ILPD.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the ILPD data\n",
    "liverdata = pd.read_csv('liver_patient_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Gender', 'Total_Bilirubin', 'Direct_Bilirubin',\n",
       "       'Alkaline_Phosphotase', 'Alamine_Aminotransferase',\n",
       "       'Aspartate_Aminotransferase', 'Total_Protiens', 'Albumin',\n",
       "       'Albumin_and_Globulin_Ratio', 'Dataset'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datatype=liverdata.dtypes.index\n",
    "datatype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>187</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>10.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>699</td>\n",
       "      <td>64</td>\n",
       "      <td>100</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>490</td>\n",
       "      <td>60</td>\n",
       "      <td>68</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>182</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>195</td>\n",
       "      <td>27</td>\n",
       "      <td>59</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>46</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>208</td>\n",
       "      <td>19</td>\n",
       "      <td>14</td>\n",
       "      <td>7.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>26</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>154</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>29</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>202</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>202</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>7.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>55</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>290</td>\n",
       "      <td>53</td>\n",
       "      <td>58</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>57</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>210</td>\n",
       "      <td>51</td>\n",
       "      <td>59</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>72</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>260</td>\n",
       "      <td>31</td>\n",
       "      <td>56</td>\n",
       "      <td>7.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>64</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>310</td>\n",
       "      <td>61</td>\n",
       "      <td>58</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.90</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>74</td>\n",
       "      <td>Female</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>214</td>\n",
       "      <td>22</td>\n",
       "      <td>30</td>\n",
       "      <td>8.1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>61</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>145</td>\n",
       "      <td>53</td>\n",
       "      <td>41</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.87</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>25</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>183</td>\n",
       "      <td>91</td>\n",
       "      <td>53</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.70</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>38</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>342</td>\n",
       "      <td>168</td>\n",
       "      <td>441</td>\n",
       "      <td>7.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>165</td>\n",
       "      <td>15</td>\n",
       "      <td>23</td>\n",
       "      <td>7.3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.92</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>40</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>293</td>\n",
       "      <td>232</td>\n",
       "      <td>245</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>40</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>293</td>\n",
       "      <td>232</td>\n",
       "      <td>245</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>610</td>\n",
       "      <td>17</td>\n",
       "      <td>28</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.55</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>482</td>\n",
       "      <td>22</td>\n",
       "      <td>34</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>542</td>\n",
       "      <td>116</td>\n",
       "      <td>66</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>40</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>231</td>\n",
       "      <td>16</td>\n",
       "      <td>55</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>63</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>194</td>\n",
       "      <td>52</td>\n",
       "      <td>45</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.85</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>34</td>\n",
       "      <td>Male</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289</td>\n",
       "      <td>875</td>\n",
       "      <td>731</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>34</td>\n",
       "      <td>Male</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289</td>\n",
       "      <td>875</td>\n",
       "      <td>731</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>34</td>\n",
       "      <td>Male</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>240</td>\n",
       "      <td>1680</td>\n",
       "      <td>850</td>\n",
       "      <td>7.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>128</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.95</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>84</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>188</td>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.10</td>\n",
       "      <td>2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>46</td>\n",
       "      <td>Male</td>\n",
       "      <td>10.2</td>\n",
       "      <td>4.2</td>\n",
       "      <td>232</td>\n",
       "      <td>58</td>\n",
       "      <td>140</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>73</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>220</td>\n",
       "      <td>20</td>\n",
       "      <td>43</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>55</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>290</td>\n",
       "      <td>139</td>\n",
       "      <td>87</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>180</td>\n",
       "      <td>25</td>\n",
       "      <td>27</td>\n",
       "      <td>6.1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>189</td>\n",
       "      <td>80</td>\n",
       "      <td>125</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>275</td>\n",
       "      <td>382</td>\n",
       "      <td>330</td>\n",
       "      <td>7.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>26</td>\n",
       "      <td>Male</td>\n",
       "      <td>42.8</td>\n",
       "      <td>19.7</td>\n",
       "      <td>390</td>\n",
       "      <td>75</td>\n",
       "      <td>138</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>66</td>\n",
       "      <td>Male</td>\n",
       "      <td>15.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>356</td>\n",
       "      <td>321</td>\n",
       "      <td>562</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>66</td>\n",
       "      <td>Male</td>\n",
       "      <td>16.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>315</td>\n",
       "      <td>233</td>\n",
       "      <td>384</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>66</td>\n",
       "      <td>Male</td>\n",
       "      <td>17.3</td>\n",
       "      <td>8.5</td>\n",
       "      <td>388</td>\n",
       "      <td>173</td>\n",
       "      <td>367</td>\n",
       "      <td>7.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>64</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>298</td>\n",
       "      <td>31</td>\n",
       "      <td>83</td>\n",
       "      <td>7.2</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>38</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>165</td>\n",
       "      <td>22</td>\n",
       "      <td>34</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.90</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>43</td>\n",
       "      <td>Male</td>\n",
       "      <td>22.5</td>\n",
       "      <td>11.8</td>\n",
       "      <td>143</td>\n",
       "      <td>22</td>\n",
       "      <td>143</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>50</td>\n",
       "      <td>Female</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>191</td>\n",
       "      <td>22</td>\n",
       "      <td>31</td>\n",
       "      <td>7.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>52</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>251</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>20</td>\n",
       "      <td>Female</td>\n",
       "      <td>16.7</td>\n",
       "      <td>8.4</td>\n",
       "      <td>200</td>\n",
       "      <td>91</td>\n",
       "      <td>101</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>16</td>\n",
       "      <td>Male</td>\n",
       "      <td>7.7</td>\n",
       "      <td>4.1</td>\n",
       "      <td>268</td>\n",
       "      <td>213</td>\n",
       "      <td>168</td>\n",
       "      <td>7.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>16</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>236</td>\n",
       "      <td>131</td>\n",
       "      <td>90</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>90</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>215</td>\n",
       "      <td>46</td>\n",
       "      <td>134</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>32</td>\n",
       "      <td>Male</td>\n",
       "      <td>15.6</td>\n",
       "      <td>9.5</td>\n",
       "      <td>134</td>\n",
       "      <td>54</td>\n",
       "      <td>125</td>\n",
       "      <td>5.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>32</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>612</td>\n",
       "      <td>50</td>\n",
       "      <td>88</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>32</td>\n",
       "      <td>Male</td>\n",
       "      <td>12.1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>515</td>\n",
       "      <td>48</td>\n",
       "      <td>92</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>32</td>\n",
       "      <td>Male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>13.7</td>\n",
       "      <td>560</td>\n",
       "      <td>41</td>\n",
       "      <td>88</td>\n",
       "      <td>7.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>32</td>\n",
       "      <td>Male</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>289</td>\n",
       "      <td>58</td>\n",
       "      <td>80</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>32</td>\n",
       "      <td>Male</td>\n",
       "      <td>12.7</td>\n",
       "      <td>8.4</td>\n",
       "      <td>190</td>\n",
       "      <td>28</td>\n",
       "      <td>47</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>60</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>34</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.37</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>40</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>98</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>52</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>245</td>\n",
       "      <td>48</td>\n",
       "      <td>49</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>184</td>\n",
       "      <td>29</td>\n",
       "      <td>32</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>38</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>216</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>583 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "0     65  Female              0.7               0.1                   187   \n",
       "1     62    Male             10.9               5.5                   699   \n",
       "2     62    Male              7.3               4.1                   490   \n",
       "3     58    Male              1.0               0.4                   182   \n",
       "4     72    Male              3.9               2.0                   195   \n",
       "5     46    Male              1.8               0.7                   208   \n",
       "6     26  Female              0.9               0.2                   154   \n",
       "7     29  Female              0.9               0.3                   202   \n",
       "8     17    Male              0.9               0.3                   202   \n",
       "9     55    Male              0.7               0.2                   290   \n",
       "10    57    Male              0.6               0.1                   210   \n",
       "11    72    Male              2.7               1.3                   260   \n",
       "12    64    Male              0.9               0.3                   310   \n",
       "13    74  Female              1.1               0.4                   214   \n",
       "14    61    Male              0.7               0.2                   145   \n",
       "15    25    Male              0.6               0.1                   183   \n",
       "16    38    Male              1.8               0.8                   342   \n",
       "17    33    Male              1.6               0.5                   165   \n",
       "18    40  Female              0.9               0.3                   293   \n",
       "19    40  Female              0.9               0.3                   293   \n",
       "20    51    Male              2.2               1.0                   610   \n",
       "21    51    Male              2.9               1.3                   482   \n",
       "22    62    Male              6.8               3.0                   542   \n",
       "23    40    Male              1.9               1.0                   231   \n",
       "24    63    Male              0.9               0.2                   194   \n",
       "25    34    Male              4.1               2.0                   289   \n",
       "26    34    Male              4.1               2.0                   289   \n",
       "27    34    Male              6.2               3.0                   240   \n",
       "28    20    Male              1.1               0.5                   128   \n",
       "29    84  Female              0.7               0.2                   188   \n",
       "..   ...     ...              ...               ...                   ...   \n",
       "553   46    Male             10.2               4.2                   232   \n",
       "554   73    Male              1.8               0.9                   220   \n",
       "555   55    Male              0.8               0.2                   290   \n",
       "556   51    Male              0.7               0.1                   180   \n",
       "557   51    Male              2.9               1.2                   189   \n",
       "558   51    Male              4.0               2.5                   275   \n",
       "559   26    Male             42.8              19.7                   390   \n",
       "560   66    Male             15.2               7.7                   356   \n",
       "561   66    Male             16.6               7.6                   315   \n",
       "562   66    Male             17.3               8.5                   388   \n",
       "563   64    Male              1.4               0.5                   298   \n",
       "564   38  Female              0.6               0.1                   165   \n",
       "565   43    Male             22.5              11.8                   143   \n",
       "566   50  Female              1.0               0.3                   191   \n",
       "567   52    Male              2.7               1.4                   251   \n",
       "568   20  Female             16.7               8.4                   200   \n",
       "569   16    Male              7.7               4.1                   268   \n",
       "570   16    Male              2.6               1.2                   236   \n",
       "571   90    Male              1.1               0.3                   215   \n",
       "572   32    Male             15.6               9.5                   134   \n",
       "573   32    Male              3.7               1.6                   612   \n",
       "574   32    Male             12.1               6.0                   515   \n",
       "575   32    Male             25.0              13.7                   560   \n",
       "576   32    Male             15.0               8.2                   289   \n",
       "577   32    Male             12.7               8.4                   190   \n",
       "578   60    Male              0.5               0.1                   500   \n",
       "579   40    Male              0.6               0.1                    98   \n",
       "580   52    Male              0.8               0.2                   245   \n",
       "581   31    Male              1.3               0.5                   184   \n",
       "582   38    Male              1.0               0.3                   216   \n",
       "\n",
       "     Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "0                          16                          18             6.8   \n",
       "1                          64                         100             7.5   \n",
       "2                          60                          68             7.0   \n",
       "3                          14                          20             6.8   \n",
       "4                          27                          59             7.3   \n",
       "5                          19                          14             7.6   \n",
       "6                          16                          12             7.0   \n",
       "7                          14                          11             6.7   \n",
       "8                          22                          19             7.4   \n",
       "9                          53                          58             6.8   \n",
       "10                         51                          59             5.9   \n",
       "11                         31                          56             7.4   \n",
       "12                         61                          58             7.0   \n",
       "13                         22                          30             8.1   \n",
       "14                         53                          41             5.8   \n",
       "15                         91                          53             5.5   \n",
       "16                        168                         441             7.6   \n",
       "17                         15                          23             7.3   \n",
       "18                        232                         245             6.8   \n",
       "19                        232                         245             6.8   \n",
       "20                         17                          28             7.3   \n",
       "21                         22                          34             7.0   \n",
       "22                        116                          66             6.4   \n",
       "23                         16                          55             4.3   \n",
       "24                         52                          45             6.0   \n",
       "25                        875                         731             5.0   \n",
       "26                        875                         731             5.0   \n",
       "27                       1680                         850             7.2   \n",
       "28                         20                          30             3.9   \n",
       "29                         13                          21             6.0   \n",
       "..                        ...                         ...             ...   \n",
       "553                        58                         140             7.0   \n",
       "554                        20                          43             6.5   \n",
       "555                       139                          87             7.0   \n",
       "556                        25                          27             6.1   \n",
       "557                        80                         125             6.2   \n",
       "558                       382                         330             7.5   \n",
       "559                        75                         138             7.5   \n",
       "560                       321                         562             6.5   \n",
       "561                       233                         384             6.9   \n",
       "562                       173                         367             7.8   \n",
       "563                        31                          83             7.2   \n",
       "564                        22                          34             5.9   \n",
       "565                        22                         143             6.6   \n",
       "566                        22                          31             7.8   \n",
       "567                        20                          40             6.0   \n",
       "568                        91                         101             6.9   \n",
       "569                       213                         168             7.1   \n",
       "570                       131                          90             5.4   \n",
       "571                        46                         134             6.9   \n",
       "572                        54                         125             5.6   \n",
       "573                        50                          88             6.2   \n",
       "574                        48                          92             6.6   \n",
       "575                        41                          88             7.9   \n",
       "576                        58                          80             5.3   \n",
       "577                        28                          47             5.4   \n",
       "578                        20                          34             5.9   \n",
       "579                        35                          31             6.0   \n",
       "580                        48                          49             6.4   \n",
       "581                        29                          32             6.8   \n",
       "582                        21                          24             7.3   \n",
       "\n",
       "     Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
       "0        3.3                        0.90        1  \n",
       "1        3.2                        0.74        1  \n",
       "2        3.3                        0.89        1  \n",
       "3        3.4                        1.00        1  \n",
       "4        2.4                        0.40        1  \n",
       "5        4.4                        1.30        1  \n",
       "6        3.5                        1.00        1  \n",
       "7        3.6                        1.10        1  \n",
       "8        4.1                        1.20        2  \n",
       "9        3.4                        1.00        1  \n",
       "10       2.7                        0.80        1  \n",
       "11       3.0                        0.60        1  \n",
       "12       3.4                        0.90        2  \n",
       "13       4.1                        1.00        1  \n",
       "14       2.7                        0.87        1  \n",
       "15       2.3                        0.70        2  \n",
       "16       4.4                        1.30        1  \n",
       "17       3.5                        0.92        2  \n",
       "18       3.1                        0.80        1  \n",
       "19       3.1                        0.80        1  \n",
       "20       2.6                        0.55        1  \n",
       "21       2.4                        0.50        1  \n",
       "22       3.1                        0.90        1  \n",
       "23       1.6                        0.60        1  \n",
       "24       3.9                        1.85        2  \n",
       "25       2.7                        1.10        1  \n",
       "26       2.7                        1.10        1  \n",
       "27       4.0                        1.20        1  \n",
       "28       1.9                        0.95        2  \n",
       "29       3.2                        1.10        2  \n",
       "..       ...                         ...      ...  \n",
       "553      2.7                        0.60        1  \n",
       "554      3.0                        0.80        1  \n",
       "555      3.0                        0.70        1  \n",
       "556      3.1                        1.00        1  \n",
       "557      3.1                        1.00        1  \n",
       "558      4.0                        1.10        1  \n",
       "559      2.6                        0.50        1  \n",
       "560      2.2                        0.40        1  \n",
       "561      2.0                        0.40        1  \n",
       "562      2.6                        0.50        1  \n",
       "563      2.6                        0.50        1  \n",
       "564      2.9                        0.90        2  \n",
       "565      2.1                        0.46        1  \n",
       "566      4.0                        1.00        2  \n",
       "567      1.7                        0.39        1  \n",
       "568      3.5                        1.02        1  \n",
       "569      4.0                        1.20        1  \n",
       "570      2.6                        0.90        1  \n",
       "571      3.0                        0.70        1  \n",
       "572      4.0                        2.50        1  \n",
       "573      1.9                        0.40        1  \n",
       "574      2.4                        0.50        1  \n",
       "575      2.5                        2.50        1  \n",
       "576      2.2                        0.70        1  \n",
       "577      2.6                        0.90        1  \n",
       "578      1.6                        0.37        2  \n",
       "579      3.2                        1.10        1  \n",
       "580      3.2                        1.00        1  \n",
       "581      3.4                        1.00        1  \n",
       "582      4.4                        1.50        2  \n",
       "\n",
       "[583 rows x 11 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liverdata.shape\n",
    "liverdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 583 entries, 0 to 582\n",
      "Data columns (total 11 columns):\n",
      "Age                           583 non-null int64\n",
      "Gender                        583 non-null object\n",
      "Total_Bilirubin               583 non-null float64\n",
      "Direct_Bilirubin              583 non-null float64\n",
      "Alkaline_Phosphotase          583 non-null int64\n",
      "Alamine_Aminotransferase      583 non-null int64\n",
      "Aspartate_Aminotransferase    583 non-null int64\n",
      "Total_Protiens                583 non-null float64\n",
      "Albumin                       583 non-null float64\n",
      "Albumin_and_Globulin_Ratio    579 non-null float64\n",
      "Dataset                       583 non-null int64\n",
      "dtypes: float64(5), int64(5), object(1)\n",
      "memory usage: 50.2+ KB\n"
     ]
    }
   ],
   "source": [
    "liverdata.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liverdata.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                           0\n",
       "Gender                        0\n",
       "Total_Bilirubin               0\n",
       "Direct_Bilirubin              0\n",
       "Alkaline_Phosphotase          0\n",
       "Alamine_Aminotransferase      0\n",
       "Aspartate_Aminotransferase    0\n",
       "Total_Protiens                0\n",
       "Albumin                       0\n",
       "Albumin_and_Globulin_Ratio    4\n",
       "Dataset                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liverdata.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#albumin_and_globulin_ratio has null values\n",
    "liverdata['Albumin_and_Globulin_Ratio'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9470639032815201"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liverdata['Albumin_and_Globulin_Ratio'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "liverdata[\"Albumin_and_Globulin_Ratio\"].fillna(liverdata['Albumin_and_Globulin_Ratio'].mean(), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                           0\n",
       "Gender                        0\n",
       "Total_Bilirubin               0\n",
       "Direct_Bilirubin              0\n",
       "Alkaline_Phosphotase          0\n",
       "Alamine_Aminotransferase      0\n",
       "Aspartate_Aminotransferase    0\n",
       "Total_Protiens                0\n",
       "Albumin                       0\n",
       "Albumin_and_Globulin_Ratio    0\n",
       "Dataset                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liverdata.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(10,10))\n",
    "sns.heatmap(liverdata.corr(),linewidths=0.5,linecolor='black',vmax=1.0,square=True,cmap=\"YlGnBu\",annot=True)\n",
    "plt.title('Correlation of Liver Disease Features')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>187</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>10.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>699</td>\n",
       "      <td>64</td>\n",
       "      <td>100</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>490</td>\n",
       "      <td>60</td>\n",
       "      <td>68</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.890000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>182</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>195</td>\n",
       "      <td>27</td>\n",
       "      <td>59</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>46</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>208</td>\n",
       "      <td>19</td>\n",
       "      <td>14</td>\n",
       "      <td>7.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>26</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>154</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>29</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>202</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>202</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>7.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>55</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>290</td>\n",
       "      <td>53</td>\n",
       "      <td>58</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>57</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>210</td>\n",
       "      <td>51</td>\n",
       "      <td>59</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>72</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>260</td>\n",
       "      <td>31</td>\n",
       "      <td>56</td>\n",
       "      <td>7.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>64</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>310</td>\n",
       "      <td>61</td>\n",
       "      <td>58</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>74</td>\n",
       "      <td>Female</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>214</td>\n",
       "      <td>22</td>\n",
       "      <td>30</td>\n",
       "      <td>8.1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>61</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>145</td>\n",
       "      <td>53</td>\n",
       "      <td>41</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.870000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>25</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>183</td>\n",
       "      <td>91</td>\n",
       "      <td>53</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>38</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>342</td>\n",
       "      <td>168</td>\n",
       "      <td>441</td>\n",
       "      <td>7.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>165</td>\n",
       "      <td>15</td>\n",
       "      <td>23</td>\n",
       "      <td>7.3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>40</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>293</td>\n",
       "      <td>232</td>\n",
       "      <td>245</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>40</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>293</td>\n",
       "      <td>232</td>\n",
       "      <td>245</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>610</td>\n",
       "      <td>17</td>\n",
       "      <td>28</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>482</td>\n",
       "      <td>22</td>\n",
       "      <td>34</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>542</td>\n",
       "      <td>116</td>\n",
       "      <td>66</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>40</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>231</td>\n",
       "      <td>16</td>\n",
       "      <td>55</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>63</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>194</td>\n",
       "      <td>52</td>\n",
       "      <td>45</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.850000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>34</td>\n",
       "      <td>Male</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289</td>\n",
       "      <td>875</td>\n",
       "      <td>731</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>34</td>\n",
       "      <td>Male</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289</td>\n",
       "      <td>875</td>\n",
       "      <td>731</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>34</td>\n",
       "      <td>Male</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>240</td>\n",
       "      <td>1680</td>\n",
       "      <td>850</td>\n",
       "      <td>7.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>128</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>84</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>188</td>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>37</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>147</td>\n",
       "      <td>27</td>\n",
       "      <td>46</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>47</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>192</td>\n",
       "      <td>38</td>\n",
       "      <td>24</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>47</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>265</td>\n",
       "      <td>40</td>\n",
       "      <td>28</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>50</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>175</td>\n",
       "      <td>20</td>\n",
       "      <td>19</td>\n",
       "      <td>7.1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>70</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>400</td>\n",
       "      <td>56</td>\n",
       "      <td>44</td>\n",
       "      <td>5.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>26</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>120</td>\n",
       "      <td>45</td>\n",
       "      <td>51</td>\n",
       "      <td>7.9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>26</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>173</td>\n",
       "      <td>38</td>\n",
       "      <td>62</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>68</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>186</td>\n",
       "      <td>18</td>\n",
       "      <td>15</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>65</td>\n",
       "      <td>Female</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>202</td>\n",
       "      <td>26</td>\n",
       "      <td>13</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>46</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>290</td>\n",
       "      <td>26</td>\n",
       "      <td>21</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>61</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>196</td>\n",
       "      <td>61</td>\n",
       "      <td>85</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>61</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.1</td>\n",
       "      <td>282</td>\n",
       "      <td>85</td>\n",
       "      <td>231</td>\n",
       "      <td>8.5</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>50</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>157</td>\n",
       "      <td>149</td>\n",
       "      <td>156</td>\n",
       "      <td>7.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2110</td>\n",
       "      <td>48</td>\n",
       "      <td>89</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>40</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>285</td>\n",
       "      <td>32</td>\n",
       "      <td>27</td>\n",
       "      <td>7.7</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>60</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>360</td>\n",
       "      <td>230</td>\n",
       "      <td>298</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>22</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>300</td>\n",
       "      <td>57</td>\n",
       "      <td>40</td>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>35</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>158</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>35</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>190</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>40</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>196</td>\n",
       "      <td>69</td>\n",
       "      <td>48</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>48</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>165</td>\n",
       "      <td>32</td>\n",
       "      <td>30</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>230</td>\n",
       "      <td>24</td>\n",
       "      <td>46</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.947064</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>29</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>205</td>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>8.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>28</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>316</td>\n",
       "      <td>25</td>\n",
       "      <td>23</td>\n",
       "      <td>8.5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>54</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>218</td>\n",
       "      <td>20</td>\n",
       "      <td>19</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>54</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>290</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>55</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>272</td>\n",
       "      <td>22</td>\n",
       "      <td>79</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>55</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>190</td>\n",
       "      <td>25</td>\n",
       "      <td>28</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>40</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>202</td>\n",
       "      <td>37</td>\n",
       "      <td>29</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>498</td>\n",
       "      <td>28</td>\n",
       "      <td>25</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "0     65  Female              0.7               0.1                   187   \n",
       "1     62    Male             10.9               5.5                   699   \n",
       "2     62    Male              7.3               4.1                   490   \n",
       "3     58    Male              1.0               0.4                   182   \n",
       "4     72    Male              3.9               2.0                   195   \n",
       "5     46    Male              1.8               0.7                   208   \n",
       "6     26  Female              0.9               0.2                   154   \n",
       "7     29  Female              0.9               0.3                   202   \n",
       "8     17    Male              0.9               0.3                   202   \n",
       "9     55    Male              0.7               0.2                   290   \n",
       "10    57    Male              0.6               0.1                   210   \n",
       "11    72    Male              2.7               1.3                   260   \n",
       "12    64    Male              0.9               0.3                   310   \n",
       "13    74  Female              1.1               0.4                   214   \n",
       "14    61    Male              0.7               0.2                   145   \n",
       "15    25    Male              0.6               0.1                   183   \n",
       "16    38    Male              1.8               0.8                   342   \n",
       "17    33    Male              1.6               0.5                   165   \n",
       "18    40  Female              0.9               0.3                   293   \n",
       "19    40  Female              0.9               0.3                   293   \n",
       "20    51    Male              2.2               1.0                   610   \n",
       "21    51    Male              2.9               1.3                   482   \n",
       "22    62    Male              6.8               3.0                   542   \n",
       "23    40    Male              1.9               1.0                   231   \n",
       "24    63    Male              0.9               0.2                   194   \n",
       "25    34    Male              4.1               2.0                   289   \n",
       "26    34    Male              4.1               2.0                   289   \n",
       "27    34    Male              6.2               3.0                   240   \n",
       "28    20    Male              1.1               0.5                   128   \n",
       "29    84  Female              0.7               0.2                   188   \n",
       "..   ...     ...              ...               ...                   ...   \n",
       "220   37    Male              0.8               0.2                   147   \n",
       "221   47    Male              0.9               0.2                   192   \n",
       "222   47    Male              0.9               0.2                   265   \n",
       "223   50    Male              1.1               0.3                   175   \n",
       "224   70    Male              1.7               0.5                   400   \n",
       "225   26    Male              0.6               0.2                   120   \n",
       "226   26    Male              1.3               0.4                   173   \n",
       "227   68  Female              0.7               0.2                   186   \n",
       "228   65  Female              1.0               0.3                   202   \n",
       "229   46    Male              0.6               0.2                   290   \n",
       "230   61    Male              1.5               0.6                   196   \n",
       "231   61    Male              0.8               0.1                   282   \n",
       "232   50    Male              2.7               1.6                   157   \n",
       "233   33    Male              2.0               1.4                  2110   \n",
       "234   40  Female              0.9               0.2                   285   \n",
       "235   60    Male              1.5               0.6                   360   \n",
       "236   22    Male              0.8               0.2                   300   \n",
       "237   35  Female              0.9               0.3                   158   \n",
       "238   35  Female              0.9               0.2                   190   \n",
       "239   40    Male              0.9               0.3                   196   \n",
       "240   48    Male              0.7               0.2                   165   \n",
       "241   51    Male              0.8               0.2                   230   \n",
       "242   29  Female              0.8               0.2                   205   \n",
       "243   28  Female              0.9               0.2                   316   \n",
       "244   54    Male              0.8               0.2                   218   \n",
       "245   54    Male              0.9               0.2                   290   \n",
       "246   55    Male              1.8               9.0                   272   \n",
       "247   55    Male              0.9               0.2                   190   \n",
       "248   40    Male              0.7               0.1                   202   \n",
       "249   33    Male              1.2               0.3                   498   \n",
       "\n",
       "     Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "0                          16                          18             6.8   \n",
       "1                          64                         100             7.5   \n",
       "2                          60                          68             7.0   \n",
       "3                          14                          20             6.8   \n",
       "4                          27                          59             7.3   \n",
       "5                          19                          14             7.6   \n",
       "6                          16                          12             7.0   \n",
       "7                          14                          11             6.7   \n",
       "8                          22                          19             7.4   \n",
       "9                          53                          58             6.8   \n",
       "10                         51                          59             5.9   \n",
       "11                         31                          56             7.4   \n",
       "12                         61                          58             7.0   \n",
       "13                         22                          30             8.1   \n",
       "14                         53                          41             5.8   \n",
       "15                         91                          53             5.5   \n",
       "16                        168                         441             7.6   \n",
       "17                         15                          23             7.3   \n",
       "18                        232                         245             6.8   \n",
       "19                        232                         245             6.8   \n",
       "20                         17                          28             7.3   \n",
       "21                         22                          34             7.0   \n",
       "22                        116                          66             6.4   \n",
       "23                         16                          55             4.3   \n",
       "24                         52                          45             6.0   \n",
       "25                        875                         731             5.0   \n",
       "26                        875                         731             5.0   \n",
       "27                       1680                         850             7.2   \n",
       "28                         20                          30             3.9   \n",
       "29                         13                          21             6.0   \n",
       "..                        ...                         ...             ...   \n",
       "220                        27                          46             5.0   \n",
       "221                        38                          24             7.3   \n",
       "222                        40                          28             8.0   \n",
       "223                        20                          19             7.1   \n",
       "224                        56                          44             5.7   \n",
       "225                        45                          51             7.9   \n",
       "226                        38                          62             8.0   \n",
       "227                        18                          15             6.4   \n",
       "228                        26                          13             5.3   \n",
       "229                        26                          21             6.0   \n",
       "230                        61                          85             6.7   \n",
       "231                        85                         231             8.5   \n",
       "232                       149                         156             7.9   \n",
       "233                        48                          89             6.2   \n",
       "234                        32                          27             7.7   \n",
       "235                       230                         298             4.5   \n",
       "236                        57                          40             7.9   \n",
       "237                        20                          16             8.0   \n",
       "238                        40                          35             7.3   \n",
       "239                        69                          48             6.8   \n",
       "240                        32                          30             8.0   \n",
       "241                        24                          46             6.5   \n",
       "242                        30                          23             8.2   \n",
       "243                        25                          23             8.5   \n",
       "244                        20                          19             6.3   \n",
       "245                        15                          18             6.1   \n",
       "246                        22                          79             6.1   \n",
       "247                        25                          28             5.9   \n",
       "248                        37                          29             5.0   \n",
       "249                        28                          25             7.0   \n",
       "\n",
       "     Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
       "0        3.3                    0.900000        1  \n",
       "1        3.2                    0.740000        1  \n",
       "2        3.3                    0.890000        1  \n",
       "3        3.4                    1.000000        1  \n",
       "4        2.4                    0.400000        1  \n",
       "5        4.4                    1.300000        1  \n",
       "6        3.5                    1.000000        1  \n",
       "7        3.6                    1.100000        1  \n",
       "8        4.1                    1.200000        0  \n",
       "9        3.4                    1.000000        1  \n",
       "10       2.7                    0.800000        1  \n",
       "11       3.0                    0.600000        1  \n",
       "12       3.4                    0.900000        0  \n",
       "13       4.1                    1.000000        1  \n",
       "14       2.7                    0.870000        1  \n",
       "15       2.3                    0.700000        0  \n",
       "16       4.4                    1.300000        1  \n",
       "17       3.5                    0.920000        0  \n",
       "18       3.1                    0.800000        1  \n",
       "19       3.1                    0.800000        1  \n",
       "20       2.6                    0.550000        1  \n",
       "21       2.4                    0.500000        1  \n",
       "22       3.1                    0.900000        1  \n",
       "23       1.6                    0.600000        1  \n",
       "24       3.9                    1.850000        0  \n",
       "25       2.7                    1.100000        1  \n",
       "26       2.7                    1.100000        1  \n",
       "27       4.0                    1.200000        1  \n",
       "28       1.9                    0.950000        0  \n",
       "29       3.2                    1.100000        0  \n",
       "..       ...                         ...      ...  \n",
       "220      2.5                    1.000000        1  \n",
       "221      4.3                    1.400000        1  \n",
       "222      4.0                    1.000000        1  \n",
       "223      4.5                    1.700000        0  \n",
       "224      3.1                    1.100000        1  \n",
       "225      4.0                    1.000000        1  \n",
       "226      4.0                    1.000000        1  \n",
       "227      3.8                    1.400000        1  \n",
       "228      2.6                    0.900000        0  \n",
       "229      3.0                    1.000000        1  \n",
       "230      3.8                    1.300000        0  \n",
       "231      4.3                    1.000000        1  \n",
       "232      3.1                    0.600000        1  \n",
       "233      3.0                    0.900000        1  \n",
       "234      3.5                    0.800000        1  \n",
       "235      2.0                    0.800000        1  \n",
       "236      3.8                    0.900000        0  \n",
       "237      4.0                    1.000000        1  \n",
       "238      4.7                    1.800000        0  \n",
       "239      3.1                    0.800000        1  \n",
       "240      4.0                    1.000000        0  \n",
       "241      3.1                    0.947064        1  \n",
       "242      4.1                    1.000000        1  \n",
       "243      5.5                    1.800000        1  \n",
       "244      2.5                    0.600000        1  \n",
       "245      2.8                    0.800000        1  \n",
       "246      2.7                    0.700000        1  \n",
       "247      2.7                    0.800000        1  \n",
       "248      2.6                    1.000000        1  \n",
       "249      3.0                    0.700000        1  \n",
       "\n",
       "[250 rows x 11 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## output value has '1' for liver disease and '2' for no liver disease so making it 0 for no disease\n",
    "\n",
    "def partition(x):\n",
    "    if x == 2:\n",
    "        return 0\n",
    "    return 1\n",
    "\n",
    "liverdata['Dataset'] = liverdata['Dataset'].map(partition)\n",
    "\n",
    "\n",
    "liverdata.head(250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>187</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>10.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>699</td>\n",
       "      <td>64</td>\n",
       "      <td>100</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>490</td>\n",
       "      <td>60</td>\n",
       "      <td>68</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.890000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>182</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>195</td>\n",
       "      <td>27</td>\n",
       "      <td>59</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>208</td>\n",
       "      <td>19</td>\n",
       "      <td>14</td>\n",
       "      <td>7.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>154</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>202</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>202</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>7.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>290</td>\n",
       "      <td>53</td>\n",
       "      <td>58</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>210</td>\n",
       "      <td>51</td>\n",
       "      <td>59</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>260</td>\n",
       "      <td>31</td>\n",
       "      <td>56</td>\n",
       "      <td>7.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>310</td>\n",
       "      <td>61</td>\n",
       "      <td>58</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>214</td>\n",
       "      <td>22</td>\n",
       "      <td>30</td>\n",
       "      <td>8.1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>145</td>\n",
       "      <td>53</td>\n",
       "      <td>41</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.870000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>183</td>\n",
       "      <td>91</td>\n",
       "      <td>53</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>342</td>\n",
       "      <td>168</td>\n",
       "      <td>441</td>\n",
       "      <td>7.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>165</td>\n",
       "      <td>15</td>\n",
       "      <td>23</td>\n",
       "      <td>7.3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>293</td>\n",
       "      <td>232</td>\n",
       "      <td>245</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>293</td>\n",
       "      <td>232</td>\n",
       "      <td>245</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>610</td>\n",
       "      <td>17</td>\n",
       "      <td>28</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>482</td>\n",
       "      <td>22</td>\n",
       "      <td>34</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>542</td>\n",
       "      <td>116</td>\n",
       "      <td>66</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>231</td>\n",
       "      <td>16</td>\n",
       "      <td>55</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>194</td>\n",
       "      <td>52</td>\n",
       "      <td>45</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.850000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289</td>\n",
       "      <td>875</td>\n",
       "      <td>731</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289</td>\n",
       "      <td>875</td>\n",
       "      <td>731</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>240</td>\n",
       "      <td>1680</td>\n",
       "      <td>850</td>\n",
       "      <td>7.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>128</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>188</td>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>147</td>\n",
       "      <td>27</td>\n",
       "      <td>46</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>192</td>\n",
       "      <td>38</td>\n",
       "      <td>24</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>265</td>\n",
       "      <td>40</td>\n",
       "      <td>28</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>175</td>\n",
       "      <td>20</td>\n",
       "      <td>19</td>\n",
       "      <td>7.1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>400</td>\n",
       "      <td>56</td>\n",
       "      <td>44</td>\n",
       "      <td>5.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>120</td>\n",
       "      <td>45</td>\n",
       "      <td>51</td>\n",
       "      <td>7.9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>173</td>\n",
       "      <td>38</td>\n",
       "      <td>62</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>186</td>\n",
       "      <td>18</td>\n",
       "      <td>15</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>202</td>\n",
       "      <td>26</td>\n",
       "      <td>13</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>290</td>\n",
       "      <td>26</td>\n",
       "      <td>21</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>196</td>\n",
       "      <td>61</td>\n",
       "      <td>85</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.1</td>\n",
       "      <td>282</td>\n",
       "      <td>85</td>\n",
       "      <td>231</td>\n",
       "      <td>8.5</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>157</td>\n",
       "      <td>149</td>\n",
       "      <td>156</td>\n",
       "      <td>7.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2110</td>\n",
       "      <td>48</td>\n",
       "      <td>89</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>285</td>\n",
       "      <td>32</td>\n",
       "      <td>27</td>\n",
       "      <td>7.7</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>360</td>\n",
       "      <td>230</td>\n",
       "      <td>298</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>300</td>\n",
       "      <td>57</td>\n",
       "      <td>40</td>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>158</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>190</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>196</td>\n",
       "      <td>69</td>\n",
       "      <td>48</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>165</td>\n",
       "      <td>32</td>\n",
       "      <td>30</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>230</td>\n",
       "      <td>24</td>\n",
       "      <td>46</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.947064</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>205</td>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>8.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>316</td>\n",
       "      <td>25</td>\n",
       "      <td>23</td>\n",
       "      <td>8.5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>218</td>\n",
       "      <td>20</td>\n",
       "      <td>19</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>290</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>272</td>\n",
       "      <td>22</td>\n",
       "      <td>79</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>190</td>\n",
       "      <td>25</td>\n",
       "      <td>28</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>202</td>\n",
       "      <td>37</td>\n",
       "      <td>29</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>498</td>\n",
       "      <td>28</td>\n",
       "      <td>25</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "0     65       1              0.7               0.1                   187   \n",
       "1     62       0             10.9               5.5                   699   \n",
       "2     62       0              7.3               4.1                   490   \n",
       "3     58       0              1.0               0.4                   182   \n",
       "4     72       0              3.9               2.0                   195   \n",
       "5     46       0              1.8               0.7                   208   \n",
       "6     26       1              0.9               0.2                   154   \n",
       "7     29       1              0.9               0.3                   202   \n",
       "8     17       0              0.9               0.3                   202   \n",
       "9     55       0              0.7               0.2                   290   \n",
       "10    57       0              0.6               0.1                   210   \n",
       "11    72       0              2.7               1.3                   260   \n",
       "12    64       0              0.9               0.3                   310   \n",
       "13    74       1              1.1               0.4                   214   \n",
       "14    61       0              0.7               0.2                   145   \n",
       "15    25       0              0.6               0.1                   183   \n",
       "16    38       0              1.8               0.8                   342   \n",
       "17    33       0              1.6               0.5                   165   \n",
       "18    40       1              0.9               0.3                   293   \n",
       "19    40       1              0.9               0.3                   293   \n",
       "20    51       0              2.2               1.0                   610   \n",
       "21    51       0              2.9               1.3                   482   \n",
       "22    62       0              6.8               3.0                   542   \n",
       "23    40       0              1.9               1.0                   231   \n",
       "24    63       0              0.9               0.2                   194   \n",
       "25    34       0              4.1               2.0                   289   \n",
       "26    34       0              4.1               2.0                   289   \n",
       "27    34       0              6.2               3.0                   240   \n",
       "28    20       0              1.1               0.5                   128   \n",
       "29    84       1              0.7               0.2                   188   \n",
       "..   ...     ...              ...               ...                   ...   \n",
       "220   37       0              0.8               0.2                   147   \n",
       "221   47       0              0.9               0.2                   192   \n",
       "222   47       0              0.9               0.2                   265   \n",
       "223   50       0              1.1               0.3                   175   \n",
       "224   70       0              1.7               0.5                   400   \n",
       "225   26       0              0.6               0.2                   120   \n",
       "226   26       0              1.3               0.4                   173   \n",
       "227   68       1              0.7               0.2                   186   \n",
       "228   65       1              1.0               0.3                   202   \n",
       "229   46       0              0.6               0.2                   290   \n",
       "230   61       0              1.5               0.6                   196   \n",
       "231   61       0              0.8               0.1                   282   \n",
       "232   50       0              2.7               1.6                   157   \n",
       "233   33       0              2.0               1.4                  2110   \n",
       "234   40       1              0.9               0.2                   285   \n",
       "235   60       0              1.5               0.6                   360   \n",
       "236   22       0              0.8               0.2                   300   \n",
       "237   35       1              0.9               0.3                   158   \n",
       "238   35       1              0.9               0.2                   190   \n",
       "239   40       0              0.9               0.3                   196   \n",
       "240   48       0              0.7               0.2                   165   \n",
       "241   51       0              0.8               0.2                   230   \n",
       "242   29       1              0.8               0.2                   205   \n",
       "243   28       1              0.9               0.2                   316   \n",
       "244   54       0              0.8               0.2                   218   \n",
       "245   54       0              0.9               0.2                   290   \n",
       "246   55       0              1.8               9.0                   272   \n",
       "247   55       0              0.9               0.2                   190   \n",
       "248   40       0              0.7               0.1                   202   \n",
       "249   33       0              1.2               0.3                   498   \n",
       "\n",
       "     Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "0                          16                          18             6.8   \n",
       "1                          64                         100             7.5   \n",
       "2                          60                          68             7.0   \n",
       "3                          14                          20             6.8   \n",
       "4                          27                          59             7.3   \n",
       "5                          19                          14             7.6   \n",
       "6                          16                          12             7.0   \n",
       "7                          14                          11             6.7   \n",
       "8                          22                          19             7.4   \n",
       "9                          53                          58             6.8   \n",
       "10                         51                          59             5.9   \n",
       "11                         31                          56             7.4   \n",
       "12                         61                          58             7.0   \n",
       "13                         22                          30             8.1   \n",
       "14                         53                          41             5.8   \n",
       "15                         91                          53             5.5   \n",
       "16                        168                         441             7.6   \n",
       "17                         15                          23             7.3   \n",
       "18                        232                         245             6.8   \n",
       "19                        232                         245             6.8   \n",
       "20                         17                          28             7.3   \n",
       "21                         22                          34             7.0   \n",
       "22                        116                          66             6.4   \n",
       "23                         16                          55             4.3   \n",
       "24                         52                          45             6.0   \n",
       "25                        875                         731             5.0   \n",
       "26                        875                         731             5.0   \n",
       "27                       1680                         850             7.2   \n",
       "28                         20                          30             3.9   \n",
       "29                         13                          21             6.0   \n",
       "..                        ...                         ...             ...   \n",
       "220                        27                          46             5.0   \n",
       "221                        38                          24             7.3   \n",
       "222                        40                          28             8.0   \n",
       "223                        20                          19             7.1   \n",
       "224                        56                          44             5.7   \n",
       "225                        45                          51             7.9   \n",
       "226                        38                          62             8.0   \n",
       "227                        18                          15             6.4   \n",
       "228                        26                          13             5.3   \n",
       "229                        26                          21             6.0   \n",
       "230                        61                          85             6.7   \n",
       "231                        85                         231             8.5   \n",
       "232                       149                         156             7.9   \n",
       "233                        48                          89             6.2   \n",
       "234                        32                          27             7.7   \n",
       "235                       230                         298             4.5   \n",
       "236                        57                          40             7.9   \n",
       "237                        20                          16             8.0   \n",
       "238                        40                          35             7.3   \n",
       "239                        69                          48             6.8   \n",
       "240                        32                          30             8.0   \n",
       "241                        24                          46             6.5   \n",
       "242                        30                          23             8.2   \n",
       "243                        25                          23             8.5   \n",
       "244                        20                          19             6.3   \n",
       "245                        15                          18             6.1   \n",
       "246                        22                          79             6.1   \n",
       "247                        25                          28             5.9   \n",
       "248                        37                          29             5.0   \n",
       "249                        28                          25             7.0   \n",
       "\n",
       "     Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
       "0        3.3                    0.900000        1  \n",
       "1        3.2                    0.740000        1  \n",
       "2        3.3                    0.890000        1  \n",
       "3        3.4                    1.000000        1  \n",
       "4        2.4                    0.400000        1  \n",
       "5        4.4                    1.300000        1  \n",
       "6        3.5                    1.000000        1  \n",
       "7        3.6                    1.100000        1  \n",
       "8        4.1                    1.200000        0  \n",
       "9        3.4                    1.000000        1  \n",
       "10       2.7                    0.800000        1  \n",
       "11       3.0                    0.600000        1  \n",
       "12       3.4                    0.900000        0  \n",
       "13       4.1                    1.000000        1  \n",
       "14       2.7                    0.870000        1  \n",
       "15       2.3                    0.700000        0  \n",
       "16       4.4                    1.300000        1  \n",
       "17       3.5                    0.920000        0  \n",
       "18       3.1                    0.800000        1  \n",
       "19       3.1                    0.800000        1  \n",
       "20       2.6                    0.550000        1  \n",
       "21       2.4                    0.500000        1  \n",
       "22       3.1                    0.900000        1  \n",
       "23       1.6                    0.600000        1  \n",
       "24       3.9                    1.850000        0  \n",
       "25       2.7                    1.100000        1  \n",
       "26       2.7                    1.100000        1  \n",
       "27       4.0                    1.200000        1  \n",
       "28       1.9                    0.950000        0  \n",
       "29       3.2                    1.100000        0  \n",
       "..       ...                         ...      ...  \n",
       "220      2.5                    1.000000        1  \n",
       "221      4.3                    1.400000        1  \n",
       "222      4.0                    1.000000        1  \n",
       "223      4.5                    1.700000        0  \n",
       "224      3.1                    1.100000        1  \n",
       "225      4.0                    1.000000        1  \n",
       "226      4.0                    1.000000        1  \n",
       "227      3.8                    1.400000        1  \n",
       "228      2.6                    0.900000        0  \n",
       "229      3.0                    1.000000        1  \n",
       "230      3.8                    1.300000        0  \n",
       "231      4.3                    1.000000        1  \n",
       "232      3.1                    0.600000        1  \n",
       "233      3.0                    0.900000        1  \n",
       "234      3.5                    0.800000        1  \n",
       "235      2.0                    0.800000        1  \n",
       "236      3.8                    0.900000        0  \n",
       "237      4.0                    1.000000        1  \n",
       "238      4.7                    1.800000        0  \n",
       "239      3.1                    0.800000        1  \n",
       "240      4.0                    1.000000        0  \n",
       "241      3.1                    0.947064        1  \n",
       "242      4.1                    1.000000        1  \n",
       "243      5.5                    1.800000        1  \n",
       "244      2.5                    0.600000        1  \n",
       "245      2.8                    0.800000        1  \n",
       "246      2.7                    0.700000        1  \n",
       "247      2.7                    0.800000        1  \n",
       "248      2.6                    1.000000        1  \n",
       "249      3.0                    0.700000        1  \n",
       "\n",
       "[250 rows x 11 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## output value has '1' for liver disease and '2' for no liver disease so making it 0 for no disease \n",
    "\n",
    "def partition(x):\n",
    "    if x =='Male':\n",
    "        return 0\n",
    "    return 1\n",
    "\n",
    "liverdata['Gender'] = liverdata['Gender'].map(partition)\n",
    "\n",
    "\n",
    "liverdata.head(250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>202</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>7.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>310</td>\n",
       "      <td>61</td>\n",
       "      <td>58</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>183</td>\n",
       "      <td>91</td>\n",
       "      <td>53</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>165</td>\n",
       "      <td>15</td>\n",
       "      <td>23</td>\n",
       "      <td>7.3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>194</td>\n",
       "      <td>52</td>\n",
       "      <td>45</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.85</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>128</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>188</td>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>187</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>410</td>\n",
       "      <td>59</td>\n",
       "      <td>57</td>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>410</td>\n",
       "      <td>59</td>\n",
       "      <td>57</td>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>145</td>\n",
       "      <td>18</td>\n",
       "      <td>36</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>160</td>\n",
       "      <td>42</td>\n",
       "      <td>110</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>630</td>\n",
       "      <td>25</td>\n",
       "      <td>47</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>208</td>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>275</td>\n",
       "      <td>48</td>\n",
       "      <td>178</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>198</td>\n",
       "      <td>26</td>\n",
       "      <td>23</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>175</td>\n",
       "      <td>24</td>\n",
       "      <td>54</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>145</td>\n",
       "      <td>20</td>\n",
       "      <td>24</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>215</td>\n",
       "      <td>24</td>\n",
       "      <td>17</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>162</td>\n",
       "      <td>52</td>\n",
       "      <td>41</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>146</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>148</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>320</td>\n",
       "      <td>28</td>\n",
       "      <td>56</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>161</td>\n",
       "      <td>27</td>\n",
       "      <td>28</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>243</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>225</td>\n",
       "      <td>14</td>\n",
       "      <td>23</td>\n",
       "      <td>6.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>145</td>\n",
       "      <td>32</td>\n",
       "      <td>92</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>145</td>\n",
       "      <td>32</td>\n",
       "      <td>92</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>158</td>\n",
       "      <td>29</td>\n",
       "      <td>39</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>158</td>\n",
       "      <td>29</td>\n",
       "      <td>39</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>190</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>280</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>430</td>\n",
       "      <td>28</td>\n",
       "      <td>32</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>155</td>\n",
       "      <td>21</td>\n",
       "      <td>17</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>588</td>\n",
       "      <td>74</td>\n",
       "      <td>113</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>174</td>\n",
       "      <td>21</td>\n",
       "      <td>47</td>\n",
       "      <td>4.6</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>165</td>\n",
       "      <td>22</td>\n",
       "      <td>18</td>\n",
       "      <td>8.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>527</td>\n",
       "      <td>178</td>\n",
       "      <td>250</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>175</td>\n",
       "      <td>48</td>\n",
       "      <td>22</td>\n",
       "      <td>8.1</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>23.2</td>\n",
       "      <td>12.6</td>\n",
       "      <td>574</td>\n",
       "      <td>43</td>\n",
       "      <td>47</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>195</td>\n",
       "      <td>24</td>\n",
       "      <td>65</td>\n",
       "      <td>7.8</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>198</td>\n",
       "      <td>42</td>\n",
       "      <td>30</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>216</td>\n",
       "      <td>179</td>\n",
       "      <td>232</td>\n",
       "      <td>7.8</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>63</td>\n",
       "      <td>31</td>\n",
       "      <td>27</td>\n",
       "      <td>5.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>198</td>\n",
       "      <td>30</td>\n",
       "      <td>58</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>205</td>\n",
       "      <td>36</td>\n",
       "      <td>34</td>\n",
       "      <td>7.1</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>171</td>\n",
       "      <td>42</td>\n",
       "      <td>37</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>157</td>\n",
       "      <td>54</td>\n",
       "      <td>68</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>210</td>\n",
       "      <td>62</td>\n",
       "      <td>56</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>258</td>\n",
       "      <td>80</td>\n",
       "      <td>113</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>350</td>\n",
       "      <td>17</td>\n",
       "      <td>24</td>\n",
       "      <td>7.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>182</td>\n",
       "      <td>24</td>\n",
       "      <td>19</td>\n",
       "      <td>8.9</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.6</td>\n",
       "      <td>458</td>\n",
       "      <td>198</td>\n",
       "      <td>143</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.2</td>\n",
       "      <td>375</td>\n",
       "      <td>85</td>\n",
       "      <td>68</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>405</td>\n",
       "      <td>79</td>\n",
       "      <td>50</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>215</td>\n",
       "      <td>50</td>\n",
       "      <td>30</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>206</td>\n",
       "      <td>44</td>\n",
       "      <td>33</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>650</td>\n",
       "      <td>70</td>\n",
       "      <td>138</td>\n",
       "      <td>6.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>198</td>\n",
       "      <td>40</td>\n",
       "      <td>28</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>230</td>\n",
       "      <td>32</td>\n",
       "      <td>44</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>417 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "8     17       0              0.9               0.3                   202   \n",
       "12    64       0              0.9               0.3                   310   \n",
       "15    25       0              0.6               0.1                   183   \n",
       "17    33       0              1.6               0.5                   165   \n",
       "24    63       0              0.9               0.2                   194   \n",
       "28    20       0              1.1               0.5                   128   \n",
       "29    84       1              0.7               0.2                   188   \n",
       "32    57       0              1.0               0.3                   187   \n",
       "33    38       1              2.6               1.2                   410   \n",
       "34    38       1              2.6               1.2                   410   \n",
       "36    17       1              0.7               0.2                   145   \n",
       "41    62       0              0.6               0.1                   160   \n",
       "42    42       0              6.8               3.2                   630   \n",
       "44    85       1              1.0               0.3                   208   \n",
       "45    35       0              1.8               0.6                   275   \n",
       "56    33       0              0.8               0.2                   198   \n",
       "57    48       1              0.9               0.2                   175   \n",
       "59    64       0              1.1               0.5                   145   \n",
       "69    60       0              0.8               0.2                   215   \n",
       "75    29       1              0.7               0.1                   162   \n",
       "78    70       0              1.4               0.6                   146   \n",
       "82    49       0              0.7               0.1                   148   \n",
       "86    13       0              0.6               0.1                   320   \n",
       "100   27       0              0.6               0.2                   161   \n",
       "101   27       0              0.7               0.2                   243   \n",
       "103   55       1              0.8               0.2                   225   \n",
       "105   36       0              5.3               2.3                   145   \n",
       "106   36       0              5.3               2.3                   145   \n",
       "107   36       0              0.8               0.2                   158   \n",
       "108   36       0              0.8               0.2                   158   \n",
       "..   ...     ...              ...               ...                   ...   \n",
       "298   31       1              1.1               0.3                   190   \n",
       "301   51       1              0.9               0.2                   280   \n",
       "302   50       1              1.7               0.6                   430   \n",
       "304   55       1              0.8               0.2                   155   \n",
       "306   48       0              1.6               1.0                   588   \n",
       "307   30       0              0.8               0.2                   174   \n",
       "308   45       1              0.8               0.2                   165   \n",
       "309   48       1              1.1               0.7                   527   \n",
       "310   51       0              0.8               0.2                   175   \n",
       "311   54       1             23.2              12.6                   574   \n",
       "314   26       0              2.0               0.9                   195   \n",
       "317   35       0              0.7               0.2                   198   \n",
       "318   38       0              3.7               2.2                   216   \n",
       "320   30       1              0.7               0.2                    63   \n",
       "321   30       1              0.8               0.2                   198   \n",
       "322   36       0              1.7               0.5                   205   \n",
       "324   60       0              2.6               1.2                   171   \n",
       "330   26       0              2.0               0.9                   157   \n",
       "331   26       0              1.7               0.6                   210   \n",
       "332   26       0              7.1               3.3                   258   \n",
       "334   13       1              0.7               0.2                   350   \n",
       "335   13       1              0.7               0.1                   182   \n",
       "336   75       0              6.7               3.6                   458   \n",
       "337   75       0              2.5               1.2                   375   \n",
       "338   75       0              1.8               0.8                   405   \n",
       "339   75       0              1.4               0.4                   215   \n",
       "340   75       0              0.9               0.2                   206   \n",
       "341   36       1              0.8               0.2                   650   \n",
       "343   70       0              3.1               1.6                   198   \n",
       "345   60       0              2.9               1.3                   230   \n",
       "\n",
       "     Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "8                          22                          19             7.4   \n",
       "12                         61                          58             7.0   \n",
       "15                         91                          53             5.5   \n",
       "17                         15                          23             7.3   \n",
       "24                         52                          45             6.0   \n",
       "28                         20                          30             3.9   \n",
       "29                         13                          21             6.0   \n",
       "32                         19                          23             5.2   \n",
       "33                         59                          57             5.6   \n",
       "34                         59                          57             5.6   \n",
       "36                         18                          36             7.2   \n",
       "41                         42                         110             4.9   \n",
       "42                         25                          47             6.1   \n",
       "44                         17                          15             7.0   \n",
       "45                         48                         178             6.5   \n",
       "56                         26                          23             8.0   \n",
       "57                         24                          54             5.5   \n",
       "59                         20                          24             5.5   \n",
       "69                         24                          17             6.3   \n",
       "75                         52                          41             5.2   \n",
       "78                         12                          24             6.2   \n",
       "82                         14                          12             5.4   \n",
       "86                         28                          56             7.2   \n",
       "100                        27                          28             3.7   \n",
       "101                        21                          23             5.3   \n",
       "103                        14                          23             6.1   \n",
       "105                        32                          92             5.1   \n",
       "106                        32                          92             5.1   \n",
       "107                        29                          39             6.0   \n",
       "108                        29                          39             6.0   \n",
       "..                        ...                         ...             ...   \n",
       "298                        26                          15             7.9   \n",
       "301                        21                          30             6.7   \n",
       "302                        28                          32             6.8   \n",
       "304                        21                          17             6.9   \n",
       "306                        74                         113             7.3   \n",
       "307                        21                          47             4.6   \n",
       "308                        22                          18             8.2   \n",
       "309                       178                         250             8.0   \n",
       "310                        48                          22             8.1   \n",
       "311                        43                          47             7.2   \n",
       "314                        24                          65             7.8   \n",
       "317                        42                          30             6.8   \n",
       "318                       179                         232             7.8   \n",
       "320                        31                          27             5.8   \n",
       "321                        30                          58             5.2   \n",
       "322                        36                          34             7.1   \n",
       "324                        42                          37             5.4   \n",
       "330                        54                          68             6.1   \n",
       "331                        62                          56             5.4   \n",
       "332                        80                         113             6.2   \n",
       "334                        17                          24             7.4   \n",
       "335                        24                          19             8.9   \n",
       "336                       198                         143             6.2   \n",
       "337                        85                          68             6.4   \n",
       "338                        79                          50             6.1   \n",
       "339                        50                          30             5.9   \n",
       "340                        44                          33             6.2   \n",
       "341                        70                         138             6.6   \n",
       "343                        40                          28             5.6   \n",
       "345                        32                          44             5.6   \n",
       "\n",
       "     Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
       "8        4.1                        1.20        0  \n",
       "12       3.4                        0.90        0  \n",
       "15       2.3                        0.70        0  \n",
       "17       3.5                        0.92        0  \n",
       "24       3.9                        1.85        0  \n",
       "28       1.9                        0.95        0  \n",
       "29       3.2                        1.10        0  \n",
       "32       2.9                        1.20        0  \n",
       "33       3.0                        0.80        0  \n",
       "34       3.0                        0.80        0  \n",
       "36       3.9                        1.18        0  \n",
       "41       2.6                        1.10        0  \n",
       "42       2.3                        0.60        0  \n",
       "44       3.6                        1.00        0  \n",
       "45       3.2                        0.90        0  \n",
       "56       4.0                        1.00        0  \n",
       "57       2.7                        0.90        0  \n",
       "59       3.2                        1.39        0  \n",
       "69       3.0                        0.90        0  \n",
       "75       2.5                        0.90        0  \n",
       "78       3.8                        1.58        0  \n",
       "82       2.8                        1.00        0  \n",
       "86       3.6                        1.00        0  \n",
       "100      1.6                        0.76        0  \n",
       "101      2.3                        0.70        0  \n",
       "103      3.3                        1.20        0  \n",
       "105      2.6                        1.00        0  \n",
       "106      2.6                        1.00        0  \n",
       "107      2.2                        0.50        0  \n",
       "108      2.2                        0.50        0  \n",
       "..       ...                         ...      ...  \n",
       "298      3.8                        0.90        1  \n",
       "301      3.2                        0.80        1  \n",
       "302      3.5                        1.00        1  \n",
       "304      3.8                        1.40        1  \n",
       "306      2.4                        0.40        1  \n",
       "307      2.3                        1.00        1  \n",
       "308      4.1                        1.00        1  \n",
       "309      4.2                        1.10        1  \n",
       "310      4.6                        1.30        1  \n",
       "311      3.5                        0.90        1  \n",
       "314      4.3                        1.20        1  \n",
       "317      3.4                        1.00        1  \n",
       "318      4.5                        1.30        1  \n",
       "320      3.4                        1.40        1  \n",
       "321      2.8                        1.10        1  \n",
       "322      3.9                        1.20        1  \n",
       "324      2.7                        1.00        1  \n",
       "330      2.7                        0.80        1  \n",
       "331      2.2                        0.60        1  \n",
       "332      2.9                        0.80        1  \n",
       "334      4.0                        1.10        1  \n",
       "335      4.9                        1.20        1  \n",
       "336      3.2                        1.00        1  \n",
       "337      2.9                        0.80        1  \n",
       "338      2.9                        0.90        1  \n",
       "339      2.6                        0.70        1  \n",
       "340      2.9                        0.80        1  \n",
       "341      3.1                        0.80        1  \n",
       "343      2.0                        0.50        1  \n",
       "345      2.0                        0.50        1  \n",
       "\n",
       "[417 rows x 11 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_class_0, count_class_1 = liverdata['Dataset'].value_counts()\n",
    "\n",
    "# Divide by class\n",
    "data_class0 = liverdata[liverdata['Dataset'] == 0]\n",
    "data_class1 = liverdata[liverdata['Dataset'] == 1]\n",
    "\n",
    "liverdata = pd.concat([data_class0,data_class1.head(250)], axis=0)\n",
    "liverdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(417, 11)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liverdata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = liverdata.drop('Dataset',axis=1)\n",
    "Y = liverdata['Dataset']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>202</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>7.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>310</td>\n",
       "      <td>61</td>\n",
       "      <td>58</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>183</td>\n",
       "      <td>91</td>\n",
       "      <td>53</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>165</td>\n",
       "      <td>15</td>\n",
       "      <td>23</td>\n",
       "      <td>7.3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>194</td>\n",
       "      <td>52</td>\n",
       "      <td>45</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>128</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>188</td>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>187</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>410</td>\n",
       "      <td>59</td>\n",
       "      <td>57</td>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>410</td>\n",
       "      <td>59</td>\n",
       "      <td>57</td>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>145</td>\n",
       "      <td>18</td>\n",
       "      <td>36</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>160</td>\n",
       "      <td>42</td>\n",
       "      <td>110</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>630</td>\n",
       "      <td>25</td>\n",
       "      <td>47</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>208</td>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>275</td>\n",
       "      <td>48</td>\n",
       "      <td>178</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>198</td>\n",
       "      <td>26</td>\n",
       "      <td>23</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>175</td>\n",
       "      <td>24</td>\n",
       "      <td>54</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>145</td>\n",
       "      <td>20</td>\n",
       "      <td>24</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>215</td>\n",
       "      <td>24</td>\n",
       "      <td>17</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>162</td>\n",
       "      <td>52</td>\n",
       "      <td>41</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>146</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>148</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>320</td>\n",
       "      <td>28</td>\n",
       "      <td>56</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>161</td>\n",
       "      <td>27</td>\n",
       "      <td>28</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>243</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>225</td>\n",
       "      <td>14</td>\n",
       "      <td>23</td>\n",
       "      <td>6.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>145</td>\n",
       "      <td>32</td>\n",
       "      <td>92</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>145</td>\n",
       "      <td>32</td>\n",
       "      <td>92</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>158</td>\n",
       "      <td>29</td>\n",
       "      <td>39</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>158</td>\n",
       "      <td>29</td>\n",
       "      <td>39</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.50</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>190</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>280</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>430</td>\n",
       "      <td>28</td>\n",
       "      <td>32</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>155</td>\n",
       "      <td>21</td>\n",
       "      <td>17</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>588</td>\n",
       "      <td>74</td>\n",
       "      <td>113</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>174</td>\n",
       "      <td>21</td>\n",
       "      <td>47</td>\n",
       "      <td>4.6</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>165</td>\n",
       "      <td>22</td>\n",
       "      <td>18</td>\n",
       "      <td>8.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>527</td>\n",
       "      <td>178</td>\n",
       "      <td>250</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>175</td>\n",
       "      <td>48</td>\n",
       "      <td>22</td>\n",
       "      <td>8.1</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>23.2</td>\n",
       "      <td>12.6</td>\n",
       "      <td>574</td>\n",
       "      <td>43</td>\n",
       "      <td>47</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>195</td>\n",
       "      <td>24</td>\n",
       "      <td>65</td>\n",
       "      <td>7.8</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>198</td>\n",
       "      <td>42</td>\n",
       "      <td>30</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>216</td>\n",
       "      <td>179</td>\n",
       "      <td>232</td>\n",
       "      <td>7.8</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>63</td>\n",
       "      <td>31</td>\n",
       "      <td>27</td>\n",
       "      <td>5.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>198</td>\n",
       "      <td>30</td>\n",
       "      <td>58</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>205</td>\n",
       "      <td>36</td>\n",
       "      <td>34</td>\n",
       "      <td>7.1</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>171</td>\n",
       "      <td>42</td>\n",
       "      <td>37</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>157</td>\n",
       "      <td>54</td>\n",
       "      <td>68</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>210</td>\n",
       "      <td>62</td>\n",
       "      <td>56</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>258</td>\n",
       "      <td>80</td>\n",
       "      <td>113</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>350</td>\n",
       "      <td>17</td>\n",
       "      <td>24</td>\n",
       "      <td>7.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>182</td>\n",
       "      <td>24</td>\n",
       "      <td>19</td>\n",
       "      <td>8.9</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.6</td>\n",
       "      <td>458</td>\n",
       "      <td>198</td>\n",
       "      <td>143</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.2</td>\n",
       "      <td>375</td>\n",
       "      <td>85</td>\n",
       "      <td>68</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>405</td>\n",
       "      <td>79</td>\n",
       "      <td>50</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>215</td>\n",
       "      <td>50</td>\n",
       "      <td>30</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>206</td>\n",
       "      <td>44</td>\n",
       "      <td>33</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>650</td>\n",
       "      <td>70</td>\n",
       "      <td>138</td>\n",
       "      <td>6.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>198</td>\n",
       "      <td>40</td>\n",
       "      <td>28</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>230</td>\n",
       "      <td>32</td>\n",
       "      <td>44</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>417 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "8     17       0              0.9               0.3                   202   \n",
       "12    64       0              0.9               0.3                   310   \n",
       "15    25       0              0.6               0.1                   183   \n",
       "17    33       0              1.6               0.5                   165   \n",
       "24    63       0              0.9               0.2                   194   \n",
       "28    20       0              1.1               0.5                   128   \n",
       "29    84       1              0.7               0.2                   188   \n",
       "32    57       0              1.0               0.3                   187   \n",
       "33    38       1              2.6               1.2                   410   \n",
       "34    38       1              2.6               1.2                   410   \n",
       "36    17       1              0.7               0.2                   145   \n",
       "41    62       0              0.6               0.1                   160   \n",
       "42    42       0              6.8               3.2                   630   \n",
       "44    85       1              1.0               0.3                   208   \n",
       "45    35       0              1.8               0.6                   275   \n",
       "56    33       0              0.8               0.2                   198   \n",
       "57    48       1              0.9               0.2                   175   \n",
       "59    64       0              1.1               0.5                   145   \n",
       "69    60       0              0.8               0.2                   215   \n",
       "75    29       1              0.7               0.1                   162   \n",
       "78    70       0              1.4               0.6                   146   \n",
       "82    49       0              0.7               0.1                   148   \n",
       "86    13       0              0.6               0.1                   320   \n",
       "100   27       0              0.6               0.2                   161   \n",
       "101   27       0              0.7               0.2                   243   \n",
       "103   55       1              0.8               0.2                   225   \n",
       "105   36       0              5.3               2.3                   145   \n",
       "106   36       0              5.3               2.3                   145   \n",
       "107   36       0              0.8               0.2                   158   \n",
       "108   36       0              0.8               0.2                   158   \n",
       "..   ...     ...              ...               ...                   ...   \n",
       "298   31       1              1.1               0.3                   190   \n",
       "301   51       1              0.9               0.2                   280   \n",
       "302   50       1              1.7               0.6                   430   \n",
       "304   55       1              0.8               0.2                   155   \n",
       "306   48       0              1.6               1.0                   588   \n",
       "307   30       0              0.8               0.2                   174   \n",
       "308   45       1              0.8               0.2                   165   \n",
       "309   48       1              1.1               0.7                   527   \n",
       "310   51       0              0.8               0.2                   175   \n",
       "311   54       1             23.2              12.6                   574   \n",
       "314   26       0              2.0               0.9                   195   \n",
       "317   35       0              0.7               0.2                   198   \n",
       "318   38       0              3.7               2.2                   216   \n",
       "320   30       1              0.7               0.2                    63   \n",
       "321   30       1              0.8               0.2                   198   \n",
       "322   36       0              1.7               0.5                   205   \n",
       "324   60       0              2.6               1.2                   171   \n",
       "330   26       0              2.0               0.9                   157   \n",
       "331   26       0              1.7               0.6                   210   \n",
       "332   26       0              7.1               3.3                   258   \n",
       "334   13       1              0.7               0.2                   350   \n",
       "335   13       1              0.7               0.1                   182   \n",
       "336   75       0              6.7               3.6                   458   \n",
       "337   75       0              2.5               1.2                   375   \n",
       "338   75       0              1.8               0.8                   405   \n",
       "339   75       0              1.4               0.4                   215   \n",
       "340   75       0              0.9               0.2                   206   \n",
       "341   36       1              0.8               0.2                   650   \n",
       "343   70       0              3.1               1.6                   198   \n",
       "345   60       0              2.9               1.3                   230   \n",
       "\n",
       "     Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "8                          22                          19             7.4   \n",
       "12                         61                          58             7.0   \n",
       "15                         91                          53             5.5   \n",
       "17                         15                          23             7.3   \n",
       "24                         52                          45             6.0   \n",
       "28                         20                          30             3.9   \n",
       "29                         13                          21             6.0   \n",
       "32                         19                          23             5.2   \n",
       "33                         59                          57             5.6   \n",
       "34                         59                          57             5.6   \n",
       "36                         18                          36             7.2   \n",
       "41                         42                         110             4.9   \n",
       "42                         25                          47             6.1   \n",
       "44                         17                          15             7.0   \n",
       "45                         48                         178             6.5   \n",
       "56                         26                          23             8.0   \n",
       "57                         24                          54             5.5   \n",
       "59                         20                          24             5.5   \n",
       "69                         24                          17             6.3   \n",
       "75                         52                          41             5.2   \n",
       "78                         12                          24             6.2   \n",
       "82                         14                          12             5.4   \n",
       "86                         28                          56             7.2   \n",
       "100                        27                          28             3.7   \n",
       "101                        21                          23             5.3   \n",
       "103                        14                          23             6.1   \n",
       "105                        32                          92             5.1   \n",
       "106                        32                          92             5.1   \n",
       "107                        29                          39             6.0   \n",
       "108                        29                          39             6.0   \n",
       "..                        ...                         ...             ...   \n",
       "298                        26                          15             7.9   \n",
       "301                        21                          30             6.7   \n",
       "302                        28                          32             6.8   \n",
       "304                        21                          17             6.9   \n",
       "306                        74                         113             7.3   \n",
       "307                        21                          47             4.6   \n",
       "308                        22                          18             8.2   \n",
       "309                       178                         250             8.0   \n",
       "310                        48                          22             8.1   \n",
       "311                        43                          47             7.2   \n",
       "314                        24                          65             7.8   \n",
       "317                        42                          30             6.8   \n",
       "318                       179                         232             7.8   \n",
       "320                        31                          27             5.8   \n",
       "321                        30                          58             5.2   \n",
       "322                        36                          34             7.1   \n",
       "324                        42                          37             5.4   \n",
       "330                        54                          68             6.1   \n",
       "331                        62                          56             5.4   \n",
       "332                        80                         113             6.2   \n",
       "334                        17                          24             7.4   \n",
       "335                        24                          19             8.9   \n",
       "336                       198                         143             6.2   \n",
       "337                        85                          68             6.4   \n",
       "338                        79                          50             6.1   \n",
       "339                        50                          30             5.9   \n",
       "340                        44                          33             6.2   \n",
       "341                        70                         138             6.6   \n",
       "343                        40                          28             5.6   \n",
       "345                        32                          44             5.6   \n",
       "\n",
       "     Albumin  Albumin_and_Globulin_Ratio  \n",
       "8        4.1                        1.20  \n",
       "12       3.4                        0.90  \n",
       "15       2.3                        0.70  \n",
       "17       3.5                        0.92  \n",
       "24       3.9                        1.85  \n",
       "28       1.9                        0.95  \n",
       "29       3.2                        1.10  \n",
       "32       2.9                        1.20  \n",
       "33       3.0                        0.80  \n",
       "34       3.0                        0.80  \n",
       "36       3.9                        1.18  \n",
       "41       2.6                        1.10  \n",
       "42       2.3                        0.60  \n",
       "44       3.6                        1.00  \n",
       "45       3.2                        0.90  \n",
       "56       4.0                        1.00  \n",
       "57       2.7                        0.90  \n",
       "59       3.2                        1.39  \n",
       "69       3.0                        0.90  \n",
       "75       2.5                        0.90  \n",
       "78       3.8                        1.58  \n",
       "82       2.8                        1.00  \n",
       "86       3.6                        1.00  \n",
       "100      1.6                        0.76  \n",
       "101      2.3                        0.70  \n",
       "103      3.3                        1.20  \n",
       "105      2.6                        1.00  \n",
       "106      2.6                        1.00  \n",
       "107      2.2                        0.50  \n",
       "108      2.2                        0.50  \n",
       "..       ...                         ...  \n",
       "298      3.8                        0.90  \n",
       "301      3.2                        0.80  \n",
       "302      3.5                        1.00  \n",
       "304      3.8                        1.40  \n",
       "306      2.4                        0.40  \n",
       "307      2.3                        1.00  \n",
       "308      4.1                        1.00  \n",
       "309      4.2                        1.10  \n",
       "310      4.6                        1.30  \n",
       "311      3.5                        0.90  \n",
       "314      4.3                        1.20  \n",
       "317      3.4                        1.00  \n",
       "318      4.5                        1.30  \n",
       "320      3.4                        1.40  \n",
       "321      2.8                        1.10  \n",
       "322      3.9                        1.20  \n",
       "324      2.7                        1.00  \n",
       "330      2.7                        0.80  \n",
       "331      2.2                        0.60  \n",
       "332      2.9                        0.80  \n",
       "334      4.0                        1.10  \n",
       "335      4.9                        1.20  \n",
       "336      3.2                        1.00  \n",
       "337      2.9                        0.80  \n",
       "338      2.9                        0.90  \n",
       "339      2.6                        0.70  \n",
       "340      2.9                        0.80  \n",
       "341      3.1                        0.80  \n",
       "343      2.0                        0.50  \n",
       "345      2.0                        0.50  \n",
       "\n",
       "[417 rows x 10 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of patients diagnosed with liver disease:  250\n",
      "Number of patients not diagnosed with liver disease:  167\n"
     ]
    },
    {
     "data": {
      "image/png": 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9jM5F7AFezOjw7I8PcbO3tvZV1Z1Jfh34beAzSd4HPALcXlUX9vl7SE+F5yCkQ0gyA3wS+FiNjsX+DLC7qh4D3gAc0W19FDhu7KbNfUmeD+ytqk8BVwMvBb4MnJHkF7o9xyR54SHuV1pSPoKQnuinkmxjdJhoP/AZ4IPdzz4B3JTkfOAO4Ifd+t3A/iRfZ/QZyofadybw9iT/C/wAuKiq9iX5Q+CGJEd3+/4c+A9gE/B3SXZX1St7+n2lQ/IktSSpyUNMkqQmAyFJajIQkqQmAyFJajIQkqQmAyFJajIQkqQmAyFJavo/n7KmHYAPwgUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=liverdata, x = 'Dataset', label='Count')\n",
    "\n",
    "LD,NLD = liverdata['Dataset'].value_counts()\n",
    "print('Number of patients diagnosed with liver disease: ',LD)\n",
    "print('Number of patients not diagnosed with liver disease: ',NLD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "liverdata.hist(bins=11,figsize=(12,12))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Archan\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:334: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "# split data\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.3, random_state = 99)\n",
    "\n",
    "\n",
    "#scaling of the data using min-max scaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "sc = MinMaxScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#X_train, X_test, Y_train, Y_test = X_train.astype(np.float64), X_test.astype(np.float64), Y_train.astype(np.float64), Y_test.astype(np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Libraries to Build Ensemble Model : Random Forest Classifier \n",
    "# Create the parameter grid based on the results of random search \n",
    "\n",
    "params_grid = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],\n",
    "                     'C': [1, 10, 100, 1000]},\n",
    "                    {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]\n",
    "\n",
    "#parameters = {'kernel': ['linear'], 'C': [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Archan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n",
      "C:\\Users\\Archan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.001, kernel='rbf',\n",
       "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid=[{'kernel': ['rbf'], 'gamma': [0.001, 0.0001], 'C': [1, 10, 100, 1000]}, {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV\n",
    "\n",
    "# Performing CV to tune parameters for best SVM fit \n",
    "svm_model = GridSearchCV(SVC(kernel=\"rbf\", probability=True, C=0.1, gamma=0.001), params_grid, cv=5)\n",
    "svm_model.fit(X_train, Y_train)\n",
    "\n",
    "\n",
    "#model = SVC(kernel=\"rbf\", probability=True, C=0.1, gamma=0.001, max_iter=10000)\n",
    "#svm_model = GridSearchCV(model, parameters, n_jobs=-1, cv=3)\n",
    "\n",
    "svm_model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_predict = svm_model.predict(X_test)\n",
    "cm = confusion_matrix(Y_test,Y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Prediction_of_Disease</th>\n",
       "      <th>Prediction_of_Healthy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>is_Liver_Disease</th>\n",
       "      <td>38</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_Liver_Disease</th>\n",
       "      <td>8</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Prediction_of_Disease  Prediction_of_Healthy\n",
       "is_Liver_Disease                     38                     40\n",
       "No_Liver_Disease                      8                     40"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = np.array(confusion_matrix(Y_test,Y_predict,labels=[1,0]))\n",
    "\n",
    "confusion=pd.DataFrame(cm, index=['is_Liver_Disease', 'No_Liver_Disease'], columns=['Prediction_of_Disease', 'Prediction_of_Healthy']) \n",
    "confusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.50      0.83      0.62        48\n",
      "           1       0.83      0.49      0.61        78\n",
      "\n",
      "   micro avg       0.62      0.62      0.62       126\n",
      "   macro avg       0.66      0.66      0.62       126\n",
      "weighted avg       0.70      0.62      0.62       126\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(Y_test,Y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 61.90%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "accuracy = accuracy_score(Y_test,Y_predict)\n",
    "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/17\n",
      "291/291 [==============================] - 2s 7ms/step - loss: 0.6922 - acc: 0.5808\n",
      "Epoch 2/17\n",
      "291/291 [==============================] - 0s 186us/step - loss: 0.6907 - acc: 0.5911\n",
      "Epoch 3/17\n",
      "291/291 [==============================] - 0s 207us/step - loss: 0.6891 - acc: 0.5911\n",
      "Epoch 4/17\n",
      "291/291 [==============================] - 0s 192us/step - loss: 0.6875 - acc: 0.5911\n",
      "Epoch 5/17\n",
      "291/291 [==============================] - 0s 193us/step - loss: 0.6859 - acc: 0.5911\n",
      "Epoch 6/17\n",
      "291/291 [==============================] - 0s 186us/step - loss: 0.6841 - acc: 0.5911\n",
      "Epoch 7/17\n",
      "291/291 [==============================] - 0s 217us/step - loss: 0.6820 - acc: 0.5911\n",
      "Epoch 8/17\n",
      "291/291 [==============================] - 0s 224us/step - loss: 0.6799 - acc: 0.5911\n",
      "Epoch 9/17\n",
      "291/291 [==============================] - 0s 193us/step - loss: 0.6776 - acc: 0.5911\n",
      "Epoch 10/17\n",
      "291/291 [==============================] - 0s 189us/step - loss: 0.6752 - acc: 0.5911\n",
      "Epoch 11/17\n",
      "291/291 [==============================] - 0s 224us/step - loss: 0.6733 - acc: 0.5911\n",
      "Epoch 12/17\n",
      "291/291 [==============================] - 0s 186us/step - loss: 0.6714 - acc: 0.5911\n",
      "Epoch 13/17\n",
      "291/291 [==============================] - 0s 196us/step - loss: 0.6694 - acc: 0.5911\n",
      "Epoch 14/17\n",
      "291/291 [==============================] - 0s 193us/step - loss: 0.6682 - acc: 0.5911\n",
      "Epoch 15/17\n",
      "291/291 [==============================] - 0s 176us/step - loss: 0.6666 - acc: 0.5911\n",
      "Epoch 16/17\n",
      "291/291 [==============================] - 0s 155us/step - loss: 0.6652 - acc: 0.5911\n",
      "Epoch 17/17\n",
      "291/291 [==============================] - 0s 189us/step - loss: 0.6640 - acc: 0.5911\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "from keras.layers.convolutional import Convolution2D\n",
    "from keras.utils import to_categorical\n",
    "\n",
    "# Initialising the ANN\n",
    "myliverclassifier = Sequential() \n",
    "myliverclassifier.add(Dense(units = 16, kernel_initializer = 'uniform', activation = 'relu', input_dim = 10))\n",
    "#hidden layer\n",
    "myliverclassifier.add(Dense(units = 8, kernel_initializer = 'uniform', activation = 'relu'))\n",
    "myliverclassifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))\n",
    "#output layer\n",
    "myliverclassifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))\n",
    "\n",
    "\n",
    "\n",
    "#myliverclassifier.add(Dense(60, input_dim=10, kernel_initializer='normal', activation='relu'))\n",
    "#myliverclassifier.add(Dense(1, kernel_initializer='normal', activation='sigmoid'))\n",
    "\n",
    "\n",
    "# compile ANN\n",
    "myliverclassifier.compile(optimizer = 'rmsprop', loss = 'binary_crossentropy', metrics = ['accuracy'])\n",
    "\n",
    "# Fitting the dat\n",
    "history =myliverclassifier.fit(X_train, Y_train, batch_size = 20, epochs =17 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = myliverclassifier.predict(X_test)\n",
    "\n",
    "y_predict = [ 1 if y>=0.5 else 0 for y in y_predict ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 61.90%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "accuracy = accuracy_score(Y_test,Y_predict)\n",
    "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "liverdata=pd.read_csv('liver_patient_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Gender', 'Total_Bilirubin', 'Direct_Bilirubin',\n",
       "       'Alkaline_Phosphotase', 'Alamine_Aminotransferase',\n",
       "       'Aspartate_Aminotransferase', 'Total_Protiens', 'Albumin',\n",
       "       'Albumin_and_Globulin_Ratio', 'Dataset'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datatype=liverdata.dtypes.index\n",
    "datatype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>187</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>10.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>699</td>\n",
       "      <td>64</td>\n",
       "      <td>100</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>490</td>\n",
       "      <td>60</td>\n",
       "      <td>68</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>182</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>195</td>\n",
       "      <td>27</td>\n",
       "      <td>59</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>46</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>208</td>\n",
       "      <td>19</td>\n",
       "      <td>14</td>\n",
       "      <td>7.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>26</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>154</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>29</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>202</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>202</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>7.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>55</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>290</td>\n",
       "      <td>53</td>\n",
       "      <td>58</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>57</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>210</td>\n",
       "      <td>51</td>\n",
       "      <td>59</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>72</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>260</td>\n",
       "      <td>31</td>\n",
       "      <td>56</td>\n",
       "      <td>7.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>64</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>310</td>\n",
       "      <td>61</td>\n",
       "      <td>58</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.90</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>74</td>\n",
       "      <td>Female</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>214</td>\n",
       "      <td>22</td>\n",
       "      <td>30</td>\n",
       "      <td>8.1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>61</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>145</td>\n",
       "      <td>53</td>\n",
       "      <td>41</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.87</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>25</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>183</td>\n",
       "      <td>91</td>\n",
       "      <td>53</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.70</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>38</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>342</td>\n",
       "      <td>168</td>\n",
       "      <td>441</td>\n",
       "      <td>7.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>165</td>\n",
       "      <td>15</td>\n",
       "      <td>23</td>\n",
       "      <td>7.3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.92</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>40</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>293</td>\n",
       "      <td>232</td>\n",
       "      <td>245</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>40</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>293</td>\n",
       "      <td>232</td>\n",
       "      <td>245</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>610</td>\n",
       "      <td>17</td>\n",
       "      <td>28</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.55</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>482</td>\n",
       "      <td>22</td>\n",
       "      <td>34</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>542</td>\n",
       "      <td>116</td>\n",
       "      <td>66</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>40</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>231</td>\n",
       "      <td>16</td>\n",
       "      <td>55</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>63</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>194</td>\n",
       "      <td>52</td>\n",
       "      <td>45</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.85</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>34</td>\n",
       "      <td>Male</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289</td>\n",
       "      <td>875</td>\n",
       "      <td>731</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>34</td>\n",
       "      <td>Male</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289</td>\n",
       "      <td>875</td>\n",
       "      <td>731</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>34</td>\n",
       "      <td>Male</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>240</td>\n",
       "      <td>1680</td>\n",
       "      <td>850</td>\n",
       "      <td>7.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>128</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.95</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>84</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>188</td>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.10</td>\n",
       "      <td>2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>46</td>\n",
       "      <td>Male</td>\n",
       "      <td>10.2</td>\n",
       "      <td>4.2</td>\n",
       "      <td>232</td>\n",
       "      <td>58</td>\n",
       "      <td>140</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>73</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>220</td>\n",
       "      <td>20</td>\n",
       "      <td>43</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>55</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>290</td>\n",
       "      <td>139</td>\n",
       "      <td>87</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>180</td>\n",
       "      <td>25</td>\n",
       "      <td>27</td>\n",
       "      <td>6.1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>189</td>\n",
       "      <td>80</td>\n",
       "      <td>125</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>275</td>\n",
       "      <td>382</td>\n",
       "      <td>330</td>\n",
       "      <td>7.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>26</td>\n",
       "      <td>Male</td>\n",
       "      <td>42.8</td>\n",
       "      <td>19.7</td>\n",
       "      <td>390</td>\n",
       "      <td>75</td>\n",
       "      <td>138</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>66</td>\n",
       "      <td>Male</td>\n",
       "      <td>15.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>356</td>\n",
       "      <td>321</td>\n",
       "      <td>562</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>66</td>\n",
       "      <td>Male</td>\n",
       "      <td>16.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>315</td>\n",
       "      <td>233</td>\n",
       "      <td>384</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>66</td>\n",
       "      <td>Male</td>\n",
       "      <td>17.3</td>\n",
       "      <td>8.5</td>\n",
       "      <td>388</td>\n",
       "      <td>173</td>\n",
       "      <td>367</td>\n",
       "      <td>7.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>64</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>298</td>\n",
       "      <td>31</td>\n",
       "      <td>83</td>\n",
       "      <td>7.2</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>38</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>165</td>\n",
       "      <td>22</td>\n",
       "      <td>34</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.90</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>43</td>\n",
       "      <td>Male</td>\n",
       "      <td>22.5</td>\n",
       "      <td>11.8</td>\n",
       "      <td>143</td>\n",
       "      <td>22</td>\n",
       "      <td>143</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>50</td>\n",
       "      <td>Female</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>191</td>\n",
       "      <td>22</td>\n",
       "      <td>31</td>\n",
       "      <td>7.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>52</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>251</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>20</td>\n",
       "      <td>Female</td>\n",
       "      <td>16.7</td>\n",
       "      <td>8.4</td>\n",
       "      <td>200</td>\n",
       "      <td>91</td>\n",
       "      <td>101</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>16</td>\n",
       "      <td>Male</td>\n",
       "      <td>7.7</td>\n",
       "      <td>4.1</td>\n",
       "      <td>268</td>\n",
       "      <td>213</td>\n",
       "      <td>168</td>\n",
       "      <td>7.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>16</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>236</td>\n",
       "      <td>131</td>\n",
       "      <td>90</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>90</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>215</td>\n",
       "      <td>46</td>\n",
       "      <td>134</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>32</td>\n",
       "      <td>Male</td>\n",
       "      <td>15.6</td>\n",
       "      <td>9.5</td>\n",
       "      <td>134</td>\n",
       "      <td>54</td>\n",
       "      <td>125</td>\n",
       "      <td>5.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>32</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>612</td>\n",
       "      <td>50</td>\n",
       "      <td>88</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>32</td>\n",
       "      <td>Male</td>\n",
       "      <td>12.1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>515</td>\n",
       "      <td>48</td>\n",
       "      <td>92</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>32</td>\n",
       "      <td>Male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>13.7</td>\n",
       "      <td>560</td>\n",
       "      <td>41</td>\n",
       "      <td>88</td>\n",
       "      <td>7.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>32</td>\n",
       "      <td>Male</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>289</td>\n",
       "      <td>58</td>\n",
       "      <td>80</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>32</td>\n",
       "      <td>Male</td>\n",
       "      <td>12.7</td>\n",
       "      <td>8.4</td>\n",
       "      <td>190</td>\n",
       "      <td>28</td>\n",
       "      <td>47</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>60</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>34</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.37</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>40</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>98</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>52</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>245</td>\n",
       "      <td>48</td>\n",
       "      <td>49</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>184</td>\n",
       "      <td>29</td>\n",
       "      <td>32</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>38</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>216</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>583 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "0     65  Female              0.7               0.1                   187   \n",
       "1     62    Male             10.9               5.5                   699   \n",
       "2     62    Male              7.3               4.1                   490   \n",
       "3     58    Male              1.0               0.4                   182   \n",
       "4     72    Male              3.9               2.0                   195   \n",
       "5     46    Male              1.8               0.7                   208   \n",
       "6     26  Female              0.9               0.2                   154   \n",
       "7     29  Female              0.9               0.3                   202   \n",
       "8     17    Male              0.9               0.3                   202   \n",
       "9     55    Male              0.7               0.2                   290   \n",
       "10    57    Male              0.6               0.1                   210   \n",
       "11    72    Male              2.7               1.3                   260   \n",
       "12    64    Male              0.9               0.3                   310   \n",
       "13    74  Female              1.1               0.4                   214   \n",
       "14    61    Male              0.7               0.2                   145   \n",
       "15    25    Male              0.6               0.1                   183   \n",
       "16    38    Male              1.8               0.8                   342   \n",
       "17    33    Male              1.6               0.5                   165   \n",
       "18    40  Female              0.9               0.3                   293   \n",
       "19    40  Female              0.9               0.3                   293   \n",
       "20    51    Male              2.2               1.0                   610   \n",
       "21    51    Male              2.9               1.3                   482   \n",
       "22    62    Male              6.8               3.0                   542   \n",
       "23    40    Male              1.9               1.0                   231   \n",
       "24    63    Male              0.9               0.2                   194   \n",
       "25    34    Male              4.1               2.0                   289   \n",
       "26    34    Male              4.1               2.0                   289   \n",
       "27    34    Male              6.2               3.0                   240   \n",
       "28    20    Male              1.1               0.5                   128   \n",
       "29    84  Female              0.7               0.2                   188   \n",
       "..   ...     ...              ...               ...                   ...   \n",
       "553   46    Male             10.2               4.2                   232   \n",
       "554   73    Male              1.8               0.9                   220   \n",
       "555   55    Male              0.8               0.2                   290   \n",
       "556   51    Male              0.7               0.1                   180   \n",
       "557   51    Male              2.9               1.2                   189   \n",
       "558   51    Male              4.0               2.5                   275   \n",
       "559   26    Male             42.8              19.7                   390   \n",
       "560   66    Male             15.2               7.7                   356   \n",
       "561   66    Male             16.6               7.6                   315   \n",
       "562   66    Male             17.3               8.5                   388   \n",
       "563   64    Male              1.4               0.5                   298   \n",
       "564   38  Female              0.6               0.1                   165   \n",
       "565   43    Male             22.5              11.8                   143   \n",
       "566   50  Female              1.0               0.3                   191   \n",
       "567   52    Male              2.7               1.4                   251   \n",
       "568   20  Female             16.7               8.4                   200   \n",
       "569   16    Male              7.7               4.1                   268   \n",
       "570   16    Male              2.6               1.2                   236   \n",
       "571   90    Male              1.1               0.3                   215   \n",
       "572   32    Male             15.6               9.5                   134   \n",
       "573   32    Male              3.7               1.6                   612   \n",
       "574   32    Male             12.1               6.0                   515   \n",
       "575   32    Male             25.0              13.7                   560   \n",
       "576   32    Male             15.0               8.2                   289   \n",
       "577   32    Male             12.7               8.4                   190   \n",
       "578   60    Male              0.5               0.1                   500   \n",
       "579   40    Male              0.6               0.1                    98   \n",
       "580   52    Male              0.8               0.2                   245   \n",
       "581   31    Male              1.3               0.5                   184   \n",
       "582   38    Male              1.0               0.3                   216   \n",
       "\n",
       "     Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "0                          16                          18             6.8   \n",
       "1                          64                         100             7.5   \n",
       "2                          60                          68             7.0   \n",
       "3                          14                          20             6.8   \n",
       "4                          27                          59             7.3   \n",
       "5                          19                          14             7.6   \n",
       "6                          16                          12             7.0   \n",
       "7                          14                          11             6.7   \n",
       "8                          22                          19             7.4   \n",
       "9                          53                          58             6.8   \n",
       "10                         51                          59             5.9   \n",
       "11                         31                          56             7.4   \n",
       "12                         61                          58             7.0   \n",
       "13                         22                          30             8.1   \n",
       "14                         53                          41             5.8   \n",
       "15                         91                          53             5.5   \n",
       "16                        168                         441             7.6   \n",
       "17                         15                          23             7.3   \n",
       "18                        232                         245             6.8   \n",
       "19                        232                         245             6.8   \n",
       "20                         17                          28             7.3   \n",
       "21                         22                          34             7.0   \n",
       "22                        116                          66             6.4   \n",
       "23                         16                          55             4.3   \n",
       "24                         52                          45             6.0   \n",
       "25                        875                         731             5.0   \n",
       "26                        875                         731             5.0   \n",
       "27                       1680                         850             7.2   \n",
       "28                         20                          30             3.9   \n",
       "29                         13                          21             6.0   \n",
       "..                        ...                         ...             ...   \n",
       "553                        58                         140             7.0   \n",
       "554                        20                          43             6.5   \n",
       "555                       139                          87             7.0   \n",
       "556                        25                          27             6.1   \n",
       "557                        80                         125             6.2   \n",
       "558                       382                         330             7.5   \n",
       "559                        75                         138             7.5   \n",
       "560                       321                         562             6.5   \n",
       "561                       233                         384             6.9   \n",
       "562                       173                         367             7.8   \n",
       "563                        31                          83             7.2   \n",
       "564                        22                          34             5.9   \n",
       "565                        22                         143             6.6   \n",
       "566                        22                          31             7.8   \n",
       "567                        20                          40             6.0   \n",
       "568                        91                         101             6.9   \n",
       "569                       213                         168             7.1   \n",
       "570                       131                          90             5.4   \n",
       "571                        46                         134             6.9   \n",
       "572                        54                         125             5.6   \n",
       "573                        50                          88             6.2   \n",
       "574                        48                          92             6.6   \n",
       "575                        41                          88             7.9   \n",
       "576                        58                          80             5.3   \n",
       "577                        28                          47             5.4   \n",
       "578                        20                          34             5.9   \n",
       "579                        35                          31             6.0   \n",
       "580                        48                          49             6.4   \n",
       "581                        29                          32             6.8   \n",
       "582                        21                          24             7.3   \n",
       "\n",
       "     Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
       "0        3.3                        0.90        1  \n",
       "1        3.2                        0.74        1  \n",
       "2        3.3                        0.89        1  \n",
       "3        3.4                        1.00        1  \n",
       "4        2.4                        0.40        1  \n",
       "5        4.4                        1.30        1  \n",
       "6        3.5                        1.00        1  \n",
       "7        3.6                        1.10        1  \n",
       "8        4.1                        1.20        2  \n",
       "9        3.4                        1.00        1  \n",
       "10       2.7                        0.80        1  \n",
       "11       3.0                        0.60        1  \n",
       "12       3.4                        0.90        2  \n",
       "13       4.1                        1.00        1  \n",
       "14       2.7                        0.87        1  \n",
       "15       2.3                        0.70        2  \n",
       "16       4.4                        1.30        1  \n",
       "17       3.5                        0.92        2  \n",
       "18       3.1                        0.80        1  \n",
       "19       3.1                        0.80        1  \n",
       "20       2.6                        0.55        1  \n",
       "21       2.4                        0.50        1  \n",
       "22       3.1                        0.90        1  \n",
       "23       1.6                        0.60        1  \n",
       "24       3.9                        1.85        2  \n",
       "25       2.7                        1.10        1  \n",
       "26       2.7                        1.10        1  \n",
       "27       4.0                        1.20        1  \n",
       "28       1.9                        0.95        2  \n",
       "29       3.2                        1.10        2  \n",
       "..       ...                         ...      ...  \n",
       "553      2.7                        0.60        1  \n",
       "554      3.0                        0.80        1  \n",
       "555      3.0                        0.70        1  \n",
       "556      3.1                        1.00        1  \n",
       "557      3.1                        1.00        1  \n",
       "558      4.0                        1.10        1  \n",
       "559      2.6                        0.50        1  \n",
       "560      2.2                        0.40        1  \n",
       "561      2.0                        0.40        1  \n",
       "562      2.6                        0.50        1  \n",
       "563      2.6                        0.50        1  \n",
       "564      2.9                        0.90        2  \n",
       "565      2.1                        0.46        1  \n",
       "566      4.0                        1.00        2  \n",
       "567      1.7                        0.39        1  \n",
       "568      3.5                        1.02        1  \n",
       "569      4.0                        1.20        1  \n",
       "570      2.6                        0.90        1  \n",
       "571      3.0                        0.70        1  \n",
       "572      4.0                        2.50        1  \n",
       "573      1.9                        0.40        1  \n",
       "574      2.4                        0.50        1  \n",
       "575      2.5                        2.50        1  \n",
       "576      2.2                        0.70        1  \n",
       "577      2.6                        0.90        1  \n",
       "578      1.6                        0.37        2  \n",
       "579      3.2                        1.10        1  \n",
       "580      3.2                        1.00        1  \n",
       "581      3.4                        1.00        1  \n",
       "582      4.4                        1.50        2  \n",
       "\n",
       "[583 rows x 11 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liverdata.shape\n",
    "liverdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>187</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.9</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
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       "      <td>5.5</td>\n",
       "      <td>699</td>\n",
       "      <td>64</td>\n",
       "      <td>100</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>490</td>\n",
       "      <td>60</td>\n",
       "      <td>68</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>182</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>195</td>\n",
       "      <td>27</td>\n",
       "      <td>59</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>208</td>\n",
       "      <td>19</td>\n",
       "      <td>14</td>\n",
       "      <td>7.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>154</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>202</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>202</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>7.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>290</td>\n",
       "      <td>53</td>\n",
       "      <td>58</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>210</td>\n",
       "      <td>51</td>\n",
       "      <td>59</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>260</td>\n",
       "      <td>31</td>\n",
       "      <td>56</td>\n",
       "      <td>7.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>310</td>\n",
       "      <td>61</td>\n",
       "      <td>58</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>214</td>\n",
       "      <td>22</td>\n",
       "      <td>30</td>\n",
       "      <td>8.1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>145</td>\n",
       "      <td>53</td>\n",
       "      <td>41</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.87</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>183</td>\n",
       "      <td>91</td>\n",
       "      <td>53</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>342</td>\n",
       "      <td>168</td>\n",
       "      <td>441</td>\n",
       "      <td>7.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>165</td>\n",
       "      <td>15</td>\n",
       "      <td>23</td>\n",
       "      <td>7.3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.92</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>293</td>\n",
       "      <td>232</td>\n",
       "      <td>245</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>293</td>\n",
       "      <td>232</td>\n",
       "      <td>245</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>610</td>\n",
       "      <td>17</td>\n",
       "      <td>28</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.55</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>482</td>\n",
       "      <td>22</td>\n",
       "      <td>34</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>542</td>\n",
       "      <td>116</td>\n",
       "      <td>66</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>231</td>\n",
       "      <td>16</td>\n",
       "      <td>55</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>194</td>\n",
       "      <td>52</td>\n",
       "      <td>45</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.85</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289</td>\n",
       "      <td>875</td>\n",
       "      <td>731</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289</td>\n",
       "      <td>875</td>\n",
       "      <td>731</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>240</td>\n",
       "      <td>1680</td>\n",
       "      <td>850</td>\n",
       "      <td>7.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>128</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.95</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>188</td>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>10.2</td>\n",
       "      <td>4.2</td>\n",
       "      <td>232</td>\n",
       "      <td>58</td>\n",
       "      <td>140</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>220</td>\n",
       "      <td>20</td>\n",
       "      <td>43</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>290</td>\n",
       "      <td>139</td>\n",
       "      <td>87</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>180</td>\n",
       "      <td>25</td>\n",
       "      <td>27</td>\n",
       "      <td>6.1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>189</td>\n",
       "      <td>80</td>\n",
       "      <td>125</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>275</td>\n",
       "      <td>382</td>\n",
       "      <td>330</td>\n",
       "      <td>7.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>42.8</td>\n",
       "      <td>19.7</td>\n",
       "      <td>390</td>\n",
       "      <td>75</td>\n",
       "      <td>138</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>15.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>356</td>\n",
       "      <td>321</td>\n",
       "      <td>562</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>16.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>315</td>\n",
       "      <td>233</td>\n",
       "      <td>384</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>17.3</td>\n",
       "      <td>8.5</td>\n",
       "      <td>388</td>\n",
       "      <td>173</td>\n",
       "      <td>367</td>\n",
       "      <td>7.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>298</td>\n",
       "      <td>31</td>\n",
       "      <td>83</td>\n",
       "      <td>7.2</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>165</td>\n",
       "      <td>22</td>\n",
       "      <td>34</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>22.5</td>\n",
       "      <td>11.8</td>\n",
       "      <td>143</td>\n",
       "      <td>22</td>\n",
       "      <td>143</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>191</td>\n",
       "      <td>22</td>\n",
       "      <td>31</td>\n",
       "      <td>7.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>251</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>16.7</td>\n",
       "      <td>8.4</td>\n",
       "      <td>200</td>\n",
       "      <td>91</td>\n",
       "      <td>101</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>7.7</td>\n",
       "      <td>4.1</td>\n",
       "      <td>268</td>\n",
       "      <td>213</td>\n",
       "      <td>168</td>\n",
       "      <td>7.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>236</td>\n",
       "      <td>131</td>\n",
       "      <td>90</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>215</td>\n",
       "      <td>46</td>\n",
       "      <td>134</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>15.6</td>\n",
       "      <td>9.5</td>\n",
       "      <td>134</td>\n",
       "      <td>54</td>\n",
       "      <td>125</td>\n",
       "      <td>5.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>612</td>\n",
       "      <td>50</td>\n",
       "      <td>88</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>12.1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>515</td>\n",
       "      <td>48</td>\n",
       "      <td>92</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>13.7</td>\n",
       "      <td>560</td>\n",
       "      <td>41</td>\n",
       "      <td>88</td>\n",
       "      <td>7.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>289</td>\n",
       "      <td>58</td>\n",
       "      <td>80</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>12.7</td>\n",
       "      <td>8.4</td>\n",
       "      <td>190</td>\n",
       "      <td>28</td>\n",
       "      <td>47</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>34</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.37</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>98</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>245</td>\n",
       "      <td>48</td>\n",
       "      <td>49</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>184</td>\n",
       "      <td>29</td>\n",
       "      <td>32</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>216</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>583 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "0     65       0              0.7               0.1                   187   \n",
       "1     62       1             10.9               5.5                   699   \n",
       "2     62       1              7.3               4.1                   490   \n",
       "3     58       1              1.0               0.4                   182   \n",
       "4     72       1              3.9               2.0                   195   \n",
       "5     46       1              1.8               0.7                   208   \n",
       "6     26       0              0.9               0.2                   154   \n",
       "7     29       0              0.9               0.3                   202   \n",
       "8     17       1              0.9               0.3                   202   \n",
       "9     55       1              0.7               0.2                   290   \n",
       "10    57       1              0.6               0.1                   210   \n",
       "11    72       1              2.7               1.3                   260   \n",
       "12    64       1              0.9               0.3                   310   \n",
       "13    74       0              1.1               0.4                   214   \n",
       "14    61       1              0.7               0.2                   145   \n",
       "15    25       1              0.6               0.1                   183   \n",
       "16    38       1              1.8               0.8                   342   \n",
       "17    33       1              1.6               0.5                   165   \n",
       "18    40       0              0.9               0.3                   293   \n",
       "19    40       0              0.9               0.3                   293   \n",
       "20    51       1              2.2               1.0                   610   \n",
       "21    51       1              2.9               1.3                   482   \n",
       "22    62       1              6.8               3.0                   542   \n",
       "23    40       1              1.9               1.0                   231   \n",
       "24    63       1              0.9               0.2                   194   \n",
       "25    34       1              4.1               2.0                   289   \n",
       "26    34       1              4.1               2.0                   289   \n",
       "27    34       1              6.2               3.0                   240   \n",
       "28    20       1              1.1               0.5                   128   \n",
       "29    84       0              0.7               0.2                   188   \n",
       "..   ...     ...              ...               ...                   ...   \n",
       "553   46       1             10.2               4.2                   232   \n",
       "554   73       1              1.8               0.9                   220   \n",
       "555   55       1              0.8               0.2                   290   \n",
       "556   51       1              0.7               0.1                   180   \n",
       "557   51       1              2.9               1.2                   189   \n",
       "558   51       1              4.0               2.5                   275   \n",
       "559   26       1             42.8              19.7                   390   \n",
       "560   66       1             15.2               7.7                   356   \n",
       "561   66       1             16.6               7.6                   315   \n",
       "562   66       1             17.3               8.5                   388   \n",
       "563   64       1              1.4               0.5                   298   \n",
       "564   38       0              0.6               0.1                   165   \n",
       "565   43       1             22.5              11.8                   143   \n",
       "566   50       0              1.0               0.3                   191   \n",
       "567   52       1              2.7               1.4                   251   \n",
       "568   20       0             16.7               8.4                   200   \n",
       "569   16       1              7.7               4.1                   268   \n",
       "570   16       1              2.6               1.2                   236   \n",
       "571   90       1              1.1               0.3                   215   \n",
       "572   32       1             15.6               9.5                   134   \n",
       "573   32       1              3.7               1.6                   612   \n",
       "574   32       1             12.1               6.0                   515   \n",
       "575   32       1             25.0              13.7                   560   \n",
       "576   32       1             15.0               8.2                   289   \n",
       "577   32       1             12.7               8.4                   190   \n",
       "578   60       1              0.5               0.1                   500   \n",
       "579   40       1              0.6               0.1                    98   \n",
       "580   52       1              0.8               0.2                   245   \n",
       "581   31       1              1.3               0.5                   184   \n",
       "582   38       1              1.0               0.3                   216   \n",
       "\n",
       "     Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "0                          16                          18             6.8   \n",
       "1                          64                         100             7.5   \n",
       "2                          60                          68             7.0   \n",
       "3                          14                          20             6.8   \n",
       "4                          27                          59             7.3   \n",
       "5                          19                          14             7.6   \n",
       "6                          16                          12             7.0   \n",
       "7                          14                          11             6.7   \n",
       "8                          22                          19             7.4   \n",
       "9                          53                          58             6.8   \n",
       "10                         51                          59             5.9   \n",
       "11                         31                          56             7.4   \n",
       "12                         61                          58             7.0   \n",
       "13                         22                          30             8.1   \n",
       "14                         53                          41             5.8   \n",
       "15                         91                          53             5.5   \n",
       "16                        168                         441             7.6   \n",
       "17                         15                          23             7.3   \n",
       "18                        232                         245             6.8   \n",
       "19                        232                         245             6.8   \n",
       "20                         17                          28             7.3   \n",
       "21                         22                          34             7.0   \n",
       "22                        116                          66             6.4   \n",
       "23                         16                          55             4.3   \n",
       "24                         52                          45             6.0   \n",
       "25                        875                         731             5.0   \n",
       "26                        875                         731             5.0   \n",
       "27                       1680                         850             7.2   \n",
       "28                         20                          30             3.9   \n",
       "29                         13                          21             6.0   \n",
       "..                        ...                         ...             ...   \n",
       "553                        58                         140             7.0   \n",
       "554                        20                          43             6.5   \n",
       "555                       139                          87             7.0   \n",
       "556                        25                          27             6.1   \n",
       "557                        80                         125             6.2   \n",
       "558                       382                         330             7.5   \n",
       "559                        75                         138             7.5   \n",
       "560                       321                         562             6.5   \n",
       "561                       233                         384             6.9   \n",
       "562                       173                         367             7.8   \n",
       "563                        31                          83             7.2   \n",
       "564                        22                          34             5.9   \n",
       "565                        22                         143             6.6   \n",
       "566                        22                          31             7.8   \n",
       "567                        20                          40             6.0   \n",
       "568                        91                         101             6.9   \n",
       "569                       213                         168             7.1   \n",
       "570                       131                          90             5.4   \n",
       "571                        46                         134             6.9   \n",
       "572                        54                         125             5.6   \n",
       "573                        50                          88             6.2   \n",
       "574                        48                          92             6.6   \n",
       "575                        41                          88             7.9   \n",
       "576                        58                          80             5.3   \n",
       "577                        28                          47             5.4   \n",
       "578                        20                          34             5.9   \n",
       "579                        35                          31             6.0   \n",
       "580                        48                          49             6.4   \n",
       "581                        29                          32             6.8   \n",
       "582                        21                          24             7.3   \n",
       "\n",
       "     Albumin Albumin_and_Globulin_Ratio  Dataset  \n",
       "0        3.3                        0.9        1  \n",
       "1        3.2                       0.74        1  \n",
       "2        3.3                       0.89        1  \n",
       "3        3.4                          1        1  \n",
       "4        2.4                        0.4        1  \n",
       "5        4.4                        1.3        1  \n",
       "6        3.5                          1        1  \n",
       "7        3.6                        1.1        1  \n",
       "8        4.1                        1.2        2  \n",
       "9        3.4                          1        1  \n",
       "10       2.7                        0.8        1  \n",
       "11       3.0                        0.6        1  \n",
       "12       3.4                        0.9        2  \n",
       "13       4.1                          1        1  \n",
       "14       2.7                       0.87        1  \n",
       "15       2.3                        0.7        2  \n",
       "16       4.4                        1.3        1  \n",
       "17       3.5                       0.92        2  \n",
       "18       3.1                        0.8        1  \n",
       "19       3.1                        0.8        1  \n",
       "20       2.6                       0.55        1  \n",
       "21       2.4                        0.5        1  \n",
       "22       3.1                        0.9        1  \n",
       "23       1.6                        0.6        1  \n",
       "24       3.9                       1.85        2  \n",
       "25       2.7                        1.1        1  \n",
       "26       2.7                        1.1        1  \n",
       "27       4.0                        1.2        1  \n",
       "28       1.9                       0.95        2  \n",
       "29       3.2                        1.1        2  \n",
       "..       ...                        ...      ...  \n",
       "553      2.7                        0.6        1  \n",
       "554      3.0                        0.8        1  \n",
       "555      3.0                        0.7        1  \n",
       "556      3.1                          1        1  \n",
       "557      3.1                          1        1  \n",
       "558      4.0                        1.1        1  \n",
       "559      2.6                        0.5        1  \n",
       "560      2.2                        0.4        1  \n",
       "561      2.0                        0.4        1  \n",
       "562      2.6                        0.5        1  \n",
       "563      2.6                        0.5        1  \n",
       "564      2.9                        0.9        2  \n",
       "565      2.1                       0.46        1  \n",
       "566      4.0                          1        2  \n",
       "567      1.7                       0.39        1  \n",
       "568      3.5                       1.02        1  \n",
       "569      4.0                        1.2        1  \n",
       "570      2.6                        0.9        1  \n",
       "571      3.0                        0.7        1  \n",
       "572      4.0                        2.5        1  \n",
       "573      1.9                        0.4        1  \n",
       "574      2.4                        0.5        1  \n",
       "575      2.5                        2.5        1  \n",
       "576      2.2                        0.7        1  \n",
       "577      2.6                        0.9        1  \n",
       "578      1.6                       0.37        2  \n",
       "579      3.2                        1.1        1  \n",
       "580      3.2                          1        1  \n",
       "581      3.4                          1        1  \n",
       "582      4.4                        1.5        2  \n",
       "\n",
       "[583 rows x 11 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liverdata[\"Albumin_and_Globulin_Ratio\"].fillna(\"0.6\", inplace = True)\n",
    "liverdata['Gender']=liverdata.Gender.map(dict(Female=0,Male=1))\n",
    "liverdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of patients diagnosed with liver disease:  416\n",
      "Number of patients not diagnosed with liver disease:  167\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEKCAYAAAAIO8L1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAEvlJREFUeJzt3X2wpnV93/H3x+VBLURUDnbdXbM0rk0wras9pUyYtATyAORhSUYcaCNby8zaGWwlWitmOtWkZcZMoiTmgc5akMWxIBENW4e0pYilthE8S1Zk2Vi3SuVkt+wxIIJW2oVv/7h/OxyW355zL9nr3AfO+zVzz31d3+t3Xfu9Z3b3M9dzqgpJkg71okk3IElangwISVKXASFJ6jIgJEldBoQkqcuAkCR1GRCSpC4DQpLUZUBIkrqOmXQDfxknn3xyrV+/ftJtSNLzyo4dO75VVVOLjXteB8T69euZmZmZdBuS9LyS5H+NM85DTJKkLgNCktRlQEiSugwISVKXASFJ6jIgJEldBoQkqcuAkCR1GRCSpK7n9Z3UR8Pfes/1k25By9CO37xk0i1IE+cehCSpy4CQJHUZEJKkrsEDIsmqJH+a5LNt/tQkdyX5WpJPJjmu1Y9v83va8vVD9yZJOryl2IN4J7B73vxvAFdV1QbgEeDSVr8UeKSqXgtc1cZJkiZk0IBIshb4WeDftvkAZwOfakO2ARe06U1tnrb8nDZekjQBQ+9B/Dbwz4Gn2vwrgW9X1YE2PwusadNrgAcB2vJH23hJ0gQMFhBJfg7YX1U75pc7Q2uMZfO3uyXJTJKZubm5o9CpJKlnyD2IM4FfSPIAcCOjQ0u/DZyU5OANemuBvW16FlgH0Ja/DHj40I1W1daqmq6q6ampRV+pKkl6jgYLiKp6X1Wtrar1wEXA56rqHwB3AG9uwzYDt7Tp7W2etvxzVfWsPQhJ0tKYxH0Q7wXelWQPo3MM17T6NcArW/1dwBUT6E2S1CzJs5iq6vPA59v014HTO2O+D1y4FP1IkhbnndSSpC4DQpLUZUBIkroMCElSlwEhSeoyICRJXQaEJKnLgJAkdRkQkqQuA0KS1GVASJK6DAhJUpcBIUnqMiAkSV0GhCSpy4CQJHUNFhBJXpzk7iRfTrIrya+1+nVJvpFkZ/tsbPUk+UiSPUnuTfKmoXqTJC1uyDfKPQGcXVWPJzkW+EKSP27L3lNVnzpk/HnAhvb5O8DV7VuSNAGD7UHUyONt9tj2qQVW2QRc39b7InBSktVD9SdJWtig5yCSrEqyE9gP3FZVd7VFV7bDSFclOb7V1gAPzlt9ttUkSRMwaEBU1ZNVtRFYC5ye5EeB9wE/DPxt4BXAe9vw9DZxaCHJliQzSWbm5uYG6lyStCRXMVXVt4HPA+dW1b52GOkJ4GPA6W3YLLBu3mprgb2dbW2tqumqmp6amhq4c0lauYa8imkqyUlt+iXATwJ/dvC8QpIAFwD3tVW2A5e0q5nOAB6tqn1D9SdJWtiQVzGtBrYlWcUoiG6qqs8m+VySKUaHlHYC/7iNvxU4H9gDfA9424C9SZIWMVhAVNW9wBs79bMPM76Ay4bqR5J0ZLyTWpLUZUBIkroMCElSlwEhSeoyICRJXQaEJKnLgJAkdRkQkqQuA0KS1GVASJK6DAhJUpcBIUnqMiAkSV0GhCSpy4CQJHUZEJKkriFfOfriJHcn+XKSXUl+rdVPTXJXkq8l+WSS41r9+Da/py1fP1RvkqTFDbkH8QRwdlW9AdgInNveNf0bwFVVtQF4BLi0jb8UeKSqXgtc1cZJkiZksICokcfb7LHtU8DZwKdafRtwQZve1OZpy89JkqH6kyQtbNBzEElWJdkJ7AduA/4n8O2qOtCGzAJr2vQa4EGAtvxR4JWdbW5JMpNkZm5ubsj2JWlFGzQgqurJqtoIrAVOB36kN6x99/YW6lmFqq1VNV1V01NTU0evWUnSMyzJVUxV9W3g88AZwElJjmmL1gJ72/QssA6gLX8Z8PBS9CdJerYhr2KaSnJSm34J8JPAbuAO4M1t2Gbglja9vc3Tln+uqp61ByFJWhrHLD7kOVsNbEuyilEQ3VRVn01yP3Bjkn8N/ClwTRt/DfDxJHsY7TlcNGBvkqRFDBYQVXUv8MZO/euMzkccWv8+cOFQ/UiSjox3UkuSugwISVKXASFJ6jIgJEldBoQkqcuAkCR1GRCSpC4DQpLUZUBIkroMCElSlwEhSeoyICRJXQaEJKnLgJAkdRkQkqQuA0KS1DXkK0fXJbkjye4ku5K8s9U/kOTPk+xsn/PnrfO+JHuSfDXJzwzVmyRpcUO+cvQA8O6quifJicCOJLe1ZVdV1W/NH5zkNEavGX098GrgPyd5XVU9OWCPkqTDGGwPoqr2VdU9bfoxYDewZoFVNgE3VtUTVfUNYA+dV5NKkpbGkpyDSLKe0fup72qldyS5N8m1SV7eamuAB+etNksnUJJsSTKTZGZubm7AriVpZRs8IJKcANwMXF5V3wGuBn4I2AjsAz50cGhn9XpWoWprVU1X1fTU1NRAXUuSBg2IJMcyCodPVNWnAarqoap6sqqeAj7K04eRZoF181ZfC+wdsj9J0uENeRVTgGuA3VX14Xn11fOG/SJwX5veDlyU5PgkpwIbgLuH6k+StLCxrmJKcntVnbNY7RBnAm8FvpJkZ6v9KnBxko2MDh89ALwdoKp2JbkJuJ/RFVCXeQWTJE3OggGR5MXAS4GT28nkg+cJfoDRpaiHVVVfoH9e4dYF1rkSuHKh7UqSlsZiexBvBy5nFAY7ePo//O8Avz9gX5KkCVswIKrqd4DfSfJPqup3l6gnSdIyMNY5iKr63SQ/Bqyfv05VXT9QX5KkCRv3JPXHGd27sBM4eOK4AANCkl6gxn0W0zRwWlU968Y1SdIL07j3QdwH/NUhG5EkLS/j7kGcDNyf5G7giYPFqvqFQbqSJE3cuAHxgSGbkCQtP+NexfRfhm5EkrS8jHsV02M8/WTV44Bjge9W1Q8M1ZgkabLG3YM4cf58kgvwZT6S9IL2nJ7mWlV/BJx9lHuRJC0j4x5i+qV5sy9idF+E90RI0gvYuFcx/fy86QOMHtO96ah3I0laNsY9B/G2oRuRJC0vY52DSLI2yWeS7E/yUJKbk6wdujlJ0uSMe5L6Y4xeCfpqYA3w71vtsJKsS3JHkt1JdiV5Z6u/IsltSb7Wvl/e6knykSR7ktyb5E3P/WdJkv6yxg2Iqar6WFUdaJ/rgKlF1jkAvLuqfgQ4A7gsyWnAFcDtVbUBuL3NA5zH6D3UG4AtwNVH9lMkSUfTuAHxrSS/nGRV+/wy8BcLrVBV+6rqnjb9GLCb0d7HJmBbG7YNuKBNbwKur5EvAiclWX2Ev0eSdJSMGxD/CHgL8L+BfcCbgbFPXCdZD7wRuAt4VVXtg1GIAKe0YWuAB+etNttqh25rS5KZJDNzc3PjtiBJOkLjBsS/AjZX1VRVncIoMD4wzopJTgBuBi6vqu8sNLRTe9a9FlW1taqmq2p6amqxo1ySpOdq3ID4m1X1yMGZqnqY0R7BgpIcyygcPlFVn27lhw4eOmrf+1t9Flg3b/W1wN4x+5MkHWXjBsSLDl5tBKMrkVjkHookAa4BdlfVh+ct2g5sbtObgVvm1S9pVzOdATx68FCUJGnpjXsn9YeA/57kU4wO+7wFuHKRdc4E3gp8JcnOVvtV4IPATUkuBb4JXNiW3QqcD+wBvscRnOOQJB19495JfX2SGUYP6AvwS1V1/yLrfIH+eQWAczrjC7hsnH4kScMbdw+CFggLhoIk6YXjOT3uW5L0wmdASJK6DAhJUpcBIUnqMiAkSV0GhCSpy4CQJHUZEJKkLgNCktRlQEiSusZ+1IakpfXNX/8bk25By9Br/uVXluzPcg9CktRlQEiSugwISVLXYAGR5Nok+5PcN6/2gSR/nmRn+5w/b9n7kuxJ8tUkPzNUX5Kk8Qy5B3EdcG6nflVVbWyfWwGSnAZcBLy+rfMHSVYN2JskaRGDBURV3Qk8PObwTcCNVfVEVX2D0WtHTx+qN0nS4iZxDuIdSe5th6Be3mprgAfnjZltNUnShCx1QFwN/BCwEdgHfKjVe++urt4GkmxJMpNkZm5ubpguJUlLGxBV9VBVPVlVTwEf5enDSLPAunlD1wJ7D7ONrVU1XVXTU1NTwzYsSSvYkgZEktXzZn8ROHiF03bgoiTHJzkV2ADcvZS9SZKeabBHbSS5ATgLODnJLPB+4KwkGxkdPnoAeDtAVe1KchNwP3AAuKyqnhyqN0nS4gYLiKq6uFO+ZoHxVwJXDtWPJOnIeCe1JKnLgJAkdRkQkqQuA0KS1GVASJK6DAhJUpcBIUnqMiAkSV0GhCSpy4CQJHUZEJKkLgNCktRlQEiSugwISVKXASFJ6jIgJEldgwVEkmuT7E9y37zaK5LcluRr7fvlrZ4kH0myJ8m9Sd40VF+SpPEMuQdxHXDuIbUrgNuragNwe5sHOI/Re6g3AFuAqwfsS5I0hsECoqruBB4+pLwJ2NamtwEXzKtfXyNfBE5Ksnqo3iRJi1vqcxCvqqp9AO37lFZfAzw4b9xsq0mSJmS5nKROp1bdgcmWJDNJZubm5gZuS5JWrqUOiIcOHjpq3/tbfRZYN2/cWmBvbwNVtbWqpqtqempqatBmJWklW+qA2A5sbtObgVvm1S9pVzOdATx68FCUJGkyjhlqw0luAM4CTk4yC7wf+CBwU5JLgW8CF7bhtwLnA3uA7wFvG6ovSdJ4BguIqrr4MIvO6Ywt4LKhepEkHbnlcpJakrTMGBCSpC4DQpLUZUBIkroMCElSlwEhSeoyICRJXQaEJKnLgJAkdRkQkqQuA0KS1GVASJK6DAhJUpcBIUnqMiAkSV0GhCSpa7AXBi0kyQPAY8CTwIGqmk7yCuCTwHrgAeAtVfXIJPqTJE12D+InqmpjVU23+SuA26tqA3B7m5ckTchyOsS0CdjWprcBF0ywF0la8SYVEAX8pyQ7kmxptVdV1T6A9n3KhHqTJDGhcxDAmVW1N8kpwG1J/mzcFVugbAF4zWteM1R/krTiTWQPoqr2tu/9wGeA04GHkqwGaN/7D7Pu1qqarqrpqamppWpZklacJQ+IJH8lyYkHp4GfBu4DtgOb27DNwC1L3Zsk6WmTOMT0KuAzSQ7++f+uqv5Dki8BNyW5FPgmcOEEepMkNUseEFX1deANnfpfAOcsdT+SpL7ldJmrJGkZMSAkSV0GhCSpy4CQJHUZEJKkLgNCktRlQEiSugwISVKXASFJ6jIgJEldBoQkqcuAkCR1GRCSpC4DQpLUZUBIkroMCElS17ILiCTnJvlqkj1Jrph0P5K0Ui2rgEiyCvh94DzgNODiJKdNtitJWpmWVUAApwN7qurrVfV/gRuBTRPuSZJWpOUWEGuAB+fNz7aaJGmJHTPpBg6RTq2eMSDZAmxps48n+ergXa0cJwPfmnQTy0F+a/OkW9Az+XfzoPf3/ps8Yj84zqDlFhCzwLp582uBvfMHVNVWYOtSNrVSJJmpqulJ9yEdyr+bk7HcDjF9CdiQ5NQkxwEXAdsn3JMkrUjLag+iqg4keQfwH4FVwLVVtWvCbUnSirSsAgKgqm4Fbp10HyuUh+60XPl3cwJSVYuPkiStOMvtHIQkaZkwIESSa5PsT3LfpHuR5kuyLskdSXYn2ZXknZPuaSXxEJNI8neBx4Hrq+pHJ92PdFCS1cDqqronyYnADuCCqrp/wq2tCO5BiKq6E3h40n1Ih6qqfVV1T5t+DNiNT1dYMgaEpOeFJOuBNwJ3TbaTlcOAkLTsJTkBuBm4vKq+M+l+VgoDQtKyluRYRuHwiar69KT7WUkMCEnLVpIA1wC7q+rDk+5npTEgRJIbgD8B/nqS2SSXTronqTkTeCtwdpKd7XP+pJtaKbzMVZLU5R6EJKnLgJAkdRkQkqQuA0KS1GVASJK6DAhpniRPtkspdyX5cpJ3JVnw30mS9Un+/gC9XJ7kpUd7u9K4DAjpmf5PVW2sqtcDPwWcD7x/kXXWA0c9IIDLAQNCE2NASIdRVfuBLcA7MrI+yX9Nck/7/Fgb+kHgx9uex68cblyS1UnubOPuS/Ljrf7TSf6kjf3DJCck+afAq4E7ktwxid8veaOcNE+Sx6vqhENqjwA/DDwGPFVV30+yAbihqqaTnAX8s6r6uTb+pYcZ927gxVV1ZZJVjPYOjgc+DZxXVd9N8l7g+Kr69SQPANNV9a2l+fXSMx0z6Qak54G072OB30uyEXgSeN1hxh9u3JeAa9vD5/6oqnYm+XvAacB/Gz12iOMYPfZEmjgDQlpAkr/G6D/5/YzORTwEvIHR4dnvH2a1X+mNq6o729v7fhb4eJLfBB4Bbquqi4f8HdJz4TkI6TCSTAH/Bvi9Gh2LfRmwr6qeYvQAuVVt6GPAifNW7Y5L8oPA/qr6KKMnlL4J+CJwZpLXtjEvTfK6w2xXWlLuQUjP9JIkOxkdJjoAfBw4+JjpPwBuTnIhcAfw3Va/FziQ5MvAdQuMOwt4T5L/x+gd4JdU1VySfwjckOT4Nu5fAP8D2Ar8cZJ9VfUTA/1e6bA8SS1J6vIQkySpy4CQJHUZEJKkLgNCktRlQEiSugwISVKXASFJ6jIgJEld/x+q/hwxWva6NgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=liverdata, x = 'Dataset', label='Count')\n",
    "\n",
    "LD,NLD = liverdata['Dataset'].value_counts()\n",
    "print('Number of patients diagnosed with liver disease: ',LD)\n",
    "print('Number of patients not diagnosed with liver disease: ',NLD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Archan\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype uint8, int64, float64, object were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\Archan\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:464: DataConversionWarning: Data with input dtype uint8, int64, float64, object were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n",
      "C:\\Users\\Archan\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:22: DataConversionWarning: Data with input dtype uint8, int64, float64, object were all converted to float64 by StandardScaler.\n"
     ]
    }
   ],
   "source": [
    "liverdata_sex = pd.get_dummies(liverdata['Gender'])\n",
    "liverdata_new = pd.concat([liverdata, liverdata_sex], axis=1)\n",
    "liverdata_gender = liverdata_new.drop(labels=['Gender' ],axis=1 )\n",
    "liverdata_gender.columns = ['Age', 'Total_Bilirubin', 'Direct_Bilirubin','Alkaline_Phosphotase','Alamine_Aminotransferase','Aspartate_Aminotransferase','Total_Protiens','Albumin','Albumin_and_Globulin_Ratio','1','0','Dataset']\n",
    "\n",
    "X = liverdata_gender.drop('Dataset',axis=1)\n",
    "Y = liverdata_gender['Dataset']\n",
    "\n",
    "\n",
    "# split data\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.3, random_state = 99)\n",
    "\n",
    "\n",
    "#scaling of the data using min-max scaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Libraries to Build Ensemble Model : Random Forest Classifier \n",
    "# Create the parameter grid based on the results of random search \n",
    "\n",
    "params_grid = [{'kernel': ['linear'], 'C': [1, 10, 100, 1000]},\n",
    "               {'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],\n",
    "                     'C': [1, 10, 100, 1000]}]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.001, kernel='rbf',\n",
       "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid=[{'kernel': ['linear'], 'C': [1, 10, 100, 1000]}, {'kernel': ['rbf'], 'gamma': [0.001, 0.0001], 'C': [1, 10, 100, 1000]}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV\n",
    "\n",
    "# Performing CV to tune parameters for best SVM fit \n",
    "svm_model = GridSearchCV(SVC(kernel=\"rbf\", probability=True, C=0.1, gamma=0.001), params_grid, cv=5)\n",
    "svm_model.fit(X_train, Y_train)\n",
    "\n",
    "\n",
    "#model = SVC(kernel=\"rbf\", probability=True, C=0.1, gamma=0.001, max_iter=10000)\n",
    "#svm_model = GridSearchCV(model, parameters, n_jobs=-1, cv=3)\n",
    "\n",
    "svm_model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_predict = svm_model.predict(X_test)\n",
    "cm = confusion_matrix(Y_test,Y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Prediction_of_Disease</th>\n",
       "      <th>Prediction_of_Healthy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>is_Liver_Disease</th>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_Liver_Disease</th>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Prediction_of_Disease  Prediction_of_Healthy\n",
       "is_Liver_Disease                    132                      0\n",
       "No_Liver_Disease                      0                     43"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = np.array(confusion_matrix(Y_test,Y_predict,labels=[1,0]))\n",
    "\n",
    "confusion=pd.DataFrame(cm, index=['is_Liver_Disease', 'No_Liver_Disease'], columns=['Prediction_of_Disease', 'Prediction_of_Healthy']) \n",
    "confusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        43\n",
      "           1       1.00      1.00      1.00       132\n",
      "\n",
      "   micro avg       1.00      1.00      1.00       175\n",
      "   macro avg       1.00      1.00      1.00       175\n",
      "weighted avg       1.00      1.00      1.00       175\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(Y_test,Y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 100.00%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "accuracy = accuracy_score(Y_test,Y_predict)\n",
    "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/17\n",
      "408/408 [==============================] - 3s 7ms/step - loss: 0.6880 - acc: 0.7475\n",
      "Epoch 2/17\n",
      "408/408 [==============================] - 0s 214us/step - loss: 0.6773 - acc: 0.7574\n",
      "Epoch 3/17\n",
      "408/408 [==============================] - 0s 228us/step - loss: 0.6592 - acc: 0.7574\n",
      "Epoch 4/17\n",
      "408/408 [==============================] - 0s 199us/step - loss: 0.6286 - acc: 0.7574\n",
      "Epoch 5/17\n",
      "408/408 [==============================] - 0s 199us/step - loss: 0.5825 - acc: 0.7574\n",
      "Epoch 6/17\n",
      "408/408 [==============================] - 0s 184us/step - loss: 0.5235 - acc: 0.7574\n",
      "Epoch 7/17\n",
      "408/408 [==============================] - 0s 209us/step - loss: 0.4561 - acc: 0.7574\n",
      "Epoch 8/17\n",
      "408/408 [==============================] - 0s 216us/step - loss: 0.3896 - acc: 0.7574\n",
      "Epoch 9/17\n",
      "408/408 [==============================] - 0s 243us/step - loss: 0.3315 - acc: 0.7574\n",
      "Epoch 10/17\n",
      "408/408 [==============================] - 0s 238us/step - loss: 0.2831 - acc: 0.7574\n",
      "Epoch 11/17\n",
      "408/408 [==============================] - 0s 248us/step - loss: 0.2457 - acc: 0.7574\n",
      "Epoch 12/17\n",
      "408/408 [==============================] - 0s 209us/step - loss: 0.2196 - acc: 0.7574\n",
      "Epoch 13/17\n",
      "408/408 [==============================] - 0s 204us/step - loss: 0.2013 - acc: 0.7574\n",
      "Epoch 14/17\n",
      "408/408 [==============================] - 0s 231us/step - loss: 0.1883 - acc: 0.7574\n",
      "Epoch 15/17\n",
      "408/408 [==============================] - 0s 228us/step - loss: 0.1785 - acc: 0.7574\n",
      "Epoch 16/17\n",
      "408/408 [==============================] - 0s 211us/step - loss: 0.1705 - acc: 0.9020\n",
      "Epoch 17/17\n",
      "408/408 [==============================] - 0s 231us/step - loss: 0.1637 - acc: 0.9926\n"
     ]
    }
   ],
   "source": [
    "myliverclassifier = Sequential() # Initialising the ANN\n",
    "#input layer\n",
    "myliverclassifier.add(Dense(units = 16, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))\n",
    "#hidden layer\n",
    "myliverclassifier.add(Dense(units = 8, kernel_initializer = 'uniform', activation = 'relu'))\n",
    "myliverclassifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))\n",
    "#output layer\n",
    "myliverclassifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))\n",
    "\n",
    "# compile ANN\n",
    "myliverclassifier.compile(optimizer = 'rmsprop', loss = 'binary_crossentropy', metrics = ['accuracy'])\n",
    "\n",
    "# Fitting the dat\n",
    "history =myliverclassifier.fit(X_train, Y_train, batch_size = 20, epochs = 17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a95e018eb8>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use(\"ggplot\")\n",
    "plt.figure()\n",
    "N = 17\n",
    "plt.plot(np.arange(0, N), history.history[\"loss\"], label=\"train_loss\")\n",
    "plt.plot(np.arange(0, N), history.history[\"acc\"], label=\"train_acc\")\n",
    "plt.title(\"Training Loss and Accuracy\")\n",
    "plt.xlabel(\"Epoch #\")\n",
    "plt.ylabel(\"Loss/Accuracy\")\n",
    "plt.legend(loc=\"upper left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = myliverclassifier.predict(X_test)\n",
    "y_predict = [ 1 if y>=0.5 else 0 for y in y_predict ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99        43\n",
      "           1       0.99      1.00      1.00       132\n",
      "\n",
      "   micro avg       0.99      0.99      0.99       175\n",
      "   macro avg       1.00      0.99      0.99       175\n",
      "weighted avg       0.99      0.99      0.99       175\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(Y_test, y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 99.43%\n"
     ]
    }
   ],
   "source": [
    "github"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}