Heart_AR_0.30.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.stats import gaussian_kde\n",
    "\n",
    "import seaborn\n",
    "import plotly\n",
    "import plotly.graph_objs as go\n",
    "from plotly import tools\n",
    "import plotly.figure_factory as ff\n",
    "\n",
    "\n",
    "##############  OFFLINE  ##############\n",
    "plotly.offline.init_notebook_mode(connected=True)\n",
    "#to plot offline: ##plotly.offline.iplot##\n",
    "\n",
    "##############  ONLINE  ##############\n",
    "#plotly.tools.set_credentials_file(username='arnaurovira23', api_key='7y46ugRJ8Rl6XCkCwBGM')\n",
    "#to plot online: ##py.iplot##\n",
    "import plotly.plotly as py\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### target dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>patient_id</th>\n",
       "      <th>heart_disease_present</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0z64un</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ryoo3j</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>yt1s1x</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>l2xjde</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>oyt4ek</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  patient_id  heart_disease_present\n",
       "0     0z64un                      0\n",
       "1     ryoo3j                      0\n",
       "2     yt1s1x                      1\n",
       "3     l2xjde                      1\n",
       "4     oyt4ek                      0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_target = pd.read_csv('train_lables.txt')\n",
    "df_target.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " How many unique values are in target column? 2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f' How many unique values are in target column? {len(df_target.heart_disease_present.unique())}')\n",
    "df_target['heart_disease_present'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "180\n"
     ]
    }
   ],
   "source": [
    "#Shape of target dataset\n",
    "num_target = df_target.shape[0]\n",
    "print(num_target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### train dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>patient_id</th>\n",
       "      <th>slope_of_peak_exercise_st_segment</th>\n",
       "      <th>thal</th>\n",
       "      <th>resting_blood_pressure</th>\n",
       "      <th>chest_pain_type</th>\n",
       "      <th>num_major_vessels</th>\n",
       "      <th>fasting_blood_sugar_gt_120_mg_per_dl</th>\n",
       "      <th>resting_ekg_results</th>\n",
       "      <th>serum_cholesterol_mg_per_dl</th>\n",
       "      <th>oldpeak_eq_st_depression</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>max_heart_rate_achieved</th>\n",
       "      <th>exercise_induced_angina</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0z64un</td>\n",
       "      <td>1</td>\n",
       "      <td>normal</td>\n",
       "      <td>128</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>308</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ryoo3j</td>\n",
       "      <td>2</td>\n",
       "      <td>normal</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>214</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>158</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>yt1s1x</td>\n",
       "      <td>1</td>\n",
       "      <td>normal</td>\n",
       "      <td>125</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>304</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>77</td>\n",
       "      <td>162</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>l2xjde</td>\n",
       "      <td>1</td>\n",
       "      <td>reversible_defect</td>\n",
       "      <td>152</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>223</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>181</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>oyt4ek</td>\n",
       "      <td>3</td>\n",
       "      <td>reversible_defect</td>\n",
       "      <td>178</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>270</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>145</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  patient_id  slope_of_peak_exercise_st_segment               thal  \\\n",
       "0     0z64un                                  1             normal   \n",
       "1     ryoo3j                                  2             normal   \n",
       "2     yt1s1x                                  1             normal   \n",
       "3     l2xjde                                  1  reversible_defect   \n",
       "4     oyt4ek                                  3  reversible_defect   \n",
       "\n",
       "   resting_blood_pressure  chest_pain_type  num_major_vessels  \\\n",
       "0                     128                2                  0   \n",
       "1                     110                3                  0   \n",
       "2                     125                4                  3   \n",
       "3                     152                4                  0   \n",
       "4                     178                1                  0   \n",
       "\n",
       "   fasting_blood_sugar_gt_120_mg_per_dl  resting_ekg_results  \\\n",
       "0                                     0                    2   \n",
       "1                                     0                    0   \n",
       "2                                     0                    2   \n",
       "3                                     0                    0   \n",
       "4                                     0                    2   \n",
       "\n",
       "   serum_cholesterol_mg_per_dl  oldpeak_eq_st_depression  sex  age  \\\n",
       "0                          308                       0.0    1   45   \n",
       "1                          214                       1.6    0   54   \n",
       "2                          304                       0.0    1   77   \n",
       "3                          223                       0.0    1   40   \n",
       "4                          270                       4.2    1   59   \n",
       "\n",
       "   max_heart_rate_achieved  exercise_induced_angina  \n",
       "0                      170                        0  \n",
       "1                      158                        0  \n",
       "2                      162                        1  \n",
       "3                      181                        0  \n",
       "4                      145                        0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.read_csv('train_values.txt')\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>slope_of_peak_exercise_st_segment</th>\n",
       "      <th>resting_blood_pressure</th>\n",
       "      <th>chest_pain_type</th>\n",
       "      <th>num_major_vessels</th>\n",
       "      <th>fasting_blood_sugar_gt_120_mg_per_dl</th>\n",
       "      <th>resting_ekg_results</th>\n",
       "      <th>serum_cholesterol_mg_per_dl</th>\n",
       "      <th>oldpeak_eq_st_depression</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>max_heart_rate_achieved</th>\n",
       "      <th>exercise_induced_angina</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.550000</td>\n",
       "      <td>131.311111</td>\n",
       "      <td>3.155556</td>\n",
       "      <td>0.694444</td>\n",
       "      <td>0.161111</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>249.211111</td>\n",
       "      <td>1.010000</td>\n",
       "      <td>0.688889</td>\n",
       "      <td>54.811111</td>\n",
       "      <td>149.483333</td>\n",
       "      <td>0.316667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.618838</td>\n",
       "      <td>17.010443</td>\n",
       "      <td>0.938454</td>\n",
       "      <td>0.969347</td>\n",
       "      <td>0.368659</td>\n",
       "      <td>0.998742</td>\n",
       "      <td>52.717969</td>\n",
       "      <td>1.121357</td>\n",
       "      <td>0.464239</td>\n",
       "      <td>9.334737</td>\n",
       "      <td>22.063513</td>\n",
       "      <td>0.466474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>213.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>245.500000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>281.250000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>166.250000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       slope_of_peak_exercise_st_segment  resting_blood_pressure  \\\n",
       "count                         180.000000              180.000000   \n",
       "mean                            1.550000              131.311111   \n",
       "std                             0.618838               17.010443   \n",
       "min                             1.000000               94.000000   \n",
       "25%                             1.000000              120.000000   \n",
       "50%                             1.000000              130.000000   \n",
       "75%                             2.000000              140.000000   \n",
       "max                             3.000000              180.000000   \n",
       "\n",
       "       chest_pain_type  num_major_vessels  \\\n",
       "count       180.000000         180.000000   \n",
       "mean          3.155556           0.694444   \n",
       "std           0.938454           0.969347   \n",
       "min           1.000000           0.000000   \n",
       "25%           3.000000           0.000000   \n",
       "50%           3.000000           0.000000   \n",
       "75%           4.000000           1.000000   \n",
       "max           4.000000           3.000000   \n",
       "\n",
       "       fasting_blood_sugar_gt_120_mg_per_dl  resting_ekg_results  \\\n",
       "count                            180.000000           180.000000   \n",
       "mean                               0.161111             1.050000   \n",
       "std                                0.368659             0.998742   \n",
       "min                                0.000000             0.000000   \n",
       "25%                                0.000000             0.000000   \n",
       "50%                                0.000000             2.000000   \n",
       "75%                                0.000000             2.000000   \n",
       "max                                1.000000             2.000000   \n",
       "\n",
       "       serum_cholesterol_mg_per_dl  oldpeak_eq_st_depression         sex  \\\n",
       "count                   180.000000                180.000000  180.000000   \n",
       "mean                    249.211111                  1.010000    0.688889   \n",
       "std                      52.717969                  1.121357    0.464239   \n",
       "min                     126.000000                  0.000000    0.000000   \n",
       "25%                     213.750000                  0.000000    0.000000   \n",
       "50%                     245.500000                  0.800000    1.000000   \n",
       "75%                     281.250000                  1.600000    1.000000   \n",
       "max                     564.000000                  6.200000    1.000000   \n",
       "\n",
       "              age  max_heart_rate_achieved  exercise_induced_angina  \n",
       "count  180.000000               180.000000               180.000000  \n",
       "mean    54.811111               149.483333                 0.316667  \n",
       "std      9.334737                22.063513                 0.466474  \n",
       "min     29.000000                96.000000                 0.000000  \n",
       "25%     48.000000               132.000000                 0.000000  \n",
       "50%     55.000000               152.000000                 0.000000  \n",
       "75%     62.000000               166.250000                 1.000000  \n",
       "max     77.000000               202.000000                 1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Shpae of dataset train: 180\n"
     ]
    }
   ],
   "source": [
    "#Shape of train dataset\n",
    "num_train = df_train.shape[0]\n",
    "print(f' Shpae of dataset train: {num_train}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['slope_of_peak_exercise_st_segment', 'thal', 'resting_blood_pressure', 'chest_pain_type', 'num_major_vessels', 'fasting_blood_sugar_gt_120_mg_per_dl', 'resting_ekg_results', 'serum_cholesterol_mg_per_dl', 'oldpeak_eq_st_depression', 'sex', 'age', 'max_heart_rate_achieved', 'exercise_induced_angina']\n",
      "13\n"
     ]
    }
   ],
   "source": [
    "features_all = df_train.columns.tolist()\n",
    "features_all.remove('patient_id')\n",
    "print(features_all)\n",
    "print(len(features_all))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How many NaN values are in df_train set? 0\n"
     ]
    }
   ],
   "source": [
    "print(f'How many NaN values are in df_train set? {df_train.isnull().sum().sum()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How many unique values are in thal column? 3\n",
      "['normal' 'reversible_defect' 'fixed_defect']\n"
     ]
    }
   ],
   "source": [
    "print(f'How many unique values are in thal column? {len(df_train.thal.unique())}')\n",
    "print(df_train['thal'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 180 entries, 0 to 179\n",
      "Data columns (total 14 columns):\n",
      "patient_id                              180 non-null object\n",
      "slope_of_peak_exercise_st_segment       180 non-null int64\n",
      "thal                                    180 non-null object\n",
      "resting_blood_pressure                  180 non-null int64\n",
      "chest_pain_type                         180 non-null int64\n",
      "num_major_vessels                       180 non-null int64\n",
      "fasting_blood_sugar_gt_120_mg_per_dl    180 non-null int64\n",
      "resting_ekg_results                     180 non-null int64\n",
      "serum_cholesterol_mg_per_dl             180 non-null int64\n",
      "oldpeak_eq_st_depression                180 non-null float64\n",
      "sex                                     180 non-null int64\n",
      "age                                     180 non-null int64\n",
      "max_heart_rate_achieved                 180 non-null int64\n",
      "exercise_induced_angina                 180 non-null int64\n",
      "dtypes: float64(1), int64(11), object(2)\n",
      "memory usage: 19.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>patient_id</th>\n",
       "      <th>slope_of_peak_exercise_st_segment</th>\n",
       "      <th>thal</th>\n",
       "      <th>resting_blood_pressure</th>\n",
       "      <th>chest_pain_type</th>\n",
       "      <th>num_major_vessels</th>\n",
       "      <th>fasting_blood_sugar_gt_120_mg_per_dl</th>\n",
       "      <th>resting_ekg_results</th>\n",
       "      <th>serum_cholesterol_mg_per_dl</th>\n",
       "      <th>oldpeak_eq_st_depression</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>max_heart_rate_achieved</th>\n",
       "      <th>exercise_induced_angina</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>olalu7</td>\n",
       "      <td>2</td>\n",
       "      <td>reversible_defect</td>\n",
       "      <td>170</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>288</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>z9n6mx</td>\n",
       "      <td>1</td>\n",
       "      <td>normal</td>\n",
       "      <td>138</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>183</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5k4413</td>\n",
       "      <td>2</td>\n",
       "      <td>reversible_defect</td>\n",
       "      <td>120</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>177</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>120</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mrg7q5</td>\n",
       "      <td>1</td>\n",
       "      <td>normal</td>\n",
       "      <td>102</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>318</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>uki4do</td>\n",
       "      <td>2</td>\n",
       "      <td>normal</td>\n",
       "      <td>138</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>166</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  patient_id  slope_of_peak_exercise_st_segment               thal  \\\n",
       "0     olalu7                                  2  reversible_defect   \n",
       "1     z9n6mx                                  1             normal   \n",
       "2     5k4413                                  2  reversible_defect   \n",
       "3     mrg7q5                                  1             normal   \n",
       "4     uki4do                                  2             normal   \n",
       "\n",
       "   resting_blood_pressure  chest_pain_type  num_major_vessels  \\\n",
       "0                     170                1                  0   \n",
       "1                     138                4                  0   \n",
       "2                     120                4                  0   \n",
       "3                     102                3                  1   \n",
       "4                     138                4                  1   \n",
       "\n",
       "   fasting_blood_sugar_gt_120_mg_per_dl  resting_ekg_results  \\\n",
       "0                                     0                    2   \n",
       "1                                     0                    0   \n",
       "2                                     0                    2   \n",
       "3                                     0                    0   \n",
       "4                                     0                    2   \n",
       "\n",
       "   serum_cholesterol_mg_per_dl  oldpeak_eq_st_depression  sex  age  \\\n",
       "0                          288                       0.2    1   59   \n",
       "1                          183                       1.4    0   35   \n",
       "2                          177                       2.5    1   43   \n",
       "3                          318                       0.0    0   60   \n",
       "4                          166                       3.6    1   61   \n",
       "\n",
       "   max_heart_rate_achieved  exercise_induced_angina  \n",
       "0                      159                        0  \n",
       "1                      182                        0  \n",
       "2                      120                        1  \n",
       "3                      160                        0  \n",
       "4                      125                        1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv('test_values.txt')\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Shpae of dataset test: 90\n"
     ]
    }
   ],
   "source": [
    "#Shape of test dataset\n",
    "num_test = df_test.shape[0]\n",
    "print(f' Shpae of dataset test: {num_test}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>slope_of_peak_exercise_st_segment</th>\n",
       "      <th>resting_blood_pressure</th>\n",
       "      <th>chest_pain_type</th>\n",
       "      <th>num_major_vessels</th>\n",
       "      <th>fasting_blood_sugar_gt_120_mg_per_dl</th>\n",
       "      <th>resting_ekg_results</th>\n",
       "      <th>serum_cholesterol_mg_per_dl</th>\n",
       "      <th>oldpeak_eq_st_depression</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>max_heart_rate_achieved</th>\n",
       "      <th>exercise_induced_angina</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.655556</td>\n",
       "      <td>131.411111</td>\n",
       "      <td>3.211111</td>\n",
       "      <td>0.622222</td>\n",
       "      <td>0.122222</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>250.555556</td>\n",
       "      <td>1.130000</td>\n",
       "      <td>0.655556</td>\n",
       "      <td>53.677778</td>\n",
       "      <td>150.066667</td>\n",
       "      <td>0.355556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.602637</td>\n",
       "      <td>19.552806</td>\n",
       "      <td>0.977206</td>\n",
       "      <td>0.894148</td>\n",
       "      <td>0.329377</td>\n",
       "      <td>0.999438</td>\n",
       "      <td>49.835936</td>\n",
       "      <td>1.193847</td>\n",
       "      <td>0.477849</td>\n",
       "      <td>8.641197</td>\n",
       "      <td>25.351817</td>\n",
       "      <td>0.481363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>212.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>46.250000</td>\n",
       "      <td>136.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>243.500000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>156.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>274.750000</td>\n",
       "      <td>1.875000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>164.750000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       slope_of_peak_exercise_st_segment  resting_blood_pressure  \\\n",
       "count                          90.000000               90.000000   \n",
       "mean                            1.655556              131.411111   \n",
       "std                             0.602637               19.552806   \n",
       "min                             1.000000               94.000000   \n",
       "25%                             1.000000              120.000000   \n",
       "50%                             2.000000              130.000000   \n",
       "75%                             2.000000              140.000000   \n",
       "max                             3.000000              200.000000   \n",
       "\n",
       "       chest_pain_type  num_major_vessels  \\\n",
       "count        90.000000          90.000000   \n",
       "mean          3.211111           0.622222   \n",
       "std           0.977206           0.894148   \n",
       "min           1.000000           0.000000   \n",
       "25%           3.000000           0.000000   \n",
       "50%           4.000000           0.000000   \n",
       "75%           4.000000           1.000000   \n",
       "max           4.000000           3.000000   \n",
       "\n",
       "       fasting_blood_sugar_gt_120_mg_per_dl  resting_ekg_results  \\\n",
       "count                             90.000000            90.000000   \n",
       "mean                               0.122222             0.966667   \n",
       "std                                0.329377             0.999438   \n",
       "min                                0.000000             0.000000   \n",
       "25%                                0.000000             0.000000   \n",
       "50%                                0.000000             0.000000   \n",
       "75%                                0.000000             2.000000   \n",
       "max                                1.000000             2.000000   \n",
       "\n",
       "       serum_cholesterol_mg_per_dl  oldpeak_eq_st_depression        sex  \\\n",
       "count                    90.000000                 90.000000  90.000000   \n",
       "mean                    250.555556                  1.130000   0.655556   \n",
       "std                      49.835936                  1.193847   0.477849   \n",
       "min                     166.000000                  0.000000   0.000000   \n",
       "25%                     212.250000                  0.000000   0.000000   \n",
       "50%                     243.500000                  0.800000   1.000000   \n",
       "75%                     274.750000                  1.875000   1.000000   \n",
       "max                     409.000000                  4.000000   1.000000   \n",
       "\n",
       "             age  max_heart_rate_achieved  exercise_induced_angina  \n",
       "count  90.000000                90.000000                90.000000  \n",
       "mean   53.677778               150.066667                 0.355556  \n",
       "std     8.641197                25.351817                 0.481363  \n",
       "min    34.000000                71.000000                 0.000000  \n",
       "25%    46.250000               136.500000                 0.000000  \n",
       "50%    54.000000               156.500000                 0.000000  \n",
       "75%    60.000000               164.750000                 1.000000  \n",
       "max    76.000000               195.000000                 1.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 90 entries, 0 to 89\n",
      "Data columns (total 14 columns):\n",
      "patient_id                              90 non-null object\n",
      "slope_of_peak_exercise_st_segment       90 non-null int64\n",
      "thal                                    90 non-null object\n",
      "resting_blood_pressure                  90 non-null int64\n",
      "chest_pain_type                         90 non-null int64\n",
      "num_major_vessels                       90 non-null int64\n",
      "fasting_blood_sugar_gt_120_mg_per_dl    90 non-null int64\n",
      "resting_ekg_results                     90 non-null int64\n",
      "serum_cholesterol_mg_per_dl             90 non-null int64\n",
      "oldpeak_eq_st_depression                90 non-null float64\n",
      "sex                                     90 non-null int64\n",
      "age                                     90 non-null int64\n",
      "max_heart_rate_achieved                 90 non-null int64\n",
      "exercise_induced_angina                 90 non-null int64\n",
      "dtypes: float64(1), int64(11), object(2)\n",
      "memory usage: 9.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How many NaN values are in df_test set? 0\n"
     ]
    }
   ],
   "source": [
    "print(f'How many NaN values are in df_test set? {df_test.isnull().sum().sum()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How many unique values are in thal column? 3\n",
      "['reversible_defect' 'normal' 'fixed_defect']\n"
     ]
    }
   ],
   "source": [
    "print(f'How many unique values are in thal column? {len(df_test.thal.unique())}')\n",
    "print(df_test['thal'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['slope_of_peak_exercise_st_segment', 'thal', 'resting_blood_pressure', 'chest_pain_type', 'num_major_vessels', 'fasting_blood_sugar_gt_120_mg_per_dl', 'resting_ekg_results', 'serum_cholesterol_mg_per_dl', 'oldpeak_eq_st_depression', 'sex', 'age', 'max_heart_rate_achieved', 'exercise_induced_angina']\n",
      "13\n"
     ]
    }
   ],
   "source": [
    "features_all_test = df_test.columns.tolist()\n",
    "features_all_test.remove('patient_id')\n",
    "print(features_all_test)\n",
    "print(len(features_all_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### merge train & target dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>patient_id</th>\n",
       "      <th>slope_of_peak_exercise_st_segment</th>\n",
       "      <th>thal</th>\n",
       "      <th>resting_blood_pressure</th>\n",
       "      <th>chest_pain_type</th>\n",
       "      <th>num_major_vessels</th>\n",
       "      <th>fasting_blood_sugar_gt_120_mg_per_dl</th>\n",
       "      <th>resting_ekg_results</th>\n",
       "      <th>serum_cholesterol_mg_per_dl</th>\n",
       "      <th>oldpeak_eq_st_depression</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>max_heart_rate_achieved</th>\n",
       "      <th>exercise_induced_angina</th>\n",
       "      <th>heart_disease_present</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0z64un</td>\n",
       "      <td>1</td>\n",
       "      <td>normal</td>\n",
       "      <td>128</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>308</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ryoo3j</td>\n",
       "      <td>2</td>\n",
       "      <td>normal</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>214</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>158</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>yt1s1x</td>\n",
       "      <td>1</td>\n",
       "      <td>normal</td>\n",
       "      <td>125</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>304</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>77</td>\n",
       "      <td>162</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>l2xjde</td>\n",
       "      <td>1</td>\n",
       "      <td>reversible_defect</td>\n",
       "      <td>152</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>223</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>181</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>oyt4ek</td>\n",
       "      <td>3</td>\n",
       "      <td>reversible_defect</td>\n",
       "      <td>178</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>270</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>145</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  patient_id  slope_of_peak_exercise_st_segment               thal  \\\n",
       "0     0z64un                                  1             normal   \n",
       "1     ryoo3j                                  2             normal   \n",
       "2     yt1s1x                                  1             normal   \n",
       "3     l2xjde                                  1  reversible_defect   \n",
       "4     oyt4ek                                  3  reversible_defect   \n",
       "\n",
       "   resting_blood_pressure  chest_pain_type  num_major_vessels  \\\n",
       "0                     128                2                  0   \n",
       "1                     110                3                  0   \n",
       "2                     125                4                  3   \n",
       "3                     152                4                  0   \n",
       "4                     178                1                  0   \n",
       "\n",
       "   fasting_blood_sugar_gt_120_mg_per_dl  resting_ekg_results  \\\n",
       "0                                     0                    2   \n",
       "1                                     0                    0   \n",
       "2                                     0                    2   \n",
       "3                                     0                    0   \n",
       "4                                     0                    2   \n",
       "\n",
       "   serum_cholesterol_mg_per_dl  oldpeak_eq_st_depression  sex  age  \\\n",
       "0                          308                       0.0    1   45   \n",
       "1                          214                       1.6    0   54   \n",
       "2                          304                       0.0    1   77   \n",
       "3                          223                       0.0    1   40   \n",
       "4                          270                       4.2    1   59   \n",
       "\n",
       "   max_heart_rate_achieved  exercise_induced_angina  heart_disease_present  \n",
       "0                      170                        0                      0  \n",
       "1                      158                        0                      0  \n",
       "2                      162                        1                      1  \n",
       "3                      181                        0                      1  \n",
       "4                      145                        0                      0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final = df_train.merge(df_target, on = 'patient_id' , how = 'left')\n",
    "df_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "patient_id                               object\n",
       "slope_of_peak_exercise_st_segment         int64\n",
       "thal                                     object\n",
       "resting_blood_pressure                    int64\n",
       "chest_pain_type                           int64\n",
       "num_major_vessels                         int64\n",
       "fasting_blood_sugar_gt_120_mg_per_dl      int64\n",
       "resting_ekg_results                       int64\n",
       "serum_cholesterol_mg_per_dl               int64\n",
       "oldpeak_eq_st_depression                float64\n",
       "sex                                       int64\n",
       "age                                       int64\n",
       "max_heart_rate_achieved                   int64\n",
       "exercise_induced_angina                   int64\n",
       "heart_disease_present                     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### features dataset final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1]\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "df_final['heart_disease_present'].value_counts()\n",
    "targets = df_final['heart_disease_present'].unique()\n",
    "num_targets = len(targets)\n",
    "print(targets)\n",
    "print(num_targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['slope_of_peak_exercise_st_segment', 'resting_blood_pressure', 'chest_pain_type', 'num_major_vessels', 'fasting_blood_sugar_gt_120_mg_per_dl', 'resting_ekg_results', 'serum_cholesterol_mg_per_dl', 'oldpeak_eq_st_depression', 'sex', 'age', 'max_heart_rate_achieved', 'exercise_induced_angina']\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "features_num = df_final.select_dtypes(include=['int64','float']).columns.to_list()\n",
    "features_num.remove('heart_disease_present')\n",
    "print(features_num)\n",
    "print(len(features_num))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    100\n",
       "1     80\n",
       "Name: heart_disease_present, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['heart_disease_present'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "marker": {
          "color": [
           "rgba(204,204,204,1)",
           "rgba(222,45,38,0.8)"
          ]
         },
         "type": "bar",
         "uid": "41bbe050-91b6-4c13-b22a-b88e6a67a6de",
         "y": [
          100,
          80
         ]
        }
       ],
       "layout": {
        "height": 400,
        "title": {
         "text": "heart_disease_present Count"
        },
        "width": 400,
        "xaxis": {
         "dtick": 1
        }
       }
      },
      "text/html": [
       "<div id=\"f3f2448b-1664-4e7d-9e0b-19a16179135f\" style=\"height: 400px; width: 400px;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"f3f2448b-1664-4e7d-9e0b-19a16179135f\", [{\"marker\": {\"color\": [\"rgba(204,204,204,1)\", \"rgba(222,45,38,0.8)\"]}, \"y\": [100, 80], \"type\": \"bar\", \"uid\": \"41bbe050-91b6-4c13-b22a-b88e6a67a6de\"}], {\"height\": 400, \"title\": {\"text\": \"heart_disease_present Count\"}, \"width\": 400, \"xaxis\": {\"dtick\": 1}}, {\"showLink\": false, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<div id=\"f3f2448b-1664-4e7d-9e0b-19a16179135f\" style=\"height: 400px; width: 400px;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"f3f2448b-1664-4e7d-9e0b-19a16179135f\", [{\"marker\": {\"color\": [\"rgba(204,204,204,1)\", \"rgba(222,45,38,0.8)\"]}, \"y\": [100, 80], \"type\": \"bar\", \"uid\": \"41bbe050-91b6-4c13-b22a-b88e6a67a6de\"}], {\"height\": 400, \"title\": {\"text\": \"heart_disease_present Count\"}, \"width\": 400, \"xaxis\": {\"dtick\": 1}}, {\"showLink\": false, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "targets_plot = [go.Bar(y = df_final['heart_disease_present'].value_counts(),\\\n",
    "                     marker = dict(color = ['rgba(204,204,204,1)', 'rgba(222,45,38,0.8)']))]\n",
    "\n",
    "layout = go.Layout(title='heart_disease_present Count',width=400, height=400, xaxis= dict(dtick=1))\n",
    "fig = go.Figure(targets_plot, layout=layout)\n",
    "plotly.offline.iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train vs test comparision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2880x1440 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3, 4, figsize=(40, 20))\n",
    "axs = axs.flatten()\n",
    "    \n",
    "for i, (ax, feature) in enumerate(zip(axs, features_num)):\n",
    "    sns.kdeplot(df_train[feature], label=\"train\", ax=ax)\n",
    "    sns.kdeplot(df_test[feature], label=\"test\", ax=ax)\n",
    "    ax.set_title(feature)\n",
    "    sns.set(font_scale=3) \n",
    "    sns.set_style(\"ticks\")\n",
    "    ax.legend(frameon=False)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_count = ['resting_ekg_results','slope_of_peak_exercise_st_segment','chest_pain_type','num_major_vessels','fasting_blood_sugar_gt_120_mg_per_dl','sex','exercise_induced_angina']\n",
    "features_kde = [ 'resting_blood_pressure', 'serum_cholesterol_mg_per_dl', 'oldpeak_eq_st_depression', 'age', 'max_heart_rate_achieved']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-1bf94b19a7cc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mncols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0maxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeature\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeatures_kde\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkdeplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_final\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf_final\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'heart_disease_present'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfeature\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols = 5, figsize=(20, 6))\n",
    "axs = axs.flatten()\n",
    "    \n",
    "for i, (ax, feature) in enumerate(zip(axs, features_kde)):\n",
    "    sns.kdeplot(df_final[df_final['heart_disease_present']==0][feature], label = 0, ax=ax)\n",
    "    sns.kdeplot(df_final[df_final['heart_disease_present']==1][feature], label = 1, ax=ax)\n",
    "    ax.set_title(feature)\n",
    "    sns.set(font_scale=1.5) \n",
    "    sns.set_style(\"ticks\")\n",
    "    ax.legend(frameon=False)\n",
    "    \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2,4,figsize=(30,15))\n",
    "axs = axs.flatten()\n",
    "\n",
    "for i,(ax, feature) in enumerate (zip(axs, features_count)):\n",
    "    gb_0 = df_final[[feature, 'heart_disease_present']][df_final['heart_disease_present'] == 0].groupby(feature).count().rename(columns = {'heart_disease_present':'0'})\n",
    "    gb_1 = df_final[[feature, 'heart_disease_present']][df_final['heart_disease_present'] == 1].groupby(feature).count().rename(columns = {'heart_disease_present':'1'})\n",
    "    gb_plot = gb_0.merge(gb_1, on = feature).reset_index()\n",
    "    gb_plot = pd.melt(gb_plot, id_vars = feature, value_vars=['0','1']).rename(columns = {'variable':'heart_disease_present'})\n",
    "    sns.barplot(data = gb_plot, x = feature , y='value' ,hue = 'heart_disease_present', ax = ax)\n",
    "    sns.set(font_scale=1.5) \n",
    "    sns.set_style(\"ticks\")\n",
    "    ax.legend(frameon=False)\n",
    "    \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### combine target and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_combine = pd.concat([df_final, df_test], sort = False)\n",
    "len_train = df_train.shape[0]\n",
    "len_test = df_test.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### dummies EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_dummies = ['thal_fixed_defect','thal_normal','thal_reversible_defect']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_combine = pd.get_dummies(df_combine, columns = ['thal'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>heart_disease_present</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thal</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fixed_defect</th>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>normal</th>\n",
       "      <td>0.204082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reversible_defect</th>\n",
       "      <td>0.756757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   heart_disease_present\n",
       "thal                                    \n",
       "fixed_defect                    0.500000\n",
       "normal                          0.204082\n",
       "reversible_defect               0.756757"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final[['thal','heart_disease_present']].groupby('thal').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize=(20,6),ncols=3)\n",
    "for i,feature in enumerate (feature_dummies):\n",
    "    count = df_combine[:len_train][[feature,'heart_disease_present']].groupby('heart_disease_present').sum().reset_index()\n",
    "    sns.barplot(data = count, x = 'heart_disease_present', y = feature, ax = axs[i])\n",
    "    sns.set(font_scale=1) \n",
    "    sns.set_style(\"ticks\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "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>patient_id</th>\n",
       "      <th>resting_blood_pressure</th>\n",
       "      <th>fasting_blood_sugar_gt_120_mg_per_dl</th>\n",
       "      <th>resting_ekg_results</th>\n",
       "      <th>serum_cholesterol_mg_per_dl</th>\n",
       "      <th>oldpeak_eq_st_depression</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>max_heart_rate_achieved</th>\n",
       "      <th>exercise_induced_angina</th>\n",
       "      <th>...</th>\n",
       "      <th>slope_of_peak_exercise_st_segment_2</th>\n",
       "      <th>slope_of_peak_exercise_st_segment_3</th>\n",
       "      <th>chest_pain_type_1</th>\n",
       "      <th>chest_pain_type_2</th>\n",
       "      <th>chest_pain_type_3</th>\n",
       "      <th>chest_pain_type_4</th>\n",
       "      <th>num_major_vessels_0</th>\n",
       "      <th>num_major_vessels_1</th>\n",
       "      <th>num_major_vessels_2</th>\n",
       "      <th>num_major_vessels_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0z64un</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>308</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ryoo3j</td>\n",
       "      <td>110</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>214</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>158</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>yt1s1x</td>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>304</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>77</td>\n",
       "      <td>162</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>l2xjde</td>\n",
       "      <td>152</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>223</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>181</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>oyt4ek</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>270</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>145</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  patient_id  resting_blood_pressure  fasting_blood_sugar_gt_120_mg_per_dl  \\\n",
       "0     0z64un                     128                                     0   \n",
       "1     ryoo3j                     110                                     0   \n",
       "2     yt1s1x                     125                                     0   \n",
       "3     l2xjde                     152                                     0   \n",
       "4     oyt4ek                     178                                     0   \n",
       "\n",
       "   resting_ekg_results  serum_cholesterol_mg_per_dl  oldpeak_eq_st_depression  \\\n",
       "0                    2                          308                       0.0   \n",
       "1                    0                          214                       1.6   \n",
       "2                    2                          304                       0.0   \n",
       "3                    0                          223                       0.0   \n",
       "4                    2                          270                       4.2   \n",
       "\n",
       "   sex  age  max_heart_rate_achieved  exercise_induced_angina  ...  \\\n",
       "0    1   45                      170                        0  ...   \n",
       "1    0   54                      158                        0  ...   \n",
       "2    1   77                      162                        1  ...   \n",
       "3    1   40                      181                        0  ...   \n",
       "4    1   59                      145                        0  ...   \n",
       "\n",
       "   slope_of_peak_exercise_st_segment_2  slope_of_peak_exercise_st_segment_3  \\\n",
       "0                                    0                                    0   \n",
       "1                                    1                                    0   \n",
       "2                                    0                                    0   \n",
       "3                                    0                                    0   \n",
       "4                                    0                                    1   \n",
       "\n",
       "   chest_pain_type_1  chest_pain_type_2  chest_pain_type_3  chest_pain_type_4  \\\n",
       "0                  0                  1                  0                  0   \n",
       "1                  0                  0                  1                  0   \n",
       "2                  0                  0                  0                  1   \n",
       "3                  0                  0                  0                  1   \n",
       "4                  1                  0                  0                  0   \n",
       "\n",
       "   num_major_vessels_0  num_major_vessels_1  num_major_vessels_2  \\\n",
       "0                    1                    0                    0   \n",
       "1                    1                    0                    0   \n",
       "2                    0                    0                    0   \n",
       "3                    1                    0                    0   \n",
       "4                    1                    0                    0   \n",
       "\n",
       "   num_major_vessels_3  \n",
       "0                    0  \n",
       "1                    0  \n",
       "2                    1  \n",
       "3                    0  \n",
       "4                    0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combine = pd.get_dummies(df_combine, columns=['slope_of_peak_exercise_st_segment','chest_pain_type','num_major_vessels'])\n",
    "df_combine.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['resting_blood_pressure',\n",
       " 'fasting_blood_sugar_gt_120_mg_per_dl',\n",
       " 'resting_ekg_results',\n",
       " 'serum_cholesterol_mg_per_dl',\n",
       " 'oldpeak_eq_st_depression',\n",
       " 'sex',\n",
       " 'age',\n",
       " 'max_heart_rate_achieved',\n",
       " 'exercise_induced_angina',\n",
       " 'heart_disease_present',\n",
       " 'thal_fixed_defect',\n",
       " 'thal_normal',\n",
       " 'thal_reversible_defect',\n",
       " 'slope_of_peak_exercise_st_segment_1',\n",
       " 'slope_of_peak_exercise_st_segment_2',\n",
       " 'slope_of_peak_exercise_st_segment_3',\n",
       " 'chest_pain_type_1',\n",
       " 'chest_pain_type_2',\n",
       " 'chest_pain_type_3',\n",
       " 'chest_pain_type_4',\n",
       " 'num_major_vessels_0',\n",
       " 'num_major_vessels_1',\n",
       " 'num_major_vessels_2',\n",
       " 'num_major_vessels_3']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_corr = df_train_final = df_combine[:len_train].select_dtypes(include=['int','float','uint8']).columns.to_list()\n",
    "features_corr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Pearson correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most negatively correlated variables:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>feature</th>\n",
       "      <th>pcorr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>thal_normal</td>\n",
       "      <td>-0.528812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>num_major_vessels_0</td>\n",
       "      <td>-0.411532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>max_heart_rate_achieved</td>\n",
       "      <td>-0.375352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>slope_of_peak_exercise_st_segment_1</td>\n",
       "      <td>-0.365428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chest_pain_type_3</td>\n",
       "      <td>-0.296427</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               feature     pcorr\n",
       "0                          thal_normal -0.528812\n",
       "1                  num_major_vessels_0 -0.411532\n",
       "2              max_heart_rate_achieved -0.375352\n",
       "3  slope_of_peak_exercise_st_segment_1 -0.365428\n",
       "4                    chest_pain_type_3 -0.296427"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute Pearson correlation for every pair of features\n",
    "# Use only training data\n",
    "pcorrs = pd.DataFrame(df_combine[:len_train][features_corr].corr()['heart_disease_present'].sort_values()).reset_index()\n",
    "pcorrs = pcorrs.rename(columns = {'index':'feature', 'heart_disease_present':'pcorr'})\n",
    "\n",
    "print('Most negatively correlated variables:')\n",
    "pcorrs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most positively correlated variables:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>feature</th>\n",
       "      <th>pcorr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>oldpeak_eq_st_depression</td>\n",
       "      <td>0.382930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>exercise_induced_angina</td>\n",
       "      <td>0.448647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>chest_pain_type_4</td>\n",
       "      <td>0.506362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>thal_reversible_defect</td>\n",
       "      <td>0.525145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>heart_disease_present</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     feature     pcorr\n",
       "19  oldpeak_eq_st_depression  0.382930\n",
       "20   exercise_induced_angina  0.448647\n",
       "21         chest_pain_type_4  0.506362\n",
       "22    thal_reversible_defect  0.525145\n",
       "23     heart_disease_present  1.000000"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Most positively correlated variables:')\n",
    "pcorrs.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Spearman correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most negatively correlated variables:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>feature</th>\n",
       "      <th>scorr</th>\n",
       "      <th>pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>thal_normal</td>\n",
       "      <td>-0.528812</td>\n",
       "      <td>2.336881e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>num_major_vessels_0</td>\n",
       "      <td>-0.411532</td>\n",
       "      <td>9.514609e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>slope_of_peak_exercise_st_segment_1</td>\n",
       "      <td>-0.365428</td>\n",
       "      <td>4.556177e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>max_heart_rate_achieved</td>\n",
       "      <td>-0.363189</td>\n",
       "      <td>5.417041e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>chest_pain_type_3</td>\n",
       "      <td>-0.296427</td>\n",
       "      <td>5.334416e-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                feature     scorr        pvalue\n",
       "11                          thal_normal -0.528812  2.336881e-14\n",
       "20                  num_major_vessels_0 -0.411532  9.514609e-09\n",
       "13  slope_of_peak_exercise_st_segment_1 -0.365428  4.556177e-07\n",
       "7               max_heart_rate_achieved -0.363189  5.417041e-07\n",
       "18                    chest_pain_type_3 -0.296427  5.334416e-05"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute Spearman correlation for every features\n",
    "# Use only training data\n",
    "from scipy.stats import spearmanr\n",
    "feats = []\n",
    "scorr = []\n",
    "pvalues = []\n",
    "\n",
    "for feat in features_corr:\n",
    "    \n",
    "    # Calculate spearman correlation\n",
    "    scorr.append(spearmanr(df_combine[:len_train][feat], df_combine[:len_train]['heart_disease_present']).correlation)\n",
    "    pvalues.append(spearmanr(df_combine[:len_train][feat], df_combine[:len_train]['heart_disease_present']).pvalue)\n",
    "\n",
    "scorrs = pd.DataFrame({'feature': features_corr, 'scorr':scorr, 'pvalue':pvalues}).sort_values('scorr')\n",
    "\n",
    "print('Most negatively correlated variables:')\n",
    "scorrs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most positively correlated variables:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>feature</th>\n",
       "      <th>scorr</th>\n",
       "      <th>pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>oldpeak_eq_st_depression</td>\n",
       "      <td>0.365369</td>\n",
       "      <td>4.576916e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>exercise_induced_angina</td>\n",
       "      <td>0.448647</td>\n",
       "      <td>2.679484e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>chest_pain_type_4</td>\n",
       "      <td>0.506362</td>\n",
       "      <td>4.111484e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>thal_reversible_defect</td>\n",
       "      <td>0.525145</td>\n",
       "      <td>3.787200e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>heart_disease_present</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     feature     scorr        pvalue\n",
       "4   oldpeak_eq_st_depression  0.365369  4.576916e-07\n",
       "8    exercise_induced_angina  0.448647  2.679484e-10\n",
       "19         chest_pain_type_4  0.506362  4.111484e-13\n",
       "12    thal_reversible_defect  0.525145  3.787200e-14\n",
       "9      heart_disease_present  1.000000  0.000000e+00"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Most positively correlated variables:')\n",
    "scorrs.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Compute features for which Pearson and Spearman correlation differs the most"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>feature</th>\n",
       "      <th>pcorr</th>\n",
       "      <th>scorr</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>serum_cholesterol_mg_per_dl</td>\n",
       "      <td>0.079775</td>\n",
       "      <td>0.127817</td>\n",
       "      <td>8.728123e-02</td>\n",
       "      <td>-4.804202e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>age</td>\n",
       "      <td>0.138255</td>\n",
       "      <td>0.151047</td>\n",
       "      <td>4.296794e-02</td>\n",
       "      <td>-1.279257e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>max_heart_rate_achieved</td>\n",
       "      <td>-0.375352</td>\n",
       "      <td>-0.363189</td>\n",
       "      <td>5.417041e-07</td>\n",
       "      <td>-1.216271e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>thal_normal</td>\n",
       "      <td>-0.528812</td>\n",
       "      <td>-0.528812</td>\n",
       "      <td>2.336881e-14</td>\n",
       "      <td>-6.661338e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>thal_reversible_defect</td>\n",
       "      <td>0.525145</td>\n",
       "      <td>0.525145</td>\n",
       "      <td>3.787200e-14</td>\n",
       "      <td>-6.661338e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>chest_pain_type_2</td>\n",
       "      <td>-0.260494</td>\n",
       "      <td>-0.260494</td>\n",
       "      <td>4.128682e-04</td>\n",
       "      <td>-3.885781e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>thal_fixed_defect</td>\n",
       "      <td>0.024112</td>\n",
       "      <td>0.024112</td>\n",
       "      <td>7.479896e-01</td>\n",
       "      <td>-2.949030e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>slope_of_peak_exercise_st_segment_2</td>\n",
       "      <td>0.309931</td>\n",
       "      <td>0.309931</td>\n",
       "      <td>2.296541e-05</td>\n",
       "      <td>-2.775558e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>num_major_vessels_1</td>\n",
       "      <td>0.153704</td>\n",
       "      <td>0.153704</td>\n",
       "      <td>3.939363e-02</td>\n",
       "      <td>-1.665335e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>num_major_vessels_3</td>\n",
       "      <td>0.241196</td>\n",
       "      <td>0.241196</td>\n",
       "      <td>1.107334e-03</td>\n",
       "      <td>-8.326673e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>slope_of_peak_exercise_st_segment_1</td>\n",
       "      <td>-0.365428</td>\n",
       "      <td>-0.365428</td>\n",
       "      <td>4.556177e-07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chest_pain_type_3</td>\n",
       "      <td>-0.296427</td>\n",
       "      <td>-0.296427</td>\n",
       "      <td>5.334416e-05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>fasting_blood_sugar_gt_120_mg_per_dl</td>\n",
       "      <td>0.003379</td>\n",
       "      <td>0.003379</td>\n",
       "      <td>9.640919e-01</td>\n",
       "      <td>6.938894e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>slope_of_peak_exercise_st_segment_3</td>\n",
       "      <td>0.119523</td>\n",
       "      <td>0.119523</td>\n",
       "      <td>1.100140e-01</td>\n",
       "      <td>2.775558e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>chest_pain_type_1</td>\n",
       "      <td>-0.076785</td>\n",
       "      <td>-0.076785</td>\n",
       "      <td>3.055934e-01</td>\n",
       "      <td>9.714451e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>heart_disease_present</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.110223e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>sex</td>\n",
       "      <td>0.335421</td>\n",
       "      <td>0.335421</td>\n",
       "      <td>4.167617e-06</td>\n",
       "      <td>2.220446e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>exercise_induced_angina</td>\n",
       "      <td>0.448647</td>\n",
       "      <td>0.448647</td>\n",
       "      <td>2.679484e-10</td>\n",
       "      <td>2.220446e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>num_major_vessels_2</td>\n",
       "      <td>0.226987</td>\n",
       "      <td>0.226987</td>\n",
       "      <td>2.182287e-03</td>\n",
       "      <td>3.053113e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>chest_pain_type_4</td>\n",
       "      <td>0.506362</td>\n",
       "      <td>0.506362</td>\n",
       "      <td>4.111484e-13</td>\n",
       "      <td>8.881784e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>num_major_vessels_0</td>\n",
       "      <td>-0.411532</td>\n",
       "      <td>-0.411532</td>\n",
       "      <td>9.514609e-09</td>\n",
       "      <td>9.436896e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>resting_ekg_results</td>\n",
       "      <td>0.145933</td>\n",
       "      <td>0.145620</td>\n",
       "      <td>5.111659e-02</td>\n",
       "      <td>3.131310e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>resting_blood_pressure</td>\n",
       "      <td>0.078506</td>\n",
       "      <td>0.071805</td>\n",
       "      <td>3.381193e-01</td>\n",
       "      <td>6.700952e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>oldpeak_eq_st_depression</td>\n",
       "      <td>0.382930</td>\n",
       "      <td>0.365369</td>\n",
       "      <td>4.576916e-07</td>\n",
       "      <td>1.756124e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 feature     pcorr     scorr        pvalue  \\\n",
       "10           serum_cholesterol_mg_per_dl  0.079775  0.127817  8.728123e-02   \n",
       "12                                   age  0.138255  0.151047  4.296794e-02   \n",
       "2                max_heart_rate_achieved -0.375352 -0.363189  5.417041e-07   \n",
       "0                            thal_normal -0.528812 -0.528812  2.336881e-14   \n",
       "22                thal_reversible_defect  0.525145  0.525145  3.787200e-14   \n",
       "5                      chest_pain_type_2 -0.260494 -0.260494  4.128682e-04   \n",
       "8                      thal_fixed_defect  0.024112  0.024112  7.479896e-01   \n",
       "17   slope_of_peak_exercise_st_segment_2  0.309931  0.309931  2.296541e-05   \n",
       "14                   num_major_vessels_1  0.153704  0.153704  3.939363e-02   \n",
       "16                   num_major_vessels_3  0.241196  0.241196  1.107334e-03   \n",
       "3    slope_of_peak_exercise_st_segment_1 -0.365428 -0.365428  4.556177e-07   \n",
       "4                      chest_pain_type_3 -0.296427 -0.296427  5.334416e-05   \n",
       "7   fasting_blood_sugar_gt_120_mg_per_dl  0.003379  0.003379  9.640919e-01   \n",
       "11   slope_of_peak_exercise_st_segment_3  0.119523  0.119523  1.100140e-01   \n",
       "6                      chest_pain_type_1 -0.076785 -0.076785  3.055934e-01   \n",
       "23                 heart_disease_present  1.000000  1.000000  0.000000e+00   \n",
       "18                                   sex  0.335421  0.335421  4.167617e-06   \n",
       "20               exercise_induced_angina  0.448647  0.448647  2.679484e-10   \n",
       "15                   num_major_vessels_2  0.226987  0.226987  2.182287e-03   \n",
       "21                     chest_pain_type_4  0.506362  0.506362  4.111484e-13   \n",
       "1                    num_major_vessels_0 -0.411532 -0.411532  9.514609e-09   \n",
       "13                   resting_ekg_results  0.145933  0.145620  5.111659e-02   \n",
       "9                 resting_blood_pressure  0.078506  0.071805  3.381193e-01   \n",
       "19              oldpeak_eq_st_depression  0.382930  0.365369  4.576916e-07   \n",
       "\n",
       "            diff  \n",
       "10 -4.804202e-02  \n",
       "12 -1.279257e-02  \n",
       "2  -1.216271e-02  \n",
       "0  -6.661338e-16  \n",
       "22 -6.661338e-16  \n",
       "5  -3.885781e-16  \n",
       "8  -2.949030e-16  \n",
       "17 -2.775558e-16  \n",
       "14 -1.665335e-16  \n",
       "16 -8.326673e-17  \n",
       "3   0.000000e+00  \n",
       "4   0.000000e+00  \n",
       "7   6.938894e-18  \n",
       "11  2.775558e-17  \n",
       "6   9.714451e-17  \n",
       "23  1.110223e-16  \n",
       "18  2.220446e-16  \n",
       "20  2.220446e-16  \n",
       "15  3.053113e-16  \n",
       "21  8.881784e-16  \n",
       "1   9.436896e-16  \n",
       "13  3.131310e-04  \n",
       "9   6.700952e-03  \n",
       "19  1.756124e-02  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrs = pcorrs.merge(scorrs, on = 'feature')\n",
    "corrs['diff'] = corrs['pcorr'] - corrs['scorr']\n",
    "corrs.sort_values('diff')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a21679b00>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10))\n",
    "sns.set(font_scale=0.75)\n",
    "sns.heatmap(df_combine[:len_train][features_corr].corr(), square=True, cmap='RdYlGn',annot=True,annot_kws={'size': 5},fmt='.2f')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### drop most related features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>patient_id</th>\n",
       "      <th>fasting_blood_sugar_gt_120_mg_per_dl</th>\n",
       "      <th>resting_ekg_results</th>\n",
       "      <th>serum_cholesterol_mg_per_dl</th>\n",
       "      <th>oldpeak_eq_st_depression</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>max_heart_rate_achieved</th>\n",
       "      <th>exercise_induced_angina</th>\n",
       "      <th>heart_disease_present</th>\n",
       "      <th>...</th>\n",
       "      <th>slope_of_peak_exercise_st_segment_2</th>\n",
       "      <th>slope_of_peak_exercise_st_segment_3</th>\n",
       "      <th>chest_pain_type_1</th>\n",
       "      <th>chest_pain_type_2</th>\n",
       "      <th>chest_pain_type_3</th>\n",
       "      <th>chest_pain_type_4</th>\n",
       "      <th>num_major_vessels_0</th>\n",
       "      <th>num_major_vessels_1</th>\n",
       "      <th>num_major_vessels_2</th>\n",
       "      <th>num_major_vessels_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0z64un</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>308</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ryoo3j</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>214</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>158</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>yt1s1x</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>304</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>77</td>\n",
       "      <td>162</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>l2xjde</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>223</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>181</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>oyt4ek</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>270</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>145</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  patient_id  fasting_blood_sugar_gt_120_mg_per_dl  resting_ekg_results  \\\n",
       "0     0z64un                                     0                    2   \n",
       "1     ryoo3j                                     0                    0   \n",
       "2     yt1s1x                                     0                    2   \n",
       "3     l2xjde                                     0                    0   \n",
       "4     oyt4ek                                     0                    2   \n",
       "\n",
       "   serum_cholesterol_mg_per_dl  oldpeak_eq_st_depression  sex  age  \\\n",
       "0                          308                       0.0    1   45   \n",
       "1                          214                       1.6    0   54   \n",
       "2                          304                       0.0    1   77   \n",
       "3                          223                       0.0    1   40   \n",
       "4                          270                       4.2    1   59   \n",
       "\n",
       "   max_heart_rate_achieved  exercise_induced_angina  heart_disease_present  \\\n",
       "0                      170                        0                    0.0   \n",
       "1                      158                        0                    0.0   \n",
       "2                      162                        1                    1.0   \n",
       "3                      181                        0                    1.0   \n",
       "4                      145                        0                    0.0   \n",
       "\n",
       "   ...  slope_of_peak_exercise_st_segment_2  \\\n",
       "0  ...                                    0   \n",
       "1  ...                                    1   \n",
       "2  ...                                    0   \n",
       "3  ...                                    0   \n",
       "4  ...                                    0   \n",
       "\n",
       "   slope_of_peak_exercise_st_segment_3  chest_pain_type_1  chest_pain_type_2  \\\n",
       "0                                    0                  0                  1   \n",
       "1                                    0                  0                  0   \n",
       "2                                    0                  0                  0   \n",
       "3                                    0                  0                  0   \n",
       "4                                    1                  1                  0   \n",
       "\n",
       "   chest_pain_type_3  chest_pain_type_4  num_major_vessels_0  \\\n",
       "0                  0                  0                    1   \n",
       "1                  1                  0                    1   \n",
       "2                  0                  1                    0   \n",
       "3                  0                  1                    1   \n",
       "4                  0                  0                    1   \n",
       "\n",
       "   num_major_vessels_1  num_major_vessels_2  num_major_vessels_3  \n",
       "0                    0                    0                    0  \n",
       "1                    0                    0                    0  \n",
       "2                    0                    0                    1  \n",
       "3                    0                    0                    0  \n",
       "4                    0                    0                    0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combine.drop(columns = ['thal_normal','resting_blood_pressure'], inplace = True)\n",
    "df_combine.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Anomanar datasets per model prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train_final = df_combine[:len_train]\n",
    "df_test_final = df_combine[len_train:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model and prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import log_loss, confusion_matrix, classification_report, roc_auc_score, roc_curve, make_scorer, f1_score\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler, Normalizer, StandardScaler, PolynomialFeatures\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import StratifiedKFold, KFold\n",
    "\n",
    "# Since the dataset is class-balanced, we can use macro averaging for the F1 scorer\n",
    "scorer = make_scorer(f1_score, greater_is_better=True, average = 'macro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(180, 21)\n",
      "(90, 21)\n",
      "(180,)\n"
     ]
    }
   ],
   "source": [
    "# Extract training data and labels\n",
    "train_set = df_train_final.drop(columns = ['patient_id','heart_disease_present'])\n",
    "test_set = df_test_final.drop(columns = ['patient_id','heart_disease_present'])\n",
    "test_ids = df_test_final['patient_id'].values\n",
    "train_labels = np.asarray(df_train_final['heart_disease_present'])\n",
    "\n",
    "[num_examples,num_feat] = train_set.shape\n",
    "\n",
    "# Submission base which is used for making submissions to the competition\n",
    "submission_base = df_combine[len_train:][['patient_id']].copy()\n",
    "\n",
    "print(train_set.shape)\n",
    "print(test_set.shape)\n",
    "print(train_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using stratified kfold cross validation\n",
    "nfolds = 10\n",
    "strkfold = StratifiedKFold(n_splits = nfolds, shuffle = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Routine to train a model with cross-validation and append the results to a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_results = pd.DataFrame(columns = ['model','cv_scores'])\n",
    "\n",
    "def run_cv_model_f1(model, train_set, train_labels, name, model_results = None):\n",
    "    \n",
    "    cv_scores = cross_val_score(model, train_set, train_labels, cv=strkfold, scoring=scorer)\n",
    "    \n",
    "    print(f'{strkfold.n_splits}-Fold Cross Validation F1 Score = \\\n",
    "            {round(cv_scores.mean(), 4)} with std = {round(cv_scores.std(), 4)}')\n",
    "    \n",
    "    if model_results is not None:\n",
    "        model_results = model_results.append(pd.DataFrame({'model':name,'cv_scores':cv_scores}),ignore_index=True)\n",
    "    return model_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_logloss = pd.DataFrame(columns = ['model','Neglogloss_scores'])\n",
    "\n",
    "def run_cv_model_logloss(model, train_set, train_labels, name, model_logloss = None):\n",
    "    \n",
    "    cv_scores = cross_val_score(model, train_set, train_labels, cv=strkfold, scoring='neg_log_loss')\n",
    "    \n",
    "    print(f'{strkfold.n_splits}-Fold Cross Validation Neg Log Loss = \\\n",
    "            {round(-1*(cv_scores.mean()), 4)} with std = {round(cv_scores.std(), 4)}')\n",
    "    \n",
    "    if model_logloss is not None:\n",
    "        model_logloss = model_logloss.append(pd.DataFrame({'model':name,'Neglogloss_scores':-1*(cv_scores)}),ignore_index=True)\n",
    "    return model_logloss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Routine to plot feature importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_feature_importance (df, n = 10, threshold = None):\n",
    "    \"\"\"Plots n most important features. Also plots the cumulative importance if\n",
    "    threshold is specified and prints the number of features needed to reach threshold cumulative importance.\n",
    "    Intended for use with any tree-based feature importances. \n",
    "    \n",
    "    Args:\n",
    "        df (dataframe): Dataframe of feature importances. Columns must be \"feature\" and \"importance\".\n",
    "    \n",
    "        n (int): Number of most important features to plot. Default is 10.\n",
    "    \n",
    "        threshold (float): Threshold for cumulative importance plot. If not provided, no plot is made. Default is None.\n",
    "        \n",
    "    Returns:\n",
    "        df (dataframe): Dataframe ordered by feature importances with a normalized column (sums to 1) \n",
    "                        and a cumulative importance column\n",
    "    \n",
    "    Note:\n",
    "    \n",
    "        * Normalization in this case means sums to 1. \n",
    "        * Cumulative importance is calculated by summing features from most to least important\n",
    "        * A threshold of x will show the most important features needed to reach 100*x% of cumulative importance\n",
    "    \n",
    "    \"\"\" \n",
    "    \n",
    "    # Sort features with most important at the head\n",
    "    df = df.sort_values('importance', ascending = False).reset_index(drop = True)\n",
    "    \n",
    "    # Normalize the feature importances to add up to one and calculate cumulative importance\n",
    "    df['importance_normalized'] = df['importance']/df['importance'].sum()\n",
    "    df['cumulative_importance'] = np.cumsum(df['importance_normalized'])\n",
    "    \n",
    "    # Bar plot of n most important features\n",
    "    trace0 = go.Bar(y = df.loc[:n, 'feature'],\\\n",
    "                   x = df.loc[:n, 'importance_normalized'],\\\n",
    "                   marker = dict(color = 'rgba(50, 171, 96, 0.6)',\\\n",
    "                                line = dict(color = 'rgba(50, 171, 96, 1.0)',\\\n",
    "                                           width = 1),),\\\n",
    "                    orientation = 'h')\n",
    "    \n",
    "    layout = go.Layout(title = 'Normalized importance of features',\\\n",
    "                      yaxis = dict(autorange = 'reversed',\\\n",
    "                                  tickangle = 0,\\\n",
    "                                  automargin = True),\\\n",
    "                      width = 800)\n",
    "    \n",
    "    fig = go.Figure(data = [trace0], layout = layout)\n",
    "    \n",
    "    plotly.offline.iplot(fig, filename='norm-importance')\n",
    "    \n",
    "    if threshold:\n",
    "        # Number of features needed for threshold cumulative importance\n",
    "        # This is the index (will need to add 1 for the actual number)\n",
    "        importance_index = np.min(np.where(df['cumulative_importance'] > threshold))\n",
    "        \n",
    "        # Cumulative importance plot\n",
    "        trace0 = go.Scatter(x = df.index,\\\n",
    "                           y = df['cumulative_importance'],\\\n",
    "                           line = dict(color = 'blue'))\n",
    "        trace1 = go.Scatter(x = [importance_index + 1, importance_index + 1],\\\n",
    "                           y = [0, 1.02],\\\n",
    "                           line = dict(color = 'red',\\\n",
    "                                      dash = 'dash'))\n",
    "        \n",
    "        fig1 = go.Figure(data = [trace0, trace1])\n",
    "        fig1['layout'].update(title = 'Cumulative Feature Importance',\\\n",
    "                             xaxis = dict(title = 'Number of Features'),\\\n",
    "                             yaxis = dict(title = 'Cumulative Importance'),\\\n",
    "                             showlegend = False)\n",
    "        \n",
    "        plotly.offline.iplot(fig1, filename='basic-line')\n",
    "        \n",
    "        print(f'{importance_index + 1} features required for {100 * threshold}% of cumulative importance.')\n",
    "    \n",
    "    return df    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Routine to plot scores of model_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_scores(model):\n",
    "    #     model.set_index('model', inplace=True)\n",
    "    \n",
    "    trace0 = go.Bar(\n",
    "                    y=model['cv_mean'],\n",
    "                    x=model['model'],\n",
    "                    marker=dict(\n",
    "                        color='orange',\n",
    "                        line=dict(\n",
    "                            color='black',\n",
    "                            width=1),\n",
    "                    ),\n",
    "                    error_y=dict(\n",
    "                            type='data',\n",
    "                            array=model['cv_std'],\n",
    "                            visible=True,\n",
    "                            color ='black')\n",
    "                    )\n",
    "    \n",
    "    layout = go.Layout(title='F1 scores of models',\n",
    "                      width=800, xaxis = dict(tickfont=dict(size=14), automargin=True))\n",
    "\n",
    "    fig = go.Figure(data=[trace0], layout=layout)\n",
    "    plotly.offline.iplot(fig, filename='F1-scores')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Routine to plot f1 and logloss score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_f1_logloss(f1,logloss):\n",
    "    \n",
    "    \"plot f1 scores\"\n",
    "    trace = []\n",
    "    for model in f1['model'].unique():\n",
    "        trace.append(go.Box(y = f1[f1['model']==model]['cv_scores'], name = model ,pointpos = 0,boxpoints = 'all'))\n",
    "    \n",
    "    layout = go.Layout(autosize=True, showlegend=False, xaxis = dict(tickfont=dict(size=12)), title = f\"Box plot of F1 socres\")\n",
    "\n",
    "    fig = go.Figure(data=trace, layout=layout)\n",
    "\n",
    "    plotly.offline.iplot(fig, filename=\"Box Plot Styling Outliers\")\n",
    "    \n",
    "    \"plot LogLoss\"\n",
    "    trace1 = []\n",
    "    for model1 in logloss['model'].unique():\n",
    "        trace1.append(go.Box(y = logloss[logloss['model']==model1]['Neglogloss_scores'], name = model1 ,pointpos = 0,boxpoints = 'all'))\n",
    "    \n",
    "    layout1 = go.Layout(autosize=True, showlegend=False, xaxis = dict(tickfont=dict(size=12)), title = f\"Box plot of LogLoss\")\n",
    "\n",
    "    fig1 = go.Figure(data=trace1, layout=layout1)\n",
    "\n",
    "    plotly.offline.iplot(fig1, filename=\"Box Plot Styling Outliers\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare multiple models\n",
    "Run several models for classification (without fine tuning) to determine a good baseline model that works for this dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_logloss = pd.DataFrame(columns = ['model','Neglogloss_scores'])\n",
    "model_results = pd.DataFrame(columns = ['model','cv_scores'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC, SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.linear_model import LogisticRegressionCV, RidgeClassifierCV, LogisticRegression\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.calibration import CalibratedClassifierCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10-Fold Cross Validation F1 Score =             0.4123 with std = 0.08\n",
      "10-Fold Cross Validation Neg Log Loss =             0.6964 with std = 0.0167\n"
     ]
    }
   ],
   "source": [
    "# Run SVC\n",
    "model_results = run_cv_model_f1(SVC(probability=True), train_set, train_labels, 'SVC', model_results)\n",
    "model_logloss = run_cv_model_logloss(SVC(probability=True), train_set, train_labels, 'SVC', model_logloss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10-Fold Cross Validation F1 Score =             0.8026 with std = 0.1293\n",
      "10-Fold Cross Validation Neg Log Loss =             0.4297 with std = 0.132\n"
     ]
    }
   ],
   "source": [
    "# Run Random Forest Classifier\n",
    "model_results = run_cv_model_f1(RandomForestClassifier(n_estimators = 100, random_state = 10, n_jobs = -1), train_set, train_labels, 'RF', model_results)\n",
    "model_logloss = run_cv_model_logloss(RandomForestClassifier(n_estimators = 100, random_state = 10, n_jobs = -1), train_set, train_labels, 'RF', model_logloss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10-Fold Cross Validation F1 Score =             0.8352 with std = 0.0696\n",
      "10-Fold Cross Validation Neg Log Loss =             0.4781 with std = 0.2837\n"
     ]
    }
   ],
   "source": [
    "# Run  LDA\n",
    "model_results = run_cv_model_f1(LinearDiscriminantAnalysis(), train_set, train_labels, 'LDA', model_results)\n",
    "model_logloss = run_cv_model_logloss(LinearDiscriminantAnalysis(), train_set, train_labels, 'LDA', model_logloss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10-Fold Cross Validation F1 Score =             0.817 with std = 0.0972\n",
      "10-Fold Cross Validation Neg Log Loss =             0.4413 with std = 0.1919\n"
     ]
    }
   ],
   "source": [
    "# Run  Logistic Regression\n",
    "model_results = run_cv_model_f1(LogisticRegressionCV(), train_set, train_labels, 'LR', model_results)\n",
    "model_logloss = run_cv_model_logloss(LogisticRegressionCV(), train_set, train_labels, 'LR', model_logloss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10-Fold Cross Validation F1 Score =             0.6137 with std = 0.1121\n",
      "10-Fold Cross Validation Neg Log Loss =             5.0459 with std = 3.3033\n",
      "10-Fold Cross Validation F1 Score =             0.5395 with std = 0.0794\n",
      "10-Fold Cross Validation Neg Log Loss =             1.9471 with std = 1.2177\n",
      "10-Fold Cross Validation F1 Score =             0.5484 with std = 0.0911\n",
      "10-Fold Cross Validation Neg Log Loss =             1.0565 with std = 0.7771\n",
      "10-Fold Cross Validation F1 Score =             0.5778 with std = 0.1173\n",
      "10-Fold Cross Validation Neg Log Loss =             0.6567 with std = 0.0757\n"
     ]
    }
   ],
   "source": [
    "# Run  KNN\n",
    "kn = [3, 5, 10, 20]\n",
    "for n in kn:\n",
    "    model_results = run_cv_model_f1(KNeighborsClassifier(n_neighbors=n),\n",
    "                                 train_set, train_labels, f'KNN-{n}', model_results)\n",
    "    model_logloss = run_cv_model_logloss(KNeighborsClassifier(n_neighbors=n),\n",
    "                                 train_set, train_labels, f'KNN-{n}', model_logloss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10-Fold Cross Validation F1 Score =             0.8115 with std = 0.1053\n",
      "10-Fold Cross Validation Neg Log Loss =             0.4195 with std = 0.1078\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "from xgboost import plot_importance\n",
    "import os\n",
    "os.environ['KMP_DUPLICATE_LIB_OK']='True'\n",
    "\n",
    "\n",
    "model_results = run_cv_model_f1(XGBClassifier(colsample_bytree=0.1,n_estimators=500, reg_lambda=1, learning_rate=0.08, min_child_weight = 2, max_depth = 8, gamma=2, subsample=.2,scale_pos_weight = 1, eval_metric='logloss',reg_alpha=0.00001), train_set, train_labels, 'XGB', model_results)\n",
    "model_logloss = run_cv_model_logloss(XGBClassifier(colsample_bytree=0.1,n_estimators=500, reg_lambda=1, learning_rate=0.08, min_child_weight = 2, max_depth = 8, gamma=2, subsample=.2,scale_pos_weight = 1, eval_metric='logloss',reg_alpha=0.00001), train_set, train_labels, 'XGB', model_logloss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10-Fold Cross Validation F1 Score =             0.8193 with std = 0.0919\n",
      "10-Fold Cross Validation Neg Log Loss =             0.4502 with std = 0.0854\n"
     ]
    }
   ],
   "source": [
    "# LinearSVC\n",
    "clf_SVC = CalibratedClassifierCV(LinearSVC(penalty=\"l1\", dual=False))\n",
    "model_results = run_cv_model_f1(clf_SVC, train_set, train_labels, 'LinearSVC', model_results)\n",
    "model_logloss = run_cv_model_logloss(clf_SVC, train_set, train_labels, 'LinearSVC', model_logloss)"
   ]
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_f1_logloss(model_results,model_logloss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cv_scores</th>\n",
       "      <th>Neglogloss_scores</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>XGB</th>\n",
       "      <td>0.811531</td>\n",
       "      <td>0.419503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF</th>\n",
       "      <td>0.802601</td>\n",
       "      <td>0.429687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LR</th>\n",
       "      <td>0.816993</td>\n",
       "      <td>0.441333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.819273</td>\n",
       "      <td>0.450248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDA</th>\n",
       "      <td>0.835195</td>\n",
       "      <td>0.478087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN-20</th>\n",
       "      <td>0.577775</td>\n",
       "      <td>0.656674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVC</th>\n",
       "      <td>0.412254</td>\n",
       "      <td>0.696368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN-10</th>\n",
       "      <td>0.548412</td>\n",
       "      <td>1.056464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN-5</th>\n",
       "      <td>0.539463</td>\n",
       "      <td>1.947082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN-3</th>\n",
       "      <td>0.613705</td>\n",
       "      <td>5.045918</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           cv_scores  Neglogloss_scores\n",
       "model                                  \n",
       "XGB         0.811531           0.419503\n",
       "RF          0.802601           0.429687\n",
       "LR          0.816993           0.441333\n",
       "LinearSVC   0.819273           0.450248\n",
       "LDA         0.835195           0.478087\n",
       "KNN-20      0.577775           0.656674\n",
       "SVC         0.412254           0.696368\n",
       "KNN-10      0.548412           1.056464\n",
       "KNN-5       0.539463           1.947082\n",
       "KNN-3       0.613705           5.045918"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_final = model_results.merge(model_logloss, on = 'model')\n",
    "model_final.groupby('model').mean().sort_values(by = 'Neglogloss_scores')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### RFECV feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.feature_selection import RFECV\n",
    "#features_selection = np.array(train_set.columns)\n",
    "#estimator = XGBClassifier(colsample_bytree=0.1,n_estimators=500, reg_lambda=1, learning_rate=0.08, min_child_weight = 2, max_depth = 8, gamma=2, subsample=.2,scale_pos_weight = 1, eval_metric='logloss',reg_alpha=0.00001)\n",
    "#selector = RFECV(estimator, step = 1, cv = strkfold, scoring= scorer, n_jobs = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#selector = selector.fit(train_set, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "#features_selected = [feat for (i,feat) in enumerate(features_selection) if selector.ranking_[i]==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train_set = train_set[features_selected]\n",
    "#test_set = test_set[features_selected]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Preprocessing with pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import FunctionTransformer, PolynomialFeatures, Normalizer, StandardScaler\n",
    "\n",
    "pipeline = Pipeline([('logtransform',FunctionTransformer(np.log1p, validate=True)),\\\n",
    "                     ('polynomialFeatures', PolynomialFeatures(include_bias=True, degree = 2))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set_PF = pipeline.fit_transform(train_set)\n",
    "test_set_PF = pipeline.fit_transform(test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import itertools\n",
    "\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Oranges):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    Source: http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    plt.figure(figsize = (10, 10))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title, size = 24)\n",
    "    plt.colorbar(aspect=4)\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45, size = 14)\n",
    "    plt.yticks(tick_marks, classes, size = 14)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    \n",
    "    # Labeling the plot\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt), fontsize = 20,\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "        \n",
    "    plt.grid(None)\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label', size = 18)\n",
    "    plt.xlabel('Predicted label', size = 18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split data into train and test sets\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(train_set_PF, train_labels, test_size=0.2, random_state=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Feature Selection with XGB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_selection = XGBClassifier(colsample_bytree=0.1,n_estimators=500, reg_lambda=1, learning_rate=0.08, min_child_weight = 2, max_depth = 8, gamma=2, subsample=.2,scale_pos_weight = 1, eval_metric='logloss',reg_alpha=0.00001)\n",
    "kfold = KFold(n_splits=10, random_state=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select features using threshold\n",
    "model_selection.fit(X_train,y_train)\n",
    "selection = SelectFromModel(model_selection, threshold=0.01, prefit=True)\n",
    "select_X_train = selection.transform(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Prediction with LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train model\n",
    "model_predict =  LogisticRegression()\n",
    "model_predict.fit(select_X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# eval model\n",
    "select_X_valid = selection.transform(X_valid)\n",
    "y_pred = model_predict.predict(select_X_valid)\n",
    "y_proba = model_predict.predict_proba(select_X_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#logloss\n",
    "from sklearn.metrics import accuracy_score, log_loss\n",
    "\n",
    "#accuracy = accuracy_score(y_valid, y_pred)\n",
    "#print(accuracy)\n",
    "\n",
    "#logi = log_loss(y_valid, y_proba)\n",
    "#print(logi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_preds = model_predict.predict_proba(select_X_valid)\n",
    "preds_df = pd.DataFrame(valid_preds, columns = targets)\n",
    "\n",
    "preds_df['prediction'] = preds_df[targets].idxmax(axis = 1)\n",
    "preds_df['confidence'] = preds_df[targets].max(axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.86111\n",
      "logloss score: 0.30214\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy score:', round(accuracy_score(y_valid, preds_df['prediction']), 5))\n",
    "print('logloss score:', round(log_loss(y_valid,  preds_df[[0,1]]), 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### plot confussion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_valid, preds_df['prediction'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(cm, classes = targets, title = 'heart_disease_present Matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model predicts heart disease 1 instead of heart disease 0 on 3 (0.14%) examples\n",
      "Model predicts heart disease 0 instead of heart disease 1 on 2 (0.14%) examples\n"
     ]
    }
   ],
   "source": [
    "cm_norm = cm.astype('float') / cm.sum(axis = 1)[:, np.newaxis]\n",
    "\n",
    "for i in range(len(targets)):\n",
    "    for j in range(len(targets)):\n",
    "        if cm_norm[i,j] > 0.1 and i!=j:\n",
    "            print(f'Model predicts heart disease {j} instead of heart disease {i} on {cm[i,j]} ({cm_norm[i,j]:.2}%) examples')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of best model so far"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LR and feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_final = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select features using threshold\n",
    "model_selection.fit(train_set_PF, train_labels)\n",
    "selection = SelectFromModel(model_selection, threshold=0.01, prefit=True)\n",
    "select_train_set = selection.transform(train_set_PF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_final.fit(select_train_set, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = pd.DataFrame()\n",
    "\n",
    "select_test_set = selection.transform(test_set_PF)\n",
    "\n",
    "# Make predictions from the fold as probabilities\n",
    "probabilites = model_final.predict_proba(select_test_set)\n",
    "\n",
    "# Record each prediction for each class as a separate column\n",
    "for j in range(len(targets)):\n",
    "    predictions[j] = probabilites[:,j]\n",
    "    \n",
    "# Add needed information for predictions \n",
    "predictions['patient_id'] = test_ids\n",
    "\n",
    "# Find the class and associated probability\n",
    "predictions['heart_disease_present'] = predictions[targets].idxmax(axis = 1)\n",
    "predictions['confidence'] = predictions[targets].max(axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>heart_disease_present</th>\n",
       "      <th>confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.589290</td>\n",
       "      <td>0.410710</td>\n",
       "      <td>olalu7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.589290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.940070</td>\n",
       "      <td>0.059930</td>\n",
       "      <td>z9n6mx</td>\n",
       "      <td>0</td>\n",
       "      <td>0.940070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.035096</td>\n",
       "      <td>0.964904</td>\n",
       "      <td>5k4413</td>\n",
       "      <td>1</td>\n",
       "      <td>0.964904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.936101</td>\n",
       "      <td>0.063899</td>\n",
       "      <td>mrg7q5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.936101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.018528</td>\n",
       "      <td>0.981472</td>\n",
       "      <td>uki4do</td>\n",
       "      <td>1</td>\n",
       "      <td>0.981472</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1 patient_id  heart_disease_present  confidence\n",
       "0  0.589290  0.410710     olalu7                      0    0.589290\n",
       "1  0.940070  0.059930     z9n6mx                      0    0.940070\n",
       "2  0.035096  0.964904     5k4413                      1    0.964904\n",
       "3  0.936101  0.063899     mrg7q5                      0    0.936101\n",
       "4  0.018528  0.981472     uki4do                      1    0.981472"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Investigate distribution of labels in train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "marker": {
          "color": "orange",
          "line": {
           "color": "black",
           "width": 1
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         },
         "name": "train",
         "type": "bar",
         "uid": "36793d4a-86b3-43ae-9003-53b9c0ba3123",
         "x": [
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          1
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         "y": [
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        {
         "marker": {
          "color": "blue",
          "line": {
           "color": "black",
           "width": 1
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         "name": "predicted",
         "type": "bar",
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         "x": [
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          1
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         "y": [
          0.5666666666666667,
          0.43333333333333335
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       ],
       "layout": {
        "title": {
         "text": "Label distribution in train and test (predicted)"
        },
        "width": 800,
        "xaxis": {
         "automargin": true,
         "tickfont": {
          "size": 14
         }
        },
        "yaxis": {
         "title": {
          "text": "Frequencies"
         }
        }
       }
      },
      "text/html": [
       "<div id=\"000c4884-50c0-4d10-8573-0b1e1db62783\" style=\"height: 525px; width: 800px;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"000c4884-50c0-4d10-8573-0b1e1db62783\", [{\"marker\": {\"color\": \"orange\", \"line\": {\"color\": \"black\", \"width\": 1}}, \"name\": \"train\", \"x\": [0, 1], \"y\": [0.5555555555555556, 0.4444444444444444], \"type\": \"bar\", \"uid\": \"36793d4a-86b3-43ae-9003-53b9c0ba3123\"}, {\"marker\": {\"color\": \"blue\", \"line\": {\"color\": \"black\", \"width\": 1}}, \"name\": \"predicted\", \"x\": [0, 1], \"y\": [0.5666666666666667, 0.43333333333333335], \"type\": \"bar\", \"uid\": \"8b971e55-37d1-4da7-a520-4f35384b35e2\"}], {\"title\": {\"text\": \"Label distribution in train and test (predicted)\"}, \"width\": 800, \"xaxis\": {\"automargin\": true, \"tickfont\": {\"size\": 14}}, \"yaxis\": {\"title\": {\"text\": \"Frequencies\"}}}, {\"showLink\": false, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<div id=\"000c4884-50c0-4d10-8573-0b1e1db62783\" style=\"height: 525px; width: 800px;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"000c4884-50c0-4d10-8573-0b1e1db62783\", [{\"marker\": {\"color\": \"orange\", \"line\": {\"color\": \"black\", \"width\": 1}}, \"name\": \"train\", \"x\": [0, 1], \"y\": [0.5555555555555556, 0.4444444444444444], \"type\": \"bar\", \"uid\": \"36793d4a-86b3-43ae-9003-53b9c0ba3123\"}, {\"marker\": {\"color\": \"blue\", \"line\": {\"color\": \"black\", \"width\": 1}}, \"name\": \"predicted\", \"x\": [0, 1], \"y\": [0.5666666666666667, 0.43333333333333335], \"type\": \"bar\", \"uid\": \"8b971e55-37d1-4da7-a520-4f35384b35e2\"}], {\"title\": {\"text\": \"Label distribution in train and test (predicted)\"}, \"width\": 800, \"xaxis\": {\"automargin\": true, \"tickfont\": {\"size\": 14}}, \"yaxis\": {\"title\": {\"text\": \"Frequencies\"}}}, {\"showLink\": false, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trace0 = go.Bar(x = targets, y = df_final['heart_disease_present'].value_counts()/num_train, name = 'train',\n",
    "                marker=dict(\n",
    "                        color='orange',\n",
    "                        line=dict(\n",
    "                            color='black',\n",
    "                            width=1),\n",
    "                    ))\n",
    "trace1 = go.Bar(x = targets, y = predictions['heart_disease_present'].value_counts()/num_test, name = 'predicted',\n",
    "               marker=dict(\n",
    "                        color='blue',\n",
    "                        line=dict(\n",
    "                            color='black',\n",
    "                            width=1),\n",
    "                    ))\n",
    "\n",
    "    \n",
    "layout = go.Layout(title='Label distribution in train and test (predicted)',\n",
    "                      width=800, xaxis = dict(tickfont=dict(size=14), automargin=True),\n",
    "                   yaxis = dict(title='Frequencies'))\n",
    "\n",
    "fig = go.Figure(data=[trace0, trace1], layout=layout)\n",
    "plotly.offline.iplot(fig, filename='label-dist')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = submission_base.merge(predictions, on = 'patient_id', how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = submission.drop(columns =[0,'heart_disease_present','confidence'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = submission.rename(columns = {1:'heart_disease_present'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>heart_disease_present</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>olalu7</td>\n",
       "      <td>0.410710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>z9n6mx</td>\n",
       "      <td>0.059930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5k4413</td>\n",
       "      <td>0.964904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mrg7q5</td>\n",
       "      <td>0.063899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>uki4do</td>\n",
       "      <td>0.981472</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  patient_id  heart_disease_present\n",
       "0     olalu7               0.410710\n",
       "1     z9n6mx               0.059930\n",
       "2     5k4413               0.964904\n",
       "3     mrg7q5               0.063899\n",
       "4     uki4do               0.981472"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('Arnau_submission.csv',index=False)"
   ]
  }
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