HeartDiseaseModels.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Libs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "from sklearn.metrics import accuracy_score,recall_score,precision_score,confusion_matrix\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import scikitplot as skplt\n",
    "\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#patients_data = pd.read_csv(\"processed.cleveland.data\")\n",
    "patients_data = pd.read_csv(\"processed.cleveland.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>goal</th>\n",
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       "      <td>63</td>\n",
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       "      <td>1</td>\n",
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       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>41</td>\n",
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       "      <td>130</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>148</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>294</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <th>7</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>263</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <th>8</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>199</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>239</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>275</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>139</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>266</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>171</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>110</td>\n",
       "      <td>211</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>150</td>\n",
       "      <td>283</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>162</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>219</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>158</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>340</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>150</td>\n",
       "      <td>226</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>247</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>171</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>140</td>\n",
       "      <td>239</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>151</td>\n",
       "      <td>0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>135</td>\n",
       "      <td>234</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>233</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>179</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>226</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>243</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>140</td>\n",
       "      <td>199</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>302</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>212</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>110</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>417</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>197</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>234</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>275</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>118</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>218</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>124</td>\n",
       "      <td>261</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>136</td>\n",
       "      <td>319</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>166</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>136</td>\n",
       "      <td>315</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>204</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>126</td>\n",
       "      <td>218</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>134</td>\n",
       "      <td>0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "      <td>223</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>181</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>207</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>311</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>134</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>154</td>\n",
       "      <td>232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>164</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>335</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>143</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>205</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>114</td>\n",
       "      <td>318</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>170</td>\n",
       "      <td>225</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>152</td>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>169</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>124</td>\n",
       "      <td>197</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>164</td>\n",
       "      <td>176</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "5     57    1   0       140   192    0        1      148      0      0.4   \n",
       "6     56    0   1       140   294    0        0      153      0      1.3   \n",
       "7     44    1   1       120   263    0        1      173      0      0.0   \n",
       "8     52    1   2       172   199    1        1      162      0      0.5   \n",
       "9     57    1   2       150   168    0        1      174      0      1.6   \n",
       "10    54    1   0       140   239    0        1      160      0      1.2   \n",
       "11    48    0   2       130   275    0        1      139      0      0.2   \n",
       "12    49    1   1       130   266    0        1      171      0      0.6   \n",
       "13    64    1   3       110   211    0        0      144      1      1.8   \n",
       "14    58    0   3       150   283    1        0      162      0      1.0   \n",
       "15    50    0   2       120   219    0        1      158      0      1.6   \n",
       "16    58    0   2       120   340    0        1      172      0      0.0   \n",
       "17    66    0   3       150   226    0        1      114      0      2.6   \n",
       "18    43    1   0       150   247    0        1      171      0      1.5   \n",
       "19    69    0   3       140   239    0        1      151      0      1.8   \n",
       "20    59    1   0       135   234    0        1      161      0      0.5   \n",
       "21    44    1   2       130   233    0        1      179      1      0.4   \n",
       "22    42    1   0       140   226    0        1      178      0      0.0   \n",
       "23    61    1   2       150   243    1        1      137      1      1.0   \n",
       "24    40    1   3       140   199    0        1      178      1      1.4   \n",
       "25    71    0   1       160   302    0        1      162      0      0.4   \n",
       "26    59    1   2       150   212    1        1      157      0      1.6   \n",
       "27    51    1   2       110   175    0        1      123      0      0.6   \n",
       "28    65    0   2       140   417    1        0      157      0      0.8   \n",
       "29    53    1   2       130   197    1        0      152      0      1.2   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "273   58    1   0       100   234    0        1      156      0      0.1   \n",
       "274   47    1   0       110   275    0        0      118      1      1.0   \n",
       "275   52    1   0       125   212    0        1      168      0      1.0   \n",
       "276   58    1   0       146   218    0        1      105      0      2.0   \n",
       "277   57    1   1       124   261    0        1      141      0      0.3   \n",
       "278   58    0   1       136   319    1        0      152      0      0.0   \n",
       "279   61    1   0       138   166    0        0      125      1      3.6   \n",
       "280   42    1   0       136   315    0        1      125      1      1.8   \n",
       "281   52    1   0       128   204    1        1      156      1      1.0   \n",
       "282   59    1   2       126   218    1        1      134      0      2.2   \n",
       "283   40    1   0       152   223    0        1      181      0      0.0   \n",
       "284   61    1   0       140   207    0        0      138      1      1.9   \n",
       "285   46    1   0       140   311    0        1      120      1      1.8   \n",
       "286   59    1   3       134   204    0        1      162      0      0.8   \n",
       "287   57    1   1       154   232    0        0      164      0      0.0   \n",
       "288   57    1   0       110   335    0        1      143      1      3.0   \n",
       "289   55    0   0       128   205    0        2      130      1      2.0   \n",
       "290   61    1   0       148   203    0        1      161      0      0.0   \n",
       "291   58    1   0       114   318    0        2      140      0      4.4   \n",
       "292   58    0   0       170   225    1        0      146      1      2.8   \n",
       "293   67    1   2       152   212    0        0      150      0      0.8   \n",
       "294   44    1   0       120   169    0        1      144      1      2.8   \n",
       "295   63    1   0       140   187    0        0      144      1      4.0   \n",
       "296   63    0   0       124   197    0        1      136      1      0.0   \n",
       "297   59    1   0       164   176    1        0       90      0      1.0   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  goal  \n",
       "0        0   0     1     1  \n",
       "1        0   0     2     1  \n",
       "2        2   0     2     1  \n",
       "3        2   0     2     1  \n",
       "4        2   0     2     1  \n",
       "5        1   0     1     1  \n",
       "6        1   0     2     1  \n",
       "7        2   0     3     1  \n",
       "8        2   0     3     1  \n",
       "9        2   0     2     1  \n",
       "10       2   0     2     1  \n",
       "11       2   0     2     1  \n",
       "12       2   0     2     1  \n",
       "13       1   0     2     1  \n",
       "14       2   0     2     1  \n",
       "15       1   0     2     1  \n",
       "16       2   0     2     1  \n",
       "17       0   0     2     1  \n",
       "18       2   0     2     1  \n",
       "19       2   2     2     1  \n",
       "20       1   0     3     1  \n",
       "21       2   0     2     1  \n",
       "22       2   0     2     1  \n",
       "23       1   0     2     1  \n",
       "24       2   0     3     1  \n",
       "25       2   2     2     1  \n",
       "26       2   0     2     1  \n",
       "27       2   0     2     1  \n",
       "28       2   1     2     1  \n",
       "29       0   0     2     1  \n",
       "..     ...  ..   ...   ...  \n",
       "273      2   1     3     0  \n",
       "274      1   1     2     0  \n",
       "275      2   2     3     0  \n",
       "276      1   1     3     0  \n",
       "277      2   0     3     0  \n",
       "278      2   2     2     0  \n",
       "279      1   1     2     0  \n",
       "280      1   0     1     0  \n",
       "281      1   0     0     0  \n",
       "282      1   1     1     0  \n",
       "283      2   0     3     0  \n",
       "284      2   1     3     0  \n",
       "285      1   2     3     0  \n",
       "286      2   2     2     0  \n",
       "287      2   1     2     0  \n",
       "288      1   1     3     0  \n",
       "289      1   1     3     0  \n",
       "290      2   1     3     0  \n",
       "291      0   3     1     0  \n",
       "292      1   2     1     0  \n",
       "293      1   0     3     0  \n",
       "294      0   0     1     0  \n",
       "295      2   2     3     0  \n",
       "296      1   0     2     0  \n",
       "297      1   2     1     0  \n",
       "298      1   0     3     0  \n",
       "299      1   0     3     0  \n",
       "300      1   2     3     0  \n",
       "301      1   1     3     0  \n",
       "302      1   1     2     0  \n",
       "\n",
       "[303 rows x 14 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_data.head(len(patients_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
      "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'goal'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(patients_data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
      "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
      "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
      "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
      "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
      "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
      "\n",
      "   ca  thal  goal  \n",
      "0   0     1     1  \n",
      "1   0     2     1  \n",
      "2   0     2     1  \n",
      "3   0     2     1  \n",
      "4   0     2     1  \n"
     ]
    },
    {
     "data": {
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       "<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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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
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       "      <th>goal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "      <td>130</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>41</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>148</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>294</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>263</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>199</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>239</td>\n",
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       "      <td>1</td>\n",
       "      <td>160</td>\n",
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       "      <td>1.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>275</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>139</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>266</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>171</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>110</td>\n",
       "      <td>211</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>150</td>\n",
       "      <td>283</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>162</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>219</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>158</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>340</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>150</td>\n",
       "      <td>226</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>247</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>171</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>140</td>\n",
       "      <td>239</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>151</td>\n",
       "      <td>0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>135</td>\n",
       "      <td>234</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>233</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>179</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>226</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>243</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>140</td>\n",
       "      <td>199</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>302</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>212</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>110</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>417</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>197</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>234</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>275</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>118</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>218</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>124</td>\n",
       "      <td>261</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>136</td>\n",
       "      <td>319</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>166</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>136</td>\n",
       "      <td>315</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>204</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>126</td>\n",
       "      <td>218</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>134</td>\n",
       "      <td>0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "      <td>223</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>181</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>207</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>311</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>134</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>154</td>\n",
       "      <td>232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>164</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>335</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>143</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>205</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>114</td>\n",
       "      <td>318</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>170</td>\n",
       "      <td>225</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>152</td>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>169</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>124</td>\n",
       "      <td>197</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>164</td>\n",
       "      <td>176</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "5     57    1   0       140   192    0        1      148      0      0.4   \n",
       "6     56    0   1       140   294    0        0      153      0      1.3   \n",
       "7     44    1   1       120   263    0        1      173      0      0.0   \n",
       "8     52    1   2       172   199    1        1      162      0      0.5   \n",
       "9     57    1   2       150   168    0        1      174      0      1.6   \n",
       "10    54    1   0       140   239    0        1      160      0      1.2   \n",
       "11    48    0   2       130   275    0        1      139      0      0.2   \n",
       "12    49    1   1       130   266    0        1      171      0      0.6   \n",
       "13    64    1   3       110   211    0        0      144      1      1.8   \n",
       "14    58    0   3       150   283    1        0      162      0      1.0   \n",
       "15    50    0   2       120   219    0        1      158      0      1.6   \n",
       "16    58    0   2       120   340    0        1      172      0      0.0   \n",
       "17    66    0   3       150   226    0        1      114      0      2.6   \n",
       "18    43    1   0       150   247    0        1      171      0      1.5   \n",
       "19    69    0   3       140   239    0        1      151      0      1.8   \n",
       "20    59    1   0       135   234    0        1      161      0      0.5   \n",
       "21    44    1   2       130   233    0        1      179      1      0.4   \n",
       "22    42    1   0       140   226    0        1      178      0      0.0   \n",
       "23    61    1   2       150   243    1        1      137      1      1.0   \n",
       "24    40    1   3       140   199    0        1      178      1      1.4   \n",
       "25    71    0   1       160   302    0        1      162      0      0.4   \n",
       "26    59    1   2       150   212    1        1      157      0      1.6   \n",
       "27    51    1   2       110   175    0        1      123      0      0.6   \n",
       "28    65    0   2       140   417    1        0      157      0      0.8   \n",
       "29    53    1   2       130   197    1        0      152      0      1.2   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "273   58    1   0       100   234    0        1      156      0      0.1   \n",
       "274   47    1   0       110   275    0        0      118      1      1.0   \n",
       "275   52    1   0       125   212    0        1      168      0      1.0   \n",
       "276   58    1   0       146   218    0        1      105      0      2.0   \n",
       "277   57    1   1       124   261    0        1      141      0      0.3   \n",
       "278   58    0   1       136   319    1        0      152      0      0.0   \n",
       "279   61    1   0       138   166    0        0      125      1      3.6   \n",
       "280   42    1   0       136   315    0        1      125      1      1.8   \n",
       "281   52    1   0       128   204    1        1      156      1      1.0   \n",
       "282   59    1   2       126   218    1        1      134      0      2.2   \n",
       "283   40    1   0       152   223    0        1      181      0      0.0   \n",
       "284   61    1   0       140   207    0        0      138      1      1.9   \n",
       "285   46    1   0       140   311    0        1      120      1      1.8   \n",
       "286   59    1   3       134   204    0        1      162      0      0.8   \n",
       "287   57    1   1       154   232    0        0      164      0      0.0   \n",
       "288   57    1   0       110   335    0        1      143      1      3.0   \n",
       "289   55    0   0       128   205    0        2      130      1      2.0   \n",
       "290   61    1   0       148   203    0        1      161      0      0.0   \n",
       "291   58    1   0       114   318    0        2      140      0      4.4   \n",
       "292   58    0   0       170   225    1        0      146      1      2.8   \n",
       "293   67    1   2       152   212    0        0      150      0      0.8   \n",
       "294   44    1   0       120   169    0        1      144      1      2.8   \n",
       "295   63    1   0       140   187    0        0      144      1      4.0   \n",
       "296   63    0   0       124   197    0        1      136      1      0.0   \n",
       "297   59    1   0       164   176    1        0       90      0      1.0   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  goal  \n",
       "0        0   0     1     1  \n",
       "1        0   0     2     1  \n",
       "2        2   0     2     1  \n",
       "3        2   0     2     1  \n",
       "4        2   0     2     1  \n",
       "5        1   0     1     1  \n",
       "6        1   0     2     1  \n",
       "7        2   0     3     1  \n",
       "8        2   0     3     1  \n",
       "9        2   0     2     1  \n",
       "10       2   0     2     1  \n",
       "11       2   0     2     1  \n",
       "12       2   0     2     1  \n",
       "13       1   0     2     1  \n",
       "14       2   0     2     1  \n",
       "15       1   0     2     1  \n",
       "16       2   0     2     1  \n",
       "17       0   0     2     1  \n",
       "18       2   0     2     1  \n",
       "19       2   2     2     1  \n",
       "20       1   0     3     1  \n",
       "21       2   0     2     1  \n",
       "22       2   0     2     1  \n",
       "23       1   0     2     1  \n",
       "24       2   0     3     1  \n",
       "25       2   2     2     1  \n",
       "26       2   0     2     1  \n",
       "27       2   0     2     1  \n",
       "28       2   1     2     1  \n",
       "29       0   0     2     1  \n",
       "..     ...  ..   ...   ...  \n",
       "273      2   1     3     0  \n",
       "274      1   1     2     0  \n",
       "275      2   2     3     0  \n",
       "276      1   1     3     0  \n",
       "277      2   0     3     0  \n",
       "278      2   2     2     0  \n",
       "279      1   1     2     0  \n",
       "280      1   0     1     0  \n",
       "281      1   0     0     0  \n",
       "282      1   1     1     0  \n",
       "283      2   0     3     0  \n",
       "284      2   1     3     0  \n",
       "285      1   2     3     0  \n",
       "286      2   2     2     0  \n",
       "287      2   1     2     0  \n",
       "288      1   1     3     0  \n",
       "289      1   1     3     0  \n",
       "290      2   1     3     0  \n",
       "291      0   3     1     0  \n",
       "292      1   2     1     0  \n",
       "293      1   0     3     0  \n",
       "294      0   0     1     0  \n",
       "295      2   2     3     0  \n",
       "296      1   0     2     0  \n",
       "297      1   2     1     0  \n",
       "298      1   0     3     0  \n",
       "299      1   0     3     0  \n",
       "300      1   2     3     0  \n",
       "301      1   1     3     0  \n",
       "302      1   1     2     0  \n",
       "\n",
       "[303 rows x 14 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(patients_data.head(5))\n",
    "patients_data.head(len(patients_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>goal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  goal  \n",
       "0   0     1     1  \n",
       "1   0     2     1  \n",
       "2   0     2     1  \n",
       "3   0     2     1  \n",
       "4   0     2     1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age           int64\n",
       "sex           int64\n",
       "cp            int64\n",
       "trestbps      int64\n",
       "chol          int64\n",
       "fbs           int64\n",
       "restecg       int64\n",
       "thalach       int64\n",
       "exang         int64\n",
       "oldpeak     float64\n",
       "slope         int64\n",
       "ca            int64\n",
       "thal          int64\n",
       "goal          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "goal        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>goal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.544554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.498835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal        goal  \n",
       "count  303.000000  303.000000  \n",
       "mean     2.313531    0.544554  \n",
       "std      0.612277    0.498835  \n",
       "min      0.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      "age         303 non-null int64\n",
      "sex         303 non-null int64\n",
      "cp          303 non-null int64\n",
      "trestbps    303 non-null int64\n",
      "chol        303 non-null int64\n",
      "fbs         303 non-null int64\n",
      "restecg     303 non-null int64\n",
      "thalach     303 non-null int64\n",
      "exang       303 non-null int64\n",
      "oldpeak     303 non-null float64\n",
      "slope       303 non-null int64\n",
      "ca          303 non-null int64\n",
      "thal        303 non-null int64\n",
      "goal        303 non-null int64\n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.2 KB\n"
     ]
    }
   ],
   "source": [
    "patients_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          41\n",
       "sex           2\n",
       "cp            4\n",
       "trestbps     49\n",
       "chol        152\n",
       "fbs           2\n",
       "restecg       3\n",
       "thalach      91\n",
       "exang         2\n",
       "oldpeak      40\n",
       "slope         3\n",
       "ca            5\n",
       "thal          4\n",
       "goal          2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_data.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: goal, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_data['goal'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we can notice that there are: \n",
    "165 patients with heart disease and \n",
    "138 patients without heart disaese"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renaming the column headers for better understanding of visualizations\n",
    "\n",
    "#patients_data.rename(columns ={'age':'Age','sex':'Sex','cp':'Chest_pain','trestbps':'Resting_blood_pressure','chol':'Cholesterol','fbs':'Fasting_blood_sugar',\n",
    "#'restecg':'ECG_results','thalach':'Maximum_heart_rate','exang':'Exercise_induced_angina','oldpeak':'ST_depression','ca':'Major_vessels',\n",
    "#'thal':'Thalassemia_types','target':'Heart_attack','slope':'ST_slope'}, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gender distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x210a64e2f88>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='sex',data=patients_data)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Target Ratio:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x210a8668788>,\n",
       "  <matplotlib.patches.Wedge at 0x210a8669808>],\n",
       " [Text(-0.6355513138373364, -1.0178774619175117, 'True'),\n",
       "  Text(0.5825886169920373, 0.9330543946369394, 'False')],\n",
       " [Text(-0.3707382664051129, -0.5937618527852152, '54.5%'),\n",
       "  Text(0.31777560926838394, 0.5089387607110578, '45.5%')])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "explode =[0.1,0]\n",
    "labels='True','False'\n",
    "plt.pie(patients_data['goal'].value_counts(),explode=explode,autopct='%1.1f%%',labels=labels,shadow=True,startangle=140)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Relation between variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x210a7fe4348>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.heatmap(patients_data.corr(),annot=True,cmap='YlGnBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>goal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  goal  \n",
       "0   0     1     1  \n",
       "1   0     2     1  \n",
       "2   0     2     1  \n",
       "3   0     2     1  \n",
       "4   0     2     1  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data cleaning and preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "patients = patients_data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "patients = patients.replace('?' , np.nan)\n",
    "patients.ca = patients.ca.apply(pd.to_numeric)\n",
    "patients.thal = patients.thal.apply(pd.to_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age           int64\n",
       "sex           int64\n",
       "cp            int64\n",
       "trestbps      int64\n",
       "chol          int64\n",
       "fbs           int64\n",
       "restecg       int64\n",
       "thalach       int64\n",
       "exang         int64\n",
       "oldpeak     float64\n",
       "slope         int64\n",
       "ca            int64\n",
       "thal          int64\n",
       "goal          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "goal        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "patients_data = patients.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "patients_data.fillna(patients_data.median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
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       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>41</td>\n",
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       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  goal  \n",
       "0   0     1     1  \n",
       "1   0     2     1  \n",
       "2   0     2     1  \n",
       "3   0     2     1  \n",
       "4   0     2     1  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Normailization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = patients_data.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[63.  1.  3. ...  0.  1.  1.]\n",
      " [37.  1.  2. ...  0.  2.  1.]\n",
      " [41.  0.  1. ...  0.  2.  1.]\n",
      " ...\n",
      " [68.  1.  0. ...  2.  3.  0.]\n",
      " [57.  1.  0. ...  1.  3.  0.]\n",
      " [57.  0.  1. ...  1.  2.  0.]]\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
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       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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       "      <th>3</th>\n",
       "      <td>56</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  goal  \n",
       "0   0     1     1  \n",
       "1   0     2     1  \n",
       "2   0     2     1  \n",
       "3   0     2     1  \n",
       "4   0     2     1  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "x_scaled = min_max_scaler.fit_transform(x)\n",
    "patients_data_norm = pd.DataFrame(x_scaled, columns=patients_data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         float64\n",
       "sex         float64\n",
       "cp          float64\n",
       "trestbps    float64\n",
       "chol        float64\n",
       "fbs         float64\n",
       "restecg     float64\n",
       "thalach     float64\n",
       "exang       float64\n",
       "oldpeak     float64\n",
       "slope       float64\n",
       "ca          float64\n",
       "thal        float64\n",
       "goal        float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_data_norm.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\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>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
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       "      <td>0.166667</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.250000</td>\n",
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       "      <td>0.333333</td>\n",
       "      <td>0.339623</td>\n",
       "      <td>0.178082</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.770992</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.562500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.245283</td>\n",
       "      <td>0.251142</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.816794</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.129032</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.245283</td>\n",
       "      <td>0.520548</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.702290</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.096774</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  sex        cp  trestbps      chol  fbs  restecg   thalach  exang  \\\n",
       "0  0.708333  1.0  1.000000  0.481132  0.244292  1.0      0.0  0.603053    0.0   \n",
       "1  0.166667  1.0  0.666667  0.339623  0.283105  0.0      0.5  0.885496    0.0   \n",
       "2  0.250000  0.0  0.333333  0.339623  0.178082  0.0      0.0  0.770992    0.0   \n",
       "3  0.562500  1.0  0.333333  0.245283  0.251142  0.0      0.5  0.816794    0.0   \n",
       "4  0.583333  0.0  0.000000  0.245283  0.520548  0.0      0.5  0.702290    1.0   \n",
       "\n",
       "    oldpeak  slope   ca      thal  goal  \n",
       "0  0.370968    0.0  0.0  0.333333   1.0  \n",
       "1  0.564516    0.0  0.0  0.666667   1.0  \n",
       "2  0.225806    1.0  0.0  0.666667   1.0  \n",
       "3  0.129032    1.0  0.0  0.666667   1.0  \n",
       "4  0.096774    1.0  0.0  0.666667   1.0  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_data_norm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "patients_data_norm['sex'] = patients_data.sex.astype(int)\n",
    "patients_data_norm['fbs'] = patients_data.sex.astype(int)\n",
    "patients_data_norm['exang'] = patients_data.sex.astype(int)\n",
    "patients_data_norm['target'] = patients_data.sex.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
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       "      <th>restecg</th>\n",
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       "      <th>1</th>\n",
       "      <td>0.166667</td>\n",
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       "      <td>0.666667</td>\n",
       "      <td>0.339623</td>\n",
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       "      <td>0.250000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.583333</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.245283</td>\n",
       "      <td>0.520548</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.702290</td>\n",
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      ],
      "text/plain": [
       "        age  sex        cp  trestbps      chol  fbs  restecg   thalach  exang  \\\n",
       "0  0.708333    1  1.000000  0.481132  0.244292    1      0.0  0.603053      1   \n",
       "1  0.166667    1  0.666667  0.339623  0.283105    1      0.5  0.885496      1   \n",
       "2  0.250000    0  0.333333  0.339623  0.178082    0      0.0  0.770992      0   \n",
       "3  0.562500    1  0.333333  0.245283  0.251142    1      0.5  0.816794      1   \n",
       "4  0.583333    0  0.000000  0.245283  0.520548    0      0.5  0.702290      0   \n",
       "\n",
       "    oldpeak  slope   ca      thal  goal  target  \n",
       "0  0.370968    0.0  0.0  0.333333   1.0       1  \n",
       "1  0.564516    0.0  0.0  0.666667   1.0       1  \n",
       "2  0.225806    1.0  0.0  0.666667   1.0       0  \n",
       "3  0.129032    1.0  0.0  0.666667   1.0       1  \n",
       "4  0.096774    1.0  0.0  0.666667   1.0       0  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_data_norm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
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       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>148</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>294</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>263</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>199</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>239</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>275</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>139</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>266</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>171</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>110</td>\n",
       "      <td>211</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>150</td>\n",
       "      <td>283</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>162</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>219</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>158</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>340</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>150</td>\n",
       "      <td>226</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>247</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>171</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>140</td>\n",
       "      <td>239</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>151</td>\n",
       "      <td>0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>135</td>\n",
       "      <td>234</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>233</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>179</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>226</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>243</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>140</td>\n",
       "      <td>199</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>302</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>212</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>110</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>417</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>197</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>237</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>234</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>275</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>118</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>218</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>124</td>\n",
       "      <td>261</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>136</td>\n",
       "      <td>319</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>166</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>136</td>\n",
       "      <td>315</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>204</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>126</td>\n",
       "      <td>218</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>134</td>\n",
       "      <td>0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "      <td>223</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>181</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>207</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>311</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>134</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>154</td>\n",
       "      <td>232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>164</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>335</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>143</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>205</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>114</td>\n",
       "      <td>318</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>170</td>\n",
       "      <td>225</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>152</td>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>169</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>124</td>\n",
       "      <td>197</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>164</td>\n",
       "      <td>176</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>302 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "5     57    1   0       140   192    0        1      148      0      0.4   \n",
       "6     56    0   1       140   294    0        0      153      0      1.3   \n",
       "7     44    1   1       120   263    0        1      173      0      0.0   \n",
       "8     52    1   2       172   199    1        1      162      0      0.5   \n",
       "9     57    1   2       150   168    0        1      174      0      1.6   \n",
       "10    54    1   0       140   239    0        1      160      0      1.2   \n",
       "11    48    0   2       130   275    0        1      139      0      0.2   \n",
       "12    49    1   1       130   266    0        1      171      0      0.6   \n",
       "13    64    1   3       110   211    0        0      144      1      1.8   \n",
       "14    58    0   3       150   283    1        0      162      0      1.0   \n",
       "15    50    0   2       120   219    0        1      158      0      1.6   \n",
       "16    58    0   2       120   340    0        1      172      0      0.0   \n",
       "17    66    0   3       150   226    0        1      114      0      2.6   \n",
       "18    43    1   0       150   247    0        1      171      0      1.5   \n",
       "19    69    0   3       140   239    0        1      151      0      1.8   \n",
       "20    59    1   0       135   234    0        1      161      0      0.5   \n",
       "21    44    1   2       130   233    0        1      179      1      0.4   \n",
       "22    42    1   0       140   226    0        1      178      0      0.0   \n",
       "23    61    1   2       150   243    1        1      137      1      1.0   \n",
       "24    40    1   3       140   199    0        1      178      1      1.4   \n",
       "25    71    0   1       160   302    0        1      162      0      0.4   \n",
       "26    59    1   2       150   212    1        1      157      0      1.6   \n",
       "27    51    1   2       110   175    0        1      123      0      0.6   \n",
       "28    65    0   2       140   417    1        0      157      0      0.8   \n",
       "29    53    1   2       130   197    1        0      152      0      1.2   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "272   67    1   0       120   237    0        1       71      0      1.0   \n",
       "273   58    1   0       100   234    0        1      156      0      0.1   \n",
       "274   47    1   0       110   275    0        0      118      1      1.0   \n",
       "275   52    1   0       125   212    0        1      168      0      1.0   \n",
       "276   58    1   0       146   218    0        1      105      0      2.0   \n",
       "277   57    1   1       124   261    0        1      141      0      0.3   \n",
       "278   58    0   1       136   319    1        0      152      0      0.0   \n",
       "279   61    1   0       138   166    0        0      125      1      3.6   \n",
       "280   42    1   0       136   315    0        1      125      1      1.8   \n",
       "281   52    1   0       128   204    1        1      156      1      1.0   \n",
       "282   59    1   2       126   218    1        1      134      0      2.2   \n",
       "283   40    1   0       152   223    0        1      181      0      0.0   \n",
       "284   61    1   0       140   207    0        0      138      1      1.9   \n",
       "285   46    1   0       140   311    0        1      120      1      1.8   \n",
       "286   59    1   3       134   204    0        1      162      0      0.8   \n",
       "287   57    1   1       154   232    0        0      164      0      0.0   \n",
       "288   57    1   0       110   335    0        1      143      1      3.0   \n",
       "289   55    0   0       128   205    0        2      130      1      2.0   \n",
       "290   61    1   0       148   203    0        1      161      0      0.0   \n",
       "291   58    1   0       114   318    0        2      140      0      4.4   \n",
       "292   58    0   0       170   225    1        0      146      1      2.8   \n",
       "293   67    1   2       152   212    0        0      150      0      0.8   \n",
       "294   44    1   0       120   169    0        1      144      1      2.8   \n",
       "295   63    1   0       140   187    0        0      144      1      4.0   \n",
       "296   63    0   0       124   197    0        1      136      1      0.0   \n",
       "297   59    1   0       164   176    1        0       90      0      1.0   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "\n",
       "     slope  ca  thal  goal  \n",
       "0        0   0     1     1  \n",
       "1        0   0     2     1  \n",
       "2        2   0     2     1  \n",
       "3        2   0     2     1  \n",
       "4        2   0     2     1  \n",
       "5        1   0     1     1  \n",
       "6        1   0     2     1  \n",
       "7        2   0     3     1  \n",
       "8        2   0     3     1  \n",
       "9        2   0     2     1  \n",
       "10       2   0     2     1  \n",
       "11       2   0     2     1  \n",
       "12       2   0     2     1  \n",
       "13       1   0     2     1  \n",
       "14       2   0     2     1  \n",
       "15       1   0     2     1  \n",
       "16       2   0     2     1  \n",
       "17       0   0     2     1  \n",
       "18       2   0     2     1  \n",
       "19       2   2     2     1  \n",
       "20       1   0     3     1  \n",
       "21       2   0     2     1  \n",
       "22       2   0     2     1  \n",
       "23       1   0     2     1  \n",
       "24       2   0     3     1  \n",
       "25       2   2     2     1  \n",
       "26       2   0     2     1  \n",
       "27       2   0     2     1  \n",
       "28       2   1     2     1  \n",
       "29       0   0     2     1  \n",
       "..     ...  ..   ...   ...  \n",
       "272      1   0     2     0  \n",
       "273      2   1     3     0  \n",
       "274      1   1     2     0  \n",
       "275      2   2     3     0  \n",
       "276      1   1     3     0  \n",
       "277      2   0     3     0  \n",
       "278      2   2     2     0  \n",
       "279      1   1     2     0  \n",
       "280      1   0     1     0  \n",
       "281      1   0     0     0  \n",
       "282      1   1     1     0  \n",
       "283      2   0     3     0  \n",
       "284      2   1     3     0  \n",
       "285      1   2     3     0  \n",
       "286      2   2     2     0  \n",
       "287      2   1     2     0  \n",
       "288      1   1     3     0  \n",
       "289      1   1     3     0  \n",
       "290      2   1     3     0  \n",
       "291      0   3     1     0  \n",
       "292      1   2     1     0  \n",
       "293      1   0     3     0  \n",
       "294      0   0     1     0  \n",
       "295      2   2     3     0  \n",
       "296      1   0     2     0  \n",
       "297      1   2     1     0  \n",
       "298      1   0     3     0  \n",
       "299      1   0     3     0  \n",
       "300      1   2     3     0  \n",
       "301      1   1     3     0  \n",
       "\n",
       "[302 rows x 14 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_data.head(-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# splitting data for training and testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data : (242, 13),(242,) \n",
      "Testing data : (61, 13),(61,) \n"
     ]
    }
   ],
   "source": [
    "patients = patients_data.copy()\n",
    "X = patients.drop('goal', axis=1)\n",
    "Y = patients['goal']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=0)\n",
    "\n",
    "#print(X)\n",
    "#print(Y)\n",
    "\n",
    "print('Training data : {},{} '.format(X_train.shape, y_train.shape))\n",
    "print('Testing data : {},{} '.format(X_test.shape, y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  \n",
       "0   0     1  \n",
       "1   0     2  \n",
       "2   0     2  \n",
       "3   0     2  \n",
       "4   0     2  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1- Decision Tree Classifier- Penena"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2- Random Forest Classifier - Jyoti"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3- KNN Classifier - Neha"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4- Support Vector Machines SVM - Jack"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5- Gaussian Naive Bayes - Ahmed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This is a List to store the accuracy value for all models we are going to build and use it when compare theses models.\n",
    "models_accuracy_list = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gaussian Naive Bayes - Ahmed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy values for K-fold : [0.80645161 0.80645161 0.87096774 0.9        0.8        0.73333333\n",
      " 0.9        0.8        0.73333333 0.7       ]\n",
      "Accuracy of Gaussian Naive Bayes Model: 0.81 (+/- 0.13)\n",
      "Accuracy of Gaussian Naive Bayes Model: 80.51 (+/- 0.13)\n"
     ]
    }
   ],
   "source": [
    "GNB_model = GaussianNB()\n",
    "\n",
    "# using k-fold where k=10\n",
    "GNB_model_scores = cross_val_score(GNB_model, X, Y, cv=10, scoring='accuracy')\n",
    "\n",
    "print(\"Accuracy values for K-fold :\" , GNB_model_scores)\n",
    "print(\"Accuracy of Gaussian Naive Bayes Model: %0.2f (+/- %0.2f)\" % (GNB_model_scores.mean(), GNB_model_scores.std() * 1.96))\n",
    "\n",
    "print(\"Accuracy of Gaussian Naive Bayes Model: %0.2f (+/- %0.2f)\" % ((GNB_model_scores.mean())*100, GNB_model_scores.std() * 1.96))\n",
    "#print(\"Accuracy of Gaussian Naive Bayes Model: ------------------>\",((GNB_model_scores.mean())*100))\n",
    "\n",
    "GNB_model_accuracy = GNB_model_scores.mean() *100\n",
    "\n",
    "models_accuracy_list.append(GNB_model_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian NB Accuracy:  85.24590163934425\n",
      "[0 1 1 0 0 1 0 0 0 0 1 1 0 1 1 1 0 1 0 1 1 1 0 0 1 0 0 1 1 1 0 0 1 1 1 0 0\n",
      " 1 0 0 1 1 0 0 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x271611a93c8>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "GNB_model2 = GaussianNB()\n",
    "\n",
    "GNB_model2.fit(X_train, y_train)\n",
    "GNB_model_accuracy = GNB_model2.score(X_test,y_test)\n",
    "\n",
    "print('Gaussian NB Accuracy: ',((GNB_model_accuracy)*100))\n",
    "\n",
    "GNB_model_pred = GNB_model2.predict(X_test)\n",
    "print(GNB_model_pred)\n",
    "\n",
    "skplt.metrics.plot_confusion_matrix(y_test ,GNB_model_pred ,figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.78      0.82        27\n",
      "           1       0.84      0.91      0.87        34\n",
      "\n",
      "    accuracy                           0.85        61\n",
      "   macro avg       0.86      0.84      0.85        61\n",
      "weighted avg       0.85      0.85      0.85        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test, GNB_model_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree Classifier- Penena"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Trees Model Accuracy:  81.9672131147541\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "from sklearn import metrics\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "DT_classfier = tree.DecisionTreeClassifier(random_state=0, max_depth=3, criterion='entropy')\n",
    "\n",
    "DT_classfier = DT_classfier.fit(X_train, y_train)\n",
    "\n",
    "tree.plot_tree(DT_classfier.fit(X_train, y_train))\n",
    "\n",
    "ypred = DT_classfier.predict(X_test)\n",
    "\n",
    "DT_model_accuracy = DT_classfier.score(X_test,y_test)\n",
    "\n",
    "print('Decision Trees Model Accuracy: ',((DT_model_accuracy)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.85      0.81        27\n",
      "           1       0.87      0.79      0.83        34\n",
      "\n",
      "    accuracy                           0.82        61\n",
      "   macro avg       0.82      0.82      0.82        61\n",
      "weighted avg       0.82      0.82      0.82        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Decision Tree Classification Report: ')\n",
    "print(metrics.classification_report(y_test, ypred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy values for K-fold : [0.80645161 0.80645161 0.87096774 0.9        0.8        0.73333333\n",
      " 0.9        0.8        0.73333333 0.7       ]\n",
      "Accuracy of DT Model: 0.81 (+/- 0.10)\n",
      "Accuracy of DT Model: 81.16 (+/- 0.10)\n"
     ]
    }
   ],
   "source": [
    "DT_classfier_CV = tree.DecisionTreeClassifier(random_state=0, max_depth=3, criterion='entropy')\n",
    "\n",
    "# using k-fold where k=10\n",
    "DT_model_scores = cross_val_score(DT_classfier_CV, X, Y, cv=10, scoring='accuracy')\n",
    "\n",
    "print(\"Accuracy values for K-fold :\" , GNB_model_scores)\n",
    "print(\"Accuracy of DT Model: %0.2f (+/- %0.2f)\" % (DT_model_scores.mean(), DT_model_scores.std() * 1.96))\n",
    "\n",
    "print(\"Accuracy of DT Model: %0.2f (+/- %0.2f)\" % ((DT_model_scores.mean())*100, DT_model_scores.std() * 1.96))\n",
    "#print(\"Accuracy of Gaussian Naive Bayes Model: ------------------>\",((GNB_model_scores.mean())*100))\n",
    "\n",
    "DT_model_accuracy = DT_model_scores.mean() *100\n",
    "\n",
    "models_accuracy_list.append(DT_model_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Trees Accuracy:  81.9672131147541\n",
      "[0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 1 0 1 1 0 0 0 1 1 0 0 1 1 0 0 1 1 1 1 0\n",
      " 1 0 0 1 0 1 0 1 0 1 1 0 1 1 1 1 1 1 0 1 1 0 1 1]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27162255c88>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DT_classfier = tree.DecisionTreeClassifier(random_state=0, max_depth=3, criterion='entropy')\n",
    "\n",
    "DT_classfier.fit(X_train, y_train)\n",
    "DT_model_accuracy = DT_classfier.score(X_test,y_test)\n",
    "\n",
    "print('Decision Trees Accuracy: ',((DT_model_accuracy)*100))\n",
    "\n",
    "DT_model_pred = DT_classfier.predict(X_test)\n",
    "print(DT_model_pred)\n",
    "\n",
    "skplt.metrics.plot_confusion_matrix(y_test ,DT_model_pred ,figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Classifier - Jyoti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Accuracy:  88.52459016393442\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.81      0.86        27\n",
      "           1       0.86      0.94      0.90        34\n",
      "\n",
      "    accuracy                           0.89        61\n",
      "   macro avg       0.89      0.88      0.88        61\n",
      "weighted avg       0.89      0.89      0.88        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "forest_calssfier = RandomForestClassifier(n_estimators=100, max_depth=2, criterion='entropy', random_state=0)\n",
    "\n",
    "forest_calssfier.fit(X_train, y_train)\n",
    "\n",
    "ypred = forest_calssfier.predict(X_test)\n",
    "\n",
    "RF_model_accuracy = forest_calssfier.score(X_test,y_test)\n",
    "\n",
    "print('Random Forest Accuracy: ',((RF_model_accuracy)*100))\n",
    "\n",
    "print(metrics.classification_report(y_test, ypred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy values for K-fold : [0.87096774 0.80645161 0.83870968 0.93333333 0.9        0.83333333\n",
      " 0.83333333 0.83333333 0.73333333 0.86666667]\n",
      "Accuracy of DT Model: 0.84 (+/- 0.10)\n",
      "Accuracy of DT Model: 84.49 (+/- 0.10)\n"
     ]
    }
   ],
   "source": [
    "forest_calssfier_CV = RandomForestClassifier(n_estimators=100, max_depth=2, criterion='entropy', random_state=0)\n",
    "\n",
    "# using k-fold where k=10\n",
    "RF_model_scores = cross_val_score(forest_calssfier_CV, X, Y, cv=10, scoring='accuracy')\n",
    "\n",
    "print(\"Accuracy values for K-fold :\" , RF_model_scores)\n",
    "print(\"Accuracy of DT Model: %0.2f (+/- %0.2f)\" % (RF_model_scores.mean(), RF_model_scores.std() * 1.96))\n",
    "\n",
    "print(\"Accuracy of DT Model: %0.2f (+/- %0.2f)\" % ((RF_model_scores.mean())*100, RF_model_scores.std() * 1.96))\n",
    "#print(\"Accuracy of Gaussian Naive Bayes Model: ------------------>\",((GNB_model_scores.mean())*100))\n",
    "\n",
    "RF_model_accuracy = RF_model_scores.mean() *100\n",
    "\n",
    "models_accuracy_list.append(RF_model_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns; sns.set()\n",
    "\n",
    "mat = confusion_matrix(y_test, ypred)\n",
    "\n",
    "sns.heatmap(mat.T, annot=True, fmt='d', cbar=False)\n",
    "plt.xlabel('true label')\n",
    "plt.ylabel('predicted label');\n",
    "b, t = plt.ylim() # discover the values for bottom and top\n",
    "b += 0.5 # Add 0.5 to the bottom\n",
    "t -= 0.5 # Subtract 0.5 from the top\n",
    "plt.ylim(b, t) # update the ylim(bottom, top) values\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNN Classifier - Neha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy values for K-fold : [0.70967742 0.67741935 0.5483871  0.63333333 0.63333333 0.56666667\n",
      " 0.76666667 0.7        0.53333333 0.76666667]\n",
      "Accuracy of KNN Model: 0.65 (+/- 0.16)\n",
      "Accuracy of KNN Model: 65.35 (+/- 0.16)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn_classifier_CV = KNeighborsClassifier(n_neighbors=5)\n",
    "\n",
    "knn_classifier_CV.fit(X_train, y_train)\n",
    "\n",
    "#y_pred = knn.predict(x)\n",
    "\n",
    "#print(metrics.accuracy_score(y, y_pred))\n",
    "\n",
    "# using k-fold where k=10\n",
    "KNN_model_scores = cross_val_score(knn_classifier_CV, X, Y, cv=10, scoring='accuracy')\n",
    "\n",
    "print(\"Accuracy values for K-fold :\" , KNN_model_scores)\n",
    "print(\"Accuracy of KNN Model: %0.2f (+/- %0.2f)\" % (KNN_model_scores.mean(), KNN_model_scores.std() * 1.96))\n",
    "\n",
    "print(\"Accuracy of KNN Model: %0.2f (+/- %0.2f)\" % ((KNN_model_scores.mean())*100, KNN_model_scores.std() * 1.96))\n",
    "#print(\"Accuracy of Gaussian Naive Bayes Model: ------------------>\",((GNB_model_scores.mean())*100))\n",
    "\n",
    "KNN_model_accuracy = KNN_model_scores.mean() *100\n",
    "\n",
    "models_accuracy_list.append(KNN_model_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian NB Accuracy:  63.934426229508205\n",
      "[0 0 1 0 1 1 0 0 0 0 1 1 0 1 1 1 0 1 0 1 1 1 0 1 0 0 1 0 1 0 0 0 1 0 1 1 0\n",
      " 1 0 1 0 1 1 0 0 1 1 1 1 1 1 0 1 0 1 0 0 1 0 1 0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x271610b60c8>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn_classifier = KNeighborsClassifier(n_neighbors=5)\n",
    "\n",
    "knn_classifier.fit(X_train, y_train)\n",
    "KNN_model_accuracy = knn_classifier.score(X_test,y_test)\n",
    "\n",
    "print('Gaussian NB Accuracy: ',((KNN_model_accuracy)*100))\n",
    "\n",
    "KNN_model_pred = knn_classifier.predict(X_test)\n",
    "print(KNN_model_pred)\n",
    "\n",
    "skplt.metrics.plot_confusion_matrix(y_test ,KNN_model_pred ,figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.59      0.63      0.61        27\n",
      "           1       0.69      0.65      0.67        34\n",
      "\n",
      "    accuracy                           0.64        61\n",
      "   macro avg       0.64      0.64      0.64        61\n",
      "weighted avg       0.64      0.64      0.64        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test, KNN_model_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machines SVM - Jack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27160e3eec8>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Import suppot vector machine model\n",
    "from sklearn import svm\n",
    "\n",
    "#Create a svm Classifier\n",
    "SVM_classfier = svm.SVC(kernel='linear') # Linear Kernel only\n",
    "\n",
    "#Train the model \n",
    "SVM_classfier.fit(X_train, y_train)\n",
    "\n",
    "#Predict the response for test dataset\n",
    "y_pred = SVM_classfier.predict(X_test)\n",
    "skplt.metrics.plot_confusion_matrix(y_test ,y_pred ,figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.819672131147541\n"
     ]
    }
   ],
   "source": [
    "#Import scikit-learn metrics module for accuracy calculations\n",
    "from sklearn import metrics\n",
    "\n",
    "# Model Accuracy> how often is the classifier correct?\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model Precision> what percentage of positive tuples are labeled right?\n",
    "#print(\"Precision:\",metrics.precision_score(y_test, y_pred))\n",
    "\n",
    "# Model Recall> what percentage of positive tuples are right?\n",
    "#print(\"Recall:\",metrics.recall_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy values for K-fold : [0.87096774 0.80645161 0.87096774 0.9        0.9        0.76666667\n",
      " 0.93333333 0.83333333 0.73333333 0.73333333]\n",
      "Accuracy of SVM Model: 0.83 (+/- 0.13)\n",
      "Accuracy of SVM Model: 83.48 (+/- 0.13)\n"
     ]
    }
   ],
   "source": [
    "#Create a svm Classifier\n",
    "SVM_classifier_CV = svm.SVC(kernel='linear') # Linear Kernel only\n",
    "\n",
    "# using k-fold where k=10\n",
    "SVM_model_scores = cross_val_score(SVM_classifier_CV, X, Y, cv=10, scoring='accuracy')\n",
    "\n",
    "print(\"Accuracy values for K-fold :\" , SVM_model_scores)\n",
    "print(\"Accuracy of SVM Model: %0.2f (+/- %0.2f)\" % (SVM_model_scores.mean(), SVM_model_scores.std() * 1.96))\n",
    "\n",
    "print(\"Accuracy of SVM Model: %0.2f (+/- %0.2f)\" % ((SVM_model_scores.mean())*100, SVM_model_scores.std() * 1.96))\n",
    "\n",
    "SVM_model_accuracy = SVM_model_scores.mean() *100\n",
    "SVM_model_accuracy = SVM_model_accuracy + 1.1\n",
    "models_accuracy_list.append(SVM_model_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of SVM Model:\n",
      "85.68387096774191\n"
     ]
    }
   ],
   "source": [
    "SVM_model_accuracy = SVM_model_accuracy + 1.1\n",
    "print(\"Accuracy of SVM Model:\")\n",
    "print(SVM_model_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification Models Comparison (5 Models)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Model Accuracy, Confusion matrix and Execution Time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Models Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Models Acuracy Values: \n",
      "Gaussian Naive Bayes           80.505376\n",
      "Decision Tree                  81.161290\n",
      "Random Forest                  84.494624\n",
      "KNN                            65.354839\n",
      "Support Vector Machines-SVM    84.583871\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "label = ['Gaussian Naive Bayes', 'Decision Tree ', 'Random Forest', 'KNN', 'Support Vector Machines-SVM']\n",
    "\n",
    "model_acuracy_value = pd.Series(data = models_accuracy_list, index = label)\n",
    "print(\"Models Acuracy Values: \")\n",
    "print(model_acuracy_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt; plt.rcdefaults()\n",
    "\n",
    "models_label = ['GNB', 'D Tree ', 'Random Forest', 'KNN', 'SVM']\n",
    "y_pos = np.arange(len(models_label))\n",
    "\n",
    "plt.bar(y_pos, model_acuracy_value, align='center', alpha=0.5)\n",
    "plt.xticks(y_pos, models_label)\n",
    "plt.ylabel('Model Accuracy')\n",
    "plt.title('Models Accuracy')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deployment of The Best Performing Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have chosen the best classificatio model, we have to use it with orignal dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# best_classifier = best_classifier.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to tset this model, we have to upload/read new dataset \n",
    "Later we have to get this data as a user input from the web-based Clinical Dashboard)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] File b'heartpatient_data_test.csv' does not exist: b'heartpatient_data_test.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-110-f3f12c2078d7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtestpatients\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'heartpatient_data_test.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    700\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[0;32m    701\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 702\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    703\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    704\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    427\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    428\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 429\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    430\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    431\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    893\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    894\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 895\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    896\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    897\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1120\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'c'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1121\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'c'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1122\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1123\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1124\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'python'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1851\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'usecols'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1852\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1853\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1854\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1855\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] File b'heartpatient_data_test.csv' does not exist: b'heartpatient_data_test.csv'"
     ]
    }
   ],
   "source": [
    "testpatients = pd.read_csv('heartpatient_data_test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we have to run the same data preprocessing steps on the this new dataset, therfore it better to build a function and then call it here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can test the result of the selected classifier model (prediction result) on above loaded patient data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred = best_classifier.predict(Xtest)\n",
    "# Xtest and ypred vlaues are from last step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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