models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1dae3d07",
   "metadata": {},
   "source": [
    "# UCI Heart failure clinical records Data Set\n",
    "\n",
    "\"Heart failure is the state in which muscles in the heart wall get fade and enlarge, limiting heart\n",
    "pumping of blood. The ventricles of heart can get inflexible and do not fill properly between\n",
    "beats. With the passage of time heart fails in fulfilling the proper demand of blood in body and\n",
    "as a consequence person starts feeling difficulty in breathing.\"- ref: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0181001\n",
    "\n",
    "This data set contains 13 columns:\n",
    "\n",
    "Here is a description of each feature in the data:\n",
    "(ref: http://archive.ics.uci.edu/ml/datasets/Heart+failure+clinical+records)\n",
    "\n",
    "### Features:\n",
    "* *age* - Age of the patient (years)\n",
    "* *anaemia* - If the patient has anaemia (0-No / 1-Yes)\n",
    "* *creatinine_phosphokinase* - level of the CPK enzyme in the blood (mcg/L) \n",
    "\n",
    "**Note**: Creatinine phosphokinase is an enzyme in the body, and it is involved in maintaining blood pressure and enhances contractility of skeletal, cardiac and smooth muscles.\n",
    "\n",
    "For men, the normal range is from 38 to 174 (U/L), whereas for women, the normal range is from 26 to 140 (U/L).\n",
    "\n",
    "(ref: https://www.healthcheckup.com/general/high-cpk-levels-causes-symptoms-side-effects-treatment/)\n",
    "\n",
    "* *diabetes* - If the patient has diabetes (0-No / 1-Yes)                   \n",
    "* *ejection_fraction* - Percentage of blood leaving the heart at each contraction (percentage)                   \n",
    "* *high_blood_pressure* - If a patient has hypertension (0-No / 1-Yes)      \n",
    "* *platelets* - Platelets in the blood (kiloplatelets/mL)                       \n",
    "* *serum_creatinine* - Level of creatinine in the blood (mg/dL) \n",
    "\n",
    "**Note**: serum_creatinine is a measurement for the function of kidneys. \n",
    "\n",
    "The normal range varies between different ages, and normally it's higher in men than in women.\n",
    "\n",
    "For adualt men (from 41 to 50 years old), the normal range is from 0.7 to 1.4 (mg/dL), and for men from 51 to 60 years old, the normal range is from 0.7 to 1.3 (mg/dL).\n",
    "\n",
    "However, for adualt women (from 41 to 50 years old), the normal range is from 0.4 to 0.9 (mg/dL), and for women from 51 to 60 years old, the normal range is from 0.6 to 1.3 (mg/dL).\n",
    "\n",
    "(ref: https://www.healthcheckup.com/general/normal-creatinine-levels-by-age/) \n",
    "\n",
    "* *serum_sodium* - Level of sodium in the blood (mEq/L)  \n",
    "\n",
    "**Note**: serum_sodium is also a measurement for the function of kidneys.\n",
    "\n",
    "The normal blood sodium levels range from 135 to 145 (mEq/L) \n",
    "\n",
    "(ref: https://www.healthcheckup.com/general/normal-sodium-levels-in-blood/)\n",
    "\n",
    "* *sex* - Gender of the patient (0-Female / 1-Male)                                \n",
    "* *smoking* - If the patient smokes (0-No / 1-Yes)                       \n",
    "* *time* - Follow-up period (days)        \n",
    "\n",
    "### Target\n",
    "\n",
    "*DEATH_EVENT* - If the patient died during the follow-up period (0-No / 1-Yes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "a7c9e75b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import tree\n",
    "from sklearn import ensemble\n",
    "import sklearn.neural_network as nn\n",
    "import sklearn.model_selection as ms\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "# disable warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "497e88a7",
   "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>anaemia</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoking</th>\n",
       "      <th>time</th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>582</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>265000.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>130</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7861</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>263358.03</td>\n",
       "      <td>1.1</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>162000.00</td>\n",
       "      <td>1.3</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>210000.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65.0</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>327000.00</td>\n",
       "      <td>2.7</td>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "0  75.0        0                       582         0                 20   \n",
       "1  55.0        0                      7861         0                 38   \n",
       "2  65.0        0                       146         0                 20   \n",
       "3  50.0        1                       111         0                 20   \n",
       "4  65.0        1                       160         1                 20   \n",
       "\n",
       "   high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "0                    1  265000.00               1.9           130    1   \n",
       "1                    0  263358.03               1.1           136    1   \n",
       "2                    0  162000.00               1.3           129    1   \n",
       "3                    0  210000.00               1.9           137    1   \n",
       "4                    0  327000.00               2.7           116    0   \n",
       "\n",
       "   smoking  time  DEATH_EVENT  \n",
       "0        0     4            1  \n",
       "1        0     6            1  \n",
       "2        1     7            1  \n",
       "3        0     7            1  \n",
       "4        0     8            1  "
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read in the json files\n",
    "heart_failur_df = pd.read_csv('heart_failure_clinical_records_dataset.csv')\n",
    "heart_failur_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a14ecb6f",
   "metadata": {},
   "source": [
    "# Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "60f41430",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(299, 13)"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_failur_df.shape # 299 rows & 13 columns including the target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "5bd8768d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 299 entries, 0 to 298\n",
      "Data columns (total 13 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   age                       299 non-null    float64\n",
      " 1   anaemia                   299 non-null    int64  \n",
      " 2   creatinine_phosphokinase  299 non-null    int64  \n",
      " 3   diabetes                  299 non-null    int64  \n",
      " 4   ejection_fraction         299 non-null    int64  \n",
      " 5   high_blood_pressure       299 non-null    int64  \n",
      " 6   platelets                 299 non-null    float64\n",
      " 7   serum_creatinine          299 non-null    float64\n",
      " 8   serum_sodium              299 non-null    int64  \n",
      " 9   sex                       299 non-null    int64  \n",
      " 10  smoking                   299 non-null    int64  \n",
      " 11  time                      299 non-null    int64  \n",
      " 12  DEATH_EVENT               299 non-null    int64  \n",
      "dtypes: float64(3), int64(10)\n",
      "memory usage: 30.5 KB\n"
     ]
    }
   ],
   "source": [
    "heart_failur_df.info() # no null values, all features are numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "ab2ee0d1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                         False\n",
       "anaemia                     False\n",
       "creatinine_phosphokinase    False\n",
       "diabetes                    False\n",
       "ejection_fraction           False\n",
       "high_blood_pressure         False\n",
       "platelets                   False\n",
       "serum_creatinine            False\n",
       "serum_sodium                False\n",
       "sex                         False\n",
       "smoking                     False\n",
       "time                        False\n",
       "DEATH_EVENT                 False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# double-check if there is any missing values\n",
    "# no data cleaning needed\n",
    "heart_failur_df.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "c7864de8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "About 32.11% of the data is positive class.\n"
     ]
    }
   ],
   "source": [
    "# check distribution for the target 'DEATH_EVENT'\n",
    "target_dist = heart_failur_df['DEATH_EVENT'].value_counts()\n",
    "positive_perc = target_dist[1] / len(heart_failur_df) * 100\n",
    "\n",
    "print(f\"About {round(positive_perc, 2)}% of the data is positive class.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "9ef28821",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualise the distribution of a couple of numerical features\n",
    "plt.rcParams['figure.figsize'] = (15, 5)\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.set(style = 'whitegrid')\n",
    "sns.distplot(heart_failur_df['age'])\n",
    "plt.title('Distribution of Age', fontsize = 20)\n",
    "plt.xlabel('Range of Age')\n",
    "plt.ylabel('Count')\n",
    "\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.set(style = 'whitegrid')\n",
    "sns.distplot(heart_failur_df['creatinine_phosphokinase'])\n",
    "plt.title('Distribution of Creatinine Phosphokinase (CPK)', fontsize = 20)\n",
    "plt.xlabel('Level of the CPK (mcg/L) ')\n",
    "plt.ylabel('Count')\n",
    "plt.show()\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.set(style = 'whitegrid')\n",
    "sns.distplot(heart_failur_df['ejection_fraction'])\n",
    "plt.title('Distribution of Ejection Fraction', fontsize = 20)\n",
    "plt.xlabel('Percentage of blood leaving the heart at each contraction (percentage)')\n",
    "plt.ylabel('Count')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.set(style = 'whitegrid')\n",
    "sns.distplot(heart_failur_df['platelets'])\n",
    "plt.title('Distribution of Platelets', fontsize = 20)\n",
    "plt.xlabel('Platelets in the blood (kiloplatelets/mL)')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "31b735cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1, 2, 1)\n",
    "sns.set(style = 'whitegrid')\n",
    "sns.distplot(heart_failur_df['serum_creatinine'])\n",
    "plt.title('Distribution of Serum Creatinine', fontsize = 20)\n",
    "plt.xlabel('Level of creatinine in the blood (MG/dL)')\n",
    "plt.ylabel('Count')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.set(style = 'whitegrid')\n",
    "sns.distplot(heart_failur_df['serum_sodium'])\n",
    "plt.title('Distribution of Serum Sodium', fontsize = 20)\n",
    "plt.xlabel('Level of sodium in the blood (mEq/L)')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "3a23a1b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualise gender column\n",
    "labels = ['Male', 'Female']\n",
    "size = heart_failur_df['sex'].value_counts()\n",
    "colors = ['lightgreen', 'orange']\n",
    "explode = [0, 0.1]\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (9, 9)\n",
    "plt.pie(size, colors = colors, explode = explode, labels = labels, shadow = True, autopct = '%.2f%%')\n",
    "plt.title('Sex', fontsize = 20)\n",
    "plt.axis('off')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "c6d1748a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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        "plotlyServerURL": "https://plot.ly"
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   "source": [
    "# visualise how \"age\" related to the target \"DEATH_EVENT\"\n",
    "fig = px.histogram(heart_failur_df, x=\"age\", color=\"DEATH_EVENT\")\n",
    "fig.show()"
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   "execution_count": 194,
   "id": "6fafa556",
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   "source": [
    "# visualise how \"sex\" related to the target \"DEATH_EVENT\"\n",
    "len_total = len(heart_failur_df)\n",
    "len_female = len(heart_failur_df[heart_failur_df[\"sex\"]==0])\n",
    "len_male = len_total - len_female\n",
    "\n",
    "# in the \"sex\" column, 0-Female, 1-male\n",
    "# in the \"DEATH_EVENT\" column, 0-survived 1-dead\n",
    "male_died = len(heart_failur_df.loc[(heart_failur_df[\"DEATH_EVENT\"]==1) &(heart_failur_df['sex']==1)])\n",
    "male_survived = len_male - male_died\n",
    "\n",
    "female_died = len(heart_failur_df.loc[(heart_failur_df[\"DEATH_EVENT\"]==1) & (heart_failur_df['sex']==0)])\n",
    "female_survived = len_female - female_died\n",
    "\n",
    "labels = ['Male died','Male survived','Female died','Female survived']\n",
    "values = [male_died, male_survived, female_died, female_survived]\n",
    "\n",
    "fig = go.Figure(data=[go.Pie(labels=labels, values=values,textinfo='label+percent',hole=0.4)])\n",
    "fig.update_layout(\n",
    "    title_text=\"Distribution of DEATH EVENT based on the gender of the patients\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "c5bbef17",
   "metadata": {},
   "outputs": [
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       "age                      -0.101012           0.060098             0.093289   \n",
       "anaemia                  -0.012729           0.031557             0.038182   \n",
       "creatinine_phosphokinase -0.009639          -0.044080            -0.070590   \n",
       "diabetes                  1.000000          -0.004850            -0.012732   \n",
       "ejection_fraction        -0.004850           1.000000             0.024445   \n",
       "\n",
       "                          platelets  serum_creatinine  serum_sodium       sex  \\\n",
       "age                       -0.052354          0.159187     -0.045966  0.065430   \n",
       "anaemia                   -0.043786          0.052174      0.041882 -0.094769   \n",
       "creatinine_phosphokinase   0.024463         -0.016408      0.059550  0.079791   \n",
       "diabetes                   0.092193         -0.046975     -0.089551 -0.157730   \n",
       "ejection_fraction          0.072177         -0.011302      0.175902 -0.148386   \n",
       "\n",
       "                           smoking      time  DEATH_EVENT  \n",
       "age                       0.018668 -0.224068     0.253729  \n",
       "anaemia                  -0.107290 -0.141414     0.066270  \n",
       "creatinine_phosphokinase  0.002421 -0.009346     0.062728  \n",
       "diabetes                 -0.147173  0.033726    -0.001943  \n",
       "ejection_fraction        -0.067315  0.041729    -0.268603  "
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get correlation coefficients\n",
    "corr_df = heart_failur_df.corr()\n",
    "corr_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "d3b01129",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1368x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the correlation figure\n",
    "f = plt.figure(figsize=(19, 15))\n",
    "plt.matshow(heart_failur_df.corr(), fignum=f.number)\n",
    "plt.xticks(range(heart_failur_df.shape[1]), heart_failur_df.columns, fontsize=14, rotation=90)\n",
    "plt.yticks(range(heart_failur_df.shape[1]), heart_failur_df.columns, fontsize=14)\n",
    "cb = plt.colorbar()\n",
    "cb.ax.tick_params(labelsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "c50f83fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time                       -0.526964\n",
       "ejection_fraction          -0.268603\n",
       "serum_sodium               -0.195204\n",
       "platelets                  -0.049139\n",
       "smoking                    -0.012623\n",
       "sex                        -0.004316\n",
       "diabetes                   -0.001943\n",
       "creatinine_phosphokinase    0.062728\n",
       "anaemia                     0.066270\n",
       "high_blood_pressure         0.079351\n",
       "age                         0.253729\n",
       "serum_creatinine            0.294278\n",
       "DEATH_EVENT                 1.000000\n",
       "Name: DEATH_EVENT, dtype: float64"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort based on correlations from high to low\n",
    "# surprisingly 'time' feature has the highest correlation to the target\n",
    "# the description for this feature is very vague and not sure what it is used to measure\n",
    "# but in the baseline model we will be using all the 12 features.\n",
    "corr_df['DEATH_EVENT'].sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4592069",
   "metadata": {},
   "source": [
    "Apart from 'time', 'serum_creatinine', 'ejection_fraction', 'age', 'serum_sodium' are all highly correlated to the target 'DEATH_EVENT'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe081503",
   "metadata": {},
   "source": [
    "# Build Models\n",
    "\n",
    "\n",
    "## Split data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "a770057c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# try if we normalize numerical features before training model\n",
    "# ref: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html\n",
    "# https://stats.stackexchange.com/questions/59392/should-you-ever-standardise-binary-variables\n",
    "\n",
    "# cols_norm = ['age', 'creatinine_phosphokinase', 'ejection_fraction', 'platelets', 'serum_creatinine', 'serum_sodium', 'time']\n",
    "\n",
    "# for col in cols_norm:\n",
    "#     scaler = MinMaxScaler()\n",
    "#     heart_failur_df[col] = scaler.fit_transform(heart_failur_df.loc[:, [col]])\n",
    "\n",
    "# The performance of the model (with default parameters) seems not improved after normalization (except for MLPClassifier) \n",
    "# but since without tuning, its performance is not good so the code for normalization have been commented out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "0e6899f4",
   "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>anaemia</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoking</th>\n",
       "      <th>time</th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>582</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>265000.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>130</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7861</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>263358.03</td>\n",
       "      <td>1.1</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>162000.00</td>\n",
       "      <td>1.3</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>210000.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65.0</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>327000.00</td>\n",
       "      <td>2.7</td>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "0  75.0        0                       582         0                 20   \n",
       "1  55.0        0                      7861         0                 38   \n",
       "2  65.0        0                       146         0                 20   \n",
       "3  50.0        1                       111         0                 20   \n",
       "4  65.0        1                       160         1                 20   \n",
       "\n",
       "   high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "0                    1  265000.00               1.9           130    1   \n",
       "1                    0  263358.03               1.1           136    1   \n",
       "2                    0  162000.00               1.3           129    1   \n",
       "3                    0  210000.00               1.9           137    1   \n",
       "4                    0  327000.00               2.7           116    0   \n",
       "\n",
       "   smoking  time  DEATH_EVENT  \n",
       "0        0     4            1  \n",
       "1        0     6            1  \n",
       "2        1     7            1  \n",
       "3        0     7            1  \n",
       "4        0     8            1  "
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_failur_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "43d8b2fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split dataset into training set/testing set\n",
    "def train_test_split(df, train_frac= 0.7, seed=1):\n",
    "    \"\"\"Shuffle the data and randomly split into train and test sets;\n",
    "       separate the class labels from the features.\n",
    "    \n",
    "    :param df: Dataframe of all the data\n",
    "    :param train_frac: The decimal fraction of data that should be training data\n",
    "    :param seed: Random seed for shuffling and reproducibility, default = 1\n",
    "    :return: Two tuples (in order): (train_features, train_labels), (test_features, test_labels)\n",
    "    \"\"\"\n",
    "    \n",
    "    # shuffle and split the data\n",
    "    # https://numpy.org/doc/stable/reference/random/generated/numpy.random.shuffle.html\n",
    "    np.random.seed(seed) #from the function input, seed = 1, this is for the output consistency\n",
    "    df_np = df.to_numpy()\n",
    "    np.random.shuffle(df_np) #perform shuffle\n",
    "    \n",
    "    #define training data size\n",
    "    train_size = int(train_frac * df_np.shape[0])\n",
    "    x_train = df_np[:train_size, :-1]\n",
    "    y_train = df_np[:train_size,-1] #only keep the last label column\n",
    "    \n",
    "    # define testing set\n",
    "    x_test = df_np[train_size:, :-1]\n",
    "    y_test = df_np[train_size:, -1]\n",
    "    \n",
    "    return (x_train, y_train), (x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "8783c30c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# shuffle the data and split it into training and testing data with 7:3\n",
    "(x_train, y_train), (x_test, y_test) = train_test_split(heart_failur_df, train_frac=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "d0876ce7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total number of rows for training set is: 209\n",
      "The total number of rows for testing set is: 90\n"
     ]
    }
   ],
   "source": [
    "print('The total number of rows for training set is:', len(x_train))\n",
    "print('The total number of rows for testing set is:', len(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "5c4c3c03",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert array to df type\n",
    "x_train = pd.DataFrame(x_train)\n",
    "y_train = pd.DataFrame(y_train)\n",
    "x_test = pd.DataFrame(x_test)\n",
    "y_test = pd.DataFrame(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "e8fbe2fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_name = heart_failur_df.columns[:-1]\n",
    "x_train.columns = columns_name\n",
    "x_test.columns = columns_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "6adee106",
   "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>anaemia</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoking</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>189000.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>139.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>582.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>543000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>418000.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>139.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>218000.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>136.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>290000.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>134.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>194.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "0  50.0      1.0                     115.0       0.0               20.0   \n",
       "1  45.0      0.0                     582.0       1.0               55.0   \n",
       "2  53.0      1.0                      91.0       0.0               20.0   \n",
       "3  52.0      0.0                     132.0       0.0               30.0   \n",
       "4  65.0      1.0                     135.0       0.0               35.0   \n",
       "\n",
       "   high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "0                  0.0   189000.0               0.8         139.0  1.0   \n",
       "1                  0.0   543000.0               1.0         132.0  0.0   \n",
       "2                  1.0   418000.0               1.4         139.0  0.0   \n",
       "3                  0.0   218000.0               0.7         136.0  1.0   \n",
       "4                  1.0   290000.0               0.8         134.0  1.0   \n",
       "\n",
       "   smoking   time  \n",
       "0      0.0  146.0  \n",
       "1      0.0  250.0  \n",
       "2      0.0   43.0  \n",
       "3      1.0  112.0  \n",
       "4      0.0  194.0  "
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "f75b6d95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape (rows, cols):  (209, 12)\n"
     ]
    }
   ],
   "source": [
    "print('Data shape (rows, cols): ', x_train.shape) # 12 features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7575ef3",
   "metadata": {},
   "source": [
    "Train individual classifier models as well as an ensemble model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "a70bc4d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_clf = LogisticRegression(random_state=42)\n",
    "rf_clf = ensemble.RandomForestClassifier(random_state=42)\n",
    "dt_clf = tree.DecisionTreeClassifier(random_state=42)\n",
    "ext_clf = ensemble.ExtraTreesClassifier(random_state=42)\n",
    "mlp_clf = nn.MLPClassifier(random_state=42)\n",
    "\n",
    "voting_classifier = ensemble.VotingClassifier([\n",
    "                                            ('lr_clf', LogisticRegression(random_state=42)),\n",
    "                                            ('rf_clf', ensemble.RandomForestClassifier(random_state=42)),\n",
    "                                            ('dt_clf', tree.DecisionTreeClassifier(random_state=42)),\n",
    "                                            ('ext_clf', ensemble.ExtraTreesClassifier(random_state=42)),\n",
    "                                            ('mlp_clf', nn.MLPClassifier(random_state=42))\n",
    "                                            ], voting='hard')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "10951e71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('lr_clf', LogisticRegression(random_state=42)),\n",
       "                             ('rf_clf',\n",
       "                              RandomForestClassifier(random_state=42)),\n",
       "                             ('dt_clf',\n",
       "                              DecisionTreeClassifier(random_state=42)),\n",
       "                             ('ext_clf', ExtraTreesClassifier(random_state=42)),\n",
       "                             ('mlp_clf', MLPClassifier(random_state=42))])"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_clf.fit(x_train, y_train)\n",
    "rf_clf.fit(x_train, y_train)\n",
    "dt_clf.fit(x_train, y_train)\n",
    "ext_clf.fit(x_train, y_train)\n",
    "mlp_clf.fit(x_train, y_train)\n",
    "voting_classifier.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "211689dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression\n",
      "Avg Accuracy: 78.33635703918722\n",
      "Std Accuracy: 6.879051126823437\n",
      "Avg F1: 62.39544718087329\n",
      "Std F1: 11.28538044823316\n",
      "\n",
      "RandomForestClassifier\n",
      "Avg Accuracy: 84.1718069666183\n",
      "Std Accuracy: 4.430766828532929\n",
      "Avg F1: 73.42752513024831\n",
      "Std F1: 7.674472703015124\n",
      "\n",
      "DecisionTreeClassifier\n",
      "Avg Accuracy: 75.74746008708273\n",
      "Std Accuracy: 5.173310436889572\n",
      "Avg F1: 61.61001088526723\n",
      "Std F1: 8.404474584731469\n",
      "\n",
      "ExtraTreesClassifier\n",
      "Avg Accuracy: 78.4887518142235\n",
      "Std Accuracy: 5.60462681819869\n",
      "Avg F1: 59.857939639564165\n",
      "Std F1: 9.611015887097931\n",
      "\n",
      "MLPClassifier\n",
      "Avg Accuracy: 67.47369375907112\n",
      "Std Accuracy: 6.53568266649667\n",
      "Avg F1: 0.0\n",
      "Std F1: 0.0\n",
      "\n",
      "VotingClassifier\n",
      "Avg Accuracy: 80.64132801161102\n",
      "Std Accuracy: 5.923488318201983\n",
      "Avg F1: 64.22971697064428\n",
      "Std F1: 9.607840830269968\n",
      "\n"
     ]
    }
   ],
   "source": [
    "estimators = [lr_clf, rf_clf, dt_clf, ext_clf, mlp_clf, voting_classifier]\n",
    "\n",
    "cv = ms.RepeatedKFold(n_splits=4, n_repeats=10, random_state=42)\n",
    "\n",
    "for estimator in estimators:\n",
    "    cv_accuracy = ms.cross_val_score(estimator, x_train, y_train, cv=cv, n_jobs=-1, scoring='accuracy')\n",
    "    f1_score = ms.cross_val_score(estimator, x_train, y_train, cv=cv, n_jobs=-1, scoring='f1')\n",
    "    print(estimator.__class__.__name__)\n",
    "    print(f'Avg Accuracy: {np.mean(cv_accuracy) * 100}')\n",
    "    print(f'Std Accuracy: {np.std(cv_accuracy) * 100}')\n",
    "    print(f'Avg F1: {np.mean(f1_score) * 100}')\n",
    "    print(f'Std F1: {np.std(f1_score) * 100}')\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d54bc949",
   "metadata": {},
   "source": [
    "From the result above, MLPClassifier with default parameters performs the worst, so we drop it from the ensemble model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "fafeb4bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('lr_clf', LogisticRegression(random_state=42)),\n",
       "                             ('rf_clf',\n",
       "                              RandomForestClassifier(random_state=42)),\n",
       "                             ('dt_clf',\n",
       "                              DecisionTreeClassifier(random_state=42)),\n",
       "                             ('ext_clf',\n",
       "                              ExtraTreesClassifier(random_state=42))])"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# re-train the ensemble model\n",
    "new_voting_classifier = ensemble.VotingClassifier([\n",
    "                                            ('lr_clf', LogisticRegression(random_state=42)),\n",
    "                                            ('rf_clf', ensemble.RandomForestClassifier(random_state=42)),\n",
    "                                            ('dt_clf', tree.DecisionTreeClassifier(random_state=42)),\n",
    "                                            ('ext_clf', ensemble.ExtraTreesClassifier(random_state=42))\n",
    "                                            ], voting='hard')\n",
    "\n",
    "new_voting_classifier.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "008fca81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VotingClassifier\n",
      "Avg Accuracy: 80.64132801161102\n",
      "Std Accuracy: 5.923488318201983\n",
      "Avg F1: 64.22971697064428\n",
      "Std F1: 9.607840830269968\n"
     ]
    }
   ],
   "source": [
    "new_cv_accuracy = ms.cross_val_score(new_voting_classifier, x_train, y_train, cv=cv, n_jobs=-1, scoring='accuracy')\n",
    "\n",
    "new_f1_score = ms.cross_val_score(new_voting_classifier, x_train, y_train, cv=cv, n_jobs=-1, scoring='f1')\n",
    "\n",
    "print(new_voting_classifier.__class__.__name__)\n",
    "print(f'Avg Accuracy: {np.mean(new_cv_accuracy) * 100}')\n",
    "print(f'Std Accuracy: {np.std(new_cv_accuracy) * 100}')\n",
    "print(f'Avg F1: {np.mean(new_f1_score) * 100}')\n",
    "print(f'Std F1: {np.std(new_f1_score) * 100}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "b9a42d37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression\n",
      "87.77777777777777\n",
      "\n",
      "RandomForestClassifier\n",
      "88.88888888888889\n",
      "\n",
      "DecisionTreeClassifier\n",
      "81.11111111111111\n",
      "\n",
      "ExtraTreesClassifier\n",
      "84.44444444444444\n",
      "\n",
      "VotingClassifier\n",
      "86.66666666666667\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# check ferformance on testing data\n",
    "estimators = [lr_clf, rf_clf, dt_clf, ext_clf, new_voting_classifier]\n",
    "\n",
    "for estimator in estimators:\n",
    "    print(estimator.__class__.__name__)\n",
    "    print(estimator.score(x_test, y_test) * 100) # accuracy\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "1f6bb344",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pick RandomForestClassifier model to deploy\n",
    "#y_pred = rf_clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "7e534034",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save RandomForestClassifier model to disk\n",
    "pickle_out = open(\"random_forest_classifier.pkl\", \"wb\")\n",
    "pickle.dump(rf_clf, pickle_out)\n",
    "pickle_out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "f3dc0b1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rf_clf.predict([[50,0,500,0,20,0,250000,1.5,110,0,0,2]])\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "347ec3fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dead'"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "death_event[y_pred[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "ce132532",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prob = rf_clf.predict_proba([[50,0,500,0,20,0,250000,1.5,110,0,0,2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "70145f90",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the RandomForestClassifier model from disk\n",
    "#loaded_rf_model = pickle.load(open(\"random_forest_classifier.pkl\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "36bf8735",
   "metadata": {},
   "outputs": [],
   "source": [
    "death_event = {\n",
    "            0: 'survival',\n",
    "            1: 'dead'\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "cdc921e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dead'"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "death_event[np.argmax(y_prob)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "c5820162",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.86"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(max(y_prob[0]), 2)"
   ]
  },
  {
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