Linear Regression - Auto vs MPG.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression - car mpg with other attributes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8 variables: \n",
    "\n",
    "1.- MPG (miles per gallon)\n",
    "\n",
    "2.- cylinders\n",
    "\n",
    "3.- engine displacement (cu. inches)\n",
    "\n",
    "4.- horsepower\n",
    "\n",
    "5.- vehicle weight (lbs.)\n",
    "\n",
    "6.- time to accelerate from O to 60 mph (sec.)\n",
    "\n",
    "7.- model year (modulo 100), and\n",
    "\n",
    "8.- origin of car (1. American, 2. European,3. Japanese).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"color:blue;\"> Importing Libraries</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imporitng libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "#%import matplotlib.style\n",
    "#%plt.style.use('classic')\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix,accuracy_score\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the file into pandas dataframe\n",
    "data_df = pd.read_csv(\"car-mpg.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data Size= (398, 10)\n"
     ]
    },
    {
     "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_type</th>\n",
       "      <th>car_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>0</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>0</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>0</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>0</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>0</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cyl   disp   hp    wt   acc  yr  origin  car_type  \\\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",
       "                    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  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check head data records\n",
    "print('Data Size=',data_df.shape)\n",
    "data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data Size= (398, 9)\n"
     ]
    },
    {
     "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_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cyl   disp   hp    wt   acc  yr  origin  car_type\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"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping car name\n",
    "data_df.drop(columns='car_name', inplace=True)\n",
    "print('Data Size=',data_df.shape)\n",
    "data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 398 entries, 0 to 397\n",
      "Data columns (total 9 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   mpg       398 non-null    float64\n",
      " 1   cyl       398 non-null    int64  \n",
      " 2   disp      398 non-null    float64\n",
      " 3   hp        398 non-null    object \n",
      " 4   wt        398 non-null    int64  \n",
      " 5   acc       398 non-null    float64\n",
      " 6   yr        398 non-null    int64  \n",
      " 7   origin    398 non-null    int64  \n",
      " 8   car_type  398 non-null    int64  \n",
      "dtypes: float64(3), int64(5), object(1)\n",
      "memory usage: 28.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# Data Information\n",
    "data_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"color:blue;\"> Analyzing the data hp=horsepower</p>"
   ]
  },
  {
   "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>hp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        hp\n",
       "32   False\n",
       "126  False\n",
       "330  False\n",
       "336  False\n",
       "354  False\n",
       "374  False"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets find the string in the numerical column\n",
    "temp = pd.DataFrame(data_df.hp.str.isdigit())\n",
    "temp[temp.hp == False]"
   ]
  },
  {
   "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>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_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>200.0</td>\n",
       "      <td>?</td>\n",
       "      <td>2875</td>\n",
       "      <td>17.0</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>40.9</td>\n",
       "      <td>4</td>\n",
       "      <td>85.0</td>\n",
       "      <td>?</td>\n",
       "      <td>1835</td>\n",
       "      <td>17.3</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>23.6</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>?</td>\n",
       "      <td>2905</td>\n",
       "      <td>14.3</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>34.5</td>\n",
       "      <td>4</td>\n",
       "      <td>100.0</td>\n",
       "      <td>?</td>\n",
       "      <td>2320</td>\n",
       "      <td>15.8</td>\n",
       "      <td>81</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>23.0</td>\n",
       "      <td>4</td>\n",
       "      <td>151.0</td>\n",
       "      <td>?</td>\n",
       "      <td>3035</td>\n",
       "      <td>20.5</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cyl   disp hp    wt   acc  yr  origin  car_type\n",
       "32   25.0    4   98.0  ?  2046  19.0  71       1         1\n",
       "126  21.0    6  200.0  ?  2875  17.0  74       1         0\n",
       "330  40.9    4   85.0  ?  1835  17.3  80       2         1\n",
       "336  23.6    4  140.0  ?  2905  14.3  80       1         1\n",
       "354  34.5    4  100.0  ?  2320  15.8  81       2         1\n",
       "374  23.0    4  151.0  ?  3035  20.5  82       1         1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking thos rows i the original data\n",
    "data_df.iloc[[32,126,330,336,354,374]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\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>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\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>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\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>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cyl   disp   hp    wt   acc  yr  origin  car_type\n",
       "32   25.0    4   98.0  NaN  2046  19.0  71       1         1\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       2         1\n",
       "336  23.6    4  140.0  NaN  2905  14.3  80       1         1\n",
       "354  34.5    4  100.0  NaN  2320  15.8  81       2         1\n",
       "374  23.0    4  151.0  NaN  3035  20.5  82       1         1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replacing the \"?\" by the hp median\n",
    "# To find the mediam of hp replace \"?\" by nan first\n",
    "data_df = data_df.replace('?', np.nan)\n",
    "data_df.iloc[[32,126,330,336,354,374]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hp median= 93.5\n"
     ]
    }
   ],
   "source": [
    "# median value\n",
    "med = data_df.hp.median()\n",
    "print('hp median=', med)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df.hp = data_df.hp.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 398 entries, 0 to 397\n",
      "Data columns (total 9 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   mpg       398 non-null    float64\n",
      " 1   cyl       398 non-null    int64  \n",
      " 2   disp      398 non-null    float64\n",
      " 3   hp        392 non-null    float64\n",
      " 4   wt        398 non-null    int64  \n",
      " 5   acc       398 non-null    float64\n",
      " 6   yr        398 non-null    int64  \n",
      " 7   origin    398 non-null    int64  \n",
      " 8   car_type  398 non-null    int64  \n",
      "dtypes: float64(4), int64(5)\n",
      "memory usage: 28.1 KB\n"
     ]
    }
   ],
   "source": [
    "data_df.info()"
   ]
  },
  {
   "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>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_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>25.0</td>\n",
       "      <td>4</td>\n",
       "      <td>98.0</td>\n",
       "      <td>93.5</td>\n",
       "      <td>2046</td>\n",
       "      <td>19.0</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>200.0</td>\n",
       "      <td>93.5</td>\n",
       "      <td>2875</td>\n",
       "      <td>17.0</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>40.9</td>\n",
       "      <td>4</td>\n",
       "      <td>85.0</td>\n",
       "      <td>93.5</td>\n",
       "      <td>1835</td>\n",
       "      <td>17.3</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>23.6</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>93.5</td>\n",
       "      <td>2905</td>\n",
       "      <td>14.3</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>34.5</td>\n",
       "      <td>4</td>\n",
       "      <td>100.0</td>\n",
       "      <td>93.5</td>\n",
       "      <td>2320</td>\n",
       "      <td>15.8</td>\n",
       "      <td>81</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>23.0</td>\n",
       "      <td>4</td>\n",
       "      <td>151.0</td>\n",
       "      <td>93.5</td>\n",
       "      <td>3035</td>\n",
       "      <td>20.5</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cyl   disp    hp    wt   acc  yr  origin  car_type\n",
       "32   25.0    4   98.0  93.5  2046  19.0  71       1         1\n",
       "126  21.0    6  200.0  93.5  2875  17.0  74       1         0\n",
       "330  40.9    4   85.0  93.5  1835  17.3  80       2         1\n",
       "336  23.6    4  140.0  93.5  2905  14.3  80       1         1\n",
       "354  34.5    4  100.0  93.5  2320  15.8  81       2         1\n",
       "374  23.0    4  151.0  93.5  3035  20.5  82       1         1"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replacing \"NaN\" with median value\n",
    "data_df = data_df.apply(lambda x:x.fillna(x.median()))\n",
    "data_df.iloc[[32,126,330,336,354,374]]"
   ]
  },
  {
   "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>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_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\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",
       "      <th>mean</th>\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>1.572864</td>\n",
       "      <td>0.530151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\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>0.802055</td>\n",
       "      <td>0.499718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\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>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\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>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\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>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\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>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\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>3.000000</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    car_type  \n",
       "count  398.000000  398.000000  398.000000  398.000000  \n",
       "mean    15.568090   76.010050    1.572864    0.530151  \n",
       "std      2.757689    3.697627    0.802055    0.499718  \n",
       "min      8.000000   70.000000    1.000000    0.000000  \n",
       "25%     13.825000   73.000000    1.000000    0.000000  \n",
       "50%     15.500000   76.000000    1.000000    1.000000  \n",
       "75%     17.175000   79.000000    2.000000    1.000000  \n",
       "max     24.800000   82.000000    3.000000    1.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"color:blue;\"> Analyzing every feature</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.pairplot(data_df, diag_kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2235c3d3f88>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAFgCAYAAAD93q3tAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3dd5gcZ5nu/+9T1XHyjDTKkiU5SE5YskfOxhkbbEzOsGDSObuwCwtsgN9vAS+7bDjALmeXwx7wEpYFlmQwwZi1ccY4jLOsYNmycprR5O7pUFXv+aPbY41mRsFSq0aa+3NdujT9Vr31Pl1d03dXmC5zziEiInKkeXEXICIiU5MCSEREYqEAEhGRWCiAREQkFgogERGJRSLuAvZ09dVXu1tvvTXuMkREDieLu4DJalLtAXV3d8ddgoiIHCGTKoBERGTqUACJiEgsFEAiIhILBZCIiMRCASQiIrFQAImISCwUQCIiEgsFkIiIxEIBJCIisajpV/GY2Z8C7wcc8BRwvXOucDiWHUWOvlzAcDEknfSoS/vUZfyR6X1DZYqliFLgyKQ82ltSI9OCIKBnKKJQikgnPRI+TGt6cXp3f4kwglI5Ip3ymLFHX4BdfSWKpYhU0sP3YfoefXf3lwiqfTNpj6QPxTI456jP+BRKlXFTCY90wihFjuFiRDbtkfCMcugoliOyKQ/fg2K5csPARMIIAkfkHMmER8KD0EEUQimIyKQ8EgkolaEcVpYfRZW+2bRPc32C/lxAsRRRKEdkkh6pBHi+R6lcWU8J3yiVK88rm/ZozB765tE3VGa4GOH7Rjbl0VhXWWYYOvpzAfliSCbtU5/2yKb9/Sxtf2MFFEohZkY27dFUN6m+aUpE9lKz31Azmwv8CXCKc27YzH4IvBX41uFY/vaeEn/x9WfZPRDgefC+V87mqo5p1Gd8egbK/OieXdx8fzfOwZxpKf72vYuZ1ZYGYEdvwCf/fT3d/WU8D95+2Uyu6mhjenOKrr4Sv/h9Nz+5t4vIwazWFJ9/32JmT6v03ba7yCdvfI5dfWU8gzddMoNrzplGe3OKrv4Stz60m/+6cxeRg79//2J+fE8Xnc8M0lyf4G+uX8Tnv7eR7T0l/ujVc1gwM8Nff2cD+WIlCP/8LfP57WO93P/0AH/73sX8/PddPLh6kM++exG/frCbB9cMAnDKcXV84k0L+N3T/XzzN9uJIpg3PcXnrl/MX33rea6/aja3P9rD71cNALB0QR3//zsWcu+Tfdz4622EEbQ2Jvjb6xfz1PP9NGR95kxL808/2cymXUUArjlnGn9w5Sya6l/6JtIzUOavvrWe9dsrnzmuPLOV971qDs31CTZ3FfjLG9fTnwtI+MaHXzuXl5/e8pJDqHewzA3f2cDazXkALjytmQ+/Zh7NDQohkcmq1ofgEkDWzBJAHbDtcCx0KB/wlZu3sHsgACCK4MZbtpMrhAD05wN+9rtK+ABs213iO7ftYGg4YHd/ma/9ahvd/eWRvv95+05KQWXm4VLEj+6phA/Ajt4S3/zNdnoHyvQMlPj3W7axq6/a18EP7txFsRQBlb2e791RCZ+l8+vY3FWk85lKaFx5Vis/uruL7T0lAFYsbeILP9xMvljpWyxH/NNPtnDtudM5fnaWXX0lHlw9yOLZGfoGyyPhA7BqY54H1wzw+HNDRJXuLD+xke/fuQvPYLgUjoQPwJpNee58vJcn1g8RVufvHQz4ys+3cvycLAtmZrjtkd6R8AH41YO72dVXesmvURBG/Pz33SPhA3Dbo71s7S7SnyvzpR9vpj8XVOd1/OvPto68fgcrjBy3P9o7Ej4A963s57ntwy+5fhGpvZoFkHNuK/AFYBOwHeh3zv333vOZ2QfNrNPMOru6ug5o2aXAsWHn6CN5zlUOwQBs6SqO6bNhZ4F8MaQcRmzcOfYoYFf1zXb77nH67ihQDCqHqTbuGtt3RzVUdvaWR9pmtqZGjTOzNTWmb/dAedTjoeGQhG/MbE2O9B2vH8CazXlm7nFocGZLZbzKuGOfw9rNeWa0jj6UuHFngYZsgnwhGneMvdfxwSiWHeu25se0r98+TBgx5jUIQkehGuQHKwgdazaPHWu88UVk8qhZAJlZK/AaYBEwB6g3s3fuPZ9z7mvOuQ7nXEd7e/sBLbsu7XH2kqZRbZmUx7SmJAAnzs3i7fXMzl7aREt9grq0R8eSxlHTUgkbOcS2cFaWhG9j+jZkfeozHh0njR436RsLZmYAmNeeJpmo9H1mS57lJ7w4ztrNec468cXHUQQnzcuOWtbc6Sn6hwLWbR1mWbXvuq3DLDt+dL0ALz+9hQ07X/yEv2ZznhVLGnl22zBnLG4YM//FL2th447RewRnndTIjp4iTXX+qNoAzODU4+rHLOdA1aU9Xv6yljHtZxzfQDo59jVorBt9Du9gpJMel5wxdqy9txERmVxqeQjuCuB551yXc64M3AScfzgWnEn7vPsVs7jkjBaSCWPBjDR///7FNNVV3sAaMj6f/YNFzJ6WIp30uLqjjVefO51U0qepPsnbLp3FpctaSCWM+e1p/ua9i0knK8GRTMAN717EnGkp0knjyjNbed2F7TRkEzTWJXnTxTO4fHkrqYQxd3qav37PIpLV981UwvjcexYxrz3N7oEyvg9/+OrKOY9H1w1yVUcbV61oI500bnmom79863GcsbiBhG+cclwdn37XIu54vJe+XEAQRvzxa+cSho7tPUX+8Lo5tDYkaMj6vO2yGSycleE9V83muJkZkgkj4cNVHW2cd3IT67cP84evnkNrY4KGjM+7rpjJaYvqec9Vs1k4K0PSNy44tZnrr5pN0jeeWj/EifOyvPq8aWRSHjNaknz6nQtpOYTzJ2bGeSc386aL26lLe0xvTvLJtx9HW2OS+ozPh66bx/mnNpH0jcWzM/z9+4+n+RAuGjhjcQPvvHwm9RmPtsYEH3/T/FEXnojI5GPuhRMlh3vBZucA3wBWAMNULj7odM79y0R9Ojo6XGdn5wGPkS+EFMoRnkFLQ3LUtCiKKueIDDLJF6++ekHvYJlyWHnue1/lFgQBPYOVq6kSCaN1r2X3DJYJQ0fkYFqjRyIxetk7e0t4BgnfaKzzGchXzm00ZX0Gh0OCat/pTT69gyERlTtWtbek2NVbwqp96zMefbkQAxoyHkPFCOcg4RmNWY9cMaJYdngG9RmfUjkidI4wgnTCqD49GrM+yUTls0ZXfwkc+L7hGzTWJcgXQ4aLEZ5XOZTpe0ZzfQLPO/T7aBXLEblC5Tk01Sfw91hmrhBSLEd4ZocUdi8oBxGDw5V13VyXwPd1HzCZFLQhTqBmAQRgZjcAbwEC4DHg/c65sScoqg42gEREjgIKoAnU9BpV59xngM/UcgwRETk66ZsQREQkFgogERGJhQJIRERioQASEZFYKIBERCQWCiAREYmFAkhERGKhABIRkVgogEREJBYKIBERiYUCSEREYqEAEhGRWCiAREQkFgogERGJhQJIRERioQASEZFYKIBERCQWCiAREYmFAkhERGKhABIRkVgogEREJBYKIBERiYUCSEREYqEAEhGRWCiAREQkFgogERGJhQJIRERioQASEZFYKIBERCQWCiAREYmFAkhERGKhABIRkVgogEREJBYKIBERiYUCSEREYqEAEhGRWCiAREQkFgogERGJhQJIRERioQASEZFYKIBERCQWCiAREYmFAkhERGKhABIRkVgogEREJBYKIBERiYUCSEREYlGzADKzJWb2+B7/Bszso7UaT0REji6JWi3YObcWWAZgZj6wFfhprcYTEZGjy5E6BHc58JxzbuMRGk9ERCa5IxVAbwW+P94EM/ugmXWaWWdXV9cRKkdEROJmzrnaDmCWArYBpzrndu5r3o6ODtfZ2VnTekREjjCLu4DJ6kjsAb0SeHR/4SMiIlPLkQigtzHB4TcREZm6ahpAZlYHXAncVMtxRETk6FOzy7ABnHN5YFotxxARkaOTvglBRERioQASEZFYKIBERCQWCiAREYmFAkhERGKhABIRkVgogEREJBYKIBERiYUCSEREYqEAEhGRWCiAREQkFgogERGJhQJIRERioQASEZFYKIBERCQWCiAREYmFAkhERGKhABIRkVgogEREJBYKIBERiYUCSEREYqEAEhGRWCiAREQkFgogERGJhQJIRERioQASEZFYKIBERCQWCiAREYmFAkhERGKhABIRkVgogEREJBYKIBERiYUCSEREYqEAEhGRWCiAREQkFgogERGJhQJIRERioQASEZFYKIBERCQWCiAREYmFAkhERGKhABIRkVgogEREJBYKIBERiYUCSEREYqEAEhGRWCiAREQkFolaLtzMWoAbgdMAB7zXOff7Wo45mQ3my+QKEU+sH6K9OcWCGWmmN6dGpnf1lcjni7Sn8njbV+E3tuE1Tifs3Yo/6wRcEFLYuBKvoY1E+wL6onq6+kps7ipyynH11KU9tnQVGByOWDq/jsg5nniuMta89jT5YsiazXkWzcriGUQOWhoSPL0hx6JZGbJpnyfXD9HakOC4mRnWbsmTTnrMbE3hmSNfdLQ1JiiVHas2Vvo0Nfh0D5TYsDPP6QubiULHqk15li6ooyHj4/vw1Po89VmPRbMyFEqOVRtyLJiZYVpTknyxTDJbYEvxObJ+lvn1C2hKNhFGjr6hgFUbc2RTHgtnp9jhnsEw5tctoCnZPO46HsgHdPWVeH57gaUL6mhtTFCfObDNfCAf0N1fZv22YZYsqKOtMUl9xt9vP1cuEuX7CTatxGudhd86F69+/Pr2VopKDJUHeXZoHW2paczMzKIx2XhAfY+E3HBA71DAmk15Fs/O0t6SpLGupm8bMoXUekv6MnCrc+6NZpYC6mo83qS2vafMJ/7vs5QDB8BpC+v5i7csYHpLiq7+Et+/YwfvWFak+J9/DkEJAH/eyWTOfyvR7i0M/eCzEBQBGH7d3/D1R1u4b+UAAJ4H/9/bj2Pu9Aw3fGcd6aRxw7sX8dVfbKPjpEaWHd/Av968daSW15w/nVee3cb/+Ke1zJue4uNvWsD//Oe1FEoRAMfPyfKeq2bxV998nuNnZ7n+6tkkfHh8V4Ev/mgzAO9/5Ww27ipw2yO9AJjt4GNvnM+9T/Xxv3+6hY+9cT4nzs3ylZu3cMLcOi4/s5Uv/HATrvL0ue68aVx3aYovPPs3DIfDAMzKzOYjJ32C4aEMH/7fzzBUCAGY357mY+9q5V82/TUz07P46JJPjAmhXCHkx3fv4kf3dI20feLN83n5y1pI+vve2c8XQ27+XTffu2PnSNtHXj+Py5e3kkzsu2+4cz2D//kXEFVqTZywgvpr/xSvbv8htH14G19a+w8ELgBgaeMpvGfR+ydFCAVBxP2rBvjSjzePtL35khm85eIZ1B1AMIvsT80OwZlZE/By4N8BnHMl51xfrcab7Lr7S3zz1u0j4QOwckOOroEyAPlCxLzmiOSD3xkJH4Bwy2oskaT42G9GwgcvwXDd7JHwAYgi+O5vd9I7WObck5voz4Xc8Xgf553SxJVntfGd23eMqucXD3QThI5CKeKtl87ku7/dORI+AM9tG2ZoOGR2W4rntg8zkA/wPPjmrdtH5jl1Yf1I+AA4B9+9fSevOKsN5+A7t+2gPxdy0ctaeEVHG9/+zfaR8AEIXMDdu38zEj4AOwrb6Rnu48f37BoJH4DNXUU2bDHmZxews7iDZwfXjVnHw8WQn9zXNarta7/cxmA+HDPv3vKFkB/ctXNU24237L9vlB8gf/vXR8IHIHj2YVx+YB+9KnJBjpu2/HAkfADWDK5ioNy/375HwkA+5Ou3bBvV9uN7dpEvRhP0EDk4tTwHtBjoAr5pZo+Z2Y1mVr/3TGb2QTPrNLPOrq6usUs5RgShY2h47JvZC23DxZDGNFAcGjOPK+ZwUfnFBt+nWB77JjCQDzGDunTl0+lgPqAu7ZNJeWPGjiIIq4sYb3qlf0g2XdlEBodD0gmPwT3mC8d5HxrIByOfjoeGQxK+UZf2qUuPHaMuYwyFg2OWUQrL9A4GY9oHchEZP1OpJxj7Bh9Glee1p3wxqhz83Y8wGvt8hkvR/rtGIa4w9jm40vA4M+81pgvJBbkx7XsGcpwclWDeU2W7OYAVKnIAahlACeBM4KvOueVADvjLvWdyzn3NOdfhnOtob2+vYTnxmtaY4FXntI1qa6rzWTir8oY6rSnJo5sgOPVVo+axTANe8wxSC894sbFcpNnPM6MlOWreK85spS7j0flM5c350mWtdD4zyGPrBrnkjNZR8y6ZX8cLR6Xufaqfa8+dNmp6NuVx4twsG3YWyKY8TpqXZVdfmSvPenE5/bmABTPSe9XQxsNrK+NffmYrvsHDawboXDvIFWeOfv6bd4ZcNO3yUW1JSzKjfhqvu3D0tpBMGMtPyrAh9zwJS3B68xnsLZ0ylswffZT3suUtZNL738wzKY/TF43+fPTy01vIpPbd17JNpM+8dnRbQxte0/635YZEA5fMuGxUW2OikfbMjP32PRKyKY9Ll43ebpYuqNvvOhE5UOZcbT7NmNks4AHn3MLq44uAv3TOXTNRn46ODtfZ2VmTeiaD3QMlnngux3939jC9OcnbL5vJ9CafVKpyKm5HT4n+nl5m5tbiP30rXsN00iuuo7jqbtLLribYtYHyY7+C+jYyl7ybrqCJn9zbxeZdBS44tZkVS5tYuX6Ie1f286aLZ5Dwje/ctoMZrUneecUs7n68jwfXDHDSvCyXLW+lbyigHDhuvr+b979qNjt7S/zi97tprk/w1ktncMdjvezqK/O6C6ezq6/E9OYU05uS/O7pfn63sp8VSxq5ZFkrP7l3J89vL3Lhac2cfFw937x1O8tPaOSC05oplSK+/uttNNcnuP6q2fx+1QD3rezj+DlZXn9hO1v7BojqdnJ//21kvSzXzLmOaenplIrGs9uG+eHdXdSlPd52+XRWBXezo7iZa+Zcx/R0O0kvOWYd9wyW+dl9XazelOfcU5q4fHkbLQ0Hdqqzd7DMz+/vZuWGHCuWNvKKs9poaRg7xt6i/ADl5x6m9OTteK1zyVzwZrymdsxsv31zwRCrBp7md133Mj3dzitnX0tbqu2A+h4JfUMBtz/aw4OrBzjluHpee8F0Whv3v05klMnxYk5CNQsgADO7F3i/c26tmX0WqHfO/dlE8x/rAfSC7r4SqaRHU/3YN8bBfJkghKZEAUukwEXgHF668sl+eGAAL5EkXZelXI7IF0PCCOrSHhERw0VHOmk0ZJPkCgGFUkR9xieT8unPFQkCI139BJvwjUzKJ1cIiaIIzzMKJUcyAUnPyBUj0knDzAhChxk01ycJI0e+GJJJeiQTHrlCieFSSHM2CeaRK4SkEkZd9eqz3HCA7xmZtE8UOXJ79C0HEaUgAr9EwvNJ+6P3qHKFAM8zsimf4SCPZ2Pn2Vs5iCiUIurSPr5/cL/7QRAxXI6oSx1cX+ccrpjD/BSWTO2/w17yQY6ElyTlHXzfWgtDR74Ukk16JPZzQYaMSwE0gVoH0DIql2GngPXA9c653onmnyoBJCJTigJoAjW9DNs59zjQUcsxRETk6KT9aRERiYUCSEREYqEAEhGRWCiAREQkFgogERGJhQJIRERioQASEZFYKIBERCQWCiAREYmFAkhERGKhABIRkVgogEREZISZ3WJmLfuZ56/N7IpDHaumX0YqIiJHB6vchMqcc6/a37zOuU8fjjG1ByQiMkWY2cfMbGX130fNbKGZrTaz/wM8Csw3sw1mNr06/1+Z2Rozu83Mvm9mn6i2f8vM3lj9eYOZ3WBmj5rZU2a29EDrUQCJiEwBZnYWcD1wDnAu8AGgFVgC/IdzbrlzbuMe83cAbwCWA69n37fW6XbOnQl8FfjEgdakABIRmRouBH7qnMs554aAm4CLgI3OuQcmmP9m59ywc24Q+MU+ln1T9f9HgIUHWpACSERkapjozqy5g5x/PMXq/yEHcW2BAkhEZGq4B3itmdWZWT3wOuDefcx/H/BqM8uYWQNwzeEuaJ9JZWav39d059xN+5ouIiKTg3PuUTP7FvBQtelGoHcf8z9sZj8HngA2Ap1A/+GsyZxzE080++Y++jrn3HsPZzEdHR2us7PzcC5SRCRuB3Moa1Ixswbn3JCZ1VHZg/qgc+7Rw7X8fe4BOeeurxbhO+fCwzWoiIgcFb5mZqcAGeDbhzN84MBPFj1rZj8GvumcW3U4CxARkcnJOff2Wi7/QC9CeBnwDHCjmT1gZh80s6Ya1iUiIse4Awog59ygc+7rzrnzgT8HPgNsN7Nvm9kJNa1QRESOSQcUQGbmm9l1ZvZT4MvAF4HFVP4w6ZYa1iciIseoAz0HtA64E/hfzrn792j/sZm9/PCXJSIix7oDPQf0OPDxF8LHzFrN7BsAzrk/qVVxIiJy+JjZN8xsl5mtjLsWOPA9oEXOub4XHjjnes1seY1qEhGZ8l75ySfeDnweWABsAj71678743uHuNhvAf8K/MchLuewONA9IM/MWl94YGZt6F5CIiI1UQ2frwPHUflD1uOAr1fbXzLn3D1Az6FXeHgcaIh8Ebi/+rdADngz8Lc1q0pEZGr7PFC3V1tdtf1Q94ImjQMKIOfcf5hZJ3AZlTR+vf4gVUSkZhYcZPtR6YAPo1UDR6EjIlJ7m6gcdhuv/Zih2zGIiEw+nwLye7Xlq+3HDAWQiMgkU73a7QNUboPgqv9/4FCvgjOz7wO/B5aY2RYze98hF3so9ezrdgxHmm7HICLHoKP2dgy1pj0gERGJhQJIRERioQASEZFYKIBERCQWCiAREYmFAkhERGKhABIRmQLMbL6Z3Wlmq83saTP7SNw16RutRUQmod7PXzPmdgytn/rVofwhakDlvm6Pmlkj8IiZ3Rbn93pqD0hEZJKphs+Y2zFU218S59x259yj1Z8HgdXA3MNQ7kumABIRmXz2dTuGQ2ZmC4HlwIOHY3kvlQJIRGTyqdntGMysAfgJ8FHn3MChLu9QKIBERCafiW67cEi3YzCzJJXw+a5z7qZDWdbhUNOLEMxsAzAIhEDgnOuo5XhyaKLhQcKerQTrHsSfdzKJ2Uvw6pvjLgsXlImGeiitugtL1ZNacj7W0IqZvuPxYEXDA4Tdmwme6yQx/zT82Sfg1cX/GssYn6JyDmjPw3CHdDsGq/zC/Duw2jn3pUMr7/Co6bdhVwOowznXfSDz69uw4+OCMsVHb2H49q+NtCVPuZi6q/4QL9sYY2UQ9mxl4MYPQ1ACwBraaHrvl/Ea2mKt62jjykUKD95E4Z7/HGlLLbuausvei2XqY6zsmPeSPikd7qvgzOxC4F7gKSCqNn/KOXfLS13modJl2AKAKwxSuPe7o9rKq+6GS98DMQaQC8oU7v/RSPgAuKEeys8/Rvr0y2Or62jkCrnKutxD6Yn/JnPh2/AVQJNONWwO6f4/e3LO3cckuzVErc8BOeC/zewRM/vgeDOY2QfNrNPMOru6umpcjkzMcFE4ptUR8/2inMOFwdj2sHzkaznqOXB7vcYuqrSLxKDWAXSBc+5M4JXAh8zs5XvP4Jz7mnOuwznX0d7eXuNyZCKWridz9mtGtSUWLceS2ZgqqrBkisx5bwB7cVO1dD3J43U68aCl60gtf9WopuSSC2J/jWXqOmJ3RDWzzwJDzrkvTDSPzgHFK8oPEGxZRWnVPSQXnE5yyXl49S1xl0VUKuAGdlF46GYsU0/6rGvxGqdhnh93aUedKN9PsPEpSmvvJ7loGckTzp4Ur/ExblId9ppMahZAZlYPeM65werPtwF/7Zy7daI+CqDJwYUh5k++N/fKIULDPP31wKGarK/xMUoBNIFaXoQwE/hp9VLZBPC9fYWPTB6T9Y1JezyHz2R9jWVqqVkAOefWA2fUavkiInJ007EMEZEpwMwyZvaQmT1RvR3DDXHXpL8DEhGZhD70yAfG/CHqV876+qH8XVARuMw5N1T9Sp77zOzXzrkHDkO5L4n2gEREJplq+Iy5HUO1/SVxFUPVh8nqv1j/CEwBJCIy+dTkdgxm5pvZ48Au4DbnnG7HICIio9TkdgzOudA5twyYB5xtZqcdyvIOlQJIRGTyqcntGF7gnOsD7gKuPhzLe6kUQCIik8+nqNx+YU+HejuGdjNrqf6cBa4A1rzkCg8DBZCIyCRTvdrtA8BGKhcKbAQ+cIhXwc0G7jSzJ4GHqZwD+uUhF3sIjth3wR0IfRWPiByD9FU8E9AekIiIxEIBJCIisVAAiYhILBRAIiISCwWQiIjEQgEkIiKxUACJiEgsFEAiIhILBZCIiMRCASQiIrFQAImISCwUQCIiEgsFkIiIxEIBJCIisVAAiYhILBRAIiISCwWQiIjEQgEkIiKxUACJiEgsFEAiIhILBZCIiMRCASQiIrFQAImISCwUQCIiEgsFkIiIxEIBJCIisVAAiYhILBRAIiISCwWQiIjEQgEkIiKxUACJiEgsFEAiIhILBZCIiMRCASQiIrFQAImISCwUQCIiEgsFkIiIxCIRdwFy+DgXQRRifnLc6WGpgHkJvMTYlz0My7igTCJdN27fcjkgIiKVSGJmREEZnMNLpgjDkDAokUpnKQcRCd8ws5G+xUIJz/PA80gmPJxzBKHDDKIoJIgi6tJpykEEzuGAVNKvjFssEWCkkwk8zyiUQpK+4QDnHKUwoD6dxjlHOXCAw/c9fM8olMqVOqIIP5HAuQjf9/DMo1SqjJtNGng+QehIJl78PFYqFgEjlU5V6giLeJbARRFhCA5HpjotCkOiKCSRTBFFjlK5DGakkj6RC7EIQiJ8LwEOCkFAJpEgIiJyIelEpjJGVMZFER5GIlFZ9nCxTCLh4QjxzacUBCR8H89BGDl8c0ReAt8MFwWERCT8JJ5VnksUOcKo8txyxSJJzyOVHLt9lIIS4DAzfAzPTxK5Sn0JL8lwuUDS80mMs22FkSOMQhK+BzicA9/zx92ORm1TYRk/qmxDL3BhAOZh3oF9Nq7Mb4CBizD/xW3bBWXwfYgiMMOZ4Zw7oNr2VlkXEQlPb5mHU83Xppn5QCew1Tl3ba3Hm6qioV6KT95GuHM9qZddQVN3cbAAABYBSURBVGLOErxsIwDloT7o3Ubp0V9hDa2kO66Dhmn4fuUXMRzcTemJ2wi7nid18svx5i4l0TgNgMHBYYatyLND61idf5SljadwTuZkyk/+lnDXelKnXII1t5MrOB7qb+Wep4c4+bh6Lj2jBd+V2NXv+On9PTRkPa5aMY2GjMe9K/vZvKvAmy6ewe9X97Nm4zAXnNZMa2OC1ZvynL2kiShyNNcneHpDjvtX9XPi3DouPK2ZlRuGWDgzg+95PPrsIGs357ngtGZOnJulfyjk1s7dvPniGZQCx8/u6yKV8nj9he0QFUn6QMIw53Hfyn6e3pDj7CWNnHliPes39TIUZli2uI7BfMBN93WTShivvaidUmIHw0E/c7zFPLvLcdujfcyZluKKM1tpSBv2xM34pSEGV7yNgeHKuOYZr7uwnWxdidW5x9mW28qV01/PA6v6eWzdMGeelOWcpc38ZvePWNp8CgvrF3F/173sLO7kguazmZueS18ug19fYmXPwyxvPZvV/atYO7SSE+tP4YzWM/Ae+S3ernUkXnYVm20O9a0N3D94CwnP56L2i7FSI/es7OGC05rpXDvAw2vynH58hgtPbcVPlWlON1AIhhkIBrhj520ELuDSGVeQwCftp+ns6eSsthWsHVzNyv4nWdRwPGe1riDpJalP1BOEEd39ZX56Xxf5Ysi1FzTT0uzo7LuX5W1nMS01nbSfHrOt5oIcXn6Q8tN34e14nuSpl5Ccu5RocDfFh3+O1TWT7rgOr7ENmyAsouIwUd92SuseJL3kfIpP3IYbHiC94jq8xmkEW5+h9PQd+LNOJHniOQxmfO7q/R0DwQCXzriC9swMsn52v79Xzjn6yn3cvetO+so9XDLjcmamZ5FN7L+v7J8552o7gNnHgA6gaX8B1NHR4To7O2taz7EoyvUx+L1PEXVtHGnLXvVHpJddjfk+5eceYegHnx6ZZvUtNF7/Zfym6YQD3eR+dAPhzvUj0zOXvofkma8mkc6wuWuAB3O3c+fuXwPw4bnvY+6v/4tw+7qR+VNvuIGfbpzFd+/cPdJ28cuaee0F0/nYvz3HC5tYQ8bn7z6wmI/86zr+9I3zufXhHp7ekBvp84aL2hkuRjy7Lc/7XjmbNZuH+eat20emr1jSyDsun8nz2wvc/WQfjz83tEff6QQhDOUDXnfRDP7kK88QRdV1kfL46keX8JuHd/KKjhl85eatdD4zONL32nOn8Y4L6/nCz3t41xWz+Pi/PUtY7ZtOenzpj+cwNxlw5xr48s07RvrNnZ7mz988n5mJPiIHQ8lW/ujLzxKElSecShhf/dMT+b9bP8cHFnyM79wyyD1PDoz0v+zMZt76ijpy1s33Nn6H3aXukWnXz347J7Uu54vP/iNvnvcuOnsf4KHe+0emL2/p4PXpi7D/+DMAkq/8U/5t3Qm86lKPr2z6HC3JVq6f9We0ZOr57m+7uPWhvpG+557cwIdeN4uWugx9QS9/u+ozlKISAB4enzzl0/xg0/d43Zw381Dv/dzddcdI31OaTuUt899FW7qF3QMh//Of1pIvVlaWZ/APfzSHxtYc/2vN5/nkyZ9mbt089lSKSvT1bSLzs38h3PHsSHvmkncT7d5C6anfAmDZRpre/xW86gehvQXb1zH4rY/T+Af/yNAPP4sbrryeqdOvwGubQ+Hu/xiZ1599Ijte9Q6+vOVrI20fO+kvOL7xhHGXvaf+cj9/t+oGBoMXt5c/OfHjLGlaut++e7D9zzI11fQckJnNA64BbqzlOFOdyw+MCh+A4gM/wQ0PUB7spfDgTaPnz/URbF1T+bk0PCp8AIoP/xwKAwwOFhiOitzf++Ib0CyvZVT4AOSTLfz0/t5RbdObU/zo7i72/HwzVAhZ+XyOE+ZmmTMtPSp8AG55cDfnn9rMM1uGaa5P8LP7ukZNn9WW4ub7u1kwMzMqfAB++cBuzj25ibdcOoOf3tc1Ej4Aw6WIe5/qY+mCJoBR4QPwm4d7KFmGN5zbxK8f6hkJH4BiOeKhp4sUCiE//t3o57i1u8jAcEiQnYbX0MovH9g9Ej4ApcBx2yM9vHbuG0i6Bu59amBU/7se6yflGilFpVHhA3Bb373koyG6S13MyLTT2fvAqOmP9z2Ca2sfeRw+/BMuWGz09iZoSbbSV+5lyLqInMftj/SP6vvA6iGCwCeIAjp7HhwJH4CIiDt3/pYZ6Rk0phr4Xfc9o/quGngaiMiHwzywqn8kfAAiB7fcn6MYBMzKzOa2nbdSjsqj+g+Hw6SCcFT4ABQf+hnJJeePPHbDgwSbVjKeqFygcP8P8WcuIti+biR8AJJLzqP48M9GzR9uX0e7NWJ75MBvdvyK4WB43OXvaVNu46jwqfS9hXyQ329f2b9aX4Twz8CfA9FEM5jZB82s08w6u7q6JppN9mW8wxSJZOXYuBmMc9zekpVDI+Mda7fq+QfPqxwzT9geR2ptnE0mCkedP4HKeYFUcuy8yYQRhpVPy3tLJY0gqryBmxnJxOiZgtCRShg2Tt+Eb4RR5XB/KjF2hnTSCILKOY69+yd8A+coR5UaxqsL58ZdbtI3wOGZjds3nTSCqFx5GfZ60r5fqcW3sa9fwkti1XXtYOSczgsMjz0/WFsiRSmsrl8XVJZhCYyx43peZbNwQNJLsbeUlyJ04cgyRo9bOb9n2LivbzpZqTVwAWkvPepN/8UCxtteUxAGo9uSYw/fAZX1kkxDGIw93xkF4I99TphH5exiddFeasw6HU9ynHM+SUuO/7zkoNUsgMzsWmCXc+6Rfc3nnPuac67DOdfR3t6+r1llApZtIHHcy0a1ZS95D1bXTLKhhcxFbx/1S++1zcWfsQgAl0yTWLR8VN/MRe+Auhbq69M0p+q5fNp1I9PWl7fhn7Bi1PyNXoF3X9Y6qq1YinjrpTNGhciMliRL59fz3PZh1m7Jc8GpzaP6vOnlM7jz8V5WLGlk++4if3DlrFHTh4ZDXntBO2s25bjkjJZR095++UzufaqP//PLrbzh5e2k93hzbG1McO7JzWzbPYxzjivPHF3rmy5uJ2tFvntXL9ecM41s6sW+zfUJli1JUNeQ5j1XTBsVXqccV0c25ZEa3EqxazPXnDOduvSLfZvqfC5b1sYPt/yAYXp49fmjx33NhW3kXDehC1lQd9xIu2Fc13YFGavnuLpFbM5t4tL2V4zqe9H0y/C3bxzp4V3wLh7aaKTrcwwGg8zJziVRbiVyIW+8uG1U36tXtJBMhmSTaZa3nkVjonFkWtpLc8mMy8gFQ3QXunnFrFeO6ntO23ngPLJ+lhUnNdHW+OIbdDrp8arzG8EL6S/3ccXMq8actK/36yn5Ht4JHaPaMxe/i9Kqu0ceey2zSMxZwngskSJ7/lsIe7biTZ+H1zxzZFpp1T1kL37XqPkTJ57D1vDFw8MJS3DNnOvGPT+1tznZucxIv7h8D59r575G54AOk5qdAzKzvwPeBQRABmgCbnLOvXOiPjoH9NJF1cNq4a7nSS45H69pOl66HoBybgiv0E9p1T1YYxvJxWfhN00f6RsOdlf67nie1NLzcPVtJBsrb1r5fJF8VKSn3M3a3FOc0HACJ/gzcVvWEHRtIHXSeUTAUL5ET3IeD64dYsn8ek6YmyVpZYYKxj0rB6jP+Jy+qIFMymPDzgLPbctz8RmtbNqV55ktBc5e0sRwMWKoEDK7LU1d2iOZMLr6Sjz8zCAnza1j0ewMqzbkOGleHcPliO6+Muu25VlxUhONdT5BCA+vHeC8kxvxfY+7n+wjm/I475RmnAuIwggvkcCo1LBqY46OExuZ1Zrguc0DNDXXM6ctQbHsuO+pHlK+cc5prUTJfroLO1jgL2T3cJJ7Vw4we1qKU4+rJ5sISWx4CJfvJzr9Kgpl496VPfieccGprZAcpCfo4vmh9XQ0XsqWrhJPPJdj2QkNzJ2e5KGB21nafAptqWk8N7iOXcPbWd58Bk1eI0OFOoLkMNuD55iXXkhf0MuagdWc1LiEWelZJNY/jdezmcSS89lWqKexJcvK3H3Up5pY0ngyrlzHyudzLDkuxY7dZR5ZN8Tpi+o5bmaaVMrRmK4jDEMGwn4e732UsitzVusKojAk6SfZmN/I3OwC+oNenu5/iuMbTmBOZh4ZP002UYdzjp7BMg+u6WeoEHDBaY2ksyWeyT3N0qZTaUo0jXvVWD7I4Q3nCLY/g+3cSOqkc/Eb23GlYUqr78WrbyG5+Cy8htYxfV/gghJRro/y84+TXHAqweZVRMMDpJZeiKWyRIPdlJ95AH/m8Xits8hl06wuPMdgMMjy1g6akk2kxtn7G89AuZ81A6vpK/VyZlsHTcnmA+5bpd2lCdT8IgQAM7sE+IQuQhCRKUgBNAH9IaqIiMTiiPxVlXPuLuCuIzGWiIgcHbQHJCIisVAAiYhILBRAIiISCwWQiIjEQgEkIiKxUACJiEgsFEAiIhILBZCIiMRCASQiIrFQAImISCwUQCIiEgsFkIiIxEIBJCIisVAAiYhILBRAIiISCwWQiIjEQgEkIiKxUACJiEgsFEAiIhILBZCIiMRCASQiIrFQAImISCwUQCIiEgsFkIiIxEIBJCIisVAAiYhILBRAIiISCwWQiIjEQgEkIiKxUACJiEgsFEAiIhILBZCIiMRCASQiIrFQAImISCwUQCIiEgsFkIiIxEIBJCIisVAAiYhILBRAIiISCwWQiIjEQgEkIiKxUACJiEgsFEAiIhILBZCIiMRCASQiIrFQAImISCwUQMeQUlQiFwwRuWjc6fkgRzEsHvI4oQvJBUOUo/JLXkYxLJAPcodci4gcvRK1WrCZZYB7gHR1nB875z5Tq/Gmut5SD7ds+wU7CttZ0XYOZ7Z20JBsBCrBs3ZwDXftuoOmRBPXzn0N01Pt+J5/0OMMlQd5YPf9PN73KMfVLeTKWa+kJdVywP2DKKC72MXPt/2UfJDn8plXcnzDidQl6g66FhE5utUsgIAicJlzbsjMksB9ZvZr59wDNRxzShoo9/PFNf9Ab7kHgPW55xgKcrxi1tUkvARrB9dw4/p/G5n/6YGn+PSpn6Ml1XpQ4xTDAr/YdjP3dd8NwPO59TwzuJY/PuljNCWbDmgZg8EAf7/6byi7EgDrhtbyoRM+winNpx1ULSJy9KvZIThXMVR9mKz+c7UabyobLA+OhM8L7uu+m1yQIxfkuGvXHaOmFaMiG3LPH/Q4hajIA7t/N6ptW2HrQR3WWzuwZiR8XnDnrt8yHAwfdD0icnSr6TkgM/PN7HFgF3Cbc+7Bceb5oJl1mllnV1dXLcs5ZqX99Ji2xkQjnhkJS9CYaBwzvWGctv0xoD7RsFebkTiIQ3lNyeYxbc3J5pd0OFBEjm41DSDnXOicWwbMA842szHHWZxzX3POdTjnOtrb22tZzjEr62dZ0XrOyGMPnzfOfyuNySbSfppXz30tae/FkFpUv5iZmZkHPU5DopE3z38bho20XTrjCjJ+5oCXMa9uPvOzC0bVfvXsa0h5qYOuR0SObubckTkqZmafAXLOuS9MNE9HR4fr7Ow8IvUca4aCQXqKPXQVd7Ko/ngakg2kqqETRiGDwQDP59bTmGhiZmYmjQd4zmZvhbDAUDDIhqH1zMrOoSXVSsNee0X7M1AeYGdhO7kgx8L6xTQmG/FNe0ByzLL9zzI11SyAzKwdKDvn+swsC/w38A/OuV9O1EcBJCLHIAXQBGp5Fdxs4Ntm5lM51PfDfYWPiIhMLTULIOfck8DyWi1fRESObvomBBERiYUCSEREYqEAEhGRWCiAREQkFgogERGJhQJIRERioQASEZFYKIBERCQWR+y74A6EmXUBGw/zYqcD3Yd5mYfDZKxrMtYEk7OuyVgTqK6DcaRq6nbOXX0ExjnqTKoAqgUz63TOdcRdx94mY12TsSaYnHVNxppAdR2MyVjTVKNDcCIiEgsFkIiIxGIqBNDX4i5gApOxrslYE0zOuiZjTaC6DsZkrGlKOebPAYmIyOQ0FfaARERkElIAiYhILI6pADKzb5jZLjNbuUdbm5ndZmbrqv+3TpK6PmtmW83s8eq/Vx3hmuab2Z1mttrMnjazj1TbY1tf+6gp7nWVMbOHzOyJal03VNvjXFcT1RTrutqjPt/MHjOzX1YfT4bfw71rmhTraio7pgII+Baw9x98/SXwW+fcicBvq4+PtG8xti6Af3LOLav+u+UI1xQAH3fOnQycC3zIzE4h3vU1UU0Q77oqApc5584AlgFXm9m5xLuuJqoJ4l1XL/gIsHqPx5Ph93DvmmByrKsp65gKIOfcPUDPXs2vAb5d/fnbwGuPaFFMWFesnHPbnXOPVn8epPKLOZcY19c+aoqVqxiqPkxW/zniXVcT1RQ7M5sHXAPcuEdzrL+HE9QkMTumAmgCM51z26HyBgfMiLmePX3YzJ6sHqI74ockXmBmC4HlwINMkvW1V00Q87qqHr55HNgF3Oaci31dTVATxL9d/TPw50C0R1vc29V4NUH862pKmwoBNFl9FTieyuGT7cAX4yjCzBqAnwAfdc4NxFHD3sapKfZ15ZwLnXPLgHnA2WZ22pGu4QBrinVdmdm1wC7n3CNHctx92UdNsW9XU91UCKCdZjYboPr/rpjrAcA5t7P6BhIBXwfOPtI1mFmSyhv9d51zN1WbY11f49U0GdbVC5xzfcBdVM7pTYpta8+aJsG6ugC4zsw2AP8FXGZm/0m862rcmibBuprypkIA/Rx4d/XndwM3x1jLiBd+GateB6ycaN4ajW/AvwOrnXNf2mNSbOtropomwbpqN7OW6s9Z4ApgDfGuq3FrintdOec+6Zyb55xbCLwVuMM5905iXFcT1RT3uhJIxF3A4WRm3wcuAaab2RbgM8DfAz80s/cBm4A3TZK6LjGzZVROHG8A/scRLusC4F3AU9XzCACfIt71NVFNb4t5Xc0Gvm1mPpUPbT90zv3SzH5PfOtqopq+E/O6mkjsv4fj+MdJuq6mDH0Vj4iIxGIqHIITEZFJSAEkIiKxUACJiEgsFEAiIhILBZCIiMRCASQiIrFQAImISCyOqT9EFal+iemtwH1UbunwBPBN4AYqX4D5DuBVVL4DbC4wH/hH59zXzcwD/hW4GHieyge0bzjnfnxkn4XI1KAAkmPRCVT+0v6DwMPA24ELgeuofLPC48DLqARUPfCYmf0KOB9YCJxOJaxWA984wrWLTBk6BCfHouedc09Vv2TyaSo3QnPAU1QCBuBm59ywc64buJPKF1FeCPzIORc553ZU20WkRhRAciwq7vFztMfjiBf3+vf+DioHWI3rEpE9KIBkqnqNmWXMbBqVL4p9mMp5ozeYmWdmM6vtIlIjOgckU9VDwK+ABcDnnHPbzOwnwOVUvpb/GSp3Y+2Pr0SRY5u+DVumHDP7LDDknPvCONManHND1T2jh4ALqueDROQw0x6QyGi/rN7oLUVlz0jhI1Ij2gMSEZFY6CIEERGJhQJIRERioQASEZFYKIBERCQWCiAREYnF/wMSYe47OBDVmwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 424.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1.- Analyzing variable cyl\n",
    "sns.relplot(x='mpg', y='cyl', hue='origin', data=data_df, palette=\"muted\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"color:red;\"> How many cars or percent are the cars with 3 & 5 cylinders</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</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_type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cyl</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     mpg  disp   hp   wt  acc   yr  origin  car_type\n",
       "cyl                                                 \n",
       "3      4     4    4    4    4    4       4         4\n",
       "4    204   204  204  204  204  204     204       204\n",
       "5      3     3    3    3    3    3       3         3\n",
       "6     84    84   84   84   84   84      84        84\n",
       "8    103   103  103  103  103  103     103       103"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.groupby('cyl').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Car with 3 cylinders = 1.01 %\n",
      "Car with 5 cylinders = 0.75 %\n"
     ]
    }
   ],
   "source": [
    "cyl3 = data_df.cyl[data_df.cyl==3].count()\n",
    "print('Car with 3 cylinders =', round(cyl3/data_df.shape[0]*100,2),'%')\n",
    "cyl5 = data_df.cyl[data_df.cyl==5].count()\n",
    "print('Car with 5 cylinders =', round(cyl5/data_df.shape[0]*100,2),'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2235e57b348>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2.- Analyzing variable disp\n",
    "sns.lmplot(x='mpg', y='disp', data=data_df, hue='cyl', palette=\"muted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# locating and dropping data point disp >250 & mpg >35 outside range\n",
    "d_ind = data_df.loc[(data_df.disp>250) & (data_df.mpg>35) ].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data_df=data_df.drop[index='data_df.iloc[(data_df.disp>250) & (data_df.mpg>35) ]'] \n",
    "data_df=data_df.drop(d_ind)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(397, 9)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2790</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2130</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2295</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2625</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2720</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>397 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cyl   disp     hp    wt   acc  yr  origin  car_type\n",
       "0    18.0    8  307.0  130.0  3504  12.0  70       1         0\n",
       "1    15.0    8  350.0  165.0  3693  11.5  70       1         0\n",
       "2    18.0    8  318.0  150.0  3436  11.0  70       1         0\n",
       "3    16.0    8  304.0  150.0  3433  12.0  70       1         0\n",
       "4    17.0    8  302.0  140.0  3449  10.5  70       1         0\n",
       "..    ...  ...    ...    ...   ...   ...  ..     ...       ...\n",
       "392  27.0    4  140.0   86.0  2790  15.6  82       1         1\n",
       "393  44.0    4   97.0   52.0  2130  24.6  82       2         1\n",
       "394  32.0    4  135.0   84.0  2295  11.6  82       1         1\n",
       "395  28.0    4  120.0   79.0  2625  18.6  82       1         1\n",
       "396  31.0    4  119.0   82.0  2720  19.4  82       1         1\n",
       "\n",
       "[397 rows x 9 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2235e659e88>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='mpg', y='disp', data=data_df, hue='cyl', palette=\"muted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2235e723548>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3.- Analyzing variable hp\n",
    "sns.lmplot(x='mpg', y='hp', data=data_df, hue='cyl', palette=\"muted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2235e7c1908>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4.- Analyzing the variable wt\n",
    "sns.lmplot(x='mpg', y='wt', data=data_df, hue='cyl', palette=\"muted\") #style='car_type',"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2235e86ac48>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 5.- Analyzing the variable acc\n",
    "sns.lmplot(x='mpg', y='acc', data=data_df, hue='cyl',palette=\"muted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2235e611888>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 6.- Analyzing variable yr\n",
    "sns.lmplot(x='mpg', y='yr', data=data_df, hue='origin', palette=\"muted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2235e983588>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='mpg', y='car_type', data=data_df, hue='origin', palette=\"muted\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"color:blue;\">Creating Dummy Variables </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg         129\n",
       "cyl           5\n",
       "disp         82\n",
       "hp           94\n",
       "wt          350\n",
       "acc          95\n",
       "yr           13\n",
       "origin        3\n",
       "car_type      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating dummy variable for cyl, origin, car_type\n",
    "mpg_df = pd.get_dummies(data_df, columns=['cyl','origin','car_type','yr'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New mpg_df shape = (397, 28)\n"
     ]
    },
    {
     "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mpg</th>\n",
       "      <td>18.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disp</th>\n",
       "      <td>307.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hp</th>\n",
       "      <td>130.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wt</th>\n",
       "      <td>3504.0</td>\n",
       "      <td>3693.0</td>\n",
       "      <td>3436.0</td>\n",
       "      <td>3433.0</td>\n",
       "      <td>3449.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acc</th>\n",
       "      <td>12.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cyl_3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cyl_4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cyl_5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cyl_6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cyl_8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin_1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin_2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin_3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>car_type_0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>car_type_1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_70</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_71</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_72</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_73</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_74</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_75</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_76</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_77</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_78</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_79</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_80</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_81</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_82</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0       1       2       3       4\n",
       "mpg           18.0    15.0    18.0    16.0    17.0\n",
       "disp         307.0   350.0   318.0   304.0   302.0\n",
       "hp           130.0   165.0   150.0   150.0   140.0\n",
       "wt          3504.0  3693.0  3436.0  3433.0  3449.0\n",
       "acc           12.0    11.5    11.0    12.0    10.5\n",
       "cyl_3          0.0     0.0     0.0     0.0     0.0\n",
       "cyl_4          0.0     0.0     0.0     0.0     0.0\n",
       "cyl_5          0.0     0.0     0.0     0.0     0.0\n",
       "cyl_6          0.0     0.0     0.0     0.0     0.0\n",
       "cyl_8          1.0     1.0     1.0     1.0     1.0\n",
       "origin_1       1.0     1.0     1.0     1.0     1.0\n",
       "origin_2       0.0     0.0     0.0     0.0     0.0\n",
       "origin_3       0.0     0.0     0.0     0.0     0.0\n",
       "car_type_0     1.0     1.0     1.0     1.0     1.0\n",
       "car_type_1     0.0     0.0     0.0     0.0     0.0\n",
       "yr_70          1.0     1.0     1.0     1.0     1.0\n",
       "yr_71          0.0     0.0     0.0     0.0     0.0\n",
       "yr_72          0.0     0.0     0.0     0.0     0.0\n",
       "yr_73          0.0     0.0     0.0     0.0     0.0\n",
       "yr_74          0.0     0.0     0.0     0.0     0.0\n",
       "yr_75          0.0     0.0     0.0     0.0     0.0\n",
       "yr_76          0.0     0.0     0.0     0.0     0.0\n",
       "yr_77          0.0     0.0     0.0     0.0     0.0\n",
       "yr_78          0.0     0.0     0.0     0.0     0.0\n",
       "yr_79          0.0     0.0     0.0     0.0     0.0\n",
       "yr_80          0.0     0.0     0.0     0.0     0.0\n",
       "yr_81          0.0     0.0     0.0     0.0     0.0\n",
       "yr_82          0.0     0.0     0.0     0.0     0.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"New mpg_df shape =\",mpg_df.shape)\n",
    "mpg_df.head().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"color:blue;\">Applying label encoder to years </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import label encoder \n",
    "# from sklearn import preprocessing \n",
    "  \n",
    "# # label_encoder object knows how to understand word labels. \n",
    "# label_encoder = preprocessing.LabelEncoder() \n",
    "  \n",
    "# # Encode labels in column 'species'. \n",
    "# mpg_df['years']= label_encoder.fit_transform(mpg_df['yr']) \n",
    "  \n",
    "# mpg_df['years'].unique() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mpg</th>\n",
       "      <td>18.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disp</th>\n",
       "      <td>307.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hp</th>\n",
       "      <td>130.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wt</th>\n",
       "      <td>3504.0</td>\n",
       "      <td>3693.0</td>\n",
       "      <td>3436.0</td>\n",
       "      <td>3433.0</td>\n",
       "      <td>3449.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acc</th>\n",
       "      <td>12.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cyl_3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cyl_4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cyl_5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cyl_6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cyl_8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin_1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin_2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin_3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>car_type_0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>car_type_1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_70</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_71</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_72</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_73</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_74</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_75</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_76</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_77</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_78</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_79</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_80</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_81</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_82</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0       1       2       3       4\n",
       "mpg           18.0    15.0    18.0    16.0    17.0\n",
       "disp         307.0   350.0   318.0   304.0   302.0\n",
       "hp           130.0   165.0   150.0   150.0   140.0\n",
       "wt          3504.0  3693.0  3436.0  3433.0  3449.0\n",
       "acc           12.0    11.5    11.0    12.0    10.5\n",
       "cyl_3          0.0     0.0     0.0     0.0     0.0\n",
       "cyl_4          0.0     0.0     0.0     0.0     0.0\n",
       "cyl_5          0.0     0.0     0.0     0.0     0.0\n",
       "cyl_6          0.0     0.0     0.0     0.0     0.0\n",
       "cyl_8          1.0     1.0     1.0     1.0     1.0\n",
       "origin_1       1.0     1.0     1.0     1.0     1.0\n",
       "origin_2       0.0     0.0     0.0     0.0     0.0\n",
       "origin_3       0.0     0.0     0.0     0.0     0.0\n",
       "car_type_0     1.0     1.0     1.0     1.0     1.0\n",
       "car_type_1     0.0     0.0     0.0     0.0     0.0\n",
       "yr_70          1.0     1.0     1.0     1.0     1.0\n",
       "yr_71          0.0     0.0     0.0     0.0     0.0\n",
       "yr_72          0.0     0.0     0.0     0.0     0.0\n",
       "yr_73          0.0     0.0     0.0     0.0     0.0\n",
       "yr_74          0.0     0.0     0.0     0.0     0.0\n",
       "yr_75          0.0     0.0     0.0     0.0     0.0\n",
       "yr_76          0.0     0.0     0.0     0.0     0.0\n",
       "yr_77          0.0     0.0     0.0     0.0     0.0\n",
       "yr_78          0.0     0.0     0.0     0.0     0.0\n",
       "yr_79          0.0     0.0     0.0     0.0     0.0\n",
       "yr_80          0.0     0.0     0.0     0.0     0.0\n",
       "yr_81          0.0     0.0     0.0     0.0     0.0\n",
       "yr_82          0.0     0.0     0.0     0.0     0.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg_df.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg           129\n",
       "disp           82\n",
       "hp             94\n",
       "wt            350\n",
       "acc            95\n",
       "cyl_3           2\n",
       "cyl_4           2\n",
       "cyl_5           2\n",
       "cyl_6           2\n",
       "cyl_8           2\n",
       "origin_1        2\n",
       "origin_2        2\n",
       "origin_3        2\n",
       "car_type_0      2\n",
       "car_type_1      2\n",
       "yr_70           2\n",
       "yr_71           2\n",
       "yr_72           2\n",
       "yr_73           2\n",
       "yr_74           2\n",
       "yr_75           2\n",
       "yr_76           2\n",
       "yr_77           2\n",
       "yr_78           2\n",
       "yr_79           2\n",
       "yr_80           2\n",
       "yr_81           2\n",
       "yr_82           2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg_df.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"color:blue;\"> Filtering data before analysis </p>\n",
    "\n",
    "1.- drop column yr because it was changed to label encoder\n",
    "\n",
    "2.- drop car with 3 & 5 cylinders because data is under 1%\n",
    "\n",
    "3.- drop car type because no enough information about labeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpg_df = mpg_df.drop(['car_type_0','car_type_1', 'cyl_3', 'cyl_5'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg            9320.8\n",
       "disp          76721.5\n",
       "hp            41428.0\n",
       "wt          1179214.0\n",
       "acc            6179.1\n",
       "cyl_4           204.0\n",
       "cyl_6            83.0\n",
       "cyl_8           103.0\n",
       "origin_1        248.0\n",
       "origin_2         70.0\n",
       "origin_3         79.0\n",
       "yr_70            29.0\n",
       "yr_71            28.0\n",
       "yr_72            28.0\n",
       "yr_73            40.0\n",
       "yr_74            27.0\n",
       "yr_75            30.0\n",
       "yr_76            34.0\n",
       "yr_77            28.0\n",
       "yr_78            36.0\n",
       "yr_79            29.0\n",
       "yr_80            29.0\n",
       "yr_81            29.0\n",
       "yr_82            30.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg_df.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"color:blue;\"> Split Data X & y (70:30)</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(397, 23)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = mpg_df.drop(columns=['mpg'])\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(397,)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = mpg_df['mpg']\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"color:blue;\"> First linear regression model </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 40,
     "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": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.38525395e-02, -4.50961132e-02, -5.71852553e-03, -1.24515885e-02,\n",
       "        3.91727503e+00,  1.58930659e+00,  4.60826298e+00, -1.29005240e+00,\n",
       "        7.80069659e-01,  5.09982745e-01, -3.26035906e+00, -2.12492075e+00,\n",
       "       -3.61785275e+00, -3.95749838e+00, -1.76734736e+00, -2.02420992e+00,\n",
       "       -1.50444049e+00,  3.28435780e-01, -1.99019109e-02,  2.39456139e+00,\n",
       "        6.72681996e+00,  4.03543990e+00,  4.79127359e+00])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regression_model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The coefficient for disp is 0.013852539493295188\n",
      "The coefficient for hp is -0.04509611315731536\n",
      "The coefficient for wt is -0.005718525528256035\n",
      "The coefficient for acc is -0.012451588546767211\n",
      "The coefficient for cyl_4 is 3.917275029798788\n",
      "The coefficient for cyl_6 is 1.5893065872931322\n",
      "The coefficient for cyl_8 is 4.60826298347598\n",
      "The coefficient for origin_1 is -1.2900524036479886\n",
      "The coefficient for origin_2 is 0.7800696588777515\n",
      "The coefficient for origin_3 is 0.5099827447702289\n",
      "The coefficient for yr_70 is -3.2603590629365358\n",
      "The coefficient for yr_71 is -2.124920748962672\n",
      "The coefficient for yr_72 is -3.617852748251408\n",
      "The coefficient for yr_73 is -3.957498381210529\n",
      "The coefficient for yr_74 is -1.7673473583949941\n",
      "The coefficient for yr_75 is -2.0242099236440305\n",
      "The coefficient for yr_76 is -1.5044404941298612\n",
      "The coefficient for yr_77 is 0.328435779583827\n",
      "The coefficient for yr_78 is -0.019901910917290523\n",
      "The coefficient for yr_79 is 2.394561392557756\n",
      "The coefficient for yr_80 is 6.7268199606969326\n",
      "The coefficient for yr_81 is 4.035439901686982\n",
      "The coefficient for yr_82 is 4.791273593921829\n"
     ]
    }
   ],
   "source": [
    "# Let us explore the coefficients for each of the independent attributes\n",
    "for idx, col_name in enumerate(X_train.columns):\n",
    "    print(\"The coefficient for {} is {}\".format(col_name, regression_model.coef_[idx]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The intercept for our model is 39.92284312238915\n"
     ]
    }
   ],
   "source": [
    "# Let us check the intercept for the model\n",
    "\n",
    "intercept = regression_model.intercept_\n",
    "\n",
    "print(\"The intercept for our model is {}\".format(intercept))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8747607293705761"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train data score\n",
    "regression_model.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8627133433846215"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test data score\n",
    "regression_model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>350</th>\n",
       "      <th>59</th>\n",
       "      <th>120</th>\n",
       "      <th>12</th>\n",
       "      <th>349</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>disp</th>\n",
       "      <td>105.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hp</th>\n",
       "      <td>63.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wt</th>\n",
       "      <td>2215.0</td>\n",
       "      <td>2254.0</td>\n",
       "      <td>2868.0</td>\n",
       "      <td>3761.0</td>\n",
       "      <td>1985.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acc</th>\n",
       "      <td>14.9</td>\n",
       "      <td>23.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cyl_4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cyl_6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cyl_8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin_1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin_2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin_3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_70</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_71</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_72</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_73</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_74</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_75</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_76</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_77</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_78</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_79</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_80</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_81</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_82</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mpg</th>\n",
       "      <td>34.7</td>\n",
       "      <td>23.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>34.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             350     59      120     12      349\n",
       "disp       105.0    97.0   121.0   400.0    91.0\n",
       "hp          63.0    54.0   112.0   150.0    68.0\n",
       "wt        2215.0  2254.0  2868.0  3761.0  1985.0\n",
       "acc         14.9    23.5    15.5     9.5    16.0\n",
       "cyl_4        1.0     1.0     1.0     0.0     1.0\n",
       "cyl_6        0.0     0.0     0.0     0.0     0.0\n",
       "cyl_8        0.0     0.0     0.0     1.0     0.0\n",
       "origin_1     1.0     0.0     0.0     1.0     0.0\n",
       "origin_2     0.0     1.0     1.0     0.0     0.0\n",
       "origin_3     0.0     0.0     0.0     0.0     1.0\n",
       "yr_70        0.0     0.0     0.0     1.0     0.0\n",
       "yr_71        0.0     0.0     0.0     0.0     0.0\n",
       "yr_72        0.0     1.0     0.0     0.0     0.0\n",
       "yr_73        0.0     0.0     1.0     0.0     0.0\n",
       "yr_74        0.0     0.0     0.0     0.0     0.0\n",
       "yr_75        0.0     0.0     0.0     0.0     0.0\n",
       "yr_76        0.0     0.0     0.0     0.0     0.0\n",
       "yr_77        0.0     0.0     0.0     0.0     0.0\n",
       "yr_78        0.0     0.0     0.0     0.0     0.0\n",
       "yr_79        0.0     0.0     0.0     0.0     0.0\n",
       "yr_80        0.0     0.0     0.0     0.0     0.0\n",
       "yr_81        1.0     0.0     0.0     0.0     1.0\n",
       "yr_82        0.0     0.0     0.0     0.0     0.0\n",
       "mpg         34.7    23.0    19.0    15.0    34.1"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train = pd.concat([X_train, y_train], axis=1)\n",
    "data_train.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# evaluating statsmodel and P values\n",
    "import statsmodels.formula.api as smf\n",
    "lm1 = smf.ols(formula= 'mpg ~ disp+hp+wt+acc+cyl_4+cyl_6+cyl_8+origin_1+origin_2+origin_3\\\n",
    "             + yr_70+yr_71+yr_72+yr_73+yr_74+yr_75+yr_76+yr_77\\\n",
    "             +yr_78+yr_79+yr_80+yr_81+yr_82', data = data_train).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    mpg   R-squared:                       0.875\n",
      "Model:                            OLS   Adj. R-squared:                  0.864\n",
      "Method:                 Least Squares   F-statistic:                     84.81\n",
      "Date:                Thu, 06 Aug 2020   Prob (F-statistic):          3.37e-102\n",
      "Time:                        16:28:54   Log-Likelihood:                -675.71\n",
      "No. Observations:                 277   AIC:                             1395.\n",
      "Df Residuals:                     255   BIC:                             1475.\n",
      "Df Model:                          21                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     28.3089      1.840     15.382      0.000      24.685      31.933\n",
      "disp           0.0139      0.009      1.590      0.113      -0.003       0.031\n",
      "hp            -0.0451      0.015     -2.930      0.004      -0.075      -0.015\n",
      "wt            -0.0057      0.001     -7.259      0.000      -0.007      -0.004\n",
      "acc           -0.0125      0.101     -0.123      0.902      -0.211       0.187\n",
      "cyl_4          3.9173      1.568      2.498      0.013       0.829       7.005\n",
      "cyl_6          1.5893      1.727      0.920      0.358      -1.812       4.991\n",
      "cyl_8          4.6083      2.117      2.177      0.030       0.440       8.777\n",
      "origin_1       8.1463      0.749     10.879      0.000       6.672       9.621\n",
      "origin_2      10.2164      0.733     13.937      0.000       8.773      11.660\n",
      "origin_3       9.9463      0.639     15.556      0.000       8.687      11.205\n",
      "yr_70         -1.0827      0.722     -1.499      0.135      -2.505       0.339\n",
      "yr_71          0.0527      0.585      0.090      0.928      -1.100       1.206\n",
      "yr_72         -1.4402      0.688     -2.094      0.037      -2.794      -0.086\n",
      "yr_73         -1.7799      0.583     -3.051      0.003      -2.929      -0.631\n",
      "yr_74          0.4103      0.615      0.667      0.505      -0.801       1.621\n",
      "yr_75          0.1534      0.653      0.235      0.815      -1.133       1.440\n",
      "yr_76          0.6732      0.587      1.148      0.252      -0.482       1.828\n",
      "yr_77          2.5060      0.683      3.667      0.000       1.160       3.852\n",
      "yr_78          2.1577      0.576      3.745      0.000       1.023       3.292\n",
      "yr_79          4.5722      0.664      6.885      0.000       3.264       5.880\n",
      "yr_80          8.9044      0.697     12.780      0.000       7.532      10.277\n",
      "yr_81          6.2130      0.707      8.794      0.000       4.822       7.604\n",
      "yr_82          6.9689      0.695     10.026      0.000       5.600       8.338\n",
      "==============================================================================\n",
      "Omnibus:                       11.095   Durbin-Watson:                   2.061\n",
      "Prob(Omnibus):                  0.004   Jarque-Bera (JB):               14.856\n",
      "Skew:                           0.312   Prob(JB):                     0.000594\n",
      "Kurtosis:                       3.948   Cond. No.                     5.62e+19\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The smallest eigenvalue is 8.3e-31. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n"
     ]
    }
   ],
   "source": [
    "print(lm1.summary())  #Inferential statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"color:blue;\"> Second Linear Regression Model </p>"
   ]
  },
  {
   "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>hp</th>\n",
       "      <th>cyl_4</th>\n",
       "      <th>cyl_8</th>\n",
       "      <th>origin_1</th>\n",
       "      <th>origin_2</th>\n",
       "      <th>origin_3</th>\n",
       "      <th>yr_72</th>\n",
       "      <th>yr_73</th>\n",
       "      <th>yr_77</th>\n",
       "      <th>yr_78</th>\n",
       "      <th>yr_79</th>\n",
       "      <th>yr_80</th>\n",
       "      <th>yr_81</th>\n",
       "      <th>yr_82</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>63.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>54.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>112.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>150.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>68.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        hp  cyl_4  cyl_8  origin_1  origin_2  origin_3  yr_72  yr_73  yr_77  \\\n",
       "350   63.0      1      0         1         0         0      0      0      0   \n",
       "59    54.0      1      0         0         1         0      1      0      0   \n",
       "120  112.0      1      0         0         1         0      0      1      0   \n",
       "12   150.0      0      1         1         0         0      0      0      0   \n",
       "349   68.0      1      0         0         0         1      0      0      0   \n",
       "\n",
       "     yr_78  yr_79  yr_80  yr_81  yr_82  \n",
       "350      0      0      0      1      0  \n",
       "59       0      0      0      0      0  \n",
       "120      0      0      0      0      0  \n",
       "12       0      0      0      0      0  \n",
       "349      0      0      0      1      0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# anaylsing the coefficient of each attributes and statmodel\n",
    "# drop features = 'hp','wt','disp','acc', 'cyl_6','yr_70','yr_71','yr_74','yr_75','yr_76'\n",
    "X_train2 = X_train.drop(columns=['wt','disp','acc', 'cyl_6','yr_70','yr_71','yr_74','yr_75','yr_76'])\n",
    "X_test2 = X_test.drop(columns=['wt','disp','acc', 'cyl_6','yr_70','yr_71','yr_74','yr_75','yr_76'])\n",
    "X_train2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train score = 0.8346491658441959\n",
      "test score = 0.8350104933846316\n"
     ]
    }
   ],
   "source": [
    "regression_model2 = LinearRegression()\n",
    "regression_model2.fit(X_train2, y_train)\n",
    "# Train data score\n",
    "print('train score =',regression_model2.score(X_train2, y_train))\n",
    "print('test score =',regression_model2.score(X_test2, y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The coefficient for hp is -0.09429395169251238\n",
      "The coefficient for cyl_4 is 4.99215460100784\n",
      "The coefficient for cyl_8 is 2.386987750836776\n",
      "The coefficient for origin_1 is -1.6726176025891257\n",
      "The coefficient for origin_2 is 0.37951771345091684\n",
      "The coefficient for origin_3 is 1.2930998891382068\n",
      "The coefficient for yr_72 is -1.8094084804875872\n",
      "The coefficient for yr_73 is -1.2681537336661906\n",
      "The coefficient for yr_77 is 2.5639582675417087\n",
      "The coefficient for yr_78 is 2.2868823398899623\n",
      "The coefficient for yr_79 is 4.301612920241482\n",
      "The coefficient for yr_80 is 7.8503317250691165\n",
      "The coefficient for yr_81 is 5.883367066169152\n",
      "The coefficient for yr_82 is 5.967834168330686\n"
     ]
    }
   ],
   "source": [
    "# Let us explore the coefficients for each of the independent attributes\n",
    "for idx, col_name in enumerate(X_train2.columns):\n",
    "    print(\"The coefficient for {} is {}\".format(col_name, regression_model2.coef_[idx]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The intercept for our model is 29.04476432398969\n"
     ]
    }
   ],
   "source": [
    "# Let us check the intercept for the model\n",
    "\n",
    "intercept = regression_model2.intercept_\n",
    "\n",
    "print(\"The intercept for our model is {}\".format(intercept))"
   ]
  },
  {
   "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>350</th>\n",
       "      <th>59</th>\n",
       "      <th>120</th>\n",
       "      <th>12</th>\n",
       "      <th>349</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>hp</th>\n",
       "      <td>63.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cyl_4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cyl_8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin_1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin_2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin_3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_72</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_73</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_77</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_78</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_79</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_80</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_81</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_82</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mpg</th>\n",
       "      <td>34.7</td>\n",
       "      <td>23.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>34.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           350   59     120    12    349\n",
       "hp        63.0  54.0  112.0  150.0  68.0\n",
       "cyl_4      1.0   1.0    1.0    0.0   1.0\n",
       "cyl_8      0.0   0.0    0.0    1.0   0.0\n",
       "origin_1   1.0   0.0    0.0    1.0   0.0\n",
       "origin_2   0.0   1.0    1.0    0.0   0.0\n",
       "origin_3   0.0   0.0    0.0    0.0   1.0\n",
       "yr_72      0.0   1.0    0.0    0.0   0.0\n",
       "yr_73      0.0   0.0    1.0    0.0   0.0\n",
       "yr_77      0.0   0.0    0.0    0.0   0.0\n",
       "yr_78      0.0   0.0    0.0    0.0   0.0\n",
       "yr_79      0.0   0.0    0.0    0.0   0.0\n",
       "yr_80      0.0   0.0    0.0    0.0   0.0\n",
       "yr_81      1.0   0.0    0.0    0.0   1.0\n",
       "yr_82      0.0   0.0    0.0    0.0   0.0\n",
       "mpg       34.7  23.0   19.0   15.0  34.1"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train2 = pd.concat([X_train2, y_train], axis=1)\n",
    "data_train2.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# evaluating statsmodel and P values\n",
    "import statsmodels.formula.api as smf\n",
    "lm1 = smf.ols(formula= 'mpg ~ hp+cyl_4+cyl_8+origin_1+origin_2+origin_3\\\n",
    "             + yr_72+yr_73+yr_77+yr_78+yr_79+yr_80+yr_81+yr_82', data = data_train2).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    mpg   R-squared:                       0.835\n",
      "Model:                            OLS   Adj. R-squared:                  0.826\n",
      "Method:                 Least Squares   F-statistic:                     102.1\n",
      "Date:                Thu, 06 Aug 2020   Prob (F-statistic):           1.11e-94\n",
      "Time:                        16:28:54   Log-Likelihood:                -714.19\n",
      "No. Observations:                 277   AIC:                             1456.\n",
      "Df Residuals:                     263   BIC:                             1507.\n",
      "Df Model:                          13                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     21.7836      0.935     23.310      0.000      19.943      23.624\n",
      "hp            -0.0943      0.011     -8.424      0.000      -0.116      -0.072\n",
      "cyl_4          4.9922      0.632      7.893      0.000       3.747       6.238\n",
      "cyl_8          2.3870      0.874      2.731      0.007       0.666       4.108\n",
      "origin_1       5.5886      0.419     13.338      0.000       4.764       6.414\n",
      "origin_2       7.6407      0.532     14.355      0.000       6.593       8.689\n",
      "origin_3       8.5543      0.492     17.399      0.000       7.586       9.522\n",
      "yr_72         -1.8094      0.842     -2.149      0.033      -3.467      -0.152\n",
      "yr_73         -1.2682      0.712     -1.780      0.076      -2.671       0.134\n",
      "yr_77          2.5640      0.860      2.980      0.003       0.870       4.258\n",
      "yr_78          2.2869      0.715      3.200      0.002       0.880       3.694\n",
      "yr_79          4.3016      0.842      5.108      0.000       2.643       5.960\n",
      "yr_80          7.8503      0.847      9.273      0.000       6.183       9.517\n",
      "yr_81          5.8834      0.890      6.613      0.000       4.131       7.635\n",
      "yr_82          5.9678      0.850      7.018      0.000       4.293       7.642\n",
      "==============================================================================\n",
      "Omnibus:                       10.485   Durbin-Watson:                   1.910\n",
      "Prob(Omnibus):                  0.005   Jarque-Bera (JB):               10.933\n",
      "Skew:                           0.410   Prob(JB):                      0.00423\n",
      "Kurtosis:                       3.525   Cond. No.                     9.18e+17\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The smallest eigenvalue is 3.96e-30. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n"
     ]
    }
   ],
   "source": [
    "print(lm1.summary())  #Inferential statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict mileage (mpg) for a set of attributes not in the training or test set\n",
    "y_pred2 = regression_model2.predict(X_test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of data points in the test set = 120\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, y_pred2)\n",
    "print('number of data points in the test set =', y_test.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"color:blue;\"> Thrid Linear Regression Model </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observations\n",
    "\n",
    "1.- Best score was using dummies for years, cylinders, and origin\n",
    "\n",
    "2.- Cars with 3 and 5 cylinders were dropped because they had less than 1% total data points\n",
    "\n",
    "3.- Using the linear regression coeficientes as reference the follow features were dropped:\n",
    "['wt','disp','acc', 'cyl_6','yr_70','yr_71','yr_74','yr_75','yr_76']\n",
    "\n",
    "4.- Linear regression interceptor in the second model is 29.04 (First model was 39.9)\n",
    "\n",
    "4.- Second Linear Regression Model: Train Score =83.4% and Test score = 83.5% (mpg mean = 23.5)\n",
    "\n",
    "5.- Label encoder used for years gave lower score\n",
    "\n"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}