Model_3.ipynb

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  "metadata": {
    "colab": {
      "name": "Model 3.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOydV2DsWzH0VXDlou6djfR",
      "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/khushi0111/LNB_DS_Internship/blob/main/Model_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "DS404 Khushi Agarwal"
      ],
      "metadata": {
        "id": "BzGLC5duo11S"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "STARTUPS DATASET (Company Profit Prediction)"
      ],
      "metadata": {
        "id": "nuV3WJH_pI1c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# importing libraries\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns \n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "YzvWS9v0prVV"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Data Preparation and Cleaning\n",
        "\n",
        "url=\"https://drive.google.com/file/d/1w2jaPgbZGOeyDqeVjUX7BfxI3AOwBpoq/view?usp=sharing\"\n",
        "url='https://drive.google.com/uc?id=' + url.split('/')[-2]\n",
        "df = pd.read_csv(url)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "MEJ0Tz1qp2yX",
        "outputId": "3d25cc28-94ce-46a9-c064-b4e685657c15"
      },
      "execution_count": 2,
      "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": [
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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",
              "      <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",
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      "execution_count": 3,
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        {
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          "data": {
            "text/plain": [
              "Index(['R&D Spend', 'Administration', 'Marketing Spend', 'State', 'Profit'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 3
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      ]
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      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50, 5)"
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          "metadata": {},
          "execution_count": 4
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        "outputId": "c014811b-fdd1-4bdc-b4e7-8bf2e0139e2b"
      },
      "execution_count": 5,
      "outputs": [
        {
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          "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"
          ]
        }
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        "outputId": "66c20c6d-2621-4358-b654-96433987d6a2"
      },
      "execution_count": 6,
      "outputs": [
        {
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              "           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"
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              "      <th>count</th>\n",
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              "      <td>112012.639200</td>\n",
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              "      <th>std</th>\n",
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              "      <td>14681.400000</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>103730.875000</td>\n",
              "      <td>129300.132500</td>\n",
              "      <td>90138.902500</td>\n",
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              "      <th>50%</th>\n",
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              "      <td>122699.795000</td>\n",
              "      <td>212716.240000</td>\n",
              "      <td>107978.190000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>101602.800000</td>\n",
              "      <td>144842.180000</td>\n",
              "      <td>299469.085000</td>\n",
              "      <td>139765.977500</td>\n",
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              "      <th>max</th>\n",
              "      <td>165349.200000</td>\n",
              "      <td>182645.560000</td>\n",
              "      <td>471784.100000</td>\n",
              "      <td>192261.830000</td>\n",
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      "execution_count": 8,
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            "text/plain": [
              "    R&D Spend  Administration  Marketing Spend  State  Profit\n",
              "0       False           False            False  False   False\n",
              "1       False           False            False  False   False\n",
              "2       False           False            False  False   False\n",
              "3       False           False            False  False   False\n",
              "4       False           False            False  False   False\n",
              "5       False           False            False  False   False\n",
              "6       False           False            False  False   False\n",
              "7       False           False            False  False   False\n",
              "8       False           False            False  False   False\n",
              "9       False           False            False  False   False\n",
              "10      False           False            False  False   False\n",
              "11      False           False            False  False   False\n",
              "12      False           False            False  False   False\n",
              "13      False           False            False  False   False\n",
              "14      False           False            False  False   False\n",
              "15      False           False            False  False   False\n",
              "16      False           False            False  False   False\n",
              "17      False           False            False  False   False\n",
              "18      False           False            False  False   False\n",
              "19      False           False            False  False   False\n",
              "20      False           False            False  False   False\n",
              "21      False           False            False  False   False\n",
              "22      False           False            False  False   False\n",
              "23      False           False            False  False   False\n",
              "24      False           False            False  False   False\n",
              "25      False           False            False  False   False\n",
              "26      False           False            False  False   False\n",
              "27      False           False            False  False   False\n",
              "28      False           False            False  False   False\n",
              "29      False           False            False  False   False\n",
              "30      False           False            False  False   False\n",
              "31      False           False            False  False   False\n",
              "32      False           False            False  False   False\n",
              "33      False           False            False  False   False\n",
              "34      False           False            False  False   False\n",
              "35      False           False            False  False   False\n",
              "36      False           False            False  False   False\n",
              "37      False           False            False  False   False\n",
              "38      False           False            False  False   False\n",
              "39      False           False            False  False   False\n",
              "40      False           False            False  False   False\n",
              "41      False           False            False  False   False\n",
              "42      False           False            False  False   False\n",
              "43      False           False            False  False   False\n",
              "44      False           False            False  False   False\n",
              "45      False           False            False  False   False\n",
              "46      False           False            False  False   False\n",
              "47      False           False            False  False   False\n",
              "48      False           False            False  False   False\n",
              "49      False           False            False  False   False"
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              "      <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>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e8752a8-ae31-457d-a100-d43557754f2f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0e8752a8-ae31-457d-a100-d43557754f2f 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-0e8752a8-ae31-457d-a100-d43557754f2f');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wuPQEEgfrF59",
        "outputId": "e4a1e0ca-edcf-47ee-df0d-0325bcc51fe3"
      },
      "execution_count": 9,
      "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": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.corr(method ='pearson'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tIQZ_dqhrMMc",
        "outputId": "ca15f371-fa7a-4cf7-b3e1-c776eb2c2a70"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 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\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "WbTH7LswrSZi",
        "outputId": "f694c6bd-f128-4231-d164-4b7953c5b4e0"
      },
      "execution_count": 11,
      "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"
            ],
            "text/html": [
              "\n",
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              "  <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>R&amp;D Spend</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.241955</td>\n",
              "      <td>0.724248</td>\n",
              "      <td>0.972900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Administration</th>\n",
              "      <td>0.241955</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.032154</td>\n",
              "      <td>0.200717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Marketing Spend</th>\n",
              "      <td>0.724248</td>\n",
              "      <td>-0.032154</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.747766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Profit</th>\n",
              "      <td>0.972900</td>\n",
              "      <td>0.200717</td>\n",
              "      <td>0.747766</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3c3a8280-69d9-4924-8595-fcc387909d48')\"\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-3c3a8280-69d9-4924-8595-fcc387909d48 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-3c3a8280-69d9-4924-8595-fcc387909d48');\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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Data visualization\n",
        "\n",
        "sns.pairplot(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 743
        },
        "id": "iIeKwvg2rY4z",
        "outputId": "0dc6ff11-dcf3-4cab-999d-c53e7354fea6"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7fd666515850>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.corr(),annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "EpQmKMOxrv-Z",
        "outputId": "183d1a75-f571-4687-d7c3-d5879b5f5d1e"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd66353f5d0>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "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": [
        "#Data preparation and model training\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression"
      ],
      "metadata": {
        "id": "oewr04GVr4Ub"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.iloc[:, :-1].values\n",
        "y = df.iloc[:, 4].values"
      ],
      "metadata": {
        "id": "L23WAdiEsNSI"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labelencoder = LabelEncoder()\n",
        "X[:, 3] = labelencoder.fit_transform(X[:, 3])\n",
        "X1 = pd.DataFrame(X)\n",
        "X1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "h0eVWY5YsTF6",
        "outputId": "6b312ea4-e07d-48c5-8206-73302da56311"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           0          1          2  3\n",
              "0   165349.2   136897.8   471784.1  2\n",
              "1   162597.7  151377.59  443898.53  0\n",
              "2  153441.51  101145.55  407934.54  1\n",
              "3  144372.41  118671.85  383199.62  2\n",
              "4  142107.34   91391.77  366168.42  1"
            ],
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          element.innerHTML = '';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=0)\n",
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uyJ9Guazsc9z",
        "outputId": "1d3100ad-554c-4a79-c7b5-8e59b2d4867d"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[130298.13, 145530.06, 323876.68, 1],\n",
              "       [119943.24, 156547.42, 256512.92, 1],\n",
              "       [1000.23, 124153.04, 1903.93, 2],\n",
              "       [542.05, 51743.15, 0.0, 2],\n",
              "       [65605.48, 153032.06, 107138.38, 2],\n",
              "       [114523.61, 122616.84, 261776.23, 2],\n",
              "       [61994.48, 115641.28, 91131.24, 1],\n",
              "       [63408.86, 129219.61, 46085.25, 0],\n",
              "       [78013.11, 121597.55, 264346.06, 0],\n",
              "       [23640.93, 96189.63, 148001.11, 0],\n",
              "       [76253.86, 113867.3, 298664.47, 0],\n",
              "       [15505.73, 127382.3, 35534.17, 2],\n",
              "       [120542.52, 148718.95, 311613.29, 2],\n",
              "       [91992.39, 135495.07, 252664.93, 0],\n",
              "       [64664.71, 139553.16, 137962.62, 0],\n",
              "       [131876.9, 99814.71, 362861.36, 2],\n",
              "       [94657.16, 145077.58, 282574.31, 2],\n",
              "       [28754.33, 118546.05, 172795.67, 0],\n",
              "       [0.0, 116983.8, 45173.06, 0],\n",
              "       [162597.7, 151377.59, 443898.53, 0],\n",
              "       [93863.75, 127320.38, 249839.44, 1],\n",
              "       [44069.95, 51283.14, 197029.42, 0],\n",
              "       [77044.01, 99281.34, 140574.81, 2],\n",
              "       [134615.46, 147198.87, 127716.82, 0],\n",
              "       [67532.53, 105751.03, 304768.73, 1],\n",
              "       [28663.76, 127056.21, 201126.82, 1],\n",
              "       [78389.47, 153773.43, 299737.29, 2],\n",
              "       [86419.7, 153514.11, 0.0, 2],\n",
              "       [123334.88, 108679.17, 304981.62, 0],\n",
              "       [38558.51, 82982.09, 174999.3, 0],\n",
              "       [1315.46, 115816.21, 297114.46, 1],\n",
              "       [144372.41, 118671.85, 383199.62, 2],\n",
              "       [165349.2, 136897.8, 471784.1, 2],\n",
              "       [0.0, 135426.92, 0.0, 0],\n",
              "       [22177.74, 154806.14, 28334.72, 0]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LinearRegression()\n",
        "lr.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uDCcXr2isvn1",
        "outputId": "17ff80f6-ebc6-4a88-c6cd-cec342a941cb"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(lr.intercept_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PuZ6sGRas0zi",
        "outputId": "5f7146cf-1985-4690-8ddc-c335e25d4904"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "42346.06536079345\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = lr.predict(x_test)\n",
        "pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DmigPxvrtIGd",
        "outputId": "9363d7cd-6029-4271-a874-af534cb9aada"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([104055.1842384 , 132557.60289702, 133633.01284474,  72336.28081054,\n",
              "       179658.27210893, 114689.63133397,  66514.82249033,  98461.69321326,\n",
              "       114294.70487032, 169090.51127461,  96281.907934  ,  88108.30057881,\n",
              "       110687.1172322 ,  90536.34203081, 127785.3793861 ])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(y_test,pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "NDjnbUyntMsZ",
        "outputId": "08f248e6-5bd0-437e-82e5-cf619d22d765"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fd663608110>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "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": [
        "testScore = lr.score(x_test, y_test)\n",
        "print(testScore)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wtrdy-y2te0n",
        "outputId": "378f9309-9ede-427e-ffbb-c0153ec8f11c"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9355139722149948\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainScore = lr.score(x_train, y_train)\n",
        "print(trainScore)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KxCsd9dDtqkT",
        "outputId": "2bdd1f25-477e-46bf-8464-ce48b8d4bb0f"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9515496105627431\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prediction and actual values on test cases\n",
        "\n",
        "data = pd.DataFrame(data={'Prediction':pred.flatten(),'Actual':y_test.flatten()})\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "PW95HuuAt5vM",
        "outputId": "43a49f84-e34e-47a7-a354-0ef2e607fdba"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Prediction     Actual\n",
              "0   104055.184238  103282.38\n",
              "1   132557.602897  144259.40\n",
              "2   133633.012845  146121.95\n",
              "3    72336.280811   77798.83\n",
              "4   179658.272109  191050.39\n",
              "5   114689.631334  105008.31\n",
              "6    66514.822490   81229.06\n",
              "7    98461.693213   97483.56\n",
              "8   114294.704870  110352.25\n",
              "9   169090.511275  166187.94\n",
              "10   96281.907934   96778.92\n",
              "11   88108.300579   96479.51\n",
              "12  110687.117232  105733.54\n",
              "13   90536.342031   96712.80\n",
              "14  127785.379386  124266.90"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-51aa004f-753b-4aee-835d-3c56a1062f45\">\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>Prediction</th>\n",
              "      <th>Actual</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>104055.184238</td>\n",
              "      <td>103282.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>132557.602897</td>\n",
              "      <td>144259.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>133633.012845</td>\n",
              "      <td>146121.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>72336.280811</td>\n",
              "      <td>77798.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>179658.272109</td>\n",
              "      <td>191050.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>114689.631334</td>\n",
              "      <td>105008.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>66514.822490</td>\n",
              "      <td>81229.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>98461.693213</td>\n",
              "      <td>97483.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>114294.704870</td>\n",
              "      <td>110352.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>169090.511275</td>\n",
              "      <td>166187.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>96281.907934</td>\n",
              "      <td>96778.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>88108.300579</td>\n",
              "      <td>96479.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>110687.117232</td>\n",
              "      <td>105733.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>90536.342031</td>\n",
              "      <td>96712.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>127785.379386</td>\n",
              "      <td>124266.90</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-51aa004f-753b-4aee-835d-3c56a1062f45')\"\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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-51aa004f-753b-4aee-835d-3c56a1062f45 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-51aa004f-753b-4aee-835d-3c56a1062f45');\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",
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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Saving Trained Model"
      ],
      "metadata": {
        "id": "aYRTAV7Up-hl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# saving model\n",
        "import pickle\n",
        "\n",
        "with open(\"model3\",\"wb\") as f:\n",
        "    pickle.dump(lr,f)"
      ],
      "metadata": {
        "id": "VtXe72kWqFca"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# loading saved model\n",
        "\n",
        "with open(\"model3\",\"rb\") as f:\n",
        "    m3=pickle.load(f)"
      ],
      "metadata": {
        "id": "61wLvH_eqgUi"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "m3.predict(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jxhs4o_dwNcV",
        "outputId": "699c1b96-3f45-4740-b655-387e5dfd1adb"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([104055.1842384 , 132557.60289702, 133633.01284474,  72336.28081054,\n",
              "       179658.27210893, 114689.63133397,  66514.82249033,  98461.69321326,\n",
              "       114294.70487032, 169090.51127461,  96281.907934  ,  88108.30057881,\n",
              "       110687.1172322 ,  90536.34203081, 127785.3793861 ])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    }
  ]
}