Python/ml/Startup_Profit_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STARTUP PROFIT PREDICTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Link to the Dataset: [Startup Details](https://www.kaggle.com/sonalisingh1411/startup50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sb\n",
    "from sklearn import preprocessing \n",
    "from scipy import stats\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting our Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>California</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Florida</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>New York</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Florida</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>131876.90</td>\n",
       "      <td>99814.71</td>\n",
       "      <td>362861.36</td>\n",
       "      <td>New York</td>\n",
       "      <td>156991.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>134615.46</td>\n",
       "      <td>147198.87</td>\n",
       "      <td>127716.82</td>\n",
       "      <td>California</td>\n",
       "      <td>156122.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>130298.13</td>\n",
       "      <td>145530.06</td>\n",
       "      <td>323876.68</td>\n",
       "      <td>Florida</td>\n",
       "      <td>155752.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120542.52</td>\n",
       "      <td>148718.95</td>\n",
       "      <td>311613.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>152211.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>123334.88</td>\n",
       "      <td>108679.17</td>\n",
       "      <td>304981.62</td>\n",
       "      <td>California</td>\n",
       "      <td>149759.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>101913.08</td>\n",
       "      <td>110594.11</td>\n",
       "      <td>229160.95</td>\n",
       "      <td>Florida</td>\n",
       "      <td>146121.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100671.96</td>\n",
       "      <td>91790.61</td>\n",
       "      <td>249744.55</td>\n",
       "      <td>California</td>\n",
       "      <td>144259.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>93863.75</td>\n",
       "      <td>127320.38</td>\n",
       "      <td>249839.44</td>\n",
       "      <td>Florida</td>\n",
       "      <td>141585.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>91992.39</td>\n",
       "      <td>135495.07</td>\n",
       "      <td>252664.93</td>\n",
       "      <td>California</td>\n",
       "      <td>134307.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>119943.24</td>\n",
       "      <td>156547.42</td>\n",
       "      <td>256512.92</td>\n",
       "      <td>Florida</td>\n",
       "      <td>132602.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>114523.61</td>\n",
       "      <td>122616.84</td>\n",
       "      <td>261776.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>129917.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>78013.11</td>\n",
       "      <td>121597.55</td>\n",
       "      <td>264346.06</td>\n",
       "      <td>California</td>\n",
       "      <td>126992.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>94657.16</td>\n",
       "      <td>145077.58</td>\n",
       "      <td>282574.31</td>\n",
       "      <td>New York</td>\n",
       "      <td>125370.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>91749.16</td>\n",
       "      <td>114175.79</td>\n",
       "      <td>294919.57</td>\n",
       "      <td>Florida</td>\n",
       "      <td>124266.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>86419.70</td>\n",
       "      <td>153514.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>122776.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>76253.86</td>\n",
       "      <td>113867.30</td>\n",
       "      <td>298664.47</td>\n",
       "      <td>California</td>\n",
       "      <td>118474.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>78389.47</td>\n",
       "      <td>153773.43</td>\n",
       "      <td>299737.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>111313.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>73994.56</td>\n",
       "      <td>122782.75</td>\n",
       "      <td>303319.26</td>\n",
       "      <td>Florida</td>\n",
       "      <td>110352.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>67532.53</td>\n",
       "      <td>105751.03</td>\n",
       "      <td>304768.73</td>\n",
       "      <td>Florida</td>\n",
       "      <td>108733.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>77044.01</td>\n",
       "      <td>99281.34</td>\n",
       "      <td>140574.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>108552.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>64664.71</td>\n",
       "      <td>139553.16</td>\n",
       "      <td>137962.62</td>\n",
       "      <td>California</td>\n",
       "      <td>107404.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>75328.87</td>\n",
       "      <td>144135.98</td>\n",
       "      <td>134050.07</td>\n",
       "      <td>Florida</td>\n",
       "      <td>105733.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>72107.60</td>\n",
       "      <td>127864.55</td>\n",
       "      <td>353183.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>105008.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>66051.52</td>\n",
       "      <td>182645.56</td>\n",
       "      <td>118148.20</td>\n",
       "      <td>Florida</td>\n",
       "      <td>103282.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65605.48</td>\n",
       "      <td>153032.06</td>\n",
       "      <td>107138.38</td>\n",
       "      <td>New York</td>\n",
       "      <td>101004.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>61994.48</td>\n",
       "      <td>115641.28</td>\n",
       "      <td>91131.24</td>\n",
       "      <td>Florida</td>\n",
       "      <td>99937.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>61136.38</td>\n",
       "      <td>152701.92</td>\n",
       "      <td>88218.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>97483.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>63408.86</td>\n",
       "      <td>129219.61</td>\n",
       "      <td>46085.25</td>\n",
       "      <td>California</td>\n",
       "      <td>97427.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>55493.95</td>\n",
       "      <td>103057.49</td>\n",
       "      <td>214634.81</td>\n",
       "      <td>Florida</td>\n",
       "      <td>96778.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>46426.07</td>\n",
       "      <td>157693.92</td>\n",
       "      <td>210797.67</td>\n",
       "      <td>California</td>\n",
       "      <td>96712.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>46014.02</td>\n",
       "      <td>85047.44</td>\n",
       "      <td>205517.64</td>\n",
       "      <td>New York</td>\n",
       "      <td>96479.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>28663.76</td>\n",
       "      <td>127056.21</td>\n",
       "      <td>201126.82</td>\n",
       "      <td>Florida</td>\n",
       "      <td>90708.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>44069.95</td>\n",
       "      <td>51283.14</td>\n",
       "      <td>197029.42</td>\n",
       "      <td>California</td>\n",
       "      <td>89949.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>20229.59</td>\n",
       "      <td>65947.93</td>\n",
       "      <td>185265.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>81229.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>38558.51</td>\n",
       "      <td>82982.09</td>\n",
       "      <td>174999.30</td>\n",
       "      <td>California</td>\n",
       "      <td>81005.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>28754.33</td>\n",
       "      <td>118546.05</td>\n",
       "      <td>172795.67</td>\n",
       "      <td>California</td>\n",
       "      <td>78239.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>27892.92</td>\n",
       "      <td>84710.77</td>\n",
       "      <td>164470.71</td>\n",
       "      <td>Florida</td>\n",
       "      <td>77798.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>23640.93</td>\n",
       "      <td>96189.63</td>\n",
       "      <td>148001.11</td>\n",
       "      <td>California</td>\n",
       "      <td>71498.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>15505.73</td>\n",
       "      <td>127382.30</td>\n",
       "      <td>35534.17</td>\n",
       "      <td>New York</td>\n",
       "      <td>69758.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>22177.74</td>\n",
       "      <td>154806.14</td>\n",
       "      <td>28334.72</td>\n",
       "      <td>California</td>\n",
       "      <td>65200.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1000.23</td>\n",
       "      <td>124153.04</td>\n",
       "      <td>1903.93</td>\n",
       "      <td>New York</td>\n",
       "      <td>64926.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1315.46</td>\n",
       "      <td>115816.21</td>\n",
       "      <td>297114.46</td>\n",
       "      <td>Florida</td>\n",
       "      <td>49490.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.00</td>\n",
       "      <td>135426.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>California</td>\n",
       "      <td>42559.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>542.05</td>\n",
       "      <td>51743.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>35673.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.00</td>\n",
       "      <td>116983.80</td>\n",
       "      <td>45173.06</td>\n",
       "      <td>California</td>\n",
       "      <td>14681.40</td>\n",
       "    </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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r'startups.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# no null values are present\n",
    "# but, we need to encode 'State' attribute\n",
    "label_encoder = preprocessing.LabelEncoder()  # encoding data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>2</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>0</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>1</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>2</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>1</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      2  192261.83\n",
       "1  162597.70       151377.59        443898.53      0  191792.06\n",
       "2  153441.51       101145.55        407934.54      1  191050.39\n",
       "3  144372.41       118671.85        383199.62      2  182901.99\n",
       "4  142107.34        91391.77        366168.42      1  166187.94"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['State'] = df['State'].astype('|S')\n",
    "df['State'] = label_encoder.fit_transform(df['State'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R&D Spend          False\n",
       "Administration     False\n",
       "Marketing Spend    False\n",
       "State              False\n",
       "Profit             False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for null values\n",
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['R&D Spend', 'Administration', 'Marketing Spend', 'State', 'Profit'], dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>VIF</th>\n",
       "      <th>Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.386322</td>\n",
       "      <td>R&amp;D Spend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.815916</td>\n",
       "      <td>Administration</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.674608</td>\n",
       "      <td>Marketing Spend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.382637</td>\n",
       "      <td>State</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        VIF         Features\n",
       "0  8.386322        R&D Spend\n",
       "1  4.815916   Administration\n",
       "2  7.674608  Marketing Spend\n",
       "3  2.382637            State"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking vif\n",
    "variables = df[['R&D Spend', 'Administration', 'Marketing Spend', 'State']]\n",
    "vif = pd.DataFrame()\n",
    "vif['VIF'] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
    "vif['Features'] = variables.columns\n",
    "\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# as vif for all attributes<10, we need not drop any of them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='R&D Spend'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking for outliners\n",
    "sb.boxplot(x=df['R&D Spend'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# no outliners in 'R&D Spend' attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Administration'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(x=df['Administration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# no outliners in 'Administration' attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Marketing Spend'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(x=df['Marketing Spend'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# no outliners in 'Marketing Spend' attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='State'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAK1klEQVR4nO3d34/l9V3H8debXVqWtljJIuK0um22atrEH4i1pdXAjaFrGm6MsfHCJk1IL9zaC40mNSb6BxgjMSakMaaJoY2xemEgsSrVCKVmQVhoQLMloB0whUKg7W6RHx8vzlk7jLs75+zO9/temccj2TBzzvfHew6ffe7Z7+w5U2OMADC/S7oHANirBBigiQADNBFggCYCDNBk/zobHzx4cBw6dGiiUQBen+67775nxhhXbb99rQAfOnQox44d272pAPaAqnriTLe7BAHQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMECTtX4m3Pm69dZbc+LEiTlOxR60ubmZJNnY2GiehNerw4cP5+jRo7t+3FkCfOLEiTzw8CN55fIr5zgde8y+k88nSf7rxVmWM3vMvpPPTnbs2VbsK5dfmVM/emSu07GHHHj0jiSxvpjE6fU1BdeAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGa7J/jJJubm7nkOyfnOBXArrrkOy9kc/PlaY49yVG3OXXqVOrVl+Y4FcCuqldfyqlTpyY5tksQAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigyY4BrqpbqupYVR17+umn55gJYE/YMcBjjNvGGNeNMa676qqr5pgJYE9wCQKgiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzSZJcAHDhzIuOTSOU4FsKvGJZfmwIEDkxx7lgBvbGzk1cuumONUALvq1cuuyMbGxiTHdgkCoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE32z3WifSefzYFH75jrdOwh+05+I0msLyax7+SzSa6e5NizBPjw4cNznIY9anPz5STJxsY0v0nY666erGGzBPjo0aNznAbg/xXXgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNaoyx+sZVTyd54jzPdTDJM+e575TMtR5zrcdc63m9zvVDY4yrtt+4VoAvRFUdG2NcN8vJ1mCu9ZhrPeZaz16byyUIgCYCDNBkzgDfNuO51mGu9ZhrPeZaz56aa7ZrwAC8lksQAE0EGKDJBQe4qm6qqn+rqhNV9dtnuL+q6o+W9x+vqmtX3XfiuX5lOc/xqrqnqn58y32PV9VDVfVAVR2bea4bqur55bkfqKrfXXXfief6zS0zPVxVr1TVlcv7pny8/rSqvl5VD5/l/q71tdNcXetrp7m61tdOc3Wtr7dX1V1V9UhVfaWqfv0M20y3xsYY5/0ryb4kX03yziRvSPJgkndv2+ZIkjuTVJL3JfnyqvtOPNf1Sb53+fGHTs+1/PzxJAd3Y5bzmOuGJH9zPvtOOde27T+c5B+mfryWx/65JNcmefgs98++vlaca/b1teJcs6+vVeZqXF/XJLl2+fFbkvz7nA270GfA701yYozx2Bjjv5N8NsnN27a5OclnxsK9Sd5aVdesuO9kc40x7hljPLf89N4kb9ulc1/QXBPtu9vH/kiS23fp3Oc0xvinJM+eY5OO9bXjXE3ra5XH62xaH69t5lxfT40x7l9+/M0kjyTZ2LbZZGvsQgO8keQ/t3z+tfzf4c+2zSr7TjnXVh/L4k+400aSv62q+6rqll2aaZ253l9VD1bVnVX1njX3nXKuVNXlSW5K8pdbbp7q8VpFx/pa11zra1Vzr6+Vda6vqjqU5CeTfHnbXZOtsf1rT/ladYbbtv+7trNts8q+52vlY1fVjVn8Bvnglps/MMZ4sqq+L8kXqurR5Z/gc8x1fxavG/9WVR1J8tdJ3rXivlPOddqHk9w9xtj6bGaqx2sVHetrZTOvr1V0rK91tKyvqnpzFtH/5Bjjhe13n2GXXVljF/oM+GtJ3r7l87cleXLFbVbZd8q5UlU/luTTSW4eY3zj9O1jjCeX//16kr/K4q8as8w1xnhhjPGt5cd3JLm0qg6usu+Uc23xy9n218MJH69VdKyvlTSsrx01ra91zL6+qurSLOL752OMz59hk+nW2AVewN6f5LEk78h3L0K/Z9s2v5DXXsD+l1X3nXiuH0xyIsn1225/U5K3bPn4niQ3zTjX9+e7L5B5b5L/WD52rY/XcrvvyeI63pvmeLy2nONQzv5NpdnX14pzzb6+Vpxr9vW1ylxd62v5tX8myR+eY5vJ1thufAFHsvjO4VeTfGp528eTfHzLF/jHy/sfSnLdufbdxQd2p7k+neS5JA8sfx1b3v7O5QP5YJKvNMz1a8vzPpjFN2+uP9e+c821/PyjST67bb+pH6/bkzyV5KUsnnF87CJZXzvN1bW+dpqra32dc67G9fXBLC4bHN/y/+rIXGvMS5EBmnglHEATAQZoIsAATQQYoIkAAzQRYC5aVfWp5TtUHV++E9bPVNUnly9X3WnflbaDTv4ZGhelqnp/kj9IcsMY48Xlq7XekMU/xL9ujHHOHxFeVY+vsh108gyYi9U1SZ4ZY7yYJMuQ/mKSH0hyV1XdlSRV9SdVdWz5TPn3lrd94gzb/XxVfamq7q+qv1i+9h9aeQbMRWkZyH9OcnmSv0vyuTHGP25/ZltVV44xnq2qfUn+PsknxhjHt263fPb8+SQfGmN8u6p+K8kbxxi/3/Clwf+60HdDg0mMxbt1/VSSn01yY5LPneUnDvzS8i0K92fxrPndWbysdKv3LW+/u6qSxaWML001O6xKgLlojTFeSfLFJF+sqoeS/OrW+6vqHUl+I8lPjzGeq6o/S3LZGQ5VSb4wxvjItBPDelwD5qJUVT9SVe/actNPJHkiyTez+NExSXJFkm8neb6qrs7iR/+ctnW7e5N8oKoOL499eVX98ITjw0o8A+Zi9eYkt1bVW5O8nMVbO96SxY+rubOqnhpj3FhV/5rFu2Q9luTuLfvftm27jya5vareuLz/d7J4Fyto45twAE1cggBoIsAATQQYoIkAAzQRYIAmAgzQRIABmvwPTlCiSrSfDakAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(x=df['State'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# no outliners in 'State' attribute"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using Pearson's correlation method\n",
    "corr = df.corr(method='pearson')\n",
    "sb.heatmap(corr, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1fe86d901c0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.pairplot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 'Profit' is almost linearly dependent on 'R&D Spend' and 'Marketing Spend'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting Data for Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df.values\n",
    "X,y = data[:,:-1], data[:,-1]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)  # splitting in the ration 80:20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(random_state=0)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = DecisionTreeRegressor(random_state=0)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making Predictions and Checking Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9609785649021075"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictions are 96.09% accurate."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}