2_cluster_onr.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK**<br>\n",
    "1. Analisa kira kira variabel apa yang bisa dipakai untuk klusterisasi tipe tipe pelanggan<br>\n",
    "2. Visualisasikan mana pelanggan yang perlu diperhatikan dan tidak perlu diperhatikan\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A. Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Library \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A.1 Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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: 0.1</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0.1  Unnamed: 0 InvoiceNo StockCode  \\\n",
       "0             0           0    536365    85123A   \n",
       "1             1           1    536365     71053   \n",
       "2             2           2    536365    84406B   \n",
       "3             3           3    536365    84029G   \n",
       "4             4           4    536365    84029E   \n",
       "\n",
       "                           Description  Quantity   InvoiceDate  UnitPrice  \\\n",
       "0   WHITE HANGING HEART T-LIGHT HOLDER         6  12/1/10 8:26       2.55   \n",
       "1                  WHITE METAL LANTERN         6  12/1/10 8:26       3.39   \n",
       "2       CREAM CUPID HEARTS COAT HANGER         8  12/1/10 8:26       2.75   \n",
       "3  KNITTED UNION FLAG HOT WATER BOTTLE         6  12/1/10 8:26       3.39   \n",
       "4       RED WOOLLY HOTTIE WHITE HEART.         6  12/1/10 8:26       3.39   \n",
       "\n",
       "   CustomerID         Country  \n",
       "0     17850.0  United Kingdom  \n",
       "1     17850.0  United Kingdom  \n",
       "2     17850.0  United Kingdom  \n",
       "3     17850.0  United Kingdom  \n",
       "4     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Online_Retail.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A.2 Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unnamed: 0.1         0\n",
      "Unnamed: 0           0\n",
      "InvoiceNo            0\n",
      "StockCode            0\n",
      "Description       1454\n",
      "Quantity             0\n",
      "InvoiceDate          0\n",
      "UnitPrice            0\n",
      "CustomerID      135080\n",
      "Country              0\n",
      "dtype: int64\n",
      "(541909, 10)\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().sum())\n",
    "print(df.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# B. Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B.1 Data Preprocessing + Analisis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "B.1.1 Menghapus baris yang memiliki nilai null, mereset index nya lalu menghitung nilai null dan menampilkan panjang data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "index           0\n",
      "Unnamed: 0.1    0\n",
      "Unnamed: 0      0\n",
      "InvoiceNo       0\n",
      "StockCode       0\n",
      "Description     0\n",
      "Quantity        0\n",
      "InvoiceDate     0\n",
      "UnitPrice       0\n",
      "CustomerID      0\n",
      "Country         0\n",
      "dtype: int64\n",
      "(406829, 11)\n"
     ]
    }
   ],
   "source": [
    "df_cln = df.dropna().reset_index()\n",
    "print(df_cln.isnull().sum())\n",
    "print(df_cln.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "B.1.2 Melihat Informasi dan Deskripsi Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 541909 entries, 0 to 541908\n",
      "Data columns (total 10 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   Unnamed: 0.1  541909 non-null  int64  \n",
      " 1   Unnamed: 0    541909 non-null  int64  \n",
      " 2   InvoiceNo     541909 non-null  object \n",
      " 3   StockCode     541909 non-null  object \n",
      " 4   Description   540455 non-null  object \n",
      " 5   Quantity      541909 non-null  int64  \n",
      " 6   InvoiceDate   541909 non-null  object \n",
      " 7   UnitPrice     541909 non-null  float64\n",
      " 8   CustomerID    406829 non-null  float64\n",
      " 9   Country       541909 non-null  object \n",
      "dtypes: float64(2), int64(3), object(5)\n",
      "memory usage: 41.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>541909.000000</td>\n",
       "      <td>541909.000000</td>\n",
       "      <td>541909.000000</td>\n",
       "      <td>541909.000000</td>\n",
       "      <td>406829.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>270954.000000</td>\n",
       "      <td>270954.000000</td>\n",
       "      <td>9.552250</td>\n",
       "      <td>4.611114</td>\n",
       "      <td>15287.690570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>156435.797851</td>\n",
       "      <td>156435.797851</td>\n",
       "      <td>218.081158</td>\n",
       "      <td>96.759853</td>\n",
       "      <td>1713.600303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-80995.000000</td>\n",
       "      <td>-11062.060000</td>\n",
       "      <td>12346.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>135477.000000</td>\n",
       "      <td>135477.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>13953.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>270954.000000</td>\n",
       "      <td>270954.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.080000</td>\n",
       "      <td>15152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>406431.000000</td>\n",
       "      <td>406431.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.130000</td>\n",
       "      <td>16791.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>541908.000000</td>\n",
       "      <td>541908.000000</td>\n",
       "      <td>80995.000000</td>\n",
       "      <td>38970.000000</td>\n",
       "      <td>18287.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0.1     Unnamed: 0       Quantity      UnitPrice  \\\n",
       "count  541909.000000  541909.000000  541909.000000  541909.000000   \n",
       "mean   270954.000000  270954.000000       9.552250       4.611114   \n",
       "std    156435.797851  156435.797851     218.081158      96.759853   \n",
       "min         0.000000       0.000000  -80995.000000  -11062.060000   \n",
       "25%    135477.000000  135477.000000       1.000000       1.250000   \n",
       "50%    270954.000000  270954.000000       3.000000       2.080000   \n",
       "75%    406431.000000  406431.000000      10.000000       4.130000   \n",
       "max    541908.000000  541908.000000   80995.000000   38970.000000   \n",
       "\n",
       "          CustomerID  \n",
       "count  406829.000000  \n",
       "mean    15287.690570  \n",
       "std      1713.600303  \n",
       "min     12346.000000  \n",
       "25%     13953.000000  \n",
       "50%     15152.000000  \n",
       "75%     16791.000000  \n",
       "max     18287.000000  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "B.1.3 Menghitung banyak barang yang sudah dibeli setiap customer lalu menjadikannya DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer_Id</th>\n",
       "      <th>Total_Pembelian</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347.0</td>\n",
       "      <td>4310.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348.0</td>\n",
       "      <td>1797.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349.0</td>\n",
       "      <td>1757.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12350.0</td>\n",
       "      <td>334.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4367</th>\n",
       "      <td>18280.0</td>\n",
       "      <td>180.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4368</th>\n",
       "      <td>18281.0</td>\n",
       "      <td>80.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4369</th>\n",
       "      <td>18282.0</td>\n",
       "      <td>176.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4370</th>\n",
       "      <td>18283.0</td>\n",
       "      <td>2094.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4371</th>\n",
       "      <td>18287.0</td>\n",
       "      <td>1837.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4372 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Customer_Id  Total_Pembelian\n",
       "0         12346.0             0.00\n",
       "1         12347.0          4310.00\n",
       "2         12348.0          1797.24\n",
       "3         12349.0          1757.55\n",
       "4         12350.0           334.40\n",
       "...           ...              ...\n",
       "4367      18280.0           180.60\n",
       "4368      18281.0            80.82\n",
       "4369      18282.0           176.60\n",
       "4370      18283.0          2094.88\n",
       "4371      18287.0          1837.28\n",
       "\n",
       "[4372 rows x 2 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cln ['CustomerID'] = df_cln['CustomerID']\n",
    "df_cln ['TotalPembelian'] = df_cln['Quantity'] * df_cln['UnitPrice']\n",
    "totalbeli = df_cln.groupby('CustomerID')['TotalPembelian'].sum().reset_index()\n",
    "totalbeli.columns = ['Customer_Id', 'Total_Pembelian']\n",
    "totalbeli = pd.DataFrame(totalbeli)\n",
    "totalbeli \n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "B.1.4 Menghitung sudah berapa banyak setiap customer melakukan transaksi lalu menjadikannya DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer_Id</th>\n",
       "      <th>Jumlah_Transaksi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346.0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347.0</td>\n",
       "      <td>182</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348.0</td>\n",
       "      <td>31</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349.0</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12350.0</td>\n",
       "      <td>17</td>\n",
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       "      <th>4367</th>\n",
       "      <td>18280.0</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>4368</th>\n",
       "      <td>18281.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4369</th>\n",
       "      <td>18282.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4370</th>\n",
       "      <td>18283.0</td>\n",
       "      <td>756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4371</th>\n",
       "      <td>18287.0</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4372 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Customer_Id  Jumlah_Transaksi\n",
       "0         12346.0                 2\n",
       "1         12347.0               182\n",
       "2         12348.0                31\n",
       "3         12349.0                73\n",
       "4         12350.0                17\n",
       "...           ...               ...\n",
       "4367      18280.0                10\n",
       "4368      18281.0                 7\n",
       "4369      18282.0                13\n",
       "4370      18283.0               756\n",
       "4371      18287.0                70\n",
       "\n",
       "[4372 rows x 2 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Banyaktransaksi = df_cln.groupby('CustomerID')['InvoiceNo'].count().reset_index()\n",
    "Banyaktransaksi.columns = ['Customer_Id', 'Jumlah_Transaksi']\n",
    "Banyaktransaksi = pd.DataFrame(Banyaktransaksi)\n",
    "Banyaktransaksi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "B.1.5 Menghitung kapan terakhir kali customer membeli barang di toko, dihitung dari tanggal paling baru di data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          12/1/10 8:26\n",
       "1          12/1/10 8:26\n",
       "2          12/1/10 8:26\n",
       "3          12/1/10 8:26\n",
       "4          12/1/10 8:26\n",
       "              ...      \n",
       "406824    12/9/11 12:50\n",
       "406825    12/9/11 12:50\n",
       "406826    12/9/11 12:50\n",
       "406827    12/9/11 12:50\n",
       "406828    12/9/11 12:50\n",
       "Name: InvoiceDate, Length: 406829, dtype: object"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cln['InvoiceDate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer_Id</th>\n",
       "      <th>Terakhir_Pembelian</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346.0</td>\n",
       "      <td>325</td>\n",
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       "      <td>12347.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348.0</td>\n",
       "      <td>74</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349.0</td>\n",
       "      <td>18</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12350.0</td>\n",
       "      <td>309</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4367</th>\n",
       "      <td>18280.0</td>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4368</th>\n",
       "      <td>18281.0</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4369</th>\n",
       "      <td>18282.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4370</th>\n",
       "      <td>18283.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4371</th>\n",
       "      <td>18287.0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4372 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Customer_Id  Terakhir_Pembelian\n",
       "0         12346.0                 325\n",
       "1         12347.0                   1\n",
       "2         12348.0                  74\n",
       "3         12349.0                  18\n",
       "4         12350.0                 309\n",
       "...           ...                 ...\n",
       "4367      18280.0                 277\n",
       "4368      18281.0                 180\n",
       "4369      18282.0                   7\n",
       "4370      18283.0                   3\n",
       "4371      18287.0                  42\n",
       "\n",
       "[4372 rows x 2 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cln['InvoiceDate'] = pd.to_datetime(df_cln['InvoiceDate'],format = '%m/%d/%y %H:%M')\n",
    "tanggal_max = max(df_cln['InvoiceDate'])\n",
    "df_cln['Terakhir_Pembelian'] = tanggal_max - df_cln['InvoiceDate']\n",
    "terakhirpembelian = df_cln.groupby('CustomerID')['Terakhir_Pembelian'].min().reset_index()\n",
    "terakhirpembelian.columns = ['Customer_Id', 'Terakhir_Pembelian']\n",
    "# konversi terakhir pembelian dari tipe data timedelta ke jumlah hari menggunakan metode .dt.days.\n",
    "terakhirpembelian['Terakhir_Pembelian'] = terakhirpembelian['Terakhir_Pembelian'].dt.days\n",
    "terakhirpembelian = pd.DataFrame(terakhirpembelian)\n",
    "terakhirpembelian\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "B.1.6 Menggabungkan 3 data diatas berdasarkan customer id menggunakan merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer_Id</th>\n",
       "      <th>Total_Pembelian</th>\n",
       "      <th>Jumlah_Transaksi</th>\n",
       "      <th>Terakhir_Pembelian</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346.0</td>\n",
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       "      <td>2</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347.0</td>\n",
       "      <td>4310.00</td>\n",
       "      <td>182</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348.0</td>\n",
       "      <td>1797.24</td>\n",
       "      <td>31</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349.0</td>\n",
       "      <td>1757.55</td>\n",
       "      <td>73</td>\n",
       "      <td>18</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12350.0</td>\n",
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       "      <td>17</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4367</th>\n",
       "      <td>18280.0</td>\n",
       "      <td>180.60</td>\n",
       "      <td>10</td>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4368</th>\n",
       "      <td>18281.0</td>\n",
       "      <td>80.82</td>\n",
       "      <td>7</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4369</th>\n",
       "      <td>18282.0</td>\n",
       "      <td>176.60</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4370</th>\n",
       "      <td>18283.0</td>\n",
       "      <td>2094.88</td>\n",
       "      <td>756</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4371</th>\n",
       "      <td>18287.0</td>\n",
       "      <td>1837.28</td>\n",
       "      <td>70</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4372 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Customer_Id  Total_Pembelian  Jumlah_Transaksi  Terakhir_Pembelian\n",
       "0         12346.0             0.00                 2                 325\n",
       "1         12347.0          4310.00               182                   1\n",
       "2         12348.0          1797.24                31                  74\n",
       "3         12349.0          1757.55                73                  18\n",
       "4         12350.0           334.40                17                 309\n",
       "...           ...              ...               ...                 ...\n",
       "4367      18280.0           180.60                10                 277\n",
       "4368      18281.0            80.82                 7                 180\n",
       "4369      18282.0           176.60                13                   7\n",
       "4370      18283.0          2094.88               756                   3\n",
       "4371      18287.0          1837.28                70                  42\n",
       "\n",
       "[4372 rows x 4 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data = totalbeli.merge(Banyaktransaksi, on='Customer_Id').merge(terakhirpembelian, on='Customer_Id')\n",
    "new_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "B.1.7 Dari data tersebut yang saya butuhkan hanya 3 kolom terakhir jadi agar memudahkan, saya menghapus kolom Customer_Id "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Pembelian</th>\n",
       "      <th>Jumlah_Transaksi</th>\n",
       "      <th>Terakhir_Pembelian</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>2</td>\n",
       "      <td>325</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4310.00</td>\n",
       "      <td>182</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1797.24</td>\n",
       "      <td>31</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1757.55</td>\n",
       "      <td>73</td>\n",
       "      <td>18</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>334.40</td>\n",
       "      <td>17</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4367</th>\n",
       "      <td>180.60</td>\n",
       "      <td>10</td>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4368</th>\n",
       "      <td>80.82</td>\n",
       "      <td>7</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4369</th>\n",
       "      <td>176.60</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4370</th>\n",
       "      <td>2094.88</td>\n",
       "      <td>756</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4371</th>\n",
       "      <td>1837.28</td>\n",
       "      <td>70</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4372 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Total_Pembelian  Jumlah_Transaksi  Terakhir_Pembelian\n",
       "0                0.00                 2                 325\n",
       "1             4310.00               182                   1\n",
       "2             1797.24                31                  74\n",
       "3             1757.55                73                  18\n",
       "4              334.40                17                 309\n",
       "...               ...               ...                 ...\n",
       "4367           180.60                10                 277\n",
       "4368            80.82                 7                 180\n",
       "4369           176.60                13                   7\n",
       "4370          2094.88               756                   3\n",
       "4371          1837.28                70                  42\n",
       "\n",
       "[4372 rows x 3 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = new_data.drop('Customer_Id', axis = 1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "B.1.8 Visualisasi Boxplot untuk cek Outlier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "B.1.9 Menangani Outlier dengan iqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1318.3625"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1 = df.Total_Pembelian.quantile(0.25)\n",
    "q3 = df.Total_Pembelian.quantile(0.75)\n",
    "iqr = q3 - q1\n",
    "iqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = df[~((df.Total_Pembelian < (q1 - 1.5*iqr))| (df.Total_Pembelian > (q3 + 1.5*iqr)))]\n",
    "\n",
    "\n",
    "# outlier = df[(df.Total_Pembelian < (q1 - 1.5*iqr))| (df.Total_Pembelian > (q3 + 1.5*iqr))]\n",
    "# median_value = df['Total_Pembelian'].mode()\n",
    "# df.loc[outlier.index, 'Total_Pembelian'] = median_value\n",
    "\n",
    "# mengatasi outlier kolom total pembelian\n",
    "q1 = df.Total_Pembelian.quantile(0.25)\n",
    "q3 = df.Total_Pembelian.quantile(0.75)\n",
    "iqr = q3 - q1\n",
    "df = df[(df.Total_Pembelian >= q1 - 1.5*iqr) & (df.Total_Pembelian <= q3 + 1.5*iqr)]\n",
    "\n",
    "# mengatasi outlier kolom jumlah transaksi\n",
    "q1 = df.Jumlah_Transaksi.quantile(0.25)\n",
    "q3 = df.Jumlah_Transaksi.quantile(0.75)\n",
    "iqr = q3 - q1\n",
    "df = df[(df.Jumlah_Transaksi >= q1 - 1.5*iqr) & (df.Jumlah_Transaksi <= q3 + 1.5*iqr)]\n",
    "\n",
    "# mengatasi outlier kolom terakhir pembelian\n",
    "q1 = df.Terakhir_Pembelian.quantile(0.25)\n",
    "q3 = df.Terakhir_Pembelian.quantile(0.75)\n",
    "iqr = q3 - q1\n",
    "df = df[(df.Terakhir_Pembelian >= q1 - 1.5*iqr) & (df.Terakhir_Pembelian <= q3 + 1.5*iqr)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3697, 3)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "B.1.10 Mengubah skala menggunakan minmaxscaler, serta memilih variabel yang dipakai untuk tipe tipe pelanggan yaitu 3 kolom yang Customer_id nya dihapus tadi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.30884115, 0.00564972, 0.87131367],\n",
       "       [0.65739071, 0.16949153, 0.19839142],\n",
       "       [0.64969339, 0.40677966, 0.04825737],\n",
       "       ...,\n",
       "       [0.32451506, 0.03389831, 0.48257373],\n",
       "       [0.34309025, 0.06779661, 0.01876676],\n",
       "       [0.66515591, 0.38983051, 0.11260054]])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = MinMaxScaler()\n",
    "x = df\n",
    "df_scaled = scaler.fit_transform(x)\n",
    "df_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# C. Permodelan Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C.1 Define Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import silhouette_score\n",
    "# model \n",
    "wcss = [] # untuk menampung within cluster sum of squares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# membuat for loop untuk setiap cluster di range 1 sampai 10\n",
    "for i in range (1,11) : \n",
    "    kmeans = KMeans(n_clusters=i, n_init=10) # n_cluster = jumlah cluster n_init = mengontrol jumlah kali pemilihan pusat klaster akan dilakukan dengan inisialisasi yang berbeda\n",
    "    kmeans.fit(x)\n",
    "    wcss.append(kmeans.inertia_) # inertia untuk memberi nilai wcss "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "C.1.1 Menentukan jumlah pusat cluster dengan Elbow Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'wcss')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,11), wcss)\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('Number Of Cluster')\n",
    "plt.xlabel('wcss')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "C.1.2 Menentukan jumlah pusat cluster dengan silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters=2, the silhouette score is 0.6770650364586208\n",
      "For n_clusters=3, the silhouette score is 0.593017271418894\n",
      "For n_clusters=4, the silhouette score is 0.5227172677339016\n",
      "For n_clusters=5, the silhouette score is 0.48340882863313245\n",
      "For n_clusters=6, the silhouette score is 0.4502475165368625\n",
      "For n_clusters=7, the silhouette score is 0.39099533004455367\n",
      "For n_clusters=8, the silhouette score is 0.403932914981845\n",
      "For n_clusters=9, the silhouette score is 0.4033878112877099\n",
      "For n_clusters=10, the silhouette score is 0.40515844444532273\n"
     ]
    }
   ],
   "source": [
    "for i in range (2,11) : \n",
    "    kmeans = KMeans(n_clusters=i, n_init=10, random_state=42) # n_cluster = jumlah cluster n_init = mengontrol jumlah kali pemilihan pusat klaster akan dilakukan dengan inisialisasi yang berbeda\n",
    "    kmeans.fit(x)\n",
    "    silhouette_avg = silhouette_score(x, kmeans.labels_)\n",
    "    print(\"For n_clusters={0}, the silhouette score is {1}\".format(i, silhouette_avg))  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "C.1.3 Dilihat dari silhouette score yang paling tinggi adalah n_cluster=2, maka jumlah pusat cluster nya 2\n",
    "<br>Menghitung dengan jumlah pusat cluster nya 3, dimana itu merupakan optimal number cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, ..., 1, 1, 0], dtype=int32)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=2, n_init=10, random_state=42)\n",
    "y_kmeans = kmeans.fit_predict(x) # untuk mengmbalikan var dependen yang akan dibuat\n",
    "y_kmeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "C.1.4 Menentukan nama cluster dengan menghitung rata-rata setiap fitur dalam setiap cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Total_Pembelian  Jumlah_Transaksi  Terakhir_Pembelian\n",
      "0      1995.356238         92.035156           49.399740\n",
      "1       450.425327         34.759986          117.398088\n"
     ]
    }
   ],
   "source": [
    "cluster_means = x.groupby(y_kmeans).mean()\n",
    "print(cluster_means)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "C.1.5 Visualisasi Tipe Tipe Pelanggan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x.iloc[y_kmeans == 0,0], x.iloc[y_kmeans ==0,1], s = 100, c = 'red', label = 'Perlu Diperhatikan')\n",
    "plt.scatter(x.iloc[y_kmeans == 1,0], x.iloc[y_kmeans ==1,1], s = 100, c = 'green', label = 'Tidak Perlu Diperhatikan')\n",
    "plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], s = 300, c = 'yellow', label = 'centroid')\n",
    "plt.title ('Visualisasi Pelanggan')\n",
    "plt.xlabel('Total Pembelian')\n",
    "plt.ylabel('Jumlah Transaksi')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C.2 Algorithm Used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fork/.local/lib/python3.10/site-packages/sklearn/base.py:465: UserWarning: X does not have valid feature names, but KMeans was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int32)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdata = [[800, 12, 75]]\n",
    "kmeans.predict(newdata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# D. Evaluasi Model Prediksi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D.1 Hasil Akurasi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjusted Rand Index (ARI): 1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import adjusted_rand_score \n",
    "ari_score = adjusted_rand_score(y_kmeans,kmeans.labels_)\n",
    "print(\"Adjusted Rand Index (ARI):\", ari_score)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "silhouette score :  0.6770650364586208\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import silhouette_score\n",
    "sil = silhouette_score(x,kmeans.labels_)\n",
    "print('silhouette score : ',sil)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D.2 Visualisasi Hasil Akurasi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XGVPG6lHs7OywceNGdO/eHaNHj4arq+tDF1A3atQIISEh2LFjB8aOHasTZP6uSZMmAO7difiwPt64cQNpaWmYOXMmpk+fri1/nEtDf2djY4OePXuiZ8+e+OCDD5CQkIC3334b27dvr7Q/vXv3hlwux5dffvnYC6Hr1KmjN9fLy8sN/j2sW7cuIiMjERkZiVu3buG5557DjBkztAHoYXP5119/RUhIiMnhxhzuz8/MzEyds0vXr1/XuwuNah5eAqMaY/v27Qb/zywlJQUAHnnHx+N64YUXkJeXp3NnSEVFBT7++GM4Ojqia9euAO79YymXy/XW2HzyySc6793d3dGtWzd8+umnBr8s/n4broODAwD9UHV/m6Hyl19+Genp6di8ebPetqKiIlRUVFR6rHl5eQYvJZaXlyMtLQ02NjbaMy4DBw7E0aNHsWHDBr369/+cjB27ypgyVsZwdnZGamoqmjZtiqFDhyItLe2h9efMmYP4+PiHrvkIDw+Hs7MzEhIScPfu3Ur7eP9sw4Nz+GF35T1KYWGhXtn9MzplZWWVtvP29sbo0aOxZcsWg09R12g0eP/99x/6IMQmTZrozfWlS5fqnQF68BZ/R0dHNG3aVKd/lc3zl19+GWq1GrNnz9b7/IqKCoPz35x69uyJWrVqYcmSJTrlixYtsujnUtXgGSCqMcaPH4/bt29jwIABaN68OcrLy7F3716sWbNG+/h+S3j99dfx6aefYsSIETh06BB8fX2xbt067NmzB0lJSdqFpC4uLhg0aBA+/vhjyGQyNGnSBD///LPBNSqLFy9G586d0aZNG4wePRqNGzdGfn4+0tPT8eeff+Lo0aMA7n2ZyeVyzJs3D8XFxVAqlejRowfc3d0RGBiIJUuWYM6cOWjatCnc3d3Ro0cPTJ06FRs3bsSLL76IESNGIDAwEKWlpTh+/DjWrVuHCxcuwM3NzeCx/vnnn+jUqRN69OiBnj17wsPDA1evXsU333yDo0ePYuLEidq2U6dOxbp16zBo0CCMHDkSgYGBKCwsxMaNG5GcnIy2bdsaPXYPY+xYGat+/frYunUrQkJC0L9/f6SlpaFTp04G63bt2vWRIc3Z2RlLlizBq6++iqeffhpDhgxB/fr1kZOTg02bNiEkJASLFi2Cs7MznnvuOcyfPx93796Fl5cXtmzZguzsbJP6/3ezZs3Crl270KdPH/j4+ODq1av45JNP0LBhQ3Tu3Pmhbd9//31kZWVhwoQJWL9+PV588UXUqVMHOTk5WLt2Lc6cOaOz5utBo0aNwhtvvIGBAwciLCwMR48exebNm/XmVsuWLdGtWzcEBgaibt26OHjwINatW6fz8ySBgYEA7j2FPDw8HHK5HEOGDEHXrl0xZswYJCYm4siRI+jVqxdsbW1x7tw5rF27FgsXLsS///3vxx6/R1GpVIiOjsb777+Pfv364fnnn8fRo0fxyy+/wM3N7aFnaKkGsN4NaESm+eWXX8TIkSNF8+bNhaOjo1AoFKJp06Zi/PjxIj8/X6eusbfBt2rVSu9zIiIihI+Pj05Zfn6+iIyMFG5ubkKhUIg2bdro3NZ+37Vr18TAgQNF7dq1RZ06dcSYMWO0t1Q/WD8rK0sMHz5ceHh4CFtbW+Hl5SVefPFFsW7dOp16y5YtE40bNxZyuVznGPLy8kSfPn2Ek5OTAKBzS/zNmzdFXFycaNq0qVAoFMLNzU08++yz4r333jP42ID7SkpKxMKFC0V4eLho2LChsLW1FU5OTiI4OFgsW7ZM55ZhIYS4fv26GDdunPDy8hIKhUI0bNhQREREiIKCApPG7u+3oBti7FgZAkBERUXplZ8+fVq4ubmJunXrihMnTjyyD/c9eBv8fdu3bxfh4eHCxcVF2NnZiSZNmogRI0aIgwcPauv8+eefYsCAAcLV1VW4uLiIQYMGiStXrujdUn7/NvgHb29/8Hb0tLQ08dJLL4kGDRoIhUIhGjRoIIYOHar3CITKVFRUiM8++0x06dJFuLi4CFtbW+Hj4yMiIyN1bpE3dBu8Wq0W06ZNE25ubqJ27doiPDxcZGZm6v3dmzNnjujUqZNwdXUV9vb2onnz5uLdd9/VmYcVFRVi/Pjxon79+kImk+ndEr906VIRGBgo7O3thZOTk2jTpo146623xJUrV7R1fHx8Kr3dvrLb4A8cOKBTz9C/ExUVFeKdd94RHh4ewt7eXvTo0UOcPn1a1KtXT7zxxhtGjDJVVzIhuNqLiIjIWEVFRahTpw7mzJmjfRAp1TxcA0RERFSJv/76S6/s/rqtv//8DNU8XANERERUiTVr1mDVqlV44YUX4OjoiN27d+Obb75Br169DP5mHtUcDEBERESVCAgIQK1atTB//nyUlJRoF0bPmTPH2l2jf4hrgIiIiEhyuAaIiIiIJIcBiIiIiCSHa4AM0Gg0uHLlCpycnPigKyIiohpCCIGbN2+iQYMGej/C/CAGIAOuXLkCb29va3eDiIiIHsOlS5fQsGHDh9ZhADLg/uP5L126BGdnZyv3hoiIiIxRUlICb29vo35mhwHIgPuXvZydnRmAiIiIahhjlq9wETQRERFJDgMQERERSQ4DEBEREUkOAxARERFJDgMQERERSQ4DEBEREUkOAxARERFJDgMQERERSQ4DEBEREUkOAxARERFJDgMQERERSQ4DEBEREUkOAxARERFJDgMQERERSU61CECLFy+Gr68v7OzsEBQUhP3791dat1u3bpDJZHqvPn36aOusX78evXr1Qr169SCTyXDkyJEqOAoiIiKqKawegNasWYOYmBjEx8fj8OHDaNu2LcLDw3H16lWD9devX4/c3Fzt68SJE5DL5Rg0aJC2TmlpKTp37ox58+ZV1WEQERFRDSITQghrdiAoKAgdO3bEokWLAAAajQbe3t4YP348YmNjH9k+KSkJ06dPR25uLhwcHHS2XbhwAX5+fvjjjz/Qrl07o/tUUlICFxcXFBcXw9nZ2aTjISIiIusw5fvbqmeAysvLcejQIYSGhmrLbGxsEBoaivT0dKP2sXz5cgwZMkQv/JiirKwMJSUlOi8iIiJ6clk1ABUUFECtVkOlUumUq1Qq5OXlPbL9/v37ceLECYwaNeof9SMxMREuLi7al7e39z/aHxEREVVvVl8D9E8sX74cbdq0QadOnf7RfuLi4lBcXKx9Xbp0yUw9JCIiouqoljU/3M3NDXK5HPn5+Trl+fn58PDweGjb0tJSfPvtt5g1a9Y/7odSqYRSqfzH+yEiIqKawapngBQKBQIDA5GWlqYt02g0SEtLQ3Bw8EPbrl27FmVlZXjllVcs3U0iIiJ6wlj1DBAAxMTEICIiAh06dECnTp2QlJSE0tJSREZGAgCGDx8OLy8vJCYm6rRbvnw5+vfvj3r16unts7CwEDk5Obhy5QoAICMjAwDg4eHxyDNLRERE9OSzegAaPHgwrl27hunTpyMvLw/t2rVDamqqdmF0Tk4ObGx0T1RlZGRg9+7d2LJli8F9bty4URugAGDIkCEAgPj4eMyYMcMyB0JEREQ1htWfA1Qd8TlARERENU+NeQ4QERERkTUwABEREZHkMAARERGR5DAAERERkeQwABEREZHkMAARERGR5DAAERERkeQwABEREZHkMAARERGR5DAAERERkeQwABEREZHkMAARERGR5DAAERERkeQwABEREZHkMAARERGR5DAAERERkeQwABEREZHkMAARERGR5DAAERERkeQwABEREZHkMAARERGR5DAAERERkeQwABEREZHkMAARERGR5DAAERERkeQwABEREZHkMAARERGR5DAAERERkeQwABEREZHkMAARERGR5DAAERERkeQwABEREZHkMAARERGR5DAAERERkeQwABEREZHkMAARERGR5DAAERERkeQwABEREZHkMAARERGR5FSLALR48WL4+vrCzs4OQUFB2L9/f6V1u3XrBplMpvfq06ePto4QAtOnT4enpyfs7e0RGhqKc+fOVcWhEBERUQ1g9QC0Zs0axMTEID4+HocPH0bbtm0RHh6Oq1evGqy/fv165Obmal8nTpyAXC7HoEGDtHXmz5+Pjz76CMnJyfj999/h4OCA8PBw3Llzp6oOi4iIiKoxmRBCWLMDQUFB6NixIxYtWgQA0Gg08Pb2xvjx4xEbG/vI9klJSZg+fTpyc3Ph4OAAIQQaNGiAyZMnY8qUKQCA4uJiqFQqrFq1CkOGDHnkPktKSuDi4oLi4mI4Ozv/swMkIiKiKmHK97dVzwCVl5fj0KFDCA0N1ZbZ2NggNDQU6enpRu1j+fLlGDJkCBwcHAAA2dnZyMvL09mni4sLgoKCKt1nWVkZSkpKdF5ERET05LJqACooKIBarYZKpdIpV6lUyMvLe2T7/fv348SJExg1apS27H47U/aZmJgIFxcX7cvb29vUQyEiIqIaxOprgP6J5cuXo02bNujUqdM/2k9cXByKi4u1r0uXLpmph0RERFQdWTUAubm5QS6XIz8/X6c8Pz8fHh4eD21bWlqKb7/9Fq+99ppO+f12puxTqVTC2dlZ50VERERPLqsGIIVCgcDAQKSlpWnLNBoN0tLSEBwc/NC2a9euRVlZGV555RWdcj8/P3h4eOjss6SkBL///vsj90lERETSUMvaHYiJiUFERAQ6dOiATp06ISkpCaWlpYiMjAQADB8+HF5eXkhMTNRpt3z5cvTv3x/16tXTKZfJZJg4cSLmzJmDZs2awc/PD++88w4aNGiA/v37V9VhERERUTVm9QA0ePBgXLt2DdOnT0deXh7atWuH1NRU7SLmnJwc2NjonqjKyMjA7t27sWXLFoP7fOutt1BaWorXX38dRUVF6Ny5M1JTU2FnZ2fx4yEiIqLqz+rPAaqO+BwgIiKimqfGPAeIiIiIyBoYgIiIiEhyGICIiIhIchiAiIiISHIYgIiIiEhyGICIiIhIchiAiIiISHIYgIiIiEhyGICIiIhIchiAiIiISHIYgIiIiEhyGICIiIhIchiAiIiISHIYgIiIiEhyGICIiIhIchiAiIiISHIYgIiIiEhyGICIiIhIchiAiIiISHIYgIiIiEhyGICIiIhIchiAiIiISHIYgIiIiEhyGICIiIhIchiAiIiISHIYgIiIiEhyGICIiIhIchiAiIiISHIYgIiIiEhyGICIiIhIchiAiIiISHIYgIiIiEhyGICIiIhIchiAiIiISHIYgIiIiEhyGICIiIhIchiAiIiISHIYgIiIiEhyrB6AFi9eDF9fX9jZ2SEoKAj79+9/aP2ioiJERUXB09MTSqUS/v7+SElJ0W6/efMmJk6cCB8fH9jb2+PZZ5/FgQMHLH0YREREVINYNQCtWbMGMTExiI+Px+HDh9G2bVuEh4fj6tWrBuuXl5cjLCwMFy5cwLp165CRkYFly5bBy8tLW2fUqFHYunUrvvjiCxw/fhy9evVCaGgoLl++XFWHRURERNWcTAghrPXhQUFB6NixIxYtWgQA0Gg08Pb2xvjx4xEbG6tXPzk5GQsWLMCZM2dga2urt/2vv/6Ck5MTfvzxR/Tp00dbHhgYiN69e2POnDlG9aukpAQuLi4oLi6Gs7PzYx4dERERVSVTvr+tdgaovLwchw4dQmho6P86Y2OD0NBQpKenG2yzceNGBAcHIyoqCiqVCq1bt0ZCQgLUajUAoKKiAmq1GnZ2djrt7O3tsXv3bssdDBEREdUoVgtABQUFUKvVUKlUOuUqlQp5eXkG25w/fx7r1q2DWq1GSkoK3nnnHbz//vvaMztOTk4IDg7G7NmzceXKFajVanz55ZdIT09Hbm5upX0pKytDSUmJzouIiIieXFZfBG0KjUYDd3d3LF26FIGBgRg8eDDefvttJCcna+t88cUXEELAy8sLSqUSH330EYYOHQobm8oPNTExES4uLtqXt7d3VRwOERERWYnVApCbmxvkcjny8/N1yvPz8+Hh4WGwjaenJ/z9/SGXy7VlLVq0QF5eHsrLywEATZo0wc6dO3Hr1i1cunQJ+/fvx927d9G4ceNK+xIXF4fi4mLt69KlS2Y4QiIiIqqurBaAFAoFAgMDkZaWpi3TaDRIS0tDcHCwwTYhISHIzMyERqPRlp09exaenp5QKBQ6dR0cHODp6YkbN25g8+bNeOmllyrti1KphLOzs86LiIiInlxWvQQWExODZcuWYfXq1Th9+jTGjh2L0tJSREZGAgCGDx+OuLg4bf2xY8eisLAQ0dHROHv2LDZt2oSEhARERUVp62zevBmpqanIzs7G1q1b0b17dzRv3ly7TyIiIqJa1vzwwYMH49q1a5g+fTry8vLQrl07pKamahdG5+Tk6Kzd8fb2xubNmzFp0iQEBATAy8sL0dHRmDZtmrZOcXEx4uLi8Oeff6Ju3boYOHAg3n33XYO3zRMREZE0WfU5QNUVnwNERERU89SI5wARERERWQsDEBEREUnOPwpAd+7cMVc/iIiIiKqMyQFIo9Fg9uzZ8PLygqOjI86fPw8AeOedd7B8+XKzd5CIiIjI3EwOQHPmzMGqVaswf/58nWfvtG7dGp999plZO0dERERkCSYHoM8//xxLly7FsGHDdJ7I3LZtW5w5c8asnSMiIiKyBJMD0OXLl9G0aVO9co1Gg7t375qlU0RERESWZHIAatmyJX777Te98nXr1qF9+/Zm6RQRERGRJZn8JOjp06cjIiICly9fhkajwfr165GRkYHPP/8cP//8syX6SERERGRWJp8Beumll/DTTz/h119/hYODA6ZPn47Tp0/jp59+QlhYmCX6SERERGRWJp0BqqioQEJCAkaOHImtW7daqk9EREREFmXSGaBatWph/vz5qKiosFR/iIiIiCzO5EtgPXv2xM6dOy3RFyIiIqIqYfIi6N69eyM2NhbHjx9HYGAgHBwcdLb369fPbJ0jIiIisgSZEEKY0sDGpvKTRjKZDGq1+h93ytpKSkrg4uKC4uJiODs7W7s7REREZARTvr9NPgOk0Wgeu2NERERE1cE/+jV4IiIioprosQLQzp070bdvXzRt2hRNmzZFv379DD4dmoiIiKg6MjkAffnllwgNDUXt2rUxYcIETJgwAfb29ujZsye+/vprS/SRiIiIyKxMXgTdokULvP7665g0aZJO+QcffIBly5bh9OnTZu2gNXARNBERUc1jyve3yWeAzp8/j759++qV9+vXD9nZ2abujoiIiKjKmRyAvL29kZaWplf+66+/wtvb2yydIiIiIrIkk2+Dnzx5MiZMmIAjR47g2WefBQDs2bMHq1atwsKFC83eQSIiIiJzMzkAjR07Fh4eHnj//ffx3XffAbi3LmjNmjV46aWXzN5BIiIiInMzeRG0FHARNBERUc1j0UXQBw4cwO+//65X/vvvv+PgwYOm7o6IiIioypkcgKKionDp0iW98suXLyMqKsosnSIiIiKyJJMD0KlTp/D000/rlbdv3x6nTp0yS6eIiIiILMnkAKRUKpGfn69Xnpubi1q1TF5TTURERFTlTA5AvXr1QlxcHIqLi7VlRUVF+O9//4uwsDCzdo6IiIjIEkw+ZfPee+/hueeeg4+PD9q3bw8AOHLkCFQqFb744guzd5CIiIjI3EwOQF5eXjh27Bi++uorHD16FPb29oiMjMTQoUNha2triT4SERERmdVjLdpxcHDA66+/bu6+EBEREVUJo9cAnT17Fvv379cpS0tLQ/fu3dGpUyckJCSYvXNERERElmB0AJo2bRp+/vln7fvs7Gz07dsXCoUCwcHBSExMRFJSkiX6SERERGRWRl8CO3jwIN566y3t+6+++gr+/v7YvHkzACAgIAAff/wxJk6caPZOEhEREZmT0WeACgoK0LBhQ+377du3o2/fvtr33bp1w4ULF8zaOSIiIiJLMDoA1a1bF7m5uQAAjUaDgwcP4plnntFuLy8vB39XlYiIiGoCowNQt27dMHv2bFy6dAlJSUnQaDTo1q2bdvupU6fg6+trgS4SERERmZfRAejdd9/FmTNn4OPjg2nTpmH+/PlwcHDQbv/iiy/Qo0cPkzuwePFi+Pr6ws7ODkFBQXp3mj2oqKgIUVFR8PT0hFKphL+/P1JSUrTb1Wo13nnnHfj5+cHe3h5NmjTB7NmzeXaKiIiItIxeBO3r64vTp0/j5MmTqF+/Pho0aKCzfebMmTprhIyxZs0axMTEIDk5GUFBQUhKSkJ4eDgyMjLg7u6uV7+8vBxhYWFwd3fHunXr4OXlhYsXL8LV1VVbZ968eViyZAlWr16NVq1a4eDBg4iMjISLiwsmTJhgUv+IiIjoySQTVjw1EhQUhI4dO2LRokUA7q0t8vb2xvjx4xEbG6tXPzk5GQsWLMCZM2cqfer0iy++CJVKheXLl2vLBg4cCHt7e3z55ZdG9aukpAQuLi4oLi6Gs7PzYxwZERERVTVTvr9N/jFUcykvL8ehQ4cQGhr6v87Y2CA0NBTp6ekG22zcuBHBwcGIioqCSqVC69atkZCQALVara3z7LPPIi0tDWfPngUAHD16FLt370bv3r0r7UtZWRlKSkp0XkRERPTkeqyfwjCHgoICqNVqqFQqnXKVSoUzZ84YbHP+/Hls27YNw4YNQ0pKCjIzM/Hmm2/i7t27iI+PBwDExsaipKQEzZs3h1wuh1qtxrvvvothw4ZV2pfExETMnDnTfAdHRERE1ZrVzgA9Do1GA3d3dyxduhSBgYEYPHgw3n77bSQnJ2vrfPfdd/jqq6/w9ddf4/Dhw1i9ejXee+89rF69utL9xsXFobi4WPu6dOlSVRwOERERWYnVzgC5ublBLpcjPz9fpzw/Px8eHh4G23h6esLW1hZyuVxb1qJFC+Tl5aG8vBwKhQJTp05FbGwshgwZAgBo06YNLl68iMTERERERBjcr1KphFKpNNORERERUXX3WGeAfvvtN7zyyisIDg7G5cuXAdy7DX737t1G70OhUCAwMBBpaWnaMo1Gg7S0NAQHBxtsExISgszMTGg0Gm3Z2bNn4enpCYVCAQC4ffs2bGx0D0sul+u0ISIiImkzOQB9//33CA8Ph729Pf744w+UlZUBAIqLi03+RfiYmBgsW7YMq1evxunTpzF27FiUlpYiMjISADB8+HDExcVp648dOxaFhYWIjo7G2bNnsWnTJiQkJCAqKkpbp2/fvnj33XexadMmXLhwARs2bMAHH3yAAQMGmHqoRERE9KQSJmrXrp1YvXq1EEIIR0dHkZWVJYQQ4vDhw0KlUpm6O/Hxxx+LRo0aCYVCITp16iT27dun3da1a1cRERGhU3/v3r0iKChIKJVK0bhxY/Huu++KiooK7faSkhIRHR0tGjVqJOzs7ETjxo3F22+/LcrKyozuU3FxsQAgiouLTT4eIiIisg5Tvr9Nfg5Q7dq1tT974eTkhKNHj6Jx48Y4f/48WrZsiTt37lgmqVUhPgeIiIio5rHoc4A8PDyQmZmpV7579240btzY1N0RERERVTmTA9Do0aMRHR2N33//HTKZDFeuXMFXX32FKVOmYOzYsZboIxEREZFZmXwbfGxsLDQaDXr27Inbt2/jueeeg1KpxJQpUzB+/HhL9JGIiIjIrB77t8DKy8uRmZmJW7duoWXLlnB0dDR336zGUmuA1BqB/dmFuHrzDtyd7NDJry7kNjKz7Z+kifOKLIHziizFknPLlO9vk88AjRw5EgsXLoSTkxNatmypLS8tLcX48eOxYsUK03ssAakncjHzp1PILf7fInFPFzvE922J51t7WrFnVJNxXpElcF6RpVSnuWXyGSC5XI7c3Fy4u7vrlBcUFMDDwwMVFRVm7aA1mPsMUOqJXIz98jAeHOj7eXfJK0/zHxUyGecVWQLnFVlKVcwti5wBKikpgRACQgjcvHkTdnZ22m1qtRopKSl6oYjuneqb+dMpvT9wABC49wc/Y+MphDR14+llMppaIxC/8STnFZkV5xVZijFza+ZPpxDW0qPK5pbRZ4BsbGwgk1XeKZlMhpkzZ+Ltt982W+esxZxngNKzrmPosn1m6hkREdGT65vRzyC4Sb3Hbm+RM0Dbt2+HEAI9evTA999/j7p162q3KRQK+Pj4oEGDBo/d6SfV1Zs1/8GQREREVaEqvzONDkBdu3YFAGRnZ6NRo0YGzwbl5OSgUaNG5uvdE8Ddye7RlQCsiuyITn51H12RCMD+7EKMWHngkfU4r8gUnFdkKcbOLWO/M83B5LvAGjdubHAR9PXr1+Hn5we1Wm22zj0JOvnVhaeLHfKK7xi89ikD4OFihy7N6vOaOhmtS7P6nFdkdpxXZCnGzq2qDNYmPwm6siVDt27d0lkYTffIbWSI73vvcQEP/nNx/31835b8x4RMwnlFlsB5RZZSHeeW0YugY2JiAAALFy7E6NGjUbt2be02tVqN33//HXK5HHv27LFMT6uQJR6EWJ2efUBPDs4rsgTOK7IUS88tU76/jQ5A3bt3BwDs3LkTwcHBUCgU2m0KhQK+vr6YMmUKmjVr9g+6Xj3wSdBUk3BekSVwXpGlVJcnQZv8IMTIyEgsXLjQrMGgurFUACIiIiLLMeX72+Q1QCtXroSzszMyMzOxefNm/PXXXwAqXxtEREREVN2YHIAKCwvRs2dP+Pv744UXXkBubi4A4LXXXsPkyZPN3kEiIiIiczM5AE2cOBG2trbIycnRWQg9ePBgpKammrVzRERERJZg8nOAtmzZgs2bN6Nhw4Y65c2aNcPFixfN1jEiIiIiSzH5DFBpaanOmZ/7CgsLoVQqzdIpIiIiIksyOQB16dIFn3/+ufa9TCaDRqPB/PnztbfKExEREVVnJl8Cmz9/Pnr27ImDBw+ivLwcb731Fk6ePInCwsIn4iGIRERE9OQz+QxQ69atcfbsWXTu3BkvvfQSSktL8a9//Qt//PEHmjRpYok+EhEREZmVyQ9ClAI+CJGIiKjmMeX72+RLYLt27Xro9ueee87UXRIRERFVKZMDULdu3fTKZLL//YaHWq3+Rx0iIiIisjST1wDduHFD53X16lWkpqaiY8eO2LJliyX6SERERGRWJp8BcnFx0SsLCwuDQqFATEwMDh06ZJaOEREREVmKyWeAKqNSqZCRkWGu3RERERFZjMlngI4dO6bzXgiB3NxczJ07F+3atTNXv4iIiIgsxuQA1K5dO8hkMjx49/wzzzyDFStWmK1jRERERJZicgDKzs7WeW9jY4P69evDzs7ObJ0iIiIisiSTA5CPj48l+kFERERUZR5rEfTOnTvRt29fNG3aFE2bNkW/fv3w22+/mbtvRERERBZhcgD68ssvERoaitq1a2PChAmYMGEC7O3t0bNnT3z99deW6CMRERGRWZn8W2AtWrTA66+/jkmTJumUf/DBB1i2bBlOnz5t1g5aA38LjIiIqOYx5fvb5DNA58+fR9++ffXK+/Xrp7dAmoiIiKg6MjkAeXt7Iy0tTa/8119/hbe3t1k6RURERGRJJt8FNnnyZEyYMAFHjhzBs88+CwDYs2cPVq1ahYULF5q9g0RERETmZvIZoLFjx+Lbb7/F8ePHMXHiREycOBEnTpzAmjVrMGbMmMfqxOLFi+Hr6ws7OzsEBQVh//79D61fVFSEqKgoeHp6QqlUwt/fHykpKdrtvr6+kMlkeq+oqKjH6h8RERE9WUw+AwQAAwYMwIABA8zSgTVr1iAmJgbJyckICgpCUlISwsPDkZGRAXd3d7365eXlCAsLg7u7O9atWwcvLy9cvHgRrq6u2joHDhyAWq3Wvj9x4gTCwsIwaNAgs/SZiIiIajaT7wK7r7y8HFevXoVGo9Epb9SokUn7CQoKQseOHbFo0SIAgEajgbe3N8aPH4/Y2Fi9+snJyViwYAHOnDkDW1tboz5j4sSJ+Pnnn3Hu3DnIZLJH1uddYERERDWPRe8CO3fuHLp06QJ7e3v4+PjAz88Pfn5+8PX1hZ+fn0n7Ki8vx6FDhxAaGvq/DtnYIDQ0FOnp6QbbbNy4EcHBwYiKioJKpULr1q2RkJCgc8bnwc/48ssvMXLkyErDT1lZGUpKSnReRERE9OQy+RLYiBEjUKtWLfz888/w9PQ06oxKZQoKCqBWq6FSqXTKVSoVzpw5Y7DN+fPnsW3bNgwbNgwpKSnIzMzEm2++ibt37yI+Pl6v/g8//ICioiKMGDGi0n4kJiZi5syZj30cREREVLOYHICOHDmCQ4cOoXnz5pbozyNpNBq4u7tj6dKlkMvlCAwMxOXLl7FgwQKDAWj58uXo3bs3GjRoUOk+4+LiEBMTo31fUlLCW/qJiIieYCYHoJYtW6KgoMAsH+7m5ga5XI78/Hyd8vz8fHh4eBhs4+npCVtbW8jlcm1ZixYtkJeXh/LycigUCm35xYsX8euvv2L9+vUP7YdSqYRSqfwHR0JEREQ1iVFrgP6+NmbevHl46623sGPHDly/fv0frZ1RKBQIDAzUebCiRqNBWloagoODDbYJCQlBZmamzuLrs2fPwtPTUyf8AMDKlSvh7u6OPn36mNQvIiIierIZdQbI1dVVZ62PEAI9e/bUqSOEgEwmq3QxcmViYmIQERGBDh06oFOnTkhKSkJpaSkiIyMBAMOHD4eXlxcSExMB3HsO0aJFixAdHY3x48fj3LlzSEhIwIQJE3T2q9FosHLlSkRERKBWrce625+IiIieUEYlg+3bt1usA4MHD8a1a9cwffp05OXloV27dkhNTdUujM7JyYGNzf9OVHl7e2Pz5s2YNGkSAgIC4OXlhejoaEybNk1nv7/++itycnIwcuRIi/WdiIiIaqbHfg7Qk4zPASIiIqp5TPn+NuoM0LFjx4z+8ICAAKPrEhEREVmDUQGoXbt2kMlkeNTJosdZA0RERERU1YwKQNnZ2ZbuBxEREVGVMSoA+fj4WLofRERERFXGqAC0ceNG9O7dG7a2tti4ceND6/br188sHSMiIiKyFKPuArOxsUFeXh7c3d11bknX29kTsgaId4ERERHVPGa/C+zvT13++38TERER1URG/RQGERER0ZPE6ACUnp6On3/+Wafs888/h5+fH9zd3fH666+jrKzM7B0kIiIiMjejA9CsWbNw8uRJ7fvjx4/jtddeQ2hoKGJjY/HTTz9pf6+LiIiIqDozOgAdOXJE5wdQv/32WwQFBWHZsmWIiYnBRx99hO+++84inSQiIiIyJ6MD0I0bN7Q/UAoAO3fuRO/evbXvO3bsiEuXLpm3d0REREQWYHQAUqlU2idCl5eX4/Dhw3jmmWe022/evAlbW1vz95CIiIjIzIwOQC+88AJiY2Px22+/IS4uDrVr10aXLl20248dO4YmTZpYpJNERERE5mTUc4AAYPbs2fjXv/6Frl27wtHREatXr4ZCodBuX7FiBXr16mWRThIRERGZk1FPgv674uJiODo6Qi6X65QXFhbC0dFRJxTVVHwSNBERUc1j9idB/52Li4vB8rp165q6KyIiIiKr4JOgiYiISHIYgIiIiEhyGICIiIhIchiAiIiISHIYgIiIiEhyGICIiIhIchiAiIiISHIYgIiIiEhyGICIiIhIchiAiIiISHIYgIiIiEhyGICIiIhIchiAiIiISHIYgIiIiEhyGICIiIhIchiAiIiISHIYgIiIiEhyGICIiIhIchiAiIiISHIYgIiIiEhyGICIiIhIcqwegBYvXgxfX1/Y2dkhKCgI+/fvf2j9oqIiREVFwdPTE0qlEv7+/khJSdGpc/nyZbzyyiuoV68e7O3t0aZNGxw8eNCSh0FEREQ1SC1rfviaNWsQExOD5ORkBAUFISkpCeHh4cjIyIC7u7te/fLycoSFhcHd3R3r1q2Dl5cXLl68CFdXV22dGzduICQkBN27d8cvv/yC+vXr49y5c6hTp04VHhkRERFVZzIhhLDWhwcFBaFjx45YtGgRAECj0cDb2xvjx49HbGysXv3k5GQsWLAAZ86cga2trcF9xsbGYs+ePfjtt98eu18lJSVwcXFBcXExnJ2dH3s/REREVHVM+f622iWw8vJyHDp0CKGhof/rjI0NQkNDkZ6ebrDNxo0bERwcjKioKKhUKrRu3RoJCQlQq9U6dTp06IBBgwbB3d0d7du3x7Jlyyx+PERERFRzWC0AFRQUQK1WQ6VS6ZSrVCrk5eUZbHP+/HmsW7cOarUaKSkpeOedd/D+++9jzpw5OnWWLFmCZs2aYfPmzRg7diwmTJiA1atXV9qXsrIylJSU6LyIiIjoyWXVNUCm0mg0cHd3x9KlSyGXyxEYGIjLly9jwYIFiI+P19bp0KEDEhISAADt27fHiRMnkJycjIiICIP7TUxMxMyZM6vsOIiIiMi6rHYGyM3NDXK5HPn5+Trl+fn58PDwMNjG09MT/v7+kMvl2rIWLVogLy8P5eXl2jotW7bUadeiRQvk5ORU2pe4uDgUFxdrX5cuXXrcwyIiIqIawGoBSKFQIDAwEGlpadoyjUaDtLQ0BAcHG2wTEhKCzMxMaDQabdnZs2fh6ekJhUKhrZORkaHT7uzZs/Dx8am0L0qlEs7OzjovIiIienJZ9TlAMTExWLZsGVavXo3Tp09j7NixKC0tRWRkJABg+PDhiIuL09YfO3YsCgsLER0djbNnz2LTpk1ISEhAVFSUts6kSZOwb98+JCQkIDMzE19//TWWLl2qU4eIiIikzaprgAYPHoxr165h+vTpyMvLQ7t27ZCamqpdGJ2TkwMbm/9lNG9vb2zevBmTJk1CQEAAvLy8EB0djWnTpmnrdOzYERs2bEBcXBxmzZoFPz8/JCUlYdiwYVV+fERERFQ9WfU5QNUVnwNERERU89SI5wARERERWQsDEBEREUkOAxARERFJDgMQERERSQ4DEBEREUkOAxARERFJDgMQERERSQ4DEBEREUkOAxARERFJDgMQERERSQ4DEBEREUkOAxARERFJDgMQERERSQ4DEBEREUkOAxARERFJDgMQERERSQ4DEBEREUkOAxARERFJDgMQERERSQ4DEBEREUkOAxARERFJDgMQERERSQ4DEBEREUkOAxARERFJDgMQERERSQ4DEBEREUkOAxARERFJDgMQERERSQ4DEBEREUkOAxARERFJDgMQERERSQ4DEBEREUkOAxARERFJDgMQERERSQ4DEBEREUkOAxARERFJDgMQERERSQ4DEBEREUkOAxARERFJTrUIQIsXL4avry/s7OwQFBSE/fv3P7R+UVERoqKi4OnpCaVSCX9/f6SkpGi3z5gxAzKZTOfVvHlzSx8GERER1RC1rN2BNWvWICYmBsnJyQgKCkJSUhLCw8ORkZEBd3d3vfrl5eUICwuDu7s71q1bBy8vL1y8eBGurq469Vq1aoVff/1V+75WLasfKhEREVUTVk8FH3zwAUaPHo3IyEgAQHJyMjZt2oQVK1YgNjZWr/6KFStQWFiIvXv3wtbWFgDg6+urV69WrVrw8PCwaN+JiIioZrLqJbDy8nIcOnQIoaGh2jIbGxuEhoYiPT3dYJuNGzciODgYUVFRUKlUaN26NRISEqBWq3XqnTt3Dg0aNEDjxo0xbNgw5OTkVNqPsrIylJSU6LyIiIjoyWXVAFRQUAC1Wg2VSqVTrlKpkJeXZ7DN+fPnsW7dOqjVaqSkpOCdd97B+++/jzlz5mjrBAUFYdWqVUhNTcWSJUuQnZ2NLl264ObNmwb3mZiYCBcXF+3L29vbfAdJRERE1Y7VL4GZSqPRwN3dHUuXLoVcLkdgYCAuX76MBQsWID4+HgDQu3dvbf2AgAAEBQXBx8cH3333HV577TW9fcbFxSEmJkb7vqSkhCGIiIjoCWbVAOTm5ga5XI78/Hyd8vz8/ErX73h6esLW1hZyuVxb1qJFC+Tl5aG8vBwKhUKvjaurK/z9/ZGZmWlwn0qlEkql8h8cCREREdUkVr0EplAoEBgYiLS0NG2ZRqNBWloagoODDbYJCQlBZmYmNBqNtuzs2bPw9PQ0GH4A4NatW8jKyoKnp6d5D4CIiIhqJKs/BygmJgbLli3D6tWrcfr0aYwdOxalpaXau8KGDx+OuLg4bf2xY8eisLAQ0dHROHv2LDZt2oSEhARERUVp60yZMgU7d+7EhQsXsHfvXgwYMAByuRxDhw6t8uMjIiKi6sfqa4AGDx6Ma9euYfr06cjLy0O7du2QmpqqXRidk5MDG5v/5TRvb29s3rwZkyZNQkBAALy8vBAdHY1p06Zp6/z5558YOnQorl+/jvr166Nz587Yt28f6tevX+XHR0RERNWPTAghrN2J6qakpAQuLi4oLi6Gs7OztbtDRERERjDl+9vql8CIiIiIqhoDEBEREUkOAxARERFJDgMQERERSQ4DEBEREUkOAxARERFJDgMQERERSQ4DEBEREUkOAxARERFJDgMQERERSQ4DEBEREUkOAxARERFJDgMQERERSQ4DEBEREUkOAxARERFJDgMQERERSQ4DEBEREUkOAxARERFJDgMQERERSQ4DEBEREUkOAxARERFJDgMQERERSQ4DEBEREUkOAxARERFJDgMQERERSQ4DEBEREUkOAxARERFJDgMQERERSQ4DEBEREUkOAxARERFJDgMQERERSQ4DEBEREUkOAxARERFJDgMQERERSQ4DEBEREUkOAxARERFJDgMQERERSQ4DEBEREUkOAxARERFJTrUIQIsXL4avry/s7OwQFBSE/fv3P7R+UVERoqKi4OnpCaVSCX9/f6SkpBisO3fuXMhkMkycONECPSciIqKaqJa1O7BmzRrExMQgOTkZQUFBSEpKQnh4ODIyMuDu7q5Xv7y8HGFhYXB3d8e6devg5eWFixcvwtXVVa/ugQMH8OmnnyIgIKAKjoSIiIhqCqufAfrggw8wevRoREZGomXLlkhOTkbt2rWxYsUKg/VXrFiBwsJC/PDDDwgJCYGvry+6du2Ktm3b6tS7desWhg0bhmXLlqFOnTpVcShERERUQ1g1AJWXl+PQoUMIDQ3VltnY2CA0NBTp6ekG22zcuBHBwcGIioqCSqVC69atkZCQALVarVMvKioKffr00dl3ZcrKylBSUqLzIiIioieXVS+BFRQUQK1WQ6VS6ZSrVCqcOXPGYJvz589j27ZtGDZsGFJSUpCZmYk333wTd+/eRXx8PADg22+/xeHDh3HgwAGj+pGYmIiZM2fqlTMIERER1Rz3v7eFEI+sa/U1QKbSaDRwd3fH0qVLIZfLERgYiMuXL2PBggWIj4/HpUuXEB0dja1bt8LOzs6ofcbFxSEmJkb7/vLly2jZsiW8vb0tdRhERERkITdv3oSLi8tD61g1ALm5uUEulyM/P1+nPD8/Hx4eHgbbeHp6wtbWFnK5XFvWokUL5OXlaS+pXb16FU8//bR2u1qtxq5du7Bo0SKUlZXptAUApVIJpVKpfe/o6IhLly7ByckJMpnMHIeqVVJSAm9vb1y6dAnOzs5m3feThmNlPI6V8ThWxuNYGY9jZRpLjZcQAjdv3kSDBg0eWdeqAUihUCAwMBBpaWno378/gHtneNLS0jBu3DiDbUJCQvD1119Do9HAxubeEqazZ8/C09MTCoUCPXv2xPHjx3XaREZGonnz5pg2bZpe+DHExsYGDRs2/GcH9wjOzs78S2IkjpXxOFbG41gZj2NlPI6VaSwxXo8683Of1S+BxcTEICIiAh06dECnTp2QlJSE0tJSREZGAgCGDx8OLy8vJCYmAgDGjh2LRYsWITo6GuPHj8e5c+eQkJCACRMmAACcnJzQunVrnc9wcHBAvXr19MqJiIhImqwegAYPHoxr165h+vTpyMvLQ7t27ZCamqpdGJ2Tk6M90wMA3t7e2Lx5MyZNmoSAgAB4eXkhOjoa06ZNs9YhEBERUQ1j9QAEAOPGjav0kteOHTv0yoKDg7Fv3z6j929oH9aiVCoRHx+vs+aIDONYGY9jZTyOlfE4VsbjWJmmOoyXTBhzrxgRERHRE8TqT4ImIiIiqmoMQERERCQ5DEBEREQkOQxAREREJDkMQGaUmJiIjh07wsnJCe7u7ujfvz8yMjIe2W7t2rVo3rw57Ozs0KZNG6SkpFRBb63rccZq1apVkMlkOi9jf+6kJluyZAkCAgK0DwwLDg7GL7/88tA2UpxTgOljJdU5ZcjcuXMhk8kwceLEh9aT6tz6O2PGSspza8aMGXrH3rx584e2sca8YgAyo507dyIqKgr79u3D1q1bcffuXfTq1QulpaWVttm7dy+GDh2K1157DX/88Qf69++P/v3748SJE1XY86r3OGMF3HtqaG5urvZ18eLFKuqx9TRs2BBz587FoUOHcPDgQfTo0QMvvfQSTp48abC+VOcUYPpYAdKcUw86cOAAPv30UwQEBDy0npTn1n3GjhUg7bnVqlUrnWPfvXt3pXWtNq8EWczVq1cFALFz585K67z88suiT58+OmVBQUFizJgxlu5etWLMWK1cuVK4uLhUXaeqsTp16ojPPvvM4DbOKV0PGyvOKSFu3rwpmjVrJrZu3Sq6du0qoqOjK60r9bllylhJeW7Fx8eLtm3bGl3fWvOKZ4AsqLi4GABQt27dSuukp6cjNDRUpyw8PBzp6ekW7Vt1Y8xYAcCtW7fg4+MDb2/vR/6f/ZNIrVbj22+/RWlpKYKDgw3W4Zy6x5ixAjinoqKi0KdPH705Y4jU55YpYwVIe26dO3cODRo0QOPGjTFs2DDk5ORUWtda86paPAn6SaTRaDBx4kSEhIQ89DfI8vLytD/7cZ9KpUJeXp6lu1htGDtWTz31FFasWIGAgAAUFxfjvffew7PPPouTJ09a/Mdrre348eMIDg7GnTt34OjoiA0bNqBly5YG60p9TpkyVlKeUwDw7bff4vDhwzhw4IBR9aU8t0wdKynPraCgIKxatQpPPfUUcnNzMXPmTHTp0gUnTpyAk5OTXn1rzSsGIAuJiorCiRMnHnrdk+4xdqyCg4N1/k/+2WefRYsWLfDpp59i9uzZlu6mVT311FM4cuQIiouLsW7dOkRERGDnzp2VfrFLmSljJeU5denSJURHR2Pr1q2SWZz7uB5nrKQ8t3r37q3974CAAAQFBcHHxwffffcdXnvtNSv2TBcDkAWMGzcOP//8M3bt2vXIpO/h4YH8/Hydsvz8fHh4eFiyi9WGKWP1IFtbW7Rv3x6ZmZkW6l31oVAo0LRpUwBAYGAgDhw4gIULF+LTTz/Vqyv1OWXKWD1ISnPq0KFDuHr1Kp5++mltmVqtxq5du7Bo0SKUlZVBLpfrtJHq3HqcsXqQlObWg1xdXeHv71/psVtrXnENkBkJITBu3Dhs2LAB27Ztg5+f3yPbBAcHIy0tTads69atD12z8CR4nLF6kFqtxvHjx+Hp6WmBHlZvGo0GZWVlBrdJdU5V5mFj9SApzamePXvi+PHjOHLkiPbVoUMHDBs2DEeOHDH4hS7VufU4Y/UgKc2tB926dQtZWVmVHrvV5pVFl1hLzNixY4WLi4vYsWOHyM3N1b5u376trfPqq6+K2NhY7fs9e/aIWrVqiffee0+cPn1axMfHC1tbW3H8+HFrHEKVeZyxmjlzpti8ebPIysoShw4dEkOGDBF2dnbi5MmT1jiEKhMbGyt27twpsrOzxbFjx0RsbKyQyWRiy5YtQgjOqb8zdaykOqcq8+CdTZxblXvUWEl5bk2ePFns2LFDZGdniz179ojQ0FDh5uYmrl69KoSoPvOKAciMABh8rVy5Uluna9euIiIiQqfdd999J/z9/YVCoRCtWrUSmzZtqtqOW8HjjNXEiRNFo0aNhEKhECqVSrzwwgvi8OHDVd/5KjZy5Ejh4+MjFAqFqF+/vujZs6f2C10Izqm/M3WspDqnKvPglzrnVuUeNVZSnluDBw8Wnp6eQqFQCC8vLzF48GCRmZmp3V5d5pVMCCEse46JiIiIqHrhGiAiIiKSHAYgIiIikhwGICIiIpIcBiAiIiKSHAYgIiIikhwGICIiIpIcBiAiIiKSHAYgIiIikhwGICJ6bBcuXIBMJsORI0es3RWtM2fO4JlnnoGdnR3atWv3j/Ylk8nwww8/mKVf1UFaWhpatGgBtVoNAJgxY8ZDxyg1NRXt2rWDRqOpoh4SVR0GIKIabMSIEZDJZJg7d65O+Q8//ACZTGalXllXfHw8HBwckJGRofcDi3+Xl5eH8ePHo3HjxlAqlfD29kbfvn0f2uaf2LFjB2QyGYqKiiyyf2O89dZb+L//+z+jfrwTAJ5//nnY2triq6++snDPiKoeAxBRDWdnZ4d58+bhxo0b1u6K2ZSXlz9226ysLHTu3Bk+Pj6oV6+ewToXLlxAYGAgtm3bhgULFuD48eNITU1F9+7dERUV9difXRWEEKioqDC53e7du5GVlYWBAwea1G7EiBH46KOPTP48ouqOAYiohgsNDYWHhwcSExMrrWPoUkdSUhJ8fX2170eMGIH+/fsjISEBKpUKrq6umDVrFioqKjB16lTUrVsXDRs2xMqVK/X2f+bMGTz77LOws7ND69atsXPnTp3tJ06cQO/eveHo6AiVSoVXX30VBQUF2u3dunXDuHHjMHHiRLi5uSE8PNzgcWg0GsyaNQsNGzaEUqlEu3btkJqaqt0uk8lw6NAhzJo1CzKZDDNmzDC4nzfffBMymQz79+/HwIED4e/vj1atWiEmJgb79u0z2MbQGZwjR45AJpPhwoULAICLFy+ib9++qFOnDhwcHNCqVSukpKTgwoUL6N69OwCgTp06kMlkGDFihPaYEhMT4efnB3t7e7Rt2xbr1q3T+9xffvkFgYGBUCqV2L17N44ePYru3bvDyckJzs7OCAwMxMGDBw32HQC+/fZbhIWFwc7OrtI6WVlZaNy4McaNG4f7PxPZt29fHDx4EFlZWZW2I6qJGICIaji5XI6EhAR8/PHH+PPPP//RvrZt24YrV65g165d+OCDDxAfH48XX3wRderUwe+//4433ngDY8aM0fucqVOnYvLkyfjjjz8QHByMvn374vr16wCAoqIi9OjRA+3bt8fBgweRmpqK/Px8vPzyyzr7WL16NRQKBfbs2YPk5GSD/Vu4cCHef/99vPfeezh27BjCw8PRr18/nDt3DgCQm5uLVq1aYfLkycjNzcWUKVP09lFYWIjU1FRERUXBwcFBb7urq+vjDB0AICoqCmVlZdi1axeOHz+OefPmwdHREd7e3vj+++8BABkZGcjNzcXChQsBAImJifj888+RnJyMkydPYtKkSXjllVf0QmRsbCzmzp2L06dPIyAgAMOGDUPDhg1x4MABHDp0CLGxsbC1ta20b7/99hs6dOhQ6fZjx46hc+fO+M9//oNFixZpL6E2atQIKpUKv/3222OPC1G1ZPHfmycii4mIiBAvvfSSEEKIZ555RowcOVIIIcSGDRvE3/96x8fHi7Zt2+q0/fDDD4WPj4/Ovnx8fIRardaWPfXUU6JLly7a9xUVFcLBwUF88803QgghsrOzBQAxd+5cbZ27d++Khg0binnz5gkhhJg9e7bo1auXzmdfunRJABAZGRlCCCG6du0q2rdv/8jjbdCggXj33Xd1yjp27CjefPNN7fu2bduK+Pj4Svfx+++/CwBi/fr1j/w8AGLDhg1CCCG2b98uAIgbN25ot//xxx8CgMjOzhZCCNGmTRsxY8YMg/sy1P7OnTuidu3aYu/evTp1X3vtNTF06FCddj/88INOHScnJ7Fq1apHHsN9Li4u4vPPP9cpuz8v9uzZI+rUqSPee+89g23bt29f6XER1VS1rJa8iMis5s2bhx49ehg862GsVq1awcbmfyeGVSoVWrdurX0vl8tRr149XL16VaddcHCw9r9r1aqFDh064PTp0wCAo0ePYvv27XB0dNT7vKysLPj7+wMAAgMDH9q3kpISXLlyBSEhITrlISEhOHr0qJFHCO2lHUuYMGECxo4diy1btiA0NBQDBw5EQEBApfUzMzNx+/ZthIWF6ZSXl5ejffv2OmUPnr2JiYnBqFGj8MUXXyA0NBSDBg1CkyZNKv2sv/76y+Dlr5ycHISFheHdd9/FxIkTDba1t7fH7du3K903UU3ES2BET4jnnnsO4eHhiIuL09tmY2Oj98V/9+5dvXoPXkKRyWQGy0y5LfrWrVvo27cvjhw5ovM6d+4cnnvuOW09Q5ejLKFZs2aQyWQ4c+aMSe3uB8O/j+ODYzhq1CicP38er776Ko4fP44OHTrg448/rnSft27dAgBs2rRJZ2xOnTqlsw4I0B+fGTNm4OTJk+jTpw+2bduGli1bYsOGDZV+lpubm8GF8vXr10enTp3wzTffoKSkxGDbwsJC1K9fv9J9E9VEDEBET5C5c+fip59+Qnp6uk55/fr1kZeXp/Plbc5n9/x94XBFRQUOHTqEFi1aAACefvppnDx5Er6+vmjatKnOy5TQ4+zsjAYNGmDPnj065Xv27EHLli2N3k/dunURHh6OxYsXo7S0VG97Zbep3w8Aubm52jJDY+jt7Y033ngD69evx+TJk7Fs2TIAgEKhAADtM3gAoGXLllAqlcjJydEbG29v70cei7+/PyZNmoQtW7bgX//6l8EF6ve1b98ep06d0iu3t7fHzz//DDs7O4SHh+PmzZs62+/cuYOsrCy9M1JENR0DENETpE2bNhg2bJjebcvdunXDtWvXMH/+fGRlZWHx4sX45ZdfzPa5ixcvxoYNG3DmzBlERUXhxo0bGDlyJIB7C4MLCwsxdOhQHDhwAFlZWdi8eTMiIyN1woAxpk6dinnz5mHNmjXIyMhAbGwsjhw5gujoaJP7q1ar0alTJ3z//fc4d+4cTp8+jY8++kjnct7f3Q8lM2bMwLlz57Bp0ya8//77OnUmTpyIzZs3Izs7G4cPH8b27du1QdDHxwcymQw///wzrl27hlu3bsHJyQlTpkzBpEmTsHr1amRlZeHw4cP4+OOPsXr16kr7/9dff2HcuHHYsWMHLl68iD179uDAgQPazzIkPDwcu3fvNrjNwcEBmzZtQq1atdC7d2/tmSngXrhVKpWVjgtRTcUARPSEmTVrlt4lqhYtWuCTTz7B4sWL0bZtW+zfv/8frRV60Ny5czF37ly0bdsWu3fvxsaNG+Hm5gYA2rM2arUavXr1Qps2bTBx4kS4urrqrDcyxoQJExATE4PJkyejTZs2SE1NxcaNG9GsWTOT9tO4cWMcPnwY3bt3x+TJk9G6dWuEhYUhLS0NS5YsMdjG1tYW33zzDc6cOYOAgADMmzcPc+bM0amjVqsRFRWFFi1a4Pnnn4e/vz8++eQTAICXlxdmzpyJ2NhYqFQqjBs3DgAwe/ZsvPPOO0hMTNS227RpE/z8/Crtv1wux/Xr1zF8+HD4+/vj5ZdfRu/evTFz5sxK2wwbNgwnT55ERkaGwe2Ojo745ZdfIIRAnz59tGfHvvnmGwwbNgy1a9eufECJaiCZsOSKQCIiqjamTp2KkpISfPrpp0bVLygowFNPPYWDBw8+NJAR1UQ8A0REJBFvv/02fHx8jF7EfuHCBXzyyScMP/RE4hkgIiIikhyeASIiIiLJYQAiIiIiyWEAIiIiIslhACIiIiLJYQAiIiIiyWEAIiIiIslhACIiIiLJYQAiIiIiyWEAIiIiIsn5f9t3afV2mfk1AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "silhouette_scores = []\n",
    "ari_scores = []\n",
    "for k in range(2, 6):\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "    silhouette_avg = sil\n",
    "    silhouette_scores.append(silhouette_avg)\n",
    "    ari = ari_score\n",
    "    ari_scores.append(ari)\n",
    "\n",
    "# Memplot Silhouette Score untuk berbagai nilai k\n",
    "plt.plot(range(2, 6), silhouette_scores, marker='o')\n",
    "plt.xlabel('Number of Clusters (k)')\n",
    "plt.ylabel('Silhouette Score')\n",
    "plt.title('Silhouette Score KMeans Clustering')\n",
    "plt.show()\n",
    "# Memplot ARI untuk berbagai nilai k\n",
    "plt.plot(range(2, 6), ari_scores, marker='o')\n",
    "plt.xlabel('Number of Clusters (k)')\n",
    "plt.ylabel('Adjusted Rand Index')\n",
    "plt.title('Adjusted Rand Index KMeans Clustering')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### COMMENTS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "cara menentukan kemiripan data dengan pengukuran jarak data<br>\n",
    "menggunakan euclidean distance = <br>\n",
    "d = akar pangkat 2 dari sigma/penjumlahan dari selisih antara (xi dan yi) ^ 2<br>\n",
    "xi = atribut data yang pertama<br>\n",
    "yi = atribut data yang kedua<br>\n",
    "<br>\n",
    "contoh ada 2 data dengan 3 kolom, maka :<br>\n",
    "d1 = akar (kolom1 data lama- kolom1 data baru)^2 + (kolom2 data lama- kolom2 data baru)^2 +(kolom3 data lama- kolom3 data baru)^2<br>\n",
    "dst sampai baris di data lama habis lalu dari setiap baris tsb dilihat mana yang nilai nya paling kecil, maka kolom data baru tsb masuk ke golongan tsb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "nah dari data tsb bisa disimpulkan nama nama cluster nya<br>\n",
    "0 = customer perlu diperhatikan<br>\n",
    "1 = customer tidak perlu diperhatikan"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}