Breast Cancer Prediction (Random Forest Hyper Parameter Tuning).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the required libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "# To ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.990</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.300100</td>\n",
       "      <td>0.147100</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.16220</td>\n",
       "      <td>0.66560</td>\n",
       "      <td>0.71190</td>\n",
       "      <td>0.26540</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.570</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.086900</td>\n",
       "      <td>0.070170</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.12380</td>\n",
       "      <td>0.18660</td>\n",
       "      <td>0.24160</td>\n",
       "      <td>0.18600</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.690</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.197400</td>\n",
       "      <td>0.127900</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.14440</td>\n",
       "      <td>0.42450</td>\n",
       "      <td>0.45040</td>\n",
       "      <td>0.24300</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.420</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.241400</td>\n",
       "      <td>0.105200</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.20980</td>\n",
       "      <td>0.86630</td>\n",
       "      <td>0.68690</td>\n",
       "      <td>0.25750</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.290</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.198000</td>\n",
       "      <td>0.104300</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.13740</td>\n",
       "      <td>0.20500</td>\n",
       "      <td>0.40000</td>\n",
       "      <td>0.16250</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>843786</td>\n",
       "      <td>M</td>\n",
       "      <td>12.450</td>\n",
       "      <td>15.70</td>\n",
       "      <td>82.57</td>\n",
       "      <td>477.1</td>\n",
       "      <td>0.12780</td>\n",
       "      <td>0.17000</td>\n",
       "      <td>0.157800</td>\n",
       "      <td>0.080890</td>\n",
       "      <td>...</td>\n",
       "      <td>23.75</td>\n",
       "      <td>103.40</td>\n",
       "      <td>741.6</td>\n",
       "      <td>0.17910</td>\n",
       "      <td>0.52490</td>\n",
       "      <td>0.53550</td>\n",
       "      <td>0.17410</td>\n",
       "      <td>0.3985</td>\n",
       "      <td>0.12440</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>844359</td>\n",
       "      <td>M</td>\n",
       "      <td>18.250</td>\n",
       "      <td>19.98</td>\n",
       "      <td>119.60</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>0.09463</td>\n",
       "      <td>0.10900</td>\n",
       "      <td>0.112700</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>...</td>\n",
       "      <td>27.66</td>\n",
       "      <td>153.20</td>\n",
       "      <td>1606.0</td>\n",
       "      <td>0.14420</td>\n",
       "      <td>0.25760</td>\n",
       "      <td>0.37840</td>\n",
       "      <td>0.19320</td>\n",
       "      <td>0.3063</td>\n",
       "      <td>0.08368</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>84458202</td>\n",
       "      <td>M</td>\n",
       "      <td>13.710</td>\n",
       "      <td>20.83</td>\n",
       "      <td>90.20</td>\n",
       "      <td>577.9</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>0.16450</td>\n",
       "      <td>0.093660</td>\n",
       "      <td>0.059850</td>\n",
       "      <td>...</td>\n",
       "      <td>28.14</td>\n",
       "      <td>110.60</td>\n",
       "      <td>897.0</td>\n",
       "      <td>0.16540</td>\n",
       "      <td>0.36820</td>\n",
       "      <td>0.26780</td>\n",
       "      <td>0.15560</td>\n",
       "      <td>0.3196</td>\n",
       "      <td>0.11510</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>844981</td>\n",
       "      <td>M</td>\n",
       "      <td>13.000</td>\n",
       "      <td>21.82</td>\n",
       "      <td>87.50</td>\n",
       "      <td>519.8</td>\n",
       "      <td>0.12730</td>\n",
       "      <td>0.19320</td>\n",
       "      <td>0.185900</td>\n",
       "      <td>0.093530</td>\n",
       "      <td>...</td>\n",
       "      <td>30.73</td>\n",
       "      <td>106.20</td>\n",
       "      <td>739.3</td>\n",
       "      <td>0.17030</td>\n",
       "      <td>0.54010</td>\n",
       "      <td>0.53900</td>\n",
       "      <td>0.20600</td>\n",
       "      <td>0.4378</td>\n",
       "      <td>0.10720</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>84501001</td>\n",
       "      <td>M</td>\n",
       "      <td>12.460</td>\n",
       "      <td>24.04</td>\n",
       "      <td>83.97</td>\n",
       "      <td>475.9</td>\n",
       "      <td>0.11860</td>\n",
       "      <td>0.23960</td>\n",
       "      <td>0.227300</td>\n",
       "      <td>0.085430</td>\n",
       "      <td>...</td>\n",
       "      <td>40.68</td>\n",
       "      <td>97.65</td>\n",
       "      <td>711.4</td>\n",
       "      <td>0.18530</td>\n",
       "      <td>1.05800</td>\n",
       "      <td>1.10500</td>\n",
       "      <td>0.22100</td>\n",
       "      <td>0.4366</td>\n",
       "      <td>0.20750</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>845636</td>\n",
       "      <td>M</td>\n",
       "      <td>16.020</td>\n",
       "      <td>23.24</td>\n",
       "      <td>102.70</td>\n",
       "      <td>797.8</td>\n",
       "      <td>0.08206</td>\n",
       "      <td>0.06669</td>\n",
       "      <td>0.032990</td>\n",
       "      <td>0.033230</td>\n",
       "      <td>...</td>\n",
       "      <td>33.88</td>\n",
       "      <td>123.80</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>0.11810</td>\n",
       "      <td>0.15510</td>\n",
       "      <td>0.14590</td>\n",
       "      <td>0.09975</td>\n",
       "      <td>0.2948</td>\n",
       "      <td>0.08452</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>84610002</td>\n",
       "      <td>M</td>\n",
       "      <td>15.780</td>\n",
       "      <td>17.89</td>\n",
       "      <td>103.60</td>\n",
       "      <td>781.0</td>\n",
       "      <td>0.09710</td>\n",
       "      <td>0.12920</td>\n",
       "      <td>0.099540</td>\n",
       "      <td>0.066060</td>\n",
       "      <td>...</td>\n",
       "      <td>27.28</td>\n",
       "      <td>136.50</td>\n",
       "      <td>1299.0</td>\n",
       "      <td>0.13960</td>\n",
       "      <td>0.56090</td>\n",
       "      <td>0.39650</td>\n",
       "      <td>0.18100</td>\n",
       "      <td>0.3792</td>\n",
       "      <td>0.10480</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>846226</td>\n",
       "      <td>M</td>\n",
       "      <td>19.170</td>\n",
       "      <td>24.80</td>\n",
       "      <td>132.40</td>\n",
       "      <td>1123.0</td>\n",
       "      <td>0.09740</td>\n",
       "      <td>0.24580</td>\n",
       "      <td>0.206500</td>\n",
       "      <td>0.111800</td>\n",
       "      <td>...</td>\n",
       "      <td>29.94</td>\n",
       "      <td>151.70</td>\n",
       "      <td>1332.0</td>\n",
       "      <td>0.10370</td>\n",
       "      <td>0.39030</td>\n",
       "      <td>0.36390</td>\n",
       "      <td>0.17670</td>\n",
       "      <td>0.3176</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>846381</td>\n",
       "      <td>M</td>\n",
       "      <td>15.850</td>\n",
       "      <td>23.95</td>\n",
       "      <td>103.70</td>\n",
       "      <td>782.7</td>\n",
       "      <td>0.08401</td>\n",
       "      <td>0.10020</td>\n",
       "      <td>0.099380</td>\n",
       "      <td>0.053640</td>\n",
       "      <td>...</td>\n",
       "      <td>27.66</td>\n",
       "      <td>112.00</td>\n",
       "      <td>876.5</td>\n",
       "      <td>0.11310</td>\n",
       "      <td>0.19240</td>\n",
       "      <td>0.23220</td>\n",
       "      <td>0.11190</td>\n",
       "      <td>0.2809</td>\n",
       "      <td>0.06287</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>84667401</td>\n",
       "      <td>M</td>\n",
       "      <td>13.730</td>\n",
       "      <td>22.61</td>\n",
       "      <td>93.60</td>\n",
       "      <td>578.3</td>\n",
       "      <td>0.11310</td>\n",
       "      <td>0.22930</td>\n",
       "      <td>0.212800</td>\n",
       "      <td>0.080250</td>\n",
       "      <td>...</td>\n",
       "      <td>32.01</td>\n",
       "      <td>108.80</td>\n",
       "      <td>697.7</td>\n",
       "      <td>0.16510</td>\n",
       "      <td>0.77250</td>\n",
       "      <td>0.69430</td>\n",
       "      <td>0.22080</td>\n",
       "      <td>0.3596</td>\n",
       "      <td>0.14310</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>84799002</td>\n",
       "      <td>M</td>\n",
       "      <td>14.540</td>\n",
       "      <td>27.54</td>\n",
       "      <td>96.73</td>\n",
       "      <td>658.8</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.15950</td>\n",
       "      <td>0.163900</td>\n",
       "      <td>0.073640</td>\n",
       "      <td>...</td>\n",
       "      <td>37.13</td>\n",
       "      <td>124.10</td>\n",
       "      <td>943.2</td>\n",
       "      <td>0.16780</td>\n",
       "      <td>0.65770</td>\n",
       "      <td>0.70260</td>\n",
       "      <td>0.17120</td>\n",
       "      <td>0.4218</td>\n",
       "      <td>0.13410</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>848406</td>\n",
       "      <td>M</td>\n",
       "      <td>14.680</td>\n",
       "      <td>20.13</td>\n",
       "      <td>94.74</td>\n",
       "      <td>684.5</td>\n",
       "      <td>0.09867</td>\n",
       "      <td>0.07200</td>\n",
       "      <td>0.073950</td>\n",
       "      <td>0.052590</td>\n",
       "      <td>...</td>\n",
       "      <td>30.88</td>\n",
       "      <td>123.40</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>0.14640</td>\n",
       "      <td>0.18710</td>\n",
       "      <td>0.29140</td>\n",
       "      <td>0.16090</td>\n",
       "      <td>0.3029</td>\n",
       "      <td>0.08216</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>84862001</td>\n",
       "      <td>M</td>\n",
       "      <td>16.130</td>\n",
       "      <td>20.68</td>\n",
       "      <td>108.10</td>\n",
       "      <td>798.8</td>\n",
       "      <td>0.11700</td>\n",
       "      <td>0.20220</td>\n",
       "      <td>0.172200</td>\n",
       "      <td>0.102800</td>\n",
       "      <td>...</td>\n",
       "      <td>31.48</td>\n",
       "      <td>136.80</td>\n",
       "      <td>1315.0</td>\n",
       "      <td>0.17890</td>\n",
       "      <td>0.42330</td>\n",
       "      <td>0.47840</td>\n",
       "      <td>0.20730</td>\n",
       "      <td>0.3706</td>\n",
       "      <td>0.11420</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>849014</td>\n",
       "      <td>M</td>\n",
       "      <td>19.810</td>\n",
       "      <td>22.15</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1260.0</td>\n",
       "      <td>0.09831</td>\n",
       "      <td>0.10270</td>\n",
       "      <td>0.147900</td>\n",
       "      <td>0.094980</td>\n",
       "      <td>...</td>\n",
       "      <td>30.88</td>\n",
       "      <td>186.80</td>\n",
       "      <td>2398.0</td>\n",
       "      <td>0.15120</td>\n",
       "      <td>0.31500</td>\n",
       "      <td>0.53720</td>\n",
       "      <td>0.23880</td>\n",
       "      <td>0.2768</td>\n",
       "      <td>0.07615</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8510426</td>\n",
       "      <td>B</td>\n",
       "      <td>13.540</td>\n",
       "      <td>14.36</td>\n",
       "      <td>87.46</td>\n",
       "      <td>566.3</td>\n",
       "      <td>0.09779</td>\n",
       "      <td>0.08129</td>\n",
       "      <td>0.066640</td>\n",
       "      <td>0.047810</td>\n",
       "      <td>...</td>\n",
       "      <td>19.26</td>\n",
       "      <td>99.70</td>\n",
       "      <td>711.2</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.17730</td>\n",
       "      <td>0.23900</td>\n",
       "      <td>0.12880</td>\n",
       "      <td>0.2977</td>\n",
       "      <td>0.07259</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>8510653</td>\n",
       "      <td>B</td>\n",
       "      <td>13.080</td>\n",
       "      <td>15.71</td>\n",
       "      <td>85.63</td>\n",
       "      <td>520.0</td>\n",
       "      <td>0.10750</td>\n",
       "      <td>0.12700</td>\n",
       "      <td>0.045680</td>\n",
       "      <td>0.031100</td>\n",
       "      <td>...</td>\n",
       "      <td>20.49</td>\n",
       "      <td>96.09</td>\n",
       "      <td>630.5</td>\n",
       "      <td>0.13120</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.18900</td>\n",
       "      <td>0.07283</td>\n",
       "      <td>0.3184</td>\n",
       "      <td>0.08183</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>8510824</td>\n",
       "      <td>B</td>\n",
       "      <td>9.504</td>\n",
       "      <td>12.44</td>\n",
       "      <td>60.34</td>\n",
       "      <td>273.9</td>\n",
       "      <td>0.10240</td>\n",
       "      <td>0.06492</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.020760</td>\n",
       "      <td>...</td>\n",
       "      <td>15.66</td>\n",
       "      <td>65.13</td>\n",
       "      <td>314.9</td>\n",
       "      <td>0.13240</td>\n",
       "      <td>0.11480</td>\n",
       "      <td>0.08867</td>\n",
       "      <td>0.06227</td>\n",
       "      <td>0.2450</td>\n",
       "      <td>0.07773</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>8511133</td>\n",
       "      <td>M</td>\n",
       "      <td>15.340</td>\n",
       "      <td>14.26</td>\n",
       "      <td>102.50</td>\n",
       "      <td>704.4</td>\n",
       "      <td>0.10730</td>\n",
       "      <td>0.21350</td>\n",
       "      <td>0.207700</td>\n",
       "      <td>0.097560</td>\n",
       "      <td>...</td>\n",
       "      <td>19.08</td>\n",
       "      <td>125.10</td>\n",
       "      <td>980.9</td>\n",
       "      <td>0.13900</td>\n",
       "      <td>0.59540</td>\n",
       "      <td>0.63050</td>\n",
       "      <td>0.23930</td>\n",
       "      <td>0.4667</td>\n",
       "      <td>0.09946</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>851509</td>\n",
       "      <td>M</td>\n",
       "      <td>21.160</td>\n",
       "      <td>23.04</td>\n",
       "      <td>137.20</td>\n",
       "      <td>1404.0</td>\n",
       "      <td>0.09428</td>\n",
       "      <td>0.10220</td>\n",
       "      <td>0.109700</td>\n",
       "      <td>0.086320</td>\n",
       "      <td>...</td>\n",
       "      <td>35.59</td>\n",
       "      <td>188.00</td>\n",
       "      <td>2615.0</td>\n",
       "      <td>0.14010</td>\n",
       "      <td>0.26000</td>\n",
       "      <td>0.31550</td>\n",
       "      <td>0.20090</td>\n",
       "      <td>0.2822</td>\n",
       "      <td>0.07526</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>852552</td>\n",
       "      <td>M</td>\n",
       "      <td>16.650</td>\n",
       "      <td>21.38</td>\n",
       "      <td>110.00</td>\n",
       "      <td>904.6</td>\n",
       "      <td>0.11210</td>\n",
       "      <td>0.14570</td>\n",
       "      <td>0.152500</td>\n",
       "      <td>0.091700</td>\n",
       "      <td>...</td>\n",
       "      <td>31.56</td>\n",
       "      <td>177.00</td>\n",
       "      <td>2215.0</td>\n",
       "      <td>0.18050</td>\n",
       "      <td>0.35780</td>\n",
       "      <td>0.46950</td>\n",
       "      <td>0.20950</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.09564</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>852631</td>\n",
       "      <td>M</td>\n",
       "      <td>17.140</td>\n",
       "      <td>16.40</td>\n",
       "      <td>116.00</td>\n",
       "      <td>912.7</td>\n",
       "      <td>0.11860</td>\n",
       "      <td>0.22760</td>\n",
       "      <td>0.222900</td>\n",
       "      <td>0.140100</td>\n",
       "      <td>...</td>\n",
       "      <td>21.40</td>\n",
       "      <td>152.40</td>\n",
       "      <td>1461.0</td>\n",
       "      <td>0.15450</td>\n",
       "      <td>0.39490</td>\n",
       "      <td>0.38530</td>\n",
       "      <td>0.25500</td>\n",
       "      <td>0.4066</td>\n",
       "      <td>0.10590</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>852763</td>\n",
       "      <td>M</td>\n",
       "      <td>14.580</td>\n",
       "      <td>21.53</td>\n",
       "      <td>97.41</td>\n",
       "      <td>644.8</td>\n",
       "      <td>0.10540</td>\n",
       "      <td>0.18680</td>\n",
       "      <td>0.142500</td>\n",
       "      <td>0.087830</td>\n",
       "      <td>...</td>\n",
       "      <td>33.21</td>\n",
       "      <td>122.40</td>\n",
       "      <td>896.9</td>\n",
       "      <td>0.15250</td>\n",
       "      <td>0.66430</td>\n",
       "      <td>0.55390</td>\n",
       "      <td>0.27010</td>\n",
       "      <td>0.4264</td>\n",
       "      <td>0.12750</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>852781</td>\n",
       "      <td>M</td>\n",
       "      <td>18.610</td>\n",
       "      <td>20.25</td>\n",
       "      <td>122.10</td>\n",
       "      <td>1094.0</td>\n",
       "      <td>0.09440</td>\n",
       "      <td>0.10660</td>\n",
       "      <td>0.149000</td>\n",
       "      <td>0.077310</td>\n",
       "      <td>...</td>\n",
       "      <td>27.26</td>\n",
       "      <td>139.90</td>\n",
       "      <td>1403.0</td>\n",
       "      <td>0.13380</td>\n",
       "      <td>0.21170</td>\n",
       "      <td>0.34460</td>\n",
       "      <td>0.14900</td>\n",
       "      <td>0.2341</td>\n",
       "      <td>0.07421</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>852973</td>\n",
       "      <td>M</td>\n",
       "      <td>15.300</td>\n",
       "      <td>25.27</td>\n",
       "      <td>102.40</td>\n",
       "      <td>732.4</td>\n",
       "      <td>0.10820</td>\n",
       "      <td>0.16970</td>\n",
       "      <td>0.168300</td>\n",
       "      <td>0.087510</td>\n",
       "      <td>...</td>\n",
       "      <td>36.71</td>\n",
       "      <td>149.30</td>\n",
       "      <td>1269.0</td>\n",
       "      <td>0.16410</td>\n",
       "      <td>0.61100</td>\n",
       "      <td>0.63350</td>\n",
       "      <td>0.20240</td>\n",
       "      <td>0.4027</td>\n",
       "      <td>0.09876</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>853201</td>\n",
       "      <td>M</td>\n",
       "      <td>17.570</td>\n",
       "      <td>15.05</td>\n",
       "      <td>115.00</td>\n",
       "      <td>955.1</td>\n",
       "      <td>0.09847</td>\n",
       "      <td>0.11570</td>\n",
       "      <td>0.098750</td>\n",
       "      <td>0.079530</td>\n",
       "      <td>...</td>\n",
       "      <td>19.52</td>\n",
       "      <td>134.90</td>\n",
       "      <td>1227.0</td>\n",
       "      <td>0.12550</td>\n",
       "      <td>0.28120</td>\n",
       "      <td>0.24890</td>\n",
       "      <td>0.14560</td>\n",
       "      <td>0.2756</td>\n",
       "      <td>0.07919</td>\n",
       "      <td>NaN</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>921362</td>\n",
       "      <td>B</td>\n",
       "      <td>7.691</td>\n",
       "      <td>25.44</td>\n",
       "      <td>48.34</td>\n",
       "      <td>170.4</td>\n",
       "      <td>0.08668</td>\n",
       "      <td>0.11990</td>\n",
       "      <td>0.092520</td>\n",
       "      <td>0.013640</td>\n",
       "      <td>...</td>\n",
       "      <td>31.89</td>\n",
       "      <td>54.49</td>\n",
       "      <td>223.6</td>\n",
       "      <td>0.15960</td>\n",
       "      <td>0.30640</td>\n",
       "      <td>0.33930</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.2790</td>\n",
       "      <td>0.10660</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>921385</td>\n",
       "      <td>B</td>\n",
       "      <td>11.540</td>\n",
       "      <td>14.44</td>\n",
       "      <td>74.65</td>\n",
       "      <td>402.9</td>\n",
       "      <td>0.09984</td>\n",
       "      <td>0.11200</td>\n",
       "      <td>0.067370</td>\n",
       "      <td>0.025940</td>\n",
       "      <td>...</td>\n",
       "      <td>19.68</td>\n",
       "      <td>78.78</td>\n",
       "      <td>457.8</td>\n",
       "      <td>0.13450</td>\n",
       "      <td>0.21180</td>\n",
       "      <td>0.17970</td>\n",
       "      <td>0.06918</td>\n",
       "      <td>0.2329</td>\n",
       "      <td>0.08134</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>921386</td>\n",
       "      <td>B</td>\n",
       "      <td>14.470</td>\n",
       "      <td>24.99</td>\n",
       "      <td>95.81</td>\n",
       "      <td>656.4</td>\n",
       "      <td>0.08837</td>\n",
       "      <td>0.12300</td>\n",
       "      <td>0.100900</td>\n",
       "      <td>0.038900</td>\n",
       "      <td>...</td>\n",
       "      <td>31.73</td>\n",
       "      <td>113.50</td>\n",
       "      <td>808.9</td>\n",
       "      <td>0.13400</td>\n",
       "      <td>0.42020</td>\n",
       "      <td>0.40400</td>\n",
       "      <td>0.12050</td>\n",
       "      <td>0.3187</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>921644</td>\n",
       "      <td>B</td>\n",
       "      <td>14.740</td>\n",
       "      <td>25.42</td>\n",
       "      <td>94.70</td>\n",
       "      <td>668.6</td>\n",
       "      <td>0.08275</td>\n",
       "      <td>0.07214</td>\n",
       "      <td>0.041050</td>\n",
       "      <td>0.030270</td>\n",
       "      <td>...</td>\n",
       "      <td>32.29</td>\n",
       "      <td>107.40</td>\n",
       "      <td>826.4</td>\n",
       "      <td>0.10600</td>\n",
       "      <td>0.13760</td>\n",
       "      <td>0.16110</td>\n",
       "      <td>0.10950</td>\n",
       "      <td>0.2722</td>\n",
       "      <td>0.06956</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>922296</td>\n",
       "      <td>B</td>\n",
       "      <td>13.210</td>\n",
       "      <td>28.06</td>\n",
       "      <td>84.88</td>\n",
       "      <td>538.4</td>\n",
       "      <td>0.08671</td>\n",
       "      <td>0.06877</td>\n",
       "      <td>0.029870</td>\n",
       "      <td>0.032750</td>\n",
       "      <td>...</td>\n",
       "      <td>37.17</td>\n",
       "      <td>92.48</td>\n",
       "      <td>629.6</td>\n",
       "      <td>0.10720</td>\n",
       "      <td>0.13810</td>\n",
       "      <td>0.10620</td>\n",
       "      <td>0.07958</td>\n",
       "      <td>0.2473</td>\n",
       "      <td>0.06443</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>922297</td>\n",
       "      <td>B</td>\n",
       "      <td>13.870</td>\n",
       "      <td>20.70</td>\n",
       "      <td>89.77</td>\n",
       "      <td>584.8</td>\n",
       "      <td>0.09578</td>\n",
       "      <td>0.10180</td>\n",
       "      <td>0.036880</td>\n",
       "      <td>0.023690</td>\n",
       "      <td>...</td>\n",
       "      <td>24.75</td>\n",
       "      <td>99.17</td>\n",
       "      <td>688.6</td>\n",
       "      <td>0.12640</td>\n",
       "      <td>0.20370</td>\n",
       "      <td>0.13770</td>\n",
       "      <td>0.06845</td>\n",
       "      <td>0.2249</td>\n",
       "      <td>0.08492</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>922576</td>\n",
       "      <td>B</td>\n",
       "      <td>13.620</td>\n",
       "      <td>23.23</td>\n",
       "      <td>87.19</td>\n",
       "      <td>573.2</td>\n",
       "      <td>0.09246</td>\n",
       "      <td>0.06747</td>\n",
       "      <td>0.029740</td>\n",
       "      <td>0.024430</td>\n",
       "      <td>...</td>\n",
       "      <td>29.09</td>\n",
       "      <td>97.58</td>\n",
       "      <td>729.8</td>\n",
       "      <td>0.12160</td>\n",
       "      <td>0.15170</td>\n",
       "      <td>0.10490</td>\n",
       "      <td>0.07174</td>\n",
       "      <td>0.2642</td>\n",
       "      <td>0.06953</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>922577</td>\n",
       "      <td>B</td>\n",
       "      <td>10.320</td>\n",
       "      <td>16.35</td>\n",
       "      <td>65.31</td>\n",
       "      <td>324.9</td>\n",
       "      <td>0.09434</td>\n",
       "      <td>0.04994</td>\n",
       "      <td>0.010120</td>\n",
       "      <td>0.005495</td>\n",
       "      <td>...</td>\n",
       "      <td>21.77</td>\n",
       "      <td>71.12</td>\n",
       "      <td>384.9</td>\n",
       "      <td>0.12850</td>\n",
       "      <td>0.08842</td>\n",
       "      <td>0.04384</td>\n",
       "      <td>0.02381</td>\n",
       "      <td>0.2681</td>\n",
       "      <td>0.07399</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>922840</td>\n",
       "      <td>B</td>\n",
       "      <td>10.260</td>\n",
       "      <td>16.58</td>\n",
       "      <td>65.85</td>\n",
       "      <td>320.8</td>\n",
       "      <td>0.08877</td>\n",
       "      <td>0.08066</td>\n",
       "      <td>0.043580</td>\n",
       "      <td>0.024380</td>\n",
       "      <td>...</td>\n",
       "      <td>22.04</td>\n",
       "      <td>71.08</td>\n",
       "      <td>357.4</td>\n",
       "      <td>0.14610</td>\n",
       "      <td>0.22460</td>\n",
       "      <td>0.17830</td>\n",
       "      <td>0.08333</td>\n",
       "      <td>0.2691</td>\n",
       "      <td>0.09479</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>923169</td>\n",
       "      <td>B</td>\n",
       "      <td>9.683</td>\n",
       "      <td>19.34</td>\n",
       "      <td>61.05</td>\n",
       "      <td>285.7</td>\n",
       "      <td>0.08491</td>\n",
       "      <td>0.05030</td>\n",
       "      <td>0.023370</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>...</td>\n",
       "      <td>25.59</td>\n",
       "      <td>69.10</td>\n",
       "      <td>364.2</td>\n",
       "      <td>0.11990</td>\n",
       "      <td>0.09546</td>\n",
       "      <td>0.09350</td>\n",
       "      <td>0.03846</td>\n",
       "      <td>0.2552</td>\n",
       "      <td>0.07920</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>923465</td>\n",
       "      <td>B</td>\n",
       "      <td>10.820</td>\n",
       "      <td>24.21</td>\n",
       "      <td>68.89</td>\n",
       "      <td>361.6</td>\n",
       "      <td>0.08192</td>\n",
       "      <td>0.06602</td>\n",
       "      <td>0.015480</td>\n",
       "      <td>0.008160</td>\n",
       "      <td>...</td>\n",
       "      <td>31.45</td>\n",
       "      <td>83.90</td>\n",
       "      <td>505.6</td>\n",
       "      <td>0.12040</td>\n",
       "      <td>0.16330</td>\n",
       "      <td>0.06194</td>\n",
       "      <td>0.03264</td>\n",
       "      <td>0.3059</td>\n",
       "      <td>0.07626</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>923748</td>\n",
       "      <td>B</td>\n",
       "      <td>10.860</td>\n",
       "      <td>21.48</td>\n",
       "      <td>68.51</td>\n",
       "      <td>360.5</td>\n",
       "      <td>0.07431</td>\n",
       "      <td>0.04227</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>24.77</td>\n",
       "      <td>74.08</td>\n",
       "      <td>412.3</td>\n",
       "      <td>0.10010</td>\n",
       "      <td>0.07348</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.2458</td>\n",
       "      <td>0.06592</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>923780</td>\n",
       "      <td>B</td>\n",
       "      <td>11.130</td>\n",
       "      <td>22.44</td>\n",
       "      <td>71.49</td>\n",
       "      <td>378.4</td>\n",
       "      <td>0.09566</td>\n",
       "      <td>0.08194</td>\n",
       "      <td>0.048240</td>\n",
       "      <td>0.022570</td>\n",
       "      <td>...</td>\n",
       "      <td>28.26</td>\n",
       "      <td>77.80</td>\n",
       "      <td>436.6</td>\n",
       "      <td>0.10870</td>\n",
       "      <td>0.17820</td>\n",
       "      <td>0.15640</td>\n",
       "      <td>0.06413</td>\n",
       "      <td>0.3169</td>\n",
       "      <td>0.08032</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>924084</td>\n",
       "      <td>B</td>\n",
       "      <td>12.770</td>\n",
       "      <td>29.43</td>\n",
       "      <td>81.35</td>\n",
       "      <td>507.9</td>\n",
       "      <td>0.08276</td>\n",
       "      <td>0.04234</td>\n",
       "      <td>0.019970</td>\n",
       "      <td>0.014990</td>\n",
       "      <td>...</td>\n",
       "      <td>36.00</td>\n",
       "      <td>88.10</td>\n",
       "      <td>594.7</td>\n",
       "      <td>0.12340</td>\n",
       "      <td>0.10640</td>\n",
       "      <td>0.08653</td>\n",
       "      <td>0.06498</td>\n",
       "      <td>0.2407</td>\n",
       "      <td>0.06484</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>924342</td>\n",
       "      <td>B</td>\n",
       "      <td>9.333</td>\n",
       "      <td>21.94</td>\n",
       "      <td>59.01</td>\n",
       "      <td>264.0</td>\n",
       "      <td>0.09240</td>\n",
       "      <td>0.05605</td>\n",
       "      <td>0.039960</td>\n",
       "      <td>0.012820</td>\n",
       "      <td>...</td>\n",
       "      <td>25.05</td>\n",
       "      <td>62.86</td>\n",
       "      <td>295.8</td>\n",
       "      <td>0.11030</td>\n",
       "      <td>0.08298</td>\n",
       "      <td>0.07993</td>\n",
       "      <td>0.02564</td>\n",
       "      <td>0.2435</td>\n",
       "      <td>0.07393</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>924632</td>\n",
       "      <td>B</td>\n",
       "      <td>12.880</td>\n",
       "      <td>28.92</td>\n",
       "      <td>82.50</td>\n",
       "      <td>514.3</td>\n",
       "      <td>0.08123</td>\n",
       "      <td>0.05824</td>\n",
       "      <td>0.061950</td>\n",
       "      <td>0.023430</td>\n",
       "      <td>...</td>\n",
       "      <td>35.74</td>\n",
       "      <td>88.84</td>\n",
       "      <td>595.7</td>\n",
       "      <td>0.12270</td>\n",
       "      <td>0.16200</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.06493</td>\n",
       "      <td>0.2372</td>\n",
       "      <td>0.07242</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>924934</td>\n",
       "      <td>B</td>\n",
       "      <td>10.290</td>\n",
       "      <td>27.61</td>\n",
       "      <td>65.67</td>\n",
       "      <td>321.4</td>\n",
       "      <td>0.09030</td>\n",
       "      <td>0.07658</td>\n",
       "      <td>0.059990</td>\n",
       "      <td>0.027380</td>\n",
       "      <td>...</td>\n",
       "      <td>34.91</td>\n",
       "      <td>69.57</td>\n",
       "      <td>357.6</td>\n",
       "      <td>0.13840</td>\n",
       "      <td>0.17100</td>\n",
       "      <td>0.20000</td>\n",
       "      <td>0.09127</td>\n",
       "      <td>0.2226</td>\n",
       "      <td>0.08283</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>924964</td>\n",
       "      <td>B</td>\n",
       "      <td>10.160</td>\n",
       "      <td>19.59</td>\n",
       "      <td>64.73</td>\n",
       "      <td>311.7</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.07504</td>\n",
       "      <td>0.005025</td>\n",
       "      <td>0.011160</td>\n",
       "      <td>...</td>\n",
       "      <td>22.88</td>\n",
       "      <td>67.88</td>\n",
       "      <td>347.3</td>\n",
       "      <td>0.12650</td>\n",
       "      <td>0.12000</td>\n",
       "      <td>0.01005</td>\n",
       "      <td>0.02232</td>\n",
       "      <td>0.2262</td>\n",
       "      <td>0.06742</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>925236</td>\n",
       "      <td>B</td>\n",
       "      <td>9.423</td>\n",
       "      <td>27.88</td>\n",
       "      <td>59.26</td>\n",
       "      <td>271.3</td>\n",
       "      <td>0.08123</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>34.24</td>\n",
       "      <td>66.50</td>\n",
       "      <td>330.6</td>\n",
       "      <td>0.10730</td>\n",
       "      <td>0.07158</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.2475</td>\n",
       "      <td>0.06969</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>925277</td>\n",
       "      <td>B</td>\n",
       "      <td>14.590</td>\n",
       "      <td>22.68</td>\n",
       "      <td>96.39</td>\n",
       "      <td>657.1</td>\n",
       "      <td>0.08473</td>\n",
       "      <td>0.13300</td>\n",
       "      <td>0.102900</td>\n",
       "      <td>0.037360</td>\n",
       "      <td>...</td>\n",
       "      <td>27.27</td>\n",
       "      <td>105.90</td>\n",
       "      <td>733.5</td>\n",
       "      <td>0.10260</td>\n",
       "      <td>0.31710</td>\n",
       "      <td>0.36620</td>\n",
       "      <td>0.11050</td>\n",
       "      <td>0.2258</td>\n",
       "      <td>0.08004</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>925291</td>\n",
       "      <td>B</td>\n",
       "      <td>11.510</td>\n",
       "      <td>23.93</td>\n",
       "      <td>74.52</td>\n",
       "      <td>403.5</td>\n",
       "      <td>0.09261</td>\n",
       "      <td>0.10210</td>\n",
       "      <td>0.111200</td>\n",
       "      <td>0.041050</td>\n",
       "      <td>...</td>\n",
       "      <td>37.16</td>\n",
       "      <td>82.28</td>\n",
       "      <td>474.2</td>\n",
       "      <td>0.12980</td>\n",
       "      <td>0.25170</td>\n",
       "      <td>0.36300</td>\n",
       "      <td>0.09653</td>\n",
       "      <td>0.2112</td>\n",
       "      <td>0.08732</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>925292</td>\n",
       "      <td>B</td>\n",
       "      <td>14.050</td>\n",
       "      <td>27.15</td>\n",
       "      <td>91.38</td>\n",
       "      <td>600.4</td>\n",
       "      <td>0.09929</td>\n",
       "      <td>0.11260</td>\n",
       "      <td>0.044620</td>\n",
       "      <td>0.043040</td>\n",
       "      <td>...</td>\n",
       "      <td>33.17</td>\n",
       "      <td>100.20</td>\n",
       "      <td>706.7</td>\n",
       "      <td>0.12410</td>\n",
       "      <td>0.22640</td>\n",
       "      <td>0.13260</td>\n",
       "      <td>0.10480</td>\n",
       "      <td>0.2250</td>\n",
       "      <td>0.08321</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>925311</td>\n",
       "      <td>B</td>\n",
       "      <td>11.200</td>\n",
       "      <td>29.37</td>\n",
       "      <td>70.67</td>\n",
       "      <td>386.0</td>\n",
       "      <td>0.07449</td>\n",
       "      <td>0.03558</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>38.30</td>\n",
       "      <td>75.19</td>\n",
       "      <td>439.6</td>\n",
       "      <td>0.09267</td>\n",
       "      <td>0.05494</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1566</td>\n",
       "      <td>0.05905</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>925622</td>\n",
       "      <td>M</td>\n",
       "      <td>15.220</td>\n",
       "      <td>30.62</td>\n",
       "      <td>103.40</td>\n",
       "      <td>716.9</td>\n",
       "      <td>0.10480</td>\n",
       "      <td>0.20870</td>\n",
       "      <td>0.255000</td>\n",
       "      <td>0.094290</td>\n",
       "      <td>...</td>\n",
       "      <td>42.79</td>\n",
       "      <td>128.70</td>\n",
       "      <td>915.0</td>\n",
       "      <td>0.14170</td>\n",
       "      <td>0.79170</td>\n",
       "      <td>1.17000</td>\n",
       "      <td>0.23560</td>\n",
       "      <td>0.4089</td>\n",
       "      <td>0.14090</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>926125</td>\n",
       "      <td>M</td>\n",
       "      <td>20.920</td>\n",
       "      <td>25.09</td>\n",
       "      <td>143.00</td>\n",
       "      <td>1347.0</td>\n",
       "      <td>0.10990</td>\n",
       "      <td>0.22360</td>\n",
       "      <td>0.317400</td>\n",
       "      <td>0.147400</td>\n",
       "      <td>...</td>\n",
       "      <td>29.41</td>\n",
       "      <td>179.10</td>\n",
       "      <td>1819.0</td>\n",
       "      <td>0.14070</td>\n",
       "      <td>0.41860</td>\n",
       "      <td>0.65990</td>\n",
       "      <td>0.25420</td>\n",
       "      <td>0.2929</td>\n",
       "      <td>0.09873</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>926424</td>\n",
       "      <td>M</td>\n",
       "      <td>21.560</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.243900</td>\n",
       "      <td>0.138900</td>\n",
       "      <td>...</td>\n",
       "      <td>26.40</td>\n",
       "      <td>166.10</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.21130</td>\n",
       "      <td>0.41070</td>\n",
       "      <td>0.22160</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>926682</td>\n",
       "      <td>M</td>\n",
       "      <td>20.130</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.144000</td>\n",
       "      <td>0.097910</td>\n",
       "      <td>...</td>\n",
       "      <td>38.25</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.32150</td>\n",
       "      <td>0.16280</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>926954</td>\n",
       "      <td>M</td>\n",
       "      <td>16.600</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.092510</td>\n",
       "      <td>0.053020</td>\n",
       "      <td>...</td>\n",
       "      <td>34.12</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.34030</td>\n",
       "      <td>0.14180</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>927241</td>\n",
       "      <td>M</td>\n",
       "      <td>20.600</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.351400</td>\n",
       "      <td>0.152000</td>\n",
       "      <td>...</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.93870</td>\n",
       "      <td>0.26500</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>92751</td>\n",
       "      <td>B</td>\n",
       "      <td>7.760</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0      842302         M       17.990         10.38          122.80     1001.0   \n",
       "1      842517         M       20.570         17.77          132.90     1326.0   \n",
       "2    84300903         M       19.690         21.25          130.00     1203.0   \n",
       "3    84348301         M       11.420         20.38           77.58      386.1   \n",
       "4    84358402         M       20.290         14.34          135.10     1297.0   \n",
       "5      843786         M       12.450         15.70           82.57      477.1   \n",
       "6      844359         M       18.250         19.98          119.60     1040.0   \n",
       "7    84458202         M       13.710         20.83           90.20      577.9   \n",
       "8      844981         M       13.000         21.82           87.50      519.8   \n",
       "9    84501001         M       12.460         24.04           83.97      475.9   \n",
       "10     845636         M       16.020         23.24          102.70      797.8   \n",
       "11   84610002         M       15.780         17.89          103.60      781.0   \n",
       "12     846226         M       19.170         24.80          132.40     1123.0   \n",
       "13     846381         M       15.850         23.95          103.70      782.7   \n",
       "14   84667401         M       13.730         22.61           93.60      578.3   \n",
       "15   84799002         M       14.540         27.54           96.73      658.8   \n",
       "16     848406         M       14.680         20.13           94.74      684.5   \n",
       "17   84862001         M       16.130         20.68          108.10      798.8   \n",
       "18     849014         M       19.810         22.15          130.00     1260.0   \n",
       "19    8510426         B       13.540         14.36           87.46      566.3   \n",
       "20    8510653         B       13.080         15.71           85.63      520.0   \n",
       "21    8510824         B        9.504         12.44           60.34      273.9   \n",
       "22    8511133         M       15.340         14.26          102.50      704.4   \n",
       "23     851509         M       21.160         23.04          137.20     1404.0   \n",
       "24     852552         M       16.650         21.38          110.00      904.6   \n",
       "25     852631         M       17.140         16.40          116.00      912.7   \n",
       "26     852763         M       14.580         21.53           97.41      644.8   \n",
       "27     852781         M       18.610         20.25          122.10     1094.0   \n",
       "28     852973         M       15.300         25.27          102.40      732.4   \n",
       "29     853201         M       17.570         15.05          115.00      955.1   \n",
       "..        ...       ...          ...           ...             ...        ...   \n",
       "539    921362         B        7.691         25.44           48.34      170.4   \n",
       "540    921385         B       11.540         14.44           74.65      402.9   \n",
       "541    921386         B       14.470         24.99           95.81      656.4   \n",
       "542    921644         B       14.740         25.42           94.70      668.6   \n",
       "543    922296         B       13.210         28.06           84.88      538.4   \n",
       "544    922297         B       13.870         20.70           89.77      584.8   \n",
       "545    922576         B       13.620         23.23           87.19      573.2   \n",
       "546    922577         B       10.320         16.35           65.31      324.9   \n",
       "547    922840         B       10.260         16.58           65.85      320.8   \n",
       "548    923169         B        9.683         19.34           61.05      285.7   \n",
       "549    923465         B       10.820         24.21           68.89      361.6   \n",
       "550    923748         B       10.860         21.48           68.51      360.5   \n",
       "551    923780         B       11.130         22.44           71.49      378.4   \n",
       "552    924084         B       12.770         29.43           81.35      507.9   \n",
       "553    924342         B        9.333         21.94           59.01      264.0   \n",
       "554    924632         B       12.880         28.92           82.50      514.3   \n",
       "555    924934         B       10.290         27.61           65.67      321.4   \n",
       "556    924964         B       10.160         19.59           64.73      311.7   \n",
       "557    925236         B        9.423         27.88           59.26      271.3   \n",
       "558    925277         B       14.590         22.68           96.39      657.1   \n",
       "559    925291         B       11.510         23.93           74.52      403.5   \n",
       "560    925292         B       14.050         27.15           91.38      600.4   \n",
       "561    925311         B       11.200         29.37           70.67      386.0   \n",
       "562    925622         M       15.220         30.62          103.40      716.9   \n",
       "563    926125         M       20.920         25.09          143.00     1347.0   \n",
       "564    926424         M       21.560         22.39          142.00     1479.0   \n",
       "565    926682         M       20.130         28.25          131.20     1261.0   \n",
       "566    926954         M       16.600         28.08          108.30      858.1   \n",
       "567    927241         M       20.600         29.33          140.10     1265.0   \n",
       "568     92751         B        7.760         24.54           47.92      181.0   \n",
       "\n",
       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0            0.11840           0.27760        0.300100             0.147100   \n",
       "1            0.08474           0.07864        0.086900             0.070170   \n",
       "2            0.10960           0.15990        0.197400             0.127900   \n",
       "3            0.14250           0.28390        0.241400             0.105200   \n",
       "4            0.10030           0.13280        0.198000             0.104300   \n",
       "5            0.12780           0.17000        0.157800             0.080890   \n",
       "6            0.09463           0.10900        0.112700             0.074000   \n",
       "7            0.11890           0.16450        0.093660             0.059850   \n",
       "8            0.12730           0.19320        0.185900             0.093530   \n",
       "9            0.11860           0.23960        0.227300             0.085430   \n",
       "10           0.08206           0.06669        0.032990             0.033230   \n",
       "11           0.09710           0.12920        0.099540             0.066060   \n",
       "12           0.09740           0.24580        0.206500             0.111800   \n",
       "13           0.08401           0.10020        0.099380             0.053640   \n",
       "14           0.11310           0.22930        0.212800             0.080250   \n",
       "15           0.11390           0.15950        0.163900             0.073640   \n",
       "16           0.09867           0.07200        0.073950             0.052590   \n",
       "17           0.11700           0.20220        0.172200             0.102800   \n",
       "18           0.09831           0.10270        0.147900             0.094980   \n",
       "19           0.09779           0.08129        0.066640             0.047810   \n",
       "20           0.10750           0.12700        0.045680             0.031100   \n",
       "21           0.10240           0.06492        0.029560             0.020760   \n",
       "22           0.10730           0.21350        0.207700             0.097560   \n",
       "23           0.09428           0.10220        0.109700             0.086320   \n",
       "24           0.11210           0.14570        0.152500             0.091700   \n",
       "25           0.11860           0.22760        0.222900             0.140100   \n",
       "26           0.10540           0.18680        0.142500             0.087830   \n",
       "27           0.09440           0.10660        0.149000             0.077310   \n",
       "28           0.10820           0.16970        0.168300             0.087510   \n",
       "29           0.09847           0.11570        0.098750             0.079530   \n",
       "..               ...               ...             ...                  ...   \n",
       "539          0.08668           0.11990        0.092520             0.013640   \n",
       "540          0.09984           0.11200        0.067370             0.025940   \n",
       "541          0.08837           0.12300        0.100900             0.038900   \n",
       "542          0.08275           0.07214        0.041050             0.030270   \n",
       "543          0.08671           0.06877        0.029870             0.032750   \n",
       "544          0.09578           0.10180        0.036880             0.023690   \n",
       "545          0.09246           0.06747        0.029740             0.024430   \n",
       "546          0.09434           0.04994        0.010120             0.005495   \n",
       "547          0.08877           0.08066        0.043580             0.024380   \n",
       "548          0.08491           0.05030        0.023370             0.009615   \n",
       "549          0.08192           0.06602        0.015480             0.008160   \n",
       "550          0.07431           0.04227        0.000000             0.000000   \n",
       "551          0.09566           0.08194        0.048240             0.022570   \n",
       "552          0.08276           0.04234        0.019970             0.014990   \n",
       "553          0.09240           0.05605        0.039960             0.012820   \n",
       "554          0.08123           0.05824        0.061950             0.023430   \n",
       "555          0.09030           0.07658        0.059990             0.027380   \n",
       "556          0.10030           0.07504        0.005025             0.011160   \n",
       "557          0.08123           0.04971        0.000000             0.000000   \n",
       "558          0.08473           0.13300        0.102900             0.037360   \n",
       "559          0.09261           0.10210        0.111200             0.041050   \n",
       "560          0.09929           0.11260        0.044620             0.043040   \n",
       "561          0.07449           0.03558        0.000000             0.000000   \n",
       "562          0.10480           0.20870        0.255000             0.094290   \n",
       "563          0.10990           0.22360        0.317400             0.147400   \n",
       "564          0.11100           0.11590        0.243900             0.138900   \n",
       "565          0.09780           0.10340        0.144000             0.097910   \n",
       "566          0.08455           0.10230        0.092510             0.053020   \n",
       "567          0.11780           0.27700        0.351400             0.152000   \n",
       "568          0.05263           0.04362        0.000000             0.000000   \n",
       "\n",
       "        ...       texture_worst  perimeter_worst  area_worst  \\\n",
       "0       ...               17.33           184.60      2019.0   \n",
       "1       ...               23.41           158.80      1956.0   \n",
       "2       ...               25.53           152.50      1709.0   \n",
       "3       ...               26.50            98.87       567.7   \n",
       "4       ...               16.67           152.20      1575.0   \n",
       "5       ...               23.75           103.40       741.6   \n",
       "6       ...               27.66           153.20      1606.0   \n",
       "7       ...               28.14           110.60       897.0   \n",
       "8       ...               30.73           106.20       739.3   \n",
       "9       ...               40.68            97.65       711.4   \n",
       "10      ...               33.88           123.80      1150.0   \n",
       "11      ...               27.28           136.50      1299.0   \n",
       "12      ...               29.94           151.70      1332.0   \n",
       "13      ...               27.66           112.00       876.5   \n",
       "14      ...               32.01           108.80       697.7   \n",
       "15      ...               37.13           124.10       943.2   \n",
       "16      ...               30.88           123.40      1138.0   \n",
       "17      ...               31.48           136.80      1315.0   \n",
       "18      ...               30.88           186.80      2398.0   \n",
       "19      ...               19.26            99.70       711.2   \n",
       "20      ...               20.49            96.09       630.5   \n",
       "21      ...               15.66            65.13       314.9   \n",
       "22      ...               19.08           125.10       980.9   \n",
       "23      ...               35.59           188.00      2615.0   \n",
       "24      ...               31.56           177.00      2215.0   \n",
       "25      ...               21.40           152.40      1461.0   \n",
       "26      ...               33.21           122.40       896.9   \n",
       "27      ...               27.26           139.90      1403.0   \n",
       "28      ...               36.71           149.30      1269.0   \n",
       "29      ...               19.52           134.90      1227.0   \n",
       "..      ...                 ...              ...         ...   \n",
       "539     ...               31.89            54.49       223.6   \n",
       "540     ...               19.68            78.78       457.8   \n",
       "541     ...               31.73           113.50       808.9   \n",
       "542     ...               32.29           107.40       826.4   \n",
       "543     ...               37.17            92.48       629.6   \n",
       "544     ...               24.75            99.17       688.6   \n",
       "545     ...               29.09            97.58       729.8   \n",
       "546     ...               21.77            71.12       384.9   \n",
       "547     ...               22.04            71.08       357.4   \n",
       "548     ...               25.59            69.10       364.2   \n",
       "549     ...               31.45            83.90       505.6   \n",
       "550     ...               24.77            74.08       412.3   \n",
       "551     ...               28.26            77.80       436.6   \n",
       "552     ...               36.00            88.10       594.7   \n",
       "553     ...               25.05            62.86       295.8   \n",
       "554     ...               35.74            88.84       595.7   \n",
       "555     ...               34.91            69.57       357.6   \n",
       "556     ...               22.88            67.88       347.3   \n",
       "557     ...               34.24            66.50       330.6   \n",
       "558     ...               27.27           105.90       733.5   \n",
       "559     ...               37.16            82.28       474.2   \n",
       "560     ...               33.17           100.20       706.7   \n",
       "561     ...               38.30            75.19       439.6   \n",
       "562     ...               42.79           128.70       915.0   \n",
       "563     ...               29.41           179.10      1819.0   \n",
       "564     ...               26.40           166.10      2027.0   \n",
       "565     ...               38.25           155.00      1731.0   \n",
       "566     ...               34.12           126.70      1124.0   \n",
       "567     ...               39.42           184.60      1821.0   \n",
       "568     ...               30.37            59.16       268.6   \n",
       "\n",
       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0             0.16220            0.66560          0.71190   \n",
       "1             0.12380            0.18660          0.24160   \n",
       "2             0.14440            0.42450          0.45040   \n",
       "3             0.20980            0.86630          0.68690   \n",
       "4             0.13740            0.20500          0.40000   \n",
       "5             0.17910            0.52490          0.53550   \n",
       "6             0.14420            0.25760          0.37840   \n",
       "7             0.16540            0.36820          0.26780   \n",
       "8             0.17030            0.54010          0.53900   \n",
       "9             0.18530            1.05800          1.10500   \n",
       "10            0.11810            0.15510          0.14590   \n",
       "11            0.13960            0.56090          0.39650   \n",
       "12            0.10370            0.39030          0.36390   \n",
       "13            0.11310            0.19240          0.23220   \n",
       "14            0.16510            0.77250          0.69430   \n",
       "15            0.16780            0.65770          0.70260   \n",
       "16            0.14640            0.18710          0.29140   \n",
       "17            0.17890            0.42330          0.47840   \n",
       "18            0.15120            0.31500          0.53720   \n",
       "19            0.14400            0.17730          0.23900   \n",
       "20            0.13120            0.27760          0.18900   \n",
       "21            0.13240            0.11480          0.08867   \n",
       "22            0.13900            0.59540          0.63050   \n",
       "23            0.14010            0.26000          0.31550   \n",
       "24            0.18050            0.35780          0.46950   \n",
       "25            0.15450            0.39490          0.38530   \n",
       "26            0.15250            0.66430          0.55390   \n",
       "27            0.13380            0.21170          0.34460   \n",
       "28            0.16410            0.61100          0.63350   \n",
       "29            0.12550            0.28120          0.24890   \n",
       "..                ...                ...              ...   \n",
       "539           0.15960            0.30640          0.33930   \n",
       "540           0.13450            0.21180          0.17970   \n",
       "541           0.13400            0.42020          0.40400   \n",
       "542           0.10600            0.13760          0.16110   \n",
       "543           0.10720            0.13810          0.10620   \n",
       "544           0.12640            0.20370          0.13770   \n",
       "545           0.12160            0.15170          0.10490   \n",
       "546           0.12850            0.08842          0.04384   \n",
       "547           0.14610            0.22460          0.17830   \n",
       "548           0.11990            0.09546          0.09350   \n",
       "549           0.12040            0.16330          0.06194   \n",
       "550           0.10010            0.07348          0.00000   \n",
       "551           0.10870            0.17820          0.15640   \n",
       "552           0.12340            0.10640          0.08653   \n",
       "553           0.11030            0.08298          0.07993   \n",
       "554           0.12270            0.16200          0.24390   \n",
       "555           0.13840            0.17100          0.20000   \n",
       "556           0.12650            0.12000          0.01005   \n",
       "557           0.10730            0.07158          0.00000   \n",
       "558           0.10260            0.31710          0.36620   \n",
       "559           0.12980            0.25170          0.36300   \n",
       "560           0.12410            0.22640          0.13260   \n",
       "561           0.09267            0.05494          0.00000   \n",
       "562           0.14170            0.79170          1.17000   \n",
       "563           0.14070            0.41860          0.65990   \n",
       "564           0.14100            0.21130          0.41070   \n",
       "565           0.11660            0.19220          0.32150   \n",
       "566           0.11390            0.30940          0.34030   \n",
       "567           0.16500            0.86810          0.93870   \n",
       "568           0.08996            0.06444          0.00000   \n",
       "\n",
       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \\\n",
       "0                 0.26540          0.4601                  0.11890   \n",
       "1                 0.18600          0.2750                  0.08902   \n",
       "2                 0.24300          0.3613                  0.08758   \n",
       "3                 0.25750          0.6638                  0.17300   \n",
       "4                 0.16250          0.2364                  0.07678   \n",
       "5                 0.17410          0.3985                  0.12440   \n",
       "6                 0.19320          0.3063                  0.08368   \n",
       "7                 0.15560          0.3196                  0.11510   \n",
       "8                 0.20600          0.4378                  0.10720   \n",
       "9                 0.22100          0.4366                  0.20750   \n",
       "10                0.09975          0.2948                  0.08452   \n",
       "11                0.18100          0.3792                  0.10480   \n",
       "12                0.17670          0.3176                  0.10230   \n",
       "13                0.11190          0.2809                  0.06287   \n",
       "14                0.22080          0.3596                  0.14310   \n",
       "15                0.17120          0.4218                  0.13410   \n",
       "16                0.16090          0.3029                  0.08216   \n",
       "17                0.20730          0.3706                  0.11420   \n",
       "18                0.23880          0.2768                  0.07615   \n",
       "19                0.12880          0.2977                  0.07259   \n",
       "20                0.07283          0.3184                  0.08183   \n",
       "21                0.06227          0.2450                  0.07773   \n",
       "22                0.23930          0.4667                  0.09946   \n",
       "23                0.20090          0.2822                  0.07526   \n",
       "24                0.20950          0.3613                  0.09564   \n",
       "25                0.25500          0.4066                  0.10590   \n",
       "26                0.27010          0.4264                  0.12750   \n",
       "27                0.14900          0.2341                  0.07421   \n",
       "28                0.20240          0.4027                  0.09876   \n",
       "29                0.14560          0.2756                  0.07919   \n",
       "..                    ...             ...                      ...   \n",
       "539               0.05000          0.2790                  0.10660   \n",
       "540               0.06918          0.2329                  0.08134   \n",
       "541               0.12050          0.3187                  0.10230   \n",
       "542               0.10950          0.2722                  0.06956   \n",
       "543               0.07958          0.2473                  0.06443   \n",
       "544               0.06845          0.2249                  0.08492   \n",
       "545               0.07174          0.2642                  0.06953   \n",
       "546               0.02381          0.2681                  0.07399   \n",
       "547               0.08333          0.2691                  0.09479   \n",
       "548               0.03846          0.2552                  0.07920   \n",
       "549               0.03264          0.3059                  0.07626   \n",
       "550               0.00000          0.2458                  0.06592   \n",
       "551               0.06413          0.3169                  0.08032   \n",
       "552               0.06498          0.2407                  0.06484   \n",
       "553               0.02564          0.2435                  0.07393   \n",
       "554               0.06493          0.2372                  0.07242   \n",
       "555               0.09127          0.2226                  0.08283   \n",
       "556               0.02232          0.2262                  0.06742   \n",
       "557               0.00000          0.2475                  0.06969   \n",
       "558               0.11050          0.2258                  0.08004   \n",
       "559               0.09653          0.2112                  0.08732   \n",
       "560               0.10480          0.2250                  0.08321   \n",
       "561               0.00000          0.1566                  0.05905   \n",
       "562               0.23560          0.4089                  0.14090   \n",
       "563               0.25420          0.2929                  0.09873   \n",
       "564               0.22160          0.2060                  0.07115   \n",
       "565               0.16280          0.2572                  0.06637   \n",
       "566               0.14180          0.2218                  0.07820   \n",
       "567               0.26500          0.4087                  0.12400   \n",
       "568               0.00000          0.2871                  0.07039   \n",
       "\n",
       "     Unnamed: 32  \n",
       "0            NaN  \n",
       "1            NaN  \n",
       "2            NaN  \n",
       "3            NaN  \n",
       "4            NaN  \n",
       "5            NaN  \n",
       "6            NaN  \n",
       "7            NaN  \n",
       "8            NaN  \n",
       "9            NaN  \n",
       "10           NaN  \n",
       "11           NaN  \n",
       "12           NaN  \n",
       "13           NaN  \n",
       "14           NaN  \n",
       "15           NaN  \n",
       "16           NaN  \n",
       "17           NaN  \n",
       "18           NaN  \n",
       "19           NaN  \n",
       "20           NaN  \n",
       "21           NaN  \n",
       "22           NaN  \n",
       "23           NaN  \n",
       "24           NaN  \n",
       "25           NaN  \n",
       "26           NaN  \n",
       "27           NaN  \n",
       "28           NaN  \n",
       "29           NaN  \n",
       "..           ...  \n",
       "539          NaN  \n",
       "540          NaN  \n",
       "541          NaN  \n",
       "542          NaN  \n",
       "543          NaN  \n",
       "544          NaN  \n",
       "545          NaN  \n",
       "546          NaN  \n",
       "547          NaN  \n",
       "548          NaN  \n",
       "549          NaN  \n",
       "550          NaN  \n",
       "551          NaN  \n",
       "552          NaN  \n",
       "553          NaN  \n",
       "554          NaN  \n",
       "555          NaN  \n",
       "556          NaN  \n",
       "557          NaN  \n",
       "558          NaN  \n",
       "559          NaN  \n",
       "560          NaN  \n",
       "561          NaN  \n",
       "562          NaN  \n",
       "563          NaN  \n",
       "564          NaN  \n",
       "565          NaN  \n",
       "566          NaN  \n",
       "567          NaN  \n",
       "568          NaN  \n",
       "\n",
       "[569 rows x 33 columns]"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading the csv file and putting it into 'df' object.\n",
    "df = pd.read_csv(r\"C:\\Users\\ANKIT VAITY\\Desktop\\Machine Learning\\Mini Projects\\3. Breast Cancer\\breast-cancer-wisconsin-data\\data.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
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       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
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       "      <th>Unnamed: 32</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    id  diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0   41          1          370             1             455        452   \n",
       "1   42          1          426           186             486        513   \n",
       "2  488          1          406           324             478        487   \n",
       "3  489          1           98           297             164         95   \n",
       "4  490          1          418            60             493        506   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0              444               531             521                  526   \n",
       "1              120               215             321                  396   \n",
       "2              403               459             478                  516   \n",
       "3              471               533             507                  493   \n",
       "4              332               414             480                  492   \n",
       "\n",
       "      ...       texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0     ...                  41              497         516               358   \n",
       "1     ...                 199              465         511               158   \n",
       "2     ...                 261              455         491               287   \n",
       "3     ...                 298              281         194               408   \n",
       "4     ...                  28              453         470               239   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0                516              520                   483             485   \n",
       "1                210              278                   401             222   \n",
       "2                464              438                   468             445   \n",
       "3                524              511                   479             499   \n",
       "4                245              415                   363              81   \n",
       "\n",
       "   fractal_dimension_worst  Unnamed: 32  \n",
       "0                      506            0  \n",
       "1                      375          374  \n",
       "2                      361          375  \n",
       "3                      533          376  \n",
       "4                      216          377  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# apply Label encoder to df\n",
    "from sklearn import preprocessing\n",
    "\n",
    "le = preprocessing.LabelEncoder()\n",
    "df = df.apply(le.fit_transform)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 33 columns):\n",
      "id                         569 non-null int64\n",
      "diagnosis                  569 non-null int64\n",
      "radius_mean                569 non-null int64\n",
      "texture_mean               569 non-null int64\n",
      "perimeter_mean             569 non-null int64\n",
      "area_mean                  569 non-null int64\n",
      "smoothness_mean            569 non-null int64\n",
      "compactness_mean           569 non-null int64\n",
      "concavity_mean             569 non-null int64\n",
      "concave points_mean        569 non-null int64\n",
      "symmetry_mean              569 non-null int64\n",
      "fractal_dimension_mean     569 non-null int64\n",
      "radius_se                  569 non-null int64\n",
      "texture_se                 569 non-null int64\n",
      "perimeter_se               569 non-null int64\n",
      "area_se                    569 non-null int64\n",
      "smoothness_se              569 non-null int64\n",
      "compactness_se             569 non-null int64\n",
      "concavity_se               569 non-null int64\n",
      "concave points_se          569 non-null int64\n",
      "symmetry_se                569 non-null int64\n",
      "fractal_dimension_se       569 non-null int64\n",
      "radius_worst               569 non-null int64\n",
      "texture_worst              569 non-null int64\n",
      "perimeter_worst            569 non-null int64\n",
      "area_worst                 569 non-null int64\n",
      "smoothness_worst           569 non-null int64\n",
      "compactness_worst          569 non-null int64\n",
      "concavity_worst            569 non-null int64\n",
      "concave points_worst       569 non-null int64\n",
      "symmetry_worst             569 non-null int64\n",
      "fractal_dimension_worst    569 non-null int64\n",
      "Unnamed: 32                569 non-null int64\n",
      "dtypes: int64(33)\n",
      "memory usage: 146.8 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparing y\n",
    "ydata = {'id':df['id'].tolist(), 'diagnosis':df['diagnosis'].tolist()} \n",
    "y = pd.DataFrame(ydata)\n",
    "\n",
    "# Preparing x\n",
    "list = ['Unnamed: 32','id','diagnosis']\n",
    "x = df.drop(list,axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(455, 30)\n",
      "(455, 2)\n",
      "(114, 30)\n",
      "(114, 2)\n"
     ]
    }
   ],
   "source": [
    "# Importing train-test-split \n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# split data into train and test/validation sets\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.20, random_state=101)\n",
    "print(x_train.shape)\n",
    "print(y_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_mod = y_train.drop('id',axis = 1)\n",
    "y_test_mod = y_test.drop('id',axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GridSearchCV to find optimal min_samples_split\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Create the parameter grid based on the results of random search \n",
    "param_grid = {\n",
    "    'max_depth': [4,8,10],\n",
    "    'min_samples_leaf': range(30, 100, 20),\n",
    "    'min_samples_split': range(30, 100, 20),\n",
    "    'n_estimators': [100,200, 300], \n",
    "    'max_features': [5, 10]\n",
    "}\n",
    "# Create a based model\n",
    "rf = RandomForestClassifier()\n",
    "# Instantiate the grid search model\n",
    "grid_search = GridSearchCV(estimator = rf, param_grid = param_grid, \n",
    "                          cv = 3, n_jobs = -1,verbose = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 288 candidates, totalling 864 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   11.7s\n",
      "[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:   37.0s\n",
      "[Parallel(n_jobs=-1)]: Done 442 tasks      | elapsed:  1.3min\n",
      "[Parallel(n_jobs=-1)]: Done 792 tasks      | elapsed:  2.3min\n",
      "[Parallel(n_jobs=-1)]: Done 864 out of 864 | elapsed:  2.5min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'max_depth': [4, 8, 10], 'min_samples_leaf': range(30, 100, 20), 'min_samples_split': range(30, 100, 20), 'n_estimators': [100, 200, 300], 'max_features': [5, 10]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=1)"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the grid search to the data\n",
    "grid_search.fit(x_train, y_train_mod)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We can get accuracy of 0.9384615384615385 using {'max_depth': 10, 'max_features': 5, 'min_samples_leaf': 30, 'min_samples_split': 90, 'n_estimators': 300}\n"
     ]
    }
   ],
   "source": [
    "# printing the optimal accuracy score and hyperparameters\n",
    "print('We can get accuracy of',grid_search.best_score_,'using',grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing random forest classifier from sklearn library\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Running the random forest with default parameters.\n",
    "rfc = RandomForestClassifier(bootstrap=True,\n",
    "                             max_depth=10,\n",
    "                             min_samples_leaf=30, \n",
    "                             min_samples_split=90,\n",
    "                             max_features=5,\n",
    "                             n_estimators=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=10, max_features=5, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=30, min_samples_split=90,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=300, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# fit\n",
    "rfc.fit(x_train,y_train_mod)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making predictions\n",
    "y_pred = rfc.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing classification report and confusion matrix from sklearn metrics\n",
    "from sklearn.metrics import classification_report,confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.96      0.99      0.97        72\n",
      "          1       0.97      0.93      0.95        42\n",
      "\n",
      "avg / total       0.97      0.96      0.96       114\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Let's check the report of our default model\n",
    "print(classification_report(y_test_mod,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9649122807017544\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test_mod,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0,\n",
       "       0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0,\n",
       "       0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "real_y = pd.DataFrame(y_test)\n",
    "pred_y = pd.DataFrame(y_pred)\n",
    "pred_y = pred_y.rename(columns={0: \"predicted\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "# intialise data of lists. \n",
    "data = {'Index':real_y.index,'Actual Value':real_y['diagnosis'].tolist(), 'Predicted Value':pred_y['predicted'].tolist()} \n",
    "  \n",
    "# Create DataFrame \n",
    "data = pd.DataFrame(data) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data preparation\n",
    "\n",
    "# list of all the \"yes-no\" binary categorical variables\n",
    "# we'll map yes to 1 and no to 0\n",
    "vars_list =  [\"Actual Value\",\"Predicted Value\"]\n",
    "\n",
    "# defining the map function\n",
    "def conversion_map(x):\n",
    "    return x.map({1: \"M\",0: \"B\"})\n",
    "\n",
    "# applying the function to the housing variables list\n",
    "data[vars_list] = data[vars_list].apply(conversion_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>Actual Value</th>\n",
       "      <th>Predicted Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>107</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>437</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>195</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>141</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>319</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>295</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>114</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>202</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>268</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>342</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>257</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>281</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>106</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>58</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>156</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>416</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>124</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>269</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>385</td>\n",
       "      <td>M</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>109</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>557</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>48</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>529</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>83</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>471</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>138</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>296</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>118</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>379</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>267</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>173</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>31</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>293</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>460</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>489</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>147</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>100</td>\n",
       "      <td>M</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>64</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>204</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>104</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>412</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>278</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>128</td>\n",
       "      <td>B</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>62</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>12</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>237</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>546</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>502</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>314</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>409</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>488</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>485</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>438</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>434</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>19</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>313</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>139</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>495</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>317</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>114 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Index Actual Value Predicted Value\n",
       "0      107            B               B\n",
       "1      437            B               B\n",
       "2      195            B               B\n",
       "3      141            M               M\n",
       "4      319            B               B\n",
       "5      295            B               B\n",
       "6      114            B               B\n",
       "7      202            M               M\n",
       "8      268            B               B\n",
       "9      342            B               B\n",
       "10     257            M               M\n",
       "11     281            B               B\n",
       "12     106            B               B\n",
       "13      58            B               B\n",
       "14     156            M               M\n",
       "15     416            B               B\n",
       "16     124            B               B\n",
       "17     269            B               B\n",
       "18      24            M               M\n",
       "19     385            M               B\n",
       "20     109            B               B\n",
       "21     557            B               B\n",
       "22      48            B               B\n",
       "23     529            B               B\n",
       "24      83            M               M\n",
       "25     471            B               B\n",
       "26     138            M               M\n",
       "27     296            B               B\n",
       "28     118            M               M\n",
       "29     379            M               M\n",
       "..     ...          ...             ...\n",
       "84     267            B               B\n",
       "85     173            B               B\n",
       "86      31            M               M\n",
       "87     293            B               B\n",
       "88     460            M               M\n",
       "89     489            M               M\n",
       "90     147            B               B\n",
       "91     100            M               B\n",
       "92      64            M               M\n",
       "93     204            B               B\n",
       "94     104            B               B\n",
       "95     412            B               B\n",
       "96     278            B               B\n",
       "97     128            B               M\n",
       "98      62            M               M\n",
       "99      12            M               M\n",
       "100    237            M               M\n",
       "101    546            B               B\n",
       "102    502            B               B\n",
       "103    314            B               B\n",
       "104    409            B               B\n",
       "105    488            B               B\n",
       "106    485            B               B\n",
       "107    438            B               B\n",
       "108    434            B               B\n",
       "109     19            B               B\n",
       "110    313            B               B\n",
       "111    139            B               B\n",
       "112    495            B               B\n",
       "113    317            M               M\n",
       "\n",
       "[114 rows x 3 columns]"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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