PYTN_KampusMerdeka_fp3_Nindya Eka Apsari.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src = \"https://global-uploads.webflow.com/5f0d53c042a9ed6288de7f8d/5f6337ae2cfaa10946ceeb06_Hacktiv8%20logo%20horizontal%2001%20black-p-500.png\" width = 400>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Project 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perkenalan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Teammate:\n",
    "1. Nindya Eka Apsari\n",
    "\n",
    "2. Faradilla Ardiyani\n",
    "\n",
    "\n",
    "PYTN-KS002\n",
    "\n",
    "Studi Independen - Kampus Merdeka"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Latar Belakang"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Penyakit kardiovaskular (CVD) adalah penyebab kematian nomor 1 secara global, diperkirakan merenggut 17,9 juta jiwa setiap tahun, yang menyumbang 31% dari semua kematian di seluruh dunia. Gagal jantung adalah peristiwa umum yang disebabkan oleh CVD dan dataset ini berisi 12 fitur yang dapat digunakan untuk memprediksi kematian akibat gagal jantung.\n",
    "\n",
    "Sebagian besar penyakit kardiovaskular dapat dicegah dengan mengatasi faktor risiko perilaku seperti penggunaan tembakau, diet tidak sehat dan obesitas, aktivitas fisik dan penggunaan alkohol yang berbahaya menggunakan strategi di seluruh populasi.\n",
    "\n",
    "Orang dengan penyakit kardiovaskular atau yang berisiko kardiovaskular tinggi (karena adanya satu atau lebih faktor risiko seperti hipertensi, diabetes, hiperlipidaemia atau penyakit yang sudah mapan) memerlukan deteksi dini dan manajemen di mana model pembelajaran mesin dapat sangat membantu."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analisis ini menggunakan dataset yang bersumber dari Kaggle dengan judul [\"Heart Failure Prediction\"](https://www.kaggle.com/andrewmvd/heart-failure-clinical-data)\n",
    "\n",
    "\n",
    "Dataset ini berisikan faktor-faktor kematian oleh gagal jantung yang memiliki 13 atribut dengan 299 rows. Atribut yang menjadi target yaitu atribut Death Event/Kematian, sedangkan atribut lainnya merupakan prediktor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Objektif yang ingin dicapai"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Objective yang ingin dicapai dalam analisis ini yaitu:\n",
    "\n",
    "- mengetahui faktor-faktor yang berpengaruh signifikan dalam memprediksi kematian oleh penyakit gagal jantung\n",
    "\n",
    "- dapat memprediksi kematian berdasarkan faktor-faktor yang mempengaruhinya menggunakan metode ensemble\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melakukan import libraries yang digunakan dalam project ini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T10:27:35.393844Z",
     "start_time": "2022-01-08T10:27:31.037780Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Libraries sudah siap!\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score,f1_score, precision_score, recall_score\n",
    "import pickle\n",
    "\n",
    "print('Libraries sudah siap!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Loading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melakukan loading dataset menggunakan library Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T10:27:38.853564Z",
     "start_time": "2022-01-08T10:27:38.818325Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('heart_failure_clinical_records_dataset.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melakukan pengaturan yakni tidak memberi batasan maksimal kolom yang ditampilkan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T10:27:42.422707Z",
     "start_time": "2022-01-08T10:27:42.410642Z"
    }
   },
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_columns\", None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melihat 5 baris teratas dari dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T10:27:45.001322Z",
     "start_time": "2022-01-08T10:27:44.958320Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
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       "    <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",
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       "    <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",
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       "      <td>7</td>\n",
       "      <td>1</td>\n",
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       "    <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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melihat 5 baris terbawah dari dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T10:27:48.287847Z",
     "start_time": "2022-01-08T10:27:48.268823Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>294</th>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>155000.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>143</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>270</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1820</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>139</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>271</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2060</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>742000.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>138</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>278</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2413</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>140000.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>280</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>196</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>395000.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>285</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "294  62.0        0                        61         1                 38   \n",
       "295  55.0        0                      1820         0                 38   \n",
       "296  45.0        0                      2060         1                 60   \n",
       "297  45.0        0                      2413         0                 38   \n",
       "298  50.0        0                       196         0                 45   \n",
       "\n",
       "     high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "294                    1   155000.0               1.1           143    1   \n",
       "295                    0   270000.0               1.2           139    0   \n",
       "296                    0   742000.0               0.8           138    0   \n",
       "297                    0   140000.0               1.4           140    1   \n",
       "298                    0   395000.0               1.6           136    1   \n",
       "\n",
       "     smoking  time  DEATH_EVENT  \n",
       "294        1   270            0  \n",
       "295        0   271            0  \n",
       "296        0   278            0  \n",
       "297        1   280            0  \n",
       "298        1   285            0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melihat informasi kolom, baris, dan data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T10:27:51.110243Z",
     "start_time": "2022-01-08T10:27:51.083617Z"
    }
   },
   "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": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dataset memiliki baris yang banyak yaitu 299 baris, dan memiliki tipe data yang bervariasi yakni float dan integer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melihat matriks dataset dengan [.shape]()\n",
    "\n",
    "Terlihat dataset memiliki 299 baris dan 13 kolom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T10:27:54.938377Z",
     "start_time": "2022-01-08T10:27:54.928764Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(299, 13)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Menggunakan [.columns]() untuk melihat rincian nama kolom yang ada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T10:29:44.622027Z",
     "start_time": "2022-01-08T10:29:44.611027Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'anaemia', 'creatinine_phosphokinase', 'diabetes',\n",
       "       'ejection_fraction', 'high_blood_pressure', 'platelets',\n",
       "       'serum_creatinine', 'serum_sodium', 'sex', 'smoking', 'time',\n",
       "       'DEATH_EVENT'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mengecek missing value dengan [.isnull()]() serta menjumlahkannya dengan menambah [.sum]()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T18:54:59.094198Z",
     "start_time": "2022-01-08T18:54:58.934172Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                         0\n",
       "anaemia                     0\n",
       "creatinine_phosphokinase    0\n",
       "diabetes                    0\n",
       "ejection_fraction           0\n",
       "high_blood_pressure         0\n",
       "platelets                   0\n",
       "serum_creatinine            0\n",
       "serum_sodium                0\n",
       "sex                         0\n",
       "smoking                     0\n",
       "time                        0\n",
       "DEATH_EVENT                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diperoleh bahwa tidak terdapat missing value pada semua atribut."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Menggunakan [.describe()]() untuk mengecek statistik deskriptif dari masing-masing atribut yang bertipe numeric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T10:30:03.678989Z",
     "start_time": "2022-01-08T10:30:03.636977Z"
    }
   },
   "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>count</th>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.00000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.00000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>60.833893</td>\n",
       "      <td>0.431438</td>\n",
       "      <td>581.839465</td>\n",
       "      <td>0.418060</td>\n",
       "      <td>38.083612</td>\n",
       "      <td>0.351171</td>\n",
       "      <td>263358.029264</td>\n",
       "      <td>1.39388</td>\n",
       "      <td>136.625418</td>\n",
       "      <td>0.648829</td>\n",
       "      <td>0.32107</td>\n",
       "      <td>130.260870</td>\n",
       "      <td>0.32107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.894809</td>\n",
       "      <td>0.496107</td>\n",
       "      <td>970.287881</td>\n",
       "      <td>0.494067</td>\n",
       "      <td>11.834841</td>\n",
       "      <td>0.478136</td>\n",
       "      <td>97804.236869</td>\n",
       "      <td>1.03451</td>\n",
       "      <td>4.412477</td>\n",
       "      <td>0.478136</td>\n",
       "      <td>0.46767</td>\n",
       "      <td>77.614208</td>\n",
       "      <td>0.46767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25100.000000</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>116.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>212500.000000</td>\n",
       "      <td>0.90000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>262000.000000</td>\n",
       "      <td>1.10000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>582.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>303500.000000</td>\n",
       "      <td>1.40000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7861.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>850000.000000</td>\n",
       "      <td>9.40000</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>285.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age     anaemia  creatinine_phosphokinase    diabetes  \\\n",
       "count  299.000000  299.000000                299.000000  299.000000   \n",
       "mean    60.833893    0.431438                581.839465    0.418060   \n",
       "std     11.894809    0.496107                970.287881    0.494067   \n",
       "min     40.000000    0.000000                 23.000000    0.000000   \n",
       "25%     51.000000    0.000000                116.500000    0.000000   \n",
       "50%     60.000000    0.000000                250.000000    0.000000   \n",
       "75%     70.000000    1.000000                582.000000    1.000000   \n",
       "max     95.000000    1.000000               7861.000000    1.000000   \n",
       "\n",
       "       ejection_fraction  high_blood_pressure      platelets  \\\n",
       "count         299.000000           299.000000     299.000000   \n",
       "mean           38.083612             0.351171  263358.029264   \n",
       "std            11.834841             0.478136   97804.236869   \n",
       "min            14.000000             0.000000   25100.000000   \n",
       "25%            30.000000             0.000000  212500.000000   \n",
       "50%            38.000000             0.000000  262000.000000   \n",
       "75%            45.000000             1.000000  303500.000000   \n",
       "max            80.000000             1.000000  850000.000000   \n",
       "\n",
       "       serum_creatinine  serum_sodium         sex    smoking        time  \\\n",
       "count         299.00000    299.000000  299.000000  299.00000  299.000000   \n",
       "mean            1.39388    136.625418    0.648829    0.32107  130.260870   \n",
       "std             1.03451      4.412477    0.478136    0.46767   77.614208   \n",
       "min             0.50000    113.000000    0.000000    0.00000    4.000000   \n",
       "25%             0.90000    134.000000    0.000000    0.00000   73.000000   \n",
       "50%             1.10000    137.000000    1.000000    0.00000  115.000000   \n",
       "75%             1.40000    140.000000    1.000000    1.00000  203.000000   \n",
       "max             9.40000    148.000000    1.000000    1.00000  285.000000   \n",
       "\n",
       "       DEATH_EVENT  \n",
       "count    299.00000  \n",
       "mean       0.32107  \n",
       "std        0.46767  \n",
       "min        0.00000  \n",
       "25%        0.00000  \n",
       "50%        0.00000  \n",
       "75%        1.00000  \n",
       "max        1.00000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melihat banyaknya unique values untuk tiap atribut."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-06T05:16:43.809903Z",
     "start_time": "2022-01-06T05:16:43.793911Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age :  47\n",
      "anaemia :  2\n",
      "creatinine_phosphokinase :  208\n",
      "diabetes :  2\n",
      "ejection_fraction :  17\n",
      "high_blood_pressure :  2\n",
      "platelets :  176\n",
      "serum_creatinine :  40\n",
      "serum_sodium :  27\n",
      "sex :  2\n",
      "smoking :  2\n",
      "time :  148\n",
      "DEATH_EVENT :  2\n"
     ]
    }
   ],
   "source": [
    "for col in df.columns:\n",
    "    print(col,': ', df[col].nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bagian ini berisi proses penyiapan data berupa data cleaning sebelum dilakukan explorasi data lebih lanjut."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melakukan pengecekan duplikasi data.\n",
    "\n",
    "Diperoleh tidak ada duplikasi data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-06T05:16:47.318173Z",
     "start_time": "2022-01-06T05:16:47.304158Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melakukan pengecekan missing value pada atribut DEATH EVENT.\n",
    "\n",
    "Diperoleh bahwa tidak terdapat missing value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T10:30:19.972658Z",
     "start_time": "2022-01-08T10:30:19.956588Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['DEATH_EVENT'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melakukan crosstab atribut smoking dan Death Event\n",
    "\n",
    "Diperoleh hasil bahwa jumlah pasien merokok yang meninggal paling sedikit dibandingkan perbandingan yang lain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T10:38:19.933075Z",
     "start_time": "2022-01-08T10:38:19.794490Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x21d9b005f08>,\n",
       "  <matplotlib.axis.XTick at 0x21d9aad7388>],\n",
       " [Text(0, 0, 'Tidak Merokok'), Text(1, 0, 'Merokok')])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.smoking, df.DEATH_EVENT).plot(kind='bar')\n",
    "plt.ylabel('Banyaknya Kematian')\n",
    "plt.xlabel('Merokok')\n",
    "plt.title('Merokok dan Kematian')\n",
    "plt.xticks(ticks=(0,1),labels=['Tidak Merokok','Merokok'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Memberikan nama baru untuk setiap atribut."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T19:00:00.243478Z",
     "start_time": "2022-01-08T19:00:00.235759Z"
    }
   },
   "outputs": [],
   "source": [
    "df2 = df.rename(columns={'age':'Usia', 'anaemia':'Anaemia','creatinine_phosphokinase':'Kreatinin_fosfokinase', 'diabetes':'Diabetes',\n",
    "                         'ejection_fraction':'Fraksi_ejeksi', 'high_blood_pressure':'Tekanan_darah_tinggi','platelets':'Platelets', \n",
    "                        'serum_creatinine':'Kreatinin_serum', 'serum_sodium':'Sodium_serum','sex':'Gender','smoking':'Perokok', \n",
    "                         'time':'Waktu', 'DEATH_EVENT':'Kematian'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Menghilangkan atribut Waktu dikarenakan tidak relevan dengan variabel target. Waktu menunjukkan seberapa lama pasien dapat bertahan, sehingga atribut tersebut tidak cocok sebagai variabel independen, melainkan lebih cocok menjadi variabel target (untuk kasus yang lain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T19:00:06.985683Z",
     "start_time": "2022-01-08T19:00:06.969680Z"
    }
   },
   "outputs": [],
   "source": [
    "df2.drop(columns=['Waktu'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T19:00:09.285566Z",
     "start_time": "2022-01-08T19:00:09.261997Z"
    }
   },
   "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>Usia</th>\n",
       "      <th>Anaemia</th>\n",
       "      <th>Kreatinin_fosfokinase</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Fraksi_ejeksi</th>\n",
       "      <th>Tekanan_darah_tinggi</th>\n",
       "      <th>Platelets</th>\n",
       "      <th>Kreatinin_serum</th>\n",
       "      <th>Sodium_serum</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Perokok</th>\n",
       "      <th>Kematian</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>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>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>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>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>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Usia  Anaemia  Kreatinin_fosfokinase  Diabetes  Fraksi_ejeksi  \\\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",
       "   Tekanan_darah_tinggi  Platelets  Kreatinin_serum  Sodium_serum  Gender  \\\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",
       "   Perokok  Kematian  \n",
       "0        0         1  \n",
       "1        0         1  \n",
       "2        1         1  \n",
       "3        0         1  \n",
       "4        0         1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melihat banyaknya nilai dari masing-masing atribut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T19:00:18.428377Z",
     "start_time": "2022-01-08T19:00:18.388836Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Usia : \n",
      "60.000    33\n",
      "50.000    27\n",
      "65.000    26\n",
      "70.000    25\n",
      "45.000    19\n",
      "55.000    17\n",
      "75.000    11\n",
      "53.000    10\n",
      "58.000    10\n",
      "63.000     8\n",
      "80.000     7\n",
      "72.000     7\n",
      "40.000     7\n",
      "42.000     7\n",
      "85.000     6\n",
      "68.000     5\n",
      "52.000     5\n",
      "62.000     5\n",
      "51.000     4\n",
      "59.000     4\n",
      "61.000     4\n",
      "73.000     4\n",
      "49.000     4\n",
      "69.000     3\n",
      "46.000     3\n",
      "64.000     3\n",
      "82.000     3\n",
      "90.000     3\n",
      "78.000     2\n",
      "60.667     2\n",
      "54.000     2\n",
      "77.000     2\n",
      "57.000     2\n",
      "66.000     2\n",
      "44.000     2\n",
      "67.000     2\n",
      "95.000     2\n",
      "48.000     2\n",
      "43.000     1\n",
      "86.000     1\n",
      "81.000     1\n",
      "79.000     1\n",
      "41.000     1\n",
      "94.000     1\n",
      "87.000     1\n",
      "47.000     1\n",
      "56.000     1\n",
      "Name: Usia, dtype: int64\n",
      "  \n",
      "  \n",
      "Anaemia : \n",
      "0    170\n",
      "1    129\n",
      "Name: Anaemia, dtype: int64\n",
      "  \n",
      "  \n",
      "Kreatinin_fosfokinase : \n",
      "582     47\n",
      "66       4\n",
      "129      4\n",
      "231      3\n",
      "69       3\n",
      "        ..\n",
      "748      1\n",
      "1876     1\n",
      "936      1\n",
      "292      1\n",
      "2413     1\n",
      "Name: Kreatinin_fosfokinase, Length: 208, dtype: int64\n",
      "  \n",
      "  \n",
      "Diabetes : \n",
      "0    174\n",
      "1    125\n",
      "Name: Diabetes, dtype: int64\n",
      "  \n",
      "  \n",
      "Fraksi_ejeksi : \n",
      "35    49\n",
      "38    40\n",
      "40    37\n",
      "25    36\n",
      "30    34\n",
      "60    31\n",
      "50    21\n",
      "45    20\n",
      "20    18\n",
      "55     3\n",
      "15     2\n",
      "62     2\n",
      "17     2\n",
      "65     1\n",
      "14     1\n",
      "80     1\n",
      "70     1\n",
      "Name: Fraksi_ejeksi, dtype: int64\n",
      "  \n",
      "  \n",
      "Tekanan_darah_tinggi : \n",
      "0    194\n",
      "1    105\n",
      "Name: Tekanan_darah_tinggi, dtype: int64\n",
      "  \n",
      "  \n",
      "Platelets : \n",
      "263358.03    25\n",
      "221000.00     4\n",
      "279000.00     4\n",
      "271000.00     4\n",
      "305000.00     4\n",
      "             ..\n",
      "227000.00     1\n",
      "289000.00     1\n",
      "300000.00     1\n",
      "217000.00     1\n",
      "742000.00     1\n",
      "Name: Platelets, Length: 176, dtype: int64\n",
      "  \n",
      "  \n",
      "Kreatinin_serum : \n",
      "1.00    50\n",
      "1.10    32\n",
      "0.90    32\n",
      "1.20    24\n",
      "0.80    24\n",
      "1.30    20\n",
      "0.70    19\n",
      "1.18    11\n",
      "1.40     9\n",
      "1.70     9\n",
      "1.83     8\n",
      "1.60     6\n",
      "1.90     5\n",
      "2.10     5\n",
      "1.50     5\n",
      "0.60     4\n",
      "1.80     4\n",
      "2.30     3\n",
      "2.70     3\n",
      "2.50     3\n",
      "3.50     2\n",
      "2.40     2\n",
      "3.00     2\n",
      "0.50     1\n",
      "5.00     1\n",
      "0.75     1\n",
      "6.10     1\n",
      "3.40     1\n",
      "3.70     1\n",
      "9.00     1\n",
      "4.00     1\n",
      "3.20     1\n",
      "5.80     1\n",
      "2.90     1\n",
      "2.00     1\n",
      "2.20     1\n",
      "6.80     1\n",
      "9.40     1\n",
      "4.40     1\n",
      "3.80     1\n",
      "Name: Kreatinin_serum, dtype: int64\n",
      "  \n",
      "  \n",
      "Sodium_serum : \n",
      "136    40\n",
      "137    38\n",
      "140    35\n",
      "134    32\n",
      "138    23\n",
      "139    22\n",
      "135    16\n",
      "132    14\n",
      "141    12\n",
      "142    11\n",
      "133    10\n",
      "145     9\n",
      "130     9\n",
      "144     5\n",
      "131     5\n",
      "127     3\n",
      "143     3\n",
      "128     2\n",
      "129     2\n",
      "121     1\n",
      "116     1\n",
      "146     1\n",
      "126     1\n",
      "124     1\n",
      "113     1\n",
      "125     1\n",
      "148     1\n",
      "Name: Sodium_serum, dtype: int64\n",
      "  \n",
      "  \n",
      "Gender : \n",
      "1    194\n",
      "0    105\n",
      "Name: Gender, dtype: int64\n",
      "  \n",
      "  \n",
      "Perokok : \n",
      "0    203\n",
      "1     96\n",
      "Name: Perokok, dtype: int64\n",
      "  \n",
      "  \n",
      "Kematian : \n",
      "0    203\n",
      "1     96\n",
      "Name: Kematian, dtype: int64\n",
      "  \n",
      "  \n"
     ]
    }
   ],
   "source": [
    "for col in df2.columns:\n",
    "    print(col,': ')\n",
    "    print(df2[col].value_counts())\n",
    "    print(\"  \")\n",
    "    print(\"  \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Membuat dataframe yang serupa dengan dataframe sebelumnya"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T19:00:21.675829Z",
     "start_time": "2022-01-08T19:00:21.665239Z"
    }
   },
   "outputs": [],
   "source": [
    "df3 = df2.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melihat 5 baris teratas dari dataset terbaru"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T19:00:23.356252Z",
     "start_time": "2022-01-08T19:00:23.332253Z"
    }
   },
   "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>Usia</th>\n",
       "      <th>Anaemia</th>\n",
       "      <th>Kreatinin_fosfokinase</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Fraksi_ejeksi</th>\n",
       "      <th>Tekanan_darah_tinggi</th>\n",
       "      <th>Platelets</th>\n",
       "      <th>Kreatinin_serum</th>\n",
       "      <th>Sodium_serum</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Perokok</th>\n",
       "      <th>Kematian</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>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>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>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>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>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Usia  Anaemia  Kreatinin_fosfokinase  Diabetes  Fraksi_ejeksi  \\\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",
       "   Tekanan_darah_tinggi  Platelets  Kreatinin_serum  Sodium_serum  Gender  \\\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",
       "   Perokok  Kematian  \n",
       "0        0         1  \n",
       "1        0         1  \n",
       "2        1         1  \n",
       "3        0         1  \n",
       "4        0         1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descriptive Statistics & Query"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melihat statistik descriptif pada data numerik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Usia</th>\n",
       "      <th>Kreatinin_fosfokinase</th>\n",
       "      <th>Fraksi_ejeksi</th>\n",
       "      <th>Platelets</th>\n",
       "      <th>Kreatinin_serum</th>\n",
       "      <th>Sodium_serum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.00000</td>\n",
       "      <td>299.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>60.833893</td>\n",
       "      <td>581.839465</td>\n",
       "      <td>38.083612</td>\n",
       "      <td>263358.029264</td>\n",
       "      <td>1.39388</td>\n",
       "      <td>136.625418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.894809</td>\n",
       "      <td>970.287881</td>\n",
       "      <td>11.834841</td>\n",
       "      <td>97804.236869</td>\n",
       "      <td>1.03451</td>\n",
       "      <td>4.412477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>25100.000000</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>113.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>116.500000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>212500.000000</td>\n",
       "      <td>0.90000</td>\n",
       "      <td>134.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>262000.000000</td>\n",
       "      <td>1.10000</td>\n",
       "      <td>137.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>582.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>303500.000000</td>\n",
       "      <td>1.40000</td>\n",
       "      <td>140.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>7861.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>850000.000000</td>\n",
       "      <td>9.40000</td>\n",
       "      <td>148.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Usia  Kreatinin_fosfokinase  Fraksi_ejeksi      Platelets  \\\n",
       "count  299.000000             299.000000     299.000000     299.000000   \n",
       "mean    60.833893             581.839465      38.083612  263358.029264   \n",
       "std     11.894809             970.287881      11.834841   97804.236869   \n",
       "min     40.000000              23.000000      14.000000   25100.000000   \n",
       "25%     51.000000             116.500000      30.000000  212500.000000   \n",
       "50%     60.000000             250.000000      38.000000  262000.000000   \n",
       "75%     70.000000             582.000000      45.000000  303500.000000   \n",
       "max     95.000000            7861.000000      80.000000  850000.000000   \n",
       "\n",
       "       Kreatinin_serum  Sodium_serum  \n",
       "count        299.00000    299.000000  \n",
       "mean           1.39388    136.625418  \n",
       "std            1.03451      4.412477  \n",
       "min            0.50000    113.000000  \n",
       "25%            0.90000    134.000000  \n",
       "50%            1.10000    137.000000  \n",
       "75%            1.40000    140.000000  \n",
       "max            9.40000    148.000000  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num = df3.drop(columns=['Anaemia', 'Diabetes', 'Tekanan_darah_tinggi', 'Gender', 'Perokok', 'Kematian'])\n",
    "num.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diperoleh bahwa:\n",
    "- Usia rata-rata pasien yakni berkisar 60 tahun\n",
    "- Rata-rata nilai kreatinin fosfokinase pada pasien yaitu 581,84\n",
    "- Fraksi ejeksi maksimal yang ada pada pasien yaitu 80\n",
    "- Kreatinin serum minimal yang ada pada pasien yaitu 0,5\n",
    "- Sodium serum rata-rata pada pasien yaitu 136,63\n",
    "\n",
    "Selanjutnya melihat statistik descriptif pada data kategorik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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",
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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>Anaemia</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Tekanan_darah_tinggi</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Perokok</th>\n",
       "      <th>Kematian</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>299</td>\n",
       "      <td>299</td>\n",
       "      <td>299</td>\n",
       "      <td>299</td>\n",
       "      <td>299</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>170</td>\n",
       "      <td>174</td>\n",
       "      <td>194</td>\n",
       "      <td>194</td>\n",
       "      <td>203</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Anaemia  Diabetes  Tekanan_darah_tinggi  Gender  Perokok  Kematian\n",
       "count       299       299                   299     299      299       299\n",
       "unique        2         2                     2       2        2         2\n",
       "top           0         0                     0       1        0         0\n",
       "freq        170       174                   194     194      203       203"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kat = df3.drop(columns=['Usia', 'Kreatinin_fosfokinase', 'Fraksi_ejeksi', 'Platelets', 'Kreatinin_serum', 'Sodium_serum']).astype('category')\n",
    "kat.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diperoleh bahwa:\n",
    "- Pasien yang tidak mengidap anemia yaitu 170 pasien\n",
    "- Pasien yang tidak mengidap diabetes yaitu 174 pasien\n",
    "- Pasien dengan tidak mengidap tekanan darah tinggi yaitu 194 pasien\n",
    "- Pasien yang tidak merokok yakni 203 pasien\n",
    "- Pasien yang tidak meninggal berjumlah 203 orang"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selanjutnya, melakukan grouping pada atribut Usia dan Kematian untuk melihat rata-ratanya"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T19:00:34.619505Z",
     "start_time": "2022-01-08T19:00:34.604015Z"
    }
   },
   "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>Usia</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kematian</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58.761906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65.215281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Usia\n",
       "Kematian           \n",
       "0         58.761906\n",
       "1         65.215281"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean1 = df3[['Usia','Kematian']].groupby('Kematian').mean().sort_values(by='Usia', ascending=True)\n",
    "mean1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diperoleh bahwa rata-rata usia pasien yang meninggal yakni 65 tahun, sedangkan rata-rata pasien yang tidak meninggal/bertahan berusia 58 tahun"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selanjutnya, melakukan crosstab pada atribut Perokok dan Kematian untuk melihat proporsi dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T19:01:12.957794Z",
     "start_time": "2022-01-08T19:01:12.904644Z"
    }
   },
   "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>Kematian</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perokok</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>137</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>66</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Kematian    0   1\n",
       "Perokok          \n",
       "0         137  66\n",
       "1          66  30"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df3.Perokok, df3.Kematian)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diperoleh bahwa banyaknya kasus kematian pasien yang merokok yakni sebesar 30 pasien, sedangkan proporsi pasien yang merokok dapat bertahan dan tidak merokok namun meninggal adalah sama yakni 66 pasien. Untuk pasien yang tidak merokok dan tidak meninggal memiliki jumlah yang paling besar yakni 137 pasien"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melakukan visualisasi Boxplot untuk melihat distribusi data, outlier data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T10:39:03.213749Z",
     "start_time": "2022-01-08T10:39:02.193687Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\lenovo\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\lenovo\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\lenovo\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\lenovo\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\lenovo\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\lenovo\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\lenovo\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\lenovo\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\lenovo\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\lenovo\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\lenovo\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JOJ/R3QFm+ytGp3hvZ3TKNdO1wMWM7vtzQVWtAxjW/dTwtoWNjG69czuje2/flORLVXXcPOttAM555C0PwJaOxLSd8W0GktryFE9SWwZKUlsGSlJbBkpSWwZKUlsGSlJbBkpSWwZKUlv/D1d/eYKC5S6jAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in df3:\n",
    "    plt.figure(figsize=(5,5))\n",
    "    sns.boxplot(df3[feature])\n",
    "    plt.title(feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diperoleh bahwa masih terdapat atribut dengan nilai outlier yang banyak."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selanjutnya melakukan visualisasi histogram untuk melihat proporsi data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T19:04:04.665934Z",
     "start_time": "2022-01-08T19:04:04.525657Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Kematian\", data=df3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diperoleh bahwa proporsi data antara pasien yang meninggal maupun yang tidak adalah tidak berbeda signifikan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melakukan visualisasi korelasi antar variabel untuk melihat hubungan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T19:04:15.595518Z",
     "start_time": "2022-01-08T19:04:14.626840Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 9))\n",
    "sns.heatmap(df3.corr(), vmin=-1, vmax=1, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T19:04:42.908567Z",
     "start_time": "2022-01-08T19:04:42.884553Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Kematian                 1.000000\n",
       "Kreatinin_serum          0.294278\n",
       "Usia                     0.253729\n",
       "Tekanan_darah_tinggi     0.079351\n",
       "Anaemia                  0.066270\n",
       "Kreatinin_fosfokinase    0.062728\n",
       "Diabetes                -0.001943\n",
       "Gender                  -0.004316\n",
       "Perokok                 -0.012623\n",
       "Platelets               -0.049139\n",
       "Sodium_serum            -0.195204\n",
       "Fraksi_ejeksi           -0.268603\n",
       "Name: Kematian, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.corr()['Kematian'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diperoleh:\n",
    "- Atribut yang memiliki korelasi tertinggi 4 teratas yaitu atribut kreatinin serum, usia, fraksi ejeksi, dan sodium serum\n",
    "- Pada atribut fraksi ejeksi dan sodium serum memiliki korelasi negatif dengan Kematian\n",
    "- Untuk atribut kreatinin serum dan usia memiliki korelasi positif dengan Kematian"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melakukan inisiasi variabel X dan Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T19:07:02.166623Z",
     "start_time": "2022-01-08T19:07:02.150611Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(299, 11)\n",
      "(299,)\n"
     ]
    }
   ],
   "source": [
    "X = df3.drop(['Kematian'], axis=1)\n",
    "y = df3['Kematian']\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melakukan feature selection menggunakan library SequentialFeatureSelector dengan Metode Rergesi Linier, banyak atribut 10, metode forward, evaluasi dengan R squared, dan cv dengan k=3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T19:08:08.015221Z",
     "start_time": "2022-01-08T19:08:07.841345Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesClassifier()"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "et=ExtraTreesClassifier()\n",
    "et.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diperoleh hasil dengan 10 feature/atribut yang paling berpengaruh dalam model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T19:08:20.877523Z",
     "start_time": "2022-01-08T19:08:20.830220Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.14451308, 0.03356173, 0.10852457, 0.04622997, 0.16760153,\n",
       "       0.02945819, 0.10702931, 0.16867687, 0.12103371, 0.03601042,\n",
       "       0.03736064])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "et.feature_importances_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selain itu juga melakukan feature selection menggunakan library F regression dengan k=10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T19:08:42.370087Z",
     "start_time": "2022-01-08T19:08:42.147944Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "feat_importances=pd.Series(et.feature_importances_,index=X.columns)\n",
    "feat_importances.nlargest(12).plot(kind='barh')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setelah diperoleh feature yang paling berpengaruh pada model, dilakukan inisiasi final pada variabel X yang akan dijadikan training.\n",
    "\n",
    "Dalam hal ini dipilih 4 atribut yang paling berpengaruh signifikan berdasarkan kedua uji feature selection yang telah dilakukan, yakni atribut Kreatinin serum, fraksi ejeksi, usia, sodium serum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T19:10:10.710856Z",
     "start_time": "2022-01-08T19:10:10.702153Z"
    }
   },
   "outputs": [],
   "source": [
    "X_fit = df3[['Usia', 'Fraksi_ejeksi', 'Kreatinin_serum', 'Sodium_serum']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melihat 5 baris teratas dari dataframe X_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T19:10:13.420259Z",
     "start_time": "2022-01-08T19:10:13.391570Z"
    }
   },
   "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>Usia</th>\n",
       "      <th>Fraksi_ejeksi</th>\n",
       "      <th>Kreatinin_serum</th>\n",
       "      <th>Sodium_serum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75.0</td>\n",
       "      <td>20</td>\n",
       "      <td>1.9</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55.0</td>\n",
       "      <td>38</td>\n",
       "      <td>1.1</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65.0</td>\n",
       "      <td>20</td>\n",
       "      <td>1.3</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.0</td>\n",
       "      <td>20</td>\n",
       "      <td>1.9</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65.0</td>\n",
       "      <td>20</td>\n",
       "      <td>2.7</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Usia  Fraksi_ejeksi  Kreatinin_serum  Sodium_serum\n",
       "0  75.0             20              1.9           130\n",
       "1  55.0             38              1.1           136\n",
       "2  65.0             20              1.3           129\n",
       "3  50.0             20              1.9           137\n",
       "4  65.0             20              2.7           116"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_fit.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melihat rincian datafram X_fit, yakni memiliki tipe data numerik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T19:10:20.741468Z",
     "start_time": "2022-01-08T19:10:20.703099Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 299 entries, 0 to 298\n",
      "Data columns (total 4 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Usia             299 non-null    float64\n",
      " 1   Fraksi_ejeksi    299 non-null    int64  \n",
      " 2   Kreatinin_serum  299 non-null    float64\n",
      " 3   Sodium_serum     299 non-null    int64  \n",
      "dtypes: float64(2), int64(2)\n",
      "memory usage: 9.5 KB\n"
     ]
    }
   ],
   "source": [
    "X_fit.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melihat dimensi X_fit, yakni memiliki 637976 baris dan 8 atribut."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T19:10:24.616352Z",
     "start_time": "2022-01-08T19:10:24.600115Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(299, 4)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_fit.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setelah diperoleh atribut yang akan dilakukan training, kemudian membagi data menjadi Training dan Testing dengan proporsi 0.8 dan 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T19:13:59.655413Z",
     "start_time": "2022-01-08T19:13:59.647417Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X_fit, y, test_size=0.30, random_state=325)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Defining"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Metode pengujian pada training ini menggunakan metode random forest classifier, extra tree classifier, dan gradien boosting classifier. Alasan penggunaan metode ini yaitu tipe data prediksi yang berupa data kategorik, sehingga metode tersebut cocok digunakan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model pertama yakni model random forest classifier dengan n estimator 10, max depth 10, min samples split 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T19:14:02.842804Z",
     "start_time": "2022-01-08T19:14:02.835237Z"
    }
   },
   "outputs": [],
   "source": [
    "clf1 = RandomForestClassifier(n_estimators=10, max_depth=10,min_samples_split=2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model kedua yakni model extra tree classifier dengan n estimator 10, max depth 5, min samples split 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T19:14:04.056766Z",
     "start_time": "2022-01-08T19:14:04.034248Z"
    }
   },
   "outputs": [],
   "source": [
    "clf2 = ExtraTreesClassifier(n_estimators=10, max_depth=5, min_samples_split=3, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model ketiga yakni model gradient boosting classifier dengan n estimator 100, learning rate 0.1, max depth 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T19:14:05.253102Z",
     "start_time": "2022-01-08T19:14:05.245510Z"
    }
   },
   "outputs": [],
   "source": [
    "clf3 = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1,\n",
    "                                  max_depth=1, random_state=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cell pada bagian ini hanya berisi code untuk melatih model dan output yang dihasilkan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T19:14:07.031543Z",
     "start_time": "2022-01-08T19:14:06.983533Z"
    }
   },
   "outputs": [],
   "source": [
    "model1 = clf1.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T19:14:08.013142Z",
     "start_time": "2022-01-08T19:14:07.983723Z"
    }
   },
   "outputs": [],
   "source": [
    "model2 = clf2.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T19:14:08.902625Z",
     "start_time": "2022-01-08T19:14:08.846293Z"
    }
   },
   "outputs": [],
   "source": [
    "model3 = clf3.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pada bagian ini, dilakukan evaluasi model yang harus menunjukkan bagaimana performa model berdasarkan metrics yang dipilih. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T19:14:11.168368Z",
     "start_time": "2022-01-08T19:14:11.141938Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.6777777777777778\n",
      "Train score: 0.9712918660287081\n"
     ]
    }
   ],
   "source": [
    "y_pred1 = clf1.predict(X_test)\n",
    "print(\"Test score:\", clf1.score(X_test, y_test))\n",
    "print(\"Train score:\", clf1.score(X_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.84      0.78        62\n",
      "           1       0.47      0.32      0.38        28\n",
      "\n",
      "    accuracy                           0.68        90\n",
      "   macro avg       0.60      0.58      0.58        90\n",
      "weighted avg       0.65      0.68      0.66        90\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cm1 = confusion_matrix(y_test, y_pred1)\n",
    "sns.heatmap(cm1, annot=True, fmt=\"d\", linewidths=.5, cmap = 'Reds')\n",
    "plt.show()\n",
    "print(classification_report(y_test, y_pred1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ringkasan Evaluasi Model\n",
      "Akurasi : 0.6777777777777778\n",
      "Presisi : 0.47368421052631576\n",
      "Recall : 0.32142857142857145\n",
      "F1 Score : 0.3829787234042553\n"
     ]
    }
   ],
   "source": [
    "print('Ringkasan Evaluasi Model')\n",
    "print('Akurasi :', accuracy_score(y_test, y_pred1))\n",
    "print('Presisi :', precision_score(y_test,y_pred1))\n",
    "print('Recall :', recall_score(y_test,y_pred1))\n",
    "print('F1 Score :', f1_score(y_test,y_pred1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extra Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T19:14:12.937301Z",
     "start_time": "2022-01-08T19:14:12.913416Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.6666666666666666\n",
      "Train score: 0.8421052631578947\n"
     ]
    }
   ],
   "source": [
    "y_pred2 = clf2.predict(X_test)\n",
    "print(\"Test score:\", clf2.score(X_test, y_test))\n",
    "print(\"Train score:\", clf2.score(X_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.82      0.77        62\n",
      "           1       0.45      0.32      0.38        28\n",
      "\n",
      "    accuracy                           0.67        90\n",
      "   macro avg       0.59      0.57      0.57        90\n",
      "weighted avg       0.64      0.67      0.65        90\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cm2 = confusion_matrix(y_test, y_pred2)\n",
    "sns.heatmap(cm2, annot=True, fmt=\"d\", linewidths=.5, cmap = 'Reds')\n",
    "plt.show()\n",
    "print(classification_report(y_test, y_pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ringkasan Evaluasi Model\n",
      "Akurasi : 0.6666666666666666\n",
      "Presisi : 0.45\n",
      "Recall : 0.32142857142857145\n",
      "F1 Score : 0.375\n"
     ]
    }
   ],
   "source": [
    "print('Ringkasan Evaluasi Model')\n",
    "print('Akurasi :', accuracy_score(y_test, y_pred2))\n",
    "print('Presisi :', precision_score(y_test,y_pred2))\n",
    "print('Recall :', recall_score(y_test,y_pred2))\n",
    "print('F1 Score :', f1_score(y_test,y_pred2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-08T19:14:14.623447Z",
     "start_time": "2022-01-08T19:14:14.607847Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.7111111111111111\n",
      "Train score: 0.8277511961722488\n"
     ]
    }
   ],
   "source": [
    "y_pred3 = clf3.predict(X_test)\n",
    "print(\"Test score:\", clf3.score(X_test, y_test))\n",
    "print(\"Train score:\", clf3.score(X_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.85      0.80        62\n",
      "           1       0.55      0.39      0.46        28\n",
      "\n",
      "    accuracy                           0.71        90\n",
      "   macro avg       0.65      0.62      0.63        90\n",
      "weighted avg       0.69      0.71      0.70        90\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cm3 = confusion_matrix(y_test, y_pred3)\n",
    "sns.heatmap(cm1, annot=True, fmt=\"d\", linewidths=.5, cmap = 'Reds')\n",
    "plt.show()\n",
    "print(classification_report(y_test, y_pred3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ringkasan Evaluasi Model\n",
      "Akurasi : 0.7111111111111111\n",
      "Presisi : 0.55\n",
      "Recall : 0.39285714285714285\n",
      "F1 Score : 0.45833333333333337\n"
     ]
    }
   ],
   "source": [
    "print('Ringkasan Evaluasi Model')\n",
    "print('Akurasi :', accuracy_score(y_test, y_pred3))\n",
    "print('Presisi :', precision_score(y_test,y_pred3))\n",
    "print('Recall :', recall_score(y_test,y_pred3))\n",
    "print('F1 Score :', f1_score(y_test,y_pred3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diperoleh bahwa:\n",
    "- Model ketiga memiliki evaluasi model paling baik dibanding kedua model lainnya\n",
    "- Model pertama dan kedua diindikasikan adanya overfitting\n",
    "- Model yang dipilih untuk deployment yakni model ketiga"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Inference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bagian ini diisi dengan model inference, di mana model yang sudah kita latih akan dicoba pada data selain data yang sudah tersedia. Data yang dimaksud bisa berupa data buatan oleh student, ataupun data yang ada pada internet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-26T03:19:58.578308Z",
     "start_time": "2021-11-26T03:19:58.556390Z"
    }
   },
   "outputs": [],
   "source": [
    "X_new = {'Usia':[50,55,60], 'Fraksi_ejeksi':[35,30,25], 'Kreatinin_serum':[1.25,2,3], 'Sodium_serum':[130,133,136]}\n",
    "X_new = pd.DataFrame(X_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melakukan prediksi pada data baru"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-26T03:19:58.594312Z",
     "start_time": "2021-11-26T03:19:58.578308Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediksi Pasien yaitu: \n",
      "[0 1 1]\n"
     ]
    }
   ],
   "source": [
    "y_pred_new = clf3.predict(X_new)\n",
    "print('Prediksi Pasien yaitu: ')\n",
    "print(y_pred_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dari analisis yang telah dilakukan, diperoleh kesimpulan sebagai berikut:\n",
    "- Terdapat 13 atribut yang diasumsikan memengaruhi tarif taksi online. Setelah dilakukan analisis, diambil 4 atribut/faktor yang paling berpengaruh dalam model.\n",
    "- Faktor-faktor yang berpengaruh signifikan terhadap kematian pasien gagal jantung yaitu Usia, Fraksi Ejeksi, Kreatinin Serum, Sodium Serum.\n",
    "- Model ensemble yang paling optimal yaitu model ketiga, diperoleh akurasi testing 71%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-26T03:19:58.610327Z",
     "start_time": "2021-11-26T03:19:58.594312Z"
    }
   },
   "outputs": [],
   "source": [
    "pickle.dump(clf3, open('modelfp3.pkl','wb'))"
   ]
  }
 ],
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