Online Retail Store Clustering Problem.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# K-Means Algorithm Implementation\n",
    "</br>\n",
    "<b>Overview</b>\n",
    "<p>The dataset contains all the transactions occurring between 1/12/2010 and 9/12/2011 for a UK-based and registered non-store online retail. The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.</p>\n",
    "\n",
    "Steps:\n",
    "<li>Fetch Data</li>\n",
    "<li>Clean Data</li>\n",
    "<li>Data Preparation</li>\n",
    "<li>Data Modelling</li>\n",
    "<li>Model Analysis</li>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "29f8a95c-839e-44da-9b67-813ed693983d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import datetime as dt\n",
    "\n",
    "import sklearn\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0d50448a-6c08-4c61-b4e0-6b1da49b90c5",
   "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>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>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>01-12-2010 08:26</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>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>01-12-2010 08:26</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>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>01-12-2010 08:26</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>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>01-12-2010 08:26</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>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>01-12-2010 08:26</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": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "        InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0  01-12-2010 08:26       2.55     17850.0  United Kingdom  \n",
       "1  01-12-2010 08:26       3.39     17850.0  United Kingdom  \n",
       "2  01-12-2010 08:26       2.75     17850.0  United Kingdom  \n",
       "3  01-12-2010 08:26       3.39     17850.0  United Kingdom  \n",
       "4  01-12-2010 08:26       3.39     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fetch data\n",
    "\n",
    "retail_df = pd.read_csv('Online+Retail.csv', sep=',', encoding=\"ISO-8859-1\", header=0)\n",
    "retail_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "23157198-7ce6-40c4-9443-1e4e19d8806f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(541909, 8)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retail_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "80d46216-808c-4056-bbd9-edb6d9b46f98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 541909 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count   Dtype  \n",
      "---  ------       --------------   -----  \n",
      " 0   InvoiceNo    541909 non-null  object \n",
      " 1   StockCode    541909 non-null  object \n",
      " 2   Description  540455 non-null  object \n",
      " 3   Quantity     541909 non-null  int64  \n",
      " 4   InvoiceDate  541909 non-null  object \n",
      " 5   UnitPrice    541909 non-null  float64\n",
      " 6   CustomerID   406829 non-null  float64\n",
      " 7   Country      541909 non-null  object \n",
      "dtypes: float64(2), int64(1), object(5)\n",
      "memory usage: 33.1+ MB\n"
     ]
    }
   ],
   "source": [
    "retail_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8e9503c5-6d61-4bd1-b1c5-a1c748e6cf7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo       0.000000\n",
       "StockCode       0.000000\n",
       "Description     0.268311\n",
       "Quantity        0.000000\n",
       "InvoiceDate     0.000000\n",
       "UnitPrice       0.000000\n",
       "CustomerID     24.926694\n",
       "Country         0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#clean data\n",
    "\n",
    "#missing vals\n",
    "(retail_df.isnull().sum()/len(retail_df))*100 #percent of missing vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "742dedc6-e842-4956-adf0-8aca75dce124",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop rows with missing vals\n",
    "retail_df = retail_df.dropna()\n",
    "retail_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3f1d5cb-09a3-4c1a-9a07-67900f3f97ca",
   "metadata": {},
   "source": [
    "Data preparation \n",
    "\n",
    "- Recency (R): Number of days since last purchase\n",
    "- Frequency (F): Number of transactions\n",
    "- Monetary (M): Total amount of transactions (revenue contributed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "78d72ef8-28c8-49a4-aeb5-0cb4e9b98651",
   "metadata": {},
   "outputs": [],
   "source": [
    "# new column amount\n",
    "retail_df['amount'] = retail_df['Quantity']* retail_df['UnitPrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5056c6e9-921c-450f-afc2-c88ec2a935d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>01-12-2010 08:26</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>15.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>01-12-2010 08:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>01-12-2010 08:26</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>01-12-2010 08:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>01-12-2010 08:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "        InvoiceDate  UnitPrice  CustomerID         Country  amount  \n",
       "0  01-12-2010 08:26       2.55     17850.0  United Kingdom   15.30  \n",
       "1  01-12-2010 08:26       3.39     17850.0  United Kingdom   20.34  \n",
       "2  01-12-2010 08:26       2.75     17850.0  United Kingdom   22.00  \n",
       "3  01-12-2010 08:26       3.39     17850.0  United Kingdom   20.34  \n",
       "4  01-12-2010 08:26       3.39     17850.0  United Kingdom   20.34  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retail_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fce30f48-da86-48d5-841c-f61315e4ebb5",
   "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>CustomerID</th>\n",
       "      <th>amount</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID   amount\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"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# monetary\n",
    "grouped_df = retail_df.groupby('CustomerID')['amount'].sum()\n",
    "grouped_df = grouped_df.reset_index()\n",
    "grouped_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d15f897c-01b1-42ff-8851-2af85c5b262b",
   "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>CustomerID</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347.0</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348.0</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   CustomerID  frequency\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"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# frequency\n",
    "frequency = retail_df.groupby('CustomerID')['InvoiceNo'].count()\n",
    "frequency = frequency.reset_index()\n",
    "frequency.columns = ['CustomerID', 'frequency']\n",
    "frequency.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3aa4cd5f-e0b7-48f5-a1c3-88a7c65d0337",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64\n",
      "float64\n",
      "CustomerID\n",
      "12346.0    1\n",
      "16282.0    1\n",
      "16295.0    1\n",
      "16293.0    1\n",
      "16292.0    1\n",
      "          ..\n",
      "14321.0    1\n",
      "14323.0    1\n",
      "14326.0    1\n",
      "14327.0    1\n",
      "18287.0    1\n",
      "Name: count, Length: 4372, dtype: int64\n",
      "CustomerID\n",
      "12346.0    1\n",
      "16282.0    1\n",
      "16295.0    1\n",
      "16293.0    1\n",
      "16292.0    1\n",
      "          ..\n",
      "14321.0    1\n",
      "14323.0    1\n",
      "14326.0    1\n",
      "14327.0    1\n",
      "18287.0    1\n",
      "Name: count, Length: 4372, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(grouped_df['CustomerID'].dtype)\n",
    "print(frequency['CustomerID'].dtype)\n",
    "print(grouped_df['CustomerID'].value_counts())\n",
    "print(frequency['CustomerID'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d475ee3f-b0ee-4efa-a7be-9fd273149e34",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>amount</th>\n",
       "      <th>frequency</th>\n",
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       "  <tbody>\n",
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       "      <td>12348</td>\n",
       "      <td>1797.24</td>\n",
       "      <td>31</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349</td>\n",
       "      <td>1757.55</td>\n",
       "      <td>73</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>17</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   CustomerID   amount  frequency\n",
       "0       12346     0.00          2\n",
       "1       12347  4310.00        182\n",
       "2       12348  1797.24         31\n",
       "3       12349  1757.55         73\n",
       "4       12350   334.40         17"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_df['CustomerID'] = grouped_df['CustomerID'].astype(int)\n",
    "frequency['CustomerID'] = frequency['CustomerID'].astype(int)\n",
    "\n",
    "# merge the two dfs\n",
    "grouped_df = pd.merge(grouped_df, frequency, on='CustomerID', how='inner')\n",
    "grouped_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "961c81c2-27cf-49da-a4c3-f99da925b895",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
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       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>01-12-2010 08:26</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>15.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
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       "      <td>6</td>\n",
       "      <td>01-12-2010 08:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>01-12-2010 08:26</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
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       "      <td>6</td>\n",
       "      <td>01-12-2010 08:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>01-12-2010 08:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "        InvoiceDate  UnitPrice  CustomerID         Country  amount  \n",
       "0  01-12-2010 08:26       2.55     17850.0  United Kingdom   15.30  \n",
       "1  01-12-2010 08:26       3.39     17850.0  United Kingdom   20.34  \n",
       "2  01-12-2010 08:26       2.75     17850.0  United Kingdom   22.00  \n",
       "3  01-12-2010 08:26       3.39     17850.0  United Kingdom   20.34  \n",
       "4  01-12-2010 08:26       3.39     17850.0  United Kingdom   20.34  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retail_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "77e1270c-49ef-48f4-9f55-80dd9f4f1e9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# recency\n",
    "# how recent last transactions happened (minimum last dates a customer did a transaction)\n",
    "\n",
    "#convert to datetime\n",
    "retail_df['InvoiceDate'] = pd.to_datetime(retail_df['InvoiceDate'], format='%d-%m-%Y %H:%M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c8fd8401-b734-49e4-8ebc-16b251dd18f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 406829 entries, 0 to 541908\n",
      "Data columns (total 9 columns):\n",
      " #   Column       Non-Null Count   Dtype         \n",
      "---  ------       --------------   -----         \n",
      " 0   InvoiceNo    406829 non-null  object        \n",
      " 1   StockCode    406829 non-null  object        \n",
      " 2   Description  406829 non-null  object        \n",
      " 3   Quantity     406829 non-null  int64         \n",
      " 4   InvoiceDate  406829 non-null  datetime64[ns]\n",
      " 5   UnitPrice    406829 non-null  float64       \n",
      " 6   CustomerID   406829 non-null  float64       \n",
      " 7   Country      406829 non-null  object        \n",
      " 8   amount       406829 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(3), int64(1), object(4)\n",
      "memory usage: 31.0+ MB\n"
     ]
    }
   ],
   "source": [
    "retail_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "48860301-abd7-4a3d-acb1-3b345908c291",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-12-09 12:50:00')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute the max date\n",
    "max_date = max(retail_df['InvoiceDate'])\n",
    "max_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1c2cd7f9-d140-4871-87a7-a6e00d60962c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "      <td>373 days 04:24:00</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>22.00</td>\n",
       "      <td>373 days 04:24:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "      <td>373 days 04:24:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "      <td>373 days 04:24:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  amount  \\\n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom   15.30   \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom   20.34   \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom   22.00   \n",
       "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom   20.34   \n",
       "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom   20.34   \n",
       "\n",
       "               diff  \n",
       "0 373 days 04:24:00  \n",
       "1 373 days 04:24:00  \n",
       "2 373 days 04:24:00  \n",
       "3 373 days 04:24:00  \n",
       "4 373 days 04:24:00  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#compute the diff\n",
    "retail_df['diff'] = max_date - retail_df['InvoiceDate']\n",
    "retail_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4a8bcc01-ce6e-444a-9431-e3a886d206e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64\n",
      "int32\n"
     ]
    }
   ],
   "source": [
    "last_purchase = retail_df.groupby('CustomerID')['diff'].min()\n",
    "last_purchase = last_purchase.reset_index()\n",
    "print(last_purchase['CustomerID'].dtype)\n",
    "last_purchase['CustomerID'] = last_purchase['CustomerID'].astype(int)\n",
    "print(last_purchase['CustomerID'].dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2b78f247-f1de-4d62-9ede-68e7b89cb493",
   "metadata": {},
   "outputs": [
    {
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       "      <td>74 days 23:37:00</td>\n",
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       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>12350</td>\n",
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       "      <td>17</td>\n",
       "      <td>309 days 20:49:00</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   CustomerID   amount  frequency           recency\n",
       "0       12346     0.00          2 325 days 02:33:00\n",
       "1       12347  4310.00        182   1 days 20:58:00\n",
       "2       12348  1797.24         31  74 days 23:37:00\n",
       "3       12349  1757.55         73  18 days 02:59:00\n",
       "4       12350   334.40         17 309 days 20:49:00"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merge the dfs\n",
    "grouped_df = pd.merge(grouped_df, last_purchase, on='CustomerID', how='inner')\n",
    "grouped_df.columns = ['CustomerID', 'amount', 'frequency', 'recency']\n",
    "grouped_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "151c7504-8948-45e5-a703-c1e6764f5af4",
   "metadata": {},
   "outputs": [
    {
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       "      <td>12349</td>\n",
       "      <td>1757.55</td>\n",
       "      <td>73</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12350</td>\n",
       "      <td>334.40</td>\n",
       "      <td>17</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID   amount  frequency  recency\n",
       "0       12346     0.00          2      325\n",
       "1       12347  4310.00        182        1\n",
       "2       12348  1797.24         31       74\n",
       "3       12349  1757.55         73       18\n",
       "4       12350   334.40         17      309"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime as dt\n",
    "#number of days only\n",
    "grouped_df['recency'] = grouped_df['recency'].dt.days\n",
    "grouped_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10bc8c65-1741-448e-a8c4-928927dbe1a1",
   "metadata": {},
   "source": [
    "remove outliers\n",
    "1. outlier treatment\n",
    "2. rescaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f1a5ab50-3674-42f4-94a2-8d99ed31ad1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='amount'>"
      ]
     },
     "execution_count": 21,
     "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": [
    "# outlier treatment\n",
    "sns.boxplot(grouped_df['amount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "57cfd5a1-4ddc-4a8a-87db-15b7ef4709fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='frequency'>"
      ]
     },
     "execution_count": 22,
     "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": [
    "# outlier treatment\n",
    "sns.boxplot(grouped_df['frequency'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fb28a544-39f6-45e6-af67-b2e358b5af26",
   "metadata": {},
   "outputs": [],
   "source": [
    "#removing statistical outliers\n",
    "Q1 = grouped_df.amount.quantile(0.05) #5th percentile\n",
    "Q3 = grouped_df.amount.quantile(0.95) #95th percentile\n",
    "IQR = Q3 - Q1\n",
    "grouped_df = grouped_df[( grouped_df.amount >= Q1 - 1.5*IQR) & (grouped_df.amount <= Q3 + 1.5*IQR)] \n",
    "\n",
    "Q1 = grouped_df.recency.quantile(0.05) #5th percentile\n",
    "Q3 = grouped_df.recency.quantile(0.95) #95th percentile\n",
    "IQR = Q3 - Q1\n",
    "grouped_df = grouped_df[( grouped_df.recency >= Q1 - 1.5*IQR) & (grouped_df.recency <= Q3 + 1.5*IQR)] \n",
    "\n",
    "Q1 = grouped_df.frequency.quantile(0.05) #5th percentile\n",
    "Q3 = grouped_df.frequency.quantile(0.95) #95th percentile\n",
    "IQR = Q3 - Q1\n",
    "grouped_df = grouped_df[( grouped_df.frequency >= Q1 - 1.5*IQR) & (grouped_df.frequency <= Q3 + 1.5*IQR)] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d78da05-bd49-4c9e-9cc5-3e1342fac915",
   "metadata": {},
   "source": [
    "types of outliers\n",
    "- statistical\n",
    "- domain specific"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "405d68f5-bd06-4d3e-be03-93197f77d8ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4293, 3)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rescaling \n",
    "rfm_df = grouped_df[['amount', 'frequency', 'recency']] \n",
    "rfm_df.head()\n",
    "\n",
    "#instantiate\n",
    "scaler = StandardScaler()\n",
    "\n",
    "#fit transformation\n",
    "rfm_df_scaled = scaler.fit_transform(rfm_df)\n",
    "rfm_df_scaled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "12e6ada0-5960-4cc2-92c8-9cec0c9103e4",
   "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>amount</th>\n",
       "      <th>frequency</th>\n",
       "      <th>recency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.723738</td>\n",
       "      <td>-0.752888</td>\n",
       "      <td>2.301611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.731617</td>\n",
       "      <td>1.042467</td>\n",
       "      <td>-0.906466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.300128</td>\n",
       "      <td>-0.463636</td>\n",
       "      <td>-0.183658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.277517</td>\n",
       "      <td>-0.044720</td>\n",
       "      <td>-0.738141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.533235</td>\n",
       "      <td>-0.603275</td>\n",
       "      <td>2.143188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     amount  frequency   recency\n",
       "0 -0.723738  -0.752888  2.301611\n",
       "1  1.731617   1.042467 -0.906466\n",
       "2  0.300128  -0.463636 -0.183658\n",
       "3  0.277517  -0.044720 -0.738141\n",
       "4 -0.533235  -0.603275  2.143188"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_df_scaled = pd.DataFrame(rfm_df_scaled)\n",
    "rfm_df_scaled.columns = ['amount', 'frequency', 'recency']\n",
    "rfm_df_scaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "267e1fe6-6937-4348-927d-4870b9f4b054",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(max_iter=50, n_clusters=4)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KMeans<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KMeans(max_iter=50, n_clusters=4)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(max_iter=50, n_clusters=4)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# modelling\n",
    "\n",
    "# k-means with some arbitrary K\n",
    "kmeans = KMeans (n_clusters=4, max_iter=50)\n",
    "kmeans.fit(rfm_df_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7d6210d2-4ded-4fbc-82d9-bcd8577e5926",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 0, ..., 2, 0, 0])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ab445e35-aa0f-427b-808c-3d4c4c7da3fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2556250e340>]"
      ]
     },
     "execution_count": 28,
     "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": [
    "#optimal clusters\n",
    "\n",
    "ssd = []\n",
    "range_n_clusters = [2,3,4,5,6,7,8]\n",
    "\n",
    "for num_clusters in range_n_clusters:\n",
    "    kmeans = KMeans( n_clusters= num_clusters, max_iter=50)\n",
    "    kmeans.fit(rfm_df_scaled)\n",
    "\n",
    "    ssd.append(kmeans.inertia_)\n",
    "\n",
    "plt.plot(ssd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2a46dd1d-33b8-4992-912e-10272df1db68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters=2, the silhouette score is 0.4085588204735244\n",
      "For n_clusters=3, the silhouette score is 0.39439948764516547\n",
      "For n_clusters=4, the silhouette score is 0.4026061774443087\n",
      "For n_clusters=5, the silhouette score is 0.32850394958407436\n",
      "For n_clusters=6, the silhouette score is 0.40778084099523626\n",
      "For n_clusters=7, the silhouette score is 0.4077237881679317\n",
      "For n_clusters=8, the silhouette score is 0.4017678996579704\n"
     ]
    }
   ],
   "source": [
    "# silhouette analysis\n",
    "range_n_clusters = [2,3,4,5,6,7,8]\n",
    "\n",
    "for n_clusters in range_n_clusters:\n",
    "    kmeans = KMeans(n_clusters= num_clusters, max_iter=50)\n",
    "    kmeans.fit(rfm_df_scaled)\n",
    "\n",
    "    cluster_labels = kmeans.labels_\n",
    "\n",
    "    silhouette_avg = silhouette_score(rfm_df_scaled, cluster_labels)\n",
    "    print(\"For n_clusters={0}, the silhouette score is {1}\".format(n_clusters, silhouette_avg))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d215f704-efb2-4739-a1a2-0f0056b75f96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(max_iter=50, n_clusters=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KMeans<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KMeans(max_iter=50, n_clusters=3)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(max_iter=50, n_clusters=3)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# final model\n",
    "\n",
    "kmeans = KMeans(n_clusters=3, max_iter=50)\n",
    "kmeans.fit(rfm_df_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e76c29a8-2c18-41cb-9265-f70f0a124e3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 0, ..., 1, 0, 0])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "98d1cf91-0123-42c4-9a8d-7b46277c1971",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>amount</th>\n",
       "      <th>frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>cluster_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>325</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347</td>\n",
       "      <td>4310.00</td>\n",
       "      <td>182</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348</td>\n",
       "      <td>1797.24</td>\n",
       "      <td>31</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349</td>\n",
       "      <td>1757.55</td>\n",
       "      <td>73</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12350</td>\n",
       "      <td>334.40</td>\n",
       "      <td>17</td>\n",
       "      <td>309</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID   amount  frequency  recency  cluster_id\n",
       "0       12346     0.00          2      325           1\n",
       "1       12347  4310.00        182        1           2\n",
       "2       12348  1797.24         31       74           0\n",
       "3       12349  1757.55         73       18           0\n",
       "4       12350   334.40         17      309           1"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_df['cluster_id'] = kmeans.labels_\n",
    "grouped_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "44c1b5e4-834d-4a55-92b1-5eedc0281684",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='cluster_id', ylabel='amount'>"
      ]
     },
     "execution_count": 33,
     "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( x='cluster_id', y='amount', data= grouped_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "08afa653-3505-49c1-b900-373344fab634",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='cluster_id', ylabel='recency'>"
      ]
     },
     "execution_count": 34,
     "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( x='cluster_id', y='recency', data= grouped_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c78f2254-d3fa-48c7-a7f6-050417dc79c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='cluster_id', ylabel='frequency'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dd2natGnq0aOH0tLS9MADD6hTp04aMmSIpEMjQZdddlng8ld9fb3GjBmja6+9ljuxAACAsWHnggsu0CuvvKIJEyZo6tSpSktL05NPPqmCgoJAn3vvvVc1NTUaNWqUqqur1a9fP61YsSJoL4/FixdrzJgxGjBgQGBRwaefftqIjwQAACKMxe/3+40uwmjHu0U8AAAnauvWrRo1alSL2ZcqnI7397ehc3YAAADCjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMzdCw8+CDD8pisQQ9evbsGWivra1VYWGh2rdvr3bt2ik/P19VVVVB71FeXq7BgwerTZs2Sk5O1vjx43Xw4MHm/igAACBCtTK6gF69eundd98NvG7V6r8ljR07Vv/85z+1dOlS2Ww2jRkzRkOHDtV//vMfSZLX69XgwYNlt9v14YcfqrKyUsOHD1d0dLSmT5/e7J8FAABEHsPDTqtWrWS324847nK59Nxzz2nJkiW69NJLJUlFRUU666yztHr1avXp00fvvPOONm/erHfffVcpKSk677zz9NBDD+m+++7Tgw8+qJiYmOb+OAAAIMIYPmfnyy+/VKdOndStWzcVFBSovLxckrRu3TrV19crNzc30Ldnz55KTU1VaWmpJKm0tFTnnHOOUlJSAn3y8vLkdru1adOm5v0gAAAgIhk6spOTk6MFCxbozDPPVGVlpaZMmaKLLrpIn332mZxOp2JiYpSYmBj0MykpKXI6nZIkp9MZFHQa2hvajsXj8cjj8QReu93uEH0iAAAQaQwNO4MGDQo8z8zMVE5Ojrp27aqXX35ZrVu3Dtt5Z8yYoSlTpoTt/QEAQOQw/DLW4RITE5Wenq6vvvpKdrtddXV1qq6uDupTVVUVmONjt9uPuDur4fXR5gE1mDBhglwuV+Cxc+fO0H4QAAAQMSIq7Bw4cEBff/21OnbsqOzsbEVHR6u4uDjQvmXLFpWXl8vhcEiSHA6HNm7cqN27dwf6rFy5UgkJCcrIyDjmeWJjY5WQkBD0AAAA5mToZax77rlHV1xxhbp27aqKigpNnjxZVqtV1113nWw2m0aOHKlx48YpKSlJCQkJuv322+VwONSnTx9J0sCBA5WRkaEbb7xRM2fOlNPp1MSJE1VYWKjY2FgjPxoAAIgQhoadXbt26brrrtN3332nDh06qF+/flq9erU6dOggSXriiScUFRWl/Px8eTwe5eXlac6cOYGft1qtWr58uUaPHi2Hw6G2bdtqxIgRmjp1qlEfCQAARBiL3+/3G12E0dxut2w2m1wuF5e0AABhtXXrVo0aNUrz589Xenq60eWc1I7393dEzdkBAAAINcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwtUaHnW+++SYcdQAAAIRFo8POGWecoUsuuUSLFi1SbW1tOGoCAAAImUaHnU8++USZmZkaN26c7Ha7fve73+mjjz4KR20AAAAnrNFh57zzztNTTz2liooK/fWvf1VlZaX69euns88+W7NmzdKePXvCUScAAECTNHmCcqtWrTR06FAtXbpUjzzyiL766ivdc8896tKli4YPH67KyspQ1gkAANAkTQ47H3/8sW677TZ17NhRs2bN0j333KOvv/5aK1euVEVFha688spQ1gkAANAkrRr7A7NmzVJRUZG2bNmiyy+/XM8//7wuv/xyRUUdyk1paWlasGCBTj/99FDXCgAA0GiNDjtz587VLbfcoptuukkdO3Y8ap/k5GQ999xzJ1wcAADAiWp02Pnyyy//Z5+YmBiNGDGiSQUBAACEUqPn7BQVFWnp0qVHHF+6dKkWLlzY5EL++Mc/ymKx6K677gocq62tVWFhodq3b6927dopPz9fVVVVQT9XXl6uwYMHq02bNkpOTtb48eN18ODBJtcBAADMpdFhZ8aMGTr11FOPOJ6cnKzp06c3qYi1a9fqz3/+szIzM4OOjx07Vm+88YaWLl2qVatWqaKiQkOHDg20e71eDR48WHV1dfrwww+1cOFCLViwQJMmTWpSHQAAwHwaHXbKy8uVlpZ2xPGuXbuqvLy80QUcOHBABQUFevbZZ3XKKacEjrtcLj333HOaNWuWLr30UmVnZ6uoqEgffvihVq9eLUl65513tHnzZi1atEjnnXeeBg0apIceekizZ89WXV1do2sBAADm0+iwk5ycrLKysiOOf/rpp2rfvn2jCygsLNTgwYOVm5sbdHzdunWqr68POt6zZ0+lpqaqtLRUklRaWqpzzjlHKSkpgT55eXlyu93atGlTo2sBAADm0+gJytddd53uuOMOxcfHq3///pKkVatW6c4779S1117bqPd68cUX9cknn2jt2rVHtDmdTsXExCgxMTHoeEpKipxOZ6DP4UGnob2h7Vg8Ho88Hk/gtdvtblTdAADg5NHosPPQQw9p+/btGjBggFq1OvTjPp9Pw4cPb9ScnZ07d+rOO+/UypUrFRcX19gyTsiMGTM0ZcqUZj0nAAAwRqMvY8XExOill17SF198ocWLF2vZsmX6+uuv9de//lUxMTHH/T7r1q3T7t27df7556tVq1Zq1aqVVq1apaefflqtWrVSSkqK6urqVF1dHfRzVVVVstvtkiS73X7E3VkNrxv6HM2ECRPkcrkCj507dx533QAA4OTS6JGdBunp6UpPT2/yiQcMGKCNGzcGHbv55pvVs2dP3XffferSpYuio6NVXFys/Px8SdKWLVtUXl4uh8MhSXI4HHr44Ye1e/duJScnS5JWrlyphIQEZWRkHPPcsbGxio2NbXLtAADg5NHosOP1erVgwQIVFxdr9+7d8vl8Qe3vvffecb1PfHy8zj777KBjbdu2Vfv27QPHR44cqXHjxikpKUkJCQm6/fbb5XA41KdPH0nSwIEDlZGRoRtvvFEzZ86U0+nUxIkTVVhYSJgBAACSmhB27rzzTi1YsECDBw/W2WefLYvFEo66JElPPPGEoqKilJ+fL4/Ho7y8PM2ZMyfQbrVatXz5co0ePVoOh0Nt27bViBEjNHXq1LDVBAAATi4Wv9/vb8wPnHrqqYHNP83C7XbLZrPJ5XIpISHB6HIAACa2detWjRo1SvPnzz+h6SA4/t/fTZqgfMYZZ5xQcQAAAM2l0WHn7rvv1lNPPaVGDggBAAAYotFzdj744AP961//0ltvvaVevXopOjo6qH3ZsmUhKw4AAOBENTrsJCYm6qqrrgpHLQAAACHX6LBTVFQUjjoAAADCotFzdiTp4MGDevfdd/XnP/9Z+/fvlyRVVFTowIEDIS0OAADgRDV6ZGfHjh267LLLVF5eLo/Ho1/96leKj4/XI488Io/Ho3nz5oWjTgAAgCZp9MjOnXfeqd69e+v7779X69atA8evuuoqFRcXh7Q4AACAE9XokZ1///vf+vDDD4/Y9PP000/Xt99+G7LCAAAAQqHRIzs+n09er/eI47t27VJ8fHxIigIAAAiVRo/sDBw4UE8++aTmz58vSbJYLDpw4IAmT55sqi0kAAChV1VVJZfLZXQZhtqxY0fQP1sym82mlJSUsJ+n0Xtj7dq1S3l5efL7/fryyy/Vu3dvffnllzr11FNVUlKi5OTkcNUaNuyNBQDhV1VVpRtuHK76Oo/RpSBCRMfEatELzzc58Bzv7+9Gj+x07txZn376qV588UWVlZXpwIEDGjlypAoKCoImLAMAcDiXy6X6Oo9+7PZL+eJsRpcDg0XVuqRvVsnlcoV9dKfRYUeSWrVqpRtuuCHUtQAAWgBfnE2+tqcaXQZakEaHneeff/5n24cPH97kYgAAAEKt0WHnzjvvDHpdX1+vH374QTExMWrTpg1hBwAARJRG33r+/fffBz0OHDigLVu2qF+/fvrb3/4WjhoBAACarEl7Y/1Ujx499Mc//vGIUR8AAACjhSTsSIcmLVdUVITq7QAAAEKi0XN2Xn/99aDXfr9flZWVeuaZZ9S3b9+QFQYAABAKjQ47Q4YMCXptsVjUoUMHXXrppXr88cdDVRcAAEBINDrs+Hy+cNQBAAAQFiGbswMAABCJGj2yM27cuOPuO2vWrMa+PQAAQEg1OuysX79e69evV319vc4880xJ0tatW2W1WnX++ecH+lksltBVCQAA0ESNDjtXXHGF4uPjtXDhQp1yyimSDi00ePPNN+uiiy7S3XffHfIiAQAAmqrRc3Yef/xxzZgxIxB0JOmUU07RtGnTuBsLAABEnEaHHbfbrT179hxxfM+ePdq/f39IigIAAAiVRoedq666SjfffLOWLVumXbt2adeuXfrHP/6hkSNHaujQoeGoEQAAoMkaPWdn3rx5uueee3T99dervr7+0Ju0aqWRI0fq0UcfDXmBAAAAJ6LRYadNmzaaM2eOHn30UX399deSpO7du6tt27YhLw4AAOBENXlRwcrKSlVWVqpHjx5q27at/H5/KOsCAAAIiUaHne+++04DBgxQenq6Lr/8clVWVkqSRo4cyW3nAAAg4jQ67IwdO1bR0dEqLy9XmzZtAseHDRumFStWhLQ4AACAE9XoOTvvvPOO3n77bXXu3DnoeI8ePbRjx46QFQYAABAKjR7ZqampCRrRabBv3z7FxsaGpCgAAIBQaXTYueiii/T8888HXlssFvl8Ps2cOVOXXHJJo95r7ty5yszMVEJCghISEuRwOPTWW28F2mtra1VYWKj27durXbt2ys/PV1VVVdB7lJeXa/DgwWrTpo2Sk5M1fvx4HTx4sLEfCwAAmFSjL2PNnDlTAwYM0Mcff6y6ujrde++92rRpk/bt26f//Oc/jXqvzp07649//KN69Oghv9+vhQsX6sorr9T69evVq1cvjR07Vv/85z+1dOlS2Ww2jRkzRkOHDg2cx+v1avDgwbLb7frwww9VWVmp4cOHKzo6WtOnT2/sRwMAACZk8TfhnnGXy6VnnnlGn376qQ4cOKDzzz9fhYWF6tix4wkXlJSUpEcffVRXX321OnTooCVLlujqq6+WJH3xxRc666yzVFpaqj59+uitt97Sr3/9a1VUVCglJUXSoUUP77vvPu3Zs0cxMTHHdU632y2bzSaXy6WEhIQT/gwAgCNt3bpVo0aNUk3G/ydf21ONLgcGi6rZq7abX9f8+fOVnp7epPc43t/fjRrZqa+v12WXXaZ58+bp/vvvb1Jhx+L1erV06VLV1NTI4XBo3bp1qq+vV25ubqBPz549lZqaGgg7paWlOueccwJBR5Ly8vI0evRobdq0SVlZWSGtEQAAnHwaFXaio6NVVlYW0gI2btwoh8Oh2tpatWvXTq+88ooyMjK0YcMGxcTEKDExMah/SkqKnE6nJMnpdAYFnYb2hrZj8Xg88ng8gddutztEnwYAAESaRk9QvuGGG/Tcc8+FrIAzzzxTGzZs0Jo1azR69GiNGDFCmzdvDtn7H82MGTNks9kCjy5duoT1fAAAwDiNnqB88OBB/fWvf9W7776r7OzsI/bEmjVrVqPeLyYmRmeccYYkKTs7W2vXrtVTTz2lYcOGqa6uTtXV1UGjO1VVVbLb7ZIku92ujz76KOj9Gu7WauhzNBMmTNC4ceMCr91uN4HnBHm9XpWVlWnfvn1KSkpSZmamrFar0WUBAHB8YaesrExnn322oqKi9Nlnn+n888+XdGiy2eEsFssJF+Tz+eTxeJSdna3o6GgVFxcrPz9fkrRlyxaVl5fL4XBIkhwOhx5++GHt3r1bycnJkqSVK1cqISFBGRkZxzxHbGwsawKFUElJiebMmRN06dBut+u2225T//79DawMAIDjDDtZWVmqrKxUcnKyduzYobVr16p9+/YnfPIJEyZo0KBBSk1N1f79+7VkyRK9//77evvtt2Wz2TRy5EiNGzdOSUlJSkhI0O233y6Hw6E+ffpIkgYOHKiMjAzdeOONmjlzppxOpyZOnKjCwkLCTDMpKSnR5MmT5XA49MADDygtLU3btm3T4sWLNXnyZE2ZMoXAAwAw1HGFncTERG3btk3Jycnavn27fD5fSE6+e/duDR8+XJWVlbLZbMrMzNTbb7+tX/3qV5KkJ554QlFRUcrPz5fH41FeXp7mzJkT+Hmr1arly5dr9OjRcjgcatu2rUaMGKGpU6eGpD78PK/Xqzlz5sjhcGjatGmKijo0BaxXr16aNm2aJk6cqLlz56pv375c0gIAGOa4wk5+fr5++ctfqmPHjrJYLOrdu/cxf3l98803x33y/zXROS4uTrNnz9bs2bOP2adr16568803j/ucCJ2ysjI5nU498MADgaDTICoqSgUFBSosLFRZWRnLAAAADHNcYWf+/PkaOnSovvrqK91xxx269dZbFR8fH+7aEOH27dsnSUpLSztqe8Pxhn4AABjhuO/GuuyyyyRJ69at05133knYgZKSkiRJ27ZtU69evY5o37ZtW1A/AACM0Oh1doqKigg6kCRlZmbKbrdr8eLFR8zj8vl8Wrx4sTp27KjMzEyDKgQAoAnr7AANrFarbrvtNk2ePFn333+/LrzwQsXGxsrj8eijjz7S6tWrNWXKFCYnAwAMRdjBCenfv7+GDRuml19+WaWlpYHjVqtVw4YN47ZzAIDhGn0ZCzhcSUmJXnrpJUVHRwcdb9WqlV566SWVlJQYVBkAAIcwsoMm83q9mjVrlvx+v7KyspSTkxO4jLVmzRqtXr1aTzzxBOvsAAAMRdhBk23YsEHV1dVKTU3V9u3btXr16kCb3W5XamqqysvLtWHDBmVnZxtYKYBIEvVjtdElIAI05/eAsIMm27BhgyRp586d6tOnj4YNG6a4uDjV1tYGJig39CPsAGjQehuXt9G8CDtosobbzTt37qxvvvkmaIJySkqKTjvtNO3atStk24sAMIcf0/rL1zrR6DJgsKgfq5st+BJ20GQJCQmSDo3sOBwOTZo0KbAR6KJFiwLhp6EfAEiSr3WifG1PNboMtCCEHTRZYmJi4Lnf79fWrVu1Y8cOeTwe+f3+o/YDAKC5EXbQZG63O/B89erVQROUj9UPAIDmxjo7aLLDR2x+us7O4a8Z2QEAGImwgyY7fIPP+vr6oLbDX7MRKADASIQdhERUVNTPvgYAwCjM2UGT7d27N/A8OztbrVu31v79+xUfH68ff/xRa9euPaIfAADNjbCDJvv8888lSaeddlog2BzutNNO07fffqvPP/9ceXl5zV0eAACSuIyFEPj2228bdRwAgOZE2EGT2e32kPYDACAcCDtosuPdBoLtIgAARiLsoMk+/fTTkPYDACAcCDtosoYJyqHqBwBAOBB20GS1tbUh7QcAQDgQdtBkVqs16HVGRoYef/xxZWRk/Gw/AACaE+vsoMm6dOmirVu3Bl5v3rxZd99991H7AQBgFEZ20GR1dXUh7QcAQDgQdtBkFoslpP0AAAgHwg6arFOnTiHtBwBAOBB20GRHm59zIv0AAAgHwg6abMWKFSHtBwBAOBB20GRvv/12SPsBoeb1erV+/XoVFxdr/fr18nq9RpcEwADceo4mq6qqCmk/IJRKSko0Z84cOZ3OwDG73a7bbrtN/fv3N7AyAM2NkR00GbeeI1KVlJRo8uTJ6tatm2bPnq0333xTs2fPVrdu3TR58mSVlJQYXSKAZkTYQZMd78rIrKCM5uT1ejVnzhw5HA5NmzZNvXr1Ups2bdSrVy9NmzZNDodDc+fO5ZIW0IIQdtBkhB1EorKyMjmdThUUFCgqKvg/cVFRUSooKFBlZaXKysoMqhBAczM07MyYMUMXXHCB4uPjlZycrCFDhmjLli1BfWpra1VYWKj27durXbt2ys/PP2IOSHl5uQYPHqw2bdooOTlZ48eP18GDB5vzo7RIPp8vpP2AUNi3b58kKS0t7ajtDccb+gEwP0PDzqpVq1RYWKjVq1dr5cqVqq+v18CBA1VTUxPoM3bsWL3xxhtaunSpVq1apYqKCg0dOjTQ7vV6NXjwYNXV1enDDz/UwoULtWDBAk2aNMmIj9SiEHYQiZKSkiRJ27ZtO2p7w/GGfgDMz9Cws2LFCt10003q1auXzj33XC1YsEDl5eVat26dJMnlcum5557TrFmzdOmllyo7O1tFRUX68MMPtXr1aknSO++8o82bN2vRokU677zzNGjQID300EOaPXs2E2PDzO/3h7QfEAqZmZmy2+1avHjxEUHb5/Np8eLF6tixozIzMw2qEEBzi6g5Oy6XS9J//+Jat26d6uvrlZubG+jTs2dPpaamqrS0VJJUWlqqc845RykpKYE+eXl5crvd2rRpUzNWDyASWK1W3XbbbSotLdXEiRO1adMm/fDDD9q0aZMmTpyo0tJSjR49mrlkQAsSMevs+Hw+3XXXXerbt6/OPvtsSZLT6VRMTIwSExOD+qakpATWznA6nUFBp6G9oe1oPB6PPB5P4LXb7Q7VxwAQAfr3768pU6Zozpw5KiwsDBzv2LGjpkyZwjo7QAsTMWGnsLBQn332mT744IOwn2vGjBmaMmVK2M9jdlFRUcd1++5P74gBmkP//v3Vt29flZWVad++fUpKSlJmZiYjOkALFBG/hcaMGaPly5frX//6lzp37hw4brfbVVdXp+rq6qD+VVVVstvtgT4/vTur4XVDn5+aMGGCXC5X4LFz584QfpqWgwnKiHRWq1VZWVkaMGCAsrKyCDpAC2Vo2PH7/RozZoxeeeUVvffee0fcKpqdna3o6GgVFxcHjm3ZskXl5eVyOBySJIfDoY0bN2r37t2BPitXrlRCQoIyMjKOet7Y2FglJCQEPdB4x7soG4u3AQCMZOhlrMLCQi1ZskSvvfaa4uPjA3NsbDabWrduLZvNppEjR2rcuHFKSkpSQkKCbr/9djkcDvXp00eSNHDgQGVkZOjGG2/UzJkz5XQ6NXHiRBUWFio2NtbIj2d6FovluO60slgszVANcCSv18tlrAgUVesyugREgOb8HhgadubOnStJuvjii4OOFxUV6aabbpIkPfHEE4qKilJ+fr48Ho/y8vI0Z86cQF+r1arly5dr9OjRcjgcatu2rUaMGKGpU6c218dosVq1aqX6+vrj6gc0NzYCjTw2m03RMbHSN6uMLgURIjomVjabLeznsfhZBEVut1s2m00ul4tLWo1w4403Htd8py5duuiFF15ohoqAQxo2AnU4HCooKFBaWpq2bdumxYsXq7S0lDuyDFRVVRVYZqSl2rFjhx5++GHdf//96tq1q9HlGMpmsx1xR3VjHO/vb/7kNona2lqVl5c36zkvuOCC4wo7F1xwgbZu3doMFf1Xamqq4uLimvWciAw/3Qi04W7Aho1AJ06cqLlz56pv375c0jJASkrKCf1yM5OuXbsqPT3d6DJaBMKOSZSXl2vUqFFGl3FUy5Yt07Jly5r1nPPnz+c/Ii1Uw0agDzzwwDE3Ai0sLFRZWZmysrIMqhJAcyLsmERqaqrmz5/f7Of9+9//rnfeeeeY7QMHDtTVV1/djBUdkpqa2uznRGRgI1AAP0XYMYm4uDhDRjL+8Ic/KCkpSS+++GLQcYvFomHDhun3v/99s9eElu3wjUB79ux5xN1YbAQKtDyEHZyw3//+97rlllv0l7/8RS+//LJ+85vf6Le//a1iYmKMLg0tUMNGoE8//bS+//77oDW4kpOTdcopp7ARKNDCRMQKyjj5xcTEBDZszc3NJejAMFarVRdffLG2bNmivXv3BrXt3btXW7Zs0S9/+UsmJwMtCGEHgKl4vV6tWLFCkhQdHR3U1vB6xYoVrOwNtCCEHQCmsmHDBlVXV+ucc87Ra6+9psLCQl111VUqLCzUa6+9pnPOOUfV1dXasGGD0aUCaCbM2QFgKg0hJjs7WzfddFPQCsr/+Mc/lJeXp40bN2rDhg3Kzs42qEoAzYmwA8CUFixYoJycHKWnp2v//v2Kj4+Xx+PRwoULjS4NQDMj7AAwlYa7rKxWq9asWXNEu9Vqldfr5W4soAUh7AAwlYZVkxsmIGdnZysrK0vr16/XunXrAsd/uroyAPMi7AAwlcPX1ZGkdevWad26df+zHwDz4k8bAKbywQcfBJ7HxsYGtR3++vB+AMyNkR0AplJbWytJSkhI0NKlS7V58+bAdhEZGRm65ppr5Ha7A/0AmB9hB4CpxMXFSZLcbrcefPBBXXjhhYqNjdX27dv18ssvy+12B/UDYH6EHQCm0q9fP/3nP/+RxWJRaWmpSktLA20Wi0UWi0V+v1/9+vUzsEoAzYmwA8BU7Ha7JMnv90uSOnfurISEBLndbu3ateuIfgDMj7ADwFR69eolq9Uqv98vn88XFHCkQ7ecWywW9erVy6AKATQ37sYCYCqbNm2S1+uVz+c7arvP55PX69WmTZuauTIARiHsADCV410/h3V2gJaDsAPAVD777LPA85iYmKC2w18f3g+AuRF2AJjKN998E3jeMEn5aK8P7wfA3JigDMBUampqAs/btm2rgQMHqlOnTqqoqNA777yj6urqI/oBMDfCDgBTSUxMDDx3u916+eWXA68P3/zz8H4AzI2wA8BUkpKSAs+tVqsuueQSpaena+vWrSopKQncpXV4PwDmRtgBYCopKSmB5/X19SouLlZxcfHP9gNgbkxQBmAqx3t5istYQMtB2AFgKsd7eYrLWEDLQdgBYCqEHQA/RdgB0GJYLBajSwBgAMIOAFPZu3dv4PlPV1COjo4+aj8A5kbYAWAqn3/++THbDh/Z+bl+AMyFsAPAVBrW0bFYLKqrqwtq83g8gcBzrF3RAZgPYQeAqTSskvzTfbEaNBw/fDVlAObGv+0ATCUtLS3wvFWr4HVTD5+zc3g/AOZmaNgpKSnRFVdcoU6dOslisejVV18Navf7/Zo0aZI6duyo1q1bKzc3V19++WVQn3379qmgoEAJCQlKTEzUyJEjdeDAgWb8FAAiyTvvvBN4fvDgwaC2+vr6o/YDYG6Ghp2amhqde+65mj179lHbZ86cqaefflrz5s3TmjVr1LZtW+Xl5am2tjbQp6CgQJs2bdLKlSu1fPlylZSUaNSoUc31EQBEmOO9y4q7sYCWw9C9sQYNGqRBgwYdtc3v9+vJJ5/UxIkTdeWVV0qSnn/+eaWkpOjVV1/Vtddeq88//1wrVqzQ2rVr1bt3b0nSn/70J11++eV67LHH1KlTp2b7LAAiQ0JCgqqqqo6rH4CWIWLn7Gzbtk1Op1O5ubmBYzabTTk5OSotLZUklZaWKjExMRB0JCk3N1dRUVFas2bNMd/b4/HI7XYHPQCYQ/fu3UPaD8DJL2LDjtPplHTkzsQpKSmBNqfTqeTk5KD2Vq1aKSkpKdDnaGbMmCGbzRZ4dOnSJcTVAzDKF198EdJ+AE5+ERt2wmnChAlyuVyBx86dO40uCUCI7N+/P6T9AJz8Ijbs2O12STri2ntVVVWgzW63a/fu3UHtBw8e1L59+wJ9jiY2NlYJCQlBDwAAYE4RG3bS0tJkt9tVXFwcOOZ2u7VmzRo5HA5JksPhUHV1tdatWxfo895778nn8yknJ6fZawZgvJqampD2A3DyM/RurAMHDuirr74KvN62bZs2bNigpKQkpaam6q677tK0adPUo0cPpaWl6YEHHlCnTp00ZMgQSdJZZ52lyy67TLfeeqvmzZun+vp6jRkzRtdeey13YgEt1OFLU4SiH4CTn6Fh5+OPP9Yll1wSeD1u3DhJ0ogRI7RgwQLde++9qqmp0ahRo1RdXa1+/fppxYoViouLC/zM4sWLNWbMGA0YMEBRUVHKz8/X008/3eyfpaqqSi6Xq9nPG0l27NgR9M+WzGazHTG5HgBgDIv/WBvItCBut1s2m00ul6tJ83eqqqp0w43DVV/nCUN1OBlFx8Rq0QvPE3gMcPHFFx933/fffz9sdQDHsnXrVo0aNUrz589Xenq60eWc1I7397ehIztm4XK5VF/n0Y/dfilfnM3ocmCwqFqX9M0quVwuwo4BrFarvF7vcfUD0DIQdkLIF2eTr+2pRpcBtGhnnXWWPvvss+PqB6BliNi7sQCgKY53kVAWEwVaDsIOAFPZtGlTSPsBOPkRdgCYSkVFRUj7ATj5EXYAmMrBgwdD2g/AyY+wA8DU2rRpo0GDBqlNmzZGlwLAINyNBcDUfvjhB7311ltGlwHAQIzsAAAAU2NkB0DY1NbWqry83Ogyjmnr1q3Ner7U1NSg7W4ANA/CDoCwKS8v16hRo4wu45iauza2BwCMQdgBEDapqamaP39+s56zurpa99577//sN3PmTCUmJoa/oMOkpqY26/kAHELYARA2cXFxhoxkxMXFqba29mfbL7zwwmasCICRmKAMwHRWrFhxzLkxcXFxWrFiRTNXBMBIhB0AprRixQq9+OKLSkhIkCQlJCToxRdfJOgALRBhB4Bp2e12PfbYY5Kkxx57THa73eCKABiBsAMAAEyNsAMAAEyNu7FCKOrHaqNLQATgewAAkYWwE0Ktt5UYXQIAAPgJwk4I/ZjWX77WiUaXAYNF/VhN8AWACELYCSFf60T52p5qdBkAAOAwTFAGAACmRtgBAACmRtgBAACmxpwdwMSqqqrkcrmMLsNQO3bsCPpnS2az2ZSSkmJ0GUCzI+wAJlVVVaUbbhyu+jqP0aVEhIcfftjoEgwXHROrRS88T+BBi0PYAUzK5XKpvs6jH7v9Ur44m9HlwGBRtS7pm1VyuVyEHbQ4hB3A5HxxNpZEANCiEXZCKKq2Zc+NwCF8DwAgshB2QsBmsyk6Jlb6ZpXRpSBCRMfEymbj0hEARALCTgikpKRo0QvPc9fLjh16+OGHdf/996tr165Gl2OoSLrrhY1JIfE9QMtG2AmRlJSUiPnlZrSuXbsqPT3d6DLw/2OfLgAtHWEHMDk2qIXEBrVo2Qg7gMmxQS2Alo7tIgAAgKmZZmRn9uzZevTRR+V0OnXuuefqT3/6ky688EKjywIMx63wkPgeoGUzRdh56aWXNG7cOM2bN085OTl68sknlZeXpy1btig5Odno8gBDsCQCfoolEaTa2lqVl5cbWkMk7deWmpqquLg4o8sIO4vf7/cbXcSJysnJ0QUXXKBnnnlGkuTz+dSlSxfdfvvt+r//+7//+fNut1s2m00ul0sJCQnhLte0tm7dqlGjRmn+/PncjRUh2AiUJREOF0lLIhil4b9TOORk/+/18f7+PulHdurq6rRu3TpNmDAhcCwqKkq5ubkqLS096s94PB55PP/dHNHtdoe9znDjr5VgLeWvlf/F6CURIuF7GUn4XhovNTVV8+fPN7qMiJGammp0Cc3ipA87e/fuldfrPeI/6CkpKfriiy+O+jMzZszQlClTmqO8ZlNeXh4xf61Ewu7SJ/tfK2bB9zIY30vjxcXF8f9BC3TSh52mmDBhgsaNGxd47Xa71aVLFwMrOnH8tRKspfy1Eun4XgbjewkY46QPO6eeeqqsVquqqqqCjldVVclutx/1Z2JjYxUbG9sc5TUb/lpBJOJ7CSASnPTr7MTExCg7O1vFxcWBYz6fT8XFxXI4HAZWBgAAIsFJP7IjSePGjdOIESPUu3dvXXjhhXryySdVU1Ojm2++2ejSAACAwUwRdoYNG6Y9e/Zo0qRJcjqdOu+887RixYoWf4slAAAwyTo7J4p1dgAAOPkc7+/vk37ODgAAwM8h7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMzxXYRJ6phEWm3221wJQAA4Hg1/N7+X5tBEHYk7d+/X5LUpUsXgysBAACNtX//ftlstmO2szeWJJ/Pp4qKCsXHx8tisRhdzknL7XarS5cu2rlzJ3uMIWLwvUSk4TsZOn6/X/v371enTp0UFXXsmTmM7EiKiopS586djS7DNBISEvgXGBGH7yUiDd/J0Pi5EZ0GTFAGAACmRtgBAACmRthByMTGxmry5MmKjY01uhQggO8lIg3fyebHBGUAAGBqjOwAAABTI+wAAABTI+wAAABTI+wgZGbPnq3TTz9dcXFxysnJ0UcffWR0SWjBSkpKdMUVV6hTp06yWCx69dVXjS4JLdyMGTN0wQUXKD4+XsnJyRoyZIi2bNlidFktAmEHIfHSSy9p3Lhxmjx5sj755BOde+65ysvL0+7du40uDS1UTU2Nzj33XM2ePdvoUgBJ0qpVq1RYWKjVq1dr5cqVqq+v18CBA1VTU2N0aabH3VgIiZycHF1wwQV65plnJB3agqNLly66/fbb9X//938GV4eWzmKx6JVXXtGQIUOMLgUI2LNnj5KTk7Vq1Sr179/f6HJMjZEdnLC6ujqtW7dOubm5gWNRUVHKzc1VaWmpgZUBQORyuVySpKSkJIMrMT/CDk7Y3r175fV6lZKSEnQ8JSVFTqfToKoAIHL5fD7ddddd6tu3r84++2yjyzE9NgIFAKCZFRYW6rPPPtMHH3xgdCktAmEHJ+zUU0+V1WpVVVVV0PGqqirZ7XaDqgKAyDRmzBgtX75cJSUl6ty5s9HltAhcxsIJi4mJUXZ2toqLiwPHfD6fiouL5XA4DKwMACKH3+/XmDFj9Morr+i9995TWlqa0SW1GIzsICTGjRunESNGqHfv3rrwwgv15JNPqqamRjfffLPRpaGFOnDggL766qvA623btmnDhg1KSkpSamqqgZWhpSosLNSSJUv02muvKT4+PjCn0WazqXXr1gZXZ27ceo6QeeaZZ/Too4/K6XTqvPPO09NPP62cnByjy0IL9f777+uSSy454viIESO0YMGC5i8ILZ7FYjnq8aKiIt10003NW0wLQ9gBAACmxpwdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAIbZvn27LBaLNmzYYHQpjXbTTTdpyJAhP9vn4osv1l133dUs9QA4NsIOANNYsGCBEhMTm+VcTz31FNtOACcJNgIFgJ/wer2yWCyKijr234M2m60ZKwJwIhjZARB2Pp9PM2fO1BlnnKHY2Filpqbq4YcfPqLf0UZmXn311aANFD/99FNdcsklio+PV0JCgrKzs/Xxxx/r/fff18033yyXyyWLxSKLxaIHH3xQkuTxeHTPPffotNNOU9u2bZWTk6P333//iPO+/vrrysjIUGxsrMrLy3/2M/30MlZNTY2GDx+udu3aqWPHjnr88ccb/b8TgPBgZAdA2E2YMEHPPvusnnjiCfXr10+VlZX64osvmvReBQUFysrK0ty5c2W1WrVhwwZFR0frF7/4hZ588klNmjRJW7ZskSS1a9dOkjRmzBht3rxZL774ojp16qRXXnlFl112mTZu3KgePXpIkn744Qc98sgj+stf/qL27dsrOTm5UXWNHz9eq1at0muvvabk5GT94Q9/0CeffKLzzjuvSZ8TQOgQdgCE1f79+/XUU0/pmWee0YgRIyRJ3bt3V79+/bR9+/ZGv195ebnGjx+vnj17SlIgrEiHLi1ZLBbZ7fag/kVFRSovL1enTp0kSffcc49WrFihoqIiTZ8+XZJUX1+vOXPm6Nxzz210TQcOHNBzzz2nRYsWacCAAZKkhQsXqnPnzo1+LwChR9gBEFaff/65PB5PIAScqHHjxum3v/2tXnjhBeXm5uqaa65R9+7dj9l/48aN8nq9Sk9PDzru8XjUvn37wOuYmBhlZmY2qaavv/5adXV1ysnJCRxLSkrSmWee2aT3AxBahB0AYdW6devj7hsVFSW/3x90rL6+Puj1gw8+qOuvv17//Oc/9dZbb2ny5Ml68cUXddVVVx31PQ8cOCCr1ap169bJarUGtTVc5mqo8/C5QQDMgwnKAMKqR48eat26tYqLi/9n3w4dOmj//v2qqakJHDvaGjzp6ekaO3as3nnnHQ0dOlRFRUWSDo3OeL3eoL5ZWVnyer3avXu3zjjjjKDH4Ze7TkT37t0VHR2tNWvWBI59//332rp1a0jeH8CJYWQHQFjFxcXpvvvu07333quYmBj17dtXe/bs0aZNm464tJWTk6M2bdroD3/4g+644w6tWbMmaC2bH3/8UePHj9fVV1+ttLQ07dq1S2vXrlV+fr4k6fTTT9eBAwdUXFysc889V23atFF6eroKCgo0fPhwPf7448rKytKePXtUXFyszMxMDR48+IQ/Y7t27TRy5EiNHz8+MLn5/vvv/9lb1wE0H/5NBBB2DzzwgO6++25NmjRJZ511loYNG6bdu3cf0S8pKUmLFi3Sm2++qXPOOUd/+9vfArePS5LVatV3332n4cOHKz09Xb/5zW80aNAgTZkyRZL0i1/8Qr///e81bNgwdejQQTNnzpQkFRUVafjw4br77rt15plnasiQIVq7dq1SU1ND9hkfffRRXXTRRbriiiuUm5urfv36KTs7O2TvD6DpLP6fXiAHAAAwEUZ2AACAqRF2AOAo2rVrd8zHv//9b6PLA9AIXMYCgKP46quvjtl22mmnNeqWegDGIuwAAABT4zIWAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwtf8HYaieiWRx7BoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot( x='cluster_id', y='frequency', data= grouped_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e66d9a3-a826-4fc7-bb77-e92d5f4a7822",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}