Final Project - Priscilla Widjaja.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# __Import Libraries__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import classification_report, accuracy_score, confusion_matrix, recall_score, roc_auc_score, precision_score, f1_score\n",
    "\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import make_scorer\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# __Load Dataset__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
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       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
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       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>services</td>\n",
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       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>services</td>\n",
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       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
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       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
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       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
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       "      <th>...</th>\n",
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       "      <th>41183</th>\n",
       "      <td>73</td>\n",
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       "      <td>no</td>\n",
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       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
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       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41184</th>\n",
       "      <td>46</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
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       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41185</th>\n",
       "      <td>56</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
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       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41186</th>\n",
       "      <td>44</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41187</th>\n",
       "      <td>74</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>41188 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age          job  marital            education  default housing loan  \\\n",
       "0       56    housemaid  married             basic.4y       no      no   no   \n",
       "1       57     services  married          high.school  unknown      no   no   \n",
       "2       37     services  married          high.school       no     yes   no   \n",
       "3       40       admin.  married             basic.6y       no      no   no   \n",
       "4       56     services  married          high.school       no      no  yes   \n",
       "...    ...          ...      ...                  ...      ...     ...  ...   \n",
       "41183   73      retired  married  professional.course       no     yes   no   \n",
       "41184   46  blue-collar  married  professional.course       no      no   no   \n",
       "41185   56      retired  married    university.degree       no     yes   no   \n",
       "41186   44   technician  married  professional.course       no      no   no   \n",
       "41187   74      retired  married  professional.course       no     yes   no   \n",
       "\n",
       "         contact month day_of_week  ...  campaign  pdays  previous  \\\n",
       "0      telephone   may         mon  ...         1    999         0   \n",
       "1      telephone   may         mon  ...         1    999         0   \n",
       "2      telephone   may         mon  ...         1    999         0   \n",
       "3      telephone   may         mon  ...         1    999         0   \n",
       "4      telephone   may         mon  ...         1    999         0   \n",
       "...          ...   ...         ...  ...       ...    ...       ...   \n",
       "41183   cellular   nov         fri  ...         1    999         0   \n",
       "41184   cellular   nov         fri  ...         1    999         0   \n",
       "41185   cellular   nov         fri  ...         2    999         0   \n",
       "41186   cellular   nov         fri  ...         1    999         0   \n",
       "41187   cellular   nov         fri  ...         3    999         1   \n",
       "\n",
       "          poutcome emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  \\\n",
       "0      nonexistent          1.1          93.994          -36.4      4.857   \n",
       "1      nonexistent          1.1          93.994          -36.4      4.857   \n",
       "2      nonexistent          1.1          93.994          -36.4      4.857   \n",
       "3      nonexistent          1.1          93.994          -36.4      4.857   \n",
       "4      nonexistent          1.1          93.994          -36.4      4.857   \n",
       "...            ...          ...             ...            ...        ...   \n",
       "41183  nonexistent         -1.1          94.767          -50.8      1.028   \n",
       "41184  nonexistent         -1.1          94.767          -50.8      1.028   \n",
       "41185  nonexistent         -1.1          94.767          -50.8      1.028   \n",
       "41186  nonexistent         -1.1          94.767          -50.8      1.028   \n",
       "41187      failure         -1.1          94.767          -50.8      1.028   \n",
       "\n",
       "       nr.employed    y  \n",
       "0           5191.0   no  \n",
       "1           5191.0   no  \n",
       "2           5191.0   no  \n",
       "3           5191.0   no  \n",
       "4           5191.0   no  \n",
       "...            ...  ...  \n",
       "41183       4963.6  yes  \n",
       "41184       4963.6   no  \n",
       "41185       4963.6   no  \n",
       "41186       4963.6  yes  \n",
       "41187       4963.6   no  \n",
       "\n",
       "[41188 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank = pd.read_csv('bank-additional-full.csv', sep = ';')\n",
    "bank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41188 entries, 0 to 41187\n",
      "Data columns (total 21 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   age             41188 non-null  int64  \n",
      " 1   job             41188 non-null  object \n",
      " 2   marital         41188 non-null  object \n",
      " 3   education       41188 non-null  object \n",
      " 4   default         41188 non-null  object \n",
      " 5   housing         41188 non-null  object \n",
      " 6   loan            41188 non-null  object \n",
      " 7   contact         41188 non-null  object \n",
      " 8   month           41188 non-null  object \n",
      " 9   day_of_week     41188 non-null  object \n",
      " 10  duration        41188 non-null  int64  \n",
      " 11  campaign        41188 non-null  int64  \n",
      " 12  pdays           41188 non-null  int64  \n",
      " 13  previous        41188 non-null  int64  \n",
      " 14  poutcome        41188 non-null  object \n",
      " 15  emp.var.rate    41188 non-null  float64\n",
      " 16  cons.price.idx  41188 non-null  float64\n",
      " 17  cons.conf.idx   41188 non-null  float64\n",
      " 18  euribor3m       41188 non-null  float64\n",
      " 19  nr.employed     41188 non-null  float64\n",
      " 20  y               41188 non-null  object \n",
      "dtypes: float64(5), int64(5), object(11)\n",
      "memory usage: 6.6+ MB\n"
     ]
    }
   ],
   "source": [
    "bank.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',\n",
       "       'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays',\n",
       "       'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx',\n",
       "       'cons.conf.idx', 'euribor3m', 'nr.employed', 'y'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank1 = bank.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# __Drop Duration__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank1 = bank1.drop(columns=['duration'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# __Drop Unknown__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',\n",
    "       'contact', 'month', 'day_of_week','campaign', 'pdays',\n",
    "       'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx',\n",
    "       'cons.conf.idx', 'euribor3m', 'nr.employed', 'y']\n",
    "\n",
    "for i in columns:\n",
    "    bank1.drop(index=bank1[bank1[i] == 'unknown'].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>59</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41183</th>\n",
       "      <td>73</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41184</th>\n",
       "      <td>46</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41185</th>\n",
       "      <td>56</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41186</th>\n",
       "      <td>44</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41187</th>\n",
       "      <td>74</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>3</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30488 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age          job  marital            education default housing loan  \\\n",
       "0       56    housemaid  married             basic.4y      no      no   no   \n",
       "2       37     services  married          high.school      no     yes   no   \n",
       "3       40       admin.  married             basic.6y      no      no   no   \n",
       "4       56     services  married          high.school      no      no  yes   \n",
       "6       59       admin.  married  professional.course      no      no   no   \n",
       "...    ...          ...      ...                  ...     ...     ...  ...   \n",
       "41183   73      retired  married  professional.course      no     yes   no   \n",
       "41184   46  blue-collar  married  professional.course      no      no   no   \n",
       "41185   56      retired  married    university.degree      no     yes   no   \n",
       "41186   44   technician  married  professional.course      no      no   no   \n",
       "41187   74      retired  married  professional.course      no     yes   no   \n",
       "\n",
       "         contact month day_of_week  campaign  pdays  previous     poutcome  \\\n",
       "0      telephone   may         mon         1    999         0  nonexistent   \n",
       "2      telephone   may         mon         1    999         0  nonexistent   \n",
       "3      telephone   may         mon         1    999         0  nonexistent   \n",
       "4      telephone   may         mon         1    999         0  nonexistent   \n",
       "6      telephone   may         mon         1    999         0  nonexistent   \n",
       "...          ...   ...         ...       ...    ...       ...          ...   \n",
       "41183   cellular   nov         fri         1    999         0  nonexistent   \n",
       "41184   cellular   nov         fri         1    999         0  nonexistent   \n",
       "41185   cellular   nov         fri         2    999         0  nonexistent   \n",
       "41186   cellular   nov         fri         1    999         0  nonexistent   \n",
       "41187   cellular   nov         fri         3    999         1      failure   \n",
       "\n",
       "       emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  nr.employed  \\\n",
       "0               1.1          93.994          -36.4      4.857       5191.0   \n",
       "2               1.1          93.994          -36.4      4.857       5191.0   \n",
       "3               1.1          93.994          -36.4      4.857       5191.0   \n",
       "4               1.1          93.994          -36.4      4.857       5191.0   \n",
       "6               1.1          93.994          -36.4      4.857       5191.0   \n",
       "...             ...             ...            ...        ...          ...   \n",
       "41183          -1.1          94.767          -50.8      1.028       4963.6   \n",
       "41184          -1.1          94.767          -50.8      1.028       4963.6   \n",
       "41185          -1.1          94.767          -50.8      1.028       4963.6   \n",
       "41186          -1.1          94.767          -50.8      1.028       4963.6   \n",
       "41187          -1.1          94.767          -50.8      1.028       4963.6   \n",
       "\n",
       "         y  \n",
       "0       no  \n",
       "2       no  \n",
       "3       no  \n",
       "4       no  \n",
       "6       no  \n",
       "...    ...  \n",
       "41183  yes  \n",
       "41184   no  \n",
       "41185   no  \n",
       "41186  yes  \n",
       "41187   no  \n",
       "\n",
       "[30488 rows x 20 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>41188.00000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.02406</td>\n",
       "      <td>258.285010</td>\n",
       "      <td>2.567593</td>\n",
       "      <td>962.475454</td>\n",
       "      <td>0.172963</td>\n",
       "      <td>0.081886</td>\n",
       "      <td>93.575664</td>\n",
       "      <td>-40.502600</td>\n",
       "      <td>3.621291</td>\n",
       "      <td>5167.035911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.42125</td>\n",
       "      <td>259.279249</td>\n",
       "      <td>2.770014</td>\n",
       "      <td>186.910907</td>\n",
       "      <td>0.494901</td>\n",
       "      <td>1.570960</td>\n",
       "      <td>0.578840</td>\n",
       "      <td>4.628198</td>\n",
       "      <td>1.734447</td>\n",
       "      <td>72.251528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.400000</td>\n",
       "      <td>92.201000</td>\n",
       "      <td>-50.800000</td>\n",
       "      <td>0.634000</td>\n",
       "      <td>4963.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32.00000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.800000</td>\n",
       "      <td>93.075000</td>\n",
       "      <td>-42.700000</td>\n",
       "      <td>1.344000</td>\n",
       "      <td>5099.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>38.00000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>93.749000</td>\n",
       "      <td>-41.800000</td>\n",
       "      <td>4.857000</td>\n",
       "      <td>5191.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47.00000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>93.994000</td>\n",
       "      <td>-36.400000</td>\n",
       "      <td>4.961000</td>\n",
       "      <td>5228.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>98.00000</td>\n",
       "      <td>4918.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>94.767000</td>\n",
       "      <td>-26.900000</td>\n",
       "      <td>5.045000</td>\n",
       "      <td>5228.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age      duration      campaign         pdays      previous  \\\n",
       "count  41188.00000  41188.000000  41188.000000  41188.000000  41188.000000   \n",
       "mean      40.02406    258.285010      2.567593    962.475454      0.172963   \n",
       "std       10.42125    259.279249      2.770014    186.910907      0.494901   \n",
       "min       17.00000      0.000000      1.000000      0.000000      0.000000   \n",
       "25%       32.00000    102.000000      1.000000    999.000000      0.000000   \n",
       "50%       38.00000    180.000000      2.000000    999.000000      0.000000   \n",
       "75%       47.00000    319.000000      3.000000    999.000000      0.000000   \n",
       "max       98.00000   4918.000000     56.000000    999.000000      7.000000   \n",
       "\n",
       "       emp.var.rate  cons.price.idx  cons.conf.idx     euribor3m   nr.employed  \n",
       "count  41188.000000    41188.000000   41188.000000  41188.000000  41188.000000  \n",
       "mean       0.081886       93.575664     -40.502600      3.621291   5167.035911  \n",
       "std        1.570960        0.578840       4.628198      1.734447     72.251528  \n",
       "min       -3.400000       92.201000     -50.800000      0.634000   4963.600000  \n",
       "25%       -1.800000       93.075000     -42.700000      1.344000   5099.100000  \n",
       "50%        1.100000       93.749000     -41.800000      4.857000   5191.000000  \n",
       "75%        1.400000       93.994000     -36.400000      4.961000   5228.100000  \n",
       "max        1.400000       94.767000     -26.900000      5.045000   5228.100000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# __Drop Outliers__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_outlier(yourList):\n",
    "    q1 = yourList.describe()['25%']\n",
    "    q2 = yourList.describe()['50%']\n",
    "    q3 = yourList.describe()['75%']\n",
    "    iqr = q3 - q1\n",
    "    batas_bawah = q1 - 1.5 * iqr\n",
    "    batas_atas = q3 + 1.5 * iqr\n",
    "    outlier = []\n",
    "    for i in yourList:\n",
    "        if i < batas_bawah or i > batas_atas:\n",
    "            outlier.append(i)\n",
    "    return outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GTi0+fkCSKshwl6QKMtwlqYIMd0mqIMNdkirIcJekCjLcJamCDHdJqiDDXZIqyHCXpAoy3CWpggx3SaogHxymnjYpD9xaP/HHeMXpL53wY+jUYrirZ030EyFh9MtjMo4jdVrbwzIR0R8R/xsR34qIbRHxiaL94oh4ICJ2RsTnI6Kvc+VKksajzJj7s8BbMvNy4ArgHRHxRmAF8MnMnAnsBxaUL1OS1Ioyc6hmZh4sVl9a/CXwFuDOon0EmF+qQklSy0qNuUfEacBm4LXA3wPfBw5k5nPFJruBC8fYdxgYBqjVajQajTKlSBPGc1O9qFS4Z+bzwBURcTZwF3Bps83G2Hc1sBpG51CdjKnMpJatv3dSptmTOq0j97ln5gGgAbwRODsijn5pXAQ81oljSJLGr8zdMq8seuxExOnAW4EdwEbgfcVmQ8C6skVKklpTZljmAmCkGHd/CbA2M78cEduBz0XEXwIPAnd0oE5JUgvaDvfM/DZwZZP2R4CryhQlSSrHZ8tIUgUZ7pJUQYa7JFWQ4S5JFWS4S1IFGe6SVEGGuyRVkOEuSRVkuEtSBRnuklRBhrskVZDhLkkVZLhLUgUZ7pJUQYa7JFVQmZmYXh0RGyNiR0Rsi4jri/ZzIuL+iNhZLGd0rlxJ0niU6bk/B9yQmZcyOnfqhyNiFrAM2JCZM4ENxbokaRK1He6ZuSczv1m8forR+VMvBOYBI8VmI8D8skVKklrTkTH3iBhgdMq9B4BaZu6B0S8A4PxOHEOSNH5lJsgGICLOAr4IfDQzfx4R491vGBgGqNVqNBqNsqVIE8JzU72oVLhHxEsZDfbPZOaXiua9EXFBZu6JiAuAJ5rtm5mrgdUA9Xo9BwcHy5QiTYz19+K5qV5U5m6ZAO4AdmTmbce9dQ8wVLweAta1X54kqR1leu5vBj4IbImIh4q2G4HlwNqIWAA8ClxdrkRJUqvaDvfM/Bow1gD73HY/V5JUnr9QlaQKMtwlqYJK3wop9ZLx3qr7gn1WtH6czGx9J6mD7LnrlJKZLf1t3Lix5X0Mdk0FhrskVZDhLkkVZLhLUgUZ7pJUQYa7JFWQ4S5JFWS4S1IFGe6SVEExFX5wERE/AX7Y7TqkJs4DftrtIqQx/FpmvrLZG1Mi3KWpKiI2ZWa923VIrXJYRpIqyHCXpAoy3KWTW93tAqR2OOYuSRVkz12SKshwl6QKMtwlqYIMd0mqIMNdp7yIuDsiNkfEtogYLtoWRMT3IqIREbdHxN8V7a+MiC9GxDeKvzd3t3qpOe+W0SkvIs7JzH0RcTrwDeD3gP8G3gA8BXwF+FZmLoqIzwL/kJlfi4jXAP+RmZd2rXhpDNO6XYA0BXwkIt5bvH418EHgq5m5DyAivgD8RvH+W4FZEXF035dHxMsy86nJLFh6MYa7TmkRMchoYL8pMw9FRAP4LjBWb/wlxbbPTE6FUnscc9ep7hXA/iLYXwe8ETgD+N2ImBER04A/PG77+4BFR1ci4opJrVYaJ8Ndp7r1wLSI+DbwF8DXgR8DNwMPAP8JbAeeLLb/CFCPiG9HxHbguskvWXpxXlCVmoiIszLzYNFzvwv4dGbe1e26pPGy5y419+cR8RCwFfgBcHeX65FaYs9dkirInrskVZDhLkkVZLhLUgUZ7pJUQYa7JFXQ/wEg27xbG0TmiQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median = 38.0\n",
      "IQR = 15.0\n",
      "Batas Bawah = 9.5\n",
      "Batas Atas = 69.5\n",
      "Jumlah Outlier = 469\n",
      "Outlier = [70, 76, 73, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 95, 70, 70, 70, 77, 75, 70, 70, 73, 80, 80, 80, 72, 72, 82, 73, 71, 70, 70, 70, 71, 70, 70, 71, 75, 73, 73, 71, 75, 75, 78, 75, 70, 78, 75, 78, 85, 85, 85, 80, 71, 85, 85, 79, 77, 83, 81, 71, 81, 73, 71, 71, 88, 81, 81, 81, 71, 83, 75, 78, 88, 77, 72, 79, 79, 74, 75, 72, 72, 70, 79, 74, 74, 74, 74, 76, 76, 82, 75, 70, 73, 73, 73, 73, 76, 70, 85, 80, 70, 74, 74, 76, 88, 74, 81, 76, 74, 76, 73, 72, 70, 71, 70, 70, 76, 72, 73, 80, 74, 71, 74, 80, 74, 73, 74, 71, 76, 76, 87, 79, 70, 74, 88, 81, 80, 80, 78, 71, 71, 72, 73, 73, 73, 79, 72, 71, 83, 76, 76, 72, 71, 71, 76, 75, 71, 78, 78, 91, 91, 76, 73, 76, 73, 71, 83, 70, 70, 70, 70, 70, 70, 70, 81, 70, 70, 78, 71, 71, 75, 83, 71, 82, 82, 82, 80, 80, 86, 77, 75, 77, 70, 80, 71, 71, 71, 80, 80, 71, 71, 74, 74, 74, 83, 85, 70, 70, 70, 88, 88, 71, 98, 98, 73, 72, 71, 71, 81, 71, 75, 81, 73, 80, 75, 72, 82, 82, 78, 78, 72, 78, 71, 79, 71, 73, 81, 81, 76, 71, 71, 79, 79, 72, 72, 72, 72, 78, 80, 77, 77, 78, 75, 72, 77, 72, 82, 83, 83, 94, 78, 86, 78, 72, 77, 73, 81, 76, 83, 83, 80, 73, 86, 70, 71, 81, 74, 85, 84, 85, 70, 70, 76, 76, 76, 76, 76, 73, 73, 78, 72, 85, 74, 73, 80, 73, 82, 75, 70, 75, 80, 78, 78, 86, 84, 76, 82, 75, 71, 71, 80, 74, 82, 82, 74, 74, 74, 85, 85, 85, 75, 85, 70, 80, 80, 80, 80, 80, 77, 80, 73, 71, 71, 71, 79, 79, 72, 78, 82, 77, 77, 92, 70, 76, 77, 71, 74, 80, 79, 76, 77, 92, 84, 70, 79, 74, 72, 72, 76, 76, 89, 78, 78, 70, 74, 83, 81, 76, 76, 86, 83, 83, 75, 73, 72, 72, 72, 79, 73, 83, 83, 73, 78, 78, 80, 70, 78, 72, 72, 78, 78, 82, 72, 72, 71, 84, 77, 74, 76, 80, 71, 71, 81, 77, 75, 71, 77, 71, 72, 77, 73, 73, 92, 70, 77, 92, 75, 71, 70, 76, 76, 72, 76, 72, 71, 70, 81, 71, 78, 75, 88, 76, 88, 88, 85, 89, 86, 80, 83, 83, 82, 71, 84, 82, 86, 86, 82, 77, 75, 70, 74, 71, 71, 81, 70, 81, 74, 84, 74, 74, 84, 81, 80, 73, 74]\n"
     ]
    }
   ],
   "source": [
    "bank.boxplot(['age'])\n",
    "plt.savefig('Age Boxplot.png')\n",
    "plt.show()\n",
    "    \n",
    "q1 = bank['age'].describe()['25%']\n",
    "q2 = bank['age'].describe()['50%']\n",
    "q3 = bank['age'].describe()['75%']\n",
    "iqr = q3 - q1\n",
    "batas_bawah = q1 - 1.5 * iqr\n",
    "batas_atas = q3 + 1.5 * iqr\n",
    "\n",
    "print(\"Median =\", bank['age'].describe()['50%'])\n",
    "print(\"IQR =\", (bank['age'].describe()['75%']) - (bank['age'].describe()['25%']))\n",
    "print(\"Batas Bawah =\", batas_bawah)\n",
    "print(\"Batas Atas =\", batas_atas)\n",
    "print(\"Jumlah Outlier =\", len(find_outlier(bank['age'])))\n",
    "print(\"Outlier =\", find_outlier(bank['age']))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>59</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41181</th>\n",
       "      <td>37</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41182</th>\n",
       "      <td>29</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>single</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>success</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41184</th>\n",
       "      <td>46</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41185</th>\n",
       "      <td>56</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41186</th>\n",
       "      <td>44</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30111 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age          job  marital            education default housing loan  \\\n",
       "0       56    housemaid  married             basic.4y      no      no   no   \n",
       "2       37     services  married          high.school      no     yes   no   \n",
       "3       40       admin.  married             basic.6y      no      no   no   \n",
       "4       56     services  married          high.school      no      no  yes   \n",
       "6       59       admin.  married  professional.course      no      no   no   \n",
       "...    ...          ...      ...                  ...     ...     ...  ...   \n",
       "41181   37       admin.  married    university.degree      no     yes   no   \n",
       "41182   29   unemployed   single             basic.4y      no     yes   no   \n",
       "41184   46  blue-collar  married  professional.course      no      no   no   \n",
       "41185   56      retired  married    university.degree      no     yes   no   \n",
       "41186   44   technician  married  professional.course      no      no   no   \n",
       "\n",
       "         contact month day_of_week  campaign  pdays  previous     poutcome  \\\n",
       "0      telephone   may         mon         1    999         0  nonexistent   \n",
       "2      telephone   may         mon         1    999         0  nonexistent   \n",
       "3      telephone   may         mon         1    999         0  nonexistent   \n",
       "4      telephone   may         mon         1    999         0  nonexistent   \n",
       "6      telephone   may         mon         1    999         0  nonexistent   \n",
       "...          ...   ...         ...       ...    ...       ...          ...   \n",
       "41181   cellular   nov         fri         1    999         0  nonexistent   \n",
       "41182   cellular   nov         fri         1      9         1      success   \n",
       "41184   cellular   nov         fri         1    999         0  nonexistent   \n",
       "41185   cellular   nov         fri         2    999         0  nonexistent   \n",
       "41186   cellular   nov         fri         1    999         0  nonexistent   \n",
       "\n",
       "       emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  nr.employed  \\\n",
       "0               1.1          93.994          -36.4      4.857       5191.0   \n",
       "2               1.1          93.994          -36.4      4.857       5191.0   \n",
       "3               1.1          93.994          -36.4      4.857       5191.0   \n",
       "4               1.1          93.994          -36.4      4.857       5191.0   \n",
       "6               1.1          93.994          -36.4      4.857       5191.0   \n",
       "...             ...             ...            ...        ...          ...   \n",
       "41181          -1.1          94.767          -50.8      1.028       4963.6   \n",
       "41182          -1.1          94.767          -50.8      1.028       4963.6   \n",
       "41184          -1.1          94.767          -50.8      1.028       4963.6   \n",
       "41185          -1.1          94.767          -50.8      1.028       4963.6   \n",
       "41186          -1.1          94.767          -50.8      1.028       4963.6   \n",
       "\n",
       "         y  \n",
       "0       no  \n",
       "2       no  \n",
       "3       no  \n",
       "4       no  \n",
       "6       no  \n",
       "...    ...  \n",
       "41181  yes  \n",
       "41182   no  \n",
       "41184   no  \n",
       "41185   no  \n",
       "41186  yes  \n",
       "\n",
       "[30111 rows x 20 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank2 = bank1[bank1['age'] < 69.5]\n",
    "bank2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank2.to_csv('bank.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# __Encoding__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label encoder order is alphabetical\n",
    "labelencoder_X = LabelEncoder()\n",
    "bank2['job']      = labelencoder_X.fit_transform(bank2['job'])\n",
    "bank2['default']  = labelencoder_X.fit_transform(bank2['default']) \n",
    "bank2['housing']  = labelencoder_X.fit_transform(bank2['housing']) \n",
    "bank2['loan']     = labelencoder_X.fit_transform(bank2['loan']) \n",
    "bank2['contact']  = labelencoder_X.fit_transform(bank2['contact']) \n",
    "bank2['marital']  = labelencoder_X.fit_transform(bank2['marital']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank2['education'].replace(['basic.4y', 'basic.6y','basic.9y', 'high.school', 'university.degree', 'professional.course','illiterate'], [1,2,3,4,5,6,7], inplace  = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank2['month'].replace(['jan','feb','mar', 'apr','may', 'jun', 'jul','aug', 'sep','oct', 'nov', 'dec'], [1,2,3,4,5,6,7,8,9,10,11,12], inplace  = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank2['day_of_week'].replace(['mon', 'tue', 'wed', 'thu', 'fri'], [1,2,3,4,5], inplace  = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank2['poutcome'].replace(['nonexistent', 'failure', 'success'], [1,2,3], inplace  = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank2['y'].replace(['no','yes'], [0,1], inplace  = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41181</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41182</th>\n",
       "      <td>29</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41184</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>41185</th>\n",
       "      <td>56</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41186</th>\n",
       "      <td>44</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30111 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  job  marital  education  default  housing  loan  contact  month  \\\n",
       "0       56    3        1          1        0        0     0        1      5   \n",
       "2       37    7        1          4        0        1     0        1      5   \n",
       "3       40    0        1          2        0        0     0        1      5   \n",
       "4       56    7        1          4        0        0     1        1      5   \n",
       "6       59    0        1          6        0        0     0        1      5   \n",
       "...    ...  ...      ...        ...      ...      ...   ...      ...    ...   \n",
       "41181   37    0        1          5        0        1     0        0     11   \n",
       "41182   29   10        2          1        0        1     0        0     11   \n",
       "41184   46    1        1          6        0        0     0        0     11   \n",
       "41185   56    5        1          5        0        1     0        0     11   \n",
       "41186   44    9        1          6        0        0     0        0     11   \n",
       "\n",
       "       day_of_week  campaign  pdays  previous  poutcome  emp.var.rate  \\\n",
       "0                1         1    999         0         1           1.1   \n",
       "2                1         1    999         0         1           1.1   \n",
       "3                1         1    999         0         1           1.1   \n",
       "4                1         1    999         0         1           1.1   \n",
       "6                1         1    999         0         1           1.1   \n",
       "...            ...       ...    ...       ...       ...           ...   \n",
       "41181            5         1    999         0         1          -1.1   \n",
       "41182            5         1      9         1         3          -1.1   \n",
       "41184            5         1    999         0         1          -1.1   \n",
       "41185            5         2    999         0         1          -1.1   \n",
       "41186            5         1    999         0         1          -1.1   \n",
       "\n",
       "       cons.price.idx  cons.conf.idx  euribor3m  nr.employed  y  \n",
       "0              93.994          -36.4      4.857       5191.0  0  \n",
       "2              93.994          -36.4      4.857       5191.0  0  \n",
       "3              93.994          -36.4      4.857       5191.0  0  \n",
       "4              93.994          -36.4      4.857       5191.0  0  \n",
       "6              93.994          -36.4      4.857       5191.0  0  \n",
       "...               ...            ...        ...          ... ..  \n",
       "41181          94.767          -50.8      1.028       4963.6  1  \n",
       "41182          94.767          -50.8      1.028       4963.6  0  \n",
       "41184          94.767          -50.8      1.028       4963.6  0  \n",
       "41185          94.767          -50.8      1.028       4963.6  0  \n",
       "41186          94.767          -50.8      1.028       4963.6  1  \n",
       "\n",
       "[30111 rows x 20 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Scaling__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "kolom = ['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',\n",
    "       'contact', 'month', 'day_of_week', 'campaign', 'pdays',\n",
    "       'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx',\n",
    "       'cons.conf.idx', 'euribor3m', 'nr.employed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in kolom:\n",
    "    bank2[i] = RobustScaler().fit_transform(bank2[[i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
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       "      <th>default</th>\n",
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       "      <th>euribor3m</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "    <tr>\n",
       "      <th>41186</th>\n",
       "      <td>0.571429</td>\n",
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       "      <td>-1.053385</td>\n",
       "      <td>-1.762791</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30111 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            age       job  marital  education  default  housing  loan  \\\n",
       "0      1.428571  0.142857      0.0       -1.5      0.0     -1.0   0.0   \n",
       "2      0.071429  0.714286      0.0        0.0      0.0      0.0   0.0   \n",
       "3      0.285714 -0.285714      0.0       -1.0      0.0     -1.0   0.0   \n",
       "4      1.428571  0.714286      0.0        0.0      0.0     -1.0   1.0   \n",
       "6      1.642857 -0.285714      0.0        1.0      0.0     -1.0   0.0   \n",
       "...         ...       ...      ...        ...      ...      ...   ...   \n",
       "41181  0.071429 -0.285714      0.0        0.5      0.0      0.0   0.0   \n",
       "41182 -0.500000  1.142857      1.0       -1.5      0.0      0.0   0.0   \n",
       "41184  0.714286 -0.142857      0.0        1.0      0.0     -1.0   0.0   \n",
       "41185  1.428571  0.428571      0.0        0.5      0.0      0.0   0.0   \n",
       "41186  0.571429  1.000000      0.0        1.0      0.0     -1.0   0.0   \n",
       "\n",
       "       contact     month  day_of_week  campaign  pdays  previous  poutcome  \\\n",
       "0          1.0 -0.333333         -1.0      -0.5    0.0       0.0       0.0   \n",
       "2          1.0 -0.333333         -1.0      -0.5    0.0       0.0       0.0   \n",
       "3          1.0 -0.333333         -1.0      -0.5    0.0       0.0       0.0   \n",
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       "6          1.0 -0.333333         -1.0      -0.5    0.0       0.0       0.0   \n",
       "...        ...       ...          ...       ...    ...       ...       ...   \n",
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       "41186      0.0  1.666667          1.0      -0.5    0.0       0.0       0.0   \n",
       "\n",
       "       emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  nr.employed  y  \n",
       "0            0.0000        0.598477       0.857143   0.000275     0.000000  0  \n",
       "2            0.0000        0.598477       0.857143   0.000275     0.000000  0  \n",
       "3            0.0000        0.598477       0.857143   0.000275     0.000000  0  \n",
       "4            0.0000        0.598477       0.857143   0.000275     0.000000  0  \n",
       "6            0.0000        0.598477       0.857143   0.000275     0.000000  0  \n",
       "...             ...             ...            ...        ...          ... ..  \n",
       "41181       -0.6875        1.439608      -1.428571  -1.053385    -1.762791  1  \n",
       "41182       -0.6875        1.439608      -1.428571  -1.053385    -1.762791  0  \n",
       "41184       -0.6875        1.439608      -1.428571  -1.053385    -1.762791  0  \n",
       "41185       -0.6875        1.439608      -1.428571  -1.053385    -1.762791  0  \n",
       "41186       -0.6875        1.439608      -1.428571  -1.053385    -1.762791  1  \n",
       "\n",
       "[30111 rows x 20 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Features Selection**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#get correlations of each features in dataset\n",
    "corrmat = bank2.corr()\n",
    "top_corr_features = corrmat.index\n",
    "plt.figure(figsize=(20,20))\n",
    "\n",
    "#plot heat map\n",
    "g=sns.heatmap(bank2[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank2 = bank2.drop(columns=['emp.var.rate', 'euribor3m'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = bank2.drop(['y'], axis=1)\n",
    "y = bank2['y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.17090820e-01 9.46421352e-02 3.94883695e-02 8.16467796e-02\n",
      " 1.56867661e-06 3.15845800e-02 2.24019565e-02 1.72503243e-02\n",
      " 2.74353969e-02 9.11919950e-02 1.16151156e-01 6.79269988e-02\n",
      " 1.53170558e-02 2.81212997e-02 3.25338806e-02 3.75847636e-02\n",
      " 7.96309206e-02]\n"
     ]
    }
   ],
   "source": [
    "model = ExtraTreesClassifier()\n",
    "model.fit(x,y)\n",
    "print(model.feature_importances_) #use inbuilt class feature_importances of tree based classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot graph of feature importances for better visualization\n",
    "feat_importances = pd.Series(model.feature_importances_, index=x.columns)\n",
    "feat_importances.nlargest(10).plot(kind='barh')\n",
    "plt.show()\n",
    "# feat_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank3 = bank2[['age','campaign','job','day_of_week', 'nr.employed','education','pdays','marital','poutcome','cons.conf.idx', 'y']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>campaign</th>\n",
       "      <th>job</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>education</th>\n",
       "      <th>pdays</th>\n",
       "      <th>marital</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.428571</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.071429</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.285714</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.285714</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.428571</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.642857</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.285714</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41181</th>\n",
       "      <td>0.071429</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.285714</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.762791</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.428571</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41182</th>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>1.142857</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.762791</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>-990.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.428571</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41184</th>\n",
       "      <td>0.714286</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.142857</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.762791</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.428571</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41185</th>\n",
       "      <td>1.428571</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.762791</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.428571</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41186</th>\n",
       "      <td>0.571429</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.762791</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.428571</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30111 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            age  campaign       job  day_of_week  nr.employed  education  \\\n",
       "0      1.428571      -0.5  0.142857         -1.0     0.000000       -1.5   \n",
       "2      0.071429      -0.5  0.714286         -1.0     0.000000        0.0   \n",
       "3      0.285714      -0.5 -0.285714         -1.0     0.000000       -1.0   \n",
       "4      1.428571      -0.5  0.714286         -1.0     0.000000        0.0   \n",
       "6      1.642857      -0.5 -0.285714         -1.0     0.000000        1.0   \n",
       "...         ...       ...       ...          ...          ...        ...   \n",
       "41181  0.071429      -0.5 -0.285714          1.0    -1.762791        0.5   \n",
       "41182 -0.500000      -0.5  1.142857          1.0    -1.762791       -1.5   \n",
       "41184  0.714286      -0.5 -0.142857          1.0    -1.762791        1.0   \n",
       "41185  1.428571       0.0  0.428571          1.0    -1.762791        0.5   \n",
       "41186  0.571429      -0.5  1.000000          1.0    -1.762791        1.0   \n",
       "\n",
       "       pdays  marital  poutcome  cons.conf.idx  y  \n",
       "0        0.0      0.0       0.0       0.857143  0  \n",
       "2        0.0      0.0       0.0       0.857143  0  \n",
       "3        0.0      0.0       0.0       0.857143  0  \n",
       "4        0.0      0.0       0.0       0.857143  0  \n",
       "6        0.0      0.0       0.0       0.857143  0  \n",
       "...      ...      ...       ...            ... ..  \n",
       "41181    0.0      0.0       0.0      -1.428571  1  \n",
       "41182 -990.0      1.0       2.0      -1.428571  0  \n",
       "41184    0.0      0.0       0.0      -1.428571  0  \n",
       "41185    0.0      0.0       0.0      -1.428571  0  \n",
       "41186    0.0      0.0       0.0      -1.428571  1  \n",
       "\n",
       "[30111 rows x 11 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Splitting Dataset__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = bank3.drop(['y'], axis=1)\n",
    "y = bank3['y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size = 0.1, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Handling Imbalance Target**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Check Target Proportion**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    26422\n",
       "1     3689\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank3['y'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead 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>Data (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>87.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>12.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Data (%)\n",
       "No      87.75\n",
       "Yes     12.25"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = [round(i/len(bank3)*100, 2) for i in bank3['y'].value_counts()]\n",
    "pd.DataFrame(data=x, index=['No', 'Yes'], columns=['Data (%)'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## __Oversampling : SMOTE__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm = SMOTE(random_state = 0)\n",
    "x_train_sm, y_train_sm = sm.fit_sample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## __Building Machine Learning__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# menyimpan model algoritma ke dalam variabel 'models'\n",
    "models = [\n",
    "    ['Logistic Regression : ', LogisticRegression()],\n",
    "    ['DecisionTree : ', DecisionTreeClassifier()],\n",
    "    ['RandomForest : ', RandomForestClassifier()],\n",
    "    ['KNeighbors Classifier : ', KNeighborsClassifier()]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression()\n",
      " \n",
      "               Score (%)\n",
      "accuracy       74.136786\n",
      "recall         72.797927\n",
      "precision      29.424084\n",
      "roc_auc_score  73.565757\n",
      "f1_score       41.909023\n",
      " \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.74      0.83      2626\n",
      "           1       0.29      0.73      0.42       386\n",
      "\n",
      "    accuracy                           0.74      3012\n",
      "   macro avg       0.62      0.74      0.63      3012\n",
      "weighted avg       0.87      0.74      0.78      3012\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier()\n",
      " \n",
      "               Score (%)\n",
      "accuracy       82.304117\n",
      "recall         33.160622\n",
      "precision      31.761787\n",
      "roc_auc_score  61.344210\n",
      "f1_score       32.446134\n",
      " \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.90      0.90      2626\n",
      "           1       0.32      0.33      0.32       386\n",
      "\n",
      "    accuracy                           0.82      3012\n",
      "   macro avg       0.61      0.61      0.61      3012\n",
      "weighted avg       0.83      0.82      0.82      3012\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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SpNKYsUqSVKJ0jFWSpPKYsUqSVCLHWCVJKlEvXU6haQZWSVKl+k3GGhEL6HgV/wAyM9/VslZJkvqNfhNYM3PlpdkQSVL/1G+7giNiLeCdiz5n5uMtaZEkqV/paxlrt6+Ni4i9ImIm8AhwE7U3Avyxxe2SJOltqSfvY/0WMAp4MDOHATsAf2lpqyRJ/UZmNLz1Zj0JrK9n5nxgmYhYJjNvAEa0uF2SpH5iab3ofGnpyRjr8xGxEnAzcH5EPEXtRbCSJL1l7b08A21UTzLWvYFXgGOBq4GHgE+2slGSpP6jr3UFd5uxZuZLdR8ntrAtkqR+qK/NCu42sC6xUMRywDuAl1wgQpJUhn73HOuSC0VExGhgZMtaJEnqV/paxtqTMdY3yMzfA59oQVskSf1Qe0bDW2/Wk67gfes+LgN8kI7XEJYkqWG9fTJSo3ryuE39DOA2aisv7d2S1kiS+p1WjLFGxARgT+CpzNy8KPsBtZj2GrUnXA7JzOcjYgNgBvBAcfqUzDy8OGdr4FxgBeAq4OjMrlvck8B6dma+YaWliNgGeKonX06SpK60qGv3XOBnwKS6ssnACZnZFhH/DZwAHFcceygzO1r86AxgHDCFWmDdlW6W9e3JGOtPe1gmSVLDWvEca2beDDy7RNm1mblogaMpwJCurhERg4B3ZeatRZY6CRjd3b27eh/rR4CPAmtGxBfrDr0LGNDdhSVJ6omKHrf5LPCbus/DIuIu4EXgxMy8BRgMzK6rM7so61JXXcHLASsVdeofuXkR+FTP2t28IRvu3upbSC03/5UFVTdB6vWa6QqOiHHUumgXGZ+Z43t47tepzRk6vyiaC6yXmfOLMdXfR8RmQEcN6/Z/A7p60flNwE0RMSkzH1miUR/qSeMlSepOM7OCiyDao0BaLyLGUpvUtMOiSUiZ+SrwarH/t4h4CNiYWoZa3108BJjT3T16Msb6u4hYnPpGxHbAhJ5+CUmSurK0nmONiF2pTVbaKzNfritfMyIGFPvvAYYDD2fmXGBBRIyKiAAOAi7r7j49CayHU0uL14mI3YHTAftpJUm9VkRcANwKbBIRsyPiUGqzhFcGJkfEtIg4s6i+HTA9Iu4GfgccnpmLJj59HjgbmEXtEZ0uZwQDRDeP4yxq4EeAXwL/BPbIzKcb+YLNWHuV97oIhd72HGNVX9H22pMtW8Vhyrr7Nvz3ftScS3rtqhJdzQq+gjcO0g4EXgDOiQgyc69WN06S1Pf19iUKG9XVrOAfLrVWSJL6rX6zpGExK/hNilWX/g3o8LgkSY1or7oBJevJkoZExAhqwfTTwCPAxa1slCSp/8gOHxd9++pqjHVjYAxwADCf2goVkZkfX0ptkyT1A+19bKpqVxnr34FbgE9m5iyAiDh2qbRKktRvtPexjLWr51j3A/4XuCEizoqIHeh4eSdJkpqWRMNbb9ZpYM3MSzPzX4H3AjcCxwJrR8QZEbHzUmqfJKmPa29i6826XXkpM1/KzPMzc09q6yROA45vecskSf1Cv8lYO5KZz2bmLzPzE61qkCSpf+lrGWuPHreRJKlVenugbJSBVZJUqd7etduobruCI+LIiFhtaTRGktT/tEfjW2/WkzHWdYA7IuKiiNi1eCedJEmlaCca3nqznswKPpHaS1/PAQ4GZkbEdyNiwxa3TZLUD2QTW2/Wo1nBWXtp6/8WWxuwGvC7iPh+C9smSdLbTreTlyLiKGAs8Ay1t6h/JTNfj4hlgJnAV1vbRElSX9YfZwWvAeybmY/VF2Zme0Ts2ZpmSZL6i/Y+NnWn28Camd/o4tiMcpsjSepvevuYaaN8jlWSVKn+2BUsSVLL9PbnUhvV0FrBkiSVrRXPsUbEhIh4KiLurStbPSImR8TM4udqRXlExOkRMSsipkfEVnXnjC3qz4yIsT35PgZWSVKlWvQc67nArkuUHQ9cn5nDgev5vze17UZtvYbhwDjgDKgFYuAk4MPASOCknqxEaGCVJFWqFUsaZubNwLNLFO8NTCz2JwKj68onZc0UYNWIGATsAkwu3uz2HDCZNwfrN3GMVZJUqaU4eWntzJwLkJlzI2Ktonww8ERdvdlFWWflXTJjlSRVqpmu4IgYFxF31m3j3kITOsqBs4vyLpmxSpIq1cys4MwcD4xv8LR5ETGoyFYHAU8V5bOBoXX1hgBzivLtlyi/sbubmLFKkirV3sTWpMupLdFL8fOyuvKDitnBo4AXii7ja4CdI2K1YtLSzkVZl8xYJUmVasUYa0RcQC3bXCMiZlOb3XsqcFFEHAo8DuxfVL8K2B2YBbwMHAKQmc9GxLeAO4p6p2TmkhOi3sTAKkmqVLZggYjMPKCTQzt0UDeBIzq5zgRgQiP3NrBKkirlkoaSJJXIwCpJUon62tttnBUsSVKJzFglSZXqa2+3MbBKkirlGKskSSUysEqSVKK+NnnJwCpJqpRjrJIklciuYEmSSmRXsCRJJWrvY6HVwCpJqpRdwZIklahv5asGVklSxcxYJUkqkY/bSJJUIicvSZJUor4VVg2skqSKOcYqSVKJ+lpXsC86lySpRGaskqRK9a181YxVklSx9ia27kTEJhExrW57MSKOiYhvRsSTdeW7151zQkTMiogHImKXZr+PGaskqVKtGGPNzAeAEQARMQB4ErgUOAT4cWb+sL5+RGwKjAE2A9YFrouIjTNzYaP3NmOVJFUqm9gatAPwUGY+1kWdvYELM/PVzHwEmAWMbPxWBlZJUsVa0RW8hDHABXWfj4yI6RExISJWK8oGA0/U1ZldlDXMwCpJqlQ28U9EjIuIO+u2cR1dOyKWA/YCflsUnQFsSK2beC7wo0VVO2xaExxjlSRVqpkFIjJzPDC+B1V3A6Zm5rzivHmLDkTEWcCVxcfZwNC684YAc5pomhmrJKla7WTDWwMOoK4bOCIG1R3bB7i32L8cGBMRy0fEMGA4cHsz38fA2oetO3gdLrliIrfc/gdumnIFnzv8QAC+fPyRTJtxE9ffcinX33IpO+y0HQDLLrssp59xKjf+9XJuuf0PHPXFDntWpKXurPE/Ys7su5l21/WLy/77eydy7z03MfVvk/ndb89mlVXeBdR+jyec8xPumnod90y/keO+emRVzVYPtWryUkQMBHYCLqkr/n5E3BMR04GPA8cCZOZ9wEXA/cDVwBHNzAgGA2uf1ta2kJNO/G+2HbkHu+84hkM+9xk23mRDAH75i4nssO0+7LDtPlw/+WYA9hq9K8sv/w62/+he7Pyx/Tjw4H9l6HpNjd1LpZo06SL22PMzbyi77vqb2WLEJ9hq652YOfNhjj+uFkA/9ak9WX755dhyqx0Z+eFd+dx//Dvrrz+kimarh1qVsWbmy5n57sx8oa7swMx8f2Z+IDP3ysy5dce+k5kbZuYmmfnHZr+PgbUPe2re09xz9/0AvPSPl5j5wEOss+7andbPTAYOHMiAAQN45zvfyeuvv86CBf9YWs2VOnXLn2/j2eeef0PZ5OtuZuHCWkIx5bapDB5c6+HLTFZcsfZ7vMIKK/Da66/z4ov+HvdmS2FW8FK11ANrRByytO8pGLreYDb/wPuYeufdAHz2c5/hhr9cxk9+9h1WWbXWhXbFZdfw8ssvM/3BW5h6358446cTeP65F7q6rNQrHHLwGK6+5gYALr74D7z00svMfvwuHnnodk477UyeWyIoq3dpZlZwb1ZFxnpyBffs1wauOJBzfnU6/3XC9/jHgpeYeM4FfHjETnziX0Yzb97TnPzt4wDYcuv3s3BhO1tssh0f+sCOHH7kIay/gV1o6t1OOP4o2tra+PWva8NoIz80goULFzJ0/a3YaONRHHvsYQwbtl7FrVRX+lrG2pLHbYpB4Q4PAZ32RRbPIY0DWPmda7PCcqu2oHX9y7LLLsuEX53OxRddwVVXTAbg6afnLz5+3sTfct5vzgBg3/335E/X3UJbWxvPPPMsd0yZyhZbbs5jj86upO1Sdw48cH/22H1Hdtrl04vLxozZh2uuvZG2tjaefno+f/3rHWy99RY88sjjFbZUXentGWijWpWxrg0cBHyyg21+Zydl5vjM/GBmftCgWo4f/+zbzHzgIX7583MXl6219pqL93ffc0f+PmMmAE/Onsu/bDcKgIEDV2CrD23BrAcfXqrtlXpql5235ytf/k9G73swr7zyz8XlTzzxJB/ffhug9nv84Q9vxQMPzKqqmeoBM9aeuRJYKTOnLXkgIm5s0T21hJGjtuLTB4zm/nsf4PpbLgXgu6f8mH0+tQebv/99ZCZPPP4kXz7mJAAmnPVr/ucX3+WmKVcQEVx4/iXcf9+DVX4FCYDzfvVzPrbdR1hjjdV59OE7OfmUH3LcV49k+eWX5+o/XgjAbbdN5Ygjj+cXZ5zLOWf/mLun/YmIYOLE33DPPTMq/gbqSnv2rYw1spd+obVXeW/vbJjUgPmvLKi6CVIp2l57sqMl/0px4Pr7Nvz3/lePXdKy9rxVLmkoSapUX8uiDKySpEq14n2sVTKwSpIq1ddmBRtYJUmV6u2zfBtlYJUkVcquYEmSSmRXsCRJJbIrWJKkEvXW9RSaZWCVJFXKMVZJkkpkV7AkSSVy8pIkSSWyK1iSpBI5eUmSpBI5xipJUon62hjrMlU3QJLUv7WTDW89ERGPRsQ9ETEtIu4sylaPiMkRMbP4uVpRHhFxekTMiojpEbFVs9/HwCpJ6ss+npkjMvODxefjgeszczhwffEZYDdgeLGNA85o9oYGVklSpTKz4e0t2BuYWOxPBEbXlU/KminAqhExqJkbGFglSZVqpis4IsZFxJ1127gOLp3AtRHxt7rja2fmXIDi51pF+WDgibpzZxdlDXPykiSpUs1MXsrM8cD4bqptk5lzImItYHJE/L2LutFh05pgYJUkVaq9Rc+xZuac4udTEXEpMBKYFxGDMnNu0dX7VFF9NjC07vQhwJxm7mtXsCSpUtnE1p2IWDEiVl60D+wM3AtcDowtqo0FLiv2LwcOKmYHjwJeWNRl3CgzVklSpVq0pOHawKURAbVY9+vMvDoi7gAuiohDgceB/Yv6VwG7A7OAl4FDmr2xgVWSVKlWBNbMfBjYooPy+cAOHZQncEQZ9zawSpIq5VrBkiSVyLfbSJJUor62VrCBVZJUKbuCJUkqkV3BkiSVyIxVkqQSmbFKklQiJy9JklSiVq0VXBXXCpYkqURmrJKkStkVLElSifpaV7CBVZJUKTNWSZJKZMYqSVKJzFglSSqRGaskSSUyY5UkqUSZ7VU3oVQGVklSpVwrWJKkEvl2G0mSStTXMlbXCpYkVSozG966ExFDI+KGiJgREfdFxNFF+Tcj4smImFZsu9edc0JEzIqIByJil2a/jxmrJKlSLXrcpg34UmZOjYiVgb9FxOTi2I8z84f1lSNiU2AMsBmwLnBdRGycmQsbvbEZqySpUtnEP91eM3NuZk4t9hcAM4DBXZyyN3BhZr6amY8As4CRzXwfA6skqVKt6AquFxEbAFsCtxVFR0bE9IiYEBGrFWWDgSfqTptN14G4UwZWSVKl2smGt4gYFxF31m3jOrp2RKwEXAwck5kvAmcAGwIjgLnAjxZV7eD0pvqoHWOVJFWqmcdtMnM8ML6rOhHxDmpB9fzMvKQ4b17d8bOAK4uPs4GhdacPAeY03DDMWCVJfVBEBHAOMCMzT6srH1RXbR/g3mL/cmBMRCwfEcOA4cDtzdzbjFWSVKkWzQreBjgQuCciphVlXwMOiIgR1Lp5HwUOA8jM+yLiIuB+ajOKj2hmRjBA9NYVL9Ze5b29s2FSA+a/sqDqJkilaHvtyY7GIEux2kobNfz3/rl/zGpZe94qM1ZJUqX62spLBlZJUqV6a89pswyskqRK+aJzSZJK5IvOJUkqkRmrJEklcoxVkqQS2RUsSVKJzFglSSqRgVWSpBL1rbDai5c0VOtFxLjiDRHS25q/y+pNfLtN/9bh+wultyF/l9VrGFglSSqRgVWSpBIZWPs3x6TUV/i7rF7DyUuSJJXIjFWSpBIZWPupiNg1Ih6IiFkRcXzV7ZGaERETIuKpiLi36rZIixhY+6GIGAD8HNgN2BQ4ICI2rbZVUlPOBXatuhFSPQNr/zQSmJWZD2fmazHew0MAAAMMSURBVMCFwN4Vt0lqWGbeDDxbdTukegbW/mkw8ETd59lFmSTpLTKw9k/RQZnTwyWpBAbW/mk2MLTu8xBgTkVtkaQ+xcDaP90BDI+IYRGxHDAGuLziNklSn2Bg7Ycysw04ErgGmAFclJn3VdsqqXERcQFwK7BJRMyOiEOrbpPkykuSJJXIjFWSpBIZWCVJKpGBVZKkEhlYJUkqkYFVkqQSGVilHoqIhRExLSLujYjfRsTAt3Ct7SPiymJ/r0VvGIqIcyPiUz28xuERcVCzbZDUGgZWqedeycwRmbk58BpweP3BqGn4v6nMvDwzT23ivDMzc1Kj50lqLQOr+r2I+FZEHF33+TsRcVQ3p90CbBQRG0TEjIj4BTAVGBoRO0fErRExtchsVyquu2tE/D0i/gzsW3e/gyPiZ52069yIWCYiTo2I+yNiekT8sDj+zYj4cgn/CiSVyMAqwTnAWIAi4xwDnN9Z5YhYltq7bO8pijYBJmXmlsBLwInAjpm5FXAn8MWIeCdwFvBJYFtgna4aFBHfB9YCDgFWBfYBNsvMDwDfbu5rSloaDKzq9zLzUWB+RGwJ7AzclZnzO6i6QkRMoxYsH6cWkAEey8wpxf4oai+P/0tRdyywPvBe4JHMnJm15c7O66JJ/wWsmpmHFXVfBP4JnB0R+wIvv4WvK6nFlq26AVIvcTZwMLVMckIndV7JzBH1BREBtSx1cREwOTMPWKLeCHr+ar47gK0jYvXMfDYz2yJiJLADtWz6SOATPbyWpKXMjFWquRTYFfgQtZcTNGsKsE1EbAQQEQMjYmPg78CwiNiwqHdAZxcArgZOBf4QESsXY7SrZOZVwDHAiC7OlVQxM1YJyMzXIuIG4PnMXPgWrvN0RBwMXBARyxfFJ2bmgxExjlqwfAb4M7B5F9f5bUSsTO11fv8GXFaM0wZwbLPtk9R6vt1GYvGkpanA/pk5s+r2SHr7sitY/V5EbArMAq43qEp6q8xYJUkqkRmrJEklMrBKklQiA6skSSUysEqSVCIDqyRJJTKwSpJUov8PhZWtLbQrqSoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier()\n",
      " \n",
      "               Score (%)\n",
      "accuracy       86.420983\n",
      "recall         36.269430\n",
      "precision      46.204620\n",
      "roc_auc_score  65.031135\n",
      "f1_score       40.638607\n",
      " \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.94      0.92      2626\n",
      "           1       0.46      0.36      0.41       386\n",
      "\n",
      "    accuracy                           0.86      3012\n",
      "   macro avg       0.69      0.65      0.66      3012\n",
      "weighted avg       0.85      0.86      0.86      3012\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighborsClassifier()\n",
      " \n",
      "               Score (%)\n",
      "accuracy       74.169987\n",
      "recall         61.658031\n",
      "precision      27.419355\n",
      "roc_auc_score  68.833585\n",
      "f1_score       37.958533\n",
      " \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.76      0.84      2626\n",
      "           1       0.27      0.62      0.38       386\n",
      "\n",
      "    accuracy                           0.74      3012\n",
      "   macro avg       0.60      0.69      0.61      3012\n",
      "weighted avg       0.85      0.74      0.78      3012\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name, model in models:\n",
    "    model = model\n",
    "     # model belajar dari data train\n",
    "    model.fit(x_train_sm, y_train_sm)\n",
    "    # membuat prediksi dan fungsinya untuk mengevaluasi model (dibandingkan dengan y aktual)\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    hasil = pd.DataFrame(data = [accuracy_score(y_test, y_pred)*100, recall_score(y_test, y_pred)*100,\n",
    "                        precision_score(y_test, y_pred)*100, roc_auc_score(y_test, y_pred)*100,\n",
    "                        f1_score(y_test, y_pred)*100],\n",
    "                        index = ['accuracy', 'recall', 'precision', 'roc_auc_score', 'f1_score'],\n",
    "                        columns = ['Score (%)'])\n",
    "    print(model)\n",
    "    print(\" \")\n",
    "    print(hasil)\n",
    "    print(\" \")\n",
    "    # Confusion matrix report\n",
    "    print(classification_report(y_test, y_pred))\n",
    "    # untuk menampilkan visualisasi confusion matrix\n",
    "    f, ax = plt.subplots(figsize=(8,5))\n",
    "    sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, fmt='.0f', ax=ax)\n",
    "    plt.xlabel('y Prediksi')\n",
    "    plt.ylabel('y Aktual')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# __Hyperparameter Tuning : Grid Search__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## __Logistic Regression__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'penalty': ['l1', 'l2', 'elasticnet', 'none'],\n",
       " 'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],\n",
       " 'max_iter': [1, 10, 100, 1000, 10000]}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# parameter model logistic regression yang akan dituned + value di setiap parameter\n",
    "penalty = ['l1', 'l2', 'elasticnet', 'none']\n",
    "solver = ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']\n",
    "max_iter = [1, 10, 100, 1000, 10000]\n",
    "\n",
    "# simpan dalam variabel dengan nama 'param'\n",
    "param = {'penalty': penalty, 'solver': solver, 'max_iter': max_iter}\n",
    "param"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_LR = LogisticRegression()\n",
    "model_LR_GS = GridSearchCV(model_LR, param, cv = 5, scoring=make_scorer(f1_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_iter': 10, 'penalty': 'l2', 'solver': 'newton-cg'}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_LR_GS.fit(x_train_sm, y_train_sm)\n",
    "model_LR_GS.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "model_LR_GS.fit(X_train, y_train)\n",
    "model_LR_GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=10, solver='newton-cg')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_LogReg_GS = LogisticRegression(solver='newton-cg', penalty = 'l2', max_iter = 10)\n",
    "model_LogReg_GS.fit(x_train_sm, y_train_sm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Score (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>71.679947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>74.870466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>27.655502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roc_auc_score</th>\n",
       "      <td>73.040717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1_score</th>\n",
       "      <td>40.391335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Score (%)\n",
       "accuracy       71.679947\n",
       "recall         74.870466\n",
       "precision      27.655502\n",
       "roc_auc_score  73.040717\n",
       "f1_score       40.391335"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_GS = model_LogReg_GS.predict(X_test)\n",
    "\n",
    "pd.DataFrame(data = [accuracy_score(y_test, y_pred_GS)*100, recall_score(y_test, y_pred_GS)*100,\n",
    "                    precision_score(y_test, y_pred_GS)*100, roc_auc_score(y_test, y_pred_GS)*100,\n",
    "                    f1_score(y_test, y_pred_GS)*100],\n",
    "            index = ['accuracy', 'recall', 'precision', 'roc_auc_score', 'f1_score'],\n",
    "            columns = ['Score (%)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.71      0.81      2626\n",
      "           1       0.28      0.75      0.40       386\n",
      "\n",
      "    accuracy                           0.72      3012\n",
      "   macro avg       0.61      0.73      0.61      3012\n",
      "weighted avg       0.86      0.72      0.76      3012\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion matrix report\n",
    "print(classification_report(y_test, y_pred_GS))\n",
    "# untuk menampilkan visualisasi confusion matrix\n",
    "f, ax = plt.subplots(figsize=(8,5))\n",
    "sns.heatmap(confusion_matrix(y_test, y_pred_GS), annot=True, fmt='.0f', ax=ax)\n",
    "plt.xlabel('y Prediksi')\n",
    "plt.ylabel('y Aktual')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## __Random Forest Classifier__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': [True, False],\n",
       " 'max_features': ['auto', 'sqrt', 'log2'],\n",
       " 'min_samples_leaf': [1, 2, 4],\n",
       " 'min_samples_split': [2, 5, 10]}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# parameter model Random Forest Classifier yang akan dituned + value di setiap parameter\n",
    "\n",
    "bootstrap = [True, False]\n",
    "max_features = ['auto', 'sqrt', 'log2']\n",
    "min_samples_leaf = [1, 2, 4]\n",
    "min_samples_split = [2, 5, 10]\n",
    "\n",
    "\n",
    "# simpan dalam variabel dengan nama 'param'\n",
    "param = {'bootstrap': bootstrap,\n",
    " 'max_features': max_features, \n",
    " 'min_samples_leaf': min_samples_leaf,\n",
    " 'min_samples_split': min_samples_split}\n",
    "param"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_RF =  RandomForestClassifier()\n",
    "model_RF_GS = GridSearchCV(model_RF, param, cv = 5,scoring=make_scorer(f1_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': False,\n",
       " 'max_features': 'sqrt',\n",
       " 'min_samples_leaf': 2,\n",
       " 'min_samples_split': 5}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_RF_GS.fit(x_train_sm, y_train_sm)\n",
    "model_RF_GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=False, max_features='sqrt', min_samples_leaf=2,\n",
       "                       min_samples_split=5)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_RF_GS = RandomForestClassifier(bootstrap=False, max_features='sqrt', min_samples_leaf=2, min_samples_split=5)\n",
    "model_RF_GS.fit(x_train_sm, y_train_sm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>Score (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>87.051793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>37.305699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>49.315068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roc_auc_score</th>\n",
       "      <td>65.834876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1_score</th>\n",
       "      <td>42.477876</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Score (%)\n",
       "accuracy       87.051793\n",
       "recall         37.305699\n",
       "precision      49.315068\n",
       "roc_auc_score  65.834876\n",
       "f1_score       42.477876"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_GS = model_RF_GS.predict(X_test)\n",
    "\n",
    "pd.DataFrame(data = [accuracy_score(y_test, y_pred_GS)*100, recall_score(y_test, y_pred_GS)*100,\n",
    "                    precision_score(y_test, y_pred_GS)*100, roc_auc_score(y_test, y_pred_GS)*100,\n",
    "                    f1_score(y_test, y_pred_GS)*100],\n",
    "            index = ['accuracy', 'recall', 'precision', 'roc_auc_score', 'f1_score'],\n",
    "            columns = ['Score (%)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.94      0.93      2626\n",
      "           1       0.49      0.37      0.42       386\n",
      "\n",
      "    accuracy                           0.87      3012\n",
      "   macro avg       0.70      0.66      0.68      3012\n",
      "weighted avg       0.86      0.87      0.86      3012\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion matrix report\n",
    "print(classification_report(y_test, y_pred_GS))\n",
    "# untuk menampilkan visualisasi confusion matrix\n",
    "f, ax = plt.subplots(figsize=(8,5))\n",
    "sns.heatmap(confusion_matrix(y_test, y_pred_GS), annot=True, fmt='.0f', ax=ax)\n",
    "plt.xlabel('y Prediksi')\n",
    "plt.ylabel('y Aktual')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## __Decision Tree Classifier__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_samples_split': [2, 3, 4],\n",
       " 'min_samples_leaf': [1, 5, 10, 20, 50, 100],\n",
       " 'max_depth': [4, 6, 8, 12]}"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# parameter model decision tree yang akan dituned + value di setiap parameter\n",
    "min_samples_split = [2, 3, 4]\n",
    "min_samples_leaf = [1, 5, 10, 20, 50, 100]\n",
    "max_depth = [4,6,8,12]\n",
    "\n",
    "# simpan dalam variabel dengan nama 'param'\n",
    "param = {'min_samples_split': min_samples_split, 'min_samples_leaf': min_samples_leaf, 'max_depth': max_depth}\n",
    "param"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_DT = DecisionTreeClassifier()\n",
    "model_DT_GS = GridSearchCV(model_DT, param, cv = 5,scoring=make_scorer(f1_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 12, 'min_samples_leaf': 1, 'min_samples_split': 2}"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_DT_GS.fit(x_train_sm, y_train_sm)\n",
    "model_DT_GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=12)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_DT_GS = DecisionTreeClassifier(max_depth = 12, min_samples_leaf = 1, min_samples_split = 2, criterion = 'entropy')\n",
    "model_DT_GS.fit(x_train_sm, y_train_sm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>Score (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>85.424967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>43.963554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roc_auc_score</th>\n",
       "      <td>70.316070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1_score</th>\n",
       "      <td>46.787879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Score (%)\n",
       "accuracy       85.424967\n",
       "recall         50.000000\n",
       "precision      43.963554\n",
       "roc_auc_score  70.316070\n",
       "f1_score       46.787879"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_GS = model_DT_GS.predict(X_test)\n",
    "\n",
    "pd.DataFrame(data = [accuracy_score(y_test, y_pred_GS)*100, recall_score(y_test, y_pred_GS)*100,\n",
    "                    precision_score(y_test, y_pred_GS)*100, roc_auc_score(y_test, y_pred_GS)*100,\n",
    "                    f1_score(y_test, y_pred_GS)*100],\n",
    "            index = ['accuracy', 'recall', 'precision', 'roc_auc_score', 'f1_score'],\n",
    "            columns = ['Score (%)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.91      0.92      2626\n",
      "           1       0.44      0.50      0.47       386\n",
      "\n",
      "    accuracy                           0.85      3012\n",
      "   macro avg       0.68      0.70      0.69      3012\n",
      "weighted avg       0.86      0.85      0.86      3012\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion matrix report\n",
    "print(classification_report(y_test, y_pred_GS))\n",
    "# untuk menampilkan visualisasi confusion matrix\n",
    "f, ax = plt.subplots(figsize=(8,5))\n",
    "sns.heatmap(confusion_matrix(y_test, y_pred_GS), annot=True, fmt='.0f', ax=ax)\n",
    "plt.xlabel('y Prediksi')\n",
    "plt.ylabel('y Aktual')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## __KNeighbors Classifier__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## __Deciding K Value__\n",
    "Berapa nilai K (jumlah tetangga) yang digunakan untuk memprediksi suatu data?\n",
    "\n",
    "1. sqrt(n) : akar pangkat 2 dari total data training\n",
    "2. pilih nilai yang ganjil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "218"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# misalnya hasilnya genap, perlu ditambah + 1\n",
    "k = round(len(x_train_sm) ** 0.5)\n",
    "k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'p': [1, 2],\n",
       " 'metric': ['euclidean', 'manhattan', 'minkowski'],\n",
       " 'weights': ['uniform', 'distance']}"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# parameter model yang akan dituned + value di setiap parameter\n",
    "\n",
    "p = [1,2]\n",
    "metric = ['euclidean', 'manhattan', 'minkowski']\n",
    "weights = ['uniform', 'distance']\n",
    "\n",
    "\n",
    "# simpan dalam variabel dengan nama 'param'\n",
    "param = {'p': p, 'metric': metric, 'weights':weights}\n",
    "param"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_k = KNeighborsClassifier()\n",
    "model_k_GS = GridSearchCV(model_k, param, cv = 5,scoring=make_scorer(f1_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'metric': 'manhattan', 'p': 1, 'weights': 'distance'}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_k_GS.fit(x_train_sm, y_train_sm)\n",
    "model_k_GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(leaf_size=1000, metric='manhattan', n_neighbors=219, p=1,\n",
       "                     weights='distance')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_k_GS = KNeighborsClassifier(n_neighbors = 219, leaf_size=1000, p=1, metric='manhattan', weights='distance')\n",
    "model_k_GS.fit(x_train_sm, y_train_sm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>Score (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>81.673307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>62.176166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>37.151703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roc_auc_score</th>\n",
       "      <td>73.357694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1_score</th>\n",
       "      <td>46.511628</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Score (%)\n",
       "accuracy       81.673307\n",
       "recall         62.176166\n",
       "precision      37.151703\n",
       "roc_auc_score  73.357694\n",
       "f1_score       46.511628"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_GS = model_k_GS.predict(X_test)\n",
    "\n",
    "pd.DataFrame(data = [accuracy_score(y_test, y_pred_GS)*100, recall_score(y_test, y_pred_GS)*100,\n",
    "                    precision_score(y_test, y_pred_GS)*100, roc_auc_score(y_test, y_pred_GS)*100,\n",
    "                    f1_score(y_test, y_pred_GS)*100],\n",
    "            index = ['accuracy', 'recall', 'precision', 'roc_auc_score', 'f1_score'],\n",
    "            columns = ['Score (%)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.85      0.89      2626\n",
      "           1       0.37      0.62      0.47       386\n",
      "\n",
      "    accuracy                           0.82      3012\n",
      "   macro avg       0.65      0.73      0.68      3012\n",
      "weighted avg       0.87      0.82      0.84      3012\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion matrix report\n",
    "print(classification_report(y_test, y_pred_GS))\n",
    "# untuk menampilkan visualisasi confusion matrix\n",
    "f, ax = plt.subplots(figsize=(8,5))\n",
    "sns.heatmap(confusion_matrix(y_test, y_pred_GS), annot=True, fmt='.0f', ax=ax)\n",
    "plt.xlabel('y Prediksi')\n",
    "plt.ylabel('y Aktual')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "# **``Saving Model``**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# __Encoding__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank4 = bank1.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>59</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age        job  marital            education default housing loan  \\\n",
       "0   56  housemaid  married             basic.4y      no      no   no   \n",
       "2   37   services  married          high.school      no     yes   no   \n",
       "3   40     admin.  married             basic.6y      no      no   no   \n",
       "4   56   services  married          high.school      no      no  yes   \n",
       "6   59     admin.  married  professional.course      no      no   no   \n",
       "\n",
       "     contact month day_of_week  campaign  pdays  previous     poutcome  \\\n",
       "0  telephone   may         mon         1    999         0  nonexistent   \n",
       "2  telephone   may         mon         1    999         0  nonexistent   \n",
       "3  telephone   may         mon         1    999         0  nonexistent   \n",
       "4  telephone   may         mon         1    999         0  nonexistent   \n",
       "6  telephone   may         mon         1    999         0  nonexistent   \n",
       "\n",
       "   emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
       "0           1.1          93.994          -36.4      4.857       5191.0  no  \n",
       "2           1.1          93.994          -36.4      4.857       5191.0  no  \n",
       "3           1.1          93.994          -36.4      4.857       5191.0  no  \n",
       "4           1.1          93.994          -36.4      4.857       5191.0  no  \n",
       "6           1.1          93.994          -36.4      4.857       5191.0  no  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label encoder order is alphabetical\n",
    "labelencoder_X = LabelEncoder()\n",
    "bank4['job']      = labelencoder_X.fit_transform(bank4['job'])\n",
    "bank4['default']  = labelencoder_X.fit_transform(bank4['default']) \n",
    "bank4['housing']  = labelencoder_X.fit_transform(bank4['housing']) \n",
    "bank4['loan']     = labelencoder_X.fit_transform(bank4['loan']) \n",
    "bank4['contact']  = labelencoder_X.fit_transform(bank4['contact']) \n",
    "bank4['marital']  = labelencoder_X.fit_transform(bank4['marital']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank4['education'].replace(['basic.4y', 'basic.6y','basic.9y', 'high.school', 'university.degree', 'professional.course','illiterate'], [1,2,3,4,5,6,7], inplace  = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank4['month'].replace(['jan','feb','mar', 'apr','may', 'jun', 'jul','aug', 'sep','oct', 'nov', 'dec'], [1,2,3,4,5,6,7,8,9,10,11,12], inplace  = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank4['day_of_week'].replace(['mon', 'tue', 'wed', 'thu', 'fri'], [1,2,3,4,5], inplace  = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank4['poutcome'].replace(['nonexistent', 'failure', 'success'], [1,2,3], inplace  = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank4['y'].replace(['no','yes'], [0,1], inplace  = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30488, 20)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "       age  job  marital  education  default  housing  loan  contact  month  \\\n",
       "0       56    3        1          1        0        0     0        1      5   \n",
       "2       37    7        1          4        0        1     0        1      5   \n",
       "3       40    0        1          2        0        0     0        1      5   \n",
       "4       56    7        1          4        0        0     1        1      5   \n",
       "6       59    0        1          6        0        0     0        1      5   \n",
       "...    ...  ...      ...        ...      ...      ...   ...      ...    ...   \n",
       "41183   73    5        1          6        0        1     0        0     11   \n",
       "41184   46    1        1          6        0        0     0        0     11   \n",
       "41185   56    5        1          5        0        1     0        0     11   \n",
       "41186   44    9        1          6        0        0     0        0     11   \n",
       "41187   74    5        1          6        0        1     0        0     11   \n",
       "\n",
       "       day_of_week  campaign  pdays  previous  poutcome  emp.var.rate  \\\n",
       "0                1         1    999         0         1           1.1   \n",
       "2                1         1    999         0         1           1.1   \n",
       "3                1         1    999         0         1           1.1   \n",
       "4                1         1    999         0         1           1.1   \n",
       "6                1         1    999         0         1           1.1   \n",
       "...            ...       ...    ...       ...       ...           ...   \n",
       "41183            5         1    999         0         1          -1.1   \n",
       "41184            5         1    999         0         1          -1.1   \n",
       "41185            5         2    999         0         1          -1.1   \n",
       "41186            5         1    999         0         1          -1.1   \n",
       "41187            5         3    999         1         2          -1.1   \n",
       "\n",
       "       cons.price.idx  cons.conf.idx  euribor3m  nr.employed  y  \n",
       "0              93.994          -36.4      4.857       5191.0  0  \n",
       "2              93.994          -36.4      4.857       5191.0  0  \n",
       "3              93.994          -36.4      4.857       5191.0  0  \n",
       "4              93.994          -36.4      4.857       5191.0  0  \n",
       "6              93.994          -36.4      4.857       5191.0  0  \n",
       "...               ...            ...        ...          ... ..  \n",
       "41183          94.767          -50.8      1.028       4963.6  1  \n",
       "41184          94.767          -50.8      1.028       4963.6  0  \n",
       "41185          94.767          -50.8      1.028       4963.6  0  \n",
       "41186          94.767          -50.8      1.028       4963.6  1  \n",
       "41187          94.767          -50.8      1.028       4963.6  0  \n",
       "\n",
       "[30488 rows x 20 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-50.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41184</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>6</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41185</th>\n",
       "      <td>56</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>5</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41186</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>6</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41187</th>\n",
       "      <td>74</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>6</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30488 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  campaign  job  day_of_week  nr.employed  education  pdays  \\\n",
       "0       56         1    3            1       5191.0          1    999   \n",
       "2       37         1    7            1       5191.0          4    999   \n",
       "3       40         1    0            1       5191.0          2    999   \n",
       "4       56         1    7            1       5191.0          4    999   \n",
       "6       59         1    0            1       5191.0          6    999   \n",
       "...    ...       ...  ...          ...          ...        ...    ...   \n",
       "41183   73         1    5            5       4963.6          6    999   \n",
       "41184   46         1    1            5       4963.6          6    999   \n",
       "41185   56         2    5            5       4963.6          5    999   \n",
       "41186   44         1    9            5       4963.6          6    999   \n",
       "41187   74         3    5            5       4963.6          6    999   \n",
       "\n",
       "       marital  poutcome  cons.conf.idx  y  \n",
       "0            1         1          -36.4  0  \n",
       "2            1         1          -36.4  0  \n",
       "3            1         1          -36.4  0  \n",
       "4            1         1          -36.4  0  \n",
       "6            1         1          -36.4  0  \n",
       "...        ...       ...            ... ..  \n",
       "41183        1         1          -50.8  1  \n",
       "41184        1         1          -50.8  0  \n",
       "41185        1         1          -50.8  0  \n",
       "41186        1         1          -50.8  1  \n",
       "41187        1         2          -50.8  0  \n",
       "\n",
       "[30488 rows x 11 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank5 = bank4[['age','campaign','job','day_of_week', 'nr.employed','education','pdays','marital','poutcome','cons.conf.idx', 'y']]\n",
    "bank5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Splitting Dataset__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = bank5.drop(['y'], axis=1)\n",
    "y = bank5['y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size = 0.1, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## __Over Sampling : SMOTE__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm = SMOTE(random_state = 0)\n",
    "x_train_sm, y_train_sm = sm.fit_sample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=12)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_DT_GS_noscale = DecisionTreeClassifier(max_depth = 12, min_samples_leaf = 1, min_samples_split = 2, criterion = 'entropy')\n",
    "model_DT_GS_noscale.fit(x_train_sm, y_train_sm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>Score (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>80.813381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>56.521739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>32.859400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roc_auc_score</th>\n",
       "      <td>70.334723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1_score</th>\n",
       "      <td>41.558442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Score (%)\n",
       "accuracy       80.813381\n",
       "recall         56.521739\n",
       "precision      32.859400\n",
       "roc_auc_score  70.334723\n",
       "f1_score       41.558442"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_GS = model_DT_GS_noscale.predict(X_test)\n",
    "\n",
    "pd.DataFrame(data = [accuracy_score(y_test, y_pred_GS)*100, recall_score(y_test, y_pred_GS)*100,\n",
    "                    precision_score(y_test, y_pred_GS)*100, roc_auc_score(y_test, y_pred_GS)*100,\n",
    "                    f1_score(y_test, y_pred_GS)*100],\n",
    "            index = ['accuracy', 'recall', 'precision', 'roc_auc_score', 'f1_score'],\n",
    "            columns = ['Score (%)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.84      0.89      2681\n",
      "           1       0.33      0.57      0.42       368\n",
      "\n",
      "    accuracy                           0.81      3049\n",
      "   macro avg       0.63      0.70      0.65      3049\n",
      "weighted avg       0.86      0.81      0.83      3049\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion matrix report\n",
    "print(classification_report(y_test, y_pred_GS))\n",
    "# untuk menampilkan visualisasi confusion matrix\n",
    "f, ax = plt.subplots(figsize=(8,5))\n",
    "sns.heatmap(confusion_matrix(y_test, y_pred_GS), annot=True, fmt='.0f', ax=ax)\n",
    "plt.xlabel('y Prediksi')\n",
    "plt.ylabel('y Aktual')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min  age 17\n",
      "Max  age 95\n",
      "Min  campaign 1\n",
      "Max  campaign 43\n",
      "Min  nr.employed 4963.6\n",
      "Max  nr.employed 5228.1\n",
      "Min  pdays 0\n",
      "Max  pdays 999\n",
      "Min  cons.conf.idx -50.8\n",
      "Max  cons.conf.idx -26.9\n",
      "Unique Value job [ 0.14285714  0.71428571 -0.28571429  1.         -0.14285714  1.14285714\n",
      "  0.42857143  0.          0.28571429  0.85714286  0.57142857]\n",
      "Unique Value day_of_week [-1.  -0.5  0.   0.5  1. ]\n",
      "Unique Value education [-1.5  0.  -1.   1.  -0.5  0.5  1.5]\n",
      "Unique Value marital [ 0.  1. -1.]\n",
      "Unique Value poutcome [0. 1. 2.]\n"
     ]
    }
   ],
   "source": [
    "columns=['age','campaign','nr.employed','pdays','cons.conf.idx']\n",
    "\n",
    "for i in columns:\n",
    "    print(\"Min \", i, bank4[i].min())\n",
    "    print(\"Max \", i, bank4[i].max())\n",
    "    \n",
    "columns2=['job','day_of_week', 'education','marital','poutcome']\n",
    "for i in columns2:\n",
    "    print(\"Unique Value\", i, bank2[i].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "# **``Saving Model``**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **1. Saving Model using Joblib**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model_DT_Joblib']"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# menyimpan model\n",
    "import joblib\n",
    "\n",
    "joblib.dump(model_DT_GS_noscale, 'model_DT_Joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **2. Using Joblib Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Ketik Age (17-69):  75\n",
      "Ketik Campaign (1-43):  42\n",
      "Ketik Number of Employees (4963-5228):  4968\n",
      "Ketik Job (0=admin, 1=blue-collar, 2=entrepreneur, 3=housemaid, 4=management, 5=retired, 6=self-employed, 7=services, 8=student, 9=technician, 10=unemployed):  2\n",
      "Ketik Day of Week (1=mon, 2=tue, 3=wed, 4=thu, 5=fri]):  3\n",
      "Ketik Education (1=basic.4y, 2=basic.6y, 3=basic.9y, 4=high.school, 5=university.degree, 6=professional.course,7=illiterate):  4\n",
      "Ketik Days Last Contacted (0-999):  210\n",
      "Marital (0=divorced, 1=married, 2=single):  1\n",
      "Outcome (0=nonexistent, 1=failure, 2=success):  2\n",
      "Consumer Confidence Index ((-51)-(-26)):  -45\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hasil Prediksi:  Not Subscribe\n",
      "Probabilitas Prediksi:  100.0\n"
     ]
    }
   ],
   "source": [
    "# simulasi menggunakan model\n",
    "\n",
    "model_joblib = joblib.load('model_DT_Joblib')\n",
    "\n",
    "# user input data\n",
    "\n",
    "age = int(input('Ketik Age (17-69): '))\n",
    "campaign = int(input('Ketik Campaign (1-43): '))\n",
    "number_of_employees = int(input('Ketik Number of Employees (4963-5228): '))\n",
    "job = int(input('Ketik Job (0=admin, 1=blue-collar, 2=entrepreneur, 3=housemaid, 4=management, 5=retired, 6=self-employed, 7=services, 8=student, 9=technician, 10=unemployed): '))\n",
    "day_of_week = int(input('Ketik Day of Week (1=mon, 2=tue, 3=wed, 4=thu, 5=fri]): '))\n",
    "education = int(input('Ketik Education (1=basic.4y, 2=basic.6y, 3=basic.9y, 4=high.school, 5=university.degree, 6=professional.course,7=illiterate): '))\n",
    "days_last_contacted = int(input('Ketik Days Last Contacted (0-999): '))\n",
    "marital = int(input('Marital (0=divorced, 1=married, 2=single): '))\n",
    "outcome = int(input('Outcome (0=nonexistent, 1=failure, 2=success): '))\n",
    "Consumer_Confidence_Index = int(input('Consumer Confidence Index ((-51)-(-26)): '))\n",
    "\n",
    "df = [[age,campaign,job,day_of_week, number_of_employees,education,days_last_contacted,marital,outcome,Consumer_Confidence_Index]]\n",
    "\n",
    "if model_joblib.predict(df)[0] == 1:\n",
    "    hasil = 'Subscribe'\n",
    "else:\n",
    "    hasil = 'Not Subscribe'\n",
    "\n",
    "print('Hasil Prediksi: ', hasil)\n",
    "print('Probabilitas Prediksi: ', round(model_joblib.predict_proba(df).tolist()[0][0]*100, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
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  "language_info": {
   "codemirror_mode": {
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