Linear-Regression-Using-Sklearn-in-Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YYEsYKchvB17"
   },
   "source": [
    "\n",
    "### Linear Regression of Automobile Data w/Cross Validation\n",
    "### By Randy Leon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FAfbtMxuu-yx"
   },
   "source": [
    "## INTRODUCTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CMsQk8nUvFtz"
   },
   "source": [
    "Presented to me was a data set with 205 observations and 26 attributes describing automobile data sourced from the UCI Machine Learning Repository.\n",
    "\n",
    "I am tasked with constructing two (2) different linear regression models with three (3) explanatory variables each in order to predict \"curb-weight\". (our response variable)\n",
    "\n",
    "I plan to achieve this by completing the following steps:\n",
    "\n",
    "1) Load the data into this notebook.\n",
    "\n",
    "2) Perform sufficient exploratory data analysis to understand, clean and analyze the data.\n",
    "\n",
    "3) Construct and evaluate my linear regression models, making sure to make use of k-fold cross validation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pMRJoNQS3CLM"
   },
   "source": [
    "**Data Set Description from https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.names**\n",
    "\n",
    "\n",
    "1. Source Information:\n",
    "   -- Creator/Donor: Jeffrey C. Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu)\n",
    "   -- Date: 19 May 1987\n",
    "   -- Sources:\n",
    "     \n",
    "     a) 1985 Model Import Car and Truck Specifications, 1985 Ward's Automotive Yearbook.\n",
    "\n",
    "     b) Personal Auto Manuals, Insurance Services Office, 160 Water Street, New York, NY 10038 \n",
    "\n",
    "     c) Insurance Collision Report, Insurance Institute for Highway Safety, Watergate 600, Washington, DC 20037\n",
    "     \n",
    "     \n",
    "\n",
    "2. Relevant Information:\n",
    "   -- Description\n",
    "      This data set consists of three types of entities: (a) the\n",
    "      specification of an auto in terms of various characteristics, (b)\n",
    "      its assigned insurance risk rating, (c) its normalized losses in use\n",
    "      as compared to other cars.  The second rating corresponds to the\n",
    "      degree to which the auto is more risky than its price indicates.\n",
    "      Cars are initially assigned a risk factor symbol associated with its\n",
    "      price.   Then, if it is more risky (or less), this symbol is\n",
    "      adjusted by moving it up (or down) the scale.  Actuarians call this\n",
    "      process \"symboling\".  A value of +3 indicates that the auto is\n",
    "      risky, -3 that it is probably pretty safe.\n",
    "\n",
    "      The third factor is the relative average loss payment per insured\n",
    "      vehicle year.  This value is normalized for all autos within a\n",
    "      particular size classification (two-door small, station wagons,\n",
    "      sports/speciality, etc...), and represents the average loss per car\n",
    "      per year.\n",
    "\n",
    "   -- Note: Several of the attributes in the database could be used as a class attribute.\n",
    "   \n",
    "   \n",
    "\n",
    "3. Number of Instances: 205\n",
    "\n",
    "\n",
    "\n",
    "4. Number of Attributes: 26 total\n",
    "   -- 15 continuous\n",
    "   -- 1 integer\n",
    "   -- 10 nominal\n",
    "\n",
    "\n",
    "\n",
    "5. Attribute Information:     \n",
    "     Attribute:                Attribute Range:\n",
    "     ------------------        -----------------------------------------------\n",
    "  1. symboling:                -3, -2, -1, 0, 1, 2, 3.\n",
    "  2. normalized-losses:        continuous from 65 to 256.\n",
    "  3. make:                     alfa-romero, audi, bmw, chevrolet, dodge honda,isuzu, jaguar, mazda, mercedes-benz, mercury,mitsubishi, nissan, peugot, plymouth, porsche,renault, saab, subaru, toyota, volkswagen, volvo\n",
    "  4. fuel-type:                diesel, gas.\n",
    "  5. aspiration:               std, turbo.\n",
    "  6. num-of-doors:             four, two.\n",
    "  7. body-style:               hardtop, wagon, sedan, hatchback, convertible.\n",
    "  8. drive-wheels:             4wd, fwd, rwd.\n",
    "  9. engine-location:          front, rear.\n",
    " 10. wheel-base:               continuous from 86.6 to 120.9.\n",
    " 11. length:                   continuous from 141.1 to 208.1.\n",
    " 12. width:                    continuous from 60.3 to 72.3.\n",
    " 13. height:                   continuous from 47.8 to 59.8.\n",
    " 14. curb-weight:              continuous from 1488 to 4066.\n",
    " 15. engine-type:              dohc, dohcv, l, ohc, ohcf, ohcv, rotor.\n",
    " 16. num-of-cylinders:         eight, five, four, six, three, twelve, two.\n",
    " 17. engine-size:              continuous from 61 to 326.\n",
    " 18. fuel-system:              1bbl, 2bbl, 4bbl, idi, mfi, mpfi, spdi, spfi.\n",
    " 19. bore:                     continuous from 2.54 to 3.94.\n",
    " 20. stroke:                   continuous from 2.07 to 4.17.\n",
    " 21. compression-ratio:        continuous from 7 to 23.\n",
    " 22. horsepower:               continuous from 48 to 288.\n",
    " 23. peak-rpm:                 continuous from 4150 to 6600.\n",
    " 24. city-mpg:                 continuous from 13 to 49.\n",
    " 25. highway-mpg:              continuous from 16 to 54.\n",
    " 26. price:                    continuous from 5118 to 45400.\n",
    " \n",
    " \n",
    "\n",
    "6. Missing Attribute Values: (denoted by \"?\")\n",
    "   Attribute #:   Number of instances missing a value:\n",
    "   2.             41\n",
    "   6.             2\n",
    "   19.            4\n",
    "   20.            4\n",
    "   22.            2\n",
    "   23.            2\n",
    "   26.            4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_UlbaTzuxO3J"
   },
   "source": [
    "## EXPLORATORY DATA ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "o0LZlv7QxSAP"
   },
   "outputs": [],
   "source": [
    "# import dependencies needed\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import sklearn as sk\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas import Series, DataFrame\n",
    "from matplotlib import rcParams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "id": "ChfVzBJ-yFVs"
   },
   "outputs": [],
   "source": [
    "#Read data From GitHub and put into a df. Column names were found on UC Irvine's website.\n",
    "df1 = pd.read_csv('https://raw.githubusercontent.com/randleon/Linear-Regression-Using-Sklearn-in-Python/main/Linear-Regression-Using-Sklearn-in-Python_data.csv',\n",
    "names=['symboling',\n",
    "'normalized-losses',\n",
    "'make',\n",
    "'fuel-type',\n",
    "'aspiration',\n",
    "'num-of-doors',\n",
    "'body-style',\n",
    "'drive-wheels',\n",
    "'engine-location',\n",
    "'wheel-base',\n",
    "'length',\n",
    "'width',\n",
    "'height',\n",
    "'curb-weight',\n",
    "'engine-type',\n",
    "'num-of-cylinders',\n",
    "'engine-size',\n",
    "'fuel-system',\n",
    "'bore',\n",
    "'stroke',\n",
    "'compression-ratio',\n",
    "'horsepower',\n",
    "'peak-rpm',\n",
    "'city-mpg',\n",
    "'highway-mpg',\n",
    "'price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</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>3.4</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</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>3.4</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</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling normalized-losses         make fuel-type aspiration num-of-doors  \\\n",
       "0          3                 ?  alfa-romero       gas        std          two   \n",
       "1          3                 ?  alfa-romero       gas        std          two   \n",
       "2          1                 ?  alfa-romero       gas        std          two   \n",
       "3          2               164         audi       gas        std         four   \n",
       "4          2               164         audi       gas        std         four   \n",
       "\n",
       "    body-style drive-wheels engine-location  wheel-base  ...  engine-size  \\\n",
       "0  convertible          rwd           front        88.6  ...          130   \n",
       "1  convertible          rwd           front        88.6  ...          130   \n",
       "2    hatchback          rwd           front        94.5  ...          152   \n",
       "3        sedan          fwd           front        99.8  ...          109   \n",
       "4        sedan          4wd           front        99.4  ...          136   \n",
       "\n",
       "   fuel-system  bore  stroke compression-ratio horsepower  peak-rpm city-mpg  \\\n",
       "0         mpfi  3.47    2.68               9.0        111      5000       21   \n",
       "1         mpfi  3.47    2.68               9.0        111      5000       21   \n",
       "2         mpfi  2.68    3.47               9.0        154      5000       19   \n",
       "3         mpfi  3.19     3.4              10.0        102      5500       24   \n",
       "4         mpfi  3.19     3.4               8.0        115      5500       18   \n",
       "\n",
       "  highway-mpg  price  \n",
       "0          27  13495  \n",
       "1          27  16500  \n",
       "2          26  16500  \n",
       "3          30  13950  \n",
       "4          22  17450  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sanity check on initial data pull from source.\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "nltAeKP4hyKA",
    "outputId": "062cc08c-68e9-4753-fc53-2c02ec7eea99"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>130</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>130</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>94.5</td>\n",
       "      <td>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "      <td>2823</td>\n",
       "      <td>152</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99.8</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2337</td>\n",
       "      <td>109</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99.4</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2824</td>\n",
       "      <td>136</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   wheel-base  length  width  height  curb-weight  engine-size horsepower  \\\n",
       "0        88.6   168.8   64.1    48.8         2548          130        111   \n",
       "1        88.6   168.8   64.1    48.8         2548          130        111   \n",
       "2        94.5   171.2   65.5    52.4         2823          152        154   \n",
       "3        99.8   176.6   66.2    54.3         2337          109        102   \n",
       "4        99.4   176.6   66.4    54.3         2824          136        115   \n",
       "\n",
       "  peak-rpm  city-mpg  \n",
       "0     5000        21  \n",
       "1     5000        21  \n",
       "2     5000        19  \n",
       "3     5500        24  \n",
       "4     5500        18  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#choosing only relevant explanatory variables.\n",
    "\n",
    "df2 = df1.drop(labels=['symboling',\n",
    "'normalized-losses',\n",
    "'make',\n",
    "'fuel-type',\n",
    "'aspiration',\n",
    "'num-of-doors',\n",
    "'body-style',\n",
    "'drive-wheels',\n",
    "'engine-location',\n",
    "'engine-type',\n",
    "'num-of-cylinders',\n",
    "'fuel-system',\n",
    "'bore',\n",
    "'stroke',\n",
    "'compression-ratio',\n",
    "'price',\n",
    "'highway-mpg'], axis=1)\n",
    "df2.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KLJC3avOooh0",
    "outputId": "0bb03ad2-5f40-4a56-f892-aea276ec411f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "wheel-base     float64\n",
       "length         float64\n",
       "width          float64\n",
       "height         float64\n",
       "curb-weight      int64\n",
       "engine-size      int64\n",
       "horsepower      object\n",
       "peak-rpm        object\n",
       "city-mpg         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking my data types\n",
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K4MyFSJEoxUz"
   },
   "source": [
    "I will have to change horsepower and peak-rpm to floating point variables to conduct proper analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zRilueY4nVtp",
    "outputId": "a6738dba-a80f-4511-9aee-72d905980085"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['111' '154' '102' '115' '110' '140' '160' '101' '121' '182' '48' '70'\n",
      " '68' '88' '145' '58' '76' '60' '86' '100' '78' '90' '176' '262' '135'\n",
      " '84' '64' '120' '72' '123' '155' '184' '175' '116' '69' '55' '97' '152'\n",
      " '200' '95' '142' '143' '207' '288' '?' '73' '82' '94' '62' '56' '112'\n",
      " '92' '161' '156' '52' '85' '114' '162' '134' '106']\n",
      "['5000' '5500' '5800' '4250' '5400' '5100' '4800' '6000' '4750' '4650'\n",
      " '4200' '4350' '4500' '5200' '4150' '5600' '5900' '5750' '?' '5250' '4900'\n",
      " '4400' '6600' '5300']\n"
     ]
    }
   ],
   "source": [
    "#taking a closer look at the possible variables in my object-type columns.\n",
    "print(df2[\"horsepower\"].unique())\n",
    "\n",
    "print(df2[\"peak-rpm\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "DdHwqMU_qGiw"
   },
   "outputs": [],
   "source": [
    "#changing columns that ought to be numeric into numeric values(floats).\n",
    "df2['horsepower'] = pd.to_numeric(df2['horsepower'],errors = 'coerce')\n",
    "df2['peak-rpm'] = pd.to_numeric(df2['peak-rpm'],errors = 'coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "F51mCxb6oeAu",
    "outputId": "5f4afde2-17bd-4954-bf4b-bf210e4db949"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[111. 154. 102. 115. 110. 140. 160. 101. 121. 182.  48.  70.  68.  88.\n",
      " 145.  58.  76.  60.  86. 100.  78.  90. 176. 262. 135.  84.  64. 120.\n",
      "  72. 123. 155. 184. 175. 116.  69.  55.  97. 152. 200.  95. 142. 143.\n",
      " 207. 288.  nan  73.  82.  94.  62.  56. 112.  92. 161. 156.  52.  85.\n",
      " 114. 162. 134. 106.]\n",
      "[5000. 5500. 5800. 4250. 5400. 5100. 4800. 6000. 4750. 4650. 4200. 4350.\n",
      " 4500. 5200. 4150. 5600. 5900. 5750.   nan 5250. 4900. 4400. 6600. 5300.]\n"
     ]
    }
   ],
   "source": [
    "#Zeros are now null values for columns horsepower and peak-rpm.\n",
    "print(df2[\"horsepower\"].unique())\n",
    "\n",
    "print(df2[\"peak-rpm\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 591
    },
    "id": "9yOzzIkyuowp",
    "outputId": "84161893-f2f9-40e6-e89d-c0e4c6a61f2d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 205 entries, 0 to 204\n",
      "Data columns (total 9 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   wheel-base   205 non-null    float64\n",
      " 1   length       205 non-null    float64\n",
      " 2   width        205 non-null    float64\n",
      " 3   height       205 non-null    float64\n",
      " 4   curb-weight  205 non-null    int64  \n",
      " 5   engine-size  205 non-null    int64  \n",
      " 6   horsepower   203 non-null    float64\n",
      " 7   peak-rpm     203 non-null    float64\n",
      " 8   city-mpg     205 non-null    int64  \n",
      "dtypes: float64(6), int64(3)\n",
      "memory usage: 14.5 KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>205.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>98.756585</td>\n",
       "      <td>174.049268</td>\n",
       "      <td>65.907805</td>\n",
       "      <td>53.724878</td>\n",
       "      <td>2555.565854</td>\n",
       "      <td>126.907317</td>\n",
       "      <td>104.256158</td>\n",
       "      <td>5125.369458</td>\n",
       "      <td>25.219512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.021776</td>\n",
       "      <td>12.337289</td>\n",
       "      <td>2.145204</td>\n",
       "      <td>2.443522</td>\n",
       "      <td>520.680204</td>\n",
       "      <td>41.642693</td>\n",
       "      <td>39.714369</td>\n",
       "      <td>479.334560</td>\n",
       "      <td>6.542142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>86.600000</td>\n",
       "      <td>141.100000</td>\n",
       "      <td>60.300000</td>\n",
       "      <td>47.800000</td>\n",
       "      <td>1488.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>4150.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>94.500000</td>\n",
       "      <td>166.300000</td>\n",
       "      <td>64.100000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>2145.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>4800.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>97.000000</td>\n",
       "      <td>173.200000</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>54.100000</td>\n",
       "      <td>2414.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>5200.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>102.400000</td>\n",
       "      <td>183.100000</td>\n",
       "      <td>66.900000</td>\n",
       "      <td>55.500000</td>\n",
       "      <td>2935.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>120.900000</td>\n",
       "      <td>208.100000</td>\n",
       "      <td>72.300000</td>\n",
       "      <td>59.800000</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>6600.000000</td>\n",
       "      <td>49.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       wheel-base      length       width      height  curb-weight  \\\n",
       "count  205.000000  205.000000  205.000000  205.000000   205.000000   \n",
       "mean    98.756585  174.049268   65.907805   53.724878  2555.565854   \n",
       "std      6.021776   12.337289    2.145204    2.443522   520.680204   \n",
       "min     86.600000  141.100000   60.300000   47.800000  1488.000000   \n",
       "25%     94.500000  166.300000   64.100000   52.000000  2145.000000   \n",
       "50%     97.000000  173.200000   65.500000   54.100000  2414.000000   \n",
       "75%    102.400000  183.100000   66.900000   55.500000  2935.000000   \n",
       "max    120.900000  208.100000   72.300000   59.800000  4066.000000   \n",
       "\n",
       "       engine-size  horsepower     peak-rpm    city-mpg  \n",
       "count   205.000000  203.000000   203.000000  205.000000  \n",
       "mean    126.907317  104.256158  5125.369458   25.219512  \n",
       "std      41.642693   39.714369   479.334560    6.542142  \n",
       "min      61.000000   48.000000  4150.000000   13.000000  \n",
       "25%      97.000000   70.000000  4800.000000   19.000000  \n",
       "50%     120.000000   95.000000  5200.000000   24.000000  \n",
       "75%     141.000000  116.000000  5500.000000   30.000000  \n",
       "max     326.000000  288.000000  6600.000000   49.000000  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Show the non-null counts, and datatypes to get an idea as to what the data is we're working with.\n",
    "df2.info()\n",
    "\n",
    "df2.shape\n",
    "\n",
    "#as well as summary statistics for the data.\n",
    "df2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZyRvYZtRiOeT",
    "outputId": "020149b3-805a-4c59-a8be-e0d2a5935eff"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "wheel-base     0\n",
       "length         0\n",
       "width          0\n",
       "height         0\n",
       "curb-weight    0\n",
       "engine-size    0\n",
       "horsepower     2\n",
       "peak-rpm       2\n",
       "city-mpg       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for null data\n",
    "df2.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yFZwUUuV9xy8"
   },
   "source": [
    "Since I only have two null values, I think it makes sense to just delete the rows since they make up less than 1% of the total dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "iuThu4cU94WO",
    "outputId": "1870b359-8796-43b4-db44-7b9cf36e1cad"
   },
   "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>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>130</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>130</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>94.5</td>\n",
       "      <td>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "      <td>2823</td>\n",
       "      <td>152</td>\n",
       "      <td>154.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99.8</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2337</td>\n",
       "      <td>109</td>\n",
       "      <td>102.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99.4</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2824</td>\n",
       "      <td>136</td>\n",
       "      <td>115.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   wheel-base  length  width  height  curb-weight  engine-size  horsepower  \\\n",
       "0        88.6   168.8   64.1    48.8         2548          130       111.0   \n",
       "1        88.6   168.8   64.1    48.8         2548          130       111.0   \n",
       "2        94.5   171.2   65.5    52.4         2823          152       154.0   \n",
       "3        99.8   176.6   66.2    54.3         2337          109       102.0   \n",
       "4        99.4   176.6   66.4    54.3         2824          136       115.0   \n",
       "\n",
       "   peak-rpm  city-mpg  \n",
       "0    5000.0        21  \n",
       "1    5000.0        21  \n",
       "2    5000.0        19  \n",
       "3    5500.0        24  \n",
       "4    5500.0        18  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#making a new dataframe rid of any null values.\n",
    "df3 = df2.dropna()\n",
    "\n",
    "df3.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5E1uFyMk-Zja"
   },
   "source": [
    "## Visualizations and Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "zYiy9swH687_",
    "outputId": "c776aca7-dcdd-4cdb-b02c-2e43697d99e8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1be956133d0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#showing plot charts to visualize data.\n",
    "sns.displot(df3, x=\"wheel-base\", bins=30, kde=True)\n",
    "sns.displot(df3, x=\"length\", bins=30, kde=True)\n",
    "sns.displot(df3, x=\"width\", bins=30, kde=True)\n",
    "sns.displot(df3, x=\"height\", bins=30, kde=True)\n",
    "sns.displot(df3, x=\"curb-weight\", bins=30, kde=True)\n",
    "sns.displot(df3, x=\"engine-size\", bins=30, kde=True)\n",
    "sns.displot(df3, x=\"horsepower\", bins=30, kde=True)\n",
    "sns.displot(df3, x=\"peak-rpm\", bins=30, kde=True)\n",
    "sns.displot(df3, x=\"city-mpg\", bins=30, kde=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8OHn3XljEyFr"
   },
   "source": [
    "Some of these variables (including our explanatory variable) feature a right-skewed distribution, including:\n",
    "horsepower, engine-size, wheel-base, and city-mpg - what this tells me is that in this data set, there might be more smaller cars with less power than there are bigger cars with more power.\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 777
    },
    "id": "JPMjirXqivw8",
    "outputId": "d2bb8cc2-3d63-43fb-ccdb-20a114870af6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#let's bring up a correlation matrix to see what relationships between our continous variables we can gain insights on.\n",
    "\n",
    "# make correlation.\n",
    "corr = df3.corr()\n",
    "\n",
    "#changing size of plot using .plt\n",
    "fig, ax = plt.subplots(figsize=(13,13)) \n",
    "\n",
    "#plot the heatmap\n",
    "sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns, annot=True, cmap=sns.diverging_palette(220, 20, as_cmap=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "701OV7pQBFS_"
   },
   "source": [
    "Points to be made from correlation matrix:\n",
    "\n",
    "1) Curb weight has the strongest positive relationship with the variable length, followed by width, engine-size, wheel-base, and lastly height.\n",
    "\n",
    "2) Negative relationships fall in the order of city-mpg, then peak-rpm. This makes sense, the heavier an automobile, the less fuel efficient it tends to be; More mass means more force required to move the automobile - this is Newton's Second Law. \n",
    "\n",
    "3) Peak-rpm has too weak of a relationship to make a any valid inferences about (-0.27).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 215
    },
    "id": "V3HfbqMsjY3l",
    "outputId": "203f39af-8d93-4294-ae46-5867a0df96e3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1be954c9520>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x180 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#let's being up a pairplot to see relationships between all variables vs our response variable, \"curb-weight\" in order of strength, starting with positive relationships.\n",
    "sns.pairplot(data=df2,\n",
    "                  y_vars=['curb-weight'],\n",
    "                  x_vars=['length','width','engine-size','wheel-base','height','horsepower','city-mpg','peak-rpm'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dB8mtmuRmSg4"
   },
   "source": [
    "Points to be made from pairplots:\n",
    "\n",
    "\n",
    "1) Some relationships tend to show quadratic relationships versus linear ones; the pairplots of curb-weight vs. length, engine-size, and city-mpg show curved plots.\n",
    "\n",
    "\n",
    "2) Height and peak-rpm's plots look very scattered with no real pattern, confiming what we saw in the correlation matrix above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6OgBZ7knsLq-"
   },
   "source": [
    "## REGRESSION MODEL CONSTRUCTION & EVALUATION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7FyzQF0czaF3"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "Jh9AY_5tIZZJ",
    "outputId": "dd0f48ce-c687-4b79-9afd-9aebc41e62fd"
   },
   "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>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>130</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>130</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>94.5</td>\n",
       "      <td>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "      <td>2823</td>\n",
       "      <td>152</td>\n",
       "      <td>154.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99.8</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2337</td>\n",
       "      <td>109</td>\n",
       "      <td>102.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99.4</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2824</td>\n",
       "      <td>136</td>\n",
       "      <td>115.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   wheel-base  length  width  height  curb-weight  engine-size  horsepower  \\\n",
       "0        88.6   168.8   64.1    48.8         2548          130       111.0   \n",
       "1        88.6   168.8   64.1    48.8         2548          130       111.0   \n",
       "2        94.5   171.2   65.5    52.4         2823          152       154.0   \n",
       "3        99.8   176.6   66.2    54.3         2337          109       102.0   \n",
       "4        99.4   176.6   66.4    54.3         2824          136       115.0   \n",
       "\n",
       "   peak-rpm  city-mpg  \n",
       "0    5000.0        21  \n",
       "1    5000.0        21  \n",
       "2    5000.0        19  \n",
       "3    5500.0        24  \n",
       "4    5500.0        18  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sanity check on latest df.\n",
    "df3.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "Z1w7LR32swUn"
   },
   "outputs": [],
   "source": [
    "#let's clone our database to run k-fold validation\n",
    "\n",
    "df4 = df3.copy()\n",
    "df5 = df3.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "HWVVd1SqtCTe",
    "outputId": "b969ecfe-dd64-42e3-92d6-fc11fb53b96d"
   },
   "outputs": [
    {
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       "      <th>height</th>\n",
       "      <th>engine-size</th>\n",
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       "      <td>5500.0</td>\n",
       "      <td>24</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99.4</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>136</td>\n",
       "      <td>115.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   wheel-base  length  width  height  engine-size  horsepower  peak-rpm  \\\n",
       "0        88.6   168.8   64.1    48.8          130       111.0    5000.0   \n",
       "1        88.6   168.8   64.1    48.8          130       111.0    5000.0   \n",
       "2        94.5   171.2   65.5    52.4          152       154.0    5000.0   \n",
       "3        99.8   176.6   66.2    54.3          109       102.0    5500.0   \n",
       "4        99.4   176.6   66.4    54.3          136       115.0    5500.0   \n",
       "\n",
       "   city-mpg  \n",
       "0        21  \n",
       "1        21  \n",
       "2        19  \n",
       "3        24  \n",
       "4        18  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now getting a database with only response variables\n",
    "df4.drop(columns=['curb-weight'], axis=1, inplace=True)\n",
    "\n",
    "df6 = df4.copy()\n",
    "df7 = df4.copy()\n",
    "df8 = df4.copy()\n",
    "\n",
    "#sanity check\n",
    "df4.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "m04Ir4LHG1bD",
    "outputId": "168ae01b-3fc2-4a6c-b4bf-11a3c8941ba9"
   },
   "outputs": [
    {
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       "   curb-weight\n",
       "0         2548\n",
       "1         2548\n",
       "2         2823\n",
       "3         2337\n",
       "4         2824"
      ]
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     "execution_count": 50,
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   "source": [
    "#getting a database with only our explanatory variable\n",
    "df5.drop(columns=['wheel-base','length', 'width', 'height', 'engine-size', 'horsepower', 'peak-rpm', 'city-mpg'], axis=1, inplace=True)\n",
    "\n",
    "df5.head(5)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "id": "7yLXqffwaJV8"
   },
   "outputs": [],
   "source": [
    "#sanity checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "id": "mdrXYgO4aCCV",
    "outputId": "24f701a9-4435-4ff5-de52-9866321ba6c3"
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   "outputs": [
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       "      <td>54.3</td>\n",
       "      <td>136</td>\n",
       "      <td>115.0</td>\n",
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      "text/plain": [
       "   wheel-base  length  width  height  engine-size  horsepower  peak-rpm  \\\n",
       "0        88.6   168.8   64.1    48.8          130       111.0    5000.0   \n",
       "1        88.6   168.8   64.1    48.8          130       111.0    5000.0   \n",
       "2        94.5   171.2   65.5    52.4          152       154.0    5000.0   \n",
       "3        99.8   176.6   66.2    54.3          109       102.0    5500.0   \n",
       "4        99.4   176.6   66.4    54.3          136       115.0    5500.0   \n",
       "\n",
       "   city-mpg  \n",
       "0        21  \n",
       "1        21  \n",
       "2        19  \n",
       "3        24  \n",
       "4        18  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
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    "df6.head(5)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "id": "sf8EJP-3aEuV",
    "outputId": "71ae6b0b-78ce-4989-d2e8-faa2bc4bc5b7"
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   "outputs": [
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      "text/plain": [
       "   wheel-base  length  width  height  engine-size  horsepower  peak-rpm  \\\n",
       "0        88.6   168.8   64.1    48.8          130       111.0    5000.0   \n",
       "1        88.6   168.8   64.1    48.8          130       111.0    5000.0   \n",
       "2        94.5   171.2   65.5    52.4          152       154.0    5000.0   \n",
       "3        99.8   176.6   66.2    54.3          109       102.0    5500.0   \n",
       "4        99.4   176.6   66.4    54.3          136       115.0    5500.0   \n",
       "\n",
       "   city-mpg  \n",
       "0        21  \n",
       "1        21  \n",
       "2        19  \n",
       "3        24  \n",
       "4        18  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
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   "source": [
    "df7.head(5)"
   ]
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  {
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   "execution_count": 54,
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    "colab": {
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    },
    "id": "FJeD-z24aGvK",
    "outputId": "cbdc69ae-5d73-4c79-fb1a-dfc3542d8b2f"
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   "outputs": [
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>94.5</td>\n",
       "      <td>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "      <td>152</td>\n",
       "      <td>154.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99.8</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "      <td>109</td>\n",
       "      <td>102.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99.4</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>136</td>\n",
       "      <td>115.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   wheel-base  length  width  height  engine-size  horsepower  peak-rpm  \\\n",
       "0        88.6   168.8   64.1    48.8          130       111.0    5000.0   \n",
       "1        88.6   168.8   64.1    48.8          130       111.0    5000.0   \n",
       "2        94.5   171.2   65.5    52.4          152       154.0    5000.0   \n",
       "3        99.8   176.6   66.2    54.3          109       102.0    5500.0   \n",
       "4        99.4   176.6   66.4    54.3          136       115.0    5500.0   \n",
       "\n",
       "   city-mpg  \n",
       "0        21  \n",
       "1        21  \n",
       "2        19  \n",
       "3        24  \n",
       "4        18  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "id": "srl3dywPTNgb"
   },
   "outputs": [],
   "source": [
    "# intending to perform three different linear regressions, so now to generate particular copies of dbs with selected variables\n",
    "\n",
    "\n",
    "# I will perform a linear regression using the three strongest correlated variables to the response variable, \n",
    "# a linear regression using the three weakest correlated variables to the response variable,\n",
    "# and a linear regression using every variable against to the response variable.\n",
    "\n",
    "\n",
    "#three strongest correlated variables in first db, (length, width, and engine-size)\n",
    "df6.drop(columns=['wheel-base', 'height', 'horsepower', 'peak-rpm', 'city-mpg'], axis=1, inplace=True)\n",
    "dfb = df5.copy()\n",
    "\n",
    "#three weakest correlated variables in second db, (height, peak-rpm, and city-mpg)\n",
    "df7.drop(columns=['wheel-base','length', 'width', 'engine-size', 'horsepower'], axis=1, inplace=True)\n",
    "dfd = df5.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "id": "6o5QkniVatSF"
   },
   "outputs": [],
   "source": [
    "#sanity checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 112
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    "id": "lNXH7J5navS2",
    "outputId": "290f6907-932d-4bd6-cdc2-2f6b5ecb1ed2"
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   "outputs": [
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       "   height  peak-rpm  city-mpg\n",
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       "   wheel-base  length  width  height  engine-size  horsepower  peak-rpm  \\\n",
       "0        88.6   168.8   64.1    48.8          130       111.0    5000.0   \n",
       "1        88.6   168.8   64.1    48.8          130       111.0    5000.0   \n",
       "\n",
       "   city-mpg  \n",
       "0        21  \n",
       "1        21  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df8.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "id": "BYlv-C4VPSjG"
   },
   "outputs": [],
   "source": [
    "#loading linear regression f(x) from sklearn 'linear model' sub-library\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "#loading the cross_val_score f(x) from the sklearn.model_selection module\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "#loading the train_test_split f(x)\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#now to split our first sets of data into training and testing subsets\n",
    "#we will set aside only 20% of our data for testing purposes\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(df6, dfb, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QgOMOws1m6No",
    "outputId": "25b2ed4f-1451-49ad-d421-c11cc76f700f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "162"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking results\n",
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mtgVrgKtolDN",
    "outputId": "04d3e30a-4d08-470b-c160-daca35a66dd8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ulEUE-WTrPAl"
   },
   "source": [
    "The data set has successfully been split into training and testing subsets. We first separated the response variable from the explanatory variables and then used the train_test_split() function to randomly sample items from the data set for inclusion in our training and testing subsets. We have set aside 20% of the data for testing purposes.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WiXtwWVhrpxs"
   },
   "source": [
    "Using scikit-learn's Cross Validation Capabilities.\n",
    "\n",
    "scikit-learn provides us with an easy-to-use cross validation capability via the cross_val_score() function. To make use of it, we must first split their data into training and testing subsets (as we did above) and select the machine learning model they believe to be appropriate for their task at hand. We continue our example from above by constructing a small linear regression model for purposes of predicting curb-weight from the other variables.\n",
    "\n",
    "I will only be using 5-fold cross validation since our data set is rather small (only 205 rows)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SMHotenlrw4H",
    "outputId": "bbaff984-4356-4479-f69b-51bea22865c1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.93287481 0.9158332  0.89941048 0.91534031 0.90848798]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9143893555022489"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assigning the model function you want to use to a variable\n",
    "LRmodel = LinearRegression()\n",
    "\n",
    "# fit the model using 5-fold cross validation\n",
    "scores = cross_val_score(LRmodel, X_train, y_train, cv=5)\n",
    "\n",
    "# print out the R^2 metrics derived from the K-fold cross validation of our linear regression model\n",
    "print (scores)\n",
    "\n",
    "np.mean(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6fczmPzfsn35"
   },
   "source": [
    "We are presented with an average cross-validated R^2 score of 0.9143 which in my opinion is a good score, considering that our highest correlation with any one variable for curb-weight was 0.88 in our seaborn heatmap."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KUZjw9LktYIm"
   },
   "source": [
    "Let's try to repeat the process using the three WEAKEST relationships as shown from our Seaborn Heatmap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "id": "Tv_raeFgr_1A"
   },
   "outputs": [],
   "source": [
    "#splitting our second sets of data into training and testing subsets\n",
    "#we will set aside only 20% of our data for testing purposes\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(df7, dfb, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MZTgfyxatqpI",
    "outputId": "0de3d185-4bf9-45f7-d8f9-7ace1b2d05a3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.72367631 0.54815223 0.7046352  0.73830831 0.78066651]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6990877115278682"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assigning the model function you want to use to a variable\n",
    "LRmodel = LinearRegression()\n",
    "\n",
    "# fit the model using 5-fold cross validation\n",
    "scores = cross_val_score(LRmodel, X_train, y_train, cv=5)\n",
    "\n",
    "# print out the R^2 metrics derived from the K-fold cross validation of our linear regression model\n",
    "print (scores)\n",
    "\n",
    "np.mean(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "id5qbzszuij1"
   },
   "source": [
    "We are presented with an average cross-validated R^2 score of 0.6991 which in my opinion is a bad score, considering that we have a stronger correlation with the 4th strongest variable to curb-weight, which is wheel-base.\n",
    "\n",
    "This is to be expected since I chose the weakest related variables to curb-weight.\n",
    "\n",
    "Let's run this one more time using every variable except for the response one, of course."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "id": "BSYcIO5dv7dB"
   },
   "outputs": [],
   "source": [
    "#splitting our last sets of data into training and testing subsets\n",
    "#we will set aside only 20% of our data for testing purposes\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(df8, dfb, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kuJpzEPiwH-p",
    "outputId": "ee0b5f26-2cd5-4159-e73d-b792e46ee8cf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.93773815 0.92697423 0.92795439 0.93836374 0.90900917]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9280079355220296"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assigning the model function you want to use to a variable\n",
    "LRmodel = LinearRegression()\n",
    "\n",
    "# fit the model using 5-fold cross validation\n",
    "scores = cross_val_score(LRmodel, X_train, y_train, cv=5)\n",
    "\n",
    "# print out the R^2 metrics derived from the K-fold cross validation of our linear regression model\n",
    "print (scores)\n",
    "\n",
    "np.mean(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CONCLUSION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uyw5rJ2uwQtx"
   },
   "source": [
    "Interestingly enough, we are presented with an average cross-validated R^2 score of 0.9280 which is our best score yet for our linear regression.\n",
    "\n",
    "Perhaps this goes back to the more information the better, perhaps if we had more continous variables in our dataset to run linear regression on, we would achieve a higher score. Granted, I think these results are satisfactory, and perhaps a stronger model could be argued to be overfit.\n",
    "\n",
    "The difference between using our strongest three variables and all of them is not that much, approximately .01 of a difference. This probably alludes to our future lesson of feature selection, showing how only our top three variables were needed for a decent regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "bUKrVRySxNNx"
   },
   "outputs": [],
   "source": []
  }
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