Machine Learning with the heart/cardio.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data= pd.read_csv('cardio_values.csv')\n",
    "\n",
    "test = pd.read_csv('test_values.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>heart_disease_present</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",
       "      <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",
       "      <td>0.444444</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",
       "      <td>0.498290</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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "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   \n",
       "\n",
       "       heart_disease_present  \n",
       "count             180.000000  \n",
       "mean                0.444444  \n",
       "std                 0.498290  \n",
       "min                 0.000000  \n",
       "25%                 0.000000  \n",
       "50%                 0.000000  \n",
       "75%                 1.000000  \n",
       "max                 1.000000  "
      ]
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     "execution_count": 3,
     "metadata": {},
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   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "    <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",
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       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <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",
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       "      <td>158</td>\n",
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       "      <td>0</td>\n",
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       "    <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",
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       "      <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",
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       "      <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",
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      ],
      "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": 4,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <td>olalu7</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>4</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0</td>\n",
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       "      <td>2.5</td>\n",
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       "    <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",
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       "      <td>0</td>\n",
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       "    <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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['max_heart_rate_diff'] = data['max_heart_rate_achieved'] - (data['max_heart_rate_achieved'] - data['age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['max_heart_rate_diff'] = test['max_heart_rate_achieved'] - (test['max_heart_rate_achieved'] - test['age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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>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>max_heart_rate_diff</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",
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       "      <td>59</td>\n",
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       "    <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",
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       "      <td>35</td>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
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       "    <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",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mrg7q5</td>\n",
       "      <td>1</td>\n",
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       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>60</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",
       "      <td>61</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  max_heart_rate_diff  \n",
       "0                      159                        0                   59  \n",
       "1                      182                        0                   35  \n",
       "2                      120                        1                   43  \n",
       "3                      160                        0                   60  \n",
       "4                      125                        1                   61  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29\n",
      "77\n"
     ]
    }
   ],
   "source": [
    "print(data['max_heart_rate_diff'].min())\n",
    "print(data['max_heart_rate_diff'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34\n",
      "76\n"
     ]
    }
   ],
   "source": [
    "print(test['max_heart_rate_diff'].min())\n",
    "print(test['max_heart_rate_diff'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    100\n",
       "1     80\n",
       "Name: heart_disease_present, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.heart_disease_present.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged = pd.concat([data, test], ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                                      0\n",
       "chest_pain_type                          0\n",
       "exercise_induced_angina                  0\n",
       "fasting_blood_sugar_gt_120_mg_per_dl     0\n",
       "heart_disease_present                   90\n",
       "max_heart_rate_achieved                  0\n",
       "max_heart_rate_diff                      0\n",
       "num_major_vessels                        0\n",
       "oldpeak_eq_st_depression                 0\n",
       "patient_id                               0\n",
       "resting_blood_pressure                   0\n",
       "resting_ekg_results                      0\n",
       "serum_cholesterol_mg_per_dl              0\n",
       "sex                                      0\n",
       "slope_of_peak_exercise_st_segment        0\n",
       "thal                                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>114</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>9n6let</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>226</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>69</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>146</td>\n",
       "      <td>69</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>jxmtyg</td>\n",
       "      <td>140</td>\n",
       "      <td>2</td>\n",
       "      <td>254</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>reversible_defect</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>59</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>182</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51s2ff</td>\n",
       "      <td>138</td>\n",
       "      <td>2</td>\n",
       "      <td>271</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>156</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>wi9mcs</td>\n",
       "      <td>138</td>\n",
       "      <td>2</td>\n",
       "      <td>257</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  chest_pain_type  exercise_induced_angina  \\\n",
       "175   67                4                        0   \n",
       "176   55                4                        1   \n",
       "177   64                3                        1   \n",
       "178   48                3                        0   \n",
       "179   54                3                        0   \n",
       "180   59                1                        0   \n",
       "181   35                4                        0   \n",
       "182   43                4                        1   \n",
       "183   60                3                        0   \n",
       "184   61                4                        1   \n",
       "185   43                3                        0   \n",
       "186   66                1                        0   \n",
       "187   69                3                        0   \n",
       "188   59                4                        0   \n",
       "189   47                3                        0   \n",
       "\n",
       "     fasting_blood_sugar_gt_120_mg_per_dl  heart_disease_present  \\\n",
       "175                                     1                    1.0   \n",
       "176                                     0                    1.0   \n",
       "177                                     0                    1.0   \n",
       "178                                     1                    0.0   \n",
       "179                                     0                    0.0   \n",
       "180                                     0                    NaN   \n",
       "181                                     0                    NaN   \n",
       "182                                     0                    NaN   \n",
       "183                                     0                    NaN   \n",
       "184                                     0                    NaN   \n",
       "185                                     0                    NaN   \n",
       "186                                     0                    NaN   \n",
       "187                                     0                    NaN   \n",
       "188                                     0                    NaN   \n",
       "189                                     0                    NaN   \n",
       "\n",
       "     max_heart_rate_achieved  max_heart_rate_diff  num_major_vessels  \\\n",
       "175                      163                   67                  2   \n",
       "176                      117                   55                  0   \n",
       "177                      131                   64                  0   \n",
       "178                      175                   48                  2   \n",
       "179                      163                   54                  1   \n",
       "180                      159                   59                  0   \n",
       "181                      182                   35                  0   \n",
       "182                      120                   43                  0   \n",
       "183                      160                   60                  1   \n",
       "184                      125                   61                  1   \n",
       "185                      165                   43                  0   \n",
       "186                      114                   66                  0   \n",
       "187                      146                   69                  3   \n",
       "188                      182                   59                  0   \n",
       "189                      156                   47                  0   \n",
       "\n",
       "     oldpeak_eq_st_depression patient_id  resting_blood_pressure  \\\n",
       "175                       0.2     5qfar3                     125   \n",
       "176                       3.4     2s2b1f                     180   \n",
       "177                       1.8     nsd00i                     125   \n",
       "178                       0.0     0xw93k                     124   \n",
       "179                       0.0     2nx10r                     160   \n",
       "180                       0.2     olalu7                     170   \n",
       "181                       1.4     z9n6mx                     138   \n",
       "182                       2.5     5k4413                     120   \n",
       "183                       0.0     mrg7q5                     102   \n",
       "184                       3.6     uki4do                     138   \n",
       "185                       0.2     kev1sk                     122   \n",
       "186                       2.6     9n6let                     150   \n",
       "187                       2.0     jxmtyg                     140   \n",
       "188                       0.0     51s2ff                     138   \n",
       "189                       0.0     wi9mcs                     138   \n",
       "\n",
       "     resting_ekg_results  serum_cholesterol_mg_per_dl  sex  \\\n",
       "175                    0                          254    1   \n",
       "176                    1                          327    0   \n",
       "177                    0                          309    1   \n",
       "178                    0                          255    1   \n",
       "179                    0                          201    0   \n",
       "180                    2                          288    1   \n",
       "181                    0                          183    0   \n",
       "182                    2                          177    1   \n",
       "183                    0                          318    0   \n",
       "184                    2                          166    1   \n",
       "185                    0                          213    0   \n",
       "186                    0                          226    0   \n",
       "187                    2                          254    1   \n",
       "188                    2                          271    1   \n",
       "189                    2                          257    1   \n",
       "\n",
       "     slope_of_peak_exercise_st_segment               thal  \n",
       "175                                  2  reversible_defect  \n",
       "176                                  2             normal  \n",
       "177                                  2  reversible_defect  \n",
       "178                                  1             normal  \n",
       "179                                  1             normal  \n",
       "180                                  2  reversible_defect  \n",
       "181                                  1             normal  \n",
       "182                                  2  reversible_defect  \n",
       "183                                  1             normal  \n",
       "184                                  2             normal  \n",
       "185                                  2             normal  \n",
       "186                                  3             normal  \n",
       "187                                  2  reversible_defect  \n",
       "188                                  1             normal  \n",
       "189                                  1             normal  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged.iloc[175:190]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting the minimum value in the age column\n",
    "merged.age.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting the maximum value in the age column\n",
    "merged.age.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([45, 54, 77, 40, 59, 42, 60, 57, 50, 66, 64, 38, 29, 58, 71, 52, 67,\n",
       "       70, 68, 51, 41, 65, 53, 48, 62, 74, 61, 63, 46, 43, 56, 44, 35, 55,\n",
       "       49, 47, 37, 69, 39, 34, 76], dtype=int64)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking all the unique values in the age column\n",
    "merged.age.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    45\n",
       "1    54\n",
       "2    77\n",
       "3    40\n",
       "4    59\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged.age.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this creates categories for the age column form the lowest value upto the highest value and labels them from 0-9\n",
    "merged.age = pd.cut(merged.age, bins=[0,40,60,100], labels=[1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "94"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#gets the minimum value in the resting BP\n",
    "merged.resting_blood_pressure.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#gets the maximum value in the resting BP\n",
    "merged.resting_blood_pressure.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([128, 110, 125, 152, 178, 130, 150, 170, 120, 140, 138, 144, 136,\n",
       "       160, 108, 106, 156, 180, 112, 122, 124, 135, 105, 115, 126, 172,\n",
       "       145, 118, 134, 100, 155, 132, 102,  94, 117, 142, 129, 158, 174,\n",
       "       101, 146, 104, 200, 192, 123, 165, 148], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting the unique values in the resting BP column\n",
    "merged.resting_blood_pressure.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this creates categories for the age column form 50 to 400, creating the categories in a specified way\n",
    "merged.resting_blood_pressure = pd.cut(merged.resting_blood_pressure, bins=[0,110,140,201,400], labels=range(1,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['normal', 'reversible_defect', 'fixed_defect'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged.thal.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creates a dictionary that maps the keys to the values\n",
    "mapper={'normal':1, 'reversible_defect':2, 'fixed_defect':3}\n",
    "\n",
    "# maps the keys of the dict to the values\n",
    "merged.thal=merged.thal.map(mapper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drops all the duplicate entries in the data\n",
    "merged.drop_duplicates(keep='first',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                                      0\n",
       "chest_pain_type                          0\n",
       "exercise_induced_angina                  0\n",
       "fasting_blood_sugar_gt_120_mg_per_dl     0\n",
       "heart_disease_present                   90\n",
       "max_heart_rate_achieved                  0\n",
       "max_heart_rate_diff                      0\n",
       "num_major_vessels                        0\n",
       "oldpeak_eq_st_depression                 0\n",
       "patient_id                               0\n",
       "resting_blood_pressure                   0\n",
       "resting_ekg_results                      0\n",
       "serum_cholesterol_mg_per_dl              0\n",
       "sex                                      0\n",
       "slope_of_peak_exercise_st_segment        0\n",
       "thal                                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checks if there are any empty spaces in the data\n",
    "merged.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged.serum_cholesterol_mg_per_dl = pd.cut(merged.serum_cholesterol_mg_per_dl, bins=[0,200,400,1000],labels=[1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29\n",
      "77\n",
      "9.109066523898205\n",
      "54.43333333333333\n"
     ]
    }
   ],
   "source": [
    "print(merged.max_heart_rate_diff.min())\n",
    "print(merged.max_heart_rate_diff.max())\n",
    "print(merged.max_heart_rate_diff.std())\n",
    "print(merged.max_heart_rate_diff.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged.max_heart_rate_diff = pd.cut(merged.max_heart_rate_diff, bins=[0,44,64,100],labels=[1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged.oldpeak_eq_st_depression = pd.cut(merged.oldpeak_eq_st_depression, bins=3, labels=[1,2,3])"
   ]
  },
  {
   "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>age</th>\n",
       "      <th>chest_pain_type</th>\n",
       "      <th>exercise_induced_angina</th>\n",
       "      <th>fasting_blood_sugar_gt_120_mg_per_dl</th>\n",
       "      <th>heart_disease_present</th>\n",
       "      <th>max_heart_rate_achieved</th>\n",
       "      <th>max_heart_rate_diff</th>\n",
       "      <th>num_major_vessels</th>\n",
       "      <th>oldpeak_eq_st_depression</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>resting_blood_pressure</th>\n",
       "      <th>resting_ekg_results</th>\n",
       "      <th>serum_cholesterol_mg_per_dl</th>\n",
       "      <th>sex</th>\n",
       "      <th>slope_of_peak_exercise_st_segment</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>172</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>bwoyg6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>j8i7ve</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>178</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>t2zn1n</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>152</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>oxf8kj</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>171</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>aeiv0y</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  chest_pain_type  exercise_induced_angina  \\\n",
       "265   2                3                        0   \n",
       "266   2                4                        1   \n",
       "267   2                1                        0   \n",
       "268   2                4                        1   \n",
       "269   2                4                        0   \n",
       "\n",
       "     fasting_blood_sugar_gt_120_mg_per_dl  heart_disease_present  \\\n",
       "265                                     0                    NaN   \n",
       "266                                     0                    NaN   \n",
       "267                                     0                    NaN   \n",
       "268                                     0                    NaN   \n",
       "269                                     0                    NaN   \n",
       "\n",
       "     max_heart_rate_achieved max_heart_rate_diff  num_major_vessels  \\\n",
       "265                      172                   2                  0   \n",
       "266                      125                   1                  0   \n",
       "267                      178                   1                  2   \n",
       "268                      152                   2                  0   \n",
       "269                      171                   1                  0   \n",
       "\n",
       "    oldpeak_eq_st_depression patient_id resting_blood_pressure  \\\n",
       "265                        1     bwoyg6                      2   \n",
       "266                        1     j8i7ve                      2   \n",
       "267                        1     t2zn1n                      3   \n",
       "268                        1     oxf8kj                      2   \n",
       "269                        1     aeiv0y                      3   \n",
       "\n",
       "     resting_ekg_results serum_cholesterol_mg_per_dl  sex  \\\n",
       "265                    0                           2    0   \n",
       "266                    0                           2    1   \n",
       "267                    2                           2    1   \n",
       "268                    2                           2    0   \n",
       "269                    0                           2    1   \n",
       "\n",
       "     slope_of_peak_exercise_st_segment  thal  \n",
       "265                                  1     1  \n",
       "266                                  2     3  \n",
       "267                                  1     1  \n",
       "268                                  2     1  \n",
       "269                                  1     1  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged.drop('max_heart_rate_achieved', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = merged.iloc[:180]\n",
    "test = merged.iloc[180:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "    age  chest_pain_type  exercise_induced_angina  \\\n",
       "175   3                4                        0   \n",
       "176   2                4                        1   \n",
       "177   3                3                        1   \n",
       "178   2                3                        0   \n",
       "179   2                3                        0   \n",
       "\n",
       "     fasting_blood_sugar_gt_120_mg_per_dl  heart_disease_present  \\\n",
       "175                                     1                    1.0   \n",
       "176                                     0                    1.0   \n",
       "177                                     0                    1.0   \n",
       "178                                     1                    0.0   \n",
       "179                                     0                    0.0   \n",
       "\n",
       "    max_heart_rate_diff  num_major_vessels oldpeak_eq_st_depression  \\\n",
       "175                   3                  2                        1   \n",
       "176                   2                  0                        2   \n",
       "177                   2                  0                        1   \n",
       "178                   2                  2                        1   \n",
       "179                   2                  1                        1   \n",
       "\n",
       "    patient_id resting_blood_pressure  resting_ekg_results  \\\n",
       "175     5qfar3                      2                    0   \n",
       "176     2s2b1f                      3                    1   \n",
       "177     nsd00i                      2                    0   \n",
       "178     0xw93k                      2                    0   \n",
       "179     2nx10r                      3                    0   \n",
       "\n",
       "    serum_cholesterol_mg_per_dl  sex  slope_of_peak_exercise_st_segment  thal  \n",
       "175                           2    1                                  2     2  \n",
       "176                           2    0                                  2     1  \n",
       "177                           2    1                                  2     2  \n",
       "178                           2    1                                  1     1  \n",
       "179                           2    0                                  1     1  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "    age  chest_pain_type  exercise_induced_angina  \\\n",
       "180   2                1                        0   \n",
       "181   1                4                        0   \n",
       "182   2                4                        1   \n",
       "183   2                3                        0   \n",
       "184   3                4                        1   \n",
       "\n",
       "     fasting_blood_sugar_gt_120_mg_per_dl  heart_disease_present  \\\n",
       "180                                     0                    NaN   \n",
       "181                                     0                    NaN   \n",
       "182                                     0                    NaN   \n",
       "183                                     0                    NaN   \n",
       "184                                     0                    NaN   \n",
       "\n",
       "    max_heart_rate_diff  num_major_vessels oldpeak_eq_st_depression  \\\n",
       "180                   2                  0                        1   \n",
       "181                   1                  0                        1   \n",
       "182                   1                  0                        2   \n",
       "183                   2                  1                        1   \n",
       "184                   2                  1                        2   \n",
       "\n",
       "    patient_id resting_blood_pressure  resting_ekg_results  \\\n",
       "180     olalu7                      3                    2   \n",
       "181     z9n6mx                      2                    0   \n",
       "182     5k4413                      2                    2   \n",
       "183     mrg7q5                      1                    0   \n",
       "184     uki4do                      2                    2   \n",
       "\n",
       "    serum_cholesterol_mg_per_dl  sex  slope_of_peak_exercise_st_segment  thal  \n",
       "180                           2    1                                  2     2  \n",
       "181                           1    0                                  1     1  \n",
       "182                           1    1                                  2     2  \n",
       "183                           2    0                                  1     1  \n",
       "184                           1    1                                  2     1  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:3643: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self[name] = value\n"
     ]
    }
   ],
   "source": [
    "data.heart_disease_present = data.heart_disease_present.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                                     0\n",
       "chest_pain_type                         0\n",
       "exercise_induced_angina                 0\n",
       "fasting_blood_sugar_gt_120_mg_per_dl    0\n",
       "heart_disease_present                   0\n",
       "max_heart_rate_diff                     0\n",
       "num_major_vessels                       0\n",
       "oldpeak_eq_st_depression                0\n",
       "patient_id                              0\n",
       "resting_blood_pressure                  0\n",
       "resting_ekg_results                     0\n",
       "serum_cholesterol_mg_per_dl             0\n",
       "sex                                     0\n",
       "slope_of_peak_exercise_st_segment       0\n",
       "thal                                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "test.drop('heart_disease_present', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                                     0\n",
       "chest_pain_type                         0\n",
       "exercise_induced_angina                 0\n",
       "fasting_blood_sugar_gt_120_mg_per_dl    0\n",
       "max_heart_rate_diff                     0\n",
       "num_major_vessels                       0\n",
       "oldpeak_eq_st_depression                0\n",
       "patient_id                              0\n",
       "resting_blood_pressure                  0\n",
       "resting_ekg_results                     0\n",
       "serum_cholesterol_mg_per_dl             0\n",
       "sex                                     0\n",
       "slope_of_peak_exercise_st_segment       0\n",
       "thal                                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "data.drop('patient_id', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#getting the target/labels for your predictions\n",
    "y = data.heart_disease_present\n",
    "y = y.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#getting the features for yoyr predictions\n",
    "X = data.drop('heart_disease_present', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "test.drop('patient_id',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#isntanciating the random forest classifier object\n",
    "forest = RandomForestClassifier(n_estimators=100, n_jobs=-1, random_state=2, min_samples_leaf=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=2, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                       n_jobs=-1, oob_score=False, random_state=2, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#making predictions\n",
    "pred = forest.predict_proba(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.35129445, 0.19249618, 0.91473265, 0.08161143, 0.8591163 ])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred[:,1][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "sol = pd.read_csv('submission_format.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "sol['heart_disease_present'] = pred[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "sol.to_csv('solution.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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