diff --git "a/h8dsft_Milestone1P2_xyla_ramadhan.ipynb" "b/h8dsft_Milestone1P2_xyla_ramadhan.ipynb"
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Phase 2 Milestone 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Perkenalan"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nama : Raden Mas Xyla Ramadhan\n",
+    "\n",
+    "Batch 13\n",
+    "\n",
+    "Link Backend \n",
+    "- https://tf-serving-xyla-backend.herokuapp.com/v1/models/churn_model\n",
+    "\n",
+    "Link Frontend \n",
+    "- https://tf-serving-xyla-frontend.herokuapp.com/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Assignment Objectives\n",
+    "\n",
+    "Milestones 1 ini dibuat guna mengevaluasi Pembelajaran Phase 2 :\n",
+    "\n",
+    "- Mampu memahami konsep Artificial Neural Network.\n",
+    "- Mampu mempersiapkan data untuk digunakan dalam model Artificial Neural Network.\n",
+    "- Mampu mengimplementasikan Artificial Neural Network dengan data yang diberikan.\n",
+    "- Mampu membuat model improvement terhadap model yang dibuat\n",
+    "- Mampu menganalsis model improvement"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import tensorflow as tf\n",
+    "from tensorflow import math\n",
+    "from tensorflow import keras\n",
+    "from feature_engine.outliers import Winsorizer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.preprocessing import OneHotEncoder, LabelEncoder\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.metrics import classification_report, confusion_matrix\n",
+    "\n",
+    "from tensorflow.python.keras.layers import Input, Dense, Dropout\n",
+    "from tensorflow.python.keras.models import Model, Sequential\n",
+    "\n",
+    "\n",
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Loading\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Membuat dataset dari file 'h8dsft_Milestone1P2_dataset_xyla_ramadhan.csv'\n",
+    "data = pd.read_csv('h8dsft_Milestone1P2_dataset_xyla_ramadhan.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>customerID</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>SeniorCitizen</th>\n",
+       "      <th>Partner</th>\n",
+       "      <th>Dependents</th>\n",
+       "      <th>tenure</th>\n",
+       "      <th>PhoneService</th>\n",
+       "      <th>MultipleLines</th>\n",
+       "      <th>InternetService</th>\n",
+       "      <th>OnlineSecurity</th>\n",
+       "      <th>...</th>\n",
+       "      <th>DeviceProtection</th>\n",
+       "      <th>TechSupport</th>\n",
+       "      <th>StreamingTV</th>\n",
+       "      <th>StreamingMovies</th>\n",
+       "      <th>Contract</th>\n",
+       "      <th>PaperlessBilling</th>\n",
+       "      <th>PaymentMethod</th>\n",
+       "      <th>MonthlyCharges</th>\n",
+       "      <th>TotalCharges</th>\n",
+       "      <th>Churn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7590-VHVEG</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>1</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No phone service</td>\n",
+       "      <td>DSL</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Electronic check</td>\n",
+       "      <td>29.85</td>\n",
+       "      <td>29.85</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5575-GNVDE</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>34</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>DSL</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>One year</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Mailed check</td>\n",
+       "      <td>56.95</td>\n",
+       "      <td>1889.5</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3668-QPYBK</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>DSL</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Mailed check</td>\n",
+       "      <td>53.85</td>\n",
+       "      <td>108.15</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7795-CFOCW</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>45</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No phone service</td>\n",
+       "      <td>DSL</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>One year</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Bank transfer (automatic)</td>\n",
+       "      <td>42.30</td>\n",
+       "      <td>1840.75</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9237-HQITU</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Fiber optic</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Electronic check</td>\n",
+       "      <td>70.70</td>\n",
+       "      <td>151.65</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
+       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
+       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
+       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
+       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
+       "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
+       "\n",
+       "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
+       "0  No phone service             DSL             No  ...               No   \n",
+       "1                No             DSL            Yes  ...              Yes   \n",
+       "2                No             DSL            Yes  ...               No   \n",
+       "3  No phone service             DSL            Yes  ...              Yes   \n",
+       "4                No     Fiber optic             No  ...               No   \n",
+       "\n",
+       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
+       "0          No          No              No  Month-to-month              Yes   \n",
+       "1          No          No              No        One year               No   \n",
+       "2          No          No              No  Month-to-month              Yes   \n",
+       "3         Yes          No              No        One year               No   \n",
+       "4          No          No              No  Month-to-month              Yes   \n",
+       "\n",
+       "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
+       "0           Electronic check          29.85         29.85    No  \n",
+       "1               Mailed check          56.95        1889.5    No  \n",
+       "2               Mailed check          53.85        108.15   Yes  \n",
+       "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
+       "4           Electronic check          70.70        151.65   Yes  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Characteristics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 7043 entries, 0 to 7042\n",
+      "Data columns (total 21 columns):\n",
+      " #   Column            Non-Null Count  Dtype  \n",
+      "---  ------            --------------  -----  \n",
+      " 0   customerID        7043 non-null   object \n",
+      " 1   gender            7043 non-null   object \n",
+      " 2   SeniorCitizen     7043 non-null   int64  \n",
+      " 3   Partner           7043 non-null   object \n",
+      " 4   Dependents        7043 non-null   object \n",
+      " 5   tenure            7043 non-null   int64  \n",
+      " 6   PhoneService      7043 non-null   object \n",
+      " 7   MultipleLines     7043 non-null   object \n",
+      " 8   InternetService   7043 non-null   object \n",
+      " 9   OnlineSecurity    7043 non-null   object \n",
+      " 10  OnlineBackup      7043 non-null   object \n",
+      " 11  DeviceProtection  7043 non-null   object \n",
+      " 12  TechSupport       7043 non-null   object \n",
+      " 13  StreamingTV       7043 non-null   object \n",
+      " 14  StreamingMovies   7043 non-null   object \n",
+      " 15  Contract          7043 non-null   object \n",
+      " 16  PaperlessBilling  7043 non-null   object \n",
+      " 17  PaymentMethod     7043 non-null   object \n",
+      " 18  MonthlyCharges    7043 non-null   float64\n",
+      " 19  TotalCharges      7043 non-null   object \n",
+      " 20  Churn             7043 non-null   object \n",
+      "dtypes: float64(1), int64(2), object(18)\n",
+      "memory usage: 1.1+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Insight yang didapat adalah terdapat 21 kolom dan 7043 baris\n",
+    "\n",
+    "Tipe Kolom dalam dataset: float64(1), int64(2), object(18)\n",
+    "\n",
+    "namun ada data yang kurang tepat yang perlu di ubah seperti\n",
+    "- SeniorCitizen berbentuk int64 akan diubah ke 'object' karena berbentuk categorical\n",
+    "- TotalCharges berbentuk object akan diubah ke bentuk 'float64'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.TotalCharges = data.TotalCharges.replace(' ', np.nan) # replace empty space with NaN\n",
+    "data.TotalCharges = data.TotalCharges.astype(float) # convert to float"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.SeniorCitizen = data.SeniorCitizen.map({1: 'Yes', 0: 'No'}) # convert to string"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Missing Value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "customerID           0\n",
+       "gender               0\n",
+       "SeniorCitizen        0\n",
+       "Partner              0\n",
+       "Dependents           0\n",
+       "tenure               0\n",
+       "PhoneService         0\n",
+       "MultipleLines        0\n",
+       "InternetService      0\n",
+       "OnlineSecurity       0\n",
+       "OnlineBackup         0\n",
+       "DeviceProtection     0\n",
+       "TechSupport          0\n",
+       "StreamingTV          0\n",
+       "StreamingMovies      0\n",
+       "Contract             0\n",
+       "PaperlessBilling     0\n",
+       "PaymentMethod        0\n",
+       "MonthlyCharges       0\n",
+       "TotalCharges        11\n",
+       "Churn                0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "cek apakah Missing value perlu di drop atau bisa pakai simple imputer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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>customerID</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>SeniorCitizen</th>\n",
+       "      <th>Partner</th>\n",
+       "      <th>Dependents</th>\n",
+       "      <th>tenure</th>\n",
+       "      <th>PhoneService</th>\n",
+       "      <th>MultipleLines</th>\n",
+       "      <th>InternetService</th>\n",
+       "      <th>OnlineSecurity</th>\n",
+       "      <th>...</th>\n",
+       "      <th>DeviceProtection</th>\n",
+       "      <th>TechSupport</th>\n",
+       "      <th>StreamingTV</th>\n",
+       "      <th>StreamingMovies</th>\n",
+       "      <th>Contract</th>\n",
+       "      <th>PaperlessBilling</th>\n",
+       "      <th>PaymentMethod</th>\n",
+       "      <th>MonthlyCharges</th>\n",
+       "      <th>TotalCharges</th>\n",
+       "      <th>Churn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>488</th>\n",
+       "      <td>4472-LVYGI</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No phone service</td>\n",
+       "      <td>DSL</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Two year</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Bank transfer (automatic)</td>\n",
+       "      <td>52.55</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>753</th>\n",
+       "      <td>3115-CZMZD</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>...</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>Two year</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Mailed check</td>\n",
+       "      <td>20.25</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>936</th>\n",
+       "      <td>5709-LVOEQ</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>DSL</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Two year</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Mailed check</td>\n",
+       "      <td>80.85</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1082</th>\n",
+       "      <td>4367-NUYAO</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>...</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>Two year</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Mailed check</td>\n",
+       "      <td>25.75</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1340</th>\n",
+       "      <td>1371-DWPAZ</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No phone service</td>\n",
+       "      <td>DSL</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Two year</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Credit card (automatic)</td>\n",
+       "      <td>56.05</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3331</th>\n",
+       "      <td>7644-OMVMY</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>...</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>Two year</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Mailed check</td>\n",
+       "      <td>19.85</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3826</th>\n",
+       "      <td>3213-VVOLG</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>...</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>Two year</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Mailed check</td>\n",
+       "      <td>25.35</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4380</th>\n",
+       "      <td>2520-SGTTA</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>...</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>Two year</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Mailed check</td>\n",
+       "      <td>20.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5218</th>\n",
+       "      <td>2923-ARZLG</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>...</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>No internet service</td>\n",
+       "      <td>One year</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Mailed check</td>\n",
+       "      <td>19.70</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6670</th>\n",
+       "      <td>4075-WKNIU</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>DSL</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Two year</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Mailed check</td>\n",
+       "      <td>73.35</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6754</th>\n",
+       "      <td>2775-SEFEE</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>DSL</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Two year</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Bank transfer (automatic)</td>\n",
+       "      <td>61.90</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>11 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      customerID  gender SeniorCitizen Partner Dependents  tenure  \\\n",
+       "488   4472-LVYGI  Female            No     Yes        Yes       0   \n",
+       "753   3115-CZMZD    Male            No      No        Yes       0   \n",
+       "936   5709-LVOEQ  Female            No     Yes        Yes       0   \n",
+       "1082  4367-NUYAO    Male            No     Yes        Yes       0   \n",
+       "1340  1371-DWPAZ  Female            No     Yes        Yes       0   \n",
+       "3331  7644-OMVMY    Male            No     Yes        Yes       0   \n",
+       "3826  3213-VVOLG    Male            No     Yes        Yes       0   \n",
+       "4380  2520-SGTTA  Female            No     Yes        Yes       0   \n",
+       "5218  2923-ARZLG    Male            No     Yes        Yes       0   \n",
+       "6670  4075-WKNIU  Female            No     Yes        Yes       0   \n",
+       "6754  2775-SEFEE    Male            No      No        Yes       0   \n",
+       "\n",
+       "     PhoneService     MultipleLines InternetService       OnlineSecurity  ...  \\\n",
+       "488            No  No phone service             DSL                  Yes  ...   \n",
+       "753           Yes                No              No  No internet service  ...   \n",
+       "936           Yes                No             DSL                  Yes  ...   \n",
+       "1082          Yes               Yes              No  No internet service  ...   \n",
+       "1340           No  No phone service             DSL                  Yes  ...   \n",
+       "3331          Yes                No              No  No internet service  ...   \n",
+       "3826          Yes               Yes              No  No internet service  ...   \n",
+       "4380          Yes                No              No  No internet service  ...   \n",
+       "5218          Yes                No              No  No internet service  ...   \n",
+       "6670          Yes               Yes             DSL                   No  ...   \n",
+       "6754          Yes               Yes             DSL                  Yes  ...   \n",
+       "\n",
+       "         DeviceProtection          TechSupport          StreamingTV  \\\n",
+       "488                   Yes                  Yes                  Yes   \n",
+       "753   No internet service  No internet service  No internet service   \n",
+       "936                   Yes                   No                  Yes   \n",
+       "1082  No internet service  No internet service  No internet service   \n",
+       "1340                  Yes                  Yes                  Yes   \n",
+       "3331  No internet service  No internet service  No internet service   \n",
+       "3826  No internet service  No internet service  No internet service   \n",
+       "4380  No internet service  No internet service  No internet service   \n",
+       "5218  No internet service  No internet service  No internet service   \n",
+       "6670                  Yes                  Yes                  Yes   \n",
+       "6754                   No                  Yes                   No   \n",
+       "\n",
+       "          StreamingMovies  Contract PaperlessBilling  \\\n",
+       "488                    No  Two year              Yes   \n",
+       "753   No internet service  Two year               No   \n",
+       "936                   Yes  Two year               No   \n",
+       "1082  No internet service  Two year               No   \n",
+       "1340                   No  Two year               No   \n",
+       "3331  No internet service  Two year               No   \n",
+       "3826  No internet service  Two year               No   \n",
+       "4380  No internet service  Two year               No   \n",
+       "5218  No internet service  One year              Yes   \n",
+       "6670                   No  Two year               No   \n",
+       "6754                   No  Two year              Yes   \n",
+       "\n",
+       "                  PaymentMethod MonthlyCharges  TotalCharges  Churn  \n",
+       "488   Bank transfer (automatic)          52.55           NaN     No  \n",
+       "753                Mailed check          20.25           NaN     No  \n",
+       "936                Mailed check          80.85           NaN     No  \n",
+       "1082               Mailed check          25.75           NaN     No  \n",
+       "1340    Credit card (automatic)          56.05           NaN     No  \n",
+       "3331               Mailed check          19.85           NaN     No  \n",
+       "3826               Mailed check          25.35           NaN     No  \n",
+       "4380               Mailed check          20.00           NaN     No  \n",
+       "5218               Mailed check          19.70           NaN     No  \n",
+       "6670               Mailed check          73.35           NaN     No  \n",
+       "6754  Bank transfer (automatic)          61.90           NaN     No  \n",
+       "\n",
+       "[11 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[data.TotalCharges.isna()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "setelah dilihat dari dataset karena data yang dipakai tidak spesifik berapa jumlah harga yang dipatok per layanan maka saya tidak bisa mengkalkulasikannya secara manual, dan missing value hanya (11/7043) 0,15 % dari seluruh jumlah data yang artinya tidak terlalu signifikan, oleh karena itu saya akan drop column missing value. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.dropna(inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Duplicated Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.duplicated().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "tidak ada duplikasi data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Info"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 7032 entries, 0 to 7042\n",
+      "Data columns (total 21 columns):\n",
+      " #   Column            Non-Null Count  Dtype  \n",
+      "---  ------            --------------  -----  \n",
+      " 0   customerID        7032 non-null   object \n",
+      " 1   gender            7032 non-null   object \n",
+      " 2   SeniorCitizen     7032 non-null   object \n",
+      " 3   Partner           7032 non-null   object \n",
+      " 4   Dependents        7032 non-null   object \n",
+      " 5   tenure            7032 non-null   int64  \n",
+      " 6   PhoneService      7032 non-null   object \n",
+      " 7   MultipleLines     7032 non-null   object \n",
+      " 8   InternetService   7032 non-null   object \n",
+      " 9   OnlineSecurity    7032 non-null   object \n",
+      " 10  OnlineBackup      7032 non-null   object \n",
+      " 11  DeviceProtection  7032 non-null   object \n",
+      " 12  TechSupport       7032 non-null   object \n",
+      " 13  StreamingTV       7032 non-null   object \n",
+      " 14  StreamingMovies   7032 non-null   object \n",
+      " 15  Contract          7032 non-null   object \n",
+      " 16  PaperlessBilling  7032 non-null   object \n",
+      " 17  PaymentMethod     7032 non-null   object \n",
+      " 18  MonthlyCharges    7032 non-null   float64\n",
+      " 19  TotalCharges      7032 non-null   float64\n",
+      " 20  Churn             7032 non-null   object \n",
+      "dtypes: float64(2), int64(1), object(18)\n",
+      "memory usage: 1.2+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Insight yang didapat adalah terdapat 21 kolom dan 7032 baris setelah data cleaning\n",
+    "\n",
+    "Tipe Kolom dalam dataset: float64(2), int64(1), object(18) setelah data manipulation"
+   ]
+  },
+  {
+   "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 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>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>tenure</th>\n",
+       "      <td>7032.0</td>\n",
+       "      <td>32.421786</td>\n",
+       "      <td>24.545260</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>9.0000</td>\n",
+       "      <td>29.000</td>\n",
+       "      <td>55.0000</td>\n",
+       "      <td>72.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MonthlyCharges</th>\n",
+       "      <td>7032.0</td>\n",
+       "      <td>64.798208</td>\n",
+       "      <td>30.085974</td>\n",
+       "      <td>18.25</td>\n",
+       "      <td>35.5875</td>\n",
+       "      <td>70.350</td>\n",
+       "      <td>89.8625</td>\n",
+       "      <td>118.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalCharges</th>\n",
+       "      <td>7032.0</td>\n",
+       "      <td>2283.300441</td>\n",
+       "      <td>2266.771362</td>\n",
+       "      <td>18.80</td>\n",
+       "      <td>401.4500</td>\n",
+       "      <td>1397.475</td>\n",
+       "      <td>3794.7375</td>\n",
+       "      <td>8684.80</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 count         mean          std    min       25%       50%  \\\n",
+       "tenure          7032.0    32.421786    24.545260   1.00    9.0000    29.000   \n",
+       "MonthlyCharges  7032.0    64.798208    30.085974  18.25   35.5875    70.350   \n",
+       "TotalCharges    7032.0  2283.300441  2266.771362  18.80  401.4500  1397.475   \n",
+       "\n",
+       "                      75%      max  \n",
+       "tenure            55.0000    72.00  \n",
+       "MonthlyCharges    89.8625   118.75  \n",
+       "TotalCharges    3794.7375  8684.80  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe().T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Insight yang didapat adalah \n",
+    "- minimal montly charge adalah 18.25 dan min Total Charge adalah 18.80\n",
+    "- maximal montly charge adalah 118 dan min Total Charge adalah 8684.80\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exploratory Data Analysis (EDA)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "dari data.info() kita dapat memisahkan menjadi beberapa kategori \n",
+    "- Output Variable : Churn\n",
+    "- Demographic Information: gender,SeniorCitizen,Partner,Dependents\n",
+    "- Service Information: PhoneService, MultipleLines, InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport, StreamingTV, StreamingMovies\n",
+    "- Account Information: tenure, Contract, PaperlessBilling, PaymentMethod, MonthlyCharges, TotalCharges"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Churn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15, 6))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "fig = sns.countplot(x='Churn', data=data,palette=['#98C1D9','#E63946'])\n",
+    "for p in fig.patches:\n",
+    "    fig.annotate(str(p.get_height()), (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')\n",
+    "plt.title('Churn Count')\n",
+    "plt.xlabel('Churn')\n",
+    "plt.ylabel('Count')\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.pie(data.Churn.value_counts(), labels=data.Churn.value_counts().index, autopct='%1.1f%%',colors =[\"#98C1D9\",\"#E63946\"])\n",
+    "plt.title('Churn Percentage')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Insight yang didapat adalah 73,4% atau 5163 customer tidak churn dan 26,6 persen dari total customer adalah churn (meninggalkan perusahaan)\n",
+    "\n",
+    "saya sebagai data analis akan menganalisis apa saja faktor yang membuat customer meninggalkan perusahaan atau Churn dimulai dari demographic information"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Demographic Information"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "gender,SeniorCitizen,Partner,Dependents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Count')"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1152x1152 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(16, 16))\n",
+    "\n",
+    "plt.subplot(2, 2, 1)\n",
+    "fig = sns.countplot(x='gender', data=data, hue='Churn',palette=['#003049','#52B788'])\n",
+    "for p in fig.patches:\n",
+    "    fig.annotate(str(p.get_height()), (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')\n",
+    "plt.title('Gender Count')\n",
+    "plt.xlabel('gender')\n",
+    "plt.ylabel('Count')\n",
+    "\n",
+    "plt.subplot(2, 2, 2)\n",
+    "fig = sns.countplot(x='SeniorCitizen', data=data, hue='Churn', palette=['#003049','#52B788'])\n",
+    "for p in fig.patches:\n",
+    "    fig.annotate(str(p.get_height()), (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')\n",
+    "plt.title('Senior Citizen Count')\n",
+    "plt.xlabel('Senior Citizen')\n",
+    "plt.ylabel('Count')\n",
+    "\n",
+    "plt.subplot(2, 2, 3)\n",
+    "fig = sns.countplot(x='Partner', data=data, hue='Churn', palette=['#003049','#52B788'])\n",
+    "for p in fig.patches:\n",
+    "    fig.annotate(str(p.get_height()), (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')\n",
+    "plt.title('Partner Count')\n",
+    "plt.xlabel('Partner')\n",
+    "plt.ylabel('Count')\n",
+    "\n",
+    "plt.subplot(2, 2, 4)\n",
+    "fig = sns.countplot(x='Dependents', data=data, hue='Churn',palette=['#003049','#52B788'])\n",
+    "for p in fig.patches:\n",
+    "    fig.annotate(str(p.get_height()), (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')\n",
+    "plt.title('Dependents Count')\n",
+    "plt.xlabel('Dependents')\n",
+    "plt.ylabel('Count')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Insight yang di dapat adalah \n",
+    "- presentase churn antara laki dan perempuan sama \n",
+    "- senior citizen memiliki presentase churn rate yang tinggi dapat dikatakan bahwa senior citizen cenderun churn\n",
+    "- customer yang tidak memiliki partner memiliki partner memliki presentase chrun yang lebih besar daripada yang memiliki partner\n",
+    "- customer yang memiliki dependets cenderung tidak churn "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Service Information"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "PhoneService, MultipleLines, InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport, StreamingTV, StreamingMovies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x1440 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(25, 20))\n",
+    "\n",
+    "plt.subplot(3, 3, 1)\n",
+    "fig = sns.countplot(x='PhoneService', data=data, hue='Churn',palette=['#432371','#FAAE7B'])\n",
+    "for p in fig.patches:\n",
+    "    fig.annotate(str(p.get_height()), (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')\n",
+    "plt.title('Phone Service Count')\n",
+    "plt.xlabel('Phone Service')\n",
+    "plt.ylabel('Count')\n",
+    "\n",
+    "plt.subplot(3, 3, 2)\n",
+    "fig = sns.countplot(x='MultipleLines', data=data, hue='Churn', palette=['#432371','#FAAE7B'])\n",
+    "for p in fig.patches:\n",
+    "    fig.annotate(str(p.get_height()), (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')\n",
+    "plt.title('MultipleLines Count')\n",
+    "plt.xlabel('MultipleLines')\n",
+    "plt.ylabel('Count')\n",
+    "\n",
+    "plt.subplot(3, 3, 3)\n",
+    "fig = sns.countplot(x='InternetService', data=data, hue='Churn', palette=['#432371','#FAAE7B'])\n",
+    "for p in fig.patches:\n",
+    "    fig.annotate(str(p.get_height()), (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')\n",
+    "plt.title('Internet Service Count')\n",
+    "plt.xlabel('Internet Service')\n",
+    "plt.ylabel('Count')\n",
+    "\n",
+    "plt.subplot(3, 3, 4)\n",
+    "fig = sns.countplot(x='OnlineSecurity', data=data, hue='Churn',palette=['#432371','#FAAE7B'])\n",
+    "for p in fig.patches:\n",
+    "    fig.annotate(str(p.get_height()), (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')\n",
+    "plt.title('Online Security Count')\n",
+    "plt.xlabel('Online Security')\n",
+    "plt.ylabel('Count')\n",
+    "\n",
+    "plt.subplot(3, 3, 5)\n",
+    "fig = sns.countplot(x='OnlineBackup', data=data, hue='Churn', palette=['#432371','#FAAE7B'])\n",
+    "for p in fig.patches:\n",
+    "    fig.annotate(str(p.get_height()), (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')\n",
+    "plt.title('Online Backup Count')\n",
+    "plt.xlabel('Online Backup')\n",
+    "plt.ylabel('Count')\n",
+    "\n",
+    "plt.subplot(3, 3, 6)\n",
+    "fig = sns.countplot(x='DeviceProtection', data=data, hue='Churn', palette=['#432371','#FAAE7B'])\n",
+    "for p in fig.patches:\n",
+    "    fig.annotate(str(p.get_height()), (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')\n",
+    "plt.title('Device Protection Count')\n",
+    "plt.xlabel('Device Protection')\n",
+    "plt.ylabel('Count')\n",
+    "\n",
+    "plt.subplot(3, 3, 7)\n",
+    "fig = sns.countplot(x='TechSupport', data=data, hue='Churn', palette=['#432371','#FAAE7B'])\n",
+    "for p in fig.patches:\n",
+    "    fig.annotate(str(p.get_height()), (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')\n",
+    "plt.title('Tech Support Count')\n",
+    "plt.xlabel('Tech Support')\n",
+    "plt.ylabel('Count')\n",
+    "\n",
+    "plt.subplot(3, 3, 8)\n",
+    "fig = sns.countplot(x='StreamingTV', data=data, hue='Churn', palette=['#432371','#FAAE7B'])\n",
+    "for p in fig.patches:\n",
+    "    fig.annotate(str(p.get_height()), (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')\n",
+    "plt.title('Streaming TV Count')\n",
+    "plt.xlabel('Streaming TV')\n",
+    "plt.ylabel('Count')\n",
+    "\n",
+    "plt.subplot(3, 3, 9)\n",
+    "fig = sns.countplot(x='StreamingMovies', data=data, hue='Churn', palette=['#432371','#FAAE7B'])\n",
+    "for p in fig.patches:\n",
+    "    fig.annotate(str(p.get_height()), (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')\n",
+    "plt.title('Streaming Movies Count')\n",
+    "plt.xlabel('Streaming Movies')\n",
+    "plt.ylabel('Count')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Insight yang didapat adalah\n",
+    "- Phone service memiliki presentase churn rate 1:3 baik yang mempunyai layanan telpon ataupun tidak.\n",
+    "- Multiple line adalah pertanyaan sambungan dari phone service memiliki presentase churn yang sama 1:3\n",
+    "- Fiber optic memiliki presentase churn rate yang tinggi dibandingkan internet service yang lain \n",
+    "- Jika customer tidak punya layanan keamanan seperti (Online Security, Online Backup, Device Protection, Tech Support) mereka cenderung Churn\n",
+    "- Streaming TV dan Streaming movies memiliki presentase churn rate yang sama 1:2 baik yang mempunyai layanannya maupun tidak\n",
+    "\n",
+    "Disini saya dapat menyimpulkan bahwa layanan keamanan seperti (Online Security, Online Backup, Device Protection, Tech Support) sangat penting terhadap churn rate, oleh karena itu saya sarankan kepada provider dapat memberikan layanan tersebut dengan harga yang lebih murah agar customer tidak churn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Account Information:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " tenure, Contract, PaperlessBilling, PaymentMethod, MonthlyCharges, TotalCharges"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x1080 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(25, 15))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "fig = sns.histplot(data['tenure'],bins=int(180/5), color = '#003049',)\n",
+    "for p in fig.patches:\n",
+    "    fig.annotate(str(p.get_height()), (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')\n",
+    "plt.title('Number of Customers by their tenure')\n",
+    "plt.xlabel('Number of of Customers')\n",
+    "plt.ylabel('Tenure (months)')\n",
+    "\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.boxplot(x='tenure', y='Churn', data=data)\n",
+    "plt.title('Tenure Distribution by Churn')\n",
+    "plt.xlabel('Tenure')\n",
+    "plt.ylabel('Churn')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Insight yang didapat adalah\n",
+    "- customer yang hanya berlangganan sebulan cendurung churn karena mereka mungkin hanya ingin mencoba layanan apakah worth it atau tidak.\n",
+    "- customer yang yang berlangganan dari bulan 10 sampai 60 atau lebih cenderung tidak churn karena mereka adalah pelanggan tetap"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Tenure vs Contract"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Two Year Contract')"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1440x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, (ax1,ax2,ax3) = plt.subplots(nrows=1, ncols=3, sharey = True, figsize = (20,6))\n",
+    "\n",
+    "ax = sns.histplot(data[data['Contract']=='Month-to-month']['tenure'],ax=ax1,color ='brown')\n",
+    "ax.set_ylabel('Number of Customers')\n",
+    "ax.set_xlabel('Tenure (months)')\n",
+    "ax.set_title('Month to Month Contract')\n",
+    "\n",
+    "ax = sns.histplot(data[data['Contract']=='One year']['tenure'],ax=ax2,color ='magenta')\n",
+    "ax.set_xlabel('Tenure (months)')\n",
+    "ax.set_title('One Year Contract')\n",
+    "\n",
+    "ax = sns.histplot(data[data['Contract']=='Two year']['tenure'],ax=ax3,color ='lightblue')\n",
+    "\n",
+    "ax.set_xlabel('Tenure (months)')\n",
+    "ax.set_title('Two Year Contract')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Insight yang didapat adalah\n",
+    "- customer banyak memakai Month to month contract daripada one atau two year contract.\n",
+    "- karena one atau two year contract hanyalah untuk customer tetap yang puas terhadap jasa provider tersebut.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1440x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20, 6))\n",
+    "plt.subplot(1, 3, 1)\n",
+    "fig = sns.countplot(x='Contract', data=data, hue='Churn',palette=['#7B6D8D','#FFE6A7'])\n",
+    "for p in fig.patches:\n",
+    "    fig.annotate(str(p.get_height()), (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')\n",
+    "plt.title('Contract Count')\n",
+    "plt.xlabel('Contract')\n",
+    "plt.ylabel('Count')\n",
+    "plt.subplot(1, 3, 2)\n",
+    "fig = sns.countplot(x='PaperlessBilling', data=data, hue='Churn',palette=['#7B6D8D','#FFE6A7'])\n",
+    "for p in fig.patches:\n",
+    "    fig.annotate(str(p.get_height()), (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')\n",
+    "plt.title('Paperless Billing Count')\n",
+    "plt.xlabel('Paperless Billing')\n",
+    "plt.ylabel('Count')\n",
+    "plt.subplot(1, 3, 3)\n",
+    "fig = sns.countplot(x='PaymentMethod', data=data, hue='Churn',palette=['#7B6D8D','#FFE6A7'])\n",
+    "for p in fig.patches:\n",
+    "    fig.annotate(str(p.get_height()), (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')\n",
+    "plt.title('Payment Method Count')\n",
+    "plt.xlabel('Payment Method')\n",
+    "fig.set_xticklabels(['Electronic check', 'Mailed check', 'Bank transfer', 'Credit card'])\n",
+    "plt.ylabel('Count')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Insight yang didapat adalah\n",
+    "- Customer yang mempunyai kontrak month to month cenderung churn karena mereka baru mencoba apakah layanan ini bagus atau tidak\n",
+    "- Customer yang memakai paperless billing cenderung churn \n",
+    "- Customer yang memakai payment method electronic check cenderung churn\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Monthly Charges vs Total Charges"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='MonthlyCharges', ylabel='TotalCharges'>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data[['MonthlyCharges', 'TotalCharges']].plot.scatter(x = 'MonthlyCharges',  y='TotalCharges',color='#167451')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Monthly Charges Mean: 64.80\n",
+      "Total Charges Mean: 2283.30\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'Monthly Charges Mean: {data.MonthlyCharges.mean():.2f}')\n",
+    "print(f'Total Charges Mean: {data.TotalCharges.mean():.2f}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Insight yang didapat adalah \n",
+    "- Dapat dilihat dari scatter plot bahwa kolom MonthlyCharges memiliki korelasi yang positive terhadap kolom TotalCharges.\n",
+    "- Rata rata customer memiliki tagihan 64.80 dollar tiap bulannya.\n",
+    "- Total rata rata customer memiliki tagihan 2283.30 dollar."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(25, 6))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.boxplot(x='MonthlyCharges', y='Churn',data=data)\n",
+    "plt.title(f'Monthly Charges by Churn')\n",
+    "plt.xlabel('Monthly Charges')\n",
+    "plt.ylabel('Churn')\n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.boxplot(x='TotalCharges', y='Churn',data=data)\n",
+    "plt.title(f'Total Charges by Churn')\n",
+    "plt.xlabel('Total Charges')\n",
+    "plt.ylabel('Churn')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "------------------------------------------------------\n",
+      "Monthly Charges for Non-Churn Customer: 61.31\n",
+      "Monthly Charges for Churn Customer: 74.44\n",
+      "------------------------------------------------------\n",
+      "Total Charges for Non-Churn Customer: 2555.34\n",
+      "Total Charges for Churn Customer: 1531.80\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('------------------------------------------------------')\n",
+    "print(f\"Monthly Charges for Non-Churn Customer: {data.MonthlyCharges[data.Churn == 'No'].mean():.2f}\")\n",
+    "print(f\"Monthly Charges for Churn Customer: {data.MonthlyCharges[data.Churn == 'Yes'].mean():.2f}\")\n",
+    "print('------------------------------------------------------')\n",
+    "print(f\"Total Charges for Non-Churn Customer: {data.TotalCharges[data.Churn == 'No'].mean():.2f}\")\n",
+    "print(f\"Total Charges for Churn Customer: {data.TotalCharges[data.Churn == 'Yes'].mean():.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Insight yang didapat adalah \n",
+    "- Dari grafik boxplot MonthlyCharge  Customer dengan range monthly charge 58 - 95 adalah churn dan Customer dengan range monthly charge 26 - 87 adalah tidak churn\n",
+    "\n",
+    "bisa disimpulkan bahwa orang churn rata rata karena tagihan perbulan yang mahal, membuat mereka meninggalkan perusahan (churn)\n",
+    "\n",
+    "- Dari grafik boxplot TotalCharge bisa dilihat dari outlier yang churn dari range ~5800 sampai 8684.80 customer meninggalkan perusahaan (churn)\n",
+    "\n",
+    "bisa disimpulkan bahwa customer yang sudah mengeluarkan uang lebih dari 5800 dollar terhadap layanan service perusahan mereka akan meninggalkan perusahaan (churn)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Feature Engineering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Setelah melakukan EDA kita mengetahui bahwa customerID hanyalah untuk mengidentifikasikan customer dan tidaklah penting untuk proses modelling nanti. oleh karena itu saya akan menghapus column customerID"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.drop(columns=['customerID'], inplace=True, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Mengubah tipe data pada kolom `Churn`, dimana:\n",
+    "- **Yes** akan menjadi **1**\n",
+    "- **No** akan mnejadi **0**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.Churn = data.Churn.map({'Yes': 1, 'No': 0})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Split Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pada bagian ini akan dilakukan splitting data yang akan menjadi training set, validation set, dan testing set dimana pembagiannya menjadi data train_val dan data test di split dulu 90 : 10 lalu Data train_val displit menjadi Data train dan data validation 80 : 20\n",
+    "\n",
+    "Data test akan dijadikan inference untuk prediksi model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#split 90 : 10 \n",
+    "df_train_val, df_test = train_test_split(data, test_size=0.1, random_state=31, stratify=data.Churn)\n",
+    "\n",
+    "#split 80 : 20\n",
+    "df_train, df_val = train_test_split(df_train_val, test_size=0.2, random_state=31, stratify=df_train_val.Churn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Handling Missing Value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "cek missing value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "gender              0\n",
+       "SeniorCitizen       0\n",
+       "Partner             0\n",
+       "Dependents          0\n",
+       "tenure              0\n",
+       "PhoneService        0\n",
+       "MultipleLines       0\n",
+       "InternetService     0\n",
+       "OnlineSecurity      0\n",
+       "OnlineBackup        0\n",
+       "DeviceProtection    0\n",
+       "TechSupport         0\n",
+       "StreamingTV         0\n",
+       "StreamingMovies     0\n",
+       "Contract            0\n",
+       "PaperlessBilling    0\n",
+       "PaymentMethod       0\n",
+       "MonthlyCharges      0\n",
+       "TotalCharges        0\n",
+       "Churn               0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Checking Missing Value\n",
+    "df_train_val.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "tidak ada missing value di dalam dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Handling Outliers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Cek Distribusi Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1440x1080 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#cek distribution\n",
+    "df_train_val[['tenure','MonthlyCharges','TotalCharges']].hist(bins=50, figsize=(20, 15))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Looping to check the Distribution of each column\n",
+    "dist_check = df_train_val[['tenure','MonthlyCharges','TotalCharges']].skew(numeric_only=True)\n",
+    "\n",
+    "normal_dist = []\n",
+    "skew_dist = []\n",
+    "for x in range(len(dist_check)):\n",
+    "    if dist_check.values[x] < -0.5:\n",
+    "        skew_dist.append(dist_check.index[x])\n",
+    "    elif dist_check.values[x] > 0.5:\n",
+    "        skew_dist.append(dist_check.index[x])\n",
+    "    else:\n",
+    "        normal_dist.append(dist_check.index[x])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Skewed distribution : ['TotalCharges']\n",
+      "\n",
+      "Normal distribution : ['tenure', 'MonthlyCharges']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Skewed distribution\n",
+    "print('Skewed distribution :', skew_dist)\n",
+    "print(\"\")\n",
+    "# Normal distribution\n",
+    "print('Normal distribution :',normal_dist)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Buat Function untuk Cek outlier Normal Distributin dan Skew\n",
+    "def detect_normal(df, variable):\n",
+    "    \n",
+    "    threshold = 1.5\n",
+    "    mean_ = np.mean(df[variable])\n",
+    "    std_ = np.std(df[variable])\n",
+    "\n",
+    "    upper_count = df[\n",
+    "                    ((df[variable]-mean_)/std_) > threshold\n",
+    "                    ][variable].count()\n",
+    "\n",
+    "    bottom_count = df[\n",
+    "                    ((df[variable]-mean_)/std_) < -threshold\n",
+    "                    ][variable].count()\n",
+    "\n",
+    "    total_count = upper_count + bottom_count\n",
+    "\n",
+    "    percentage = (total_count/len(df[variable])*100)\n",
+    "\n",
+    "    a = ({'Variable':variable, 'Total Outliers':total_count, 'Percentage':percentage})\n",
+    "\n",
+    "    return print(a)\n",
+    "\n",
+    "def detect_skew(df, variable, distance):\n",
+    "    \n",
+    "    for i in variable:\n",
+    "        \n",
+    "        IQR = df[variable].quantile(0.75) - df[variable].quantile(0.25)\n",
+    "        lower_boundary = df[variable].quantile(0.25) - (IQR * distance)\n",
+    "        upper_boundary = df[variable].quantile(0.75) + (IQR * distance)\n",
+    "\n",
+    "        upper_boundary_count = df[(df[variable] > upper_boundary)][variable].count()\n",
+    "\n",
+    "        lower_boundary_count = df[(df[variable] < lower_boundary)][variable].count()\n",
+    "        \n",
+    "        total_count = upper_boundary_count + lower_boundary_count\n",
+    "\n",
+    "        percentage = (total_count/len(df[variable])*100)\n",
+    "\n",
+    "        text = ({'Variable':variable, 'Total Outliers':total_count, 'Percentage':percentage})\n",
+    "\n",
+    "    return print(text)   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train\n",
+      "{'Variable': 'tenure', 'Total Outliers': 461, 'Percentage': 9.107072303437375}\n",
+      "{'Variable': 'MonthlyCharges', 'Total Outliers': 597, 'Percentage': 11.793757408139076}\n",
+      "validation\n",
+      "{'Variable': 'tenure', 'Total Outliers': 106, 'Percentage': 8.372827804107425}\n",
+      "{'Variable': 'MonthlyCharges', 'Total Outliers': 171, 'Percentage': 13.507109004739338}\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Cek outlier di distribusi normal\n",
+    "print('train')\n",
+    "for x in normal_dist:\n",
+    "    detect_normal(df_train, variable=x)\n",
+    "print('validation')\n",
+    "for y in normal_dist:\n",
+    "    detect_normal(df_val, variable=y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train\n",
+      "{'Variable': 'TotalCharges', 'Total Outliers': 0, 'Percentage': 0.0}\n",
+      "validation\n",
+      "{'Variable': 'TotalCharges', 'Total Outliers': 0, 'Percentage': 0.0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Cek outlier di skew\n",
+    "print('train')\n",
+    "for i in skew_dist:\n",
+    "    detect_skew(df_train, variable=i, distance=1.5)\n",
+    "print('validation')\n",
+    "for j in skew_dist:\n",
+    "    detect_skew(df_val, variable=j, distance=1.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Censoring"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Censoring normal distribution with gaussian capping method\n",
+    "windsoriser = Winsorizer(capping_method='gaussian', \n",
+    "                          tail='both',  #Censor both the right and left end outliers\n",
+    "                          fold=1.5, \n",
+    "                          variables=['tenure', 'MonthlyCharges'])\n",
+    "\n",
+    "windsoriser.fit(df_train)\n",
+    "windsoriser.fit(df_val)\n",
+    "\n",
+    "df_train_cen = windsoriser.transform(df_train)\n",
+    "df_val_cen = windsoriser.transform(df_val)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Feature Selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Training \n",
+    "X_train = df_train_cen.drop(['Churn'], axis=1) # Features\n",
+    "y_train = df_train_cen['Churn'] # Target\n",
+    "\n",
+    "# Validation\n",
+    "X_val = df_val_cen.drop(['Churn'], axis=1) # Features\n",
+    "y_val = df_val_cen['Churn'] # Target"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Pipeline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pada bagian ini akan dibuat pipeline untuk melakukan scaling dan encoding, dimana:\n",
+    "- **Scaling** menggunakan `StandardScaler()`\n",
+    "- **Encoding** menggunakan `OneHotEncoder()`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "alasan menggunakan onehotencoder adalah karena onehotencoder memiliki akurasi yang lebih bagus dari encoder lainnya ketika jumlah label tidak terlalu banyak \n",
+    "\n",
+    "sumber \n",
+    "https://towardsdatascience.com/choosing-the-right-encoding-method-label-vs-onehot-encoder-a4434493149b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_pipe = Pipeline([('scaler', StandardScaler())])\n",
+    "\n",
+    "cat_pipe = Pipeline([('encoder', OneHotEncoder(handle_unknown='ignore'))])\n",
+    "\n",
+    "preprocessor = ColumnTransformer([\n",
+    "    ('num', num_pipe, X_train.select_dtypes(include=['float64', 'int64']).columns),\n",
+    "    ('cat', cat_pipe, X_train.select_dtypes(include=['object']).columns)\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "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>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
+       "                                 Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler())]),\n",
+       "                                 Index([&#x27;tenure&#x27;, &#x27;MonthlyCharges&#x27;, &#x27;TotalCharges&#x27;], dtype=&#x27;object&#x27;)),\n",
+       "                                (&#x27;cat&#x27;,\n",
+       "                                 Pipeline(steps=[(&#x27;encoder&#x27;,\n",
+       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
+       "                                 Index([&#x27;gender&#x27;, &#x27;SeniorCitizen&#x27;, &#x27;Partner&#x27;, &#x27;Dependents&#x27;, &#x27;PhoneService&#x27;,\n",
+       "       &#x27;MultipleLines&#x27;, &#x27;InternetService&#x27;, &#x27;OnlineSecurity&#x27;, &#x27;OnlineBackup&#x27;,\n",
+       "       &#x27;DeviceProtection&#x27;, &#x27;TechSupport&#x27;, &#x27;StreamingTV&#x27;, &#x27;StreamingMovies&#x27;,\n",
+       "       &#x27;Contract&#x27;, &#x27;PaperlessBilling&#x27;, &#x27;PaymentMethod&#x27;],\n",
+       "      dtype=&#x27;object&#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-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
+       "                                 Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler())]),\n",
+       "                                 Index([&#x27;tenure&#x27;, &#x27;MonthlyCharges&#x27;, &#x27;TotalCharges&#x27;], dtype=&#x27;object&#x27;)),\n",
+       "                                (&#x27;cat&#x27;,\n",
+       "                                 Pipeline(steps=[(&#x27;encoder&#x27;,\n",
+       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
+       "                                 Index([&#x27;gender&#x27;, &#x27;SeniorCitizen&#x27;, &#x27;Partner&#x27;, &#x27;Dependents&#x27;, &#x27;PhoneService&#x27;,\n",
+       "       &#x27;MultipleLines&#x27;, &#x27;InternetService&#x27;, &#x27;OnlineSecurity&#x27;, &#x27;OnlineBackup&#x27;,\n",
+       "       &#x27;DeviceProtection&#x27;, &#x27;TechSupport&#x27;, &#x27;StreamingTV&#x27;, &#x27;StreamingMovies&#x27;,\n",
+       "       &#x27;Contract&#x27;, &#x27;PaperlessBilling&#x27;, &#x27;PaymentMethod&#x27;],\n",
+       "      dtype=&#x27;object&#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-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;tenure&#x27;, &#x27;MonthlyCharges&#x27;, &#x27;TotalCharges&#x27;], dtype=&#x27;object&#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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" 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-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;gender&#x27;, &#x27;SeniorCitizen&#x27;, &#x27;Partner&#x27;, &#x27;Dependents&#x27;, &#x27;PhoneService&#x27;,\n",
+       "       &#x27;MultipleLines&#x27;, &#x27;InternetService&#x27;, &#x27;OnlineSecurity&#x27;, &#x27;OnlineBackup&#x27;,\n",
+       "       &#x27;DeviceProtection&#x27;, &#x27;TechSupport&#x27;, &#x27;StreamingTV&#x27;, &#x27;StreamingMovies&#x27;,\n",
+       "       &#x27;Contract&#x27;, &#x27;PaperlessBilling&#x27;, &#x27;PaymentMethod&#x27;],\n",
+       "      dtype=&#x27;object&#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-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "ColumnTransformer(transformers=[('num',\n",
+       "                                 Pipeline(steps=[('scaler', StandardScaler())]),\n",
+       "                                 Index(['tenure', 'MonthlyCharges', 'TotalCharges'], dtype='object')),\n",
+       "                                ('cat',\n",
+       "                                 Pipeline(steps=[('encoder',\n",
+       "                                                  OneHotEncoder(handle_unknown='ignore'))]),\n",
+       "                                 Index(['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'PhoneService',\n",
+       "       'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup',\n",
+       "       'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies',\n",
+       "       'Contract', 'PaperlessBilling', 'PaymentMethod'],\n",
+       "      dtype='object'))])"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "preprocessor"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Setelah pipeline dibuat, selanjutkan akan dilakukan scaling dan encoding terhadap `X_train` dan `X_val`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train = preprocessor.fit_transform(X_train)\n",
+    "X_val = preprocessor.transform(X_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.2590246 ,  1.20674295,  0.1473109 , ...,  0.        ,\n",
+       "         0.        ,  0.        ],\n",
+       "       [-1.12397806, -0.30011212, -0.8904118 , ...,  0.        ,\n",
+       "         0.        ,  1.        ],\n",
+       "       [ 0.5647406 , -1.34176361, -0.48861553, ...,  0.        ,\n",
+       "         1.        ,  0.        ],\n",
+       "       ...,\n",
+       "       [ 0.81187016, -0.52428631,  0.12542394, ...,  0.        ,\n",
+       "         1.        ,  0.        ],\n",
+       "       [-1.20635458, -1.00803061, -0.96198504, ...,  1.        ,\n",
+       "         0.        ,  0.        ],\n",
+       "       [-1.2887311 , -0.52765735, -0.99094453, ...,  0.        ,\n",
+       "         0.        ,  1.        ]])"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Cek shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X_train shape: (5062, 46)\n",
+      "X_val shape: (1266, 46)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Cek shape\n",
+    "print(f'X_train shape: {X_train.shape}')\n",
+    "print(f'X_val shape: {X_val.shape}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Input Pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Training\n",
+    "train_data = tf.data.Dataset.from_tensor_slices((X_train, y_train)).batch(31)\n",
+    "\n",
+    "# Validation\n",
+    "val_data = tf.data.Dataset.from_tensor_slices((X_val, y_val)).batch(31)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Definition"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "untuk model awal saya menggunakan \n",
+    "- Optimizer = Adam \n",
+    "- Epoch = 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Adam Optimizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optim_adam = tf.keras.optimizers.Adam(\n",
+    "    learning_rate=0.001,\n",
+    "    beta_1=0.9,\n",
+    "    beta_2=0.999,\n",
+    "    epsilon=1e-07,\n",
+    "    amsgrad=False,\n",
+    "    name='Adam',\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sequential API"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Defining the model\n",
+    "model_seq = keras.models.Sequential([\n",
+    "    keras.layers.Dense(32,input_shape=(X_train.shape[1],),activation='relu'),\n",
+    "    keras.layers.Dense(16, activation=\"relu\"),\n",
+    "    keras.layers.Dense(8, activation=\"relu\"),\n",
+    "    keras.layers.Dense(1, activation=\"sigmoid\")\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Functional API"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Defining the model\n",
+    "input_ = keras.layers.Input(shape=(X_train.shape[1],))\n",
+    "hidden1 = keras.layers.Dense(32, activation=\"relu\")(input_)\n",
+    "hidden2 = keras.layers.Dense(16, activation=\"relu\")(hidden1)\n",
+    "output_ = keras.layers.Dense(1, activation=\"sigmoid\")(hidden2)\n",
+    "model_func = keras.models.Model(inputs=input_, outputs=output_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\Xyla\\OneDrive\\Documents\\Hacktiv8\\ftds\\ftds13\\Assignments\\p2---ftds013---m1-Xylverize\\h8dsft_Milestone1P2_xyla_ramadhan.ipynb Cell 102\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Xyla/OneDrive/Documents/Hacktiv8/ftds/ftds13/Assignments/p2---ftds013---m1-Xylverize/h8dsft_Milestone1P2_xyla_ramadhan.ipynb#Y203sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m model_seq\u001b[39m.\u001b[39mcompile(loss\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mbinary_crossentropy\u001b[39m\u001b[39m\"\u001b[39m, optimizer\u001b[39m=\u001b[39moptim_adam, metrics\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39maccuracy\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Xyla/OneDrive/Documents/Hacktiv8/ftds/ftds13/Assignments/p2---ftds013---m1-Xylverize/h8dsft_Milestone1P2_xyla_ramadhan.ipynb#Y203sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m \u001b[39m#Training model\u001b[39;00m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Xyla/OneDrive/Documents/Hacktiv8/ftds/ftds13/Assignments/p2---ftds013---m1-Xylverize/h8dsft_Milestone1P2_xyla_ramadhan.ipynb#Y203sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m history_seq \u001b[39m=\u001b[39m model_seq\u001b[39m.\u001b[39;49mfit(train_data, epochs\u001b[39m=\u001b[39;49m\u001b[39m100\u001b[39;49m, validation_data\u001b[39m=\u001b[39;49mval_data,verbose\u001b[39m=\u001b[39;49m\u001b[39m0\u001b[39;49m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Xyla/OneDrive/Documents/Hacktiv8/ftds/ftds13/Assignments/p2---ftds013---m1-Xylverize/h8dsft_Milestone1P2_xyla_ramadhan.ipynb#Y203sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m model_seq\u001b[39m.\u001b[39msummary()\n",
+      "File \u001b[1;32mc:\\Users\\Xyla\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\utils\\traceback_utils.py:64\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     62\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m     63\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m---> 64\u001b[0m   \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m     65\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:  \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[0;32m     66\u001b[0m   filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n",
+      "File \u001b[1;32mc:\\Users\\Xyla\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\engine\\training.py:1409\u001b[0m, in \u001b[0;36mModel.fit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1402\u001b[0m \u001b[39mwith\u001b[39;00m tf\u001b[39m.\u001b[39mprofiler\u001b[39m.\u001b[39mexperimental\u001b[39m.\u001b[39mTrace(\n\u001b[0;32m   1403\u001b[0m     \u001b[39m'\u001b[39m\u001b[39mtrain\u001b[39m\u001b[39m'\u001b[39m,\n\u001b[0;32m   1404\u001b[0m     epoch_num\u001b[39m=\u001b[39mepoch,\n\u001b[0;32m   1405\u001b[0m     step_num\u001b[39m=\u001b[39mstep,\n\u001b[0;32m   1406\u001b[0m     batch_size\u001b[39m=\u001b[39mbatch_size,\n\u001b[0;32m   1407\u001b[0m     _r\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m):\n\u001b[0;32m   1408\u001b[0m   callbacks\u001b[39m.\u001b[39mon_train_batch_begin(step)\n\u001b[1;32m-> 1409\u001b[0m   tmp_logs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtrain_function(iterator)\n\u001b[0;32m   1410\u001b[0m   \u001b[39mif\u001b[39;00m data_handler\u001b[39m.\u001b[39mshould_sync:\n\u001b[0;32m   1411\u001b[0m     context\u001b[39m.\u001b[39masync_wait()\n",
+      "File \u001b[1;32mc:\\Users\\Xyla\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    148\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m    149\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> 150\u001b[0m   \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m    151\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m    152\u001b[0m   filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n",
+      "File \u001b[1;32mc:\\Users\\Xyla\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py:915\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    912\u001b[0m compiler \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mxla\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_jit_compile \u001b[39melse\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mnonXla\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    914\u001b[0m \u001b[39mwith\u001b[39;00m OptionalXlaContext(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_jit_compile):\n\u001b[1;32m--> 915\u001b[0m   result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_call(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwds)\n\u001b[0;32m    917\u001b[0m new_tracing_count \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mexperimental_get_tracing_count()\n\u001b[0;32m    918\u001b[0m without_tracing \u001b[39m=\u001b[39m (tracing_count \u001b[39m==\u001b[39m new_tracing_count)\n",
+      "File \u001b[1;32mc:\\Users\\Xyla\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py:947\u001b[0m, in \u001b[0;36mFunction._call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    944\u001b[0m   \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock\u001b[39m.\u001b[39mrelease()\n\u001b[0;32m    945\u001b[0m   \u001b[39m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[0;32m    946\u001b[0m   \u001b[39m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[1;32m--> 947\u001b[0m   \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_stateless_fn(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwds)  \u001b[39m# pylint: disable=not-callable\u001b[39;00m\n\u001b[0;32m    948\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_stateful_fn \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m    949\u001b[0m   \u001b[39m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[0;32m    950\u001b[0m   \u001b[39m# in parallel.\u001b[39;00m\n\u001b[0;32m    951\u001b[0m   \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock\u001b[39m.\u001b[39mrelease()\n",
+      "File \u001b[1;32mc:\\Users\\Xyla\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:2453\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2450\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock:\n\u001b[0;32m   2451\u001b[0m   (graph_function,\n\u001b[0;32m   2452\u001b[0m    filtered_flat_args) \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[1;32m-> 2453\u001b[0m \u001b[39mreturn\u001b[39;00m graph_function\u001b[39m.\u001b[39;49m_call_flat(\n\u001b[0;32m   2454\u001b[0m     filtered_flat_args, captured_inputs\u001b[39m=\u001b[39;49mgraph_function\u001b[39m.\u001b[39;49mcaptured_inputs)\n",
+      "File \u001b[1;32mc:\\Users\\Xyla\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:1860\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1856\u001b[0m possible_gradient_type \u001b[39m=\u001b[39m gradients_util\u001b[39m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[0;32m   1857\u001b[0m \u001b[39mif\u001b[39;00m (possible_gradient_type \u001b[39m==\u001b[39m gradients_util\u001b[39m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[0;32m   1858\u001b[0m     \u001b[39mand\u001b[39;00m executing_eagerly):\n\u001b[0;32m   1859\u001b[0m   \u001b[39m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[1;32m-> 1860\u001b[0m   \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_build_call_outputs(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_inference_function\u001b[39m.\u001b[39;49mcall(\n\u001b[0;32m   1861\u001b[0m       ctx, args, cancellation_manager\u001b[39m=\u001b[39;49mcancellation_manager))\n\u001b[0;32m   1862\u001b[0m forward_backward \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[0;32m   1863\u001b[0m     args,\n\u001b[0;32m   1864\u001b[0m     possible_gradient_type,\n\u001b[0;32m   1865\u001b[0m     executing_eagerly)\n\u001b[0;32m   1866\u001b[0m forward_function, args_with_tangents \u001b[39m=\u001b[39m forward_backward\u001b[39m.\u001b[39mforward()\n",
+      "File \u001b[1;32mc:\\Users\\Xyla\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:497\u001b[0m, in \u001b[0;36m_EagerDefinedFunction.call\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m    495\u001b[0m \u001b[39mwith\u001b[39;00m _InterpolateFunctionError(\u001b[39mself\u001b[39m):\n\u001b[0;32m    496\u001b[0m   \u001b[39mif\u001b[39;00m cancellation_manager \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m--> 497\u001b[0m     outputs \u001b[39m=\u001b[39m execute\u001b[39m.\u001b[39;49mexecute(\n\u001b[0;32m    498\u001b[0m         \u001b[39mstr\u001b[39;49m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msignature\u001b[39m.\u001b[39;49mname),\n\u001b[0;32m    499\u001b[0m         num_outputs\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_num_outputs,\n\u001b[0;32m    500\u001b[0m         inputs\u001b[39m=\u001b[39;49margs,\n\u001b[0;32m    501\u001b[0m         attrs\u001b[39m=\u001b[39;49mattrs,\n\u001b[0;32m    502\u001b[0m         ctx\u001b[39m=\u001b[39;49mctx)\n\u001b[0;32m    503\u001b[0m   \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    504\u001b[0m     outputs \u001b[39m=\u001b[39m execute\u001b[39m.\u001b[39mexecute_with_cancellation(\n\u001b[0;32m    505\u001b[0m         \u001b[39mstr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39msignature\u001b[39m.\u001b[39mname),\n\u001b[0;32m    506\u001b[0m         num_outputs\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_outputs,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    509\u001b[0m         ctx\u001b[39m=\u001b[39mctx,\n\u001b[0;32m    510\u001b[0m         cancellation_manager\u001b[39m=\u001b[39mcancellation_manager)\n",
+      "File \u001b[1;32mc:\\Users\\Xyla\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py:54\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     52\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m     53\u001b[0m   ctx\u001b[39m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 54\u001b[0m   tensors \u001b[39m=\u001b[39m pywrap_tfe\u001b[39m.\u001b[39;49mTFE_Py_Execute(ctx\u001b[39m.\u001b[39;49m_handle, device_name, op_name,\n\u001b[0;32m     55\u001b[0m                                       inputs, attrs, num_outputs)\n\u001b[0;32m     56\u001b[0m \u001b[39mexcept\u001b[39;00m core\u001b[39m.\u001b[39m_NotOkStatusException \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m     57\u001b[0m   \u001b[39mif\u001b[39;00m name \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "#Compile model\n",
+    "model_seq.compile(loss=\"binary_crossentropy\", optimizer=optim_adam, metrics='accuracy')\n",
+    "\n",
+    "#Training model\n",
+    "history_seq = model_seq.fit(train_data, epochs=100, validation_data=val_data,verbose=0)\n",
+    "\n",
+    "model_seq.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"model\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " input_1 (InputLayer)        [(None, 46)]              0         \n",
+      "                                                                 \n",
+      " dense_4 (Dense)             (None, 32)                1504      \n",
+      "                                                                 \n",
+      " dense_5 (Dense)             (None, 16)                528       \n",
+      "                                                                 \n",
+      " dense_6 (Dense)             (None, 1)                 17        \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 2,049\n",
+      "Trainable params: 2,049\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Compile model\n",
+    "model_func.compile(loss=\"binary_crossentropy\", optimizer=optim_adam, metrics='accuracy')\n",
+    "\n",
+    "#Training model\n",
+    "history_func = model_func.fit(train_data, epochs=100, validation_data=val_data, verbose=0)\n",
+    "\n",
+    "model_func.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Evaluation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sequential API"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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>loss</th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>val_loss</th>\n",
+       "      <th>val_accuracy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>0.308890</td>\n",
+       "      <td>0.862307</td>\n",
+       "      <td>0.579305</td>\n",
+       "      <td>0.764613</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>0.307711</td>\n",
+       "      <td>0.863295</td>\n",
+       "      <td>0.580769</td>\n",
+       "      <td>0.763033</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>0.307515</td>\n",
+       "      <td>0.863493</td>\n",
+       "      <td>0.592543</td>\n",
+       "      <td>0.760664</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>0.306758</td>\n",
+       "      <td>0.865073</td>\n",
+       "      <td>0.586848</td>\n",
+       "      <td>0.763033</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>0.306430</td>\n",
+       "      <td>0.863493</td>\n",
+       "      <td>0.593730</td>\n",
+       "      <td>0.759874</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        loss  accuracy  val_loss  val_accuracy\n",
+       "95  0.308890  0.862307  0.579305      0.764613\n",
+       "96  0.307711  0.863295  0.580769      0.763033\n",
+       "97  0.307515  0.863493  0.592543      0.760664\n",
+       "98  0.306758  0.865073  0.586848      0.763033\n",
+       "99  0.306430  0.863493  0.593730      0.759874"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics_seq = pd.DataFrame(history_seq.history)\n",
+    "metrics_seq.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Bz+1zfi65/HUL99POjFtpo1O5sLLVCWflLUdXuVBERIqGgpqIiBxbmdRoQ95UxAlGkXxIiu474H7+dmCA8YacfU2+MLQ/O74wRah6tOqgJzh++V9ycPx1PAEI1zlBqGI2NJ8zGor8Zc7+qJExjIxn32bn6PY6oezs90LjmU4/qZr5R1/NL5d1wlqszwldJXXOLJuW04mICApqIiKnJmud0DS21PrYgg6FpXaefEPefFNet9cpX56OO8ErOTzB7YDnJ9oTNcLtGw1OZc1OADqw6ERZk7NscCTAWOvMsB1YuGLjfzvl0SvyfbFazx/tj1Ux27kdbRCy1rlNZzU/l9tZ+hiqmr5riojISUNBTUTkRJbLOjNIhcCyzTn2vZxvnjuJbGq079Qr4fYf3Hy3rCnfhLdk4qa8wcrRcHY0jXmNcZoVlzc7VQnHsvbYzEgZo5kuERE5rhTURERmUiYJ/bug92UnXBWOO5wlgh7/+JLkY0uVR7pg/0vO3qkRpU1QuwCWXeucMxm39+AmwyP3g5XO67ns6B6r7JijzYI37AQxj/+Y/4iOiMKUiIicJBTUROTUNNwFL/3RaVhr3AcvtStrdoo6uFzOLE28f7QIRaR79H5icOICD2OLSWQzBweeXMZZPjjUOb6QRbAKqk+DORc6RSQySee8Qt+r/P3YfqcJ79xLRotq1C5wZqimi9ujSoAiIiIzRP8GFpHilstB/04nzIydVfIEj2y/UC7rNPZ96Y+w7QGnch84Ycfjd/Y8HbgU0OVxglO8f+Jlgi5PvhGwN7+Py33Afc/oY09gzH6v/N4vT8DZN1V9uhPOquZpv5KIiIgACmoiUkxyOejdPtqbas/zTqBKRSY+3+0b7Q81bp/UAXuiBtph+0POUkLjglmvhiu+DPOvgvolznK5XM55vVAOPn+M9TpLAceWaR+5f7h+WyIiIiJHSUFNRGZGOuEUvejeCF0bnUC294XRsuueADQsgzPf5ZRE9wQgE3fel47llwLG8o+jkIw4gS457CxJHFt9MFAB86+E+a+D0y6feNbK5XKWGpbUQtOZx/MnISIiInIQBTWRE1EuO2a/Unz83iVv0KmoF645fMPb5PCYZsD5JryFSn1l42eofCXO9Q9sQDxyP9LtnFNSN1rRb+wMlNvn9KPq3gjdm5wiGDbrjMMbcma2Vlzv9KZqOhNqFk7P/iib78elIhMiIiJyAlFQE5kuuaxTva/nRRjeO1pAIpserZ43Ukgikzw4YBVmipL50umTFKg4krLqwar8Mr1aJzwFK50mviPhLN73yr5zuM4pvFE511lOmIrmr78Ldj/rLBs8sHFx+WxoWAqL3wL1S51b1dyjbxp8OApoIiIicgJSUBM5UtmMUz79wGa7+186dGNfGFNEwj9aYn1s6fVQlfOc2zem8ES+QMXYQhTe0Jj3BcbfT8fHVyUcud+51glm4VqnAXDTWVDZmm8G3OrcD1U7YTE5DImhA5oXDznXH6mMWNoIHt/hf1axXie8pRNQMx+CFdP2qxARERE5WSmoiRxKrG90D1V3/rbvxfGBrKLVKY1+2uVQu9C5X95yQNjKB7QTYXbHF3ZupQ2v/FpuD5TWOzcRERERmTIFNZERmaRTZbD9aWh/xilsMbxn9PVwnbNk79UfcZbr1S5yZoh84Zkbs4iIiIiclBTU5NQVH4CO1dD2lBPMOteOzpRVz4e5FzsFLhry+6hK6mZ0uCIiIiJy6lBQk5NbLguDHU5vrr4d0Puys7+sdzv07QSssySxcQW86iaYfT7MPs+pmCgiIiIiMkMU1OTkkE5A70v54h75Ah/7tznhLJsaPc8bhup5Tl+u5dc7oaxlpZYvioiIiEhRUVCTUdm0U7K9d/uYmaeXYbiLg0qsj+XxO2XgQ1VO+fex9wMVTlBKDo+5DY3eT0XGNC2OH9zQGOP08Aoc0NPLX+ZUPhzqdEJZ/y6wOWc8xg1V86BmgdPguPp0qD4Nqk5zCmScCAU9REREROSUpqB2MssknRDTvckpzz6uZ1c+HGXiTu+r/l1Ob62RBsQA/nIn4FSfdugeV+m4Ux1xpC9XfIBDB7vAmCbKYWeWyxdylhseWGreWkgNjw96kX2j90sbndmxZe8crbhYfZoTHkVERERETlAKaieKTGp8iDpQfMAJZCMl5Ls2Okv/DnyP2zfas8sbGO3H1bAclrx9dOap+jSnp9bRzD7lspAYhHi/My6PH/wlziyYr+TwvbdERERERE5xCmrFJpeDgbZ84NoEXRucY//OqV+jrMWpVLjojU61woZlzsyTN3jombHp4nI7Sx9DVcf+s0RERERETkIKaseLtTC811kiGO9zZpvG3e93imF0b3aW+gFgnJmtxuWw/DpnBmwy3jDUn+GUkw9WHpevJCIiIiIix4aC2rGWy8Kme2DVt6Fny8TneIJOuKpshRXXj/btqlusaoQiIiIiIqcgBbVjJZuBjb+BVd9yZspqFsLr/85ZghiqciojBiud+97gTI9WRERERESKiILadMum4YX/cmbQ+nc6M2PX3gGLrwGXa6ZHJyIiIiIiJ4ApBTVjzOuB7wFu4MfW2r874PXZwH8AFflzvmCtvX96h1rkMklY90t44h+dMvWNK+C6X8DCN05bQLPWkunpId3WRmrktquN1K5dmGCQpq/fhn/+/Gn5LBERERERmTmHDWrGGDfwA+BKoANYbYy511q7ecxpXwJ+Za39oTHmDOB+YM4xGG/xSUZg7R0M3/XP9K9P4a2vJHjJZwle/i58p52GmUJIy0YipHa1kdnfQ3ZggNzgIJmBgcL97MAAmb5+0u3t5GKx0Td6vfhaWvDNmUN84wZ2Xnc9Td/4BmVXve4YfmERERERETnWpjKj9ipgu7V2B4Ax5i7gGmBsULNAWf5+ObBnOgdZlGJ98OztZFbdTvdTlqG2EJ7aBuK7sgy8cBf80124SkoILl9OYMVygitW4K2rI9W+Oz8TtqswK5bt7T34+i4X7rIy3BUVuMvL8TY0EFq5Et+cOfhaW/HNacXb2IjxOL/CdPc+Om+9lc5PfYrERz9K7a23YNzHoRS/iIiIiIhMu6kEtWZg95jHHcCrDzjnq8AfjTG3AGHgtdMyumI02AlP/wC75g6Gd2TpWldHNmWpufnj1HzkJvB4SO1qI75+PfH164ivf4He23/k9Ecbw1Nbi6+1lZLLLnWCV2sr3oYG3OXluCsqcJWWTmk2boS3vo7ZP7uT7q99jd7bbyexZTPN3/427rKyw79ZRERERESKynQVE7kBuMNa+w/GmPOBnxljllprx6UTY8xHgI8AzJ49e5o++jh66P/BU/9EOgZd2xYR2dxLYOkiZn/96wQWLiic5p83F/+8uVS87a0A5KJR4ps2ke3rwzd7Nt7ZrbhLpr/svsvno/FrXyOwZCldX/86O6+9lln//M9HvG/NZrPE179A5LHHiKxaRWbfPqre824qb3zvMRm3iIiIiIiMZ6y1hz7BCV5ftdZelX/8/wFYa78x5pxNwOuttbvzj3cA51lr90123ZUrV9o1a9a88m9wvPS3Yb+7nMHk+XT/Xy82lab21luoet/7CssPi0nsuefouPVT5GKxKe1by/T3E33iCSKPrSL6+ONkBwfB7SZ01lmYUJDoqsdxV1RQ9aEPUvXud+MKhY7TNxEREREROTkZY9Zaa1dO9NpUEsZqYL4xZi7QCVwPvOuAc9qBK4A7jDGLgQDQc/RDLj6Z1b9mz6oqonvbCJ5zDo23fQ3/3LkzPaxJhc4+m7m/uZuO/L613uXLJ92zlkskSG7dCrkc7upqSi67jJJLLib8mtfgLi8HIL5hIz3//E/0/MM/0vfTO6i+6SYqr78OV1A94EREREREptthZ9QAjDFvBL6LU3r/J9barxtj/gZYY629N1/p8d+AEpzCIn9prf3joa55Is2oJV58kY73/RmZSJa6z3+Ryve8+4j2j82kXCpFz/e+R3LLlslPcrkJrlhByaWXEFiy5JDfLfb88+z/p38m+tRTuGtrqLnpI5RfczWusjKMMcfgG4iIiIiInJwONaM2paB2LJwoQW3wvvvY+8W/wu2K0fKpawi+/1szPaSiEFu9mp7v/xOx1audJ9zuQiGUkUqV444VFbgryg96zQSDCngnMZvLFYrrpPfuIbB4McEVK/BUVc300ERERERm3Ctd+nhKspkM+77zHfr+/ScE5zfRckYbnjfdNNPDKhqhc8+l9Wd3Elu9mviGjWTz/d6yAwNkBwdJ791LYssWsgMD2ERi0uu4wmECS5cSXLGCYL6Ngaem5ojHY60l3d7uVNtct574+vWk2trwNjbim5Ovqtnain/OHLytrXhqa6c1INpMhvi6dUQeW+UUYOntJbh0KcEzVxBcsYLAsmW4S0qm7fOKVXZggPiGDYXfQfyFF8gNDR10nnf27Pzv3Pm9BxYuxPh8MzBiERERkeKkGbUJZPr72fPnf070qaepuOF6GpqfxSR74ZPPzvTQTki5ZJLsQD7IDY6GuezAAJm9e4m/sIHEiy9CJgOAt7nZ+Qv8mSvwtsyCyfJUNkvypZcKoSA7MACACYUILluG/7R5pLu6nZ51u3dDOl14qysUwptvi+AEudH+dO7KyimFuExfH9HHH3eqYz7xpBNIPB5CZ5+Nt7GR+MaNpF5+2TnZGPynn0YgH06OJoyOsKl0/uc49mc6et8dLnH67I18v9Y5zveapqBoczkye/cW+gCmdjnH5M4dpNvanZNcLvzz548L4N7GRhJbtoyG6XXryPQ4W1mNz4d//vz872BOIVz7WltxV1RM8DNIjf7HgcFBcrEYrtLS0dnbsjL1ERQREZGip6WPRyDx4ot03HwLme5uGr7yZSpefwn8wwK4+HNw2RdnengnrVwiQWLz5nEzMZm9e6f0Xt/pp42ZnVmB//TTD/pLus1mSe/d64SKQrNx55ju6IRstnCuq7QUX2srrkMEm9zQEIktW8Ba3DU1lFx8MSUXX0z4gtfgLi0tnJcdHCS+YWO+p9564utfIDc4eIQ/nckZv/+A5aRlZIcjpHbtItPVNe5cd00NvlmzMH7/0X2YtWT7ekm178Ymk6NjCAQKoSqwZIkzg7h06WFbOVhryXR1FYJb8qWXnN/Hnj3j+g66Kyrwzp4NmUxh1jYXix16rMbgKitzltuWj112WzHmudGfm7e5acoBXURERGS6KKhN0eB997H3r76Eu6yMln/6PsEVK2DNT+F3n4aPPQkNS2d6iKeUdHc3mX2HKB5qDL7Zs15xU2+bTpPq6HBCwphZotyYMHLQR/u8hM49l5KLLyFwxuIpF5ex1pJuayM7HDnq8RqvpxAyDlV1MxePk2rfXQikqV27SHd0YvMzl0fDXV7uzHiNmY301NVNa3GdXCpFevfucbN16d3t4PXiyc+YufIBa+SxCQbJDQ87s2z9A+OW4R54PxeZ+Gc/EtAPnNXzNjaSi0YnvFZ2YBBXKEj4wosInX2Wlm+KiIjIEVFQm6Luv/8m8fXrafned/HU1jpP/uxt0L8LbnkO9F/bRU54Np0mOzQ0Grb6+0nng3ohGO7ZA4f7Z6MxuMvKyMZikE7jCocJX3ABJZdcQsnFF43+M0RERERkEiomMkV1f/5ZyOVG/6t4rA92roLzb1ZIEzlJGK8XT3U1nurqSc/JJZOFWb10VxfukpKDqpi6Sksxbje5aJToM88QefQxIqtWMfxHpzNJYMkSSi65mMDy5c4sXUsLxus94vHmEglS7e2FGd/krl2kd7WRam/H+P0HzXD65szB29iI8egf7yIiIicy/Zt8jIP+YrP195DLwBnXzMyARGRGuPx+/Kefjv/00w9/bjhM6RVXUHrFFVhrSW7d6lT/fOwx9v/r7aP77dxuvM3N44rXeJuayMVj45dmDg6SyS+xzOzrcfYajpndc1dX42ttJfya12BTSZK7dhFfu3b8vj2vF9/s2YReda6zd/K889ScXkRE5ASjpY+H8svroHszfPoFzaiJyBHLDg2RfPnl/JLKXeMqZdoJCqK4wuHxM3c11aOVO0eqko4pVjPCWkt2//4x199FcttLRFevxsZiGJ+P0Ktf7SzLvORifLNmHfV3suk0mX37cFdX4woEjvo6x5O1ltzwMLnhYTwNDaoIKiIiRUN71I5GYgi+dRqcexO8/m9nejQichKx1pLpcWbLXOGwE8zKy49qaeSh5FIpYn9aTWTVY0Qee6zQPsE3bx6hc8/FU109cTP6igqnemjbrjFVUtucJZednYVWGp6GhtHiK4VWF614Z83CdRwLq2SHh4m/8ILTWL1998GFZIaGCpVdvc3NVL773VT82TtecSGi6ZBqayPy2CpSHbtxlx34exh97CotVVVSEZGTkILa0Xjh1/DfH4YP/hFmv3qmRyMi8oqldu0ismoVkUcfI75x44TNyCdigsFxFTG9TU1kevc7VVLzBVhG+hgC4HLhbWo6aO+cr7UVb3MzxuMhl0o5FToP6K2YGxrGBANjWk5UjO4LDIcglyO5fftoK4/160nt2OEsDzUGT0PDpOHT+HwM3/97YmvWYEIhKt72NqpufA++OXMm/e7WWtLt7cTXryfT23dwiBrp2zfFPYG5VIr4mjVO/8XHVpHatcv5kYVCh2w74QqHCz/LkR6J/jlz8La24qmsnNJnT5VTMbad1K420p2duIKBg36W7vJyTCiEMQabzToFesb+PgcGnb6OZeUEz1yBb86caa0OKyJyslBQOxr/9R7oWAOf2Qz6l4uInIQKf8EeOLilgSsUyoerOXjqag87m5MdGDioCfrIbNy4lggeD8brxcbjRz5grxfjchX6+LkrK53+iWeuILh8OYFlyyZcGnqg+KZN9N/5Mwbvvx8yGUouuYSq995I6PzzyUUixF94gcQLLxTC4LgQOglXaelBQWbsfVwuos88Teypp8lNshzVZjL538cBrSD6+0nv2TPaZqOzc1yvQVd5+bgZTafJ/cSN7nOJxJjft/M5Y6+dams7qAfjZIzXiwkEyA0PH/7nU1ZGcPny0d/XsmUTNrMXETnVKKgdqVQUvnkanPUeeNO3Z3o0IiInLGst2b4+JwjsdIKATSZxV1YcHGrKy3GVlWFHwsTY5Yv5UGHTaQJLziC4YgXeWbNe0XLATE8P/Xf9F/133UW2txd3dTXZvr7C7JzvtHlOsFixguCKM/E2NuTHNFFPvTH3xzw3dtbS09SYb9/wygq82FSKVEfnuB6JIyE5s2fvuHPd1dV4qqoKgdxO0h/SXVFx8OxnvlqpTaUm/b65eGLCpZojx0xPD/H1LxRmP5MvvVQImb45c0aD24oV+BcsULVSETnlKKgdqU2/hV+/D973vzD34pkejYiIHEO5ZJKh++4n+sQT+OefTnDFiinPzh3OyKylTSScQibHeJ/Z2HYOIwEuOzBwwFLS8jFLSsvx1tcft9mtbCRKYuPGQnCLr19PtrcXcJbYjoTw4IoVBM5Ygqe6alorllpryUWjkMloRk9EioKC2pG6+4Ow4zH4863g1n/dExERORastaQ79xBfv64Q3BKbt0A6XTjH+P0TLis1wcCkwdep9Bk5eKZzcLBQDCd4zjmUX301Za+/ylmeKiIyAxTUjkQ64VR7XPoOuPr7Mz0aERGRU0oumSS5ZQuJbdsKRUnGFigZuW8TiUNexzXSqH6CPYO5RJyh++8ntf1ljNdLyWWXUX7N1ZRcdBFmihVLbSZTCH9jl+fm4jG8jU3OEtKW5ilf70RlrSX6+ONkevtG24hUVh529jgXjRaW7OaiUULnnYevpeWYjDE7POzsnR2zTLjw2YODh3xv+JKLqb35FoLLlh6TsYkoqB2Jrb+H/7we3vMbOP21Mz0aEREROQastSQ2b2bo3nsZ/N19zj7F8nJK3/gGgitWkBsannwf4sDA+CI5k3G58o3u5xSqoHqbm0dDY0X5EVUNParvmc06BWPa2/HU1OJfMH9aluBaa4k89hj7v/9PJDZvHveaq6xstOpr/jtn+3pJ7trltPloayPT03PQNX2nnVbYxxk6+6wJQ65NpUhs3eoU+3nhBRIbN5I7RGi3iQTZ/v7RJ4zB09hQGJunqnrSXrm5aJTBe+4hOzhIyRVXUHvzJwksXjzFn5DI1CioHYl7PgZb74e/2A6ek/u/gomIiIgzOxZ96ikGf/s/DP/f/40WXTEGV1nZ+IIpY/b3jW0f4S4vx11ZgcvvJ93ZOa4B/Ugl1Fw0OuHnu0pLx7WRmJQBd2nZwbOE+UIuWEuqrX18/8Pdu7FjlpJ6GhspufhiSi7JF7UJhY7sZ2Ut0SeepOef/onECy/gnTWLmk98guCKFaTaR2et0m1tJHftIrO3yynQA7irqsaF1pHqpMbrJfrkk0QefYzY6tXYdBpXOEz4ggsoueRiXOFwoQprYtMmbCrlfJe6OoIrluMqnbwnovF68c1qwTvS0mLWLFyBwJS/bzYSoe/OO+n76R3khocpfd3rqLn5kwQWLJj8Z5TNkt7bBTaHt6VFPRDlkBTUpiqTgm+fDgvfBG/74UyPRkRERI6zbCRKpmffaI88t3tarmutJdvbS3pv18RVNPM3m99DN/HgsmSHhwvvm6zNhfH78c2ePdqyYc4cvLNmk2pvI7pqFdEnn3LaRHi9hF71KkouuYTwRRfiaz70Us3oM8/Q8/1/Iv7cc3iaGqn5+MepeOtbMV7vpO/JJZOk9+zBU109pSbzuWiU6DPPEHn0MSKrVpHp7i58p8CSJaOVWM9cgbeh4bDXmy7ZoSH67vgP+v7jP8jFYpS94Q1Uvf995GLx8aG8rY10e3shHLsrKsZVN52oUJG11mlxMraCa/c+vA3140LtVArg2FyO3PAwrlDokL8XKR4KalO1/SH4+Tvghrtg4RtmejQiIiIik8olk+P27pGz+Fpn46mvP2SD8VwqRXztWiKPrSLy2GOkdu4svOYKhXDlZ+g8FRW48jN3qe0vE1u9Gk99PTUf/xgVb3/7Md9/Z60lue0lpy3HwgVFETwy/f30/fQO+n7+c+yYJvXG58PXOrvQkN7X2go5S/wFpz1F6uWX8yfmW38sX4FNpwsBb2wrD1wu3NVVZHv7xvVMdJeX482Hb3dFBbnBoYND/9CQ8x63O7/sNt9bsRD4WvE2NR3xf4DIxWIkNm0ivmEjnppqSq+4Alc4/Ip+luJQUJuqe2+Bjf8Nn3sZvFOfFhcRERE5UaXa24k++yzZ3l6y/QMTzviZUJDqD3yQindei8vvn+khz7hMXx+RVavw1tU5e90aGw8ZjrNDQ8Q3bBitbrphIybgH10C2jqmYX2+CE0ulSLd0VFYOjt2xi03OFgI1AdVRS2vIDs4MGamr218qPR68c6aNS68jYzBU18PxpDatWtcG43k1m2QzY5eIxik9MrXUn71NYTPP2/S4GetJbllC5FVq4g8+hjJnTvxNjcd/L3zM4an4jJRBbWp2vEo9O2AlR+c6ZGIiIiIiLxi1loyPT2kD1iimdrVRqq9fXRPJmACASck5mf4XOEwwRXLCSxf7izhXL6cVFsbg/9zL0O//z25oSE8tbWUveUtlF9zNYGFC8lGokSfepLIqlVEH1tVKBwTWLaMwOLFpPfudZaIdnSMmzF0lZcTmD+fwIr8Z604E2993aTfKxeNEt+4qRAmM93dhM49l5JLLiZ09tknTMVVBTURERERERnH5nJkurvHFb7JxeMEly0luGIFvnnzJp0tyyWTRB59jMF77yWyahWk03hnzSLd1QXpNK7S0nxBmEsouehCPDU14z87lSLV0UmqbVfh8xNbtpDcvKWwx8/T2FjYlxhYvJh0Z6cTzF54geS2bYWg55szB09NDfH160eL0bzmNZRcegnhiy7CWzd54JtpCmoiIiIiInJMZPr7Gfr974k+tgr//NMJX3wxobPOOqp9hblUiuSWLU4gy1f7THd2Fl53lZYSHJnhO9Mp0OKprHTeO1KMJr//cqQYTeCMM5yKnR/76PR84WmkoCYiIiIiIiekTE8PiRdfLPQlPNR+wBFOMZpthQqi7ooKZv3gn4/DaI+MgpqIiIiIiJyybDY7be02ptOhgtrh46iIiIiIiMgJrBhD2uEoqImIiIiIiBQZBTUREREREZEio6AmIiIiIiJSZBTUREREREREioyCmoiIiIiISJFRUBMRERERESkyCmoiIiIiIiJFRkFNRERERESkyCioiYiIiIiIFBkFNRERERERkSKjoCYiIiIiIlJkFNRERERERESKjIKaiIiIiIhIkVFQExERERERKTIKaiIiIiIiIkVGQU1ERERERKTIKKiJiIiIiIgUGQU1ERERERGRIqOgJiIiIiIiUmQU1ERERERERIqMgpqIiIiIiEiRUVATEREREREpMgpqIiIiIiIiRUZBTUREREREpMgoqImIiIiIiBQZBTUREREREZEio6AmIiIiIiJSZBTUREREREREioyCmoiIiIiISJFRUBMRERERESkyCmoiIiIiIiJFRkFNRERERESkyCioiYiIiIiIFBkFNRERERERkSKjoCYiIiIiIlJkFNRERERERESKzJSCmjHm9caYrcaY7caYL0zw+neMMevyt23GmIFpH6mIiIiIiMgpwnO4E4wxbuAHwJVAB7DaGHOvtXbzyDnW2s+MOf8W4KxjMFYREREREZFTwlRm1F4FbLfW7rDWpoC7gGsOcf4NwH9Ox+BERERERERORVMJas3A7jGPO/LPHcQY0wrMBR5+5UMTERERERE5NU13MZHrgbuttdmJXjTGfMQYs8YYs6anp2eaP1pEREREROTkMJWg1gnMGvO4Jf/cRK7nEMserbU/stautNaurK2tnfooRURERERETiFTCWqrgfnGmLnGGB9OGLv3wJOMMYuASuDp6R2iiIiIiIjIqeWwQc1amwFuBh4AtgC/stZuMsb8jTHm6jGnXg/cZa21x2aoIiIiIiIip4bDlucHsNbeD9x/wHNfPuDxV6dvWCIiIiIiIqeu6S4mIiIiIiIiIq+QgpqIiIiIiEiRUVATEREREREpMgpqIiIiIiIiRUZBTUREREREpMgoqImIiIiIiBQZBTUREREREZEio6AmIiIiIiJSZBTUREREREREioyCmoiIiIiISJFRUBMRERERESkyCmoiIiIiIiJFRkFNRERERESkyCioiYiIiIiIFBkFNRERERERkSKjoCYiIiIiIlJkFNRERERERESKjIKaiIiIiIhIkVFQExERERERKTIKaiIiIiIiIkVGQU1ERERERKTIKKiJiIiIiIgUGQU1ERERERGRIqOgJiIiIiIiUmQU1ERERERERIqMgpqIiIiIiEiRUVATEREREREpMgpqIiIiIiIiRUZBTUREREREpMgoqImIiIiIiBQZBTUREREREZEio6AmIiIiIiJSZBTUREREREREioyCmoiIiIiISJFRUBMRERERESkyCmoiIiIiIiJFRkFNRERERESkyCioiYiIiIiIFBkFNRERERERkSKjoCYiIiIiIlJkFNRERERERESKjIKaiIiIiIhIkVFQExERERERKTIKaiIiIiIiIkVGQU1ERERERKTIKKiJiIiIiIgUGQU1ERERERGRIqOgJiIiIiIiUmQU1ERERERERIqMgpqIiIiIiEiRUVATEREREREpMgpqIiIiIiIiRUZBTUREREREpMgoqImIiIiIiBQZBTUREREREZEio6AmIiIiIiJSZBTUREREREREioyCmoiIiIiISJFRUBMRERERESkyCmoiIiIiIiJFRkFNRERERESkyCioiYiIiIiIFJkpBTVjzOuNMVuNMduNMV+Y5Jx3GmM2G2M2GWN+Ob3DFBEREREROXV4DneCMcYN/AC4EugAVhtj7rXWbh5zznzg/wMusNb2G2PqjtWARURERERETnZTmVF7FbDdWrvDWpsC7gKuOeCcm4AfWGv7Aay1+6Z3mCIiIiIiIqeOqQS1ZmD3mMcd+efGWgAsMMY8aYx5xhjz+okuZIz5iDFmjTFmTU9Pz9GNWERERERE5CQ3XcVEPMB84FLgBuDfjDEVB55krf2RtXaltXZlbW3tNH20iIiIiIjIyWUqQa0TmDXmcUv+ubE6gHuttWlr7U5gG05wExERERERkSM0laC2GphvjJlrjPEB1wP3HnDOb3Fm0zDG1OAshdwxfcMUERERERE5culcmp7Yibft6rBBzVqbAW4GHgC2AL+y1m4yxvyNMebq/GkPAL3GmM3AI8DnrLW9x2rQIiIiIiJy6tgX20cmlzni963vWc87//edfPrRT5OzuWMwsmPnsOX5Aay19wP3H/Dcl8fct8Bn8zcREREREZFp8cddf+Rzqz5Ha1krN595M69tfS0uc+j5pkgqwvef/z53vXgXdaE6bj3r1sO+p9hMKaiJiIiIiIgcb0/teYrPP/55FlctJpFJ8OeP/TmLqxbzqbM/xWuaXoMx5qD3PNz+MF9/9uv0xHq4YdEN3HLWLZT4SmZg9K+MgpqIiIiIiBSdDT0b+PQjn2Zu+Vxuv/J2Srwl3LfzPv5l3b/wsYc+xjn153DrWbdydv3ZgLM88u/+9Hc82PYg8yvn851Lv8Py2uUz/C2OnnFWLR5/K1eutGvWrJmRzxYRERERkeK1Y2AH7/3Deyn1lnLnG+6kNjTa2iudTfObl37D7S/czv74fi5qvoiVDSv5txf+jVQ2xcfP/DjvW/I+vC7vDH6DqTHGrLXWrpzwNQU1EREREREpFnsie7jx9zeSsznufMOdzCqdNeF58UycX275JT/Z+BOGUkO8uuHV/PX5f01rWetxHvHRO1RQ09JHEREREREpCn2JPj764EeJp+P89PU/nTSkAQQ9QT607ENcu/Ba2gbbWFqzdMI9aycqBTUREREREZlxkVSEjz/0cbqiXdx+5e0srFo4pfeV+cpYVrvsGI/u+FNQExERERGRGZXMJvnUI59iW982vnf59woFQk5lCmoiIiIiInJcpHNpuiJd7I7spmO4g47hDnYP72Zb/zbah9v5xkXf4OKWi2d6mEVBQU1ERERERI5af6KfnYM7aR9uZzA5SCQdIZKKEElHiKajDKeGiaaj9CX66Ip2kbXZwnt9Lh/Npc3MKZ/DJ878BG+a96YZ/CbFRUFNREREREQOqzfeyws9L7BraBc7B3eyc3Anu4Z2MZAcGHeewRD2hinxlVDiLSHsDVPmL2NW6SzeNO9NtJS0MKt0Fi2lLdSF6nAZ18x8oSKnoCYiIiIiIof0YNuDfPnJLxNJRwCoDlQzt3wuV7Zeydzyucwpm8OcsjlUBioJeUMKX9NAQU1ERERERCaUzqb5x7X/yM+3/JzlNcv5i3P/gtMqTqPMVzbTQzvpKaiJiIiIiMhB9kb28heP/QUv7H+B9yx+D58957N43d6ZHtYpQ0FNRERERETGWdWxii8+8UWyuSz/eOk/cmXrlTM9pFOOFo+KiIiIiJwg9sf3s27fumN2/Uwuw3fXfpdP/t8naQw38l9v/i+FtBmiGTURERERkRPA7qHdfOiPH2JvdC8XNl/I51Z+jnkV86bl2jmbY0vfFr61+lus7V7Lny34Mz5/7ucJeALTcn05cgpqIiIiIiJFrm2ojQ8+8EGS2SQ3LbuJ/3zxP3n7vW/nuoXX8YkzP0G5v/yIr9kT6+GpPU/x1J6neGbvM/Ql+gh6gvzthX/LW057yzH4FnIkFNRERERERIrYjsEdfPiBD5PJZfj31/07C6sW8p4z3sMPnv8Bd229i9/t+B2fOPMTvHPhO/G6Ji72Ya2lL9HH1r6tPL33aZ7c8yQv9b8EOKX2L2i6gPObzueC5guoClQdz68nkzDW2hn54JUrV9o1a9bMyGeLiIiIiJwItvdv50N//BAGw49f92NOrzx93Ovb+rfxzdXf5Nm9zzK3fC6fW/k5zqg+g5cHXmb7wHZ2DO5g+8B2Xh54udCY2uvycnbd2bym+TVc0HQB8yvnq+/ZDDHGrLXWrpzwNQU1EREREZFjb/fwbv7zxf/kt9t/S2O4kesXXc+b5r6JkDc04flb+7Zy0x9vwuPy8OOrfsy88on3o1lreazjMb695tu0DbWNe63UW8ppFacVbqdXnM6K2hWTfqYcXwpqIiIiIiIzwFrL6q7V/HzLz3l096O4jZvLZl9G+1A7W/u3Uuot5ZrTr+H6RdfTWtZaeN+W3i3c9OBNBNwB/v2qfx/32mTS2TT3vnwvsUysEMpqg7UYY47hN5RXQkFNREREROQ4SmQS3L/zfn6x5Rds699Ghb+Caxdcy3ULr6M+XI+1lnU96/jPF/+TB3c9SMZmeE3Ta7hh0Q1UB6r56EMfpcRbwr9f9e/MKp01019HjhEFNRERERGRaTCYHOSJzidYt28dqVyKTC5D1mbJ5rJkbbbweEPPBvqT/cyvnM97Fr+HN85946Sl7vfH93P3trv59dZfsy++D4Dmkmb+/ap/p7mk+Xh+PTnOFNRERERERI6CtZbtA9tZ1bGKVR2rWNezjpzNEfaGCXvCuF1uPC4PbjN6dLvcNJc0c/3C6zm34dwpLz1M59I80v4IT+15io8u/yiNJY3H+NvJTFNQExERERGZoqHUEOv2rePxjsdZ1bGKPdE9ACyuWszFLRdzccvFLK1ZqkqJ8oodKqipj5qIiIiInLKsteyJ7uH5fc/zfPfzPLfvOV4eeBmLJegJcl7jedy0/CYuar6I+nD9TA9XTiEKaiIiIiJySkln0zzQ9gCP7X6M5/Y9x76Ysy+sxFvCiroVvH7O6zmr7ixW1K3A7/bP8GjlVKWgJiIiIiKnhMHkIL/e9mv+c8t/si++j/pQPefUn8PZdWdzVt1ZnF5xOm6Xe6aHKQIoqImIiIjISa5tqI2fb/45//Py/xDPxDm/8Xz+3wX/jwuaLlCPMSlaCmoiIiIictKx1rK2ey13br6TR3c/isfl4U3z3sSNZ9zIgsoFMz08kcNSUBMRERGRk0rbUBvf+NM3eLLzSSr8Fdy0/CZuWHQDNcGamR6ayJQpqImIiIhIUbHWEk1HGUwNUuGvIOwNT+l9sXSMH2/4MXdsugO/289frPwL3rnwnQQ9wWM8YpHpp6AmIiIiIsedtZbHOx/nDzv/QH+yn6HUEEPJIQaTgwylhsjaLAA+l48Lmy/kDXPfwMUtFxPyhia81oNtD/KtNd+iK9rF1addzWfO+Yxm0OSEpqAmIiIiIsfVun3r+M7a7/DcvueoClTRGG6kzFdGU7iJMl8Z5f5yyv3llPpKean/JR7Y9QAP736YoCfIpS2XctXcq7iw+UL8bj87BnfwjWe/wTN7n2Fh5UL+/qK/5+z6s2f6K4q8YsZaOyMfvHLlSrtmzZoZ+WwREREROf6292/ne89/j0d3P0p1oJqPr/g4b1/wdrwu7yHfl81leW7fc/xh5x94sO1B+pP9lHhLOLv+bJ7a8xRBd5Cbz7qZdy58Jx6X5iHkxGGMWWutXTnhawpqIiIiInIsdUW7+MG6H3Dvy/cS8oT4wNIP8J7F75lwGePhpHNp/rT3T/xh1x94qvMpXtP8Gj599qepDlYfg5GLHFuHCmr6Tw4iIiIiMu2stWzp28L/vvy//Grrr7BY3rP4PXx42YepDFQe9XW9Li8XNF/ABc0XTONoRYqPgpqIiIiITIuRJYoPtz/Mw+0Psye6B5dx8eZ5b+aTZ36SppKmmR6iyAlDQU1EREREJhRJRbh/5/1s2L+BUl8p5b5yKvwVlAecY4W/gjJfGS/1v8T/tf8fj+5+lP5kPz6Xj/ObzudjKz7GJbMuoSpQNdNfReSEo6AmIiIiIgXWWtb3rOc3L/2GB3Y9QDwTpypQRSKTIJaJTfq+Em8JF7VcxBWzr+DC5gun3PtMRCamoCYiIiJyErPW8vy+5+lL9FEbqqUuWEdNsAave3ylxcHkIL/b8Tvu3nY32we2E/QEeePcN/JnC/6MJdVLMMaQyqYYTA4ykBxgIDlQuN8YbuRVDa866JoicvQU1EREREROUut71vO9577H6q7VB71W6a+kJlRDXbAOr9vLU51PkcqlWFq9lK+c/xXeMPcNB82K+dw+akO11IZqj9dXEDllKaiJiIiInGRe6n+J7z//fR7d/ShVgSq+8KovcHbd2fTEe+iJ9Rx0HEoN8bb5b+PPFvwZi6oWzfTwRQQFNREREZGTxu7h3fzLun/hvh33EfaGueWsW8b1K1vM4hkeoYhMlYKaiIiIyAkmk8swnBpmKDXEUHKIodQQj+x+hN+89Bvcxs37l76fDy39EOX+8pkeqogcJQU1ERERkSJmreX3O3/PL7b8gp64s0wxmo4edJ7HeHj7/Lfz0RUfpS5UNwMjFZHppKAmIiIiUqRe6HmBb67+Jut71nN6xemsrF9Jmb+MMl/+NuZ+S2mLAprISURBTURERKTIdEW7+N5z3+N3O35HdaCav3nN33D1aVfjdrlnemgicpwoqImIiIhMkbWWVC5FMpsklXWOyUzSOeZvsXSMaCZKLB0r3I+mnccel4e55XOZWz6XeeXzqA/VY4wpXD+eiXPHxjv46aafks1l+fCyD/PhZR9W82iRU5CCmoiIiMgh9MR6eKLzCR7vfJyn9zxNJB054muEPCHC3jCJTILh9HDh+aAnWAhujeFG/vfl/6U71s3rWl/HZ875DC2lLdP5VUTkBKKgJiIiIjJGNpdlw/4NPN75OI93PM6Wvi0A1IXquGrOVbSUtuB3+/G7/fjcPgLuAD63r/BcyOuEspFwFvAEcBkX4MzI9SZ62Tm4s3DbMbiD57qfY290L4urFvP3F/8959SfM5M/AhEpAgpqIiIicsobTg3zZOeTPNrxKE90PsFgchC3cbOidgWfOvtTXNR8EQsqF4xbpng0jDHUBGuoCdZwbsO5415LZpP4XL5X/BkicnJQUBMREZFTUsdwB491PMYjux9hbddaMjZDpb+SS1ou4aKWizi/8fzj2ofM7/Yft88SkeKnoCYiIiKnhJEljY91PMajux9l+8B2AOaVz+O9S97LZbMuY1nNMlVWFJGioKAmIiIiJ4yczXHPS/ewc3An8yvns7BqIaeVn4bX7Z3w/IHEAE/ueZJVHat4cs+ThSWN59Sfw+dWfo5LZ13K7LLZx/lbiIgcnoKaiIiInBC6ol389ZN/zTN7n8FjPGRsBgCP8TC3Yi4LKxeysHIh8yrm8WLfizze8Tgv7H+BnM1RFaiasSWNIiJHQ0FNREREit7vd/6erz3zNTK5DF85/yu89fS30j7czra+bWzt38rWvq38qetP/G7H7wrvWVK9hI8u/ygXNV/EkpolhcqLIiInAgU1ERERKVqDyUG+/uzX+f3O37O8djnfuPAbhaWK88rnMa98Hq+f+/rC+QOJAXYM7mB22WxqgjUzNWwRkVdMQU1ERESK0tN7nuZLT36Jvngft5x1Cx9c+kE8rkP/1aUiUMHZgbOP0whFRI4dBTURERE5ptK5NLF0jGg6WrjF0jFy5CZ9zxOdT/CLLb9gbvlcvn/591lSveQ4jlhEZOYpqImIiMgrls6l2d6/nU29m9i4fyObezfTHesmmo6SzCaP6prvWvQuPn3Opwl6gtM8WhGR4jeloGaMeT3wPcAN/Nha+3cHvP5+4FtAZ/6pf7bW/ngaxykiIiJFIJVNsS+2j554D+1D7Wzq3cSm/Zt4se9FUrkUAGW+MpZUL2FZzTLC3vC4W8gbosRbQsgbOmRxjwp/Ba1lrcfra4mIFJ3DBjVjjBv4AXAl0AGsNsbca63dfMCp/2WtvfkYjFFERESOMWstA8kB9sX20R3rpivaxb7YPucWd449sR4GkgPj3hf0BDmj+gxuWHQDS2qWsLR6KS2lLRhjZuaLiIicJKYyo/YqYLu1dgeAMeYu4BrgwKAmIiIiRSpnc+yL7aNjuIPOSCcdkQ46hjvYG93rhLNod2FGbITLuKgOVFMXqqO5pJmzas+iLlRXuDWGG2kta8Xtcs/QtxIROXlNJag1A7vHPO4AXj3Bee8wxlwMbAM+Y63dPcE5IiIiMg2stfxux+/41/X/SiaXwef24Xf78bl94+6nc2k6hzvpjHSSzqUL73cZF/WhehrDjSytWcprZ7+WulAd9eF65xiqpyZYc9gqiyIicmxM1z99/xf4T2tt0hjzUeA/gMsPPMkY8xHgIwCzZ8+epo8WERE5teyP7+dvnv4bHtn9CEurlzKvYh6pbIpkNlk4RlIRktkkLuNifuV8Lpt1GS2lLbSUtNBS2kJjuBGv2zvTX0VERCYxlaDWCcwa87iF0aIhAFhre8c8/DHwzYkuZK39EfAjgJUrV9ojGqmIiIjwwK4HuO2Z24ilY/z5OX/OjWfcqKWHIiInoakEtdXAfGPMXJyAdj3wrrEnGGMarbV78w+vBrZM6yhFREROcQOJAb7+7Nf5w64/sLR6KbddeBunVZw208MSEZFj5LBBzVqbMcbcDDyAU57/J9baTcaYvwHWWGvvBW41xlwNZIA+4P3HcMwiIiInPGstqVzKafxsc4S8IQLuwITVEh9pf4T/9/T/YzA1yC1n3cIHl35Qe8dERE5yxtqZWYG4cuVKu2bNmhn5bBERkemSzqZ5eu/T7InsIZaJEU1HiaWdYzQdJZqJEk/HiWVixDNxYun8MeMEtLHcxk3IGyLsDRd6jRkM63vWs7ByIV+/8OssrFo4Q99URESmmzFmrbV25USv6T/HiYiIHIWtfVv57fbfct+O++hP9heeNxinsbMnVAhdIW+IhlADQU+QkDdE0BMcd99lXOMCXiQdGQ17mSgfX/Fxblp2k4p/iIicQhTUREREpmgwOch9O+7jt9t/y5a+LXhcHi6bdRlvPf2tnFF9BiGPE7zU7FlERF4pBTUREREglo4RSUeIpCNEU85MVjQVLTz3/L7nebj9YdK5NIuqFvGFV32BN859I5WBypkeuoiInIQU1ERE5JRkrWX7wHYebn+YR3Y/wqbeTYc8v9xfzrULruWtp7+VxdWLj9MoRUTkVKWgJiIip4xsLsu6nnU83P4wD7c/TEekA4Dltcv5xIpPUB2spsRbQomvhJAnRImvpFDYo9RXqkqLIiJy3OjfOCIickKIpCJ0RbtoLGkk7A1P6T2xdIxt/dvY1LuJTfs38UTnE/Qn+/G6vLy68dV8cNkHubTlUmpDtcd49CIiIkdGQU1ERIrOcGqYLb1b2NK3hU29m9jSu4VdQ7sKr1cFqphVOotZpbOYXTqbltIWZpfNJp1Ns6VvC5t7N7O5dzO7hnYVSuBXBao4v+l8Lp99ORc2XzjlsCciIjITFNRERGRGpLNp9kb30hHpoDPSScdwB7uHd7O1byvtw+2F8xrCDZxRdQZvmvcmWkpb6I52s3t4N7uHd7O2ey337bgPy/ieoHXBOhZXL+aqOVexuGoxZ1SfQV2oTtUYRUTkhKGgJiIix9zYvWFb+rbQMdxBd6x7XMNnj8tDU7iJBZULCgU7FlctpjpYfchrp7IpOiIddAw7+83OqD6DmmDNMf0+IiIix5qC2hg/eWInL+2L8I23L5vpoYiInPDS2TR/6voTD7U/xMPtD9OX6MPr8rKkegnn1J9DS2kLzSXNNJc0M6t0FrXBWtwu9xF/js/tY175POaVzzsG30JERGRmKKiNsbs/xj3Pd3DbW5fidml5jIjIkYqkIjyz9xkean+IVbtXMZweJuQJcXHLxVwx+wouarlIe8NERESmQEFtjKVN5STSOV7uibCgvnSmhyMiUvTimTjP73ue1V2r+dPeP7GpdxNZm6XcX84VrVfw2tmv5bym8/C7/TM9VBERkROKgtoYy1rKAdjYOaigJiIntWQ2SU+sh554T+G4L7avcN9aS5m/jHJ/OWW+8Ue/28+m/Zt4tutZ1vesJ5PL4DEeltYs5UPLPsR5jedxVt1Z6jkmIiLyCujfomPMqwkT8LrY0DnI289umenhiIhMi3gmzpbeLWzcv9G59W5k9/Dug87zuDzUBmupDdZijGHfwD4Gk4MMpYbI5DLjzjUYFlcv5sbFN/Kqxldxdt3ZhLyh4/WVRERETnoKamN43C7OaCxjU+fQTA9FROSIZXNZumNO6fq2oTY2925mw/4NvDzwMlmbBZxS90url3L1aVdTH6qnNuQEs7pQHeX+clzGddB1rbXEM3GGUkMMJgeJpCOcXnE65f7y4/0VRUREThkKagdY2lzOb9Z2kMtZXCooIiJFIJ1NM5weZjjl3IZSQwynhumKdrF7eHehNH1npHPczFeZr4ylNUu5dNalLKtZxtKapUdVtt4YQ8gbIuQN0RBumM6vJiIiIpNQUDvA0uZy7ny6jZ29UU6rLZnp4YjIKWY4Ncyjux/lwbYH2bR/E8PpYeKZ+KTnl3pLaSltYWHlQq6YfQWzSmfRUtrCrNJZNIWb1OBZRETkBKWgdoClTaMFRRTUROR4GEwO8ujuR/lj2x95es/TpHNp6kP1nN90PpWBSkp9pYVbma+MEm8JJb4S6kP1Wn4oIiJyklJQO8D8+hJ8HhcbOwe55szmmR6OiJyABhIDbOnbwrb+bSQyCTwuT+HmdXkLx1g6xiMdj/DsnmfJ2AxN4SbetehdXDnnSpbVLJtwv5iIiIicGhTUDuB1u1jcUMpGFRQRkcPI2Rxd0S5e7HuRF/teZEvfFl7se5GuaNeUr9Fc0syNZ9zI6+a8jiXVS7RUUURERAAFtQktaS7nf9fvwVqrvzSJnKJi6RhP73maNd1rGEgOEElFGEoNEUlHGE4NE0lFiKQjWCwALuNiTtkczqo7i8VVi1lUtYhFVYso9ZWSyWXI5DKkc+nC/UwugzGG5pJm/XNGREREDqKgNoFlzeX88tl22vtitFaHZ3o4InKcdEe7eazjMR7d/SjP7n2WVC5F0BOkKlBFqa+UEm8JzSXNhfulvlLqQnUsrFrI/Ir5k/YRU+NnEREROVL628MERgqKbOgcVFATOUnlbI6+RB8dwx08vfdpHt39KJt7NwPQUtLCdYuu47JZl3Fm3Zl4Xd6ZHayIiIicchTUJrCgoQSv27Cxc4g3L2+a6eGIyFHK5rJs6t1U2De2N7q3cOyOdRd6jhkMy2uX86mzP8Vlsy5jXvk8LUcUERGRGaWgNgG/x82C+lI2dg7O9FBE5Aj1Jfp4svNJnuh8gqf2PMVAcgAAt3FTF6qjMdzI8trlNIQbaAw30hBqYFntsqNqBC0iIiJyrCioTWJZczl/2NSlgiIiMyyby9I+3E4ikyiUqzfGYDC4jAuDYTA1yFN7nuKJjifY1LsJi6UqUMVFzRdxYfOFnF1/NrXBWtwu9wx/GxEREZGpUVCbxJLmcu5avZvOgTgtlRMXCBCR6ZXJZdg5uJPNvZsLt639W4ln4od9r8u4WFazjE+c+Qkuar6IxdWL1YdMRERETlgKapNY1uwUFNnYOaigJnIMpLIpXhp4ia19W9nSu4UtfVvY2reVRDYBQNATZHHVYt4x/x0sqlpEia8ELFgsOZvDjvzPWnxuH+fUnUNFoGJmv5SIiIjINFFQm8SihlLcLqegyOuXNs70cEROKNlclkQ2QTwTJ5FJkMgk6E/2FxpDv9j3IjsGdpCxTjGPkCfEoqpF/NmCP+OM6jNYUr2E1rJWLVUUERGRU5aC2iQCXjfz60rYoIIiIgdJ59LsGtzFtv5tbOvfxkv9L7FjcAdDqSESmQTpXHrS99YEa1hUtYiLWy5mUdUiFlctpqW0RcsURURERMZQUDuEpc3lPPLiPhUUkVOStZbeRC8dwx3sieyhM9LJjsEdvNT/Ei8Pvlwobe9xeZhXPo/ltcup9FcS8AQIeAIE3cHC/YAnQJm3jAVVC1RdUURERGQKFNQOYWlTGXev7aBrKEFjeXCmhyMyjrWWZDZJLBMjnokTSzvHZDZZuKWzaZLZJKlcilTWuWVtlmwuS87myFrnmLEZcrkcsUyMPdE9dA53sje6l2Q2Oe4z60J1LKhcwAXNFzC/cj4LKhcwt2wuXrcaQouIiIhMJwW1Q1jWMlJQZEhBTY67WDpG21AbbUNt7Braxa6hXbQNttEd6y6Es5zNveLPcRu3c3O5CbgDNJY0Mr9yPpfOupSmkiaaS5ppLmmmMdxIyKvCOiIiIiLHg4LaISxuLMNlYEPnIFeeUT/Tw5ETRCwdozvWzUBygP5EP4PJQfqT/QwkBgrHdC5dqFg48j8ArLP/q2O4g33xfeOu2xhupLWslfmV8wl7wwQ9QULekHP0hAr3g54gXpcXv9uPz+1zbi5f4bHb5cZlXLiNW/vCRERERIqUgtohhHweTqstYZMKisgY6Vya4dQwHcMd7B7efdBtf3z/hO/zuXxUBCqo8FcQcAecJw2Ykf/l90G6jZvzms5jTtkcWstamVM+h1mlswh6NKsrIiIicqpQUDuMpc3lPLl94r94y4nJWks0HR03y9Wf6C/MgI08jqajhX1fsUzMuaVjE1Y0rAvVMbt0Nhc1X8Ss0lk0ljRS6a+kIlDhHP0VBD1BFaURERERkSlRUDuMpc3l3PN8J/uGEtSVBWZ6OKc8ay2dkU429W5ia99W4pk4LuPCYJxlfAZcuDDGkLM5BpODDCQHGEgOOEsQ80sRR/p3Hcjj8hQCVqm3lPJAOY2exnHLC0OeECW+EprCTcwqnUVLaQsBj/5siIiIiMj0UVA7jKVNZQBs2jOkoHac5WyOPZE9bO7dzObezWzq3cSWvi0MJp2lqB7jwe/xj+7zspaczRX2fBkM5f5yKvwVlPvLmVM2hxW1K6jwV1AZqKTcX05VoMp57K+kMlBJ2BvWrJeIiIiIzDgFtcNY0uxUftzQOchli+pmeDQnHmstA8kBumPddEW76I520xUbPfbGe0llU6Rz6dFb1jlmbbZwHY/Lw/yK+bx29ms5o/oMltQsYX7FfHxu3wx+OxERERGRY0NBbYyn9zxNV7SLt57+1sKsSonfw7yaMBtVUARrLUOpIXpiPfTE87dYDwPJAYZTw4VbJB0p3B9KDR20p8tt3NSF6mgINzCvfB5+jx+fy4fX5cXr9jrH/P3qQDVLqpcwv1KhTEREREROHQpqY/zPy//DfTvu44G2B/jq+V+lIdwAOPvU1uzqm+HRTb90Lj2uZPxgarCwl2vs4/5EP/vj++mJ9ZDKpQ66jt/tp9RX6ty8pZT5ymguaabEV0Kpr5TaYC0N4QbqQ/U0hBuoDlTjdrln4BuLiIiIiJwYFNTG+NsL/5blNcv57nPf5W3/8zY+d+7neNvpb2Npcxn3rt9DbyRJdYl/pod5WCPLDduG2mgfbqd9qJ19sX2FaoYjt+H08KTXCLgDlPnLCvu3zqw7k7pgHTXBGmpDtdQGawtHNUEWEREREZleCmpjuIyLdy1+Fxe1XMSXn/wyX3nqKzyw6wHe0nQrABv3DHHJgtoZHqUjmU3SHe1mb3Qve6N76Yx0OsFsqJ324XaGU6MhzGVcVAeqqQw4BTOWVC9xysYHKqnyVxVKyJf7ywvFN1TFUERERERk5iioTWBW6Sz+/ap/57+2/hffWfsd1u37EN6Kq9jQseC4BLVsLsv++H66Yl10RcffRoJZX2L8UkyDoamkidmls3nj3Dcyu3Q2rWWtzC6bTUtJC16395iPW0REREREpoeC2iRcxsUNi27gwuYL+cpTX2F15r/5r91bWdj2oUIp98pAJeW+8kPut8rmssQyMaLpKNF0tLAHbDA5yFBqaHRPWHLACWfRLnpiPQf1+Qp6gjSEG2gMN7KoalHhfmO4kcaSRupD9Sq2ISIiIiJykjDW2hn54JUrV9o1a9bMyGcfqZzN8Y6f/wPbM3eBa3wxDYOhzF9Gpb+SMl8ZyWySaDpKLBMjlo6RyCYOeW2P8VDmL6PcX05NsIaGUAMN4dHbSAGOMl+Z+nuJiIiIiJxEjDFrrbUrJ3pNM2pT4DIurpz1Np7/4zzuvnkhaYYL1RDHFugYTg1TFawi7A0T8oQKx5DXuZV4S8btAyv3lavBsoiIiIiIHERBbYqWNZdDLkAy1sAFpy+d6eGIiIiIiMhJzDXTAzhRLG0qB+CRF/cxU8tFRURERETk1KCgNkWVYR+vXVzHj5/Yycd+vpbeSHKmhyQiIiIiIicpBbUjcPuNK/n/3rCIR17s4XXfWcUfN3XN9JBEREREROQkpKB2BNwuw0cvOY17b7mA+rIAH/nZWv7i1+sZSqRnemgiIiIiInISUVA7CosayvjtJy/g5stO57+f6+AN332cp17eP9PDEhERERGRk4SC2lHyeVz8xVULufvjr8HncfGuf3uWr967iX1Dh+6bJiIiIiIicjhqeD0N4qksf/+HF7njqV24DFw0v5a3n93MVUsaCHjdMz08EREREREpQodqeK2gNo129ET47+c6uef5TjoH4pT6PbxpeSPvOKeFla2VamwtIiIiIiIFCmrHWS5neWZHL3c/18EfNnYRS2WZXRXiLSsaedXcas6eXUFpwDvTwxQRERERkRmkoDaDoskMf9jYxW+e6+CZHb3kLLgMLGwo49w5lZzTWsm5c6poqgjO9FBFREREROQ4UlArEpFkhnXtA6ze1cfatn6ea+8nlsoC0FQe4KzZlSxrKWdZczlLm8spD2rWTURERETkZHWooOY53oM5lZX4PVw4v4YL59cAkMnmeLFrmDW7+ljd1s8LHQPct2Fv4fw51SGWNpezvMUJbmc0llER8s3U8EVERERE5DjRjFqR6Y+m2LhnkBc6BtnY6Rw7B+KF1xvLAyxuLGNRQymLGstY3FDK3JowHrc6LYiIiIiInEg0o3YCqQz7uGh+LRfNry081xdNsaFzkBf3DrFl7xAvdg2zalsPmZwTsn0eF6fXljC3JkxrdYg5NWHmVIeZUx2ittSvapMiIiIiIieYKQU1Y8zrge8BbuDH1tq/m+S8dwB3A+daazVdNk2qwj4uWVDLJQtGw1syk+XlfdF8cBtiW3eETXsG+cOmLrK50VnSkM9Na3WYebVh5teVML+ulAX1JbRWh/F5NAsnIiIiIlKMDhvUjDFu4AfAlUAHsNoYc6+1dvMB55UCnwKePRYDlfH8HjdnNJVxRlPZuOfT2Rx7BuLs6o3R1htl1/4Yu3qjbOwc5P4NexlZ6epxGebUjIS3EmZVhWipDNFSGaShPIBXSylFRERERGbMVGbUXgVst9buADDG3AVcA2w+4LyvAX8PfG5aRyhHxOt20VodprU6DNSOey2RzrJ9X4Tt+yJs6x7mpX0RXuwa5oFNXYyZhMNloKEsQEtliObKIC2FW4hZlSEaKxTkRERERESOpakEtWZg95jHHcCrx55gjDkbmGWtvc8Yo6BWpAJeN0vzpf/HSmay7B1I0NEfp3MgRmd/nI7+OB0Dcf60s4//WRefNMi1VAZprgzSVOHcmisCNJYHCfu1/VFERERE5Gi94r9NG2NcwD8C75/CuR8BPgIwe/bsV/rRMk38HrdTgKQmPOHr6WyOrkEnyO3ujzkhLn98ZkcvXUOJcUEOoCLkpancCW+t1SHm1oSZVxNmbm2YhrKACpyIiIiIiBzCVIJaJzBrzOOW/HMjSoGlwKP5v3w3APcaY64+sKCItfZHwI/AKc//CsYtx5HX7WJWVYhZVSHOp/qg1zPZHN3DSfYMxNkzEKczf9wzkGB3X4wntveQSOcK5we9TjCcVxNmTk2I1qows6tDzK4K0VAWwOVSiBMRERGRU9tUgtpqYL4xZi5OQLseeNfIi9baQaBm5LEx5lHgL1T18dThcbtorgjSXBGc8PVcztI1lGDn/ig79kfZ2RNlV2+UzXuHDqpS6XO7aKkKMrsqRGs+HI4ssZxVGaI85D1eX0tEREREZMYcNqhZazPGmJuBB3DK8//EWrvJGPM3wBpr7b3HepByYnO5TGEP2wWn14x7LZ3NsXcgQXtfjLa+KO19Mdp7Y7T3xVi7q5/hZGbc+aUBz7jgNtI3bl5NmKaKIG7NxomIiIjIScBYOzMrEFeuXGnXrNGkm0zOWstgPF3YE7e7b3Rv3Mh+uVgqWzjf53YxqyrI3JGG3zVhZudn5ZorguobJyIiIiJFxRiz1lq7cqLXVJpPipYxhoqQj4qQ76BKleAEuZ7hJDv3O0spd+yPsmu/0ztu1Uv7SWVyY64FjWUBWqqcFgOzqoL5ozM7V18W0GyciIiIiBQNBTU5YRljqCsLUFcW4NXzxhc5GdkXt7vPWUa5uz9OR1+M3f0xnty+n+7hBGMnkz355Zlje8Y1V4y0HQjQUB7A73Ef528oIiIiIqcqBTU5KY3dF3dgiAOn+feegfi4ZZQjSywf2dpDz3DyoPfUlvppKg8Urju7KsTsaqfoSUtlSEsrRURERGTaKKjJKSngdTOvtoR5tSUTvj4S5PYOJugciLN3IOG0HBiMs617mEe39hBPj+6PcxloLA8ypybE7KqRvXGjyysrQ171jhMRERGRKVNQE5nA4YLcyP64tr4Ybb0x2nujhfsPbOqiL5oad37Y5y60GphV5bQyaCwP0lAeoLE8QF2pH49bM3IiIiIi4lBQEzkKY/fHnTun6qDXhxPpQpXK3f1xdvfF8pUrYzz18v5x1SrBmZGrKw0UgtvoMegcywLUlwW0vFJERETkFKGgJnIMlAa8nNHk5YymsoNes9YyFM+wd8hZWtk1mGBvfpll11CCbd3DrNrWQ/SAMAdQU+KjsdzZHzfSGHxWZYjZVSGa1IJARERE5KShoCZynBljKA95KQ95WdRwcJAbMZxIOyEuH+a6hhKFPXNb9g7x4OZuUtnRFgQj++SaK4P5ipWjhU+cpZYBSgPe4/EVRUREROQVUlATKVKlAS+lAS/z60snfD2Xs3QPJ2jvdZZXtvc5Sys7B+Ks3tVH12CCTM4ecE0PTeVBGiucpZUj++TGPhfy6R8LIiIiIjNNfyMTOUG5XIbGcqcoyasneD2bs+wbdqpVdo5UrRyIs2cgQddQnA0dg/QeUPQEoCrso6kiQHNFkOaKUGGGriV/rFAFSxEREZFjTkFN5CTlHhPkzmmd+JxEOkv3UKIQ3vYMJOjodwLdyz1RVm3bP64NAUDI5x6zpNKZjWuudD6nrsxPTdhPWdCjMCciIiLyChRVUEun03R0dJBIJGZ6KAIEAgFaWlrwerWv6WQV8LpprQ7TWh2e8HVrLf2xNJ39cToHYuNm5joH4mzeM8j+yMGzcl63oTrsp7rER02Jc6wrDTCvNszpdSWcXldCmfbLiYiIiEyqqIJaR0cHpaWlzJkzR/81foZZa+nt7aWjo4O5c+fO9HBkhhhjqAr7qAr7WNZSPuE5iXSWvYNOgNsfSdIznKQ3mqI3kmR/xDlu3xdh33CCdHZ0z1xtqZ/Ta0sKwe202hLm1YZpKAvgcun//yIiInJqK6qglkgkFNKKhDGG6upqenp6ZnooUuQCXjdza8LMrZl4Vm5EJpujoz/O9n0RtvdE2L4vwss9EX67rpPhRKZwXjB/vXm1YebVlnBabZh5NSU0Vwap1P44EREROUUUVVAD9JewIqLfhUwnj9vFnJowc2rCvJb6wvPWWnqGk7zcE+Xlngg7eqLs2B/hhY5B7t+wl7GFK31uF3Vl/kIDcOfmp6E8QEtliFmVQWpK/JqRExERkRNe0QW1mVZSUkIkEpnpYYicMowx1JUFqCsLcP5p1eNeS6SztPXG2Lk/UmgIvm8oSddggi17h3hk6z5iBzQG93lctFQGaakM0VIZZFZliNbqEKfVljCnJoTf4z6eX09ERETkqCioiUjRCnjdLGwoZWHDxL3kYLQxeMdAnI6+GB39cXb3O8eNnYP0jWlB4DIwuyrk7InL74sb2R9XHlRxExERESkeCmqTsNbyl3/5l/z+97/HGMOXvvQlrrvuOvbu3ct1113H0NAQmUyGH/7wh7zmNa/hQx/6EGvWrMEYwwc/+EE+85nPzPRXEDklHK4xeCSZYdd+Z1nly/n9cS/vc1oPpLK5wnk1Jb7CnriRwibzakpoqQzicbuO19cRERERAYo4qP2//93E5j1D03rNM5rK+Mpblkzp3P/+7/9m3bp1rF+/nv3793Puuedy8cUX88tf/pKrrrqKv/qrvyKbzRKLxVi3bh2dnZ1s3LgRgIGBgWkdt4gcvRK/h6XN5SxtHl+1cmxxk5G9cS/3RHhgUzd90d2F87xuQ32Z0y+uqSJAY0WQpvIATRVO77jmyqBm40RERGTaFW1Qm2lPPPEEN9xwA263m/r6ei655BJWr17Nueeeywc/+EHS6TRvfetbOfPMM5k3bx47duzglltu4U1vehOve93rZnr4InIYkxU3AeiPptixP8LLPVF27o+yZyDO3oEEa9r66XphL5mxFU6AypCX1uowc6pDzrEmf6wOq1KliIiIHJWiDWpTnfk63i6++GJWrVrFfffdx/vf/34++9nP8t73vpf169fzwAMP8K//+q/86le/4ic/+clMD1VEjlJl2Mc54SrOaa066LVszrI/knTC22CC3X0x2vpitPVGWb2rn/9Zvwc7Jsf5PS7qywLUlfqpK/NTVxooHOvL/DSWB2gsDxL2F+0/jkVERGQG6G8Gk7jooou4/fbbed/73kdfXx+rVq3iW9/6Fm1tbbS0tHDTTTeRTCZ57rnneOMb34jP5+Md73gHCxcu5D3vec9MD19EjhG3yxRaA5w1wevJTJbdfXHaeqPs6o3RPZSgO1+t8sWuYR7ftp/hZOag95UHvTSWB2iuCNJY4YS3lspgYaauIuQ79l9OREREioaC2iTe9ra38fTTT7NixQqMMXzzm9+koaGB//iP/+Bb3/oWXq+XkpIS7rzzTjo7O/nABz5ALucUJvjGN74xw6MXkZni97g5vc6pJjmZWCrDvqEk3UNOy4HO/NLKvYNxOgcSrG3vZyCWHvee8qCXOdUhZo9ZYtlYHqAq7KM67KMy7MOroiciIiInDWOtPfxZx8DKlSvtmjVrxj23ZcsWFi9ePCPjkYnpdyIyM2KpDB39cdp6Y/nZuShtvTF29Ubp7I+Tm+Af3eVBL9VhnxPeSnzUlvqpH7PUcuRYHfapKbiIiEgRMMastdaunOg1zaiJiBShkM/DgvpSFkzQdiCVydE5EKd7KEFfNEVvJElvNOXczz/e0RPl2Z19B83MgbN8s7bEX6hi2VwRLOyVG1l6WR32qQiKiIjIDFJQExE5wfg8LubWhJlbEz7suYl0lp7hJPuGk+wbSjjH4QRdg0n2DsbZvGeIBzd3k8rkxr3P73HRlA9xTRUBmitC+WOQpoogVSU+SnwezcyJiIgcIwpqIiInsYDXzayqELOqQpOeY62lL5pi72CCPQNx5zbo7J3r7I/z6NYe9g0nD3qfyzjLLcuDXspDPsqDXiqCXqrCPlqrQ8ypDtNaHaKlMoTPo/1zIiIiR0JBTUTkFGeMobrET3WJ/6DG4COSmSxdgwk6+50QNxBLMRBLMxhPMxB3joOxFO29UXqGk0RT2cJ7XQaaK4O0VjnBrb4sQInf49wCHsL5+6UB51hT4lewExGRU56CmoiIHJbf46a1Okxr9eGXW47M0O0qFEIZPd63Ye+E++bGMgbqSv2FZZbNlUFa8vebKoLUlvqpDPlwa9mliIicxBTURERkWo2doTuntfKg1zPZHNFkluFkmmgySySZJpLMEklkGE6knZYF/XE6B+Js6Bzkj5u6SWXH76FzGagu8VNT4qcmX+GytsRPdYmPqrCf6nzlS6d9gZ+gz328vr6IiMi0UFATEZHjyuN2UR5yUR7yTun8XM6yP5KkI99vbn8kyf5Ikp7h/DGSYkdPlJ5I8qCiKCOCXjdVYR91ZU7LgvoyP3X5xuX1ZX7qywLUlfopD3pV7VJERIqCgtoMyWQyeDz68YuIHI7LZagrC1BXFoDZk59nrSWSzBTaFPRFRlsW9EWT9EZS9ESS7Ngf4ekdvQzGD16C6fO4qC3xU1vqp67UX+g9V1vqd8JdqRPu1ItORESONSWFCbz1rW9l9+7dJBIJPvWpT/GRj3yEP/zhD3zxi18km81SU1PD//3f/xGJRLjllltYs2YNxhi+8pWv8I53vIOSkhIikQgAd999N7/73e+44447eP/7308gEOD555/nggsu4Prrr+dTn/oUiUSCYDDIT3/6UxYuXEg2m+Xzn/88f/jDH3C5XNx0000sWbKE73//+/z2t78F4MEHH+Rf/uVfuOeee2bwJyUiUjyMMZQGvJQGvFPaS5dIZ9k3lKRrKEF3/tYTSdIz5LQz2NUbZfWuPvon2FPncZl8kBudkSsPegn5PIT9bufocxPyO8ew30NtqZ+qkAKeiIhMTfEGtd9/Abo2TO81G5bBG/7usKf95Cc/oaqqing8zrnnnss111zDTTfdxKpVq5g7dy59fX0AfO1rX6O8vJwNG5xx9vf3H/baHR0dPPXUU7jdboaGhnj88cfxeDw89NBDfPGLX+Q3v/kNP/rRj9i1axfr1q3D4/HQ19dHZWUln/jEJ+jp6aG2tpaf/vSnfPCDH3xlPw8RkVNYwOtmdnWI2dWTty4Ap+Ll/kiK7qEE+4ZG+tAl6M7f37k/ytMv9zKczGDtoT/T4zKF2brakSWYpQGqS3yFSpjhfAXMsN8JfaV+LwGvS0syRUROMcUb1GbQ97///cJM1e7du/nRj37ExRdfzNy5cwGoqqoC4KGHHuKuu+4qvK+y8uBN8we69tprcbudTe2Dg4O8733v46WXXsIYQzqdLlz3Yx/7WGFp5Mjn3Xjjjfz85z/nAx/4AE8//TR33nnnNH1jERGZjN/jpjnf/PtQrLUk0jmiqQyxZNY5pjLEUlmGExl6hpNO2Ms3IO/oj/Fcez990dRhxxD0ugv76+rG7KkbOVaV+KgK+agI+dTaQETkJFG8QW0KM1/HwqOPPspDDz3E008/TSgU4tJLL+XMM8/kxRdfnPI1xv5Xz0QiMe61cHh0Oc5f//Vfc9lll3HPPfewa9cuLr300kNe9wMf+ABvectbCAQCXHvttdrjJiJSRIwxBH1up8JkydTfl8rkGIiliCQzo7dEhmjKOQ4nM/RFUnTng96mPUP835Z9xNPZCa9X6vdQEfZSFfJRGfZRGfJREfJSGfJRGfJSERrzXNgJeKqKKSJSfPQ3/QMMDg5SWVlJKBTixRdf5JlnniGRSLBq1Sp27txZWPpYVVXFlVdeyQ9+8AO++93vAs7Sx8rKSurr69myZQsLFy7knnvuobS0dNLPam5uBuCOO+4oPH/llVdy++23c9lllxWWPlZVVdHU1ERTUxO33XYbDz300LH+UYiIyHHg87icYilH8J6Rwikjyy/7o2n6YikGoin6Yin6oyn6Yml6Iym274swEEsTSWYmvd5IVczKsBPoqvIBryrsozzopSzooSzgpSzozR+dxyGfW0syRUSOEQW1A7z+9a/nX//1X1m8eDELFy7kvPPOo7a2lh/96Ee8/e1vJ5fLUVdXx4MPPsiXvvQlPvnJT7J06VLcbjdf+cpXePvb387f/d3f8eY3v5na2lpWrlxZKCxyoL/8y7/kfe97H7fddhtvetObCs9/+MMfZtu2bSxfvhyv18tNN93EzTffDMC73/1uenp6WLx48XH5eYiISPEZWzjl9LqpTd+lMjkG4ikGYmn6oyn6Y2n6YynnFk3RF3Ue90VTtPXG6I+mGD5EuANnz11FfqbOmb3Lz9yFJ5i9yz8uD3rxurU8U0TkcIw93M7nY2TlypV2zZo1457bsmWLAshh3HzzzZx11ll86EMfOi6fp9+JiMipK5XJMZxIM5TIMBRPM5RIMxTP5I9pBuJpBmIp+qNjQl8+CGZyk//9otTvoTzkdZqWh33U5JuVjxxr8w3Ta0qcoKdKmSJysjLGrLXWrpzoNc2onUDOOeccwuEw//AP/zDTQxERkVOAz+OiOh+ajsTI0syBWNqZwcuHuMF4uhDqBmJOj7s9gwk2dA7SG02RnSDcuV2GqrAT3mpKnfBWW+LPL9UcWaI5sgfPmbFTsBORk4GC2glk7dq1Mz0EERGRwxq7NHNW1dTek8tZBuNp9keS7I+k8sf8bXj08cv7IvREkqQyuQmv4zJQHnRm66rDPqpLfFSHnZm6kRm86pLRBuYqpCIixUpBTURERGacy2WcGbKwj/n1hz7XWksslaUvOn655ch+u978/f2RFFu7humN9jIwQeNygNKAx2lePtLXrixAZchH0Osi6HMT8Dq3oNep6Bn0uikPeqkp8SvkicgxpaAmIiIiJxRjTL4huIdZVYduWD4inc3RH00VZuucfnajTcy7h5Ksbe9n31CS5CSzdQcK+dyFGbua/LGuzM+SpjLOnFVJQ3nglXxNETnFKaiJiIjISc/rzrdBKDt0eBppXJ5IZ4mns+OPqRzxdJb+WIreSIreSJLeqBP89gw4e+32R0b32tWX+TlzVgUrZlVw5qwKlrdUUOLXX71EZGr0TwsRERGRvLGNyyuP4v2JdJYte4dYt3uA9bsHWLd7gAc2deevDfWlAVzG+RxjnOdcxmBwjlVhH7OrQrRUhZhVGWRWVYjZVSHqywK4VSRF5JSioCYiIiIyTQJeN2fNruSs2aMxrz+aYn2HE9o6++NYwFpn9s4COWuxFrLWsn84ybM7+7hnXSdjOyh53YamiiD1pQFqy/zUljjLLGtL/NTm99jVlPgoC3rxe1xqRC5yElBQewVKSkombWa9a9cu3vzmN7Nx48bjPCoREREpJpVhH5curOPShXVTfk8qk2PPQJzd/THa+2Ls7ovT0R9j33CSLXuGWDWcnLQhuc/toizooTTgpSzgoSzopSzgzRdHceH3uPF7XAS8o8eg101dmZ+WyiBNFUFCPv0VUWSm6f+FIiIiIkXG53ExpybMnJrwpOfEU1l6hpP0RBL0DDttDYYTow3JxzYq7xyIk0hlSWRyJNNZkpncIZuSV4V9NFUEaK4I0lwRoqHcT3nQS3k+9JUVjk4g1LJMkelXtEHt7//097zY9+K0XnNR1SI+/6rPT/r6F77wBWbNmsUnP/lJAL761a/i8Xh45JFH6O/vJ51Oc9ttt3HNNdcc0ecmEgk+/vGPs2bNGjweD//4j//IZZddxqZNm/jABz5AKpUil8vxm9/8hqamJt75znfS0dFBNpvlr//6r7nuuute0fcWERGRk0/Q52Z2dYjZ1VOrfHmgTDZHMuPcoskM3UMJOgfidPTH6RyIs2cgzo6eKI+/tJ9YKnvIa1WGvDSWB2mqCNBYHqSxIkBTeZDG8gBNFUHqyvz4PWpnIHIkijaozYTrrruOT3/604Wg9qtf/YoHHniAW2+9lbKyMvbv3895553H1VdffURrv3/wgx9gjGHDhg28+OKLvO51r2Pbtm3867/+K5/61Kd497vfTSqVIpvNcv/999PU1MR9990HwODg4DH5riIiInJq87hdeNwuwn5nBm1WVYiVE5xnrSWSzBRm6Abj42fsBuNpeiJJ9uZD3upd/QzGD+5bVxnyUlcaoK7MT31ZoNB0vLrEh9/jxudx4XUb/B4XPrfz2OdxURXyUR7yHvsfiEiRKdqgdqiZr2PlrLPOYt++fezZs4eenh4qKytpaGjgM5/5DKtWrcLlctHZ2Ul3dzcNDQ1Tvu4TTzzBLbfcAsCiRYtobW1l27ZtnH/++Xz961+no6ODt7/97cyfP59ly5bx53/+53z+85/nzW9+MxdddNGx+roiIiIih2WMoTTgpTTgpbkiOKX3RJMZ9g4m2DsYZ+9AotCrrnsoQfdwkpf37WffcPKQyy/HKgt4aK0OM7s6RGu+Eubs6hCt1WGqQj4CXhVQkZNP0Qa1mXLttddy991309XVxXXXXccvfvELenp6WLt2LV6vlzlz5pBIJKbls971rnfx6le/mvvuu483vvGN3H777Vx++eU899xz3H///XzpS1/iiiuu4Mtf/vK0fJ6IiIjI8RD2ezi9roTT60omPSeXs/TFUvRFUyTTOVLZHKnMmGMmRyqbZf9wira+KG29MTZ2DvLAxq6DAp7HZSgNePKB0jP+vt9DScBpkF6ab5ReMnILjB5L/V4FPikqCmoHuO6667jpppvYv38/jz32GL/61a+oq6vD6/XyyCOP0NbWdsTXvOiii/jFL37B5ZdfzrZt22hvb2fhwoXs2LGDefPmceutt9Le3s4LL7zAokWLqKqq4j3veQ8VFRX8+Mc/PgbfUkRERGRmuVyGmhI/NSX+I3pfJptj72CCtl6nIuZA3CmiMpxI54/O/d19MYYTGSJJ55adwuyd22UKIa404KGlMsSihlIWNJSyqKGUuTVhvG7X0X5lkSOioHaAJUuWMDw8THNzM42Njbz73e/mLW95C8uWLWPlypUsWrToiK/5iU98go9//OMsW7YMj8fDHXfcgd/v51e/+hU/+9nP8Hq9NDQ08MUvfpHVq1fzuc99DpfLhdfr5Yc//OEx+JYiIiIiJyaP28WsqhCzqqZeRMVaSzKTYziRIZoPbiMhLprMMJzMEDngtaFEml37ozyydV8h5HndhtNqS1hQX8qc6hCxVJaB/D69wZhzHIinGIilyeasUykz5FTLrMhXzawIOf3uqsM+qkt8VIV91JT4qQ77qAj5VEFTCoy1U1sbPN1Wrlxp16xZM+65LVu2sHjx4hkZj0xMvxMRERE5lSUzWXb0RNnaNczW7mHn2DVM50CcoNdNRT6IOSHMS0XQKX7idhknwB0Q4gZjTiGWibgMheA2qyrE3Jowc6rDzKlx7teXBnApyJ1UjDFrrbUT1fHRjJqIiIiIyGT8HjeLG8tY3Fg27vlszh717Fc2ZxmIpeiNptgfSdIbSdEbSeYfp+gZTrBzf5THtvaQyuYK7wt4XcypDtNSGaQy5MzGVYR8VIa8VIZ9VObvV4R8VIS8WqZ5glNQe4U2bNjAjTfeOO45v9/Ps88+O0MjEhEREZFj7ZUsUXS7DNUlfqpL/CyoL530vGzOsmcgTltvjJ29UXbtd24d/XE2dg7RF0uRyuQmfX+J31OY6avMtzmoDHlpKHP62zWWB2muCFJfrj53xUhB7RVatmwZ69atm+lhiIiIiMhJxu0yhf14F86vOeh1ay3xdJb+WJr+aIr+WIr+WJrB/HEgNrrcsj+WYs9gPH/ewX3uakv9NJUHqC0NUBrwEPa7KfF7KfG7x1XKrC7x01wZpL7Uj0czdseUgpqIiIiIyAnIGEPI5yHk80y5xx1APJV1etwNJugccHrd7RmIs2cwTkd/rFBkJZrMjlt6OZbbZfIzc87sXHNFkMbyAG6Xi5y1WGvJ5iw5CzlryVmLx+WivixAQ3mAxvIAdQp7h6SgJiIiIiJyCgn63MyrLWFe7eR97kYkM1miyaxTHTORYX8kSedAnD0DcTr743QOxFnb1s99L+ydcgPzES4DNSV+Gsud8FYW8OJxu/C6DW6Xwet2OUeXweN2URrwjCne4ssXb3Een4yBT0FNREREREQm5Pe48XvcVIV9hzwvm7P0RpLkLLhc4DIGlzG4jcG4wG0MqUyO7uEEewcTdA06x+7BBHuHnOIpkUSGdM6SyebI5CyZrCWTy5HOHj4AhnxuAl43fo8Ln8eF3+PKj915vLChlK+8Zcl0/ViOCwU1ERERERF5RdwuQ11Z4JDnhP1QGfaxqKHskOcdyFpLJmeJJDIMxNMMxFJO/7qRtgexNEOJNMlMlmQ6RyqbI5nOkcxkC/djyewr+XozYkpBzRjzeuB7gBv4sbX27w54/WPAJ4EsEAE+Yq3dPM1jLTolJSVEIpGZHoaIiIiIyEnLGIPXbZwWBGEfEJ7pIR0Xh13MaYxxAz8A3gCcAdxgjDnjgNN+aa1dZq09E/gm8I/TPVCZXCYzcdNEERERERE5MU1lRu1VwHZr7Q4AY8xdwDVAYcbMWjs05vwwcGQ7CSfQ9bd/S3LLi6/0MuP4Fy+i4YtfnPT1L3zhC8yaNYtPfvKTAHz1q1/F4/HwyCOP0N/fTzqd5rbbbuOaa6457GdFIhGuueaaCd9355138u1vfxtjDMuXL+dnP/sZ3d3dfOxjH2PHjh0A/PCHP6SpqYk3v/nNbNy4EYBvf/vbRCIRvvrVr3LppZdy5pln8sQTT3DDDTewYMECbrvtNlKpFNXV1fziF7+gvr6eSCTCLbfcwpo1azDG8JWvfIXBwUFeeOEFvvvd7wLwb//2b2zevJnvfOc7r+THKyIiIiIi02QqQa0Z2D3mcQfw6gNPMsZ8Evgs4AMun5bRHWfXXXcdn/70pwtB7Ve/+hUPPPAAt956K2VlZezfv5/zzjuPq6++GmMO3eQwEAhwzz33HPS+zZs3c9ttt/HUU09RU1NDX18fALfeeiuXXHIJ99xzD9lslkgkQn9//yE/I5VKsWbNGgD6+/t55plnMMbw4x//mG9+85v8wz/8A1/72tcoLy9nw4YNhfO8Xi9f//rX+da3voXX6+WnP/0pt99++yv98YmIiIiIyDSZtmIi1tofAD8wxrwL+BLwvgPPMcZ8BPgIwOzZsw95vUPNfB0rZ511Fvv27WPPnj309PRQWVlJQ0MDn/nMZ1i1ahUul4vOzk66u7tpaGg45LWstXzxi1886H0PP/ww1157LTU1TtPCqqoqAB5++GHuvPNOANxuN+Xl5YcNatddd13hfkdHB9dddx179+4llUoxd+5cAB566CHuuuuuwnmVlZUAXH755fzud79j8eLFpNNpli1bdoQ/LREREREROVamEtQ6gVljHrfkn5vMXcAPJ3rBWvsj4EcAK1eufMXLI4+Fa6+9lrvvvpuuri6uu+46fvGLX9DT08PatWvxer3MmTOHRCJx2Osc7fvG8ng85HKjTQYPfH84PLqR8pZbbuGzn/0sV199NY8++ihf/epXD3ntD3/4w/zt3/4tixYt4gMf+MARjUtERERERI6tqXSGWw3MN8bMNcb4gOuBe8eeYIyZP+bhm4CXpm+Ix9d1113HXXfdxd133821117L4OAgdXV1eL1eHnnkEdra2qZ0ncned/nll/PrX/+a3t5egMLSxyuuuIIf/tDJt9lslsHBQerr69m3bx+9vb0kk0l+97vfHfLzmpubAfiP//iPwvNXXnklP/jBDwqPR2bpXv3qV7N7925++ctfcsMNN0z1xyMiIiIiIsfBYYOatTYD3Aw8AGwBfmWt3WSM+RtjzNX50242xmwyxqzD2ad20LLHE8WSJUsYHh6mubmZxsZG3v3ud7NmzRqWLVvGnXfeyaJFi6Z0ncnet2TJEv7qr/6KSy65hBUrVvDZz34WgO9973s88sgjLFu2jHPOOYfNmzfj9Xr58pe/zKte9SquvPLKQ372V7/6Va699lrOOeecwrJKgC996Uv09/ezdOlSVqxYwSOPPFJ47Z3vfCcXXHBBYTmkiIiIiIgUB2PtzKxAXLlypR0phDFiy5YtLF68eEbGcyp685vfzGc+8xmuuOKKSc/R70RERERE5Ngwxqy11q6c6LWpLH2Uk8zAwAALFiwgGAweMqSJiIiIiMjMmLaqj6eqDRs2cOONN457zu/38+yzz87QiA6voqKCbdu2zfQwRERERERkEgpqr9CyZctYt27dTA9DREREREROIkW39HGm9szJwfS7EBERERGZGUUV1AKBAL29vQoIRcBaS29vL4FAYKaHIiIiIiJyyimqpY8tLS10dHTQ09Mz00MRnODc0tIy08MQERERETnlFFVQ83q9zJ07d6aHISIiIiIiMqOKaumjiIiIiIiIKKiJiIiIiIgUHQU1ERERERGRImNmqsKiMaYHaJuRDz+0GmD/TA9CTnr6cybHg/6cybGmP2NyPOjPmRwPM/XnrNVaWzvRCzMW1IqVMWaNtXblTI9DTm76cybHg/6cybGmP2NyPOjPmRwPxfjnTEsfRUREREREioyCmoiIiIiISJFRUDvY/9/evYRKXYZxHP/+0KQbZBeQUiMjKSToQoRRRFgLK8kW0YWikKJN0IUiqk20aBFENwohyi4QXTApaRFEBbVJyoQuWiRWppgGXSnoQk+L/ytnOHoWNeOZOafvBw4z7zuzeBYvvznP/N/3P48PuwD9L7jONBlcZ9rXXGOaDK4zTYaRW2eeUZMkSZKkEeMVNUmSJEkaMTZqPZIsTfJ5ks1J7hh2PZr6ksxP8naSjUk+TXJTmz8syRtJvmiPhw67Vk19SWYk2ZDktTZekGRdy7QXk8wado2a2pLMTrI6yWdJNiU5wzzTICW5pX1efpLk+ST7m2XqV5JVSXYl+aRnbq/Zlc4jbb19lOTUYdVto9YkmQE8BpwPLAKuSLJouFVpGvgLuLWqFgGLgRvauroDeLOqFgJvtrHUr5uATT3j+4AHq+o44Afg2qFUpenkYeD1qjoBOIluvZlnGogkc4EbgdOq6kRgBnA5Zpn69zSwdNzcRNl1PrCw/V0PrJykGvdgozbmdGBzVW2pqj+AF4DlQ65JU1xV7aiqD9vzX+j+qZlLt7aeaW97Brh4KAVq2kgyD7gQeKKNAywBVre3uM7UlySHAGcDTwJU1R9V9SPmmQZrJnBAkpnAgcAOzDL1qareAb4fNz1Rdi0Hnq3Oe8DsJEdOSqHj2KiNmQt80zPe1uakgUhyDHAKsA6YU1U72kvfAnOGVZemjYeA24G/2/hw4Meq+quNzTT1awHwHfBU22L7RJKDMM80IFW1Hbgf2ErXoP0ErMcs074xUXaNTE9goyZNgiQHAy8DN1fVz72vVXfrVW+/qv8syTJgV1WtH3YtmtZmAqcCK6vqFOBXxm1zNM/Uj3ZGaDndlwJHAQex53Y1aeBGNbts1MZsB+b3jOe1OakvSfaja9Keq6o1bXrn7svo7XHXsOrTtHAmcFGSr+i2bS+hO0s0u20fAjNN/dsGbKuqdW28mq5xM880KOcBX1bVd1X1J7CGLt/MMu0LE2XXyPQENmpj3gcWtjsLzaI7vLp2yDVpimvnhJ4ENlXVAz0vrQWuac+vAV6d7No0fVTVnVU1r6qOocuut6rqSuBt4JL2NteZ+lJV3wLfJDm+TZ0LbMQ80+BsBRYnObB9fu5eY2aZ9oWJsmstcHW7++Ni4KeeLZKTyh+87pHkArpzHjOAVVV173Ar0lSX5CzgXeBjxs4O3UV3Tu0l4Gjga+DSqhp/yFX615KcA9xWVcuSHEt3he0wYANwVVX9PsTyNMUlOZnuhjWzgC3ACrovfc0zDUSSe4DL6O6avAG4ju58kFmm/yzJ88A5wBHATuBu4BX2kl3tS4JH6bbd/gasqKoPhlC2jZokSZIkjRq3PkqSJEnSiLFRkyRJkqQRY6MmSZIkSSPGRk2SJEmSRoyNmiRJkiSNGBs1SZIkSRoxNmqSJEmSNGJs1CRJkiRpxPwDJGREpaeAsTIAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#model metrics\n",
+    "metrics_seq.plot(figsize=(15, 8))\n",
+    "plt.title('Model Metrics')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "159/159 [==============================] - 0s 481us/step\n",
+      "40/40 [==============================] - 0s 487us/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "#predict train and val data for classification report and confusion matrix\n",
+    "y_pred_train_seq = model_seq.predict(X_train)\n",
+    "y_pred_train_seq = y_pred_train_seq.round()\n",
+    "\n",
+    "y_pred_val_seq = model_seq.predict(X_val)\n",
+    "y_pred_val_seq = y_pred_val_seq.round()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------Training Set------------------------\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.89      0.92      0.91      3717\n",
+      "           1       0.76      0.69      0.72      1345\n",
+      "\n",
+      "    accuracy                           0.86      5062\n",
+      "   macro avg       0.83      0.81      0.81      5062\n",
+      "weighted avg       0.86      0.86      0.86      5062\n",
+      "\n",
+      "---------------------Validation Set----------------------\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.83      0.84      0.84       929\n",
+      "           1       0.55      0.53      0.54       337\n",
+      "\n",
+      "    accuracy                           0.76      1266\n",
+      "   macro avg       0.69      0.69      0.69      1266\n",
+      "weighted avg       0.76      0.76      0.76      1266\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "#classification_report\n",
+    "print('----------------------Training Set------------------------')\n",
+    "print(classification_report(y_train, y_pred_train_seq))\n",
+    "print('---------------------Validation Set----------------------')\n",
+    "print(classification_report(y_val, y_pred_val_seq))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(33.0, 0.5, 'Actual')"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Visualising the prediction for the validation test\n",
+    "cm = math.confusion_matrix(labels=y_val,predictions=y_pred_val_seq)\n",
+    "\n",
+    "sns.heatmap(cm,annot=True,fmt='d',cmap='Blues')\n",
+    "plt.xlabel('Predicted')\n",
+    "plt.ylabel('Actual')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Model Sequential API menghasilkan akurasi yang cukup baik yaitu **0.86** pada training set dan **0.76** pada validation set. \n",
+    "\n",
+    "Namun, recall dari model ini rendah dalam memprediksi kelas 1, hal ini disebabkan karena adanya label imbalance pada dataset. \n",
+    "\n",
+    "Grafik metric dapat diketahui model mengalami **overfitting** ini ditandai saat training loss dari model turun tetapi validation lossnya naik. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Functional API "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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>loss</th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>val_loss</th>\n",
+       "      <th>val_accuracy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>0.322702</td>\n",
+       "      <td>0.847491</td>\n",
+       "      <td>0.549358</td>\n",
+       "      <td>0.780411</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>0.322083</td>\n",
+       "      <td>0.846898</td>\n",
+       "      <td>0.551627</td>\n",
+       "      <td>0.781201</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>0.321576</td>\n",
+       "      <td>0.847491</td>\n",
+       "      <td>0.552874</td>\n",
+       "      <td>0.781991</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>0.320871</td>\n",
+       "      <td>0.845713</td>\n",
+       "      <td>0.555364</td>\n",
+       "      <td>0.781991</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>0.320323</td>\n",
+       "      <td>0.846306</td>\n",
+       "      <td>0.554808</td>\n",
+       "      <td>0.778831</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        loss  accuracy  val_loss  val_accuracy\n",
+       "95  0.322702  0.847491  0.549358      0.780411\n",
+       "96  0.322083  0.846898  0.551627      0.781201\n",
+       "97  0.321576  0.847491  0.552874      0.781991\n",
+       "98  0.320871  0.845713  0.555364      0.781991\n",
+       "99  0.320323  0.846306  0.554808      0.778831"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics_func = pd.DataFrame(history_func.history)\n",
+    "metrics_func.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "metrics_func.plot(figsize=(15, 8))\n",
+    "plt.title('Model Metrics')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "159/159 [==============================] - 0s 525us/step\n",
+      "40/40 [==============================] - 0s 538us/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "#predict train and val data for classification report and confusion matrix\n",
+    "y_pred_train_func = model_func.predict(X_train)\n",
+    "y_pred_train_func = y_pred_train_func.round()\n",
+    "\n",
+    "y_pred_val_func = model_func.predict(X_val)\n",
+    "y_pred_val_func = y_pred_val_func.round()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------Training Set------------------------\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.88      0.93      0.90      3717\n",
+      "           1       0.76      0.65      0.70      1345\n",
+      "\n",
+      "    accuracy                           0.85      5062\n",
+      "   macro avg       0.82      0.79      0.80      5062\n",
+      "weighted avg       0.85      0.85      0.85      5062\n",
+      "\n",
+      "---------------------Validation Set----------------------\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.83      0.88      0.85       929\n",
+      "           1       0.60      0.51      0.55       337\n",
+      "\n",
+      "    accuracy                           0.78      1266\n",
+      "   macro avg       0.72      0.69      0.70      1266\n",
+      "weighted avg       0.77      0.78      0.77      1266\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('----------------------Training Set------------------------')\n",
+    "print(classification_report(y_train, y_pred_train_func))\n",
+    "print('---------------------Validation Set----------------------')\n",
+    "print(classification_report(y_val, y_pred_val_func))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(33.0, 0.5, 'Actual')"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Visualising the prediction for the testset\n",
+    "cm = math.confusion_matrix(labels=y_val,predictions=y_pred_val_func)\n",
+    "\n",
+    "sns.heatmap(cm,annot=True,fmt='d')\n",
+    "plt.xlabel('Predicted')\n",
+    "plt.ylabel('Actual')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Model Sequential API menghasilkan akurasi yang cukup baik yaitu **0.85** pada training set dan **0.78** pada validation set. \n",
+    "\n",
+    "Namun, recall dari model ini rendah dalam memprediksi kelas 1, hal ini disebabkan karena adanya label imbalance pada dataset. \n",
+    "\n",
+    "Grafik metric dapat diketahui model mengalami **overfitting** ini ditandai saat training loss dari model turun tetapi validation lossnya naik. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Conclusion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Untuk Model Improvement saya akan menggunakan Model Sequential API karena walupun akurasi dari kedua model hampir sama persis, tapi Sequential API mempunyai Recall terhadap label 1 yang lebih bagus sedikit dibandingkan Functional API\n",
+    "\n",
+    "dilihat dari Confusion Matrix nya \n",
+    "\n",
+    "- True Positive = Not Churn\n",
+    "- True Negative = Churn\n",
+    "\n",
+    "Sequential API berhasil memprediksi True Negative sebanyak 178\n",
+    "\n",
+    "dan Functional API berhasil memprediksi True Negative sebanyak 172\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Improvement\t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "untuk model improvement saya mencoba tweaking menggunakan \n",
+    "\n",
+    "- Optimizer = Nadam karena Nadam merupakan extension dari Adam dan ada Nesterov nya\n",
+    "\n",
+    "- Epoch = 200 karena 2 kali lebih banyak dari base model\n",
+    "\n",
+    "- Kernel Regulizer = l2  (Least absolute deviations(l1) and Least square errors(l2)) alasan saya memakai l2 karena l2 loss lebih barik dari l1 loss\n",
+    "\n",
+    "- Kernel Initializer = Glorot uniform alasannya adalah karena untuk mempertahankan smooth distribution  yang sama baik untuk forward pass maupun back propagation\n",
+    "\n",
+    "- Drop out 0.3 (untuk menghindari overfitting)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nadam Optimizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optim_nadam = tf.keras.optimizers.Nadam(\n",
+    "    learning_rate=0.001, \n",
+    "    beta_1=0.9, \n",
+    "    beta_2=0.999, \n",
+    "    epsilon=1e-07, \n",
+    "    name=\"Nadam\",\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sequential Improvement"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Defining the model\n",
+    "model_seq_improve = keras.models.Sequential([\n",
+    "    keras.layers.Dense(64,input_shape=(X_train.shape[1],)),\n",
+    "    keras.layers.Dense(32, activation=\"relu\",kernel_initializer='GlorotUniform', kernel_regularizer='l2'),\n",
+    "    keras.layers.Dropout(0.3),\n",
+    "    keras.layers.Dense(16, activation=\"relu\", kernel_initializer='GlorotUniform',kernel_regularizer='l2'),\n",
+    "    keras.layers.Dropout(0.3),\n",
+    "    keras.layers.Dense(1, activation=\"sigmoid\",kernel_initializer='GlorotUniform', kernel_regularizer='l2')\n",
+    "])\n",
+    "model_seq_improve.compile(loss=\"binary_crossentropy\", optimizer=optim_nadam, metrics='accuracy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential_1\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " dense_7 (Dense)             (None, 64)                3008      \n",
+      "                                                                 \n",
+      " dense_8 (Dense)             (None, 32)                2080      \n",
+      "                                                                 \n",
+      " dropout (Dropout)           (None, 32)                0         \n",
+      "                                                                 \n",
+      " dense_9 (Dense)             (None, 16)                528       \n",
+      "                                                                 \n",
+      " dropout_1 (Dropout)         (None, 16)                0         \n",
+      "                                                                 \n",
+      " dense_10 (Dense)            (None, 1)                 17        \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 5,633\n",
+      "Trainable params: 5,633\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Training the model\n",
+    "history_seq_improve = model_seq_improve.fit(train_data, epochs=200,validation_data=val_data,verbose=0)\n",
+    "\n",
+    "model_seq_improve.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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>loss</th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>val_loss</th>\n",
+       "      <th>val_accuracy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>195</th>\n",
+       "      <td>0.434498</td>\n",
+       "      <td>0.799091</td>\n",
+       "      <td>0.435471</td>\n",
+       "      <td>0.792259</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>196</th>\n",
+       "      <td>0.430446</td>\n",
+       "      <td>0.801067</td>\n",
+       "      <td>0.435631</td>\n",
+       "      <td>0.791469</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>0.432421</td>\n",
+       "      <td>0.805018</td>\n",
+       "      <td>0.435210</td>\n",
+       "      <td>0.793049</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>198</th>\n",
+       "      <td>0.433422</td>\n",
+       "      <td>0.801659</td>\n",
+       "      <td>0.434820</td>\n",
+       "      <td>0.793839</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199</th>\n",
+       "      <td>0.435570</td>\n",
+       "      <td>0.801659</td>\n",
+       "      <td>0.435196</td>\n",
+       "      <td>0.791469</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         loss  accuracy  val_loss  val_accuracy\n",
+       "195  0.434498  0.799091  0.435471      0.792259\n",
+       "196  0.430446  0.801067  0.435631      0.791469\n",
+       "197  0.432421  0.805018  0.435210      0.793049\n",
+       "198  0.433422  0.801659  0.434820      0.793839\n",
+       "199  0.435570  0.801659  0.435196      0.791469"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics_seq_improve = pd.DataFrame(history_seq_improve.history)\n",
+    "metrics_seq_improve.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "metrics_seq_improve.plot(figsize=(15, 8))\n",
+    "plt.title('Model Metrics')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------Training Set------------------------\n",
+      "159/159 [==============================] - 0s 563us/step\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.85      0.89      0.87      3717\n",
+      "           1       0.65      0.57      0.61      1345\n",
+      "\n",
+      "    accuracy                           0.81      5062\n",
+      "   macro avg       0.75      0.73      0.74      5062\n",
+      "weighted avg       0.80      0.81      0.80      5062\n",
+      "\n",
+      "---------------------Validation Set----------------------\n",
+      "40/40 [==============================] - 0s 564us/step\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.84      0.88      0.86       929\n",
+      "           1       0.62      0.55      0.58       337\n",
+      "\n",
+      "    accuracy                           0.79      1266\n",
+      "   macro avg       0.73      0.72      0.72      1266\n",
+      "weighted avg       0.78      0.79      0.79      1266\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('----------------------Training Set------------------------')\n",
+    "print(classification_report(y_train, model_seq_improve.predict(X_train).round()))\n",
+    "print('---------------------Validation Set----------------------')\n",
+    "print(classification_report(y_val, model_seq_improve.predict(X_val).round()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "40/40 [==============================] - 0s 590us/step\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(33.0, 0.5, 'Actual')"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Visualising the prediction for the testset\n",
+    "cm = math.confusion_matrix(labels=y_val,predictions=model_seq_improve.predict(X_val).round())\n",
+    "\n",
+    "sns.heatmap(cm,annot=True,fmt='d')\n",
+    "plt.xlabel('Predicted')\n",
+    "plt.ylabel('Actual')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Dari confusion matrix diatas\n",
+    "\n",
+    "Model Improvement mengalami peningkatan yaitu \n",
+    "\n",
+    "- Sequential API berhasil memprediksi True Positive sebanyak 784 \n",
+    "- Sequential API Improve model berhasil memprediksi True Positive sebanyak 816\n",
+    "\n",
+    "Sequential API Improve model berhasil memperbaiki True Positive sebanyak 32 \n",
+    "\n",
+    "- Sequential API berhasil memprediksi True Negative sebanyak 178 \n",
+    "- Sequential API Improve model berhasil memprediksi True Negative sebanyak 186\n",
+    "\n",
+    "Sequential API Improve model berhasil memperbaiki True Negative sebanyak 8 \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Setelah dilakukan peningkatan terhadap model dengan mengubah optimizer menjadi **nadam**, menambahkan **2 regularisasi Dropout** dengan **rate = 0.3**, menambahkan **1 hidden layer** dengan **64 neuron**, dan menambahkan epoch pada training model menjadi 200 epoch, model **tidak lagi mengalami overfitting** dan sudah menjadi model good fit\n",
+    "\n",
+    "Secara keseluruhan model sudah baik dan menghasilkan akurasi sebasar **0.81** pada training set dan **0.79** pada validation set."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Saving"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Assets written to: model\\assets\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Save the model\n",
+    "model_seq_improve.save('model')\n",
+    "\n",
+    "# Save preprocessing pipeline\n",
+    "with open('preprocessor.pkl', 'wb') as file:\n",
+    "    pickle.dump(preprocessor, file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Inference"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Setelah model dilatih dan dievaluasi, sekarang model akan diuji menggunakan test set (inference)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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>gender</th>\n",
+       "      <th>SeniorCitizen</th>\n",
+       "      <th>Partner</th>\n",
+       "      <th>Dependents</th>\n",
+       "      <th>tenure</th>\n",
+       "      <th>PhoneService</th>\n",
+       "      <th>MultipleLines</th>\n",
+       "      <th>InternetService</th>\n",
+       "      <th>OnlineSecurity</th>\n",
+       "      <th>OnlineBackup</th>\n",
+       "      <th>DeviceProtection</th>\n",
+       "      <th>TechSupport</th>\n",
+       "      <th>StreamingTV</th>\n",
+       "      <th>StreamingMovies</th>\n",
+       "      <th>Contract</th>\n",
+       "      <th>PaperlessBilling</th>\n",
+       "      <th>PaymentMethod</th>\n",
+       "      <th>MonthlyCharges</th>\n",
+       "      <th>TotalCharges</th>\n",
+       "      <th>Churn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4481</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>42</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No phone service</td>\n",
+       "      <td>DSL</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Bank transfer (automatic)</td>\n",
+       "      <td>40.15</td>\n",
+       "      <td>1626.05</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2679</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Fiber optic</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Electronic check</td>\n",
+       "      <td>74.65</td>\n",
+       "      <td>301.40</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5614</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>68</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Fiber optic</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>One year</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Bank transfer (automatic)</td>\n",
+       "      <td>85.50</td>\n",
+       "      <td>5696.60</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>646</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>DSL</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Mailed check</td>\n",
+       "      <td>45.70</td>\n",
+       "      <td>45.70</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5837</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>2</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No phone service</td>\n",
+       "      <td>DSL</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Electronic check</td>\n",
+       "      <td>51.40</td>\n",
+       "      <td>96.80</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      gender SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
+       "4481    Male            No     Yes         No      42           No   \n",
+       "2679    Male            No     Yes         No       4          Yes   \n",
+       "5614    Male            No     Yes        Yes      68          Yes   \n",
+       "646     Male            No      No         No       1          Yes   \n",
+       "5837  Female            No      No         No       2           No   \n",
+       "\n",
+       "         MultipleLines InternetService OnlineSecurity OnlineBackup  \\\n",
+       "4481  No phone service             DSL            Yes           No   \n",
+       "2679               Yes     Fiber optic             No           No   \n",
+       "5614               Yes     Fiber optic            Yes          Yes   \n",
+       "646                 No             DSL             No           No   \n",
+       "5837  No phone service             DSL             No          Yes   \n",
+       "\n",
+       "     DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
+       "4481               No          No         Yes              No  Month-to-month   \n",
+       "2679               No          No          No              No  Month-to-month   \n",
+       "5614               No          No          No              No        One year   \n",
+       "646                No          No          No              No  Month-to-month   \n",
+       "5837               No          No         Yes             Yes  Month-to-month   \n",
+       "\n",
+       "     PaperlessBilling              PaymentMethod  MonthlyCharges  \\\n",
+       "4481              Yes  Bank transfer (automatic)           40.15   \n",
+       "2679              Yes           Electronic check           74.65   \n",
+       "5614              Yes  Bank transfer (automatic)           85.50   \n",
+       "646               Yes               Mailed check           45.70   \n",
+       "5837               No           Electronic check           51.40   \n",
+       "\n",
+       "      TotalCharges  Churn  \n",
+       "4481       1626.05      0  \n",
+       "2679        301.40      1  \n",
+       "5614       5696.60      0  \n",
+       "646          45.70      1  \n",
+       "5837         96.80      0  "
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'X_test' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\Xyla\\OneDrive\\Documents\\Hacktiv8\\ftds\\ftds13\\Assignments\\p2---ftds013---m1-Xylverize\\h8dsft_Milestone1P2_xyla_ramadhan.ipynb Cell 141\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Xyla/OneDrive/Documents/Hacktiv8/ftds/ftds13/Assignments/p2---ftds013---m1-Xylverize/h8dsft_Milestone1P2_xyla_ramadhan.ipynb#Y304sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m X_test\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'X_test' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "X_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "22/22 [==============================] - 0s 714us/step\n"
+     ]
+    },
+    {
+     "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>Actual</th>\n",
+       "      <th>Predicted</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4481</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2679</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5614</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>646</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5837</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6506</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1272</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1188</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2199</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6282</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2316</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6897</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1555</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3348</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6682</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5376</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6708</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1460</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4004</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Actual  Predicted\n",
+       "4481       0          0\n",
+       "2679       1          1\n",
+       "5614       0          0\n",
+       "646        1          0\n",
+       "5837       0          1\n",
+       "6506       1          1\n",
+       "1272       0          0\n",
+       "1188       0          0\n",
+       "2199       0          0\n",
+       "6282       0          0\n",
+       "2316       0          0\n",
+       "6897       0          0\n",
+       "19         0          1\n",
+       "1555       1          0\n",
+       "3348       0          0\n",
+       "6682       0          0\n",
+       "5376       0          0\n",
+       "6708       1          1\n",
+       "1460       1          1\n",
+       "4004       0          0"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Split features and target\n",
+    "X_test = df_test.drop(['Churn'], axis=1) # Features\n",
+    "y_test = df_test['Churn'] # Target\n",
+    "\n",
+    "\n",
+    "# Transform the test set\n",
+    "X_test = preprocessor.transform(X_test)\n",
+    "\n",
+    "# Predict the test set\n",
+    "y_pred = model_seq_improve.predict(X_test).round()\n",
+    "\n",
+    "# Create dataframe\n",
+    "predictions = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred.flatten().astype(int)})\n",
+    "predictions.head(20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "dengan inference 20 data \n",
+    "\n",
+    "- Label 0 atau Not Churn berhasil memprediksi 12/14 akurasi 85 %\n",
+    "- Label 1 atau Churn berhasil memprediksi 4/6 akurasi 66 %"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "23/23 [==============================] - 0s 1ms/step - loss: 0.4310 - accuracy: 0.8026\n",
+      "Test Set - Loss     : 0.43\n",
+      "Test Set - Accuracy : 0.80\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create a test dataset pipeline\n",
+    "test_data = tf.data.Dataset.from_tensor_slices((X_test, y_test)).batch(31)\n",
+    "\n",
+    "# Evaluate test set\n",
+    "results = model_seq_improve.evaluate(test_data)\n",
+    "print(f'Test Set - Loss     : {results[0]:.2f}')\n",
+    "print(f'Test Set - Accuracy : {results[1]:.2f}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion EDA Analysis & Model Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## EDA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Insight yang didapat adalah 73,4% atau 5163 customer tidak churn dan 26,6 persen dari total customer adalah churn (meninggalkan perusahaan)\n",
+    "\n",
+    "saya sebagai data analis akan menganalisis apa saja faktor yang membuat customer meninggalkan perusahaan atau Churn dimulai dari demographic information\n",
+    "\n",
+    "- presentase churn antara laki dan perempuan sama \n",
+    "- senior citizen memiliki presentase churn rate yang tinggi dapat dikatakan bahwa senior citizen cenderun churn\n",
+    "- customer yang tidak memiliki partner memiliki partner memliki presentase chrun yang lebih besar daripada yang memiliki partner\n",
+    "- customer yang memiliki dependets cenderung tidak churn \n",
+    "\n",
+    "\n",
+    "- Phone service memiliki presentase churn rate 1:3 baik yang mempunyai layanan telpon ataupun tidak.\n",
+    "- Multiple line adalah pertanyaan sambungan dari phone service memiliki presentase churn yang sama 1:3\n",
+    "- Fiber optic memiliki presentase churn rate yang tinggi dibandingkan internet service yang lain \n",
+    "- Jika customer tidak punya layanan keamanan seperti (Online Security, Online Backup, Device Protection, Tech Support) mereka cenderung Churn\n",
+    "- Streaming TV dan Streaming movies memiliki presentase churn rate yang sama 1:2 baik yang mempunyai layanannya maupun tidak\n",
+    "\n",
+    "Disini saya dapat menyimpulkan bahwa layanan keamanan seperti (Online Security, Online Backup, Device Protection, Tech Support) sangat penting terhadap churn rate, oleh karena itu saya sarankan \n",
+    "kepada provider dapat memberikan layanan tersebut dengan harga yang lebih murah agar customer tidak churn\n",
+    "\n",
+    "\n",
+    "- Phone service memiliki presentase churn rate 1:3 baik yang mempunyai layanan telpon ataupun tidak.\n",
+    "- Multiple line adalah pertanyaan sambungan dari phone service memiliki presentase churn yang sama 1:3\n",
+    "- Fiber optic memiliki presentase churn rate yang tinggi dibandingkan internet service yang lain \n",
+    "- Jika customer tidak punya layanan keamanan seperti (Online Security, Online Backup, Device Protection, Tech Support) mereka cenderung Churn\n",
+    "- Streaming TV dan Streaming movies memiliki presentase churn rate yang sama 1:2 baik yang mempunyai layanannya maupun tidak\n",
+    "\n",
+    "Disini saya dapat menyimpulkan bahwa layanan keamanan seperti (Online Security, Online Backup, Device Protection, Tech Support) sangat penting terhadap churn rate, oleh karena itu saya sarankan kepada provider dapat memberikan layanan \n",
+    "tersebut dengan harga yang lebih murah agar customer tidak churn\n",
+    "\n",
+    "- customer yang hanya berlangganan sebulan cendurung churn karena mereka mungkin hanya ingin mencoba layanan apakah worth it atau tidak.\n",
+    "- customer yang yang berlangganan dari bulan 10 sampai 60 atau lebih cenderung tidak churn karena mereka adalah pelanggan tetap\n",
+    "\n",
+    "- customer banyak memakai Month to month contract daripada one atau two year contract.\n",
+    "- karena one atau two year contract hanyalah untuk customer tetap yang puas terhadap jasa provider tersebut.\n",
+    "\n",
+    "- Customer yang mempunyai kontrak month to month cenderung churn karena mereka baru mencoba apakah layanan ini bagus atau tidak\n",
+    "- Customer yang memakai paperless billing cenderung churn \n",
+    "- Customer yang memakai payment method electronic check cenderung churn\n",
+    "\n",
+    "- Dapat dilihat dari scatter plot bahwa kolom MonthlyCharges memiliki korelasi yang positive terhadap kolom TotalCharges.\n",
+    "- Rata rata customer memiliki tagihan 64.80 dollar tiap bulannya.\n",
+    "- Total rata rata customer memiliki tagihan 2283.30 dollar.\n",
+    "\n",
+    "- Dari grafik boxplot MonthlyCharge  Customer dengan range monthly charge 58 - 95 adalah churn dan Customer dengan range monthly charge 26 - 87 adalah tidak churn\n",
+    "\n",
+    "bisa disimpulkan bahwa orang churn rata rata karena tagihan perbulan yang mahal, membuat mereka meninggalkan perusahan (churn)\n",
+    "\n",
+    "- Dari grafik boxplot TotalCharge bisa dilihat dari outlier yang churn dari range ~5800 sampai 8684.80 customer meninggalkan perusahaan (churn)\n",
+    "\n",
+    "bisa disimpulkan bahwa customer yang sudah mengeluarkan uang lebih dari 5800 dollar terhadap layanan service perusahan mereka akan meninggalkan perusahaan (churn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Analysis Improvement"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Dari Grafik confusion matrix\n",
+    "\n",
+    "Model Improvement mengalami peningkatan yaitu \n",
+    "\n",
+    "- Sequential API berhasil memprediksi True Positive sebanyak 784 \n",
+    "- Sequential API Improve model berhasil memprediksi True Positive sebanyak 816\n",
+    "\n",
+    "Sequential API Improve model berhasil memperbaiki True Positive sebanyak 32 \n",
+    "\n",
+    "- Sequential API berhasil memprediksi True Negative sebanyak 178 \n",
+    "- Sequential API Improve model berhasil memprediksi True Negative sebanyak 186\n",
+    "\n",
+    "Sequential API Improve model berhasil memperbaiki True Negative sebanyak 8 "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Inference"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "dengan inference 20 data \n",
+    "\n",
+    "- Label 0 atau Not Churn berhasil memprediksi 12/14 akurasi 85 %\n",
+    "- Label 1 atau Churn berhasil memprediksi 4/6 akurasi 66 %\n",
+    "\n",
+    "Test Set - Loss     : 0.43\n",
+    "\n",
+    "Test Set - Accuracy : 0.80"
+   ]
+  }
+ ],
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