Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression with the mpg dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(398, 9)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = sns.load_dataset('mpg')\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg             False\n",
       "cylinders       False\n",
       "displacement    False\n",
       "horsepower       True\n",
       "weight          False\n",
       "acceleration    False\n",
       "model_year      False\n",
       "origin          False\n",
       "name            False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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",
       "      <th>name</th>\n",
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       "  </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>usa</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>165.0</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</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>150.0</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</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>150.0</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</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>140.0</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>ford torino</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                       name  \n",
       "0          70    usa  chevrolet chevelle malibu  \n",
       "1          70    usa          buick skylark 320  \n",
       "2          70    usa         plymouth satellite  \n",
       "3          70    usa              amc rebel sst  \n",
       "4          70    usa                ford torino  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['horsepower'] = df['horsepower'].fillna(df['horsepower'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mpg</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.775396</td>\n",
       "      <td>-0.804203</td>\n",
       "      <td>-0.771437</td>\n",
       "      <td>-0.831741</td>\n",
       "      <td>0.420289</td>\n",
       "      <td>0.579267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cylinders</th>\n",
       "      <td>-0.775396</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.950721</td>\n",
       "      <td>0.838939</td>\n",
       "      <td>0.896017</td>\n",
       "      <td>-0.505419</td>\n",
       "      <td>-0.348746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>displacement</th>\n",
       "      <td>-0.804203</td>\n",
       "      <td>0.950721</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.893646</td>\n",
       "      <td>0.932824</td>\n",
       "      <td>-0.543684</td>\n",
       "      <td>-0.370164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>horsepower</th>\n",
       "      <td>-0.771437</td>\n",
       "      <td>0.838939</td>\n",
       "      <td>0.893646</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.860574</td>\n",
       "      <td>-0.684259</td>\n",
       "      <td>-0.411651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weight</th>\n",
       "      <td>-0.831741</td>\n",
       "      <td>0.896017</td>\n",
       "      <td>0.932824</td>\n",
       "      <td>0.860574</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.417457</td>\n",
       "      <td>-0.306564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acceleration</th>\n",
       "      <td>0.420289</td>\n",
       "      <td>-0.505419</td>\n",
       "      <td>-0.543684</td>\n",
       "      <td>-0.684259</td>\n",
       "      <td>-0.417457</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.288137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year</th>\n",
       "      <td>0.579267</td>\n",
       "      <td>-0.348746</td>\n",
       "      <td>-0.370164</td>\n",
       "      <td>-0.411651</td>\n",
       "      <td>-0.306564</td>\n",
       "      <td>0.288137</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   mpg  cylinders  displacement  horsepower    weight  \\\n",
       "mpg           1.000000  -0.775396     -0.804203   -0.771437 -0.831741   \n",
       "cylinders    -0.775396   1.000000      0.950721    0.838939  0.896017   \n",
       "displacement -0.804203   0.950721      1.000000    0.893646  0.932824   \n",
       "horsepower   -0.771437   0.838939      0.893646    1.000000  0.860574   \n",
       "weight       -0.831741   0.896017      0.932824    0.860574  1.000000   \n",
       "acceleration  0.420289  -0.505419     -0.543684   -0.684259 -0.417457   \n",
       "model_year    0.579267  -0.348746     -0.370164   -0.411651 -0.306564   \n",
       "\n",
       "              acceleration  model_year  \n",
       "mpg               0.420289    0.579267  \n",
       "cylinders        -0.505419   -0.348746  \n",
       "displacement     -0.543684   -0.370164  \n",
       "horsepower       -0.684259   -0.411651  \n",
       "weight           -0.417457   -0.306564  \n",
       "acceleration      1.000000    0.288137  \n",
       "model_year        0.288137    1.000000  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr(method='pearson', min_periods=1, numeric_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['horsepower'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(398, 9)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df['horsepower'].values\n",
    "type(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xarray = df['horsepower'].values\n",
    "Yarray = df['weight'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(Xarray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
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       "       104.46938776, 100.        , 105.        , 100.        ,\n",
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       "        71.        ,  97.        ,  97.        ,  70.        ,\n",
       "        90.        ,  95.        ,  88.        ,  98.        ,\n",
       "       115.        ,  53.        ,  86.        ,  81.        ,\n",
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       "        78.        ,  97.        , 110.        , 110.        ,\n",
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       "        60.        , 110.        , 140.        , 139.        ,\n",
       "       105.        ,  95.        ,  85.        ,  88.        ,\n",
       "       100.        ,  90.        , 105.        ,  85.        ,\n",
       "       110.        , 120.        , 145.        , 165.        ,\n",
       "       139.        , 140.        ,  68.        ,  95.        ,\n",
       "        97.        ,  75.        ,  95.        , 105.        ,\n",
       "        85.        ,  97.        , 103.        , 125.        ,\n",
       "       115.        , 133.        ,  71.        ,  68.        ,\n",
       "       115.        ,  85.        ,  88.        ,  90.        ,\n",
       "       110.        , 130.        , 129.        , 138.        ,\n",
       "       135.        , 155.        , 142.        , 125.        ,\n",
       "       150.        ,  71.        ,  65.        ,  80.        ,\n",
       "        80.        ,  77.        , 125.        ,  71.        ,\n",
       "        90.        ,  70.        ,  70.        ,  65.        ,\n",
       "        69.        ,  90.        , 115.        , 115.        ,\n",
       "        90.        ,  76.        ,  60.        ,  70.        ,\n",
       "        65.        ,  90.        ,  88.        ,  90.        ,\n",
       "        90.        ,  78.        ,  90.        ,  75.        ,\n",
       "        92.        ,  75.        ,  65.        , 105.        ,\n",
       "        65.        ,  48.        ,  48.        ,  67.        ,\n",
       "        67.        ,  67.        , 104.46938776,  67.        ,\n",
       "        62.        , 132.        , 100.        ,  88.        ,\n",
       "       104.46938776,  72.        ,  84.        ,  84.        ,\n",
       "        92.        , 110.        ,  84.        ,  58.        ,\n",
       "        64.        ,  60.        ,  67.        ,  65.        ,\n",
       "        62.        ,  68.        ,  63.        ,  65.        ,\n",
       "        65.        ,  74.        , 104.46938776,  75.        ,\n",
       "        75.        , 100.        ,  74.        ,  80.        ,\n",
       "        76.        , 116.        , 120.        , 110.        ,\n",
       "       105.        ,  88.        ,  85.        ,  88.        ,\n",
       "        88.        ,  88.        ,  85.        ,  84.        ,\n",
       "        90.        ,  92.        , 104.46938776,  74.        ,\n",
       "        68.        ,  68.        ,  63.        ,  70.        ,\n",
       "        88.        ,  75.        ,  70.        ,  67.        ,\n",
       "        67.        ,  67.        , 110.        ,  85.        ,\n",
       "        92.        , 112.        ,  96.        ,  84.        ,\n",
       "        90.        ,  86.        ,  52.        ,  84.        ,\n",
       "        79.        ,  82.        ])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(199, 2)\n"
     ]
    },
    {
     "data": {
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       "       [225.        , 190.        ],\n",
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       "       [ 95.        ,  71.        ],\n",
       "       [ 70.        ,  75.        ],\n",
       "       [ 72.        , 102.        ],\n",
       "       [150.        ,  88.        ],\n",
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       "       [180.        , 145.        ],\n",
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       "       [105.        , 100.        ],\n",
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       "       [170.        , 190.        ],\n",
       "       [149.        ,  78.        ],\n",
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       "       [ 89.        ,  63.        ],\n",
       "       [ 83.        ,  67.        ],\n",
       "       [ 78.        ,  97.        ],\n",
       "       [110.        , 110.        ],\n",
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       "       [ 52.        ,  70.        ],\n",
       "       [ 60.        , 110.        ],\n",
       "       [140.        , 139.        ],\n",
       "       [105.        ,  95.        ],\n",
       "       [ 85.        ,  88.        ],\n",
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       "       [110.        , 120.        ],\n",
       "       [145.        , 165.        ],\n",
       "       [139.        , 140.        ],\n",
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       "       [ 97.        ,  75.        ],\n",
       "       [ 95.        , 105.        ],\n",
       "       [ 85.        ,  97.        ],\n",
       "       [103.        , 125.        ],\n",
       "       [115.        , 133.        ],\n",
       "       [ 71.        ,  68.        ],\n",
       "       [115.        ,  85.        ],\n",
       "       [ 88.        ,  90.        ],\n",
       "       [110.        , 130.        ],\n",
       "       [129.        , 138.        ],\n",
       "       [135.        , 155.        ],\n",
       "       [142.        , 125.        ],\n",
       "       [150.        ,  71.        ],\n",
       "       [ 65.        ,  80.        ],\n",
       "       [ 80.        ,  77.        ],\n",
       "       [125.        ,  71.        ],\n",
       "       [ 90.        ,  70.        ],\n",
       "       [ 70.        ,  65.        ],\n",
       "       [ 69.        ,  90.        ],\n",
       "       [115.        , 115.        ],\n",
       "       [ 90.        ,  76.        ],\n",
       "       [ 60.        ,  70.        ],\n",
       "       [ 65.        ,  90.        ],\n",
       "       [ 88.        ,  90.        ],\n",
       "       [ 90.        ,  78.        ],\n",
       "       [ 90.        ,  75.        ],\n",
       "       [ 92.        ,  75.        ],\n",
       "       [ 65.        , 105.        ],\n",
       "       [ 65.        ,  48.        ],\n",
       "       [ 48.        ,  67.        ],\n",
       "       [ 67.        ,  67.        ],\n",
       "       [104.46938776,  67.        ],\n",
       "       [ 62.        , 132.        ],\n",
       "       [100.        ,  88.        ],\n",
       "       [104.46938776,  72.        ],\n",
       "       [ 84.        ,  84.        ],\n",
       "       [ 92.        , 110.        ],\n",
       "       [ 84.        ,  58.        ],\n",
       "       [ 64.        ,  60.        ],\n",
       "       [ 67.        ,  65.        ],\n",
       "       [ 62.        ,  68.        ],\n",
       "       [ 63.        ,  65.        ],\n",
       "       [ 65.        ,  74.        ],\n",
       "       [104.46938776,  75.        ],\n",
       "       [ 75.        , 100.        ],\n",
       "       [ 74.        ,  80.        ],\n",
       "       [ 76.        , 116.        ],\n",
       "       [120.        , 110.        ],\n",
       "       [105.        ,  88.        ],\n",
       "       [ 85.        ,  88.        ],\n",
       "       [ 88.        ,  88.        ],\n",
       "       [ 85.        ,  84.        ],\n",
       "       [ 90.        ,  92.        ],\n",
       "       [104.46938776,  74.        ],\n",
       "       [ 68.        ,  68.        ],\n",
       "       [ 63.        ,  70.        ],\n",
       "       [ 88.        ,  75.        ],\n",
       "       [ 70.        ,  67.        ],\n",
       "       [ 67.        ,  67.        ],\n",
       "       [110.        ,  85.        ],\n",
       "       [ 92.        , 112.        ],\n",
       "       [ 96.        ,  84.        ],\n",
       "       [ 90.        ,  86.        ],\n",
       "       [ 52.        ,  84.        ],\n",
       "       [ 79.        ,  82.        ]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = Xarray.reshape(-1, 2)\n",
    "print(X.shape)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = Xarray.reshape(-1, 1)\n",
    "Y = Yarray.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [ 79.        ],\n",
       "       [ 82.        ]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model1 = LinearRegression()\n",
    "model1.fit(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[977.34076621]\n",
      "[[19.07816155]]\n"
     ]
    }
   ],
   "source": [
    "print(model1.intercept_)\n",
    "print(model1.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = model1.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3457.50176806],\n",
       "       [4125.23742241],\n",
       "       [3839.06499912],\n",
       "       [3839.06499912],\n",
       "       [3648.28338359],\n",
       "       [4754.81675365],\n",
       "       [5174.53630781],\n",
       "       [5079.14550004],\n",
       "       [5269.92711557],\n",
       "       [4602.19146122],\n",
       "       [4220.62823017],\n",
       "       [4029.84661464],\n",
       "       [3839.06499912],\n",
       "       [5269.92711557],\n",
       "       [2789.76611372],\n",
       "       [2789.76611372],\n",
       "       [2827.92243682],\n",
       "       [2598.98449819],\n",
       "       [2656.21898285],\n",
       "       [1854.93619764],\n",
       "       [2637.1408213 ],\n",
       "       [2694.37530596],\n",
       "       [2789.76611372],\n",
       "       [3133.17302167],\n",
       "       [2694.37530596],\n",
       "       [5079.14550004],\n",
       "       [4792.97307675],\n",
       "       [4983.75469228],\n",
       "       [4659.42594588],\n",
       "       [2656.21898285],\n",
       "       [2694.37530596],\n",
       "       [2789.76611372],\n",
       "       [2970.42462312],\n",
       "       [2885.15692148],\n",
       "       [2980.54772925],\n",
       "       [2885.15692148],\n",
       "       [2656.21898285],\n",
       "       [2885.15692148],\n",
       "       [4125.23742241],\n",
       "       [4316.01903793],\n",
       "       [3896.29948378],\n",
       "       [3839.06499912],\n",
       "       [4411.4098457 ],\n",
       "       [4220.62823017],\n",
       "       [4316.01903793],\n",
       "       [3075.93853701],\n",
       "       [2350.96839801],\n",
       "       [2885.15692148],\n",
       "       [2656.21898285],\n",
       "       [2618.06265974],\n",
       "       [2694.37530596],\n",
       "       [2312.8120749 ],\n",
       "       [2427.28104422],\n",
       "       [2217.42126714],\n",
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       "       [2541.75001353]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       [2579.90633664],\n",
       "       [2484.51552888],\n",
       "       [2541.75001353]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, Y,  color='gray')\n",
    "plt.plot(X, Y_pred, color='red', linewidth=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Need to split dataset into Training and Testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split \n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(318, 1)\n",
      "(80, 1)\n",
      "(318, 1)\n",
      "(80, 1)\n",
      "318.40000000000003\n",
      "79.60000000000001\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(Y_train.shape)\n",
    "print(Y_test.shape)\n",
    "print(0.8 * df.shape[0])\n",
    "print(0.2 * df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[942.42861007]\n",
      "[[19.48576276]]\n"
     ]
    }
   ],
   "source": [
    "model2 = LinearRegression()\n",
    "model2.fit(X_train, Y_train)\n",
    "print(model2.intercept_)\n",
    "print(model2.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Compare with model1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[977.34076621]\n",
      "[[19.07816155]]\n"
     ]
    }
   ],
   "source": [
    "print(model1.intercept_)\n",
    "print(model1.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = model2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test, Y_test,  color='gray')\n",
    "plt.scatter(X_test, Y_pred, color='red', linewidth=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now check model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 368.1337619437148\n",
      "Mean Squared Error: 201176.0818221282\n",
      "Root Mean Squared Error: 448.52656757669126\n",
      "R-squared: 0.7232751145017162\n"
     ]
    }
   ],
   "source": [
    "print('Mean Absolute Error:', metrics.mean_absolute_error(Y_test, Y_pred))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(Y_test, Y_pred))  \n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(Y_test, Y_pred)))\n",
    "print('R-squared:', metrics.r2_score(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pick another training set and build another model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(238, 1)\n",
      "(160, 1)\n",
      "(238, 1)\n",
      "(160, 1)\n",
      "238.79999999999998\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.4, random_state=0)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(Y_train.shape)\n",
    "print(Y_test.shape)\n",
    "print(0.6 * df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[885.63517611]\n",
      "[[20.05543059]]\n"
     ]
    }
   ],
   "source": [
    "model3 = LinearRegression()\n",
    "model3.fit(X_train, Y_train)\n",
    "print(model3.intercept_)\n",
    "print(model3.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y_pred = model3.predict(X_test)\n",
    "plt.scatter(X_test, Y_test,  color='gray')\n",
    "plt.scatter(X_test, Y_pred, color='red', linewidth=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 343.7676698648614\n",
      "Mean Squared Error: 188633.05036591884\n",
      "Root Mean Squared Error: 434.3190651651374\n",
      "R-squared: 0.7285281245558992\n"
     ]
    }
   ],
   "source": [
    "print('Mean Absolute Error:', metrics.mean_absolute_error(Y_test, Y_pred))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(Y_test, Y_pred))  \n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(Y_test, Y_pred)))\n",
    "print('R-squared:', metrics.r2_score(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Note that the RMSE changes. If the RMSE is still good when we pick various training set, it means model passes (one of the ) model validation test."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## how are we going to do this automatically?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Idea is to pick random testing set, and display the model performance metric.  First we need some random number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "323\n",
      "222\n",
      "307\n",
      "783\n",
      "648\n",
      "612\n",
      "814\n",
      "403\n",
      "838\n",
      "960\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "for i in range(10):\n",
    "    print(random.randint(1, 1000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now we can use different random_state value in the train_test_split so that it will generate different training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 0, R-squared: 0.782959728786109,  Slope: [[18.91437704]], Intercept: [993.7773375],  \n",
      "Trial 1, R-squared: 0.7508859670424688,  Slope: [[19.36590281]], Intercept: [948.44750382],  \n",
      "Trial 2, R-squared: 0.7933132989752376,  Slope: [[19.5657829]], Intercept: [936.75953823],  \n",
      "Trial 3, R-squared: 0.7642013901362446,  Slope: [[18.86591382]], Intercept: [1006.263687],  \n",
      "Trial 4, R-squared: 0.7310157851930557,  Slope: [[18.91580313]], Intercept: [991.26632435],  \n",
      "Trial 5, R-squared: 0.6829736137848603,  Slope: [[19.44275652]], Intercept: [930.01050274],  \n",
      "Trial 6, R-squared: 0.6686304340382099,  Slope: [[19.09426519]], Intercept: [986.60088564],  \n",
      "Trial 7, R-squared: 0.7628932661940503,  Slope: [[18.56106263]], Intercept: [1027.86771744],  \n",
      "Trial 8, R-squared: 0.6622219224727108,  Slope: [[19.35223898]], Intercept: [932.9961973],  \n",
      "Trial 9, R-squared: 0.7751374384654208,  Slope: [[18.81162967]], Intercept: [989.20691921],  \n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state = random.randint(1,1000))\n",
    "    model = LinearRegression()\n",
    "    model.fit(X_train, Y_train)\n",
    "    Y_pred = model.predict(X_test)\n",
    "    print('Trial {}, R-squared: {},  Slope: {}, Intercept: {},  '.format(i, metrics.r2_score(Y_test, Y_pred), \\\n",
    "                                                                      model.coef_, model.intercept_))\n",
    "    #plt.scatter(X_test, Y_test,  color='gray')\n",
    "    #plt.scatter(X_test, Y_pred, color='red', linewidth=2)\n",
    "    #plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collect the R-squared and plot it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>trial</th>\n",
       "      <th>Rsquare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.780990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.789332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.662606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.698546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.764358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.713347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.768140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.778284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0.759422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0.749581</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   trial   Rsquare\n",
       "0      0  0.780990\n",
       "1      1  0.789332\n",
       "2      2  0.662606\n",
       "3      3  0.698546\n",
       "4      4  0.764358\n",
       "5      5  0.713347\n",
       "6      6  0.768140\n",
       "7      7  0.778284\n",
       "8      8  0.759422\n",
       "9      9  0.749581"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rsquare = []\n",
    "for i in range(10):\n",
    "    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state = random.randint(1,1000))\n",
    "    model = LinearRegression()\n",
    "    model.fit(X_train, Y_train)\n",
    "    Y_pred = model.predict(X_test)\n",
    "    rsquare.append(metrics.r2_score(Y_test, Y_pred))\n",
    "    \n",
    "rdf = pd.DataFrame( {'trial': range(10), 'Rsquare': rsquare})\n",
    "rdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='trial'>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rdf.plot.bar(x='trial', y='Rsquare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7464606204688761\n"
     ]
    }
   ],
   "source": [
    "print(rdf['Rsquare'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The above concept of re-running the same model but with different training set drawn from the data set is called Cross-Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# We have just did a 10-fold Cross Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now Add a second variable, say, the horsepower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(318, 2) (80, 2)\n",
      "(318, 1) (80, 1)\n"
     ]
    }
   ],
   "source": [
    "X = df[['weight','horsepower']].values.reshape(-1, 2)\n",
    "Y = df['mpg'].values.reshape(-1, 1)\n",
    " \n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)\n",
    "print(X_train.shape, X_test.shape)\n",
    "print(Y_train.shape, Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       [3205.        ,  139.        ],\n",
       "       [4080.        ,  140.        ],\n",
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       "       [2855.        ,   85.        ],\n",
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       "       [3900.        ,  125.        ],\n",
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       "       [2200.        ,   70.        ],\n",
       "       [2150.        ,   70.        ],\n",
       "       [2020.        ,   65.        ],\n",
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       "       [2670.        ,   90.        ],\n",
       "       [2595.        ,  115.        ],\n",
       "       [2700.        ,  115.        ],\n",
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       "       [1875.        ,   64.        ],\n",
       "       [1760.        ,   60.        ],\n",
       "       [2065.        ,   67.        ],\n",
       "       [1975.        ,   65.        ],\n",
       "       [2050.        ,   62.        ],\n",
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       "       [2215.        ,   63.        ],\n",
       "       [2045.        ,   65.        ],\n",
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       "       [2320.        ,  104.46938776],\n",
       "       [2210.        ,   75.        ],\n",
       "       [2350.        ,   75.        ],\n",
       "       [2615.        ,  100.        ],\n",
       "       [2635.        ,   74.        ],\n",
       "       [3230.        ,   80.        ],\n",
       "       [3160.        ,   76.        ],\n",
       "       [2900.        ,  116.        ],\n",
       "       [2930.        ,  120.        ],\n",
       "       [3415.        ,  110.        ],\n",
       "       [3725.        ,  105.        ],\n",
       "       [3060.        ,   88.        ],\n",
       "       [3465.        ,   85.        ],\n",
       "       [2605.        ,   88.        ],\n",
       "       [2640.        ,   88.        ],\n",
       "       [2395.        ,   88.        ],\n",
       "       [2575.        ,   85.        ],\n",
       "       [2525.        ,   84.        ],\n",
       "       [2735.        ,   90.        ],\n",
       "       [2865.        ,   92.        ],\n",
       "       [3035.        ,  104.46938776],\n",
       "       [1980.        ,   74.        ],\n",
       "       [2025.        ,   68.        ],\n",
       "       [1970.        ,   68.        ],\n",
       "       [2125.        ,   63.        ],\n",
       "       [2125.        ,   70.        ],\n",
       "       [2160.        ,   88.        ],\n",
       "       [2205.        ,   75.        ],\n",
       "       [2245.        ,   70.        ],\n",
       "       [1965.        ,   67.        ],\n",
       "       [1965.        ,   67.        ],\n",
       "       [1995.        ,   67.        ],\n",
       "       [2945.        ,  110.        ],\n",
       "       [3015.        ,   85.        ],\n",
       "       [2585.        ,   92.        ],\n",
       "       [2835.        ,  112.        ],\n",
       "       [2665.        ,   96.        ],\n",
       "       [2370.        ,   84.        ],\n",
       "       [2950.        ,   90.        ],\n",
       "       [2790.        ,   86.        ],\n",
       "       [2130.        ,   52.        ],\n",
       "       [2295.        ,   84.        ],\n",
       "       [2625.        ,   79.        ],\n",
       "       [2720.        ,   82.        ]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weight</th>\n",
       "      <th>horsepower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3504</td>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3693</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3436</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3433</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3449</td>\n",
       "      <td>140.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   weight  horsepower\n",
       "0    3504       130.0\n",
       "1    3693       165.0\n",
       "2    3436       150.0\n",
       "3    3433       150.0\n",
       "4    3449       140.0"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['weight', 'horsepower']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.00521591 -0.06185685]]\n",
      "[45.44711105]\n"
     ]
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train, Y_train)\n",
    "Y_pred = model.predict(X_test)\n",
    "print(model.coef_)\n",
    "print(model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y_test, Y_pred, color='red', linewidth=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: 0.7030552754471302\n"
     ]
    }
   ],
   "source": [
    "print('R-squared:', metrics.r2_score(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Do a 10-fold cross validation again with two variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 0, R-squared: 0.7144936823332535,  Slope: [[-0.00594165 -0.04420883]], Intercept: [45.88589493],  \n",
      "Trial 1, R-squared: 0.7087603144543813,  Slope: [[-0.00608047 -0.04235069]], Intercept: [46.04523585],  \n",
      "Trial 2, R-squared: 0.7351269644147336,  Slope: [[-0.00592012 -0.04863489]], Intercept: [46.10644824],  \n",
      "Trial 3, R-squared: 0.6083611345181243,  Slope: [[-0.00633674 -0.03458477]], Intercept: [45.96923374],  \n",
      "Trial 4, R-squared: 0.6576889743699001,  Slope: [[-0.00632087 -0.03520986]], Intercept: [45.96951422],  \n",
      "Trial 5, R-squared: 0.743607034267757,  Slope: [[-0.00613194 -0.03902896]], Intercept: [45.80033424],  \n",
      "Trial 6, R-squared: 0.6846251326208389,  Slope: [[-0.00574052 -0.05124888]], Intercept: [45.95493465],  \n",
      "Trial 7, R-squared: 0.7751936534831565,  Slope: [[-0.00596223 -0.04463239]], Intercept: [46.0204638],  \n",
      "Trial 8, R-squared: 0.6650562560636957,  Slope: [[-0.00585038 -0.04413667]], Intercept: [45.41691719],  \n",
      "Trial 9, R-squared: 0.6592463076734482,  Slope: [[-0.00583151 -0.04193864]], Intercept: [45.06104922],  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='trial'>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rsquare = []\n",
    "for i in range(10):\n",
    "    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state = random.randint(1,1000))\n",
    "    model = LinearRegression()\n",
    "    model.fit(X_train, Y_train)\n",
    "    Y_pred = model.predict(X_test)\n",
    "    rsquare.append(metrics.r2_score(Y_test, Y_pred))\n",
    "    print('Trial {}, R-squared: {},  Slope: {}, Intercept: {},  '.format(i, metrics.r2_score(Y_test, Y_pred), \\\n",
    "                                                                      model.coef_, model.intercept_))\n",
    "\n",
    "    \n",
    "rdf = pd.DataFrame( {'trial': range(10), 'Rsquare': rsquare})\n",
    "rdf.plot.bar(x='trial', y='Rsquare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6952159454199289\n"
     ]
    }
   ],
   "source": [
    "print(rdf['Rsquare'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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",
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