Assignment 5-Multi_Linear_Regression-Startup-50.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "376fe1f5",
   "metadata": {},
   "source": [
    "# Do transformations for getting better predictions of profit andmake a table containing R^2 value for each prepared model.\n",
    "\n",
    "\n",
    "\"\"\"R&D Spend -- Research and devolop spend in the past few years\n",
    "Administration -- spend on administration in the past few years\n",
    "Marketing Spend -- spend on Marketing in the past few years\n",
    "State -- states from which data is collected\n",
    "Profit  -- profit of each state in the past few years\"\"\"\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c64897a5",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7cecd9ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the required libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.formula.api as smf\n",
    "from statsmodels.graphics.regressionplots import influence_plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9692016d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading the Data\n",
    "\n",
    "df=pd.read_csv(\"50_Startups (1).csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7aa7e4ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>California</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Florida</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>New York</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Florida</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>131876.90</td>\n",
       "      <td>99814.71</td>\n",
       "      <td>362861.36</td>\n",
       "      <td>New York</td>\n",
       "      <td>156991.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>134615.46</td>\n",
       "      <td>147198.87</td>\n",
       "      <td>127716.82</td>\n",
       "      <td>California</td>\n",
       "      <td>156122.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>130298.13</td>\n",
       "      <td>145530.06</td>\n",
       "      <td>323876.68</td>\n",
       "      <td>Florida</td>\n",
       "      <td>155752.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120542.52</td>\n",
       "      <td>148718.95</td>\n",
       "      <td>311613.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>152211.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>123334.88</td>\n",
       "      <td>108679.17</td>\n",
       "      <td>304981.62</td>\n",
       "      <td>California</td>\n",
       "      <td>149759.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>101913.08</td>\n",
       "      <td>110594.11</td>\n",
       "      <td>229160.95</td>\n",
       "      <td>Florida</td>\n",
       "      <td>146121.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100671.96</td>\n",
       "      <td>91790.61</td>\n",
       "      <td>249744.55</td>\n",
       "      <td>California</td>\n",
       "      <td>144259.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>93863.75</td>\n",
       "      <td>127320.38</td>\n",
       "      <td>249839.44</td>\n",
       "      <td>Florida</td>\n",
       "      <td>141585.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>91992.39</td>\n",
       "      <td>135495.07</td>\n",
       "      <td>252664.93</td>\n",
       "      <td>California</td>\n",
       "      <td>134307.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>119943.24</td>\n",
       "      <td>156547.42</td>\n",
       "      <td>256512.92</td>\n",
       "      <td>Florida</td>\n",
       "      <td>132602.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>114523.61</td>\n",
       "      <td>122616.84</td>\n",
       "      <td>261776.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>129917.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>78013.11</td>\n",
       "      <td>121597.55</td>\n",
       "      <td>264346.06</td>\n",
       "      <td>California</td>\n",
       "      <td>126992.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>94657.16</td>\n",
       "      <td>145077.58</td>\n",
       "      <td>282574.31</td>\n",
       "      <td>New York</td>\n",
       "      <td>125370.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>91749.16</td>\n",
       "      <td>114175.79</td>\n",
       "      <td>294919.57</td>\n",
       "      <td>Florida</td>\n",
       "      <td>124266.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>86419.70</td>\n",
       "      <td>153514.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>122776.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>76253.86</td>\n",
       "      <td>113867.30</td>\n",
       "      <td>298664.47</td>\n",
       "      <td>California</td>\n",
       "      <td>118474.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>78389.47</td>\n",
       "      <td>153773.43</td>\n",
       "      <td>299737.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>111313.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>73994.56</td>\n",
       "      <td>122782.75</td>\n",
       "      <td>303319.26</td>\n",
       "      <td>Florida</td>\n",
       "      <td>110352.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>67532.53</td>\n",
       "      <td>105751.03</td>\n",
       "      <td>304768.73</td>\n",
       "      <td>Florida</td>\n",
       "      <td>108733.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>77044.01</td>\n",
       "      <td>99281.34</td>\n",
       "      <td>140574.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>108552.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>64664.71</td>\n",
       "      <td>139553.16</td>\n",
       "      <td>137962.62</td>\n",
       "      <td>California</td>\n",
       "      <td>107404.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>75328.87</td>\n",
       "      <td>144135.98</td>\n",
       "      <td>134050.07</td>\n",
       "      <td>Florida</td>\n",
       "      <td>105733.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>72107.60</td>\n",
       "      <td>127864.55</td>\n",
       "      <td>353183.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>105008.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>66051.52</td>\n",
       "      <td>182645.56</td>\n",
       "      <td>118148.20</td>\n",
       "      <td>Florida</td>\n",
       "      <td>103282.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65605.48</td>\n",
       "      <td>153032.06</td>\n",
       "      <td>107138.38</td>\n",
       "      <td>New York</td>\n",
       "      <td>101004.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>61994.48</td>\n",
       "      <td>115641.28</td>\n",
       "      <td>91131.24</td>\n",
       "      <td>Florida</td>\n",
       "      <td>99937.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>61136.38</td>\n",
       "      <td>152701.92</td>\n",
       "      <td>88218.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>97483.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>63408.86</td>\n",
       "      <td>129219.61</td>\n",
       "      <td>46085.25</td>\n",
       "      <td>California</td>\n",
       "      <td>97427.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>55493.95</td>\n",
       "      <td>103057.49</td>\n",
       "      <td>214634.81</td>\n",
       "      <td>Florida</td>\n",
       "      <td>96778.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>46426.07</td>\n",
       "      <td>157693.92</td>\n",
       "      <td>210797.67</td>\n",
       "      <td>California</td>\n",
       "      <td>96712.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>46014.02</td>\n",
       "      <td>85047.44</td>\n",
       "      <td>205517.64</td>\n",
       "      <td>New York</td>\n",
       "      <td>96479.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>28663.76</td>\n",
       "      <td>127056.21</td>\n",
       "      <td>201126.82</td>\n",
       "      <td>Florida</td>\n",
       "      <td>90708.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>44069.95</td>\n",
       "      <td>51283.14</td>\n",
       "      <td>197029.42</td>\n",
       "      <td>California</td>\n",
       "      <td>89949.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>20229.59</td>\n",
       "      <td>65947.93</td>\n",
       "      <td>185265.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>81229.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>38558.51</td>\n",
       "      <td>82982.09</td>\n",
       "      <td>174999.30</td>\n",
       "      <td>California</td>\n",
       "      <td>81005.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>28754.33</td>\n",
       "      <td>118546.05</td>\n",
       "      <td>172795.67</td>\n",
       "      <td>California</td>\n",
       "      <td>78239.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>27892.92</td>\n",
       "      <td>84710.77</td>\n",
       "      <td>164470.71</td>\n",
       "      <td>Florida</td>\n",
       "      <td>77798.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>23640.93</td>\n",
       "      <td>96189.63</td>\n",
       "      <td>148001.11</td>\n",
       "      <td>California</td>\n",
       "      <td>71498.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>15505.73</td>\n",
       "      <td>127382.30</td>\n",
       "      <td>35534.17</td>\n",
       "      <td>New York</td>\n",
       "      <td>69758.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>22177.74</td>\n",
       "      <td>154806.14</td>\n",
       "      <td>28334.72</td>\n",
       "      <td>California</td>\n",
       "      <td>65200.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1000.23</td>\n",
       "      <td>124153.04</td>\n",
       "      <td>1903.93</td>\n",
       "      <td>New York</td>\n",
       "      <td>64926.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1315.46</td>\n",
       "      <td>115816.21</td>\n",
       "      <td>297114.46</td>\n",
       "      <td>Florida</td>\n",
       "      <td>49490.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.00</td>\n",
       "      <td>135426.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>California</td>\n",
       "      <td>42559.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>542.05</td>\n",
       "      <td>51743.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>35673.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.00</td>\n",
       "      <td>116983.80</td>\n",
       "      <td>45173.06</td>\n",
       "      <td>California</td>\n",
       "      <td>14681.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    R&D Spend  Administration  Marketing Spend       State     Profit\n",
       "0   165349.20       136897.80        471784.10    New York  192261.83\n",
       "1   162597.70       151377.59        443898.53  California  191792.06\n",
       "2   153441.51       101145.55        407934.54     Florida  191050.39\n",
       "3   144372.41       118671.85        383199.62    New York  182901.99\n",
       "4   142107.34        91391.77        366168.42     Florida  166187.94\n",
       "5   131876.90        99814.71        362861.36    New York  156991.12\n",
       "6   134615.46       147198.87        127716.82  California  156122.51\n",
       "7   130298.13       145530.06        323876.68     Florida  155752.60\n",
       "8   120542.52       148718.95        311613.29    New York  152211.77\n",
       "9   123334.88       108679.17        304981.62  California  149759.96\n",
       "10  101913.08       110594.11        229160.95     Florida  146121.95\n",
       "11  100671.96        91790.61        249744.55  California  144259.40\n",
       "12   93863.75       127320.38        249839.44     Florida  141585.52\n",
       "13   91992.39       135495.07        252664.93  California  134307.35\n",
       "14  119943.24       156547.42        256512.92     Florida  132602.65\n",
       "15  114523.61       122616.84        261776.23    New York  129917.04\n",
       "16   78013.11       121597.55        264346.06  California  126992.93\n",
       "17   94657.16       145077.58        282574.31    New York  125370.37\n",
       "18   91749.16       114175.79        294919.57     Florida  124266.90\n",
       "19   86419.70       153514.11             0.00    New York  122776.86\n",
       "20   76253.86       113867.30        298664.47  California  118474.03\n",
       "21   78389.47       153773.43        299737.29    New York  111313.02\n",
       "22   73994.56       122782.75        303319.26     Florida  110352.25\n",
       "23   67532.53       105751.03        304768.73     Florida  108733.99\n",
       "24   77044.01        99281.34        140574.81    New York  108552.04\n",
       "25   64664.71       139553.16        137962.62  California  107404.34\n",
       "26   75328.87       144135.98        134050.07     Florida  105733.54\n",
       "27   72107.60       127864.55        353183.81    New York  105008.31\n",
       "28   66051.52       182645.56        118148.20     Florida  103282.38\n",
       "29   65605.48       153032.06        107138.38    New York  101004.64\n",
       "30   61994.48       115641.28         91131.24     Florida   99937.59\n",
       "31   61136.38       152701.92         88218.23    New York   97483.56\n",
       "32   63408.86       129219.61         46085.25  California   97427.84\n",
       "33   55493.95       103057.49        214634.81     Florida   96778.92\n",
       "34   46426.07       157693.92        210797.67  California   96712.80\n",
       "35   46014.02        85047.44        205517.64    New York   96479.51\n",
       "36   28663.76       127056.21        201126.82     Florida   90708.19\n",
       "37   44069.95        51283.14        197029.42  California   89949.14\n",
       "38   20229.59        65947.93        185265.10    New York   81229.06\n",
       "39   38558.51        82982.09        174999.30  California   81005.76\n",
       "40   28754.33       118546.05        172795.67  California   78239.91\n",
       "41   27892.92        84710.77        164470.71     Florida   77798.83\n",
       "42   23640.93        96189.63        148001.11  California   71498.49\n",
       "43   15505.73       127382.30         35534.17    New York   69758.98\n",
       "44   22177.74       154806.14         28334.72  California   65200.33\n",
       "45    1000.23       124153.04          1903.93    New York   64926.08\n",
       "46    1315.46       115816.21        297114.46     Florida   49490.75\n",
       "47       0.00       135426.92             0.00  California   42559.73\n",
       "48     542.05        51743.15             0.00    New York   35673.41\n",
       "49       0.00       116983.80         45173.06  California   14681.40"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b5dbb817",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3fed28c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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": [
       "    R&D Spend  Administration  Marketing Spend       State    Profit\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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5ae3cb66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50 entries, 0 to 49\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   R&D Spend        50 non-null     float64\n",
      " 1   Administration   50 non-null     float64\n",
      " 2   Marketing Spend  50 non-null     float64\n",
      " 3   State            50 non-null     object \n",
      " 4   Profit           50 non-null     float64\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 2.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b920b648",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>73721.615600</td>\n",
       "      <td>121344.639600</td>\n",
       "      <td>211025.097800</td>\n",
       "      <td>112012.639200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>45902.256482</td>\n",
       "      <td>28017.802755</td>\n",
       "      <td>122290.310726</td>\n",
       "      <td>40306.180338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>51283.140000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14681.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>39936.370000</td>\n",
       "      <td>103730.875000</td>\n",
       "      <td>129300.132500</td>\n",
       "      <td>90138.902500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>73051.080000</td>\n",
       "      <td>122699.795000</td>\n",
       "      <td>212716.240000</td>\n",
       "      <td>107978.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>101602.800000</td>\n",
       "      <td>144842.180000</td>\n",
       "      <td>299469.085000</td>\n",
       "      <td>139765.977500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>165349.200000</td>\n",
       "      <td>182645.560000</td>\n",
       "      <td>471784.100000</td>\n",
       "      <td>192261.830000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           R&D Spend  Administration  Marketing Spend         Profit\n",
       "count      50.000000       50.000000        50.000000      50.000000\n",
       "mean    73721.615600   121344.639600    211025.097800  112012.639200\n",
       "std     45902.256482    28017.802755    122290.310726   40306.180338\n",
       "min         0.000000    51283.140000         0.000000   14681.400000\n",
       "25%     39936.370000   103730.875000    129300.132500   90138.902500\n",
       "50%     73051.080000   122699.795000    212716.240000  107978.190000\n",
       "75%    101602.800000   144842.180000    299469.085000  139765.977500\n",
       "max    165349.200000   182645.560000    471784.100000  192261.830000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "34f1b828",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R&D Spend          0\n",
       "Administration     0\n",
       "Marketing Spend    0\n",
       "State              0\n",
       "Profit             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking null values\n",
    "\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2d9579c5",
   "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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [R&D Spend, Administration, Marketing Spend, State, Profit]\n",
       "Index: []"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking duplicates records\n",
    "\n",
    "df[df.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4ddcbdf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>California</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Florida</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>New York</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Florida</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>131876.90</td>\n",
       "      <td>99814.71</td>\n",
       "      <td>362861.36</td>\n",
       "      <td>New York</td>\n",
       "      <td>156991.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>134615.46</td>\n",
       "      <td>147198.87</td>\n",
       "      <td>127716.82</td>\n",
       "      <td>California</td>\n",
       "      <td>156122.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>130298.13</td>\n",
       "      <td>145530.06</td>\n",
       "      <td>323876.68</td>\n",
       "      <td>Florida</td>\n",
       "      <td>155752.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120542.52</td>\n",
       "      <td>148718.95</td>\n",
       "      <td>311613.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>152211.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>123334.88</td>\n",
       "      <td>108679.17</td>\n",
       "      <td>304981.62</td>\n",
       "      <td>California</td>\n",
       "      <td>149759.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>101913.08</td>\n",
       "      <td>110594.11</td>\n",
       "      <td>229160.95</td>\n",
       "      <td>Florida</td>\n",
       "      <td>146121.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100671.96</td>\n",
       "      <td>91790.61</td>\n",
       "      <td>249744.55</td>\n",
       "      <td>California</td>\n",
       "      <td>144259.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>93863.75</td>\n",
       "      <td>127320.38</td>\n",
       "      <td>249839.44</td>\n",
       "      <td>Florida</td>\n",
       "      <td>141585.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>91992.39</td>\n",
       "      <td>135495.07</td>\n",
       "      <td>252664.93</td>\n",
       "      <td>California</td>\n",
       "      <td>134307.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>119943.24</td>\n",
       "      <td>156547.42</td>\n",
       "      <td>256512.92</td>\n",
       "      <td>Florida</td>\n",
       "      <td>132602.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>114523.61</td>\n",
       "      <td>122616.84</td>\n",
       "      <td>261776.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>129917.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>78013.11</td>\n",
       "      <td>121597.55</td>\n",
       "      <td>264346.06</td>\n",
       "      <td>California</td>\n",
       "      <td>126992.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>94657.16</td>\n",
       "      <td>145077.58</td>\n",
       "      <td>282574.31</td>\n",
       "      <td>New York</td>\n",
       "      <td>125370.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>91749.16</td>\n",
       "      <td>114175.79</td>\n",
       "      <td>294919.57</td>\n",
       "      <td>Florida</td>\n",
       "      <td>124266.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>86419.70</td>\n",
       "      <td>153514.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>122776.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>76253.86</td>\n",
       "      <td>113867.30</td>\n",
       "      <td>298664.47</td>\n",
       "      <td>California</td>\n",
       "      <td>118474.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>78389.47</td>\n",
       "      <td>153773.43</td>\n",
       "      <td>299737.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>111313.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>73994.56</td>\n",
       "      <td>122782.75</td>\n",
       "      <td>303319.26</td>\n",
       "      <td>Florida</td>\n",
       "      <td>110352.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>67532.53</td>\n",
       "      <td>105751.03</td>\n",
       "      <td>304768.73</td>\n",
       "      <td>Florida</td>\n",
       "      <td>108733.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>77044.01</td>\n",
       "      <td>99281.34</td>\n",
       "      <td>140574.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>108552.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>64664.71</td>\n",
       "      <td>139553.16</td>\n",
       "      <td>137962.62</td>\n",
       "      <td>California</td>\n",
       "      <td>107404.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>75328.87</td>\n",
       "      <td>144135.98</td>\n",
       "      <td>134050.07</td>\n",
       "      <td>Florida</td>\n",
       "      <td>105733.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>72107.60</td>\n",
       "      <td>127864.55</td>\n",
       "      <td>353183.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>105008.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>66051.52</td>\n",
       "      <td>182645.56</td>\n",
       "      <td>118148.20</td>\n",
       "      <td>Florida</td>\n",
       "      <td>103282.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65605.48</td>\n",
       "      <td>153032.06</td>\n",
       "      <td>107138.38</td>\n",
       "      <td>New York</td>\n",
       "      <td>101004.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>61994.48</td>\n",
       "      <td>115641.28</td>\n",
       "      <td>91131.24</td>\n",
       "      <td>Florida</td>\n",
       "      <td>99937.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>61136.38</td>\n",
       "      <td>152701.92</td>\n",
       "      <td>88218.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>97483.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>63408.86</td>\n",
       "      <td>129219.61</td>\n",
       "      <td>46085.25</td>\n",
       "      <td>California</td>\n",
       "      <td>97427.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>55493.95</td>\n",
       "      <td>103057.49</td>\n",
       "      <td>214634.81</td>\n",
       "      <td>Florida</td>\n",
       "      <td>96778.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>46426.07</td>\n",
       "      <td>157693.92</td>\n",
       "      <td>210797.67</td>\n",
       "      <td>California</td>\n",
       "      <td>96712.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>46014.02</td>\n",
       "      <td>85047.44</td>\n",
       "      <td>205517.64</td>\n",
       "      <td>New York</td>\n",
       "      <td>96479.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>28663.76</td>\n",
       "      <td>127056.21</td>\n",
       "      <td>201126.82</td>\n",
       "      <td>Florida</td>\n",
       "      <td>90708.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>44069.95</td>\n",
       "      <td>51283.14</td>\n",
       "      <td>197029.42</td>\n",
       "      <td>California</td>\n",
       "      <td>89949.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>20229.59</td>\n",
       "      <td>65947.93</td>\n",
       "      <td>185265.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>81229.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>38558.51</td>\n",
       "      <td>82982.09</td>\n",
       "      <td>174999.30</td>\n",
       "      <td>California</td>\n",
       "      <td>81005.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>28754.33</td>\n",
       "      <td>118546.05</td>\n",
       "      <td>172795.67</td>\n",
       "      <td>California</td>\n",
       "      <td>78239.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>27892.92</td>\n",
       "      <td>84710.77</td>\n",
       "      <td>164470.71</td>\n",
       "      <td>Florida</td>\n",
       "      <td>77798.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>23640.93</td>\n",
       "      <td>96189.63</td>\n",
       "      <td>148001.11</td>\n",
       "      <td>California</td>\n",
       "      <td>71498.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>15505.73</td>\n",
       "      <td>127382.30</td>\n",
       "      <td>35534.17</td>\n",
       "      <td>New York</td>\n",
       "      <td>69758.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>22177.74</td>\n",
       "      <td>154806.14</td>\n",
       "      <td>28334.72</td>\n",
       "      <td>California</td>\n",
       "      <td>65200.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1000.23</td>\n",
       "      <td>124153.04</td>\n",
       "      <td>1903.93</td>\n",
       "      <td>New York</td>\n",
       "      <td>64926.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1315.46</td>\n",
       "      <td>115816.21</td>\n",
       "      <td>297114.46</td>\n",
       "      <td>Florida</td>\n",
       "      <td>49490.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.00</td>\n",
       "      <td>135426.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>California</td>\n",
       "      <td>42559.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>542.05</td>\n",
       "      <td>51743.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>35673.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.00</td>\n",
       "      <td>116983.80</td>\n",
       "      <td>45173.06</td>\n",
       "      <td>California</td>\n",
       "      <td>14681.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    R&D Spend  Administration  Marketing Spend       State     Profit\n",
       "0   165349.20       136897.80        471784.10    New York  192261.83\n",
       "1   162597.70       151377.59        443898.53  California  191792.06\n",
       "2   153441.51       101145.55        407934.54     Florida  191050.39\n",
       "3   144372.41       118671.85        383199.62    New York  182901.99\n",
       "4   142107.34        91391.77        366168.42     Florida  166187.94\n",
       "5   131876.90        99814.71        362861.36    New York  156991.12\n",
       "6   134615.46       147198.87        127716.82  California  156122.51\n",
       "7   130298.13       145530.06        323876.68     Florida  155752.60\n",
       "8   120542.52       148718.95        311613.29    New York  152211.77\n",
       "9   123334.88       108679.17        304981.62  California  149759.96\n",
       "10  101913.08       110594.11        229160.95     Florida  146121.95\n",
       "11  100671.96        91790.61        249744.55  California  144259.40\n",
       "12   93863.75       127320.38        249839.44     Florida  141585.52\n",
       "13   91992.39       135495.07        252664.93  California  134307.35\n",
       "14  119943.24       156547.42        256512.92     Florida  132602.65\n",
       "15  114523.61       122616.84        261776.23    New York  129917.04\n",
       "16   78013.11       121597.55        264346.06  California  126992.93\n",
       "17   94657.16       145077.58        282574.31    New York  125370.37\n",
       "18   91749.16       114175.79        294919.57     Florida  124266.90\n",
       "19   86419.70       153514.11             0.00    New York  122776.86\n",
       "20   76253.86       113867.30        298664.47  California  118474.03\n",
       "21   78389.47       153773.43        299737.29    New York  111313.02\n",
       "22   73994.56       122782.75        303319.26     Florida  110352.25\n",
       "23   67532.53       105751.03        304768.73     Florida  108733.99\n",
       "24   77044.01        99281.34        140574.81    New York  108552.04\n",
       "25   64664.71       139553.16        137962.62  California  107404.34\n",
       "26   75328.87       144135.98        134050.07     Florida  105733.54\n",
       "27   72107.60       127864.55        353183.81    New York  105008.31\n",
       "28   66051.52       182645.56        118148.20     Florida  103282.38\n",
       "29   65605.48       153032.06        107138.38    New York  101004.64\n",
       "30   61994.48       115641.28         91131.24     Florida   99937.59\n",
       "31   61136.38       152701.92         88218.23    New York   97483.56\n",
       "32   63408.86       129219.61         46085.25  California   97427.84\n",
       "33   55493.95       103057.49        214634.81     Florida   96778.92\n",
       "34   46426.07       157693.92        210797.67  California   96712.80\n",
       "35   46014.02        85047.44        205517.64    New York   96479.51\n",
       "36   28663.76       127056.21        201126.82     Florida   90708.19\n",
       "37   44069.95        51283.14        197029.42  California   89949.14\n",
       "38   20229.59        65947.93        185265.10    New York   81229.06\n",
       "39   38558.51        82982.09        174999.30  California   81005.76\n",
       "40   28754.33       118546.05        172795.67  California   78239.91\n",
       "41   27892.92        84710.77        164470.71     Florida   77798.83\n",
       "42   23640.93        96189.63        148001.11  California   71498.49\n",
       "43   15505.73       127382.30         35534.17    New York   69758.98\n",
       "44   22177.74       154806.14         28334.72  California   65200.33\n",
       "45    1000.23       124153.04          1903.93    New York   64926.08\n",
       "46    1315.46       115816.21        297114.46     Florida   49490.75\n",
       "47       0.00       135426.92             0.00  California   42559.73\n",
       "48     542.05        51743.15             0.00    New York   35673.41\n",
       "49       0.00       116983.80         45173.06  California   14681.40"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73cfbb5e",
   "metadata": {},
   "source": [
    "# Correlation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "398c4f63",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Removing the warnings\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "14368712",
   "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>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.241955</td>\n",
       "      <td>0.724248</td>\n",
       "      <td>0.972900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Administration</th>\n",
       "      <td>0.241955</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.032154</td>\n",
       "      <td>0.200717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marketing Spend</th>\n",
       "      <td>0.724248</td>\n",
       "      <td>-0.032154</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.747766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Profit</th>\n",
       "      <td>0.972900</td>\n",
       "      <td>0.200717</td>\n",
       "      <td>0.747766</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 R&D Spend  Administration  Marketing Spend    Profit\n",
       "R&D Spend         1.000000        0.241955         0.724248  0.972900\n",
       "Administration    0.241955        1.000000        -0.032154  0.200717\n",
       "Marketing Spend   0.724248       -0.032154         1.000000  0.747766\n",
       "Profit            0.972900        0.200717         0.747766  1.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8bca6e2",
   "metadata": {},
   "source": [
    "#  Scatterplot between variables along with histograms\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c3835ed1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1f7c189add0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(style=\"darkgrid\")\n",
    "sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c7922259",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renaming the columns \n",
    "\n",
    "df1=df.rename({\"R&D Spend\":\"RDS\",\"Administration\":\"ADMN\",\"Marketing Spend\":\"MS\"},axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8ecf4f95",
   "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>RDS</th>\n",
       "      <th>ADMN</th>\n",
       "      <th>MS</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": [
       "          RDS       ADMN         MS       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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0bbeae4",
   "metadata": {},
   "source": [
    "# Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "38b082da",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=smf.ols(\"Profit~RDS+ADMN+MS\",data=df1).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "761f8419",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x1f7c241f910>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd3dab9a",
   "metadata": {},
   "source": [
    "#  Model Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9b8d6497",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept    50122.192990\n",
       "RDS              0.805715\n",
       "ADMN            -0.026816\n",
       "MS               0.027228\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Co-efficient of model\n",
    "\n",
    "model.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3250b913",
   "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>Thu, 30 Nov 2023</td> <th>  Prob (F-statistic):</th> <td>4.53e-30</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:44:50</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>RDS</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>ADMN</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>MS</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/>Notes:<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/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &      Profit      & \\textbf{  R-squared:         } &     0.951   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.948   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     296.0   \\\\\n",
       "\\textbf{Date:}             & Thu, 30 Nov 2023 & \\textbf{  Prob (F-statistic):} &  4.53e-30   \\\\\n",
       "\\textbf{Time:}             &     15:44:50     & \\textbf{  Log-Likelihood:    } &   -525.39   \\\\\n",
       "\\textbf{No. Observations:} &          50      & \\textbf{  AIC:               } &     1059.   \\\\\n",
       "\\textbf{Df Residuals:}     &          46      & \\textbf{  BIC:               } &     1066.   \\\\\n",
       "\\textbf{Df Model:}         &           3      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept} &    5.012e+04  &     6572.353     &     7.626  &         0.000        &     3.69e+04    &     6.34e+04     \\\\\n",
       "\\textbf{RDS}       &       0.8057  &        0.045     &    17.846  &         0.000        &        0.715    &        0.897     \\\\\n",
       "\\textbf{ADMN}      &      -0.0268  &        0.051     &    -0.526  &         0.602        &       -0.130    &        0.076     \\\\\n",
       "\\textbf{MS}        &       0.0272  &        0.016     &     1.655  &         0.105        &       -0.006    &        0.060     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 14.838 & \\textbf{  Durbin-Watson:     } &    1.282  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.001 & \\textbf{  Jarque-Bera (JB):  } &   21.442  \\\\\n",
       "\\textbf{Skew:}          & -0.949 & \\textbf{  Prob(JB):          } & 2.21e-05  \\\\\n",
       "\\textbf{Kurtosis:}      &  5.586 & \\textbf{  Cond. No.          } & 1.40e+06  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
       " [2] The condition number is large, 1.4e+06. This might indicate that there are \\newline\n",
       " 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:                Thu, 30 Nov 2023   Prob (F-statistic):           4.53e-30\n",
       "Time:                        15:44:50   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",
       "RDS            0.8057      0.045     17.846      0.000       0.715       0.897\n",
       "ADMN          -0.0268      0.051     -0.526      0.602      -0.130       0.076\n",
       "MS             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",
       "Notes:\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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Summary of model\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0679efad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since the p value for Administration and Marketing Spend is > 0.05 building the  simple linear regression and multi linear regression models  to see insignificant variables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1f2a8ebd",
   "metadata": {},
   "outputs": [],
   "source": [
    "slr_adm=smf.ols(\"Profit~ADMN\",data=df1).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "251915e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x1f7c2499ad0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slr_adm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f92b0c4f",
   "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>   0.040</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 30 Nov 2023</td> <th>  Prob (F-statistic):</th>  <td> 0.162</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:44:50</td>     <th>  Log-Likelihood:    </th> <td> -599.63</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    50</td>      <th>  AIC:               </th> <td>   1203.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    48</td>      <th>  BIC:               </th> <td>   1207.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</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> 7.697e+04</td> <td> 2.53e+04</td> <td>    3.040</td> <td> 0.004</td> <td> 2.61e+04</td> <td> 1.28e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ADMN</th>      <td>    0.2887</td> <td>    0.203</td> <td>    1.419</td> <td> 0.162</td> <td>   -0.120</td> <td>    0.698</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.126</td> <th>  Durbin-Watson:     </th> <td>   0.099</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.939</td> <th>  Jarque-Bera (JB):  </th> <td>   0.110</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.093</td> <th>  Prob(JB):          </th> <td>   0.947</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.866</td> <th>  Cond. No.          </th> <td>5.59e+05</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 5.59e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &      Profit      & \\textbf{  R-squared:         } &     0.040   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.020   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     2.015   \\\\\n",
       "\\textbf{Date:}             & Thu, 30 Nov 2023 & \\textbf{  Prob (F-statistic):} &    0.162    \\\\\n",
       "\\textbf{Time:}             &     15:44:50     & \\textbf{  Log-Likelihood:    } &   -599.63   \\\\\n",
       "\\textbf{No. Observations:} &          50      & \\textbf{  AIC:               } &     1203.   \\\\\n",
       "\\textbf{Df Residuals:}     &          48      & \\textbf{  BIC:               } &     1207.   \\\\\n",
       "\\textbf{Df Model:}         &           1      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept} &    7.697e+04  &     2.53e+04     &     3.040  &         0.004        &     2.61e+04    &     1.28e+05     \\\\\n",
       "\\textbf{ADMN}      &       0.2887  &        0.203     &     1.419  &         0.162        &       -0.120    &        0.698     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       &  0.126 & \\textbf{  Durbin-Watson:     } &    0.099  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.939 & \\textbf{  Jarque-Bera (JB):  } &    0.110  \\\\\n",
       "\\textbf{Skew:}          &  0.093 & \\textbf{  Prob(JB):          } &    0.947  \\\\\n",
       "\\textbf{Kurtosis:}      &  2.866 & \\textbf{  Cond. No.          } & 5.59e+05  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
       " [2] The condition number is large, 5.59e+05. This might indicate that there are \\newline\n",
       " 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:                       0.040\n",
       "Model:                            OLS   Adj. R-squared:                  0.020\n",
       "Method:                 Least Squares   F-statistic:                     2.015\n",
       "Date:                Thu, 30 Nov 2023   Prob (F-statistic):              0.162\n",
       "Time:                        15:44:50   Log-Likelihood:                -599.63\n",
       "No. Observations:                  50   AIC:                             1203.\n",
       "Df Residuals:                      48   BIC:                             1207.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept   7.697e+04   2.53e+04      3.040      0.004    2.61e+04    1.28e+05\n",
       "ADMN           0.2887      0.203      1.419      0.162      -0.120       0.698\n",
       "==============================================================================\n",
       "Omnibus:                        0.126   Durbin-Watson:                   0.099\n",
       "Prob(Omnibus):                  0.939   Jarque-Bera (JB):                0.110\n",
       "Skew:                           0.093   Prob(JB):                        0.947\n",
       "Kurtosis:                       2.866   Cond. No.                     5.59e+05\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 5.59e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slr_adm.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c9f6c746",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x1f7c2f8cf90>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Building the simple linear model to check the column Marketing State.\n",
    "\n",
    "slr_mar=smf.ols(\"Profit~MS\",data=df1).fit()\n",
    "\n",
    "slr_mar\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "962bfdf8",
   "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>   0.559</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.550</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   60.88</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 30 Nov 2023</td> <th>  Prob (F-statistic):</th> <td>4.38e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:44:50</td>     <th>  Log-Likelihood:    </th> <td> -580.18</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    50</td>      <th>  AIC:               </th> <td>   1164.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    48</td>      <th>  BIC:               </th> <td>   1168.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</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>     6e+04</td> <td> 7684.530</td> <td>    7.808</td> <td> 0.000</td> <td> 4.46e+04</td> <td> 7.55e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MS</th>        <td>    0.2465</td> <td>    0.032</td> <td>    7.803</td> <td> 0.000</td> <td>    0.183</td> <td>    0.310</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 4.420</td> <th>  Durbin-Watson:     </th> <td>   1.178</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.110</td> <th>  Jarque-Bera (JB):  </th> <td>   3.882</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.336</td> <th>  Prob(JB):          </th> <td>   0.144</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.188</td> <th>  Cond. No.          </th> <td>4.89e+05</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 4.89e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &      Profit      & \\textbf{  R-squared:         } &     0.559   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.550   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     60.88   \\\\\n",
       "\\textbf{Date:}             & Thu, 30 Nov 2023 & \\textbf{  Prob (F-statistic):} &  4.38e-10   \\\\\n",
       "\\textbf{Time:}             &     15:44:50     & \\textbf{  Log-Likelihood:    } &   -580.18   \\\\\n",
       "\\textbf{No. Observations:} &          50      & \\textbf{  AIC:               } &     1164.   \\\\\n",
       "\\textbf{Df Residuals:}     &          48      & \\textbf{  BIC:               } &     1168.   \\\\\n",
       "\\textbf{Df Model:}         &           1      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept} &        6e+04  &     7684.530     &     7.808  &         0.000        &     4.46e+04    &     7.55e+04     \\\\\n",
       "\\textbf{MS}        &       0.2465  &        0.032     &     7.803  &         0.000        &        0.183    &        0.310     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       &  4.420 & \\textbf{  Durbin-Watson:     } &    1.178  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.110 & \\textbf{  Jarque-Bera (JB):  } &    3.882  \\\\\n",
       "\\textbf{Skew:}          & -0.336 & \\textbf{  Prob(JB):          } &    0.144  \\\\\n",
       "\\textbf{Kurtosis:}      &  4.188 & \\textbf{  Cond. No.          } & 4.89e+05  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
       " [2] The condition number is large, 4.89e+05. This might indicate that there are \\newline\n",
       " 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:                       0.559\n",
       "Model:                            OLS   Adj. R-squared:                  0.550\n",
       "Method:                 Least Squares   F-statistic:                     60.88\n",
       "Date:                Thu, 30 Nov 2023   Prob (F-statistic):           4.38e-10\n",
       "Time:                        15:44:50   Log-Likelihood:                -580.18\n",
       "No. Observations:                  50   AIC:                             1164.\n",
       "Df Residuals:                      48   BIC:                             1168.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept       6e+04   7684.530      7.808      0.000    4.46e+04    7.55e+04\n",
       "MS             0.2465      0.032      7.803      0.000       0.183       0.310\n",
       "==============================================================================\n",
       "Omnibus:                        4.420   Durbin-Watson:                   1.178\n",
       "Prob(Omnibus):                  0.110   Jarque-Bera (JB):                3.882\n",
       "Skew:                          -0.336   Prob(JB):                        0.144\n",
       "Kurtosis:                       4.188   Cond. No.                     4.89e+05\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 4.89e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slr_mar.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b5306f04",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building the Multi Linear regression model of Adminstration and Marketing together to check for collinearity problem\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0ef2f54e",
   "metadata": {},
   "outputs": [],
   "source": [
    "slr_am=smf.ols(\"Profit~ADMN+MS\",data=df1).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "83476003",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x1f7c245ee50>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slr_am"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c070c60c",
   "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>   0.610</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.593</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   36.71</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 30 Nov 2023</td> <th>  Prob (F-statistic):</th> <td>2.50e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:44:50</td>     <th>  Log-Likelihood:    </th> <td> -577.13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    50</td>      <th>  AIC:               </th> <td>   1160.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    47</td>      <th>  BIC:               </th> <td>   1166.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</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> 2.022e+04</td> <td> 1.77e+04</td> <td>    1.143</td> <td> 0.259</td> <td>-1.54e+04</td> <td> 5.58e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ADMN</th>      <td>    0.3237</td> <td>    0.131</td> <td>    2.468</td> <td> 0.017</td> <td>    0.060</td> <td>    0.588</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MS</th>        <td>    0.2488</td> <td>    0.030</td> <td>    8.281</td> <td> 0.000</td> <td>    0.188</td> <td>    0.309</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 6.584</td> <th>  Durbin-Watson:     </th> <td>   1.279</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.037</td> <th>  Jarque-Bera (JB):  </th> <td>   6.524</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.512</td> <th>  Prob(JB):          </th> <td>  0.0383</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.443</td> <th>  Cond. No.          </th> <td>1.30e+06</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.3e+06. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &      Profit      & \\textbf{  R-squared:         } &     0.610   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.593   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     36.71   \\\\\n",
       "\\textbf{Date:}             & Thu, 30 Nov 2023 & \\textbf{  Prob (F-statistic):} &  2.50e-10   \\\\\n",
       "\\textbf{Time:}             &     15:44:50     & \\textbf{  Log-Likelihood:    } &   -577.13   \\\\\n",
       "\\textbf{No. Observations:} &          50      & \\textbf{  AIC:               } &     1160.   \\\\\n",
       "\\textbf{Df Residuals:}     &          47      & \\textbf{  BIC:               } &     1166.   \\\\\n",
       "\\textbf{Df Model:}         &           2      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept} &    2.022e+04  &     1.77e+04     &     1.143  &         0.259        &    -1.54e+04    &     5.58e+04     \\\\\n",
       "\\textbf{ADMN}      &       0.3237  &        0.131     &     2.468  &         0.017        &        0.060    &        0.588     \\\\\n",
       "\\textbf{MS}        &       0.2488  &        0.030     &     8.281  &         0.000        &        0.188    &        0.309     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       &  6.584 & \\textbf{  Durbin-Watson:     } &    1.279  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.037 & \\textbf{  Jarque-Bera (JB):  } &    6.524  \\\\\n",
       "\\textbf{Skew:}          & -0.512 & \\textbf{  Prob(JB):          } &   0.0383  \\\\\n",
       "\\textbf{Kurtosis:}      &  4.443 & \\textbf{  Cond. No.          } & 1.30e+06  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
       " [2] The condition number is large, 1.3e+06. This might indicate that there are \\newline\n",
       " 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:                       0.610\n",
       "Model:                            OLS   Adj. R-squared:                  0.593\n",
       "Method:                 Least Squares   F-statistic:                     36.71\n",
       "Date:                Thu, 30 Nov 2023   Prob (F-statistic):           2.50e-10\n",
       "Time:                        15:44:50   Log-Likelihood:                -577.13\n",
       "No. Observations:                  50   AIC:                             1160.\n",
       "Df Residuals:                      47   BIC:                             1166.\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept   2.022e+04   1.77e+04      1.143      0.259   -1.54e+04    5.58e+04\n",
       "ADMN           0.3237      0.131      2.468      0.017       0.060       0.588\n",
       "MS             0.2488      0.030      8.281      0.000       0.188       0.309\n",
       "==============================================================================\n",
       "Omnibus:                        6.584   Durbin-Watson:                   1.279\n",
       "Prob(Omnibus):                  0.037   Jarque-Bera (JB):                6.524\n",
       "Skew:                          -0.512   Prob(JB):                       0.0383\n",
       "Kurtosis:                       4.443   Cond. No.                     1.30e+06\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.3e+06. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slr_am.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afbd7166",
   "metadata": {},
   "source": [
    "# Model Validation Techniques\n",
    "\n",
    "# Two Techniques\n",
    "\n",
    "# 1) Collinearity Check - VIF\n",
    "\n",
    "# 2) Residual Analysis\n",
    "\n",
    "# Calculating Variance Inflation Factor (VIF) = 1/1-r^2\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6553f5c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.468903069994701"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rsq_rds=smf.ols(\"RDS~ADMN+MS\",data=df1).fit().rsquared\n",
    "vif_rds=1/(1-rsq_rds)\n",
    "vif_rds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "626a49e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.175091007055045"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rsq_adm=smf.ols(\"ADMN~RDS+MS\",data=df1).fit().rsquared\n",
    "vif_adm=1/(1-rsq_adm)\n",
    "vif_adm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3e9139fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.3267732905308773"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rsq_ms=smf.ols(\"MS~RDS+ADMN\",data=df1).fit().rsquared\n",
    "\n",
    "vif_ms=1/(1-rsq_ms)\n",
    "vif_ms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0a1a2d3e",
   "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>Variables</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RDS</td>\n",
       "      <td>2.468903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ADM</td>\n",
       "      <td>1.175091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MKS</td>\n",
       "      <td>2.326773</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Variables       VIF\n",
       "0       RDS  2.468903\n",
       "1       ADM  1.175091\n",
       "2       MKS  2.326773"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sorting the values \n",
    "\n",
    "d1={\"Variables\":[\"RDS\",\"ADM\",\"MKS\"],\"VIF\":[vif_rds,vif_adm,vif_ms]}\n",
    "Vif_frame=pd.DataFrame(d1)\n",
    "Vif_frame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b925cdb",
   "metadata": {},
   "source": [
    "# Residual Analysis\n",
    "\n",
    "# Test for Normality of Residuals (Q-Q Plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0171d8bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "sns.set_style(style=\"darkgrid\")\n",
    "qqplot=sm.qqplot(model.resid,line=\"q\")\n",
    "plt.title(\"Normal Q-Q plot of Residuals\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "558a132e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([14, 15, 48, 49], dtype=int64)]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(np.where(model.resid<-10000))\n",
    "\n",
    "# Residual Plot for Homoscedasticity or Heteroscedasticity (plotting model's standardized fitted values vs standardized residual values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bea0c288",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_standardized_values (vals):\n",
    "    return (vals-vals.mean())/vals.std()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "dc22ce45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(get_standardized_values(model.fittedvalues),\n",
    "           get_standardized_values(model.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": 38,
   "id": "bc973d28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Regression Plots for RDS\n",
    "\n",
    "fig=plt.figure(figsize=(15,8))\n",
    "fig=sm.graphics.plot_regress_exog(model,\"RDS\",fig=fig)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3b89cf51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Regression Plots for ADMN\n",
    "\n",
    "fig=plt.figure(figsize=(15,8))\n",
    "fig=sm.graphics.plot_regress_exog(model,\"ADMN\",fig=fig)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "fa2603de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Regression Plots for MS\n",
    "\n",
    "fig=plt.figure(figsize=(15,8))\n",
    "fig=sm.graphics.plot_regress_exog(model,\"MS\",fig=fig)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d72a745",
   "metadata": {},
   "source": [
    "# Model Deletion Diagnostics\n",
    "\n",
    "# Detecting Influuencers / Outliers\n",
    "\n",
    "# Cooks Distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "96aff3ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_influence=model.get_influence()\n",
    "(c,_)=model_influence.cooks_distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e375eb3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the influencers values using the stem plot\n",
    "\n",
    "sns.set_style(style=\"darkgrid\")\n",
    "fig=plt.subplots(figsize=(20,7))\n",
    "plt.stem(np.arange(len(df1)),np.round(c,3))\n",
    "plt.xlabel(\"Row Index\")\n",
    "plt.ylabel(\"Cooks Distance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d5dfbae",
   "metadata": {},
   "source": [
    "#  High Influence Points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cd82a450",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(figsize=(15,10))\n",
    "fig=influence_plot(model,ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5238801b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.36"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k=df1.shape[1]\n",
    "n=df1.shape[0]\n",
    "leverage_cutoff=3*((k+1)/n)\n",
    "leverage_cutoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b5b891f6",
   "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>RDS</th>\n",
       "      <th>ADMN</th>\n",
       "      <th>MS</th>\n",
       "      <th>State</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>California</td>\n",
       "      <td>14681.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    RDS      ADMN        MS       State   Profit\n",
       "49  0.0  116983.8  45173.06  California  14681.4"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1.index.isin([49])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d393ad33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 5)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "527554bf",
   "metadata": {},
   "source": [
    "#  Improving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "82fccb41",
   "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>RDS</th>\n",
       "      <th>ADMN</th>\n",
       "      <th>MS</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": [
       "          RDS       ADMN         MS       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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Discard the data points which are influencers and reasign the row number (reset_index())\n",
    "\n",
    "df2=df1.copy()\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d25137f5",
   "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>RDS</th>\n",
       "      <th>ADMN</th>\n",
       "      <th>MS</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          RDS       ADMN         MS       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"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Discard the data points which are influencers and reassign the row number (reset_index(drop=True))\n",
    "\n",
    "df2=df1.drop(df1.index[[49]],axis=0).reset_index(drop=True)\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b110b500",
   "metadata": {},
   "source": [
    "#  Model Deletion Diagnostics and Final model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "373123fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model=smf.ols(\"Profit~RDS+ADMN+MS\",data=df2).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b3648a28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x1f7c2491790>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "bba64b5c",
   "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>   0.961</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.959</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   372.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 30 Nov 2023</td> <th>  Prob (F-statistic):</th> <td>8.85e-32</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:45:00</td>     <th>  Log-Likelihood:    </th> <td> -506.28</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    49</td>      <th>  AIC:               </th> <td>   1021.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    45</td>      <th>  BIC:               </th> <td>   1028.</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> 5.238e+04</td> <td> 5542.657</td> <td>    9.451</td> <td> 0.000</td> <td> 4.12e+04</td> <td> 6.35e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RDS</th>       <td>    0.7830</td> <td>    0.038</td> <td>   20.470</td> <td> 0.000</td> <td>    0.706</td> <td>    0.860</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ADMN</th>      <td>   -0.0222</td> <td>    0.043</td> <td>   -0.518</td> <td> 0.607</td> <td>   -0.109</td> <td>    0.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MS</th>        <td>    0.0252</td> <td>    0.014</td> <td>    1.825</td> <td> 0.075</td> <td>   -0.003</td> <td>    0.053</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.082</td> <th>  Durbin-Watson:     </th> <td>   1.598</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.960</td> <th>  Jarque-Bera (JB):  </th> <td>   0.232</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.082</td> <th>  Prob(JB):          </th> <td>   0.890</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.706</td> <th>  Cond. No.          </th> <td>1.41e+06</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.41e+06. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &      Profit      & \\textbf{  R-squared:         } &     0.961   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.959   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     372.8   \\\\\n",
       "\\textbf{Date:}             & Thu, 30 Nov 2023 & \\textbf{  Prob (F-statistic):} &  8.85e-32   \\\\\n",
       "\\textbf{Time:}             &     15:45:00     & \\textbf{  Log-Likelihood:    } &   -506.28   \\\\\n",
       "\\textbf{No. Observations:} &          49      & \\textbf{  AIC:               } &     1021.   \\\\\n",
       "\\textbf{Df Residuals:}     &          45      & \\textbf{  BIC:               } &     1028.   \\\\\n",
       "\\textbf{Df Model:}         &           3      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept} &    5.238e+04  &     5542.657     &     9.451  &         0.000        &     4.12e+04    &     6.35e+04     \\\\\n",
       "\\textbf{RDS}       &       0.7830  &        0.038     &    20.470  &         0.000        &        0.706    &        0.860     \\\\\n",
       "\\textbf{ADMN}      &      -0.0222  &        0.043     &    -0.518  &         0.607        &       -0.109    &        0.064     \\\\\n",
       "\\textbf{MS}        &       0.0252  &        0.014     &     1.825  &         0.075        &       -0.003    &        0.053     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       &  0.082 & \\textbf{  Durbin-Watson:     } &    1.598  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.960 & \\textbf{  Jarque-Bera (JB):  } &    0.232  \\\\\n",
       "\\textbf{Skew:}          & -0.082 & \\textbf{  Prob(JB):          } &    0.890  \\\\\n",
       "\\textbf{Kurtosis:}      &  2.706 & \\textbf{  Cond. No.          } & 1.41e+06  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
       " [2] The condition number is large, 1.41e+06. This might indicate that there are \\newline\n",
       " 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:                       0.961\n",
       "Model:                            OLS   Adj. R-squared:                  0.959\n",
       "Method:                 Least Squares   F-statistic:                     372.8\n",
       "Date:                Thu, 30 Nov 2023   Prob (F-statistic):           8.85e-32\n",
       "Time:                        15:45:00   Log-Likelihood:                -506.28\n",
       "No. Observations:                  49   AIC:                             1021.\n",
       "Df Residuals:                      45   BIC:                             1028.\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept   5.238e+04   5542.657      9.451      0.000    4.12e+04    6.35e+04\n",
       "RDS            0.7830      0.038     20.470      0.000       0.706       0.860\n",
       "ADMN          -0.0222      0.043     -0.518      0.607      -0.109       0.064\n",
       "MS             0.0252      0.014      1.825      0.075      -0.003       0.053\n",
       "==============================================================================\n",
       "Omnibus:                        0.082   Durbin-Watson:                   1.598\n",
       "Prob(Omnibus):                  0.960   Jarque-Bera (JB):                0.232\n",
       "Skew:                          -0.082   Prob(JB):                        0.890\n",
       "Kurtosis:                       2.706   Cond. No.                     1.41e+06\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.41e+06. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13fbf055",
   "metadata": {},
   "source": [
    "# Model Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f296088c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# New data for predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f94882e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RDS</th>\n",
       "      <th>ADMN</th>\n",
       "      <th>MS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78000</td>\n",
       "      <td>127864.55</td>\n",
       "      <td>249839.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     RDS       ADMN         MS\n",
       "1  78000  127864.55  249839.44"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data=pd.DataFrame({'RDS':78000,'ADMN':127864.55,\"MS\":249839.44},index=[1])\n",
    "new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "542545e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    116922.172202\n",
       "dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Manual Prediction\n",
    "\n",
    "final_model.predict(new_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "28b9f740",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     190716.676999\n",
       "1     187537.122227\n",
       "2     180575.526396\n",
       "3     172461.144642\n",
       "4     170863.486721\n",
       "5     162582.583177\n",
       "6     157741.338633\n",
       "7     159347.735318\n",
       "8     151328.826941\n",
       "9     154236.846778\n",
       "10    135507.792682\n",
       "11    135472.855621\n",
       "12    129355.599449\n",
       "13    127780.129139\n",
       "14    149295.404796\n",
       "15    145937.941975\n",
       "16    117437.627921\n",
       "17    130408.626295\n",
       "18    129129.234457\n",
       "19    116641.003121\n",
       "20    117097.731866\n",
       "21    117911.019038\n",
       "22    115248.217796\n",
       "23    110603.139045\n",
       "24    114051.073877\n",
       "25    103398.054385\n",
       "26    111547.638935\n",
       "27    114916.165026\n",
       "28    103027.229434\n",
       "29    103057.621761\n",
       "30    100656.410227\n",
       "31     99088.213693\n",
       "32    100325.741335\n",
       "33     98962.303136\n",
       "34     90552.307809\n",
       "35     91709.288672\n",
       "36     77080.554255\n",
       "37     90722.503244\n",
       "38     71433.021956\n",
       "39     85147.375646\n",
       "40     76625.510303\n",
       "41     76492.145175\n",
       "42     72492.394974\n",
       "43     62592.049718\n",
       "44     67025.731107\n",
       "45     50457.297206\n",
       "46     58338.443625\n",
       "47     49375.776655\n",
       "48     51658.096812\n",
       "dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Automatic Prediction\n",
    "\n",
    "pred_y=final_model.predict(df2)\n",
    "pred_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "30a64bb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [Model_x, index, Model_y]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "Prepared_Models = pd.DataFrame({'Model': ['final_model'], 'index': [1]})\n",
    "new_data = pd.DataFrame({'Model': ['new_model'], 'index': [2]})\n",
    "\n",
    "merged_data = pd.merge(Prepared_Models, new_data, on='index')\n",
    "print(merged_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5f014b16",
   "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",
       "\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>Prepared_Models</th>\n",
       "      <th>Rsquared</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Model</td>\n",
       "      <td>0.950746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>final_model</td>\n",
       "      <td>0.961316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Prepared_Models  Rsquared\n",
       "0           Model  0.950746\n",
       "1     final_model  0.961316"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d2={'Prepared_Models':['Model','final_model'],'Rsquared':[model.rsquared,final_model.rsquared]}\n",
    "table=pd.DataFrame(d2)\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdfd9c84",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf85876f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dccb3f42",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "212ba558",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e43a1ca1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1eb06a16",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9d494a8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c5a9cf2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b417ed2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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