MLR(50_startups).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyforest\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading datset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nimport sklearn\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('50_Startups.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renameing columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>RD_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>"
      ],
      "text/plain": [
       "     RD_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"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_rename=data.rename({\"R&D Spend\":\"RD_Spend\",\"Marketing Spend\":\"Marketing_Spend\"},axis=1)\n",
    "data_rename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# droping catogarical column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>RD_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>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</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>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>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>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>166187.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    RD_Spend  Administration  Marketing_Spend     Profit\n",
       "0  165349.20       136897.80        471784.10  192261.83\n",
       "1  162597.70       151377.59        443898.53  191792.06\n",
       "2  153441.51       101145.55        407934.54  191050.39\n",
       "3  144372.41       118671.85        383199.62  182901.99\n",
       "4  142107.34        91391.77        366168.42  166187.94"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d=data_rename.drop(columns = 'State')\n",
    "d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# No NaN values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50 entries, 0 to 49\n",
      "Data columns (total 4 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   RD_Spend         50 non-null     float64\n",
      " 1   Administration   50 non-null     float64\n",
      " 2   Marketing_Spend  50 non-null     float64\n",
      " 3   Profit           50 non-null     float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 1.7 KB\n"
     ]
    }
   ],
   "source": [
    "d.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correlation between indipendent variables Marketing_spend and RD_spend is comparitivly is 0.72 (not very high)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>RD_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>RD_Spend</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.241955</td>\n",
       "      <td>0.724248</td>\n",
       "      <td>0.972900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Administration</th>\n",
       "      <td>0.241955</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.032154</td>\n",
       "      <td>0.200717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marketing_Spend</th>\n",
       "      <td>0.724248</td>\n",
       "      <td>-0.032154</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.747766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Profit</th>\n",
       "      <td>0.972900</td>\n",
       "      <td>0.200717</td>\n",
       "      <td>0.747766</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 RD_Spend  Administration  Marketing_Spend    Profit\n",
       "RD_Spend         1.000000        0.241955         0.724248  0.972900\n",
       "Administration   0.241955        1.000000        -0.032154  0.200717\n",
       "Marketing_Spend  0.724248       -0.032154         1.000000  0.747766\n",
       "Profit           0.972900        0.200717         0.747766  1.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import pandas as pd\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import pandas as pd\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1f468a97588>"
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(style='darkgrid')\n",
    "sns.pairplot(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Buliding reggresion model for prediction of profit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Profit</td>      <th>  R-squared:         </th> <td>   0.951</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.948</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   296.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 17 May 2021</td> <th>  Prob (F-statistic):</th> <td>4.53e-30</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:09:15</td>     <th>  Log-Likelihood:    </th> <td> -525.39</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    50</td>      <th>  AIC:               </th> <td>   1059.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    46</td>      <th>  BIC:               </th> <td>   1066.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>       <td> 5.012e+04</td> <td> 6572.353</td> <td>    7.626</td> <td> 0.000</td> <td> 3.69e+04</td> <td> 6.34e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RD_Spend</th>        <td>    0.8057</td> <td>    0.045</td> <td>   17.846</td> <td> 0.000</td> <td>    0.715</td> <td>    0.897</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Administration</th>  <td>   -0.0268</td> <td>    0.051</td> <td>   -0.526</td> <td> 0.602</td> <td>   -0.130</td> <td>    0.076</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Marketing_Spend</th> <td>    0.0272</td> <td>    0.016</td> <td>    1.655</td> <td> 0.105</td> <td>   -0.006</td> <td>    0.060</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>14.838</td> <th>  Durbin-Watson:     </th> <td>   1.282</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.001</td> <th>  Jarque-Bera (JB):  </th> <td>  21.442</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.949</td> <th>  Prob(JB):          </th> <td>2.21e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.586</td> <th>  Cond. No.          </th> <td>1.40e+06</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.4e+06. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 Profit   R-squared:                       0.951\n",
       "Model:                            OLS   Adj. R-squared:                  0.948\n",
       "Method:                 Least Squares   F-statistic:                     296.0\n",
       "Date:                Mon, 17 May 2021   Prob (F-statistic):           4.53e-30\n",
       "Time:                        15:09:15   Log-Likelihood:                -525.39\n",
       "No. Observations:                  50   AIC:                             1059.\n",
       "Df Residuals:                      46   BIC:                             1066.\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===================================================================================\n",
       "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------\n",
       "Intercept        5.012e+04   6572.353      7.626      0.000    3.69e+04    6.34e+04\n",
       "RD_Spend            0.8057      0.045     17.846      0.000       0.715       0.897\n",
       "Administration     -0.0268      0.051     -0.526      0.602      -0.130       0.076\n",
       "Marketing_Spend     0.0272      0.016      1.655      0.105      -0.006       0.060\n",
       "==============================================================================\n",
       "Omnibus:                       14.838   Durbin-Watson:                   1.282\n",
       "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               21.442\n",
       "Skew:                          -0.949   Prob(JB):                     2.21e-05\n",
       "Kurtosis:                       5.586   Cond. No.                     1.40e+06\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.4e+06. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "model1=smf.ols(\"Profit~RD_Spend+Administration+Marketing_Spend\",data=d).fit()\n",
    "model1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept          50122.192990\n",
       "RD_Spend               0.805715\n",
       "Administration        -0.026816\n",
       "Marketing_Spend        0.027228\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# P values , administration and markiting_spend have p values > 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept          1.057379e-09\n",
       "RD_Spend           2.634968e-22\n",
       "Administration     6.017551e-01\n",
       "Marketing_Spend    1.047168e-01\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.pvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rsquared,adj.rsquared and aic values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9507459940683246, 0.9475337762901719, 1058.7714985998055)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1_values=(model.rsquared,model.rsquared_adj,model.aic)\n",
    "model1_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "\n",
    "Here rsquared value is 0.95 which idicates the model we build is good, but we can improve the model by \n",
    "model deletion diagnostics by removing influencers and outliers in our data using cooks distance.We can also try to reduce AIC \n",
    "value, lesser the aic value better the model.\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept         0.003824\n",
       "Administration    0.162217\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slr_model=smf.ols('Profit~Administration',data=d).fit()\n",
    "slr_model.pvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model for only marketing_spend , which shows the p value is <0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept          4.294735e-10\n",
       "Marketing_Spend    4.381073e-10\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slr_model2=smf.ols('Profit~Marketing_Spend',data=d).fit()\n",
    "slr_model2.pvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finding VIF values for each variable\n",
    "# As the vif value of variables are < 10 ,we can say variables are good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variables</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RD_Spend</td>\n",
       "      <td>2.468903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Administration</td>\n",
       "      <td>1.175091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Marketing_Spend</td>\n",
       "      <td>2.326773</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         variables       VIF\n",
       "0         RD_Spend  2.468903\n",
       "1   Administration  1.175091\n",
       "2  Marketing_Spend  2.326773"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rsq_rd=smf.ols(\"RD_Spend~Administration+Marketing_Spend\",data=d).fit().rsquared\n",
    "vif_rd=1/(1-rsq_rd)\n",
    "rsq_admin=smf.ols(\"Administration~RD_Spend+Marketing_Spend\",data=d).fit().rsquared\n",
    "vif_admin=1/(1-rsq_admin)\n",
    "rsq_mark=smf.ols(\"Marketing_Spend~Administration+RD_Spend\",data=d).fit().rsquared\n",
    "vif_mark=1/(1-rsq_mark)\n",
    "vif={'variables':['RD_Spend','Administration','Marketing_Spend'],'VIF':[vif_rd,vif_admin,vif_mark]}\n",
    "vif_df=pd.DataFrame(vif)\n",
    "vif_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# QQplot for residuals checking if there are following normal distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "qqplot=sm.qqplot(model1.resid,line='q') \n",
    "plt.title(\"Normal Q-Q plot of residuals\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data point which is far away form line(outlier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[array([49], dtype=int64)]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(np.where(model.resid<-30000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Residual Plot for finding weather there is  Homoscedasticity or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_standardized_values( vals ):\n",
    "    return (vals - vals.mean())/vals.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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aTtZJtOB7eHhgx44daNWqVbMb02q1WLx4MS5fvgwHBwcsXboUnTp1avZ+5c7YAi73MWRTnYHU9er1/ayVNVqczC3BV/H9m52XTM8azkKtkbmv3YmO4cfExJik2APAkSNHUF1djYyMDCQmJmLFihUm2a/cGTseLfcxZFOcgTx6ncLYdsjy5H4Wao2kuHZn1ANQmuv06dMICQkBAPTu3Rvnzp1r8OttbVXw9HSRItojbdqYvM05oc/hg33nUKl+ZDza3gZzQp/T21Z0sC9cXRyx5vAV3LpXiWc8nJA4zA+je7UzW0ZjPOPhhAI9dwY+4+GkyyWWcd3JvCd6iA3tz9QsfQwbQ84ZG/M3IBdyPo51bG1t9P5PVNZose5knskWV5O04JeVlcHNzU33ua2tLWpqamBnpz+GRiNIMjf1UZ6eLiZvc1AnTywY1v2JU7VBnTwNtjWok+cTa2vUfa05Mhrj7YGd9D568O2BnRqdUexWcic7GwR39kTIqmNmOb219DFsDDlnbMzfgFzI+TjW8fR0Mfg/cetepdH5vb3d9W43WPAjIyN1T7qqIwgCVCoV/vGPfxjVeB03NzeUl5frPtdqtQaLfUvTki4CmmIWi6HrFEDtXcOPz9Jp6kVBpd/PYC51x5CzdExHimt3BqvtZ599ZrJG6vTp0wfHjh1DWFgYzpw5Az8/aW4nJtNr7hvYwC6t8eXZ209sH9+rLea/7IfwtOxmXxRsaCaJVOuPt2Qj/H0QHewr+96ztZge0lnvWZMpr90ZLPjPPvssACAvLw/ffvst1Go1gNq18T/++OMmNTZs2DCcPHkSUVFREAQBSUlJTdoPWb+TuSV6t+/95TZ6PethkouCDc0kYcEnuZHi/g/R8ZTExEQMGzYMP/30E55++mlUVDT93dzGxqbJbxbUshgq3FoBSDp0Fe6OtrhfpXnidWNObzmTRJ44zGaYuYd+Radluri44K233oKPjw9WrFiBO3fumC0MKUdDhbuyRguVStXsqamWXJ6B9OOyIZYlWvBVKhWKi4tRXl6OioqKZvXwierou9fgUfcra5q9/LPc72dQIi49bVmiQzozZ87E4cOHMWbMGLz88ssYM2aMFLlIYlKfZtfte/HBy3ofy+jj7tjs01uuiSM/HGazLNGC369fP936OUOHDjV7IJKepdZFqdu3OWcmtKTpsC2B3JcNaelEC/6QIUPqzcd3c3PDvn37zBqKpGXJdVHYC1cWKaYekmGiBf/bb78FUHvT1blz53SfU8th6dNs9sKVg2/wltWo9fDr9O3b1yw3ZJFl8TSbpMQ3eMsRLfhr1qzRDekUFRXBxkZ0Yo9FcY6v8XiaTaQMogW/S5cuuo979OihW+1SjvhQhqbhaTY7CpbEYy8dgwX/1KlTAID27dvX237p0iXZPvWKD2VoOiWfZrOjYDk89tIyWPDT09MBAPn5+VCr1fjjH/+ICxcuwNXVFdu3b5csoDEsffGR5OvxXuSc0OdQXlHV4BO3pOgoPPrULxtV7dISbf/by1XCej/spElLdLXM+Ph4pKSkwM7ODhqNBvHx8ZKFMxYvPpI++nqR8/fmQNAKqNFz01cdc3cUHs9VdwNaXS/X1cURgzp5mjWDuTR2mIadNGmJXoEtLi7WfazRaHD37l2zBmoO3kpP+ujrRao1DRd7wPwdBX256lTWaLHm8BWztm8uxqyXw/WOpCV60XbChAkYOXIk/Pz8cPXqVcTFxUmRq0l48ZH0aUpvUYqOglgusaeCyZUxwzScISYt0YI/efJkDB8+HPn5+ejUqRO8vLykyNVkSr74SPo19HQtfdpK1FEQy/WMh5NZ2zcXY4Zp2EmTlsGCn5KSgunTpyMhIeGJRx2uWbPG7MGITEVfL9LeVvXEGL6Tnc0TK3Kac8qgvlyPZkkcZp1PhDP2WpoUnTRO/axlsOAPGTIEABAVFSVZGCJz0NeLfHSWjqEiYO4pg4/m0jdLZ3Svdlb5+EC5DdPsP1vAqZ//pRIEocFLV4WFhXjw4AFsbGywYcMGxMTEwN/fX5JwarVG8j/4x59wL8eeweMZ5UjuGRuTLzwtW29Pta27I76K72+uaDpyP4aA4Yym+r8xxX7GbPh/KNBzPUSq32NjmPp37e3trnd7ox5xOHPmTOzcuROhoaFISkqS7Tx8U+NNIcrGKYNNZ4phGlP9/xm6+K3E32OjnnjVr18/3L9/HyNHjpT9WjqmxKfzKBunDFqWqf7/DF38VuLvUbR619TUYPXq1fjTn/6EH3/8EWq1WopcssAenrLxvg7LMtX/X+IwP/4e/0u04C9fvhwdOnRAfHw87t69i5UrV0qRSxbYw1O2Ef4+zX6uLjWdqf7/Rvdqx9/jf4mO4Xfo0AEODg5ITU3FCy+8AFdXVylyyYLcZhuQ9Hhfh+WY8v+Pv8daoj38RYsWoaCgAN9//z3Ky8sxb948KXLJAnt4RJbD/z/TE+3h5+fnY9myZTh9+jSGDBmCtLQ0KXLJBnsGRJbD/z/TEu3hP7pgWllZmaJm6RARtSSiPfzZs2cjOjoaxcXFiIyMxAcffCBFLiIiMjHRgn/r1i189913uHv3Llq3bv3EujrGOnz4ML799luux0NEJDHRgr9r1y6MHj3aJKtkLl26FCdOnJBsaQaixpDj8hlE5iC6ls7EiRNRXV0NX19f3fh9U3vnBw4cgJeXFzIyMvD555+Lfr1Wq4VGI/KUChOztbWBRqP/oRRywYzNV5dv/9kCfLDvHCrVj0z9s7fBsjEBGN2rnQUTyv8YAsxoKqbOaG9vq3e7aA//vffeM7qxzMxMbN26td62pKQkhIWFITs7u9H70WgEiy+eJkfM2Hx1+VZ/d7lesQeASrUWq7+7bPHHC8r9GALMaCqyWTzt+eefN7qxiIgIREREGP19RFLj8hmkJJxjSYrG5TNISVjwSdG4QBopicEhnffff9/gNy1fvrzJDfbv3x/9+8vjoQNEfKYqKYnBgh8WFgYASE9PR1BQEPr06YOcnBzk5ORIFo5ICrx9n5TC4JBOSEgIQkJCUFlZibi4OPTt2xexsbG6ZRaIiMi6iI7hV1RU4IcffkBZWRmysrJQVcXZC0RE1kh0WuayZcuwevVq3LhxA926dVPUA1CIiFoS0YLftWtXzJs3D3l5eejRowd8fDjWSURkjUQL/t///nccPnwY9+7dw9ixY5GXl4dFixZJkY2IiExIdAz/m2++webNm+Hu7o6pU6fi7NmzUuQiIiITEy34giBApVLplkV2cHAweygiIjI90SGdkSNHYvLkySgoKEBcXBxefvllKXIREZGJiRb8mJgYDBgwAFeuXIGvry/atbPskrFNxTXPiUjpRId0Xn75ZajVaowYMQI9evTAzJkzpchlUgcvFiLp0FXcflAFAcDtB1VIOnQVBy8WWjoaEZFkRAu+s7MzlixZgh9//BFA7Zi+tUnJuoHKmsfWPK/RIiXrhmUCERFZgOiQTuvWrZGcnIwZM2agpKQEdnai3yI7XPOciKiRs3Q8PDzwxRdfYPfu3bh8+bIUuUyKa54TETWi4CcmJgIAXF1dkZqaijfeeMPsoUyNa54TETUwpJOZmYmIiAj861//wtGjR6XMZHJc85yIqIGC37ZtWwBAly5dJAtjTlzznIiUzmDBd3JywqlTp9C+fXsp8xARkZkYLPjp6ekAgPz8fKjVavzxj3/EhQsX4Orqiu3bt0sWkIiITMNgwf/ss88AAPHx8UhJSYGdnR00Gg3i4+MlC0dERKYjOqm+uLhY97FGo+EjDolaMC5B0rKJFvwJEyZg5MiR8PPzw9WrV9nDJ2qh6pYgqbsrvW4JEgAs+i1Eo554tXPnTuTn56NTp07w8vKSIhcRSayhJUhG+Pvo7f1HB/taKC01heiNV8nJyWjTpg2CgoJY7IlasIaWIDG0AOH+swXShqRmEe3hq1QqzJgxA76+vrCxqX1/SEhIMHswIpKWj7sjbusp+j7ujgZ7/2sOX8GgN5+XKiI1k2jBHz9+vBQ5iMjCpod0rjeGD/y+BMlHB/SvoXXrXqVU8cgERAt+eHg4cnJyUFNTA0EQUFRU1KSGHjx4gDlz5qCsrAxqtRrz589HUFBQk/ZFRKbX0BIkKVk39Pb+n/FwkjomNYNowZ85cybUajWKioqg0Wjw9NNPY9SoUUY3tHnzZrzwwguIjY1Fbm4uEhMTsXfv3iaFJiLzMLQEiaHef+IwPynjUTOJXrQtKSnBxo0bERgYiD179qCqqmlryMfGxiIqKgpA7Xx+R0cuTUxkLUb4+2DBK93R1t0RKgBt3R2x4JXuGN3LOh95qlSiPXwnp9pTtocPH8LJyQkqlUp0p5mZmdi6dWu9bUlJSQgMDERxcTHmzJmDBQsWiO7H1lYFT08X0a8zJVtbG8nbNBYzNp/c8wHyyxgd7PvENEy5ZdSHGX+nEkSeWbhjxw6UlJTAwcEBR44cgYuLC7Zs2dKkxi5fvoyEhATMnTsXgwcPFv16tVqD0tKKJrXVVJ6eLpK3aSxmbD655wOY0VSUmNHb213vdtEe/uTJk3UfDx48GJ07d25SgGvXruGdd97BX/7yF/To0aNJ+yAioqYzWPDff/99g9+0fPlyoxtas2YNqqursWzZMgCAm5sbUlNTjd4PERE1jcGCHxYWBqB2meSgoCD06dMHOTk5yMnJaVJDLO5ERJZlcJZOSEgIQkJCUFlZibi4OPTt2xexsbFcLZOIyEqJTsusqKjADz/8gLKyMmRlZTV5WiYREVmW6EXbZcuWYfXq1fjPf/6D7t27Y+XKlVLkIiIiE2vU8sjr1q2TIgsREZmRaMFft24dNmzYoLsBCwBOnDhh1lBERGR6ogX/wIEDyMrKgrOzsxR5iIjITEQv2rZv375e756IiKyTaA9frVYjPDwcfn61q+KpVCqsWbPG7MGIiMi0RAt+XFycFDmIiMjMRId0/Pz8UFRUhIKCAvz666/4+eefpchFREQm1qgHoHTp0gVXrlyBo6MjL94SEVkp0R6+IAj4+OOP4evri82bN6O0tFSCWEREZGqiBd/W1hZVVVV4+PAhVCoVNBqNFLmIiMjERAv+5MmTsXXrVgwcOBCDBw9G+/btpchFREQmJjqG365dO4SGhgIARowYgQsXLpg9FBERmZ7Bgv/vf/8b165dw5YtW/Daa68BALRaLXbs2IGvv/5asoBERGQaBgt+q1atcOfOHVRXV6O4uBhA7U1Xc+bMkSwcERGZjsGC7+fnBz8/P0RERMDHxwf379+HjY0N3NzcpMxHREQmYvCi7fnz5/Hqq6/Cy8sLhw4dQmhoKMaPH4+jR49KmY+IiEzEYMFftWoVVqxYAXt7e/zlL3/B+vXr8eWXXyItLU3KfEREZCIGh3S0Wi169OiBwsJCPHz4EAEBAQAAGxvRmZxERCRDBqu3nV3te0FWVhaCg4MB1K6cWV5eLk0yIiIyKYM9/ODgYERFReH27dtITU1Ffn4+Pv74Y4SFhUmZj4iITMRgwY+Pj8fQoUPh5uYGHx8f5OfnIzIyEsOGDZMyHxERmUiDd9p27dpV93HHjh3RsWNHswciIiLz4BVYIiKFYMEnIlII0cXTTKWiogKJiYm4f/8+7O3tsXLlSvj4+EjVPBGR4knWw9+1axd69uyJHTt2YPTo0Vi/fr1UTRMRESTs4cfGxuoenlJQUIBWrVpJ1TQREQFQCYIgmHqnmZmZ2Lp1a71tSUlJCAwMxJQpU3DlyhVs3rwZ/v7+De5Hq9VCozF5vAbZ2tpAo9FK2qaxmLH55J4PYEZTUWJGe3tbvdvNUvDFXL9+HW+99RaOHDnS4Nep1RqUllZIlKqWp6eL5G0aixmbT+75AGY0FSVm9PZ217tdsjH8L774Av/85z8BAK6urrC11f8ORERE5iHZGP748eMxb948fPnll9BoNEhKSpKqaSIigoQF/6mnnsLGjRulao6IiB7DG6+IiBSCBZ+ISCFY8ImIFIIFn4hIIVjwiYgUggWfiEghWPCJiBSCBZ+ISCFY8ImIFIIFn4hIIVjwiYgUggWfiEghWPCJiBSCBZ+ISCFY8ImIFIIFn4hIIVjwiYgUggWfiEghJHvEoaUcvFiIlKwbKHxQBR93R0wP6YwR/j6WjkVEJLkWXfAPXixE0qGrqKzRAgBuP6hC0qGrAMCiT0SK06KHdFKybuiKfZ3KGi1Ssm5YJhARkQW16IJf+KDKqO1ERC1Ziy74Pu6ORm0nImrJWnTBnx7SGU529X9EJzsbTA/pbJlAREQW1KIv2tZdmOUsHSKiFl7wgdqizwJPRNTCh3SIiOh3khf869evo2/fvqiq4kwZIiIpSVrwy8rKsHLlSjg4OEjZLBERQcKCLwgCPvzwQyQkJMDZ2VmqZomI6L/MctE2MzMTW7durbetXbt2CAsLQ48ePRq9H1tbFTw9XUwdT6RNG8nbNBYzNp/c8wHMaCrM+DuVIAiC2VsBMGzYMLRt2xYAcObMGQQGBmLHjh1SNE1ERJCw4D9qyJAhOHjwIBwdeccrEZFUOC2TiEghLNLDJyIi6bGHT0SkECz4REQKwYJPRKQQLPhERArBgg/g8OHDSExM1Pva0qVLMW7cOMTExCAmJgYPHjyQOF2thjLu2rUL48aNw8SJE3Hs2DGJkwGVlZX4n//5H0yaNAlxcXG4e/fuE18zbdo0REVFISYmBm+++aZk2bRaLRYtWoTIyEjExMQgLy+v3uuWPnaNySiXv0EAOHv2LGJiYp7YfvToUYwfPx6RkZHYtWuXBZL9zlDGLVu2YOTIkbrjmJubK3k2tVqNOXPmYNKkSZgwYQL+9a9/1Xvd7MdRULhPPvlECA0NFd599129r0dFRQm//fabxKnqayhjUVGRMGrUKKGqqkq4f/++7mMpbdq0Sfjb3/4mCIIgfP3118Inn3zyxNeMGDFC0Gq1kuYSBEH47rvvhHnz5gmCIAg///yz8Pbbb+tek8OxE8soCPL4GxQEQUhLSxNGjRolRERE1NteXV0tvPzyy0JpaalQVVUljBs3TiguLpZVRkEQhMTERCEnJ8cCqX63e/duYenSpYIgCEJJSYkwePBg3WtSHEfF9/D79OmDxYsX631Nq9UiLy8PixYtQlRUFHbv3i1tuP9qKOMvv/yCoKAgODg4wN3dHR07dsSlS5ckzXf69GmEhIQAAAYNGoQffvih3ut37tzB/fv38fbbbyM6OlrSnvSj2Xr37o1z587pXpPDsRPLKJe/QQDo2LEjkpOTn9h+/fp1dOzYER4eHnBwcEDfvn1x6tQpCyQ0nBEAzp8/j7S0NERHR+OLL76QOFmt4cOH45133gFQu76Yra2t7jUpjmOLfwBKHX3r+yQlJSEsLAzZ2dl6v6eiogJ//vOf8dprr0Gj0WDKlCkICAgwaj0gc2csKyuDu7u77nNXV1eUlZWZJZ+hjG3atNFlcHV1fWLIQa1W4/XXX8eUKVNw7949REdHIzAwEG3atDFbzjplZWVwc3PTfW5ra4uamhrY2dlJfuyaklHqv8GGhIaG4ubNm09sl8txBAxnBICRI0di0qRJcHNzw8yZM3Hs2DG89NJLkuZzdXUFUHvMZs2ahXfffVf3mhTHUTEFPyIiAhEREUZ9j7OzM6ZMmaJb3fOFF17ApUuXzPbP1pSMbm5uKC8v131eXl5e74/G1PRlnDlzpi5DeXk5WrVqVe/1p556ClFRUbCzs0ObNm3g7++P//znP5IU/MePj1arhZ2dnd7XzH3smpJR6r/BppDLcWyIIAiYOnWqLtfgwYNx4cIFyQs+ANy6dQszZszApEmTEB4ertsuxXFU/JBOQ27cuIHo6GhoNBqo1Wr89NNP6Nmzp6Vj1RMYGIjTp0+jqqoKDx48wPXr1+Hn5ydphj59+uD//u//AADHjx9H3759673+/fff605jy8vLcfXqVXTp0kWybMePHwdQu2jfo8dGDsdOLKM1/A127doVeXl5KC0tRXV1Nf79738jKCjI0rHqKSsrw6hRo1BeXg5BEJCdnY2AgADJc9y5cwevv/465syZgwkTJtR7TYrjqJgevjE2b96Mjh07YujQoRgzZgwmTpwIe3t7jBkzBt27d7d0PAD1M8bExGDSpEkQBAGzZ8+WfFG66OhozJs3D9HR0bC3t8eaNWsAAKtWrcLw4cMxePBgnDhxAhMnToSNjQ0SEhLg5eUlSbZhw4bh5MmTiIqKgiAISEpKktWxa0xGuf4NfvXVV6ioqEBkZCTmz5+PN954A4IgYPz48fDxkcdzpB/NOHv2bEyZMgUODg4IDg7G4MGDJc+zbt063L9/HykpKUhJSQFQe9b88OFDSY4j19IhIlIIDukQESkECz4RkUKw4BMRKQQLPhGRQrDgExEpBAs+mUxaWhpiY2Px5z//GTExMbolAi5fvtzsW8SPHz+O+fPnN+l7Z8+ejezsbBw/fhwZGRkmzxAQEKBbkCsmJgaLFy9GcXGxbjmMU6dO6ZZsOHz4MAoLC5vVXmPNnz9fN7+fCOA8fDKRa9eu4ejRo0hPT4dKpcLFixcxb9487N+/H4cOHcJTTz2Ffv36WTTjoEGDzLJfDw8PbN++/YntdQX/yy+/RFhYGHr06IFt27Zh8eLFspmnTsrCgk8m4e7ujoKCAuzevRuDBg2Cv78/du/ejcLCQuzduxf29vbo2bMnCgoKsGPHDtTU1EClUmHt2rW4evUq1q9fD3t7e9y8eRNhYWGYNm0arl+/jgULFsDZ2RnOzs7w8PAAAPz973/HoUOH8PDhQ7Ru3Rpr167F119/jS+//BJarRazZs1Cbm4uMjMz4e3tjd9++w0AsGfPHuTm5mLo0KH47LPPAAAlJSWoqKjA0aNHsX37dnz99ddQqVQICwvDlClTDGYQc/PmTSQkJGDRokXIysrC+fPn8dtvv+neCHfu3ImMjAyj21u+fDl69OiBsWPHori4GG+99RYyMzOxaNEi3L59G0VFRRgyZAhmz56t+566n/u9995DVVUVRowYgaNHj+Ly5ctYunQpAMDT0xNJSUlQq9V49913IQgCqqqqsGTJEvj7+zf774PkgUM6ZBI+Pj5ITU3FTz/9hMjISAwfPhzHjh2Dj48Pxo4di9jYWAQGBuLGjRtIS0tDeno6unXrhhMnTgAACgoKkJycjIyMDGzYsAFA7Z26s2bNwpYtW3S3mGu1WpSWlmLLli3IzMyERqNBTk4OAKBVq1ZIT09H9+7dsW3bNuzatQspKSlQq9X1sgYFBWH79u1ITk6Gm5sbkpOTce3aNRw4cAA7d+7Ejh07cOTIEeTm5urN8Lh79+7VG9J5dLXLgIAAhISEYM6cORg7diz8/f2xcuVK5OfnN6m9iIgI7N27FwCwb98+jBs3Drdu3ULv3r2xceNG7N69G//4xz8a9Tv78MMP8dFHH2H79u0YNGgQNmzYgF9++QWenp5Yv349Fi1ahIqKikbti6wDe/hkEnl5eXBzc8Py5csBADk5OYiLi0P//v3rfV2bNm0wb948uLq6Ijc3F7179wYA+Pn5wc7ODnZ2dnBycgJQu45MYGAggNr1ZnJzc2FjYwN7e3skJCTAxcUFt2/fRk1NDQDA19cXAJCfn49u3brBwcEBAHT7eFR5eTlmzJiBWbNmoWfPnjhw4AAKCgoQGxsLoLaI5+Xl6c3wOH1DOoZWbKxz5cqVJrXXrVs3aDQa/Prrrzhw4AC2bNkCGxsb5OTk4Mcff4Sbmxuqq6sNtvvojfXXr1/HkiVLANSuaNq5c2cMGjQIN27cwPTp02FnZ4dp06Y1+HOQdWHBJ5O4fPkyMjIykJqaCgcHB/j6+qJVq1awtbWFSqWCVqvFgwcP8Le//Q3/+7//CwB47bXXdAVIpVI9sc+uXbvi559/xqBBg3S95kuXLuHIkSPIzMzEw4cPMW7cON0+bGxqT1g7d+6Ma9euobKyEvb29rh48SJGjx6t2291dTVmzZqFyZMnY8CAAQCALl26oFu3btiwYQNUKhW2bNmC5557Tm8GY6lUqno/pyAIzWpvwoQJWL16Nbp164ZWrVph27ZtcHd3x8cff4y8vDzs2rWrXmF3dHREcXExgNo14ev4+vpi5cqVaNeuHU6fPo3i4mJkZ2fj6aefxqZNm/Dzzz/js88+03t9gqwTCz6ZxCuvvILr169jwoQJcHFxgSAImDt3Ltzd3REQEIBVq1aha9eu6NOnDyIjI2FnZ4dWrVqhqKgI7du317vP+fPnY968edi4cSO8vLzg6OiITp06wdnZGVFRUQAAb29vFBUV1fs+Ly8vxMXFISoqCl5eXrqlhets27YN58+fR01NDdLT0wEAycnJCA4ORnR0NKqrqxEYGAgfHx+9GYzVq1cvfPrpp2jfvj2CgoIwd+5cbNq0qcntDR8+HMuWLUNqaioAIDg4GImJiThz5gwcHBzQqVOnesckJCQE6enpiI6ORs+ePXVrsi9evBjz5s3TXU9ZtmwZPD09kZCQgPT0dNTU1GDGjBlG/7wkX1w8jYhIIXjRlohIIVjwiYgUggWfiEghWPCJiBSCBZ+ISCFY8ImIFIIFn4hIIf4/TT/ye9hj7PcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(get_standardized_values(model1.fittedvalues),\n",
    "            get_standardized_values(model1.resid))\n",
    "\n",
    "plt.title('Residual Plot')\n",
    "plt.xlabel('Standardized Fitted values')\n",
    "plt.ylabel('Standardized residual values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Residual Vs Regressors plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,8))\n",
    "fig = sm.graphics.plot_regress_exog(model, \"RD_Spend\", fig=fig)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,8))\n",
    "fig = sm.graphics.plot_regress_exog(model, \"Administration\", fig=fig)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,8))\n",
    "fig = sm.graphics.plot_regress_exog(model, \"Marketing_Spend\", fig=fig)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Deletion Diagnostics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# COOK'S DISTANCE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1_influence = model1.get_influence()\n",
    "(d, _) = model1_influence.cooks_distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.subplots(figsize=(20, 7))\n",
    "plt.stem(np.arange(len(d)), np.round(d, 3))\n",
    "plt.xlabel('Row index')\n",
    "plt.ylabel('Cooks Distance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Influensing Data point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(49, 0.28808229275432634)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.argmax(d),np.max(d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# High Influence points . We can see there are 5 influencing DataPoints : 49,48,46,19,6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nimport sklearn\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.regressionplots import influence_plot\n",
    "influence_plot(model1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Most influensing DataPoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RD_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>49</th>\n",
       "      <td>0.0</td>\n",
       "      <td>116983.8</td>\n",
       "      <td>45173.06</td>\n",
       "      <td>14681.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    RD_Spend  Administration  Marketing_Spend   Profit\n",
       "49       0.0        116983.8         45173.06  14681.4"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[d.index.isin([49])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RD_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>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</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>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>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>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>166187.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    RD_Spend  Administration  Marketing_Spend     Profit\n",
       "0  165349.20       136897.80        471784.10  192261.83\n",
       "1  162597.70       151377.59        443898.53  191792.06\n",
       "2  153441.51       101145.55        407934.54  191050.39\n",
       "3  144372.41       118671.85        383199.62  182901.99\n",
       "4  142107.34        91391.77        366168.42  166187.94"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Droping the influensing DataPoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>RD_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>44</th>\n",
       "      <td>44</td>\n",
       "      <td>22177.74</td>\n",
       "      <td>154806.14</td>\n",
       "      <td>28334.72</td>\n",
       "      <td>65200.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>45</td>\n",
       "      <td>1000.23</td>\n",
       "      <td>124153.04</td>\n",
       "      <td>1903.93</td>\n",
       "      <td>64926.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>46</td>\n",
       "      <td>1315.46</td>\n",
       "      <td>115816.21</td>\n",
       "      <td>297114.46</td>\n",
       "      <td>49490.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>0.00</td>\n",
       "      <td>135426.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>42559.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>48</td>\n",
       "      <td>542.05</td>\n",
       "      <td>51743.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>35673.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  RD_Spend  Administration  Marketing_Spend    Profit\n",
       "44     44  22177.74       154806.14         28334.72  65200.33\n",
       "45     45   1000.23       124153.04          1903.93  64926.08\n",
       "46     46   1315.46       115816.21        297114.46  49490.75\n",
       "47     47      0.00       135426.92             0.00  42559.73\n",
       "48     48    542.05        51743.15             0.00  35673.41"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_drp=d.drop(d.index[49],axis=0).reset_index()\n",
    "d_drp.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RD_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>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>35673.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     RD_Spend  Administration  Marketing_Spend     Profit\n",
       "0   165349.20       136897.80        471784.10  192261.83\n",
       "1   162597.70       151377.59        443898.53  191792.06\n",
       "2   153441.51       101145.55        407934.54  191050.39\n",
       "3   144372.41       118671.85        383199.62  182901.99\n",
       "4   142107.34        91391.77        366168.42  166187.94\n",
       "5   131876.90        99814.71        362861.36  156991.12\n",
       "6   134615.46       147198.87        127716.82  156122.51\n",
       "7   130298.13       145530.06        323876.68  155752.60\n",
       "8   120542.52       148718.95        311613.29  152211.77\n",
       "9   123334.88       108679.17        304981.62  149759.96\n",
       "10  101913.08       110594.11        229160.95  146121.95\n",
       "11  100671.96        91790.61        249744.55  144259.40\n",
       "12   93863.75       127320.38        249839.44  141585.52\n",
       "13   91992.39       135495.07        252664.93  134307.35\n",
       "14  119943.24       156547.42        256512.92  132602.65\n",
       "15  114523.61       122616.84        261776.23  129917.04\n",
       "16   78013.11       121597.55        264346.06  126992.93\n",
       "17   94657.16       145077.58        282574.31  125370.37\n",
       "18   91749.16       114175.79        294919.57  124266.90\n",
       "19   86419.70       153514.11             0.00  122776.86\n",
       "20   76253.86       113867.30        298664.47  118474.03\n",
       "21   78389.47       153773.43        299737.29  111313.02\n",
       "22   73994.56       122782.75        303319.26  110352.25\n",
       "23   67532.53       105751.03        304768.73  108733.99\n",
       "24   77044.01        99281.34        140574.81  108552.04\n",
       "25   64664.71       139553.16        137962.62  107404.34\n",
       "26   75328.87       144135.98        134050.07  105733.54\n",
       "27   72107.60       127864.55        353183.81  105008.31\n",
       "28   66051.52       182645.56        118148.20  103282.38\n",
       "29   65605.48       153032.06        107138.38  101004.64\n",
       "30   61994.48       115641.28         91131.24   99937.59\n",
       "31   61136.38       152701.92         88218.23   97483.56\n",
       "32   63408.86       129219.61         46085.25   97427.84\n",
       "33   55493.95       103057.49        214634.81   96778.92\n",
       "34   46426.07       157693.92        210797.67   96712.80\n",
       "35   46014.02        85047.44        205517.64   96479.51\n",
       "36   28663.76       127056.21        201126.82   90708.19\n",
       "37   44069.95        51283.14        197029.42   89949.14\n",
       "38   20229.59        65947.93        185265.10   81229.06\n",
       "39   38558.51        82982.09        174999.30   81005.76\n",
       "40   28754.33       118546.05        172795.67   78239.91\n",
       "41   27892.92        84710.77        164470.71   77798.83\n",
       "42   23640.93        96189.63        148001.11   71498.49\n",
       "43   15505.73       127382.30         35534.17   69758.98\n",
       "44   22177.74       154806.14         28334.72   65200.33\n",
       "45    1000.23       124153.04          1903.93   64926.08\n",
       "46    1315.46       115816.21        297114.46   49490.75\n",
       "47       0.00       135426.92             0.00   42559.73\n",
       "48     542.05        51743.15             0.00   35673.41"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_new=d_drp.drop(['index'],axis=1)\n",
    "d_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Buliding reggresioin model after removing most influencing DataPoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9613162435129847, 0.9587373264138503, 1020.5652974526367)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2=smf.ols(\"Profit~RD_Spend+Administration+Marketing_Spend\",data=d_new).fit()\n",
    "model2_values=(model2.rsquared,model2.rsquared_adj,model2.aic)\n",
    "model2_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Improving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# again checing for influencing point by cook's distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2_influence = model2.get_influence()\n",
    "(d_new, _) = model2_influence.cooks_distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
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       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
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       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
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       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
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       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
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       "<IPython.core.display.Javascript object>"
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     "metadata": {},
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    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
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      "text/plain": [
       "<IPython.core.display.Javascript object>"
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      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
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      "text/plain": [
       "<IPython.core.display.Javascript object>"
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      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.subplots(figsize=(20, 7))\n",
    "plt.stem(np.arange(len(d_new)), np.round(d_new, 3))\n",
    "plt.xlabel('Row index')\n",
    "plt.ylabel('Cooks Distance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# influcencing datapoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(48, 0.39274420556321554)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.argmax(d_new),np.max(d_new))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# droping the datapoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>RD_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>43</th>\n",
       "      <td>43</td>\n",
       "      <td>15505.73</td>\n",
       "      <td>127382.30</td>\n",
       "      <td>35534.17</td>\n",
       "      <td>69758.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>44</td>\n",
       "      <td>22177.74</td>\n",
       "      <td>154806.14</td>\n",
       "      <td>28334.72</td>\n",
       "      <td>65200.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>45</td>\n",
       "      <td>1000.23</td>\n",
       "      <td>124153.04</td>\n",
       "      <td>1903.93</td>\n",
       "      <td>64926.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>46</td>\n",
       "      <td>1315.46</td>\n",
       "      <td>115816.21</td>\n",
       "      <td>297114.46</td>\n",
       "      <td>49490.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>0.00</td>\n",
       "      <td>135426.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>42559.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  RD_Spend  Administration  Marketing_Spend    Profit\n",
       "43     43  15505.73       127382.30         35534.17  69758.98\n",
       "44     44  22177.74       154806.14         28334.72  65200.33\n",
       "45     45   1000.23       124153.04          1903.93  64926.08\n",
       "46     46   1315.46       115816.21        297114.46  49490.75\n",
       "47     47      0.00       135426.92             0.00  42559.73"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_new2=d_new.drop(d_new.index[[48]],axis=0).reset_index()\n",
    "d_new2.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RD_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>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>42559.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     RD_Spend  Administration  Marketing_Spend     Profit\n",
       "0   165349.20       136897.80        471784.10  192261.83\n",
       "1   162597.70       151377.59        443898.53  191792.06\n",
       "2   153441.51       101145.55        407934.54  191050.39\n",
       "3   144372.41       118671.85        383199.62  182901.99\n",
       "4   142107.34        91391.77        366168.42  166187.94\n",
       "5   131876.90        99814.71        362861.36  156991.12\n",
       "6   134615.46       147198.87        127716.82  156122.51\n",
       "7   130298.13       145530.06        323876.68  155752.60\n",
       "8   120542.52       148718.95        311613.29  152211.77\n",
       "9   123334.88       108679.17        304981.62  149759.96\n",
       "10  101913.08       110594.11        229160.95  146121.95\n",
       "11  100671.96        91790.61        249744.55  144259.40\n",
       "12   93863.75       127320.38        249839.44  141585.52\n",
       "13   91992.39       135495.07        252664.93  134307.35\n",
       "14  119943.24       156547.42        256512.92  132602.65\n",
       "15  114523.61       122616.84        261776.23  129917.04\n",
       "16   78013.11       121597.55        264346.06  126992.93\n",
       "17   94657.16       145077.58        282574.31  125370.37\n",
       "18   91749.16       114175.79        294919.57  124266.90\n",
       "19   86419.70       153514.11             0.00  122776.86\n",
       "20   76253.86       113867.30        298664.47  118474.03\n",
       "21   78389.47       153773.43        299737.29  111313.02\n",
       "22   73994.56       122782.75        303319.26  110352.25\n",
       "23   67532.53       105751.03        304768.73  108733.99\n",
       "24   77044.01        99281.34        140574.81  108552.04\n",
       "25   64664.71       139553.16        137962.62  107404.34\n",
       "26   75328.87       144135.98        134050.07  105733.54\n",
       "27   72107.60       127864.55        353183.81  105008.31\n",
       "28   66051.52       182645.56        118148.20  103282.38\n",
       "29   65605.48       153032.06        107138.38  101004.64\n",
       "30   61994.48       115641.28         91131.24   99937.59\n",
       "31   61136.38       152701.92         88218.23   97483.56\n",
       "32   63408.86       129219.61         46085.25   97427.84\n",
       "33   55493.95       103057.49        214634.81   96778.92\n",
       "34   46426.07       157693.92        210797.67   96712.80\n",
       "35   46014.02        85047.44        205517.64   96479.51\n",
       "36   28663.76       127056.21        201126.82   90708.19\n",
       "37   44069.95        51283.14        197029.42   89949.14\n",
       "38   20229.59        65947.93        185265.10   81229.06\n",
       "39   38558.51        82982.09        174999.30   81005.76\n",
       "40   28754.33       118546.05        172795.67   78239.91\n",
       "41   27892.92        84710.77        164470.71   77798.83\n",
       "42   23640.93        96189.63        148001.11   71498.49\n",
       "43   15505.73       127382.30         35534.17   69758.98\n",
       "44   22177.74       154806.14         28334.72   65200.33\n",
       "45    1000.23       124153.04          1903.93   64926.08\n",
       "46    1315.46       115816.21        297114.46   49490.75\n",
       "47       0.00       135426.92             0.00   42559.73"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_new_2=d_new2.drop(['index'],axis=1)\n",
    "d_new_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Again building the model.Here we got higher rsquared values and lower AIC value for this model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9626766170294073, 0.960131840917776, 994.6690439260116)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3=smf.ols(\"Profit~RD_Spend+Administration+Marketing_Spend\",data=d_new2).fit()\n",
    "model3_values=(model3.rsquared,model3.rsquared_adj,model3.aic)\n",
    "model3_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Profit</td>      <th>  R-squared:         </th> <td>   0.963</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.960</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   378.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 17 May 2021</td> <th>  Prob (F-statistic):</th> <td>2.03e-31</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>18:35:53</td>     <th>  Log-Likelihood:    </th> <td> -493.33</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    48</td>      <th>  AIC:               </th> <td>   994.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    44</td>      <th>  BIC:               </th> <td>   1002.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>       <td>  5.91e+04</td> <td> 5916.711</td> <td>    9.988</td> <td> 0.000</td> <td> 4.72e+04</td> <td>  7.1e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RD_Spend</th>        <td>    0.7895</td> <td>    0.036</td> <td>   21.718</td> <td> 0.000</td> <td>    0.716</td> <td>    0.863</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Administration</th>  <td>   -0.0633</td> <td>    0.044</td> <td>   -1.442</td> <td> 0.156</td> <td>   -0.152</td> <td>    0.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Marketing_Spend</th> <td>    0.0169</td> <td>    0.014</td> <td>    1.249</td> <td> 0.218</td> <td>   -0.010</td> <td>    0.044</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.287</td> <th>  Durbin-Watson:     </th> <td>   1.809</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.866</td> <th>  Jarque-Bera (JB):  </th> <td>   0.475</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.057</td> <th>  Prob(JB):          </th> <td>   0.789</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.526</td> <th>  Cond. No.          </th> <td>1.58e+06</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.58e+06. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 Profit   R-squared:                       0.963\n",
       "Model:                            OLS   Adj. R-squared:                  0.960\n",
       "Method:                 Least Squares   F-statistic:                     378.3\n",
       "Date:                Mon, 17 May 2021   Prob (F-statistic):           2.03e-31\n",
       "Time:                        18:35:53   Log-Likelihood:                -493.33\n",
       "No. Observations:                  48   AIC:                             994.7\n",
       "Df Residuals:                      44   BIC:                             1002.\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===================================================================================\n",
       "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------\n",
       "Intercept         5.91e+04   5916.711      9.988      0.000    4.72e+04     7.1e+04\n",
       "RD_Spend            0.7895      0.036     21.718      0.000       0.716       0.863\n",
       "Administration     -0.0633      0.044     -1.442      0.156      -0.152       0.025\n",
       "Marketing_Spend     0.0169      0.014      1.249      0.218      -0.010       0.044\n",
       "==============================================================================\n",
       "Omnibus:                        0.287   Durbin-Watson:                   1.809\n",
       "Prob(Omnibus):                  0.866   Jarque-Bera (JB):                0.475\n",
       "Skew:                           0.057   Prob(JB):                        0.789\n",
       "Kurtosis:                       2.526   Cond. No.                     1.58e+06\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.58e+06. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_final=model3\n",
    "model_final.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Corelation matrix for final dataset,we can see there are no high r values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RD_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>RD_Spend</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.173065</td>\n",
       "      <td>0.692606</td>\n",
       "      <td>0.978661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Administration</th>\n",
       "      <td>0.173065</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.144120</td>\n",
       "      <td>0.110473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marketing_Spend</th>\n",
       "      <td>0.692606</td>\n",
       "      <td>-0.144120</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.718207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Profit</th>\n",
       "      <td>0.978661</td>\n",
       "      <td>0.110473</td>\n",
       "      <td>0.718207</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 RD_Spend  Administration  Marketing_Spend    Profit\n",
       "RD_Spend         1.000000        0.173065         0.692606  0.978661\n",
       "Administration   0.173065        1.000000        -0.144120  0.110473\n",
       "Marketing_Spend  0.692606       -0.144120         1.000000  0.718207\n",
       "Profit           0.978661        0.110473         0.718207  1.000000"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_new_2.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting for test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Taking test data from traing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RD_Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing_Spend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>76253.86</td>\n",
       "      <td>113867.30</td>\n",
       "      <td>298664.47</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>73994.56</td>\n",
       "      <td>122782.75</td>\n",
       "      <td>303319.26</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>77044.01</td>\n",
       "      <td>99281.34</td>\n",
       "      <td>140574.81</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>75328.87</td>\n",
       "      <td>144135.98</td>\n",
       "      <td>134050.07</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>66051.52</td>\n",
       "      <td>182645.56</td>\n",
       "      <td>118148.20</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>61994.48</td>\n",
       "      <td>115641.28</td>\n",
       "      <td>91131.24</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>63408.86</td>\n",
       "      <td>129219.61</td>\n",
       "      <td>46085.25</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>46426.07</td>\n",
       "      <td>157693.92</td>\n",
       "      <td>210797.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    RD_Spend  Administration  Marketing_Spend\n",
       "20  76253.86       113867.30        298664.47\n",
       "21  78389.47       153773.43        299737.29\n",
       "22  73994.56       122782.75        303319.26\n",
       "23  67532.53       105751.03        304768.73\n",
       "24  77044.01        99281.34        140574.81\n",
       "25  64664.71       139553.16        137962.62\n",
       "26  75328.87       144135.98        134050.07\n",
       "27  72107.60       127864.55        353183.81\n",
       "28  66051.52       182645.56        118148.20\n",
       "29  65605.48       153032.06        107138.38\n",
       "30  61994.48       115641.28         91131.24\n",
       "31  61136.38       152701.92         88218.23\n",
       "32  63408.86       129219.61         46085.25\n",
       "33  55493.95       103057.49        214634.81\n",
       "34  46426.07       157693.92        210797.67"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data=d.iloc[20:35,:3]\n",
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[118474.03],\n",
       "       [111313.02],\n",
       "       [110352.25],\n",
       "       [108733.99],\n",
       "       [108552.04],\n",
       "       [107404.34],\n",
       "       [105733.54],\n",
       "       [105008.31],\n",
       "       [103282.38],\n",
       "       [101004.64],\n",
       "       [ 99937.59],\n",
       "       [ 97483.56],\n",
       "       [ 97427.84],\n",
       "       [ 96778.92],\n",
       "       [ 96712.8 ]])"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acct_values=d.iloc[20:35,3:].values\n",
    "acct_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicted values for test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([117133.0612167 , 116309.22709875, 114863.21169632, 110864.90055894,\n",
       "       116009.98846338, 103641.27806068, 111704.1628601 , 113893.99203167,\n",
       "       101671.53647611, 103009.36638408, 102256.7293717 ,  99182.29441439,\n",
       "       101752.15766831, 100008.33434856,  89323.2954028 ])"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_values_final=model_final.predict(test_data).values\n",
    "pred_values_final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Root Mean Square Error (RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nimport sklearn\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "4723.322419138779"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse = sklearn.metrics.mean_squared_error(acct_values, pred_values_final)\n",
    "rmse = math.sqrt(mse)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# R-squared and AIC values for all the models "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.preprocessing import OneHotEncoder\\nimport seaborn as sns\\nimport sklearn\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom pathlib import Path\\nfrom sklearn.ensemble import RandomForestRegressor\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom sklearn.ensemble import GradientBoostingClassifier\\nimport numpy as np\\nfrom sklearn.ensemble import GradientBoostingRegressor'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model 1</th>\n",
       "      <th>model 2</th>\n",
       "      <th>Final Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rsquared</th>\n",
       "      <td>0.950746</td>\n",
       "      <td>0.961316</td>\n",
       "      <td>0.962677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rsquared_adj</th>\n",
       "      <td>0.947534</td>\n",
       "      <td>0.958737</td>\n",
       "      <td>0.960132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIC</th>\n",
       "      <td>1058.771499</td>\n",
       "      <td>1020.565297</td>\n",
       "      <td>994.669044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  model 1      model 2  Final Model\n",
       "rsquared         0.950746     0.961316     0.962677\n",
       "rsquared_adj     0.947534     0.958737     0.960132\n",
       "AIC           1058.771499  1020.565297   994.669044"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_values_df=pd.DataFrame({'model 1':model1_values,'model 2':model2_values,'Final Model':model3_values},index=['rsquared','rsquared_adj','AIC'])\n",
    "model_values_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "\n",
    "Here we can see the r-squared values is higher and AIC value is lesser for final model by which we can say the model is better \n",
    "than the previous models.\n",
    "\n",
    "'''"
   ]
  }
 ],
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