</td>\n",
       "      <td>34</td>\n",
       "      <td>8495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>97.3</td>\n",
       "      <td>...</td>\n",
       "      <td>97</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.00</td>\n",
       "      <td>68</td>\n",
       "      <td>4500</td>\n",
       "      <td>37</td>\n",
       "      <td>42</td>\n",
       "      <td>9495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>97.3</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.00</td>\n",
       "      <td>100</td>\n",
       "      <td>5500</td>\n",
       "      <td>26</td>\n",
       "      <td>32</td>\n",
       "      <td>9995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.50</td>\n",
       "      <td>90</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>29</td>\n",
       "      <td>11595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>3</td>\n",
       "      <td>256</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.50</td>\n",
       "      <td>90</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>29</td>\n",
       "      <td>9980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>100.4</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.50</td>\n",
       "      <td>110</td>\n",
       "      <td>5500</td>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>13295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>100.4</td>\n",
       "      <td>...</td>\n",
       "      <td>97</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.00</td>\n",
       "      <td>68</td>\n",
       "      <td>4500</td>\n",
       "      <td>33</td>\n",
       "      <td>38</td>\n",
       "      <td>13845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>wagon</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>100.4</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>9.00</td>\n",
       "      <td>88</td>\n",
       "      <td>5500</td>\n",
       "      <td>25</td>\n",
       "      <td>31</td>\n",
       "      <td>12290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>-2</td>\n",
       "      <td>103</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>104.3</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.50</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>12940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>-1</td>\n",
       "      <td>74</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>wagon</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>104.3</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.50</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>13415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>-2</td>\n",
       "      <td>103</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>104.3</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.50</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>15985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>-1</td>\n",
       "      <td>74</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>wagon</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>104.3</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.50</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>16515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>-2</td>\n",
       "      <td>103</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>104.3</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.15</td>\n",
       "      <td>7.50</td>\n",
       "      <td>162</td>\n",
       "      <td>5100</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>18420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>-1</td>\n",
       "      <td>74</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>wagon</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>104.3</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.15</td>\n",
       "      <td>7.50</td>\n",
       "      <td>162</td>\n",
       "      <td>5100</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>18950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.50</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>16845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>8.70</td>\n",
       "      <td>160</td>\n",
       "      <td>5300</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>19045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>173</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.58</td>\n",
       "      <td>2.87</td>\n",
       "      <td>8.80</td>\n",
       "      <td>134</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>21485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>145</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.00</td>\n",
       "      <td>106</td>\n",
       "      <td>4800</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>22470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.50</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>22625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>205 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     symboling normalized-losses         make fuel-type aspiration  \\\n",
       "0            3                 ?  alfa-romero       gas        std   \n",
       "1            3                 ?  alfa-romero       gas        std   \n",
       "2            1                 ?  alfa-romero       gas        std   \n",
       "3            2               164         audi       gas        std   \n",
       "4            2               164         audi       gas        std   \n",
       "5            2                 ?         audi       gas        std   \n",
       "6            1               158         audi       gas        std   \n",
       "7            1                 ?         audi       gas        std   \n",
       "8            1               158         audi       gas      turbo   \n",
       "9            0                 ?         audi       gas      turbo   \n",
       "10           2               192          bmw       gas        std   \n",
       "11           0               192          bmw       gas        std   \n",
       "12           0               188          bmw       gas        std   \n",
       "13           0               188          bmw       gas        std   \n",
       "14           1                 ?          bmw       gas        std   \n",
       "15           0                 ?          bmw       gas        std   \n",
       "16           0                 ?          bmw       gas        std   \n",
       "17           0                 ?          bmw       gas        std   \n",
       "18           2               121    chevrolet       gas        std   \n",
       "19           1                98    chevrolet       gas        std   \n",
       "20           0                81    chevrolet       gas        std   \n",
       "21           1               118        dodge       gas        std   \n",
       "22           1               118        dodge       gas        std   \n",
       "23           1               118        dodge       gas      turbo   \n",
       "24           1               148        dodge       gas        std   \n",
       "25           1               148        dodge       gas        std   \n",
       "26           1               148        dodge       gas        std   \n",
       "27           1               148        dodge       gas      turbo   \n",
       "28          -1               110        dodge       gas        std   \n",
       "29           3               145        dodge       gas      turbo   \n",
       "..         ...               ...          ...       ...        ...   \n",
       "175         -1                65       toyota       gas        std   \n",
       "176         -1                65       toyota       gas        std   \n",
       "177         -1                65       toyota       gas        std   \n",
       "178          3               197       toyota       gas        std   \n",
       "179          3               197       toyota       gas        std   \n",
       "180         -1                90       toyota       gas        std   \n",
       "181         -1                 ?       toyota       gas        std   \n",
       "182          2               122   volkswagen    diesel        std   \n",
       "183          2               122   volkswagen       gas        std   \n",
       "184          2                94   volkswagen    diesel        std   \n",
       "185          2                94   volkswagen       gas        std   \n",
       "186          2                94   volkswagen       gas        std   \n",
       "187          2                94   volkswagen    diesel      turbo   \n",
       "188          2                94   volkswagen       gas        std   \n",
       "189          3                 ?   volkswagen       gas        std   \n",
       "190          3               256   volkswagen       gas        std   \n",
       "191          0                 ?   volkswagen       gas        std   \n",
       "192          0                 ?   volkswagen    diesel      turbo   \n",
       "193          0                 ?   volkswagen       gas        std   \n",
       "194         -2               103        volvo       gas        std   \n",
       "195         -1                74        volvo       gas        std   \n",
       "196         -2               103        volvo       gas        std   \n",
       "197         -1                74        volvo       gas        std   \n",
       "198         -2               103        volvo       gas      turbo   \n",
       "199         -1                74        volvo       gas      turbo   \n",
       "200         -1                95        volvo       gas        std   \n",
       "201         -1                95        volvo       gas      turbo   \n",
       "202         -1                95        volvo       gas        std   \n",
       "203         -1                95        volvo    diesel      turbo   \n",
       "204         -1                95        volvo       gas      turbo   \n",
       "\n",
       "    num-of-doors   body-style drive-wheels engine-location  wheel-base  ...  \\\n",
       "0            two  convertible          rwd           front        88.6  ...   \n",
       "1            two  convertible          rwd           front        88.6  ...   \n",
       "2            two    hatchback          rwd           front        94.5  ...   \n",
       "3           four        sedan          fwd           front        99.8  ...   \n",
       "4           four        sedan          4wd           front        99.4  ...   \n",
       "5            two        sedan          fwd           front        99.8  ...   \n",
       "6           four        sedan          fwd           front       105.8  ...   \n",
       "7           four        wagon          fwd           front       105.8  ...   \n",
       "8           four        sedan          fwd           front       105.8  ...   \n",
       "9            two    hatchback          4wd           front        99.5  ...   \n",
       "10           two        sedan          rwd           front       101.2  ...   \n",
       "11          four        sedan          rwd           front       101.2  ...   \n",
       "12           two        sedan          rwd           front       101.2  ...   \n",
       "13          four        sedan          rwd           front       101.2  ...   \n",
       "14          four        sedan          rwd           front       103.5  ...   \n",
       "15          four        sedan          rwd           front       103.5  ...   \n",
       "16           two        sedan          rwd           front       103.5  ...   \n",
       "17          four        sedan          rwd           front       110.0  ...   \n",
       "18           two    hatchback          fwd           front        88.4  ...   \n",
       "19           two    hatchback          fwd           front        94.5  ...   \n",
       "20          four        sedan          fwd           front        94.5  ...   \n",
       "21           two    hatchback          fwd           front        93.7  ...   \n",
       "22           two    hatchback          fwd           front        93.7  ...   \n",
       "23           two    hatchback          fwd           front        93.7  ...   \n",
       "24          four    hatchback          fwd           front        93.7  ...   \n",
       "25          four        sedan          fwd           front        93.7  ...   \n",
       "26          four        sedan          fwd           front        93.7  ...   \n",
       "27             ?        sedan          fwd           front        93.7  ...   \n",
       "28          four        wagon          fwd           front       103.3  ...   \n",
       "29           two    hatchback          fwd           front        95.9  ...   \n",
       "..           ...          ...          ...             ...         ...  ...   \n",
       "175         four    hatchback          fwd           front       102.4  ...   \n",
       "176         four        sedan          fwd           front       102.4  ...   \n",
       "177         four    hatchback          fwd           front       102.4  ...   \n",
       "178          two    hatchback          rwd           front       102.9  ...   \n",
       "179          two    hatchback          rwd           front       102.9  ...   \n",
       "180         four        sedan          rwd           front       104.5  ...   \n",
       "181         four        wagon          rwd           front       104.5  ...   \n",
       "182          two        sedan          fwd           front        97.3  ...   \n",
       "183          two        sedan          fwd           front        97.3  ...   \n",
       "184         four        sedan          fwd           front        97.3  ...   \n",
       "185         four        sedan          fwd           front        97.3  ...   \n",
       "186         four        sedan          fwd           front        97.3  ...   \n",
       "187         four        sedan          fwd           front        97.3  ...   \n",
       "188         four        sedan          fwd           front        97.3  ...   \n",
       "189          two  convertible          fwd           front        94.5  ...   \n",
       "190          two    hatchback          fwd           front        94.5  ...   \n",
       "191         four        sedan          fwd           front       100.4  ...   \n",
       "192         four        sedan          fwd           front       100.4  ...   \n",
       "193         four        wagon          fwd           front       100.4  ...   \n",
       "194         four        sedan          rwd           front       104.3  ...   \n",
       "195         four        wagon          rwd           front       104.3  ...   \n",
       "196         four        sedan          rwd           front       104.3  ...   \n",
       "197         four        wagon          rwd           front       104.3  ...   \n",
       "198         four        sedan          rwd           front       104.3  ...   \n",
       "199         four        wagon          rwd           front       104.3  ...   \n",
       "200         four        sedan          rwd           front       109.1  ...   \n",
       "201         four        sedan          rwd           front       109.1  ...   \n",
       "202         four        sedan          rwd           front       109.1  ...   \n",
       "203         four        sedan          rwd           front       109.1  ...   \n",
       "204         four        sedan          rwd           front       109.1  ...   \n",
       "\n",
       "     engine-size  fuel-system  bore  stroke compression-ratio horsepower  \\\n",
       "0            130         mpfi  3.47    2.68              9.00        111   \n",
       "1            130         mpfi  3.47    2.68              9.00        111   \n",
       "2            152         mpfi  2.68    3.47              9.00        154   \n",
       "3            109         mpfi  3.19    3.40             10.00        102   \n",
       "4            136         mpfi  3.19    3.40              8.00        115   \n",
       "5            136         mpfi  3.19    3.40              8.50        110   \n",
       "6            136         mpfi  3.19    3.40              8.50        110   \n",
       "7            136         mpfi  3.19    3.40              8.50        110   \n",
       "8            131         mpfi  3.13    3.40              8.30        140   \n",
       "9            131         mpfi  3.13    3.40              7.00        160   \n",
       "10           108         mpfi  3.50    2.80              8.80        101   \n",
       "11           108         mpfi  3.50    2.80              8.80        101   \n",
       "12           164         mpfi  3.31    3.19              9.00        121   \n",
       "13           164         mpfi  3.31    3.19              9.00        121   \n",
       "14           164         mpfi  3.31    3.19              9.00        121   \n",
       "15           209         mpfi  3.62    3.39              8.00        182   \n",
       "16           209         mpfi  3.62    3.39              8.00        182   \n",
       "17           209         mpfi  3.62    3.39              8.00        182   \n",
       "18            61         2bbl  2.91    3.03              9.50         48   \n",
       "19            90         2bbl  3.03    3.11              9.60         70   \n",
       "20            90         2bbl  3.03    3.11              9.60         70   \n",
       "21            90         2bbl  2.97    3.23              9.41         68   \n",
       "22            90         2bbl  2.97    3.23              9.40         68   \n",
       "23            98         mpfi  3.03    3.39              7.60        102   \n",
       "24            90         2bbl  2.97    3.23              9.40         68   \n",
       "25            90         2bbl  2.97    3.23              9.40         68   \n",
       "26            90         2bbl  2.97    3.23              9.40         68   \n",
       "27            98         mpfi  3.03    3.39              7.60        102   \n",
       "28           122         2bbl  3.34    3.46              8.50         88   \n",
       "29           156          mfi  3.60    3.90              7.00        145   \n",
       "..           ...          ...   ...     ...               ...        ...   \n",
       "175          122         mpfi  3.31    3.54              8.70         92   \n",
       "176          122         mpfi  3.31    3.54              8.70         92   \n",
       "177          122         mpfi  3.31    3.54              8.70         92   \n",
       "178          171         mpfi  3.27    3.35              9.30        161   \n",
       "179          171         mpfi  3.27    3.35              9.30        161   \n",
       "180          171         mpfi  3.27    3.35              9.20        156   \n",
       "181          161         mpfi  3.27    3.35              9.20        156   \n",
       "182           97          idi  3.01    3.40             23.00         52   \n",
       "183          109         mpfi  3.19    3.40              9.00         85   \n",
       "184           97          idi  3.01    3.40             23.00         52   \n",
       "185          109         mpfi  3.19    3.40              9.00         85   \n",
       "186          109         mpfi  3.19    3.40              9.00         85   \n",
       "187           97          idi  3.01    3.40             23.00         68   \n",
       "188          109         mpfi  3.19    3.40             10.00        100   \n",
       "189          109         mpfi  3.19    3.40              8.50         90   \n",
       "190          109         mpfi  3.19    3.40              8.50         90   \n",
       "191          136         mpfi  3.19    3.40              8.50        110   \n",
       "192           97          idi  3.01    3.40             23.00         68   \n",
       "193          109         mpfi  3.19    3.40              9.00         88   \n",
       "194          141         mpfi  3.78    3.15              9.50        114   \n",
       "195          141         mpfi  3.78    3.15              9.50        114   \n",
       "196          141         mpfi  3.78    3.15              9.50        114   \n",
       "197          141         mpfi  3.78    3.15              9.50        114   \n",
       "198          130         mpfi  3.62    3.15              7.50        162   \n",
       "199          130         mpfi  3.62    3.15              7.50        162   \n",
       "200          141         mpfi  3.78    3.15              9.50        114   \n",
       "201          141         mpfi  3.78    3.15              8.70        160   \n",
       "202          173         mpfi  3.58    2.87              8.80        134   \n",
       "203          145          idi  3.01    3.40             23.00        106   \n",
       "204          141         mpfi  3.78    3.15              9.50        114   \n",
       "\n",
       "     peak-rpm city-mpg highway-mpg  price  \n",
       "0        5000       21          27  13495  \n",
       "1        5000       21          27  16500  \n",
       "2        5000       19          26  16500  \n",
       "3        5500       24          30  13950  \n",
       "4        5500       18          22  17450  \n",
       "5        5500       19          25  15250  \n",
       "6        5500       19          25  17710  \n",
       "7        5500       19          25  18920  \n",
       "8        5500       17          20  23875  \n",
       "9        5500       16          22      ?  \n",
       "10       5800       23          29  16430  \n",
       "11       5800       23          29  16925  \n",
       "12       4250       21          28  20970  \n",
       "13       4250       21          28  21105  \n",
       "14       4250       20          25  24565  \n",
       "15       5400       16          22  30760  \n",
       "16       5400       16          22  41315  \n",
       "17       5400       15          20  36880  \n",
       "18       5100       47          53   5151  \n",
       "19       5400       38          43   6295  \n",
       "20       5400       38          43   6575  \n",
       "21       5500       37          41   5572  \n",
       "22       5500       31          38   6377  \n",
       "23       5500       24          30   7957  \n",
       "24       5500       31          38   6229  \n",
       "25       5500       31          38   6692  \n",
       "26       5500       31          38   7609  \n",
       "27       5500       24          30   8558  \n",
       "28       5000       24          30   8921  \n",
       "29       5000       19          24  12964  \n",
       "..        ...      ...         ...    ...  \n",
       "175      4200       27          32   9988  \n",
       "176      4200       27          32  10898  \n",
       "177      4200       27          32  11248  \n",
       "178      5200       20          24  16558  \n",
       "179      5200       19          24  15998  \n",
       "180      5200       20          24  15690  \n",
       "181      5200       19          24  15750  \n",
       "182      4800       37          46   7775  \n",
       "183      5250       27          34   7975  \n",
       "184      4800       37          46   7995  \n",
       "185      5250       27          34   8195  \n",
       "186      5250       27          34   8495  \n",
       "187      4500       37          42   9495  \n",
       "188      5500       26          32   9995  \n",
       "189      5500       24          29  11595  \n",
       "190      5500       24          29   9980  \n",
       "191      5500       19          24  13295  \n",
       "192      4500       33          38  13845  \n",
       "193      5500       25          31  12290  \n",
       "194      5400       23          28  12940  \n",
       "195      5400       23          28  13415  \n",
       "196      5400       24          28  15985  \n",
       "197      5400       24          28  16515  \n",
       "198      5100       17          22  18420  \n",
       "199      5100       17          22  18950  \n",
       "200      5400       23          28  16845  \n",
       "201      5300       19          25  19045  \n",
       "202      5500       18          23  21485  \n",
       "203      4800       26          27  22470  \n",
       "204      5400       19          25  22625  \n",
       "\n",
       "[205 rows x 26 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we can drop missing values along the column \"price\" as follows\n",
    "df.dropna(subset=[\"price\"], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['symboling', 'normalized-losses', 'make', 'fuel-type', 'aspiration',\n",
      "       'num-of-doors', 'body-style', 'drive-wheels', 'engine-location',\n",
      "       'wheel-base', 'length', 'width', 'height', 'curb-weight', 'engine-type',\n",
      "       'num-of-cylinders', 'engine-size', 'fuel-system', 'bore', 'stroke',\n",
      "       'compression-ratio', 'horsepower', 'peak-rpm', 'city-mpg',\n",
      "       'highway-mpg', 'price'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#Find the name of the columns of the dataframe\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "symboling              int64\n",
      "normalized-losses     object\n",
      "make                  object\n",
      "fuel-type             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                  object\n",
      "stroke                object\n",
      "compression-ratio    float64\n",
      "horsepower            object\n",
      "peak-rpm              object\n",
      "city-mpg               int64\n",
      "highway-mpg            int64\n",
      "price                 object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# check the data type of data frame \"df\" by .dtypes\n",
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\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>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>compression-ratio</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.834146</td>\n",
       "      <td>98.756585</td>\n",
       "      <td>174.049268</td>\n",
       "      <td>65.907805</td>\n",
       "      <td>53.724878</td>\n",
       "      <td>2555.565854</td>\n",
       "      <td>126.907317</td>\n",
       "      <td>10.142537</td>\n",
       "      <td>25.219512</td>\n",
       "      <td>30.751220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.245307</td>\n",
       "      <td>6.021776</td>\n",
       "      <td>12.337289</td>\n",
       "      <td>2.145204</td>\n",
       "      <td>2.443522</td>\n",
       "      <td>520.680204</td>\n",
       "      <td>41.642693</td>\n",
       "      <td>3.972040</td>\n",
       "      <td>6.542142</td>\n",
       "      <td>6.886443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.000000</td>\n",
       "      <td>86.600000</td>\n",
       "      <td>141.100000</td>\n",
       "      <td>60.300000</td>\n",
       "      <td>47.800000</td>\n",
       "      <td>1488.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.500000</td>\n",
       "      <td>166.300000</td>\n",
       "      <td>64.100000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>2145.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>173.200000</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>54.100000</td>\n",
       "      <td>2414.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>102.400000</td>\n",
       "      <td>183.100000</td>\n",
       "      <td>66.900000</td>\n",
       "      <td>55.500000</td>\n",
       "      <td>2935.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>120.900000</td>\n",
       "      <td>208.100000</td>\n",
       "      <td>72.300000</td>\n",
       "      <td>59.800000</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>54.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        symboling  wheel-base      length       width      height  \\\n",
       "count  205.000000  205.000000  205.000000  205.000000  205.000000   \n",
       "mean     0.834146   98.756585  174.049268   65.907805   53.724878   \n",
       "std      1.245307    6.021776   12.337289    2.145204    2.443522   \n",
       "min     -2.000000   86.600000  141.100000   60.300000   47.800000   \n",
       "25%      0.000000   94.500000  166.300000   64.100000   52.000000   \n",
       "50%      1.000000   97.000000  173.200000   65.500000   54.100000   \n",
       "75%      2.000000  102.400000  183.100000   66.900000   55.500000   \n",
       "max      3.000000  120.900000  208.100000   72.300000   59.800000   \n",
       "\n",
       "       curb-weight  engine-size  compression-ratio    city-mpg  highway-mpg  \n",
       "count   205.000000   205.000000         205.000000  205.000000   205.000000  \n",
       "mean   2555.565854   126.907317          10.142537   25.219512    30.751220  \n",
       "std     520.680204    41.642693           3.972040    6.542142     6.886443  \n",
       "min    1488.000000    61.000000           7.000000   13.000000    16.000000  \n",
       "25%    2145.000000    97.000000           8.600000   19.000000    25.000000  \n",
       "50%    2414.000000   120.000000           9.000000   24.000000    30.000000  \n",
       "75%    2935.000000   141.000000           9.400000   30.000000    34.000000  \n",
       "max    4066.000000   326.000000          23.000000   49.000000    54.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</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>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>205.000000</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>52</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>39</td>\n",
       "      <td>37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "      <td>24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>?</td>\n",
       "      <td>toyota</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68</td>\n",
       "      <td>5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "      <td>32</td>\n",
       "      <td>185</td>\n",
       "      <td>168</td>\n",
       "      <td>114</td>\n",
       "      <td>96</td>\n",
       "      <td>120</td>\n",
       "      <td>202</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19</td>\n",
       "      <td>37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.834146</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.756585</td>\n",
       "      <td>...</td>\n",
       "      <td>126.907317</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.142537</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.219512</td>\n",
       "      <td>30.751220</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.245307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.021776</td>\n",
       "      <td>...</td>\n",
       "      <td>41.642693</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.972040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.542142</td>\n",
       "      <td>6.886443</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.400000</td>\n",
       "      <td>...</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120.900000</td>\n",
       "      <td>...</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         symboling normalized-losses    make fuel-type aspiration  \\\n",
       "count   205.000000               205     205       205        205   \n",
       "unique         NaN                52      22         2          2   \n",
       "top            NaN                 ?  toyota       gas        std   \n",
       "freq           NaN                41      32       185        168   \n",
       "mean      0.834146               NaN     NaN       NaN        NaN   \n",
       "std       1.245307               NaN     NaN       NaN        NaN   \n",
       "min      -2.000000               NaN     NaN       NaN        NaN   \n",
       "25%       0.000000               NaN     NaN       NaN        NaN   \n",
       "50%       1.000000               NaN     NaN       NaN        NaN   \n",
       "75%       2.000000               NaN     NaN       NaN        NaN   \n",
       "max       3.000000               NaN     NaN       NaN        NaN   \n",
       "\n",
       "       num-of-doors body-style drive-wheels engine-location  wheel-base  ...  \\\n",
       "count           205        205          205             205  205.000000  ...   \n",
       "unique            3          5            3               2         NaN  ...   \n",
       "top            four      sedan          fwd           front         NaN  ...   \n",
       "freq            114         96          120             202         NaN  ...   \n",
       "mean            NaN        NaN          NaN             NaN   98.756585  ...   \n",
       "std             NaN        NaN          NaN             NaN    6.021776  ...   \n",
       "min             NaN        NaN          NaN             NaN   86.600000  ...   \n",
       "25%             NaN        NaN          NaN             NaN   94.500000  ...   \n",
       "50%             NaN        NaN          NaN             NaN   97.000000  ...   \n",
       "75%             NaN        NaN          NaN             NaN  102.400000  ...   \n",
       "max             NaN        NaN          NaN             NaN  120.900000  ...   \n",
       "\n",
       "        engine-size  fuel-system  bore  stroke compression-ratio horsepower  \\\n",
       "count    205.000000          205   205     205        205.000000        205   \n",
       "unique          NaN            8    39      37               NaN         60   \n",
       "top             NaN         mpfi  3.62    3.40               NaN         68   \n",
       "freq            NaN           94    23      20               NaN         19   \n",
       "mean     126.907317          NaN   NaN     NaN         10.142537        NaN   \n",
       "std       41.642693          NaN   NaN     NaN          3.972040        NaN   \n",
       "min       61.000000          NaN   NaN     NaN          7.000000        NaN   \n",
       "25%       97.000000          NaN   NaN     NaN          8.600000        NaN   \n",
       "50%      120.000000          NaN   NaN     NaN          9.000000        NaN   \n",
       "75%      141.000000          NaN   NaN     NaN          9.400000        NaN   \n",
       "max      326.000000          NaN   NaN     NaN         23.000000        NaN   \n",
       "\n",
       "        peak-rpm    city-mpg highway-mpg price  \n",
       "count        205  205.000000  205.000000   205  \n",
       "unique        24         NaN         NaN   187  \n",
       "top         5500         NaN         NaN     ?  \n",
       "freq          37         NaN         NaN     4  \n",
       "mean         NaN   25.219512   30.751220   NaN  \n",
       "std          NaN    6.542142    6.886443   NaN  \n",
       "min          NaN   13.000000   16.000000   NaN  \n",
       "25%          NaN   19.000000   25.000000   NaN  \n",
       "50%          NaN   24.000000   30.000000   NaN  \n",
       "75%          NaN   30.000000   34.000000   NaN  \n",
       "max          NaN   49.000000   54.000000   NaN  \n",
       "\n",
       "[11 rows x 26 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#You can add an argument include = \"all\" inside the bracket. Let's try it again.\n",
    "# describe all the columns in \"df\" \n",
    "df.describe(include = \"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of      symboling normalized-losses         make fuel-type aspiration  \\\n",
       "0            3                 ?  alfa-romero       gas        std   \n",
       "1            3                 ?  alfa-romero       gas        std   \n",
       "2            1                 ?  alfa-romero       gas        std   \n",
       "3            2               164         audi       gas        std   \n",
       "4            2               164         audi       gas        std   \n",
       "5            2                 ?         audi       gas        std   \n",
       "6            1               158         audi       gas        std   \n",
       "7            1                 ?         audi       gas        std   \n",
       "8            1               158         audi       gas      turbo   \n",
       "9            0                 ?         audi       gas      turbo   \n",
       "10           2               192          bmw       gas        std   \n",
       "11           0               192          bmw       gas        std   \n",
       "12           0               188          bmw       gas        std   \n",
       "13           0               188          bmw       gas        std   \n",
       "14           1                 ?          bmw       gas        std   \n",
       "15           0                 ?          bmw       gas        std   \n",
       "16           0                 ?          bmw       gas        std   \n",
       "17           0                 ?          bmw       gas        std   \n",
       "18           2               121    chevrolet       gas        std   \n",
       "19           1                98    chevrolet       gas        std   \n",
       "20           0                81    chevrolet       gas        std   \n",
       "21           1               118        dodge       gas        std   \n",
       "22           1               118        dodge       gas        std   \n",
       "23           1               118        dodge       gas      turbo   \n",
       "24           1               148        dodge       gas        std   \n",
       "25           1               148        dodge       gas        std   \n",
       "26           1               148        dodge       gas        std   \n",
       "27           1               148        dodge       gas      turbo   \n",
       "28          -1               110        dodge       gas        std   \n",
       "29           3               145        dodge       gas      turbo   \n",
       "..         ...               ...          ...       ...        ...   \n",
       "175         -1                65       toyota       gas        std   \n",
       "176         -1                65       toyota       gas        std   \n",
       "177         -1                65       toyota       gas        std   \n",
       "178          3               197       toyota       gas        std   \n",
       "179          3               197       toyota       gas        std   \n",
       "180         -1                90       toyota       gas        std   \n",
       "181         -1                 ?       toyota       gas        std   \n",
       "182          2               122   volkswagen    diesel        std   \n",
       "183          2               122   volkswagen       gas        std   \n",
       "184          2                94   volkswagen    diesel        std   \n",
       "185          2                94   volkswagen       gas        std   \n",
       "186          2                94   volkswagen       gas        std   \n",
       "187          2                94   volkswagen    diesel      turbo   \n",
       "188          2                94   volkswagen       gas        std   \n",
       "189          3                 ?   volkswagen       gas        std   \n",
       "190          3               256   volkswagen       gas        std   \n",
       "191          0                 ?   volkswagen       gas        std   \n",
       "192          0                 ?   volkswagen    diesel      turbo   \n",
       "193          0                 ?   volkswagen       gas        std   \n",
       "194         -2               103        volvo       gas        std   \n",
       "195         -1                74        volvo       gas        std   \n",
       "196         -2               103        volvo       gas        std   \n",
       "197         -1                74        volvo       gas        std   \n",
       "198         -2               103        volvo       gas      turbo   \n",
       "199         -1                74        volvo       gas      turbo   \n",
       "200         -1                95        volvo       gas        std   \n",
       "201         -1                95        volvo       gas      turbo   \n",
       "202         -1                95        volvo       gas        std   \n",
       "203         -1                95        volvo    diesel      turbo   \n",
       "204         -1                95        volvo       gas      turbo   \n",
       "\n",
       "    num-of-doors   body-style drive-wheels engine-location  wheel-base  ...  \\\n",
       "0            two  convertible          rwd           front        88.6  ...   \n",
       "1            two  convertible          rwd           front        88.6  ...   \n",
       "2            two    hatchback          rwd           front        94.5  ...   \n",
       "3           four        sedan          fwd           front        99.8  ...   \n",
       "4           four        sedan          4wd           front        99.4  ...   \n",
       "5            two        sedan          fwd           front        99.8  ...   \n",
       "6           four        sedan          fwd           front       105.8  ...   \n",
       "7           four        wagon          fwd           front       105.8  ...   \n",
       "8           four        sedan          fwd           front       105.8  ...   \n",
       "9            two    hatchback          4wd           front        99.5  ...   \n",
       "10           two        sedan          rwd           front       101.2  ...   \n",
       "11          four        sedan          rwd           front       101.2  ...   \n",
       "12           two        sedan          rwd           front       101.2  ...   \n",
       "13          four        sedan          rwd           front       101.2  ...   \n",
       "14          four        sedan          rwd           front       103.5  ...   \n",
       "15          four        sedan          rwd           front       103.5  ...   \n",
       "16           two        sedan          rwd           front       103.5  ...   \n",
       "17          four        sedan          rwd           front       110.0  ...   \n",
       "18           two    hatchback          fwd           front        88.4  ...   \n",
       "19           two    hatchback          fwd           front        94.5  ...   \n",
       "20          four        sedan          fwd           front        94.5  ...   \n",
       "21           two    hatchback          fwd           front        93.7  ...   \n",
       "22           two    hatchback          fwd           front        93.7  ...   \n",
       "23           two    hatchback          fwd           front        93.7  ...   \n",
       "24          four    hatchback          fwd           front        93.7  ...   \n",
       "25          four        sedan          fwd           front        93.7  ...   \n",
       "26          four        sedan          fwd           front        93.7  ...   \n",
       "27             ?        sedan          fwd           front        93.7  ...   \n",
       "28          four        wagon          fwd           front       103.3  ...   \n",
       "29           two    hatchback          fwd           front        95.9  ...   \n",
       "..           ...          ...          ...             ...         ...  ...   \n",
       "175         four    hatchback          fwd           front       102.4  ...   \n",
       "176         four        sedan          fwd           front       102.4  ...   \n",
       "177         four    hatchback          fwd           front       102.4  ...   \n",
       "178          two    hatchback          rwd           front       102.9  ...   \n",
       "179          two    hatchback          rwd           front       102.9  ...   \n",
       "180         four        sedan          rwd           front       104.5  ...   \n",
       "181         four        wagon          rwd           front       104.5  ...   \n",
       "182          two        sedan          fwd           front        97.3  ...   \n",
       "183          two        sedan          fwd           front        97.3  ...   \n",
       "184         four        sedan          fwd           front        97.3  ...   \n",
       "185         four        sedan          fwd           front        97.3  ...   \n",
       "186         four        sedan          fwd           front        97.3  ...   \n",
       "187         four        sedan          fwd           front        97.3  ...   \n",
       "188         four        sedan          fwd           front        97.3  ...   \n",
       "189          two  convertible          fwd           front        94.5  ...   \n",
       "190          two    hatchback          fwd           front        94.5  ...   \n",
       "191         four        sedan          fwd           front       100.4  ...   \n",
       "192         four        sedan          fwd           front       100.4  ...   \n",
       "193         four        wagon          fwd           front       100.4  ...   \n",
       "194         four        sedan          rwd           front       104.3  ...   \n",
       "195         four        wagon          rwd           front       104.3  ...   \n",
       "196         four        sedan          rwd           front       104.3  ...   \n",
       "197         four        wagon          rwd           front       104.3  ...   \n",
       "198         four        sedan          rwd           front       104.3  ...   \n",
       "199         four        wagon          rwd           front       104.3  ...   \n",
       "200         four        sedan          rwd           front       109.1  ...   \n",
       "201         four        sedan          rwd           front       109.1  ...   \n",
       "202         four        sedan          rwd           front       109.1  ...   \n",
       "203         four        sedan          rwd           front       109.1  ...   \n",
       "204         four        sedan          rwd           front       109.1  ...   \n",
       "\n",
       "     engine-size  fuel-system  bore  stroke compression-ratio horsepower  \\\n",
       "0            130         mpfi  3.47    2.68              9.00        111   \n",
       "1            130         mpfi  3.47    2.68              9.00        111   \n",
       "2            152         mpfi  2.68    3.47              9.00        154   \n",
       "3            109         mpfi  3.19    3.40             10.00        102   \n",
       "4            136         mpfi  3.19    3.40              8.00        115   \n",
       "5            136         mpfi  3.19    3.40              8.50        110   \n",
       "6            136         mpfi  3.19    3.40              8.50        110   \n",
       "7            136         mpfi  3.19    3.40              8.50        110   \n",
       "8            131         mpfi  3.13    3.40              8.30        140   \n",
       "9            131         mpfi  3.13    3.40              7.00        160   \n",
       "10           108         mpfi  3.50    2.80              8.80        101   \n",
       "11           108         mpfi  3.50    2.80              8.80        101   \n",
       "12           164         mpfi  3.31    3.19              9.00        121   \n",
       "13           164         mpfi  3.31    3.19              9.00        121   \n",
       "14           164         mpfi  3.31    3.19              9.00        121   \n",
       "15           209         mpfi  3.62    3.39              8.00        182   \n",
       "16           209         mpfi  3.62    3.39              8.00        182   \n",
       "17           209         mpfi  3.62    3.39              8.00        182   \n",
       "18            61         2bbl  2.91    3.03              9.50         48   \n",
       "19            90         2bbl  3.03    3.11              9.60         70   \n",
       "20            90         2bbl  3.03    3.11              9.60         70   \n",
       "21            90         2bbl  2.97    3.23              9.41         68   \n",
       "22            90         2bbl  2.97    3.23              9.40         68   \n",
       "23            98         mpfi  3.03    3.39              7.60        102   \n",
       "24            90         2bbl  2.97    3.23              9.40         68   \n",
       "25            90         2bbl  2.97    3.23              9.40         68   \n",
       "26            90         2bbl  2.97    3.23              9.40         68   \n",
       "27            98         mpfi  3.03    3.39              7.60        102   \n",
       "28           122         2bbl  3.34    3.46              8.50         88   \n",
       "29           156          mfi  3.60    3.90              7.00        145   \n",
       "..           ...          ...   ...     ...               ...        ...   \n",
       "175          122         mpfi  3.31    3.54              8.70         92   \n",
       "176          122         mpfi  3.31    3.54              8.70         92   \n",
       "177          122         mpfi  3.31    3.54              8.70         92   \n",
       "178          171         mpfi  3.27    3.35              9.30        161   \n",
       "179          171         mpfi  3.27    3.35              9.30        161   \n",
       "180          171         mpfi  3.27    3.35              9.20        156   \n",
       "181          161         mpfi  3.27    3.35              9.20        156   \n",
       "182           97          idi  3.01    3.40             23.00         52   \n",
       "183          109         mpfi  3.19    3.40              9.00         85   \n",
       "184           97          idi  3.01    3.40             23.00         52   \n",
       "185          109         mpfi  3.19    3.40              9.00         85   \n",
       "186          109         mpfi  3.19    3.40              9.00         85   \n",
       "187           97          idi  3.01    3.40             23.00         68   \n",
       "188          109         mpfi  3.19    3.40             10.00        100   \n",
       "189          109         mpfi  3.19    3.40              8.50         90   \n",
       "190          109         mpfi  3.19    3.40              8.50         90   \n",
       "191          136         mpfi  3.19    3.40              8.50        110   \n",
       "192           97          idi  3.01    3.40             23.00         68   \n",
       "193          109         mpfi  3.19    3.40              9.00         88   \n",
       "194          141         mpfi  3.78    3.15              9.50        114   \n",
       "195          141         mpfi  3.78    3.15              9.50        114   \n",
       "196          141         mpfi  3.78    3.15              9.50        114   \n",
       "197          141         mpfi  3.78    3.15              9.50        114   \n",
       "198          130         mpfi  3.62    3.15              7.50        162   \n",
       "199          130         mpfi  3.62    3.15              7.50        162   \n",
       "200          141         mpfi  3.78    3.15              9.50        114   \n",
       "201          141         mpfi  3.78    3.15              8.70        160   \n",
       "202          173         mpfi  3.58    2.87              8.80        134   \n",
       "203          145          idi  3.01    3.40             23.00        106   \n",
       "204          141         mpfi  3.78    3.15              9.50        114   \n",
       "\n",
       "     peak-rpm city-mpg highway-mpg  price  \n",
       "0        5000       21          27  13495  \n",
       "1        5000       21          27  16500  \n",
       "2        5000       19          26  16500  \n",
       "3        5500       24          30  13950  \n",
       "4        5500       18          22  17450  \n",
       "5        5500       19          25  15250  \n",
       "6        5500       19          25  17710  \n",
       "7        5500       19          25  18920  \n",
       "8        5500       17          20  23875  \n",
       "9        5500       16          22      ?  \n",
       "10       5800       23          29  16430  \n",
       "11       5800       23          29  16925  \n",
       "12       4250       21          28  20970  \n",
       "13       4250       21          28  21105  \n",
       "14       4250       20          25  24565  \n",
       "15       5400       16          22  30760  \n",
       "16       5400       16          22  41315  \n",
       "17       5400       15          20  36880  \n",
       "18       5100       47          53   5151  \n",
       "19       5400       38          43   6295  \n",
       "20       5400       38          43   6575  \n",
       "21       5500       37          41   5572  \n",
       "22       5500       31          38   6377  \n",
       "23       5500       24          30   7957  \n",
       "24       5500       31          38   6229  \n",
       "25       5500       31          38   6692  \n",
       "26       5500       31          38   7609  \n",
       "27       5500       24          30   8558  \n",
       "28       5000       24          30   8921  \n",
       "29       5000       19          24  12964  \n",
       "..        ...      ...         ...    ...  \n",
       "175      4200       27          32   9988  \n",
       "176      4200       27          32  10898  \n",
       "177      4200       27          32  11248  \n",
       "178      5200       20          24  16558  \n",
       "179      5200       19          24  15998  \n",
       "180      5200       20          24  15690  \n",
       "181      5200       19          24  15750  \n",
       "182      4800       37          46   7775  \n",
       "183      5250       27          34   7975  \n",
       "184      4800       37          46   7995  \n",
       "185      5250       27          34   8195  \n",
       "186      5250       27          34   8495  \n",
       "187      4500       37          42   9495  \n",
       "188      5500       26          32   9995  \n",
       "189      5500       24          29  11595  \n",
       "190      5500       24          29   9980  \n",
       "191      5500       19          24  13295  \n",
       "192      4500       33          38  13845  \n",
       "193      5500       25          31  12290  \n",
       "194      5400       23          28  12940  \n",
       "195      5400       23          28  13415  \n",
       "196      5400       24          28  15985  \n",
       "197      5400       24          28  16515  \n",
       "198      5100       17          22  18420  \n",
       "199      5100       17          22  18950  \n",
       "200      5400       23          28  16845  \n",
       "201      5300       19          25  19045  \n",
       "202      5500       18          23  21485  \n",
       "203      4800       26          27  22470  \n",
       "204      5400       19          25  22625  \n",
       "\n",
       "[205 rows x 26 columns]>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at the info of \"df\"\n",
    "df.info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How to work with missing data?\n",
    "\n",
    "Steps for working with missing data:\n",
    "\n",
    "dentify missing data\n",
    "deal with missing data\n",
    "correct data format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</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>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</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>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</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>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</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>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</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>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</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>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling normalized-losses         make fuel-type aspiration num-of-doors  \\\n",
       "0          3               NaN  alfa-romero       gas        std          two   \n",
       "1          3               NaN  alfa-romero       gas        std          two   \n",
       "2          1               NaN  alfa-romero       gas        std          two   \n",
       "3          2               164         audi       gas        std         four   \n",
       "4          2               164         audi       gas        std         four   \n",
       "\n",
       "    body-style drive-wheels engine-location  wheel-base  ...  engine-size  \\\n",
       "0  convertible          rwd           front        88.6  ...          130   \n",
       "1  convertible          rwd           front        88.6  ...          130   \n",
       "2    hatchback          rwd           front        94.5  ...          152   \n",
       "3        sedan          fwd           front        99.8  ...          109   \n",
       "4        sedan          4wd           front        99.4  ...          136   \n",
       "\n",
       "   fuel-system  bore  stroke compression-ratio horsepower  peak-rpm city-mpg  \\\n",
       "0         mpfi  3.47    2.68               9.0        111      5000       21   \n",
       "1         mpfi  3.47    2.68               9.0        111      5000       21   \n",
       "2         mpfi  2.68    3.47               9.0        154      5000       19   \n",
       "3         mpfi  3.19    3.40              10.0        102      5500       24   \n",
       "4         mpfi  3.19    3.40               8.0        115      5500       18   \n",
       "\n",
       "  highway-mpg  price  \n",
       "0          27  13495  \n",
       "1          27  16500  \n",
       "2          26  16500  \n",
       "3          30  13950  \n",
       "4          22  17450  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# replace \"?\" to NaN\n",
    "df.replace(\"?\", np.nan, inplace = True)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating for Missing Data\n",
    "\n",
    "The missing values are converted to Python's default. We use Python's built-in functions to identify these missing values. \n",
    "\n",
    "There are two methods to detect missing data:\n",
    "\n",
    "1.isnull()\n",
    "2.notnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</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>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  normalized-losses   make  fuel-type  aspiration  num-of-doors  \\\n",
       "0      False               True  False      False       False         False   \n",
       "1      False               True  False      False       False         False   \n",
       "2      False               True  False      False       False         False   \n",
       "3      False              False  False      False       False         False   \n",
       "4      False              False  False      False       False         False   \n",
       "\n",
       "   body-style  drive-wheels  engine-location  wheel-base  ...  engine-size  \\\n",
       "0       False         False            False       False  ...        False   \n",
       "1       False         False            False       False  ...        False   \n",
       "2       False         False            False       False  ...        False   \n",
       "3       False         False            False       False  ...        False   \n",
       "4       False         False            False       False  ...        False   \n",
       "\n",
       "   fuel-system   bore  stroke  compression-ratio  horsepower  peak-rpm  \\\n",
       "0        False  False   False              False       False     False   \n",
       "1        False  False   False              False       False     False   \n",
       "2        False  False   False              False       False     False   \n",
       "3        False  False   False              False       False     False   \n",
       "4        False  False   False              False       False     False   \n",
       "\n",
       "   city-mpg  highway-mpg  price  \n",
       "0     False        False  False  \n",
       "1     False        False  False  \n",
       "2     False        False  False  \n",
       "3     False        False  False  \n",
       "4     False        False  False  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data = df.isnull()\n",
    "missing_data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Count missing values in each column:\n",
    "\n",
    "Using a for loop in Python, we can quickly figure out the number of missing values in each column. As mentioned above, \"True\"\n",
    "\n",
    "represents a missing value, \"False\" means the value is present in the dataset. In the body of the for loop the method \n",
    "\n",
    "\".value_counts()\" counts the number of \"True\" values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "symboling\n",
      "False    205\n",
      "Name: symboling, dtype: int64\n",
      "\n",
      "normalized-losses\n",
      "False    164\n",
      "True      41\n",
      "Name: normalized-losses, dtype: int64\n",
      "\n",
      "make\n",
      "False    205\n",
      "Name: make, dtype: int64\n",
      "\n",
      "fuel-type\n",
      "False    205\n",
      "Name: fuel-type, dtype: int64\n",
      "\n",
      "aspiration\n",
      "False    205\n",
      "Name: aspiration, dtype: int64\n",
      "\n",
      "num-of-doors\n",
      "False    203\n",
      "True       2\n",
      "Name: num-of-doors, dtype: int64\n",
      "\n",
      "body-style\n",
      "False    205\n",
      "Name: body-style, dtype: int64\n",
      "\n",
      "drive-wheels\n",
      "False    205\n",
      "Name: drive-wheels, dtype: int64\n",
      "\n",
      "engine-location\n",
      "False    205\n",
      "Name: engine-location, dtype: int64\n",
      "\n",
      "wheel-base\n",
      "False    205\n",
      "Name: wheel-base, dtype: int64\n",
      "\n",
      "length\n",
      "False    205\n",
      "Name: length, dtype: int64\n",
      "\n",
      "width\n",
      "False    205\n",
      "Name: width, dtype: int64\n",
      "\n",
      "height\n",
      "False    205\n",
      "Name: height, dtype: int64\n",
      "\n",
      "curb-weight\n",
      "False    205\n",
      "Name: curb-weight, dtype: int64\n",
      "\n",
      "engine-type\n",
      "False    205\n",
      "Name: engine-type, dtype: int64\n",
      "\n",
      "num-of-cylinders\n",
      "False    205\n",
      "Name: num-of-cylinders, dtype: int64\n",
      "\n",
      "engine-size\n",
      "False    205\n",
      "Name: engine-size, dtype: int64\n",
      "\n",
      "fuel-system\n",
      "False    205\n",
      "Name: fuel-system, dtype: int64\n",
      "\n",
      "bore\n",
      "False    201\n",
      "True       4\n",
      "Name: bore, dtype: int64\n",
      "\n",
      "stroke\n",
      "False    201\n",
      "True       4\n",
      "Name: stroke, dtype: int64\n",
      "\n",
      "compression-ratio\n",
      "False    205\n",
      "Name: compression-ratio, dtype: int64\n",
      "\n",
      "horsepower\n",
      "False    203\n",
      "True       2\n",
      "Name: horsepower, dtype: int64\n",
      "\n",
      "peak-rpm\n",
      "False    203\n",
      "True       2\n",
      "Name: peak-rpm, dtype: int64\n",
      "\n",
      "city-mpg\n",
      "False    205\n",
      "Name: city-mpg, dtype: int64\n",
      "\n",
      "highway-mpg\n",
      "False    205\n",
      "Name: highway-mpg, dtype: int64\n",
      "\n",
      "price\n",
      "False    201\n",
      "True       4\n",
      "Name: price, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for column in missing_data.columns.values.tolist():\n",
    "    print(column)\n",
    "    print (missing_data[column].value_counts())\n",
    "    print(\"\")    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the summary above, each column has 205 rows of data, seven columns containing missing data:\n",
    "\n",
    "\"normalized-losses\": 41 missing data\n",
    "\"num-of-doors\": 2 missing data\n",
    "\"bore\": 4 missing data\n",
    "\"stroke\" : 4 missing data\n",
    "\"horsepower\": 2 missing data\n",
    "\"peak-rpm\": 2 missing data\n",
    "\"price\": 4 missing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deal with missing data\n",
    "How to deal with missing data?\n",
    "drop data\n",
    "a. drop the whole row\n",
    "b. drop the whole column\n",
    "replace data\n",
    "a. replace it by mean\n",
    "b. replace it by frequency\n",
    "c. replace it based on other functions\n",
    "Whole columns should be dropped only if most entries in the column are empty. In our dataset, none of the columns are empty enough to drop entirely. We have some freedom in choosing which method to replace data; however, some methods may seem more reasonable than others. We will apply each method to many different columns:\n",
    "\n",
    "Replace by mean:\n",
    "\n",
    "\"normalized-losses\": 41 missing data, replace them with mean\n",
    "\"stroke\": 4 missing data, replace them with mean\n",
    "\"bore\": 4 missing data, replace them with mean\n",
    "\"horsepower\": 2 missing data, replace them with mean\n",
    "\"peak-rpm\": 2 missing data, replace them with mean\n",
    "Replace by frequency:\n",
    "\n",
    "\"num-of-doors\": 2 missing data, replace them with \"four\".\n",
    "Reason: 84% sedans is four doors. Since four doors is most frequent, it is most likely to occur\n",
    "Drop the whole row:\n",
    "\n",
    "\"price\": 4 missing data, simply delete the whole row\n",
    "Reason: price is what we want to predict. Any data entry without price data cannot be used for prediction; therefore any row now without price data is not useful to us"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of normalized-losses: 122.0\n"
     ]
    }
   ],
   "source": [
    "avg_norm_loss = df[\"normalized-losses\"].astype(\"float\").mean(axis=0)\n",
    "print(\"Average of normalized-losses:\", avg_norm_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replace \"NaN\" by mean value in \"normalized-losses\" column\n",
    "df[\"normalized-losses\"].replace(np.nan, avg_norm_loss, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of bore: 3.3297512437810957\n"
     ]
    }
   ],
   "source": [
    "#Calculate the mean value for 'bore' column\n",
    "avg_bore=df['bore'].astype('float').mean(axis=0)\n",
    "print(\"Average of bore:\", avg_bore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replace NaN by mean value\n",
    "df[\"bore\"].replace(np.nan, avg_bore, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avarage of stroke: 3.2554228855721337\n"
     ]
    }
   ],
   "source": [
    "#replace NaN in \"stroke\" column by mean.\n",
    "avg_stroke = df[\"stroke\"].astype(\"float\").mean(axis=0)\n",
    "print(\"avarage of stroke:\", avg_stroke)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"stroke\"].replace(np.nan,avg_stroke,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avarage of horsepower: 104.25615763546799\n"
     ]
    }
   ],
   "source": [
    "#Calculate the mean value for the 'horsepower' column:\n",
    "avg_horsepower = df[\"horsepower\"].astype(float).mean(axis=0)\n",
    "print(\"avarage of horsepower:\", avg_horsepower)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"horsepower\"].replace(np.nan,avg_horsepower,inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avarage of peak rpm is: 5125.369458128079\n"
     ]
    }
   ],
   "source": [
    "#Calculate the mean value for 'peak-rpm' column:\n",
    "avg_peakrpm = df['peak-rpm'].astype('float').mean(axis=0)\n",
    "print(\"avarage of peak rpm is:\", avg_peakrpm )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replace NaN by mean value\n",
    "df[\"peak-rpm\"].replace(np.nan,avg_peakrpm,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "four    114\n",
       "two      89\n",
       "Name: num-of-doors, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#o see which values are present in a particular column, we can use the \".value_counts()\" method:\n",
    "df['num-of-doors'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'four'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can see that four doors are the most common type. We can also use the \".idxmax()\" \n",
    "#method to calculate for us the most common type automatically:\n",
    "df['num-of-doors'].value_counts().idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replace the missing 'num-of-doors' values by the most frequent \n",
    "df[\"num-of-doors\"].replace(np.nan, \"four\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Finally, let's drop all rows that do not have price data:\n",
    "# simply drop whole row with NaN in \"price\" column\n",
    "df.dropna(subset=[\"price\"], axis=0, inplace=True)\n",
    "\n",
    "# reset index, because we droped two rows\n",
    "df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</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>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</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",
       "    </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>gas</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>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</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>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</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>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</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>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</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>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling normalized-losses         make fuel-type aspiration num-of-doors  \\\n",
       "0          3               122  alfa-romero       gas        std          two   \n",
       "1          3               122  alfa-romero       gas        std          two   \n",
       "2          1               122  alfa-romero       gas        std          two   \n",
       "3          2               164         audi       gas        std         four   \n",
       "4          2               164         audi       gas        std         four   \n",
       "\n",
       "    body-style drive-wheels engine-location  wheel-base  ...  engine-size  \\\n",
       "0  convertible          rwd           front        88.6  ...          130   \n",
       "1  convertible          rwd           front        88.6  ...          130   \n",
       "2    hatchback          rwd           front        94.5  ...          152   \n",
       "3        sedan          fwd           front        99.8  ...          109   \n",
       "4        sedan          4wd           front        99.4  ...          136   \n",
       "\n",
       "   fuel-system  bore  stroke compression-ratio horsepower  peak-rpm city-mpg  \\\n",
       "0         mpfi  3.47    2.68               9.0        111      5000       21   \n",
       "1         mpfi  3.47    2.68               9.0        111      5000       21   \n",
       "2         mpfi  2.68    3.47               9.0        154      5000       19   \n",
       "3         mpfi  3.19    3.40              10.0        102      5500       24   \n",
       "4         mpfi  3.19    3.40               8.0        115      5500       18   \n",
       "\n",
       "  highway-mpg  price  \n",
       "0          27  13495  \n",
       "1          27  16500  \n",
       "2          26  16500  \n",
       "3          30  13950  \n",
       "4          22  17450  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling              int64\n",
       "normalized-losses     object\n",
       "make                  object\n",
       "fuel-type             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                  object\n",
       "stroke                object\n",
       "compression-ratio    float64\n",
       "horsepower            object\n",
       "peak-rpm              object\n",
       "city-mpg               int64\n",
       "highway-mpg            int64\n",
       "price                 object\n",
       "dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert data types to proper format\n",
    "df[[\"bore\", \"stroke\"]] = df[[\"bore\", \"stroke\"]].astype(\"float\")\n",
    "df[[\"normalized-losses\"]] = df[[\"normalized-losses\"]].astype(\"int\")\n",
    "df[[\"price\"]] = df[[\"price\"]].astype(\"float\")\n",
    "df[[\"peak-rpm\"]] = df[[\"peak-rpm\"]].astype(\"float\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling              int64\n",
       "normalized-losses      int32\n",
       "make                  object\n",
       "fuel-type             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            object\n",
       "peak-rpm             float64\n",
       "city-mpg               int64\n",
       "highway-mpg            int64\n",
       "price                float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Standardization¶\n",
    "Data is usually collected from different agencies with different formats. (Data Standardization is also a term for a particular type of data normalization, where we subtract the mean and divide by the standard deviation)\n",
    "\n",
    "What is Standardization?\n",
    "\n",
    "Standardization is the process of transforming data into a common format which allows the researcher to make the meaningful comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</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>...</th>\n",
       "      <th>fuel-system</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",
       "    </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>gas</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>...</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</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>...</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</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>...</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</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>...</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</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>...</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  normalized-losses         make fuel-type aspiration  \\\n",
       "0          3                122  alfa-romero       gas        std   \n",
       "1          3                122  alfa-romero       gas        std   \n",
       "2          1                122  alfa-romero       gas        std   \n",
       "3          2                164         audi       gas        std   \n",
       "4          2                164         audi       gas        std   \n",
       "\n",
       "  num-of-doors   body-style drive-wheels engine-location  wheel-base  ...  \\\n",
       "0          two  convertible          rwd           front        88.6  ...   \n",
       "1          two  convertible          rwd           front        88.6  ...   \n",
       "2          two    hatchback          rwd           front        94.5  ...   \n",
       "3         four        sedan          fwd           front        99.8  ...   \n",
       "4         four        sedan          4wd           front        99.4  ...   \n",
       "\n",
       "   fuel-system  bore  stroke  compression-ratio horsepower peak-rpm  city-mpg  \\\n",
       "0         mpfi  3.47    2.68                9.0        111   5000.0        21   \n",
       "1         mpfi  3.47    2.68                9.0        111   5000.0        21   \n",
       "2         mpfi  2.68    3.47                9.0        154   5000.0        19   \n",
       "3         mpfi  3.19    3.40               10.0        102   5500.0        24   \n",
       "4         mpfi  3.19    3.40                8.0        115   5500.0        18   \n",
       "\n",
       "  highway-mpg    price  city-L/100km  \n",
       "0          27  13495.0     11.190476  \n",
       "1          27  16500.0     11.190476  \n",
       "2          26  16500.0     12.368421  \n",
       "3          30  13950.0      9.791667  \n",
       "4          22  17450.0     13.055556  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert mpg to L/100km by mathematical operation (235 divided by mpg)\n",
    "df['city-L/100km'] = 235/df[\"city-mpg\"]\n",
    "\n",
    "# check your transformed data \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Normalization¶\n",
    "Why normalization?\n",
    "\n",
    "Normalization is the process of transforming values of several variables into a similar range. Typical normalizations include scaling the variable so the variable average is 0, scaling the variable so the variance is 1, or scaling variable so the variable values range from 0 to 1\n",
    "\n",
    "Example\n",
    "\n",
    "To demonstrate normalization, let's say we want to scale the columns \"length\", \"width\" and \"height\"\n",
    "\n",
    "Target:would like to Normalize those variables so their value ranges from 0 to 1.\n",
    "\n",
    "Approach: replace original value by (original value)/(maximum value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace (original value) by (original value)/(maximum value)\n",
    "df['length'] = df['length']/df['length'].max()\n",
    "df['width'] = df['width']/df['width'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Binning¶\n",
    "Why binning?\n",
    "Binning is a process of transforming continuous numerical variables into discrete categorical 'bins', for grouped analysis.\n",
    "\n",
    "Example:\n",
    "\n",
    "In our dataset, \"horsepower\" is a real valued variable ranging from 48 to 288, it has 57 unique values. What if we only care about the price difference between cars with high horsepower, medium horsepower, and little horsepower (3 types)? Can we rearrange them into three ‘bins' to simplify analysis?\n",
    "\n",
    "We will use the Pandas method 'cut' to segment the 'horsepower' column into 3 bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"horsepower\"]=df[\"horsepower\"].astype(int, copy=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'horsepower bins')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Lets plot the histogram of horspower, to see what the distribution of horsepower looks like.\n",
    "%matplotlib inline\n",
    "import matplotlib as plt\n",
    "from matplotlib import pyplot\n",
    "plt.pyplot.hist(df[\"horsepower\"])\n",
    "\n",
    "# set x/y labels and plot title\n",
    "plt.pyplot.xlabel(\"horsepower\")\n",
    "plt.pyplot.ylabel(\"count\")\n",
    "plt.pyplot.title(\"horsepower bins\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 48.        , 119.33333333, 190.66666667, 262.        ])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = np.linspace(min(df[\"horsepower\"]), max(df[\"horsepower\"]), 4)\n",
    "bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We set group names:\n",
    "group_names = ['Low', 'Medium', 'High']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>horsepower</th>\n",
       "      <th>horsepower-binned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>111</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>111</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>154</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>115</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>110</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>110</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>110</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>140</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>101</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>101</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>121</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>121</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>121</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>182</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>182</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>182</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>48</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>70</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>70</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    horsepower horsepower-binned\n",
       "0          111               Low\n",
       "1          111               Low\n",
       "2          154            Medium\n",
       "3          102               Low\n",
       "4          115               Low\n",
       "5          110               Low\n",
       "6          110               Low\n",
       "7          110               Low\n",
       "8          140            Medium\n",
       "9          101               Low\n",
       "10         101               Low\n",
       "11         121            Medium\n",
       "12         121            Medium\n",
       "13         121            Medium\n",
       "14         182            Medium\n",
       "15         182            Medium\n",
       "16         182            Medium\n",
       "17          48               Low\n",
       "18          70               Low\n",
       "19          70               Low"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We apply the function \"cut\" the determine what each value of \"df['horsepower']\" belongs to.\n",
    "df['horsepower-binned'] = pd.cut(df['horsepower'], bins, labels=group_names, include_lowest=True )\n",
    "df[['horsepower','horsepower-binned']].head(20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low       153\n",
       "Medium     43\n",
       "High        5\n",
       "Name: horsepower-binned, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets see the number of vehicles in each bin.\n",
    "df[\"horsepower-binned\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'horsepower bins')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Lets plot the distribution of each bin.\n",
    "%matplotlib inline\n",
    "import matplotlib as plt\n",
    "from matplotlib import pyplot\n",
    "pyplot.bar(group_names, df[\"horsepower-binned\"].value_counts())\n",
    "\n",
    "# set x/y labels and plot title\n",
    "plt.pyplot.xlabel(\"horsepower\")\n",
    "plt.pyplot.ylabel(\"count\")\n",
    "plt.pyplot.title(\"horsepower bins\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\envs\\sharath\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6575: RuntimeWarning: All-NaN slice encountered\n",
      "  xmin = min(xmin, np.nanmin(xi))\n",
      "C:\\Users\\dell\\Anaconda3\\envs\\sharath\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6576: RuntimeWarning: All-NaN slice encountered\n",
      "  xmax = max(xmax, np.nanmax(xi))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'horsepower bins')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bins visualization\n",
    "#Normally, a histogram is used to visualize the distribution of bins we created above.\n",
    "%matplotlib inline\n",
    "import matplotlib as plt\n",
    "from matplotlib import pyplot\n",
    "\n",
    "a = (0,1,2)\n",
    "\n",
    "# draw historgram of attribute \"horsepower\" with bins = 3\n",
    "plt.pyplot.hist(df[\"horsepower\"], bins = 3)\n",
    "\n",
    "# set x/y labels and plot title\n",
    "plt.pyplot.xlabel(\"horsepower\")\n",
    "plt.pyplot.ylabel(\"count\")\n",
    "plt.pyplot.title(\"horsepower bins\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['symboling', 'normalized-losses', 'make', 'fuel-type', 'aspiration',\n",
       "       'num-of-doors', 'body-style', 'drive-wheels', 'engine-location',\n",
       "       'wheel-base', 'length', 'width', 'height', 'curb-weight', 'engine-type',\n",
       "       'num-of-cylinders', 'engine-size', 'fuel-system', 'bore', 'stroke',\n",
       "       'compression-ratio', 'horsepower', 'peak-rpm', 'city-mpg',\n",
       "       'highway-mpg', 'price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diesel</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   diesel  gas\n",
       "0       0    1\n",
       "1       0    1\n",
       "2       0    1\n",
       "3       0    1\n",
       "4       0    1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get indicator variables and assign it to data frame \"dummy_variable_1\"\n",
    "dummy_variable_1 = pd.get_dummies(df[\"fuel-type\"])\n",
    "dummy_variable_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diesel</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   diesel  gas\n",
       "0       0    1\n",
       "1       0    1\n",
       "2       0    1\n",
       "3       0    1\n",
       "4       0    1"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_variable_1.rename(columns={'fuel-type-diesel':'gas', 'fuel-type-diesel':'diesel'}, inplace=True)\n",
    "dummy_variable_1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have the value 0 to represent \"gas\" and 1 to represent \"diesel\" in the column \"fuel-type\". We will now insert this column back into our original dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>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",
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       "    <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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path='https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DA0101EN/automobileEDA.csv'\n",
    "df = pd.read_csv(path)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "! pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
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       "      <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",
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       "      <td>0.036233</td>\n",
       "      <td>-0.082391</td>\n",
       "      <td>0.066171</td>\n",
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       "      <td>0.196735</td>\n",
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       "    <tr>\n",
       "      <th>normalized-losses</th>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>wheel-base</th>\n",
       "      <td>-0.535987</td>\n",
       "      <td>-0.056661</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>width</th>\n",
       "      <td>-0.242423</td>\n",
       "      <td>0.086802</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>height</th>\n",
       "      <td>-0.550160</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>curb-weight</th>\n",
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       "      <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",
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       "      <td>0.608971</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <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",
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       "      <td>1.000000</td>\n",
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       "      <td>-0.034696</td>\n",
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       "      <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": [
    "#for example, we can calculate the correlation between variables of type \"int64\" or \"float64\" using the method \"corr\":\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bore</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.055390</td>\n",
       "      <td>0.001263</td>\n",
       "      <td>0.566936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stroke</th>\n",
       "      <td>-0.055390</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.187923</td>\n",
       "      <td>0.098462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compression-ratio</th>\n",
       "      <td>0.001263</td>\n",
       "      <td>0.187923</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.214514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>horsepower</th>\n",
       "      <td>0.566936</td>\n",
       "      <td>0.098462</td>\n",
       "      <td>-0.214514</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       bore    stroke  compression-ratio  horsepower\n",
       "bore               1.000000 -0.055390           0.001263    0.566936\n",
       "stroke            -0.055390  1.000000           0.187923    0.098462\n",
       "compression-ratio  0.001263  0.187923           1.000000   -0.214514\n",
       "horsepower         0.566936  0.098462          -0.214514    1.000000"
      ]
     },
     "execution_count": 38,
     "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": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\envs\\sharath\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0, 55911.98908044319)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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PNzK7wEUfvW4XdZUTK5iIyE5jzIpctp04V6WUUg5jDN2hGN2jTMQHwnEe2baf3/z+UKro42lz7aKPp80rbNHHiRhMRmJyX51SatIJxxK0940uEZ+wDP/55mF+8vL+1Mz6WdVl9oTRDw1d9DFfXreL2gpv0deaHy80qCilJoSEZegMRukLj668yit7O7h3Uwv7naKP5V43X7xwIX943nzKCljhd6oFkyQNKkqNA4VYYGsyL9IViMTpCERGlYjfezzIvZub2b7PrtElwJVnzuHLlzTmVaNrOFM1mCRpUFGqxAqxwNZkXaQrlrDoCEQJRUc+TLgrFOUnL+/jv948QjImnbuwljsuXcKSWVUFOtMTwaSqzFPQ7rOJRoOKUiVWiAW2JuMiXT2hGF2hkdfrisYtfv37Q/x0236CTtHHBXXl3LqmiYuXTC/YB78Gk4E0qChVYoVYYGsyLdI12npdxhi27D7O+i0tHOmxiz5W+z3ccNEiPnP2PLyjKPqYToNJZhpUlCqxQiywNRkW6TLGTsT3huMjHib8/tE+7t7UzFuHegC76ONV58zjhlWFKfoIEy9nMta5tqIv0qWUGlohFtia6It0haInFs4aSUBp74vw90/u4vafvpYKKBc1TefHN67gqx85pSABxet2MbO6jIb6igkVUL614R3a+sIDcm2bdrUNv/MI6Z2KUiVWiAW2JuoiXfGERWcwmlO9rldbOnl0+0GO9PYzt6acay9o4MyGafxi+0F+sf1gquhj08xK7rh0CectKkzRx4k8abEUubaJ9yopNQkVYoGtsV6ka7TdKvksnPVqSyd3bdyNxyXU+D0cD4T5+6d2YYyhN2wHpLoKL1++pJErzihM0ceJHEySSpFrm7ivllKqZEYzhHkkifhHtx/E4xLKvW5C0QTtgWjqzsTrFr6woqFgRR8nU6HHUuTaNKeilMpbereKiP3d6xbu29KSdR/LKU1/qKs/75FdR3r7cQkc7umntbs/FVD8HhcPfXklN69uHHVA8XlczKrxs6CuYlIEFChNrm1yvHJKqTGVb7dKKBqnIxAdUb2uQCSOZcH+zv5Um9/joqbcw7xpFcwZZRXhybaeSbpS5Nom36uo1AQ00UqsNNRVsK8jQG9/nGjCwue2P+QXTx84Qz2fRPxgdtHHI/zk5X2poo9uEWZU+/C6hISBay9oGOYo2U3mYJJurHNtk/vVVGoCmIglVi5qqufVfZ24BFwC0YRFW1+U6y6oT23TG47RGRjZjPjt+zq5Z1Mz+zrsOx+/18WapTM52h2mLRBmRpWfay9oYGVT/TBHOtlUCSaloq+qUiU2EUusbG3pZGaVj77wiTuVar+HrS2d3DaKhbP2dQS5d1Mzr6YVfbzijDl8+ZLFTK8qG9U5l3nd1FV4C76CoxpIX12lSmwillg52BViRlUZM6tP5DMsy2J/R5BD3f15T2DsDkV56OX9/Mebh1NFH89pqOWOtUs4ZZRFHzWYjC19lZUqsYlYYmXwOVvG0BeJM6van1dAicYtfvP7Q/zfV/YTjNh3NvNry7nt0tEXfdRgUhr6aitVYreuaeJbG94hFI1T7nXTH0uM+xIryXMORmL43C6C0QRxy+ScODfG8MIeu+jj4W676GNVmV308apzRlf00edxUV/p02BSIvqqK1Via5fP4urWbh54cS/BaIJKn5tbVjeO23wK2Of8V7EE925p4UhPP3Ocsim5JM4/OGYXfXyz1a7R5RK46pz53HDRIqaNokaXJuDHB331lSqxTbvaePy1Q8ysLmOhc6fy+GuHOGtB7bgMLAlnEuPSOdX8ny+cnfN+xwMRfvziXn73zjGSHWSrmuq5bc0SFk4fvqsvU+2vlU31k2oG/GSg74JSJTaRRn/1hWN0BqN5LesbjiV4bMdBHn31IGFnJnzjjEpuv7SJFYtzGxI8uPZXRzDCDzfu5m8qTuPKM+eO6FpUcWhQUarEJsLor5Es62sZw3PvtXH/Cy0cD0QBu+jjly5ZzJVnzM2r6GN67S8RodrvJhJP8PDW/RpUxhkNKkoVWL6z48f76K+RLOv7VmsPd29u5v2jfYBd9PEPz1vAFy9cOKKcx5Hefqb5vXjcrlQwGm+BV9k0qChVQCOZHT9eR3+NpJrwkZ5+1m/Zy+YP2lNta5fN5E/XNDJ3WvmIzsPrdrGwvoLOYJSytLub8RR41QkaVJQqoJHkR8bbAlvGGLpDMbrzWIUxEInzs1cO8KvXWokl7H2Wz6nmjrVLOGP+tBGdh9ftYlqFl+oyD19Ze8q4DLzqZBpUlCqgkeZHClH0rxBFKcOxBO19kZyrCScsw3+9dYSfvLSPbqfo48yqMm75cCMfO3UWrhFMXvS4XNRW2sEkOflxvAVelZ0GFaUKqFT5kdEWpbQsQ0cwSl84lvNzbt/Xyb2bW9h7PAjY5eivW7mQP1qxAL/Xnfc1eFz2nUmN35NxJv1YV9tVI1O0RbpExC8ir4rIGyLyjoj8D6e9UUReEZHdIvILEfE57WXO4z3O7xenHeubTvv7IvKJtPYrnLY9InJnsa5FqVyVYlEkGNmiWUmBSJzWrv6cA8r+jiDf/PVbfONXb7H3eBABPnH6bB6+eSV/ctGivAOKx+VielUZDfXlTCv3jqo0iyq9Yt6pRICPGmMCIuIFXhSRJ4G/AH5gjHlURO4Fbgbucb53GWNOEZFrge8D14jIacC1wOnAPOBZEVnmPMePgI8DrcB2EdlgjHm3iNek1JBK1U0zkm63eMLieB7DhHv6Yzz08j42vHGi6OPZC6Zx+9olLJtdnfc5e1wuppV7qSnPfGeiJqacg4qILAKWGmOeFZFywGOM6cu2vbEzfAHnodf5MsBHgT922h8C/hY7qFzl/AzwOPCvYv9Luwp41BgTAfaKyB5gpbPdHmNMi3N+jzrbalBRJVWKbpp8u93yGSYcS1j8++8P8ci2A6nFtubV+rltzRIuOSX/oo9ul1Bb7tNgMknlFFRE5E+BdUA9sARYANwLfGyY/dzATuAU7LuKZqDbGJP806gVmO/8PB84CGCMiYtIDzDdad+Wdtj0fQ4Oar8wl+tRarLJdVhyPsOEjTG8tKeD+7a0cKjbXsq3sszNn6xaxGfPmY/Pk1/vudslTCv3ahfXJJfrncpXsO8OXgEwxuwWkWH/FDPGJIBzRKQW+A1waqbNnO+Z/pWZIdoz/YvO+GeXiKzDDoosXLhwmLNWauIZrtvNGENnMEpvOJ7TMOHdx/q4Z3Mzrx88UfTxD86ex00XLWZaRX5FH5PBpMbvxZXHLHo1MeUaVCLGmGjyrwsR8ZDlAzwTY0y3iGwCVgG1IuJx7lYWAIedzVqBBqDVOf40oDOtPSl9n2ztg59/PbAeYMWKFfmvbarUBJCt260/muB4ILdhwh2BCD9+cR9Pv3M09R/8wsZ6br20icXTK/M6Hw0mU1OuQWWziPx3oFxEPg7cAfzHUDuIyEwg5gSUcuAy7OT788DVwKPAjcATzi4bnMdbnd9vNMYYEdkA/ExE/gk7Ub8UeBX7DmapiDQCh7CT+clcjVJTXsIydAQjBMLDJ+LDsQS/3NHKz7cfIByzg8/i6RXcvnYJF+RY9DHJJSe6uTSYTD25BpU7sUdnvQXcCvwWeGCYfeYCDzl5FRfwmDHmP0XkXeBREfku8Hvgx872PwYecRLxndhBAmPMOyLyGHYCPg58xelWQ0S+CjwNuIEHjTHv5Hg9Sk1quVYTtoxh46427t+yl/ZABIDaci83XbKYT52ZX9FHDSYKQHLpXxWRSiCc9mHuBsqMMROumtuKFSvMjh07Sn0aahL74bMfnLTg1tcuWzb8jgUQS1gcD0Tojw6fiH/7UA93b2pmV1rRx8+fO58vrlqU19okLhFqnGCSTxBSE4eI7DTGrMhl21z/5TyH3X2VHCJcDvwOuDj/01Nq8vrhsx9w18Y9uAQ8LntY710b9wAUNbAYY+jpj9EVGr5e19GeMOu3tLAprejjmmUzWPfhJubV5l70UYOJyiTXoOI3xiQDCs6ERi0PqtQgD7y41wko9uBEl0Dcsnjgxb1FCyrhmJ2Ij8aHTsQHI3F+9uoBHt95oujjh2bbRR/PXJB70UcRe6Gs2gqfBhN1klyDSlBEzjPGvAYgIucD/cU7LaUmpmA0weDpGy6x2wvNsgydoSi9/UOXV0lYhiffPsq/vbSXrpC97YwqH7esbuSy02bnXPRRg4nKRa5B5c+BX4pIcsjuXOCa4pySUhNXpc+eeJj+mWsZu72QgpE4HYEocWvou5PX9ndx9+ZmWtpPFH285oIGvnBBA+U51uiyV1r0UFtuL5Kl1FByCirGmO0ishz4EPZQ3l3GmNzLmSo1RdyyupG7Nu4hblm4xA4olrHbCyGesOgIRglGhh4mfKAjxL1bmtnW0plqu/y02dy8upGZ1WU5PZcGEzUSQwYVEfmoMWajiHx+0K+WigjGmF8X8dyUmnCSeZNijP7q6Y/RFRy6XldPf4yHt+5nwxuHU8OJz5xfwx1rT+FDc3Ir+igiVJV5qK3w4tVgovI03J3KpcBG4A8y/M4AGlSUGuRrly0raFI+l3pdsYTFE68f5pFt++lzJjvOnebn1jVNfHjpjJxrbVX5PdRV+DSYqBEbMqgYY74tIi7gSWPMY2N0Tkop7GHCXaEYPUMs62uM4eXmDqfml1P00efmi6sW8flzcy/6WOX3UFvuy7tIpFKDDZtTMcZYzsx1DSpKjZFc6nU1twW4e3Mzvz/QDThFH8+ax40XL6K2wpfT82gwUYWW6+ivZ0Tk68AvgGCy0RjTmX0Xpaam0awVn0u9rs5glAdf3MuTb58o+rhycR23rV2Sc9FHO2eiwUQVXq5B5cvYOZQ7BrUXd41UpSaY0awVH4jE6QhEstbrisQSPP5aKz975SD9Tn5l0fQKbr90CSsbcyv6qMFEFVuuQeU07ICyGju4vIC9SJdSKk36WvEAFT4PoWic+7a0ZA0qwy3ra4xh46527n+hhbY+u+hjjd/Dly5p5NNn5Vb0UYOJGiu5BpWHgF7gh87j65y2LxTjpJSaqPJdK364YcLvHO7hnk3NvHvELvrocQmfP28+11+4iCr/8P99K52hwWWewk6+VCqbXIPKh4wxZ6c9fl5E3ijGCSk1VkaT+8gm17Xio3G7mnA4yzDho71h7t/SwvPvnyj6+OGlM1i3pon5ORR91GCiSiXXoPJ7EVlljNkGICIXAi8V77SUKq7R5D6GMtxa8cYYukMxurMMEw5F4/zslQP8Mq3o49JZVdyxdglnN9QO+/wVPjuY+HMswaJUoeUaVC4EbhCRA87jhcB7IvIWYIwxZxXl7JQqkpHkPnIx1Frx4ViC9r7Mw4QTluGpt4/yYFrRx+lVPm6+pJHLTx++6KMGEzVe5BpUrijqWahJrRjdTKOVb+4jH4PXircsQ3tfhL5w5nJ5rx3o4p5NzTQ7RR/LPC6uWdHANSuHL/pY7nNTV+HTYKLGjVwLSu4v9omoyalY3UyjlWvuY7QCkTidWaoJH+wMcd+WFl5u7ki1ffy02dySQ9FHDSZqvMp9zVClRqBY3UyjNVzuY7RiCYuOLMOEe/tjPLxtP0+8fqLo4xnzarjjI0tYPqdmyOP6vW7qKzWYqPFLg4oqqmJ2M43GULmP0RhqWd94wmLDG4d5eOt+ep0Z83Nq/Kxb08Sly4Yu+uj32ncm5QVel0WpQtOgoopqrLqZRmJw7mO0si3ra4xha0sH924+UfSxwufm+gsX8vnzFgw5IVG7udREo0FFFdVIupkGJ/Yvaqpna0vnuEr0pxtqWd/m9gD3bGrmtbSij586ay43XbyYuiGKPmo3l5qoNKioosq3m2lwYn9fR4BX93Uys8rHjKqycZPoT8q2rG9nMMq/vbSPJ98+QrKU14pFddy+dgmNM7IXfSzzuqnXbi41gWlQUUWXTzfT4MR+b38cl0BfOM7Mav9Jif5SDVfOtqxvNG7x+M5WfvrKgVTRx4X1Fdx2aRMXNtZnzZuUed3UVXgHdBMqNRHpv2A1rgxO7EcT9lrv0bQJg8lEf6mGK/eEYnSFBtbrMsaw6f121r/QwrHeE0Ufb7x4MX9w1tysa7z7PC7qKnxUlul/RTU56L+CJwo1AAAgAElEQVRkNa4MTuz73C6iCQtf2odyMtE/VsOVk3dDBzqDzK7x84XzG1jZdKLU/HtHevnR8828e6QXsIs+fu7c+Vy/aiHVfm/GY3rdLuorNZioyUf/RatxZXBiv6bcQ1tflGq/B2PMgET/Xz/xdtGHK2/a1cbfPPE2LpdQ4XPT3hfhro27+TOWsmhGBQ+8sJfndrWltr/klOncuqYp6+g2r9tFXaWPKg0mapLSf9lqXBmc2F88vYrrLrBHfw1O9DdsGd1w5R8++wEPvLiXYDRBpc/NLasb+dplywZsc/emZkSgzLlTKve6CUbj/ODZD+jqj6WGD5/iFH08J0vRR6/bRW2FN+udi1KThQYVNe5kSux/LcN2o5kV/8NnP+CujXtwCXhcdjC6a+Me+7kuW0YsYZem398ZpMZZt8QYQ284zvFgNDUTvr7Sx82rG7n8tNkZF8vSYKKmGg0qasIazaz4B17c6wQU+w7EJRC3LO5/oYUbLl6cmhE/t6acjmAEYwztgSgR585EgOtXLeTaCxZmHP6bDCZVZZ4hZ8orNdkULaiISAPwMDAHsID1xpi7RKQe+AWwGNgHfMEY0yX2/7y7gE8CIeAmY8xrzrFuBP7aOfR3jTEPOe3nAz8ByoHfAn9mMi1SoSatkc6KD0YTDJ7ILhiC0QSdwWiq7bJTZ/Evz+9JBRMAv9fFn390KZefMeek43rdLqZVeKnWYKKmqGIuWB0H/j9jzKnAKuArInIacCfwnDFmKfCc8xjgSmCp87UOuAfACULfxl7TZSXwbRGpc/a5x9k2uZ+W6Fc5qfS5U5MSjTEYY7AMqVLzfeEYd2/awz8/tzsVULxuYemsKv7206efFFA8LhczqstYUFdOjd+rAUVNWUW7UzHGHAGOOD/3ich7wHzgKmCts9lDwCbgG077w86dxjYRqRWRuc62zxhjOgFE5BngChHZBNQYY7Y67Q8DnwWeLNY1qcnjltWN3LVxD7FEApeAZeyvq8+dz69fO8TDW/elij7Orinj1jVNXLps5knBwuOy70xq/B42v98+7taNUWqsjUlORUQWA+cCrwCznYCDMeaIiCT/180HDqbt1uq0DdXemqFdqWHd8ZFTCETiqZnvfo+Li5dM5/ndxznQaQ9JrvC5+eOVC7n6/JOLPqYHExEZt+vGKDXWih5URKQK+BXw58aY3iG6BTL9woygPdM5rMPuJmPhwoXDnbKa5Hr6Y3QFo1y7ciHXrlzI3uNB7t7UzLO72gE7aX/lGXP50iWLqa8cWPTR7RJqy33UlA/MmYzXdWOUGmtFDSoi4sUOKD81xvzaaT4mInOdu5S5QHLmWCvQkLb7AuCw0752UPsmp31Bhu1PYoxZD6wHWLFihSbyx9BQtbmy/a5Y9bzCsQQdwSgRpyZXVyjKT17ax3+9daLo4/kLa7lt7RKWzKwasG+2YJI0XteNUWqsFXP0lwA/Bt4zxvxT2q82ADcC/+B8fyKt/asi8ih2Ur7HCTxPA/8zLTl/OfBNY0yniPSJyCrsbrUbgH8p1vWogXL54B+qSwjI+LurW7t5/LVDBe1GGlyaPhq3+NVrdtHHUNQOMA115dy+dslJRR/dLmFauZcavxdXhnkoSeN53RilxpIUawSuiKwGXgDewh5SDPDfsQPAY8BC4ADwR06AEOBfsUdwhYAvGWN2OMf6srMvwPeMMf/mtK/gxJDiJ4H/NtyQ4hUrVpgdO3YU6jKnpPRgkT7p8DufOX3AB/9167exryNAb388Vb+rptxDbbmPQ939hKIJyjwuZlSVUVPuJRSN094XYWZ12YAP51A0zqxqPz9ftyrvc00vTW+MYfMH7azfspejvWHALvp4w0WL+czZA4s+5hpM0l+Trz/+BoFInIRlcLuEqjIP/3j12dr9pSY8EdlpjFmRy7bFHP31IpnzHgAfy7C9Ab6S5VgPAg9maN8BnDGK01QjcN+WFmKJBB2BgcFicP5gd1sfPaEYLpfgdglxy9DWG+FoTySV/EpEE/bdDhVU+z0EInHiCYuYZfC5XcysLqOqzJN3N9LgNeJ3He3l7uebefuwXfTR7RI+e848brho0YDZ7i6xg8m08tyCSToBMPYQZYxk/cev1GSmM+pV3jIFi+N9UWKJvgHbReMWiP1BDSACCSeaCPaoCgMYA0d7+onEfRgg5vylH7cMh7vDTK/ysnj6wBxHuvSuuAW15Vy/ahGnz5+GMYa23jAPvLiXZ987UfTx4iV20ceG+hNdU6MJJmAH2ppyL3OmlafaNFGvpiINKipvmYKFJeaktdm9bqE/Zuc0ROzgceJ3rgFrpEQSho5glNpyD4FIAmM5x8XQGYxx3QX1XLd+W8akfrIrrqbMw+Gefr732/e4fc0S9rQH+MWOg6nJi0tmVnL72iWct7Au9bwuEWqcYJKpdleuNFGvlE2DispbtmDhcw/8UF42u4a9xwP0hU90k0US2ftE4wlDjd9Ljd/H8UAktY9LTNbk/X1bWvC47CAVtwxlbheBcJTv/Ne7xJ0hXXUVXm5e3cgnTp+TChyFCiZJmqhXyqZBReUtU7Co9ntpnDGwiypZRXjONE8qoX+gM0QiYYglBt7VJD/Wj/SE+dCcGmqcv/qTyftsc0D2dwQBw5GeMNG4hWVOTFbyeVz80fkLuG5lQ2rfQgeT9Gv9y8ff4FBXP3HLwuNyUe338DefOq1gz6HURKBBZQob6XyQTMEiU8n5TFWErzp7Ho9s2097IDpgW49LsIwhmjC094XpC8eJxC3cLsHnjDJLV+Zxsa8jQLnXw97jwZNmvVaWuXnghhXMrvEDICLU+D3UVvgKGkzSGQCxnwvJMhNXqUlOg8oUNZqyIm+2dtPeF6Y/ZiHAgrpy/u6qMzLul6mK8FkLavnSQ9sxxp697hbB43aRsCxMwtAVihG3LMqcir+dwRjHAxFmVvsxxpCwDIFInOmVZbS0BzJ+eFf53Myu8eMSoXqEwSSfoHvflhamlXuZq4l6NcVpUJmich0WPFj64lZlHsEycLgnzJut3Rn3y/bB/KFZVexpD+IWsRPyll0l2OsWu2KwBf2WhRWMUelz0xWKUe5143W7CEXj9IbjdASjhGLWSc8pQGcoxrRy74jvTPINupqoV8pWzNL3ahzb3dbH8b4o8bThu8f7ouxu6xtyv/TFrVzicr7b7YMlP5jb+sIDPpg37WrjzitPpbbCi7ggYQzignKvi2jC7gJLCsctukMxvG5hWrmP9r4w7YEoveF4ajb8YPYwZcP0qrIRd3Wl1/ISsb973cJ9W1oybt9QV0F/bOD5aKJeTUUaVKao9GHBgtjDg4WThgUPFozapeLTucRuH2yoD+a1y2fxj1efzbkNdcyp8XNuQx0L6ipIVkhJzmEBuxxDImHweoSufjvXIsAnz5xDhkUXAXs02Ggc7AoRT1i0tAfYdbSXlvYA8YSV9c7j1jVNxBKGUDSOMfb3XJc2Vmoy0e6vEipW4cRc5DoseLBKn5tgNE7CGhhEyr0nf4gP1yU0ON+y+vsbyVZkpz9usX1fFwDnLqzljkuXsGRWFS/vOU6sP54KQII9v6UyS7TJ9TWv8rn54FggFdziiQQHO/tZNjvzJMzRLG2s1GSiQaVESr3+Rq7Dggf72PKZ/Ob1Iye1J9cUST/34eZuDP6Ary4b+p/jgrpybru0iYuapuNyuagq87B8Tg37OoKDrsOT8Tryec0DkTjp92zJ4BKIxLOe30iXNlZqMtHurxLJt8++0G5d04TP42bOND8fml3NnGl+fB73sN01R3ujeDLkKeIJ66RzH6pLaHC+5VhvP8f6wlmH4ZZ7Xfz4xhVcvGQG1eVeFtSVM7O6jNsuXZLzdeTzmrcHonhcdteegJNH4qSh0EqpgfROpURKPVpopN01B7tCA7qakqIJc1KSf6jnuG79ttQHvGUMXrcbryuRqgmW5BKoLfewsL6KugoftRW+AaswZnsO4KSyLvm+5i6RAbmZhDV0vkkppUGlZMZDWY+RdNc01FVwuLvffpCMKk4UyJTkz/YcB7tCTPN7iCUsIs7iWck14QH8Xhezq8tSa8d/9SNLmOVMZBzuOQaXoT8eiPD1x99gZlUZ/bFETq9504xKdrcFEHMi52QZWDqzcriXSKkpTbu/SmSijha6dU1TKplujPMFuF3DJ/mTjDHMrfHTE47R1htmX0coFVD8Xhe3XNLI8tk1xBKGebXlfO+zZ3DZ6XNyPsd/ePI9ukMxjGVPrDQWdIdi9PVHc37Nv3HFcuoqvAh2155g1xD7xhXLsz7vpl1tXLd+G6u/v5Hr1m9j0662rNsqNVnpnUqJTNTRQmuXz2L5nGr2tPWRvDEp87iorRg+yQ/2kr5tfWEWz6hk54Gu1DK+LoFqv4dvfGI5Hz11Nl+v8OL3ZhkvPIy9HSFcQqqEvQgYy9AejHHf9Wfl9JqvXT6L/3312Tm/P6UeeKHUeKFBpYQKOVpopMOTR7LfN65YnnHlx6HusuIJi85glJeaj3PPpmY+OBYA7B60Cp+bphlVfOmSxXzyrLl5B5PB15CwrFRZ/sHyec3z2TZ9EAAMLHqpQUVNJRpUCqhU805G+lfySPfL5y7r+feOcfemZvZ2BIklDD3OOvEAa5fNZN2aJhpnVlJX4RvRnUmmaxAR4gmDyNjlQw52hYjE4uw9HsRyappNr/QOO5lUqclGg0qBlLL7Y6R/JY/mr+tc/op/+q0jfPs/3iEYSRCInJig2FBXzl9+4kPE4hb//OxuDvf0n7TwVj6FHAfXMKsu89AXiafyIR6Xa9h8yKgZQ3vgRMC0DLQHYiyoHVkXnlITlQaVAill98dIhycn9+vtjw1YFKsnNLq5GLGERVtfmL/7r/do64uk8iYel11+fnqlD5/bxT/+7oOTgvDVrd08sm0/feE4ccvieF+Ev3z8Df731WdnfB0zLW0cDcfxelx4XEIsavB5XPzJqkVFfR86g3ZASe90M2ntSk0VOvqrQA52hU5a82Os5p2MtJhhQ10FxwMRDvf0E08Y3CJEExZ9kcSIRi5ZlqEjEOHXrx3ii/e/Qmt3P5axE+XTK300zqhkdo2fjmCUh7buzzgR8d7NLXSFYhjA43ZhgK5QjO8/tSvjc2aqYWZhCEUTRBMWLoFowuLhbfuLOhorkrDwukjVLhMBr8tuV2oq0aBSIKWsUjvS4cm3rmmiK+T8he2y/7IWhPpK75Az+zMNne0Lx3hx93Fu/+lrfONXb7Kvww6mlT43jdMrmFVTRpnHXpd+QV1F1iAcitkFK9ODhEug5Xgw47l4nWHMlmWckvmG5Od4JGYRSxgiMYuuYJR/ePK9XF7OEan0uUGEMo8bv9dNmcd+nK0GmVKTlQaVAinlvJO1y2fxnc+czqxqPz39MWZV+/nOZ07PKS/icwsJyxCOWcQSFrXlHqZXlmW9w0rmjvYeD9AVjPLq3g7+9OHt/NE9L3PTT17l1b2dAJzTMI3/9tFTqK3wAfZ8kfRRYtmCcJZBW1ktm13D9EofHreQMAbPoLkyyUcJA83tmQNTIdyyuhHLQNyysIzlfLfblZpKNKdSIKWedzKS4cmbdrURTdjrqfhEMAa6+u38RLY5J/dtaSEaT3A8EEGcXEnMgl3OEOH5tXbRxyvOmENdhY/zF9ZlfU2+teEdQtH4gGHJ86f5OdobQayBI7dOmZH5ji/T0sap4DEowRG3slUWG72vXbYMsNeVCUYTVPrc3LK6MdWu1FShQaUIJsra5N9/ahfGGGIJQ1wMXmeyYFcoxj9kucM62BWiIxAh00jZedP8PHbrKmbV+FM1s7IFu6FqdqWXWHG7hNoyL3deeWrG88l0nH3HgySSZYXTiokNvosptK9dtkyDiJryNKgUyESbUb1pVxsftAXAGPtz19hFIT0uqC73ZjzncCwBxpBhBV8AIvEE8/PIIWULOP+YYSY7nFwgMrnv4ONc8YPN7G4LYDllZARwCyyZoXW7lCo2DSoFMtFmVN+3pcWexzHotipuwcyqsoFtCYv3j/bxw427ae0OZz1mXzj7WiP5yFQg8i8ffyPnYcZ3XnnqSXc7VWWerHc7SqnC0UR9geS7/Gyp7W7ry5pjME7FSMsyHO7u57v/9R5/eM/LPP3OsSGPGU0YfvjsBwU/1+8/tSuvYcaZlir+xywBSClVWHqnUiDVZR52twVwp03CO9QdZums4YsslsJQ5UOOB6P0hKL8ckcr921poT0QAZwFqwSGmnpx7+aWgucVWo4HU8OMwSkQKSbrMGPQVRiVKhUNKgViUvXgGfDdZFt0vcS8bjlpQayk7lCUT/3Li7R29ae2ravwUeZxIZCa1JhJaNAwYaXU1FK07i8ReVBE2kTk7bS2ehF5RkR2O9/rnHYRkR+KyB4ReVNEzkvb50Zn+90icmNa+/ki8pazzw9F8p3hUFiBaIL5tf4B8yXm1/oJRsfnh+yy2TWUezO//XGLVEBZu2wmv1h3EV63MK3cy7QKHw1juJAYQOP0CnuxrrQJjpax25VS40sxcyo/Aa4Y1HYn8JwxZinwnPMY4EpgqfO1DrgH7CAEfBu4EFgJfDsZiJxt1qXtN/i5xlRDXQUet4ummVUsn1ND08wqPG7XmK7kmI+LmuoJD9EF5hK4+4vncc/153PeojoW1lemJivWDKozlovRLGB155WnUuF1EbMswnGLmGVR4XVp4l2pcahoQcUYswXoHNR8FfCQ8/NDwGfT2h82tm1ArYjMBT4BPGOM6TTGdAHPAFc4v6sxxmw1dv/Sw2nHKomJtpLjb986krULC+wJh588cy7lTpmRwdeXj+Rw67a+8IDh1royolKTz1jnVGYbY44AGGOOiEgykzofOJi2XavTNlR7a4b2kin1jPpNu9r4/lO7UsnrxukV3HnlqRmfvy8cY097IK/jp1/f7mO9WbfL1Ac52uHW339qF6GYhdftSs2yD8Usvv/ULk3GKzXOjJdEfabPIjOC9swHF1mH3VXGwoULR3J+OSnViKPkPI6OQJRkh9auYwHu+OlO7v7i+alz6o8m2Lm/k3/ZuCfjjPh0mV7g9PIqZCnp7nGdvOfBrhBugZb2QKq8/owqX87DrUcy+kspVRpjPU/lmNN1hfM92f/RCjSkbbcAODxM+4IM7RkZY9YbY1YYY1bMnDlz1Bcx3ty3pYXO0ImAkhSKWfzNE28TjVu8f6SPb/76TW548FVe2Tu4V/JkZZ7M4x6Si2JliB0AnJJhCHV1mYdD3WHizkTE5HDrqrLx8jeNUqpQxvp/9QbgRuAfnO9PpLV/VUQexU7K9zjdY08D/zMtOX858E1jTKeI9InIKuAV4AbgX8byQkpp8MqIu9v6ss4dOdjVzw+e/YBHtu4nELFnvM+r9ZNIGI71RbI+R2TwVHtHclGsbPkYY04+kdEOt26cXsGe9mDORSaVUqVTzCHFPwe2Ah8SkVYRuRk7mHxcRHYDH3ceA/wWaAH2APcDdwAYYzqBvwO2O1/fcdoAbgcecPZpBp4s1rWMJ5mS3sOVR7lnUzOBSJzKMje3X7qEX99+CR63sKi+POs+2T7uk4tiZbPr2MldUqMdbq2jv5SaOIp2p2KMuS7Lrz6WYVsDfCXLcR4EHszQvgM4YzTnOBFlSnrXVXg52pv9rsMl8Adnz+P2S5fQNLMKn8fFwvpK9h7PL1kP9kTI/jxXyG2oq6CtL0zTzBNdY6FonFnV/pyP4fO68VkmVcvL59XFr5Qaj7T21wSTqcaYNcw6IT/50kr+/vNnsnxuDT6P/ZbfuqaJ9iG6v6ZXZJ6LklwUKx+jHW5935YWppV7WTqrmuVzalg6q5pp5UOvTqmUKg0NKhNEcvLg0Z4w+zv7iSYs3C4hlrBoC0SH3PfDS2ek7mzSJYaIRWuWzcjYfuuaJnye/O4SRroyZVK2pYfHa7FOpaYyHX4zASTzKNF4IlVZOJawF9fKRaYKNn/zxNsZtjzhqXeO8YMM7cn5Kjf9ZHvm58pyvNEMt052n6UHxv5YYtxWK1BqKtOgMgEkl/DtCA59R5LN4NFit65pStX2yqY/20pc2AGioa6cg139g1fsZUFd9uT/SCWXDB689PB4rVag1FSm3V8TwMGuEL39sWFzJ9lkKpEy3JGGq875d1edQYXPTXLVXgNU+Nz83VWFHzsx2u4zpdTY0TuVcS6esJhd7R/2zmIomUqkDMcl9h3OUB/cFT43ljkxIqvCV7wRWbo+ilITg96pjFMJy9ARiLCvI0Rb78gDCpAxyT0Ul8DM6rIhR1fpiCylVCZ6pzLOJCxDT3+M3v4Y2/d1cs+mZg4OsS78cETspPbgJHe2BbrAToxX+z1Djq462BWidlAJfB2RpZTSoDJOWE4w6emPsa8jyL2bm9nWMnyNruHMn+ZPzRFJT3J/9py5/PvrR4CBwWVmlY+aci+haHzI0VU6IksplYkGlRKzLENv2A4mXcEoD2/dzxNvHCbhJOXPnD+Ntw71jOjYLoHvfvZMIHNJ/sYZH/DAi3sJROIYYJrfw+waf06TE3VEllIqExmva6gXy4oVK8yOHTtKfRpsfO8Yd29qprUrxKxqPwvrK9i8uz1Vx2vuND+3rmlizbKZfPT/bB7Rc3zunLn84Nrzht+QE8OO81kLZiT7KKUmHhHZaYxZkcu2eqeSg0zzPEb64WmM4bdvHuG7v30Pt9g5j3eP9PKmczdS6XPzxVWL+Py585lW4WV6ZVnOx3aJXb3XJVDj93C0N/d5LSMZXaUjspRSg2lQGUZyNrvXLQPmeXwH8vpANcbQ2x+nuz/K/S/sxbIM3eF4at13gOmVPu6/4XymV5VRX+mj2p/fWvBe14mVEYPRxJArNCqlVDFoUBnGaJfCTQ8mCcvQGYyy61gv4bQZ6xU+NzOqfETjFg31lUyv9OFKWwWrxu+hd5jy9kBqHxE7VxPNsYyLUkoVis5TGcZoihn2hWO0dvXTEYwQisT56Sv7+ZMfv5oKKD63i/nT/CyotUubLKqvZGZ12YCAAnDL6sasz5G+pWUMBoNlDBhSFYmVUmqs6J3KMEYydDYYidMVihKNWxhj2LirnftfaKHNKTVf6XPjdgk1fg/lPjfRhIUxcPvaJRmP97XLlvHYjoMc7gmftOJi8mGVz4VBUmvA11R6WTz95KV9lVKqmDSoDCOfobOhaJyuUIyIkyd553AP92xq5t0jfQB4XMLnz5vP9Rcu4t3DvTy28yBtvWEa6iuHTf5/97NnpnI7vf1ROoL2kr4VPjefOG0WOw/04HWLDu9VSpWUDinOwXBDZ8OxBJ3BKGEnmBztDXP/lhaef789tc3qU2Zw65om5teV43bJiBLxQ52HDu9VShVLPkOKNaiMQjiWoCsUpd9Zaz0UjfPzVw/yy52t9lruwNJZVdyxdglnN9QCUFXmYXpVGW7XcHWAlVJqfNB5KkUWiSfoDsUIRuwRWQnL8PQ7R3nwpX10OmueTK/yccvqRj5+2mxcInjdLqZX+TKuwKiUUpOFfsLlIRq36A5FCURODO997UAX92xqprk9CECZx8U1Kxq4ZmUD5V43InZCvq7Cd9KoLqWUmmw0qOQglrDoCkUJpM0VOdgZ4r4tLbzc3JFqu+zUWfzph5uYWW3Pgi/z2vNPyvJc010ppSYqDSpDiCcsuvtj9IXjJHNPvf0xHt62nydeP1H08Yx5NdzxkSUsn1MDgEuEukof08rzS8QrpdREp0Elg4Rl6A5F6U0LJvGExYY3DvPw1v2p2e1zavysW9PEpctmIGJ3bVX5PUyv1ES8Umpq0qCSJpG2pkkymBhj2NrSwb2bW1JL+lb43Fx/4UI+f96C1Kx1r9vFjKoyyou4pK5SSo13GlQYuNqilTbEurk9wD2bmnntQDdgV//91JlzuemSxdRV+AAQsQtN1lZ4U3crSik1VU3poJK+2mJ6MOkMRvm3l/bx5NtHUmVRzl9Ux+2XNtE080Tpk3Kfm+mVZVpjSymlHFMyqGQLJtG4xeM7W/nZqwcIORMaF9ZXcNulTVzYWJ+6ExnpjHillJrsplxQSViGg12h1MgtsPMmm95vZ/0LLRzrtYs+1vg93HTxYj591lw87hN3ItV+70ml6ZVSStmmXFCJW2ZAQHnvSC93b2rmncP2glYel/C5c+dz/aqFA+5EvG4XM6vL8Hs1Ea+UUtlM+KAiIlcAdwFu4AFjzD/kst+x3jAPvLCX53a1pdrSiz6mHV8T8UoplaMJHVRExA38CPg40ApsF5ENxph3s+1jGcODL+3lsR0nij6eMrOKOz6yhHOcoo9JmohXSqn8TOigAqwE9hhjWgBE5FHgKiBrUNl7PMj/3XYAgPpKHzdfspjLT58zYLKiJuKVUmpkJnpQmQ8cTHvcClw41A4Jy+DzuPjCigVcd8HCkyYr6ox4pZQauYkeVDJ98p+0QIyIrAPWOQ8Du7/3yfe/BzO+B8eLenbjwwwm/3XqNU4eU+E6J+I1Lsp1w4keVFqBhrTHC4DDgzcyxqwH1qe3iciOXBedmcimwnXqNU4eU+E6J/s1TvQM9HZgqYg0iogPuBbYUOJzUkqpKWtC36kYY+Ii8lXgaewhxQ8aY94p8WkppdSUNaGDCoAx5rfAb0ew6/rhN5kUpsJ16jVOHlPhOif1NYoxJ+W1lVJKqRGZ6DkVpZRS48iUCSoisk9E3hKR10Vkh9NWLyLPiMhu53tdqc8zHyLyoIi0icjbaW0Zr0lsPxSRPSLypoicV7ozz0+W6/xbETnkvJ+vi8gn0373Tec63xeRT5TmrPMjIg0i8ryIvCci74jInzntk+b9HOIaJ9t76ReRV0XkDec6/4fT3igirzjv5S+cwUWISJnzeI/z+8WlPP9RM8ZMiS9gHzBjUNv/Au50fr4T+H6pzzPPa1oDnAe8Pdw1AZ8EnsSe27MKeKXU5z/K6/xb4OsZtj0NeAMoAxqBZsBd6mvI4RrnAuc5P0jY65IAAAVtSURBVFcDHzjXMmnezyGucbK9lwJUOT97gVec9+gx4Fqn/V7gdufnO4B7nZ+vBX5R6msYzdeUuVPJ4irgIefnh4DPlvBc8maM2QJ0DmrOdk1XAQ8b2zagVkTmjs2Zjk6W68zmKuBRY0zEGLMX2INdzmdcM8YcMca85vzcB7yHXTFi0ryfQ1xjNhP1vTTGmIDz0Ot8GeCjwONO++D3MvkePw58TCZw9dqpFFQM8DsR2enMsAeYbYw5AvY/eGBWyc6ucLJdU6aSNkP9h54Ivup0/TyY1nU54a/T6f44F/sv3En5fg66Rphk76WIuEXkdaANeAb7LqvbGBN3Nkm/ltR1Or/vAaaP7RkXzlQKKpcYY84DrgS+IiJrSn1CYyynkjYTyD3AEuAc4Ajwf5z2CX2dIlIF/Ar4c2NM71CbZmibENeZ4Ron3XtpjEkYY87BrvKxEjg102bO9wl7nZlMmaBijDnsfG8DfoP9Rh9Ldhk439uyH2HCyHZNOZW0mSiMMcec/7gWcD8nukUm7HWKiBf7w/anxphfO82T6v3MdI2T8b1MMsZ0A5uwcyq1IpKcG5h+LanrdH4/jdy7e8edKRFURKRSRKqTPwOXA29jl3S50dnsRuCJ0pxhQWW7pg3ADc6ooVVAT7JbZSIalD/4HPb7CfZ1XuuMqGkElgKvjvX55cvpQ/8x8J4x5p/SfjVp3s9s1zgJ38uZIlLr/FwOXIadP3oeuNrZbPB7mXyPrwY2GidrPyGVeqTAWHwBTdijSN4A3gH+ymmfDjwH7Ha+15f6XPO8rp9jdxfEsP/auTnbNWHfYv8Iu2/3LWBFqc9/lNf5iHMdb2L/p5ybtv1fOdf5PnBlqc8/x2tcjd3l8SbwuvP1ycn0fg5xjZPtvTwL+L1zPW8D33Lam7CD4h7gl0CZ0+53Hu9xft9U6msYzZfOqFdKKVUwU6L7Syml1NjQoKKUUqpgNKgopZQqGA0qSimlCkaDilJKqYLRoKJUkYnIPBF5fPgtR/Ucv03OjVCqlHRIsVJKqYLROxWlhiAi1ztrY7wuIvc5hQIDIvI9Z72MbSIy29l2ifN4u4h8R0QCTvticdaCEZGbROTXIvKUs67G/0p7rstFZKuIvCYiv3RqZA0+n7kissU5n7dF5MNO+z4RmSEit6WtS7JXRJ7P9dhKFYIGFaWyEJFTgWuwi5GeAySALwKVwDZjzNnAFuBPnV3uAu4yxlzA0DWqznGOeyZwjdiLV80A/hq4zNiFT3cAf5Fh3z8GnnbO52zsWekpxph7nd9dgF194J/yOLZSo+YZfhOlpqyPAecD253lLcqxCzpGgf90ttkJfNz5+SJOrJHxM+Afsxz3OWNMD4CIvAssAmqxF6V6yXkuH7A1w77bgQedwoz/box5PcM2YAe4jcaY/xCRT+d4bKVGTYOKUtkJ8JAx5psDGkW+bk4kIxPk//8okvZzcn8BnjHGXDfouS4E7nMefssYs8FZtuFTwCMi8r+NMQ8P2ucm7ED11bTrOOnYShWDdn8pld1zwNUiMgtS68UvGmL7bcAfOj9fm+dzbQMuEZFTnOeqEJFlxphXjDHnOF8bnOdvM8bcj13xd8Da9CJyPvB14Hpjl5LPeuw8z0+pnGhQUSoLY8y72LmI34nIm9gr+A21ZO+fA38hIq862/Xk8VztwE3Az53n2gYsz7DpWuB1Efk9dgC7a9DvvwrUA887yfoH8ji2UqOmQ4qVKhARqQD6jTFGRK4FrjPGXFXq81JqLGlORanCOR/4V2cxqm7gyyU+H6XGnN6pKKWUKhjNqSillCoYDSpKKaUKRoOKUkqpgtGgopRSqmA0qCillCoYDSpKKaUK5v8BrZ0KeeOJPDAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Engine size as potential predictor variable of price\n",
    "#Let's find the scatterplot of \"engine-size\" and \"price\"\n",
    "\n",
    "sns.regplot(x=\"engine-size\", y=\"price\", data=df)\n",
    "plt.ylim(0,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"engine-size\", \"price\"]].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f81f56af60>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=\"highway-mpg\", y=\"price\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f81f707908>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=\"highway-mpg\", y=\"price\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>highway-mpg</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.704692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>-0.704692</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             highway-mpg     price\n",
       "highway-mpg     1.000000 -0.704692\n",
       "price          -0.704692  1.000000"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['highway-mpg', 'price']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f81f76ef98>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's see if \"Peak-rpm\" as a predictor variable of \"price\".\n",
    "sns.regplot(x=\"peak-rpm\", y=\"price\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['peak-rpm','price']].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Categorical variables¶\n",
    "These are variables that describe a 'characteristic' of a data unit, and are selected from a small group of categories. The categorical variables can have the type \"object\" or \"int64\". A good way to visualize categorical variables is by using boxplots.\n",
    "\n",
    "Let's look at the relationship between \"body-style\" and \"price\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f81f7d7438>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"body-style\", y=\"price\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the distributions of price between the different body-style categories have a significant overlap, and so body-style would not be a good predictor of price. Let's examine engine \"engine-location\" and \"price\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f81f88f6d8>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"engine-location\", y=\"price\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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.\n",
    "\n",
    "Let's examine \"drive-wheels\" and \"price\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f81f9035c0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# drive-wheels\n",
    "sns.boxplot(x=\"drive-wheels\", y=\"price\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Descriptive Statistical Analysis¶\n",
    "Let's first take a look at the variables by utilizing a description method.\n",
    "\n",
    "The describe function automatically computes basic statistics for all continuous variables. Any NaN values are automatically skipped in these statistics.\n",
    "\n",
    "This will show:\n",
    "\n",
    "the count of that variable\n",
    "the mean\n",
    "the standard deviation (std)\n",
    "the minimum value\n",
    "the IQR (Interquartile Range: 25%, 50% and 75%)\n",
    "the maximum value\n",
    "We can apply the method \"describe\" as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=['object'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Value-counts is a good way of understanding how many units of each characteristic/variable we have. We can apply the \"value_counts\" method on the column 'drive-wheels'. Don’t forget the method \"value_counts\" only works on Pandas series, not Pandas Dataframes. As a result, we only include one bracket \"df['drive-wheels']\" not two brackets \"df[['drive-wheels']]\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fwd    118\n",
       "rwd     75\n",
       "4wd      8\n",
       "Name: drive-wheels, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['drive-wheels'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>drive-wheels</th>\n",
       "    </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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can convert the series to a Dataframe as follows :\n",
    "df['drive-wheels'].value_counts().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 52,
     "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": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now let's rename the index to 'drive-wheels':\n",
    "drive_wheels_counts.index.name = 'drive-wheels'\n",
    "drive_wheels_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 54,
     "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": [
    "Basics of Grouping¶\n",
    "The \"groupby\" method groups data by different categories. The data is grouped based on one or several variables and analysis is performed on the individual groups.\n",
    "\n",
    "For example, let's group by the variable \"drive-wheels\". We see that there are 3 different categories of drive wheels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['rwd', 'fwd', '4wd'], dtype=object)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['drive-wheels'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_group_one = df[['drive-wheels','body-style','price']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4wd</td>\n",
       "      <td>10241.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fwd</td>\n",
       "      <td>9244.779661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rwd</td>\n",
       "      <td>19757.613333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  drive-wheels         price\n",
       "0          4wd  10241.000000\n",
       "1          fwd   9244.779661\n",
       "2          rwd  19757.613333"
      ]
     },
     "execution_count": 57,
     "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": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>body-style</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>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": 58,
     "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": "markdown",
   "metadata": {},
   "source": [
    "This grouped data is much easier to visualize when it is made into a pivot table. A pivot table is like an Excel spreadsheet, with one variable along the column and another along the row. We can convert the dataframe to a pivot table using the method \"pivot \" to create a pivot table from the groups.\n",
    "\n",
    "In this case, we will leave the drive-wheel variable as the rows of the table, and pivot body-style to become the columns of the table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>body-style</th>\n",
       "      <th>convertible</th>\n",
       "      <th>hardtop</th>\n",
       "      <th>hatchback</th>\n",
       "      <th>sedan</th>\n",
       "      <th>wagon</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drive-wheels</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4wd</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7603.000000</td>\n",
       "      <td>12647.333333</td>\n",
       "      <td>9095.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fwd</th>\n",
       "      <td>11595.0</td>\n",
       "      <td>8249.000000</td>\n",
       "      <td>8396.387755</td>\n",
       "      <td>9811.800000</td>\n",
       "      <td>9997.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rwd</th>\n",
       "      <td>23949.6</td>\n",
       "      <td>24202.714286</td>\n",
       "      <td>14337.777778</td>\n",
       "      <td>21711.833333</td>\n",
       "      <td>16994.222222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   price                                            \\\n",
       "body-style   convertible       hardtop     hatchback         sedan   \n",
       "drive-wheels                                                         \n",
       "4wd                  NaN           NaN   7603.000000  12647.333333   \n",
       "fwd              11595.0   8249.000000   8396.387755   9811.800000   \n",
       "rwd              23949.6  24202.714286  14337.777778  21711.833333   \n",
       "\n",
       "                            \n",
       "body-style           wagon  \n",
       "drive-wheels                \n",
       "4wd            9095.750000  \n",
       "fwd            9997.333333  \n",
       "rwd           16994.222222  "
      ]
     },
     "execution_count": 59,
     "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": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead tr th {\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>body-style</th>\n",
       "      <th>convertible</th>\n",
       "      <th>hardtop</th>\n",
       "      <th>hatchback</th>\n",
       "      <th>sedan</th>\n",
       "      <th>wagon</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drive-wheels</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4wd</th>\n",
       "      <td>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": 60,
     "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": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "im = ax.pcolor(grouped_pivot, cmap='RdBu')\n",
    "\n",
    "#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": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.5846418222655081  with a P-value of P = 8.076488270732955e-20\n"
     ]
    }
   ],
   "source": [
    "#Wheel-base vs Price\n",
    "#Let's calculate the Pearson Correlation Coefficient and P-value of 'wheel-base' and 'price'.\n",
    "from scipy import stats\n",
    "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": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.8095745670036559  with a P-value of P =  6.36905742825998e-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": [
    "Conclusion:\n",
    "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.690628380448364  with a P-value of P =  8.016477466159053e-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": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.7512653440522674  with a P-value of P = 9.200335510481426e-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": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.8344145257702846  with a P-value of P =  2.1895772388936997e-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": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.8723351674455185  with a P-value of P = 9.265491622197996e-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": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.5431553832626602  with a P-value of P =   8.049189483935364e-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": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is -0.6865710067844677  with a P-value of P =  2.3211320655676368e-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": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is -0.7046922650589529  with a P-value of P =  1.7495471144476807e-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": [
    "6. ANOVA\n",
    "ANOVA: Analysis of Variance\n",
    "The Analysis of Variance (ANOVA) is a statistical method used to test whether there are significant differences between the means of two or more groups. ANOVA returns two parameters:\n",
    "\n",
    "F-test score: ANOVA assumes the means of all groups are the same, calculates how much the actual means deviate from the assumption, and reports it as the F-test score. A larger score means there is a larger difference between the means.\n",
    "\n",
    "P-value: P-value tells how statistically significant is our calculated score value.\n",
    "\n",
    "If our price variable is strongly correlated with the variable we are analyzing, expect ANOVA to return a sizeable F-test score and a small p-value.\n",
    "\n",
    "Drive Wheels\n",
    "Since ANOVA analyzes the difference between different groups of the same variable, the groupby function will come in handy. Because the ANOVA algorithm averages the data automatically, we do not need to take the average before hand.\n",
    "\n",
    "Let's see if different types 'drive-wheels' impact 'price', we group the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rwd</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rwd</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fwd</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4wd</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fwd</td>\n",
       "      <td>15250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>4wd</td>\n",
       "      <td>7603.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    drive-wheels    price\n",
       "0            rwd  13495.0\n",
       "1            rwd  16500.0\n",
       "3            fwd  13950.0\n",
       "4            4wd  17450.0\n",
       "5            fwd  15250.0\n",
       "136          4wd   7603.0"
      ]
     },
     "execution_count": 72,
     "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": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>body-style</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rwd</td>\n",
       "      <td>convertible</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rwd</td>\n",
       "      <td>convertible</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rwd</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4wd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>15250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>fwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>17710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>fwd</td>\n",
       "      <td>wagon</td>\n",
       "      <td>18920.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>fwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>23875.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>rwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>16430.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>rwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>16925.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>rwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>20970.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>rwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>21105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>rwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>24565.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>rwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>30760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>rwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>41315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>rwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>36880.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>fwd</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>5151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>fwd</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>6295.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>fwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>6575.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>fwd</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>5572.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>fwd</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>6377.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>fwd</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>7957.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>fwd</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>6229.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>fwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>6692.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>fwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>7609.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>fwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>8558.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>fwd</td>\n",
       "      <td>wagon</td>\n",
       "      <td>8921.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>fwd</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>12964.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>fwd</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>6479.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>fwd</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>9988.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>fwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>10898.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>fwd</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>11248.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>rwd</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>16558.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>rwd</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>15998.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>rwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>15690.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>rwd</td>\n",
       "      <td>wagon</td>\n",
       "      <td>15750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>fwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>7775.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>fwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>7975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>fwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>7995.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>fwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>8195.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>fwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>8495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>fwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>9495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>fwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>9995.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>fwd</td>\n",
       "      <td>convertible</td>\n",
       "      <td>11595.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>fwd</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>9980.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>fwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>13295.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>fwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>13845.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>fwd</td>\n",
       "      <td>wagon</td>\n",
       "      <td>12290.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>rwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>12940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>rwd</td>\n",
       "      <td>wagon</td>\n",
       "      <td>13415.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>rwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>15985.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>rwd</td>\n",
       "      <td>wagon</td>\n",
       "      <td>16515.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>rwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>18420.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>rwd</td>\n",
       "      <td>wagon</td>\n",
       "      <td>18950.0</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",
       "5            fwd        sedan  15250.0\n",
       "6            fwd        sedan  17710.0\n",
       "7            fwd        wagon  18920.0\n",
       "8            fwd        sedan  23875.0\n",
       "9            rwd        sedan  16430.0\n",
       "10           rwd        sedan  16925.0\n",
       "11           rwd        sedan  20970.0\n",
       "12           rwd        sedan  21105.0\n",
       "13           rwd        sedan  24565.0\n",
       "14           rwd        sedan  30760.0\n",
       "15           rwd        sedan  41315.0\n",
       "16           rwd        sedan  36880.0\n",
       "17           fwd    hatchback   5151.0\n",
       "18           fwd    hatchback   6295.0\n",
       "19           fwd        sedan   6575.0\n",
       "20           fwd    hatchback   5572.0\n",
       "21           fwd    hatchback   6377.0\n",
       "22           fwd    hatchback   7957.0\n",
       "23           fwd    hatchback   6229.0\n",
       "24           fwd        sedan   6692.0\n",
       "25           fwd        sedan   7609.0\n",
       "26           fwd        sedan   8558.0\n",
       "27           fwd        wagon   8921.0\n",
       "28           fwd    hatchback  12964.0\n",
       "29           fwd    hatchback   6479.0\n",
       "..           ...          ...      ...\n",
       "171          fwd    hatchback   9988.0\n",
       "172          fwd        sedan  10898.0\n",
       "173          fwd    hatchback  11248.0\n",
       "174          rwd    hatchback  16558.0\n",
       "175          rwd    hatchback  15998.0\n",
       "176          rwd        sedan  15690.0\n",
       "177          rwd        wagon  15750.0\n",
       "178          fwd        sedan   7775.0\n",
       "179          fwd        sedan   7975.0\n",
       "180          fwd        sedan   7995.0\n",
       "181          fwd        sedan   8195.0\n",
       "182          fwd        sedan   8495.0\n",
       "183          fwd        sedan   9495.0\n",
       "184          fwd        sedan   9995.0\n",
       "185          fwd  convertible  11595.0\n",
       "186          fwd    hatchback   9980.0\n",
       "187          fwd        sedan  13295.0\n",
       "188          fwd        sedan  13845.0\n",
       "189          fwd        wagon  12290.0\n",
       "190          rwd        sedan  12940.0\n",
       "191          rwd        wagon  13415.0\n",
       "192          rwd        sedan  15985.0\n",
       "193          rwd        wagon  16515.0\n",
       "194          rwd        sedan  18420.0\n",
       "195          rwd        wagon  18950.0\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": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gptest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4      17450.0\n",
       "136     7603.0\n",
       "140     9233.0\n",
       "141    11259.0\n",
       "144     8013.0\n",
       "145    11694.0\n",
       "150     7898.0\n",
       "151     8778.0\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_test2.get_group('4wd')['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA results: F= 67.95406500780399 , P = 3.3945443577151245e-23\n"
     ]
    }
   ],
   "source": [
    "#we can use the function 'f_oneway' in the module 'stats' to obtain the F-test score and P-value.\n",
    "# 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": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA results: F= 130.5533160959111 , P = 2.2355306355677845e-23\n"
     ]
    }
   ],
   "source": [
    "f_val, p_val = stats.f_oneway(grouped_test2.get_group('fwd')['price'], grouped_test2.get_group('rwd')['price'])  \n",
    " \n",
    "print( \"ANOVA results: F=\", f_val, \", P =\", p_val )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA results: F= 8.580681368924756 , P = 0.004411492211225333\n"
     ]
    }
   ],
   "source": [
    "f_val, p_val = stats.f_oneway(grouped_test2.get_group('4wd')['price'], grouped_test2.get_group('rwd')['price'])  \n",
    "   \n",
    "print( \"ANOVA results: F=\", f_val, \", P =\", p_val)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA results: F= 0.665465750252303 , P = 0.41620116697845666\n"
     ]
    }
   ],
   "source": [
    "f_val, p_val = stats.f_oneway(grouped_test2.get_group('4wd')['price'], grouped_test2.get_group('fwd')['price'])  \n",
    " \n",
    "print(\"ANOVA results: F=\", f_val, \", P =\", p_val)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion: Important Variables\n",
    "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:\n",
    "\n",
    "Continuous numerical variables:\n",
    "\n",
    "Length\n",
    "Width\n",
    "Curb-weight\n",
    "Engine-size\n",
    "Horsepower\n",
    "City-mpg\n",
    "Highway-mpg\n",
    "Wheel-base\n",
    "Bore\n",
    "Categorical variables:\n",
    "\n",
    "Drive-wheels\n",
    "As we now move into building machine learning models to automate our analysis, feeding the model with variables that meaningfully affect our target variable will improve our model's prediction performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create the linear regression object\n",
    "lm = LinearRegression()\n",
    "lm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['highway-mpg']]\n",
    "Y = df['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16236.50464347, 16236.50464347, 17058.23802179, 13771.3045085 ,\n",
       "       20345.17153508])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Yhat=lm.predict(X)\n",
    "Yhat[0:5]   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38423.305858157386"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-821.73337832])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the previous section we know that other good predictors of price could be:\n",
    "\n",
    "Horsepower\n",
    "Curb-weight\n",
    "Engine-size\n",
    "Highway-mpg\n",
    "Let's develop a model using these variables as the predictor variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = df[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.fit(Z, df['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-15806.624626329198"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([53.49574423,  4.70770099, 81.53026382, 36.05748882])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm2 = LinearRegression()\n",
    "lm2.fit(df[['normalized-losses' , 'highway-mpg']],df['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1.49789586, -820.45434016])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write your code below and press Shift+Enter to execute \n",
    "lm2.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ") Model Evaluation using Visualization¶\n",
    "Now that we've developed some models, how do we evaluate our models and how do we choose the best one? One way to do this is by using visualization.\n",
    "\n",
    "import the visualization package: seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the visualization package: seaborn\n",
    "import seaborn as sns\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\envs\\sharath\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0, 48277.31607845775)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "width = 12\n",
    "height = 10\n",
    "plt.figure(figsize=(width, height))\n",
    "sns.regplot(x=\"highway-mpg\", y=\"price\", data=df)\n",
    "plt.ylim(0,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from this plot that price is negatively correlated to highway-mpg, since the regression slope is negative. One thing to keep in mind when looking at a regression plot is to pay attention to how scattered the data points are around the regression line. This will give you a good indication of the variance of the data, and whether a linear model would be the best fit or not. If the data is too far off from the line, this linear model might not be the best model for this data. Let's compare this plot to the regression plot of \"peak-rpm\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 47422.919330307624)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(width, height))\n",
    "sns.regplot(x=\"peak-rpm\", y=\"price\", data=df)\n",
    "plt.ylim(0,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>highway-mpg</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>-0.058598</td>\n",
       "      <td>-0.101616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>highway-mpg</th>\n",
       "      <td>-0.058598</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.704692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>-0.101616</td>\n",
       "      <td>-0.704692</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             peak-rpm  highway-mpg     price\n",
       "peak-rpm     1.000000    -0.058598 -0.101616\n",
       "highway-mpg -0.058598     1.000000 -0.704692\n",
       "price       -0.101616    -0.704692  1.000000"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write your code below and press Shift+Enter to execute \n",
    "df[[\"peak-rpm\",\"highway-mpg\",\"price\"]].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "width = 12\n",
    "height = 10\n",
    "plt.figure(figsize=(width, height))\n",
    "sns.residplot(df['highway-mpg'], df['price'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_hat = lm.predict(Z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\envs\\sharath\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(width, height))\n",
    "\n",
    "\n",
    "ax1 = sns.distplot(df['price'], hist=False, color=\"r\", label=\"Actual Value\")\n",
    "sns.distplot(Yhat, hist=False, color=\"b\", label=\"Fitted Values\" , ax=ax1)\n",
    "\n",
    "\n",
    "plt.title('Actual vs Fitted Values for Price')\n",
    "plt.xlabel('Price (in dollars)')\n",
    "plt.ylabel('Proportion of Cars')\n",
    "\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "def PlotPolly(model, independent_variable, dependent_variabble, Name):\n",
    "    x_new = np.linspace(15, 55, 100)\n",
    "    y_new = model(x_new)\n",
    "\n",
    "    plt.plot(independent_variable, dependent_variabble, '.', x_new, y_new, '-')\n",
    "    plt.title('Polynomial Fit with Matplotlib for Price ~ Length')\n",
    "    ax = plt.gca()\n",
    "    ax.set_facecolor((0.898, 0.898, 0.898))\n",
    "    fig = plt.gcf()\n",
    "    plt.xlabel(Name)\n",
    "    plt.ylabel('Price of Cars')\n",
    "\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df['highway-mpg']\n",
    "y = df['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        3         2\n",
      "-1.557 x + 204.8 x - 8965 x + 1.379e+05\n"
     ]
    }
   ],
   "source": [
    "# Here we use a polynomial of the 3rd order (cubic) \n",
    "f = np.polyfit(x, y, 3)\n",
    "p = np.poly1d(f)\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PlotPolly(p, x, y, 'highway-mpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PlotPolly(p, x, y, 'highway-mpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PolynomialFeatures(degree=2, include_bias=True, interaction_only=False)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr=PolynomialFeatures(degree=2)\n",
    "pr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z_pr=pr.fit_transform(Z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(201, 4)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(201, 15)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z_pr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Pipelines simplify the steps of processing the data. We use the module Pipeline to create a pipeline.\n",
    "#We also use StandardScaler as a step in our pipeline.\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We create the pipeline, by creating a list of tuples including the name of the\n",
    "#model or estimator and its corresponding constructor.\n",
    "Input=[('scale',StandardScaler()), ('polynomial', PolynomialFeatures(include_bias=False)), ('model',LinearRegression())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('scale', StandardScaler(copy=True, with_mean=True, with_std=True)), ('polynomial', PolynomialFeatures(degree=2, include_bias=False, interaction_only=False)), ('model', LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False))])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe=Pipeline(Input)\n",
    "pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\envs\\sharath\\lib\\site-packages\\sklearn\\preprocessing\\data.py:625: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\dell\\Anaconda3\\envs\\sharath\\lib\\site-packages\\sklearn\\base.py:465: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, y, **fit_params).transform(X)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('scale', StandardScaler(copy=True, with_mean=True, with_std=True)), ('polynomial', PolynomialFeatures(degree=2, include_bias=False, interaction_only=False)), ('model', LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False))])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(Z,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\envs\\sharath\\lib\\site-packages\\sklearn\\pipeline.py:331: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([13102.74784201, 13102.74784201, 18225.54572197, 10390.29636555])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypipe=pipe.predict(Z)\n",
    "ypipe[0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\envs\\sharath\\lib\\site-packages\\sklearn\\preprocessing\\data.py:625: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\dell\\Anaconda3\\envs\\sharath\\lib\\site-packages\\sklearn\\base.py:465: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, y, **fit_params).transform(X)\n",
      "C:\\Users\\dell\\Anaconda3\\envs\\sharath\\lib\\site-packages\\sklearn\\pipeline.py:331: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([13699.11161184, 13699.11161184, 19051.65470233, 10620.36193015,\n",
       "       15521.31420211, 13869.66673213, 15456.16196732, 15974.00907672,\n",
       "       17612.35917161, 10722.32509097])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a pipeline that Standardizes the data, then perform prediction using a linear regression model using\n",
    "#the features Z and targets y\n",
    "# Write your code below and press Shift+Enter to execute \n",
    "Input=[('scale',StandardScaler()),('model',LinearRegression())]\n",
    "\n",
    "pipe=Pipeline(Input)\n",
    "\n",
    "pipe.fit(Z,y)\n",
    "\n",
    "ypipe=pipe.predict(Z)\n",
    "ypipe[0:10]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R-square is:  0.4965911884339175\n"
     ]
    }
   ],
   "source": [
    "#highway_mpg_fit\n",
    "lm.fit(X, Y)\n",
    "# Find the R^2\n",
    "print('The R-square is: ', lm.score(X, Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The output of the first four predicted value is:  [16236.50464347 16236.50464347 17058.23802179 13771.3045085 ]\n"
     ]
    }
   ],
   "source": [
    "Yhat=lm.predict(X)\n",
    "print('The output of the first four predicted value is: ', Yhat[0:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean square error of price and predicted value is:  31635042.944639895\n"
     ]
    }
   ],
   "source": [
    "mse = mean_squared_error(df['price'], Yhat)\n",
    "print('The mean square error of price and predicted value is: ', mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R-square is:  0.8093562806577457\n"
     ]
    }
   ],
   "source": [
    "# fit the model \n",
    "lm.fit(Z, df['price'])\n",
    "# Find the R^2\n",
    "print('The R-square is: ', lm.score(Z, df['price']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_predict_multifit = lm.predict(Z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean square error of price and predicted value using multifit is:  11980366.87072649\n"
     ]
    }
   ],
   "source": [
    "print('The mean square error of price and predicted value using multifit is: ', \\\n",
    "      mean_squared_error(df['price'], Y_predict_multifit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R-square value is:  0.6741946663906513\n"
     ]
    }
   ],
   "source": [
    "r_squared = r2_score(y, p(x))\n",
    "print('The R-square value is: ', r_squared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20474146.42636125"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(df['price'], p(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_input=np.arange(1, 100, 1).reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.fit(X, Y)\n",
    "lm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([37601.57247984, 36779.83910151, 35958.10572319, 35136.37234487,\n",
       "       34314.63896655])"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat=lm.predict(new_input)\n",
    "yhat[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(new_input, yhat)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "      <th>city-L/100km</th>\n",
       "      <th>diesel</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>88.6</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.890278</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>130</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "      <td>11.190476</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>88.6</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.890278</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>130</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>11.190476</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>94.5</td>\n",
       "      <td>0.822681</td>\n",
       "      <td>0.909722</td>\n",
       "      <td>52.4</td>\n",
       "      <td>2823</td>\n",
       "      <td>152</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>12.368421</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>99.8</td>\n",
       "      <td>0.848630</td>\n",
       "      <td>0.919444</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2337</td>\n",
       "      <td>109</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>9.791667</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>99.4</td>\n",
       "      <td>0.848630</td>\n",
       "      <td>0.922222</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2824</td>\n",
       "      <td>136</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>13.055556</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  normalized-losses  wheel-base    length     width  height  \\\n",
       "0          3                122        88.6  0.811148  0.890278    48.8   \n",
       "1          3                122        88.6  0.811148  0.890278    48.8   \n",
       "2          1                122        94.5  0.822681  0.909722    52.4   \n",
       "3          2                164        99.8  0.848630  0.919444    54.3   \n",
       "4          2                164        99.4  0.848630  0.922222    54.3   \n",
       "\n",
       "   curb-weight  engine-size  bore  stroke  compression-ratio  horsepower  \\\n",
       "0         2548          130  3.47    2.68                9.0       111.0   \n",
       "1         2548          130  3.47    2.68                9.0       111.0   \n",
       "2         2823          152  2.68    3.47                9.0       154.0   \n",
       "3         2337          109  3.19    3.40               10.0       102.0   \n",
       "4         2824          136  3.19    3.40                8.0       115.0   \n",
       "\n",
       "   peak-rpm  city-mpg  highway-mpg    price  city-L/100km  diesel  gas  \n",
       "0    5000.0        21           27  13495.0     11.190476       0    1  \n",
       "1    5000.0        21           27  16500.0     11.190476       0    1  \n",
       "2    5000.0        19           26  16500.0     12.368421       0    1  \n",
       "3    5500.0        24           30  13950.0      9.791667       0    1  \n",
       "4    5500.0        18           22  17450.0     13.055556       0    1  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df._get_numeric_data()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "! pip install ipywidgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\envs\\sharath\\lib\\site-packages\\IPython\\html.py:14: ShimWarning: The `IPython.html` package has been deprecated since IPython 4.0. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n",
      "  \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n"
     ]
    }
   ],
   "source": [
    "from IPython.display import display\n",
    "from IPython.html import widgets \n",
    "from IPython.display import display\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "def DistributionPlot(RedFunction, BlueFunction, RedName, BlueName, Title):\n",
    "    width = 12\n",
    "    height = 10\n",
    "    plt.figure(figsize=(width, height))\n",
    "\n",
    "    ax1 = sns.distplot(RedFunction, hist=False, color=\"r\", label=RedName)\n",
    "    ax2 = sns.distplot(BlueFunction, hist=False, color=\"b\", label=BlueName, ax=ax1)\n",
    "\n",
    "    plt.title(Title)\n",
    "    plt.xlabel('Price (in dollars)')\n",
    "    plt.ylabel('Proportion of Cars')\n",
    "\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "def PollyPlot(xtrain, xtest, y_train, y_test, lr,poly_transform):\n",
    "    width = 12\n",
    "    height = 10\n",
    "    plt.figure(figsize=(width, height))\n",
    "def DistributionPlot(RedFunction, BlueFunction, RedName, BlueName, Title):\n",
    "    width = 12\n",
    "    height = 10\n",
    "    plt.figure(figsize=(width, height))\n",
    "\n",
    "    ax1 = sns.distplot(RedFunction, hist=False, color=\"r\", label=RedName)\n",
    "    ax2 = sns.distplot(BlueFunction, hist=False, color=\"b\", label=BlueName, ax=ax1)\n",
    "\n",
    "    plt.title(Title)\n",
    "    plt.xlabel('Price (in dollars)')\n",
    "    plt.ylabel('Proportion of Cars')\n",
    "\n",
    "    plt.show()\n",
    "    plt.close()\n",
    "    \n",
    "    \n",
    "    #training data \n",
    "    #testing data \n",
    "    # lr:  linear regression object \n",
    "    #poly_transform:  polynomial transformation object \n",
    " \n",
    "    xmax=max([xtrain.values.max(), xtest.values.max()])\n",
    "\n",
    "    xmin=min([xtrain.values.min(), xtest.values.min()])\n",
    "\n",
    "    x=np.arange(xmin, xmax, 0.1)\n",
    "\n",
    "\n",
    "    plt.plot(xtrain, y_train, 'ro', label='Training Data')\n",
    "    plt.plot(xtest, y_test, 'go', label='Test Data')\n",
    "    plt.plot(x, lr.predict(poly_transform.fit_transform(x.reshape(-1, 1))), label='Predicted Function')\n",
    "    plt.ylim([-10000, 60000])\n",
    "    plt.ylabel('Price')\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_data = df['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data=df.drop('price',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of test samples : 31\n",
      "number of training samples: 170\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.15, random_state=1)\n",
    "\n",
    "\n",
    "print(\"number of test samples :\", x_test.shape[0])\n",
    "print(\"number of training samples:\",x_train.shape[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "lre=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lre.fit(x_train[['horsepower']], y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6449517437659684"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lre.score(x_train[['horsepower']], y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "Rcross = cross_val_score(lre, x_data[['horsepower']], y_data, cv=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.7746232 , 0.51716687, 0.74785353, 0.04839605])"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Rcross"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean of the folds are 0.522009915042119 and the standard deviation is 0.2911839444756029\n"
     ]
    }
   ],
   "source": [
    "print(\"The mean of the folds are\", Rcross.mean(), \"and the standard deviation is\" , Rcross.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20254142.84026704, 43745493.26505169, 12539630.34014931,\n",
       "       17561927.72247591])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-1 * cross_val_score(lre,x_data[['horsepower']], y_data,cv=4,scoring='neg_mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14141.63807508, 14141.63807508, 20814.29423473, 12745.03562306,\n",
       "       14762.35027598])"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat = cross_val_predict(lre,x_data[['horsepower']], y_data,cv=4)\n",
    "yhat[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It turns out that the test data sometimes referred to as the out of sample data is a much better measure of how well your model performs in the real world. One reason for this is overfitting; let's go over some examples. It turns out these differences are more apparent in Multiple Linear Regression and Polynomial Regression so we will explore overfitting in that context.\n",
    "\n",
    "Let's create Multiple linear regression objects and train the model using 'horsepower', 'curb-weight', 'engine-size' and 'highway-mpg' as features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']], y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11927.70699817, 11236.71672034,  6436.91775515, 21890.22064982,\n",
       "       16667.18254832])"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat_train = lr.predict(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])\n",
    "yhat_train[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11349.16502418,  5914.48335385, 11243.76325987,  6662.03197043,\n",
       "       15555.76936275])"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat_test = lr.predict(x_test[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])\n",
    "yhat_test[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\envs\\sharath\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "ename": "NameError",
     "evalue": "name 'xtrain' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-156-d09fbe9898ce>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mTitle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'Distribution  Plot of  Predicted Value Using Training Data vs Training Data Distribution'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mDistributionPlot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myhat_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Actual Values (Train)\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Predicted Values (Train)\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTitle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-139-c9363a32fc1e>\u001b[0m in \u001b[0;36mDistributionPlot\u001b[1;34m(RedFunction, BlueFunction, RedName, BlueName, Title)\u001b[0m\n\u001b[0;32m     24\u001b[0m     \u001b[1;31m#poly_transform:  polynomial transformation object\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m     \u001b[0mxmax\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mxtrain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxtest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     27\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m     \u001b[0mxmin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mxtrain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxtest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'xtrain' is not defined"
     ]
    }
   ],
   "source": [
    "Title = 'Distribution  Plot of  Predicted Value Using Training Data vs Training Data Distribution'\n",
    "DistributionPlot(y_train, yhat_train, \"Actual Values (Train)\", \"Predicted Values (Train)\", Title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "ename": "NameError",
     "evalue": "name 'xtrain' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-157-8b8725ee8400>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mTitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Distribution  Plot of  Predicted Value Using Test Data vs Data Distribution of Test Data'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mDistributionPlot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0myhat_test\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"Actual Values (Test)\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"Predicted Values (Test)\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mTitle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-139-c9363a32fc1e>\u001b[0m in \u001b[0;36mDistributionPlot\u001b[1;34m(RedFunction, BlueFunction, RedName, BlueName, Title)\u001b[0m\n\u001b[0;32m     24\u001b[0m     \u001b[1;31m#poly_transform:  polynomial transformation object\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m     \u001b[0mxmax\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mxtrain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxtest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     27\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m     \u001b[0mxmin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mxtrain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxtest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'xtrain' is not defined"
     ]
    }
   ],
   "source": [
    "Title='Distribution  Plot of  Predicted Value Using Test Data vs Data Distribution of Test Data'\n",
    "DistributionPlot(y_test,yhat_test,\"Actual Values (Test)\",\"Predicted Values (Test)\",Title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.45, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PolynomialFeatures(degree=5, include_bias=True, interaction_only=False)"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr = PolynomialFeatures(degree=5)\n",
    "x_train_pr = pr.fit_transform(x_train[['horsepower']])\n",
    "x_test_pr = pr.fit_transform(x_test[['horsepower']])\n",
    "pr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly = LinearRegression()\n",
    "poly.fit(x_train_pr, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6728.77492727,  7308.09738048, 12213.83912148, 18893.06269972,\n",
       "       19995.73316497])"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat = poly.predict(x_test_pr)\n",
    "yhat[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted values: [ 6728.77492727  7308.09738048 12213.83912148 18893.06269972]\n",
      "True values: [ 6295. 10698. 13860. 13499.]\n"
     ]
    }
   ],
   "source": [
    "print(\"Predicted values:\", yhat[0:4])\n",
    "print(\"True values:\", y_test[0:4].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 864x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PollyPlot(x_train[['horsepower']], x_test[['horsepower']], y_train, y_test, poly,pr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5567716899817778"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly.score(x_train_pr, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-29.871838229908324"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly.score(x_test_pr, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(3, 0.75, 'Maximum R^2 ')"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Rsqu_test = []\n",
    "\n",
    "order = [1, 2, 3, 4]\n",
    "for n in order:\n",
    "    pr = PolynomialFeatures(degree=n)\n",
    "    \n",
    "    x_train_pr = pr.fit_transform(x_train[['horsepower']])\n",
    "    \n",
    "    x_test_pr = pr.fit_transform(x_test[['horsepower']])    \n",
    "    \n",
    "    lr.fit(x_train_pr, y_train)\n",
    "    \n",
    "    Rsqu_test.append(lr.score(x_test_pr, y_test))\n",
    "\n",
    "plt.plot(order, Rsqu_test)\n",
    "plt.xlabel('order')\n",
    "plt.ylabel('R^2')\n",
    "plt.title('R^2 Using Test Data')\n",
    "plt.text(3, 0.75, 'Maximum R^2 ')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(order, test_data):\n",
    "    x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=test_data, random_state=0)\n",
    "    pr = PolynomialFeatures(degree=order)\n",
    "    x_train_pr = pr.fit_transform(x_train[['horsepower']])\n",
    "    x_test_pr = pr.fit_transform(x_test[['horsepower']])\n",
    "    poly = LinearRegression()\n",
    "    poly.fit(x_train_pr,y_train)\n",
    "    PollyPlot(x_train[['horsepower']], x_test[['horsepower']], y_train,y_test, poly, pr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e15eac1b156c42978ed9cfa2a7f6a41f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=3, description='order', max=6), FloatSlider(value=0.45, description='tes…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.f(order, test_data)>"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact(f, order=(0, 6, 1), test_data=(0.05, 0.95, 0.05))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "pr=PolynomialFeatures(degree=2)\n",
    "x_train_pr=pr.fit_transform(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg','normalized-losses','symboling']])\n",
    "x_test_pr=pr.fit_transform(x_test[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg','normalized-losses','symboling']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "RigeModel=Ridge(alpha=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\envs\\sharath\\lib\\site-packages\\sklearn\\linear_model\\ridge.py:125: LinAlgWarning: scipy.linalg.solve\n",
      "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n",
      "Reciprocal condition number1.029716e-16\n",
      "  overwrite_a=True).T\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RigeModel.fit(x_train_pr, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "yhat = RigeModel.predict(x_test_pr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted: [ 6567.83081933  9597.97151399 20836.22326843 19347.69543463]\n",
      "test set : [ 6295. 10698. 13860. 13499.]\n"
     ]
    }
   ],
   "source": [
    "print('predicted:', yhat[0:4])\n",
    "print('test set :', y_test[0:4].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "Rsqu_test = []\n",
    "Rsqu_train = []\n",
    "dummy1 = []\n",
    "ALFA = 10 * np.array(range(0,1000))\n",
    "for alfa in ALFA:\n",
    "    RigeModel = Ridge(alpha=alfa) \n",
    "    RigeModel.fit(x_train_pr, y_train)\n",
    "    Rsqu_test.append(RigeModel.score(x_test_pr, y_test))\n",
    "    Rsqu_train.append(RigeModel.score(x_train_pr, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f821d45dd8>"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "width = 12\n",
    "height = 10\n",
    "plt.figure(figsize=(width, height))\n",
    "\n",
    "plt.plot(ALFA,Rsqu_test, label='validation data  ')\n",
    "plt.plot(ALFA,Rsqu_train, 'r', label='training Data ')\n",
    "plt.xlabel('alpha')\n",
    "plt.ylabel('R^2')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'alpha': [0.001, 0.1, 1, 10, 100, 1000, 10000, 100000, 100000]}]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters1= [{'alpha': [0.001,0.1,1, 10, 100, 1000, 10000, 100000, 100000]}]\n",
    "parameters1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RR=Ridge()\n",
    "RR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "Grid1 = GridSearchCV(RR, parameters1,cv=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\envs\\sharath\\lib\\site-packages\\sklearn\\model_selection\\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=4, error_score='raise-deprecating',\n",
       "       estimator=Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid=[{'alpha': [0.001, 0.1, 1, 10, 100, 1000, 10000, 100000, 100000]}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Grid1.fit(x_data[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']], y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=10000, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BestRR=Grid1.best_estimator_\n",
    "BestRR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8411649831036149"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BestRR.score(x_test[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']], y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Succesfully performed grid search"
   ]
  }
 ],
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