Linear Regression with Scikit learn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pydataset import data\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pima = data('Pima.tr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>npreg</th>\n",
       "      <th>glu</th>\n",
       "      <th>bp</th>\n",
       "      <th>skin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>ped</th>\n",
       "      <th>age</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>86</td>\n",
       "      <td>68</td>\n",
       "      <td>28</td>\n",
       "      <td>30.2</td>\n",
       "      <td>0.364</td>\n",
       "      <td>24</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>195</td>\n",
       "      <td>70</td>\n",
       "      <td>33</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.163</td>\n",
       "      <td>55</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>77</td>\n",
       "      <td>82</td>\n",
       "      <td>41</td>\n",
       "      <td>35.8</td>\n",
       "      <td>0.156</td>\n",
       "      <td>35</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>165</td>\n",
       "      <td>76</td>\n",
       "      <td>43</td>\n",
       "      <td>47.9</td>\n",
       "      <td>0.259</td>\n",
       "      <td>26</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>107</td>\n",
       "      <td>60</td>\n",
       "      <td>25</td>\n",
       "      <td>26.4</td>\n",
       "      <td>0.133</td>\n",
       "      <td>23</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>97</td>\n",
       "      <td>76</td>\n",
       "      <td>27</td>\n",
       "      <td>35.6</td>\n",
       "      <td>0.378</td>\n",
       "      <td>52</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>83</td>\n",
       "      <td>58</td>\n",
       "      <td>31</td>\n",
       "      <td>34.3</td>\n",
       "      <td>0.336</td>\n",
       "      <td>25</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>193</td>\n",
       "      <td>50</td>\n",
       "      <td>16</td>\n",
       "      <td>25.9</td>\n",
       "      <td>0.655</td>\n",
       "      <td>24</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>142</td>\n",
       "      <td>80</td>\n",
       "      <td>15</td>\n",
       "      <td>32.4</td>\n",
       "      <td>0.200</td>\n",
       "      <td>63</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>128</td>\n",
       "      <td>78</td>\n",
       "      <td>37</td>\n",
       "      <td>43.3</td>\n",
       "      <td>1.224</td>\n",
       "      <td>31</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9</td>\n",
       "      <td>154</td>\n",
       "      <td>78</td>\n",
       "      <td>30</td>\n",
       "      <td>30.9</td>\n",
       "      <td>0.164</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>189</td>\n",
       "      <td>60</td>\n",
       "      <td>23</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.398</td>\n",
       "      <td>59</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>12</td>\n",
       "      <td>92</td>\n",
       "      <td>62</td>\n",
       "      <td>7</td>\n",
       "      <td>27.6</td>\n",
       "      <td>0.926</td>\n",
       "      <td>44</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "      <td>66</td>\n",
       "      <td>52</td>\n",
       "      <td>41.3</td>\n",
       "      <td>0.917</td>\n",
       "      <td>29</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4</td>\n",
       "      <td>99</td>\n",
       "      <td>76</td>\n",
       "      <td>15</td>\n",
       "      <td>23.2</td>\n",
       "      <td>0.223</td>\n",
       "      <td>21</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "      <td>60</td>\n",
       "      <td>8</td>\n",
       "      <td>25.4</td>\n",
       "      <td>0.947</td>\n",
       "      <td>21</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11</td>\n",
       "      <td>143</td>\n",
       "      <td>94</td>\n",
       "      <td>33</td>\n",
       "      <td>36.6</td>\n",
       "      <td>0.254</td>\n",
       "      <td>51</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>149</td>\n",
       "      <td>68</td>\n",
       "      <td>29</td>\n",
       "      <td>29.3</td>\n",
       "      <td>0.349</td>\n",
       "      <td>42</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>139</td>\n",
       "      <td>62</td>\n",
       "      <td>17</td>\n",
       "      <td>22.1</td>\n",
       "      <td>0.207</td>\n",
       "      <td>21</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2</td>\n",
       "      <td>99</td>\n",
       "      <td>70</td>\n",
       "      <td>16</td>\n",
       "      <td>20.4</td>\n",
       "      <td>0.235</td>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.444</td>\n",
       "      <td>42</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4</td>\n",
       "      <td>83</td>\n",
       "      <td>86</td>\n",
       "      <td>19</td>\n",
       "      <td>29.3</td>\n",
       "      <td>0.317</td>\n",
       "      <td>34</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>101</td>\n",
       "      <td>64</td>\n",
       "      <td>17</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.252</td>\n",
       "      <td>21</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>87</td>\n",
       "      <td>68</td>\n",
       "      <td>34</td>\n",
       "      <td>37.6</td>\n",
       "      <td>0.401</td>\n",
       "      <td>24</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9</td>\n",
       "      <td>164</td>\n",
       "      <td>84</td>\n",
       "      <td>21</td>\n",
       "      <td>30.8</td>\n",
       "      <td>0.831</td>\n",
       "      <td>32</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>58</td>\n",
       "      <td>10</td>\n",
       "      <td>25.4</td>\n",
       "      <td>0.551</td>\n",
       "      <td>21</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>65</td>\n",
       "      <td>26</td>\n",
       "      <td>42.6</td>\n",
       "      <td>0.431</td>\n",
       "      <td>24</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5</td>\n",
       "      <td>108</td>\n",
       "      <td>72</td>\n",
       "      <td>43</td>\n",
       "      <td>36.1</td>\n",
       "      <td>0.263</td>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2</td>\n",
       "      <td>110</td>\n",
       "      <td>74</td>\n",
       "      <td>29</td>\n",
       "      <td>32.4</td>\n",
       "      <td>0.698</td>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>79</td>\n",
       "      <td>60</td>\n",
       "      <td>42</td>\n",
       "      <td>43.5</td>\n",
       "      <td>0.678</td>\n",
       "      <td>23</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3</td>\n",
       "      <td>148</td>\n",
       "      <td>66</td>\n",
       "      <td>25</td>\n",
       "      <td>32.5</td>\n",
       "      <td>0.256</td>\n",
       "      <td>22</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0</td>\n",
       "      <td>121</td>\n",
       "      <td>66</td>\n",
       "      <td>30</td>\n",
       "      <td>34.3</td>\n",
       "      <td>0.203</td>\n",
       "      <td>33</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>3</td>\n",
       "      <td>158</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>31.2</td>\n",
       "      <td>0.295</td>\n",
       "      <td>24</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2</td>\n",
       "      <td>105</td>\n",
       "      <td>80</td>\n",
       "      <td>45</td>\n",
       "      <td>33.7</td>\n",
       "      <td>0.711</td>\n",
       "      <td>29</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>13</td>\n",
       "      <td>145</td>\n",
       "      <td>82</td>\n",
       "      <td>19</td>\n",
       "      <td>22.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>57</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1</td>\n",
       "      <td>79</td>\n",
       "      <td>80</td>\n",
       "      <td>25</td>\n",
       "      <td>25.4</td>\n",
       "      <td>0.583</td>\n",
       "      <td>22</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1</td>\n",
       "      <td>71</td>\n",
       "      <td>48</td>\n",
       "      <td>18</td>\n",
       "      <td>20.4</td>\n",
       "      <td>0.323</td>\n",
       "      <td>22</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>86</td>\n",
       "      <td>17</td>\n",
       "      <td>29.3</td>\n",
       "      <td>0.695</td>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0</td>\n",
       "      <td>119</td>\n",
       "      <td>66</td>\n",
       "      <td>27</td>\n",
       "      <td>38.8</td>\n",
       "      <td>0.259</td>\n",
       "      <td>22</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>8</td>\n",
       "      <td>176</td>\n",
       "      <td>90</td>\n",
       "      <td>34</td>\n",
       "      <td>33.7</td>\n",
       "      <td>0.467</td>\n",
       "      <td>58</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1</td>\n",
       "      <td>97</td>\n",
       "      <td>68</td>\n",
       "      <td>21</td>\n",
       "      <td>27.2</td>\n",
       "      <td>1.095</td>\n",
       "      <td>22</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>4</td>\n",
       "      <td>129</td>\n",
       "      <td>60</td>\n",
       "      <td>12</td>\n",
       "      <td>27.5</td>\n",
       "      <td>0.527</td>\n",
       "      <td>31</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1</td>\n",
       "      <td>97</td>\n",
       "      <td>64</td>\n",
       "      <td>19</td>\n",
       "      <td>18.2</td>\n",
       "      <td>0.299</td>\n",
       "      <td>21</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0</td>\n",
       "      <td>86</td>\n",
       "      <td>68</td>\n",
       "      <td>32</td>\n",
       "      <td>35.8</td>\n",
       "      <td>0.238</td>\n",
       "      <td>25</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2</td>\n",
       "      <td>125</td>\n",
       "      <td>60</td>\n",
       "      <td>20</td>\n",
       "      <td>33.8</td>\n",
       "      <td>0.088</td>\n",
       "      <td>31</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>5</td>\n",
       "      <td>123</td>\n",
       "      <td>74</td>\n",
       "      <td>40</td>\n",
       "      <td>34.1</td>\n",
       "      <td>0.269</td>\n",
       "      <td>28</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2</td>\n",
       "      <td>92</td>\n",
       "      <td>76</td>\n",
       "      <td>20</td>\n",
       "      <td>24.2</td>\n",
       "      <td>1.698</td>\n",
       "      <td>28</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>3</td>\n",
       "      <td>171</td>\n",
       "      <td>72</td>\n",
       "      <td>33</td>\n",
       "      <td>33.3</td>\n",
       "      <td>0.199</td>\n",
       "      <td>24</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1</td>\n",
       "      <td>199</td>\n",
       "      <td>76</td>\n",
       "      <td>43</td>\n",
       "      <td>42.9</td>\n",
       "      <td>1.394</td>\n",
       "      <td>22</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>3</td>\n",
       "      <td>116</td>\n",
       "      <td>74</td>\n",
       "      <td>15</td>\n",
       "      <td>26.3</td>\n",
       "      <td>0.107</td>\n",
       "      <td>24</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2</td>\n",
       "      <td>83</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>32.2</td>\n",
       "      <td>0.497</td>\n",
       "      <td>22</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>8</td>\n",
       "      <td>154</td>\n",
       "      <td>78</td>\n",
       "      <td>32</td>\n",
       "      <td>32.4</td>\n",
       "      <td>0.443</td>\n",
       "      <td>45</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1</td>\n",
       "      <td>114</td>\n",
       "      <td>66</td>\n",
       "      <td>36</td>\n",
       "      <td>38.1</td>\n",
       "      <td>0.289</td>\n",
       "      <td>21</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1</td>\n",
       "      <td>106</td>\n",
       "      <td>70</td>\n",
       "      <td>28</td>\n",
       "      <td>34.2</td>\n",
       "      <td>0.142</td>\n",
       "      <td>22</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>4</td>\n",
       "      <td>127</td>\n",
       "      <td>88</td>\n",
       "      <td>11</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0.598</td>\n",
       "      <td>28</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>1</td>\n",
       "      <td>124</td>\n",
       "      <td>74</td>\n",
       "      <td>36</td>\n",
       "      <td>27.8</td>\n",
       "      <td>0.100</td>\n",
       "      <td>30</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "      <td>38</td>\n",
       "      <td>18</td>\n",
       "      <td>23.1</td>\n",
       "      <td>0.407</td>\n",
       "      <td>26</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2</td>\n",
       "      <td>123</td>\n",
       "      <td>48</td>\n",
       "      <td>32</td>\n",
       "      <td>42.1</td>\n",
       "      <td>0.520</td>\n",
       "      <td>26</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>8</td>\n",
       "      <td>167</td>\n",
       "      <td>106</td>\n",
       "      <td>46</td>\n",
       "      <td>37.6</td>\n",
       "      <td>0.165</td>\n",
       "      <td>43</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>7</td>\n",
       "      <td>184</td>\n",
       "      <td>84</td>\n",
       "      <td>33</td>\n",
       "      <td>35.5</td>\n",
       "      <td>0.355</td>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>1</td>\n",
       "      <td>96</td>\n",
       "      <td>64</td>\n",
       "      <td>27</td>\n",
       "      <td>33.2</td>\n",
       "      <td>0.289</td>\n",
       "      <td>21</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>10</td>\n",
       "      <td>129</td>\n",
       "      <td>76</td>\n",
       "      <td>28</td>\n",
       "      <td>35.9</td>\n",
       "      <td>0.280</td>\n",
       "      <td>39</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>6</td>\n",
       "      <td>92</td>\n",
       "      <td>62</td>\n",
       "      <td>32</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.085</td>\n",
       "      <td>46</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>6</td>\n",
       "      <td>109</td>\n",
       "      <td>60</td>\n",
       "      <td>27</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.206</td>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>5</td>\n",
       "      <td>139</td>\n",
       "      <td>80</td>\n",
       "      <td>35</td>\n",
       "      <td>31.6</td>\n",
       "      <td>0.361</td>\n",
       "      <td>25</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>6</td>\n",
       "      <td>134</td>\n",
       "      <td>70</td>\n",
       "      <td>23</td>\n",
       "      <td>35.4</td>\n",
       "      <td>0.542</td>\n",
       "      <td>29</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>3</td>\n",
       "      <td>106</td>\n",
       "      <td>54</td>\n",
       "      <td>21</td>\n",
       "      <td>30.9</td>\n",
       "      <td>0.292</td>\n",
       "      <td>24</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>66</td>\n",
       "      <td>40</td>\n",
       "      <td>34.3</td>\n",
       "      <td>0.196</td>\n",
       "      <td>22</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>0</td>\n",
       "      <td>135</td>\n",
       "      <td>94</td>\n",
       "      <td>46</td>\n",
       "      <td>40.6</td>\n",
       "      <td>0.284</td>\n",
       "      <td>26</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>5</td>\n",
       "      <td>158</td>\n",
       "      <td>84</td>\n",
       "      <td>41</td>\n",
       "      <td>39.4</td>\n",
       "      <td>0.395</td>\n",
       "      <td>29</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>3</td>\n",
       "      <td>112</td>\n",
       "      <td>74</td>\n",
       "      <td>30</td>\n",
       "      <td>31.6</td>\n",
       "      <td>0.197</td>\n",
       "      <td>25</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>8</td>\n",
       "      <td>181</td>\n",
       "      <td>68</td>\n",
       "      <td>36</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.615</td>\n",
       "      <td>60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>2</td>\n",
       "      <td>121</td>\n",
       "      <td>70</td>\n",
       "      <td>32</td>\n",
       "      <td>39.1</td>\n",
       "      <td>0.886</td>\n",
       "      <td>23</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>88</td>\n",
       "      <td>29</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.905</td>\n",
       "      <td>52</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>1</td>\n",
       "      <td>144</td>\n",
       "      <td>82</td>\n",
       "      <td>46</td>\n",
       "      <td>46.1</td>\n",
       "      <td>0.335</td>\n",
       "      <td>46</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>2</td>\n",
       "      <td>101</td>\n",
       "      <td>58</td>\n",
       "      <td>17</td>\n",
       "      <td>24.2</td>\n",
       "      <td>0.614</td>\n",
       "      <td>23</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>2</td>\n",
       "      <td>96</td>\n",
       "      <td>68</td>\n",
       "      <td>13</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.647</td>\n",
       "      <td>26</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>3</td>\n",
       "      <td>107</td>\n",
       "      <td>62</td>\n",
       "      <td>13</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.678</td>\n",
       "      <td>23</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>12</td>\n",
       "      <td>121</td>\n",
       "      <td>78</td>\n",
       "      <td>17</td>\n",
       "      <td>26.5</td>\n",
       "      <td>0.259</td>\n",
       "      <td>62</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>64</td>\n",
       "      <td>23</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.368</td>\n",
       "      <td>21</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>4</td>\n",
       "      <td>154</td>\n",
       "      <td>72</td>\n",
       "      <td>29</td>\n",
       "      <td>31.3</td>\n",
       "      <td>0.338</td>\n",
       "      <td>37</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>6</td>\n",
       "      <td>125</td>\n",
       "      <td>78</td>\n",
       "      <td>31</td>\n",
       "      <td>27.6</td>\n",
       "      <td>0.565</td>\n",
       "      <td>49</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>10</td>\n",
       "      <td>125</td>\n",
       "      <td>70</td>\n",
       "      <td>26</td>\n",
       "      <td>31.1</td>\n",
       "      <td>0.205</td>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>76</td>\n",
       "      <td>27</td>\n",
       "      <td>35.9</td>\n",
       "      <td>0.483</td>\n",
       "      <td>26</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>84</td>\n",
       "      <td>27</td>\n",
       "      <td>27.3</td>\n",
       "      <td>0.231</td>\n",
       "      <td>59</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>9</td>\n",
       "      <td>112</td>\n",
       "      <td>82</td>\n",
       "      <td>32</td>\n",
       "      <td>34.2</td>\n",
       "      <td>0.260</td>\n",
       "      <td>36</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>4</td>\n",
       "      <td>148</td>\n",
       "      <td>60</td>\n",
       "      <td>27</td>\n",
       "      <td>30.9</td>\n",
       "      <td>0.150</td>\n",
       "      <td>29</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>1</td>\n",
       "      <td>136</td>\n",
       "      <td>74</td>\n",
       "      <td>50</td>\n",
       "      <td>37.4</td>\n",
       "      <td>0.399</td>\n",
       "      <td>24</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>9</td>\n",
       "      <td>145</td>\n",
       "      <td>80</td>\n",
       "      <td>46</td>\n",
       "      <td>37.9</td>\n",
       "      <td>0.637</td>\n",
       "      <td>40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>56</td>\n",
       "      <td>11</td>\n",
       "      <td>22.5</td>\n",
       "      <td>0.417</td>\n",
       "      <td>22</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>1</td>\n",
       "      <td>107</td>\n",
       "      <td>72</td>\n",
       "      <td>30</td>\n",
       "      <td>30.8</td>\n",
       "      <td>0.821</td>\n",
       "      <td>24</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>12</td>\n",
       "      <td>151</td>\n",
       "      <td>70</td>\n",
       "      <td>40</td>\n",
       "      <td>41.8</td>\n",
       "      <td>0.742</td>\n",
       "      <td>38</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>1</td>\n",
       "      <td>97</td>\n",
       "      <td>70</td>\n",
       "      <td>40</td>\n",
       "      <td>38.1</td>\n",
       "      <td>0.218</td>\n",
       "      <td>30</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>5</td>\n",
       "      <td>144</td>\n",
       "      <td>82</td>\n",
       "      <td>26</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.452</td>\n",
       "      <td>58</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>2</td>\n",
       "      <td>112</td>\n",
       "      <td>86</td>\n",
       "      <td>42</td>\n",
       "      <td>38.4</td>\n",
       "      <td>0.246</td>\n",
       "      <td>28</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>2</td>\n",
       "      <td>99</td>\n",
       "      <td>52</td>\n",
       "      <td>15</td>\n",
       "      <td>24.6</td>\n",
       "      <td>0.637</td>\n",
       "      <td>21</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "      <td>56</td>\n",
       "      <td>21</td>\n",
       "      <td>25.2</td>\n",
       "      <td>0.833</td>\n",
       "      <td>23</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>48</td>\n",
       "      <td>38.9</td>\n",
       "      <td>1.162</td>\n",
       "      <td>41</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7</td>\n",
       "      <td>187</td>\n",
       "      <td>68</td>\n",
       "      <td>39</td>\n",
       "      <td>37.7</td>\n",
       "      <td>0.254</td>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>3</td>\n",
       "      <td>129</td>\n",
       "      <td>92</td>\n",
       "      <td>49</td>\n",
       "      <td>36.4</td>\n",
       "      <td>0.968</td>\n",
       "      <td>32</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>7</td>\n",
       "      <td>179</td>\n",
       "      <td>95</td>\n",
       "      <td>31</td>\n",
       "      <td>34.2</td>\n",
       "      <td>0.164</td>\n",
       "      <td>60</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6</td>\n",
       "      <td>80</td>\n",
       "      <td>66</td>\n",
       "      <td>30</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.313</td>\n",
       "      <td>41</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>2</td>\n",
       "      <td>105</td>\n",
       "      <td>58</td>\n",
       "      <td>40</td>\n",
       "      <td>34.9</td>\n",
       "      <td>0.225</td>\n",
       "      <td>25</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>3</td>\n",
       "      <td>191</td>\n",
       "      <td>68</td>\n",
       "      <td>15</td>\n",
       "      <td>30.9</td>\n",
       "      <td>0.299</td>\n",
       "      <td>34</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "      <td>80</td>\n",
       "      <td>45</td>\n",
       "      <td>36.5</td>\n",
       "      <td>0.330</td>\n",
       "      <td>26</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>4</td>\n",
       "      <td>99</td>\n",
       "      <td>72</td>\n",
       "      <td>17</td>\n",
       "      <td>25.6</td>\n",
       "      <td>0.294</td>\n",
       "      <td>28</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>68</td>\n",
       "      <td>14</td>\n",
       "      <td>24.8</td>\n",
       "      <td>0.143</td>\n",
       "      <td>21</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1</td>\n",
       "      <td>97</td>\n",
       "      <td>70</td>\n",
       "      <td>15</td>\n",
       "      <td>18.2</td>\n",
       "      <td>0.147</td>\n",
       "      <td>21</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>88</td>\n",
       "      <td>60</td>\n",
       "      <td>46.8</td>\n",
       "      <td>0.962</td>\n",
       "      <td>31</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1</td>\n",
       "      <td>167</td>\n",
       "      <td>74</td>\n",
       "      <td>17</td>\n",
       "      <td>23.4</td>\n",
       "      <td>0.447</td>\n",
       "      <td>33</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>0</td>\n",
       "      <td>180</td>\n",
       "      <td>90</td>\n",
       "      <td>26</td>\n",
       "      <td>36.5</td>\n",
       "      <td>0.314</td>\n",
       "      <td>35</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>62</td>\n",
       "      <td>12</td>\n",
       "      <td>27.2</td>\n",
       "      <td>0.580</td>\n",
       "      <td>24</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>3</td>\n",
       "      <td>120</td>\n",
       "      <td>70</td>\n",
       "      <td>30</td>\n",
       "      <td>42.9</td>\n",
       "      <td>0.452</td>\n",
       "      <td>30</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6</td>\n",
       "      <td>154</td>\n",
       "      <td>78</td>\n",
       "      <td>41</td>\n",
       "      <td>46.1</td>\n",
       "      <td>0.571</td>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>2</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>28</td>\n",
       "      <td>24.2</td>\n",
       "      <td>0.332</td>\n",
       "      <td>22</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>0</td>\n",
       "      <td>177</td>\n",
       "      <td>60</td>\n",
       "      <td>29</td>\n",
       "      <td>34.6</td>\n",
       "      <td>1.072</td>\n",
       "      <td>21</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>3</td>\n",
       "      <td>124</td>\n",
       "      <td>80</td>\n",
       "      <td>33</td>\n",
       "      <td>33.2</td>\n",
       "      <td>0.305</td>\n",
       "      <td>26</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>8</td>\n",
       "      <td>85</td>\n",
       "      <td>55</td>\n",
       "      <td>20</td>\n",
       "      <td>24.4</td>\n",
       "      <td>0.136</td>\n",
       "      <td>42</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>12</td>\n",
       "      <td>88</td>\n",
       "      <td>74</td>\n",
       "      <td>40</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0.378</td>\n",
       "      <td>48</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>9</td>\n",
       "      <td>152</td>\n",
       "      <td>78</td>\n",
       "      <td>34</td>\n",
       "      <td>34.2</td>\n",
       "      <td>0.893</td>\n",
       "      <td>33</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>0</td>\n",
       "      <td>198</td>\n",
       "      <td>66</td>\n",
       "      <td>32</td>\n",
       "      <td>41.3</td>\n",
       "      <td>0.502</td>\n",
       "      <td>28</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>0</td>\n",
       "      <td>188</td>\n",
       "      <td>82</td>\n",
       "      <td>14</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.682</td>\n",
       "      <td>22</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>5</td>\n",
       "      <td>139</td>\n",
       "      <td>64</td>\n",
       "      <td>35</td>\n",
       "      <td>28.6</td>\n",
       "      <td>0.411</td>\n",
       "      <td>26</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>7</td>\n",
       "      <td>168</td>\n",
       "      <td>88</td>\n",
       "      <td>42</td>\n",
       "      <td>38.2</td>\n",
       "      <td>0.787</td>\n",
       "      <td>40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>2</td>\n",
       "      <td>197</td>\n",
       "      <td>70</td>\n",
       "      <td>99</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.575</td>\n",
       "      <td>62</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>2</td>\n",
       "      <td>142</td>\n",
       "      <td>82</td>\n",
       "      <td>18</td>\n",
       "      <td>24.7</td>\n",
       "      <td>0.761</td>\n",
       "      <td>21</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>8</td>\n",
       "      <td>126</td>\n",
       "      <td>74</td>\n",
       "      <td>38</td>\n",
       "      <td>25.9</td>\n",
       "      <td>0.162</td>\n",
       "      <td>39</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>3</td>\n",
       "      <td>158</td>\n",
       "      <td>76</td>\n",
       "      <td>36</td>\n",
       "      <td>31.6</td>\n",
       "      <td>0.851</td>\n",
       "      <td>28</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>3</td>\n",
       "      <td>130</td>\n",
       "      <td>78</td>\n",
       "      <td>23</td>\n",
       "      <td>28.4</td>\n",
       "      <td>0.323</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>54</td>\n",
       "      <td>28</td>\n",
       "      <td>37.8</td>\n",
       "      <td>0.498</td>\n",
       "      <td>24</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>1</td>\n",
       "      <td>164</td>\n",
       "      <td>82</td>\n",
       "      <td>43</td>\n",
       "      <td>32.8</td>\n",
       "      <td>0.341</td>\n",
       "      <td>50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>4</td>\n",
       "      <td>95</td>\n",
       "      <td>60</td>\n",
       "      <td>32</td>\n",
       "      <td>35.4</td>\n",
       "      <td>0.284</td>\n",
       "      <td>28</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>36.2</td>\n",
       "      <td>0.816</td>\n",
       "      <td>28</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>4</td>\n",
       "      <td>85</td>\n",
       "      <td>58</td>\n",
       "      <td>22</td>\n",
       "      <td>27.8</td>\n",
       "      <td>0.306</td>\n",
       "      <td>28</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>0</td>\n",
       "      <td>151</td>\n",
       "      <td>90</td>\n",
       "      <td>46</td>\n",
       "      <td>42.1</td>\n",
       "      <td>0.371</td>\n",
       "      <td>21</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>6</td>\n",
       "      <td>144</td>\n",
       "      <td>72</td>\n",
       "      <td>27</td>\n",
       "      <td>33.9</td>\n",
       "      <td>0.255</td>\n",
       "      <td>40</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>3</td>\n",
       "      <td>111</td>\n",
       "      <td>90</td>\n",
       "      <td>12</td>\n",
       "      <td>28.4</td>\n",
       "      <td>0.495</td>\n",
       "      <td>29</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>1</td>\n",
       "      <td>107</td>\n",
       "      <td>68</td>\n",
       "      <td>19</td>\n",
       "      <td>26.5</td>\n",
       "      <td>0.165</td>\n",
       "      <td>24</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>6</td>\n",
       "      <td>115</td>\n",
       "      <td>60</td>\n",
       "      <td>39</td>\n",
       "      <td>33.7</td>\n",
       "      <td>0.245</td>\n",
       "      <td>40</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>5</td>\n",
       "      <td>105</td>\n",
       "      <td>72</td>\n",
       "      <td>29</td>\n",
       "      <td>36.9</td>\n",
       "      <td>0.159</td>\n",
       "      <td>28</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>7</td>\n",
       "      <td>194</td>\n",
       "      <td>68</td>\n",
       "      <td>28</td>\n",
       "      <td>35.9</td>\n",
       "      <td>0.745</td>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>4</td>\n",
       "      <td>184</td>\n",
       "      <td>78</td>\n",
       "      <td>39</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.264</td>\n",
       "      <td>31</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "      <td>85</td>\n",
       "      <td>25</td>\n",
       "      <td>37.4</td>\n",
       "      <td>0.247</td>\n",
       "      <td>24</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>7</td>\n",
       "      <td>124</td>\n",
       "      <td>70</td>\n",
       "      <td>33</td>\n",
       "      <td>25.5</td>\n",
       "      <td>0.161</td>\n",
       "      <td>37</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>62</td>\n",
       "      <td>13</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.138</td>\n",
       "      <td>23</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>7</td>\n",
       "      <td>137</td>\n",
       "      <td>90</td>\n",
       "      <td>41</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.391</td>\n",
       "      <td>39</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>9</td>\n",
       "      <td>57</td>\n",
       "      <td>80</td>\n",
       "      <td>37</td>\n",
       "      <td>32.8</td>\n",
       "      <td>0.096</td>\n",
       "      <td>41</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>74</td>\n",
       "      <td>35</td>\n",
       "      <td>39.4</td>\n",
       "      <td>0.134</td>\n",
       "      <td>30</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>2</td>\n",
       "      <td>95</td>\n",
       "      <td>54</td>\n",
       "      <td>14</td>\n",
       "      <td>26.1</td>\n",
       "      <td>0.748</td>\n",
       "      <td>22</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>12</td>\n",
       "      <td>140</td>\n",
       "      <td>85</td>\n",
       "      <td>33</td>\n",
       "      <td>37.4</td>\n",
       "      <td>0.244</td>\n",
       "      <td>41</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>0</td>\n",
       "      <td>117</td>\n",
       "      <td>66</td>\n",
       "      <td>31</td>\n",
       "      <td>30.8</td>\n",
       "      <td>0.493</td>\n",
       "      <td>22</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>8</td>\n",
       "      <td>100</td>\n",
       "      <td>74</td>\n",
       "      <td>40</td>\n",
       "      <td>39.4</td>\n",
       "      <td>0.661</td>\n",
       "      <td>43</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>9</td>\n",
       "      <td>123</td>\n",
       "      <td>70</td>\n",
       "      <td>44</td>\n",
       "      <td>33.1</td>\n",
       "      <td>0.374</td>\n",
       "      <td>40</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>60</td>\n",
       "      <td>35</td>\n",
       "      <td>34.6</td>\n",
       "      <td>0.534</td>\n",
       "      <td>21</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>14</td>\n",
       "      <td>100</td>\n",
       "      <td>78</td>\n",
       "      <td>25</td>\n",
       "      <td>36.6</td>\n",
       "      <td>0.412</td>\n",
       "      <td>46</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>14</td>\n",
       "      <td>175</td>\n",
       "      <td>62</td>\n",
       "      <td>30</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.212</td>\n",
       "      <td>38</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>52</td>\n",
       "      <td>10</td>\n",
       "      <td>27.8</td>\n",
       "      <td>0.269</td>\n",
       "      <td>22</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>1</td>\n",
       "      <td>133</td>\n",
       "      <td>102</td>\n",
       "      <td>28</td>\n",
       "      <td>32.8</td>\n",
       "      <td>0.234</td>\n",
       "      <td>45</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>0</td>\n",
       "      <td>119</td>\n",
       "      <td>64</td>\n",
       "      <td>18</td>\n",
       "      <td>34.9</td>\n",
       "      <td>0.725</td>\n",
       "      <td>23</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>5</td>\n",
       "      <td>155</td>\n",
       "      <td>84</td>\n",
       "      <td>44</td>\n",
       "      <td>38.7</td>\n",
       "      <td>0.619</td>\n",
       "      <td>34</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>1</td>\n",
       "      <td>128</td>\n",
       "      <td>48</td>\n",
       "      <td>45</td>\n",
       "      <td>40.5</td>\n",
       "      <td>0.613</td>\n",
       "      <td>24</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>2</td>\n",
       "      <td>112</td>\n",
       "      <td>68</td>\n",
       "      <td>22</td>\n",
       "      <td>34.1</td>\n",
       "      <td>0.315</td>\n",
       "      <td>26</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>74</td>\n",
       "      <td>26</td>\n",
       "      <td>24.1</td>\n",
       "      <td>0.828</td>\n",
       "      <td>23</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>2</td>\n",
       "      <td>141</td>\n",
       "      <td>58</td>\n",
       "      <td>34</td>\n",
       "      <td>25.4</td>\n",
       "      <td>0.699</td>\n",
       "      <td>24</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>7</td>\n",
       "      <td>129</td>\n",
       "      <td>68</td>\n",
       "      <td>49</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0.439</td>\n",
       "      <td>43</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>0</td>\n",
       "      <td>106</td>\n",
       "      <td>70</td>\n",
       "      <td>37</td>\n",
       "      <td>39.4</td>\n",
       "      <td>0.605</td>\n",
       "      <td>22</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>1</td>\n",
       "      <td>118</td>\n",
       "      <td>58</td>\n",
       "      <td>36</td>\n",
       "      <td>33.3</td>\n",
       "      <td>0.261</td>\n",
       "      <td>23</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>8</td>\n",
       "      <td>155</td>\n",
       "      <td>62</td>\n",
       "      <td>26</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.543</td>\n",
       "      <td>46</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     npreg  glu   bp  skin   bmi    ped  age type\n",
       "1        5   86   68    28  30.2  0.364   24   No\n",
       "2        7  195   70    33  25.1  0.163   55  Yes\n",
       "3        5   77   82    41  35.8  0.156   35   No\n",
       "4        0  165   76    43  47.9  0.259   26   No\n",
       "5        0  107   60    25  26.4  0.133   23   No\n",
       "6        5   97   76    27  35.6  0.378   52  Yes\n",
       "7        3   83   58    31  34.3  0.336   25   No\n",
       "8        1  193   50    16  25.9  0.655   24   No\n",
       "9        3  142   80    15  32.4  0.200   63   No\n",
       "10       2  128   78    37  43.3  1.224   31  Yes\n",
       "11       0  137   40    35  43.1  2.288   33  Yes\n",
       "12       9  154   78    30  30.9  0.164   45   No\n",
       "13       1  189   60    23  30.1  0.398   59  Yes\n",
       "14      12   92   62     7  27.6  0.926   44  Yes\n",
       "15       1   86   66    52  41.3  0.917   29   No\n",
       "16       4   99   76    15  23.2  0.223   21   No\n",
       "17       1  109   60     8  25.4  0.947   21   No\n",
       "18      11  143   94    33  36.6  0.254   51  Yes\n",
       "19       1  149   68    29  29.3  0.349   42  Yes\n",
       "20       0  139   62    17  22.1  0.207   21   No\n",
       "21       2   99   70    16  20.4  0.235   27   No\n",
       "22       1  100   66    29  32.0  0.444   42   No\n",
       "23       4   83   86    19  29.3  0.317   34   No\n",
       "24       0  101   64    17  21.0  0.252   21   No\n",
       "25       1   87   68    34  37.6  0.401   24   No\n",
       "26       9  164   84    21  30.8  0.831   32  Yes\n",
       "27       1   99   58    10  25.4  0.551   21   No\n",
       "28       0  140   65    26  42.6  0.431   24  Yes\n",
       "29       5  108   72    43  36.1  0.263   33   No\n",
       "30       2  110   74    29  32.4  0.698   27   No\n",
       "31       1   79   60    42  43.5  0.678   23   No\n",
       "32       3  148   66    25  32.5  0.256   22   No\n",
       "33       0  121   66    30  34.3  0.203   33  Yes\n",
       "34       3  158   64    13  31.2  0.295   24   No\n",
       "35       2  105   80    45  33.7  0.711   29  Yes\n",
       "36      13  145   82    19  22.2  0.245   57   No\n",
       "37       1   79   80    25  25.4  0.583   22   No\n",
       "38       1   71   48    18  20.4  0.323   22   No\n",
       "39       0  102   86    17  29.3  0.695   27   No\n",
       "40       0  119   66    27  38.8  0.259   22   No\n",
       "41       8  176   90    34  33.7  0.467   58  Yes\n",
       "42       1   97   68    21  27.2  1.095   22   No\n",
       "43       4  129   60    12  27.5  0.527   31   No\n",
       "44       1   97   64    19  18.2  0.299   21   No\n",
       "45       0   86   68    32  35.8  0.238   25   No\n",
       "46       2  125   60    20  33.8  0.088   31   No\n",
       "47       5  123   74    40  34.1  0.269   28   No\n",
       "48       2   92   76    20  24.2  1.698   28   No\n",
       "49       3  171   72    33  33.3  0.199   24  Yes\n",
       "50       1  199   76    43  42.9  1.394   22  Yes\n",
       "51       3  116   74    15  26.3  0.107   24   No\n",
       "52       2   83   66    23  32.2  0.497   22   No\n",
       "53       8  154   78    32  32.4  0.443   45  Yes\n",
       "54       1  114   66    36  38.1  0.289   21   No\n",
       "55       1  106   70    28  34.2  0.142   22   No\n",
       "56       4  127   88    11  34.5  0.598   28   No\n",
       "57       1  124   74    36  27.8  0.100   30   No\n",
       "58       1  109   38    18  23.1  0.407   26   No\n",
       "59       2  123   48    32  42.1  0.520   26   No\n",
       "60       8  167  106    46  37.6  0.165   43  Yes\n",
       "61       7  184   84    33  35.5  0.355   41  Yes\n",
       "62       1   96   64    27  33.2  0.289   21   No\n",
       "63      10  129   76    28  35.9  0.280   39   No\n",
       "64       6   92   62    32  32.0  0.085   46   No\n",
       "65       6  109   60    27  25.0  0.206   27   No\n",
       "66       5  139   80    35  31.6  0.361   25  Yes\n",
       "67       6  134   70    23  35.4  0.542   29  Yes\n",
       "68       3  106   54    21  30.9  0.292   24   No\n",
       "69       0  131   66    40  34.3  0.196   22  Yes\n",
       "70       0  135   94    46  40.6  0.284   26   No\n",
       "71       5  158   84    41  39.4  0.395   29  Yes\n",
       "72       3  112   74    30  31.6  0.197   25  Yes\n",
       "73       8  181   68    36  30.1  0.615   60  Yes\n",
       "74       2  121   70    32  39.1  0.886   23   No\n",
       "75       1  168   88    29  35.0  0.905   52  Yes\n",
       "76       1  144   82    46  46.1  0.335   46  Yes\n",
       "77       2  101   58    17  24.2  0.614   23   No\n",
       "78       2   96   68    13  21.1  0.647   26   No\n",
       "79       3  107   62    13  22.9  0.678   23  Yes\n",
       "80      12  121   78    17  26.5  0.259   62   No\n",
       "81       2  100   64    23  29.7  0.368   21   No\n",
       "82       4  154   72    29  31.3  0.338   37   No\n",
       "83       6  125   78    31  27.6  0.565   49  Yes\n",
       "84      10  125   70    26  31.1  0.205   41  Yes\n",
       "85       2  122   76    27  35.9  0.483   26   No\n",
       "..     ...  ...  ...   ...   ...    ...  ...  ...\n",
       "116      0  137   84    27  27.3  0.231   59   No\n",
       "117      9  112   82    32  34.2  0.260   36  Yes\n",
       "118      4  148   60    27  30.9  0.150   29  Yes\n",
       "119      1  136   74    50  37.4  0.399   24   No\n",
       "120      9  145   80    46  37.9  0.637   40  Yes\n",
       "121      1   93   56    11  22.5  0.417   22   No\n",
       "122      1  107   72    30  30.8  0.821   24   No\n",
       "123     12  151   70    40  41.8  0.742   38  Yes\n",
       "124      1   97   70    40  38.1  0.218   30   No\n",
       "125      5  144   82    26  32.0  0.452   58  Yes\n",
       "126      2  112   86    42  38.4  0.246   28   No\n",
       "127      2   99   52    15  24.6  0.637   21   No\n",
       "128      1  109   56    21  25.2  0.833   23   No\n",
       "129      1  120   80    48  38.9  1.162   41   No\n",
       "130      7  187   68    39  37.7  0.254   41  Yes\n",
       "131      3  129   92    49  36.4  0.968   32  Yes\n",
       "132      7  179   95    31  34.2  0.164   60   No\n",
       "133      6   80   66    30  26.2  0.313   41   No\n",
       "134      2  105   58    40  34.9  0.225   25   No\n",
       "135      3  191   68    15  30.9  0.299   34   No\n",
       "136      0   95   80    45  36.5  0.330   26   No\n",
       "137      4   99   72    17  25.6  0.294   28   No\n",
       "138      0  137   68    14  24.8  0.143   21   No\n",
       "139      1   97   70    15  18.2  0.147   21   No\n",
       "140      0  100   88    60  46.8  0.962   31   No\n",
       "141      1  167   74    17  23.4  0.447   33  Yes\n",
       "142      0  180   90    26  36.5  0.314   35  Yes\n",
       "143      2  122   70    27  36.8  0.340   27   No\n",
       "144      1   90   62    12  27.2  0.580   24   No\n",
       "145      3  120   70    30  42.9  0.452   30   No\n",
       "146      6  154   78    41  46.1  0.571   27   No\n",
       "147      2   56   56    28  24.2  0.332   22   No\n",
       "148      0  177   60    29  34.6  1.072   21  Yes\n",
       "149      3  124   80    33  33.2  0.305   26   No\n",
       "150      8   85   55    20  24.4  0.136   42   No\n",
       "151     12   88   74    40  35.3  0.378   48   No\n",
       "152      9  152   78    34  34.2  0.893   33  Yes\n",
       "153      0  198   66    32  41.3  0.502   28  Yes\n",
       "154      0  188   82    14  32.0  0.682   22  Yes\n",
       "155      5  139   64    35  28.6  0.411   26   No\n",
       "156      7  168   88    42  38.2  0.787   40  Yes\n",
       "157      2  197   70    99  34.7  0.575   62  Yes\n",
       "158      2  142   82    18  24.7  0.761   21   No\n",
       "159      8  126   74    38  25.9  0.162   39   No\n",
       "160      3  158   76    36  31.6  0.851   28  Yes\n",
       "161      3  130   78    23  28.4  0.323   34  Yes\n",
       "162      2  100   54    28  37.8  0.498   24   No\n",
       "163      1  164   82    43  32.8  0.341   50   No\n",
       "164      4   95   60    32  35.4  0.284   28   No\n",
       "165      2  122   52    43  36.2  0.816   28   No\n",
       "166      4   85   58    22  27.8  0.306   28   No\n",
       "167      0  151   90    46  42.1  0.371   21  Yes\n",
       "168      6  144   72    27  33.9  0.255   40   No\n",
       "169      3  111   90    12  28.4  0.495   29   No\n",
       "170      1  107   68    19  26.5  0.165   24   No\n",
       "171      6  115   60    39  33.7  0.245   40  Yes\n",
       "172      5  105   72    29  36.9  0.159   28   No\n",
       "173      7  194   68    28  35.9  0.745   41  Yes\n",
       "174      4  184   78    39  37.0  0.264   31  Yes\n",
       "175      0   95   85    25  37.4  0.247   24  Yes\n",
       "176      7  124   70    33  25.5  0.161   37   No\n",
       "177      1  111   62    13  24.0  0.138   23   No\n",
       "178      7  137   90    41  32.0  0.391   39   No\n",
       "179      9   57   80    37  32.8  0.096   41   No\n",
       "180      2  157   74    35  39.4  0.134   30   No\n",
       "181      2   95   54    14  26.1  0.748   22   No\n",
       "182     12  140   85    33  37.4  0.244   41   No\n",
       "183      0  117   66    31  30.8  0.493   22   No\n",
       "184      8  100   74    40  39.4  0.661   43  Yes\n",
       "185      9  123   70    44  33.1  0.374   40   No\n",
       "186      0  138   60    35  34.6  0.534   21  Yes\n",
       "187     14  100   78    25  36.6  0.412   46  Yes\n",
       "188     14  175   62    30  33.6  0.212   38  Yes\n",
       "189      0   74   52    10  27.8  0.269   22   No\n",
       "190      1  133  102    28  32.8  0.234   45  Yes\n",
       "191      0  119   64    18  34.9  0.725   23   No\n",
       "192      5  155   84    44  38.7  0.619   34   No\n",
       "193      1  128   48    45  40.5  0.613   24  Yes\n",
       "194      2  112   68    22  34.1  0.315   26   No\n",
       "195      1  140   74    26  24.1  0.828   23   No\n",
       "196      2  141   58    34  25.4  0.699   24   No\n",
       "197      7  129   68    49  38.5  0.439   43  Yes\n",
       "198      0  106   70    37  39.4  0.605   22   No\n",
       "199      1  118   58    36  33.3  0.261   23   No\n",
       "200      8  155   62    26  34.0  0.543   46  Yes\n",
       "\n",
       "[200 rows x 8 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fb2d072240>"
      ]
     },
     "execution_count": 7,
     "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": [
    "pima.plot(kind='scatter', x = 'skin', y = 'bmi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(pima.skin, pima.bmi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "plt.scatter(X_train, Y_train, label='Training Data', color='r', alpha = 0.7)\n",
    "plt.scatter(X_test, Y_test, label='Training Data', color='b', alpha = 0.7)\n",
    "plt.legend()\n",
    "plt.title('Train Test Data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR = LinearRegression()\n",
    "LR.fit(X_train.values.reshape(-1,1), Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NBnqnTp344he/CDQdand3zQ3H++KLL/KNb3wDgGHDhnH00UfnXNbUprNbb7218RNCbW0t77zzTrPPyXU7ESkOud4pehtwJdA18bgHsMHdk2Oj1gJ9mnuimV0IXAhwyCGHtL6kBMNfHnBA02X5nH0kdfjZFStWcPvtt7Nw4UK6d+/OOeec0zgkbqqOHXe1PGUaPjbdcLytUV9fz2uvvcbgwYN57rnnWLBgAX/729/o1KkTo0ePbracuW4nIsUjaw3dzMYC69x9UeriZjZtNo3cfaa7V7l7Va9evVpZzED//sEA9akKNfvIpk2b6Nq1K926dWPNmjX84Q9/aPdjjB49mscffxyAV199tdlPALvbsWMHV111FUcccQRHHXUUGzduZP/996dTp068/vrrvPzyywCNc4om3zzSbScixSuXJpfjgDPMbCXwKEFTy21AdzNL1vD7AqvzUsIUU6YEs41s2gQ7dwbfCzX7SGVlJUcddRRDhgxh0qRJjbMHtafvfOc7rFq1iqFDhzJ9+nSGDBnCfvvt1+y2X//61xk6dCjHHHMMO3bsaJwZ6LTTTmPr1q0MGzaMH/3oR3z2s59tfM4FF1zA0KFDmTBhQsbtRHJRqA4KRWnbtuCENDQU9LAt6rZoZicA33P3sWb2BPBLd3/UzH4GvOLuGSfKbI9ui/Pnx3f2kfr6eurr6ykvL2fFihWcfPLJrFixorF2HSfqtljckh0UOnduOrXb9Onx+X9slVdegc9+Ngh0gEWLoLKyzbvNtdtiW5LiKuBRM7seWALc14Z95SzOs4/U1dVx4oknUl9fj7tzzz33xDLMpfildlCAXd9nzIjv/2da7vDf/w3TpjVdPmlSu4R5S7QoLdx9HjAv8fO7QPopcqTFunfvzqJFi7JvKBKyQndQiKTXX4chQ/Zc/vTTcMYZhS8PEbn1v5B3q0r49HoXvzA7KBRK2msEF10EZk3DvKoK1q4NaushhTlEYIKL8vJyPvroI3r06EHQvV3izN356KOPKC8vD7so0gZTpgRt6NC0Db0QHRQKIfUawQEHwObVm6k+odueG37601BbGwR8BIQe6H379qW2tpb169eHXRQpkPLy8iaTckvxqa4OLoAWsoNCITtEJK8R/Mea/803Vly/5wYPPQTnnpufg7dB6INziYhkU9BeNe7BrejNqK7YwPwlzXclzidNEi0isVGQYT9eeCFoOtktzD/u2JvTxzrVxzu9jwjCPKp98BXoIhJ5770X1MxTtVuvmsMOC4L8+OObLJ585B+pPt455wtrm9zEWOhBAltCgS4ikdfuvWr++c8gxM32fFdoaAB3/u3nJ9G7d9B5pXfvXc07hR4ksCUU6CISee027Md11wUh3qNH0+WXXRa0nae0n1dXwxNPQE1N8D3ZVp/XTwttFHovFxGRbNrUqybDRU7WrIEDD2xRWfr3D5pZuqX0YoxKH3wFuogUhRYP+/HoozB+/B6L6/ftyt5bNrW6HFHug68mFxGJl2Tb+G5hfvdRd1B9vDNy8KY2XcBMflporn09bKqhi0jx+8c/4KCDml31+c/V02W/DgAkW0naOohYVAcJVA1dRIrXF78Y1MZ3D/PDDgN3qo519u3aocmqqFzAzAcFukRKVG/YkAhx39Ws8vvfN1335pvB+sT8uKUwiFgqBbpERpRv2JAIuPfeZu/kBHZ1ORw4sMniMGc5C4MCXSIjyjdsSIiStfFJk5ouv+WWXUGeRpQvYOaDLopKZGjSBGm0ejX06dP8uh07oKws511F9QJmPqiGLpERRnun2uwjJlkbby7Mk7XxFoR5qVGgS2QUur1TbfYRkXqRc3c1NVmbVWQXBbpERqHbO9VmH7Jrrsl+kfPYYwtfriKmNnQpWWqzD0m66dpGjYK//KWwZYkZ1dAlMgrdBFJqfZRD9f776ZtV/vWvoDauMG8zBbpERqGbQEqtj3IokiHer9+e65LNKpowvN0o0CUyCj3OdKn1US6odLXx3/0u40VO9TpqG7WhS2SEMc50KfVRzruf/AR+8IPm1+XQSyV1IujUJje9yeZONXSJDDWBFKlkbXz3MB82rEVdDtXrqO0U6BIZ+WoCuf126Ns3CIe+fYPH0kaZLnJu2RKE+NKlLdpllKd2KxZqcpFIae8mkNtvh6uvhr33DgJ98+bgMcCll7bfcUpGui6H0KQmPn9+y6eLi/LUbsVCNXSJhXQX0266KQjz8vIgi8rLg8c33RRueYtOutr47Nl7NKu0tvupmtzaToEuRS9TgHz8MeyzT9Pt99knWB6WounJ8cMfpg/yZIh/85t7rGptW7h6HbWdeQHHSKiqqvKampqCHU9Kw7hxe35U37QpCIS//jVoZknt6rxtG3TtCrW1hS9rak+O1AmGIxVc6ZpV9t03KGwWVVXBG2vqHf07dwYhrX//1jGzRe5elW071dCl6GW6mHbFFVBfH4S4e/C9vj5YHobI9uRYvTp9bXzjxuDk7Rbm6T5p6A7c8CjQpehlCpBLLw26R3ftGuRR167B47AuiEauJ0cuw9WmfvRJyNTMpbbw8GQNdDMrN7OFZrbMzF43s+sSyx80s/fMbGniqyL/xRXZU7YAufTSoHlly5bge5i9WyJTe01XG7/33pz6jmf6pKG28PDk0m1xOzDG3evMrAx40cz+X2LdFe7+ZP6KJ5JdMkBa2k0OWte9ri2mTAlqstC0Db0gtde77oLJk5tf18JradlGqtQduOHIGugeXDVN1inKEl8abV4ipTUBEsat5m1582m1dBc5+/aFDz5o1S7VZzyacmpDN7MOZrYUWAc86+4vJVb92MxeMbNbzWyfDLsQiZywLlBWV8MTTwQ9Pp54Ik9hvn59+maVDRuCGnkrwxzUTh5VOQW6uze4ewXQFxhhZkOA7wODgOHA/sBVzT3XzC40sxozq1m/fn07FVuk7d57D7Zvh0WL4IUXgu/bt0f3VvOc+q937BiEeO/ee65Lto3vt1+by6J28mhqcT90M7sG2OLuN6csOwH4nruPzfRc9UOXKKmuhsWLgwzs0AEaGoIJ5Ssro3ezT9b+6+maVR5+GM45p6BllfbXbv3QzayXmXVP/NwJ+ALwppkdlFhmwJnAa20rskhhpWZgcz9H6Y7O5pqHJn58K9UnZLmTU2FeUnLp5XIQMMvMOhC8ATzu7r81sz+ZWS/AgKXAxXksp0irpevJUlcHQ4YEAwdu3RrcCDlgQLA8amNzp/Yq+c1vcxsgS0qPbv2XWMvUVDFjRvohAyD9uieeKOzvAPCtMz7k/t/0an7l+vXQs2dhCyQFpVv/RcjckyVTT43I3NGZ6KnSXJhXHevMn+cKc2mkQJdYyxTMmXpqhH5HZ5ouhw8Mu5WqY51xZ7l6lcgeNMGFxFq2G2DS3ZAUyh2dP/sZ/Md/NL8u0TR6fuJLpDmqoUustfYGmIL2s07WxpsJ8/nzcp+Ts71FqZeP5EYXRSX2Cj1eS04+/hj237/ZVWeO/Aef7H9AqGOlF8W47SUk14uiCnSRQsowJ+e4szwyPWsyTRoSRi+fUqdeLiJRkm5clZ/+tPEmoLb0rGnv5pHI9PKRFlGgi+TL/fdnn5Nz2rTGRa3tWdPaSZkzCb2XT5EL6/qDAl2kvSVD/IIL9lyXYfKI1l7AzceokVEcTbFYLtLm4w02Vwp0kfawYUP62viqVTnNAtTanjX5aB6J2miKYYZkS4U5b6z6oUvs5bWXS4aLnK3pbtiaiTryNdlEGLMOpXutUkMSdn1PTnkXJdlmc8on1dAl1vJWs0tXG7/00pxq4+0pis0jrZHptSqmi7RhXn9QoEustevH35kzs1/kvO22Npe5paLWPNJamV6rYrpIG+YbrJpcJFLau3mkXT7+tnOzSj7EYVLmTK/V9OkhTq7dQqHMG5ugGrpERqS6323enL42/s47BW9WKQWZXqti+xRSkHljm6EaukRGPi58tXiQrSKojcdVttcqDp9C8k01dImMULvfpauNX3SRauMFUmy18ChSDV0io+Dd72bNgokTm3+SAjwUqoW3jWroEhkF6x2QrI03F+aqjUsRU6BLZOT1I/eWLembVd56S0EusaAmF4mUdv/IXVYG9fXNr1OAS8yohi7xlKyN7x7mEyaoNi6xpRq6xMfDDweB3RwFuJQABboUP/UdFwHU5NImxTI+cyz961/pL3K++qqaVaQkKdBbqZjGZ46VZIjvu++e65IhPmRI4cslEgEK9FZqyyh+qtm3Qrra+Oc/D+7Mn+c6p1LyFOit1Nrb1IupZh/6G88vfpE+yHfuDGrjf/pTUZ1TkXxSoLdSa0fxC3N6qpYINSSTIX722XuuSzarpIR8sZxTkXxToLdSa29TL5aZVwoektu3p6+NL1yY8SJnsZxTkXxToLdSa29TL5aZVwoWkh06BCFeXr7numSIDx+ecRfFck5F8k2B3gatGcS+WOZ/zHtIJmvjO3c2XT5iRIu7HBbLORXJNwV6gRXLmM95Cck5c7Jf5HzppRbvNts5Df3irkiBmBfw5ouqqiqvqakp2PGkbdoyv2fqc2sWhXcnZ/LibufOTWfBieKbqEg6ZrbI3auybZf11n8zKwcWAPsktn/S3a8xs/7Ao8D+wGLgXHff0bZiS5S0duTD+fPhyu9+wkuLOza/wQsvwOjRbStcjvIxrZ1IVOXS5LIdGOPuw4AK4FQzGwncCNzq7kcCHwMX5K+YUjT224/qE6zZMB93VqJtvEBhDuoBI6Ula6B7IHl5rCzx5cAY4MnE8lnAmXkpoRSHZNv4pk1NFv9Pl6M4faxz2pc8lBBVDxgpJTldFDWzDma2FFgHPAu8A2xw9+Rg07VAnzTPvdDMasysZv369e1RZomK555Le5HzhM81cPpYZ/IJrwPhhah6wEgpySnQ3b3B3SuAvsAIYHBzm6V57kx3r3L3ql69erW+pBIdyRA/6aQ91yXGVanbulckQrRYehWJtIcWjYfu7hvMbB4wEuhuZnsnaul9gdV5KJ9ERUMD7J3mz2XuXBgzpvFhMkRb20OmvWkmeSkVufRy6QV8kgjzTsAXCC6IPg+cRdDT5Tzg6XwWVEJSWQlLljS/LkOXQ4WoSOHlUkM/CJhlZh0Immged/ffmtkbwKNmdj2wBLgvj+WUQks3C1BVFbz8cmHLIiI5yRro7v4K8Jlmlr9L0J4ucbFgQfpqdX19MO6KiESW5hQVzckpEhMay6VU7dyZflyVuXM1J6dIEVKgl5qvfCUI8eaaT5IhntJjRUSKhwK9VCRr40891XR5dXVeauMa4VCk8BTocbZ4cfpmlU8+CUJ83rx2P6zm+BQJhy6KxlHIFzk1wqFIOFRDj4vkxMnNhfmf/lTQi5wa4VAkHAr0YvfNbwYhvlczL2UyxD//+YIWSSMcioRDgV6skrXxRx5punz48NC7HGqEQ5FwKNCLyZtvpm9W2bEjCPGFCwtfrt1ohEORcOiiaDEowjs5NTiXSOGphh5VmS5yzpsXerOKqK+9RI8CPWpuuCH7RU5VfUOnvvYSRQr0qEjWxr///abLv/CFvNbGVctsndS+9nvtFXzv3DlYLhIWBXqY3n8/fbPK9u1BiD/7bN4OH6daZqHfmNTXXqJIgZ5FXoJi4MAgxPv123NdsjbesWM7HCizuNQyw3hjUl97iSIFegbtGhSpFzn//vem6158MZSLnHGpZYbxxqS+9hJFCvQM2iUoZs3KfpHzuOParcwtEZdaZhhvTOprL1GkfugZvPdeUDNPlXNQpOs7fsklcPvtbS5be5gyJfjEAcHvVVdXnLXM/v2DT0/JQcCgMG9M6msvUaMaegYtrsGuXZv9ImdEwhziU8tU84dIQDX0DHKuwVZUwLJlze8k4jf/xKGWmXxjmjEj+PTUv3/wGhX77yXSUuYFDJyqqiqvqakp2PHaw/z5GYIiXbPKSy/BiBEFK6OIxJuZLXL3qmzbqYaexR412GeeATut+Y0jXhsXkXhTG3qukm3jp+0W5v/1XyU7roruMhWJFtXQM9m6Nein2Jxt22CffQpbnghJ9tHv3LlpH/1ivKgqEheqoTdn9uygNr57mO+7767aeAmHOcTnLlOROFGgp0o2q5xzTtPltbVBiG/ZkvciFEszRlzpePG0AAAItUlEQVTuMi11xfL3JrlRoG/Y0Hzf8UMO2VUb79OnIEUppsGy4nKXaSkrpr83yU3pBvrzzwch/qlPNV3+zDNBiL//fsGLVEzNGLqZp/gV09+b5Ka0Ar2+Hi6/PAjyMWN2Lf/BD4J17vDFL4ZWvGJqxojLXaalrJj+3iQ3RdXLJeNNPpm88w4cfzysXt10+V//CiNH5qWsrRHWmCStFYe7TEtZsf29SXZFU0NvVXvfvfcGtfEjjtgV5l/+MmzeHNTGIxTmoGYMKSz9vcVP0QR6zu19GzfCKacEQT5p0q7lDz0UhPhTT+35OTMi1IwhhaS/t/jJ2uRiZgcDDwEHAjuBme5+u5ldC0wC1ic2vdrdn8lXQbMOZbtgwZ5/iUccAXPnBj1WioSaMaSQ9PcWL7nU0OuBqe4+GBgJTDazoxLrbnX3isRX3sIcmu8mt3VzA9dsmhrUxlP/KqdNg4YGWLGiqMJcWk/9qUVyqKG7+xpgTeLnzWa2HChMx+wUqUPZHrHXu/zkrydwwPYPmm705z/D//pfhS6ahEzDEIgEWtSGbmb9gM8ALyUWTTGzV8zsfjP7VNontoNke9/Xt9zPffMO3xXmY8cGV3PcFeYlSv2pRQI5B7qZdQF+CVzm7puAu4HDgQqCGvz0NM+70MxqzKxm/fr1zW2Ss+pq+PaNhwZjqjz4YBDiv/kNdO3apv1KcVN/apFATv3QzayMIMxnu/scAHdfm7L+58Bvm3uuu88EZkIwwUVbC8yJJxZkTBUpHupPLRLIWkM3MwPuA5a7+y0pyw9K2ewrwGvtXzyR7NSfWiSQS5PLccC5wBgzW5r4+hLw32b2qpm9AnweuDyfBRX15EhH/alFAppTtEik9uRInbBawSUSf7nOKVo0d4qWOvXkEJFsFOhFQj05RCQbBXqR0IQSIpKNAr1IqCeHiGSjQC8S6skhItkU1QQXpU4j44lIJqqhi4jEhAJdRCQmFOgiIjGhQBcRiQkFuohITCjQRURiQoEuIhITJR/oGpJWROKipAM9OSTtunVNJxdWqItIMSrpQNeQtCISJyUd6LkMSasmGREpFiUd6NmGpFWTjIgUk5IO9GxD0qpJRkSKSUkHerYhaTVLkIgUk5IfPjfTkLT9+wfNLN267VqmWYJEJKpKuoaejWYJEpFiokDPQLMEiUgxKfkml2w0S5CIFAvV0EVEYkKBLiISEwp0EZGYUKCLiMSEAl1EJCYU6CIiMaFAFxGJCQW6iEhMKNBFRGJCgS4iEhNZA93MDjaz581suZm9bmaXJpbvb2bPmtmKxPdP5b+4IiKSTi419HpgqrsPBkYCk83sKGAaMNfdjwTmJh6LiEhIsga6u69x98WJnzcDy4E+wJeBWYnNZgFn5quQIiKSXYva0M2sH/AZ4CXgAHdfA0HoA73TPOdCM6sxs5r169e3rbQiIpJWzoFuZl2AXwKXufumXJ/n7jPdvcrdq3r16tWaMoqISA5yCnQzKyMI89nuPiexeK2ZHZRYfxCwLj9FFBGRXOTSy8WA+4Dl7n5LyqpfA+clfj4PeLr9i5d/8+fDuHFQVRV8nz8/7BKJiLROLjX044BzgTFmtjTx9SXgBuAkM1sBnJR4XFTmz4epU4OJoA84IPg+dapCXUSKU9Yp6Nz9RcDSrD6xfYtTWDNmQOfO0K1b8Dj5fcYMTTsnIsWnpO8Ufe896NKl6bIuXYLlIiLFpqQDvX9/qKtruqyuLlguIlJsSjrQp0yBLVtg0ybYuTP4vmVLsFxEpNiUdKBXV8P06dC7N6xdG3yfPl3t5yJSnLJeFI276moFuIjEQ0nX0EVE4kSBLiISEwp0EZGYUKCLiMSEAl1EJCYU6CIiMaFAFxGJCXP3wh3MbD3wfsEOuKeewIchHj/KdG7S07nJTOcnvfY6N4e6e9YZggoa6GEzsxp3rwq7HFGkc5Oezk1mOj/pFfrcqMlFRCQmFOgiIjFRaoE+M+wCRJjOTXo6N5np/KRX0HNTUm3oIiJxVmo1dBGR2FKgi4jERGwD3czuN7N1ZvZayrL9zexZM1uR+P6pMMsYFjM72MyeN7PlZva6mV2aWF7y58fMys1soZktS5yb6xLL+5vZS4lz85iZdQy7rGExsw5mtsTMfpt4rHMDmNlKM3vVzJaaWU1iWUH/p2Ib6MCDwKm7LZsGzHX3I4G5icelqB6Y6u6DgZHAZDM7Cp0fgO3AGHcfBlQAp5rZSOBG4NbEufkYuCDEMobtUmB5ymOdm10+7+4VKX3PC/o/FdtAd/cFwD93W/xlYFbi51nAmQUtVES4+xp3X5z4eTPBP2cfdH7wQHLq8LLElwNjgCcTy0vy3ACYWV/gNODexGND5yaTgv5PxTbQ0zjA3ddAEGpA75DLEzoz6wd8BngJnR+gsUlhKbAOeBZ4B9jg7vWJTWoJ3gBL0W3AlcDOxOMe6NwkOfBHM1tkZhcmlhX0f6rk5xQtZWbWBfglcJm7bwoqW+LuDUCFmXUHfgUMbm6zwpYqfGY2Fljn7ovM7ITk4mY2Lblzk3Ccu682s97As2b2ZqELUGo19LVmdhBA4vu6kMsTGjMrIwjz2e4+J7FY5yeFu28A5hFcZ+huZskKUF9gdVjlCtFxwBlmthJ4lKCp5TZ0bgBw99WJ7+sIKgIjKPD/VKkF+q+B8xI/nwc8HWJZQpNo97wPWO7ut6SsKvnzY2a9EjVzzKwT8AWCawzPA2clNivJc+Pu33f3vu7eD/gG8Cd3PxudG8yss5l1Tf4MnAy8RoH/p2J7p6iZPQKcQDB85VrgGuAp4HHgEOB/gHHuvvuF09gzs9HAC8Cr7GoLvZqgHb2kz4+ZDSW4eNWBoMLzuLv/yMwOI6iV7g8sAc5x9+3hlTRciSaX77n7WJ0bSJyDXyUe7g38wt1/bGY9KOD/VGwDXUSk1JRak4uISGwp0EVEYkKBLiISEwp0EZGYUKCLiMSEAl1EJCYU6CIiMfH/Aa1ebsRPZjewAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#test sets\n",
    "prediction = LR.predict(X_test.values.reshape(-1,1))\n",
    "\n",
    "plt.plot(X_test, prediction, label='Linear Regression', color='r')\n",
    "plt.scatter(X_test, Y_test, label='Training Data', color='b', alpha = 0.7)\n",
    "plt.legend()\n",
    "plt.title('Linear Regression')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": [
    "#traing sets\n",
    "prediction = LR.predict(X_train.values.reshape(-1,1))\n",
    "\n",
    "plt.plot(X_train, prediction, label='Linear Regression', color='r')\n",
    "plt.scatter(X_train, Y_train, label='Training Data', color='b', alpha = 0.7)\n",
    "plt.legend()\n",
    "plt.title('Linear Regression')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39.59190780364388"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR.predict(np.array([[50]]))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4878686207420827"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR.score(X_test.values.reshape(-1,1), Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}