{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Graded Challenge 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Perkenalan\n",
    "\n",
    ">- Nama      : Alsello Diveni Manuputty\n",
    ">- Batch     : HCK 6 Pondok Indah\n",
    ">- Phase     : 1\n",
    "------\n",
    "**Objective**\n",
    ">Pada notebook ini akan dilakukan pengerjaan Graded Challenge 3 fase 1 dimama akan dibuat model Random Forest dan satu algoritma boosting untuk memprediksi apakah seorang pasien akan meninggal atau tidak menggunakan dataset yang sudah diberi."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "SELECT  *\n",
    "FROM    `ftds-hacktiv8-project.phase1_ftds_006_hck.heart-failure`\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xgboost in c:\\users\\alsel\\anaconda3\\envs\\hack\\lib\\site-packages (1.7.6)\n",
      "Requirement already satisfied: numpy in c:\\users\\alsel\\anaconda3\\envs\\hack\\lib\\site-packages (from xgboost) (1.24.3)\n",
      "Requirement already satisfied: scipy in c:\\users\\alsel\\anaconda3\\envs\\hack\\lib\\site-packages (from xgboost) (1.10.1)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "from feature_engine.outliers import Winsorizer\n",
    "from sklearn.preprocessing import MinMaxScaler,StandardScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier\n",
    "!pip install xgboost\n",
    "import xgboost as xgb\n",
    "\n",
    "from sklearn.feature_selection import SelectKBest, chi2\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score,KFold\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import ConfusionMatrixDisplay,confusion_matrix\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mentah = pd.read_csv('h8dsft_P1G3_AlselloDM.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>anaemia</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoking</th>\n",
       "      <th>time</th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42.0</td>\n",
       "      <td>1</td>\n",
       "      <td>250</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>213000.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46.0</td>\n",
       "      <td>0</td>\n",
       "      <td>168</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>271000.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>124</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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>327000.0</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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53.0</td>\n",
       "      <td>1</td>\n",
       "      <td>91</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>418000.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>139</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>582</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>279000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>134</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>186</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "0  42.0        1                       250         1                 15   \n",
       "1  46.0        0                       168         1                 17   \n",
       "2  65.0        1                       160         1                 20   \n",
       "3  53.0        1                        91         0                 20   \n",
       "4  50.0        1                       582         1                 20   \n",
       "\n",
       "   high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "0                    0   213000.0               1.3           136    0   \n",
       "1                    1   271000.0               2.1           124    0   \n",
       "2                    0   327000.0               2.7           116    0   \n",
       "3                    1   418000.0               1.4           139    0   \n",
       "4                    1   279000.0               1.0           134    0   \n",
       "\n",
       "   smoking  time  DEATH_EVENT  \n",
       "0        0    65            1  \n",
       "1        0   100            1  \n",
       "2        0     8            1  \n",
       "3        0    43            1  \n",
       "4        0   186            0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mentah.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>anaemia</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoking</th>\n",
       "      <th>time</th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>63.0</td>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>267000.00</td>\n",
       "      <td>1.2</td>\n",
       "      <td>145</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>308</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>377000.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>186</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>220000.00</td>\n",
       "      <td>0.9</td>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>186</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>53.0</td>\n",
       "      <td>1</td>\n",
       "      <td>446</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>263358.03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>139</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>215</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>582</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>147000.00</td>\n",
       "      <td>0.8</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "294  63.0        1                       122         1                 60   \n",
       "295  45.0        0                       308         1                 60   \n",
       "296  70.0        0                        97         0                 60   \n",
       "297  53.0        1                       446         0                 60   \n",
       "298  50.0        0                       582         0                 62   \n",
       "\n",
       "     high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "294                    0  267000.00               1.2           145    1   \n",
       "295                    1  377000.00               1.0           136    1   \n",
       "296                    1  220000.00               0.9           138    1   \n",
       "297                    1  263358.03               1.0           139    1   \n",
       "298                    1  147000.00               0.8           140    1   \n",
       "\n",
       "     smoking  time  DEATH_EVENT  \n",
       "294        0   147            0  \n",
       "295        0   186            0  \n",
       "296        0   186            0  \n",
       "297        0   215            0  \n",
       "298        1   192            0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mentah.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(299, 13)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# melihat bentuk data\n",
    "mentah.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 299 entries, 0 to 298\n",
      "Data columns (total 13 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   age                       299 non-null    float64\n",
      " 1   anaemia                   299 non-null    int64  \n",
      " 2   creatinine_phosphokinase  299 non-null    int64  \n",
      " 3   diabetes                  299 non-null    int64  \n",
      " 4   ejection_fraction         299 non-null    int64  \n",
      " 5   high_blood_pressure       299 non-null    int64  \n",
      " 6   platelets                 299 non-null    float64\n",
      " 7   serum_creatinine          299 non-null    float64\n",
      " 8   serum_sodium              299 non-null    int64  \n",
      " 9   sex                       299 non-null    int64  \n",
      " 10  smoking                   299 non-null    int64  \n",
      " 11  time                      299 non-null    int64  \n",
      " 12  DEATH_EVENT               299 non-null    int64  \n",
      "dtypes: float64(3), int64(10)\n",
      "memory usage: 30.5 KB\n"
     ]
    }
   ],
   "source": [
    "# melihat info data\n",
    "mentah.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cek missing value\n",
    "mentah.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dari data loading, terlihat bahwa datanya berjumlah 13 kolom dan 299 baris. Semua data tidak memiliki null sehingga tidak perlu membersihkan missing value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Berikut deskripsi masing-masing kolom agar memudahkan analisa:\n",
    "\n",
    "- age : Age\n",
    "- anaemia : Decrease of red blood cells or hemoglobin (boolean)\n",
    "- creatinine_phosphokinase : Level of the CPK Enzyme in the blood (mcg/L)\n",
    "- diabetes : If the patient has Diabetes (boolean)\n",
    "- ejection_fraction : Percentage of blood leaving the heart at each contraction (percentage)\n",
    "- high_blood_pressure : If the patient has Hypertension (boolean)\n",
    "- platelets : Platelets in the blood (kiloplatelets/mL)\n",
    "- serum_creatinine : Level of serum creatinine in the blood (mg/dL)\n",
    "- serum_sodium : Level serum of sodium in the blood (mEq/L)\n",
    "- sex : Woman or Man (binary)\n",
    "- smoking : If the patient smokes or not (boolean)\n",
    "- time : Follow up period (days)\n",
    "- DEATH_EVENT : If the patient deceased during the follow up (boolean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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": [
    "mentah.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rename kolom untuk memudahkan pengerjaan\n",
    "mentah.rename(columns={'DEATH_EVENT':'death_event'},inplace= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean = mentah.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 13)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cek duplicates\n",
    "df_clean[df_clean.duplicated()].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dari cek duplicates, tidak ada data yang duplicate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Berhubung data sudah cukup bersih, maka akan langsung masuk ke proses EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_eda = df_clean.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Karena keseluruhan data sudah dalam representasi numerik, maka akan langsung saja di visualisasikan untuk membantu pemahaman data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualisasi pie chart untuk target\n",
    "df_eda['death_event'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dari data terlihat untuk target, ada data imbalance, dari visualisasi ini bisa di expect bahwa performa model akan kurang maksimal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualisasi kematian berdasarkan jenis kelamin\n",
    "\n",
    "sns.countplot(data=df_eda, x='death_event', hue='sex',palette='Set1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dari umurnya, pasien yang banyak meninggal adalah pasien wanita. Jumlah pasien nya juga lebih banyak wanita."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualisasi umur pasien\n",
    "\n",
    "plt.figure(figsize=(20, 5))\n",
    "sns.countplot(data=df_eda, x='age', hue='death_event',palette='Set1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Terlihat dari umur, data pasien paling banyak di umur 60 tahun. Yang palik banyak kematiannya berdasarkan umur juga di umur 60 tahun."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x700 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create subplots with separate count plots for each category of 'death_event'\n",
    "fig, ax = plt.subplots(nrows=3, ncols=2, figsize=(14, 7))\n",
    "plt.tight_layout()\n",
    "\n",
    "# Count plot for 'anaemia' grouped by 'death_event'\n",
    "sns.countplot(data=df_eda, x='anaemia', hue='death_event', ax=ax[0, 0])\n",
    "ax[0, 0].set_title('Anaemia by Death Event')\n",
    "ax[0, 0].set_xlabel('Anaemia')\n",
    "ax[0, 0].set_ylabel('Count')\n",
    "\n",
    "# Count plot for 'diabetes' grouped by 'death_event'\n",
    "sns.countplot(data=df_eda, x='diabetes', hue='death_event', ax=ax[0, 1])\n",
    "ax[0, 1].set_title('Diabetes by Death Event')\n",
    "ax[0, 1].set_xlabel('Diabetes')\n",
    "ax[0, 1].set_ylabel('Count')\n",
    "\n",
    "# Count plot for 'high_blood_pressure' grouped by 'death_event'\n",
    "sns.countplot(data=df_eda, x='high_blood_pressure', hue='death_event', ax=ax[1, 0])\n",
    "ax[1, 0].set_title('High Blood Pressure by Death Event')\n",
    "ax[1, 0].set_xlabel('High Blood Pressure')\n",
    "ax[1, 0].set_ylabel('Count')\n",
    "\n",
    "# Count plot for 'sex' grouped by 'death_event'\n",
    "sns.countplot(data=df_eda, x='sex', hue='death_event', ax=ax[1, 1])\n",
    "ax[1, 1].set_title('Sex by Death Event')\n",
    "ax[1, 1].set_xlabel('Sex')\n",
    "ax[1, 1].set_ylabel('Count')\n",
    "\n",
    "# Count plot for 'smoking' grouped by 'death_event'\n",
    "sns.countplot(data=df_eda, x='smoking', hue='death_event', ax=ax[2, 0])\n",
    "ax[2, 0].set_title('Smoking by Death Event')\n",
    "ax[2, 0].set_xlabel('Smoking')\n",
    "ax[2, 0].set_ylabel('Count')\n",
    "\n",
    "# Remove empty subplot\n",
    "fig.delaxes(ax[2, 1])\n",
    "\n",
    "# Adjust spacing between subplots\n",
    "plt.subplots_adjust(hspace=0.5)\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diatas adalah beberapa visualisasi dari hubungan beberapa kolom dibandingkan dengan kematian. Tidak ada pola tertentu yang memang bisa di kerucutkan. Kejadiannya masih random."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>anaemia</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoking</th>\n",
       "      <th>time</th>\n",
       "      <th>death_event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the dataset description\n",
    "df_eda.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**serum_creatinine** adalah kadar creatinine dalam darah. Berdasarkan riset, jumlah creatinine normal adalah 0.9 to 1.3 mg/dL untuk pria dan 0.6 to 1.1mg/dL intuk perempuan 18 sampai 60 tahun. Jumlah serum_creatinine menyatakan bahwa kerja ginjal lemah atau saluran kemih yang tersumbat, dan dehidrasi. Jika dilihat dari nilai tabel, rata-rata memiliki 1.1 namun ada yang 9.4 yang cukup jauh dari kadar seharusnya."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Berdasarkan data, dapat diketahui bahwa :\n",
    "- Rentang usia berkisar antara 49-71 tahun\n",
    "- Distribusi pada creatinine phospokinase adalah skew apabila dilihat dari nilai rata-rata dan mediannya. Dapat diketahui bahwa rentang nilai Creatinine Phospokinase pada pasien yaitu 250-582 mcg/L yang berarti diatas ambang normal dimana normalnya 10-129 mcg/L\n",
    "- Rata-rata memiliki nilai Ejection Fraction dibawah batas normal yaitu masih di dalam range 50-70. Hal ini menunjukkan bahwa rata-rata orang di dalam data ini memiliki masalah pada jantung.\n",
    "- Jumlah rata-rata platelets/sel darah putih pada pasien yaitu 263.358 kPL/mLyang berarti rata-rata pasien memiliki jumlah sel darah putih di dalam batas normal. Platelet adalah sel yang bersirkulasi dan memperbaiki kerusakan.Platelet normal berada diantara 150,000 sampai 450,000 platelets per microliter darah. Kondisi melebihi 450,000 platelet adalah kondisi bernama thrombocytosisi dan dibawah 150,000 bernama thrombcytopenia.\n",
    "- Rata-rata pasien memiliki angka serum sodium dengan nilai normal yaitu 137 mEq/dL\n",
    "- Waktu follow-up pada pasien berkisar dari 115-285 hari\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Relationship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# cek korelasi fitur dan target\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(df_eda.corr(), annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dari heatmap diatas terlihat bahwa ejection_fraction, platelets,serum_creatinine,time,sex dan smoking memiliki hubungan berbanding terbalik death_event."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yang korelasinya cukup tinggi adalah age, ejection fraction, serum creatinine, serum sodium, dan time. Cukup mengejutkan karena banyak kolom yang terasa pengaruh ternyata tidak."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_eda.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "anaemia \n",
      "\n",
      "0    170\n",
      "1    129\n",
      "Name: anaemia, dtype: int64\n",
      "------------------------------\n",
      "diabetes \n",
      "\n",
      "0    174\n",
      "1    125\n",
      "Name: diabetes, dtype: int64\n",
      "------------------------------\n",
      "high_blood_pressure \n",
      "\n",
      "0    194\n",
      "1    105\n",
      "Name: high_blood_pressure, dtype: int64\n",
      "------------------------------\n",
      "sex \n",
      "\n",
      "1    194\n",
      "0    105\n",
      "Name: sex, dtype: int64\n",
      "------------------------------\n",
      "smoking \n",
      "\n",
      "0    203\n",
      "1     96\n",
      "Name: smoking, dtype: int64\n",
      "------------------------------\n",
      "death_event \n",
      "\n",
      "0    203\n",
      "1     96\n",
      "Name: death_event, dtype: int64\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "# cek unique value masing masing kategori\n",
    "for i in ['anaemia', 'diabetes', 'high_blood_pressure', 'sex', 'smoking', 'death_event'\n",
    "]:\n",
    "    print(i, '\\n')\n",
    "    print(df_eda[i].value_counts())\n",
    "    print('-'*30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dari data kategorik terlihat tidak ada yang high cardinality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pFi1apPXr1+vo0aMaMGCAysvLzTnx8fHKycnRypUrtXLlSuXk5CghIaHG+QEAwIXl0qGiNm3aaNKkSdqwYYOioqLk5eXltPyxxx6r1vf0799f/fv3r3KZYRjKyMjQxIkTNWTIEEnSW2+9pdDQUC1cuFAjR46Uw+HQG2+8oXnz5qlv376SpPnz5ysiIkKrV69Wv379tGPHDq1cuVIbNmxQt27dJJ06R6dHjx7auXOn2rVrV+X2S0tLVVpaan4uLi6u1m8CAAC1x6Xi8tprr8nf31/r1q3TunXrnJbZbLZqF5ffk5ubq4KCAsXGxppjPj4+6t27t7KysjRy5EhlZ2frxIkTTnPCw8MVGRmprKws9evXT1999ZXsdrtZWqRTl2/b7XZlZWWds7ikp6drypQpNf4dAADAfVwqLrm5ue7OUUlBQYEkKTQ01Gk8NDRUe/fuNed4e3urcePGleacXr+goEBNmzat9P1NmzY151RlwoQJSkpKMj8XFxcrIiLCtR8DAADcwi03oKtNZ15qLZ06hHT22NnOnlPV/D/6Hh8fH567BNQD0Y+/XdcRqhRYdNQ8STC/6Gi9zZn9/PC6jgC4VbWLS1JSkp5++mn5+fk57YmoyvTp02scLCwsTNKpPSbNmjUzxwsLC829MGFhYSorK1NRUZHTXpfCwkLFxMSYc3755ZdK33/gwIFKe3MAAED9Vu3ismXLFp04ccJ8X9tatWqlsLAwZWZmqkuXLpKksrIyrVu3TlOnTpUkRUdHy8vLS5mZmYqLi5Mk5efna+vWrZo2bZokqUePHnI4HPr666/1l7/8RZK0ceNGORwOs9wAAABrqHZx+eyzz6p8XxNHjx7V7t27zc+5ubnKyclRUFCQWrRoocTERKWlpalt27Zq27at0tLS5Ovrq/j4eEmS3W7XiBEjlJycrODgYAUFBSklJUVRUVHmVUbt27fXjTfeqAcffFCvvvqqJOmhhx7SgAEDznliLgAAqJ9cuo/LmjVrzrnsXPdkqco333yjLl26mHtUkpKS1KVLF/ORAePGjVNiYqJGjRqlrl276ueff9aqVasUEBBgfseMGTM0ePBgxcXFqWfPnvL19dXy5cvl4eFhzlmwYIGioqIUGxur2NhYderUSfPmzTvfnw0AAOqYzTAM43xXuuSSS5SZmamrr77aaTwjI0OTJ0/+U97zpLi4WHa7XQ6HQ4GBgXUdB7ho1NeTXgO3LlGDshJJUoW3n4ojb6/jRFXj5Fz82bi0x2XGjBm66aabtH37dnPsn//8p5588kmtWLHCbeEAAADO5NLl0Pfdd58OHTqk2NhYrV+/XosXL1ZaWpo++eQTTngFAAC1xuX7uKSkpOjQoUPq2rWrysvLtWrVKqe70wIAALhbtYvLSy+9VGmsWbNm8vX1Va9evbRx40Zt3LhRUvWfVQQAAHA+ql1cZsyYUeW4h4eHvvzyS3355ZeS3PesIgAAgLNVu7hciOcTAQAA/B6Xrio6k2EYcuGKagAAgPPmcnF5++23FRUVpUaNGqlRo0bc1A0AANQ6l64qmj59uiZNmqRHH31UPXv2lGEY+vLLL/Xwww/r4MGDGjt2rLtzAgAAuFZcZs6cqdmzZ2v48P+7I+Mtt9yijh07KjU1leICAABqhUuHivLz86u80VxMTIzy8/NrHAoAAKAqLhWXNm3a6F//+lel8cWLF6tt27Y1DgUAAFAVlw4VTZkyRXfccYc+//xz9ezZUzabTevXr9eaNWuqLDQAAADu4NIel9tuu00bN25USEiIli1bpvfff18hISH6+uuvdeutt7o7IwAAgKQaPKsoOjpa8+fPd2cWAACA3+VycamoqNDu3btVWFioiooKp2W9evWqcTAAAICzuVRcNmzYoPj4eO3du7fSXXNtNpvKy8vdEg4AAOBMLhWXhx9+WF27dtWKFSvUrFkz2Ww2d+cCAACoxKXi8sMPP2jJkiVq06aNu/MAAACck0tXFXXr1k27d+92dxYAAIDfVe09Lt999535fvTo0UpOTlZBQYGioqLk5eXlNLdTp07uSwgAAPC/ql1crrzyStlsNqeTce+//37z/ellnJwLAABqS7WLS25ubm3mAAAA+EPVLi6XXXZZbeYAAAD4Qy7fgG7nzp2aOXOmduzYIZvNpiuuuEKjR49Wu3bt3JkPAADA5NJVRUuWLFFkZKSys7PVuXNnderUSZs3b1ZkZKTeffddd2cEAACQ5OIel3HjxmnChAl66qmnnMaffPJJjR8/XkOHDnVLOAAAgDO5tMeloKBAw4cPrzR+9913q6CgoMahAAAAquJScenTp4+++OKLSuPr16/XtddeW+NQAAAAVXHpUNGgQYM0fvx4ZWdnq3v37pJOPXjx3Xff1ZQpU/Thhx86zQUAAHAHm3H2452roUGD6u2o+TPdjK64uFh2u10Oh0OBgYF1HQe4aEQ//nZdR6hS4NYlalBWIkmq8PZTceTtdZyoatnPVz6sD1iZS3tcKioq3J0DAADgD7l0jktVDh8+7K6vAgAAqJJLxWXq1KlavHix+Xno0KEKCgrSpZdeqm+//dZt4QAAAM7kUnF59dVXFRERIUnKzMzU6tWrtXLlSvXv31+PP/64WwMCAACc5tI5Lvn5+WZx+eijjxQXF6fY2Fi1bNlS3bp1c2tAAACA01za49K4cWPt27dPkrRy5Ur17dtXkmQYxp/mKiIAAFD/uLTHZciQIYqPj1fbtm116NAh9e/fX5KUk5OjNm3auDUgAADAaS4VlxkzZqhly5bat2+fpk2bJn9/f0mnDiGNGjXKrQEBAABOc6m4eHl5KSUlpdJ4YmJiTfMAAACck0vFRZL27NmjjIwM7dixQzabTe3bt1diYqJat27tznwAAAAml07O/fTTT9WhQwd9/fXX6tSpkyIjI7Vx40Z16NBBmZmZ7s4IAAAgycU9Lk888YTGjh2r5557rtL4+PHjdcMNN7glHAAAwJlc2uOyY8cOjRgxotL4/fffr+3bt9c41JlOnjypf/zjH2rVqpUaNWqk1q1b66mnnnJ6XpJhGEpNTVV4eLgaNWqkPn36aNu2bU7fU1paqtGjRyskJER+fn4aNGiQ9u/f79asAABc7Pr06VOr57y6tMelSZMmysnJUdu2bZ3Gc3Jy1LRpU7cEO23q1Kl65ZVX9NZbb6ljx4765ptvdN9998lut2vMmDGSpGnTpmn69OmaO3euLr/8cj3zzDO64YYbtHPnTgUEBEg6deLw8uXLtWjRIgUHBys5OVkDBgxQdna2PDw83JrZ6saMGaMDBw5IOvW/9YsvvljHiQAAVvH+++/Ly8ur1r7fpeLy4IMP6qGHHtKPP/6omJgY2Ww2rV+/XlOnTlVycrJbA3711Ve65ZZbdPPNN0uSWrZsqXfeeUfffPONpFN7WzIyMjRx4kQNGTJEkvTWW28pNDRUCxcu1MiRI+VwOPTGG29o3rx55s3y5s+fr4iICK1evVr9+vWrtN3S0lKVlpaan4uLi936u+qzAwcO6JdffqnrGAAACwoKCqrV73fpUNGkSZM0efJkzZw5U71791avXr00a9YspaamauLEiW4NeM0112jNmjXatWuXJOnbb7/V+vXrddNNN0mScnNzVVBQoNjYWHMdHx8f9e7dW1lZWZKk7OxsnThxwmlOeHi4IiMjzTlnS09Pl91uN1+nH3EAAEBtW7lypa655hpdcsklCg4O1oABA7Rnzx5J0k8//SSbzab3339f1113nXx9fdW5c2d99dVX5vqHDh3SsGHD1Lx5c/n6+ioqKkrvvPOO0zYMw9C0adPUunVrNWrUSJ07d9aSJUvM5WvXrpXNZtOnn36qLl26qFGjRvrrX/+qwsJCffLJJ2rfvr0CAwM1bNgwHTt2zFzv7ENF8+fPV9euXRUQEKCwsDDFx8ersLDQ5f9uXCouNptNY8eO1f79++VwOORwOLR//36NGTNGNpvN5TBVGT9+vIYNG6YrrrhCXl5e6tKlixITEzVs2DBJUkFBgSQpNDTUab3Q0FBzWUFBgby9vdW4ceNzzjnbhAkTzN/mcDjMRxwAAFDbSkpKlJSUpE2bNmnNmjVq0KCBbr31VqfzOydOnKiUlBTl5OTo8ssv17Bhw3Ty5ElJ0m+//abo6Gh99NFH2rp1qx566CElJCRo48aN5vr/+Mc/9Oabb2r27Nnatm2bxo4dq7vvvlvr1q1zypKamqpZs2YpKytL+/btU1xcnDIyMrRw4UKtWLFCmZmZmjlz5jl/S1lZmZ5++ml9++23WrZsmXJzc3Xvvfe6/N+Ny/dxOe30OSS1ZfHixZo/f74WLlyojh07KicnR4mJiQoPD9c999xjzju7MBmG8Ycl6vfm+Pj4yMfHp+Y/AACA83Tbbbc5fX7jjTfUtGlTbd++3bxbfUpKinkaxZQpU9SxY0ft3r1bV1xxhS699FKnG8WOHj1aK1eu1Lvvvqtu3bqppKRE06dP17///W/16NFDktS6dWutX79er776qnr37m2u+8wzz6hnz56SpBEjRmjChAnas2ePed+222+/XZ999pnGjx9f5W+5//77zfetW7fWSy+9pL/85S86evSo+VvOh0t7XH755RclJCQoPDxcnp6e8vDwcHq50+OPP64nnnhCd955p6KiopSQkKCxY8cqPT1dkhQWFiZJlfacFBYWmnthwsLCVFZWpqKionPOAQCgvtizZ4/i4+PVunVrBQYGqlWrVpKkvLw8c06nTp3M982aNZMk8xBMeXm5nn32WXXq1EnBwcHy9/fXqlWrzPW3b9+u3377TTfccIP8/f3N19tvv20ekqpqO6GhofL19XW62WxoaOjvHvrZsmWLbrnlFl122WUKCAhQnz59Kv2W8+HSHpd7771XeXl5mjRpkpo1a+b2w0NnOnbsmBo0cO5XHh4e5u6yVq1aKSwsTJmZmerSpYukU7ul1q1bp6lTp0qSoqOj5eXlpczMTMXFxUk69VylrVu3atq0abWWHQAAVwwcOFARERGaM2eOwsPDVVFRocjISJWVlZlzzrxy5/S/h0//u/GFF17QjBkzlJGRoaioKPn5+SkxMdFc//S8FStW6NJLL3Xa9tlHG87eztlXDNlsNqdDWGcqKSlRbGysYmNjNX/+fDVp0kR5eXnq16+f0285Hy4Vl/Xr1+uLL77QlVde6dJGz8fAgQP17LPPqkWLFurYsaO2bNmi6dOnm7uebDabEhMTlZaWprZt26pt27ZKS0uTr6+v4uPjJUl2u10jRoxQcnKygoODFRQUpJSUFEVFRZlXGQEAUB8cOnRIO3bs0Kuvvqprr71W0ql/756PL774QrfccovuvvtuSaeKyg8//KD27dtLkjp06CAfHx/l5eU5HRZyt//85z86ePCgnnvuOfMil9NXBbvKpeISEREhwzBqtOHqmjlzpiZNmqRRo0apsLBQ4eHhGjlypCZPnmzOGTdunI4fP65Ro0apqKhI3bp106pVq5zOv5kxY4Y8PT0VFxen48eP6/rrr9fcuXO5hwsAoF5p3LixgoOD9dprr6lZs2bKy8vTE088cV7f0aZNG7333nvKyspS48aNNX36dBUUFJjFJSAgQCkpKRo7dqwqKip0zTXXqLi4WFlZWfL393c6h7QmWrRoIW9vb82cOVMPP/ywtm7dqqeffrpG3+nSOS4ZGRl64okn9NNPP9Vo49UREBCgjIwM7d27V8ePH9eePXv0zDPPyNvb25xjs9mUmpqq/Px8/fbbb1q3bp0iIyOdvqdhw4aaOXOmDh06pGPHjmn58uVc4gwAqHcaNGigRYsWKTs7W5GRkRo7dqyef/758/qOSZMm6aqrrlK/fv3Up08fhYWFafDgwU5znn76aU2ePFnp6elq3769+vXrp+XLl5vn07hDkyZNNHfuXL377rvq0KGDnnvuOf3zn/+s0XfajGruOmncuLHTuSwlJSU6efKkfH19Kx3v+vXXX2sUqj4qLi6W3W6Xw+FQYGBgXcepVfHx8eYN6E7fyA+oK9GPv13XEaoUuHWJGpSVSJIqvP1UHHl7HSeqWvbzw+s6AuBW1T5UlJGRUYsxAAAA/li1i4u7jncBAAC4yuUb0JWXl2vp0qXasWOHbDab2rdvr1tuuUWenjW+px0AAECVXGoZW7du1S233KKCggK1a9dOkrRr1y41adJEH374oaKiotwaEgAAQHLxqqIHHnhAHTt21P79+7V582Zt3rxZ+/btU6dOnfTQQw+5OyMAAIAkF/e4fPvtt/rmm2+cHlrYuHFjPfvss7r66qvdFg4AAOBMLu1xadeunXm57JkKCwvVpk2bGocCAACoikvFJS0tTY899piWLFmi/fv3a//+/VqyZIkSExM1depUFRcXmy8AAAB3celQ0YABAyRJcXFx5k3pTt/HbuDAgeZnm82m8vJyd+QEAABwrbh89tln7s4BAAB+x4W8i3R9vuOyS8Wluk+SHDVqlDp27KiQkBBXNgMAACzk5Zdf1vPPP6/8/Hx17NhRGRkZ5hOu3cWlc1yqa/78+ZznAgDARWDx4sVKTEzUxIkTtWXLFl177bXq37+/8vLy3LqdWi0u1Xx+IwAAsLjp06drxIgReuCBB9S+fXtlZGQoIiJCs2fPdut2arW4AACAP7+ysjJlZ2crNjbWaTw2NlZZWVlu3RbFBQAA1MjBgwdVXl6u0NBQp/HQ0FAVFBS4dVsUFwAA4Banb5Fy2ulbo7gTxQUAANRISEiIPDw8Ku1dKSwsrLQXpqZqtbjcfffdCgwMrM1NAACAOubt7a3o6GhlZmY6jWdmZiomJsat23K5uHzxxRe6++671aNHD/3888+SpHnz5mn9+vXmnNmzZ3MPFwAALgJJSUl6/fXX9f/+3//Tjh07NHbsWOXl5enhhx9263ZcugHde++9p4SEBN11113asmWLSktLJUlHjhxRWlqaPv74Y7eGBADgYlef72YrSXfccYcOHTqkp556Svn5+YqMjNTHH3+syy67zK3bcWmPyzPPPKNXXnlFc+bMkZeXlzkeExOjzZs3uy0cAACwjlGjRumnn35SaWmpsrOz1atXL7dvw6XisnPnzirDBAYG6vDhwzXNBAAAUCWXikuzZs20e/fuSuPr169X69ataxwKAACgKi4Vl5EjR2rMmDHauHGjbDab/vvf/2rBggVKSUnRqFGj3J0RAABAkosn544bN04Oh0PXXXedfvvtN/Xq1Us+Pj5KSUnRo48+6u6MAAAAklwsLpL07LPPauLEidq+fbsqKirUoUMH+fv7uzMbAACAE5eLiyT5+vqqa9eu7soCAADwu1wqLiUlJXruuee0Zs0aFRYWqqKiwmn5jz/+6JZwAAAAZ3KpuDzwwANat26dEhIS1KxZM7c/QAkAAKAqLhWXTz75RCtWrFDPnj3dnQcAAOCcXCoujRs3VlBQkLuzAACAc8h7KuqCbavF5O8v2LbOl0v3cXn66ac1efJkHTt2zN15AACABX3++ecaOHCgwsPDZbPZtGzZslrZjkt7XF544QXt2bNHoaGhatmypdPziiTxvCIAAC4yJSUl6ty5s+677z7ddttttbYdl4rL4MGD3RwDAABYWf/+/dW/f/9a345LxeXJJ590dw4AAIA/VKMb0KFmoh9/u64jVCmw6Kh58lN+0dF6m1OSsp8fXtcRAAAXULWLS1BQkHbt2qWQkBA1btz4d+/d8uuvv7olHAAAwJmqXVxmzJihgIAASVJGRkZt5QEAADinaheXe+65p8r3AAAAF4rL57hUVFRo9+7dVT6rqFevXjUOBgAArOPo0aPavXu3+Tk3N1c5OTkKCgpSixYt3LYdl4rLhg0bFB8fr71798owDKdlNptN5eXlbgkHAABOqc93s5Wkb775Rtddd535OSkpSdKpozRz585123ZcKi4PP/ywunbtqhUrVvCQRQAAoD59+lTamVEbXLrl/w8//KC0tDS1b99el1xyiex2u9PL3X7++WfdfffdCg4Olq+vr6688kplZ2ebyw3DUGpqqsLDw9WoUSP16dNH27Ztc/qO0tJSjR49WiEhIfLz89OgQYO0f/9+t2cFAAC1x6Xi0q1bN6fjWLWpqKhIPXv2lJeXlz755BNt375dL7zwgi655BJzzrRp0zR9+nTNmjVLmzZtUlhYmG644QYdOXLEnJOYmKilS5dq0aJFWr9+vY4ePaoBAwZwWAsAAAtx6VDR6NGjlZycrIKCAkVFRVV6VlGnTp3cEk6Spk6dqoiICL355pvmWMuWLc33hmEoIyNDEydO1JAhQyRJb731lkJDQ7Vw4UKNHDlSDodDb7zxhubNm6e+fftKkubPn6+IiAitXr1a/fr1q7Td0tJSlZaWmp+Li4vd9psAAIBrXNrjctttt2nHjh26//77dfXVV+vKK69Uly5dzP90pw8//FBdu3bV0KFD1bRpU3Xp0kVz5swxl+fm5qqgoECxsbHmmI+Pj3r37q2srCxJUnZ2tk6cOOE0Jzw8XJGRkeacs6Wnpzsd/oqIiHDr7wIAAOfPpT0uubm57s5xTj/++KNmz56tpKQk/f3vf9fXX3+txx57TD4+Pho+fLgKCgokSaGhoU7rhYaGau/evZKkgoICeXt7q3HjxpXmnF7/bBMmTDDPiJZO7XGhvAAAULdcKi6XXXaZu3OcU0VFhbp27aq0tDRJUpcuXbRt2zbNnj1bw4f/33Nqzr6yyTCMP7za6ffm+Pj4yMfHp4bpAQCAO1W7uHz44Yfq37+/vLy89OGHH/7u3EGDBtU42GnNmjVThw4dnMbat2+v9957T5IUFhYm6dRelWbNmplzCgsLzb0wYWFhKisrU1FRkdNel8LCQsXExLgtKwAAqF3VLi6DBw9WQUGBmjZtqsGDB59znrtvQNezZ0/t3LnTaWzXrl3mXp9WrVopLCxMmZmZ5vk1ZWVlWrdunaZOnSpJio6OlpeXlzIzMxUXFydJys/P19atWzVt2jS3ZQUAALWr2sXlzNv6n32L/9o0duxYxcTEKC0tTXFxcfr666/12muv6bXXXpN0qiglJiYqLS1Nbdu2Vdu2bZWWliZfX1/Fx8dLkux2u0aMGKHk5GQFBwcrKChIKSkpioqKMq8yAgAA9Z9LVxW9/fbbTpcKn1ZWVqa33367xqHOdPXVV2vp0qV65513FBkZqaeffloZGRm66667zDnjxo1TYmKiRo0apa5du+rnn3/WqlWrzKdZS6eebj148GDFxcWpZ8+e8vX11fLly+Xh4eHWvAAAoPbYDBfuz+vh4aH8/Hw1bdrUafzQoUNq2rTpn/KmbsXFxbLb7XI4HAoMDHTLd0Y/7t6S5y6BW5eoQVmJJKnC20/FkbfXcaJzy35++B9PgqXxd1Iz/I3gz8alq4rOdTXO/v37a+WW/wBQ31R4+VX5HkDtOq/i0qVLF9lsNtlsNl1//fXy9Py/1cvLy5Wbm6sbb7zR7SEBoL452q5/XUcALkrnVVxOX02Uk5Ojfv36yd/f31zm7e2tli1b6rbbbnNrQAAAgNPOq7g8+eSTkk49K+iOO+5Qw4YNayUUAABAVVw6x+Wee+5xdw4AAIA/5FJxKS8v14wZM/Svf/1LeXl5Kisrc1r+66+/uiUcAADAmVy6j8uUKVM0ffp0xcXFyeFwKCkpSUOGDFGDBg2Umprq5ogAAACnuFRcFixYoDlz5iglJUWenp4aNmyYXn/9dU2ePFkbNmxwd0YAAABJLhaXgoICRUVFSZL8/f3lcDgkSQMGDNCKFSvclw4AAOAMLhWX5s2bKz8/X5LUpk0brVq1SpK0adMm+fj4uC8dAADAGVw6OffWW2/VmjVr1K1bN40ZM0bDhg3TG2+8oby8PI0dO9bdGQHLGTNmjA4cOCBJatKkiV588cU6TgQAfw4uFZfnnnvOfH/77berefPmysrKUps2bTRo0CC3hQOs6sCBA/rll1/qOgYA/Om4VFzO1r17d3Xv3t0dXwUAAHBOLp3jIknz5s1Tz549FR4err1790qSMjIy9MEHH7gtHAAAwJlcKi6zZ89WUlKSbrrpJh0+fFjl5eWSpEsuuUQZGRnuzAcAAGByqbjMnDlTc+bM0cSJE+Xh4WGOd+3aVd9//73bwgEAAJzJpeKSm5urLl26VBr38fFRSUlJjUMBAABUxaXi0qpVK+Xk5FQa/+STT9ShQ4eaZgIAAKiSS1cVPf7443rkkUf022+/yTAMff3113rnnXeUnp6u119/3d0ZAQAAJLlYXO677z6dPHlS48aN07FjxxQfH69LL71UL774ou688053ZwQAAJDkQnE5efKkFixYoIEDB+rBBx/UwYMHVVFRoaZNm9ZGPtSBCi+/Kt8DAFDXzru4eHp66m9/+5t27NghSQoJCXF7KNSto+3613UEAACq5NLJud26ddOWLVvcnQUAAOB3uXSOy6hRo5ScnKz9+/crOjpafn7OhxM6derklnAAAABncqm43HHHHZKkxx57zByz2WwyDEM2m828ky4AAIA7uVRccnNz3Z0DAADgD7l0jsvChQu1Zs0aXXbZZU6vNWvWaNGiRe7OCAAAIMnF4vLqq6/qiiuuqDTesWNHvfLKKzUOBQAAUBWXiktBQYGaNWtWabxJkybKz8+vcSgAAICquFRcIiIi9OWXX1Ya//LLLxUeHl7jUAAAAFVx6eTcBx54QImJiTpx4oT++te/SpLWrFmjcePGKTk52a0BAQAATnOpuIwbN06//vqrRo0apbKyMklSw4YNNX78eE2YMMGtAQEAAE5zqbjYbDZNnTpVkyZN0o4dO9SoUSO1bdtWPj4+7s4HAABgcqm4nObv76+rr77aXVkAAAB+V42KCwAArhgzZowOHDgg6dQVqS+++GIdJ4JVUFwAABfcgQMH9Msvv9R1DFiQS5dDAwAA1AWKCwAAsAyKCwAAsAyKCwAAsAyKCwAAsAyKCwAAsAxLFZf09HTZbDYlJiaaY4ZhKDU1VeHh4WrUqJH69Omjbdu2Oa1XWlqq0aNHKyQkRH5+fho0aJD2799/gdMDAICaskxx2bRpk1577TV16tTJaXzatGmaPn26Zs2apU2bNiksLEw33HCDjhw5Ys5JTEzU0qVLtWjRIq1fv15Hjx7VgAEDVF5efqF/BgAAqAFLFJejR4/qrrvu0pw5c9S4cWNz3DAMZWRkaOLEiRoyZIgiIyP11ltv6dixY1q4cKEkyeFw6I033tALL7ygvn37qkuXLpo/f76+//57rV69uq5+EgAAcIElissjjzyim2++WX379nUaz83NVUFBgWJjY80xHx8f9e7dW1lZWZKk7OxsnThxwmlOeHi4IiMjzTlVKS0tVXFxsdMLAADUrXp/y/9FixZp8+bN2rRpU6VlBQUFkqTQ0FCn8dDQUO3du9ec4+3t7bSn5vSc0+tXJT09XVOmTKlpfAAA4Eb1urjs27dPY8aM0apVq9SwYcNzzrPZbE6fDcOoNHa2P5ozYcIEJSUlmZ+Li4sVERFRzeS4UPKeiqrrCFU6eThYksf/vv9vvc3ZYvL3dR0BAM5LvT5UlJ2drcLCQkVHR8vT01Oenp5at26dXnrpJXl6epp7Ws7ec1JYWGguCwsLU1lZmYqKis45pyo+Pj4KDAx0egEAgLpVr4vL9ddfr++//145OTnmq2vXrrrrrruUk5Oj1q1bKywsTJmZmeY6ZWVlWrdunWJiYiRJ0dHR8vLycpqTn5+vrVu3mnMAAIA11OtDRQEBAYqMjHQa8/PzU3BwsDmemJiotLQ0tW3bVm3btlVaWpp8fX0VHx8vSbLb7RoxYoSSk5MVHBysoKAgpaSkKCoqqtLJvgAAoH6r18WlOsaNG6fjx49r1KhRKioqUrdu3bRq1SoFBASYc2bMmCFPT0/FxcXp+PHjuv766zV37lx5eHjUYXIAAHC+LFdc1q5d6/TZZrMpNTVVqamp51ynYcOGmjlzpmbOnFm74QAAQK2q1+e4AAAAnIniAgAALIPiAgAALIPiAgAALIPiAgAALIPiAgAALIPiAgAALIPiAgAALIPiAgAALIPiAgAALIPiAgAALIPiAgAALIPiAgAALIPiAgAALIPiAgAALMOzrgMAAGpP3lNRdR2hSicPB0vy+N/3/623OSWpxeTv6zoCzsAeFwAAYBkUFwAAYBkUFwAAYBkUFwAAYBkUFwAAYBlcVQTUgiCf8irfAwBqhuIC1IK/dzlc1xEA4E+JQ0UAAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyPOs6AADg4hPkU17le+CPUFwAABfc37scrusIsKh6f6goPT1dV199tQICAtS0aVMNHjxYO3fudJpjGIZSU1MVHh6uRo0aqU+fPtq2bZvTnNLSUo0ePVohISHy8/PToEGDtH///gv5UwAAQA3V++Kybt06PfLII9qwYYMyMzN18uRJxcbGqqSkxJwzbdo0TZ8+XbNmzdKmTZsUFhamG264QUeOHDHnJCYmaunSpVq0aJHWr1+vo0ePasCAASovZxclAABWUe8PFa1cudLp85tvvqmmTZsqOztbvXr1kmEYysjI0MSJEzVkyBBJ0ltvvaXQ0FAtXLhQI0eOlMPh0BtvvKF58+apb9++kqT58+crIiJCq1evVr9+/S747wIAAOev3u9xOZvD4ZAkBQUFSZJyc3NVUFCg2NhYc46Pj4969+6trKwsSVJ2drZOnDjhNCc8PFyRkZHmnLOVlpaquLjY6QUAAOqWpYqLYRhKSkrSNddco8jISElSQUGBJCk0NNRpbmhoqLmsoKBA3t7eaty48TnnnC09PV12u918RUREuPvnAACA82Sp4vLoo4/qu+++0zvvvFNpmc1mc/psGEalsbP93pwJEybI4XCYr3379rkeHAAAuIVlisvo0aP14Ycf6rPPPlPz5s3N8bCwMEmqtOeksLDQ3AsTFhamsrIyFRUVnXPO2Xx8fBQYGOj0AgAAdaveFxfDMPToo4/q/fff17///W+1atXKaXmrVq0UFhamzMxMc6ysrEzr1q1TTEyMJCk6OlpeXl5Oc/Lz87V161ZzDgAAqP/q/VVFjzzyiBYuXKgPPvhAAQEB5p4Vu92uRo0ayWazKTExUWlpaWrbtq3atm2rtLQ0+fr6Kj4+3pw7YsQIJScnKzg4WEFBQUpJSVFUVJR5lREAAKj/6n1xmT17tiSpT58+TuNvvvmm7r33XknSuHHjdPz4cY0aNUpFRUXq1q2bVq1apYCAAHP+jBkz5Onpqbi4OB0/flzXX3+95s6dKw8Pjwv1UwAAQA3V++JiGMYfzrHZbEpNTVVqauo55zRs2FAzZ87UzJkz3ZgOAABcSPX+HBcAAIDTKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyKC4AAMAyLqri8vLLL6tVq1Zq2LChoqOj9cUXX9R1JAAAcB4umuKyePFiJSYmauLEidqyZYuuvfZa9e/fX3l5eXUdDQAAVNNFU1ymT5+uESNG6IEHHlD79u2VkZGhiIgIzZ49u66jAQCAavKs6wAXQllZmbKzs/XEE084jcfGxiorK6vKdUpLS1VaWmp+djgckqTi4mK35SovPe6277pYHfEqr+sIlubO/z/XFv5Oaoa/kZqrjb+TgIAA2Ww2t3/vxeCiKC4HDx5UeXm5QkNDncZDQ0NVUFBQ5Trp6emaMmVKpfGIiIhayQjXRNZ1AKtLt9d1AtQy/kbcoBb+ThwOhwIDA93+vReDi6K4nHZ2uzUM45yNd8KECUpKSjI/V1RU6Ndff1VwcDAtuZ4oLi5WRESE9u3bxz8AgCrwN1J/BQQE1HUEy7ooiktISIg8PDwq7V0pLCystBfmNB8fH/n4+DiNXXLJJbUVETUQGBjIP5SB38HfCP5MLoqTc729vRUdHa3MzEyn8czMTMXExNRRKgAAcL4uij0ukpSUlKSEhAR17dpVPXr00Guvvaa8vDw9/PDDdR0NAABU00VTXO644w4dOnRITz31lPLz8xUZGamPP/5Yl112WV1Hg4t8fHz05JNPVjqkB+AU/kbwZ2QzDMOo6xAAAADVcVGc4wIAAP4cKC4AAMAyKC4AAMAyKC4AAMAyKC6wpJdfflmtWrVSw4YNFR0drS+++KKuIwH1xueff66BAwcqPDxcNptNy5Ytq+tIgNtQXGA5ixcvVmJioiZOnKgtW7bo2muvVf/+/ZWXl1fX0YB6oaSkRJ07d9asWbPqOgrgdlwODcvp1q2brrrqKs2ePdsca9++vQYPHqz09PQ6TAbUPzabTUuXLtXgwYPrOgrgFuxxgaWUlZUpOztbsbGxTuOxsbHKysqqo1QAgAuF4gJLOXjwoMrLyys9HDM0NLTSQzQBAH8+FBdYks1mc/psGEalMQDAnw/FBZYSEhIiDw+PSntXCgsLK+2FAQD8+VBcYCne3t6Kjo5WZmam03hmZqZiYmLqKBUA4EK5aJ4OjT+PpKQkJSQkqGvXrurRo4dee+015eXl6eGHH67raEC9cPToUe3evdv8nJubq5ycHAUFBalFixZ1mAyoOS6HhiW9/PLLmjZtmvLz8xUZGakZM2aoV69edR0LqBfWrl2r6667rtL4Pffco7lz5174QIAbUVwAAIBlcI4LAACwDIoLAACwDIoLAACwDIoLAACwDIoLAACwDIoLAACwDIoLAACwDIoLAACwDIoLYGF9+vRRYmJirW5j7dq1stlsOnz4cK1uBwCqg+ICwHQhilBdSU1N1ZVXXlnXMQDUEMUFAABYBsUFsIiSkhINHz5c/v7+atasmV544QWn5WVlZRo3bpwuvfRS+fn5qVu3blq7dq25/NChQxo2bJiaN28uX19fRUVF6Z133jGX33vvvVq3bp1efPFF2Ww22Ww2/fTTT+by7Oxsde3aVb6+voqJidHOnTurnX358uWKjo5Ww4YN1bp1a02ZMkUnT56UJA0bNkx33nmn0/wTJ04oJCREb775piTJMAxNmzZNrVu3VqNGjdS5c2ctWbLEnH/6cNaaNWuqzDh37lxNmTJF3377rfnbeNggYFEGAEv429/+ZjRv3txYtWqV8d133xkDBgww/P39jTFjxhiGYRjx8fFGTEyM8fnnnxu7d+82nn/+ecPHx8fYtWuXYRiGsX//fuP55583tmzZYuzZs8d46aWXDA8PD2PDhg2GYRjG4cOHjR49ehgPPvigkZ+fb+Tn5xsnT540PvvsM0OS0a1bN2Pt2rXGtm3bjGuvvdaIiYmpVu6VK1cagYGBxty5c409e/YYq1atMlq2bGmkpqYahmEYy5cvNxo1amQcOXLEXGf58uVGw4YNDYfDYRiGYfz97383rrjiCmPlypXGnj17jDfffNPw8fEx1q5daxiG8YcZjx07ZiQnJxsdO3Y0f9uxY8dq/j8KgAuO4gJYwJEjRwxvb29j0aJF5tihQ4eMRo0aGWPGjDF2795t2Gw24+eff3Za7/rrrzcmTJhwzu+96aabjOTkZPNz7969zSJ02ulSsHr1anNsxYoVhiTj+PHjf5j92muvNdLS0pzG5s2bZzRr1swwDMMoKyszQkJCjLfffttcPmzYMGPo0KGGYRjG0aNHjYYNGxpZWVlO3zFixAhj2LBh1c745JNPGp07d/7DvADqN8+63NsDoHr27NmjsrIy9ejRwxwLCgpSu3btJEmbN2+WYRi6/PLLndYrLS1VcHCwJKm8vFzPPfecFi9erJ9//lmlpaUqLS2Vn59ftTJ06tTJfN+sWTNJUmFhoVq0aPG762VnZ2vTpk169tlnzbHy8nL99ttvOnbsmHx9fTV06FAtWLBACQkJKikp0QcffKCFCxdKkrZv367ffvtNN9xwg9P3lpWVqUuXLm7JCMA6KC6ABRiG8bvLKyoq5OHhoezsbHl4eDgt8/f3lyS98MILmjFjhjIyMhQVFSU/Pz8lJiaqrKysWhm8vLzM9zabzdzuH6moqNCUKVM0ZMiQSssaNmwoSbrrrrvUu3dvFRYWKjMzUw0bNlT//v2dtrFixQpdeumlTuv7+Pi4JSMA66C4ABbQpk0beXl5acOGDebeg6KiIu3atUu9e/dWly5dVF5ersLCQl177bVVfscXX3yhW265RXfffbekU/9C/+GHH9S+fXtzjre3t8rLy92a/aqrrtLOnTvVpk2bc86JiYlRRESEFi9erE8++URDhw6Vt7e3JKlDhw7y8fFRXl6eevfu7XKO2vhtAC48igtgAf7+/hoxYoQef/xxBQcHKzQ0VBMnTlSDBqcuDLz88st11113afjw4XrhhRfUpUsXHTx4UP/+978VFRWlm266SW3atNF7772nrKwsNW7cWNOnT1dBQYFTcWnZsqU2btyon376Sf7+/goKCqpx9smTJ2vAgAGKiIjQ0KFD1aBBA3333Xf6/vvv9cwzz0g6tXckPj5er7zyinbt2qXPPvvMXD8gIEApKSkaO3asKioqdM0116i4uFhZWVny9/fXPffcU60cLVu2VG5urnJyctS8eXMFBARU2mMDoP7jcmjAIp5//nn16tVLgwYNUt++fXXNNdcoOjraXP7mm29q+PDhSk5OVrt27TRo0CBt3LhRERERkqRJkybpqquuUr9+/dSnTx+FhYVp8ODBTttISUmRh4eHOnTooCZNmigvL6/Gufv166ePPvpImZmZuvrqq9W9e3dNnz5dl112mdO8u+66S9u3b9ell16qnj17Oi17+umnNXnyZKWnp6t9+/bq16+fli9frlatWlU7x2233aYbb7xR1113nZo0aeJ0KTgA67AZf3TwHAAAoJ5gjwsAALAMiguAGunYsaP8/f2rfC1YsKCu4wH4k+FQEYAa2bt3r06cOFHlstDQUAUEBFzgRAD+zCguAADAMjhUBAAALIPiAgAALIPiAgAALIPiAgAALIPiAgAALIPiAgAALIPiAgAALOP/AxOEPsCCxQKIAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 571.611x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 571.611x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PBwQAAPg5rwJmxIgR7sdxcXHavn27Dh48qDZt2rjvRAIAAGgqZ3wNzIkTJ+Tn56cvv/zSY3nbtm2JFwAAcE6cccD4+fkpJiaGz3oBAACO8eoupEceeUTTp0/XwYMHfT0PAADAaXl1DcyCBQu0c+dORUVFKSYmRq1bt/ZY//nnn/tkOAAAgPp4FTCjRo3y8RgAAACN1+iAWbBgge69914FBATorrvuUufOndWihVdnoAAAAM5KowskLS1N5eXlkqTY2Fjt37+/yYYCAABoSKOPwERFRWnVqlUaOXKkbNvWd999p+PHj9f72i5duvhsQAAAYKbExERddtllmjdvns+33eiAeeSRR/Tggw/qgQcekGVZuvzyy+u8xrZtWZbFLdYAAECrV69Wq1atmmTbjQ6Ye++9VykpKfr222/Vq1cv/fWvf1W7du2aZCgAAGC+tm3bNtm2z+gq3JCQEMXHx2vJkiW64oor1Lt373p/Tnrttdfc35kEAADO3Lp16zRo0CBdcMEFateuna677jrt2rVLkvTNN9/IsiytXr1aw4YNU1BQkHr37q2///3v7vcfOHBAKSkp6ty5s4KCgtSzZ0+99tprHvuwbVuzZ89WXFycAgMD1bt3b73xxhvu9bm5ubIsS++//7769OmjwMBAXXnllSotLdV7772n7t27KzQ0VCkpKTp69Kj7fYmJiUpNTXU/f+WVV9SvXz+FhIQoMjJSY8aMUWlpqVf/XLy6jWj8+PFyuVynfd19992nvXv3erMLAAAgqbKyUmlpadq4caM++OADtWjRQjfddJNqa2vdr8nIyNC0adNUWFiobt26KSUlRSdOnJAkHT9+XAkJCXrnnXf05Zdf6t5779W4ceP02Wefud//yCOPaMmSJVq0aJG2bdumKVOm6Pbbb1deXp7HLJmZmVq4cKHy8/O1e/du/frXv9a8efO0fPlyvfvuu8rJydGzzz57yt+lurpajz/+uL744gu9+eabKioq0p133unVPxevPgemsWzbbsrNAwBw3rv55ps9nr/00kvq0KGDtm/fruDgYEnStGnTdO2110qSZs6cqUsvvVQ7d+7UJZdcok6dOmnatGnu9z/44INat26dXn/9dfXv31+VlZXKzs7Whx9+qAEDBkj68YuaP/nkE73wwgsaOnSo+71PPPGErrjiCknShAkTNH36dO3atUtxcXGSpP/8z//URx99pIceeqje3+Xuu+92P46Li9OCBQv0q1/9SkeOHHH/Lo3FB7kAANCM7dq1S2PGjFFcXJxCQ0MVGxsrSSouLna/plevXu7HHTt2lCT3qZmamho9+eST6tWrl9q1a6fg4GCtX7/e/f7t27fr+PHjGj58uIKDg90/y5Ytc5+qqm8/ERERCgoKcsfLyWUNnRLavHmzbrzxRsXExCgkJESJiYl1fpfGatIjMAAA4Oxcf/31io6O1osvvqioqCjV1tYqPj5e1dXV7tf89E4fy7IkyX2Kae7cuXrmmWc0b9489ezZU61bt1Zqaqr7/Sdf9+6776pTp04e+/755SI/38/P7zCyLMvj1NZPVVZWKikpSUlJSXrllVcUHh6u4uJijRgxwuN3aSwCBgCAZurAgQP6xz/+oRdeeEGDBw+WJH3yySdntI2PP/5YN954o26//XZJPwbL119/re7du0uSevToIZfLpeLiYo/TRb72z3/+U/v379dTTz2l6OhoSdKmTZu83h4BAwBAM9WmTRu1a9dOixcvVseOHVVcXKzf//73Z7SNiy66SKtWrVJ+fr7atGmj7OxslZSUuAMmJCRE06ZN05QpU1RbW6tBgwapvLxc+fn5Cg4O1vjx433yu3Tp0kX+/v569tlndf/99+vLL7/U448/7vX2mvQamJiYmCb7ABsAAM53LVq00IoVK1RQUKD4+HhNmTJFc+bMOaNt/OEPf1Dfvn01YsQIJSYmKjIyss6XMj/++OOaMWOGsrKy1L17d40YMUJr1651X2/jC+Hh4Vq6dKlef/119ejRQ0899ZT++Mc/er09yz6LW4Wqq6tVWlpa53xXc/oqgfLycoWFhenw4cMKDQ312XYTfrfMZ9vCj9aEnNkfJU6vy4ytTo/QKPw9+R5/T75lyt/SL4lXp5C+/vpr3X333crPz/dYzlcJAACAc8GrgLnzzjvl5+end955Rx07dnRf8QwAAHAueBUwhYWFKigo0CWXXOLreQAAAE7Lq4t4e/Toof379/t6FgAAgEbxKmCefvpppaenKzc3VwcOHFB5ebnHDwAAQFPy6hTS1VdfLUm66qqrPJZzES8AADgXvAqYjz76yNdzAAAANJpXAdOUHzUMAABwOl5/lUBZWZleeukl/eMf/5BlWerRo4fuvvtuhYWF+XI+AACAOrwKmE2bNmnEiBEKDAzUr371K9m2rezsbD355JNav369+vbt6+s5AQCAzv0nVxfMueOc7q+xvLoLacqUKbrhhhv0zTffaPXq1VqzZo2Kiop03XXXKTU11ccjAgAA0zz33HOKjY1VQECAEhIS9PHHH/t0+14FzKZNm/TQQw/Jz+//D+D4+fkpPT39rL4aGwAAmG/lypVKTU1VRkaGNm/erMGDBys5OVnFxcU+24dXARMaGlrvELt371ZISMhZDwUAAMyVnZ2tCRMm6J577lH37t01b948RUdHa9GiRT7bh1cBc+utt2rChAlauXKldu/ere+++04rVqzQPffco5SUFJ8NBwAAzFJdXa2CggIlJSV5LE9KSqrzJdBnw6uLeP/4xz/KsizdcccdOnHihCSpVatW+u1vf6unnnrKZ8MBAACz7N+/XzU1NYqIiPBYHhERoZKSEp/tx6sjMP7+/po/f74OHTqkwsJCbd68WQcPHtQzzzwjl8vV6O1kZWXp8ssvV0hIiDp06KBRo0Zpx44dHq+xbVuZmZmKiopSYGCgEhMTtW3bNm/GBgAA54hlWR7PT35av694FTAnBQUFqWfPnurVq5eCgoLO+P15eXmaNGmSPv30U+Xk5OjEiRNKSkpSZWWl+zWzZ89Wdna2Fi5cqI0bNyoyMlLDhw9XRUXF2YwOAACaQPv27dWyZcs6R1tKS0vrHJU5G40+hTR69GgtXbpUoaGhGj16dIOvXb16daO2uW7dOo/nS5YsUYcOHVRQUKAhQ4bItm3NmzdPGRkZ7n2+/PLLioiI0PLly3Xfffc1dnwAAHAO+Pv7KyEhQTk5Obrpppvcy3NycnTjjTf6bD+NDpiwsDD3oZ/Q0FCfHgY66fDhw5Kktm3bSpKKiopUUlLicSGQy+XS0KFDlZ+fX2/AVFVVqaqqyv2cb8cGAODcSktL07hx49SvXz8NGDBAixcvVnFxse6//36f7aPRAbNkyRL346VLl/psgJNs21ZaWpoGDRqk+Ph4SXIffqrvQqBvv/223u1kZWVp5syZPp8PAIDmoLl+Mu5P3XrrrTpw4IAee+wx7dmzR/Hx8frLX/6imJgYn+3Dq2tgrrzySpWVldVZXl5eriuvvNKrQR544AFt2bJFr732Wp11Z3Ih0PTp03X48GH3z+7du72aBwAAeG/ixIn65ptvVFVV5b40xJe8uo06NzdX1dXVdZYfP37cq48KfvDBB/X2229rw4YN6ty5s3t5ZGSkpB+PxHTs2NG9vKELgVwu1xndCQUAAMxzRgGzZcsW9+Pt27d7XGFcU1OjdevWqVOnTo3enm3bevDBB7VmzRrl5uYqNjbWY31sbKwiIyOVk5OjPn36SPrxA3Ly8vL09NNPn8noAADgPHJGAXPZZZfJsixZllXvqaLAwEA9++yzjd7epEmTtHz5cr311lsKCQlxB1FYWJgCAwNlWZZSU1M1a9Ysde3aVV27dtWsWbMUFBSkMWPGnMnoAADgPHJGAVNUVCTbthUXF6f/+Z//UXh4uHudv7+/OnTooJYtWzZ6eye/EyExMdFj+ZIlS3TnnXdKktLT03Xs2DFNnDhRhw4dUv/+/bV+/Xq+cwkAgF+wMwqYk1cP19bW+mTntm2f9jWWZSkzM1OZmZk+2ScAADCfV3chZWVl6U9/+lOd5X/605+4NgUAADQ5rwLmhRde0CWXXFJn+aWXXqrnn3/+rIcCAABoiFcB8/Pbmk8KDw/Xnj17znooAACAhngVMNHR0frb3/5WZ/nf/vY3RUVFnfVQAAAADfHqg+zuuecepaam6ocffnDfTv3BBx8oPT1dU6dO9emAAADg/xU/1vOc7q/LjK3ndH+N5VXApKen6+DBg5o4caL7E3kDAgL00EMPafr06T4dEAAAmGXDhg2aM2eOCgoKtGfPHq1Zs0ajRo3y6T68OoVkWZaefvpp7du3T59++qm++OILHTx4UDNmzPDpcAAAwDyVlZXq3bu3Fi5c2GT78OoIzEklJSU6ePCghgwZIpfL1eCXLAIAgF+G5ORkJScnN+k+vDoCc+DAAV111VXq1q2bRo4c6b7z6J577uEaGAAA0OS8CpgpU6aoVatWKi4uVlBQkHv5rbfeqnXr1vlsOAAAgPp4dQpp/fr1ev/999W5c2eP5V27dtW3337rk8EAAABOxasjMJWVlR5HXk7av3+/XC7XWQ8FAADQEK8CZsiQIVq2bJn7uWVZqq2t1Zw5czRs2DCfDQcAAFAfr04hzZkzR4mJidq0aZOqq6uVnp6ubdu26eDBg/V+Qi8AAPjlOHLkiHbu3Ol+XlRUpMLCQrVt21ZdunTxyT68CpgePXpoy5YtWrRokVq2bKnKykqNHj1akyZNqvc7kgAAgG8010/G/alNmzZ5nJFJS0uTJI0fP15Lly71yT68/hyYyMhIzZw50ydDAACA80diYqJs227SfTQ6YLZs2aL4+Hi1aNFCW7ZsafC1wcHBio6OVqtWrc56QAAAgJ9rdMBcdtllKikpUYcOHXTZZZfJsqwG6yosLEzPP/+8br31Vp8MCgAAcFKjA6aoqEjh4eHuxw2pqqrS66+/roceeoiAAQAAPtfo26hjYmLc33MUExOj4uJiZWRkKCUlRX5+foqJidGGDRu0e/dudevWTRMnTlTfvn2bbHAAAPDL5dXnwKxatUojRoxQYGCgPv/8c1VVVUmSKioqNGvWLElSmzZttHr1at9NCgAA8G9eBcwTTzyh559/Xi+++KLHhboDBw7U559/7rPhAAAA6uNVwOzYsUNDhgypszw0NFRlZWVnOxMAAECDvAqYjh07enzC3kmffPKJ4uLiznooAACAhngVMPfdd58mT56szz77TJZl6V//+pdeffVVTZs2TRMnTvT1jAAAAB68+iTe9PR0HT58WMOGDdPx48c1ZMgQuVwuTZs2TQ888ICvZwQAAPDg9VcJPPnkk8rIyND27dtVW1urHj16KDg42JezAQAA1MvrgJGkoKAg9evXz1ezAAAANIpX18AAAAA4iYABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEcDZgNGzbo+uuvV1RUlCzL0ptvvumx3rZtZWZmKioqSoGBgUpMTNS2bducGRYAADQbjgZMZWWlevfurYULF9a7fvbs2crOztbChQu1ceNGRUZGavjw4aqoqDjHkwIAgObEz8mdJycnKzk5ud51tm1r3rx5ysjI0OjRoyVJL7/8siIiIrR8+XLdd99953JUAADQjDTba2CKiopUUlKipKQk9zKXy6WhQ4cqPz//lO+rqqpSeXm5xw8AADi/NNuAKSkpkSRFRER4LI+IiHCvq09WVpbCwsLcP9HR0U06JwAAOPeabcCcZFmWx3Pbtuss+6np06fr8OHD7p/du3c39YgAAOAcc/QamIZERkZK+vFITMeOHd3LS0tL6xyV+SmXyyWXy9Xk8wEAAOc02yMwsbGxioyMVE5OjntZdXW18vLyNHDgQAcnAwAATnP0CMyRI0e0c+dO9/OioiIVFhaqbdu26tKli1JTUzVr1ix17dpVXbt21axZsxQUFKQxY8Y4ODUAAHCaowGzadMmDRs2zP08LS1NkjR+/HgtXbpU6enpOnbsmCZOnKhDhw6pf//+Wr9+vUJCQpwaGQAANAOOBkxiYqJs2z7lesuylJmZqczMzHM3FAAAaPaa7TUwAAAAp0LAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDhGBMxzzz2n2NhYBQQEKCEhQR9//LHTIwEAAAc1+4BZuXKlUlNTlZGRoc2bN2vw4MFKTk5WcXGx06MBAACHNPuAyc7O1oQJE3TPPfeoe/fumjdvnqKjo7Vo0SKnRwMAAA7xc3qAhlRXV6ugoEC///3vPZYnJSUpPz+/3vdUVVWpqqrK/fzw4cOSpPLycp/OVlN1zKfbg1TRqsbpEc47vv7vfVPh78n3+Hvyrab6WwoJCZFlWU2y7fNdsw6Y/fv3q6amRhERER7LIyIiVFJSUu97srKyNHPmzDrLo6Ojm2RG+E680wOcj7LCnJ4ADuHvycea6G/p8OHDCg0NbZJtn++adcCc9PM6tW37lMU6ffp0paWluZ/X1tbq4MGDateuHZXbjJWXlys6Olq7d+/mjxk4S/w9mSMkJMTpEYzVrAOmffv2atmyZZ2jLaWlpXWOypzkcrnkcrk8ll1wwQVNNSJ8LDQ0lH/hAj7C3xPOZ836Il5/f38lJCQoJyfHY3lOTo4GDhzo0FQAAMBpzfoIjCSlpaVp3Lhx6tevnwYMGKDFixeruLhY999/v9OjAQAAhzT7gLn11lt14MABPfbYY9qzZ4/i4+P1l7/8RTExMU6PBh9yuVx69NFH65z+A3Dm+HvCL4Fl27bt9BAAAABnollfAwMAAFAfAgYAABiHgAEAAMYhYAAAgHEIGDjuueeeU2xsrAICApSQkKCPP/7Y6ZEAI23YsEHXX3+9oqKiZFmW3nzzTadHApoMAQNHrVy5UqmpqcrIyNDmzZs1ePBgJScnq7i42OnRAONUVlaqd+/eWrhwodOjAE2O26jhqP79+6tv375atGiRe1n37t01atQoZWVlOTgZYDbLsrRmzRqNGjXK6VGAJsERGDimurpaBQUFSkpK8lielJSk/Px8h6YCAJiAgIFj9u/fr5qamjpfzBkREVHnCzwBAPgpAgaOsyzL47lt23WWAQDwUwQMHNO+fXu1bNmyztGW0tLSOkdlAAD4KQIGjvH391dCQoJycnI8lufk5GjgwIEOTQUAMEGz/zZqnN/S0tI0btw49evXTwMGDNDixYtVXFys+++/3+nRAOMcOXJEO3fudD8vKipSYWGh2rZtqy5dujg4GeB73EYNxz333HOaPXu29uzZo/j4eD3zzDMaMmSI02MBxsnNzdWwYcPqLB8/fryWLl167gcCmhABAwAAjMM1MAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDBAM5aYmKjU1NQm3Udubq4sy1JZWVmT7gcAfImAAX5BzkUQOSUzM1OXXXaZ02MAOEcIGAAAYBwCBmgmKisrdccddyg4OFgdO3bU3LlzPdZXV1crPT1dnTp1UuvWrdW/f3/l5ua61x84cEApKSnq3LmzgoKC1LNnT7322mvu9Xfeeafy8vI0f/58WZYly7L0zTffuNcXFBSoX79+CgoK0sCBA7Vjx45Gz7527VolJCQoICBAcXFxmjlzpk6cOCFJSklJ0W233ebx+h9++EHt27fXkiVLJEm2bWv27NmKi4tTYGCgevfurTfeeMP9+pOnuT744IN6Z1y6dKlmzpypL774wv278eWFwHnOBtAs/Pa3v7U7d+5sr1+/3t6yZYt93XXX2cHBwfbkyZNt27btMWPG2AMHDrQ3bNhg79y5054zZ47tcrnsr776yrZt2/7uu+/sOXPm2Js3b7Z37dplL1iwwG7ZsqX96aef2rZt22VlZfaAAQPs3/zmN/aePXvsPXv22CdOnLA/+ugjW5Ldv39/Ozc31962bZs9ePBge+DAgY2ae926dXZoaKi9dOlSe9euXfb69evtCy+80M7MzLRt27bXrl1rBwYG2hUVFe73rF271g4ICLAPHz5s27ZtP/zww/Yll1xir1u3zt61a5e9ZMkS2+Vy2bm5ubZt26ed8ejRo/bUqVPtSy+91P27HT169Oz/QwHQbBEwQDNQUVFh+/v72ytWrHAvO3DggB0YGGhPnjzZ3rlzp21Zlv399997vO+qq66yp0+ffsrtjhw50p46dar7+dChQ91BdNLJOPjrX//qXvbuu+/akuxjx46ddvbBgwfbs2bN8lj25z//2e7YsaNt27ZdXV1tt2/f3l62bJl7fUpKin3LLbfYtm3bR44csQMCAuz8/HyPbUyYMMFOSUlp9IyPPvqo3bt379POC+D84Ofk0R8AP9q1a5eqq6s1YMAA97K2bdvq4osvliR9/vnnsm1b3bp183hfVVWV2rVrJ0mqqanRU089pZUrV+r7779XVVWVqqqq1Lp160bN0KtXL/fjjh07SpJKS0vVpUuXBt9XUFCgjRs36sknn3Qvq6mp0fHjx3X06FEFBQXplltu0auvvqpx48apsrJSb731lpYvXy5J2r59u44fP67hw4d7bLe6ulp9+vTxyYwAzj8EDNAM2Lbd4Pra2lq1bNlSBQUFatmypce64OBgSdLcuXP1zDPPaN68eerZs6dat26t1NRUVVdXN2qGVq1auR9bluXe7+nU1tZq5syZGj16dJ11AQEBkqSxY8dq6NChKi0tVU5OjgICApScnOyxj3fffVedOnXyeL/L5fLJjADOPwQM0AxcdNFFatWqlT799FP30YRDhw7pq6++0tChQ9WnTx/V1NSotLRUgwcPrncbH3/8sW688Ubdfvvtkn78H/avv/5a3bt3d7/G399fNTU1Pp29b9++2rFjhy666KJTvmbgwIGKjo7WypUr9d577+mWW26Rv7+/JKlHjx5yuVwqLi7W0KFDvZ6jKX43AM0XAQM0A8HBwZowYYJ+97vfqV27doqIiFBGRoZatPjxRsFu3bpp7NixuuOOOzR37lz16dNH+/fv14cffqiePXtq5MiRuuiii7Rq1Srl5+erTZs2ys7OVklJiUfAXHjhhfrss8/0zTffKDg4WG3btj3r2WfMmKHrrrtO0dHRuuWWW9SiRQtt2bJFW7du1RNPPCHpx6MlY8aM0fPPP6+vvvpKH330kfv9ISEhmjZtmqZMmaLa2loNGjRI5eXlys/PV3BwsMaPH9+oOS688EIVFRWpsLBQnTt3VkhISJ0jOADOH9xGDTQTc+bM0ZAhQ3TDDTfo6quv1qBBg5SQkOBev2TJEt1xxx2aOnWqLr74Yt1www367LPPFB0dLUn6wx/+oL59+2rEiBFKTExUZGSkRo0a5bGPadOmqWXLlurRo4fCw8NVXFx81nOPGDFC77zzjnJycnT55ZfrP/7jP5Sdna2YmBiP140dO1bbt29Xp06ddMUVV3ise/zxxzVjxgxlZWWpe/fuGjFihNauXavY2NhGz3HzzTfrmmuu0bBhwxQeHu5xCzmA849ln+7kOwAAQDPDERgAAGAcAgZAgy699FIFBwfX+/Pqq686PR6AXyhOIQFo0Lfffqsffvih3nUREREKCQk5xxMBAAEDAAAMxCkkAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMb5P/ad/+VaH5SaAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 571.611x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 571.611x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 571.611x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 571.611x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 570.986x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 570.986x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 570.986x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 570.986x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 570.986x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 570.986x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 569.861x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 569.861x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 569.861x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 569.861x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 569.861x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 569.861x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 558.875x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 558.875x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 558.875x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 558.875x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 558.875x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 558.875x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat=['anaemia','diabetes','smoking','sex']\n",
    "con=['creatinine_phosphokinase','age','ejection_fraction','platelets','serum_creatinine','serum_sodium']\n",
    "for x in cat:\n",
    "  for y in con:\n",
    "      sns.catplot(x='death_event',y=y,hue=x,kind='bar',data=df_eda)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
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       "      <th>13</th>\n",
       "      <td>72.0</td>\n",
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       "      <td>245</td>\n",
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       "      <th>35</th>\n",
       "      <td>49.0</td>\n",
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       "      <td>972</td>\n",
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       "      <td>35</td>\n",
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       "      <td>268000.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>130</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>59.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>253000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>139</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>230</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>362000.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>240</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>237000.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>55.0</td>\n",
       "      <td>1</td>\n",
       "      <td>170</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>336000.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>135</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>172000.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>133</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>52.0</td>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>218000.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "13   72.0        0                       211         0                 25   \n",
       "246  42.0        0                        64         0                 40   \n",
       "35   49.0        0                       972         1                 35   \n",
       "139  59.0        1                       176         1                 25   \n",
       "222  58.0        0                       132         1                 38   \n",
       "200  50.0        1                       298         0                 35   \n",
       "233  42.0        0                       102         1                 40   \n",
       "250  55.0        1                       170         1                 40   \n",
       "273  79.0        1                        55         0                 50   \n",
       "158  52.0        0                       132         0                 30   \n",
       "\n",
       "     high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "13                     0   274000.0               1.2           134    0   \n",
       "246                    0   189000.0               0.7           140    1   \n",
       "35                     1   268000.0               0.8           130    0   \n",
       "139                    0   221000.0               1.0           136    1   \n",
       "222                    1   253000.0               1.0           139    1   \n",
       "200                    0   362000.0               0.9           140    1   \n",
       "233                    0   237000.0               1.2           140    1   \n",
       "250                    0   336000.0               1.2           135    1   \n",
       "273                    1   172000.0               1.8           133    1   \n",
       "158                    0   218000.0               0.7           136    1   \n",
       "\n",
       "     smoking  time  death_event  \n",
       "13         0   207            0  \n",
       "246        0   245            0  \n",
       "35         0   187            0  \n",
       "139        1   150            1  \n",
       "222        0   230            0  \n",
       "200        1   240            0  \n",
       "233        0    74            0  \n",
       "250        0   250            0  \n",
       "273        0    78            0  \n",
       "158        1   112            0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ambil data inference\n",
    "inf = df_eda.sample(10, random_state=30) \n",
    "inf "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <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",
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       "  </thead>\n",
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       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>279000.00</td>\n",
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       "      <td>134</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>186</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>215</td>\n",
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       "    <tr>\n",
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       "      <td>582</td>\n",
       "      <td>0</td>\n",
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       "      <td>147000.00</td>\n",
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       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>289 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "0    42.0        1                       250         1                 15   \n",
       "1    46.0        0                       168         1                 17   \n",
       "2    65.0        1                       160         1                 20   \n",
       "3    53.0        1                        91         0                 20   \n",
       "4    50.0        1                       582         1                 20   \n",
       "..    ...      ...                       ...       ...                ...   \n",
       "294  63.0        1                       122         1                 60   \n",
       "295  45.0        0                       308         1                 60   \n",
       "296  70.0        0                        97         0                 60   \n",
       "297  53.0        1                       446         0                 60   \n",
       "298  50.0        0                       582         0                 62   \n",
       "\n",
       "     high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "0                      0  213000.00               1.3           136    0   \n",
       "1                      1  271000.00               2.1           124    0   \n",
       "2                      0  327000.00               2.7           116    0   \n",
       "3                      1  418000.00               1.4           139    0   \n",
       "4                      1  279000.00               1.0           134    0   \n",
       "..                   ...        ...               ...           ...  ...   \n",
       "294                    0  267000.00               1.2           145    1   \n",
       "295                    1  377000.00               1.0           136    1   \n",
       "296                    1  220000.00               0.9           138    1   \n",
       "297                    1  263358.03               1.0           139    1   \n",
       "298                    1  147000.00               0.8           140    1   \n",
       "\n",
       "     smoking  time  death_event  \n",
       "0          0    65            1  \n",
       "1          0   100            1  \n",
       "2          0     8            1  \n",
       "3          0    43            1  \n",
       "4          0   186            0  \n",
       "..       ...   ...          ...  \n",
       "294        0   147            0  \n",
       "295        0   186            0  \n",
       "296        0   186            0  \n",
       "297        0   215            0  \n",
       "298        1   192            0  \n",
       "\n",
       "[289 rows x 13 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop inference dari dataset\n",
    "\n",
    "df_eda = df_eda.drop(inf.index)\n",
    "df_eda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>anaemia</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoking</th>\n",
       "      <th>time</th>\n",
       "      <th>death_event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>72.0</td>\n",
       "      <td>0</td>\n",
       "      <td>211</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>274000.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>134</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>207</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>189000.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>245</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>972</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>268000.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>130</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>59.0</td>\n",
       "      <td>1</td>\n",
       "      <td>176</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>221000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>253000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>139</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>230</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>362000.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>240</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>237000.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>55.0</td>\n",
       "      <td>1</td>\n",
       "      <td>170</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>336000.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>135</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>172000.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>133</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>52.0</td>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>218000.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "0  72.0        0                       211         0                 25   \n",
       "1  42.0        0                        64         0                 40   \n",
       "2  49.0        0                       972         1                 35   \n",
       "3  59.0        1                       176         1                 25   \n",
       "4  58.0        0                       132         1                 38   \n",
       "5  50.0        1                       298         0                 35   \n",
       "6  42.0        0                       102         1                 40   \n",
       "7  55.0        1                       170         1                 40   \n",
       "8  79.0        1                        55         0                 50   \n",
       "9  52.0        0                       132         0                 30   \n",
       "\n",
       "   high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "0                    0   274000.0               1.2           134    0   \n",
       "1                    0   189000.0               0.7           140    1   \n",
       "2                    1   268000.0               0.8           130    0   \n",
       "3                    0   221000.0               1.0           136    1   \n",
       "4                    1   253000.0               1.0           139    1   \n",
       "5                    0   362000.0               0.9           140    1   \n",
       "6                    0   237000.0               1.2           140    1   \n",
       "7                    0   336000.0               1.2           135    1   \n",
       "8                    1   172000.0               1.8           133    1   \n",
       "9                    0   218000.0               0.7           136    1   \n",
       "\n",
       "   smoking  time  death_event  \n",
       "0        0   207            0  \n",
       "1        0   245            0  \n",
       "2        0   187            0  \n",
       "3        1   150            1  \n",
       "4        0   230            0  \n",
       "5        1   240            0  \n",
       "6        0    74            0  \n",
       "7        0   250            0  \n",
       "8        0    78            0  \n",
       "9        1   112            0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reset index\n",
    "\n",
    "df_eda.reset_index(drop=True, inplace=True)\n",
    "inf.reset_index(drop=True, inplace=True)\n",
    "inf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(231, 12)\n",
      "(58, 12)\n",
      "(231,)\n",
      "(58,)\n"
     ]
    }
   ],
   "source": [
    "# split\n",
    "X = df_eda.drop('death_event', axis=1)\n",
    "y = df_eda.death_event\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=69)\n",
    "\n",
    "for i in [X_train, X_test, y_train, y_test]:\n",
    "    print(i.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected features: ['age', 'anaemia', 'creatinine_phosphokinase', 'diabetes', 'ejection_fraction', 'high_blood_pressure', 'platelets', 'serum_creatinine', 'serum_sodium', 'sex', 'smoking', 'time']\n",
      "Feature scores: [4.23470132e+01 5.54270212e-01 1.57978379e+03 7.52441671e-03\n",
      " 7.86304411e+01 1.32965274e+00 2.60177978e+04 1.93883573e+01\n",
      " 1.66596955e+00 4.90376257e-04 1.20255077e-01 3.77549599e+03]\n"
     ]
    }
   ],
   "source": [
    "X = X.copy()  # fitur\n",
    "y = y.copy()  # target\n",
    "\n",
    "# selectkbest dengan function chi2\n",
    "k = 'all'  # jumlah fitur\n",
    "selector = SelectKBest(score_func=chi2, k=k)\n",
    "\n",
    "# buat variabel baru untuk menyimpan hasil selectkbest\n",
    "X_baru = selector.fit_transform(X, y)\n",
    "\n",
    "# ambil index\n",
    "index = selector.get_support(indices=True)\n",
    "feature_scores = selector.scores_\n",
    "# Print feature names\n",
    "selected_features = df_eda.columns[index].tolist()\n",
    "\n",
    "print(\"Selected features:\", selected_features)\n",
    "print(\"Feature scores:\", feature_scores)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Berdasarkan analisa selectKbest, terlihat fitur mana yang harus dipilih. sebelum itu, ada baiknya di cek vif multicolinearity nya dahulu supaya model lebih valid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "data_num = df_eda[['age', 'creatinine_phosphokinase', 'ejection_fraction',\n",
    "                    'platelets', 'serum_creatinine', 'time', 'serum_sodium']]\n",
    "\n",
    "def calc_vif(X):\n",
    "\n",
    "    # hitung VIF\n",
    "    vif = pd.DataFrame()\n",
    "    vif[\"variables\"] = X.columns\n",
    "    vif[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "\n",
    "    return(vif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>variables</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>28.891087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>creatinine_phosphokinase</td>\n",
       "      <td>1.385313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ejection_fraction</td>\n",
       "      <td>11.663433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>platelets</td>\n",
       "      <td>8.218574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>serum_creatinine</td>\n",
       "      <td>2.877972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>time</td>\n",
       "      <td>4.029306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>serum_sodium</td>\n",
       "      <td>52.366216</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  variables        VIF\n",
       "0                       age  28.891087\n",
       "1  creatinine_phosphokinase   1.385313\n",
       "2         ejection_fraction  11.663433\n",
       "3                 platelets   8.218574\n",
       "4          serum_creatinine   2.877972\n",
       "5                      time   4.029306\n",
       "6              serum_sodium  52.366216"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc_vif(data_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dari kedua analisis, akhirnya terpilih kolom kolom yang dapat dijadikan fitur. Kolom kolomnya adalah anaemia, creatinine_phosphokinase, diabetes, high_blood_pressure, serum_creatinine, dan time karena secara linear, time masih berkorelasi dan tidak memiliki skor VIF yang tinggi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setelah feature slection, akan dilakukan cek outlier dan langsung di handle jika memang ada."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_fitur(data, variable):\n",
    "    fig, axes = plt.subplots(ncols = 2, figsize = (15, 5))\n",
    "\n",
    "     # histogram\n",
    "    sns.histplot(data[variable],ax = axes[0], bins=30)\n",
    "    axes[0].set_title(f\"Histogram '{variable}'\")\n",
    "    axes[0].axvline(data[variable].mean(), color = 'red', linestyle = 'dashed', label = 'mean')\n",
    "    axes[0].axvline(data[variable].median(), color = 'green', linestyle = 'dashed', label = 'median')\n",
    "    axes[0].legend()\n",
    "\n",
    "\n",
    "     # boxplot\n",
    "    sns.boxplot(y=data[variable], ax =  axes[1])\n",
    "    axes[1].set_title(f\"Boxplot '{variable}'\")\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "    # skewness\n",
    "    print(data[variable].name + ' Kurtosis: ' + str(data[variable].kurt()))\n",
    "    print(data[variable].name + ' Skewness: ' + str(data[variable].skew()))\n",
    "    if -0.5 <= data[variable].skew() <= 0.5:\n",
    "        print(\"Columns '{}' normal distribution\".format(variable))\n",
    "    elif data[variable].skew() > 0.5:\n",
    "        print(\"Columns '{}' right skewed\".format(variable))\n",
    "    elif data[variable].skew() < -0.5:\n",
    "        print(\"Columns '{}' left skewed\".format(variable))\n",
    "\n",
    "    print(data[variable].mean())\n",
    "    print(data[variable].median())\n",
    "    print(data[variable].mode())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creatinine_phosphokinase Kurtosis: 25.8864265697024\n",
      "creatinine_phosphokinase Skewness: 4.506158627224996\n",
      "Columns 'creatinine_phosphokinase' right skewed\n",
      "557.1125541125541\n",
      "258.0\n",
      "0    582\n",
      "Name: creatinine_phosphokinase, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "plot_fitur(X_train, 'creatinine_phosphokinase')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "serum_creatinine Kurtosis: 21.752253093231637\n",
      "serum_creatinine Skewness: 4.05181984078609\n",
      "Columns 'serum_creatinine' right skewed\n",
      "1.4061471861471861\n",
      "1.1\n",
      "0    1.0\n",
      "Name: serum_creatinine, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "plot_fitur(X_train, 'serum_creatinine')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time Kurtosis: -1.2569187442285428\n",
      "time Skewness: 0.1496433083415178\n",
      "Columns 'time' normal distribution\n",
      "129.6103896103896\n",
      "112.0\n",
      "0     10\n",
      "1     30\n",
      "2     87\n",
      "3    209\n",
      "4    244\n",
      "5    250\n",
      "Name: time, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "plot_fitur(X_train, 'time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Berhubung dari semua fitur numerik yang memiliki outlier skew nya lebih dari 1, maka akan di handle dengan capping dengan range 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count of outlier:  15\n",
      "percentage of outlier:  6.493506493506493 %\n"
     ]
    }
   ],
   "source": [
    "# Deteksi outlier bill_amt_sep dengan IQR\n",
    "Q3 = X_train['serum_creatinine'].quantile(.75)\n",
    "Q1 = X_train['serum_creatinine'].quantile(.25)\n",
    "\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "upper = Q3 + (3 * IQR)\n",
    "lower = Q1 - (3 * IQR)\n",
    "\n",
    "outlier = X_train[(X_train['serum_creatinine'] > upper) | (X_train['serum_creatinine'] < lower)]\n",
    "\n",
    "print('count of outlier: ', outlier.shape[0])\n",
    "print('percentage of outlier: ', outlier.shape[0]/X_train.shape[0] * 100, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count of outlier:  13\n",
      "percentage of outlier:  5.627705627705628 %\n"
     ]
    }
   ],
   "source": [
    "# Deteksi outlier creatinine_phosphokinase dengan IQR\n",
    "Q3 = X_train['creatinine_phosphokinase'].quantile(.75)\n",
    "Q1 = X_train['creatinine_phosphokinase'].quantile(.25)\n",
    "\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "upper = Q3 + (3 * IQR)\n",
    "lower = Q1 - (3 * IQR)\n",
    "\n",
    "outlier = X_train[(X_train['creatinine_phosphokinase'] > upper) | (X_train['creatinine_phosphokinase'] < lower)]\n",
    "\n",
    "print('count of outlier: ', outlier.shape[0])\n",
    "print('percentage of outlier: ', outlier.shape[0]/X_train.shape[0] * 100, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hitung outlier dengan capping sesuai range iqr\n",
    "wins = Winsorizer(capping_method='iqr', tail='both', fold=3, variables=['creatinine_phosphokinase','serum_creatinine'])\n",
    "wins.fit(X_train)\n",
    "X_train_cleaned = wins.transform(X_train) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selanjutnya akan di cek perubahan datanya setelah handling outlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creatinine_phosphokinase Kurtosis: 2.9511810274529866\n",
      "creatinine_phosphokinase Skewness: 1.8523633657565894\n",
      "Columns 'creatinine_phosphokinase' right skewed\n",
      "468.9718614718615\n",
      "258.0\n",
      "0    582.0\n",
      "Name: creatinine_phosphokinase, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "plot_fitur(X_train_cleaned, 'creatinine_phosphokinase')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "serum_creatinine Kurtosis: 1.3238482148399884\n",
      "serum_creatinine Skewness: 1.4672196303459524\n",
      "Columns 'serum_creatinine' right skewed\n",
      "1.2985714285714287\n",
      "1.1\n",
      "0    1.0\n",
      "Name: serum_creatinine, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "plot_fitur(X_train_cleaned, 'serum_creatinine')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setelah dihandle datanya, terlihat sudah tidak ada outlier karena sudah ditarik ke ujung batasnya. Jika masih ada, tidak apa karena masih didalam range yang bisa di toleransi."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pipeline Definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pisah numerik dan kategorik\n",
    "num_col_out = ['serum_creatinine','creatinine_phosphokinase']\n",
    "num_col = ['time']\n",
    "cat_col = ['anaemia','diabetes', 'high_blood_pressure']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [0.18604651, 0.11608284],\n",
       "       [0.18604651, 0.02301202],\n",
       "       [1.        , 0.        ],\n",
       "       [0.18604651, 0.44694452],\n",
       "       [0.3255814 , 0.02965993],\n",
       "       [0.18604651, 0.2833035 ],\n",
       "       [0.23255814, 0.05420609],\n",
       "       [0.27906977, 0.91894656],\n",
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       "       [0.13953488, 0.59422143],\n",
       "       [0.23255814, 0.0490923 ],\n",
       "       [0.18604651, 0.30733828],\n",
       "       [0.04651163, 0.323191  ],\n",
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       "       [0.18604651, 1.        ],\n",
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       "       [0.13953488, 0.05420609],\n",
       "       [1.        , 0.01840961],\n",
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       "       [0.3255814 , 0.11608284],\n",
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       "       [0.3255814 , 0.17131168],\n",
       "       [0.60465116, 0.04500128],\n",
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       "       [0.60465116, 0.28586039],\n",
       "       [0.13953488, 0.28586039],\n",
       "       [0.57209302, 0.28586039],\n",
       "       [0.18604651, 0.05216057],\n",
       "       [0.        , 0.09204807],\n",
       "       [0.27906977, 0.28586039],\n",
       "       [1.        , 0.02965993],\n",
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       "       [0.13953488, 0.28586039],\n",
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       "       [0.18604651, 0.28586039],\n",
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       "       [0.18604651, 0.28586039],\n",
       "       [1.        , 0.12631041],\n",
       "       [0.09302326, 0.41523907],\n",
       "       [0.        , 0.28586039],\n",
       "       [0.18604651, 0.02914856],\n",
       "       [0.13953488, 0.16006137],\n",
       "       [0.26976744, 0.28586039],\n",
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       "       [0.18604651, 1.        ],\n",
       "       [0.3255814 , 0.13756073],\n",
       "       [0.60465116, 0.28586039],\n",
       "       [0.23255814, 0.44745589],\n",
       "       [0.04651163, 0.01687548],\n",
       "       [0.37209302, 0.15187931],\n",
       "       [0.23255814, 1.        ],\n",
       "       [0.04651163, 0.11506009],\n",
       "       [0.97674419, 0.07005881],\n",
       "       [1.        , 0.10841217],\n",
       "       [0.23255814, 0.48990028],\n",
       "       [0.23255814, 1.        ],\n",
       "       [0.18604651, 0.08591153],\n",
       "       [0.97674419, 0.02659166],\n",
       "       [0.23255814, 0.30427001],\n",
       "       [0.27906977, 0.03323958],\n",
       "       [0.18604651, 0.21631296],\n",
       "       [0.37209302, 1.        ],\n",
       "       [0.09302326, 0.15954999],\n",
       "       [0.37209302, 0.03477372],\n",
       "       [0.51162791, 0.11761698],\n",
       "       [0.55813953, 0.28586039],\n",
       "       [0.3255814 , 0.01482997],\n",
       "       [0.13953488, 1.        ],\n",
       "       [0.18604651, 0.0235234 ]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_pipeline1 = Pipeline([\n",
    "        ('outlier', Winsorizer(capping_method='iqr', tail='both', fold=3)),\n",
    "        ('imputer', SimpleImputer(strategy='most_frequent')),\n",
    "        ('scaler', MinMaxScaler())\n",
    "    ])\n",
    "\n",
    "num_tr1 = num_pipeline1.fit_transform(X_train[['serum_creatinine','creatinine_phosphokinase']])\n",
    "num_tr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [-0.7302059 ],\n",
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       "       [-0.32460912],\n",
       "       [-1.54139948],\n",
       "       [-1.26255169],\n",
       "       [-1.50337478],\n",
       "       [ 1.06962984],\n",
       "       [ 1.09497964],\n",
       "       [-0.9583541 ],\n",
       "       [ 1.46255172],\n",
       "       [ 1.08230474],\n",
       "       [ 1.08230474],\n",
       "       [ 1.90617321],\n",
       "       [-0.12181072],\n",
       "       [-1.09777799],\n",
       "       [ 1.52592622],\n",
       "       [ 1.05695494],\n",
       "       [-1.44000028],\n",
       "       [ 0.20773667],\n",
       "       [-1.08510309]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_pipeline2 = Pipeline([\n",
    "                ('imputer', SimpleImputer(strategy='most_frequent')),\n",
    "                ('scaler', StandardScaler())\n",
    "    ])\n",
    "\n",
    "num_tr2 = num_pipeline2.fit_transform(X_train[['time']])\n",
    "num_tr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       [1, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [1, 1, 1],\n",
       "       [1, 1, 0],\n",
       "       [0, 0, 1],\n",
       "       [1, 0, 1],\n",
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       "       [0, 0, 0],\n",
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       "       [0, 1, 0],\n",
       "       [1, 0, 0],\n",
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       "       [0, 0, 0],\n",
       "       [0, 1, 1],\n",
       "       [1, 0, 1],\n",
       "       [1, 0, 0],\n",
       "       [0, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 1],\n",
       "       [0, 1, 1],\n",
       "       [1, 1, 1],\n",
       "       [0, 0, 1],\n",
       "       [1, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 1],\n",
       "       [0, 0, 1],\n",
       "       [1, 0, 0],\n",
       "       [0, 0, 0],\n",
       "       [1, 0, 1],\n",
       "       [1, 0, 1],\n",
       "       [1, 1, 1],\n",
       "       [1, 1, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [0, 0, 0],\n",
       "       [0, 1, 1],\n",
       "       [0, 1, 1],\n",
       "       [1, 1, 1],\n",
       "       [1, 1, 0],\n",
       "       [0, 0, 1],\n",
       "       [0, 1, 0],\n",
       "       [0, 0, 1],\n",
       "       [1, 1, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 1, 1],\n",
       "       [0, 0, 0],\n",
       "       [0, 1, 1],\n",
       "       [1, 0, 0],\n",
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       "       [0, 1, 1],\n",
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       "       [0, 0, 1],\n",
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       "       [1, 0, 0],\n",
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       "       [0, 0, 1],\n",
       "       [0, 0, 0],\n",
       "       [1, 1, 0],\n",
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       "       [0, 1, 0],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 1, 1],\n",
       "       [1, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 0],\n",
       "       [1, 0, 1],\n",
       "       [0, 1, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 1],\n",
       "       [0, 0, 0],\n",
       "       [0, 1, 1],\n",
       "       [0, 1, 0],\n",
       "       [0, 0, 0],\n",
       "       [1, 0, 0],\n",
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       "       [0, 0, 0],\n",
       "       [1, 1, 1],\n",
       "       [0, 0, 1],\n",
       "       [1, 1, 0],\n",
       "       [1, 1, 0],\n",
       "       [1, 0, 1],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [0, 0, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 0, 1],\n",
       "       [1, 0, 1],\n",
       "       [1, 1, 1],\n",
       "       [0, 0, 0],\n",
       "       [1, 1, 1],\n",
       "       [0, 0, 0],\n",
       "       [1, 1, 0],\n",
       "       [0, 0, 0],\n",
       "       [0, 0, 0],\n",
       "       [0, 0, 0],\n",
       "       [1, 1, 0],\n",
       "       [0, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [0, 0, 0],\n",
       "       [1, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 1],\n",
       "       [0, 0, 0],\n",
       "       [0, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 1, 0],\n",
       "       [1, 0, 1],\n",
       "       [0, 0, 0],\n",
       "       [1, 0, 1],\n",
       "       [1, 1, 1],\n",
       "       [0, 0, 1],\n",
       "       [1, 0, 0],\n",
       "       [0, 0, 0],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [0, 1, 0],\n",
       "       [1, 1, 0],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 0],\n",
       "       [1, 0, 0],\n",
       "       [1, 0, 1],\n",
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       "       [0, 0, 0],\n",
       "       [0, 1, 1],\n",
       "       [0, 0, 0],\n",
       "       [0, 0, 1],\n",
       "       [1, 0, 0],\n",
       "       [0, 0, 1],\n",
       "       [0, 1, 1],\n",
       "       [1, 1, 0],\n",
       "       [0, 0, 0],\n",
       "       [0, 0, 1],\n",
       "       [0, 1, 0],\n",
       "       [0, 0, 0],\n",
       "       [0, 0, 0],\n",
       "       [0, 0, 0],\n",
       "       [0, 0, 1],\n",
       "       [0, 1, 0],\n",
       "       [1, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [1, 0, 0],\n",
       "       [0, 0, 0],\n",
       "       [1, 1, 1],\n",
       "       [1, 0, 0],\n",
       "       [0, 0, 0],\n",
       "       [0, 1, 1],\n",
       "       [1, 1, 0],\n",
       "       [0, 0, 0],\n",
       "       [1, 1, 0],\n",
       "       [1, 1, 0],\n",
       "       [0, 0, 0],\n",
       "       [0, 0, 1],\n",
       "       [1, 1, 0],\n",
       "       [0, 0, 0],\n",
       "       [1, 1, 1]], dtype=int64)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#membuat variabel berisi fungsi pipeline untuk categorical feature\n",
    "cat_pipeline = Pipeline([\n",
    "        ('imputer', SimpleImputer(strategy='most_frequent')),\n",
    "\n",
    "    ])\n",
    "\n",
    "cat_tr = cat_pipeline.fit_transform(X_train[['anaemia', 'high_blood_pressure', 'diabetes']])\n",
    "cat_tr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# preprocessing pipeline\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        (\"numerikout\", num_pipeline1, ['serum_creatinine','creatinine_phosphokinase']),\n",
    "        (\"numerik\", num_pipeline2, ['time']),\n",
    "        (\"kategorik\",cat_pipeline, ['anaemia', 'high_blood_pressure', 'diabetes'])\n",
    "        ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# random forest\n",
    "rfpipe = Pipeline([(\"preprocess\", preprocessor), (\"rf\", RandomForestClassifier(random_state= 69))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation array           : [0.85714286 0.77777778 0.63157895 0.6        0.5        0.57142857\n",
      " 0.88888889 0.75       0.73684211 0.75      ]\n",
      "Cross Validation score           : 0.7063659147869673\n",
      "Std Dev Cross Validation         : 0.11982754996674694\n"
     ]
    }
   ],
   "source": [
    "# cross validation random forest\n",
    "kf=KFold(n_splits=10)\n",
    "scoreRF_train = cross_val_score(rfpipe, X_train, y_train,scoring = \"f1\", cv=kf)\n",
    "\n",
    "print(\"Cross Validation array           :\",scoreRF_train)\n",
    "print(\"Cross Validation score           :\",scoreRF_train.mean())\n",
    "print(\"Std Dev Cross Validation         :\",scoreRF_train.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# XGboost\n",
    "xgpipe =Pipeline([(\"preprocess\",preprocessor), \n",
    "                  (\"xgb\", xgb.XGBRFClassifier(random_state=69))\n",
    "                  ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation array           : [0.85714286 0.82352941 0.6        0.6        0.5        0.66666667\n",
      " 1.         0.53333333 0.73684211 0.75      ]\n",
      "Cross Validation score           : 0.706751437417072\n",
      "Std Dev Cross Validation         : 0.14907843695823308\n"
     ]
    }
   ],
   "source": [
    "# cross validation XGBoost\n",
    "kf=KFold(n_splits=10)\n",
    "scoreXG_train = cross_val_score(xgpipe, X_train, y_train,scoring = \"f1\", cv=kf)\n",
    "\n",
    "print(\"Cross Validation array           :\",scoreXG_train)\n",
    "print(\"Cross Validation score           :\",scoreXG_train.mean())\n",
    "print(\"Std Dev Cross Validation         :\",scoreXG_train.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dari baseline model terlihat bahwa Random Forest memiliki skor f1 yang lebih tinggi. Namun XGBoost memiliki standar deviasi yang lebih kecil."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Training & Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Random Forest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;numerikout&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;outlier&#x27;,\n",
       "                                                                   Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                                              tail=&#x27;both&#x27;)),\n",
       "                                                                  (&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   MinMaxScaler())]),\n",
       "                                                  [&#x27;serum_creatinine&#x27;,\n",
       "                                                   &#x27;creatinine_phosphokinase&#x27;]),\n",
       "                                                 (&#x27;numerik&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;time&#x27;]),\n",
       "                                                 (&#x27;kategorik&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;))]),\n",
       "                                                  [&#x27;anaemia&#x27;,\n",
       "                                                   &#x27;high_blood_pressure&#x27;,\n",
       "                                                   &#x27;diabetes&#x27;])])),\n",
       "                (&#x27;rf&#x27;, RandomForestClassifier(random_state=69))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;numerikout&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;outlier&#x27;,\n",
       "                                                                   Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                                              tail=&#x27;both&#x27;)),\n",
       "                                                                  (&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   MinMaxScaler())]),\n",
       "                                                  [&#x27;serum_creatinine&#x27;,\n",
       "                                                   &#x27;creatinine_phosphokinase&#x27;]),\n",
       "                                                 (&#x27;numerik&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;time&#x27;]),\n",
       "                                                 (&#x27;kategorik&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;))]),\n",
       "                                                  [&#x27;anaemia&#x27;,\n",
       "                                                   &#x27;high_blood_pressure&#x27;,\n",
       "                                                   &#x27;diabetes&#x27;])])),\n",
       "                (&#x27;rf&#x27;, RandomForestClassifier(random_state=69))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocess: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;numerikout&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;outlier&#x27;,\n",
       "                                                  Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                             tail=&#x27;both&#x27;)),\n",
       "                                                 (&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;scaler&#x27;, MinMaxScaler())]),\n",
       "                                 [&#x27;serum_creatinine&#x27;,\n",
       "                                  &#x27;creatinine_phosphokinase&#x27;]),\n",
       "                                (&#x27;numerik&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;time&#x27;]),\n",
       "                                (&#x27;kategorik&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;))]),\n",
       "                                 [&#x27;anaemia&#x27;, &#x27;high_blood_pressure&#x27;,\n",
       "                                  &#x27;diabetes&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerikout</label><div class=\"sk-toggleable__content\"><pre>[&#x27;serum_creatinine&#x27;, &#x27;creatinine_phosphokinase&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Winsorizer</label><div class=\"sk-toggleable__content\"><pre>Winsorizer(capping_method=&#x27;iqr&#x27;, tail=&#x27;both&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerik</label><div class=\"sk-toggleable__content\"><pre>[&#x27;time&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">kategorik</label><div class=\"sk-toggleable__content\"><pre>[&#x27;anaemia&#x27;, &#x27;high_blood_pressure&#x27;, &#x27;diabetes&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=69)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocess',\n",
       "                 ColumnTransformer(transformers=[('numerikout',\n",
       "                                                  Pipeline(steps=[('outlier',\n",
       "                                                                   Winsorizer(capping_method='iqr',\n",
       "                                                                              tail='both')),\n",
       "                                                                  ('imputer',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   MinMaxScaler())]),\n",
       "                                                  ['serum_creatinine',\n",
       "                                                   'creatinine_phosphokinase']),\n",
       "                                                 ('numerik',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['time']),\n",
       "                                                 ('kategorik',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(strategy='most_frequent'))]),\n",
       "                                                  ['anaemia',\n",
       "                                                   'high_blood_pressure',\n",
       "                                                   'diabetes'])])),\n",
       "                ('rf', RandomForestClassifier(random_state=69))])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training rf\n",
    "modelRF = rfpipe.fit(X_train,y_train)\n",
    "modelRF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    'rf__n_estimators': [100, 200, 300],  # Number of trees in the forest\n",
    "    'rf__max_depth': [None, 5, 10],  # Maximum depth of the trees\n",
    "    'rf__min_samples_split': [2, 5, 10],  # Minimum number of samples required to split an internal node\n",
    "    'rf__min_samples_leaf': [1, 2, 4]  # Minimum number of samples required to be at a leaf node\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10,\n",
       "             estimator=Pipeline(steps=[(&#x27;preprocess&#x27;,\n",
       "                                        ColumnTransformer(transformers=[(&#x27;numerikout&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;outlier&#x27;,\n",
       "                                                                                          Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                                                                     tail=&#x27;both&#x27;)),\n",
       "                                                                                         (&#x27;imputer&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                         (&#x27;scaler&#x27;,\n",
       "                                                                                          MinMaxScaler())]),\n",
       "                                                                         [&#x27;serum_creatinine&#x27;,\n",
       "                                                                          &#x27;creatinine_phosphokinase&#x27;]),\n",
       "                                                                        (&#x27;numerik&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                                          Simpl...\n",
       "                                                                                          StandardScaler())]),\n",
       "                                                                         [&#x27;time&#x27;]),\n",
       "                                                                        (&#x27;kategorik&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;))]),\n",
       "                                                                         [&#x27;anaemia&#x27;,\n",
       "                                                                          &#x27;high_blood_pressure&#x27;,\n",
       "                                                                          &#x27;diabetes&#x27;])])),\n",
       "                                       (&#x27;rf&#x27;,\n",
       "                                        RandomForestClassifier(random_state=69))]),\n",
       "             param_grid={&#x27;rf__max_depth&#x27;: [None, 5, 10],\n",
       "                         &#x27;rf__min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                         &#x27;rf__min_samples_split&#x27;: [2, 5, 10],\n",
       "                         &#x27;rf__n_estimators&#x27;: [100, 200, 300]},\n",
       "             scoring=&#x27;f1&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10,\n",
       "             estimator=Pipeline(steps=[(&#x27;preprocess&#x27;,\n",
       "                                        ColumnTransformer(transformers=[(&#x27;numerikout&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;outlier&#x27;,\n",
       "                                                                                          Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                                                                     tail=&#x27;both&#x27;)),\n",
       "                                                                                         (&#x27;imputer&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                         (&#x27;scaler&#x27;,\n",
       "                                                                                          MinMaxScaler())]),\n",
       "                                                                         [&#x27;serum_creatinine&#x27;,\n",
       "                                                                          &#x27;creatinine_phosphokinase&#x27;]),\n",
       "                                                                        (&#x27;numerik&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                                          Simpl...\n",
       "                                                                                          StandardScaler())]),\n",
       "                                                                         [&#x27;time&#x27;]),\n",
       "                                                                        (&#x27;kategorik&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;))]),\n",
       "                                                                         [&#x27;anaemia&#x27;,\n",
       "                                                                          &#x27;high_blood_pressure&#x27;,\n",
       "                                                                          &#x27;diabetes&#x27;])])),\n",
       "                                       (&#x27;rf&#x27;,\n",
       "                                        RandomForestClassifier(random_state=69))]),\n",
       "             param_grid={&#x27;rf__max_depth&#x27;: [None, 5, 10],\n",
       "                         &#x27;rf__min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                         &#x27;rf__min_samples_split&#x27;: [2, 5, 10],\n",
       "                         &#x27;rf__n_estimators&#x27;: [100, 200, 300]},\n",
       "             scoring=&#x27;f1&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;numerikout&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;outlier&#x27;,\n",
       "                                                                   Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                                              tail=&#x27;both&#x27;)),\n",
       "                                                                  (&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   MinMaxScaler())]),\n",
       "                                                  [&#x27;serum_creatinine&#x27;,\n",
       "                                                   &#x27;creatinine_phosphokinase&#x27;]),\n",
       "                                                 (&#x27;numerik&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;time&#x27;]),\n",
       "                                                 (&#x27;kategorik&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;))]),\n",
       "                                                  [&#x27;anaemia&#x27;,\n",
       "                                                   &#x27;high_blood_pressure&#x27;,\n",
       "                                                   &#x27;diabetes&#x27;])])),\n",
       "                (&#x27;rf&#x27;, RandomForestClassifier(random_state=69))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocess: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;numerikout&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;outlier&#x27;,\n",
       "                                                  Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                             tail=&#x27;both&#x27;)),\n",
       "                                                 (&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;scaler&#x27;, MinMaxScaler())]),\n",
       "                                 [&#x27;serum_creatinine&#x27;,\n",
       "                                  &#x27;creatinine_phosphokinase&#x27;]),\n",
       "                                (&#x27;numerik&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;time&#x27;]),\n",
       "                                (&#x27;kategorik&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;))]),\n",
       "                                 [&#x27;anaemia&#x27;, &#x27;high_blood_pressure&#x27;,\n",
       "                                  &#x27;diabetes&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerikout</label><div class=\"sk-toggleable__content\"><pre>[&#x27;serum_creatinine&#x27;, &#x27;creatinine_phosphokinase&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Winsorizer</label><div class=\"sk-toggleable__content\"><pre>Winsorizer(capping_method=&#x27;iqr&#x27;, tail=&#x27;both&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerik</label><div class=\"sk-toggleable__content\"><pre>[&#x27;time&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">kategorik</label><div class=\"sk-toggleable__content\"><pre>[&#x27;anaemia&#x27;, &#x27;high_blood_pressure&#x27;, &#x27;diabetes&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=69)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=10,\n",
       "             estimator=Pipeline(steps=[('preprocess',\n",
       "                                        ColumnTransformer(transformers=[('numerikout',\n",
       "                                                                         Pipeline(steps=[('outlier',\n",
       "                                                                                          Winsorizer(capping_method='iqr',\n",
       "                                                                                                     tail='both')),\n",
       "                                                                                         ('imputer',\n",
       "                                                                                          SimpleImputer(strategy='most_frequent')),\n",
       "                                                                                         ('scaler',\n",
       "                                                                                          MinMaxScaler())]),\n",
       "                                                                         ['serum_creatinine',\n",
       "                                                                          'creatinine_phosphokinase']),\n",
       "                                                                        ('numerik',\n",
       "                                                                         Pipeline(steps=[('imputer',\n",
       "                                                                                          Simpl...\n",
       "                                                                                          StandardScaler())]),\n",
       "                                                                         ['time']),\n",
       "                                                                        ('kategorik',\n",
       "                                                                         Pipeline(steps=[('imputer',\n",
       "                                                                                          SimpleImputer(strategy='most_frequent'))]),\n",
       "                                                                         ['anaemia',\n",
       "                                                                          'high_blood_pressure',\n",
       "                                                                          'diabetes'])])),\n",
       "                                       ('rf',\n",
       "                                        RandomForestClassifier(random_state=69))]),\n",
       "             param_grid={'rf__max_depth': [None, 5, 10],\n",
       "                         'rf__min_samples_leaf': [1, 2, 4],\n",
       "                         'rf__min_samples_split': [2, 5, 10],\n",
       "                         'rf__n_estimators': [100, 200, 300]},\n",
       "             scoring='f1')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search = GridSearchCV(modelRF, param_grid, cv=10, scoring='f1')\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_params = grid_search.best_params_\n",
    "best_score = grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'rf__max_depth': 10, 'rf__min_samples_leaf': 2, 'rf__min_samples_split': 2, 'rf__n_estimators': 100}\n",
      "0.6995357583592878\n"
     ]
    }
   ],
   "source": [
    "print(best_params)\n",
    "print(best_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;numerikout&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;outlier&#x27;,\n",
       "                                                                   Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                                              tail=&#x27;both&#x27;)),\n",
       "                                                                  (&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   MinMaxScaler())]),\n",
       "                                                  [&#x27;serum_creatinine&#x27;,\n",
       "                                                   &#x27;creatinine_phosphokinase&#x27;]),\n",
       "                                                 (&#x27;numerik&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;time&#x27;]),\n",
       "                                                 (&#x27;kategorik&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;))]),\n",
       "                                                  [&#x27;anaemia&#x27;,\n",
       "                                                   &#x27;high_blood_pressure&#x27;,\n",
       "                                                   &#x27;diabetes&#x27;])])),\n",
       "                (&#x27;rf&#x27;,\n",
       "                 RandomForestClassifier(max_depth=10, min_samples_leaf=2,\n",
       "                                        random_state=69))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;numerikout&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;outlier&#x27;,\n",
       "                                                                   Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                                              tail=&#x27;both&#x27;)),\n",
       "                                                                  (&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   MinMaxScaler())]),\n",
       "                                                  [&#x27;serum_creatinine&#x27;,\n",
       "                                                   &#x27;creatinine_phosphokinase&#x27;]),\n",
       "                                                 (&#x27;numerik&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;time&#x27;]),\n",
       "                                                 (&#x27;kategorik&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;))]),\n",
       "                                                  [&#x27;anaemia&#x27;,\n",
       "                                                   &#x27;high_blood_pressure&#x27;,\n",
       "                                                   &#x27;diabetes&#x27;])])),\n",
       "                (&#x27;rf&#x27;,\n",
       "                 RandomForestClassifier(max_depth=10, min_samples_leaf=2,\n",
       "                                        random_state=69))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocess: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;numerikout&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;outlier&#x27;,\n",
       "                                                  Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                             tail=&#x27;both&#x27;)),\n",
       "                                                 (&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;scaler&#x27;, MinMaxScaler())]),\n",
       "                                 [&#x27;serum_creatinine&#x27;,\n",
       "                                  &#x27;creatinine_phosphokinase&#x27;]),\n",
       "                                (&#x27;numerik&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;time&#x27;]),\n",
       "                                (&#x27;kategorik&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;))]),\n",
       "                                 [&#x27;anaemia&#x27;, &#x27;high_blood_pressure&#x27;,\n",
       "                                  &#x27;diabetes&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerikout</label><div class=\"sk-toggleable__content\"><pre>[&#x27;serum_creatinine&#x27;, &#x27;creatinine_phosphokinase&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Winsorizer</label><div class=\"sk-toggleable__content\"><pre>Winsorizer(capping_method=&#x27;iqr&#x27;, tail=&#x27;both&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerik</label><div class=\"sk-toggleable__content\"><pre>[&#x27;time&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">kategorik</label><div class=\"sk-toggleable__content\"><pre>[&#x27;anaemia&#x27;, &#x27;high_blood_pressure&#x27;, &#x27;diabetes&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=10, min_samples_leaf=2, random_state=69)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocess',\n",
       "                 ColumnTransformer(transformers=[('numerikout',\n",
       "                                                  Pipeline(steps=[('outlier',\n",
       "                                                                   Winsorizer(capping_method='iqr',\n",
       "                                                                              tail='both')),\n",
       "                                                                  ('imputer',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   MinMaxScaler())]),\n",
       "                                                  ['serum_creatinine',\n",
       "                                                   'creatinine_phosphokinase']),\n",
       "                                                 ('numerik',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['time']),\n",
       "                                                 ('kategorik',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(strategy='most_frequent'))]),\n",
       "                                                  ['anaemia',\n",
       "                                                   'high_blood_pressure',\n",
       "                                                   'diabetes'])])),\n",
       "                ('rf',\n",
       "                 RandomForestClassifier(max_depth=10, min_samples_leaf=2,\n",
       "                                        random_state=69))])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mencari best estimator\n",
    "modelRF_gridsearchCV = grid_search.best_estimator_\n",
    "modelRF_gridsearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Score modelRF_gridsearchCV array   : [0.85714286 0.82352941 0.66666667 0.54545455 0.66666667 0.71428571\n",
      " 0.76923077 0.57142857 0.66666667 0.71428571]\n",
      "Cross Validation Score modelRF_gridsearchCV         : 0.70\n",
      "Std dev Cross Validation modelRF_gridsearchCV       : 0.00\n"
     ]
    }
   ],
   "source": [
    "# cross validation setelah tuning\n",
    "scores = cross_val_score(modelRF_gridsearchCV, X_train, y_train,scoring='f1', cv=10)\n",
    "print(\"Cross Validation Score modelRF_gridsearchCV array   :\",scores)\n",
    "print(\"Cross Validation Score modelRF_gridsearchCV         : {:.2f}\".format(grid_search.best_score_))\n",
    "print(\"Std dev Cross Validation modelRF_gridsearchCV       : {:.2f}\".format(grid_search.best_score_.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**XGBOOST**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;numerikout&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;outlier&#x27;,\n",
       "                                                                   Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                                              tail=&#x27;both&#x27;)),\n",
       "                                                                  (&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   MinMaxScaler())]),\n",
       "                                                  [&#x27;serum_creatinine&#x27;,\n",
       "                                                   &#x27;creatinine_phosphokinase&#x27;]),\n",
       "                                                 (&#x27;numerik&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequ...\n",
       "                                 grow_policy=None, importance_type=None,\n",
       "                                 interaction_constraints=None, max_bin=None,\n",
       "                                 max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "                                 max_delta_step=None, max_depth=None,\n",
       "                                 max_leaves=None, min_child_weight=None,\n",
       "                                 missing=nan, monotone_constraints=None,\n",
       "                                 n_estimators=100, n_jobs=None,\n",
       "                                 num_parallel_tree=None,\n",
       "                                 objective=&#x27;binary:logistic&#x27;, predictor=None,\n",
       "                                 random_state=69, reg_alpha=None, ...))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;numerikout&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;outlier&#x27;,\n",
       "                                                                   Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                                              tail=&#x27;both&#x27;)),\n",
       "                                                                  (&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   MinMaxScaler())]),\n",
       "                                                  [&#x27;serum_creatinine&#x27;,\n",
       "                                                   &#x27;creatinine_phosphokinase&#x27;]),\n",
       "                                                 (&#x27;numerik&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequ...\n",
       "                                 grow_policy=None, importance_type=None,\n",
       "                                 interaction_constraints=None, max_bin=None,\n",
       "                                 max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "                                 max_delta_step=None, max_depth=None,\n",
       "                                 max_leaves=None, min_child_weight=None,\n",
       "                                 missing=nan, monotone_constraints=None,\n",
       "                                 n_estimators=100, n_jobs=None,\n",
       "                                 num_parallel_tree=None,\n",
       "                                 objective=&#x27;binary:logistic&#x27;, predictor=None,\n",
       "                                 random_state=69, reg_alpha=None, ...))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocess: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;numerikout&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;outlier&#x27;,\n",
       "                                                  Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                             tail=&#x27;both&#x27;)),\n",
       "                                                 (&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;scaler&#x27;, MinMaxScaler())]),\n",
       "                                 [&#x27;serum_creatinine&#x27;,\n",
       "                                  &#x27;creatinine_phosphokinase&#x27;]),\n",
       "                                (&#x27;numerik&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;time&#x27;]),\n",
       "                                (&#x27;kategorik&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;))]),\n",
       "                                 [&#x27;anaemia&#x27;, &#x27;high_blood_pressure&#x27;,\n",
       "                                  &#x27;diabetes&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerikout</label><div class=\"sk-toggleable__content\"><pre>[&#x27;serum_creatinine&#x27;, &#x27;creatinine_phosphokinase&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-41\" type=\"checkbox\" ><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Winsorizer</label><div class=\"sk-toggleable__content\"><pre>Winsorizer(capping_method=&#x27;iqr&#x27;, tail=&#x27;both&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-42\" type=\"checkbox\" ><label for=\"sk-estimator-id-42\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-43\" type=\"checkbox\" ><label for=\"sk-estimator-id-43\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-44\" type=\"checkbox\" ><label for=\"sk-estimator-id-44\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerik</label><div class=\"sk-toggleable__content\"><pre>[&#x27;time&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-45\" type=\"checkbox\" ><label for=\"sk-estimator-id-45\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-46\" type=\"checkbox\" ><label for=\"sk-estimator-id-46\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-47\" type=\"checkbox\" ><label for=\"sk-estimator-id-47\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">kategorik</label><div class=\"sk-toggleable__content\"><pre>[&#x27;anaemia&#x27;, &#x27;high_blood_pressure&#x27;, &#x27;diabetes&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-48\" type=\"checkbox\" ><label for=\"sk-estimator-id-48\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-49\" type=\"checkbox\" ><label for=\"sk-estimator-id-49\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRFClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBRFClassifier(base_score=None, booster=None, callbacks=None,\n",
       "                colsample_bylevel=None, colsample_bytree=None,\n",
       "                early_stopping_rounds=None, enable_categorical=False,\n",
       "                eval_metric=None, feature_types=None, gamma=None, gpu_id=None,\n",
       "                grow_policy=None, importance_type=None,\n",
       "                interaction_constraints=None, max_bin=None,\n",
       "                max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "                max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "                min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "                n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "                objective=&#x27;binary:logistic&#x27;, predictor=None, random_state=69,\n",
       "                reg_alpha=None, ...)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocess',\n",
       "                 ColumnTransformer(transformers=[('numerikout',\n",
       "                                                  Pipeline(steps=[('outlier',\n",
       "                                                                   Winsorizer(capping_method='iqr',\n",
       "                                                                              tail='both')),\n",
       "                                                                  ('imputer',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   MinMaxScaler())]),\n",
       "                                                  ['serum_creatinine',\n",
       "                                                   'creatinine_phosphokinase']),\n",
       "                                                 ('numerik',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(strategy='most_frequ...\n",
       "                                 grow_policy=None, importance_type=None,\n",
       "                                 interaction_constraints=None, max_bin=None,\n",
       "                                 max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "                                 max_delta_step=None, max_depth=None,\n",
       "                                 max_leaves=None, min_child_weight=None,\n",
       "                                 missing=nan, monotone_constraints=None,\n",
       "                                 n_estimators=100, n_jobs=None,\n",
       "                                 num_parallel_tree=None,\n",
       "                                 objective='binary:logistic', predictor=None,\n",
       "                                 random_state=69, reg_alpha=None, ...))])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training xgboost\n",
    "modelXG = xgpipe.fit(X_train,y_train)\n",
    "modelXG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'xgb__n_estimators': [100, 200, 300],  # Number of trees in the ensemble\n",
    "    'xgb__max_depth': [3, 5, 7],  # Maximum depth of a tree\n",
    "    'xgb__learning_rate': [0.1, 0.01, 0.001],  # Learning rate for boosting\n",
    "    'xgb__subsample': [0.8, 1.0],  # Subsample ratio of the training instances\n",
    "    'xgb__colsample_bytree': [0.8, 1.0],  # Subsample ratio of columns when constructing each tree\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10,\n",
       "             estimator=Pipeline(steps=[(&#x27;preprocess&#x27;,\n",
       "                                        ColumnTransformer(transformers=[(&#x27;numerikout&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;outlier&#x27;,\n",
       "                                                                                          Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                                                                     tail=&#x27;both&#x27;)),\n",
       "                                                                                         (&#x27;imputer&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                         (&#x27;scaler&#x27;,\n",
       "                                                                                          MinMaxScaler())]),\n",
       "                                                                         [&#x27;serum_creatinine&#x27;,\n",
       "                                                                          &#x27;creatinine_phosphokinase&#x27;]),\n",
       "                                                                        (&#x27;numerik&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                                          Simpl...\n",
       "                                                        missing=nan,\n",
       "                                                        monotone_constraints=None,\n",
       "                                                        n_estimators=100,\n",
       "                                                        n_jobs=None,\n",
       "                                                        num_parallel_tree=None,\n",
       "                                                        objective=&#x27;binary:logistic&#x27;,\n",
       "                                                        predictor=None,\n",
       "                                                        random_state=69,\n",
       "                                                        reg_alpha=None, ...))]),\n",
       "             param_grid={&#x27;xgb__colsample_bytree&#x27;: [0.8, 1.0],\n",
       "                         &#x27;xgb__learning_rate&#x27;: [0.1, 0.01, 0.001],\n",
       "                         &#x27;xgb__max_depth&#x27;: [3, 5, 7],\n",
       "                         &#x27;xgb__n_estimators&#x27;: [100, 200, 300],\n",
       "                         &#x27;xgb__subsample&#x27;: [0.8, 1.0]},\n",
       "             scoring=&#x27;f1&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-50\" type=\"checkbox\" ><label for=\"sk-estimator-id-50\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10,\n",
       "             estimator=Pipeline(steps=[(&#x27;preprocess&#x27;,\n",
       "                                        ColumnTransformer(transformers=[(&#x27;numerikout&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;outlier&#x27;,\n",
       "                                                                                          Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                                                                     tail=&#x27;both&#x27;)),\n",
       "                                                                                         (&#x27;imputer&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                         (&#x27;scaler&#x27;,\n",
       "                                                                                          MinMaxScaler())]),\n",
       "                                                                         [&#x27;serum_creatinine&#x27;,\n",
       "                                                                          &#x27;creatinine_phosphokinase&#x27;]),\n",
       "                                                                        (&#x27;numerik&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                                          Simpl...\n",
       "                                                        missing=nan,\n",
       "                                                        monotone_constraints=None,\n",
       "                                                        n_estimators=100,\n",
       "                                                        n_jobs=None,\n",
       "                                                        num_parallel_tree=None,\n",
       "                                                        objective=&#x27;binary:logistic&#x27;,\n",
       "                                                        predictor=None,\n",
       "                                                        random_state=69,\n",
       "                                                        reg_alpha=None, ...))]),\n",
       "             param_grid={&#x27;xgb__colsample_bytree&#x27;: [0.8, 1.0],\n",
       "                         &#x27;xgb__learning_rate&#x27;: [0.1, 0.01, 0.001],\n",
       "                         &#x27;xgb__max_depth&#x27;: [3, 5, 7],\n",
       "                         &#x27;xgb__n_estimators&#x27;: [100, 200, 300],\n",
       "                         &#x27;xgb__subsample&#x27;: [0.8, 1.0]},\n",
       "             scoring=&#x27;f1&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-51\" type=\"checkbox\" ><label for=\"sk-estimator-id-51\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;numerikout&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;outlier&#x27;,\n",
       "                                                                   Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                                              tail=&#x27;both&#x27;)),\n",
       "                                                                  (&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   MinMaxScaler())]),\n",
       "                                                  [&#x27;serum_creatinine&#x27;,\n",
       "                                                   &#x27;creatinine_phosphokinase&#x27;]),\n",
       "                                                 (&#x27;numerik&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequ...\n",
       "                                 grow_policy=None, importance_type=None,\n",
       "                                 interaction_constraints=None, max_bin=None,\n",
       "                                 max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "                                 max_delta_step=None, max_depth=None,\n",
       "                                 max_leaves=None, min_child_weight=None,\n",
       "                                 missing=nan, monotone_constraints=None,\n",
       "                                 n_estimators=100, n_jobs=None,\n",
       "                                 num_parallel_tree=None,\n",
       "                                 objective=&#x27;binary:logistic&#x27;, predictor=None,\n",
       "                                 random_state=69, reg_alpha=None, ...))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-52\" type=\"checkbox\" ><label for=\"sk-estimator-id-52\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocess: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;numerikout&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;outlier&#x27;,\n",
       "                                                  Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                             tail=&#x27;both&#x27;)),\n",
       "                                                 (&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;scaler&#x27;, MinMaxScaler())]),\n",
       "                                 [&#x27;serum_creatinine&#x27;,\n",
       "                                  &#x27;creatinine_phosphokinase&#x27;]),\n",
       "                                (&#x27;numerik&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;time&#x27;]),\n",
       "                                (&#x27;kategorik&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;))]),\n",
       "                                 [&#x27;anaemia&#x27;, &#x27;high_blood_pressure&#x27;,\n",
       "                                  &#x27;diabetes&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-53\" type=\"checkbox\" ><label for=\"sk-estimator-id-53\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerikout</label><div class=\"sk-toggleable__content\"><pre>[&#x27;serum_creatinine&#x27;, &#x27;creatinine_phosphokinase&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-54\" type=\"checkbox\" ><label for=\"sk-estimator-id-54\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Winsorizer</label><div class=\"sk-toggleable__content\"><pre>Winsorizer(capping_method=&#x27;iqr&#x27;, tail=&#x27;both&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-55\" type=\"checkbox\" ><label for=\"sk-estimator-id-55\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-56\" type=\"checkbox\" ><label for=\"sk-estimator-id-56\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-57\" type=\"checkbox\" ><label for=\"sk-estimator-id-57\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerik</label><div class=\"sk-toggleable__content\"><pre>[&#x27;time&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-58\" type=\"checkbox\" ><label for=\"sk-estimator-id-58\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-59\" type=\"checkbox\" ><label for=\"sk-estimator-id-59\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-60\" type=\"checkbox\" ><label for=\"sk-estimator-id-60\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">kategorik</label><div class=\"sk-toggleable__content\"><pre>[&#x27;anaemia&#x27;, &#x27;high_blood_pressure&#x27;, &#x27;diabetes&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-61\" type=\"checkbox\" ><label for=\"sk-estimator-id-61\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-62\" type=\"checkbox\" ><label for=\"sk-estimator-id-62\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRFClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBRFClassifier(base_score=None, booster=None, callbacks=None,\n",
       "                colsample_bylevel=None, colsample_bytree=None,\n",
       "                early_stopping_rounds=None, enable_categorical=False,\n",
       "                eval_metric=None, feature_types=None, gamma=None, gpu_id=None,\n",
       "                grow_policy=None, importance_type=None,\n",
       "                interaction_constraints=None, max_bin=None,\n",
       "                max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "                max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "                min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "                n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "                objective=&#x27;binary:logistic&#x27;, predictor=None, random_state=69,\n",
       "                reg_alpha=None, ...)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=10,\n",
       "             estimator=Pipeline(steps=[('preprocess',\n",
       "                                        ColumnTransformer(transformers=[('numerikout',\n",
       "                                                                         Pipeline(steps=[('outlier',\n",
       "                                                                                          Winsorizer(capping_method='iqr',\n",
       "                                                                                                     tail='both')),\n",
       "                                                                                         ('imputer',\n",
       "                                                                                          SimpleImputer(strategy='most_frequent')),\n",
       "                                                                                         ('scaler',\n",
       "                                                                                          MinMaxScaler())]),\n",
       "                                                                         ['serum_creatinine',\n",
       "                                                                          'creatinine_phosphokinase']),\n",
       "                                                                        ('numerik',\n",
       "                                                                         Pipeline(steps=[('imputer',\n",
       "                                                                                          Simpl...\n",
       "                                                        missing=nan,\n",
       "                                                        monotone_constraints=None,\n",
       "                                                        n_estimators=100,\n",
       "                                                        n_jobs=None,\n",
       "                                                        num_parallel_tree=None,\n",
       "                                                        objective='binary:logistic',\n",
       "                                                        predictor=None,\n",
       "                                                        random_state=69,\n",
       "                                                        reg_alpha=None, ...))]),\n",
       "             param_grid={'xgb__colsample_bytree': [0.8, 1.0],\n",
       "                         'xgb__learning_rate': [0.1, 0.01, 0.001],\n",
       "                         'xgb__max_depth': [3, 5, 7],\n",
       "                         'xgb__n_estimators': [100, 200, 300],\n",
       "                         'xgb__subsample': [0.8, 1.0]},\n",
       "             scoring='f1')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_searchxg = GridSearchCV(modelXG, params, cv=10, scoring='f1')\n",
    "grid_searchxg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;numerikout&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;outlier&#x27;,\n",
       "                                                                   Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                                              tail=&#x27;both&#x27;)),\n",
       "                                                                  (&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   MinMaxScaler())]),\n",
       "                                                  [&#x27;serum_creatinine&#x27;,\n",
       "                                                   &#x27;creatinine_phosphokinase&#x27;]),\n",
       "                                                 (&#x27;numerik&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequ...\n",
       "                                 grow_policy=None, importance_type=None,\n",
       "                                 interaction_constraints=None,\n",
       "                                 learning_rate=0.1, max_bin=None,\n",
       "                                 max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "                                 max_delta_step=None, max_depth=7,\n",
       "                                 max_leaves=None, min_child_weight=None,\n",
       "                                 missing=nan, monotone_constraints=None,\n",
       "                                 n_estimators=200, n_jobs=None,\n",
       "                                 num_parallel_tree=None,\n",
       "                                 objective=&#x27;binary:logistic&#x27;, predictor=None,\n",
       "                                 random_state=69, ...))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-63\" type=\"checkbox\" ><label for=\"sk-estimator-id-63\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;numerikout&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;outlier&#x27;,\n",
       "                                                                   Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                                              tail=&#x27;both&#x27;)),\n",
       "                                                                  (&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   MinMaxScaler())]),\n",
       "                                                  [&#x27;serum_creatinine&#x27;,\n",
       "                                                   &#x27;creatinine_phosphokinase&#x27;]),\n",
       "                                                 (&#x27;numerik&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequ...\n",
       "                                 grow_policy=None, importance_type=None,\n",
       "                                 interaction_constraints=None,\n",
       "                                 learning_rate=0.1, max_bin=None,\n",
       "                                 max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "                                 max_delta_step=None, max_depth=7,\n",
       "                                 max_leaves=None, min_child_weight=None,\n",
       "                                 missing=nan, monotone_constraints=None,\n",
       "                                 n_estimators=200, n_jobs=None,\n",
       "                                 num_parallel_tree=None,\n",
       "                                 objective=&#x27;binary:logistic&#x27;, predictor=None,\n",
       "                                 random_state=69, ...))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-64\" type=\"checkbox\" ><label for=\"sk-estimator-id-64\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocess: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;numerikout&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;outlier&#x27;,\n",
       "                                                  Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                             tail=&#x27;both&#x27;)),\n",
       "                                                 (&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;scaler&#x27;, MinMaxScaler())]),\n",
       "                                 [&#x27;serum_creatinine&#x27;,\n",
       "                                  &#x27;creatinine_phosphokinase&#x27;]),\n",
       "                                (&#x27;numerik&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;time&#x27;]),\n",
       "                                (&#x27;kategorik&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;))]),\n",
       "                                 [&#x27;anaemia&#x27;, &#x27;high_blood_pressure&#x27;,\n",
       "                                  &#x27;diabetes&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-65\" type=\"checkbox\" ><label for=\"sk-estimator-id-65\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerikout</label><div class=\"sk-toggleable__content\"><pre>[&#x27;serum_creatinine&#x27;, &#x27;creatinine_phosphokinase&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-66\" type=\"checkbox\" ><label for=\"sk-estimator-id-66\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Winsorizer</label><div class=\"sk-toggleable__content\"><pre>Winsorizer(capping_method=&#x27;iqr&#x27;, tail=&#x27;both&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-67\" type=\"checkbox\" ><label for=\"sk-estimator-id-67\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-68\" type=\"checkbox\" ><label for=\"sk-estimator-id-68\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-69\" type=\"checkbox\" ><label for=\"sk-estimator-id-69\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerik</label><div class=\"sk-toggleable__content\"><pre>[&#x27;time&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-70\" type=\"checkbox\" ><label for=\"sk-estimator-id-70\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-71\" type=\"checkbox\" ><label for=\"sk-estimator-id-71\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-72\" type=\"checkbox\" ><label for=\"sk-estimator-id-72\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">kategorik</label><div class=\"sk-toggleable__content\"><pre>[&#x27;anaemia&#x27;, &#x27;high_blood_pressure&#x27;, &#x27;diabetes&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-73\" type=\"checkbox\" ><label for=\"sk-estimator-id-73\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-74\" type=\"checkbox\" ><label for=\"sk-estimator-id-74\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRFClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBRFClassifier(base_score=None, booster=None, callbacks=None,\n",
       "                colsample_bylevel=None, colsample_bytree=1.0,\n",
       "                early_stopping_rounds=None, enable_categorical=False,\n",
       "                eval_metric=None, feature_types=None, gamma=None, gpu_id=None,\n",
       "                grow_policy=None, importance_type=None,\n",
       "                interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
       "                max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "                max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "                min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "                n_estimators=200, n_jobs=None, num_parallel_tree=None,\n",
       "                objective=&#x27;binary:logistic&#x27;, predictor=None, random_state=69, ...)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocess',\n",
       "                 ColumnTransformer(transformers=[('numerikout',\n",
       "                                                  Pipeline(steps=[('outlier',\n",
       "                                                                   Winsorizer(capping_method='iqr',\n",
       "                                                                              tail='both')),\n",
       "                                                                  ('imputer',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   MinMaxScaler())]),\n",
       "                                                  ['serum_creatinine',\n",
       "                                                   'creatinine_phosphokinase']),\n",
       "                                                 ('numerik',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(strategy='most_frequ...\n",
       "                                 grow_policy=None, importance_type=None,\n",
       "                                 interaction_constraints=None,\n",
       "                                 learning_rate=0.1, max_bin=None,\n",
       "                                 max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "                                 max_delta_step=None, max_depth=7,\n",
       "                                 max_leaves=None, min_child_weight=None,\n",
       "                                 missing=nan, monotone_constraints=None,\n",
       "                                 n_estimators=200, n_jobs=None,\n",
       "                                 num_parallel_tree=None,\n",
       "                                 objective='binary:logistic', predictor=None,\n",
       "                                 random_state=69, ...))])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mencari best estimator\n",
    "modelXG_gridsearchCV = grid_searchxg.best_estimator_\n",
    "modelXG_gridsearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Score modelXG_gridearchCV         : 0.71\n",
      "Std dev Cross Validation modelXG_gridsearchCV       : 0.00\n"
     ]
    }
   ],
   "source": [
    "# cross validation setelah tuning\n",
    "scores = cross_val_score(modelXG_gridsearchCV, X_train, y_train,scoring='f1', cv=10)\n",
    "print(\"Cross Validation Score modelXG_gridearchCV         : {:.2f}\".format(grid_searchxg.best_score_))\n",
    "print(\"Std dev Cross Validation modelXG_gridsearchCV       : {:.2f}\".format(grid_searchxg.best_score_.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1 score train :  1.0\n",
      "f1 score test :  0.8108108108108109\n"
     ]
    }
   ],
   "source": [
    "# performance random forest\n",
    "y_pred_train_RFC = modelRF.predict(X_train)\n",
    "y_pred_test_RFC = modelRF.predict(X_test)\n",
    "print('f1 score train : ',f1_score(y_train,y_pred_train_RFC))\n",
    "print('f1 score test : ',f1_score(y_test,y_pred_test_RFC))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1 score train :  0.9103448275862069\n",
      "f1 score test :  0.8421052631578947\n"
     ]
    }
   ],
   "source": [
    "# performance rf grid search\n",
    "y_pred_train_rf_gridsearchCV = modelRF_gridsearchCV.predict(X_train)\n",
    "y_pred_test_rf_gridsearchCV = modelRF_gridsearchCV.predict(X_test)\n",
    "print('f1 score train : ',f1_score(y_train,y_pred_train_rf_gridsearchCV))\n",
    "print('f1 score test : ',f1_score(y_test,y_pred_test_rf_gridsearchCV))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix Random Forest Train Set\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                           \n",
      "confusion matrix Random Forest Test Set\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix RFGS Train Set\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                           \n",
      "confusion matrix RFGS Test Set\n"
     ]
    },
    {
     "data": {
      "image/png": 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7Vvv27XM7ZsqUKbLZbG7LoEGDvLoPiRwAYAnnK3Izizc2b96se+65R9u2bVN+fr7q6uo0cuRIVVVVuR03atQolZWVuZaNGzd6dR9a6wAAeKGystJt3W63y263Nzhu06ZNbuurVq1SfHy8ioqKdNNNN7mdn5iY2Oh4qMgBAJZgmGyrn6/IU1JSFBsb61pycnI8uv+JEyckSXFxcW7bCwoKFB8fr/T0dE2fPl0VFRVe/VxU5AAASzAkGYa58yWptLRUDofDtf1C1XiDcw1Ds2fP1g033KBevXq5tmdlZWn8+PFKTU1VcXGx5s2bp+HDh6uoqMij60okcgAAvOJwONwSuSfuvfdeffbZZ3r//ffdtk+YMMH137169VJGRoZSU1P1xhtvaNy4cR5dm0QOALAEp2yyBeDNbvfdd59effVVbdmyRe3bt7/ksUlJSUpNTdX+/fs9vj6JHABgCU39HLlhGLrvvvu0fv16FRQUKC0t7bLnHD16VKWlpUpKSvL4Pkx2AwDAD+655x7913/9l9asWaOYmBiVl5ervLxcZ86ckSSdOnVKDzzwgD788EOVlJSooKBAo0ePVtu2bXX77bd7fB8qcgCAJTgNm2xN+K713NxcSdLQoUPdtq9atUpTpkxRs2bNtGvXLj333HM6fvy4kpKSNGzYMK1bt04xMTEe34dEDgCwBMMwOWvdy3ONy5wQHR2tt956q/EBfYvWOgAAIYyKHABgCeH60RQSOQDAEkjkAACEsKae7NZUGCMHACCEUZEDACyhqWetNxUSOQDAEs4lcjNj5D4MxodorQMAEMKoyAEAlsCsdQAAQpih//umeGPPD0a01gEACGFU5AAAS6C1DgBAKAvT3jqJHABgDSYrcgVpRc4YOQAAIYyKHABgCbzZDQCAEBauk91orQMAEMKoyAEA1mDYzE1YC9KKnEQOALCEcB0jp7UOAEAIoyIHAFgDL4QBACB0heusdY8S+VNPPeXxBWfOnNnoYAAAgHc8SuRLlizx6GI2m41EDgAIXkHaHjfDo0ReXFzs7zgAAPCrcG2tN3rWem1trfbt26e6ujpfxgMAgH8YPliCkNeJ/PTp05o6dapatmypnj176uDBg5LOjY0vXLjQ5wECAICL8zqRP/zww/r0009VUFCgqKgo1/Yf/vCHWrdunU+DAwDAd2w+WIKP14+fbdiwQevWrdOgQYNks/3fD9WjRw998cUXPg0OAACfCdPnyL2uyI8cOaL4+PgG26uqqtwSOwAA8D+vE/mAAQP0xhtvuNbPJ+9nnnlGmZmZvosMAABfCtPJbl631nNycjRq1Cjt2bNHdXV1WrZsmXbv3q0PP/xQmzdv9keMAACYF6ZfP/O6Ih88eLA++OADnT59Wp06ddLbb7+thIQEffjhh+rfv78/YgQAABfRqHet9+7dW3l5eb6OBQAAvwnXz5g2KpHX19dr/fr12rt3r2w2m7p3764xY8aoeXO+wQIACFJhOmvd68z7j3/8Q2PGjFF5ebm6du0qSfrnP/+pdu3a6dVXX1Xv3r19HiQAALgwr8fIp02bpp49e+rQoUP65JNP9Mknn6i0tFR9+vTRz3/+c3/ECACAeecnu5lZgpDXFfmnn36qwsJCXXHFFa5tV1xxhebPn68BAwb4NDgAAHzFZpxbzJwfjLyuyLt27aqvvvqqwfaKigp17tzZJ0EBAOBzYfocuUeJvLKy0rUsWLBAM2fO1EsvvaRDhw7p0KFDeumllzRr1iwtWrTI3/ECAIDv8Ki13qZNG7fXrxqGoR//+Meubca3c/JHjx6t+vp6P4QJAIBJYfpCGI8S+bvvvuvvOAAA8C8rP342ZMgQf8cBAAAaodFvcDl9+rQOHjyo2tpat+19+vQxHRQAAD4XphV5oz5jettttykmJkY9e/ZUv3793BYAAIJSE89az8nJ0YABAxQTE6P4+HiNHTtW+/btcw/JMJSdna3k5GRFR0dr6NCh2r17t1f38TqRz5o1S8eOHdO2bdsUHR2tTZs2KS8vT126dNGrr77q7eUAAAhLmzdv1j333KNt27YpPz9fdXV1GjlypKqqqlzHPPnkk1q8eLGWL1+u7du3KzExUSNGjNDJkyc9vo/XrfV33nlHr7zyigYMGKCIiAilpqZqxIgRcjgcysnJ0a233urtJQEA8D8fzVqvrKx022y322W32xscvmnTJrf1VatWKT4+XkVFRbrppptkGIaWLl2quXPnaty4cZKkvLw8JSQkaM2aNfrFL37hUVheV+RVVVWKj4+XJMXFxenIkSOSzn0R7ZNPPvH2cgAANInzb3Yzs0hSSkqKYmNjXUtOTo5H9z9x4oSkc7lTkoqLi1VeXq6RI0e6jrHb7RoyZIi2bt3q8c/ldUXetWtX7du3Tx06dNA111yjlStXqkOHDvrjH/+opKQkby8HAEBIKS0tlcPhcK1fqBr/PsMwNHv2bN1www3q1auXJKm8vFySlJCQ4HZsQkKCDhw44HE8XifyWbNmqaysTJL02GOP6eabb9YLL7ygyMhIrV692tvLAQDQNHw0a93hcLglck/ce++9+uyzz/T+++832PfdF65J55L+97dditeJ/I477nD9d79+/VRSUqL//d//1dVXX622bdt6ezkAAMLafffdp1dffVVbtmxR+/btXdsTExMlnavMv9vRrqioaFClX4rXY+Tf17JlS1177bUkcQBAULPJ5Bi5l/czDEP33nuv/vrXv+qdd95RWlqa2/60tDQlJiYqPz/fta22tlabN2/W4MGDPb6PRxX57NmzPb7g4sWLPT4WAIBwdc8992jNmjV65ZVXFBMT4xoTj42NVXR0tGw2m2bNmqUFCxaoS5cu6tKlixYsWKCWLVtq0qRJHt/Ho0S+Y8cOjy7mTU/fl25P763mthYBuTfgb4cf9Pw3cyDU1NdUS8teaZqbNfFHU3JzcyVJQ4cOddu+atUqTZkyRZI0Z84cnTlzRjNmzNCxY8c0cOBAvf3224qJifH4Pnw0BQBgDU38itbzXwa9FJvNpuzsbGVnZzcuJvlgjBwAAAROoz+aAgBASAnTj6aQyAEAlvDdt7M19vxgRGsdAIAQRkUOALCGMG2tN6oif/7553X99dcrOTnZ9T7YpUuX6pVXmugRAgAAvNXE3yNvKl4n8tzcXM2ePVu33HKLjh8/rvr6eklSmzZttHTpUl/HBwAALsHrRP7000/rmWee0dy5c9WsWTPX9oyMDO3atcunwQEA4Cu++oxpsPF6jLy4uFj9+vVrsN1ut6uqqsonQQEA4HNN/Ga3puJ1RZ6WlqadO3c22P7mm2+qR48evogJAADfC9Mxcq8r8gcffFD33HOPqqurZRiGPv74Y/33f/+3cnJy9Oc//9kfMQIAgIvwOpH/7Gc/U11dnebMmaPTp09r0qRJuuqqq7Rs2TJNnDjRHzECAGBauL4QplHPkU+fPl3Tp0/X119/LafTqfj4eF/HBQCAb4Xpc+SmXgjTtm1bX8UBAAAawetEnpaWdsnvjn/55ZemAgIAwC/MPkIWLhX5rFmz3NbPnj2rHTt2aNOmTXrwwQd9FRcAAL5Fa/2c+++//4Lb//M//1OFhYWmAwIAAJ7z2dfPsrKy9PLLL/vqcgAA+BbPkV/aSy+9pLi4OF9dDgAAn+Lxs2/169fPbbKbYRgqLy/XkSNHtGLFCp8GBwAALs3rRD527Fi39YiICLVr105Dhw5Vt27dfBUXAADwgFeJvK6uTh06dNDNN9+sxMREf8UEAIDvhemsda8muzVv3ly//OUvVVNT4694AADwi3D9jKnXs9YHDhyoHTt2+CMWAADgJa/HyGfMmKFf/epXOnTokPr3769WrVq57e/Tp4/PggMAwKeCtKo2w+NEftddd2np0qWaMGGCJGnmzJmufTabTYZhyGazqb6+3vdRAgBgVpiOkXucyPPy8rRw4UIVFxf7Mx4AAOAFjxO5YZz7VSQ1NdVvwQAA4C+8EEa65FfPAAAIalZvrUtSenr6ZZP5N998YyogAADgOa8S+eOPP67Y2Fh/xQIAgN/QWpc0ceJExcfH+ysWAAD8J0xb6x6/EIbxcQAAgo/Xs9YBAAhJYVqRe5zInU6nP+MAAMCvGCMHACCUhWlF7vVHUwAAQPCgIgcAWEOYVuQkcgCAJYTrGDmtdQAAQhgVOQDAGmitAwAQumitAwCAoENFDgCwBlrrAACEsDBN5LTWAQDwgy1btmj06NFKTk6WzWbThg0b3PZPmTJFNpvNbRk0aJDX9yGRAwAsweaDxRtVVVXq27evli9fftFjRo0apbKyMteyceNGL+9Cax0AYBU+aq1XVla6bbbb7bLb7Q0Oz8rKUlZW1iUvabfblZiYaCIoKnIAgEWcf/zMzCJJKSkpio2NdS05OTmNjqmgoEDx8fFKT0/X9OnTVVFR4fU1qMgBAPBCaWmpHA6Ha/1C1bgnsrKyNH78eKWmpqq4uFjz5s3T8OHDVVRU5NU1SeQAAGvwUWvd4XC4JfLGmjBhguu/e/XqpYyMDKWmpuqNN97QuHHjPL4OiRwAYB1B+giZJCUlJSk1NVX79+/36jzGyAEACAJHjx5VaWmpkpKSvDqPihwAYAlN/a71U6dO6fPPP3etFxcXa+fOnYqLi1NcXJyys7P1ox/9SElJSSopKdEjjzyitm3b6vbbb/fqPiRyAIA1NPGb3QoLCzVs2DDX+uzZsyVJkydPVm5urnbt2qXnnntOx48fV1JSkoYNG6Z169YpJibGq/uQyAEA8IOhQ4fKMC6e/d966y2f3IdEDgCwhHD9jCmJHABgDXw0BQAABBsqcgCAJdBaBwAglIVpa51EDgCwhjBN5IyRAwAQwqjIAQCWwBg5AAChjNY6AAAINlTkAABLsBmGbJd4Zaon5wcjEjkAwBporQMAgGBDRQ4AsARmrQMAEMporQMAgGBDRQ4AsARa6wAAhLIwba2TyAEAlhCuFTlj5AAAhDAqcgCANdBaBwAgtAVre9wMWusAAIQwKnIAgDUYxrnFzPlBiEQOALAEZq0DAICgQ0UOALAGZq0DABC6bM5zi5nzgxGtdQAAQhgVOS7rtp9+rVt/elQJKbWSpAP7ovTCkgQVvusIcGRA4/S/6rCmZOxUj/gjim99Wve/OkrvfJHmdkxa3DH9vxs+VEb7MkXYDH1+9Ao98MZIlZ+MCVDUMI3WOqzqSFkL/WVBkg6X2CVJI8Z/o+xVJbpnZLoO/DMqwNEB3otucVb/PHKlNuzupqWj32qwv33sCT334/X66+7uWvHhAJ2qtSst7phq65oFIFr4SrjOWg9oIt+yZYt+//vfq6ioSGVlZVq/fr3Gjh0byJBwAR/lx7qtr16UpNt+elTd+leRyBGS3i9J1fslqRfdP/P6j/VeSaqWvJfp2nboBB2okBemz5EHdIy8qqpKffv21fLlywMZBrwQEWFoyJhjsrd0am9hq0CHA/icTYZuSjugA8di9cfbX1fBL1bphYkva3in4kCHBlxQQCvyrKwsZWVleXx8TU2NampqXOuVlZX+CAsX0KHbGS197XNF2p06UxWh307toIP7qcYRfuJanlGryLO6a8AOLf/gOi15f5Bu6HBQS0Zv0tT/GaPCfyUHOkQ0Uri21kNq1npOTo5iY2NdS0pKSqBDsoxDX9g1Y0S67r+ti15/rq0eWHZQV3epDnRYgM9FfPuvdcEXHfT8jr7ad6Stnt1+rTZ/marxfXYHODqYYvhgCUIhlcgffvhhnThxwrWUlpYGOiTLqDsbocMldu3/rKVW5SSpeE+0xk47EuiwAJ87diZKZ+sj9MXROLftxd9coSTHqQBFBVxcSM1at9vtstvtgQ4D32oRGaS/ngIm1DmbafdX7dQh7rjb9tQrTqissnVggoJPhGtrPaQSOQLjZ78u0/Z3YnTkcKSiW9dr6Jjj6jP4lH5zR8dAhwY0SnSLs7q6zQnX+lWOSnVt97VOVNtVfjJGqwqv0R9uzVfRoSR9XHqVbuhwUEM6luiu/xkTwKhhWpjOWieR47LatKvTg08fVFx8nU6fbKbivVH6zR0d9ckWXoyB0NQzoUKrxr/qWp8zdKsk6ZXdXfWbt4frnS866rd/v0nTBuzQr4e9r5Jv2mj2azdrx+GkQIUMXFRAE/mpU6f0+eefu9aLi4u1c+dOxcXF6eqrrw5gZPiuJb9iUiHCS+Ghq9R7yS8vecyG3d21YXf3JooITYHWuh8UFhZq2LBhrvXZs2dLkiZPnqzVq1cHKCoAQFjiFa2+N3ToUBlBOuYAAEAoYIwcAGAJtNYBAAhlTuPcYub8IEQiBwBYQ5iOkYfUm90AAAgVW7Zs0ejRo5WcnCybzaYNGza47TcMQ9nZ2UpOTlZ0dLSGDh2q3bu9fw0wiRwAYAk2/d84eaMWL+93uS98Pvnkk1q8eLGWL1+u7du3KzExUSNGjNDJkye9ug+tdQCANfjozW7f//LmxV4ffqkvfBqGoaVLl2ru3LkaN26cJCkvL08JCQlas2aNfvGLX3gcFhU5AABeSElJcfsSZ05OjtfXKC4uVnl5uUaOHOnaZrfbNWTIEG3dutWra1GRAwAswVePn5WWlsrhcLi2N+ZjXuXl5ZKkhIQEt+0JCQk6cOCAV9cikQMArMFHs9YdDodbIjfDZnMfeTcMo8G2y6G1DgBAE0tMTJT0f5X5eRUVFQ2q9MshkQMALMFmGKYXX0lLS1NiYqLy8/Nd22pra7V582YNHjzYq2vRWgcAWIPz28XM+V643Bc+Z82apQULFqhLly7q0qWLFixYoJYtW2rSpEle3YdEDgCAH1zuC59z5szRmTNnNGPGDB07dkwDBw7U22+/rZiYGK/uQyIHAFiC2fa4t+de7gufNptN2dnZys7ObnRMEokcAGAVYfqudRI5AMAafPRmt2DDrHUAAEIYFTkAwBJ89Wa3YEMiBwBYA611AAAQbKjIAQCWYHOeW8ycH4xI5AAAa6C1DgAAgg0VOQDAGnghDAAAoaupX9HaVGitAwAQwqjIAQDWEKaT3UjkAABrMGTue+TBmcdJ5AAAa2CMHAAABB0qcgCANRgyOUbus0h8ikQOALCGMJ3sRmsdAIAQRkUOALAGpySbyfODEIkcAGAJzFoHAABBh4ocAGANYTrZjUQOALCGME3ktNYBAAhhVOQAAGsI04qcRA4AsAYePwMAIHTx+BkAAAg6VOQAAGtgjBwAgBDmNCSbiWTsDM5ETmsdAIAQRkUOALAGWusAAIQyk4lcwZnIaa0DABDCqMgBANZAax0AgBDmNGSqPc6sdQAA4GtU5AAAazCc5xYz5wchEjkAwBoYIwcAIIQxRg4AAIINFTkAwBporQMAEMIMmUzkPovEp2itAwAQwkjkAABrON9aN7N4ITs7WzabzW1JTEz0+Y9Fax0AYA1OpyQTz4I7vT+3Z8+e+tvf/uZab9asWePvfxEkcgAAvFBZWem2brfbZbfbL3hs8+bN/VKFfxetdQCANfiotZ6SkqLY2FjXkpOTc9Fb7t+/X8nJyUpLS9PEiRP15Zdf+vzHoiIHAFiDjx4/Ky0tlcPhcG2+WDU+cOBAPffcc0pPT9dXX32lJ554QoMHD9bu3bt15ZVXNj6O7yGRAwDgBYfD4ZbILyYrK8v1371791ZmZqY6deqkvLw8zZ4922fxkMgBANYQ4Fe0tmrVSr1799b+/ftNXef7GCMHAFiCYThNL2bU1NRo7969SkpK8tFPdA6JHABgDYZxrqpu7OLl+PoDDzygzZs3q7i4WB999JH+7d/+TZWVlZo8ebJPfyxa6wAA+MGhQ4f0k5/8RF9//bXatWunQYMGadu2bUpNTfXpfUjkAABrMEyOkXtZka9du7bx9/ICiRwAYA1Op2QzMc5tcozcXxgjBwAghFGRAwCsoYlb602FRA4AsATD6ZRhorVu9vEzf6G1DgBACKMiBwBYA611AABCmNOQbOGXyGmtAwAQwqjIAQDWYBiSzDxHHpwVOYkcAGAJhtOQYaK1bpDIAQAIIMMpcxU5j58BAAAfoyIHAFgCrXUAAEJZmLbWQzqRn//tqE5nTT3jDwSz+prqQIcA+M35v99NUe2azRV1Ouu7YHzIZgRrr8ADhw4dUkpKSqDDAACYVFpaqvbt2/vl2tXV1UpLS1N5ebnpayUmJqq4uFhRUVE+iMw3QjqRO51OHT58WDExMbLZbIEOxxIqKyuVkpKi0tJSORyOQIcD+BR/v5ueYRg6efKkkpOTFRHhv/nX1dXVqq2tNX2dyMjIoEriUoi31iMiIvz2GxwuzeFw8A8dwhZ/v5tWbGys3+8RFRUVdAnYV3j8DACAEEYiBwAghJHI4RW73a7HHntMdrs90KEAPsffb4SikJ7sBgCA1VGRAwAQwkjkAACEMBI5AAAhjEQOAEAII5HDYytWrFBaWpqioqLUv39/vffee4EOCfCJLVu2aPTo0UpOTpbNZtOGDRsCHRLgMRI5PLJu3TrNmjVLc+fO1Y4dO3TjjTcqKytLBw8eDHRogGlVVVXq27evli9fHuhQAK/x+Bk8MnDgQF177bXKzc11bevevbvGjh2rnJycAEYG+JbNZtP69es1duzYQIcCeISKHJdVW1uroqIijRw50m37yJEjtXXr1gBFBQCQSOTwwNdff636+nolJCS4bU9ISPDJZwEBAI1HIofHvv+pWMMw+HwsAAQYiRyX1bZtWzVr1qxB9V1RUdGgSgcANC0SOS4rMjJS/fv3V35+vtv2/Px8DR48OEBRAQAkqXmgA0BomD17tu68805lZGQoMzNTf/rTn3Tw4EHdfffdgQ4NMO3UqVP6/PPPXevFxcXauXOn4uLidPXVVwcwMuDyePwMHluxYoWefPJJlZWVqVevXlqyZIluuummQIcFmFZQUKBhw4Y12D558mStXr266QMCvEAiBwAghDFGDgBACCORAwAQwkjkAACEMBI5AAAhjEQOAEAII5EDABDCSOQAAIQwEjkAACGMRA6YlJ2drWuuuca1PmXKFI0dO7bJ4ygpKZHNZtPOnTsvekyHDh20dOlSj6+5evVqtWnTxnRsNptNGzZsMH0dAA2RyBGWpkyZIpvNJpvNphYtWqhjx4564IEHVFVV5fd7L1u2zOPXenqSfAHgUvhoCsLWqFGjtGrVKp09e1bvvfeepk2bpqqqKuXm5jY49uzZs2rRooVP7hsbG+uT6wCAJ6jIEbbsdrsSExOVkpKiSZMm6Y477nC1d8+3w//yl7+oY8eOstvtMgxDJ06c0M9//nPFx8fL4XBo+PDh+vTTT92uu3DhQiUkJCgmJkZTp05VdXW12/7vt9adTqcWLVqkzp07y2636+qrr9b8+fMlSWlpaZKkfv36yWazaejQoa7zVq1ape7duysqKkrdunXTihUr3O7z8ccfq1+/foqKilJGRoZ27Njh9Z/R4sWL1bt3b7Vq1UopKSmaMWOGTp061eC4DRs2KD09XVFRURoxYoRKS0vd9r/22mvq37+/oqKi1LFjRz3++OOqq6vzOh4A3iORwzKio6N19uxZ1/rnn3+uF198US+//LKrtX3rrbeqvLxcGzduVFFRka699lr94Ac/0DfffCNJevHFF/XYY49p/vz5KiwsVFJSUoME+30PP/ywFi1apHnz5mnPnj1as2aNEhISJJ1LxpL0t7/9TWVlZfrrX/8qSXrmmWc0d+5czZ8/X3v37tWCBQs0b9485eXlSZKqqqp02223qWvXrioqKlJ2drYeeOABr/9MIiIi9NRTT+kf//iH8vLy9M4772jOnDlux5w+fVrz589XXl6ePvjgA1VWVmrixImu/W+99Zb+/d//XTNnztSePXu0cuVKrV692vXLCgA/M4AwNHnyZGPMmDGu9Y8++si48sorjR//+MeGYRjGY489ZrRo0cKoqKhwHfP3v//dcDgcRnV1tdu1OnXqZKxcudIwDMPIzMw07r77brf9AwcONPr27XvBe1dWVhp2u9145plnLhhncXGxIcnYsWOH2/aUlBRjzZo1btt+97vfGZmZmYZhGMbKlSuNuLg4o6qqyrU/Nzf3gtf6rtTUVGPJkiUX3f/iiy8aV155pWt91apVhiRj27Ztrm179+41JBkfffSRYRiGceONNxoLFixwu87zzz9vJCUludYlGevXr7/ofQE0HmPkCFuvv/66Wrdurbq6Op09e1ZjxozR008/7dqfmpqqdu3audaLiop06tQpXXnllW7XOXPmjL744gtJ0t69e3X33Xe77c/MzNS77757wRj27t2rmpoa/eAHP/A47iNHjqi0tFRTp07V9OnTXdvr6upc4+979+5V37591bJlS7c4vPXuu+9qwYIF2rNnjyorK1VXV6fq6mpVVVWpVatWkqTmzZsrIyPDdU63bt3Upk0b7d27V9ddd52Kioq0fft2twq8vr5e1dXVOn36tFuMAHyPRI6wNWzYMOXm5qpFixZKTk5uMJntfKI6z+l0KikpSQUFBQ2u1dhHsKKjo70+x+l0SjrXXh84cKDbvmbNmkmSDMNoVDzfdeDAAd1yyy26++679bvf/U5xcXF6//33NXXqVLchCOnc42Pfd36b0+nU448/rnHjxjU4JioqynScAC6NRI6w1apVK3Xu3Nnj46+99lqVl5erefPm6tChwwWP6d69u7Zt26af/vSnrm3btm276DW7dOmi6Oho/f3vf9e0adMa7I+MjJR0roI9LyEhQVdddZW+/PJL3XHHHRe8bo8ePfT888/rzJkzrl8WLhXHhRQWFqqurk7/8R//oYiIc9NlXnzxxQbH1dXVqbCwUNddd50kad++fTp+/Li6desm6dyf2759+7z6swbgOyRy4Fs//OEPlZmZqbFjx2rRokXq2rWrDh8+rI0bN2rs2LHKyMjQ/fffr8mTJysjI0M33HCDXnjhBe3evVsdO3a84DWjoqL00EMPac6cOYqMjNT111+vI0eOaPfu3Zo6dari4+MVHR2tTZs2qX379oqKilJsbKyys7M1c+ZMORwOZWVlqaamRoWFhTp27Jhmz56tSZMmae7cuZo6dap+85vfqKSkRH/4wx+8+nk7deqkuro6Pf300xo9erQ++OAD/fGPf2xwXIsWLXTffffpqaeeUosWLXTvvfdq0KBBrsT+6KOP6rbbblNKSorGjx+viIgIffbZZ9q1a5eeeOIJ7/9HAPAKs9aBb9lsNm3cuFE33XST7rrrLqWnp2vixIkqKSlxzTKfMGGCHn30UT300EPq37+/Dhw4oF/+8peXvO68efP0q1/9So8++qi6d++uCRMmqKKiQtK58eennnpKK1euVHJyssaMGSNJmjZtmv785z9r9erV6t27t4YMGaLVq1e7Hldr3bq1XnvtNe3Zs0f9+vXT3LlztWjRIq9+3muuuUaLFy/WokWL1KtXL73wwgvKyclpcFzLli310EMPadKkScrMzFR0dLTWrl3r2n/zzTfr9ddfV35+vgYMGKBBgwZp8eLFSk1N9SoeAI1jM3wx2AYAAAKCihwAgBBGIgcAIISRyAEACGEkcgAAQhiJHACAEEYiBwAghJHIAQAIYSRyAABCGIkcAIAQRiIHACCEkcgBAAhh/x838GhILGeT4gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion matrix random forest\n",
    "print('confusion matrix Random Forest Train Set')\n",
    "rf = confusion_matrix(y_train,y_pred_train_RFC, labels=modelRF.classes_)\n",
    "disprf = ConfusionMatrixDisplay(confusion_matrix=rf,display_labels=modelRF.classes_)\n",
    "disprf.plot()\n",
    "plt.show()\n",
    "print('                                           ')\n",
    "print('confusion matrix Random Forest Test Set')\n",
    "rf2 = confusion_matrix(y_test,y_pred_test_RFC, labels=modelRF.classes_)\n",
    "disprf2 = ConfusionMatrixDisplay(confusion_matrix=rf2,display_labels=modelRF.classes_)\n",
    "disprf2.plot()\n",
    "plt.show()\n",
    "# confusion matrix random forest grid search\n",
    "print('confusion matrix RFGS Train Set')\n",
    "rf3 = confusion_matrix(y_train,y_pred_train_rf_gridsearchCV, labels=modelRF_gridsearchCV.classes_)\n",
    "disprf3 = ConfusionMatrixDisplay(confusion_matrix=rf3,display_labels=modelRF_gridsearchCV.classes_)\n",
    "disprf3.plot()\n",
    "plt.show()\n",
    "print('                                           ')\n",
    "print('confusion matrix RFGS Test Set')\n",
    "rf4 = confusion_matrix(y_test,y_pred_test_rf_gridsearchCV, labels=modelRF_gridsearchCV.classes_)\n",
    "disprf4 = ConfusionMatrixDisplay(confusion_matrix=rf4,display_labels=modelRF_gridsearchCV.classes_)\n",
    "disprf4.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classification report RF Train Set\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       155\n",
      "           1       1.00      1.00      1.00        76\n",
      "\n",
      "    accuracy                           1.00       231\n",
      "   macro avg       1.00      1.00      1.00       231\n",
      "weighted avg       1.00      1.00      1.00       231\n",
      "\n",
      "                                  \n",
      "classification report RF Test Set\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.92      0.91        39\n",
      "           1       0.83      0.79      0.81        19\n",
      "\n",
      "    accuracy                           0.88        58\n",
      "   macro avg       0.87      0.86      0.86        58\n",
      "weighted avg       0.88      0.88      0.88        58\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# classification report RF\n",
    "print('classification report RF Train Set')\n",
    "print(classification_report(y_train,y_pred_train_RFC))\n",
    "print('                                  ')\n",
    "print('classification report RF Test Set')\n",
    "print(classification_report(y_test,y_pred_test_RFC))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classification report RFGS Train Set\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.98      0.96       155\n",
      "           1       0.96      0.87      0.91        76\n",
      "\n",
      "    accuracy                           0.94       231\n",
      "   macro avg       0.95      0.92      0.93       231\n",
      "weighted avg       0.94      0.94      0.94       231\n",
      "\n",
      "                                  \n",
      "classification report RFGS Test Set\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.92      0.92        39\n",
      "           1       0.84      0.84      0.84        19\n",
      "\n",
      "    accuracy                           0.90        58\n",
      "   macro avg       0.88      0.88      0.88        58\n",
      "weighted avg       0.90      0.90      0.90        58\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# classification report RFGS\n",
    "print('classification report RFGS Train Set')\n",
    "print(classification_report(y_train,y_pred_train_rf_gridsearchCV))\n",
    "print('                                  ')\n",
    "print('classification report RFGS Test Set')\n",
    "print(classification_report(y_test,y_pred_test_rf_gridsearchCV))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1 score train :  0.8571428571428571\n",
      "f1 score test :  0.8717948717948718\n"
     ]
    }
   ],
   "source": [
    "# performance XGBoost\n",
    "y_pred_train_XGB = modelXG.predict(X_train)\n",
    "y_pred_test_XGB = modelXG.predict(X_test)\n",
    "print('f1 score train : ',f1_score(y_train,y_pred_train_XGB))\n",
    "print('f1 score test : ',f1_score(y_test,y_pred_test_XGB))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1 score train :  0.8666666666666667\n",
      "f1 score test :  0.8421052631578947\n"
     ]
    }
   ],
   "source": [
    "# performance XGBoost GridSearch\n",
    "y_pred_train_XGB_gridsearchCV = modelXG_gridsearchCV.predict(X_train)\n",
    "y_pred_test_XGB_gridsearchCV = modelXG_gridsearchCV.predict(X_test)\n",
    "print('f1 score train : ',f1_score(y_train,y_pred_train_XGB_gridsearchCV))\n",
    "print('f1 score test : ',f1_score(y_test,y_pred_test_XGB_gridsearchCV))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix XGBoost Train Set\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                           \n",
      "confusion matrix XGBoost Test Set\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix XGBGS Train Set\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                           \n",
      "confusion matrix XGBGS Test Set\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion matrix XGB\n",
    "print('confusion matrix XGBoost Train Set')\n",
    "xg = confusion_matrix(y_train,y_pred_train_XGB, labels=modelXG.classes_)\n",
    "dispxg = ConfusionMatrixDisplay(confusion_matrix=xg,display_labels=modelXG.classes_)\n",
    "dispxg.plot()\n",
    "plt.show()\n",
    "print('                                           ')\n",
    "print('confusion matrix XGBoost Test Set')\n",
    "xg2 = confusion_matrix(y_test,y_pred_test_XGB, labels=modelXG.classes_)\n",
    "dispxg2 = ConfusionMatrixDisplay(confusion_matrix=xg2,display_labels=modelXG.classes_)\n",
    "dispxg2.plot()\n",
    "plt.show()\n",
    "# confusion matrix XGB grid search\n",
    "print('confusion matrix XGBGS Train Set')\n",
    "xg3 = confusion_matrix(y_train,y_pred_train_XGB_gridsearchCV, labels=modelXG_gridsearchCV.classes_)\n",
    "dispxg3 = ConfusionMatrixDisplay(confusion_matrix=xg3,display_labels=modelXG_gridsearchCV.classes_)\n",
    "dispxg3.plot()\n",
    "plt.show()\n",
    "print('                                           ')\n",
    "print('confusion matrix XGBGS Test Set')\n",
    "xg4 = confusion_matrix(y_test,y_pred_test_XGB_gridsearchCV, labels=modelXG_gridsearchCV.classes_)\n",
    "dispxg4 = ConfusionMatrixDisplay(confusion_matrix=xg4,display_labels=modelXG_gridsearchCV.classes_)\n",
    "dispxg4.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classification report XGBoost Train Set\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.95      0.93       155\n",
      "           1       0.89      0.83      0.86        76\n",
      "\n",
      "    accuracy                           0.91       231\n",
      "   macro avg       0.90      0.89      0.90       231\n",
      "weighted avg       0.91      0.91      0.91       231\n",
      "\n",
      "                                  \n",
      "classification report XGBoost Test Set\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.92      0.94        39\n",
      "           1       0.85      0.89      0.87        19\n",
      "\n",
      "    accuracy                           0.91        58\n",
      "   macro avg       0.90      0.91      0.90        58\n",
      "weighted avg       0.92      0.91      0.91        58\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# classification report XGBoost\n",
    "print('classification report XGBoost Train Set')\n",
    "print(classification_report(y_train,y_pred_train_XGB))\n",
    "print('                                  ')\n",
    "print('classification report XGBoost Test Set')\n",
    "print(classification_report(y_test,y_pred_test_XGB))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classification report XGBoostGS Train Set\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.94      0.94       155\n",
      "           1       0.88      0.86      0.87        76\n",
      "\n",
      "    accuracy                           0.91       231\n",
      "   macro avg       0.90      0.90      0.90       231\n",
      "weighted avg       0.91      0.91      0.91       231\n",
      "\n",
      "                                  \n",
      "classification report XGBoostGS Test Set\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.92      0.92        39\n",
      "           1       0.84      0.84      0.84        19\n",
      "\n",
      "    accuracy                           0.90        58\n",
      "   macro avg       0.88      0.88      0.88        58\n",
      "weighted avg       0.90      0.90      0.90        58\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# classification report XGBoost GS\n",
    "print('classification report XGBoostGS Train Set')\n",
    "print(classification_report(y_train,y_pred_train_XGB_gridsearchCV))\n",
    "print('                                  ')\n",
    "print('classification report XGBoostGS Test Set')\n",
    "print(classification_report(y_test,y_pred_test_XGB_gridsearchCV))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dari evaluasi masing-masing model, terlihat bahwa untuk model XGBoost adalah yang terbaik. Bahkan sebelum tuning. Secara model juga masih goodfit. Pengukurannya menggunakan metrik f1-score. Pemilihannya karena kita masih belum tahu apa yang lebih penting. Untuk pemilihannya berdasarkan kedua kategori false negative dan false positive sama-sama tidak diinginkan. Ketika seseorang diprediksi meninggal, walaupun bisa sehat-sehat saja, mental pasien akan jatuh dan akan menambah masalah baru. Ketika orang yang diprediksi sehat tetapi ternyata akan meninggal, kasihan karena pihak keluarga juga tidak bersiap-siap untuk ditinggal oleh yang bersangkutan. Oleh karena itu, metrik f1-score lah yang dipilih. Jika kita tinjau kembali, skor yang ddidapatkan di sekitar 0.86. Ini sudah cukup baik mengingat datanya juga imbalance. Pada dasarnya semua model tidak memiliki oerforma yang buruk. Hanya saja, pada machine learning, hal yang difokuskan adalah apakah modelnya bestfit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kesimpulan yang dapat diambil dari pengerjaan model ini adalah model bisa terwujud dengan akurasi yang cukup tinggi. Insight yang didapatkan juga cukup banyak. Pengguna bisa memanfaatkan model untuk memprediksi pasien-pasien yang sekiranya tidak tertolong untuk diusahakan mendapatkan pertolongan lebih. Mulai dari kecepatan, ketepatan, ketelitian, dan sebagainya dibutuhkan sekali pada keadaan darurat. Untuk menghindari itu semua, institusi kesehatan yang bersangkutan juga bisa mencoba untuk edukasi masyarakat mulai dari kelas atas hingga bawah mengenai penanganan orang sakit. Edukasi mengenai hidup sehat juga diperlukan disini. Institusi juga bisa mencoba menyebarluaskan pengetahuan mengenai kriteria tertentu yang hidupnya tidak akan lama. Memberi visualisasi yang jelas supaya sampai ke masyarakat yang keras kepala juga. Menariknya, untuk pasien-pasien yang perokok, model tidak menunjukan bahwa hal itu berhubungan. Beberapa model data yang terpikir berhubungan juga ternyata tidak. Disini juga saya menyimpulkan bahwa data ini masih kurang masuk akal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>189000.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>245</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>972</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>268000.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>130</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>59.0</td>\n",
       "      <td>1</td>\n",
       "      <td>176</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>221000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>253000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>139</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>230</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>362000.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>240</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>237000.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>55.0</td>\n",
       "      <td>1</td>\n",
       "      <td>170</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>336000.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>135</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>172000.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>133</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>52.0</td>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>218000.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "0  72.0        0                       211         0                 25   \n",
       "1  42.0        0                        64         0                 40   \n",
       "2  49.0        0                       972         1                 35   \n",
       "3  59.0        1                       176         1                 25   \n",
       "4  58.0        0                       132         1                 38   \n",
       "5  50.0        1                       298         0                 35   \n",
       "6  42.0        0                       102         1                 40   \n",
       "7  55.0        1                       170         1                 40   \n",
       "8  79.0        1                        55         0                 50   \n",
       "9  52.0        0                       132         0                 30   \n",
       "\n",
       "   high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "0                    0   274000.0               1.2           134    0   \n",
       "1                    0   189000.0               0.7           140    1   \n",
       "2                    1   268000.0               0.8           130    0   \n",
       "3                    0   221000.0               1.0           136    1   \n",
       "4                    1   253000.0               1.0           139    1   \n",
       "5                    0   362000.0               0.9           140    1   \n",
       "6                    0   237000.0               1.2           140    1   \n",
       "7                    0   336000.0               1.2           135    1   \n",
       "8                    1   172000.0               1.8           133    1   \n",
       "9                    0   218000.0               0.7           136    1   \n",
       "\n",
       "   smoking  time  death_event  \n",
       "0        0   207            0  \n",
       "1        0   245            0  \n",
       "2        0   187            0  \n",
       "3        1   150            1  \n",
       "4        0   230            0  \n",
       "5        1   240            0  \n",
       "6        0    74            0  \n",
       "7        0   250            0  \n",
       "8        0    78            0  \n",
       "9        1   112            0  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>anaemia</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>time</th>\n",
       "      <th>death_event</th>\n",
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       "      <td>211</td>\n",
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       "      <td>207</td>\n",
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       "      <td>0</td>\n",
       "      <td>64</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0.8</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>176</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>150</td>\n",
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       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>230</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>240</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>170</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>250</td>\n",
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       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>112</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   anaemia  creatinine_phosphokinase  diabetes  high_blood_pressure  \\\n",
       "0        0                       211         0                    0   \n",
       "1        0                        64         0                    0   \n",
       "2        0                       972         1                    1   \n",
       "3        1                       176         1                    0   \n",
       "4        0                       132         1                    1   \n",
       "5        1                       298         0                    0   \n",
       "6        0                       102         1                    0   \n",
       "7        1                       170         1                    0   \n",
       "8        1                        55         0                    1   \n",
       "9        0                       132         0                    0   \n",
       "\n",
       "   serum_creatinine  time  death_event  \n",
       "0               1.2   207            0  \n",
       "1               0.7   245            0  \n",
       "2               0.8   187            0  \n",
       "3               1.0   150            1  \n",
       "4               1.0   230            0  \n",
       "5               0.9   240            0  \n",
       "6               1.2    74            0  \n",
       "7               1.2   250            0  \n",
       "8               1.8    78            0  \n",
       "9               0.7   112            0  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inftest = inf[['anaemia', 'creatinine_phosphokinase', 'diabetes', 'high_blood_pressure', 'serum_creatinine', 'time','death_event']]\n",
    "inftest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>RF</th>\n",
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      "text/plain": [
       "   RF\n",
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       "1   0\n",
       "2   0\n",
       "3   0\n",
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       "5   0\n",
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       "8   0\n",
       "9   0"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Random Forest data inference\n",
    "y_pred_inf_RF = modelRF.predict(inf)\n",
    "y_pred_inf_RF_test = pd.DataFrame(y_pred_inf_RF, columns=['RF'])\n",
    "y_pred_inf_RF_test "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   RFGS\n",
       "0     0\n",
       "1     0\n",
       "2     0\n",
       "3     0\n",
       "4     0\n",
       "5     0\n",
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       "9     0"
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     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest data inference\n",
    "y_pred_inf_RFGS = modelRF_gridsearchCV.predict(inf)\n",
    "y_pred_inf_RFGS_test = pd.DataFrame(y_pred_inf_RFGS, columns=['RFGS'])\n",
    "y_pred_inf_RFGS_test "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
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    }
   ],
   "source": [
    "# XGBOOST data inference\n",
    "y_pred_inf_XG = modelXG.predict(inf)\n",
    "y_pred_inf_XG_test = pd.DataFrame(y_pred_inf_XG, columns=['XG'])\n",
    "y_pred_inf_XG_test "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>XGGS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   XGGS\n",
       "0     0\n",
       "1     0\n",
       "2     0\n",
       "3     0\n",
       "4     0\n",
       "5     0\n",
       "6     0\n",
       "7     0\n",
       "8     0\n",
       "9     0"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# XGBOOSTGS data inference\n",
    "y_pred_inf_XGGS = modelXG_gridsearchCV.predict(inf)\n",
    "y_pred_inf_XGGS_test = pd.DataFrame(y_pred_inf_XG, columns=['XGGS'])\n",
    "y_pred_inf_XGGS_test "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>time</th>\n",
       "      <th>death_event</th>\n",
       "      <th>RF</th>\n",
       "      <th>RFGS</th>\n",
       "      <th>XG</th>\n",
       "      <th>XGGS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>211</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>207</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>245</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>972</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>176</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>230</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>240</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>170</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   anaemia  creatinine_phosphokinase  diabetes  high_blood_pressure  \\\n",
       "0        0                       211         0                    0   \n",
       "1        0                        64         0                    0   \n",
       "2        0                       972         1                    1   \n",
       "3        1                       176         1                    0   \n",
       "4        0                       132         1                    1   \n",
       "5        1                       298         0                    0   \n",
       "6        0                       102         1                    0   \n",
       "7        1                       170         1                    0   \n",
       "8        1                        55         0                    1   \n",
       "9        0                       132         0                    0   \n",
       "\n",
       "   serum_creatinine  time  death_event  RF  RFGS  XG  XGGS  \n",
       "0               1.2   207            0   0     0   0     0  \n",
       "1               0.7   245            0   0     0   0     0  \n",
       "2               0.8   187            0   0     0   0     0  \n",
       "3               1.0   150            1   0     0   0     0  \n",
       "4               1.0   230            0   0     0   0     0  \n",
       "5               0.9   240            0   0     0   0     0  \n",
       "6               1.2    74            0   0     0   0     0  \n",
       "7               1.2   250            0   0     0   0     0  \n",
       "8               1.8    78            0   0     0   0     0  \n",
       "9               0.7   112            0   0     0   0     0  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge/combine scaling/encoder data with its predicted rank\n",
    "inf_final=inftest\n",
    "pd.concat([inf_final,y_pred_inf_RF_test,y_pred_inf_RFGS_test,y_pred_inf_XG_test,y_pred_inf_XGGS_test], axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Saving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "models = {\n",
    "    'model_xgboost': modelXG\n",
    "}\n",
    "\n",
    "with open('death_pred.pkl', 'wb') as f:\n",
    "    pickle.dump(models, f)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conceptual Problems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jelaskan latar belakang adanya bagging dan cara kerja bagging !\n",
    "\n",
    "- Bagging adalah singkatan dari Bootstrap Aggregating. Fungsi nya adalah untuk menurunkan variansi hasil pemetaan model. Cara kerjanya adalah dengan mengambil sampel untuk melakukan training dan selanjutnya hasil dari masing masing prediksi di ukur. Setelah diukur hasilnya akan di rata-rata sehingga hasilnya lebih men-generalisir."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jelaskan perbedaan cara kerja algoritma Random Forest dengan algoritma boosting yang Anda pilih !\n",
    "\n",
    "- Cara kerja Random Forest adalah dengan melakukan proses dengan beberapa atau banyak Decision Tree. Karena Random Forest merupakan salah satu bagging, maka tiap pohon akan dijamin random. Karena sampel data diambil dengan cara bootstrap. Setiap pohon kemudian melakukan training terhadap target dan hasilnya akan di rata-ratakan. Kalau algoritma boosting yang saya pilih adalah XGBoost. Pada dasarnya, XGBoost adalah gabungan dari hal-hal positif dari beberapa algoritma. Sebagai contoh, analisa tree-based model dipakai oleh XGBoost dalam memecahkan masalah. Selanjutnya, XGBoost menggunakan Regularisasi untuk menjaga agar model tidak overfit dan lebih meliputi keseluruhan data. XGBoost juga menggunakan hal positif dari Gradient Booster dalam melakukan analisa yaitu membuat beberapa model lemah yang di seri kan. Setiap model belajar dari kesalahan sebelumnya sehingga hasilnya makin baik. Secara hardware juga, XGBoost tidak memakan performa yang banyak. Dengan beberapa optimisasi, XGBoost menjadi salah satu algoritma paling efisien dan kuat."
   ]
  }
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