simple linear regression - car price prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
   " # import libraries"
    " # for linear algebra "
    "import numpy as np\n",
    " # for data processing "
    "import pandas as pd\n",
    " # for reading the input file "
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>car_ID</th>\n",
       "      <th>symboling</th>\n",
       "      <th>CarName</th>\n",
       "      <th>fueltype</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>doornumber</th>\n",
       "      <th>carbody</th>\n",
       "      <th>drivewheel</th>\n",
       "      <th>enginelocation</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>...</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>fuelsystem</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peakrpm</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero giulia</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero stelvio</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>alfa-romero Quadrifoglio</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>audi 100 ls</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>audi 100ls</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>201</td>\n",
       "      <td>-1</td>\n",
       "      <td>volvo 145e (sw)</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>16845.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>202</td>\n",
       "      <td>-1</td>\n",
       "      <td>volvo 144ea</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>8.7</td>\n",
       "      <td>160</td>\n",
       "      <td>5300</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>19045.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>203</td>\n",
       "      <td>-1</td>\n",
       "      <td>volvo 244dl</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>173</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.58</td>\n",
       "      <td>2.87</td>\n",
       "      <td>8.8</td>\n",
       "      <td>134</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>21485.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>204</td>\n",
       "      <td>-1</td>\n",
       "      <td>volvo 246</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>145</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.0</td>\n",
       "      <td>106</td>\n",
       "      <td>4800</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>22470.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>205</td>\n",
       "      <td>-1</td>\n",
       "      <td>volvo 264gl</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>22625.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>205 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     car_ID  symboling                   CarName fueltype aspiration  \\\n",
       "0         1          3        alfa-romero giulia      gas        std   \n",
       "1         2          3       alfa-romero stelvio      gas        std   \n",
       "2         3          1  alfa-romero Quadrifoglio      gas        std   \n",
       "3         4          2               audi 100 ls      gas        std   \n",
       "4         5          2                audi 100ls      gas        std   \n",
       "..      ...        ...                       ...      ...        ...   \n",
       "200     201         -1           volvo 145e (sw)      gas        std   \n",
       "201     202         -1               volvo 144ea      gas      turbo   \n",
       "202     203         -1               volvo 244dl      gas        std   \n",
       "203     204         -1                 volvo 246   diesel      turbo   \n",
       "204     205         -1               volvo 264gl      gas      turbo   \n",
       "\n",
       "    doornumber      carbody drivewheel enginelocation  wheelbase  ...  \\\n",
       "0          two  convertible        rwd          front       88.6  ...   \n",
       "1          two  convertible        rwd          front       88.6  ...   \n",
       "2          two    hatchback        rwd          front       94.5  ...   \n",
       "3         four        sedan        fwd          front       99.8  ...   \n",
       "4         four        sedan        4wd          front       99.4  ...   \n",
       "..         ...          ...        ...            ...        ...  ...   \n",
       "200       four        sedan        rwd          front      109.1  ...   \n",
       "201       four        sedan        rwd          front      109.1  ...   \n",
       "202       four        sedan        rwd          front      109.1  ...   \n",
       "203       four        sedan        rwd          front      109.1  ...   \n",
       "204       four        sedan        rwd          front      109.1  ...   \n",
       "\n",
       "     enginesize  fuelsystem  boreratio  stroke compressionratio horsepower  \\\n",
       "0           130        mpfi       3.47    2.68              9.0        111   \n",
       "1           130        mpfi       3.47    2.68              9.0        111   \n",
       "2           152        mpfi       2.68    3.47              9.0        154   \n",
       "3           109        mpfi       3.19    3.40             10.0        102   \n",
       "4           136        mpfi       3.19    3.40              8.0        115   \n",
       "..          ...         ...        ...     ...              ...        ...   \n",
       "200         141        mpfi       3.78    3.15              9.5        114   \n",
       "201         141        mpfi       3.78    3.15              8.7        160   \n",
       "202         173        mpfi       3.58    2.87              8.8        134   \n",
       "203         145         idi       3.01    3.40             23.0        106   \n",
       "204         141        mpfi       3.78    3.15              9.5        114   \n",
       "\n",
       "     peakrpm citympg  highwaympg    price  \n",
       "0       5000      21          27  13495.0  \n",
       "1       5000      21          27  16500.0  \n",
       "2       5000      19          26  16500.0  \n",
       "3       5500      24          30  13950.0  \n",
       "4       5500      18          22  17450.0  \n",
       "..       ...     ...         ...      ...  \n",
       "200     5400      23          28  16845.0  \n",
       "201     5300      19          25  19045.0  \n",
       "202     5500      18          23  21485.0  \n",
       "203     4800      26          27  22470.0  \n",
       "204     5400      19          25  22625.0  \n",
       "\n",
       "[205 rows x 26 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # reading the dataset "
    "data=pd.read_csv('C:\\\\Users\\\\ananya\\\\laboid\\\\notebook\\\\car price\\\\carpricedata.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    " # assigning data to the variables"
    "x = data[[\"wheelbase\",\"carwidth\",\"curbweight\",\"compressionratio\",\"horsepower\",\"highwaympg\"]].values\n",
    "y = data[[\"price\"]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  88.6,   64.1, 2548. ,    9. ,  111. ,   27. ],\n",
       "       [  88.6,   64.1, 2548. ,    9. ,  111. ,   27. ],\n",
       "       [  94.5,   65.5, 2823. ,    9. ,  154. ,   26. ],\n",
       "       ...,\n",
       "       [ 109.1,   68.9, 3012. ,    8.8,  134. ,   23. ],\n",
       "       [ 109.1,   68.9, 3217. ,   23. ,  106. ,   27. ],\n",
       "       [ 109.1,   68.9, 3062. ,    9.5,  114. ,   25. ]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[13495.   ],\n",
       "       [16500.   ],\n",
       "       [16500.   ],\n",
       "       [13950.   ],\n",
       "       [17450.   ],\n",
       "       [15250.   ],\n",
       "       [17710.   ],\n",
       "       [18920.   ],\n",
       "       [23875.   ],\n",
       "       [17859.167],\n",
       "       [16430.   ],\n",
       "       [16925.   ],\n",
       "       [20970.   ],\n",
       "       [21105.   ],\n",
       "       [24565.   ],\n",
       "       [30760.   ],\n",
       "       [41315.   ],\n",
       "       [36880.   ],\n",
       "       [ 5151.   ],\n",
       "       [ 6295.   ],\n",
       "       [ 6575.   ],\n",
       "       [ 5572.   ],\n",
       "       [ 6377.   ],\n",
       "       [ 7957.   ],\n",
       "       [ 6229.   ],\n",
       "       [ 6692.   ],\n",
       "       [ 7609.   ],\n",
       "       [ 8558.   ],\n",
       "       [ 8921.   ],\n",
       "       [12964.   ],\n",
       "       [ 6479.   ],\n",
       "       [ 6855.   ],\n",
       "       [ 5399.   ],\n",
       "       [ 6529.   ],\n",
       "       [ 7129.   ],\n",
       "       [ 7295.   ],\n",
       "       [ 7295.   ],\n",
       "       [ 7895.   ],\n",
       "       [ 9095.   ],\n",
       "       [ 8845.   ],\n",
       "       [10295.   ],\n",
       "       [12945.   ],\n",
       "       [10345.   ],\n",
       "       [ 6785.   ],\n",
       "       [ 8916.5  ],\n",
       "       [ 8916.5  ],\n",
       "       [11048.   ],\n",
       "       [32250.   ],\n",
       "       [35550.   ],\n",
       "       [36000.   ],\n",
       "       [ 5195.   ],\n",
       "       [ 6095.   ],\n",
       "       [ 6795.   ],\n",
       "       [ 6695.   ],\n",
       "       [ 7395.   ],\n",
       "       [10945.   ],\n",
       "       [11845.   ],\n",
       "       [13645.   ],\n",
       "       [15645.   ],\n",
       "       [ 8845.   ],\n",
       "       [ 8495.   ],\n",
       "       [10595.   ],\n",
       "       [10245.   ],\n",
       "       [10795.   ],\n",
       "       [11245.   ],\n",
       "       [18280.   ],\n",
       "       [18344.   ],\n",
       "       [25552.   ],\n",
       "       [28248.   ],\n",
       "       [28176.   ],\n",
       "       [31600.   ],\n",
       "       [34184.   ],\n",
       "       [35056.   ],\n",
       "       [40960.   ],\n",
       "       [45400.   ],\n",
       "       [16503.   ],\n",
       "       [ 5389.   ],\n",
       "       [ 6189.   ],\n",
       "       [ 6669.   ],\n",
       "       [ 7689.   ],\n",
       "       [ 9959.   ],\n",
       "       [ 8499.   ],\n",
       "       [12629.   ],\n",
       "       [14869.   ],\n",
       "       [14489.   ],\n",
       "       [ 6989.   ],\n",
       "       [ 8189.   ],\n",
       "       [ 9279.   ],\n",
       "       [ 9279.   ],\n",
       "       [ 5499.   ],\n",
       "       [ 7099.   ],\n",
       "       [ 6649.   ],\n",
       "       [ 6849.   ],\n",
       "       [ 7349.   ],\n",
       "       [ 7299.   ],\n",
       "       [ 7799.   ],\n",
       "       [ 7499.   ],\n",
       "       [ 7999.   ],\n",
       "       [ 8249.   ],\n",
       "       [ 8949.   ],\n",
       "       [ 9549.   ],\n",
       "       [13499.   ],\n",
       "       [14399.   ],\n",
       "       [13499.   ],\n",
       "       [17199.   ],\n",
       "       [19699.   ],\n",
       "       [18399.   ],\n",
       "       [11900.   ],\n",
       "       [13200.   ],\n",
       "       [12440.   ],\n",
       "       [13860.   ],\n",
       "       [15580.   ],\n",
       "       [16900.   ],\n",
       "       [16695.   ],\n",
       "       [17075.   ],\n",
       "       [16630.   ],\n",
       "       [17950.   ],\n",
       "       [18150.   ],\n",
       "       [ 5572.   ],\n",
       "       [ 7957.   ],\n",
       "       [ 6229.   ],\n",
       "       [ 6692.   ],\n",
       "       [ 7609.   ],\n",
       "       [ 8921.   ],\n",
       "       [12764.   ],\n",
       "       [22018.   ],\n",
       "       [32528.   ],\n",
       "       [34028.   ],\n",
       "       [37028.   ],\n",
       "       [31400.5  ],\n",
       "       [ 9295.   ],\n",
       "       [ 9895.   ],\n",
       "       [11850.   ],\n",
       "       [12170.   ],\n",
       "       [15040.   ],\n",
       "       [15510.   ],\n",
       "       [18150.   ],\n",
       "       [18620.   ],\n",
       "       [ 5118.   ],\n",
       "       [ 7053.   ],\n",
       "       [ 7603.   ],\n",
       "       [ 7126.   ],\n",
       "       [ 7775.   ],\n",
       "       [ 9960.   ],\n",
       "       [ 9233.   ],\n",
       "       [11259.   ],\n",
       "       [ 7463.   ],\n",
       "       [10198.   ],\n",
       "       [ 8013.   ],\n",
       "       [11694.   ],\n",
       "       [ 5348.   ],\n",
       "       [ 6338.   ],\n",
       "       [ 6488.   ],\n",
       "       [ 6918.   ],\n",
       "       [ 7898.   ],\n",
       "       [ 8778.   ],\n",
       "       [ 6938.   ],\n",
       "       [ 7198.   ],\n",
       "       [ 7898.   ],\n",
       "       [ 7788.   ],\n",
       "       [ 7738.   ],\n",
       "       [ 8358.   ],\n",
       "       [ 9258.   ],\n",
       "       [ 8058.   ],\n",
       "       [ 8238.   ],\n",
       "       [ 9298.   ],\n",
       "       [ 9538.   ],\n",
       "       [ 8449.   ],\n",
       "       [ 9639.   ],\n",
       "       [ 9989.   ],\n",
       "       [11199.   ],\n",
       "       [11549.   ],\n",
       "       [17669.   ],\n",
       "       [ 8948.   ],\n",
       "       [10698.   ],\n",
       "       [ 9988.   ],\n",
       "       [10898.   ],\n",
       "       [11248.   ],\n",
       "       [16558.   ],\n",
       "       [15998.   ],\n",
       "       [15690.   ],\n",
       "       [15750.   ],\n",
       "       [ 7775.   ],\n",
       "       [ 7975.   ],\n",
       "       [ 7995.   ],\n",
       "       [ 8195.   ],\n",
       "       [ 8495.   ],\n",
       "       [ 9495.   ],\n",
       "       [ 9995.   ],\n",
       "       [11595.   ],\n",
       "       [ 9980.   ],\n",
       "       [13295.   ],\n",
       "       [13845.   ],\n",
       "       [12290.   ],\n",
       "       [12940.   ],\n",
       "       [13415.   ],\n",
       "       [15985.   ],\n",
       "       [16515.   ],\n",
       "       [18420.   ],\n",
       "       [18950.   ],\n",
       "       [16845.   ],\n",
       "       [19045.   ],\n",
       "       [21485.   ],\n",
       "       [22470.   ],\n",
       "       [22625.   ]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    " # training set creation"
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  96.5 ,   62.5 , 2372.  ,    9.  ,   86.  ,   33.  ],\n",
       "       [  98.8 ,   66.5 , 2410.  ,    8.6 ,   84.  ,   32.  ],\n",
       "       [  95.3 ,   65.7 , 2380.  ,    9.4 ,  101.  ,   23.  ],\n",
       "       [ 100.4 ,   66.5 , 3095.  ,    9.  ,  152.  ,   22.  ],\n",
       "       [  96.3 ,   65.4 , 2405.  ,    8.5 ,   88.  ,   32.  ],\n",
       "       [  94.5 ,   63.6 , 1874.  ,    9.6 ,   70.  ,   43.  ],\n",
       "       [  95.7 ,   63.6 , 3110.  ,    9.  ,   62.  ,   32.  ],\n",
       "       [  94.5 ,   63.8 , 2037.  ,    9.4 ,   69.  ,   37.  ],\n",
       "       [  93.1 ,   64.2 , 1950.  ,    9.  ,   68.  ,   38.  ],\n",
       "       [  97.3 ,   65.5 , 2264.  ,   23.  ,   52.  ,   46.  ],\n",
       "       [  93.1 ,   64.2 , 1900.  ,    9.  ,   68.  ,   38.  ],\n",
       "       [  93.7 ,   63.6 , 2120.  ,    8.7 ,   73.  ,   31.  ],\n",
       "       [  97.2 ,   65.4 , 2145.  ,    9.5 ,   82.  ,   37.  ],\n",
       "       [ 107.9 ,   68.4 , 3252.  ,   21.  ,   95.  ,   33.  ],\n",
       "       [  94.5 ,   63.8 , 1971.  ,    9.4 ,   69.  ,   37.  ],\n",
       "       [ 107.9 ,   68.4 , 3020.  ,    8.4 ,   97.  ,   24.  ],\n",
       "       [  97.  ,   65.4 , 2290.  ,    9.  ,   82.  ,   32.  ],\n",
       "       [ 114.2 ,   68.4 , 3430.  ,   21.  ,   95.  ,   25.  ],\n",
       "       [ 102.9 ,   67.7 , 2976.  ,    9.3 ,  161.  ,   24.  ],\n",
       "       [ 103.5 ,   66.9 , 3055.  ,    9.  ,  121.  ,   25.  ],\n",
       "       [  93.7 ,   63.8 , 2191.  ,    7.6 ,  102.  ,   30.  ],\n",
       "       [ 107.9 ,   68.4 , 3197.  ,   21.  ,   95.  ,   33.  ],\n",
       "       [  97.3 ,   65.5 , 2212.  ,    9.  ,   85.  ,   34.  ],\n",
       "       [  89.5 ,   65.  , 2756.  ,    9.5 ,  207.  ,   25.  ],\n",
       "       [  96.  ,   65.2 , 2734.  ,    9.2 ,   90.  ,   29.  ],\n",
       "       [ 100.4 ,   66.9 , 2661.  ,    8.5 ,  110.  ,   24.  ],\n",
       "       [ 109.1 ,   68.9 , 2952.  ,    9.5 ,  114.  ,   28.  ],\n",
       "       [  95.7 ,   64.4 , 2140.  ,    9.  ,   70.  ,   34.  ],\n",
       "       [  98.8 ,   66.5 , 2410.  ,    8.6 ,   84.  ,   32.  ],\n",
       "       [  94.5 ,   65.5 , 2823.  ,    9.  ,  154.  ,   26.  ],\n",
       "       [  98.8 ,   66.5 , 2385.  ,    8.6 ,   84.  ,   32.  ],\n",
       "       [ 103.3 ,   64.6 , 2535.  ,    8.5 ,   88.  ,   30.  ],\n",
       "       [  95.9 ,   66.3 , 2818.  ,    7.  ,  145.  ,   24.  ],\n",
       "       [  94.3 ,   61.8 , 2337.  ,    8.5 ,   78.  ,   29.  ],\n",
       "       [ 101.2 ,   64.8 , 2395.  ,    8.8 ,  101.  ,   29.  ],\n",
       "       [ 114.2 ,   68.4 , 3230.  ,    8.4 ,   97.  ,   24.  ],\n",
       "       [ 120.9 ,   71.7 , 3900.  ,    8.  ,  184.  ,   16.  ],\n",
       "       [ 104.3 ,   67.2 , 2935.  ,    9.5 ,  114.  ,   28.  ],\n",
       "       [  97.3 ,   65.5 , 2261.  ,   23.  ,   52.  ,   46.  ],\n",
       "       [  95.7 ,   63.6 , 1985.  ,    9.  ,   62.  ,   39.  ],\n",
       "       [  95.1 ,   63.8 , 2008.  ,    9.4 ,   69.  ,   37.  ],\n",
       "       [  99.8 ,   66.2 , 2337.  ,   10.  ,  102.  ,   30.  ],\n",
       "       [  94.5 ,   63.8 , 2024.  ,    9.4 ,   69.  ,   37.  ],\n",
       "       [  93.7 ,   63.8 , 1918.  ,    9.4 ,   68.  ,   41.  ],\n",
       "       [  95.7 ,   64.4 , 2094.  ,    9.  ,   70.  ,   47.  ],\n",
       "       [ 104.3 ,   67.2 , 3157.  ,    7.5 ,  162.  ,   22.  ],\n",
       "       [  93.1 ,   64.2 , 1890.  ,    9.  ,   68.  ,   31.  ],\n",
       "       [  88.6 ,   64.1 , 2548.  ,    9.  ,  111.  ,   27.  ],\n",
       "       [  94.5 ,   63.8 , 1951.  ,    9.4 ,   69.  ,   37.  ],\n",
       "       [ 107.9 ,   68.4 , 3252.  ,   21.  ,   95.  ,   33.  ],\n",
       "       [  94.5 ,   63.8 , 2028.  ,    9.4 ,   69.  ,   37.  ],\n",
       "       [  98.8 ,   66.5 , 2425.  ,    8.6 ,   84.  ,   32.  ],\n",
       "       [  97.  ,   65.4 , 2385.  ,    9.  ,   82.  ,   25.  ],\n",
       "       [  96.5 ,   65.2 , 2465.  ,    9.  ,  101.  ,   28.  ],\n",
       "       [ 106.7 ,   70.3 , 3495.  ,   21.5 ,  123.  ,   25.  ],\n",
       "       [ 102.  ,   70.6 , 3950.  ,   11.5 ,  262.  ,   17.  ],\n",
       "       [ 113.  ,   69.6 , 4066.  ,    8.1 ,  176.  ,   19.  ],\n",
       "       [  96.3 ,   65.4 , 2365.  ,    8.5 ,   88.  ,   32.  ],\n",
       "       [ 109.1 ,   68.9 , 3012.  ,    8.8 ,  134.  ,   23.  ],\n",
       "       [ 101.2 ,   64.8 , 2765.  ,    9.  ,  121.  ,   28.  ],\n",
       "       [  95.7 ,   63.6 , 2280.  ,    9.  ,   62.  ,   37.  ],\n",
       "       [  93.7 ,   63.8 , 2128.  ,    7.6 ,  102.  ,   30.  ],\n",
       "       [  94.5 ,   64.  , 2221.  ,    8.5 ,   90.  ,   29.  ],\n",
       "       [  99.1 ,   66.5 , 2847.  ,    9.  ,  160.  ,   26.  ],\n",
       "       [  94.5 ,   63.6 , 1909.  ,    9.6 ,   70.  ,   43.  ],\n",
       "       [ 103.5 ,   66.9 , 3230.  ,    8.  ,  182.  ,   22.  ],\n",
       "       [  93.7 ,   64.4 , 2004.  ,    9.4 ,   68.  ,   38.  ],\n",
       "       [  97.2 ,   65.2 , 2302.  ,    8.5 ,   97.  ,   34.  ],\n",
       "       [  94.5 ,   64.  , 2300.  ,    9.4 ,  112.  ,   29.  ],\n",
       "       [ 109.1 ,   68.9 , 3217.  ,   23.  ,  106.  ,   27.  ],\n",
       "       [ 105.8 ,   71.4 , 2844.  ,    8.5 ,  110.  ,   25.  ],\n",
       "       [ 110.  ,   70.3 , 3750.  ,   21.5 ,  123.  ,   25.  ],\n",
       "       [  95.9 ,   66.3 , 2926.  ,    7.  ,  145.  ,   24.  ],\n",
       "       [  93.7 ,   63.8 , 1989.  ,    9.4 ,   68.  ,   38.  ],\n",
       "       [  95.7 ,   64.4 , 2109.  ,    9.  ,   70.  ,   37.  ],\n",
       "       [  97.3 ,   65.5 , 2319.  ,   23.  ,   68.  ,   42.  ],\n",
       "       [  95.7 ,   64.4 , 2275.  ,   22.5 ,   56.  ,   36.  ],\n",
       "       [  94.5 ,   63.8 , 1918.  ,    9.4 ,   69.  ,   37.  ],\n",
       "       [ 102.4 ,   66.5 , 2414.  ,    8.7 ,   92.  ,   32.  ],\n",
       "       [ 101.2 ,   64.8 , 2395.  ,    8.8 ,  101.  ,   29.  ],\n",
       "       [  93.7 ,   63.8 , 2128.  ,    7.6 ,  102.  ,   30.  ],\n",
       "       [ 100.4 ,   66.5 , 3296.  ,    9.  ,  152.  ,   22.  ],\n",
       "       [  96.5 ,   64.  , 2010.  ,    9.2 ,   76.  ,   34.  ],\n",
       "       [  95.3 ,   65.7 , 2385.  ,    9.4 ,  101.  ,   23.  ],\n",
       "       [ 104.9 ,   66.1 , 2670.  ,    8.  ,  120.  ,   27.  ],\n",
       "       [  88.6 ,   64.1 , 2548.  ,    9.  ,  111.  ,   27.  ],\n",
       "       [  93.7 ,   63.8 , 1967.  ,    9.4 ,   68.  ,   38.  ],\n",
       "       [  94.5 ,   64.  , 2204.  ,    9.  ,   70.  ,   34.  ],\n",
       "       [  96.5 ,   66.  , 2293.  ,    9.1 ,  100.  ,   31.  ],\n",
       "       [  91.3 ,   67.9 , 3139.  ,    7.8 ,  200.  ,   23.  ],\n",
       "       [  99.1 ,   66.5 , 2658.  ,    9.31,  110.  ,   28.  ],\n",
       "       [ 102.4 ,   66.5 , 2458.  ,    8.7 ,   92.  ,   32.  ],\n",
       "       [ 110.  ,   70.9 , 3505.  ,    8.  ,  182.  ,   20.  ],\n",
       "       [  96.5 ,   65.2 , 2289.  ,    9.  ,   86.  ,   33.  ],\n",
       "       [  99.1 ,   66.5 , 2695.  ,    9.3 ,  110.  ,   28.  ],\n",
       "       [  93.1 ,   64.2 , 1945.  ,    9.  ,   68.  ,   38.  ],\n",
       "       [  95.7 ,   64.4 , 2275.  ,   22.5 ,   56.  ,   47.  ],\n",
       "       [  89.5 ,   65.  , 2800.  ,    9.5 ,  207.  ,   25.  ],\n",
       "       [  93.7 ,   64.  , 1956.  ,    9.2 ,   76.  ,   34.  ],\n",
       "       [ 103.3 ,   64.6 , 2535.  ,    8.5 ,   88.  ,   30.  ],\n",
       "       [ 114.2 ,   68.4 , 3485.  ,   21.  ,   95.  ,   25.  ],\n",
       "       [  95.7 ,   63.6 , 2040.  ,    9.  ,   62.  ,   38.  ],\n",
       "       [  86.6 ,   63.9 , 1819.  ,    9.2 ,   76.  ,   38.  ],\n",
       "       [  98.4 ,   65.6 , 2536.  ,    9.3 ,  116.  ,   30.  ],\n",
       "       [  89.5 ,   65.  , 2756.  ,    9.5 ,  207.  ,   25.  ],\n",
       "       [ 104.5 ,   66.5 , 3131.  ,    9.2 ,  156.  ,   24.  ],\n",
       "       [  93.7 ,   64.  , 1837.  ,   10.1 ,   60.  ,   42.  ],\n",
       "       [  98.4 ,   65.6 , 2540.  ,    9.3 ,  116.  ,   30.  ],\n",
       "       [  97.2 ,   65.4 , 2190.  ,    9.5 ,   82.  ,   33.  ],\n",
       "       [ 109.1 ,   68.9 , 3062.  ,    9.5 ,  114.  ,   25.  ],\n",
       "       [  97.  ,   65.4 , 2455.  ,    9.  ,   94.  ,   31.  ],\n",
       "       [  95.9 ,   66.3 , 2811.  ,    7.  ,  145.  ,   24.  ],\n",
       "       [  97.2 ,   65.2 , 2324.  ,    8.5 ,   97.  ,   34.  ],\n",
       "       [  95.9 ,   66.3 , 2833.  ,    7.  ,  145.  ,   24.  ],\n",
       "       [ 102.4 ,   66.5 , 2414.  ,    8.7 ,   92.  ,   32.  ],\n",
       "       [  93.  ,   63.8 , 2145.  ,    7.6 ,  102.  ,   30.  ],\n",
       "       [ 107.9 ,   68.4 , 3075.  ,    8.4 ,   97.  ,   24.  ],\n",
       "       [  96.9 ,   65.4 , 2420.  ,    9.  ,   82.  ,   29.  ],\n",
       "       [ 104.3 ,   67.2 , 3042.  ,    9.5 ,  114.  ,   28.  ],\n",
       "       [  96.6 ,   70.5 , 3685.  ,    8.3 ,  155.  ,   18.  ],\n",
       "       [  93.7 ,   64.4 , 1944.  ,    9.4 ,   68.  ,   38.  ],\n",
       "       [  93.7 ,   63.8 , 1989.  ,    9.4 ,   68.  ,   38.  ],\n",
       "       [  94.5 ,   64.  , 2265.  ,    9.4 ,  112.  ,   29.  ],\n",
       "       [  96.3 ,   65.4 , 2328.  ,    8.5 ,   88.  ,   32.  ],\n",
       "       [ 109.1 ,   68.8 , 3049.  ,    8.7 ,  160.  ,   25.  ],\n",
       "       [ 102.4 ,   66.5 , 2480.  ,   22.5 ,   73.  ,   33.  ],\n",
       "       [ 104.3 ,   67.2 , 2912.  ,    9.5 ,  114.  ,   28.  ],\n",
       "       [  96.5 ,   65.2 , 2304.  ,    9.  ,   86.  ,   33.  ],\n",
       "       [  95.3 ,   65.7 , 2500.  ,    9.4 ,  135.  ,   23.  ],\n",
       "       [  93.3 ,   63.8 , 2240.  ,    8.7 ,   73.  ,   31.  ],\n",
       "       [  96.3 ,   65.4 , 2403.  ,    7.5 ,  116.  ,   30.  ],\n",
       "       [ 115.6 ,   71.7 , 3770.  ,   21.5 ,  123.  ,   25.  ],\n",
       "       [  96.3 ,   65.4 , 2403.  ,    7.5 ,  116.  ,   30.  ],\n",
       "       [  96.5 ,   63.9 , 2024.  ,    9.2 ,   76.  ,   34.  ],\n",
       "       [  93.7 ,   63.8 , 1876.  ,    9.41,   68.  ,   41.  ],\n",
       "       [  99.5 ,   67.9 , 3053.  ,    7.  ,  160.  ,   22.  ],\n",
       "       [ 100.4 ,   66.5 , 3060.  ,    9.  ,  152.  ,   25.  ],\n",
       "       [ 104.3 ,   67.2 , 3034.  ,    9.5 ,  114.  ,   28.  ],\n",
       "       [ 110.  ,   70.3 , 3515.  ,   21.5 ,  123.  ,   25.  ],\n",
       "       [ 100.4 ,   66.9 , 2579.  ,   23.  ,   68.  ,   38.  ],\n",
       "       [ 108.  ,   68.3 , 3130.  ,    7.  ,  142.  ,   24.  ],\n",
       "       [ 113.  ,   69.6 , 4066.  ,    8.1 ,  176.  ,   19.  ],\n",
       "       [  98.4 ,   65.6 , 2975.  ,    9.3 ,  116.  ,   30.  ]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  93.1,   64.2, 1905. ,    9. ,   68. ,   38. ],\n",
       "       [ 104.5,   66.5, 3151. ,    9.2,  156. ,   24. ],\n",
       "       [  99.8,   66.3, 2507. ,    8.5,  110. ,   25. ],\n",
       "       [  88.4,   60.3, 1488. ,    9.5,   48. ,   53. ],\n",
       "       [  97.3,   65.5, 2300. ,   10. ,  100. ,   32. ],\n",
       "       [  98.4,   65.6, 2679. ,    9.3,  116. ,   30. ],\n",
       "       [  93.7,   64.4, 1918. ,    9.4,   68. ,   41. ],\n",
       "       [  95.7,   63.6, 2290. ,    9. ,   62. ,   32. ],\n",
       "       [  91.3,   67.9, 3071. ,    9. ,  160. ,   25. ],\n",
       "       [  93.7,   64. , 1940. ,    9.2,   76. ,   34. ],\n",
       "       [ 101.2,   64.8, 2710. ,    9. ,  121. ,   28. ],\n",
       "       [  98.4,   72.3, 3366. ,   10. ,  288. ,   28. ],\n",
       "       [  95.3,   65.7, 2380. ,    9.4,  101. ,   23. ],\n",
       "       [ 104.9,   66.1, 2700. ,   22. ,   72. ,   39. ],\n",
       "       [  94.5,   63.6, 1909. ,    9.6,   70. ,   43. ],\n",
       "       [  98.4,   65.6, 2551. ,    9.3,  116. ,   30. ],\n",
       "       [  96.1,   66.5, 2579. ,    8.7,   90. ,   31. ],\n",
       "       [ 105.8,   71.4, 2954. ,    8.5,  110. ,   25. ],\n",
       "       [  96.5,   65.2, 2236. ,    9. ,   86. ,   33. ],\n",
       "       [  95.7,   63.6, 2015. ,    9. ,   62. ,   38. ],\n",
       "       [  96.3,   65.4, 2370. ,    7.5,  116. ,   30. ],\n",
       "       [ 107.9,   68.4, 3075. ,    8.4,   95. ,   24. ],\n",
       "       [  96.1,   66.6, 2460. ,    8.7,   90. ,   31. ],\n",
       "       [  98.4,   65.6, 2714. ,    9.3,  116. ,   30. ],\n",
       "       [ 102.9,   67.7, 3016. ,    9.3,  161. ,   24. ],\n",
       "       [  93.7,   63.4, 2050. ,    9. ,   69. ,   36. ],\n",
       "       [  95.7,   64.4, 2081. ,    9. ,   70. ,   37. ],\n",
       "       [ 114.2,   68.4, 3285. ,    8.4,   95. ,   24. ],\n",
       "       [  95.7,   64.4, 2122. ,    9. ,   70. ,   34. ],\n",
       "       [  94.5,   63.8, 1889. ,    9.4,   69. ,   37. ],\n",
       "       [  97.3,   65.5, 2209. ,    9. ,   85. ,   34. ],\n",
       "       [ 100.4,   66.9, 2563. ,    9. ,   88. ,   31. ],\n",
       "       [  94.5,   68.3, 2778. ,    9.5,  143. ,   27. ],\n",
       "       [ 102.4,   66.5, 2326. ,    8.7,   92. ,   34. ],\n",
       "       [  94.5,   63.8, 1938. ,    9.4,   69. ,   37. ],\n",
       "       [ 103.5,   67.9, 3380. ,    8. ,  182. ,   22. ],\n",
       "       [  94.5,   64.2, 2254. ,    8.5,   90. ,   29. ],\n",
       "       [  99.1,   66.5, 2808. ,    9. ,  160. ,   26. ],\n",
       "       [  93.7,   63.8, 1876. ,    9.4,   68. ,   38. ],\n",
       "       [ 112. ,   72. , 3715. ,    8. ,  184. ,   16. ],\n",
       "       [  94.5,   63.6, 1874. ,    9.6,   70. ,   43. ],\n",
       "       [  99.4,   66.4, 2824. ,    8. ,  115. ,   22. ],\n",
       "       [ 115.6,   71.7, 3740. ,    8.3,  155. ,   18. ],\n",
       "       [  99.1,   66.5, 2707. ,    9.3,  110. ,   28. ],\n",
       "       [  97. ,   65.4, 2510. ,    7.7,  111. ,   29. ],\n",
       "       [  93.7,   63.8, 2191. ,    9.4,   68. ,   38. ],\n",
       "       [  93.7,   63.8, 1989. ,    9.4,   68. ,   38. ],\n",
       "       [  95.9,   66.3, 2921. ,    7. ,  145. ,   24. ],\n",
       "       [  96.9,   65.4, 2650. ,    7.7,  111. ,   23. ],\n",
       "       [  97.3,   65.5, 2275. ,    9. ,   85. ,   34. ],\n",
       "       [ 105.8,   71.4, 3086. ,    8.3,  140. ,   20. ],\n",
       "       [  94.5,   63.8, 2017. ,   21.9,   55. ,   50. ],\n",
       "       [  94.5,   64. , 2169. ,    9. ,   70. ,   34. ],\n",
       "       [  98.8,   66.5, 2385. ,    8.6,   84. ,   32. ],\n",
       "       [ 102.7,   68. , 2910. ,    8. ,  175. ,   24. ],\n",
       "       [  99.2,   67.9, 3139. ,    9. ,  160. ,   25. ],\n",
       "       [  98.8,   66.5, 2443. ,   22.7,   64. ,   42. ],\n",
       "       [ 104.3,   67.2, 3045. ,    7.5,  162. ,   22. ],\n",
       "       [  97.2,   65.4, 2340. ,    9. ,   94. ,   32. ],\n",
       "       [  93.7,   63.8, 1967. ,    9.4,   68. ,   38. ],\n",
       "       [  86.6,   63.9, 1713. ,    9.6,   58. ,   54. ],\n",
       "       [  99.1,   66.5, 2758. ,    9.3,  110. ,   28. ]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10295.   ],\n",
       "       [ 8495.   ],\n",
       "       [11845.   ],\n",
       "       [13499.   ],\n",
       "       [ 8189.   ],\n",
       "       [ 6295.   ],\n",
       "       [ 8778.   ],\n",
       "       [ 7999.   ],\n",
       "       [ 7395.   ],\n",
       "       [ 7995.   ],\n",
       "       [ 6095.   ],\n",
       "       [ 7053.   ],\n",
       "       [ 7126.   ],\n",
       "       [16900.   ],\n",
       "       [ 7499.   ],\n",
       "       [11900.   ],\n",
       "       [ 7463.   ],\n",
       "       [13860.   ],\n",
       "       [16558.   ],\n",
       "       [24565.   ],\n",
       "       [ 8558.   ],\n",
       "       [13200.   ],\n",
       "       [ 8195.   ],\n",
       "       [32528.   ],\n",
       "       [11048.   ],\n",
       "       [13295.   ],\n",
       "       [16845.   ],\n",
       "       [ 9258.   ],\n",
       "       [10245.   ],\n",
       "       [16500.   ],\n",
       "       [ 8845.   ],\n",
       "       [ 8921.   ],\n",
       "       [12764.   ],\n",
       "       [ 6785.   ],\n",
       "       [16430.   ],\n",
       "       [12440.   ],\n",
       "       [40960.   ],\n",
       "       [15985.   ],\n",
       "       [ 7775.   ],\n",
       "       [ 5348.   ],\n",
       "       [ 8249.   ],\n",
       "       [13950.   ],\n",
       "       [ 7349.   ],\n",
       "       [ 5572.   ],\n",
       "       [ 7738.   ],\n",
       "       [18950.   ],\n",
       "       [ 5195.   ],\n",
       "       [13495.   ],\n",
       "       [ 7299.   ],\n",
       "       [17950.   ],\n",
       "       [ 7799.   ],\n",
       "       [11245.   ],\n",
       "       [ 9233.   ],\n",
       "       [12945.   ],\n",
       "       [28176.   ],\n",
       "       [36000.   ],\n",
       "       [35550.   ],\n",
       "       [ 6989.   ],\n",
       "       [21485.   ],\n",
       "       [21105.   ],\n",
       "       [ 6918.   ],\n",
       "       [ 7957.   ],\n",
       "       [ 9980.   ],\n",
       "       [18620.   ],\n",
       "       [ 6575.   ],\n",
       "       [30760.   ],\n",
       "       [ 6669.   ],\n",
       "       [ 9549.   ],\n",
       "       [ 9538.   ],\n",
       "       [22470.   ],\n",
       "       [17710.   ],\n",
       "       [28248.   ],\n",
       "       [14489.   ],\n",
       "       [ 6692.   ],\n",
       "       [ 7198.   ],\n",
       "       [ 9495.   ],\n",
       "       [ 7898.   ],\n",
       "       [ 6649.   ],\n",
       "       [10898.   ],\n",
       "       [16925.   ],\n",
       "       [ 7957.   ],\n",
       "       [14399.   ],\n",
       "       [ 7295.   ],\n",
       "       [13645.   ],\n",
       "       [18280.   ],\n",
       "       [16500.   ],\n",
       "       [ 6229.   ],\n",
       "       [ 8238.   ],\n",
       "       [10345.   ],\n",
       "       [19699.   ],\n",
       "       [11850.   ],\n",
       "       [11248.   ],\n",
       "       [36880.   ],\n",
       "       [ 9095.   ],\n",
       "       [12170.   ],\n",
       "       [ 6695.   ],\n",
       "       [ 7788.   ],\n",
       "       [37028.   ],\n",
       "       [ 7129.   ],\n",
       "       [ 8921.   ],\n",
       "       [17075.   ],\n",
       "       [ 6338.   ],\n",
       "       [ 6855.   ],\n",
       "       [ 9639.   ],\n",
       "       [34028.   ],\n",
       "       [15690.   ],\n",
       "       [ 5399.   ],\n",
       "       [ 8449.   ],\n",
       "       [ 7775.   ],\n",
       "       [22625.   ],\n",
       "       [10198.   ],\n",
       "       [12964.   ],\n",
       "       [ 8949.   ],\n",
       "       [12629.   ],\n",
       "       [ 9988.   ],\n",
       "       [ 7689.   ],\n",
       "       [16630.   ],\n",
       "       [ 8013.   ],\n",
       "       [16515.   ],\n",
       "       [35056.   ],\n",
       "       [ 6189.   ],\n",
       "       [ 6692.   ],\n",
       "       [ 9298.   ],\n",
       "       [ 8499.   ],\n",
       "       [19045.   ],\n",
       "       [10698.   ],\n",
       "       [12940.   ],\n",
       "       [ 8845.   ],\n",
       "       [15645.   ],\n",
       "       [ 7603.   ],\n",
       "       [ 9279.   ],\n",
       "       [31600.   ],\n",
       "       [ 9279.   ],\n",
       "       [ 7295.   ],\n",
       "       [ 5572.   ],\n",
       "       [17859.167],\n",
       "       [13499.   ],\n",
       "       [13415.   ],\n",
       "       [25552.   ],\n",
       "       [13845.   ],\n",
       "       [18150.   ],\n",
       "       [32250.   ],\n",
       "       [17669.   ]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6795. ],\n",
       "       [15750. ],\n",
       "       [15250. ],\n",
       "       [ 5151. ],\n",
       "       [ 9995. ],\n",
       "       [11199. ],\n",
       "       [ 5389. ],\n",
       "       [ 7898. ],\n",
       "       [17199. ],\n",
       "       [ 6529. ],\n",
       "       [20970. ],\n",
       "       [31400.5],\n",
       "       [10945. ],\n",
       "       [18344. ],\n",
       "       [ 8916.5],\n",
       "       [ 9989. ],\n",
       "       [ 9295. ],\n",
       "       [18920. ],\n",
       "       [ 7895. ],\n",
       "       [ 6488. ],\n",
       "       [ 9959. ],\n",
       "       [15580. ],\n",
       "       [ 9895. ],\n",
       "       [11549. ],\n",
       "       [15998. ],\n",
       "       [ 5118. ],\n",
       "       [ 6938. ],\n",
       "       [16695. ],\n",
       "       [ 8358. ],\n",
       "       [ 5499. ],\n",
       "       [ 7975. ],\n",
       "       [12290. ],\n",
       "       [22018. ],\n",
       "       [ 8948. ],\n",
       "       [ 6849. ],\n",
       "       [41315. ],\n",
       "       [11595. ],\n",
       "       [18150. ],\n",
       "       [ 6377. ],\n",
       "       [45400. ],\n",
       "       [ 8916.5],\n",
       "       [17450. ],\n",
       "       [34184. ],\n",
       "       [15040. ],\n",
       "       [11259. ],\n",
       "       [ 7609. ],\n",
       "       [ 7609. ],\n",
       "       [14869. ],\n",
       "       [11694. ],\n",
       "       [ 8495. ],\n",
       "       [23875. ],\n",
       "       [ 7099. ],\n",
       "       [ 8058. ],\n",
       "       [10595. ],\n",
       "       [16503. ],\n",
       "       [18399. ],\n",
       "       [10795. ],\n",
       "       [18420. ],\n",
       "       [ 9960. ],\n",
       "       [ 6229. ],\n",
       "       [ 6479. ],\n",
       "       [15510. ]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "\n",
    "X_train = sc.fit_transform(x_train)\n",
    "X_test = sc.fit_transform(x_test)\n",
    "\n",
    "Y_train = sc.fit_transform(y_train)\n",
    "Y_test = sc.fit_transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    " # first prediction by linear regression"
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr.fit(x_train, y_train)\n",
    "\n",
    "pred1 = lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5256.40575887],\n",
       "       [21896.1040049 ],\n",
       "       [13151.74698904],\n",
       "       [   94.14083013],\n",
       "       [11444.26835726],\n",
       "       [14795.42339382],\n",
       "       [ 5779.68699621],\n",
       "       [ 5268.25069922],\n",
       "       [22669.88761279],\n",
       "       [ 6002.89603081],\n",
       "       [14915.97564296],\n",
       "       [43217.10571389],\n",
       "       [11069.1499553 ],\n",
       "       [13106.95943334],\n",
       "       [ 5768.98627747],\n",
       "       [14266.94823434],\n",
       "       [11525.16116771],\n",
       "       [18027.65678953],\n",
       "       [ 9102.51195825],\n",
       "       [ 4656.63321328],\n",
       "       [12996.6245145 ],\n",
       "       [14843.50045533],\n",
       "       [11091.0392732 ],\n",
       "       [14939.92832024],\n",
       "       [22648.57748432],\n",
       "       [ 5360.91420283],\n",
       "       [ 6312.61486361],\n",
       "       [15829.63973892],\n",
       "       [ 6220.00288088],\n",
       "       [ 5109.37770384],\n",
       "       [ 9138.38970906],\n",
       "       [11577.36123558],\n",
       "       [19873.42753843],\n",
       "       [11114.90194206],\n",
       "       [ 5311.68460082],\n",
       "       [26495.83071016],\n",
       "       [ 8622.01250739],\n",
       "       [21018.07391162],\n",
       "       [ 5001.22277036],\n",
       "       [30114.16964181],\n",
       "       [ 5624.48135105],\n",
       "       [14779.07539722],\n",
       "       [26677.00962875],\n",
       "       [14504.50451019],\n",
       "       [12908.30976775],\n",
       "       [ 6301.76710814],\n",
       "       [ 5467.76724709],\n",
       "       [18825.41382721],\n",
       "       [12960.66047837],\n",
       "       [ 9410.88471316],\n",
       "       [21894.76291763],\n",
       "       [ 7561.60435087],\n",
       "       [ 6162.58536556],\n",
       "       [10082.11994922],\n",
       "       [23885.42740993],\n",
       "       [23099.99985523],\n",
       "       [11551.47062377],\n",
       "       [22091.93106309],\n",
       "       [10585.44995355],\n",
       "       [ 5376.93557905],\n",
       "       [ 4422.40747974],\n",
       "       [14715.06883155]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>curbweight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>2952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>3049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>3012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>3217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>3062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>205 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     curbweight\n",
       "0          2548\n",
       "1          2548\n",
       "2          2823\n",
       "3          2337\n",
       "4          2824\n",
       "..          ...\n",
       "200        2952\n",
       "201        3049\n",
       "202        3012\n",
       "203        3217\n",
       "204        3062\n",
       "\n",
       "[205 rows x 1 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # variables for second prediction"
    "x2=data[[\"curbweight\"]]\n",
    "x2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>curbweight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>2952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>3049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>3012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>3217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>3062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>205 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     curbweight\n",
       "0          2548\n",
       "1          2548\n",
       "2          2823\n",
       "3          2337\n",
       "4          2824\n",
       "..          ...\n",
       "200        2952\n",
       "201        3049\n",
       "202        3012\n",
       "203        3217\n",
       "204        3062\n",
       "\n",
       "[205 rows x 1 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x2=data[[\"curbweight\"]]\n",
    "x2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>16845.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>19045.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>21485.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>22470.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>22625.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>205 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       price\n",
       "0    13495.0\n",
       "1    16500.0\n",
       "2    16500.0\n",
       "3    13950.0\n",
       "4    17450.0\n",
       "..       ...\n",
       "200  16845.0\n",
       "201  19045.0\n",
       "202  21485.0\n",
       "203  22470.0\n",
       "204  22625.0\n",
       "\n",
       "[205 rows x 1 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2 = data[[\"price\"]]\n",
    "y2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train2, x_test2, y_train2, y_test2 = train_test_split(x2, y2, test_size=0.33,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    " # second prediction by linear regression"
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr.fit(x_train2,y_train2)\n",
    "\n",
    "pred2 = lr.predict(x_test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4958.61444038],\n",
       "       [20525.83673646],\n",
       "       [12479.85667332],\n",
       "       [ -251.28258809],\n",
       "       [ 9893.64879588],\n",
       "       [14628.78302558],\n",
       "       [ 5121.03329258],\n",
       "       [ 9768.71121726],\n",
       "       [19526.3361075 ],\n",
       "       [ 5395.89596555],\n",
       "       [15016.08951931],\n",
       "       [23211.9946768 ],\n",
       "       [10893.14942484],\n",
       "       [14891.15194069],\n",
       "       [ 5008.58947182],\n",
       "       [13029.58201924],\n",
       "       [13379.40723938],\n",
       "       [18064.56643764],\n",
       "       [ 9094.04829271],\n",
       "       [ 6332.9278052 ],\n",
       "       [10768.21184622],\n",
       "       [19576.31113895],\n",
       "       [11892.6500538 ],\n",
       "       [15066.06455076],\n",
       "       [18839.17942509],\n",
       "       [ 6770.20933037],\n",
       "       [ 7157.51582409],\n",
       "       [22200.00028997],\n",
       "       [ 7669.75989644],\n",
       "       [ 4758.71431458],\n",
       "       [ 8756.71683043],\n",
       "       [13179.50711359],\n",
       "       [15865.66505392],\n",
       "       [10218.48650029],\n",
       "       [ 5370.90844982],\n",
       "       [23386.90728687],\n",
       "       [ 9318.93593422],\n",
       "       [16240.47778979],\n",
       "       [ 4596.29546238],\n",
       "       [27572.31617064],\n",
       "       [ 4571.30794665],\n",
       "       [16440.37791558],\n",
       "       [27884.66011719],\n",
       "       [14978.60824572],\n",
       "       [12517.3379469 ],\n",
       "       [ 8531.82918892],\n",
       "       [ 6008.09010079],\n",
       "       [17652.27242819],\n",
       "       [14266.46404759],\n",
       "       [ 9581.30484933],\n",
       "       [19713.74247543],\n",
       "       [ 6357.91532092],\n",
       "       [ 8256.96651595],\n",
       "       [10955.61821415],\n",
       "       [17514.84109171],\n",
       "       [20375.91164212],\n",
       "       [11680.25617015],\n",
       "       [19201.49840309],\n",
       "       [10393.39911036],\n",
       "       [ 5733.22742782],\n",
       "       [ 2559.81293087],\n",
       "       [15615.78989668],\n",
       "       [10793.19936194],\n",
       "       [11267.9621607 ],\n",
       "       [10893.14942484],\n",
       "       [19826.18629619],\n",
       "       [11205.49337139],\n",
       "       [ 4571.30794665]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'price')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_train2=x_train2.sort_values(by=\"curbweight\")\n",
    "y_train2=y_train2.sort_index()\n",
    "\n",
    "plt.plot(x_train2,y_train2,)\n",
    "plt.plot(x_test2,pred2)\n",
    "\n",
    "plt.title(\"car prices\")\n",
    "plt.xlabel(\"curbweight\")\n",
    "plt.ylabel(\"price\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}