linear_regression_auto_mpg_residual.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To enable plotting graphs in Jupyter notebook\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Numerical libraries\n",
    "import numpy as np   \n",
    "\n",
    "# Import Linear Regression machine learning library\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# to handle data in form of rows and columns \n",
    "import pandas as pd    \n",
    "\n",
    "# importing ploting libraries\n",
    "import matplotlib.pyplot as plt   \n",
    "import matplotlib.style\n",
    "plt.style.use('classic')\n",
    "\n",
    "#importing seaborn for statistical plots\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading the CSV file into pandas dataframe\n",
    "mpg_df = pd.read_csv(\"auto-mpg.csv\")  "
   ]
  },
  {
   "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>mpg</th>\n",
       "      <th>cyl</th>\n",
       "      <th>disp</th>\n",
       "      <th>hp</th>\n",
       "      <th>wt</th>\n",
       "      <th>acc</th>\n",
       "      <th>yr</th>\n",
       "      <th>origin</th>\n",
       "      <th>car_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>429.0</td>\n",
       "      <td>198</td>\n",
       "      <td>4341</td>\n",
       "      <td>10.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford galaxie 500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>454.0</td>\n",
       "      <td>220</td>\n",
       "      <td>4354</td>\n",
       "      <td>9.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet impala</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>440.0</td>\n",
       "      <td>215</td>\n",
       "      <td>4312</td>\n",
       "      <td>8.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth fury iii</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>455.0</td>\n",
       "      <td>225</td>\n",
       "      <td>4425</td>\n",
       "      <td>10.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>pontiac catalina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>390.0</td>\n",
       "      <td>190</td>\n",
       "      <td>3850</td>\n",
       "      <td>8.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc ambassador dpl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>383.0</td>\n",
       "      <td>170</td>\n",
       "      <td>3563</td>\n",
       "      <td>10.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge challenger se</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>340.0</td>\n",
       "      <td>160</td>\n",
       "      <td>3609</td>\n",
       "      <td>8.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth 'cuda 340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>400.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3761</td>\n",
       "      <td>9.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet monte carlo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>455.0</td>\n",
       "      <td>225</td>\n",
       "      <td>3086</td>\n",
       "      <td>10.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>buick estate wagon (sw)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4</td>\n",
       "      <td>113.0</td>\n",
       "      <td>95</td>\n",
       "      <td>2372</td>\n",
       "      <td>15.0</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "      <td>toyota corona mark ii</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6</td>\n",
       "      <td>198.0</td>\n",
       "      <td>95</td>\n",
       "      <td>2833</td>\n",
       "      <td>15.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth duster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6</td>\n",
       "      <td>199.0</td>\n",
       "      <td>97</td>\n",
       "      <td>2774</td>\n",
       "      <td>15.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc hornet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>200.0</td>\n",
       "      <td>85</td>\n",
       "      <td>2587</td>\n",
       "      <td>16.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford maverick</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>88</td>\n",
       "      <td>2130</td>\n",
       "      <td>14.5</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "      <td>datsun pl510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>26.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>46</td>\n",
       "      <td>1835</td>\n",
       "      <td>20.5</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>volkswagen 1131 deluxe sedan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>25.0</td>\n",
       "      <td>4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>87</td>\n",
       "      <td>2672</td>\n",
       "      <td>17.5</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>peugeot 504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4</td>\n",
       "      <td>107.0</td>\n",
       "      <td>90</td>\n",
       "      <td>2430</td>\n",
       "      <td>14.5</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>audi 100 ls</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>25.0</td>\n",
       "      <td>4</td>\n",
       "      <td>104.0</td>\n",
       "      <td>95</td>\n",
       "      <td>2375</td>\n",
       "      <td>17.5</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>saab 99e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>26.0</td>\n",
       "      <td>4</td>\n",
       "      <td>121.0</td>\n",
       "      <td>113</td>\n",
       "      <td>2234</td>\n",
       "      <td>12.5</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>bmw 2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>199.0</td>\n",
       "      <td>90</td>\n",
       "      <td>2648</td>\n",
       "      <td>15.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc gremlin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8</td>\n",
       "      <td>360.0</td>\n",
       "      <td>215</td>\n",
       "      <td>4615</td>\n",
       "      <td>14.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford f250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>200</td>\n",
       "      <td>4376</td>\n",
       "      <td>15.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevy c20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>210</td>\n",
       "      <td>4382</td>\n",
       "      <td>13.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge d200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>193</td>\n",
       "      <td>4732</td>\n",
       "      <td>18.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>hi 1200d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>88</td>\n",
       "      <td>2130</td>\n",
       "      <td>14.5</td>\n",
       "      <td>71</td>\n",
       "      <td>3</td>\n",
       "      <td>datsun pl510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>90</td>\n",
       "      <td>2264</td>\n",
       "      <td>15.5</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet vega 2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>4</td>\n",
       "      <td>113.0</td>\n",
       "      <td>95</td>\n",
       "      <td>2228</td>\n",
       "      <td>14.0</td>\n",
       "      <td>71</td>\n",
       "      <td>3</td>\n",
       "      <td>toyota corona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>25.0</td>\n",
       "      <td>4</td>\n",
       "      <td>98.0</td>\n",
       "      <td>?</td>\n",
       "      <td>2046</td>\n",
       "      <td>19.0</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>ford pinto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6</td>\n",
       "      <td>232.0</td>\n",
       "      <td>100</td>\n",
       "      <td>2634</td>\n",
       "      <td>13.0</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>amc gremlin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6</td>\n",
       "      <td>225.0</td>\n",
       "      <td>105</td>\n",
       "      <td>3439</td>\n",
       "      <td>15.5</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth satellite custom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6</td>\n",
       "      <td>250.0</td>\n",
       "      <td>100</td>\n",
       "      <td>3329</td>\n",
       "      <td>15.5</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6</td>\n",
       "      <td>250.0</td>\n",
       "      <td>88</td>\n",
       "      <td>3302</td>\n",
       "      <td>15.5</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino 500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6</td>\n",
       "      <td>232.0</td>\n",
       "      <td>100</td>\n",
       "      <td>3288</td>\n",
       "      <td>15.5</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>amc matador</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165</td>\n",
       "      <td>4209</td>\n",
       "      <td>12.0</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet impala</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>400.0</td>\n",
       "      <td>175</td>\n",
       "      <td>4464</td>\n",
       "      <td>11.5</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>pontiac catalina brougham</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>351.0</td>\n",
       "      <td>153</td>\n",
       "      <td>4154</td>\n",
       "      <td>13.5</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>ford galaxie 500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150</td>\n",
       "      <td>4096</td>\n",
       "      <td>13.0</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth fury iii</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8</td>\n",
       "      <td>383.0</td>\n",
       "      <td>180</td>\n",
       "      <td>4955</td>\n",
       "      <td>11.5</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge monaco (sw)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8</td>\n",
       "      <td>400.0</td>\n",
       "      <td>170</td>\n",
       "      <td>4746</td>\n",
       "      <td>12.0</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>ford country squire (sw)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8</td>\n",
       "      <td>400.0</td>\n",
       "      <td>175</td>\n",
       "      <td>5140</td>\n",
       "      <td>12.0</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>pontiac safari (sw)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6</td>\n",
       "      <td>258.0</td>\n",
       "      <td>110</td>\n",
       "      <td>2962</td>\n",
       "      <td>13.5</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>amc hornet sportabout (sw)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>72</td>\n",
       "      <td>2408</td>\n",
       "      <td>19.0</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet vega (sw)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6</td>\n",
       "      <td>250.0</td>\n",
       "      <td>100</td>\n",
       "      <td>3282</td>\n",
       "      <td>15.0</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>pontiac firebird</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6</td>\n",
       "      <td>250.0</td>\n",
       "      <td>88</td>\n",
       "      <td>3139</td>\n",
       "      <td>14.5</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>ford mustang</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4</td>\n",
       "      <td>122.0</td>\n",
       "      <td>86</td>\n",
       "      <td>2220</td>\n",
       "      <td>14.0</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>mercury capri 2000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     mpg  cyl   disp   hp    wt   acc  yr  origin  \\\n",
       "0   18.0    8  307.0  130  3504  12.0  70       1   \n",
       "1   15.0    8  350.0  165  3693  11.5  70       1   \n",
       "2   18.0    8  318.0  150  3436  11.0  70       1   \n",
       "3   16.0    8  304.0  150  3433  12.0  70       1   \n",
       "4   17.0    8  302.0  140  3449  10.5  70       1   \n",
       "5   15.0    8  429.0  198  4341  10.0  70       1   \n",
       "6   14.0    8  454.0  220  4354   9.0  70       1   \n",
       "7   14.0    8  440.0  215  4312   8.5  70       1   \n",
       "8   14.0    8  455.0  225  4425  10.0  70       1   \n",
       "9   15.0    8  390.0  190  3850   8.5  70       1   \n",
       "10  15.0    8  383.0  170  3563  10.0  70       1   \n",
       "11  14.0    8  340.0  160  3609   8.0  70       1   \n",
       "12  15.0    8  400.0  150  3761   9.5  70       1   \n",
       "13  14.0    8  455.0  225  3086  10.0  70       1   \n",
       "14  24.0    4  113.0   95  2372  15.0  70       3   \n",
       "15  22.0    6  198.0   95  2833  15.5  70       1   \n",
       "16  18.0    6  199.0   97  2774  15.5  70       1   \n",
       "17  21.0    6  200.0   85  2587  16.0  70       1   \n",
       "18  27.0    4   97.0   88  2130  14.5  70       3   \n",
       "19  26.0    4   97.0   46  1835  20.5  70       2   \n",
       "20  25.0    4  110.0   87  2672  17.5  70       2   \n",
       "21  24.0    4  107.0   90  2430  14.5  70       2   \n",
       "22  25.0    4  104.0   95  2375  17.5  70       2   \n",
       "23  26.0    4  121.0  113  2234  12.5  70       2   \n",
       "24  21.0    6  199.0   90  2648  15.0  70       1   \n",
       "25  10.0    8  360.0  215  4615  14.0  70       1   \n",
       "26  10.0    8  307.0  200  4376  15.0  70       1   \n",
       "27  11.0    8  318.0  210  4382  13.5  70       1   \n",
       "28   9.0    8  304.0  193  4732  18.5  70       1   \n",
       "29  27.0    4   97.0   88  2130  14.5  71       3   \n",
       "30  28.0    4  140.0   90  2264  15.5  71       1   \n",
       "31  25.0    4  113.0   95  2228  14.0  71       3   \n",
       "32  25.0    4   98.0    ?  2046  19.0  71       1   \n",
       "33  19.0    6  232.0  100  2634  13.0  71       1   \n",
       "34  16.0    6  225.0  105  3439  15.5  71       1   \n",
       "35  17.0    6  250.0  100  3329  15.5  71       1   \n",
       "36  19.0    6  250.0   88  3302  15.5  71       1   \n",
       "37  18.0    6  232.0  100  3288  15.5  71       1   \n",
       "38  14.0    8  350.0  165  4209  12.0  71       1   \n",
       "39  14.0    8  400.0  175  4464  11.5  71       1   \n",
       "40  14.0    8  351.0  153  4154  13.5  71       1   \n",
       "41  14.0    8  318.0  150  4096  13.0  71       1   \n",
       "42  12.0    8  383.0  180  4955  11.5  71       1   \n",
       "43  13.0    8  400.0  170  4746  12.0  71       1   \n",
       "44  13.0    8  400.0  175  5140  12.0  71       1   \n",
       "45  18.0    6  258.0  110  2962  13.5  71       1   \n",
       "46  22.0    4  140.0   72  2408  19.0  71       1   \n",
       "47  19.0    6  250.0  100  3282  15.0  71       1   \n",
       "48  18.0    6  250.0   88  3139  14.5  71       1   \n",
       "49  23.0    4  122.0   86  2220  14.0  71       1   \n",
       "\n",
       "                        car_name  \n",
       "0      chevrolet chevelle malibu  \n",
       "1              buick skylark 320  \n",
       "2             plymouth satellite  \n",
       "3                  amc rebel sst  \n",
       "4                    ford torino  \n",
       "5               ford galaxie 500  \n",
       "6               chevrolet impala  \n",
       "7              plymouth fury iii  \n",
       "8               pontiac catalina  \n",
       "9             amc ambassador dpl  \n",
       "10           dodge challenger se  \n",
       "11            plymouth 'cuda 340  \n",
       "12         chevrolet monte carlo  \n",
       "13       buick estate wagon (sw)  \n",
       "14         toyota corona mark ii  \n",
       "15               plymouth duster  \n",
       "16                    amc hornet  \n",
       "17                 ford maverick  \n",
       "18                  datsun pl510  \n",
       "19  volkswagen 1131 deluxe sedan  \n",
       "20                   peugeot 504  \n",
       "21                   audi 100 ls  \n",
       "22                      saab 99e  \n",
       "23                      bmw 2002  \n",
       "24                   amc gremlin  \n",
       "25                     ford f250  \n",
       "26                     chevy c20  \n",
       "27                    dodge d200  \n",
       "28                      hi 1200d  \n",
       "29                  datsun pl510  \n",
       "30           chevrolet vega 2300  \n",
       "31                 toyota corona  \n",
       "32                    ford pinto  \n",
       "33                   amc gremlin  \n",
       "34     plymouth satellite custom  \n",
       "35     chevrolet chevelle malibu  \n",
       "36               ford torino 500  \n",
       "37                   amc matador  \n",
       "38              chevrolet impala  \n",
       "39     pontiac catalina brougham  \n",
       "40              ford galaxie 500  \n",
       "41             plymouth fury iii  \n",
       "42             dodge monaco (sw)  \n",
       "43      ford country squire (sw)  \n",
       "44           pontiac safari (sw)  \n",
       "45    amc hornet sportabout (sw)  \n",
       "46           chevrolet vega (sw)  \n",
       "47              pontiac firebird  \n",
       "48                  ford mustang  \n",
       "49            mercury capri 2000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check top few records to get a feel of the data structure\n",
    "mpg_df.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the car name column as it is useless for the model\n",
    "mpg_df = mpg_df.drop('car_name', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>mpg</th>\n",
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       "      <td>0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
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       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3436</td>\n",
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       "      <td>70</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>393</td>\n",
       "      <td>27.0</td>\n",
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       "      <td>140.0</td>\n",
       "      <td>86</td>\n",
       "      <td>2790</td>\n",
       "      <td>15.6</td>\n",
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       "    <tr>\n",
       "      <td>394</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>52</td>\n",
       "      <td>2130</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>395</td>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84</td>\n",
       "      <td>2295</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>396</td>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79</td>\n",
       "      <td>2625</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>397</td>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82</td>\n",
       "      <td>2720</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cyl   disp   hp    wt   acc  yr  origin\n",
       "0    18.0    8  307.0  130  3504  12.0  70       1\n",
       "1    15.0    8  350.0  165  3693  11.5  70       1\n",
       "2    18.0    8  318.0  150  3436  11.0  70       1\n",
       "3    16.0    8  304.0  150  3433  12.0  70       1\n",
       "4    17.0    8  302.0  140  3449  10.5  70       1\n",
       "..    ...  ...    ...  ...   ...   ...  ..     ...\n",
       "393  27.0    4  140.0   86  2790  15.6  82       1\n",
       "394  44.0    4   97.0   52  2130  24.6  82       2\n",
       "395  32.0    4  135.0   84  2295  11.6  82       1\n",
       "396  28.0    4  120.0   79  2625  18.6  82       1\n",
       "397  31.0    4  119.0   82  2720  19.4  82       1\n",
       "\n",
       "[398 rows x 8 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace the numbers in categorical variables with the actual country names in the origin col\n",
    "mpg_df['origin'] = mpg_df['origin'].replace({1: 'america', 2: 'europe', 3: 'asia'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <td>52</td>\n",
       "      <td>2130</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>395</td>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84</td>\n",
       "      <td>2295</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>america</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>396</td>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79</td>\n",
       "      <td>2625</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>america</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>397</td>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82</td>\n",
       "      <td>2720</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>america</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cyl   disp   hp    wt   acc  yr   origin\n",
       "0    18.0    8  307.0  130  3504  12.0  70  america\n",
       "1    15.0    8  350.0  165  3693  11.5  70  america\n",
       "2    18.0    8  318.0  150  3436  11.0  70  america\n",
       "3    16.0    8  304.0  150  3433  12.0  70  america\n",
       "4    17.0    8  302.0  140  3449  10.5  70  america\n",
       "..    ...  ...    ...  ...   ...   ...  ..      ...\n",
       "393  27.0    4  140.0   86  2790  15.6  82  america\n",
       "394  44.0    4   97.0   52  2130  24.6  82   europe\n",
       "395  32.0    4  135.0   84  2295  11.6  82  america\n",
       "396  28.0    4  120.0   79  2625  18.6  82  america\n",
       "397  31.0    4  119.0   82  2720  19.4  82  america\n",
       "\n",
       "[398 rows x 8 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert categorical variable into dummy/indicator variables. As many columns will be created as distinct values\n",
    "# This is also kown as one hot coding. The column names will be America, Europe and Asia... with one hot coding\n",
    "mpg_df = pd.get_dummies(mpg_df, columns=['origin'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>130</td>\n",
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       "      <td>2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3436</td>\n",
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       "      <td>3</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140</td>\n",
       "      <td>3449</td>\n",
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       "      <td>393</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>86</td>\n",
       "      <td>2790</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>394</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>52</td>\n",
       "      <td>2130</td>\n",
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       "      <td>396</td>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79</td>\n",
       "      <td>2625</td>\n",
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       "      <td>82</td>\n",
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       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82</td>\n",
       "      <td>2720</td>\n",
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       "      <td>82</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cyl   disp   hp    wt   acc  yr  origin_america  origin_asia  \\\n",
       "0    18.0    8  307.0  130  3504  12.0  70               1            0   \n",
       "1    15.0    8  350.0  165  3693  11.5  70               1            0   \n",
       "2    18.0    8  318.0  150  3436  11.0  70               1            0   \n",
       "3    16.0    8  304.0  150  3433  12.0  70               1            0   \n",
       "4    17.0    8  302.0  140  3449  10.5  70               1            0   \n",
       "..    ...  ...    ...  ...   ...   ...  ..             ...          ...   \n",
       "393  27.0    4  140.0   86  2790  15.6  82               1            0   \n",
       "394  44.0    4   97.0   52  2130  24.6  82               0            0   \n",
       "395  32.0    4  135.0   84  2295  11.6  82               1            0   \n",
       "396  28.0    4  120.0   79  2625  18.6  82               1            0   \n",
       "397  31.0    4  119.0   82  2720  19.4  82               1            0   \n",
       "\n",
       "     origin_europe  \n",
       "0                0  \n",
       "1                0  \n",
       "2                0  \n",
       "3                0  \n",
       "4                0  \n",
       "..             ...  \n",
       "393              0  \n",
       "394              1  \n",
       "395              0  \n",
       "396              0  \n",
       "397              0  \n",
       "\n",
       "[398 rows x 10 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
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       "      <th>max</th>\n",
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       "  <tbody>\n",
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       "      <td>mpg</td>\n",
       "      <td>398.0</td>\n",
       "      <td>23.514573</td>\n",
       "      <td>7.815984</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.500</td>\n",
       "      <td>23.0</td>\n",
       "      <td>29.000</td>\n",
       "      <td>46.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cyl</td>\n",
       "      <td>398.0</td>\n",
       "      <td>5.454774</td>\n",
       "      <td>1.701004</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.000</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>disp</td>\n",
       "      <td>398.0</td>\n",
       "      <td>193.425879</td>\n",
       "      <td>104.269838</td>\n",
       "      <td>68.0</td>\n",
       "      <td>104.250</td>\n",
       "      <td>148.5</td>\n",
       "      <td>262.000</td>\n",
       "      <td>455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>wt</td>\n",
       "      <td>398.0</td>\n",
       "      <td>2970.424623</td>\n",
       "      <td>846.841774</td>\n",
       "      <td>1613.0</td>\n",
       "      <td>2223.750</td>\n",
       "      <td>2803.5</td>\n",
       "      <td>3608.000</td>\n",
       "      <td>5140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>acc</td>\n",
       "      <td>398.0</td>\n",
       "      <td>15.568090</td>\n",
       "      <td>2.757689</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.825</td>\n",
       "      <td>15.5</td>\n",
       "      <td>17.175</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>yr</td>\n",
       "      <td>398.0</td>\n",
       "      <td>76.010050</td>\n",
       "      <td>3.697627</td>\n",
       "      <td>70.0</td>\n",
       "      <td>73.000</td>\n",
       "      <td>76.0</td>\n",
       "      <td>79.000</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>origin_america</td>\n",
       "      <td>398.0</td>\n",
       "      <td>0.625628</td>\n",
       "      <td>0.484569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>origin_asia</td>\n",
       "      <td>398.0</td>\n",
       "      <td>0.198492</td>\n",
       "      <td>0.399367</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>origin_europe</td>\n",
       "      <td>398.0</td>\n",
       "      <td>0.175879</td>\n",
       "      <td>0.381197</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count         mean         std     min       25%     50%  \\\n",
       "mpg             398.0    23.514573    7.815984     9.0    17.500    23.0   \n",
       "cyl             398.0     5.454774    1.701004     3.0     4.000     4.0   \n",
       "disp            398.0   193.425879  104.269838    68.0   104.250   148.5   \n",
       "wt              398.0  2970.424623  846.841774  1613.0  2223.750  2803.5   \n",
       "acc             398.0    15.568090    2.757689     8.0    13.825    15.5   \n",
       "yr              398.0    76.010050    3.697627    70.0    73.000    76.0   \n",
       "origin_america  398.0     0.625628    0.484569     0.0     0.000     1.0   \n",
       "origin_asia     398.0     0.198492    0.399367     0.0     0.000     0.0   \n",
       "origin_europe   398.0     0.175879    0.381197     0.0     0.000     0.0   \n",
       "\n",
       "                     75%     max  \n",
       "mpg               29.000    46.6  \n",
       "cyl                8.000     8.0  \n",
       "disp             262.000   455.0  \n",
       "wt              3608.000  5140.0  \n",
       "acc               17.175    24.8  \n",
       "yr                79.000    82.0  \n",
       "origin_america     1.000     1.0  \n",
       "origin_asia        0.000     1.0  \n",
       "origin_europe      0.000     1.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets analysze the distribution of the dependent (mpg) column\n",
    "mpg_df.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg               float64\n",
       "cyl                 int64\n",
       "disp              float64\n",
       "hp                 object\n",
       "wt                  int64\n",
       "acc               float64\n",
       "yr                  int64\n",
       "origin_america      uint8\n",
       "origin_asia         uint8\n",
       "origin_europe       uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note:  HP column is missing the describe output. That indicates something is not right with that column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>False</td>\n",
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      "text/plain": [
       "        hp\n",
       "32   False\n",
       "126  False\n",
       "330  False\n",
       "336  False\n",
       "354  False\n",
       "374  False"
      ]
     },
     "execution_count": 16,
     "metadata": {},
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    }
   ],
   "source": [
    "#Check if the hp column contains anything other than digits \n",
    "# run the \"isdigit() check on 'hp' column of the mpg_df dataframe. Result will be True or False for every row\n",
    "# capture the result in temp dataframe and dow a frequency count using value_counts()\n",
    "# There are six records with non digit values in 'hp' column\n",
    "\n",
    "temp = pd.DataFrame(mpg_df.hp.str.isdigit())  # if the string is made of digits store True else False  in the hp column \n",
    "# in temp dataframe\n",
    "\n",
    "temp[temp['hp'] == False]   # from temp take only those rows where hp has false\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On inspecting records number 32, 126 etc, we find \"?\" in the columns. Replace them with \"nan\"\n",
    "#Replace them with nan and remove the records from the data frame that have \"nan\"\n",
    "mpg_df = mpg_df.replace('?', np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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",
       "\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>mpg</th>\n",
       "      <th>cyl</th>\n",
       "      <th>disp</th>\n",
       "      <th>hp</th>\n",
       "      <th>wt</th>\n",
       "      <th>acc</th>\n",
       "      <th>yr</th>\n",
       "      <th>origin_america</th>\n",
       "      <th>origin_asia</th>\n",
       "      <th>origin_europe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>25.0</td>\n",
       "      <td>4</td>\n",
       "      <td>98.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2046</td>\n",
       "      <td>19.0</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>126</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2875</td>\n",
       "      <td>17.0</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>330</td>\n",
       "      <td>40.9</td>\n",
       "      <td>4</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1835</td>\n",
       "      <td>17.3</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>336</td>\n",
       "      <td>23.6</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2905</td>\n",
       "      <td>14.3</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>354</td>\n",
       "      <td>34.5</td>\n",
       "      <td>4</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2320</td>\n",
       "      <td>15.8</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>374</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4</td>\n",
       "      <td>151.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3035</td>\n",
       "      <td>20.5</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cyl   disp   hp    wt   acc  yr  origin_america  origin_asia  \\\n",
       "32   25.0    4   98.0  NaN  2046  19.0  71               1            0   \n",
       "126  21.0    6  200.0  NaN  2875  17.0  74               1            0   \n",
       "330  40.9    4   85.0  NaN  1835  17.3  80               0            0   \n",
       "336  23.6    4  140.0  NaN  2905  14.3  80               1            0   \n",
       "354  34.5    4  100.0  NaN  2320  15.8  81               0            0   \n",
       "374  23.0    4  151.0  NaN  3035  20.5  82               1            0   \n",
       "\n",
       "     origin_europe  \n",
       "32               0  \n",
       "126              0  \n",
       "330              1  \n",
       "336              0  \n",
       "354              1  \n",
       "374              0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let us see if we can get those records with nan\n",
    "\n",
    "mpg_df[mpg_df.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# There are various ways to handle missing values. Drop the rows, replace missing values with median values etc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#of the 398 rows 6 have NAN in the hp column. We will drop those 6 rows. Not a good idea under all situations\n",
    "#note: HP is missing becauses of the non-numeric values in the column. \n",
    "#mpg_df = mpg_df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg                 23.0\n",
       "cyl                  4.0\n",
       "disp               148.5\n",
       "hp                  93.5\n",
       "wt                2803.5\n",
       "acc                 15.5\n",
       "yr                  76.0\n",
       "origin_america       1.0\n",
       "origin_asia          0.0\n",
       "origin_europe        0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#instead of dropping the rows, lets replace the missing values with median value. \n",
    "mpg_df.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace the missing values in 'hp' with median value of 'hp' :Note, we do not need to specify the column names\n",
    "# every column's missing value is replaced with that column's median respectively  (axis =0 means columnwise)\n",
    "#mpg_df = mpg_df.fillna(mpg_df.median())\n",
    "\n",
    "mpg_df = mpg_df.apply(lambda x: x.fillna(x.median()),axis=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg               float64\n",
       "cyl                 int64\n",
       "disp              float64\n",
       "hp                 object\n",
       "wt                  int64\n",
       "acc               float64\n",
       "yr                  int64\n",
       "origin_america      int64\n",
       "origin_asia         int64\n",
       "origin_europe       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpg_df['hp'] = mpg_df['hp'].astype('float64')  # converting the hp column from object / string type to float\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>mpg</th>\n",
       "      <th>cyl</th>\n",
       "      <th>disp</th>\n",
       "      <th>hp</th>\n",
       "      <th>wt</th>\n",
       "      <th>acc</th>\n",
       "      <th>yr</th>\n",
       "      <th>origin_america</th>\n",
       "      <th>origin_asia</th>\n",
       "      <th>origin_europe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>23.514573</td>\n",
       "      <td>5.454774</td>\n",
       "      <td>193.425879</td>\n",
       "      <td>104.304020</td>\n",
       "      <td>2970.424623</td>\n",
       "      <td>15.568090</td>\n",
       "      <td>76.010050</td>\n",
       "      <td>0.625628</td>\n",
       "      <td>0.198492</td>\n",
       "      <td>0.175879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>7.815984</td>\n",
       "      <td>1.701004</td>\n",
       "      <td>104.269838</td>\n",
       "      <td>38.222625</td>\n",
       "      <td>846.841774</td>\n",
       "      <td>2.757689</td>\n",
       "      <td>3.697627</td>\n",
       "      <td>0.484569</td>\n",
       "      <td>0.399367</td>\n",
       "      <td>0.381197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>1613.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>104.250000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>2223.750000</td>\n",
       "      <td>13.825000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>148.500000</td>\n",
       "      <td>93.500000</td>\n",
       "      <td>2803.500000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>3608.000000</td>\n",
       "      <td>17.175000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>46.600000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>455.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>5140.000000</td>\n",
       "      <td>24.800000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              mpg         cyl        disp          hp           wt  \\\n",
       "count  398.000000  398.000000  398.000000  398.000000   398.000000   \n",
       "mean    23.514573    5.454774  193.425879  104.304020  2970.424623   \n",
       "std      7.815984    1.701004  104.269838   38.222625   846.841774   \n",
       "min      9.000000    3.000000   68.000000   46.000000  1613.000000   \n",
       "25%     17.500000    4.000000  104.250000   76.000000  2223.750000   \n",
       "50%     23.000000    4.000000  148.500000   93.500000  2803.500000   \n",
       "75%     29.000000    8.000000  262.000000  125.000000  3608.000000   \n",
       "max     46.600000    8.000000  455.000000  230.000000  5140.000000   \n",
       "\n",
       "              acc          yr  origin_america  origin_asia  origin_europe  \n",
       "count  398.000000  398.000000      398.000000   398.000000     398.000000  \n",
       "mean    15.568090   76.010050        0.625628     0.198492       0.175879  \n",
       "std      2.757689    3.697627        0.484569     0.399367       0.381197  \n",
       "min      8.000000   70.000000        0.000000     0.000000       0.000000  \n",
       "25%     13.825000   73.000000        0.000000     0.000000       0.000000  \n",
       "50%     15.500000   76.000000        1.000000     0.000000       0.000000  \n",
       "75%     17.175000   79.000000        1.000000     0.000000       0.000000  \n",
       "max     24.800000   82.000000        1.000000     1.000000       1.000000  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x204385ae2c8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x2000 with 110 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let us do a correlation analysis among the different dimensions and also each dimension with the dependent dimension\n",
    "# This is done using scatter matrix function which creates a dashboard reflecting useful information about the dimensions\n",
    "# The result can be stored as a .png file and opened in say, paint to get a larger view \n",
    "\n",
    "mpg_df_attr = mpg_df.iloc[:, 0:10]\n",
    "\n",
    "#axes = pd.plotting.scatter_matrix(mpg_df_attr)\n",
    "#plt.tight_layout()\n",
    "#plt.savefig('d:\\greatlakes\\mpg_pairpanel.png')\n",
    "\n",
    "sns.pairplot(mpg_df_attr, diag_kind='kde')   # to plot density curve instead of histogram\n",
    "\n",
    "#sns.pairplot(mpg_df_attr)  # to plot histogram, the default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The data distribution across various dimensions except 'Acc' do not look normal\n",
    "#Close observation between 'mpg' and other attributes indicate the relationship is not really linear\n",
    "#relation between 'mpg' and 'hp' show hetroscedacity... which will impact model accuracy\n",
    "#How about 'mpg' vs 'yr' surprising to see a positive relation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copy all the predictor variables into X dataframe. Since 'mpg' is dependent variable drop it\n",
    "X = mpg_df.drop('mpg', axis=1)\n",
    "X = X.drop({'origin_america', 'origin_asia' ,'origin_europe'}, axis=1)\n",
    "\n",
    "# Copy the 'mpg' column alone into the y dataframe. This is the dependent variable\n",
    "y = mpg_df[['mpg']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let us break the X and y dataframes into training set and test set. For this we will use\n",
    "#Sklearn package's data splitting function which is based on random function\n",
    "\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split X and y into training and test set in 75:25 ratio\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30 , random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# invoke the LinearRegression function and find the bestfit model on training data\n",
    "\n",
    "regression_model = LinearRegression()\n",
    "regression_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The coefficient for cyl is -0.18095805032306023\n",
      "The coefficient for disp is 0.010983679987754756\n",
      "The coefficient for hp is -0.00898274748809659\n",
      "The coefficient for wt is -0.007188190332770617\n",
      "The coefficient for acc is 0.029142901338762704\n",
      "The coefficient for yr is 0.7883566858707732\n"
     ]
    }
   ],
   "source": [
    "# Let us explore the coefficients for each of the independent attributes\n",
    "\n",
    "for idx, col_name in enumerate(X_train.columns):\n",
    "    print(\"The coefficient for {} is {}\".format(col_name, regression_model.coef_[0][idx]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The intercept for our model is -15.621707993406755\n"
     ]
    }
   ],
   "source": [
    "# Let us check the intercept for the model\n",
    "\n",
    "intercept = regression_model.intercept_[0]\n",
    "\n",
    "print(\"The intercept for our model is {}\".format(intercept))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.79968038605472"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "regression_model.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8268047501149659"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model score - R2 or coeff of determinant\n",
    "# R^2=1–RSS / TSS =  RegErr / TSS\n",
    "\n",
    "regression_model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20439bbe208>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Is OLS a good model ? Should we building a simple linear model ? Check the residuals for each predictor.\n",
    "\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "sns.residplot(x= X_test['hp'], y= y_test['mpg'], color='green', lowess=True )\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "sns.residplot(x= X_test['acc'], y= y_test['mpg'], color='green', lowess=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So the model explains 85% of the variability in Y using X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ---------------------------------- Using Statsmodel library to get R type outputs -----------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>cyl</th>\n",
       "      <th>disp</th>\n",
       "      <th>hp</th>\n",
       "      <th>wt</th>\n",
       "      <th>acc</th>\n",
       "      <th>yr</th>\n",
       "      <th>mpg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>350</td>\n",
       "      <td>4</td>\n",
       "      <td>105.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>2215</td>\n",
       "      <td>14.9</td>\n",
       "      <td>81</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2254</td>\n",
       "      <td>23.5</td>\n",
       "      <td>72</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>4</td>\n",
       "      <td>121.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>2868</td>\n",
       "      <td>15.5</td>\n",
       "      <td>73</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>400.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3761</td>\n",
       "      <td>9.5</td>\n",
       "      <td>70</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>349</td>\n",
       "      <td>4</td>\n",
       "      <td>91.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1985</td>\n",
       "      <td>16.0</td>\n",
       "      <td>81</td>\n",
       "      <td>34.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cyl   disp     hp    wt   acc  yr   mpg\n",
       "350    4  105.0   63.0  2215  14.9  81  34.7\n",
       "59     4   97.0   54.0  2254  23.5  72  23.0\n",
       "120    4  121.0  112.0  2868  15.5  73  19.0\n",
       "12     8  400.0  150.0  3761   9.5  70  15.0\n",
       "349    4   91.0   68.0  1985  16.0  81  34.1"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# R^2 is not a reliable metric as it always increases with addition of more attributes even if the attributes have no \n",
    "# influence on the predicted variable. Instead we use adjusted R^2 which removes the statistical chance that improves R^2\n",
    "# Scikit does not provide a facility for adjusted R^2... so we use \n",
    "# statsmodel, a library that gives results similar to\n",
    "# what you obtain in R language\n",
    "# This library expects the X and Y to be given in one single dataframe\n",
    "\n",
    "data_train = pd.concat([X_train, y_train], axis=1)\n",
    "data_train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept    2.131628e-14\n",
       "cyl          4.662937e-15\n",
       "disp        -8.364620e-17\n",
       "hp          -4.614364e-16\n",
       "wt          -5.637851e-18\n",
       "acc         -3.330669e-16\n",
       "yr          -2.220446e-16\n",
       "mpg          1.000000e+00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "lm1 = smf.ols(formula= 'mpg ~ cyl+disp+hp+wt+acc+yr+mpg', data = data_train).fit()\n",
    "lm1.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    mpg   R-squared:                       1.000\n",
      "Model:                            OLS   Adj. R-squared:                  1.000\n",
      "Method:                 Least Squares   F-statistic:                 5.028e+29\n",
      "Date:                Fri, 10 Apr 2020   Prob (F-statistic):               0.00\n",
      "Time:                        23:20:04   Log-Likelihood:                 8030.4\n",
      "No. Observations:                 278   AIC:                        -1.604e+04\n",
      "Df Residuals:                     270   BIC:                        -1.602e+04\n",
      "Df Model:                           7                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   2.132e-14   1.13e-13      0.188      0.851   -2.02e-13    2.44e-13\n",
      "cyl         4.663e-15    8.5e-15      0.549      0.584   -1.21e-14    2.14e-14\n",
      "disp       -8.365e-17   1.88e-16     -0.444      0.658   -4.55e-16    2.87e-16\n",
      "hp         -4.614e-16   3.25e-16     -1.418      0.157    -1.1e-15    1.79e-16\n",
      "wt         -5.638e-18    1.9e-17     -0.297      0.767    -4.3e-17    3.17e-17\n",
      "acc        -3.331e-16   2.39e-15     -0.139      0.889   -5.04e-15    4.37e-15\n",
      "yr          -2.22e-16   1.57e-15     -0.142      0.887   -3.31e-15    2.86e-15\n",
      "mpg            1.0000   1.19e-15    8.4e+14      0.000       1.000       1.000\n",
      "==============================================================================\n",
      "Omnibus:                       48.908   Durbin-Watson:                   0.162\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               69.665\n",
      "Skew:                           1.130   Prob(JB):                     7.45e-16\n",
      "Kurtosis:                       3.954   Cond. No.                     8.30e+04\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 8.3e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "print(lm1.summary())  #Inferential statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let us check the sum of squared errors by predicting value of y for test cases and \n",
    "# subtracting from the actual y for the test cases\n",
    "\n",
    "mse = np.mean((regression_model.predict(X_test)-y_test)**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.1821685329303415"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# underroot of mean_sq_error is standard deviation i.e. avg variance between predicted and actual\n",
    "\n",
    "import math\n",
    "\n",
    "math.sqrt(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# so there is avg of 3.0 (roundoff) mpg difference from real mpg on an avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict mileage (mpg) for a set of attributes not in the training or test set\n",
    "y_pred = regression_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2043a355a88>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Since this is regression, plot the predicted y value vs actual y values for the test data\n",
    "# A good model's prediction will be close to actual leading to high R and R2 values\n",
    "#plt.rcParams['figure.dpi'] = 500\n",
    "\n",
    "\n",
    "\n",
    "plt.scatter(y_test['mpg'], y_pred)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ------------------------------------------------- ITERATION 2  ---------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# How do we improve the model? the R^2 is .844, how do we improve it\n",
    "# The indpendent attributes have different units and scales of measurement \n",
    "# It is always a good practice to scale all the dimensions using z scores or someother methode to address the problem of different scales \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import zscore\n",
    "\n",
    "mpg_df_scaled  = mpg_df.apply(zscore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert the numpy array back into a dataframe \n",
    "\n",
    "mpg_df_scaled = pd.DataFrame(mpg_df_scaled, columns=mpg_df.columns)"
   ]
  },
  {
   "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>mpg</th>\n",
       "      <th>cyl</th>\n",
       "      <th>disp</th>\n",
       "      <th>hp</th>\n",
       "      <th>wt</th>\n",
       "      <th>acc</th>\n",
       "      <th>yr</th>\n",
       "      <th>origin_america</th>\n",
       "      <th>origin_asia</th>\n",
       "      <th>origin_europe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-0.706439</td>\n",
       "      <td>1.498191</td>\n",
       "      <td>1.090604</td>\n",
       "      <td>0.673118</td>\n",
       "      <td>0.630870</td>\n",
       "      <td>-1.295498</td>\n",
       "      <td>-1.627426</td>\n",
       "      <td>0.773559</td>\n",
       "      <td>-0.497643</td>\n",
       "      <td>-0.461968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-1.090751</td>\n",
       "      <td>1.498191</td>\n",
       "      <td>1.503514</td>\n",
       "      <td>1.589958</td>\n",
       "      <td>0.854333</td>\n",
       "      <td>-1.477038</td>\n",
       "      <td>-1.627426</td>\n",
       "      <td>0.773559</td>\n",
       "      <td>-0.497643</td>\n",
       "      <td>-0.461968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-0.706439</td>\n",
       "      <td>1.498191</td>\n",
       "      <td>1.196232</td>\n",
       "      <td>1.197027</td>\n",
       "      <td>0.550470</td>\n",
       "      <td>-1.658577</td>\n",
       "      <td>-1.627426</td>\n",
       "      <td>0.773559</td>\n",
       "      <td>-0.497643</td>\n",
       "      <td>-0.461968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-0.962647</td>\n",
       "      <td>1.498191</td>\n",
       "      <td>1.061796</td>\n",
       "      <td>1.197027</td>\n",
       "      <td>0.546923</td>\n",
       "      <td>-1.295498</td>\n",
       "      <td>-1.627426</td>\n",
       "      <td>0.773559</td>\n",
       "      <td>-0.497643</td>\n",
       "      <td>-0.461968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-0.834543</td>\n",
       "      <td>1.498191</td>\n",
       "      <td>1.042591</td>\n",
       "      <td>0.935072</td>\n",
       "      <td>0.565841</td>\n",
       "      <td>-1.840117</td>\n",
       "      <td>-1.627426</td>\n",
       "      <td>0.773559</td>\n",
       "      <td>-0.497643</td>\n",
       "      <td>-0.461968</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>393</td>\n",
       "      <td>0.446497</td>\n",
       "      <td>-0.856321</td>\n",
       "      <td>-0.513026</td>\n",
       "      <td>-0.479482</td>\n",
       "      <td>-0.213324</td>\n",
       "      <td>0.011586</td>\n",
       "      <td>1.621983</td>\n",
       "      <td>0.773559</td>\n",
       "      <td>-0.497643</td>\n",
       "      <td>-0.461968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>394</td>\n",
       "      <td>2.624265</td>\n",
       "      <td>-0.856321</td>\n",
       "      <td>-0.925936</td>\n",
       "      <td>-1.370127</td>\n",
       "      <td>-0.993671</td>\n",
       "      <td>3.279296</td>\n",
       "      <td>1.621983</td>\n",
       "      <td>-1.292726</td>\n",
       "      <td>-0.497643</td>\n",
       "      <td>2.164651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>395</td>\n",
       "      <td>1.087017</td>\n",
       "      <td>-0.856321</td>\n",
       "      <td>-0.561039</td>\n",
       "      <td>-0.531873</td>\n",
       "      <td>-0.798585</td>\n",
       "      <td>-1.440730</td>\n",
       "      <td>1.621983</td>\n",
       "      <td>0.773559</td>\n",
       "      <td>-0.497643</td>\n",
       "      <td>-0.461968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>396</td>\n",
       "      <td>0.574601</td>\n",
       "      <td>-0.856321</td>\n",
       "      <td>-0.705077</td>\n",
       "      <td>-0.662850</td>\n",
       "      <td>-0.408411</td>\n",
       "      <td>1.100822</td>\n",
       "      <td>1.621983</td>\n",
       "      <td>0.773559</td>\n",
       "      <td>-0.497643</td>\n",
       "      <td>-0.461968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>397</td>\n",
       "      <td>0.958913</td>\n",
       "      <td>-0.856321</td>\n",
       "      <td>-0.714680</td>\n",
       "      <td>-0.584264</td>\n",
       "      <td>-0.296088</td>\n",
       "      <td>1.391285</td>\n",
       "      <td>1.621983</td>\n",
       "      <td>0.773559</td>\n",
       "      <td>-0.497643</td>\n",
       "      <td>-0.461968</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          mpg       cyl      disp        hp        wt       acc        yr  \\\n",
       "0   -0.706439  1.498191  1.090604  0.673118  0.630870 -1.295498 -1.627426   \n",
       "1   -1.090751  1.498191  1.503514  1.589958  0.854333 -1.477038 -1.627426   \n",
       "2   -0.706439  1.498191  1.196232  1.197027  0.550470 -1.658577 -1.627426   \n",
       "3   -0.962647  1.498191  1.061796  1.197027  0.546923 -1.295498 -1.627426   \n",
       "4   -0.834543  1.498191  1.042591  0.935072  0.565841 -1.840117 -1.627426   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "393  0.446497 -0.856321 -0.513026 -0.479482 -0.213324  0.011586  1.621983   \n",
       "394  2.624265 -0.856321 -0.925936 -1.370127 -0.993671  3.279296  1.621983   \n",
       "395  1.087017 -0.856321 -0.561039 -0.531873 -0.798585 -1.440730  1.621983   \n",
       "396  0.574601 -0.856321 -0.705077 -0.662850 -0.408411  1.100822  1.621983   \n",
       "397  0.958913 -0.856321 -0.714680 -0.584264 -0.296088  1.391285  1.621983   \n",
       "\n",
       "     origin_america  origin_asia  origin_europe  \n",
       "0          0.773559    -0.497643      -0.461968  \n",
       "1          0.773559    -0.497643      -0.461968  \n",
       "2          0.773559    -0.497643      -0.461968  \n",
       "3          0.773559    -0.497643      -0.461968  \n",
       "4          0.773559    -0.497643      -0.461968  \n",
       "..              ...          ...            ...  \n",
       "393        0.773559    -0.497643      -0.461968  \n",
       "394       -1.292726    -0.497643       2.164651  \n",
       "395        0.773559    -0.497643      -0.461968  \n",
       "396        0.773559    -0.497643      -0.461968  \n",
       "397        0.773559    -0.497643      -0.461968  \n",
       "\n",
       "[398 rows x 10 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#browse the contents of the dataframe. Check that all the values are now z scores\n",
    "\n",
    "mpg_df_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copy all the predictor variables into X dataframe. Since 'mpg' is dependent variable drop it\n",
    "X = mpg_df_scaled.drop('mpg', axis=1)\n",
    "X = X.drop({'origin_america', 'origin_asia' ,'origin_europe'}, axis=1)\n",
    "\n",
    "# Copy the 'mpg' column alone into the y dataframe. This is the dependent variable\n",
    "y = mpg_df_scaled[['mpg']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split X and y into training and test set in 75:25 ratio\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# invoke the LinearRegression function and find the bestfit model on training data\n",
    "\n",
    "regression_model = LinearRegression()\n",
    "regression_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The coefficient for cyl is -0.03938216856283999\n",
      "The coefficient for disp is 0.14652876580191515\n",
      "The coefficient for hp is -0.043928464258872965\n",
      "The coefficient for wt is -0.7788219130487555\n",
      "The coefficient for acc is 0.010282397352733334\n",
      "The coefficient for yr is 0.3729598950344689\n"
     ]
    }
   ],
   "source": [
    "# Let us explore the coefficients for each of the independent attributes\n",
    "\n",
    "for idx, col_name in enumerate(X_train.columns):\n",
    "    print(\"The coefficient for {} is {}\".format(col_name, regression_model.coef_[0][idx]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The intercept for our model is 0.011398468343359232\n"
     ]
    }
   ],
   "source": [
    "intercept = regression_model.intercept_[0]\n",
    "\n",
    "print(\"The intercept for our model is {}\".format(intercept))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.826804750114966"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model score - R2 or coeff of determinant\n",
    "# R^2=1–RSS / TSS\n",
    "\n",
    "regression_model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let us check the sum of squared errors by predicting value of y for training cases and \n",
    "# subtracting from the actual y for the training cases\n",
    "\n",
    "mse = np.mean((regression_model.predict(X_test)-y_test)**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4076484360556728"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# underroot of mean_sq_error is standard deviation i.e. avg variance between predicted and actual\n",
    "\n",
    "import math\n",
    "\n",
    "math.sqrt(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict mileage (mpg) for a set of attributes not in the training or test set\n",
    "y_pred = regression_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2043a392548>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Since this is regression, plot the predicted y value vs actual y values for the test data\n",
    "# A good model's prediction will be close to actual leading to high R and R2 values\n",
    "plt.scatter(y_test['mpg'], y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}