startup_profits.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "alEtmoeao2UA"
      },
      "source": [
        "# startup prfits"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oICejxWpo2Ac"
      },
      "source": [
        "# importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "pxjJrrSfHo5C",
        "outputId": "989d4e0c-6b55-4515-adf7-1e0e8f004af6"
      },
      "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",
              "  <div id=\"df-4f24417a-6c0e-4c2c-a035-72d6511bd2e1\">\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",
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              "        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>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-4f24417a-6c0e-4c2c-a035-72d6511bd2e1')\"\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-4f24417a-6c0e-4c2c-a035-72d6511bd2e1 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-4f24417a-6c0e-4c2c-a035-72d6511bd2e1');\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": 4
        }
      ],
      "source": [
        "import pandas as pd\n",
        "data = pd.read_csv(\"/content/start_ups.csv\")\n",
        "data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y7aZdTDcwrit"
      },
      "source": [
        "# checking for missing data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bq2DtiOqQTOX",
        "outputId": "1548e8ef-c1f2-4627-8507-32866b57b47d"
      },
      "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": 5
        }
      ],
      "source": [
        "missing = data.isnull().sum()\n",
        "missing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "M6ZwHKCLxH6V",
        "outputId": "046784f8-c19a-4db0-8011-6fb1964d56c9"
      },
      "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-da9b9d2e-0eed-464d-addd-c5df52d561db\">\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-da9b9d2e-0eed-464d-addd-c5df52d561db')\"\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-da9b9d2e-0eed-464d-addd-c5df52d561db 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-da9b9d2e-0eed-464d-addd-c5df52d561db');\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": 6
        }
      ],
      "source": [
        "data.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ndrIsr6PQbCc"
      },
      "source": [
        "# data splitting (F\\L)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "uu_DlMqFQeE7"
      },
      "outputs": [],
      "source": [
        "x = data.iloc[:, 0:4]\n",
        "y = data.iloc[:, 4:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        },
        "id": "sYkvr-ykr-tA",
        "outputId": "ca5b17ad-3655-4f3c-8eac-5d3a7b9561b3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-2126b28b51ca>:5: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  print(x.corr())\n",
            "<ipython-input-8-2126b28b51ca>:8: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  dataplot = sb.heatmap(x.corr(), cmap=\"YlGnBu\", annot=True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 R&D Spend  Administration  Marketing Spend\n",
            "R&D Spend         1.000000        0.241955         0.724248\n",
            "Administration    0.241955        1.000000        -0.032154\n",
            "Marketing Spend   0.724248       -0.032154         1.000000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# import modules\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sb\n",
        "\n",
        "print(x.corr())\n",
        "  \n",
        "# plotting correlation heatmap\n",
        "dataplot = sb.heatmap(x.corr(), cmap=\"YlGnBu\", annot=True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "UW78EGGaQu3r"
      },
      "outputs": [],
      "source": [
        "x = pd.get_dummies(x, drop_first = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "QGl0sidJy35o",
        "outputId": "877fd239-40c2-4310-cd35-56ea52db46f0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    R&D Spend  Administration  Marketing Spend  State_Florida  State_New York\n",
              "0   165349.20       136897.80        471784.10              0               1\n",
              "1   162597.70       151377.59        443898.53              0               0\n",
              "2   153441.51       101145.55        407934.54              1               0\n",
              "3   144372.41       118671.85        383199.62              0               1\n",
              "4   142107.34        91391.77        366168.42              1               0\n",
              "5   131876.90        99814.71        362861.36              0               1\n",
              "6   134615.46       147198.87        127716.82              0               0\n",
              "7   130298.13       145530.06        323876.68              1               0\n",
              "8   120542.52       148718.95        311613.29              0               1\n",
              "9   123334.88       108679.17        304981.62              0               0\n",
              "10  101913.08       110594.11        229160.95              1               0\n",
              "11  100671.96        91790.61        249744.55              0               0\n",
              "12   93863.75       127320.38        249839.44              1               0\n",
              "13   91992.39       135495.07        252664.93              0               0\n",
              "14  119943.24       156547.42        256512.92              1               0\n",
              "15  114523.61       122616.84        261776.23              0               1\n",
              "16   78013.11       121597.55        264346.06              0               0\n",
              "17   94657.16       145077.58        282574.31              0               1\n",
              "18   91749.16       114175.79        294919.57              1               0\n",
              "19   86419.70       153514.11             0.00              0               1\n",
              "20   76253.86       113867.30        298664.47              0               0\n",
              "21   78389.47       153773.43        299737.29              0               1\n",
              "22   73994.56       122782.75        303319.26              1               0\n",
              "23   67532.53       105751.03        304768.73              1               0\n",
              "24   77044.01        99281.34        140574.81              0               1\n",
              "25   64664.71       139553.16        137962.62              0               0\n",
              "26   75328.87       144135.98        134050.07              1               0\n",
              "27   72107.60       127864.55        353183.81              0               1\n",
              "28   66051.52       182645.56        118148.20              1               0\n",
              "29   65605.48       153032.06        107138.38              0               1\n",
              "30   61994.48       115641.28         91131.24              1               0\n",
              "31   61136.38       152701.92         88218.23              0               1\n",
              "32   63408.86       129219.61         46085.25              0               0\n",
              "33   55493.95       103057.49        214634.81              1               0\n",
              "34   46426.07       157693.92        210797.67              0               0\n",
              "35   46014.02        85047.44        205517.64              0               1\n",
              "36   28663.76       127056.21        201126.82              1               0\n",
              "37   44069.95        51283.14        197029.42              0               0\n",
              "38   20229.59        65947.93        185265.10              0               1\n",
              "39   38558.51        82982.09        174999.30              0               0\n",
              "40   28754.33       118546.05        172795.67              0               0\n",
              "41   27892.92        84710.77        164470.71              1               0\n",
              "42   23640.93        96189.63        148001.11              0               0\n",
              "43   15505.73       127382.30         35534.17              0               1\n",
              "44   22177.74       154806.14         28334.72              0               0\n",
              "45    1000.23       124153.04          1903.93              0               1\n",
              "46    1315.46       115816.21        297114.46              1               0\n",
              "47       0.00       135426.92             0.00              0               0\n",
              "48     542.05        51743.15             0.00              0               1\n",
              "49       0.00       116983.80         45173.06              0               0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b00b1de7-0b74-46da-bfa2-4712caaa7b99\">\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_Florida</th>\n",
              "      <th>State_New York</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>1</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>0</td>\n",
              "      <td>0</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>1</td>\n",
              "      <td>0</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>1</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>1</td>\n",
              "      <td>0</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",
              "      <td>1</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>0</td>\n",
              "      <td>0</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>1</td>\n",
              "      <td>0</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",
              "      <td>1</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>0</td>\n",
              "      <td>0</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>1</td>\n",
              "      <td>0</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>0</td>\n",
              "      <td>0</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>1</td>\n",
              "      <td>0</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>0</td>\n",
              "      <td>0</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>1</td>\n",
              "      <td>0</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",
              "      <td>1</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>0</td>\n",
              "      <td>0</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",
              "      <td>1</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>1</td>\n",
              "      <td>0</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",
              "      <td>1</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>0</td>\n",
              "      <td>0</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",
              "      <td>1</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>1</td>\n",
              "      <td>0</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>1</td>\n",
              "      <td>0</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",
              "      <td>1</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>0</td>\n",
              "      <td>0</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>1</td>\n",
              "      <td>0</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",
              "      <td>1</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>1</td>\n",
              "      <td>0</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",
              "      <td>1</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>1</td>\n",
              "      <td>0</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",
              "      <td>1</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>0</td>\n",
              "      <td>0</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>1</td>\n",
              "      <td>0</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>0</td>\n",
              "      <td>0</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",
              "      <td>1</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>1</td>\n",
              "      <td>0</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>0</td>\n",
              "      <td>0</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",
              "      <td>1</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>0</td>\n",
              "      <td>0</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>0</td>\n",
              "      <td>0</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>1</td>\n",
              "      <td>0</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>0</td>\n",
              "      <td>0</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",
              "      <td>1</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>0</td>\n",
              "      <td>0</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",
              "      <td>1</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>1</td>\n",
              "      <td>0</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>0</td>\n",
              "      <td>0</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",
              "      <td>1</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>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b00b1de7-0b74-46da-bfa2-4712caaa7b99')\"\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-b00b1de7-0b74-46da-bfa2-4712caaa7b99 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-b00b1de7-0b74-46da-bfa2-4712caaa7b99');\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": 10
        }
      ],
      "source": [
        "x"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kvSyjXcrRptt"
      },
      "source": [
        "# Data scaling \n",
        "scale for ( x and y )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "jnUQuqEsRoYd"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "x_scaler = MinMaxScaler()\n",
        "y_scaler = MinMaxScaler()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "x3iOflBmSD6R"
      },
      "outputs": [],
      "source": [
        "x = x_scaler.fit_transform(x)\n",
        "y = y_scaler.fit_transform(y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OKDNNXFr86Zd"
      },
      "source": [
        "# save the scalar"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aE7JD05p89b3",
        "outputId": "4b892f85-f990-419d-c506-8696bcc721cc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['yscaler.save']"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "import joblib\n",
        "xscaler_filename = \"Xscaler.save\"\n",
        "yscaler_filename = \"yscaler.save\"\n",
        "joblib.dump(x_scaler, xscaler_filename) \n",
        "joblib.dump(y_scaler, yscaler_filename) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rfCvk-XgUTYW"
      },
      "source": [
        "# splitting (train test split)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "p4Innig7UW6M"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "azjP6gnfCiGk",
        "outputId": "eea93a1c-fc82-4d06-ff51-369755a11365"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "50\n",
            "40\n",
            "10\n"
          ]
        }
      ],
      "source": [
        "print(len(x))\n",
        "print(len(x_train))\n",
        "print(len(x_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "PvP1eGp8Ch-v"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y7wPfkpkUvTP"
      },
      "source": [
        "# Modeling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "TyA1W1_YUxQ0"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "yybsKkj7VnyE",
        "outputId": "2fd61a83-6d43-466d-8d34-6c408112d713"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "model1 = LinearRegression()\n",
        "model1.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Gql36IyFy-9",
        "outputId": "7c5b5238-907a-4a30-d1ca-a6689961e206"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9490275352642189"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "model1.score(x_test, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0-s0Xwjl-tKe"
      },
      "source": [
        "# extracting the weights"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "fo22W_Nk-wHI"
      },
      "outputs": [],
      "source": [
        "import pickle\n",
        "pickle.dump(model1, open('startup_weights.sav', 'wb'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "Yk6PY2sWOmoF",
        "outputId": "64f70785-7a0a-4dd4-db48-a0058494870b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ],
      "source": [
        "pickle.load(open('startup_weights.sav', 'rb'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wABGx2lAV0R9"
      },
      "source": [
        "# model evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rw1C_7dOV2kc",
        "outputId": "94a1a546-414f-43b4-987a-c98cd5b81ce7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9490275352642189\n"
          ]
        }
      ],
      "source": [
        "print(model1.score(x_test, y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "No_QsrayrK1r",
        "outputId": "b9a4cfc1-2036-4c18-d718-2d12c4634d8e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9492071221684192\n"
          ]
        }
      ],
      "source": [
        "print(model1.score(x_train, y_train))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KswK5V72xZ-f"
      },
      "source": [
        "#knn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "8yiR8lN1xdew"
      },
      "outputs": [],
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "5e6ftrpB4_cE",
        "outputId": "ab278791-0e0d-4f55-e904-3ff07b0775d2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsRegressor(n_neighbors=3)"
            ],
            "text/html": [
              "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor(n_neighbors=3)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "model2 = KNeighborsRegressor(n_neighbors=3)\n",
        "model2.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5OzrVfqV5fhL",
        "outputId": "4575bd50-f30d-4c2c-f3f9-5c2e36c76c94"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8530196957129562"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ],
      "source": [
        "model2.score(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qJMJMypW5lOM",
        "outputId": "84a3b9c9-a6e5-4b4c-e6fc-09b71bd279d6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6853662776709808"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "model2.score(x_test, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cXm1DjoD6Lp9"
      },
      "source": [
        "# random forset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "id": "bbAlWvvr6LWd",
        "outputId": "d510491d-51ad-45aa-8bee-fe6669af4081"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-65-7a1688eab01c>:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  model3.fit(x_train, y_train)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor()"
            ],
            "text/html": [
              "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "model3 = RandomForestRegressor()\n",
        "model3.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MWOXzVCJ6yHx",
        "outputId": "67eeecd0-679e-4dfb-e7e3-bafa6bc9bba5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9457782497473982"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "model3.score(x_train, y_train)\n",
        "model3.score(x_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kg0-smNv660L",
        "outputId": "1630a09f-01db-411d-e46b-238570249dd5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.98982750874332"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "model3.score(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mzM73S13dZaA"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}