Auto-mpg.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('auto-mpg.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(398, 9)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
      "mpg             398 non-null float64\n",
      "cylinders       398 non-null int64\n",
      "displacement    398 non-null float64\n",
      "horsepower      398 non-null object\n",
      "weight          398 non-null int64\n",
      "acceleration    398 non-null float64\n",
      "model year      398 non-null int64\n",
      "origin          398 non-null int64\n",
      "car name        398 non-null object\n",
      "dtypes: float64(3), int64(4), object(2)\n",
      "memory usage: 28.1+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model year</th>\n",
       "      <th>origin</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>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>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>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>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>ford torino</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement horsepower  weight  acceleration  model year  \\\n",
       "0  18.0          8         307.0        130    3504          12.0          70   \n",
       "1  15.0          8         350.0        165    3693          11.5          70   \n",
       "2  18.0          8         318.0        150    3436          11.0          70   \n",
       "3  16.0          8         304.0        150    3433          12.0          70   \n",
       "4  17.0          8         302.0        140    3449          10.5          70   \n",
       "\n",
       "   origin                   car name  \n",
       "0       1  chevrolet chevelle malibu  \n",
       "1       1          buick skylark 320  \n",
       "2       1         plymouth satellite  \n",
       "3       1              amc rebel sst  \n",
       "4       1                ford torino  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg             0\n",
       "cylinders       0\n",
       "displacement    0\n",
       "horsepower      0\n",
       "weight          0\n",
       "acceleration    0\n",
       "model year      0\n",
       "origin          0\n",
       "car name        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['130', '165', '150', '140', '198', '220', '215', '225', '190',\n",
       "       '170', '160', '95', '97', '85', '88', '46', '87', '90', '113',\n",
       "       '200', '210', '193', '?', '100', '105', '175', '153', '180', '110',\n",
       "       '72', '86', '70', '76', '65', '69', '60', '80', '54', '208', '155',\n",
       "       '112', '92', '145', '137', '158', '167', '94', '107', '230', '49',\n",
       "       '75', '91', '122', '67', '83', '78', '52', '61', '93', '148',\n",
       "       '129', '96', '71', '98', '115', '53', '81', '79', '120', '152',\n",
       "       '102', '108', '68', '58', '149', '89', '63', '48', '66', '139',\n",
       "       '103', '125', '133', '138', '135', '142', '77', '62', '132', '84',\n",
       "       '64', '74', '116', '82'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"horsepower\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 9)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data[\"horsepower\"]==\"?\"].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[data.horsepower != '?']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "print('?' in data.horsepower)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(392, 9)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg             float64\n",
       "cylinders         int64\n",
       "displacement    float64\n",
       "horsepower       object\n",
       "weight            int64\n",
       "acceleration    float64\n",
       "model year        int64\n",
       "origin            int64\n",
       "car name         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.horsepower = data.horsepower.astype('float')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg             float64\n",
       "cylinders         int64\n",
       "displacement    float64\n",
       "horsepower      float64\n",
       "weight            int64\n",
       "acceleration    float64\n",
       "model year        int64\n",
       "origin            int64\n",
       "car name         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As the all the features are in a proper datatype as they should be we can proceed for further analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model year</th>\n",
       "      <th>origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>23.445918</td>\n",
       "      <td>5.471939</td>\n",
       "      <td>194.411990</td>\n",
       "      <td>104.469388</td>\n",
       "      <td>2977.584184</td>\n",
       "      <td>15.541327</td>\n",
       "      <td>75.979592</td>\n",
       "      <td>1.576531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.805007</td>\n",
       "      <td>1.705783</td>\n",
       "      <td>104.644004</td>\n",
       "      <td>38.491160</td>\n",
       "      <td>849.402560</td>\n",
       "      <td>2.758864</td>\n",
       "      <td>3.683737</td>\n",
       "      <td>0.805518</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>2225.250000</td>\n",
       "      <td>13.775000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>22.750000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>151.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>275.750000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>3614.750000</td>\n",
       "      <td>17.025000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>2.000000</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              mpg   cylinders  displacement  horsepower       weight  \\\n",
       "count  392.000000  392.000000    392.000000  392.000000   392.000000   \n",
       "mean    23.445918    5.471939    194.411990  104.469388  2977.584184   \n",
       "std      7.805007    1.705783    104.644004   38.491160   849.402560   \n",
       "min      9.000000    3.000000     68.000000   46.000000  1613.000000   \n",
       "25%     17.000000    4.000000    105.000000   75.000000  2225.250000   \n",
       "50%     22.750000    4.000000    151.000000   93.500000  2803.500000   \n",
       "75%     29.000000    8.000000    275.750000  126.000000  3614.750000   \n",
       "max     46.600000    8.000000    455.000000  230.000000  5140.000000   \n",
       "\n",
       "       acceleration  model year      origin  \n",
       "count    392.000000  392.000000  392.000000  \n",
       "mean      15.541327   75.979592    1.576531  \n",
       "std        2.758864    3.683737    0.805518  \n",
       "min        8.000000   70.000000    1.000000  \n",
       "25%       13.775000   73.000000    1.000000  \n",
       "50%       15.500000   76.000000    1.000000  \n",
       "75%       17.025000   79.000000    2.000000  \n",
       "max       24.800000   82.000000    3.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first we should look at all the numeric features.\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    392.000000\n",
       "mean      23.445918\n",
       "std        7.805007\n",
       "min        9.000000\n",
       "25%       17.000000\n",
       "50%       22.750000\n",
       "75%       29.000000\n",
       "max       46.600000\n",
       "Name: mpg, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.mpg.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dell\\Anaconda3\\anaconda\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2bff3ed48d0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['mpg'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_scale = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scale(a):\n",
    "    b = (a-a.min())/(a.max()-a.min())\n",
    "    return b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_scale ['displacement'] = scale(data_scale['displacement'])\n",
    "data_scale['horsepower'] = scale(data_scale['horsepower'])\n",
    "data_scale ['acceleration'] = scale(data_scale['acceleration'])\n",
    "data_scale ['weight'] = scale(data_scale['weight'])\n",
    "data_scale['mpg'] = scale(data_scale['mpg'])"
   ]
  },
  {
   "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>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model year</th>\n",
       "      <th>origin</th>\n",
       "      <th>car name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.239362</td>\n",
       "      <td>8</td>\n",
       "      <td>0.617571</td>\n",
       "      <td>0.456522</td>\n",
       "      <td>0.536150</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.159574</td>\n",
       "      <td>8</td>\n",
       "      <td>0.728682</td>\n",
       "      <td>0.646739</td>\n",
       "      <td>0.589736</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.239362</td>\n",
       "      <td>8</td>\n",
       "      <td>0.645995</td>\n",
       "      <td>0.565217</td>\n",
       "      <td>0.516870</td>\n",
       "      <td>0.178571</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.186170</td>\n",
       "      <td>8</td>\n",
       "      <td>0.609819</td>\n",
       "      <td>0.565217</td>\n",
       "      <td>0.516019</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.212766</td>\n",
       "      <td>8</td>\n",
       "      <td>0.604651</td>\n",
       "      <td>0.510870</td>\n",
       "      <td>0.520556</td>\n",
       "      <td>0.148810</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        mpg  cylinders  displacement  horsepower    weight  acceleration  \\\n",
       "0  0.239362          8      0.617571    0.456522  0.536150      0.238095   \n",
       "1  0.159574          8      0.728682    0.646739  0.589736      0.208333   \n",
       "2  0.239362          8      0.645995    0.565217  0.516870      0.178571   \n",
       "3  0.186170          8      0.609819    0.565217  0.516019      0.238095   \n",
       "4  0.212766          8      0.604651    0.510870  0.520556      0.148810   \n",
       "\n",
       "   model year  origin                   car name  \n",
       "0          70       1  chevrolet chevelle malibu  \n",
       "1          70       1          buick skylark 320  \n",
       "2          70       1         plymouth satellite  \n",
       "3          70       1              amc rebel sst  \n",
       "4          70       1                ford torino  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_scale.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Country_code'] = data.origin.replace([1,2,3],['USA','Europe','Japan'])\n",
    "data_scale['Country_code'] = data.origin.replace([1,2,3],['USA','Europe','Japan'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model year</th>\n",
       "      <th>origin</th>\n",
       "      <th>car name</th>\n",
       "      <th>Country_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.239362</td>\n",
       "      <td>8</td>\n",
       "      <td>0.617571</td>\n",
       "      <td>0.456522</td>\n",
       "      <td>0.536150</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.159574</td>\n",
       "      <td>8</td>\n",
       "      <td>0.728682</td>\n",
       "      <td>0.646739</td>\n",
       "      <td>0.589736</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>buick skylark 320</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.239362</td>\n",
       "      <td>8</td>\n",
       "      <td>0.645995</td>\n",
       "      <td>0.565217</td>\n",
       "      <td>0.516870</td>\n",
       "      <td>0.178571</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth satellite</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.186170</td>\n",
       "      <td>8</td>\n",
       "      <td>0.609819</td>\n",
       "      <td>0.565217</td>\n",
       "      <td>0.516019</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.212766</td>\n",
       "      <td>8</td>\n",
       "      <td>0.604651</td>\n",
       "      <td>0.510870</td>\n",
       "      <td>0.520556</td>\n",
       "      <td>0.148810</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        mpg  cylinders  displacement  horsepower    weight  acceleration  \\\n",
       "0  0.239362          8      0.617571    0.456522  0.536150      0.238095   \n",
       "1  0.159574          8      0.728682    0.646739  0.589736      0.208333   \n",
       "2  0.239362          8      0.645995    0.565217  0.516870      0.178571   \n",
       "3  0.186170          8      0.609819    0.565217  0.516019      0.238095   \n",
       "4  0.212766          8      0.604651    0.510870  0.520556      0.148810   \n",
       "\n",
       "   model year  origin                   car name Country_code  \n",
       "0          70       1  chevrolet chevelle malibu          USA  \n",
       "1          70       1          buick skylark 320          USA  \n",
       "2          70       1         plymouth satellite          USA  \n",
       "3          70       1              amc rebel sst          USA  \n",
       "4          70       1                ford torino          USA  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_scale.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### So we have scaled the data down to values between 0 and 1 and changed the country code to USA Europe and Japan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bi-Variate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2bff619add8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Box Plot between mpg and country code\n",
    "sns.boxplot(x = 'Country_code',y = 'mpg', data = data_scale)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* So by this graph it can be concluded that cars in japan have higher average mpg compared to Europe and USA.\n",
    "* Frrom the graph max number of ouliers lie int he region of Europe and the least in japan\n",
    "* USA has the least average mpg."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2bff6398940>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Box Plot between mpg and model year of the car\n",
    "sns.boxplot(x = 'model year',y = 'mpg', data = data_scale)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The cars from model year 80 have highest average mpg.\n",
    "* The lowest average mpg can be seem from the crs whose model year was 70\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2bff64ee4a8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Box Plot between mpg and number of cylinder of the car\n",
    "sns.boxplot(x = 'cylinders',y = 'mpg', data = data_scale)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* As expected the cars with 4 cylinder have the best average mpg.\n",
    "* As the number of cylinder in creases the average mpg decreases.\n",
    "* The average mpg of cars with 3 cylinder engine is very simmilar to cars with 6 cylinder engine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model year</th>\n",
       "      <th>origin</th>\n",
       "      <th>car name</th>\n",
       "      <th>Country_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392</td>\n",
       "      <td>392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>301</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>toyota corolla</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>23.445918</td>\n",
       "      <td>5.471939</td>\n",
       "      <td>194.411990</td>\n",
       "      <td>104.469388</td>\n",
       "      <td>2977.584184</td>\n",
       "      <td>15.541327</td>\n",
       "      <td>75.979592</td>\n",
       "      <td>1.576531</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.805007</td>\n",
       "      <td>1.705783</td>\n",
       "      <td>104.644004</td>\n",
       "      <td>38.491160</td>\n",
       "      <td>849.402560</td>\n",
       "      <td>2.758864</td>\n",
       "      <td>3.683737</td>\n",
       "      <td>0.805518</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>2225.250000</td>\n",
       "      <td>13.775000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>22.750000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>151.000000</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>275.750000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>3614.750000</td>\n",
       "      <td>17.025000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               mpg   cylinders  displacement  horsepower       weight  \\\n",
       "count   392.000000  392.000000    392.000000  392.000000   392.000000   \n",
       "unique         NaN         NaN           NaN         NaN          NaN   \n",
       "top            NaN         NaN           NaN         NaN          NaN   \n",
       "freq           NaN         NaN           NaN         NaN          NaN   \n",
       "mean     23.445918    5.471939    194.411990  104.469388  2977.584184   \n",
       "std       7.805007    1.705783    104.644004   38.491160   849.402560   \n",
       "min       9.000000    3.000000     68.000000   46.000000  1613.000000   \n",
       "25%      17.000000    4.000000    105.000000   75.000000  2225.250000   \n",
       "50%      22.750000    4.000000    151.000000   93.500000  2803.500000   \n",
       "75%      29.000000    8.000000    275.750000  126.000000  3614.750000   \n",
       "max      46.600000    8.000000    455.000000  230.000000  5140.000000   \n",
       "\n",
       "        acceleration  model year      origin        car name Country_code  \n",
       "count     392.000000  392.000000  392.000000             392          392  \n",
       "unique           NaN         NaN         NaN             301            3  \n",
       "top              NaN         NaN         NaN  toyota corolla          USA  \n",
       "freq             NaN         NaN         NaN               5          245  \n",
       "mean       15.541327   75.979592    1.576531             NaN          NaN  \n",
       "std         2.758864    3.683737    0.805518             NaN          NaN  \n",
       "min         8.000000   70.000000    1.000000             NaN          NaN  \n",
       "25%        13.775000   73.000000    1.000000             NaN          NaN  \n",
       "50%        15.500000   76.000000    1.000000             NaN          NaN  \n",
       "75%        17.025000   79.000000    2.000000             NaN          NaN  \n",
       "max        24.800000   82.000000    3.000000             NaN          NaN  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include= 'all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* So by this we can get a hold of all the minimum and maximum values present in the data.\n",
    "* There doesn't seem to be ny extreme outlier that needs to be removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2bff658fb00>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 436.5x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now ploting the categorical features using lmplot() to see which features are the most useful \n",
    "var = 'displacement'\n",
    "plot = sns.lmplot(var,'mpg',data=data,hue='Country_code')\n",
    "plot.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Many of the blue points are seperable based on the mpg-displacement graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2bff615b4e0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 436.5x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'weight'\n",
    "plot = sns.lmplot(var,'mpg',data=data,hue='Country_code')\n",
    "plot.set(ylim = (0,50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Power_to_weight'] = ((data.horsepower*0.7457)/data.weight)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* This cocludes the data visvalisation part now model making can be done."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spliting The Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "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>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model year</th>\n",
       "      <th>origin</th>\n",
       "      <th>car name</th>\n",
       "      <th>Country_code</th>\n",
       "      <th>Power_to_weight</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>chevrolet chevelle malibu</td>\n",
       "      <td>USA</td>\n",
       "      <td>0.027666</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>buick skylark 320</td>\n",
       "      <td>USA</td>\n",
       "      <td>0.033317</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>plymouth satellite</td>\n",
       "      <td>USA</td>\n",
       "      <td>0.032554</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>amc rebel sst</td>\n",
       "      <td>USA</td>\n",
       "      <td>0.032582</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>ford torino</td>\n",
       "      <td>USA</td>\n",
       "      <td>0.030269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
       "0  18.0          8         307.0       130.0    3504          12.0   \n",
       "1  15.0          8         350.0       165.0    3693          11.5   \n",
       "2  18.0          8         318.0       150.0    3436          11.0   \n",
       "3  16.0          8         304.0       150.0    3433          12.0   \n",
       "4  17.0          8         302.0       140.0    3449          10.5   \n",
       "\n",
       "   model year  origin                   car name Country_code  Power_to_weight  \n",
       "0          70       1  chevrolet chevelle malibu          USA         0.027666  \n",
       "1          70       1          buick skylark 320          USA         0.033317  \n",
       "2          70       1         plymouth satellite          USA         0.032554  \n",
       "3          70       1              amc rebel sst          USA         0.032582  \n",
       "4          70       1                ford torino          USA         0.030269  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = data.mpg\n",
    "factors = ['cylinders','displacement','horsepower','acceleration','weight','origin','model year','Power_to_weight']\n",
    "X = pd.DataFrame(data[factors].copy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>weight</th>\n",
       "      <th>origin</th>\n",
       "      <th>model year</th>\n",
       "      <th>Power_to_weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>8</td>\n",
       "      <td>351.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>4657</td>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "      <td>0.023698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>8</td>\n",
       "      <td>260.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>3365</td>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "      <td>0.024377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>4</td>\n",
       "      <td>105.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>14.9</td>\n",
       "      <td>2215</td>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>0.021210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>6</td>\n",
       "      <td>225.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>3430</td>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "      <td>0.021741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>4</td>\n",
       "      <td>107.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>14.4</td>\n",
       "      <td>2210</td>\n",
       "      <td>3</td>\n",
       "      <td>81</td>\n",
       "      <td>0.025307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cylinders  displacement  horsepower  acceleration  weight  origin  \\\n",
       "159          8         351.0       148.0          13.5    4657       1   \n",
       "249          8         260.0       110.0          15.5    3365       1   \n",
       "350          4         105.0        63.0          14.9    2215       1   \n",
       "256          6         225.0       100.0          17.2    3430       1   \n",
       "355          4         107.0        75.0          14.4    2210       3   \n",
       "\n",
       "     model year  Power_to_weight  \n",
       "159          75         0.023698  \n",
       "249          78         0.024377  \n",
       "350          81         0.021210  \n",
       "256          78         0.021741  \n",
       "355          81         0.025307  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(313, 8)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(79, 8)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Rgression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [-2.60645512e-01  1.09033366e-02  1.77172522e-01 -2.84135565e-02\n",
      " -1.28706853e-02  1.56372720e+00  6.96588358e-01 -9.42682880e+02]\n",
      "Variance score: 0.8505168479276458\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create linear regression object \n",
    "reg = LinearRegression() \n",
    "  \n",
    "# train the model using the training sets \n",
    "reg.fit(X_train, Y_train) \n",
    "  \n",
    "# regression coefficients \n",
    "print('Coefficients: \\n', reg.coef_) \n",
    "  \n",
    "# variance score: 1 means perfect prediction \n",
    "print('Variance score: {}'.format(reg.score(X_test, Y_test))) \n",
    "  \n",
    "# plot for residual error \n",
    "  \n",
    "## setting plot style \n",
    "plt.style.use('fivethirtyeight') \n",
    "  \n",
    "## plotting residual errors in training data \n",
    "plt.scatter(reg.predict(X_train), reg.predict(X_train) - Y_train, \n",
    "            color = \"green\", s = 10, label = 'Train data') \n",
    "  \n",
    "## plotting residual errors in test data \n",
    "plt.scatter(reg.predict(X_test), reg.predict(X_test) - Y_test, \n",
    "            color = \"blue\", s = 10, label = 'Test data') \n",
    "  \n",
    "## plotting line for zero residual error \n",
    "plt.hlines(y = 0, xmin = 0, xmax = 50, linewidth = 2) \n",
    "  \n",
    "## plotting legend \n",
    "plt.legend(loc = 'upper right') \n",
    "  \n",
    "## plot title \n",
    "plt.title(\"Residual errors\") \n",
    "  \n",
    "## function to show plot \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted = reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.8816396895658074"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from math import sqrt\n",
    "rmse = sqrt(mean_squared_error(y_true=Y_test,y_pred=y_predicted))\n",
    "rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient Boosting Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(n_estimators=300)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb_regressor = GradientBoostingRegressor(n_estimators=300,max_depth=3)\n",
    "gb_regressor.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted_gbr = gb_regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.383552637570386"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_bgr = sqrt(mean_squared_error(y_true=Y_test,y_pred=y_predicted_gbr))\n",
    "rmse_bgr\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2bff712c3c8>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fi= pd.Series(gb_regressor.feature_importances_,index=factors)\n",
    "fi.plot.barh()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion \n",
    "* As we can see that Gradient Boosting was a better model asa it has lower root meana squared error.\n",
    "* The RMSE error I got was 2.38\n",
    "* \"Weight\" feature was the most important feature in the whole dataset.\n",
    "* The custom feature made (power_to_weight) also added more value than some of the features that were alrady present in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reference taken from : \"https://www.kaggle.com/devanshbesain/exploration-and-analysis-auto-mpg/notebook\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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