prediction-model-of-customers-leaving .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deskripsi Proyek\n",
    "Nasabah Bank Beta pergi meninggalkan perusahaan: sedikit demi sedikit, jumlah mereka berkurang setiap bulannya. Para pegawai bank menyadari bahwa akan lebih menghemat biaya jika perusahaan fokus untuk mempertahankan nasabah lama mereka yang setia daripada menarik nasabah baru.\n",
    "\n",
    "Pada kasus ini, tugas kita adalah untuk memprediksi apakah seorang nasabah akan segera meninggalkan bank atau tidak. Kamu memiliki data terkait perilaku para klien di masa lalu dan riwayat pemutusan kontrak mereka dengan bank.\n",
    "Buat sebuah model dengan skor F1 semaksimal mungkin. Untuk bisa dinyatakan lulus dari peninjauan, kamu memerlukan skor F1 minimal 0,59 untuk test dataset.\n",
    "\n",
    "Setelah itu, kamu akan membuat perubahan yang diperlukan pada pekerjaanmu dan mengirimkannya kembali untuk tinjauan kedua.\n",
    "Selain itu, ukur metrik AUC-ROC dan bandingkan metrik tersebut dengan skor F1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Memuat Librari yang diperlukan \n",
    "\n",
    "#data prep\n",
    "import pandas as pd, numpy as np\n",
    "\n",
    "#data viz\n",
    "import matplotlib.pyplot as plt, seaborn as sns\n",
    "\n",
    "#skylearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix, roc_auc_score\n",
    "\n",
    "\n",
    "# Data Statistic\n",
    "from scipy.stats import chi2_contingency"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pra-process data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Memuat data\n",
    "try:\n",
    "    data = pd.read_csv ('/datasets/Churn.csv')\n",
    "except:\n",
    "    data = pd.read_csv ('Churn.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>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1.0</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8.0</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2.0</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave          619    France  Female   42   \n",
       "1          2    15647311      Hill          608     Spain  Female   41   \n",
       "2          3    15619304      Onio          502    France  Female   42   \n",
       "3          4    15701354      Boni          699    France  Female   39   \n",
       "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0     2.0       0.00              1          1               1   \n",
       "1     1.0   83807.86              1          0               1   \n",
       "2     8.0  159660.80              3          1               0   \n",
       "3     1.0       0.00              2          0               0   \n",
       "4     2.0  125510.82              1          1               1   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0        101348.88       1  \n",
       "1        112542.58       0  \n",
       "2        113931.57       1  \n",
       "3         93826.63       0  \n",
       "4         79084.10       0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Melihat isi data\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Deskripsi data</b>\n",
    "\n",
    "RowNumber — indeks string data\n",
    "\n",
    "CustomerId — ID pelanggan\n",
    "\n",
    "Surname — nama belakang\n",
    "\n",
    "CreditScore — skor kredit\n",
    "\n",
    "Geography — negara domisili\n",
    "\n",
    "Gender — gender\n",
    "\n",
    "Age — umur\n",
    "\n",
    "Tenure — jangka waktu jatuh tempo untuk deposito tetap nasabah (tahun)\n",
    "\n",
    "Balance — saldo rekening\n",
    "\n",
    "NumOfProducts — jumlah produk bank yang digunakan oleh nasabah\n",
    "\n",
    "HasCrCard — apakah nasabah memiliki kartu kredit (1 - jika ya; 0 - jika tidak)\n",
    "\n",
    "IsActiveMember — tingkat keaktifan nasabah (1 - jika ya; 0 - jika tidak)\n",
    "\n",
    "EstimatedSalary — estimasi gaji\n",
    "\n",
    "Target =\n",
    "\n",
    "Exited — apakah nasabah telah berhenti (1 - jika ya; 0 - jika tidak)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 14 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   RowNumber        10000 non-null  int64  \n",
      " 1   CustomerId       10000 non-null  int64  \n",
      " 2   Surname          10000 non-null  object \n",
      " 3   CreditScore      10000 non-null  int64  \n",
      " 4   Geography        10000 non-null  object \n",
      " 5   Gender           10000 non-null  object \n",
      " 6   Age              10000 non-null  int64  \n",
      " 7   Tenure           9091 non-null   float64\n",
      " 8   Balance          10000 non-null  float64\n",
      " 9   NumOfProducts    10000 non-null  int64  \n",
      " 10  HasCrCard        10000 non-null  int64  \n",
      " 11  IsActiveMember   10000 non-null  int64  \n",
      " 12  EstimatedSalary  10000 non-null  float64\n",
      " 13  Exited           10000 non-null  int64  \n",
      "dtypes: float64(3), int64(8), object(3)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# Melakukan pengecekan informasi data\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0     952\n",
       "2.0     950\n",
       "8.0     933\n",
       "3.0     928\n",
       "5.0     927\n",
       "7.0     925\n",
       "4.0     885\n",
       "9.0     882\n",
       "6.0     881\n",
       "10.0    446\n",
       "0.0     382\n",
       "Name: Tenure, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Melakukan pengecekan isi data tabel \"Tenure\"\n",
    "data['Tenure'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<pre><b>Mini-Conclusion</b>\n",
    "\n",
    "<b>RowNumber</b> — is Ok\n",
    "<b>CustomerId</b> — is OK\n",
    "<b>Surname</b> — is Ok \n",
    "<b>CreditScore</b> — is OK\n",
    "<b>Geography</b> — is OK\n",
    "<b>Gender</b> — is OK\n",
    "<b>Age</b> — is OK\n",
    "\n",
    "<b>Tenure</b> — memiliki <b>data yang hilang sebanyak 909</b>, Perlu dilakukan aksi atas nilai yang kosong.\n",
    "\n",
    "<b>Balance</b> — is OK\n",
    "<b>NumOfProducts</b> — is OK\n",
    "<b>HasCrCard</b> — is OK\n",
    "<b>IsActiveMember</b> — is OK\n",
    "<b>EstimatedSalary</b> — is OK\n",
    "<b>Exited</b> — is OK\n",
    "\n",
    "</pre>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quality data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0     952\n",
       "2.0     950\n",
       "8.0     933\n",
       "3.0     928\n",
       "5.0     927\n",
       "7.0     925\n",
       "4.0     885\n",
       "9.0     882\n",
       "6.0     881\n",
       "10.0    446\n",
       "0.0     382\n",
       "Name: Tenure, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Melakukan pengecekan isi data tabel \"Tenure\"\n",
    "data['Tenure'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Melakukan filter data kolom Tenure tanpa ada data yang hilang\n",
    "data_nan_tenure = data.loc[~(data['Tenure'].isna())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mendapatkan Nilai tengah dari kolom Tenure yang telah di filter\n",
    "data_nan_tenure_mean = data_nan_tenure['Tenure'].median()\n",
    "data_nan_tenure_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert-info\">\n",
    "<b>Note</b>\n",
    "\n",
    "Data yang hilang pada kolom Tenure akan diisi oleh nilai median\n",
    "\n",
    "    \n",
    "    \n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mengisi data yang hilang menjadi nilai media\n",
    "data['Tenure'].fillna(value=data_nan_tenure_mean, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analyst"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apakah ada pengaruh terhadap Skor Kredit dengan nasabah yang berhenti?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Memisahkan data yang nasabah yang berhenti dan tidak\n",
    "data['exited_str'] = data['Exited']\n",
    "data.loc[(data['Exited'] == 1), 'exited_str'] = 'exit'\n",
    "data.loc[(data['Exited'] == 0), 'exited_str'] = 'stay'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Melihat distribusi CreditScore terhadap  yang keluar atau bertahan\n",
    "sns.violinplot(data=data, x='CreditScore', y='exited_str')\n",
    "plt.title('Total distribusi berdasarkan Skor Kredit')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>CreditScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exited_str</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>exit</th>\n",
       "      <td>645.351497</td>\n",
       "      <td>10064.403894</td>\n",
       "      <td>100.321503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stay</th>\n",
       "      <td>651.853196</td>\n",
       "      <td>9149.656542</td>\n",
       "      <td>95.653837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  mean           var         std\n",
       "           CreditScore   CreditScore CreditScore\n",
       "exited_str                                      \n",
       "exit        645.351497  10064.403894  100.321503\n",
       "stay        651.853196   9149.656542   95.653837"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mendapatkan nilai rata-rata, varian dan standar deviasi berdasarkan Skor Kredit\n",
    "data.pivot_table(index='exited_str', values='CreditScore', aggfunc=[np.mean, np.var, np.std])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert-info\">\n",
    "<b>INSIGHT</b>\n",
    "\n",
    "\n",
    "- Tidak terdapat perbedaan yang signifikan antara Skor Kredit dengan pelanggan yang berhenti ataupun masuk.\n",
    "\n",
    "Sehingga dapat disimpulkan bahwa Tidak terdapat pengaruh Skor Kredit yang kecil ataupun besar terhadap pelanggan yang berhenti ataupun masuk. \n",
    "    \n",
    "    \n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bagaimana data nasabah yang berhenti di masing-masing negara"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "France     5014\n",
       "Germany    2509\n",
       "Spain      2477\n",
       "Name: Geography, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Melakukan pengecekan isi data tabel \"Tenure\"\n",
    "data['Geography'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mendapatkan nilai rata-rata, varian dan standar deviasi berdasarkan Skor Kredit\n",
    "country = pd.pivot_table(data=data, index='Geography', columns='exited_str', values='CustomerId', aggfunc='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>exited_str</th>\n",
       "      <th>exit</th>\n",
       "      <th>stay</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geography</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>810</td>\n",
       "      <td>4204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>814</td>\n",
       "      <td>1695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>413</td>\n",
       "      <td>2064</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "exited_str  exit  stay\n",
       "Geography             \n",
       "France       810  4204\n",
       "Germany      814  1695\n",
       "Spain        413  2064"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=country, x = ['France','Germany','Spain'], y = 'exit')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert-info\">\n",
    "<b>Note</b>\n",
    "\n",
    "    \n",
    "- Negara Spanyol Merupakan negara yang dimana pelanggan berhenti lebih sedikit 2x lipat dari Prancis dan Jerman\n",
    "    \n",
    "    \n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=country, x = ['France','Germany','Spain'], y = 'stay')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "geo_exit = pd.pivot_table(data=data, index='Geography', columns='exited_str', values='CustomerId', aggfunc='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert-info\">\n",
    "<b>Note</b>\n",
    "\n",
    "    \n",
    "- Negara Prancis Merupakan negara yang dimana pelanggan bertahan paling banyak diantara Jerman dan Spanyol\n",
    "    \n",
    "    \n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P-Value: 3.8303176053541544e-66\n",
      "H1: Ada hubungan antara negara dengan orang yang churn\n",
      "Kolom Geography memiliki impact yang besar terhadap model\n"
     ]
    }
   ],
   "source": [
    "p_value = chi2_contingency(geo_exit)[1]\n",
    "alpha = 0.05\n",
    "print('P-Value:', p_value)\n",
    "if p_value > alpha:\n",
    "    print('H0: Tidak ada hubungan antara negara dengan orang yang berhenti')\n",
    "    print('Kolom Geography tidak memiliki impact yang besar terhadap model')\n",
    "else:\n",
    "    print('H1: Ada hubungan antara negara dengan orang yang churn')\n",
    "    print('Kolom Geography memiliki impact yang besar terhadap model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert-info\">\n",
    "<b>Note</b>\n",
    "\n",
    "    \n",
    "Kolom Negera Memiliki hubungungan dengan orang yang berhenti.\n",
    "Dan perlu di teliti lebih lanjut\n",
    "    \n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bagaimana distribusi data pelanggan yang berhenti setiap gender?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      5457\n",
       "Female    4543\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Melakukan pengecekan isi data tabel \"Tenure\"\n",
    "data['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>exited_str</th>\n",
       "      <th>exit</th>\n",
       "      <th>stay</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>1139</td>\n",
       "      <td>3404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>898</td>\n",
       "      <td>4559</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "exited_str  exit  stay\n",
       "Gender                \n",
       "Female      1139  3404\n",
       "Male         898  4559"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adakah perbedaan orang yang berhenti dari tiap gender?\n",
    "data_gender = pd.pivot_table(data=data, index='Gender', columns='exited_str', values='CustomerId', aggfunc='count')\n",
    "data_gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P-Value: 2.2482100097131755e-26\n",
      "H1: Ada hubungan antara Gender dengan orang yang berhenti\n",
      "Kolom Gender memiliki impact yang besar terhadap model\n"
     ]
    }
   ],
   "source": [
    "p_value = chi2_contingency(data_gender)[1]\n",
    "alpha = 0.05\n",
    "print('P-Value:', p_value)\n",
    "if p_value > alpha:\n",
    "    print('H0: Tidak ada hubungan antara Gender dengan orang yang berhenti')\n",
    "    print('Kolom Gender tidak memiliki impact yang besar terhadap model')\n",
    "else:\n",
    "    print('H1: Ada hubungan antara Gender dengan orang yang berhenti')\n",
    "    print('Kolom Gender memiliki impact yang besar terhadap model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert-info\">\n",
    "<b>Note</b>\n",
    "\n",
    "    \n",
    "Kolom Negera Memiliki hubungungan dengan orang yang berhenti.\n",
    "Dan perlu di teliti lebih lanjut\n",
    "    \n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stay    7963\n",
      "exit    2037\n",
      "Name: exited_str, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Jumlah Masing2 target\n",
    "exited = data['exited_str'].value_counts().reset_index()\n",
    "sns.barplot(data=exited, x='index', y='exited_str')\n",
    "plt.show()\n",
    "print(data['exited_str'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bagaimana distribusi data pelanggan berdasarkan Umur?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Melihat distribusi Umur terhadap  yang keluar atau bertahan\n",
    "sns.violinplot(data=data, x='Age', y='exited_str')\n",
    "plt.title('Total distribusi berdasarkan Umur')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Age</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exited_str</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>exit</th>\n",
       "      <td>44.837997</td>\n",
       "      <td>95.288084</td>\n",
       "      <td>9.761562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stay</th>\n",
       "      <td>37.408389</td>\n",
       "      <td>102.522974</td>\n",
       "      <td>10.125363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mean         var        std\n",
       "                  Age         Age        Age\n",
       "exited_str                                  \n",
       "exit        44.837997   95.288084   9.761562\n",
       "stay        37.408389  102.522974  10.125363"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mendapatkan nilai rata-rata, varian dan standar deviasi berdasarkan Umur\n",
    "data.pivot_table(index='exited_str', values='Age', aggfunc=[np.mean, np.var, np.std])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert-info\">\n",
    "<b>Note</b>\n",
    "\n",
    "    \n",
    "- Rata-rata pelanggan yang berhenti yaitu pada umur 44 Tahun\n",
    "- Rata-rata pelanggan yang tetap bertahan pada umur 37 tahun yang artinya lebih muda.\n",
    "- Terdapat pengaruh terhadap data pelanggan yang berhenti dengan umur.\n",
    "    \n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bagaimana distribusi data pelanggan berdasarkan Tenure?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Melihat distribusi Tenure terhadap yang keluar atau bertahan\n",
    "sns.violinplot(data=data, x='Tenure', y='exited_str')\n",
    "plt.title('Total distribusi berdasarkan Tenure')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Tenure</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exited_str</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>exit</th>\n",
       "      <td>4.910653</td>\n",
       "      <td>7.892799</td>\n",
       "      <td>2.809413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stay</th>\n",
       "      <td>5.020219</td>\n",
       "      <td>7.545811</td>\n",
       "      <td>2.746964</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                mean       var       std\n",
       "              Tenure    Tenure    Tenure\n",
       "exited_str                              \n",
       "exit        4.910653  7.892799  2.809413\n",
       "stay        5.020219  7.545811  2.746964"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mendapatkan nilai rata-rata, varian dan standar deviasi berdasarkan Tenure\n",
    "data.pivot_table(index='exited_str', values='Tenure', aggfunc=[np.mean, np.var, np.std])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert-info\">\n",
    "<b>Note</b>\n",
    "\n",
    "    \n",
    "- Penyebaran data waktu tempo pelunasan tergolong merata sehingga dapat dikatakan tidak terdapat hubungan antara Tenure dengan pelanggan yang Churn    \n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bagaimana distribusi data pelanggan berdasarkan Saldo Rekening ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Melihat distribusi Saldo Rekening terhadap yang keluar atau bertahan\n",
    "sns.violinplot(data=data, x='Balance', y='exited_str')\n",
    "plt.title('Total distribusi berdasarkan Saldo Rekening')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Tenure</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exited_str</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>exit</th>\n",
       "      <td>4.910653</td>\n",
       "      <td>7.892799</td>\n",
       "      <td>2.809413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stay</th>\n",
       "      <td>5.020219</td>\n",
       "      <td>7.545811</td>\n",
       "      <td>2.746964</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                mean       var       std\n",
       "              Tenure    Tenure    Tenure\n",
       "exited_str                              \n",
       "exit        4.910653  7.892799  2.809413\n",
       "stay        5.020219  7.545811  2.746964"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mendapatkan nilai rata-rata, varian dan standar deviasi berdasarkan Tenure\n",
    "data.pivot_table(index='exited_str', values='Tenure', aggfunc=[np.mean, np.var, np.std])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert-info\">\n",
    "<b>Note</b>\n",
    "\n",
    "    \n",
    "- Penyebaran antara orang yang berhenti dan bertahan hampir sama.\n",
    "- Tidak Terdapat pengaruh terhadap data pelanggan yang berhenti dengan saldo Rekening.\n",
    "    \n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bagaimana distribusi data pelanggan berdasarkan jumlah produk bank yang digunakan oleh nasabah ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Melihat distribusi Saldo Rekening terhadap yang keluar atau bertahan\n",
    "sns.violinplot(data=data, x='NumOfProducts', y='exited_str')\n",
    "plt.title('Total distribusi berdasarkan  jumlah produk bank yang digunakan oleh nasabah')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>NumOfProducts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exited_str</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>exit</th>\n",
       "      <td>1.475209</td>\n",
       "      <td>0.642435</td>\n",
       "      <td>0.801521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stay</th>\n",
       "      <td>1.544267</td>\n",
       "      <td>0.259626</td>\n",
       "      <td>0.509536</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    mean           var           std\n",
       "           NumOfProducts NumOfProducts NumOfProducts\n",
       "exited_str                                          \n",
       "exit            1.475209      0.642435      0.801521\n",
       "stay            1.544267      0.259626      0.509536"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mendapatkan nilai rata-rata, varian dan standar deviasi berdasarkan jumlah produk bank yang digunakan oleh nasabah\n",
    "data.pivot_table(index='exited_str', values='NumOfProducts', aggfunc=[np.mean, np.var, np.std])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert-info\">\n",
    "<b>Note</b>\n",
    "\n",
    "    \n",
    "- Pelanggan yang menggunakan lebih dari 2 produk bank cendrung bertahan.\n",
    "- Pelanggan yang menggunakan 1 atau kurang dari 2 produk cendrung berhenti.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bagaimana distribusi data pelanggan berdasarkan yang memiliki Kartu Kredit ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Melihat distribusi Memiliki Kartu Kredit terhadap yang keluar atau bertahan\n",
    "sns.violinplot(data=data, x='HasCrCard', y='exited_str')\n",
    "plt.title('Total distribusi berdasarkan pelanggan yang memiliki Kartu Kredit')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>HasCrCard</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exited_str</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>exit</th>\n",
       "      <td>0.699067</td>\n",
       "      <td>0.210476</td>\n",
       "      <td>0.458776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stay</th>\n",
       "      <td>0.707146</td>\n",
       "      <td>0.207117</td>\n",
       "      <td>0.455101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                mean       var       std\n",
       "           HasCrCard HasCrCard HasCrCard\n",
       "exited_str                              \n",
       "exit        0.699067  0.210476  0.458776\n",
       "stay        0.707146  0.207117  0.455101"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mendapatkan nilai rata-rata, varian dan standar deviasi berdasarkan nasabah yang memiliki Kartu Kredit\n",
    "data.pivot_table(index='exited_str', values='HasCrCard', aggfunc=[np.mean, np.var, np.std])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert-info\">\n",
    "<b>Note</b>\n",
    "\n",
    "    \n",
    "- Penyebaran antara orang yang berhenti dan bertahan hampir sama.\n",
    "- Tidak Terdapat pengaruh terhadap data pelanggan yang berhenti dengan pelanggan yang memiliki kartu kredit.\n",
    "    \n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bagaimana distribusi data pelanggan berdasarkan tingkat ke-aktifan ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Melihat distribusi Memiliki Tingkat Keaktifan terhadap yang keluar atau bertahan\n",
    "sns.violinplot(data=data, x='IsActiveMember', y='exited_str')\n",
    "plt.title('Total distribusi berdasarkan tingkat keaktifan pelanggan')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>IsActiveMember</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exited_str</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>exit</th>\n",
       "      <td>0.360825</td>\n",
       "      <td>0.230744</td>\n",
       "      <td>0.480358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stay</th>\n",
       "      <td>0.554565</td>\n",
       "      <td>0.247054</td>\n",
       "      <td>0.497045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     mean            var            std\n",
       "           IsActiveMember IsActiveMember IsActiveMember\n",
       "exited_str                                             \n",
       "exit             0.360825       0.230744       0.480358\n",
       "stay             0.554565       0.247054       0.497045"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mendapatkan nilai rata-rata, varian dan standar deviasi berdasarkan tingkat keakfifan nasabah\n",
    "data.pivot_table(index='exited_str', values='IsActiveMember', aggfunc=[np.mean, np.var, np.std])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert-info\">\n",
    "<b>Note</b>\n",
    "\n",
    "    \n",
    "- Nasabah yang tidak aktif lebih cendrung berhenti bila dibandingkan dengan yang bertahan\n",
    "    \n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert-info\">\n",
    "<b>Insight</b>\n",
    "\n",
    "    \n",
    "- Kolom yang mempengaruhi pelanggan yang berhenti yaitu : Geography, Gender, Age, NumOfProducts dan IsActiveMember\n",
    "    \n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Geography  Gender  Age  NumOfProducts  IsActiveMember  EstimatedSalary  \\\n",
       "0    France  Female   42              1               1        101348.88   \n",
       "1     Spain  Female   41              1               1        112542.58   \n",
       "2    France  Female   42              3               0        113931.57   \n",
       "3    France  Female   39              2               0         93826.63   \n",
       "4     Spain  Female   43              1               1         79084.10   \n",
       "\n",
       "   Exited  \n",
       "0       1  \n",
       "1       0  \n",
       "2       1  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Hapus kolom yang tidak digunakan dalam pembuatan model\n",
    "data = data.drop(['RowNumber', 'CustomerId', 'Surname', 'CreditScore', 'Tenure', 'Balance', 'HasCrCard', 'exited_str' ], axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# One hot encoding ke data yang memiliki tipe data kategorikal nominal\n",
    "data = pd.get_dummies(data=data, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>NumOfProducts</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "      <th>Geography_Germany</th>\n",
       "      <th>Geography_Spain</th>\n",
       "      <th>Gender_Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</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>43</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>44</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>149756.71</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>10062.80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>119346.88</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>44</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>74940.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>71725.73</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  NumOfProducts  IsActiveMember  EstimatedSalary  Exited  \\\n",
       "0   42              1               1        101348.88       1   \n",
       "1   41              1               1        112542.58       0   \n",
       "2   42              3               0        113931.57       1   \n",
       "3   39              2               0         93826.63       0   \n",
       "4   43              1               1         79084.10       0   \n",
       "5   44              2               0        149756.71       1   \n",
       "6   50              2               1         10062.80       0   \n",
       "7   29              4               0        119346.88       1   \n",
       "8   44              2               1         74940.50       0   \n",
       "9   27              1               1         71725.73       0   \n",
       "\n",
       "   Geography_Germany  Geography_Spain  Gender_Male  \n",
       "0                  0                0            0  \n",
       "1                  0                1            0  \n",
       "2                  0                0            0  \n",
       "3                  0                0            0  \n",
       "4                  0                1            0  \n",
       "5                  0                1            1  \n",
       "6                  0                0            1  \n",
       "7                  1                0            0  \n",
       "8                  0                0            1  \n",
       "9                  0                0            1  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Memisahkan Target dengan Feature\n",
    "X = data.drop(['Exited'], axis=1)\n",
    "y = data['Exited']\n",
    "\n",
    "# Memisahkan antara train dan test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>NumOfProducts</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Geography_Germany</th>\n",
       "      <th>Geography_Spain</th>\n",
       "      <th>Gender_Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5951</th>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>61018.85</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6630</th>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>89804.83</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3240</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>57541.08</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5942</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>54676.10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3789</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>199256.98</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  NumOfProducts  IsActiveMember  EstimatedSalary  Geography_Germany  \\\n",
       "5951   41              2               1         61018.85                  0   \n",
       "6630   36              2               1         89804.83                  0   \n",
       "3240   50              1               0         57541.08                  0   \n",
       "5942   53              1               0         54676.10                  1   \n",
       "3789   45              1               1        199256.98                  0   \n",
       "\n",
       "      Geography_Spain  Gender_Male  \n",
       "5951                0            1  \n",
       "6630                0            0  \n",
       "3240                0            1  \n",
       "5942                0            1  \n",
       "3789                1            0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lihat sample\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def class_eval(model, X_train, X_test, y_train, y_test):\n",
    "    # Predict Train\n",
    "    y_train_pred = model.predict(X_train)\n",
    "\n",
    "    # Predict Test\n",
    "    y_test_pred = model.predict(X_test)\n",
    "\n",
    "    # Show Metrics\n",
    "    # Confusion Matrix\n",
    "    cm_tr = confusion_matrix(y_train, y_train_pred)\n",
    "    cm_te = confusion_matrix(y_test, y_test_pred)\n",
    "\n",
    "    # Accuracy\n",
    "    accuracy_tr = (cm_tr[0][0] + cm_tr[1][1]) / (cm_tr[0][0] + cm_tr[1][1] + cm_tr[0][1] + cm_tr[1][0])\n",
    "    accuracy_te = (cm_te[0][0] + cm_te[1][1]) / (cm_te[0][0] + cm_te[1][1] + cm_te[0][1] + cm_te[1][0])\n",
    "\n",
    "    # Precision\n",
    "    precision_tr_1 = cm_tr[1][1] / (cm_tr[1][1] + cm_tr[0][1])\n",
    "    precision_tr_0 = cm_tr[0][0] / (cm_tr[0][0] + cm_tr[1][0])\n",
    "    precision_te_1 = cm_te[1][1] / (cm_te[1][1] + cm_te[0][1])\n",
    "    precision_te_0 = cm_te[0][0] / (cm_te[0][0] + cm_te[1][0])\n",
    "\n",
    "    # Recall\n",
    "    recall_tr_1 = cm_tr[1][1] / (cm_tr[1][1] + cm_tr[1][0])\n",
    "    recall_tr_0 = cm_tr[0][0] / (cm_tr[0][0] + cm_tr[0][1])\n",
    "    recall_te_1 = cm_te[1][1] / (cm_te[1][1] + cm_te[1][0])\n",
    "    recall_te_0 = cm_te[0][0] / (cm_te[0][0] + cm_te[0][1])\n",
    "\n",
    "    # F1 Score\n",
    "    f1_score_tr_1 = (2 * precision_tr_1 * recall_tr_1) / (precision_tr_1 + recall_tr_1)\n",
    "    f1_score_tr_0 = (2 * precision_tr_0 * recall_tr_0) / (precision_tr_0 + recall_tr_0)\n",
    "    f1_score_te_1 = (2 * precision_te_1 * recall_te_1) / (precision_te_1 + recall_te_1)\n",
    "    f1_score_te_0 = (2 * precision_te_0 * recall_te_0) / (precision_te_0 + recall_te_0)\n",
    "\n",
    "    # ROC AUC score\n",
    "    roc_auc_tr = roc_auc_score(y_train, y_train_pred)\n",
    "    roc_auc_te = roc_auc_score(y_test, y_test_pred)\n",
    "\n",
    "    # Show Output\n",
    "    print('----------------Training----------------')\n",
    "    print('Confusion Matrix')\n",
    "    print(cm_tr)\n",
    "    print('Accuracy         :', round(accuracy_tr * 100, 1), '%')\n",
    "    print('ROC AUC          :', round(roc_auc_tr * 100, 1), '%')\n",
    "    print('Precision Class 0:', round(precision_tr_0 * 100, 1), '%')\n",
    "    print('Precision Class 1:', round(precision_tr_1 * 100, 1), '%')\n",
    "    print('Recall Class 0   :', round(recall_tr_0 * 100, 1), '%')\n",
    "    print('Recall Class 1   :', round(recall_tr_1 * 100, 1), '%')\n",
    "    print('F1 Score Class 0 :', round(f1_score_tr_0 * 100, 1), '%')\n",
    "    print('F1 Score Class 1 :', round(f1_score_tr_1 * 100, 1), '%')\n",
    "\n",
    "    print('\\n----------------Testing----------------')\n",
    "    print('Confusion Matrix')\n",
    "    print(cm_te)\n",
    "    print('Accuracy         :', round(accuracy_te * 100, 1), '%')\n",
    "    print('ROC AUC          :', round(roc_auc_te * 100, 1), '%')\n",
    "    print('Precision Class 0:', round(precision_te_0 * 100, 1), '%')\n",
    "    print('Precision Class 1:', round(precision_te_1 * 100, 1), '%')\n",
    "    print('Recall Class 0   :', round(recall_te_0 * 100, 1), '%')\n",
    "    print('Recall Class 1   :', round(recall_te_1 * 100, 1), '%')\n",
    "    print('F1 Score Class 0 :', round(f1_score_te_0 * 100, 1), '%')\n",
    "    print('F1 Score Class 1 :', round(f1_score_te_1 * 100, 1), '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=12345)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bikin model lr\n",
    "lr = LogisticRegression(random_state=12345)\n",
    "# Training Model\n",
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------Training----------------\n",
      "Confusion Matrix\n",
      "[[6399    0]\n",
      " [1601    0]]\n",
      "Accuracy         : 80.0 %\n",
      "ROC AUC          : 50.0 %\n",
      "Precision Class 0: 80.0 %\n",
      "Precision Class 1: nan %\n",
      "Recall Class 0   : 100.0 %\n",
      "Recall Class 1   : 0.0 %\n",
      "F1 Score Class 0 : 88.9 %\n",
      "F1 Score Class 1 : nan %\n",
      "\n",
      "----------------Testing----------------\n",
      "Confusion Matrix\n",
      "[[1564    0]\n",
      " [ 436    0]]\n",
      "Accuracy         : 78.2 %\n",
      "ROC AUC          : 50.0 %\n",
      "Precision Class 0: 78.2 %\n",
      "Precision Class 1: nan %\n",
      "Recall Class 0   : 100.0 %\n",
      "Recall Class 1   : 0.0 %\n",
      "F1 Score Class 0 : 87.8 %\n",
      "F1 Score Class 1 : nan %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_62/504699899.py:18: RuntimeWarning: invalid value encountered in long_scalars\n",
      "  precision_tr_1 = cm_tr[1][1] / (cm_tr[1][1] + cm_tr[0][1])\n",
      "/tmp/ipykernel_62/504699899.py:20: RuntimeWarning: invalid value encountered in long_scalars\n",
      "  precision_te_1 = cm_te[1][1] / (cm_te[1][1] + cm_te[0][1])\n"
     ]
    }
   ],
   "source": [
    "# Evaluasi model lr\n",
    "class_eval(model=lr, X_train=X_train, X_test=X_test, y_train=y_train, y_test=y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert-info\">\n",
    "<b>Insight</b>\n",
    "\n",
    "    \n",
    "- ROC AUC  cukup rendah yaitu 50% yang artinya sama seperti melempar koin yang kemungkinannya fifty:fifty\n",
    "    \n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest hypertuning parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=9, n_estimators=1000, random_state=12345)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bikin model rf\n",
    "rf = RandomForestClassifier(max_depth=9, random_state=12345, n_estimators=1000)\n",
    "# Training Model\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------Training----------------\n",
      "Confusion Matrix\n",
      "[[6290  109]\n",
      " [ 837  764]]\n",
      "Accuracy         : 88.2 %\n",
      "ROC AUC          : 73.0 %\n",
      "Precision Class 0: 88.3 %\n",
      "Precision Class 1: 87.5 %\n",
      "Recall Class 0   : 98.3 %\n",
      "Recall Class 1   : 47.7 %\n",
      "F1 Score Class 0 : 93.0 %\n",
      "F1 Score Class 1 : 61.8 %\n",
      "\n",
      "----------------Testing----------------\n",
      "Confusion Matrix\n",
      "[[1525   39]\n",
      " [ 246  190]]\n",
      "Accuracy         : 85.8 %\n",
      "ROC AUC          : 70.5 %\n",
      "Precision Class 0: 86.1 %\n",
      "Precision Class 1: 83.0 %\n",
      "Recall Class 0   : 97.5 %\n",
      "Recall Class 1   : 43.6 %\n",
      "F1 Score Class 0 : 91.5 %\n",
      "F1 Score Class 1 : 57.1 %\n"
     ]
    }
   ],
   "source": [
    "# Evaluasi model rf\n",
    "class_eval(model=rf, X_train=X_train, X_test=X_test, y_train=y_train, y_test=y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert-info\">\n",
    "<b>Insight</b>\n",
    "\n",
    "Pada Random Forest hypertuning parameter, ROC-AUC sudah melebihi 59% dan berada di angka 70%.\n",
    "    \n",
    "</div>"
   ]
  }
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