Assignment_1_Q1(Start_Up_Predict_profit_using_Multi_LR).ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOLDcCMoTg/CjNL/v4GrQK8",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Sharminrahi/Deep-Learning/blob/main/Assignment_1_Q1(Start_Up_Predict_profit_using_Multi_LR).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Title: Sharmin Akhter\n",
        "## ID: 201891112\n",
        "## Date: 26-01-2023"
      ],
      "metadata": {
        "id": "My5J4T3hNtWE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Question 1\n",
        "Linear regression on the start-up dataset. Download the start-up dataset from\n",
        "Brightspace and analyse it in Python. The goal is to do multi-linear regression to predict the pro t of a start-up based on the features in the dataset.\n",
        "\n",
        "Your investigation should include answering the following question:\n",
        "\n",
        "(a) How many samples does this dataset have?\n",
        "\n",
        "(b) Are there any missing values in the dataset?\n",
        "\n",
        "(c) Is linear regression a suitable method for this dataset at all?\n",
        "Note that there are some categorical features in the dataset. These would need to be converted to numerical data  set.\n",
        "\n",
        "For the linear regression, use 80% of the data for training and 20% for testing. Visualize the predictions and assess whether the model works well on the test data, e.g., by computing the R2-score."
      ],
      "metadata": {
        "id": "pfmWkZfA-x_a"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import Libraries"
      ],
      "metadata": {
        "id": "-DtyxU_IQD8W"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UJue1mkdrTj1"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from google.colab import drive\n",
        "from matplotlib.pyplot import figure\n",
        "import seaborn as sns\n",
        "import geopandas as gpd\n",
        "from shapely.geometry import Point"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Mount drive\n",
        "drive.mount(\"/content/drive\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9TrLnDudtOwb",
        "outputId": "e9e446cb-5e44-4bc1-a7e7-eebe7e74e780"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load Data"
      ],
      "metadata": {
        "id": "gmMqGa9aQHP6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pickle import TRUE\n",
        "file_path = '/content/drive/MyDrive/StartUpProfits.csv'\n",
        "# Reading dataset \n",
        "df = pd.read_csv(file_path)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "eO--xFW9taA7",
        "outputId": "17148a6e-8b44-423a-ff81-c6031c5b03d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    R&D Spend  Administration  Marketing Spend       State     Profit\n",
              "0   165349.20       136897.80        471784.10    New York  192261.83\n",
              "1   162597.70       151377.59        443898.53  California  191792.06\n",
              "2   153441.51       101145.55        407934.54     Florida  191050.39\n",
              "3   144372.41       118671.85        383199.62    New York  182901.99\n",
              "4   142107.34        91391.77        366168.42     Florida  166187.94\n",
              "5   131876.90        99814.71        362861.36    New York  156991.12\n",
              "6   134615.46       147198.87        127716.82  California  156122.51\n",
              "7   130298.13       145530.06        323876.68     Florida  155752.60\n",
              "8   120542.52       148718.95        311613.29    New York  152211.77\n",
              "9   123334.88       108679.17        304981.62  California  149759.96\n",
              "10  101913.08       110594.11        229160.95     Florida  146121.95\n",
              "11  100671.96        91790.61        249744.55  California  144259.40\n",
              "12   93863.75       127320.38        249839.44     Florida  141585.52\n",
              "13   91992.39       135495.07        252664.93  California  134307.35\n",
              "14  119943.24       156547.42        256512.92     Florida  132602.65\n",
              "15  114523.61       122616.84        261776.23    New York  129917.04\n",
              "16   78013.11       121597.55        264346.06  California  126992.93\n",
              "17   94657.16       145077.58        282574.31    New York  125370.37\n",
              "18   91749.16       114175.79        294919.57     Florida  124266.90\n",
              "19   86419.70       153514.11             0.00    New York  122776.86\n",
              "20   76253.86       113867.30        298664.47  California  118474.03\n",
              "21   78389.47       153773.43        299737.29    New York  111313.02\n",
              "22   73994.56       122782.75        303319.26     Florida  110352.25\n",
              "23   67532.53       105751.03        304768.73     Florida  108733.99\n",
              "24   77044.01        99281.34        140574.81    New York  108552.04\n",
              "25   64664.71       139553.16        137962.62  California  107404.34\n",
              "26   75328.87       144135.98        134050.07     Florida  105733.54\n",
              "27   72107.60       127864.55        353183.81    New York  105008.31\n",
              "28   66051.52       182645.56        118148.20     Florida  103282.38\n",
              "29   65605.48       153032.06        107138.38    New York  101004.64\n",
              "30   61994.48       115641.28         91131.24     Florida   99937.59\n",
              "31   61136.38       152701.92         88218.23    New York   97483.56\n",
              "32   63408.86       129219.61         46085.25  California   97427.84\n",
              "33   55493.95       103057.49        214634.81     Florida   96778.92\n",
              "34   46426.07       157693.92        210797.67  California   96712.80\n",
              "35   46014.02        85047.44        205517.64    New York   96479.51\n",
              "36   28663.76       127056.21        201126.82     Florida   90708.19\n",
              "37   44069.95        51283.14        197029.42  California   89949.14\n",
              "38   20229.59        65947.93        185265.10    New York   81229.06\n",
              "39   38558.51        82982.09        174999.30  California   81005.76\n",
              "40   28754.33       118546.05        172795.67  California   78239.91\n",
              "41   27892.92        84710.77        164470.71     Florida   77798.83\n",
              "42   23640.93        96189.63        148001.11  California   71498.49\n",
              "43   15505.73       127382.30         35534.17    New York   69758.98\n",
              "44   22177.74       154806.14         28334.72  California   65200.33\n",
              "45    1000.23       124153.04          1903.93    New York   64926.08\n",
              "46    1315.46       115816.21        297114.46     Florida   49490.75\n",
              "47       0.00       135426.92             0.00  California   42559.73\n",
              "48     542.05        51743.15             0.00    New York   35673.41\n",
              "49       0.00       116983.80         45173.06  California   14681.40"
            ],
            "text/html": [
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>R&amp;D Spend</th>\n",
              "      <th>Administration</th>\n",
              "      <th>Marketing Spend</th>\n",
              "      <th>State</th>\n",
              "      <th>Profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>165349.20</td>\n",
              "      <td>136897.80</td>\n",
              "      <td>471784.10</td>\n",
              "      <td>New York</td>\n",
              "      <td>192261.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>162597.70</td>\n",
              "      <td>151377.59</td>\n",
              "      <td>443898.53</td>\n",
              "      <td>California</td>\n",
              "      <td>191792.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>153441.51</td>\n",
              "      <td>101145.55</td>\n",
              "      <td>407934.54</td>\n",
              "      <td>Florida</td>\n",
              "      <td>191050.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>144372.41</td>\n",
              "      <td>118671.85</td>\n",
              "      <td>383199.62</td>\n",
              "      <td>New York</td>\n",
              "      <td>182901.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>142107.34</td>\n",
              "      <td>91391.77</td>\n",
              "      <td>366168.42</td>\n",
              "      <td>Florida</td>\n",
              "      <td>166187.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>131876.90</td>\n",
              "      <td>99814.71</td>\n",
              "      <td>362861.36</td>\n",
              "      <td>New York</td>\n",
              "      <td>156991.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>134615.46</td>\n",
              "      <td>147198.87</td>\n",
              "      <td>127716.82</td>\n",
              "      <td>California</td>\n",
              "      <td>156122.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>130298.13</td>\n",
              "      <td>145530.06</td>\n",
              "      <td>323876.68</td>\n",
              "      <td>Florida</td>\n",
              "      <td>155752.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>120542.52</td>\n",
              "      <td>148718.95</td>\n",
              "      <td>311613.29</td>\n",
              "      <td>New York</td>\n",
              "      <td>152211.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>123334.88</td>\n",
              "      <td>108679.17</td>\n",
              "      <td>304981.62</td>\n",
              "      <td>California</td>\n",
              "      <td>149759.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>101913.08</td>\n",
              "      <td>110594.11</td>\n",
              "      <td>229160.95</td>\n",
              "      <td>Florida</td>\n",
              "      <td>146121.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>100671.96</td>\n",
              "      <td>91790.61</td>\n",
              "      <td>249744.55</td>\n",
              "      <td>California</td>\n",
              "      <td>144259.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>93863.75</td>\n",
              "      <td>127320.38</td>\n",
              "      <td>249839.44</td>\n",
              "      <td>Florida</td>\n",
              "      <td>141585.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>91992.39</td>\n",
              "      <td>135495.07</td>\n",
              "      <td>252664.93</td>\n",
              "      <td>California</td>\n",
              "      <td>134307.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>119943.24</td>\n",
              "      <td>156547.42</td>\n",
              "      <td>256512.92</td>\n",
              "      <td>Florida</td>\n",
              "      <td>132602.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>114523.61</td>\n",
              "      <td>122616.84</td>\n",
              "      <td>261776.23</td>\n",
              "      <td>New York</td>\n",
              "      <td>129917.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>78013.11</td>\n",
              "      <td>121597.55</td>\n",
              "      <td>264346.06</td>\n",
              "      <td>California</td>\n",
              "      <td>126992.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>94657.16</td>\n",
              "      <td>145077.58</td>\n",
              "      <td>282574.31</td>\n",
              "      <td>New York</td>\n",
              "      <td>125370.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>91749.16</td>\n",
              "      <td>114175.79</td>\n",
              "      <td>294919.57</td>\n",
              "      <td>Florida</td>\n",
              "      <td>124266.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>86419.70</td>\n",
              "      <td>153514.11</td>\n",
              "      <td>0.00</td>\n",
              "      <td>New York</td>\n",
              "      <td>122776.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>76253.86</td>\n",
              "      <td>113867.30</td>\n",
              "      <td>298664.47</td>\n",
              "      <td>California</td>\n",
              "      <td>118474.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>78389.47</td>\n",
              "      <td>153773.43</td>\n",
              "      <td>299737.29</td>\n",
              "      <td>New York</td>\n",
              "      <td>111313.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>73994.56</td>\n",
              "      <td>122782.75</td>\n",
              "      <td>303319.26</td>\n",
              "      <td>Florida</td>\n",
              "      <td>110352.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>67532.53</td>\n",
              "      <td>105751.03</td>\n",
              "      <td>304768.73</td>\n",
              "      <td>Florida</td>\n",
              "      <td>108733.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>77044.01</td>\n",
              "      <td>99281.34</td>\n",
              "      <td>140574.81</td>\n",
              "      <td>New York</td>\n",
              "      <td>108552.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>64664.71</td>\n",
              "      <td>139553.16</td>\n",
              "      <td>137962.62</td>\n",
              "      <td>California</td>\n",
              "      <td>107404.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>75328.87</td>\n",
              "      <td>144135.98</td>\n",
              "      <td>134050.07</td>\n",
              "      <td>Florida</td>\n",
              "      <td>105733.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>72107.60</td>\n",
              "      <td>127864.55</td>\n",
              "      <td>353183.81</td>\n",
              "      <td>New York</td>\n",
              "      <td>105008.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>66051.52</td>\n",
              "      <td>182645.56</td>\n",
              "      <td>118148.20</td>\n",
              "      <td>Florida</td>\n",
              "      <td>103282.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>65605.48</td>\n",
              "      <td>153032.06</td>\n",
              "      <td>107138.38</td>\n",
              "      <td>New York</td>\n",
              "      <td>101004.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>61994.48</td>\n",
              "      <td>115641.28</td>\n",
              "      <td>91131.24</td>\n",
              "      <td>Florida</td>\n",
              "      <td>99937.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>61136.38</td>\n",
              "      <td>152701.92</td>\n",
              "      <td>88218.23</td>\n",
              "      <td>New York</td>\n",
              "      <td>97483.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>63408.86</td>\n",
              "      <td>129219.61</td>\n",
              "      <td>46085.25</td>\n",
              "      <td>California</td>\n",
              "      <td>97427.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>55493.95</td>\n",
              "      <td>103057.49</td>\n",
              "      <td>214634.81</td>\n",
              "      <td>Florida</td>\n",
              "      <td>96778.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>46426.07</td>\n",
              "      <td>157693.92</td>\n",
              "      <td>210797.67</td>\n",
              "      <td>California</td>\n",
              "      <td>96712.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>46014.02</td>\n",
              "      <td>85047.44</td>\n",
              "      <td>205517.64</td>\n",
              "      <td>New York</td>\n",
              "      <td>96479.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>28663.76</td>\n",
              "      <td>127056.21</td>\n",
              "      <td>201126.82</td>\n",
              "      <td>Florida</td>\n",
              "      <td>90708.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>44069.95</td>\n",
              "      <td>51283.14</td>\n",
              "      <td>197029.42</td>\n",
              "      <td>California</td>\n",
              "      <td>89949.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>20229.59</td>\n",
              "      <td>65947.93</td>\n",
              "      <td>185265.10</td>\n",
              "      <td>New York</td>\n",
              "      <td>81229.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>38558.51</td>\n",
              "      <td>82982.09</td>\n",
              "      <td>174999.30</td>\n",
              "      <td>California</td>\n",
              "      <td>81005.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>28754.33</td>\n",
              "      <td>118546.05</td>\n",
              "      <td>172795.67</td>\n",
              "      <td>California</td>\n",
              "      <td>78239.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>27892.92</td>\n",
              "      <td>84710.77</td>\n",
              "      <td>164470.71</td>\n",
              "      <td>Florida</td>\n",
              "      <td>77798.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>23640.93</td>\n",
              "      <td>96189.63</td>\n",
              "      <td>148001.11</td>\n",
              "      <td>California</td>\n",
              "      <td>71498.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>15505.73</td>\n",
              "      <td>127382.30</td>\n",
              "      <td>35534.17</td>\n",
              "      <td>New York</td>\n",
              "      <td>69758.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>22177.74</td>\n",
              "      <td>154806.14</td>\n",
              "      <td>28334.72</td>\n",
              "      <td>California</td>\n",
              "      <td>65200.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>1000.23</td>\n",
              "      <td>124153.04</td>\n",
              "      <td>1903.93</td>\n",
              "      <td>New York</td>\n",
              "      <td>64926.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>1315.46</td>\n",
              "      <td>115816.21</td>\n",
              "      <td>297114.46</td>\n",
              "      <td>Florida</td>\n",
              "      <td>49490.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>0.00</td>\n",
              "      <td>135426.92</td>\n",
              "      <td>0.00</td>\n",
              "      <td>California</td>\n",
              "      <td>42559.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>542.05</td>\n",
              "      <td>51743.15</td>\n",
              "      <td>0.00</td>\n",
              "      <td>New York</td>\n",
              "      <td>35673.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>0.00</td>\n",
              "      <td>116983.80</td>\n",
              "      <td>45173.06</td>\n",
              "      <td>California</td>\n",
              "      <td>14681.40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-339ed090-4361-469c-bae5-5522287ec889')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-339ed090-4361-469c-bae5-5522287ec889 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-339ed090-4361-469c-bae5-5522287ec889');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Shape of Data"
      ],
      "metadata": {
        "id": "OG6n0PvWQKOy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iRKX3KERv3JH",
        "outputId": "a3cf9200-99f9-4666-cc42-770231226d5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Type of Data"
      ],
      "metadata": {
        "id": "-B-IY0i8QNkG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BY0Wry98v4bH",
        "outputId": "3da7a7d0-0a01-4c15-e4c6-d9e185a132e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 50 entries, 0 to 49\n",
            "Data columns (total 5 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   R&D Spend        50 non-null     float64\n",
            " 1   Administration   50 non-null     float64\n",
            " 2   Marketing Spend  50 non-null     float64\n",
            " 3   State            50 non-null     object \n",
            " 4   Profit           50 non-null     float64\n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 2.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "zm1sK5kew7dq",
        "outputId": "9e4b3358-db9d-4b0d-c1e5-8768ef54ce30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           R&D Spend  Administration  Marketing Spend         Profit\n",
              "count      50.000000       50.000000        50.000000      50.000000\n",
              "mean    73721.615600   121344.639600    211025.097800  112012.639200\n",
              "std     45902.256482    28017.802755    122290.310726   40306.180338\n",
              "min         0.000000    51283.140000         0.000000   14681.400000\n",
              "25%     39936.370000   103730.875000    129300.132500   90138.902500\n",
              "50%     73051.080000   122699.795000    212716.240000  107978.190000\n",
              "75%    101602.800000   144842.180000    299469.085000  139765.977500\n",
              "max    165349.200000   182645.560000    471784.100000  192261.830000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f6fae2b7-077d-4b4b-9012-784a3d3e1913\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>R&amp;D Spend</th>\n",
              "      <th>Administration</th>\n",
              "      <th>Marketing Spend</th>\n",
              "      <th>Profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>50.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>73721.615600</td>\n",
              "      <td>121344.639600</td>\n",
              "      <td>211025.097800</td>\n",
              "      <td>112012.639200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>45902.256482</td>\n",
              "      <td>28017.802755</td>\n",
              "      <td>122290.310726</td>\n",
              "      <td>40306.180338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>51283.140000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14681.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>39936.370000</td>\n",
              "      <td>103730.875000</td>\n",
              "      <td>129300.132500</td>\n",
              "      <td>90138.902500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>73051.080000</td>\n",
              "      <td>122699.795000</td>\n",
              "      <td>212716.240000</td>\n",
              "      <td>107978.190000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>101602.800000</td>\n",
              "      <td>144842.180000</td>\n",
              "      <td>299469.085000</td>\n",
              "      <td>139765.977500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>165349.200000</td>\n",
              "      <td>182645.560000</td>\n",
              "      <td>471784.100000</td>\n",
              "      <td>192261.830000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f6fae2b7-077d-4b4b-9012-784a3d3e1913')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f6fae2b7-077d-4b4b-9012-784a3d3e1913 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f6fae2b7-077d-4b4b-9012-784a3d3e1913');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Question 1(a)\n",
        "How many samples does this dataset have?"
      ],
      "metadata": {
        "id": "0bIjzf13xG-P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s0bZjprcxKJD",
        "outputId": "1e1e4a7c-32d0-473d-e7d2-2f30ef4fa79c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Question 1(b)\n",
        "Are there any missing values in the dataset?"
      ],
      "metadata": {
        "id": "GBKyEnAHx2Yi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kOmoiwbeyApN",
        "outputId": "3d76ba15-f80a-49f3-988e-f21f668eecd2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['R&D Spend', 'Administration', 'Marketing Spend', 'State', 'Profit'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H4NW1mwUx3WG",
        "outputId": "157c5d0e-4831-43e8-b261-141fddbde99a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "R&D Spend          0\n",
              "Administration     0\n",
              "Marketing Spend    0\n",
              "State              0\n",
              "Profit             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.isnull().sum())\n",
        "print('Count of NAN ' + str(df.isnull().sum().sum()))\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QRvfUTxayaWr",
        "outputId": "1ece43fa-9746-4cd8-d989-48f2876e9462"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R&D Spend          0\n",
            "Administration     0\n",
            "Marketing Spend    0\n",
            "State              0\n",
            "Profit             0\n",
            "dtype: int64\n",
            "Count of NAN 0\n",
            "    R&D Spend  Administration  Marketing Spend       State     Profit\n",
            "0   165349.20       136897.80        471784.10    New York  192261.83\n",
            "1   162597.70       151377.59        443898.53  California  191792.06\n",
            "2   153441.51       101145.55        407934.54     Florida  191050.39\n",
            "3   144372.41       118671.85        383199.62    New York  182901.99\n",
            "4   142107.34        91391.77        366168.42     Florida  166187.94\n",
            "5   131876.90        99814.71        362861.36    New York  156991.12\n",
            "6   134615.46       147198.87        127716.82  California  156122.51\n",
            "7   130298.13       145530.06        323876.68     Florida  155752.60\n",
            "8   120542.52       148718.95        311613.29    New York  152211.77\n",
            "9   123334.88       108679.17        304981.62  California  149759.96\n",
            "10  101913.08       110594.11        229160.95     Florida  146121.95\n",
            "11  100671.96        91790.61        249744.55  California  144259.40\n",
            "12   93863.75       127320.38        249839.44     Florida  141585.52\n",
            "13   91992.39       135495.07        252664.93  California  134307.35\n",
            "14  119943.24       156547.42        256512.92     Florida  132602.65\n",
            "15  114523.61       122616.84        261776.23    New York  129917.04\n",
            "16   78013.11       121597.55        264346.06  California  126992.93\n",
            "17   94657.16       145077.58        282574.31    New York  125370.37\n",
            "18   91749.16       114175.79        294919.57     Florida  124266.90\n",
            "19   86419.70       153514.11             0.00    New York  122776.86\n",
            "20   76253.86       113867.30        298664.47  California  118474.03\n",
            "21   78389.47       153773.43        299737.29    New York  111313.02\n",
            "22   73994.56       122782.75        303319.26     Florida  110352.25\n",
            "23   67532.53       105751.03        304768.73     Florida  108733.99\n",
            "24   77044.01        99281.34        140574.81    New York  108552.04\n",
            "25   64664.71       139553.16        137962.62  California  107404.34\n",
            "26   75328.87       144135.98        134050.07     Florida  105733.54\n",
            "27   72107.60       127864.55        353183.81    New York  105008.31\n",
            "28   66051.52       182645.56        118148.20     Florida  103282.38\n",
            "29   65605.48       153032.06        107138.38    New York  101004.64\n",
            "30   61994.48       115641.28         91131.24     Florida   99937.59\n",
            "31   61136.38       152701.92         88218.23    New York   97483.56\n",
            "32   63408.86       129219.61         46085.25  California   97427.84\n",
            "33   55493.95       103057.49        214634.81     Florida   96778.92\n",
            "34   46426.07       157693.92        210797.67  California   96712.80\n",
            "35   46014.02        85047.44        205517.64    New York   96479.51\n",
            "36   28663.76       127056.21        201126.82     Florida   90708.19\n",
            "37   44069.95        51283.14        197029.42  California   89949.14\n",
            "38   20229.59        65947.93        185265.10    New York   81229.06\n",
            "39   38558.51        82982.09        174999.30  California   81005.76\n",
            "40   28754.33       118546.05        172795.67  California   78239.91\n",
            "41   27892.92        84710.77        164470.71     Florida   77798.83\n",
            "42   23640.93        96189.63        148001.11  California   71498.49\n",
            "43   15505.73       127382.30         35534.17    New York   69758.98\n",
            "44   22177.74       154806.14         28334.72  California   65200.33\n",
            "45    1000.23       124153.04          1903.93    New York   64926.08\n",
            "46    1315.46       115816.21        297114.46     Florida   49490.75\n",
            "47       0.00       135426.92             0.00  California   42559.73\n",
            "48     542.05        51743.15             0.00    New York   35673.41\n",
            "49       0.00       116983.80         45173.06  California   14681.40\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "catagorical_idx = df.select_dtypes(include=['object', 'bool']).dtypes\n",
        "print(catagorical_idx.index)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8zRz99t2y5Wo",
        "outputId": "6c5d9a96-b142-460d-9d90-a465e2663f31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['State'], dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Question 1(c)\n",
        "Is linear regression a suitable method for this dataset at all?"
      ],
      "metadata": {
        "id": "FInCC-C9yj8T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The shape of the dataset being (50, 5) does not provide enough information to determine if linear regression is a suitable method for this dataset. However, the sample size of 50 observations is relatively small, which could be a concern if the data is complex or non-linear."
      ],
      "metadata": {
        "id": "Vvl1RLZFQlfl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Check the correlation of each feature"
      ],
      "metadata": {
        "id": "DazT7Rz9SkA8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "ZXPQ-OHaysUo",
        "outputId": "1674747a-cb33-42dc-8c18-0c47e2ad879d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 R&D Spend  Administration  Marketing Spend    Profit\n",
              "R&D Spend         1.000000        0.241955         0.724248  0.972900\n",
              "Administration    0.241955        1.000000        -0.032154  0.200717\n",
              "Marketing Spend   0.724248       -0.032154         1.000000  0.747766\n",
              "Profit            0.972900        0.200717         0.747766  1.000000"
            ],
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "# Correlation Matrix"
      ],
      "metadata": {
        "id": "F3uVlenCSoQy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = df.corr()\n",
        "x = sns.heatmap(a,xticklabels = a.columns.values,yticklabels = a.columns.values)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.hist(figsize=(10,10));"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "ErtmNacG1uoY",
        "outputId": "2ee87fed-7446-4d4a-f66c-c2130ab24a67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(8,6))\n",
        "plt.hist(df['Profit'], bins = 10)\n",
        "plt.xlabel('bin')\n",
        "plt.ylabel('frequency')\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "Pxm-vzchqbI-",
        "outputId": "7090fe3f-ceea-4670-8367-fd77bb5cc846"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Categorical data\n",
        "catagorical_idx = df.select_dtypes(include=['object', 'bool']).dtypes\n",
        "print(catagorical_idx.index)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dXB-huy-kziP",
        "outputId": "02cb6a45-ff2c-4862-91b5-00ed67ea40c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['State'], dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.State"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oQdik832p1_s",
        "outputId": "31e5531b-54d6-4c38-85a0-761eac85f43f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       New York\n",
              "1     California\n",
              "2        Florida\n",
              "3       New York\n",
              "4        Florida\n",
              "5       New York\n",
              "6     California\n",
              "7        Florida\n",
              "8       New York\n",
              "9     California\n",
              "10       Florida\n",
              "11    California\n",
              "12       Florida\n",
              "13    California\n",
              "14       Florida\n",
              "15      New York\n",
              "16    California\n",
              "17      New York\n",
              "18       Florida\n",
              "19      New York\n",
              "20    California\n",
              "21      New York\n",
              "22       Florida\n",
              "23       Florida\n",
              "24      New York\n",
              "25    California\n",
              "26       Florida\n",
              "27      New York\n",
              "28       Florida\n",
              "29      New York\n",
              "30       Florida\n",
              "31      New York\n",
              "32    California\n",
              "33       Florida\n",
              "34    California\n",
              "35      New York\n",
              "36       Florida\n",
              "37    California\n",
              "38      New York\n",
              "39    California\n",
              "40    California\n",
              "41       Florida\n",
              "42    California\n",
              "43      New York\n",
              "44    California\n",
              "45      New York\n",
              "46       Florida\n",
              "47    California\n",
              "48      New York\n",
              "49    California\n",
              "Name: State, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def label1(row):\n",
        "    if row['State'] == 'New York':\n",
        "        return 0\n",
        "    elif row['State'] == 'California':\n",
        "        return 1\n",
        "    else:\n",
        "        return 2\n",
        "\n",
        "df['State'] = df.apply(lambda row: label1(row), axis=1)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "qpmRF5-amzhI",
        "outputId": "def3103e-1cd3-4771-edfe-dbf21dfc35cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   R&D Spend  Administration  Marketing Spend  State     Profit\n",
              "0  165349.20       136897.80        471784.10      0  192261.83\n",
              "1  162597.70       151377.59        443898.53      1  191792.06\n",
              "2  153441.51       101145.55        407934.54      2  191050.39\n",
              "3  144372.41       118671.85        383199.62      0  182901.99\n",
              "4  142107.34        91391.77        366168.42      2  166187.94"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9b9d76be-d83b-485b-bf36-aa57c5466eea\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>R&amp;D Spend</th>\n",
              "      <th>Administration</th>\n",
              "      <th>Marketing Spend</th>\n",
              "      <th>State</th>\n",
              "      <th>Profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>165349.20</td>\n",
              "      <td>136897.80</td>\n",
              "      <td>471784.10</td>\n",
              "      <td>0</td>\n",
              "      <td>192261.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>162597.70</td>\n",
              "      <td>151377.59</td>\n",
              "      <td>443898.53</td>\n",
              "      <td>1</td>\n",
              "      <td>191792.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>153441.51</td>\n",
              "      <td>101145.55</td>\n",
              "      <td>407934.54</td>\n",
              "      <td>2</td>\n",
              "      <td>191050.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>144372.41</td>\n",
              "      <td>118671.85</td>\n",
              "      <td>383199.62</td>\n",
              "      <td>0</td>\n",
              "      <td>182901.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>142107.34</td>\n",
              "      <td>91391.77</td>\n",
              "      <td>366168.42</td>\n",
              "      <td>2</td>\n",
              "      <td>166187.94</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Model Selection\n",
        "Split Data"
      ],
      "metadata": {
        "id": "TkvwMlJpqqFv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G1SpAfYIrD93",
        "outputId": "0269e986-29d3-4e9c-9455-3659dafdec75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['R&D Spend', 'Administration', 'Marketing Spend', 'State', 'Profit'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the predictor for Linear Regression Model\n",
        "X = df[['R&D Spend', 'Administration', 'Marketing Spend', 'State']]\n",
        "Y = df[['Profit']]"
      ],
      "metadata": {
        "id": "bdRexveIrBN_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Slrgr4T8uL07",
        "outputId": "6da6a74a-887a-463c-800c-bb6d72a82eb7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    R&D Spend  Administration  Marketing Spend  State\n",
              "0   165349.20       136897.80        471784.10      0\n",
              "1   162597.70       151377.59        443898.53      1\n",
              "2   153441.51       101145.55        407934.54      2\n",
              "3   144372.41       118671.85        383199.62      0\n",
              "4   142107.34        91391.77        366168.42      2\n",
              "5   131876.90        99814.71        362861.36      0\n",
              "6   134615.46       147198.87        127716.82      1\n",
              "7   130298.13       145530.06        323876.68      2\n",
              "8   120542.52       148718.95        311613.29      0\n",
              "9   123334.88       108679.17        304981.62      1\n",
              "10  101913.08       110594.11        229160.95      2\n",
              "11  100671.96        91790.61        249744.55      1\n",
              "12   93863.75       127320.38        249839.44      2\n",
              "13   91992.39       135495.07        252664.93      1\n",
              "14  119943.24       156547.42        256512.92      2\n",
              "15  114523.61       122616.84        261776.23      0\n",
              "16   78013.11       121597.55        264346.06      1\n",
              "17   94657.16       145077.58        282574.31      0\n",
              "18   91749.16       114175.79        294919.57      2\n",
              "19   86419.70       153514.11             0.00      0\n",
              "20   76253.86       113867.30        298664.47      1\n",
              "21   78389.47       153773.43        299737.29      0\n",
              "22   73994.56       122782.75        303319.26      2\n",
              "23   67532.53       105751.03        304768.73      2\n",
              "24   77044.01        99281.34        140574.81      0\n",
              "25   64664.71       139553.16        137962.62      1\n",
              "26   75328.87       144135.98        134050.07      2\n",
              "27   72107.60       127864.55        353183.81      0\n",
              "28   66051.52       182645.56        118148.20      2\n",
              "29   65605.48       153032.06        107138.38      0\n",
              "30   61994.48       115641.28         91131.24      2\n",
              "31   61136.38       152701.92         88218.23      0\n",
              "32   63408.86       129219.61         46085.25      1\n",
              "33   55493.95       103057.49        214634.81      2\n",
              "34   46426.07       157693.92        210797.67      1\n",
              "35   46014.02        85047.44        205517.64      0\n",
              "36   28663.76       127056.21        201126.82      2\n",
              "37   44069.95        51283.14        197029.42      1\n",
              "38   20229.59        65947.93        185265.10      0\n",
              "39   38558.51        82982.09        174999.30      1\n",
              "40   28754.33       118546.05        172795.67      1\n",
              "41   27892.92        84710.77        164470.71      2\n",
              "42   23640.93        96189.63        148001.11      1\n",
              "43   15505.73       127382.30         35534.17      0\n",
              "44   22177.74       154806.14         28334.72      1\n",
              "45    1000.23       124153.04          1903.93      0\n",
              "46    1315.46       115816.21        297114.46      2\n",
              "47       0.00       135426.92             0.00      1\n",
              "48     542.05        51743.15             0.00      0\n",
              "49       0.00       116983.80         45173.06      1"
            ],
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              "\n",
              "  <div id=\"df-6bc3f03e-70b7-4c97-8d0a-324fb8f1e37d\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>R&amp;D Spend</th>\n",
              "      <th>Administration</th>\n",
              "      <th>Marketing Spend</th>\n",
              "      <th>State</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>165349.20</td>\n",
              "      <td>136897.80</td>\n",
              "      <td>471784.10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>162597.70</td>\n",
              "      <td>151377.59</td>\n",
              "      <td>443898.53</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>153441.51</td>\n",
              "      <td>101145.55</td>\n",
              "      <td>407934.54</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>144372.41</td>\n",
              "      <td>118671.85</td>\n",
              "      <td>383199.62</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>142107.34</td>\n",
              "      <td>91391.77</td>\n",
              "      <td>366168.42</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>131876.90</td>\n",
              "      <td>99814.71</td>\n",
              "      <td>362861.36</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>134615.46</td>\n",
              "      <td>147198.87</td>\n",
              "      <td>127716.82</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>130298.13</td>\n",
              "      <td>145530.06</td>\n",
              "      <td>323876.68</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>120542.52</td>\n",
              "      <td>148718.95</td>\n",
              "      <td>311613.29</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>123334.88</td>\n",
              "      <td>108679.17</td>\n",
              "      <td>304981.62</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>101913.08</td>\n",
              "      <td>110594.11</td>\n",
              "      <td>229160.95</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>100671.96</td>\n",
              "      <td>91790.61</td>\n",
              "      <td>249744.55</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>93863.75</td>\n",
              "      <td>127320.38</td>\n",
              "      <td>249839.44</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>91992.39</td>\n",
              "      <td>135495.07</td>\n",
              "      <td>252664.93</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>119943.24</td>\n",
              "      <td>156547.42</td>\n",
              "      <td>256512.92</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>114523.61</td>\n",
              "      <td>122616.84</td>\n",
              "      <td>261776.23</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>78013.11</td>\n",
              "      <td>121597.55</td>\n",
              "      <td>264346.06</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>94657.16</td>\n",
              "      <td>145077.58</td>\n",
              "      <td>282574.31</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>91749.16</td>\n",
              "      <td>114175.79</td>\n",
              "      <td>294919.57</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>86419.70</td>\n",
              "      <td>153514.11</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>76253.86</td>\n",
              "      <td>113867.30</td>\n",
              "      <td>298664.47</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>78389.47</td>\n",
              "      <td>153773.43</td>\n",
              "      <td>299737.29</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>73994.56</td>\n",
              "      <td>122782.75</td>\n",
              "      <td>303319.26</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>67532.53</td>\n",
              "      <td>105751.03</td>\n",
              "      <td>304768.73</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>77044.01</td>\n",
              "      <td>99281.34</td>\n",
              "      <td>140574.81</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>64664.71</td>\n",
              "      <td>139553.16</td>\n",
              "      <td>137962.62</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>75328.87</td>\n",
              "      <td>144135.98</td>\n",
              "      <td>134050.07</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>72107.60</td>\n",
              "      <td>127864.55</td>\n",
              "      <td>353183.81</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>66051.52</td>\n",
              "      <td>182645.56</td>\n",
              "      <td>118148.20</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>65605.48</td>\n",
              "      <td>153032.06</td>\n",
              "      <td>107138.38</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>61994.48</td>\n",
              "      <td>115641.28</td>\n",
              "      <td>91131.24</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>61136.38</td>\n",
              "      <td>152701.92</td>\n",
              "      <td>88218.23</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>63408.86</td>\n",
              "      <td>129219.61</td>\n",
              "      <td>46085.25</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>55493.95</td>\n",
              "      <td>103057.49</td>\n",
              "      <td>214634.81</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>46426.07</td>\n",
              "      <td>157693.92</td>\n",
              "      <td>210797.67</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>46014.02</td>\n",
              "      <td>85047.44</td>\n",
              "      <td>205517.64</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>28663.76</td>\n",
              "      <td>127056.21</td>\n",
              "      <td>201126.82</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>44069.95</td>\n",
              "      <td>51283.14</td>\n",
              "      <td>197029.42</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>20229.59</td>\n",
              "      <td>65947.93</td>\n",
              "      <td>185265.10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>38558.51</td>\n",
              "      <td>82982.09</td>\n",
              "      <td>174999.30</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>28754.33</td>\n",
              "      <td>118546.05</td>\n",
              "      <td>172795.67</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>27892.92</td>\n",
              "      <td>84710.77</td>\n",
              "      <td>164470.71</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>23640.93</td>\n",
              "      <td>96189.63</td>\n",
              "      <td>148001.11</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>15505.73</td>\n",
              "      <td>127382.30</td>\n",
              "      <td>35534.17</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>22177.74</td>\n",
              "      <td>154806.14</td>\n",
              "      <td>28334.72</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>1000.23</td>\n",
              "      <td>124153.04</td>\n",
              "      <td>1903.93</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>1315.46</td>\n",
              "      <td>115816.21</td>\n",
              "      <td>297114.46</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>0.00</td>\n",
              "      <td>135426.92</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>542.05</td>\n",
              "      <td>51743.15</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>0.00</td>\n",
              "      <td>116983.80</td>\n",
              "      <td>45173.06</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6bc3f03e-70b7-4c97-8d0a-324fb8f1e37d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6bc3f03e-70b7-4c97-8d0a-324fb8f1e37d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6bc3f03e-70b7-4c97-8d0a-324fb8f1e37d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Visualize the predictions and assess whether the model works well on the test data, e.g., by computing the R2-score."
      ],
      "metadata": {
        "id": "99nRS1WW-fup"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "X_train,X_test,Y_train, Y_test = train_test_split(X,Y, test_size = 0.20, random_state = 42)"
      ],
      "metadata": {
        "id": "KcWPcTI2qMxc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "\n",
        "# create and fit a linear regression model\n",
        "reg = LinearRegression()\n",
        "reg.fit(X_train, Y_train)\n",
        "\n",
        "# make predictions using the model\n",
        "y_pred = reg.predict(X_test)\n",
        "\n",
        "# calculate the R-squared score\n",
        "r2 = r2_score(Y_test, y_pred)\n",
        "\n",
        "# print the R-squared score\n",
        "print('R-squared score: ', r2*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BBhyQIngrXi4",
        "outputId": "0f631f33-ea85-48f8-e26f-69dc4e543575"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R-squared score:  89.90092409390613\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Training Acc {round(reg.score(X_train, Y_train) * 100, 2)}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OQYqw8OHsUZk",
        "outputId": "ff5a6bfe-975e-4a64-8f38-e1ae655deb6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Acc 95.37%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Question 1(c)\n",
        "#Part b\n",
        "Visualize the predictions and assess whether the model works well on the test data, e.g., by computing the R2-score."
      ],
      "metadata": {
        "id": "EikPlKPyLaH1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create a scatter plot of the predicted vs actual values\n",
        "plt.scatter(y_pred, Y_test)\n",
        "plt.xlabel('Predicted Values')\n",
        "plt.ylabel('Actual Values')\n",
        "plt.title('Predicted vs Actual Values')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "C1dBjmN4Mo4K",
        "outputId": "4f93efcf-af07-4c53-fb34-85a3490f5b46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create the line of best fit\n",
        "plt.plot(y_pred,reg.predict(X_test), color='red', linewidth=3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "wgZlnKd7LiLv",
        "outputId": "68f99e88-c867-45d3-996b-8fa050b80f8f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f6686e1dfd0>]"
            ]
          },
          "metadata": {},
          "execution_count": 157
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}