Profit prediction of startup.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import all th necessary library we needed\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
       "  </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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#first we will load our data in data variable\n",
    "#data.head will only print first five components of our data\n",
    "data=pd.read_csv(\"50_Startups.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#here we are finding profit so we are making it output and dropping it from input features\n",
    "# here axis =1 means we are deleting entire data of profit if axis=0 then it will delete only one row\n",
    "output=data['Profit']\n",
    "Input_features= data.drop(['Profit'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    R&D Spend  Administration  Marketing Spend       State\n",
       "0   165349.20       136897.80        471784.10    New York\n",
       "1   162597.70       151377.59        443898.53  California\n",
       "2   153441.51       101145.55        407934.54     Florida\n",
       "3   144372.41       118671.85        383199.62    New York\n",
       "4   142107.34        91391.77        366168.42     Florida\n",
       "5   131876.90        99814.71        362861.36    New York\n",
       "6   134615.46       147198.87        127716.82  California\n",
       "7   130298.13       145530.06        323876.68     Florida\n",
       "8   120542.52       148718.95        311613.29    New York\n",
       "9   123334.88       108679.17        304981.62  California\n",
       "10  101913.08       110594.11        229160.95     Florida\n",
       "11  100671.96        91790.61        249744.55  California\n",
       "12   93863.75       127320.38        249839.44     Florida\n",
       "13   91992.39       135495.07        252664.93  California\n",
       "14  119943.24       156547.42        256512.92     Florida\n",
       "15  114523.61       122616.84        261776.23    New York\n",
       "16   78013.11       121597.55        264346.06  California\n",
       "17   94657.16       145077.58        282574.31    New York\n",
       "18   91749.16       114175.79        294919.57     Florida\n",
       "19   86419.70       153514.11             0.00    New York\n",
       "20   76253.86       113867.30        298664.47  California\n",
       "21   78389.47       153773.43        299737.29    New York\n",
       "22   73994.56       122782.75        303319.26     Florida\n",
       "23   67532.53       105751.03        304768.73     Florida\n",
       "24   77044.01        99281.34        140574.81    New York\n",
       "25   64664.71       139553.16        137962.62  California\n",
       "26   75328.87       144135.98        134050.07     Florida\n",
       "27   72107.60       127864.55        353183.81    New York\n",
       "28   66051.52       182645.56        118148.20     Florida\n",
       "29   65605.48       153032.06        107138.38    New York\n",
       "30   61994.48       115641.28         91131.24     Florida\n",
       "31   61136.38       152701.92         88218.23    New York\n",
       "32   63408.86       129219.61         46085.25  California\n",
       "33   55493.95       103057.49        214634.81     Florida\n",
       "34   46426.07       157693.92        210797.67  California\n",
       "35   46014.02        85047.44        205517.64    New York\n",
       "36   28663.76       127056.21        201126.82     Florida\n",
       "37   44069.95        51283.14        197029.42  California\n",
       "38   20229.59        65947.93        185265.10    New York\n",
       "39   38558.51        82982.09        174999.30  California\n",
       "40   28754.33       118546.05        172795.67  California\n",
       "41   27892.92        84710.77        164470.71     Florida\n",
       "42   23640.93        96189.63        148001.11  California\n",
       "43   15505.73       127382.30         35534.17    New York\n",
       "44   22177.74       154806.14         28334.72  California\n",
       "45    1000.23       124153.04          1903.93    New York\n",
       "46    1315.46       115816.21        297114.46     Florida\n",
       "47       0.00       135426.92             0.00  California\n",
       "48     542.05        51743.15             0.00    New York\n",
       "49       0.00       116983.80         45173.06  California"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Input_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "    California  Florida  New York\n",
       "0            0        0         1\n",
       "1            1        0         0\n",
       "2            0        1         0\n",
       "3            0        0         1\n",
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       "5            0        0         1\n",
       "6            1        0         0\n",
       "7            0        1         0\n",
       "8            0        0         1\n",
       "9            1        0         0\n",
       "10           0        1         0\n",
       "11           1        0         0\n",
       "12           0        1         0\n",
       "13           1        0         0\n",
       "14           0        1         0\n",
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       "29           0        0         1\n",
       "30           0        1         0\n",
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       "34           1        0         0\n",
       "35           0        0         1\n",
       "36           0        1         0\n",
       "37           1        0         0\n",
       "38           0        0         1\n",
       "39           1        0         0\n",
       "40           1        0         0\n",
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       "44           1        0         0\n",
       "45           0        0         1\n",
       "46           0        1         0\n",
       "47           1        0         0\n",
       "48           0        0         1\n",
       "49           1        0         0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now State is a category but not a feature. ML algorithms see everything as numbers so we need to convert states into categories.\n",
    "#we are making dummy variable for state category\n",
    "dummy=pd.get_dummies(data['State'])\n",
    "dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    California  New York\n",
       "0            0         1\n",
       "1            1         0\n",
       "2            0         0\n",
       "3            0         1\n",
       "4            0         0\n",
       "5            0         1\n",
       "6            1         0\n",
       "7            0         0\n",
       "8            0         1\n",
       "9            1         0\n",
       "10           0         0\n",
       "11           1         0\n",
       "12           0         0\n",
       "13           1         0\n",
       "14           0         0\n",
       "15           0         1\n",
       "16           1         0\n",
       "17           0         1\n",
       "18           0         0\n",
       "19           0         1\n",
       "20           1         0\n",
       "21           0         1\n",
       "22           0         0\n",
       "23           0         0\n",
       "24           0         1\n",
       "25           1         0\n",
       "26           0         0\n",
       "27           0         1\n",
       "28           0         0\n",
       "29           0         1\n",
       "30           0         0\n",
       "31           0         1\n",
       "32           1         0\n",
       "33           0         0\n",
       "34           1         0\n",
       "35           0         1\n",
       "36           0         0\n",
       "37           1         0\n",
       "38           0         1\n",
       "39           1         0\n",
       "40           1         0\n",
       "41           0         0\n",
       "42           1         0\n",
       "43           0         1\n",
       "44           1         0\n",
       "45           0         1\n",
       "46           0         0\n",
       "47           1         0\n",
       "48           0         1\n",
       "49           1         0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This looks good but has a huge problem of Dummy Variable Trap. In this we made three categories California, NewYork and Florida. But we just need two of them. For example we take California and Newyork, If there is a zero in both of them, this implies that the state to which startup belongs is Florida.\n",
    "dummies=dummy.drop(['Florida'],axis=1)\n",
    "dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>131876.90</td>\n",
       "      <td>99814.71</td>\n",
       "      <td>362861.36</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>130298.13</td>\n",
       "      <td>145530.06</td>\n",
       "      <td>323876.68</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>123334.88</td>\n",
       "      <td>108679.17</td>\n",
       "      <td>304981.62</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100671.96</td>\n",
       "      <td>91790.61</td>\n",
       "      <td>249744.55</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>91992.39</td>\n",
       "      <td>135495.07</td>\n",
       "      <td>252664.93</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>114523.61</td>\n",
       "      <td>122616.84</td>\n",
       "      <td>261776.23</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>94657.16</td>\n",
       "      <td>145077.58</td>\n",
       "      <td>282574.31</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>86419.70</td>\n",
       "      <td>153514.11</td>\n",
       "      <td>0.00</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>78389.47</td>\n",
       "      <td>153773.43</td>\n",
       "      <td>299737.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>73994.56</td>\n",
       "      <td>122782.75</td>\n",
       "      <td>303319.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>67532.53</td>\n",
       "      <td>105751.03</td>\n",
       "      <td>304768.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>77044.01</td>\n",
       "      <td>99281.34</td>\n",
       "      <td>140574.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>64664.71</td>\n",
       "      <td>139553.16</td>\n",
       "      <td>137962.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>75328.87</td>\n",
       "      <td>144135.98</td>\n",
       "      <td>134050.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>72107.60</td>\n",
       "      <td>127864.55</td>\n",
       "      <td>353183.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>66051.52</td>\n",
       "      <td>182645.56</td>\n",
       "      <td>118148.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65605.48</td>\n",
       "      <td>153032.06</td>\n",
       "      <td>107138.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>61994.48</td>\n",
       "      <td>115641.28</td>\n",
       "      <td>91131.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>61136.38</td>\n",
       "      <td>152701.92</td>\n",
       "      <td>88218.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>63408.86</td>\n",
       "      <td>129219.61</td>\n",
       "      <td>46085.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>55493.95</td>\n",
       "      <td>103057.49</td>\n",
       "      <td>214634.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>46426.07</td>\n",
       "      <td>157693.92</td>\n",
       "      <td>210797.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>46014.02</td>\n",
       "      <td>85047.44</td>\n",
       "      <td>205517.64</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>44069.95</td>\n",
       "      <td>51283.14</td>\n",
       "      <td>197029.42</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>38558.51</td>\n",
       "      <td>82982.09</td>\n",
       "      <td>174999.30</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>27892.92</td>\n",
       "      <td>84710.77</td>\n",
       "      <td>164470.71</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>15505.73</td>\n",
       "      <td>127382.30</td>\n",
       "      <td>35534.17</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1000.23</td>\n",
       "      <td>124153.04</td>\n",
       "      <td>1903.93</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.00</td>\n",
       "      <td>135426.92</td>\n",
       "      <td>0.00</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.00</td>\n",
       "      <td>116983.80</td>\n",
       "      <td>45173.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    R&D Spend  Administration  Marketing Spend\n",
       "0   165349.20       136897.80        471784.10\n",
       "1   162597.70       151377.59        443898.53\n",
       "2   153441.51       101145.55        407934.54\n",
       "3   144372.41       118671.85        383199.62\n",
       "4   142107.34        91391.77        366168.42\n",
       "5   131876.90        99814.71        362861.36\n",
       "6   134615.46       147198.87        127716.82\n",
       "7   130298.13       145530.06        323876.68\n",
       "8   120542.52       148718.95        311613.29\n",
       "9   123334.88       108679.17        304981.62\n",
       "10  101913.08       110594.11        229160.95\n",
       "11  100671.96        91790.61        249744.55\n",
       "12   93863.75       127320.38        249839.44\n",
       "13   91992.39       135495.07        252664.93\n",
       "14  119943.24       156547.42        256512.92\n",
       "15  114523.61       122616.84        261776.23\n",
       "16   78013.11       121597.55        264346.06\n",
       "17   94657.16       145077.58        282574.31\n",
       "18   91749.16       114175.79        294919.57\n",
       "19   86419.70       153514.11             0.00\n",
       "20   76253.86       113867.30        298664.47\n",
       "21   78389.47       153773.43        299737.29\n",
       "22   73994.56       122782.75        303319.26\n",
       "23   67532.53       105751.03        304768.73\n",
       "24   77044.01        99281.34        140574.81\n",
       "25   64664.71       139553.16        137962.62\n",
       "26   75328.87       144135.98        134050.07\n",
       "27   72107.60       127864.55        353183.81\n",
       "28   66051.52       182645.56        118148.20\n",
       "29   65605.48       153032.06        107138.38\n",
       "30   61994.48       115641.28         91131.24\n",
       "31   61136.38       152701.92         88218.23\n",
       "32   63408.86       129219.61         46085.25\n",
       "33   55493.95       103057.49        214634.81\n",
       "34   46426.07       157693.92        210797.67\n",
       "35   46014.02        85047.44        205517.64\n",
       "36   28663.76       127056.21        201126.82\n",
       "37   44069.95        51283.14        197029.42\n",
       "38   20229.59        65947.93        185265.10\n",
       "39   38558.51        82982.09        174999.30\n",
       "40   28754.33       118546.05        172795.67\n",
       "41   27892.92        84710.77        164470.71\n",
       "42   23640.93        96189.63        148001.11\n",
       "43   15505.73       127382.30         35534.17\n",
       "44   22177.74       154806.14         28334.72\n",
       "45    1000.23       124153.04          1903.93\n",
       "46    1315.46       115816.21        297114.46\n",
       "47       0.00       135426.92             0.00\n",
       "48     542.05        51743.15             0.00\n",
       "49       0.00       116983.80         45173.06"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Input_features2 = Input_features.drop(['State'],axis = 1)\n",
    "Input_features2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Cal</th>\n",
       "      <th>New Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>131876.90</td>\n",
       "      <td>99814.71</td>\n",
       "      <td>362861.36</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>134615.46</td>\n",
       "      <td>147198.87</td>\n",
       "      <td>127716.82</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>130298.13</td>\n",
       "      <td>145530.06</td>\n",
       "      <td>323876.68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120542.52</td>\n",
       "      <td>148718.95</td>\n",
       "      <td>311613.29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>123334.88</td>\n",
       "      <td>108679.17</td>\n",
       "      <td>304981.62</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>101913.08</td>\n",
       "      <td>110594.11</td>\n",
       "      <td>229160.95</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100671.96</td>\n",
       "      <td>91790.61</td>\n",
       "      <td>249744.55</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>93863.75</td>\n",
       "      <td>127320.38</td>\n",
       "      <td>249839.44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>91992.39</td>\n",
       "      <td>135495.07</td>\n",
       "      <td>252664.93</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>119943.24</td>\n",
       "      <td>156547.42</td>\n",
       "      <td>256512.92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>114523.61</td>\n",
       "      <td>122616.84</td>\n",
       "      <td>261776.23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>78013.11</td>\n",
       "      <td>121597.55</td>\n",
       "      <td>264346.06</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>94657.16</td>\n",
       "      <td>145077.58</td>\n",
       "      <td>282574.31</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>91749.16</td>\n",
       "      <td>114175.79</td>\n",
       "      <td>294919.57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>86419.70</td>\n",
       "      <td>153514.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>76253.86</td>\n",
       "      <td>113867.30</td>\n",
       "      <td>298664.47</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>78389.47</td>\n",
       "      <td>153773.43</td>\n",
       "      <td>299737.29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>73994.56</td>\n",
       "      <td>122782.75</td>\n",
       "      <td>303319.26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>67532.53</td>\n",
       "      <td>105751.03</td>\n",
       "      <td>304768.73</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>77044.01</td>\n",
       "      <td>99281.34</td>\n",
       "      <td>140574.81</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>64664.71</td>\n",
       "      <td>139553.16</td>\n",
       "      <td>137962.62</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>75328.87</td>\n",
       "      <td>144135.98</td>\n",
       "      <td>134050.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>72107.60</td>\n",
       "      <td>127864.55</td>\n",
       "      <td>353183.81</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>66051.52</td>\n",
       "      <td>182645.56</td>\n",
       "      <td>118148.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65605.48</td>\n",
       "      <td>153032.06</td>\n",
       "      <td>107138.38</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>61994.48</td>\n",
       "      <td>115641.28</td>\n",
       "      <td>91131.24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>61136.38</td>\n",
       "      <td>152701.92</td>\n",
       "      <td>88218.23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>63408.86</td>\n",
       "      <td>129219.61</td>\n",
       "      <td>46085.25</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>55493.95</td>\n",
       "      <td>103057.49</td>\n",
       "      <td>214634.81</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>46426.07</td>\n",
       "      <td>157693.92</td>\n",
       "      <td>210797.67</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>46014.02</td>\n",
       "      <td>85047.44</td>\n",
       "      <td>205517.64</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>28663.76</td>\n",
       "      <td>127056.21</td>\n",
       "      <td>201126.82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>44069.95</td>\n",
       "      <td>51283.14</td>\n",
       "      <td>197029.42</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>20229.59</td>\n",
       "      <td>65947.93</td>\n",
       "      <td>185265.10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>38558.51</td>\n",
       "      <td>82982.09</td>\n",
       "      <td>174999.30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>28754.33</td>\n",
       "      <td>118546.05</td>\n",
       "      <td>172795.67</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>27892.92</td>\n",
       "      <td>84710.77</td>\n",
       "      <td>164470.71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>23640.93</td>\n",
       "      <td>96189.63</td>\n",
       "      <td>148001.11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>15505.73</td>\n",
       "      <td>127382.30</td>\n",
       "      <td>35534.17</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>22177.74</td>\n",
       "      <td>154806.14</td>\n",
       "      <td>28334.72</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1000.23</td>\n",
       "      <td>124153.04</td>\n",
       "      <td>1903.93</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1315.46</td>\n",
       "      <td>115816.21</td>\n",
       "      <td>297114.46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.00</td>\n",
       "      <td>135426.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>542.05</td>\n",
       "      <td>51743.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.00</td>\n",
       "      <td>116983.80</td>\n",
       "      <td>45173.06</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    R&D Spend  Administration  Marketing Spend  Cal  New Y\n",
       "0   165349.20       136897.80        471784.10    0      1\n",
       "1   162597.70       151377.59        443898.53    1      0\n",
       "2   153441.51       101145.55        407934.54    0      0\n",
       "3   144372.41       118671.85        383199.62    0      1\n",
       "4   142107.34        91391.77        366168.42    0      0\n",
       "5   131876.90        99814.71        362861.36    0      1\n",
       "6   134615.46       147198.87        127716.82    1      0\n",
       "7   130298.13       145530.06        323876.68    0      0\n",
       "8   120542.52       148718.95        311613.29    0      1\n",
       "9   123334.88       108679.17        304981.62    1      0\n",
       "10  101913.08       110594.11        229160.95    0      0\n",
       "11  100671.96        91790.61        249744.55    1      0\n",
       "12   93863.75       127320.38        249839.44    0      0\n",
       "13   91992.39       135495.07        252664.93    1      0\n",
       "14  119943.24       156547.42        256512.92    0      0\n",
       "15  114523.61       122616.84        261776.23    0      1\n",
       "16   78013.11       121597.55        264346.06    1      0\n",
       "17   94657.16       145077.58        282574.31    0      1\n",
       "18   91749.16       114175.79        294919.57    0      0\n",
       "19   86419.70       153514.11             0.00    0      1\n",
       "20   76253.86       113867.30        298664.47    1      0\n",
       "21   78389.47       153773.43        299737.29    0      1\n",
       "22   73994.56       122782.75        303319.26    0      0\n",
       "23   67532.53       105751.03        304768.73    0      0\n",
       "24   77044.01        99281.34        140574.81    0      1\n",
       "25   64664.71       139553.16        137962.62    1      0\n",
       "26   75328.87       144135.98        134050.07    0      0\n",
       "27   72107.60       127864.55        353183.81    0      1\n",
       "28   66051.52       182645.56        118148.20    0      0\n",
       "29   65605.48       153032.06        107138.38    0      1\n",
       "30   61994.48       115641.28         91131.24    0      0\n",
       "31   61136.38       152701.92         88218.23    0      1\n",
       "32   63408.86       129219.61         46085.25    1      0\n",
       "33   55493.95       103057.49        214634.81    0      0\n",
       "34   46426.07       157693.92        210797.67    1      0\n",
       "35   46014.02        85047.44        205517.64    0      1\n",
       "36   28663.76       127056.21        201126.82    0      0\n",
       "37   44069.95        51283.14        197029.42    1      0\n",
       "38   20229.59        65947.93        185265.10    0      1\n",
       "39   38558.51        82982.09        174999.30    1      0\n",
       "40   28754.33       118546.05        172795.67    1      0\n",
       "41   27892.92        84710.77        164470.71    0      0\n",
       "42   23640.93        96189.63        148001.11    1      0\n",
       "43   15505.73       127382.30         35534.17    0      1\n",
       "44   22177.74       154806.14         28334.72    1      0\n",
       "45    1000.23       124153.04          1903.93    0      1\n",
       "46    1315.46       115816.21        297114.46    0      0\n",
       "47       0.00       135426.92             0.00    1      0\n",
       "48     542.05        51743.15             0.00    0      1\n",
       "49       0.00       116983.80         45173.06    1      0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we will add our new columns (state) in our input features\n",
    "Input_features2['Cal'] = dummies['California']\n",
    "Input_features2['New Y'] = dummies['New York']\n",
    "Input_features2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we will spilt our data into 80:20 ratio 80 for train and 20 for test \n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(Input_features2, output, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([103015.20159767, 132582.27760838, 132447.73845188,  71976.0985127 ,\n",
       "       178537.48221077, 116161.24230153,  67851.69209695,  98791.73374676,\n",
       "       113969.43533007, 167921.06569577])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we are treating this data as linear regression\n",
    "from sklearn.linear_model import LinearRegression\n",
    "linear=LinearRegression()\n",
    "# we are fitting the data in machine\n",
    "linear.fit(X_train,y_train)\n",
    "#we are predicting an output\n",
    "test_pred=linear.predict(X_test)\n",
    "test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# to plot dot graph\n",
    "plt.scatter(Input_features2['R&D Spend'],output)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9347068473300781"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we have to find model accurasy\n",
    "# 1. r square value\n",
    "# 2. p value\n",
    "# 3. getting the anknown coeeficient the betas\n",
    "r_square=linear.score(X_test,y_test)\n",
    "r_square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.89074433e+02, 1.87171302e-01, 7.78923444e+00, 3.50569377e-01,\n",
       "        2.77002545e+00]),\n",
       " array([7.54872128e-07, 6.76707627e-01, 2.35249079e-02, 5.70141785e-01,\n",
       "        1.34612146e-01]))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting p value\n",
    "from sklearn.feature_selection import f_regression\n",
    "f = f_regression(X_test,y_test)\n",
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.73467193e-01, 3.28845975e-02, 3.66100259e-02, 9.59284160e+02,\n",
       "       1.65865321e+03])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting coef\n",
    "coeff = linear.coef_\n",
    "coeff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41594.88345830595"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding intersect beta 0\n",
    "intercept = linear.intercept_\n",
    "intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>const</th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Cal</th>\n",
       "      <th>New Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1.0</td>\n",
       "      <td>55493.95</td>\n",
       "      <td>103057.49</td>\n",
       "      <td>214634.81</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1.0</td>\n",
       "      <td>46014.02</td>\n",
       "      <td>85047.44</td>\n",
       "      <td>205517.64</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.0</td>\n",
       "      <td>75328.87</td>\n",
       "      <td>144135.98</td>\n",
       "      <td>134050.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1.0</td>\n",
       "      <td>46426.07</td>\n",
       "      <td>157693.92</td>\n",
       "      <td>210797.67</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.0</td>\n",
       "      <td>91749.16</td>\n",
       "      <td>114175.79</td>\n",
       "      <td>294919.57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>130298.13</td>\n",
       "      <td>145530.06</td>\n",
       "      <td>323876.68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.0</td>\n",
       "      <td>119943.24</td>\n",
       "      <td>156547.42</td>\n",
       "      <td>256512.92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1000.23</td>\n",
       "      <td>124153.04</td>\n",
       "      <td>1903.93</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1.0</td>\n",
       "      <td>542.05</td>\n",
       "      <td>51743.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.0</td>\n",
       "      <td>65605.48</td>\n",
       "      <td>153032.06</td>\n",
       "      <td>107138.38</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.0</td>\n",
       "      <td>114523.61</td>\n",
       "      <td>122616.84</td>\n",
       "      <td>261776.23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1.0</td>\n",
       "      <td>61994.48</td>\n",
       "      <td>115641.28</td>\n",
       "      <td>91131.24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1.0</td>\n",
       "      <td>63408.86</td>\n",
       "      <td>129219.61</td>\n",
       "      <td>46085.25</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.0</td>\n",
       "      <td>78013.11</td>\n",
       "      <td>121597.55</td>\n",
       "      <td>264346.06</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1.0</td>\n",
       "      <td>23640.93</td>\n",
       "      <td>96189.63</td>\n",
       "      <td>148001.11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.0</td>\n",
       "      <td>76253.86</td>\n",
       "      <td>113867.30</td>\n",
       "      <td>298664.47</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1.0</td>\n",
       "      <td>15505.73</td>\n",
       "      <td>127382.30</td>\n",
       "      <td>35534.17</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>120542.52</td>\n",
       "      <td>148718.95</td>\n",
       "      <td>311613.29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.0</td>\n",
       "      <td>91992.39</td>\n",
       "      <td>135495.07</td>\n",
       "      <td>252664.93</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.0</td>\n",
       "      <td>64664.71</td>\n",
       "      <td>139553.16</td>\n",
       "      <td>137962.62</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>131876.90</td>\n",
       "      <td>99814.71</td>\n",
       "      <td>362861.36</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.0</td>\n",
       "      <td>94657.16</td>\n",
       "      <td>145077.58</td>\n",
       "      <td>282574.31</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1.0</td>\n",
       "      <td>28754.33</td>\n",
       "      <td>118546.05</td>\n",
       "      <td>172795.67</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>116983.80</td>\n",
       "      <td>45173.06</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>93863.75</td>\n",
       "      <td>127320.38</td>\n",
       "      <td>249839.44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1.0</td>\n",
       "      <td>44069.95</td>\n",
       "      <td>51283.14</td>\n",
       "      <td>197029.42</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.0</td>\n",
       "      <td>77044.01</td>\n",
       "      <td>99281.34</td>\n",
       "      <td>140574.81</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>134615.46</td>\n",
       "      <td>147198.87</td>\n",
       "      <td>127716.82</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.0</td>\n",
       "      <td>67532.53</td>\n",
       "      <td>105751.03</td>\n",
       "      <td>304768.73</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1.0</td>\n",
       "      <td>28663.76</td>\n",
       "      <td>127056.21</td>\n",
       "      <td>201126.82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.0</td>\n",
       "      <td>78389.47</td>\n",
       "      <td>153773.43</td>\n",
       "      <td>299737.29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.0</td>\n",
       "      <td>86419.70</td>\n",
       "      <td>153514.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>123334.88</td>\n",
       "      <td>108679.17</td>\n",
       "      <td>304981.62</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1.0</td>\n",
       "      <td>38558.51</td>\n",
       "      <td>82982.09</td>\n",
       "      <td>174999.30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1315.46</td>\n",
       "      <td>115816.21</td>\n",
       "      <td>297114.46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>135426.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1.0</td>\n",
       "      <td>22177.74</td>\n",
       "      <td>154806.14</td>\n",
       "      <td>28334.72</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    const  R&D Spend  Administration  Marketing Spend  Cal  New Y\n",
       "33    1.0   55493.95       103057.49        214634.81    0      0\n",
       "35    1.0   46014.02        85047.44        205517.64    0      1\n",
       "26    1.0   75328.87       144135.98        134050.07    0      0\n",
       "34    1.0   46426.07       157693.92        210797.67    1      0\n",
       "18    1.0   91749.16       114175.79        294919.57    0      0\n",
       "7     1.0  130298.13       145530.06        323876.68    0      0\n",
       "14    1.0  119943.24       156547.42        256512.92    0      0\n",
       "45    1.0    1000.23       124153.04          1903.93    0      1\n",
       "48    1.0     542.05        51743.15             0.00    0      1\n",
       "29    1.0   65605.48       153032.06        107138.38    0      1\n",
       "15    1.0  114523.61       122616.84        261776.23    0      1\n",
       "30    1.0   61994.48       115641.28         91131.24    0      0\n",
       "32    1.0   63408.86       129219.61         46085.25    1      0\n",
       "16    1.0   78013.11       121597.55        264346.06    1      0\n",
       "42    1.0   23640.93        96189.63        148001.11    1      0\n",
       "20    1.0   76253.86       113867.30        298664.47    1      0\n",
       "43    1.0   15505.73       127382.30         35534.17    0      1\n",
       "8     1.0  120542.52       148718.95        311613.29    0      1\n",
       "13    1.0   91992.39       135495.07        252664.93    1      0\n",
       "25    1.0   64664.71       139553.16        137962.62    1      0\n",
       "5     1.0  131876.90        99814.71        362861.36    0      1\n",
       "17    1.0   94657.16       145077.58        282574.31    0      1\n",
       "40    1.0   28754.33       118546.05        172795.67    1      0\n",
       "49    1.0       0.00       116983.80         45173.06    1      0\n",
       "1     1.0  162597.70       151377.59        443898.53    1      0\n",
       "12    1.0   93863.75       127320.38        249839.44    0      0\n",
       "37    1.0   44069.95        51283.14        197029.42    1      0\n",
       "24    1.0   77044.01        99281.34        140574.81    0      1\n",
       "6     1.0  134615.46       147198.87        127716.82    1      0\n",
       "23    1.0   67532.53       105751.03        304768.73    0      0\n",
       "36    1.0   28663.76       127056.21        201126.82    0      0\n",
       "21    1.0   78389.47       153773.43        299737.29    0      1\n",
       "19    1.0   86419.70       153514.11             0.00    0      1\n",
       "9     1.0  123334.88       108679.17        304981.62    1      0\n",
       "39    1.0   38558.51        82982.09        174999.30    1      0\n",
       "46    1.0    1315.46       115816.21        297114.46    0      0\n",
       "3     1.0  144372.41       118671.85        383199.62    0      1\n",
       "0     1.0  165349.20       136897.80        471784.10    0      1\n",
       "47    1.0       0.00       135426.92             0.00    1      0\n",
       "44    1.0   22177.74       154806.14         28334.72    1      0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we also have one other library called states model for linear regression\n",
    "import statsmodels.api as sm\n",
    "X_train_new = sm.add_constant(X_train)#we can not find intercept using stats model so we are adding constant\n",
    "X_train_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                 Profit   R-squared:                       0.950\n",
      "Model:                            OLS   Adj. R-squared:                  0.943\n",
      "Method:                 Least Squares   F-statistic:                     129.7\n",
      "Date:                Mon, 09 Nov 2020   Prob (F-statistic):           3.91e-21\n",
      "Time:                        16:16:10   Log-Likelihood:                -421.10\n",
      "No. Observations:                  40   AIC:                             854.2\n",
      "Df Residuals:                      34   BIC:                             864.3\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "const            4.159e+04   9015.583      4.614      0.000    2.33e+04    5.99e+04\n",
      "R&D Spend           0.7735      0.055     14.025      0.000       0.661       0.886\n",
      "Administration      0.0329      0.066      0.495      0.624      -0.102       0.168\n",
      "Marketing Spend     0.0366      0.019      1.884      0.068      -0.003       0.076\n",
      "Cal               959.2842   4038.108      0.238      0.814   -7247.138    9165.706\n",
      "New Y            1658.6532   4209.221      0.394      0.696   -6895.513    1.02e+04\n",
      "==============================================================================\n",
      "Omnibus:                       15.823   Durbin-Watson:                   2.468\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               23.231\n",
      "Skew:                          -1.094   Prob(JB):                     9.03e-06\n",
      "Kurtosis:                       6.025   Cond. No.                     1.66e+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, 1.66e+06. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "model = sm.OLS(y_train,X_train_new)\n",
    "result = model.fit()\n",
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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