Predict Clicked on Ads Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e96adb2f-bd22-4f04-baa4-df8dab9b3baa",
   "metadata": {},
   "source": [
    "# Predict Clicked Ads Customer Classification by using Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e9949d48-fff0-4c73-82ff-9ad66d71f9d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import library\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "pd.set_option('display.max_rows', 200)\n",
    "pd.set_option('display.max_columns', 200)\n",
    "pd.set_option('display.width', 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bcd902e-c630-44a4-9b60-b84fd34bd988",
   "metadata": {},
   "source": [
    "## Load Dataset & Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "76840630-bb96-4956-828a-0d4e6f016c14",
   "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>Unnamed: 0</th>\n",
       "      <th>Daily Time Spent on Site</th>\n",
       "      <th>Age</th>\n",
       "      <th>Area Income</th>\n",
       "      <th>Daily Internet Usage</th>\n",
       "      <th>Male</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Clicked on Ad</th>\n",
       "      <th>city</th>\n",
       "      <th>province</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>68.95</td>\n",
       "      <td>35</td>\n",
       "      <td>432837300.0</td>\n",
       "      <td>256.09</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>3/27/2016 0:53</td>\n",
       "      <td>No</td>\n",
       "      <td>Jakarta Timur</td>\n",
       "      <td>Daerah Khusus Ibukota Jakarta</td>\n",
       "      <td>Furniture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>80.23</td>\n",
       "      <td>31</td>\n",
       "      <td>479092950.0</td>\n",
       "      <td>193.77</td>\n",
       "      <td>Laki-Laki</td>\n",
       "      <td>4/4/2016 1:39</td>\n",
       "      <td>No</td>\n",
       "      <td>Denpasar</td>\n",
       "      <td>Bali</td>\n",
       "      <td>Food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>69.47</td>\n",
       "      <td>26</td>\n",
       "      <td>418501580.0</td>\n",
       "      <td>236.50</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>3/13/2016 20:35</td>\n",
       "      <td>No</td>\n",
       "      <td>Surabaya</td>\n",
       "      <td>Jawa Timur</td>\n",
       "      <td>Electronic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>74.15</td>\n",
       "      <td>29</td>\n",
       "      <td>383643260.0</td>\n",
       "      <td>245.89</td>\n",
       "      <td>Laki-Laki</td>\n",
       "      <td>1/10/2016 2:31</td>\n",
       "      <td>No</td>\n",
       "      <td>Batam</td>\n",
       "      <td>Kepulauan Riau</td>\n",
       "      <td>House</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>68.37</td>\n",
       "      <td>35</td>\n",
       "      <td>517229930.0</td>\n",
       "      <td>225.58</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>6/3/2016 3:36</td>\n",
       "      <td>No</td>\n",
       "      <td>Medan</td>\n",
       "      <td>Sumatra Utara</td>\n",
       "      <td>Finance</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>995</th>\n",
       "      <td>995</td>\n",
       "      <td>72.97</td>\n",
       "      <td>30</td>\n",
       "      <td>499691990.0</td>\n",
       "      <td>208.58</td>\n",
       "      <td>Laki-Laki</td>\n",
       "      <td>2/11/2016 21:49</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Semarang</td>\n",
       "      <td>Jawa Tengah</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>996</td>\n",
       "      <td>51.30</td>\n",
       "      <td>45</td>\n",
       "      <td>474475190.0</td>\n",
       "      <td>134.42</td>\n",
       "      <td>Laki-Laki</td>\n",
       "      <td>4/22/2016 2:07</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Pakanbaru</td>\n",
       "      <td>Riau</td>\n",
       "      <td>Electronic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51</td>\n",
       "      <td>296910040.0</td>\n",
       "      <td>120.37</td>\n",
       "      <td>Laki-Laki</td>\n",
       "      <td>2/1/2016 17:24</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Samarinda</td>\n",
       "      <td>Kalimantan Timur</td>\n",
       "      <td>Travel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>998</td>\n",
       "      <td>55.55</td>\n",
       "      <td>19</td>\n",
       "      <td>293445530.0</td>\n",
       "      <td>187.95</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>3/24/2016 2:35</td>\n",
       "      <td>No</td>\n",
       "      <td>Tangerang</td>\n",
       "      <td>Banten</td>\n",
       "      <td>Bank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>999</td>\n",
       "      <td>45.01</td>\n",
       "      <td>26</td>\n",
       "      <td>209130600.0</td>\n",
       "      <td>178.35</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>6/3/2016 21:43</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Serang</td>\n",
       "      <td>Banten</td>\n",
       "      <td>Travel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  Daily Time Spent on Site  Age  Area Income  Daily Internet Usage       Male        Timestamp Clicked on Ad           city                       province    category\n",
       "0             0                     68.95   35  432837300.0                256.09  Perempuan   3/27/2016 0:53            No  Jakarta Timur  Daerah Khusus Ibukota Jakarta   Furniture\n",
       "1             1                     80.23   31  479092950.0                193.77  Laki-Laki    4/4/2016 1:39            No       Denpasar                           Bali        Food\n",
       "2             2                     69.47   26  418501580.0                236.50  Perempuan  3/13/2016 20:35            No       Surabaya                     Jawa Timur  Electronic\n",
       "3             3                     74.15   29  383643260.0                245.89  Laki-Laki   1/10/2016 2:31            No          Batam                 Kepulauan Riau       House\n",
       "4             4                     68.37   35  517229930.0                225.58  Perempuan    6/3/2016 3:36            No          Medan                  Sumatra Utara     Finance\n",
       "..          ...                       ...  ...          ...                   ...        ...              ...           ...            ...                            ...         ...\n",
       "995         995                     72.97   30  499691990.0                208.58  Laki-Laki  2/11/2016 21:49           Yes       Semarang                    Jawa Tengah     Finance\n",
       "996         996                     51.30   45  474475190.0                134.42  Laki-Laki   4/22/2016 2:07           Yes      Pakanbaru                           Riau  Electronic\n",
       "997         997                       NaN   51  296910040.0                120.37  Laki-Laki   2/1/2016 17:24           Yes      Samarinda               Kalimantan Timur      Travel\n",
       "998         998                     55.55   19  293445530.0                187.95  Perempuan   3/24/2016 2:35            No      Tangerang                         Banten        Bank\n",
       "999         999                     45.01   26  209130600.0                178.35  Perempuan   6/3/2016 21:43           Yes         Serang                         Banten      Travel\n",
       "\n",
       "[1000 rows x 11 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"C:/Users/LENOVO/Downloads/CLicked Ads Dataset.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74e4c1d8-222f-4aad-9e3a-1062f28685cc",
   "metadata": {},
   "source": [
    "## Statistical Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "34d05d69-daaa-4ee7-94a7-df7a97e4dbb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 11 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Unnamed: 0                1000 non-null   int64  \n",
      " 1   Daily Time Spent on Site  987 non-null    float64\n",
      " 2   Age                       1000 non-null   int64  \n",
      " 3   Area Income               987 non-null    float64\n",
      " 4   Daily Internet Usage      989 non-null    float64\n",
      " 5   Male                      997 non-null    object \n",
      " 6   Timestamp                 1000 non-null   object \n",
      " 7   Clicked on Ad             1000 non-null   object \n",
      " 8   city                      1000 non-null   object \n",
      " 9   province                  1000 non-null   object \n",
      " 10  category                  1000 non-null   object \n",
      "dtypes: float64(3), int64(2), object(6)\n",
      "memory usage: 86.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5735685-a3ff-4958-8ecf-d7ab3a33d200",
   "metadata": {},
   "source": [
    "- Terdapat data null pada kolom `Daily Time Spent on Site`,`Area Income`,`Daily Internet Usage`, dan `Male`. Sepertinya akan diisi dengan `modus` pada kolom `Male` dan sisanya diisi dengan median \n",
    "- kolom `Timestamp` seharusnya bertipe data datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "52fc591b-79ae-4782-9961-182b48ea505c",
   "metadata": {},
   "outputs": [],
   "source": [
    "cats = ['Male','Clicked on Ad','city','province','category']\n",
    "nums = ['Unnamed: 0','Daily Time Spent on Site','Age','Area Income','Daily Internet Usage','Timestamp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7700f79d-7735-400b-8b6d-3dbe6e25bc4d",
   "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>Unnamed: 0</th>\n",
       "      <th>Daily Time Spent on Site</th>\n",
       "      <th>Age</th>\n",
       "      <th>Area Income</th>\n",
       "      <th>Daily Internet Usage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>987.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>9.870000e+02</td>\n",
       "      <td>989.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>499.500000</td>\n",
       "      <td>64.929524</td>\n",
       "      <td>36.009000</td>\n",
       "      <td>3.848647e+08</td>\n",
       "      <td>179.863620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>288.819436</td>\n",
       "      <td>15.844699</td>\n",
       "      <td>8.785562</td>\n",
       "      <td>9.407999e+07</td>\n",
       "      <td>43.870142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>32.600000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>9.797550e+07</td>\n",
       "      <td>104.780000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>249.750000</td>\n",
       "      <td>51.270000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>3.286330e+08</td>\n",
       "      <td>138.710000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>499.500000</td>\n",
       "      <td>68.110000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>3.990683e+08</td>\n",
       "      <td>182.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>749.250000</td>\n",
       "      <td>78.460000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>4.583554e+08</td>\n",
       "      <td>218.790000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>999.000000</td>\n",
       "      <td>91.430000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>5.563936e+08</td>\n",
       "      <td>267.010000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  Daily Time Spent on Site          Age   Area Income  Daily Internet Usage\n",
       "count  1000.000000                987.000000  1000.000000  9.870000e+02            989.000000\n",
       "mean    499.500000                 64.929524    36.009000  3.848647e+08            179.863620\n",
       "std     288.819436                 15.844699     8.785562  9.407999e+07             43.870142\n",
       "min       0.000000                 32.600000    19.000000  9.797550e+07            104.780000\n",
       "25%     249.750000                 51.270000    29.000000  3.286330e+08            138.710000\n",
       "50%     499.500000                 68.110000    35.000000  3.990683e+08            182.650000\n",
       "75%     749.250000                 78.460000    42.000000  4.583554e+08            218.790000\n",
       "max     999.000000                 91.430000    61.000000  5.563936e+08            267.010000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[nums].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "afc5b70d-7dac-4e26-b296-f9018a3703dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Male</th>\n",
       "      <th>Clicked on Ad</th>\n",
       "      <th>city</th>\n",
       "      <th>province</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>997</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Perempuan</td>\n",
       "      <td>No</td>\n",
       "      <td>Surabaya</td>\n",
       "      <td>Daerah Khusus Ibukota Jakarta</td>\n",
       "      <td>Otomotif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>518</td>\n",
       "      <td>500</td>\n",
       "      <td>64</td>\n",
       "      <td>253</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Male Clicked on Ad      city                       province  category\n",
       "count         997          1000      1000                           1000      1000\n",
       "unique          2             2        30                             16        10\n",
       "top     Perempuan            No  Surabaya  Daerah Khusus Ibukota Jakarta  Otomotif\n",
       "freq          518           500        64                            253       112"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[cats].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b1b71297-2ee8-45a8-a00d-c8b943ecded3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value count kolom Male:\n",
      "Perempuan    518\n",
      "Laki-Laki    479\n",
      "Name: Male, dtype: int64\n",
      "\n",
      "Value count kolom Clicked on Ad:\n",
      "No     500\n",
      "Yes    500\n",
      "Name: Clicked on Ad, dtype: int64\n",
      "\n",
      "Value count kolom city:\n",
      "Surabaya             64\n",
      "Bandung              64\n",
      "Jakarta Timur        63\n",
      "Jakarta Selatan      61\n",
      "Jakarta Barat        57\n",
      "Bekasi               56\n",
      "Medan                48\n",
      "Jakarta Utara        45\n",
      "Palembang            42\n",
      "Semarang             41\n",
      "Batam                34\n",
      "Makassar             33\n",
      "Tangerang Selatan    33\n",
      "Tangerang            32\n",
      "Depok                30\n",
      "Bandar Lampung       29\n",
      "Bogor                28\n",
      "Jakarta Pusat        27\n",
      "Malang               26\n",
      "Banjarmasin          23\n",
      "Pakanbaru            23\n",
      "Denpasar             21\n",
      "Padang               19\n",
      "Balikpapan           18\n",
      "Cimahi               17\n",
      "Samarinda            16\n",
      "Tasikmalaya          15\n",
      "Pontianak            12\n",
      "Surakarta            12\n",
      "Serang               11\n",
      "Name: city, dtype: int64\n",
      "\n",
      "Value count kolom province:\n",
      "Daerah Khusus Ibukota Jakarta    253\n",
      "Jawa Barat                       210\n",
      "Jawa Timur                        90\n",
      "Banten                            76\n",
      "Jawa Tengah                       53\n",
      "Sumatra Utara                     48\n",
      "Sumatra Selatan                   42\n",
      "Kepulauan Riau                    34\n",
      "Kalimantan Timur                  34\n",
      "Sulawesi Selatan                  33\n",
      "Lampung                           29\n",
      "Kalimantan Selatan                23\n",
      "Riau                              23\n",
      "Bali                              21\n",
      "Sumatra Barat                     19\n",
      "Kalimantan Barat                  12\n",
      "Name: province, dtype: int64\n",
      "\n",
      "Value count kolom category:\n",
      "Otomotif      112\n",
      "House         109\n",
      "Health        104\n",
      "Fashion       102\n",
      "Food           99\n",
      "Furniture      98\n",
      "Travel         98\n",
      "Electronic     97\n",
      "Finance        91\n",
      "Bank           90\n",
      "Name: category, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for col in cats:\n",
    "    print(f'''Value count kolom {col}:''')\n",
    "    print(df[col].value_counts())\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5ae3ff9-9d88-496e-8d5a-5968d503c692",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95293066-99d1-4546-aa4f-760431955403",
   "metadata": {},
   "source": [
    "### Univariate, Bivariate & Multivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "25f8ff5d-ff5b-4709-b8a6-07ef4f3e5543",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = ['Age','Area Income','Daily Time Spent on Site','Daily Internet Usage']\n",
    "for i in range(0, len(features)):\n",
    "    plt.subplot(1, len(features), i+1)\n",
    "    sns.boxplot(x=df['Clicked on Ad'],y=df[features[i]], color='green', orient='v')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "687895e9-7c49-4cec-ae82-3fb3df697c54",
   "metadata": {},
   "source": [
    "Terlihat terdapat outlier pada kolom yang dipilih menjadi features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "384077e1-7056-48ce-97d4-d992db3285aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 3))\n",
    "for i in range(0, len(features)):\n",
    "    plt.subplot(1, len(features), i+1)\n",
    "    sns.kdeplot(x=df[features[i]], hue=df['Clicked on Ad'])\n",
    "    plt.xlabel(features[i])\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cea9b94-98ff-4c01-82bc-30eebefe5b4b",
   "metadata": {},
   "source": [
    "- Pada kolom `Age` data terlihat memiliki distribusi yang sedikit right skewed\n",
    "- Pada kolom `Area Income` data terlihat memiliki distribusi yang sedikit left skewed\n",
    "- Pada kolom `Daily Time Spent on Site` dan `Daily Internet Usage` data terlihat memiliki distribusi bimodal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "df2b8087-96e5-4741-aa0c-6595f65f7761",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(22, 5))\n",
    "for i in range(0, len(features)):\n",
    "    plt.subplot(1, len(features), i+1)\n",
    "    sns.histplot(x=df[features[i]], hue=df['Clicked on Ad'],multiple='stack',palette='Reds')\n",
    "    plt.xlabel(features[i])\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0bf19bc-9a2a-4b43-ad83-ff6b8ce321e2",
   "metadata": {},
   "source": [
    "Berdasarkan fitur `Age`, terbagi kedalam 3 segmen, yaitu:\n",
    "- Click on ad dibawah 50% = umur 35 kebawah\n",
    "- Click on ad 50% - 80% = Umur 35 - 45 Tahun\n",
    "- Click on ad 80% keatas = Umur 45 Keatas\n",
    "\n",
    "Berdasarkan fitur `Daily Time Spent on Site`, terbagi kedalam 3 segmen, yaitu:\n",
    "- Click on ad dibawah 20% = Time Spent diatas 70 menit\n",
    "- Click on ad 20% - 90% = Time Spent 55 - 70 menit\n",
    "- CLick on ad diatas 90% = Time Spent 55 menit kebawah\n",
    "\n",
    "Berdasarkan fitur `Daily Internet Usage`, terbagi kedalam 3 segmen, yaitu:\n",
    "- Click on ad dibawah 25% = Internet Usage diatas 180 hari\n",
    "- Click on ad 25% - 90% = Internet Usage 150 - 180 hari\n",
    "- CLick on ad diatas 90% = Internet Usage dibawah 160 hari"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "041f7cd9-808c-42ce-9beb-b685f09cac3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(22, 5))\n",
    "for i in range(0, len(features)):\n",
    "    plt.subplot(1, len(features), i+1)\n",
    "    sns.histplot(x=df[features[i]], hue=df['Clicked on Ad'],multiple='fill',palette='Reds')\n",
    "    plt.xlabel(features[i])\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "66ca1bd5-628a-4033-8d04-658594d1d84d",
   "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>Unnamed: 0</th>\n",
       "      <th>Daily Time Spent on Site</th>\n",
       "      <th>Age</th>\n",
       "      <th>Area Income</th>\n",
       "      <th>Daily Internet Usage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.032637</td>\n",
       "      <td>0.001835</td>\n",
       "      <td>-0.056862</td>\n",
       "      <td>-0.004842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daily Time Spent on Site</th>\n",
       "      <td>-0.032637</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.331424</td>\n",
       "      <td>0.308266</td>\n",
       "      <td>0.518294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.001835</td>\n",
       "      <td>-0.331424</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.179343</td>\n",
       "      <td>-0.370481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Area Income</th>\n",
       "      <td>-0.056862</td>\n",
       "      <td>0.308266</td>\n",
       "      <td>-0.179343</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.338080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daily Internet Usage</th>\n",
       "      <td>-0.004842</td>\n",
       "      <td>0.518294</td>\n",
       "      <td>-0.370481</td>\n",
       "      <td>0.338080</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Unnamed: 0  Daily Time Spent on Site       Age  Area Income  Daily Internet Usage\n",
       "Unnamed: 0                  1.000000                 -0.032637  0.001835    -0.056862             -0.004842\n",
       "Daily Time Spent on Site   -0.032637                  1.000000 -0.331424     0.308266              0.518294\n",
       "Age                         0.001835                 -0.331424  1.000000    -0.179343             -0.370481\n",
       "Area Income                -0.056862                  0.308266 -0.179343     1.000000              0.338080\n",
       "Daily Internet Usage       -0.004842                  0.518294 -0.370481     0.338080              1.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "39af9b73-d84f-4c74-9d84-ecf5ae45e2cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Dvx7sdwiF4q4Od/kdgufeSF/qdwiFIvNft/gdguc2zfjT7xAKRcr5txZ4G0U7rVpiNcYYE2eiHCo4blliNcYYE1esKdgYY4zxkPUKNsYYYzxUxPOqJVZjjDHxxRKrMcYY4yEbeckYY4zxkPUKNsYYYzwU9DuAArLEaowxJq5IEa+yWmI1xhgTV4p2WrXEaowxJs5YYjXGGGM8ZE3BxhhjjIds5CVjjDHGQ4Fg0c6slliNMcbElSLeEmyJ1RhjTHwp9tdYRSQEzAcSgUxgOPCyqmYdZJ1awCuqepmInA08rKoXRrGvm4D73MmmwBIgBEwA9gKpqvr1obZzOETkZuABQIEA8ISqjvF4H71Vtb+X23S3mwK8DdTF+XxWqGqXPH//lkAtVR3v9f4Ph6rSf+QkUhcupWxiIv2vv4imR9fMV271xi08NGw0W3fsomndmjx3Y1dKJQT5Zu4SXh03FQkICYEAj13WkZOPPdqHI9nvurcH0/zCzmSs30C/5m0jlrli0PM069KJvTt3MvzGO1g1ey4ATc/rwBWDBhIIBvlh6HAmDnwplqEflKry7AuDmDp9BmXKlOa5Pr054fjj8pXr3e85Fiz+DUVpcHRdBvTpTfly5fh66jQGvTmUgAQIBoP0fvBeWrds4cOR5CZNWhHo3hMCAbJ+nIxO/jT38uanErjgGlCFrBChz4bCskUABK6+F2nWBjK2Ehpwtx/hR1Sq3RlUfOhxCATZNeZTdg4fmmt5Yqs2JL3wGqE1fwOwZ8pkdgx9nUBKDSo/PYBA1Wqgys7Rn7Dro//5cQgReZlXRaQzMAhn3ImhqvpchDJnAy/j/I5uVNWzCrLPaGqsu1S1pbvzZOADoDLw1IFWUNU1wGWHG4yqvgO84+5rBXCOqm483O1ES0TqAE8ArVR1q4hUAKoXwq56A54nVuAZYLKqDgIQkRaQ7+/fEmgN+JpYUxf+ycr1m5nw9J3MW/E3fT/6io8fuTlfuRc+/5Yb2p9Kl9Yn8PQH4xk1fQ5XnXkybY9rQPsWjRERlqxO48G3R/HlU3f4cCT7/fjuCL57bQg3vvdmxOXNzu9EcqOG9GnUkgantuHq119iYNv2SCBAj8EvMKhjV7as/pvHZ37HvLHjWbt4SYyPILLU6TNYsWo1kz77kLkLFvH0wBcY+c6QfOV6P3APFSqUB2DAS68yYuQoet1wLe3anMy5Z56BiPDbH0u5v/dTTBg5ItaHkZsECFx+O6HBT0L6JoL/fJHQ/J9g3aqcIrpkrjMPoFZ9gjc/Sujfzncs66dvIPVLgtc94Ef0kQUCVHzkX6TffSuhtDSOGv4xe1KnEFr+Z65i+2bPIv3BO3Ovm5lJxsvPk7lkMVKuHEe99yl7f/ox37p+8arGKiJBYDDQEVgNzBSRsaq6KKxMEvBfoLOq/uXmuQIJHE5hVV0P9ALuFkd9EZkmIr+6r9PcQOuLyILwdUUkICJ/iEj1sOmlIlItmn2LyLsicpn7foWI9BeRH0XkFxFpJSITReRPEbk9bJ1/ishMEZknIn0jbDYZyAC2u8e3XVWXu+t+JyIvi8h0EVkgIqe488uLyDB3u7NFpKs7/0YRGSUiE9zjfN6d/xxQVkTmiEi+XxcR6SEi8919DAybv11EnhWRuSIyw62d5lUT58uCG/88d9367vZK4STfK939X3mg+Avbt/OW0PXU5ogIJzaoQ8bO3WzYmpGrjKry05IVdDqpCQDd2rbgm7lOsilfplTOf7Zde/fFxX1uS6dNZ+fmLQdc3qJrF2a89yEAy3+aSdmkylSqkUL9U1qzfukyNi5fQWjfPmZ+9Bktul4Qq7AP6ZvU7+nWpTMiQsvmJ7AtYzvrN+Y/v81OqqrK7j17yL77sHy5cvs/q12746NZr14jdONa2JQGoUyyZqUizU/NXWbv7v3vS5V2aq7Z/lwIO3N/X/2WeEJzQqv+IvT3asjcx+7JX1H6rPZRrZu1aSOZSxYDoDt3krliGcHqBc4nngkGJKpXFE4BlqrqMlXdC3wE5P3NuxoYpap/QU6eK5DDSqzuTpe56yUD64GOqtoKuBJ45SDrZQH/A65xZ3UA5hagRrpKVdsB04B3cWpobXESCSLSCWiE84dtCZwsImfm2cZcIA1YLiLviMhFeZaXV9XTgDuBYe68J4BvVbUNcA7wHxEp7y5rifN3aI6TzOqq6mO4tX5VvSZ8426T7UCgvbtuGxHplr1vYIaqngikAj0j/A0GA2+LyBQRecLdXg73i9QH+Njd/8eHiL/QrE/PoEaVSjnTKVUqkZae+4cqfccuKpYrQ0LQ+VqmJFXMVebrOb9xQd/Xuf2/H/Hv6/J+VPEnqXYttqzKOe8hffXfJNWuRZXaNfPMX0OV2rUibcIXaes3UCNl/49sjeTqpK2P/N/08Wf6c/r5XVm28i+uu7J7zvzJU1LpfPk13PbgI/T/12OFHvOhSFJV2BJ2DOmbnHl5y7VoS/BfrxO8/SlCIwbFMMLDF6ieQlbaupzprLR1EZNjYvOWHDViFEmD3iR4zLH5t1OzFonHNWHfwnmFGu/hEIn2Jb3cylX2q1eeTdUGVoVNr3bnhWsMVHErU7NE5PqCxn/YidWVfaqQCLwlIvOBkTjXRQ9mGJAd9M24zb5HaKz773zgJ1XNUNUNwG63at/Jfc0GfgWOx0m0OVQ1BHTGScq/Ay+JyNNhRT50y6UClcK2+5iIzAG+A8oA2Rf7vlHVraq6G1gE1DvEMbQBvlPVDaqaCYwAspP/XuAL9/0soH7elVV1InAM8JZ7fLOzWwQO4mDxF5rwk/9seWsyGqFQeJEOLY/ny6fu4LXbLueVcd95G2AhiFhTU414ASnSsftFifQ5RK4dDOjTm2lfjqZh/XqMn/xNzvyO55zJhJEjGPx8fwa9OTTiurF1gM8i76x5Mwj9+w5Cbz1L4MJrYxBXAUT8fuWezFyyiI0Xd2DzNZey8+MRJP3n1dybKFuOpIGDyHhxALpjRyEGe3hEJKqXqg5R1dZhr7zXLCJ9cfN+8AnAycAFwHnAkyLSuCDxH3ZiFZFjcDoUrcfp9JMGnIhzHa/UwdZV1VVAmoi0B04Fvjrc/YfZ4/6bFfY+ezoB5w86wK2ptVTVY1X17Qgxqar+rKoDgKuA7uGL8xZ3t9s9bLtHq+riPDGB8zc61DXsg7Vl7NP9v7YH3JaqblbVD1T1OmAm+xPzwfZ5oPj3Fwo7E3zriymH2GRkH0z9hUv6v8Ul/d8iOakC67Zsy1mWtmUbyZUr5CpfpUI5MnbuJjPk9ItLS88guXLFfNtt3ageqzZuYcv2nUcUV6xsWf03VerWyZlOqlOb9DVr2bJ6TZ75tUhfs9aPEHOMGDmKrtfcRNdrbiK5WjXWpe1vDVu3fgPJ1fPX7rIFg0G6dGzPpG+n5lvWplVL/lq9hs3p6YURdtQ0fSNUCbvqlFQV3br5wCv8uRCpVhPKVzpwGZ9lrV9HIKVGznQgpQahjblbMXXHDnSX8/9k7/RUJCEBqZzkLAwmUHngy+ye8AV7pnjaJ7TAJBDdKwqrcTp3ZqsDrIlQZoKq7nBbUFNxctoRO6zE6taG3gBec3/0KwNr3Wbe64juaT9DcZqEP3FrjIVlInCzOB2SEJHaeS9Ki0gtEWkVNqslsDJs+kq33BnAVlXd6m73HnFP4UXkpChi2SciiRHm/wScJSLVxLnI3gPI/+t0ACLSXkTKue8rAg2Bv/IUywDCs1NU8YefCfa88JxoQ8rl6rNaM7p3T0b37sm5LY5jzE/zUVXmLl9NxbJlqJ4naYoIpzSuz6TZTp7/fMY82rdwThxXrt+cU6tb9Nda9mVmkVS+7BHFFSvzxn5F2+t7ANDg1Dbs3rqNbevSWDlzFsmNjqFq/XoEExNpc1V35o31tW8Z11x+KWNGvMOYEe/Q4ax/8Pn4Cagqc+YvpGKFCiRXy90VQlVZ6TZnqypTpk3nmPpOA83KVatzPquFvy1hX+Y+qlSuHNsDyuuvP5DqtaBqCgQTCJx8Jjr/59xlqoX1Uq/TEIIJsGMb8WrfogUEj65HoFZtSEikTMfz2ZOa+yQ4UHX/55bQtDkEAujWdAAqPdmPzBXL2PnB8FiGHZVoa6xRmAk0EpEGbp+Tq9jf2pltDPAPEUlwf09PBfJVNg5HNL2Cy7rNhtm327wPvOgu+y/wmYhcDkwBomlLGIvTBFyQZuBDUtVJItIE+NH9ALYD1+LUtLMlAv/nXpvcDWwAbg9bvkVEpgOVcJquAfrhdMue5yanFcChbiUa4pb/Nfw6q6quFZHHcf52Aow/zFt9TgZeE5FMnJOkoao6U0Tqh5WZwv6m3wFHGH+BndnsWFIXLqXzU4MpUyqRZ8Oukd42+EP6XXMhyUkVeeiS9jz89mgGjfuOJnVq0P20lgBMnvMbY36aR0IwSJnEBF645RLfO8Xc8sEwGp99BhWqVWXAqsWMe6o/wUTn/Gnam8NYMH4izbp0ot/Suc7tNjc5PTOzQiE+vvuf3DtxNIFgkOnD3mftot/8PJRczjq9HVOnz6DjpVdRtkwZ+j/5eM6ynvf/k38/8SjVqx7Fo32fZceOnagqxzU6lr6PPgTAxG+nMmb8BBISEihTujQvPdvX98+KrCyyRr5B8M6+IAGyZnwN6/5CTu8MgP4wAWl5GoFT2kMoE/btJfTO8zmrB258GDm2OVSoRPCZd8ga/wE6Y7JfR+MIhch4/lmqvPIWBAPsHjua0LKllL30SgB2jfqY0u07Ue6yq9DMTHTPHrY+4XxGiSe2ouwFXdn3xxKOGjEKgO2DX2bv9FTfDiecV18XVc0UkbtxKhRBYJiqLhS3k6uqvqGqi0VkAjAPp8VzqKouOPBWD01ifW1HRFoDL6nqP2K648MkIt/h3H/7i9+x+C30zfvxcwHQQ3d1uMvvEDz3RvpSv0MoFJn/usXvEDy3aUZ83NritZSZiwqcFpc3axzVb06DBb/HQZfz/GI68pKIPAbcwf6ewcYYY0wuAb9bOAooponVHfEi36gX8UhVz/Y7BmOMKYmKeF61sYKNMcbEF9+vyReQJVZjjDFxpYjnVUusxhhj4oslVmOMMcZD9qBzY4wxxkPWK9gYY4zxUBHPq5ZYjTHGxBfrFWyMMcZ4qIjnVUusxhhj4ksguoeYxy1LrMYYY+KKNQUbY4wxHoryWatxyxKrMcaYuGI1VmOMMcZLdo3VGGOM8ZDVWI0xxhjvSLBoX2S1xGqMMSa+WI3VFHu7d/kdQaF4I32p3yF47vakY/0OoVC8vvQ7v0PwXErNhn6HELfErrEaY4wxHrIaqzHGGOMdq7EaY4wxXirinZeKdvTGGGOKHRGJ6hXltjqLyBIRWSoijx2kXBsRCYnIZQWN3xKrMcaY+BKQ6F6HICJBYDBwPtAU6CEiTQ9QbiAw0ZPwvdiIMcYY4xmR6F6HdgqwVFWXqepe4COga4Ry9wCfAeu9CN8SqzHGmLgigeheUagNrAqbXu3O278vkdrAJcAbXsVvidUYY0x8ibLGKiK9ROSXsFevvFuKsHXNM/0y8KiqhrwK33oFG2OMiSvRDmmoqkOAIQcpshqoGzZdB1iTp0xr4CO3M1Q1oIuIZKrq59HGm5clVmOMMfHFu/tYZwKNRKQB8DdwFXB1eAFVbZD9XkTeBb4oSFIFS6zGGGPijFfPY1XVTBG5G6e3bxAYpqoLReR2d7ln11XDWWI1xhgTXzwceUlVxwPj88yLmFBV9UYv9mmJ1RhjTHyxsYKNMcYY73jVFOwXS6zGGGPiij3o3BhjjPGSPd3G+E1ELgFGAU1U9Te/44lk2uLlDPh8CqEs5bK2zeh57qm5lqsq/UdPIXXxcsqWSqB/j840rZMS1bp+UlWefWEQU6fPoEyZ0jzXpzcnHH9cvnK9+z3HgsW/oSgNjq7LgD69KV+uHF9PncagN4cSkADBYJDeD95L65YtfDiS/a57ezDNL+xMxvoN9GveNmKZKwY9T7Mundi7cyfDb7yDVbPnAtD0vA5cMWgggWCQH4YOZ+LAl2IZ+kGpKs++OZzUmXMoU7oUAx68gxOObZCv3P/GTeS9z7/ir7Vp/Pjhm1SpXAmAjB07+ed/BrN2w0ZCoRA3XXoh3TudHeOjyE1Vefb5F5j6w3TKlCnDc337cEKT4/OV6/10PxYsWowCDY4+mgHP9KF8uXIMHf4+48ZPACAUCvHn8hX8+O1EkipXjvGR5FHEm4KLdn3bZOsBfI9zj1bcCWVl8e9R3/Bmr0sZ9+iNjP91CUvXbcpVJnXxclZu3MKE3jfT9/KO9P3066jX9VPq9BmsWLWaSZ99SL/HH+HpgS9ELNf7gXsY+8G7jPtgODVTUhgxchQA7dqczNgR7zJmxDv0f/Ix/vXswFiGH9GP747g1c6XHnB5s/M7kdyoIX0atWREr/u4+nUneUogQI/BL/Da+d3p27QNbXpcRs0m+U8y/JL6yxxW/r2OiUNf4pl7e9L3tbcjlmvVtDHD+j9BreRqueaP+GISxx5dmzGDB/LewD48P/R/7N2XGYvQDyj1++ms+GsVk8Z8Rr9/Pc7T/SN/f3o//ABjP/mAcZ98QM0aKYz4aCQAt95wHWM+HsGYj0fw4D130ebkk/xPqjjPY43mFa8ssRZxIlIBOB24BTexikhARP4rIgtF5AsRGZ/9KCQROVlEporILBGZKCI1CzvG+X+t4+hqSdStmkSphCDnn3Qc3y5YmqvMtwv+pGvrpogIJ9avRcauPWzYtj2qdf30Ter3dOvSGRGhZfMT2JaxnfUbN+YrV6FCecCpYezes4fskdbKlyuX01Fj167dcdFpY+m06ezcvOWAy1t07cKM9z4EYPlPMymbVJlKNVKof0pr1i9dxsblKwjt28fMjz6jRdcLYhX2IX0zYxZdz/2H81kd34htO3ayPsJxNm3YgDop1fPNF2DHrl2oKjt37aZyxQok+Hwt8JupqXS7sItzTC2asy0jg/UbIn3/KgBh378IX7MvJ0zkws7nFXbI0fFuEH5fWGIt+roBE1T1d2CziLQCLgXqA82BW4F2ACKSCLwKXKaqJwPDgGcLO8C0rdupkVQxZ7pGUkXWb92eq8z6bbnLpCRVJG3r9qjW9VPa+g3USEnOma6RXJ209fl/2AAef6Y/p5/flWUr/+K6K7vnzJ88JZXOl1/DbQ8+Qv9/HfBxkXEjqXYttqxanTOdvvpvkmrXokrtmnnmr6FK7Vp+hBhR2sbN1KxeNWe6RrWjSNu4Oer1r7noPP5ctYYzr72Ti+98hN63XU8g4O9PaNr69dSokZIzXSMlmbT1kR/Q8vhTz3B6h/NZtmIl1111Za5lu3btZtr0GXQ695xCjTdaEgxE9YpX8RuZiVYPnEch4f7bAzgDGKmqWaq6DpjiLj8OaAZMFpE5wL9wxs7MJ3xw67cmpBYoQNW8Y16T74w5UhlBolrXT5pvPO8D3yowoE9vpn05mob16zF+8jc58zuecyYTRo5g8PP9GfTm0EKL1SsRj081Yg0i4ufnm+g/q0i+/3UeTY6pR+r//svo156j3+vvsn3nTi8DPGwR/3sc6PvXtw/TJn1Jwwb1GT9pcq5lU1Kn0apli7hoBgY8ex6rX6zzUhEmIlWB9kAzEVGcIbsUGH2gVYCFqtruUNsOH9w69OWQAv061kiqyLr0jJzpdekZJFeqkKtMSuXcZdLSM0iuXJ59odAh1421ESNH8cnn4wBo3vR41qXtryGsW7+B5LBaUV7BYJAuHdvz9vsf0v2i3M2kbVq15K++a9icns5RSUmFErsXtqz+myp195+PJdWpTfqatQRLlcozvxbpa9b6EWKOEeMmMXLitwA0b3QMazfsvz6/buNmkqtWiXpboyd/R8/LuyIi1KtVgzop1Vm2ag0tjjvW87gPZsTHI/lk1OcAND+hKevWpeUsW5e2nuTq+ZuxswWDQbp06sjb771P964X5cz/cuIkLujcqdBiPmxx3MwbDauxFm2XAe+paj1Vra+qdYHlwEagu3utNQU42y2/BKguIjlNwyJyQmEH2axuDVZuSGf1pq3szQzx1ewlnNOsYa4y7Zs1ZMwvi1BV5q5YQ8UypaleqUJU68baNZdfypgR7zBmxDt0OOsffD5+AqrKnPkLqVihAsnVcnd6UVVWuk2kqsqUadM5pn49AFauWp1Tq1v42xL2Ze6jSrzUGg5g3tivaHt9DwAanNqG3Vu3sW1dGitnziK50TFUrV+PYGIiba7qzryx4w+xtcJ1zUWd+Py15/j8tec4t11rxnwzzfmsfvuDiuXLkXxU9Im1ZvVq/DhnAQAbt6Sz/O+11K2RfIi1vHfNlZfndDjqcM5ZfP7FeOeY5s13vn/VI3z//lqV835K6jSOqV8/Z3lGxnZmzprNuWefFcvDOLgifo3VaqxFWw/guTzzPgOa4DwuaQHwO/ATsFVV97qdmF4Rkco4n//LwMLCDDIhGOCJS9vTc8hnZGVlcckpzWhUoxofTXdu0bjqtBM5s0kDUhcvo3P/tymTmMizPc476Lrx4qzT2zF1+gw6XnoVZcuUof+Tj+cs63n/P/n3E49SvepRPNr3WXbs2ImqclyjY+n76EMATPx2KmPGTyAhIYEypUvz0rN9fe/AdMsHw2h89hlUqFaVAasWM+6p/gQTEwGY9uYwFoyfSLMunei3dK5zu81NdwKQFQrx8d3/5N6JowkEg0wf9j5rF8XP3V9ntTmJ1Jlz6HTL/ZQpXZr+D9yWs6xXn4H0u68nKVWP4r0xE3j703Fs3JLOxXc9ylmtT+Lf9/fijh6X8PiLb3DRHY8AysM39ci5FccvZ51xOlO/n07Hiy91vn9PP5mzrOfd9/PvPk9QvVpVHu3Tlx07djjfv8aN6Nv70Zxyk6d8x+ltT6Vc2bJ+HEJkcZw0oyHxdQ3EeEVEKqjqdre5+GfgdPd662EraFNwvAqe0c3vEDx3e1JsmyVj5fWl3/kdguekpr8tL4WmXOUCZ8XMBy6J6jcn4aXRcZmBrcZafH0hIklAKaDfkSZVY4yJOZ97WxeUJdZiSlXP9jsGY4w5IkW8KdgSqzHGmPhiNVZjjDHGQ1ZjNcYYYzxkidUYY4zxkCVWY4wxxkPBoN8RFIglVmOMMfHFaqzGGGOMhyyxGmOMMd6RIn67TdGO3hhjTPHj4SD8ItJZRJaIyFIRyffAYxG5RkTmua/pInJiQcO3Gqsxxpj44lGNVUSCwGCgI86DSWaKyFhVXRRWbDlwlqpuEZHzcR6XeWpB9muJ1RhjTHzxrin4FGCpqi4DEJGPgK5ATmJV1elh5WcAdSggawo2xhgTX7xrCq4NrAqbXu3OO5BbgK8KEDlgNVZjjDHxJvrrp72AXmGzhqjqkPAiEVaL+Eg6ETkHJ7GeEWWUB2SJ1RhjTHyJMrG6SXTIQYqsBuqGTdcB1uTfnbQAhgLnq+qm6AONzBKrMcaY+OLdNdaZQCMRaQD8DVwFXB1eQESOBkYB16nq717s1BKrObRKVfyOoFBk/usWv0Pw3OtLv/M7hEJxx7Fn+x2C5165pZ3fIRSKUkMnFnwjHiVWVc0UkbuBiUAQGKaqC0Xkdnf5G0AfoCrwX3Fqypmq2rog+7XEaowxJr54OECEqo4HxueZ90bY+1uBWz3bIZZYjTHGxBsb0tAYY4zxkCVWY4wxxkOWWI0xxhgPFfFB+C2xGmOMiS+WWI0xxhgPiSVWY4wxxjsBu8ZqjDHGeMdqrMYYY4yHrFewMcYY46Fg0O8ICsQSqzHGmPhiTcHGGGOMh6wp2BhjjPGQ3cdqjDHGeMhqrMYYY4yH7Bpr8SMil+A8Ub6Jqv5WSPtYAbRW1Y2Fsf14o6r0//BLUuf/TtlSifS/uTtN69XKV271hs08NOQTtu7YRdOja/LcrZdRKsH5mv782zIGfDyezFAWVSqU471HPH2E4hGRJq0IdO8JgQBZP05GJ3+ae3nzUwlccA2oQlaI0GdDYdkiAAJX34s0awMZWwkNuNuP8CNSVZ59czipM+dQpnQpBjx4Bycc2yBfuf+Nm8h7n3/FX2vT+PHDN6lSuRIAGTt28s//DGbtho2EQiFuuvRCunc6O8ZHkdt1bw+m+YWdyVi/gX7N20Ysc8Wg52nWpRN7d+5k+I13sGr2XACanteBKwYNJBAM8sPQ4Uwc+FIsQz8oOaE1CT1uh0CQ0LSvyPrqk9zLW7Yj2O16yHK/fx+9gS5dGFYgQMKTr8KWTWS+2ifG0R9EEe8VXLRPCwpPD+B74KpIC0WkaH/qPkid/zsr129iQv8H6Ht9N/r+b2zEci98NokbOp7GhP4PUKl8WUZNmwXAtp27eGbEOAbffS3jnrmXl26P+NHElgQIXH47odefJvTsXQROPhNq1M1VRJfMJfTcvYQG3kdoxCsEr74nZ1nWT98Q+u/TMQ760FJ/mcPKv9cxcehLPHNvT/q+9nbEcq2aNmZY/yeolVwt1/wRX0zi2KNrM2bwQN4b2Ifnh/6PvfsyYxH6Af347ghe7XzpAZc3O78TyY0a0qdRS0b0uo+rX3eSpwQC9Bj8Aq+d352+TdvQpsdl1GxyXKzCPjgJkHDNXex7+V/se7IngVPOgZpH5yqii2eT+fQdZD5zJ6F3XyThhgdyLQ906IauXRXLqKMjEt0rTllizUNEKgCnA7cQllhF5GwRmSIiHwDzRSQoIv8RkZkiMk9EbsteX0S+EZFfRWS+iHQ9xP7qi8hiEXlLRBaKyCQRKesuO1ZEvhaRue72GorjPyKywN3+lWHxTRWRT0TkdxF5TkSuEZGf3XIN3XLVReQzN+6ZInJ64fwlc/t2zmK6tmuJiHBiw7pk7NzNhvSMXGVUlZ9+W0ank08AoNtpJ/HNnMUAfPnTPDq2akqtqkkAVK1UIRZhH1y9RujGtbApDUKZZM1KRZqfmrvM3t3735cq7dRcs/25EHbm/hvEg29mzKLruf9ARGh5fCO27djJ+s1b8pVr2rABdVKq55svwI5du1BVdu7aTeWKFUgI+vtTs3TadHZGOIZsLbp2YcZ7HwKw/KeZlE2qTKUaKdQ/pTXrly5j4/IVhPbtY+ZHn9Gi6wWxCvugpMFx6Po1sHGd8/37+TsCLdvlLrQn/PtXBgj7/lWpRqDFKWRN+yom8R4WCUT3ilPWFJxfN2CCqv4uIptFpJWq/uouOwVopqrLRaQXsFVV24hIaeAHEZkErAIuUdVtIlINmCEiY1XDf1HzaQT0UNWeIvIJ0B34HzACeE5VR4tIGZwToUuBlsCJQDVgpoikuts5EWgCbAaWAUNV9RQRuQ+4B7gfGAS8pKrfi8jRwER3nUK1Pj2DGkdVzplOqVKJtPRtVE+qmDMvfftOKpYtQ4LbDJRSpRJpW7YBsCJtI5mhLG54fig7du/lug7t6HraSYUd9kFJUlXYEtaSn74Jqd+YvB+0tGhL4OIboEJlQm/0jWmMRyJt42ZqVq+aM12j2lGkbdxM8lFVolr/movO485n/o8zr72THbt28eJj9xKI816eSbVrsWXV6pzp9NV/k1S7FlVq18wzfw0NTm3tR4j5VamKbtmwf3rLRuSY4/MVk5NOI+HSm6FSEpmDnsyZn3Dl7YQ+HQplysUi2sNjYwUXOz2Al933H7nT2Yn1Z1Vd7r7vBLQQkcvc6co4CXI10F9EzgSygNpACrDuIPtcrqpz3PezgPoiUhGoraqjAVR1N4CInAF8qKohIE1EpgJtgG3ATFVd65b7E5jkbnM+cI77vgPQVPY3o1QSkYqqWqhVp0jnFXn/60Q688iOMxTKYuHKvxn20M3s2buPHgOGcOIxdalfo1qEtWIlwn/+CMep82YQmjcDGp5A4MJryXrtyfzrxZUIn9VhNLt9/+s8mhxTj+ED/sVfa9O4+Yn+tG52PBXKxeEPuCvi8alGbG48+DlyLEX5/Zs9nX2zpyONmhHsdgOZLz6GtDgVzUhHVy5FjmsRg1gPUxw380bDEmsYEakKtAeaiYgCQUBF5BG3yI7w4sA9qjoxzzZuBKoDJ6vqPreTUplD7HpP2PsQUJaI/2ty9hvNdrLCprPY/1kHgHaquutgAbk18l4Arz/ci54XdzhY8Yg++HYGI6f9AkDz+rVZt3lrzrK0LdtITqqUq3yVCuXI2LWbzFCIhGDQLePUaFOqVCKpQjnKlS5FudKlaN24Hr+tXudrYtX0jUiVsP0nVUW3bj7wCn8uRKrVhPKVYMe2wg/wMIwYN4mRE78FoHmjY1i7YVPOsnUbN5NcNbraKsDoyd/R8/KuiAj1atWgTkp1lq1aQ4vjjvU8bq9sWf03VerWyZlOqlOb9DVrCZYqlWd+LdLXrPUjxPy2bESqhDXFV6mGpm86YHH9YwFSvSZUqETg2KYETmxLoHkbSCwFZcoRvPURQkOfj0HgUbDOS8XKZcB7qlpPVeural1gOXBGhLITgTtEJBFARBqLSHmcmut6N6meA9Q7kkBUdRuwWkS6udsvLSLlgFTgSvcab3XgTODnw9j0JCCnC6qItDzA/oeoamtVbX0kSRXg6vZtGf3U3Yx+6m7OPakpY36cg6oy989VVCxbOlczsBsLpxzXgEmznF6Ln0+fTfuWTit1+5ZNmPXHSjJDIXbt2cu8ZatpWDP/9b2Y+usPpHotqJoCwQQCJ5+Jzs/zUVSruf99nYYQTIi7pApwzUWd+Py15/j8tec4t11rxnwzDVVlzm9/ULF8uaibgQFqVq/Gj3MWALBxSzrL/15L3RrJhRW6J+aN/Yq21/cAoMGpbdi9dRvb1qWxcuYskhsdQ9X69QgmJtLmqu7MGzve52gdumIJklIbqrnfv1PORufOyF0oeX/Pezn6WEhIgO3bCI16h32PXMu+x24gc8gA9Le58ZNUwa6xFjM9gOfyzPsMuBr4OM/8oUB94Fdx2pE24FyfHQGME5FfgDlAQW7XuQ54U0SeAfYBlwOjgXbAXJw2u0dUdZ2I5L+4Etm9wGARmYfz+acCtxcgxqic2bwxqfN/p3PvFylTqhTP3rS/h+ZtL79Hvxu7kZxUiYcuO4+H3/yYQaO/psnRNel+xskANKyVzBnNGtHt6dcIiHDZP1rTqHZKYYd9cFlZZI18g+CdfUECZM34Gtb9hZzeGQD9YQLS8jQCp7SHUCbs20vonf0/XoEbH0aObQ4VKhF85h2yxn+Azpjs19HkOKvNSaTOnEOnW+6nTOnS9H/gtpxlvfoMpN99PUmpehTvjZnA25+OY+OWdC6+61HOan0S/76/F3f0uITHX3yDi+54BFAevqlHzq04frnlg2E0PvsMKlSryoBVixn3VH+CiYkATHtzGAvGT6RZl070WzrXud3mpjsByAqF+Pjuf3LvxNEEgkGmD3uftYsK5Q68w5eVReYHg0m8vz8EAoR+mISuWUngLKdzVdbULwm0OoNAuw7u928PmW/29znoKBXxpmCJn+sFJl6Fpo0sll8S/eQ9v0PwXPD++O8cdSTuOPZsv0Pw3Cu3tDt0oSKo1NCJBc6KoTH/jeo3J9j1zkPuS0Q643TaDOJ06Hwuz3Jxl3cBdgI3hnVYPSLxW5c2xhhTMgUkutchuGMODAbOB5oCPUSkaZ5i5+N0PG2E06/k9QKHX9ANGGOMMZ7y7hrrKcBSVV2mqntx7vTIO7ZAV5y+NaqqM4AkEamZd0OHwxKrMcaY+BIMRvUSkV4i8kvYq1eeLdXGGVsg22p33uGWOSzWeckYY0x8ibLzkqoOAYYcbEuRVjuCMofFEqsxxpj44t2tNKuB8AG86wBrjqDMYbGmYGOMMfHFu0H4ZwKNRKSBiJTCGf897xNAxgLXu+Owt8UZqrZAo4BYjdUYY0x88WhsaVXNFJG7cQb0CQLDVHWhiNzuLn8DGI9zq81SnNttbirofi2xGmOMiS8eDhChquNxkmf4vDfC3itwl2c7xBKrMcaYeBMo2mMFW2I1xhgTX+J4HOBoWGI1xhgTX+x5rMYYY4yHrMZqjDHGeKiIP93GEqsxxpi4ItZ5yRhjjPGQNQUbY4wxHrLEaowxxnjIegUbY4wxHrIaqyn2tm/1O4JCsWnGn36H4LmUmg39DqFQvHJLO79D8Ny9b//odwiF4o2hHmzEegUbY4wxHrJewcYYY4yHrCnYGGOM8ZB1XjLGGGM8ZDVWY4wxxkPWeckYY4zxkNVYjTHGGA9Zr2BjjDHGQwGrsRpjjDGeEbvGaowxxnjIrrEaY4wxHrIaqzHGGOOhIt55qWjXt40xxhQ/gUB0rwISkaNEZLKI/OH+WyVCmboiMkVEFovIQhG575DhFzgyY4wxxksi0b0K7jHgG1VtBHzjTueVCTykqk2AtsBdItL0YBu1xGqMMSa+SCC6V8F1BYa774cD3fIWUNW1qvqr+z4DWAzUPthGLbEaY4yJL1HWWEWkl4j8EvbqdZh7SlHVteAkUCD54GFJfeAk4KeDlYt55yURCQHzgUScKvZw4GVVzTrIOrWAV1T1MhE5G3hYVS+Mcn83Aq1V9e6DlKkPnKaqH0R5GEfMjWeSqq6JsOw7nGP7JSyuL1S1WWHHVdhUlf6jviV18TLKJibQ/+ouNK2bkq/c6k3pPDT8C7bu3EXTOik8d+0FlEoIRr1+rJVqdwYVH3ocAkF2jfmUncNzP+U5sVUbkl54jdCavwHYM2UyO4a+TiClBpWfHkCgajVQZefoT9j10f/8OIR8VJVnn3+BqT9Mp0yZMjzXtw8nNDk+X7neT/djwaLFKNDg6KMZ8Ewfypcrx9Dh7zNu/AQAQqEQfy5fwY/fTiSpcuUYH0luckJrEnrcDoEgoWlfkfXVJ7mXt2xHsNv1kKWQFSL00Rvo0oVhBQIkPPkqbNlE5qt9Yhx9ZNe9PZjmF3YmY/0G+jVvG7HMFYOep1mXTuzduZPhN97BqtlzAWh6XgeuGDSQQDDID0OHM3HgS7EM/RCia+ZV1SHAkINuSeRroEaERU8cVkQiFYDPgPtVddvByvrRK3iXqrYEEJFk4AOgMvDUgVZwk9BlhRhTfeBqN5aoiEhQVUNHsK8bgQVAvsRanKUuXs7KDVuY8MStzFu5lr4jJ/Pxg9fmK/fCuFRuOPtkurRqwtOfTGLUjHlcdcZJUa8fU4EAFR/5F+l330ooLY2jhn/MntQphJb/mavYvtmzSH/wztzrZmaS8fLzZC5ZjJQrx1Hvfcren37Mt64fUr+fzoq/VjFpzGfMnb+Ap/sPZOT77+Qr1/vhB6hQoQIAA/7vJUZ8NJJeN9/ArTdcx603XAfAt1On8e6ID3xPqkiAhGvuYt+Lj8OWjST861Wy5syAtX/lFNHFs8mc86NTvE4DEm57gn1P3pqzPNChG7p2FVKmXMzDP5Af3x3Bd68N4cb33oy4vNn5nUhu1JA+jVrS4NQ2XP36Swxs2x4JBOgx+AUGdezKltV/8/jM75g3djxrFy+J8REcgIcjL6lqhwMtE5E0EampqmtFpCaw/gDlEnGS6ghVHXWoffraFKyq64FewN3iqC8i00TkV/d1Gjg1NxFZEL6uiATcnlzVw6aXiki1A+1PRN4VkVdEZLqILBOR7GT9HPAPEZkjIg+ISFBE/iMiM0Vknojc5q5/tts77ANgvjv9nYh8KiK/icgIcYcMEZGTRWSqiMwSkYkiUtPdX2tghLuvstH+rUTkBBH52V1vnog0cud/7u5jYXgziIjcIiK/u/G9JSKvufOri8hn7rHNFJHTo42hIL6d/wdd25yAiHBi/Vpk7NrNhq3bc5VRVX764y86nXgcAN3anMA385dGvX6sJZ7QnNCqvwj9vRoy97F78leUPqt9VOtmbdpI5pLFAOjOnWSuWEaw+kFboWLmm6mpdLuwCyJCyxbN2ZaRwfoNG/OVy06qqsruPXsiVjK+nDCRCzufV9ghH5I0OA5dvwY2roNQJlk/f0egZbvchfbs3v++VBlA909XqUagxSlkTfsqJvFGa+m06ezcvOWAy1t07cKM9z4EYPlPMymbVJlKNVKof0pr1i9dxsblKwjt28fMjz6jRdcLYhV2FCTKV4GNBW5w398AjMkXifOb/jawWFVfjGajvl9jVdVlbhzJOGcLHVW1FXAl8MpB1ssC/gdc487qAMxV1fy/ALnVBM4ALsRJqOD0BJumqi1V9SXgFmCrqrYB2gA9RaSBW/YU4AlVze4VdhJwP9AUOAY43T27eRW4TFVPBoYBz6rqp8AvwDXuvnYdItZwtwOD3Np+a2C1O/9mdx+tgXtFpKrbdP4kTg+2jkB4O94g4CX32LoDudsuC8n6rdupUaViznRKUkXS8iTG9B27qFi2NAnBQL4y0awfa4HqKWSlrcuZzkpbFzE5JjZvyVEjRpE06E2Cxxybfzs1a5F4XBP2LZxXqPFGK239emrU2N/MXiMlmbT1EU/kefypZzi9w/ksW7GS6666MteyXbt2M236DDqde06hxhuVKlXRLRv2T2/ZiFTJfw4uJ51GYr+hJNzXj8x39v+GJlx5O6FPh4JqvnXiWVLtWmxZtTpnOn313yTVrkWV2jXzzF9Dldq1/Agxstj1Cn4O6Cgif+D8Vj7n7F5qich4t8zpwHVAe7diM0dEuhxso/EyQET2XygReE1EWgIhoPEh1huGc4bxMnAzkL+9Kr/P3aS8SEQOdJGuE9AirEZbGWgE7AV+VtXlYWV/VtXVACIyB6dZOR1oBkx2K7BBYG0UsUX6X5s970fgCRGpA4xS1T/c+feKyCXu+7punDWAqaq62Y1rJPv/lh2ApmFjcVYSkYpub7ccbu23F8Dr91xHz/PPjCL8wzuwvP8vDlYmmvVjLlIAeQLNXLKIjRd3QHftpNRpZ5L0n1fZ1P38/ZsoW46kgYPIeHEAumNHIQccnUi540Bjtw7o24dQKES/gf/H+EmT6d71opxlU1Kn0aplC/+bgYGItZsIB6qzp7Nv9nSkUTOC3W4g88XHkBanohnp6MqlyHEtYhCrdyJ+bqoRv7saTycNMfq/raqbgHMjzF8DdHHff3+4EfmeWEXkGJwkuh7nOmsacCJOLXb3QVZFVVe5beTtgVPZX3s9mD3huz9QWMA9qjoxT6xnA3l//cK3F8L5mwqwUFXztDUd0iYg/Ablo4CNAKr6gYj8BFwATBSRW4EsnETZTlV3up2fyhzkuMD5u7Y7VG05vFNA6KuhR/Q/7oNpvzLyR6cW1vzomqzbsj93p6VnkFypQq7yVcqXJWPXHjJDWSQEA7nKpFSucMj1Yy1r/ToCKfv7RARSahDamLtmF54s905PRR59EqmchG5Nh2AClQe+zO4JX7BnytexCjuiER+P5JNRnwPQ/ISmrFuXlrNsXdp6kqtXP+C6wWCQLp068vZ77+dKrF9OnMQFnTsVWsyHZctGpErYMVSphqZvOmBx/WMBUr0mVKhE4NimBE5sS6B5G0gsBWXKEbz1EUJDn49B4AWzZfXfVKlbJ2c6qU5t0tesJViqVJ75tUhfE825f6z4fdZcML42BbvXR98AXlPndKkysNatUV6HU9M7lKE4TcKfHGFnIoAMoGLY9ETgDrdJFxFpLCLlD2N7S4DqItLOXT9RRE44wL7CfQdcK/tPM28AprjbOAZYpqqv4FwXaIHz99riJtXjcZp+AX4GzhKRKiKSgNPkm20SkNND2m0dKBRX/6MVox+5kdGP3Mi5zY9lzMyFqCpzV6yhYtnSVK+cOzGKCKccW5dJc50OFJ/PXEj75k7Taftmh14/1vYtWkDw6HoEatWGhETKdDyfPalTcpUJVN3f3JjQtDkEAk5SBSo92Y/MFcvY+cFw/HbNlZcz5uMRjPl4BB3OOYvPvxiPqjJn3nwqVqhAcvXczaaqysq/VuW8n5I6jWPq189ZnpGxnZmzZnPu2WfF8jAOSFcsQVJqQ7UUCCYQOOVsdO6M3IWS9zeFytHHQkICbN9GaNQ77HvkWvY9dgOZQwagv80tEkkVYN7Yr2h7fQ8AGpzaht1bt7FtXRorZ84iudExVK1fj2BiIm2u6s68seMPsbUYil1TcKHwo8Za1m0yzb7d5n0g+2LGf4HPRORynIQSTdvYWJwm4GiagQ9kHpApInOBd3GuQ9YHfnWT3AYi3Dh8IKq6121GfkVEKuP8nV8GFrrbf0NEdpG/5jgE53roXBFRnOuxj7vLrsRJuvuAdcAzOH+f20VkHk4yn+Hu/28R6Y9zr9UaYBGw1d3OvcBgd50EIBXn+m2hOrPpMaQuXkbnf79FmVKJPNtjf3PobW9+Sr+rOpNcuQIPXXQWD783jkHjv6dJ7WS6t21+yPV9EwqR8fyzVHnlLQgG2D12NKFlSyl7qXOtcdeojyndvhPlLrsKzcxE9+xh6xMPAZB4YivKXtCVfX8s4agRTifD7YNfZu/0VN8OJ9tZZ5zO1O+n0/HiSylbpgz9n34yZ1nPu+/n332eoHq1qjzapy87duxAVTmucSP69n40p9zkKd9xettTKVc26v55hSsri8wPBpN4f38IBAj9MAlds5LAWU6HnaypXxJodQaBdh0glAn79pD5Zn+fgz60Wz4YRuOzz6BCtaoMWLWYcU/1J5iYCMC0N4exYPxEmnXpRL+lc53bbW5yeqdnhUJ8fPc/uXfiaALBINOHvc/aRb/5eSi5FfGn20hctasfARFpjdMZ5x9+xxJPRKSCqm53a6yjgWGqOvpItnWkTcHxbmOfqDr4FSkpU3/0O4RCsffeK/wOwXP3vl08P6s3dFuBq5Katiyq3xxJOSYuq61F+rRARB7Dubfo8UOVLYGedlsGFgDLgc99jcYYY6IWs9ttCoXvnZcKQlWfY/8tMyaMqj7sdwzGGHNE4vj6aTSKdGI1xhhTDFliNcYYY7wjRbzzkiVWY4wx8cVqrMYYY4yXLLEaY4wx3rEaqzHGGOMhS6zGGGOMlyyxGmOMMd4JWGI1xhhjPGSJ1RhjjPGOXWM1xhhjPGSJ1RhjjPGSJVZjjDHGO1ZjNcYYYzxkYwUbY4wxHiriNVZRjepB7cbEhIj0UtUhfsfhpeJ4TFA8j6s4HhMU3+OKV0W7vm2Ko15+B1AIiuMxQfE8ruJ4TFB8jysuWWI1xhhjPGSJ1RhjjPGQJVYTb4rjdaDieExQPI+rOB4TFN/jikvWeckYY4zxkNVYjTHGGA9ZYjXGGGM8ZInVmEIgImVF5Di/4zDREZHyfsdQGIrrccU7S6zGN+I4VUQuFZFL3PdFe8gVQEQuAuYAE9zpliIy1tegTEQicpqILAIWu9Mnish/fQ6rwIrrcRUVlliNL0SkE/AH8DTQBbgA6Av84S4ryp4GTgHSAVR1DlDft2g8IiIpIvK2iHzlTjcVkVv8jquAXgLOAzYBqOpc4ExfI/JGcT2uIsHGCjZ+GQR0UNUV4TNFpAEwHmjiR1AeyVTVrcWg8p3Xu8A7wBPu9O/Ax8DbfgXkBVVdleezCvkVi5eK63EVBVZjNX5JAFZHmP83kBjjWLy2QESuBoIi0khEXgWm+x2UB6qp6idAFoCqZlL0f6xXichpgIpIKRF5GLf5tIgrrsdVJFiN1fhlGDBTRD4CVrnz6gJXUcRrQMA9OLW6PcAHwESgn68ReWOHiFQFFEBE2gJb/Q2pwG7HaT2pjXOiNwm4y9eIvFFcj6tIsAEijG9EpAnQFec/v+D8AIxV1UW+BlZAInK5qo481LyiRkRaAa8CzYAFQHXgMlWd52tgxsQZS6zGeExEflXVVoeaVxSJSAJwHM6J0BJV3edzSAXiXtO/B6dzWU4Lnqpe7FdMXhCRVyLM3gr8oqpjYh1PSWNNwcZ4RETOx+nhXDvPD1slINOfqLwjIpfmmdVYRLYC81V1vR8xeeBznEsP43CvHRcTZYDjgexWku7AQuAWETlHVe/3K7CSwBKrMd5ZA/wCXAzMCpufATzgS0TeugVoB0xxp88GZuAk2GdU9X2/AiuA3aoaqXZX1B0LtHc7mCEir+NcZ+0IzPczsJLAEqsxHnHvFZwrIiOyf9CKmSygiaqmgXNfK/A6cCqQChTFxDpIRJ7CSTp7smeq6q/+heSJ2kB59ncuKw/UUtWQiOw58GrGC5ZYje9EpJeqDjnQdFEhIp+o6hXAbBEJ77wggKpqC59C80r97KTqWg80VtXNIlJUr7U2B64D2rO/KVjd6aLseWCOiHyH8/07E+jvDnH4tZ+BlQSWWE08yDuSQlEdWeE+998LfY2i8EwTkS/Ifd0u1f2xTvctqoK5BDhGVff6HYiXVPVtERmPMwKYAL1VdY27+J/+RVYyWK9gYwqJe8/nmcBfqjrrUOXjnTuO86XAGe6sTUBNVS2y90eKyMfAPUW489UBiUgVoBFORyYAVDXVv4hKDquxGl+IyIMHW66qL8YqFq+4tbnHVHWBiNQEfsXpzNRQRIao6su+BlhAqqoi8ifONdUrgOXAZ/5GVWApwG8iMpPc11iL+u02t+K0oNTBeSBEW+BHin4Td5FgidX4paL773FAGyD76S8X4XSEKYoaqOoC9/1NwGRVvV5EKgI/AC/7FlkBiEhjnBGxeuDUUj/Gae06x9fAvPGU3wEUkvtw/l/NUNVzROR4nIdcmBiwxGp8oap9AURkEtBKVTPc6afZfw2vqAnvwHMu8BaAqmaISFG+R/I3YBpwkaouBRCR4nD7EKo61e3d3Mad9XMxaRberaq7RQQRKa2qv9nzgWPHBuE3fjsaCO84spei+4i1VSJyj4hcArRi//NYy1K0HyzQHVgHTBGRt0TkXIpuB7NcROQK4Gfgcpzm7Z9E5DJ/o/LEahFJwhkAY7KIjMG5z9rEgHVeMr4SkSdwftBG49zmcAnwiar29zWwIyAiycAzQE1gsKpOcuefA5ysqv/nZ3wF5fb+7YbTJNweGA6Mzj7OokhE5gIds2upIlId+FpVT/Q3Mu+IyFlAZWBCcev9HK8ssRrfuYO7/8OdTFXV2X7GYw5NRI7CqeVdqapFtkOMiMxX1eZh0wFgbvi8okhEGgKrVXWPiJwNtADeU9V0P+MqKSyxGt+JyBlAI1V9x60xVFDV5X7HZYo/EfkPTtL50J11Jc7Yx4/4F1XBicgcoDXOZZWJOJ0Dj1PVLj6GVWJYYjW+coeTa43zn76xiNQCRqrq6T6HZkoI9+ECZ+BcN05V1dE+h1Rg2U9TEpF/4nRkelVEZqvqSX7HVhJYr2Djt0uAk3Du+URV17i3pxhT6NzHxo1X1VHudFkRqa+qK/yNrMD2iUgP4AacW9igaHegK1IssRq/7XUHHlDI6SBTpLnN2T3J/4zPm/2KyRzQSOC0sOmQO69N5OJFxk3A7cCzqrrcPYH4n88xlRiWWI3fPhGRN4EkEekJ3Ix7/2cRNgbnvs+vcX6oTfxKCO8pq6p7RaSUnwF5QVUXAfeGTS8HnvMvopLFEqvxlar+n4h0BLbhjMLUR1Un+xxWQZVT1Uf9DsJEZYOIXKyqYwFEpCuw0eeYjpiIzMe5bS2b4hzPFOD/VHW3L4GVMNZ5ycQFEalE7mbTzT6GUyAi8m9guqqO9zsWc3DubSkjgFo4nZdWAddnjzBV1IhIvQizj8K51lpeVXvGOKQSyRKr8ZWI3IYzqMIunOdhZj+79BhfAysAEcnAebD0XvYPc6iqWsm/qMzBiEgFnN/DDL9jKSzWKzh2rCnY+O1h4ARVLbLNb3mpqvVqLiJEpDTOkI31gQTnyXigqs/4GFZhsSFsY8QSq/Hbn8BOv4PwmohcjPMsVoDvVPULP+MxBzQG2ArMIuyxcUWVO4pZXlWAaym6T40qcqwp2PhKRE4C3gF+IvfzMO894EpxTkSew7ldY4Q7qwcwS1Uf8y8qE4mILFDVZn7H4RURmZJnluI86u87YIiq7su3kvGcJVbjKxH5GfgemI9zjRUAVR3uW1AFJCLzgJaqmuVOB4HZqtrC38hMXiIyBHhVVef7HYspPqwp2PgtU1Uf9DuIQpAEZPdsruxjHObgzgBuFJHlOC0m2Z3n7CTIHDFLrMZvU0SkFzCO3E3BRfZ2G2AAMNttlhOca62P+xuSOYDz/Q7AFD/WFGx85dYU8irSt9sAiEhNnOusAvykqut8DsmEcR97d0BF/MQOESmtqnsONc8UDkusxpgSxz2hU5wTn7yKw4ndr6ra6lDzTOGwpmDjOxFpBjQFymTPU9X3/IvIFHeq2sDvGAqDiNQAagNl3R732ScOlYByvgVWwlhiNb5yn8d6Nk5iHY9zzet7wBKrMYfvPOBGoA7wYtj8bUBvPwIqiawp2PjKHTT8RJzbUU4UkRRgqKpedIhV45aIvK+q1x1qnjGFRUS6q+pnfsdRUtkQV8Zvu9z7PTPdgfjXA0X6+hZwQviEex/ryT7FYkqmH0TkbRH5CkBEmorILX4HVVJYYjV++0VEknCewToL+BX42deIjpCIPO4OwN9CRLa5rwyck4UxPodnDkJEkkXk6OyX3/F44B1gIs5TewB+B+73LZoSxpqCTdwQkfpAJVWd53csBSEiA1TV7lstAtwxnV/ASUDrgXrAYlU94aArxjkRmamqbcKfaCMic1S1pc+hlQjWecn4TkRq4/ygJbjTZ6pqkR0wXFUfz3tM7vwie0zFWD+gLfC1qp4kIufgjO1c1O0Qkaq4Dz0XkbY4DxswMWCJ1fhKRAYCVwKLgJA7WynCT+JwB+G/imJ0TMXYPlXdJCIBEQmo6hT3O1nUPQiMBRqKyA9AdeAyf0MqOSyxGr91A44rZiPCXELxO6biKt19yPk0YISIrAcyfY6pwFT1VxE5CzgO517WJfZkm9ixxGr8tgxIpBg8CzNMcTym4qorsAunY881OA9MKC4POT8F9wHuQCsRsYFXYsQSq/HbTmCOiHxDMXkeK8XzmIolVd0hIvWARqo6XETKAUG/4yooEXkfaAjMIfflCEusMWCJ1fhtrPsqTorjMRVLItIT6AUchZOIagNvAOf6GZcHWgNN1W778IXdbmNMIRCRssDRqrrE71jMgYnIHJwm05/CbkuZr6rNfQ2sgERkJHCvqq71O5aSyGqsxlcicjrwNPtvTcl+0HSRHX1JRC4C/g8oBTQQkZbAM6p6sa+BmUj2qOpeEWesehFJwL1FpYirBiwSkZ/JfTnCvoMxYInV+O1t4AGcUZdChyhbVDyNUwv6DkBV54hIsXyaSjEwVUR64zwNpiNwJzDO55i88LTfAZRklliN37aq6ld+B+GxTFXdml0LchWHWlBx9ChwKzAfuA3nCUtDfY2ogEQkAAxW1WZ+x1JSWWI1fpsiIv8BRpG7yepX/0IqsAUicjUQFJFGwL3AdJ9jMnm4CWiem4De8jser6hqlojMFZGjVfUvv+MpiSyxGr+d6v7bOmyeAu19iMUr9wBP4JwofIgzGHo/XyMy+RTzBFQTWOheY92RPdOuscaG9Qo2ppC4j8FTVc3wOxYTmYh8C7TBeaJSsUlA7qhL+ajq1FjHUhJZYjW+EJEH88xSYCPwvaou9yEkz4hIG2AYUNGdtRW4WVVn+ReViaQ4J6CwgS++zh74wk7yYsOex2r8UjHPqxJOc/BXInKVn4F54G3gTlWtr6r1gbtwno9p4oyqTg1/4YwTfIXfcRWUO/DFp8Cb7qzawOe+BVTC2DVW4wtV7RtpvogcBXwNfBTbiDyVoarTsidU9Xv3gecmDrn3GV+Nk1CXA5/5GpA37sId+AJAVf8QkWR/Qyo5LLGauKKqmyXPfSpF0M8i8iZOxyXFeSzedyLSCop8j+diQUQa4zzarwewCfgY59LYOb4G5p3iOvBFkWCJ1cQVEWkPbPE7jgJq6f77VJ75p1H0ezwXF7/hPCruIlVdCiAiD/gbkqeK68AXRYJ1XjK+EJH55D+DPgpYA1yvqr/FPipTUojIJTg11tOACTiXHoaqarEYIcu9R/cWoBPOMKETVbXY3Ksb7yyxGl+4PRbDKbBJVXdEKl8UuGMEz1PVle50H6A7sBK4r6j3di6ORKQ80A2nSbg9MBwYraqT/IyroETkPlUddKh5pnBYYjXGIyIyD2irqjtF5ELgRZwf7JOAy1X1PF8DNAfldpy7HLhSVYt0c72I/KqqrfLMm539BB9TuCyxGuMREZmrqie674cBS1R1oDud74fOGK+JSA+cHs5n4FxDzlYRCKlqB18CK2Gs85Ix3hERqQDsxHlQ9n/DlpXxJyRTwkwH1uI8Nu6FsPkZwDxfIiqBLLEa452XgTnANmCxqv4CICIn4fzYGVOo3Ov7K4F2fsdSkllTsDEeEpHaQDIwV1Wz3Hk1gcRiONC7iVMicikwEOe7KO5LVbWSr4GVEJZYjTGmmBGRpTj36C72O5aSyMYKNsaY4ifNkqp/rMZqjDHFjIgMAmrgDLy/J3u+qo7yK6aSxDovGVMIROQMnEd2vSMi1YEKNkCEiaFKOL3TO4XNU8ASawxYjdUYj4nIUziPwDtOVRuLSC1gpKqe7nNoxpgYsBqrMd67BGe0pV8BVHWNiFQ8+CrGFJyIvMpBnmKjqvfGMJwSyxKrMd7bq6oqIgo549EaEwu/+B2AscRqTGH4xH0ea5KI9ARuBuzJIqbQqepwv2Mwdo3VmELhPgMz/JFdk30OyRgTI5ZYjSkkIlKJsFYhVd3sYzjGmBixpmBjPCYitwHPALuALNzh5IBj/IzLlBwicpSdyPnHaqzGeExE/gDaqepGv2MxJZP7HZwDvAN8pfZDH1M2pKEx3vsT5+Z8Y/zSGBgCXAcsFZH+ItLY55hKDKuxGuMx9zFx7wA/kXs4ObuH0MSciJwD/A8oD8wFHlPVH/2Nqniza6zGeO9N4FtgPs41VmNiSkSqAtfi1FjTgHuAsUBLYCTQwLfgSgBLrMZ4L1NVH/Q7CFOi/Qi8D3RT1dVh838RkTd8iqnEsKZgYzwmIs8CK4Fx5G4Ktl6aJiZERKzDkn8ssRrjMRGJ9BQbVVW73cYUKhEZx8HHCr44huGUWJZYjTGmmBCRsw62XFWnxiqWkswSqzEeEZH2qvqtiFwaabk9ZNqYksE6LxnjnTNxegNfFGGZPWTaFDoR+URVrxCR+URoElbVFj6EVeJYYjXGO6UAVPUmvwMxJdZ97r8X+hpFCWdNwcZ4RER+VdVWfsdhjPGX1ViN8U5QRKrgDLqfj91uY2JFRNoCrwJNcFpSgsAOVa3ka2AlhCVWY7xzPDCLyInVnm5jYuk14CqcUZZaA9cDx/oaUQliidUY7yxS1ZP8DsIYAFVdKiJBVQ0B74jIdL9jKikssRpjTPGzU0RKAXNE5HlgLc4g/CYG7LFxxnhnkN8BGOO6Duf3/W5gB1AX6O5rRCWI9Qo2xphiSESqA6jqBr9jKWmsxmqMMcWEOJ4WkY3Ab8DvIrJBRPr4HVtJYonVGI+JyFF+x2BKrPuB04E2qlpVVasApwKni8gDvkZWglhTsDEeE5E/gDnAO8BX9vguEysiMhvoqKob88yvDkyyXuuxYTVWY7zXGBiC04FkqYj0F5HGPsdkSobEvEkVcq6zJvoQT4lkidUYj6ljsqr2AG4FbgB+FpGpItLO5/BM8bb3CJcZD1lTsDEeE5GqwLU4NdY04G1gLNASGKmqDfyLzhRnIhLCub0m3yKgjKparTUGbIAIY7z3I/A+0E1VV4fN/0VE3vApJlMCqGrQ7xiM1ViN8ZyIiHVYMqbkssRqjEdEZBwRHi6dTVUvjmE4xhifWFOwMd75P78DMMb4z2qsxhhjjIesxmqMR0TkE1W9QkTmE6FJWFVb+BCWMSbGrMZqjEdEpKaqrhWRepGWq+rKWMdkjIk9S6zGGGOMh2zkJWM8JiJtRWSmiGwXkb0iEhKRbX7HZYyJDUusxnjvNaAH8AdQFmdYw1d9jcgYEzPWecmYQqCqS0UkqKoh4B0Rme53TMaY2LDEaoz3dopIKWCOiDwPrAXK+xyTMSZGrCnYGO9dh/N/626cAdHrAt19jcgYEzPWK9iYQuA+WDr7OZjGmBLEaqzGeEQcT4vIRuA34HcR2SAiffyOzRgTO5ZYjfHO/cDpQBtVraqqVYBTgdNF5AFfIzPGxIw1BRvjERGZDXRU1Y155lcHJqnqSf5EZoyJJauxGuOdxLxJFXKusyb6EI8xxgeWWI3xzt4jXGaMKUasKdgYj4hICOf2mnyLgDKqarVWY0oAS6zGGGOMh6wp2BhjjPGQJVZjjDHGQ5ZYjTHGGA9ZYjXGGGM8ZInVGGOM8ZAlVmOMMcZD/w+CCU8y6vPE9wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(),cmap='Reds',annot=True,fmt='.2f')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d335abb-fe48-40f8-8026-c6a38a8b2ebe",
   "metadata": {},
   "source": [
    "- kolom `Daily Internet Usage` dengan `Daily Time Spent on Site` memiliki korelasi positif cukup kuat\n",
    "- kolom `Area Income` dengan `Daily Time Spent on Site` memiliki korelasi positif lemah\n",
    "- kolom `Daily Internet Usage` dengan `Area Income` memiliki korelasi positif lemah"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e8ab70d-8d84-4883-b384-f5bf79f1333b",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30e72395-e4c2-4c5c-8334-9da3d2bcfdc8",
   "metadata": {},
   "source": [
    "### Removing Duplicates & Imputation for Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9e16c7b2-fd82-4ee3-9555-450b7ab115ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5ba59b27-f336-4a98-b8ad-36bf8295f014",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                   0\n",
       "Daily Time Spent on Site    13\n",
       "Age                          0\n",
       "Area Income                 13\n",
       "Daily Internet Usage        11\n",
       "Male                         3\n",
       "Timestamp                    0\n",
       "Clicked on Ad                0\n",
       "city                         0\n",
       "province                     0\n",
       "category                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8071c6d5-b773-498b-ad02-2c26f8bebbe3",
   "metadata": {},
   "source": [
    "langkah yang akan dilakukan pada kolom berisi null :\n",
    "1. Pada kolom `Daily Time Spent on Site`,`Area Income`, dan `Daily Internet Usage` akan dilakukan imputasi dengan `median`\n",
    "2. Pada kolom `Male` akan dilakukan imputasi dengan `modus`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "3fcde6d0-51c2-49b0-bed1-4ad570166591",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                                0\n",
       "Daily Time Spent on Site                  0\n",
       "Age                                       0\n",
       "Area Income                               0\n",
       "Daily Internet Usage                      0\n",
       "Male                                      0\n",
       "Timestamp                                 0\n",
       "Clicked on Ad                             0\n",
       "city                                      0\n",
       "province                                  0\n",
       "category                                  0\n",
       "city_Balikpapan                           0\n",
       "city_Bandar Lampung                       0\n",
       "city_Bandung                              0\n",
       "city_Banjarmasin                          0\n",
       "city_Batam                                0\n",
       "city_Bekasi                               0\n",
       "city_Bogor                                0\n",
       "city_Cimahi                               0\n",
       "city_Denpasar                             0\n",
       "city_Depok                                0\n",
       "city_Jakarta Barat                        0\n",
       "city_Jakarta Pusat                        0\n",
       "city_Jakarta Selatan                      0\n",
       "city_Jakarta Timur                        0\n",
       "city_Jakarta Utara                        0\n",
       "city_Makassar                             0\n",
       "city_Malang                               0\n",
       "city_Medan                                0\n",
       "city_Padang                               0\n",
       "city_Pakanbaru                            0\n",
       "city_Palembang                            0\n",
       "city_Pontianak                            0\n",
       "city_Samarinda                            0\n",
       "city_Semarang                             0\n",
       "city_Serang                               0\n",
       "city_Surabaya                             0\n",
       "city_Surakarta                            0\n",
       "city_Tangerang                            0\n",
       "city_Tangerang Selatan                    0\n",
       "city_Tasikmalaya                          0\n",
       "province_Bali                             0\n",
       "province_Banten                           0\n",
       "province_Daerah Khusus Ibukota Jakarta    0\n",
       "province_Jawa Barat                       0\n",
       "province_Jawa Tengah                      0\n",
       "province_Jawa Timur                       0\n",
       "province_Kalimantan Barat                 0\n",
       "province_Kalimantan Selatan               0\n",
       "province_Kalimantan Timur                 0\n",
       "province_Kepulauan Riau                   0\n",
       "province_Lampung                          0\n",
       "province_Riau                             0\n",
       "province_Sulawesi Selatan                 0\n",
       "province_Sumatra Barat                    0\n",
       "province_Sumatra Selatan                  0\n",
       "province_Sumatra Utara                    0\n",
       "category_Bank                             0\n",
       "category_Electronic                       0\n",
       "category_Fashion                          0\n",
       "category_Finance                          0\n",
       "category_Food                             0\n",
       "category_Furniture                        0\n",
       "category_Health                           0\n",
       "category_House                            0\n",
       "category_Otomotif                         0\n",
       "category_Travel                           0\n",
       "Date                                      0\n",
       "Time                                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Imputation for null values with Median and Modus\n",
    "\n",
    "df['Daily Time Spent on Site'].fillna(df['Daily Time Spent on Site'].median(), inplace=True)\n",
    "df['Area Income'].fillna(df['Area Income'].median(), inplace=True)\n",
    "df['Daily Internet Usage'].fillna(df['Daily Internet Usage'].median(), inplace=True)\n",
    "df['Male'].fillna(df['Male'].mode()[0], inplace=True)\n",
    "\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d66fd4c-9717-46b1-a1c3-d7f737a8ebcc",
   "metadata": {},
   "source": [
    "### Feature Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28637a03-9985-46ea-b3d5-6f623c331b86",
   "metadata": {},
   "source": [
    "Feature Encoding yang akan dilakukan :\n",
    "1. Pada kolom `Male` dan `Clicked on Ad` akan dilakukan `Label Encoding`\n",
    "2. Pada kolom `City`,`Province` dan `Category` akan dilakukan `One Hot Encoding`\n",
    "3. Untuk kolom `Timestamp` akan dirubah menjadi tipe datetime dan akan diekstrak menjadi kolom `Year`,`Month`,`Day`, dan `Time`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d300e48f-044e-4ad9-8049-36eb74ccf34c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    521\n",
       "1    479\n",
       "Name: Male, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Male'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8d2c1dbc-d0b0-4fc0-904f-89b9376720d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     500\n",
       "Yes    500\n",
       "Name: Clicked on Ad, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Clicked on Ad'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e3b1e1dd-f938-4e19-94e0-7acbfebb6c67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    521\n",
       "1    479\n",
       "Name: Male, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# label Encoding for `Male` and `Clicked on Ad` column\n",
    "\n",
    "df['Male'] = df['Male'].replace(['Laki-Laki','Perempuan'],[1,0])\n",
    "df['Clicked on Ad'] = df['Clicked on Ad'].replace(['Yes','No'],[1,0])\n",
    "\n",
    "df['Male'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6bf771ab-bb81-480b-b0cc-bf06eb210008",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    500\n",
       "1    500\n",
       "Name: Clicked on Ad, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Clicked on Ad'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0109059a-2ac3-47b9-bff7-f707f208fccb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# One Hot Encoding for `City`,`Province`, and `Category' columns\n",
    "\n",
    "for i in ['city','province','category']:\n",
    "    onehots = pd.get_dummies(df[i], prefix=i)\n",
    "    df = df.join(onehots)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "73a24b1d-2c35-4ba4-943b-44b7913a0f6f",
   "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>Unnamed: 0</th>\n",
       "      <th>Daily Time Spent on Site</th>\n",
       "      <th>Age</th>\n",
       "      <th>Area Income</th>\n",
       "      <th>Daily Internet Usage</th>\n",
       "      <th>Male</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Clicked on Ad</th>\n",
       "      <th>city</th>\n",
       "      <th>province</th>\n",
       "      <th>category</th>\n",
       "      <th>city_Balikpapan</th>\n",
       "      <th>city_Bandar Lampung</th>\n",
       "      <th>city_Bandung</th>\n",
       "      <th>city_Banjarmasin</th>\n",
       "      <th>city_Batam</th>\n",
       "      <th>city_Bekasi</th>\n",
       "      <th>city_Bogor</th>\n",
       "      <th>city_Cimahi</th>\n",
       "      <th>city_Denpasar</th>\n",
       "      <th>city_Depok</th>\n",
       "      <th>city_Jakarta Barat</th>\n",
       "      <th>city_Jakarta Pusat</th>\n",
       "      <th>city_Jakarta Selatan</th>\n",
       "      <th>city_Jakarta Timur</th>\n",
       "      <th>city_Jakarta Utara</th>\n",
       "      <th>city_Makassar</th>\n",
       "      <th>city_Malang</th>\n",
       "      <th>city_Medan</th>\n",
       "      <th>city_Padang</th>\n",
       "      <th>city_Pakanbaru</th>\n",
       "      <th>city_Palembang</th>\n",
       "      <th>city_Pontianak</th>\n",
       "      <th>city_Samarinda</th>\n",
       "      <th>city_Semarang</th>\n",
       "      <th>city_Serang</th>\n",
       "      <th>city_Surabaya</th>\n",
       "      <th>city_Surakarta</th>\n",
       "      <th>city_Tangerang</th>\n",
       "      <th>city_Tangerang Selatan</th>\n",
       "      <th>city_Tasikmalaya</th>\n",
       "      <th>province_Bali</th>\n",
       "      <th>province_Banten</th>\n",
       "      <th>province_Daerah Khusus Ibukota Jakarta</th>\n",
       "      <th>province_Jawa Barat</th>\n",
       "      <th>province_Jawa Tengah</th>\n",
       "      <th>province_Jawa Timur</th>\n",
       "      <th>province_Kalimantan Barat</th>\n",
       "      <th>province_Kalimantan Selatan</th>\n",
       "      <th>province_Kalimantan Timur</th>\n",
       "      <th>province_Kepulauan Riau</th>\n",
       "      <th>province_Lampung</th>\n",
       "      <th>province_Riau</th>\n",
       "      <th>province_Sulawesi Selatan</th>\n",
       "      <th>province_Sumatra Barat</th>\n",
       "      <th>province_Sumatra Selatan</th>\n",
       "      <th>province_Sumatra Utara</th>\n",
       "      <th>category_Bank</th>\n",
       "      <th>category_Electronic</th>\n",
       "      <th>category_Fashion</th>\n",
       "      <th>category_Finance</th>\n",
       "      <th>category_Food</th>\n",
       "      <th>category_Furniture</th>\n",
       "      <th>category_Health</th>\n",
       "      <th>category_House</th>\n",
       "      <th>category_Otomotif</th>\n",
       "      <th>category_Travel</th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>68.95</td>\n",
       "      <td>35</td>\n",
       "      <td>432837300.0</td>\n",
       "      <td>256.09</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-03-27 00:53:00</td>\n",
       "      <td>No</td>\n",
       "      <td>Jakarta Timur</td>\n",
       "      <td>Daerah Khusus Ibukota Jakarta</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-03-27</td>\n",
       "      <td>00:53:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>03</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>80.23</td>\n",
       "      <td>31</td>\n",
       "      <td>479092950.0</td>\n",
       "      <td>193.77</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-04-04 01:39:00</td>\n",
       "      <td>No</td>\n",
       "      <td>Denpasar</td>\n",
       "      <td>Bali</td>\n",
       "      <td>Food</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-04</td>\n",
       "      <td>01:39:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>04</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>69.47</td>\n",
       "      <td>26</td>\n",
       "      <td>418501580.0</td>\n",
       "      <td>236.50</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-03-13 20:35:00</td>\n",
       "      <td>No</td>\n",
       "      <td>Surabaya</td>\n",
       "      <td>Jawa Timur</td>\n",
       "      <td>Electronic</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-03-13</td>\n",
       "      <td>20:35:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>03</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>74.15</td>\n",
       "      <td>29</td>\n",
       "      <td>383643260.0</td>\n",
       "      <td>245.89</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-10 02:31:00</td>\n",
       "      <td>No</td>\n",
       "      <td>Batam</td>\n",
       "      <td>Kepulauan Riau</td>\n",
       "      <td>House</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-01-10</td>\n",
       "      <td>02:31:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>01</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>68.37</td>\n",
       "      <td>35</td>\n",
       "      <td>517229930.0</td>\n",
       "      <td>225.58</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-06-03 03:36:00</td>\n",
       "      <td>No</td>\n",
       "      <td>Medan</td>\n",
       "      <td>Sumatra Utara</td>\n",
       "      <td>Finance</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-06-03</td>\n",
       "      <td>03:36:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>06</td>\n",
       "      <td>03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <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>995</th>\n",
       "      <td>995</td>\n",
       "      <td>72.97</td>\n",
       "      <td>30</td>\n",
       "      <td>499691990.0</td>\n",
       "      <td>208.58</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-02-11 21:49:00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Semarang</td>\n",
       "      <td>Jawa Tengah</td>\n",
       "      <td>Finance</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-02-11</td>\n",
       "      <td>21:49:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>02</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>996</td>\n",
       "      <td>51.30</td>\n",
       "      <td>45</td>\n",
       "      <td>474475190.0</td>\n",
       "      <td>134.42</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-04-22 02:07:00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Pakanbaru</td>\n",
       "      <td>Riau</td>\n",
       "      <td>Electronic</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-22</td>\n",
       "      <td>02:07:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>04</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>997</td>\n",
       "      <td>68.11</td>\n",
       "      <td>51</td>\n",
       "      <td>296910040.0</td>\n",
       "      <td>120.37</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-02-01 17:24:00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Samarinda</td>\n",
       "      <td>Kalimantan Timur</td>\n",
       "      <td>Travel</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-02-01</td>\n",
       "      <td>17:24:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>02</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>998</td>\n",
       "      <td>55.55</td>\n",
       "      <td>19</td>\n",
       "      <td>293445530.0</td>\n",
       "      <td>187.95</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-03-24 02:35:00</td>\n",
       "      <td>No</td>\n",
       "      <td>Tangerang</td>\n",
       "      <td>Banten</td>\n",
       "      <td>Bank</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-03-24</td>\n",
       "      <td>02:35:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>03</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>999</td>\n",
       "      <td>45.01</td>\n",
       "      <td>26</td>\n",
       "      <td>209130600.0</td>\n",
       "      <td>178.35</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-06-03 21:43:00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Serang</td>\n",
       "      <td>Banten</td>\n",
       "      <td>Travel</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-03</td>\n",
       "      <td>21:43:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>06</td>\n",
       "      <td>03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  Daily Time Spent on Site  Age  Area Income  Daily Internet Usage  Male           Timestamp Clicked on Ad           city                       province    category  city_Balikpapan  city_Bandar Lampung  city_Bandung  city_Banjarmasin  city_Batam  city_Bekasi  city_Bogor  city_Cimahi  city_Denpasar  city_Depok  city_Jakarta Barat  city_Jakarta Pusat  city_Jakarta Selatan  city_Jakarta Timur  city_Jakarta Utara  city_Makassar  city_Malang  city_Medan  city_Padang  city_Pakanbaru  city_Palembang  city_Pontianak  city_Samarinda  city_Semarang  city_Serang  city_Surabaya  city_Surakarta  city_Tangerang  city_Tangerang Selatan  city_Tasikmalaya  province_Bali  province_Banten  province_Daerah Khusus Ibukota Jakarta  province_Jawa Barat  province_Jawa Tengah  province_Jawa Timur  province_Kalimantan Barat  province_Kalimantan Selatan  province_Kalimantan Timur  province_Kepulauan Riau  province_Lampung  province_Riau  province_Sulawesi Selatan  province_Sumatra Barat  \\\n",
       "0             0                     68.95   35  432837300.0                256.09     0 2016-03-27 00:53:00            No  Jakarta Timur  Daerah Khusus Ibukota Jakarta   Furniture                0                    0             0                 0           0            0           0            0              0           0                   0                   0                     0                   1                   0              0            0           0            0               0               0               0               0              0            0              0               0               0                       0                 0              0                0                                       1                    0                     0                    0                          0                            0                          0                        0                 0              0                          0                       0   \n",
       "1             1                     80.23   31  479092950.0                193.77     1 2016-04-04 01:39:00            No       Denpasar                           Bali        Food                0                    0             0                 0           0            0           0            0              1           0                   0                   0                     0                   0                   0              0            0           0            0               0               0               0               0              0            0              0               0               0                       0                 0              1                0                                       0                    0                     0                    0                          0                            0                          0                        0                 0              0                          0                       0   \n",
       "2             2                     69.47   26  418501580.0                236.50     0 2016-03-13 20:35:00            No       Surabaya                     Jawa Timur  Electronic                0                    0             0                 0           0            0           0            0              0           0                   0                   0                     0                   0                   0              0            0           0            0               0               0               0               0              0            0              1               0               0                       0                 0              0                0                                       0                    0                     0                    1                          0                            0                          0                        0                 0              0                          0                       0   \n",
       "3             3                     74.15   29  383643260.0                245.89     1 2016-01-10 02:31:00            No          Batam                 Kepulauan Riau       House                0                    0             0                 0           1            0           0            0              0           0                   0                   0                     0                   0                   0              0            0           0            0               0               0               0               0              0            0              0               0               0                       0                 0              0                0                                       0                    0                     0                    0                          0                            0                          0                        1                 0              0                          0                       0   \n",
       "4             4                     68.37   35  517229930.0                225.58     0 2016-06-03 03:36:00            No          Medan                  Sumatra Utara     Finance                0                    0             0                 0           0            0           0            0              0           0                   0                   0                     0                   0                   0              0            0           1            0               0               0               0               0              0            0              0               0               0                       0                 0              0                0                                       0                    0                     0                    0                          0                            0                          0                        0                 0              0                          0                       0   \n",
       "..          ...                       ...  ...          ...                   ...   ...                 ...           ...            ...                            ...         ...              ...                  ...           ...               ...         ...          ...         ...          ...            ...         ...                 ...                 ...                   ...                 ...                 ...            ...          ...         ...          ...             ...             ...             ...             ...            ...          ...            ...             ...             ...                     ...               ...            ...              ...                                     ...                  ...                   ...                  ...                        ...                          ...                        ...                      ...               ...            ...                        ...                     ...   \n",
       "995         995                     72.97   30  499691990.0                208.58     1 2016-02-11 21:49:00           Yes       Semarang                    Jawa Tengah     Finance                0                    0             0                 0           0            0           0            0              0           0                   0                   0                     0                   0                   0              0            0           0            0               0               0               0               0              1            0              0               0               0                       0                 0              0                0                                       0                    0                     1                    0                          0                            0                          0                        0                 0              0                          0                       0   \n",
       "996         996                     51.30   45  474475190.0                134.42     1 2016-04-22 02:07:00           Yes      Pakanbaru                           Riau  Electronic                0                    0             0                 0           0            0           0            0              0           0                   0                   0                     0                   0                   0              0            0           0            0               1               0               0               0              0            0              0               0               0                       0                 0              0                0                                       0                    0                     0                    0                          0                            0                          0                        0                 0              1                          0                       0   \n",
       "997         997                     68.11   51  296910040.0                120.37     1 2016-02-01 17:24:00           Yes      Samarinda               Kalimantan Timur      Travel                0                    0             0                 0           0            0           0            0              0           0                   0                   0                     0                   0                   0              0            0           0            0               0               0               0               1              0            0              0               0               0                       0                 0              0                0                                       0                    0                     0                    0                          0                            0                          1                        0                 0              0                          0                       0   \n",
       "998         998                     55.55   19  293445530.0                187.95     0 2016-03-24 02:35:00            No      Tangerang                         Banten        Bank                0                    0             0                 0           0            0           0            0              0           0                   0                   0                     0                   0                   0              0            0           0            0               0               0               0               0              0            0              0               0               1                       0                 0              0                1                                       0                    0                     0                    0                          0                            0                          0                        0                 0              0                          0                       0   \n",
       "999         999                     45.01   26  209130600.0                178.35     0 2016-06-03 21:43:00           Yes         Serang                         Banten      Travel                0                    0             0                 0           0            0           0            0              0           0                   0                   0                     0                   0                   0              0            0           0            0               0               0               0               0              0            1              0               0               0                       0                 0              0                1                                       0                    0                     0                    0                          0                            0                          0                        0                 0              0                          0                       0   \n",
       "\n",
       "     province_Sumatra Selatan  province_Sumatra Utara  category_Bank  category_Electronic  category_Fashion  category_Finance  category_Food  category_Furniture  category_Health  category_House  category_Otomotif  category_Travel        Date      Time  Year Month Day  \n",
       "0                           0                       0              0                    0                 0                 0              0                   1                0               0                  0                0  2016-03-27  00:53:00  2016    03  27  \n",
       "1                           0                       0              0                    0                 0                 0              1                   0                0               0                  0                0  2016-04-04  01:39:00  2016    04  04  \n",
       "2                           0                       0              0                    1                 0                 0              0                   0                0               0                  0                0  2016-03-13  20:35:00  2016    03  13  \n",
       "3                           0                       0              0                    0                 0                 0              0                   0                0               1                  0                0  2016-01-10  02:31:00  2016    01  10  \n",
       "4                           0                       1              0                    0                 0                 1              0                   0                0               0                  0                0  2016-06-03  03:36:00  2016    06  03  \n",
       "..                        ...                     ...            ...                  ...               ...               ...            ...                 ...              ...             ...                ...              ...         ...       ...   ...   ...  ..  \n",
       "995                         0                       0              0                    0                 0                 1              0                   0                0               0                  0                0  2016-02-11  21:49:00  2016    02  11  \n",
       "996                         0                       0              0                    1                 0                 0              0                   0                0               0                  0                0  2016-04-22  02:07:00  2016    04  22  \n",
       "997                         0                       0              0                    0                 0                 0              0                   0                0               0                  0                1  2016-02-01  17:24:00  2016    02  01  \n",
       "998                         0                       0              1                    0                 0                 0              0                   0                0               0                  0                0  2016-03-24  02:35:00  2016    03  24  \n",
       "999                         0                       0              0                    0                 0                 0              0                   0                0               0                  0                1  2016-06-03  21:43:00  2016    06  03  \n",
       "\n",
       "[1000 rows x 72 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting `Timestamp` column to get `Year`,`Month`,`Day`, and `Time` columns\n",
    "\n",
    "df['Timestamp'] = pd.to_datetime(df['Timestamp'])\n",
    "df['Date'] = [d.date() for d in df['Timestamp']]\n",
    "df['Time'] = [d.time() for d in df['Timestamp']]\n",
    "df['Date'] = df['Date'].astype(str)\n",
    "df2 = df['Date'].str.split('-', expand=True)\n",
    "df2 = df2.rename(columns={0:'Year',1:'Month',2:'Day'})\n",
    "df.join(df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71b27df6-e983-44bd-ad9d-e4be8fa6f49f",
   "metadata": {},
   "source": [
    "## Data Modelling & Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cf141f7-a71c-4a98-983d-f46fb9840847",
   "metadata": {},
   "source": [
    "### Split Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5c5e1d62-71ba-4320-b2d5-23d2a262cfc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into features(X) and target(y)\n",
    "X = df[['Age','Area Income','Daily Time Spent on Site','Daily Internet Usage']].values\n",
    "y = df['Clicked on Ad'].values\n",
    "\n",
    "# Split data into Train and Test\n",
    "from sklearn.model_selection import train_test_split \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d6a57193-8824-42e9-8075-a03044f01646",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_16608/3777217377.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'X' is not defined"
     ]
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80a792f9-bb94-486f-9b53-e9c955f91c4c",
   "metadata": {},
   "source": [
    "### Fit and Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "4b22c73b-3a41-4446-8dcb-8db1201a5c7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "def eval_classification(model):\n",
    "    y_pred = model.predict(X_test)\n",
    "    y_pred_train = model.predict(X_train)\n",
    "    y_pred_proba = model.predict_proba(X_test)\n",
    "    y_pred_proba_train = model.predict_proba(X_train)\n",
    "    \n",
    "    print(\"Accuracy (Test Set): %.2f\" % accuracy_score(y_test, y_pred))\n",
    "    print(\"Accuracy (Train Set): %.2f\" % accuracy_score(y_train, y_pred_train))\n",
    "\n",
    "    print(\"Precision (Test Set): %.2f\" % precision_score(y_test, y_pred))\n",
    "    print(\"Precision (Train Set): %.2f\" % precision_score(y_train, y_pred_train))\n",
    "\n",
    "    print(\"Recall (Test Set): %.2f\" % recall_score(y_test, y_pred))\n",
    "    print(\"Recall (Train Set): %.2f\" % recall_score(y_train, y_pred_train))\n",
    "\n",
    "    print(\"F1-Score (Test Set): %.2f\" % f1_score(y_test, y_pred))\n",
    "    print(\"F1-Score (Train Set): %.2f\" % f1_score(y_train, y_pred_train))\n",
    "\n",
    "    print('Test score:' + str(model.score(X_test, y_test))) #Test score\n",
    "    print('Train score: ' + str(model.score(X_train, y_train))) #Train score\n",
    "\n",
    "\n",
    "    conf_mat = confusion_matrix(y_test,y_pred)\n",
    "    print(conf_mat)\n",
    "    print('='*100)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "b53afae6-f772-4ad3-a8a6-6aaa200a7141",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (Test Set): 0.94\n",
      "Accuracy (Train Set): 1.00\n",
      "Precision (Test Set): 0.94\n",
      "Precision (Train Set): 1.00\n",
      "Recall (Test Set): 0.94\n",
      "Recall (Train Set): 1.00\n",
      "F1-Score (Test Set): 0.94\n",
      "F1-Score (Train Set): 1.00\n",
      "Test score:0.9366666666666666\n",
      "Train score: 1.0\n",
      "[[136  10]\n",
      " [  9 145]]\n",
      "====================================================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model = DecisionTreeClassifier(random_state=42)\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "eval_classification(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38e1410b-c532-4188-82f0-e8e3e93ff331",
   "metadata": {},
   "source": [
    "### Tuning Hyperparameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "8aff76e0-6aa4-4f42-964a-f8691a454d1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (Test Set): 0.93\n",
      "Accuracy (Train Set): 0.94\n",
      "Precision (Test Set): 0.89\n",
      "Precision (Train Set): 0.91\n",
      "Recall (Test Set): 0.97\n",
      "Recall (Train Set): 0.97\n",
      "F1-Score (Test Set): 0.93\n",
      "F1-Score (Train Set): 0.94\n",
      "Test score:0.9266666666666666\n",
      "Train score: 0.9414285714285714\n",
      "[[128  18]\n",
      " [  4 150]]\n",
      "====================================================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import uniform\n",
    "import numpy as np\n",
    "\n",
    "# List of hyperparameter\n",
    "max_depth = [3] # Maximum number of levels in tree\n",
    "min_samples_split = [int(i) for i in range(100)] # Minimum number of samples required to split a node\n",
    "min_samples_leaf = [int(i) for i in range(50)] # Minimum number of samples required at each leaf node\n",
    "max_features = ['auto', 'sqrt'] # Number of features to consider at every split\n",
    "\n",
    "hyperparameters = dict(max_depth=max_depth, \n",
    "                       min_samples_split=min_samples_split, \n",
    "                       min_samples_leaf=min_samples_leaf,\n",
    "                       max_features=max_features\n",
    "                      )\n",
    "\n",
    "# Inisialisasi Model\n",
    "dt = DecisionTreeClassifier(random_state=42)\n",
    "modelt = RandomizedSearchCV(dt, hyperparameters, cv=5, random_state=42, scoring='accuracy')\n",
    "modelt.fit(X_train, y_train)\n",
    "\n",
    "# Predict & Evaluation\n",
    "y_pred = modelt.predict(X_test)#Check performa dari model\n",
    "eval_classification(modelt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "a636d1fa-892f-434f-a9cb-adad003038bd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-20T19:13:57.194185Z",
     "start_time": "2021-11-20T19:13:57.189671Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best max_depth: 3\n",
      "Best min_samples_split: 60\n",
      "Best min_samples_leaf: 8\n",
      "Best max_features: auto\n"
     ]
    }
   ],
   "source": [
    "print('Best max_depth:', modelt.best_estimator_.get_params()['max_depth'])\n",
    "print('Best min_samples_split:', modelt.best_estimator_.get_params()['min_samples_split'])\n",
    "print('Best min_samples_leaf:', modelt.best_estimator_.get_params()['min_samples_leaf'])\n",
    "print('Best max_features:', modelt.best_estimator_.get_params()['max_features'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "474e7670-1fe2-4390-bf1b-5ba8045aaa9e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-20T19:13:57.768120Z",
     "start_time": "2021-11-20T19:13:57.761109Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 0.9414285714285714\n",
      "Test score:0.9266666666666666\n"
     ]
    }
   ],
   "source": [
    "print('Train score: ' + str(modelt.score(X_train, y_train)))\n",
    "print('Test score:' + str(modelt.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "d34c01d2-e5fa-4145-a428-a464587bddd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (Test Set): 0.93\n",
      "Accuracy (Train Set): 0.94\n",
      "Precision (Test Set): 0.89\n",
      "Precision (Train Set): 0.91\n",
      "Recall (Test Set): 0.97\n",
      "Recall (Train Set): 0.97\n",
      "F1-Score (Test Set): 0.93\n",
      "F1-Score (Train Set): 0.94\n",
      "Test score:0.9266666666666666\n",
      "Train score: 0.9414285714285714\n",
      "[[128  18]\n",
      " [  4 150]]\n",
      "====================================================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "models = DecisionTreeClassifier(max_depth = 3, min_samples_split = 60, min_samples_leaf = 8, max_features = 'auto', random_state=42)\n",
    "models.fit(X_train,y_train)\n",
    "\n",
    "y_pred = models.predict(X_test)\n",
    "eval_classification(models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "a3c2eeef-e0a7-43d3-b810-2915200ccd0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 0.9414285714285714\n",
      "Test score:0.9266666666666666\n"
     ]
    }
   ],
   "source": [
    "print('Train score: ' + str(models.score(X_train, y_train))) #accuracy\n",
    "print('Test score:' + str(models.score(X_test, y_test))) #accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f859e1e2-fefa-4dd4-b48e-5e9d6019448a",
   "metadata": {},
   "source": [
    "## 2nd Model & Fit (with Normalization)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f4a7a74-0581-44b9-835b-9757ba0e3558",
   "metadata": {},
   "source": [
    "### Standarization & Normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44733739-4471-4b43-bcc1-c52b0dfb0be6",
   "metadata": {},
   "source": [
    "- Pada kolom `Age` dan `Area Income` akan dilakukan standarisasi karena distribusinya skewed\n",
    "- Pada kolom `Daily Time Spent on Site` dan `Daily Internet Usage` akan dilakukan normalisasi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "079a51db-2af4-4d19-ab26-8d4179b147d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler,StandardScaler\n",
    "\n",
    "mm = MinMaxScaler()\n",
    "ss = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "50af6c70-009f-42ca-a7dc-d1dee769a5ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standarization for `Age` and `Area Income`\n",
    "\n",
    "df['Age std'] = ss.fit_transform(df['Age'].values.reshape(len(df),1))\n",
    "df['Area Income std'] = ss.fit_transform(df['Area Income'].values.reshape(len(df),1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "11e8fb95-a34f-4ddc-a867-f96e02ed98a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalization for `Daily Time Spent on Site` and `Daily Internet Usage`\n",
    "\n",
    "df['Daily Time Spent on Site norm'] = mm.fit_transform(df['Daily Time Spent on Site'].values.reshape(len(df),1))\n",
    "df['Daily Internet Usage norm'] = mm.fit_transform(df['Daily Internet Usage'].values.reshape(len(df),1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f9272f7-e9d2-4563-90ea-e8c84a6a4f8e",
   "metadata": {},
   "source": [
    "### Split Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "d28b9235-78af-4b78-b354-a29dc3a1c121",
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = df[['Age std','Area Income std','Daily Time Spent on Site norm','Daily Internet Usage norm']].values\n",
    "y1 = df['Clicked on Ad'].values\n",
    "\n",
    "X_train1, X_test1, y_train1, y_test1 = train_test_split(X1, y1, test_size = 0.3, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "91fff002-e645-4599-ae43-5be9cf0ab6de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2 = DecisionTreeClassifier()\n",
    "\n",
    "model2.fit(X_train1, y_train1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "6b88186b-10d0-4314-82c3-ccfd700e7715",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "def eval_classification2(model):\n",
    "    y_pred = model.predict(X_test1)\n",
    "    y_pred_train = model.predict(X_train1)\n",
    "    y_pred_proba = model.predict_proba(X_test1)\n",
    "    y_pred_proba_train = model.predict_proba(X_train1)\n",
    "    \n",
    "    print(\"Accuracy (Test Set): %.2f\" % accuracy_score(y_test1, y_pred))\n",
    "    print(\"Accuracy (Train Set): %.2f\" % accuracy_score(y_train1, y_pred_train))\n",
    "\n",
    "    print(\"Precision (Test Set): %.2f\" % precision_score(y_test1, y_pred))\n",
    "    print(\"Precision (Train Set): %.2f\" % precision_score(y_train1, y_pred_train))\n",
    "\n",
    "    print(\"Recall (Test Set): %.2f\" % recall_score(y_test1, y_pred))\n",
    "    print(\"Recall (Train Set): %.2f\" % recall_score(y_train1, y_pred_train))\n",
    "\n",
    "    print(\"F1-Score (Test Set): %.2f\" % f1_score(y_test1, y_pred))\n",
    "    print(\"F1-Score (Train Set): %.2f\" % f1_score(y_train1, y_pred_train))\n",
    "\n",
    "    print('Test score:' + str(model.score(X_test1, y_test1))) #Test score\n",
    "    print('Train score: ' + str(model.score(X_train1, y_train1))) #Train score\n",
    "\n",
    "\n",
    "    conf_mat = confusion_matrix(y_test1,y_pred)\n",
    "    print(conf_mat)\n",
    "    print('='*100)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "e5a03bd0-356e-4a43-9322-f854aecdc9a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (Test Set): 0.94\n",
      "Accuracy (Train Set): 1.00\n",
      "Precision (Test Set): 0.94\n",
      "Precision (Train Set): 1.00\n",
      "Recall (Test Set): 0.94\n",
      "Recall (Train Set): 1.00\n",
      "F1-Score (Test Set): 0.94\n",
      "F1-Score (Train Set): 1.00\n",
      "Test score:0.9366666666666666\n",
      "Train score: 1.0\n",
      "[[136  10]\n",
      " [  9 145]]\n",
      "====================================================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "eval_classification2(model2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "46d16ae2-c65c-45fb-b382-dc2c376fd326",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (Test Set): 0.93\n",
      "Accuracy (Train Set): 0.94\n",
      "Precision (Test Set): 0.89\n",
      "Precision (Train Set): 0.91\n",
      "Recall (Test Set): 0.97\n",
      "Recall (Train Set): 0.97\n",
      "F1-Score (Test Set): 0.93\n",
      "F1-Score (Train Set): 0.94\n",
      "Test score:0.9266666666666666\n",
      "Train score: 0.9414285714285714\n",
      "[[128  18]\n",
      " [  4 150]]\n",
      "====================================================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model2s = DecisionTreeClassifier(max_depth = 3, min_samples_split = 60, min_samples_leaf = 8, max_features = 'auto', random_state=42)\n",
    "model2s.fit(X_train1,y_train1)\n",
    "\n",
    "eval_classification2(model2s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "1c5bec65-40e0-43e4-afd7-4ef4583c81a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "fig, axes = plt.subplots(nrows = 1,ncols = 1,figsize = (3,3), dpi=300)\n",
    "\n",
    "tree.plot_tree(models, class_names=True)\n",
    "plt.savefig('Tree1.png', dpi=300);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "74001cb7-2202-4eb2-b4fd-1597d6e87446",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature: 0, Score: 0.00000\n",
      "Feature: 1, Score: 0.11562\n",
      "Feature: 2, Score: 0.66490\n",
      "Feature: 3, Score: 0.21948\n"
     ]
    },
    {
     "data": {
      "image/png": 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ne7+SpId1Foqq+jDw4SSnVNXnZ3rnSf4GWA0sTrIV+CPgkHbfa4FzgJcl2QV8Fzi3qn7og3+SpNmTUX7vJjkUeAnNaaj/OOVUVS+evWj77pFLV9bS8y/pO8asuN1PnEuaBUk2VtXEsLZRL2a/Bzga+G/AZ4DlNKefJEkHuFELxZOr6g+B+9sBAs8Enjl7sSRJc8WoheL77dd7kjwDOBJYMSuJJElzyqgfuFuX5LHAHwLrgcMBx12SpAVg1OdRvKOd/QwwJ4cWlyTNjpFOPSV5QpJ3JrmiXX56kpfMbjRJ0lww6jWKdwEfA45pl78MvGoW8kiS5phRC8XiqvoA8AOAqtoF7J61VJKkOWPUQnF/ksfRPg41yfOAHbOWSpI0Z4x619Pv0Nzt9KQk/wwsoRleQ5J0gNvT6LHHVdXXq+q6JC+gGSQwwG1V9f2u10qSDgx7OvV0+cD8+6vqpqq60SIhSQvHngpFBub9/IQkLUB7KhQ1zbwkaYHY08Xsk5LspDmyOKydp12uqnrMrKaTJPVuTw8uWjSuIJKkuWnUz1FIkhaoXgtFkkuT3Jnkxmnak+QtSTYnuSHJyePOKEkLXd9HFO8CTutoPx1Y2U5rgLePIZMkaUCvhaKqrgK+3dHlbOCyalwDHJVk6XjSSZKg/yOKPVkGbBlY3tqu+yFJ1iSZTDK5+wGHoZKkmTLXC0WGrBv6eY6qWldVE1U1sehRR85yLElaOOZ6odgKHDuwvBzY1lMWSVqQ5nqhWA+c19799DxgR1Xd0XcoSVpIRh1mfFYk+RtgNbA4yVbgj4BDAKpqLbABOAPYDDwAXNhPUklauHotFFX1y3toL+DlY4ojSRpirp96kiT1zEIhSepkoZAkdbJQSJI6WSgkSZ0sFJKkThYKSVInC4UkqZOFQpLUyUIhSepkoZAkdbJQSJI6WSgkSZ0sFJKkThYKSVKnXp9HMVueuexIJt94Zt8xJOmA4BGFJKlTr4UiyWlJbkuyOclrhrSvTrIjyaZ2uqiPnJK0kPV26inJIuDPgBcCW4EvJllfVTdP6frZqjpr7AElSUC/RxSrgM1V9dWq+h7wPuDsHvNIkobos1AsA7YMLG9t1011SpLrk1yR5ITpNpZkTZLJJJPbt2+f6ayStGD1WSgyZF1NWb4OOL6qTgLeClw+3caqal1VTVTVxJIlS2YupSQtcH0Wiq3AsQPLy4Ftgx2qamdV3dfObwAOSbJ4fBElSX0Wii8CK5M8MckjgHOB9YMdkhydJO38Kpq8d489qSQtYL3d9VRVu5K8AvgYsAi4tKpuSvLStn0tcA7wsiS7gO8C51bV1NNTkqRZlAPx9+7ExERNTk72HUOS5o0kG6tqYlibn8yWJHWyUEiSOlkoJEmdLBSSpE4WCklSJwuFJKmThUKS1MlCIUnqZKGQJHWyUEiSOlkoJEmdLBSSpE4WCklSJwuFJKmThUKS1MlCIUnqZKGQJHXqtVAkOS3JbUk2J3nNkPYkeUvbfkOSk/vIKUkLWW+FIski4M+A04GnA7+c5OlTup0OrGynNcDbxxpSktTrEcUqYHNVfbWqvge8Dzh7Sp+zgcuqcQ1wVJKl4w4qSQtZn4ViGbBlYHlru25v+wCQZE2SySST27dvn9GgkrSQ9VkoMmRd7UOfZmXVuqqaqKqJJUuW7Hc4SVKjz0KxFTh2YHk5sG0f+kiSZlGfheKLwMokT0zyCOBcYP2UPuuB89q7n54H7KiqO8YdVJIWsoP72nFV7UryCuBjwCLg0qq6KclL2/a1wAbgDGAz8ABwYV95JWmh6q1QAFTVBppiMLhu7cB8AS8fdy5J0sP8ZLYkqZOFQpLUyUIhSepkoZAkdbJQSJI6WSgkSZ0sFJKkThYKSVInC4UkqZOFQpLUyUIhSepkoZAkdbJQSJI6WSgkSZ0sFJKkThYKSVInC4UkqVMvT7hL8iPA+4EVwO3AL1bVd4b0ux24F9gN7KqqifGllCRBf0cUrwE+WVUrgU+2y9P5qap6lkVCkvrRV6E4G3h3O/9u4EU95ZAk7UFfheIJVXUHQPv18dP0K+DjSTYmWdO1wSRrkkwmmdy+ffsMx5WkhWvWrlEk+QRw9JCm1+7FZp5fVduSPB64MsmtVXXVsI5VtQ5YBzAxMVF7HViSNNSsFYqqOnW6tiTfSrK0qu5IshS4c5ptbGu/3pnkQ8AqYGihkCTNjr5OPa0Hzm/nzwc+PLVDkkcnOeKheeBngRvHllCSBPRXKN4IvDDJvwIvbJdJckySDW2fJwBXJ7keuBb4aFX9Yy9pJWkBS9WBdzo/yb3AbX3n2A+Lgbv6DrGP5nN2MH/fzN+f46tqybCGXj5wNwa3zefPXSSZnK/553N2MH/fzD83OYSHJKmThUKS1OlALRTr+g6wn+Zz/vmcHczfN/PPQQfkxWxJ0sw5UI8oJEkzxEIhSeo0bwtFktOS3JZkc5IfGqY8jbe07TckObmPnNMZIf/Tknw+yYNJXt1Hxi4j5P/V9n2/IcnnkpzUR87pjJD/7Db7pnawyZ/oI+d09pR/oN9zkuxOcs448+3JCO//6iQ72vd/U5KL+sg5nVHe//Z72JTkpiSfGXfGGVVV824CFgFfAX4UeARwPfD0KX3OAK4AAjwP+ELfufcy/+OB5wB/DLy678z7kP/Hgce286fPw/f/cB6+hncicGvfufcm/0C/TwEbgHP6zr2X7/9q4CN9Z92P/EcBNwPHtcuP7zv3/kzz9YhiFbC5qr5aVd8D3kfzjItBZwOXVeMa4Kh2AMK5YI/5q+rOqvoi8P0+Au7BKPk/Vw8/tfAaYPmYM3YZJf991f4PBx5NM+T9XDHKv3+AVwJ/zzSDbvZo1Pxz1Sj5fwX4YFV9HZr/z2POOKPma6FYBmwZWN7artvbPn2Zy9lGsbf5X0JzdDdXjJQ/yc8nuRX4KPDiMWUbxR7zJ1kG/Dywdoy5RjXqv59Tklyf5IokJ4wn2khGyf8U4LFJPt0+T+e8saWbBfN1CI8MWTf1L75R+vRlLmcbxcj5k/wUTaGYS+f4R8pfVR8CPpTkvwKvB6YdOn/MRsl/CfB7VbU7Gda9V6Pkv45m7KH7kpwBXA6snO1gIxol/8HAfwF+BjgM+HySa6rqy7MdbjbM10KxFTh2YHk5sG0f+vRlLmcbxUj5k5wIvAM4varuHlO2UezV+19VVyV5UpLFVTUXBnwbJf8E8L62SCwGzkiyq6ouH0vCbnvMX1U7B+Y3JHnbPHv/twJ3VdX9wP1JrgJOAuZloej9Ism+TDQF7qvAE3n4YtIJU/qcyX++mH1t37n3Jv9A34uZexezR3n/jwM2Az/ed959zP9kHr6YfTLwjYeW+5725t9P2/9dzK2L2aO8/0cPvP+rgK/Pp/cf+DHgk23fR9E8S+cZfWff12leHlFU1a4krwA+RnMHwqVVdVOSl7bta2nu9DiD5pfVA8CFfeWdapT8SY4GJoHHAD9I8iqaOyt2TrfdcRnx/b8IeBzwtvav2l01R0bVHDH/LwDnJfk+8F3gl6r9DdC3EfPPWSPmPwd4WZJdNO//ufPp/a+qW5L8I3AD8APgHVU1bx+85hAekqRO8/WuJ0nSmFgoJEmdLBSSpE4WCklSJwuFJKmThULaD0le244O+tBIs8/tO5M00+bl5yikuSDJKcBZwMlV9WCSxTQfwNrX7R1cVbtmLKA0QzyikPbdUpphGh4EqKq7qmpb+wyIz7UD2l2b5Igkhyb5yyRfSvIv7RhYJLkgyd8m+Qfg40keneTSJF9s+82nUVV1gPKIQtp3HwcuSvJl4BPA+4HPt19/qaq+mOQxNJ8s/m2AqnpmkqfRFIWntNs5BTixqr6d5P8Cn6qqFyc5Crg2ySeqGTNI6oVHFNI+qqr7aEYIXQNspykQvwHcUc2zRKiqne3ppJ8A3tOuuxX4Gs1Q1ABXVtW32/mfBV6TZBPwaeBQmnGzpN54RCHth6raTfML/dNJvgS8nOFDrneN9T14tBDgF6rqthkLKe0njyikfZTkqUkGn5HwLOAW4Jgkz2n7HJHkYOAq4FfbdU+hOUoYVgw+Brwy7UiKSZ49e9+BNBqPKKR9dzjw1vZawi6akYrXAH/Zrj+M5vrEqcDbgLXtUccu4IL2Tqmp23w9zUOHbmiLxe00d1ZJvXH0WElSJ089SZI6WSgkSZ0sFJKkThYKSVInC4UkqZOFQpLUyUIhSer0/wHqy7rZ8VvEBwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importance = model2s.feature_importances_\n",
    "# summarize feature importance\n",
    "for i,v in enumerate(importance):\n",
    "\tprint('Feature: %0d, Score: %.5f' % (i,v))\n",
    "# plot feature importance\n",
    "plt.barh([x for x in range(len(importance))], importance)\n",
    "plt.xlabel('Score')\n",
    "plt.ylabel('Feature')\n",
    "plt.title('Feature Importance Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "2baaa6b9-cce2-4c4c-b632-35223f998ba0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_feature_importance(model):\n",
    "    feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n",
    "    ax = feat_importances.nlargest(25).plot(kind='barh', figsize=(10, 8))\n",
    "    ax.invert_yaxis()\n",
    "\n",
    "    plt.xlabel('Score')\n",
    "    plt.ylabel('Feature')\n",
    "    plt.title('Feature Importance Score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "a6b399ad-fea2-4e2f-aca6-9645c1993d8b",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'numpy.ndarray' object has no attribute 'columns'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_18132/147754674.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mshow_feature_importance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel2s\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_18132/1048319208.py\u001b[0m in \u001b[0;36mshow_feature_importance\u001b[1;34m(model)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mshow_feature_importance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mfeat_importances\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfeature_importances_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m     \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfeat_importances\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlargest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m25\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'barh'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minvert_yaxis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'columns'"
     ]
    }
   ],
   "source": [
    "show_feature_importance(model2s)"
   ]
  }
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