Hierarchical_Clustering_Retail_Store.ipynb

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      "name": "Hierarchical_Clustering_Retail_Store.ipynb",
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      "name": "python3",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "B0ipFe1L_rEa"
      },
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv(\"/content/OnlineRetail.csv\", sep = \",\" , encoding= \"ISO-8859-1\", header = 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "274XfvrW_5gH",
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          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "a1de9ff9-f2a0-450f-d125-89203e40677e"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>6.0</td>\n",
              "      <td>12/1/2010 8:26</td>\n",
              "      <td>2.55</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>536365</td>\n",
              "      <td>71053</td>\n",
              "      <td>WHITE METAL LANTERN</td>\n",
              "      <td>6.0</td>\n",
              "      <td>12/1/2010 8:26</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>536365</td>\n",
              "      <td>84406B</td>\n",
              "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
              "      <td>8.0</td>\n",
              "      <td>12/1/2010 8:26</td>\n",
              "      <td>2.75</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>536365</td>\n",
              "      <td>84029G</td>\n",
              "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
              "      <td>6.0</td>\n",
              "      <td>12/1/2010 8:26</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>536365</td>\n",
              "      <td>84029E</td>\n",
              "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
              "      <td>6.0</td>\n",
              "      <td>12/1/2010 8:26</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "  InvoiceNo StockCode  ... CustomerID         Country\n",
              "0    536365    85123A  ...    17850.0  United Kingdom\n",
              "1    536365     71053  ...    17850.0  United Kingdom\n",
              "2    536365    84406B  ...    17850.0  United Kingdom\n",
              "3    536365    84029G  ...    17850.0  United Kingdom\n",
              "4    536365    84029E  ...    17850.0  United Kingdom\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fcb5d1cc-3635-4d9b-a55f-a372f40f3997"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12462, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
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      "cell_type": "code",
      "metadata": {
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        "outputId": "bb447dc6-06db-4fcd-c3bd-3bf84337b12e"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 12462 entries, 0 to 12461\n",
            "Data columns (total 8 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   InvoiceNo    12462 non-null  object \n",
            " 1   StockCode    12462 non-null  object \n",
            " 2   Description  12417 non-null  object \n",
            " 3   Quantity     12461 non-null  float64\n",
            " 4   InvoiceDate  12461 non-null  object \n",
            " 5   UnitPrice    12461 non-null  float64\n",
            " 6   CustomerID   8956 non-null   float64\n",
            " 7   Country      12461 non-null  object \n",
            "dtypes: float64(3), object(5)\n",
            "memory usage: 779.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "REd97BSEAw2d",
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          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "d3d3ac18-b863-4941-a15d-4c3ea0748d98"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Quantity</th>\n",
              "      <th>UnitPrice</th>\n",
              "      <th>CustomerID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>12461.000000</td>\n",
              "      <td>12461.000000</td>\n",
              "      <td>8956.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>7.477249</td>\n",
              "      <td>3.973280</td>\n",
              "      <td>15598.129299</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>93.885564</td>\n",
              "      <td>17.253932</td>\n",
              "      <td>1748.194062</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-9360.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12395.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>14307.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.510000</td>\n",
              "      <td>15646.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>4.210000</td>\n",
              "      <td>17211.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2880.000000</td>\n",
              "      <td>940.870000</td>\n",
              "      <td>18239.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Quantity     UnitPrice    CustomerID\n",
              "count  12461.000000  12461.000000   8956.000000\n",
              "mean       7.477249      3.973280  15598.129299\n",
              "std       93.885564     17.253932   1748.194062\n",
              "min    -9360.000000      0.000000  12395.000000\n",
              "25%        1.000000      1.250000  14307.000000\n",
              "50%        2.000000      2.510000  15646.000000\n",
              "75%        6.000000      4.210000  17211.000000\n",
              "max     2880.000000    940.870000  18239.000000"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YI8D8-39BD3D"
      },
      "source": [
        "df_null = round(100*(df.isnull().sum())/len(df),2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sVR5J0rcB417",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c357862f-8e77-44c5-96c7-123a9e2499ac"
      },
      "source": [
        "df_null"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "InvoiceNo       0.00\n",
              "StockCode       0.00\n",
              "Description     0.36\n",
              "Quantity        0.01\n",
              "InvoiceDate     0.01\n",
              "UnitPrice       0.01\n",
              "CustomerID     28.13\n",
              "Country         0.01\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wLxIb4vHB9Kx"
      },
      "source": [
        "df = df.dropna()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k8ArjAQyCH3r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ae37884e-f4b8-4eb1-e543-9f1e55061ba0"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8956, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FNWOmOf7CJmy"
      },
      "source": [
        "df['CustomerID'] = df['CustomerID'].astype(str)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gQe-xOB5DeZ9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "c5016a56-895f-4b4b-fb1c-4377b1a2f666"
      },
      "source": [
        "df['Amount'] = df['Quantity']*df['UnitPrice']\n",
        "rfm = df.groupby('CustomerID')['Amount'].sum()\n",
        "rfm = rfm.reset_index()\n",
        "rfm.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12395.0</td>\n",
              "      <td>346.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12427.0</td>\n",
              "      <td>303.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12431.0</td>\n",
              "      <td>358.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12433.0</td>\n",
              "      <td>1919.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12471.0</td>\n",
              "      <td>-17.00</td>\n",
              "    </tr>\n",
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            ],
            "text/plain": [
              "  CustomerID   Amount\n",
              "0    12395.0   346.10\n",
              "1    12427.0   303.50\n",
              "2    12431.0   358.25\n",
              "3    12433.0  1919.14\n",
              "4    12471.0   -17.00"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
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        "outputId": "6f9302c6-6a05-42f0-99ed-4292d385de77"
      },
      "source": [
        "rff = df.groupby('CustomerID')['InvoiceNo'].count()\n",
        "rff = rff.reset_index()\n",
        "rff.columns = ['CustomerID','Frequency']\n",
        "rff.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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",
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              "      <th>0</th>\n",
              "      <td>12395.0</td>\n",
              "      <td>12</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12427.0</td>\n",
              "      <td>10</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12431.0</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12433.0</td>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12471.0</td>\n",
              "      <td>1</td>\n",
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            ],
            "text/plain": [
              "  CustomerID  Frequency\n",
              "0    12395.0         12\n",
              "1    12427.0         10\n",
              "2    12431.0         14\n",
              "3    12433.0         73\n",
              "4    12471.0          1"
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          "metadata": {},
          "execution_count": 14
        }
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    },
    {
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      "metadata": {
        "id": "9R5XALpDFZE_",
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        "outputId": "b681e95a-63b5-4377-99f7-13a4f5d62119"
      },
      "source": [
        "rft = pd.merge(rfm, rff, on = 'CustomerID', how = 'inner')\n",
        "rft.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Frequency</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12395.0</td>\n",
              "      <td>346.10</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12427.0</td>\n",
              "      <td>303.50</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12431.0</td>\n",
              "      <td>358.25</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12433.0</td>\n",
              "      <td>1919.14</td>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12471.0</td>\n",
              "      <td>-17.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "  CustomerID   Amount  Frequency\n",
              "0    12395.0   346.10         12\n",
              "1    12427.0   303.50         10\n",
              "2    12431.0   358.25         14\n",
              "3    12433.0  1919.14         73\n",
              "4    12471.0   -17.00          1"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mKade86PGVHh"
      },
      "source": [
        "df['InvoiceDate'] = pd.to_datetime(df['InvoiceDate'],format='%d/%m/%Y %H:%M')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "80NybsyFG9f1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6dd3e559-cef7-4087-f398-6de8c48d8dda"
      },
      "source": [
        "maxDate = max(df['InvoiceDate'])\n",
        "maxDate"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2010-06-12 13:13:00')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "avKCxdlFHJHS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "96e0a99a-f053-4001-baac-395a77170d40"
      },
      "source": [
        "df['Diff'] = maxDate - df['InvoiceDate']\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>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>Diff</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>6.0</td>\n",
              "      <td>2010-01-12 08:26:00</td>\n",
              "      <td>2.55</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>151 days 04:47:00</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>6.0</td>\n",
              "      <td>2010-01-12 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>151 days 04:47:00</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>8.0</td>\n",
              "      <td>2010-01-12 08:26:00</td>\n",
              "      <td>2.75</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>151 days 04:47:00</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>6.0</td>\n",
              "      <td>2010-01-12 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>151 days 04:47:00</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>6.0</td>\n",
              "      <td>2010-01-12 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>151 days 04:47:00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  InvoiceNo StockCode  ...         Country              Diff\n",
              "0    536365    85123A  ...  United Kingdom 151 days 04:47:00\n",
              "1    536365     71053  ...  United Kingdom 151 days 04:47:00\n",
              "2    536365    84406B  ...  United Kingdom 151 days 04:47:00\n",
              "3    536365    84029G  ...  United Kingdom 151 days 04:47:00\n",
              "4    536365    84029E  ...  United Kingdom 151 days 04:47:00\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ijql7BRVHh4L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "cfedf869-43ca-4998-82f5-48945fe92ada"
      },
      "source": [
        "rfp = df.groupby('CustomerID')['Diff'].min()\n",
        "rfp = rfp.reset_index()\n",
        "rfp.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Diff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12395.0</td>\n",
              "      <td>91 days 20:38:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12427.0</td>\n",
              "      <td>92 days 02:29:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12431.0</td>\n",
              "      <td>151 days 03:10:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12433.0</td>\n",
              "      <td>150 days 23:49:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12471.0</td>\n",
              "      <td>120 days 02:36:00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CustomerID              Diff\n",
              "0     12395.0  91 days 20:38:00\n",
              "1     12427.0  92 days 02:29:00\n",
              "2     12431.0 151 days 03:10:00\n",
              "3     12433.0 150 days 23:49:00\n",
              "4     12471.0 120 days 02:36:00"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vhZV6DLbIBWy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "b17ac429-5c27-40be-cace-7c380f382ded"
      },
      "source": [
        "rfp['Diff'] =rfp['Diff'].dt.days\n",
        "rfp.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Diff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12395.0</td>\n",
              "      <td>91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12427.0</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12431.0</td>\n",
              "      <td>151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12433.0</td>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12471.0</td>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CustomerID  Diff\n",
              "0     12395.0    91\n",
              "1     12427.0    92\n",
              "2     12431.0   151\n",
              "3     12433.0   150\n",
              "4     12471.0   120"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aILLGRwlOO4B"
      },
      "source": [
        "rft['CustomerID']= rft['CustomerID'].astype('float64')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sRipNc9KOmdF",
        "outputId": "b80076f3-bedc-42ce-edcd-da77d7ffe0fa"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 371 entries, 0 to 370\n",
            "Data columns (total 2 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   CustomerID  371 non-null    float64\n",
            " 1   Diff        371 non-null    int64  \n",
            "dtypes: float64(1), int64(1)\n",
            "memory usage: 5.9 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dmS_o751IiX1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "bdcd3638-4be5-4d50-e720-6399b8a82787"
      },
      "source": [
        "rft = pd.merge(rft, rfp, on = 'CustomerID', how = 'inner')\n",
        "rft.columns = ['CustomerID', 'Amount', 'Frequency', 'Recency']\n",
        "rft.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12395.0</td>\n",
              "      <td>346.10</td>\n",
              "      <td>12</td>\n",
              "      <td>91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12427.0</td>\n",
              "      <td>303.50</td>\n",
              "      <td>10</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12431.0</td>\n",
              "      <td>358.25</td>\n",
              "      <td>14</td>\n",
              "      <td>151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12433.0</td>\n",
              "      <td>1919.14</td>\n",
              "      <td>73</td>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12471.0</td>\n",
              "      <td>-17.00</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CustomerID   Amount  Frequency  Recency\n",
              "0     12395.0   346.10         12       91\n",
              "1     12427.0   303.50         10       92\n",
              "2     12431.0   358.25         14      151\n",
              "3     12433.0  1919.14         73      150\n",
              "4     12471.0   -17.00          1      120"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GEUbKR8OJwX1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "outputId": "e887f4eb-341f-4aab-ede8-bf19732bc5bb"
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "attributes = ['Amount', 'Frequency', 'Recency']\n",
        "plt.rcParams['figure.figsize'] = [10,8]\n",
        "sns.boxplot(data = rft[attributes], orient =\"v\", palette= \"Set2\", whis = 1.5, saturation= 1, width  = 0.7)\n",
        "plt.title(\"Outliers Detection\", fontsize = 15, fontweight = 'bold')\n",
        "plt.ylabel(\"Range\", fontweight = 'bold')\n",
        "plt.xlabel(\"Attributes\", fontweight= 'bold')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Attributes')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZpM0ZEHLLWvC"
      },
      "source": [
        "Q1 = rft.Amount.quantile(0.05)\n",
        "Q3 = rft.Amount.quantile(0.95)\n",
        "IQR = Q3 - Q1\n",
        "rft = rft[(rft.Amount >= Q1 - 1.5*IQR) & (rfm.Amount <= Q3 + 1.5*IQR)]\n",
        "\n",
        "Q1 = rft.Recency.quantile(0.05)\n",
        "Q3 = rft.Recency.quantile(0.95)\n",
        "IQR = Q3 - Q1\n",
        "rft = rft[(rft.Recency >= Q1 - 1.5*IQR) & (rft.Recency <= Q3 + 1.5 * IQR)]\n",
        "\n",
        "\n",
        "Q1 = rft.Frequency.quantile(0.05)\n",
        "Q3 = rft.Frequency.quantile(0.95)\n",
        "IQR = Q3 - Q1\n",
        "rft = rft[(rft.Frequency >= Q1 - 1.5*IQR) & (rft.Frequency <= Q3 + 1.5 * IQR)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sCTAtO6KNrQC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70da18a8-250d-4aec-ad12-9fcd4aff015b"
      },
      "source": [
        "rft_df = rft[['Amount', 'Frequency', 'Recency']]\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "rft_df_scaled = scaler.fit_transform(rft_df)\n",
        "rft_df_scaled.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(365, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8FP0MpbaTxXK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "c0bb3219-330b-4d22-e64e-cfb6081e2d2f"
      },
      "source": [
        "rft_df_scaled = pd.DataFrame(rft_df_scaled)\n",
        "rft_df_scaled.columns = ['Amount', 'Frequency', 'Recency']\n",
        "rft_df_scaled.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>-0.043768</td>\n",
              "      <td>-0.441013</td>\n",
              "      <td>0.077025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.139687</td>\n",
              "      <td>-0.523540</td>\n",
              "      <td>0.095546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.016411</td>\n",
              "      <td>-0.358485</td>\n",
              "      <td>1.188257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.498136</td>\n",
              "      <td>2.076072</td>\n",
              "      <td>1.169736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.861335</td>\n",
              "      <td>-0.894913</td>\n",
              "      <td>0.614120</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Amount  Frequency   Recency\n",
              "0 -0.043768  -0.441013  0.077025\n",
              "1 -0.139687  -0.523540  0.095546\n",
              "2 -0.016411  -0.358485  1.188257\n",
              "3  3.498136   2.076072  1.169736\n",
              "4 -0.861335  -0.894913  0.614120"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ck8OOBqkZX1k",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "outputId": "efbc109f-704c-4097-aa38-a2390149728c"
      },
      "source": [
        "from scipy.cluster.hierarchy import linkage\n",
        "from scipy.cluster.hierarchy import dendrogram\n",
        "merg = linkage(rft_df_scaled, method = \"single\", metric = \"euclidean\")\n",
        "dendrogram(merg)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gO4hQwUiZ-Um",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "outputId": "32c6362f-5cb4-4bbd-cb33-efbd4683accb"
      },
      "source": [
        "merg = linkage(rft_df_scaled, method = \"complete\", metric = \"euclidean\")\n",
        "dendrogram(merg)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zZgeL6xvatV3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cbc8aad4-ab14-4c81-fd20-334161e76b64"
      },
      "source": [
        "from scipy.cluster.hierarchy import cut_tree\n",
        "clus_lab = cut_tree(merg, n_clusters = 2).reshape(-1,)\n",
        "clus_lab"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0,\n",
              "       0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0,\n",
              "       0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
              "       0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,\n",
              "       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0,\n",
              "       1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n",
              "       1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
              "       0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,\n",
              "       1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
              "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1,\n",
              "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQfe0nk2bmJL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "8e1dc646-55ba-4a16-9268-51f8d641606c"
      },
      "source": [
        "rft['Cluster_Labels'] = clus_lab\n",
        "rft.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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_Labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12395.0</td>\n",
              "      <td>346.10</td>\n",
              "      <td>12</td>\n",
              "      <td>91</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12427.0</td>\n",
              "      <td>303.50</td>\n",
              "      <td>10</td>\n",
              "      <td>92</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12431.0</td>\n",
              "      <td>358.25</td>\n",
              "      <td>14</td>\n",
              "      <td>151</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12433.0</td>\n",
              "      <td>1919.14</td>\n",
              "      <td>73</td>\n",
              "      <td>150</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12471.0</td>\n",
              "      <td>-17.00</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CustomerID   Amount  Frequency  Recency  Cluster_Labels\n",
              "0     12395.0   346.10         12       91               0\n",
              "1     12427.0   303.50         10       92               0\n",
              "2     12431.0   358.25         14      151               0\n",
              "3     12433.0  1919.14         73      150               1\n",
              "4     12471.0   -17.00          1      120               0"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dTTwu6kbL2h8"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}