{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b4dbqhI7uo3y"
      },
      "source": [
        "# ***Customer Segmentation***\n",
        "![15e8e8b2-5581-4acd-90a5-f316613fa40d.jpg](data:image/jpeg;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iHjJHFZxsEbN"
      },
      "source": [
        "# *Problem Statement*\n",
        "In the competitive landscape of online retail, understanding customer behavior and segmenting customers effectively are crucial for targeted marketing strategies and improving customer retention. The goal of this analysis is to segment customers based on their purchasing behavior using the Recency, Frequency, and Monetary (RFM) framework. By clustering customers into distinct groups, we aim to identify key customer segments such as Loyal Customers, At-Risk Customers, Champions, and New Customers.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:11.681405Z",
          "iopub.status.busy": "2025-01-16T16:53:11.681031Z",
          "iopub.status.idle": "2025-01-16T16:53:11.699076Z",
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          "shell.execute_reply.started": "2025-01-16T16:53:11.681364Z"
        },
        "id": "CxfLOS_bkjB4"
      },
      "outputs": [],
      "source": [
        "# Importing necessary libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.express as px\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.metrics import silhouette_score\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "execution": {
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          "iopub.status.busy": "2025-01-16T16:53:11.701183Z",
          "iopub.status.idle": "2025-01-16T16:53:11.719910Z",
          "shell.execute_reply": "2025-01-16T16:53:11.718522Z",
          "shell.execute_reply.started": "2025-01-16T16:53:11.701519Z"
        },
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      "outputs": [],
      "source": [
        "sns.set(rc={'axes.facecolor': '#e0aaff'}, style='darkgrid')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sau39KCjwSoL"
      },
      "source": [
        "### Dataset Description\n",
        "The UCI Online Retail dataset is a transactional dataset that contains all the transactions occurring between 01/12/2009 and 09/12/2011 for a UK-based and registered non-store online retail."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:11.721672Z",
          "iopub.status.busy": "2025-01-16T16:53:11.721279Z",
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          "shell.execute_reply.started": "2025-01-16T16:53:11.721640Z"
        },
        "id": "XvN7gz-5lFVX"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/DataSets/online_retail.csv\",encoding='unicode_escape')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
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        "execution": {
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        },
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        "outputId": "b72e3a64-2412-44a9-9413-6e198d7c4ca4"
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      "outputs": [
        {
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            "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  12/1/2010 8:26       2.55     17850.0  United Kingdom  \n",
              "1  12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
              "2  12/1/2010 8:26       2.75     17850.0  United Kingdom  \n",
              "3  12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
              "4  12/1/2010 8:26       3.39     17850.0  United Kingdom  "
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          "metadata": {},
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        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AVegh6_I3hKN"
      },
      "source": [
        "### Dataset Attributes:\n",
        "* **InvoiceNo:** Invoice number. Nominal, a 6-digit integral number uniquely assigned\n",
        "to each transaction. If this code starts with 'C', it indicates a cancellation.\n",
        "* **StockCode:** Product (item) code. Nominal, a 5-digit integral number uniquely assigned to each distinct product.\n",
        "* **Description:** Product (item) name. Nominal.\n",
        "* **Quantity:** The quantities of each product (item) per transaction. Numeric.\n",
        "* **InvoiceDate:** Invoice Date and time. Numeric, the day and time when each transaction was generated.\n",
        "* **UnitPrice:** Unit price. Numeric, Product price per unit in sterling.\n",
        "* **CustomerID:** Customer number. Nominal, a 5-digit integral number uniquely assigned to each customer.\n",
        "* **Country:** Country name. Nominal, the name of the country where each customer resides."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
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        },
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      "outputs": [
        {
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          "name": "stdout",
          "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"
          ]
        }
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      "source": [
        "df.info()"
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    },
    {
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      "execution_count": 74,
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        {
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          "data": {
            "text/plain": [
              "(541909, 8)"
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          "metadata": {},
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      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
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        "id": "Y04AsAgq4QVu"
      },
      "source": [
        "* **Number of Instances**: 541,909\n",
        "* **Number of Attributes**: 8\n",
        "* **Missing Values**: The dataset contains missing values, particularly in the CustomerID and Description columns."
      ]
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    {
      "cell_type": "code",
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Calculating the percentage of missing values in each attribute\n",
        "null_percentages = (df.isnull().sum()/df.shape[0])*100\n",
        "null_percentages = null_percentages.reset_index()\n",
        "null_percentages.columns = ['Column Name','Percentage']\n",
        "\n",
        "plt.figure(figsize=(6,5))\n",
        "sns.barplot(data=null_percentages,x='Column Name',y='Percentage',color='#5a189a')\n",
        "plt.xlabel('Column Name',fontsize = 12)\n",
        "plt.ylabel('Percentage of Null Values',fontsize = 12)\n",
        "plt.title('Percentage of Null Values in Each Column')\n",
        "plt.xticks(rotation=90,fontsize = 10)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kwX-I1zl4s1b"
      },
      "source": [
        "* The **CustomerID** column contains nearly a quarter of missing values, which poses a challenge for customer segmentation. Since this column is crucial for uniquely identifying customers and linking their purchasing patterns, handling these missing values carefully is essential.\n",
        "*The **CustomerID** column contains nearly a quarter of missing values, which poses a challenge for customer segmentation. Since this column is crucial for uniquely identifying customers and linking their purchasing patterns, handling these missing values carefully is essential."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:13.314971Z",
          "iopub.status.busy": "2025-01-16T16:53:13.314614Z",
          "iopub.status.idle": "2025-01-16T16:53:13.453298Z",
          "shell.execute_reply": "2025-01-16T16:53:13.451550Z",
          "shell.execute_reply.started": "2025-01-16T16:53:13.314942Z"
        },
        "id": "jJS8DYNApr73",
        "outputId": "55ba7476-5031-4d5c-acea-af74b06c8eb2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Null Values in the Dataset 0.0\n"
          ]
        }
      ],
      "source": [
        "# Removing missing values\n",
        "df.dropna(subset=['CustomerID'],inplace=True)\n",
        "print(f\"Null Values in the Dataset {((df.isnull().sum()/df.shape[0])*100).sum()}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:13.454889Z",
          "iopub.status.busy": "2025-01-16T16:53:13.454463Z",
          "iopub.status.idle": "2025-01-16T16:53:14.123688Z",
          "shell.execute_reply": "2025-01-16T16:53:14.122465Z",
          "shell.execute_reply.started": "2025-01-16T16:53:13.454849Z"
        },
        "id": "0xbS8Gg2q219",
        "outputId": "0b5a632a-3dd6-4d28-e13b-5794bf21b99c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Duplicated Values in the Dataset 5225\n",
            "Percentage of Duplicates 1.28\n",
            "*********************************************\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       InvoiceNo StockCode                          Description  Quantity  \\\n",
              "485       536409     22111         SCOTTIE DOG HOT WATER BOTTLE         1   \n",
              "489       536409     22866        HAND WARMER SCOTTY DOG DESIGN         1   \n",
              "494       536409     21866          UNION JACK FLAG LUGGAGE TAG         1   \n",
              "517       536409     21866          UNION JACK FLAG LUGGAGE TAG         1   \n",
              "521       536409     22900      SET 2 TEA TOWELS I LOVE LONDON          1   \n",
              "...          ...       ...                                  ...       ...   \n",
              "440149   C574510     22360      GLASS JAR ENGLISH CONFECTIONERY        -1   \n",
              "461407   C575940     23309  SET OF 60 I LOVE LONDON CAKE CASES        -24   \n",
              "461408   C575940     23309  SET OF 60 I LOVE LONDON CAKE CASES        -24   \n",
              "529981   C580764     22667                RECIPE BOX RETROSPOT        -12   \n",
              "529980   C580764     22667                RECIPE BOX RETROSPOT        -12   \n",
              "\n",
              "             InvoiceDate  UnitPrice  CustomerID         Country  \n",
              "485      12/1/2010 11:45       4.95     17908.0  United Kingdom  \n",
              "489      12/1/2010 11:45       2.10     17908.0  United Kingdom  \n",
              "494      12/1/2010 11:45       1.25     17908.0  United Kingdom  \n",
              "517      12/1/2010 11:45       1.25     17908.0  United Kingdom  \n",
              "521      12/1/2010 11:45       2.95     17908.0  United Kingdom  \n",
              "...                  ...        ...         ...             ...  \n",
              "440149   11/4/2011 13:25       2.95     15110.0  United Kingdom  \n",
              "461407  11/13/2011 11:38       0.55     17838.0  United Kingdom  \n",
              "461408  11/13/2011 11:38       0.55     17838.0  United Kingdom  \n",
              "529981   12/6/2011 10:38       2.95     14562.0  United Kingdom  \n",
              "529980   12/6/2011 10:38       2.95     14562.0  United Kingdom  \n",
              "\n",
              "[10062 rows x 8 columns]"
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              "      <th>InvoiceNo</th>\n",
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              "      <th>485</th>\n",
              "      <td>536409</td>\n",
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              "      <td>SCOTTIE DOG HOT WATER BOTTLE</td>\n",
              "      <td>1</td>\n",
              "      <td>12/1/2010 11:45</td>\n",
              "      <td>4.95</td>\n",
              "      <td>17908.0</td>\n",
              "      <td>United Kingdom</td>\n",
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              "    <tr>\n",
              "      <th>489</th>\n",
              "      <td>536409</td>\n",
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              "      <td>HAND WARMER SCOTTY DOG DESIGN</td>\n",
              "      <td>1</td>\n",
              "      <td>12/1/2010 11:45</td>\n",
              "      <td>2.10</td>\n",
              "      <td>17908.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>494</th>\n",
              "      <td>536409</td>\n",
              "      <td>21866</td>\n",
              "      <td>UNION JACK FLAG LUGGAGE TAG</td>\n",
              "      <td>1</td>\n",
              "      <td>12/1/2010 11:45</td>\n",
              "      <td>1.25</td>\n",
              "      <td>17908.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>517</th>\n",
              "      <td>536409</td>\n",
              "      <td>21866</td>\n",
              "      <td>UNION JACK FLAG LUGGAGE TAG</td>\n",
              "      <td>1</td>\n",
              "      <td>12/1/2010 11:45</td>\n",
              "      <td>1.25</td>\n",
              "      <td>17908.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>521</th>\n",
              "      <td>536409</td>\n",
              "      <td>22900</td>\n",
              "      <td>SET 2 TEA TOWELS I LOVE LONDON</td>\n",
              "      <td>1</td>\n",
              "      <td>12/1/2010 11:45</td>\n",
              "      <td>2.95</td>\n",
              "      <td>17908.0</td>\n",
              "      <td>United Kingdom</td>\n",
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              "    <tr>\n",
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              "      <th>440149</th>\n",
              "      <td>C574510</td>\n",
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              "      <td>-1</td>\n",
              "      <td>11/4/2011 13:25</td>\n",
              "      <td>2.95</td>\n",
              "      <td>15110.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>461407</th>\n",
              "      <td>C575940</td>\n",
              "      <td>23309</td>\n",
              "      <td>SET OF 60 I LOVE LONDON CAKE CASES</td>\n",
              "      <td>-24</td>\n",
              "      <td>11/13/2011 11:38</td>\n",
              "      <td>0.55</td>\n",
              "      <td>17838.0</td>\n",
              "      <td>United Kingdom</td>\n",
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              "    <tr>\n",
              "      <th>461408</th>\n",
              "      <td>C575940</td>\n",
              "      <td>23309</td>\n",
              "      <td>SET OF 60 I LOVE LONDON CAKE CASES</td>\n",
              "      <td>-24</td>\n",
              "      <td>11/13/2011 11:38</td>\n",
              "      <td>0.55</td>\n",
              "      <td>17838.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>529981</th>\n",
              "      <td>C580764</td>\n",
              "      <td>22667</td>\n",
              "      <td>RECIPE BOX RETROSPOT</td>\n",
              "      <td>-12</td>\n",
              "      <td>12/6/2011 10:38</td>\n",
              "      <td>2.95</td>\n",
              "      <td>14562.0</td>\n",
              "      <td>United Kingdom</td>\n",
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              "    <tr>\n",
              "      <th>529980</th>\n",
              "      <td>C580764</td>\n",
              "      <td>22667</td>\n",
              "      <td>RECIPE BOX RETROSPOT</td>\n",
              "      <td>-12</td>\n",
              "      <td>12/6/2011 10:38</td>\n",
              "      <td>2.95</td>\n",
              "      <td>14562.0</td>\n",
              "      <td>United Kingdom</td>\n",
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              "  </tbody>\n",
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              "<p>10062 rows × 8 columns</p>\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 77
        }
      ],
      "source": [
        "# Duplicate Records\n",
        "print(f\"Duplicated Values in the Dataset {df.duplicated().sum()}\")\n",
        "print(f\"Percentage of Duplicates {np.round((df.duplicated().sum()/df.shape[0])*100,2)}\")\n",
        "print(\"*\"*45)\n",
        "df[df.duplicated(keep=False)].sort_values(by='InvoiceNo')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qt2iXj6r6lGf"
      },
      "source": [
        "The dataset contains **5,225** duplicate entries, accounting for **1.28%** of the total data. These duplicates will be removed to ensure the integrity and accuracy of the analysis."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:14.124987Z",
          "iopub.status.busy": "2025-01-16T16:53:14.124723Z",
          "iopub.status.idle": "2025-01-16T16:53:14.763554Z",
          "shell.execute_reply": "2025-01-16T16:53:14.762320Z",
          "shell.execute_reply.started": "2025-01-16T16:53:14.124965Z"
        },
        "id": "8YQLW_nEq_Tf",
        "outputId": "4b659eca-fe8e-4945-8150-71f22cc62ae5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Duplicated Values in the Dataset 0\n",
            "Percentage of Duplicates 0.0\n"
          ]
        }
      ],
      "source": [
        "# droping duplicates\n",
        "df.drop_duplicates(inplace=True)\n",
        "print(f\"Duplicated Values in the Dataset {df.duplicated().sum()}\")\n",
        "print(f\"Percentage of Duplicates {np.round((df.duplicated().sum()/df.shape[0])*100,2)}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8nd6xQCW7FIo"
      },
      "source": [
        "### Cancelled Transactions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:14.764989Z",
          "iopub.status.busy": "2025-01-16T16:53:14.764595Z",
          "iopub.status.idle": "2025-01-16T16:53:14.893436Z",
          "shell.execute_reply": "2025-01-16T16:53:14.892323Z",
          "shell.execute_reply.started": "2025-01-16T16:53:14.764946Z"
        },
        "id": "842Wd2LR6Chx",
        "outputId": "55b94921-4244-4164-a0b6-c84197b2efa2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Canceled Orders 8872\n",
            "Percentage of Canceled Orders 2.21\n",
            "\n",
            "*********************************************\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    InvoiceNo StockCode                       Description  Quantity  \\\n",
              "141   C536379         D                          Discount        -1   \n",
              "154   C536383    35004C   SET OF 3 COLOURED  FLYING DUCKS        -1   \n",
              "235   C536391     22556    PLASTERS IN TIN CIRCUS PARADE        -12   \n",
              "236   C536391     21984  PACK OF 12 PINK PAISLEY TISSUES        -24   \n",
              "\n",
              "         InvoiceDate  UnitPrice  CustomerID         Country  \n",
              "141   12/1/2010 9:41      27.50     14527.0  United Kingdom  \n",
              "154   12/1/2010 9:49       4.65     15311.0  United Kingdom  \n",
              "235  12/1/2010 10:24       1.65     17548.0  United Kingdom  \n",
              "236  12/1/2010 10:24       0.29     17548.0  United Kingdom  "
            ],
            "text/html": [
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>C536379</td>\n",
              "      <td>D</td>\n",
              "      <td>Discount</td>\n",
              "      <td>-1</td>\n",
              "      <td>12/1/2010 9:41</td>\n",
              "      <td>27.50</td>\n",
              "      <td>14527.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>C536383</td>\n",
              "      <td>35004C</td>\n",
              "      <td>SET OF 3 COLOURED  FLYING DUCKS</td>\n",
              "      <td>-1</td>\n",
              "      <td>12/1/2010 9:49</td>\n",
              "      <td>4.65</td>\n",
              "      <td>15311.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>235</th>\n",
              "      <td>C536391</td>\n",
              "      <td>22556</td>\n",
              "      <td>PLASTERS IN TIN CIRCUS PARADE</td>\n",
              "      <td>-12</td>\n",
              "      <td>12/1/2010 10:24</td>\n",
              "      <td>1.65</td>\n",
              "      <td>17548.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>236</th>\n",
              "      <td>C536391</td>\n",
              "      <td>21984</td>\n",
              "      <td>PACK OF 12 PINK PAISLEY TISSUES</td>\n",
              "      <td>-24</td>\n",
              "      <td>12/1/2010 10:24</td>\n",
              "      <td>0.29</td>\n",
              "      <td>17548.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-858cf712-99c2-424b-bcd6-4f53c84c2b79 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-858cf712-99c2-424b-bcd6-4f53c84c2b79');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    }\n",
              "    40% {\n",
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              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2c03f639-0e10-4894-a2a0-dfe6b0407380 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "canceled_orders",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 79
        }
      ],
      "source": [
        "canceled_orders = df[df['InvoiceNo'].str.startswith('C')] # fetching records that startswith C\n",
        "print(f\"Total Canceled Orders {canceled_orders.shape[0]}\")\n",
        "print(f\"Percentage of Canceled Orders {np.round((canceled_orders.shape[0]/df.shape[0])*100,2)}\")\n",
        "print()\n",
        "print(\"*\"*45)\n",
        "canceled_orders.head(4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JebNeviG7sVw"
      },
      "source": [
        "The **8,872** canceled orders, representing **2.21%** of the dataset, have been removed to ensure the analysis focuses on completed transactions and yields accurate insights."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:14.897439Z",
          "iopub.status.busy": "2025-01-16T16:53:14.897148Z",
          "iopub.status.idle": "2025-01-16T16:53:15.028226Z",
          "shell.execute_reply": "2025-01-16T16:53:15.026903Z",
          "shell.execute_reply.started": "2025-01-16T16:53:14.897415Z"
        },
        "id": "woWU56ji62q7",
        "outputId": "6a76fd70-44ec-436a-8837-2a92b8a94593"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of the Dataset Before Cleaning cancel orders (401604, 8)\n",
            "Shape of the Dataset After Cleaning cancel orders (392732, 8)\n"
          ]
        }
      ],
      "source": [
        "print(f\"Shape of the Dataset Before Cleaning cancel orders {df.shape}\")\n",
        "df = df[~df['InvoiceNo'].str.startswith('C')]\n",
        "print(f\"Shape of the Dataset After Cleaning cancel orders {df.shape}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:15.030204Z",
          "iopub.status.busy": "2025-01-16T16:53:15.029901Z",
          "iopub.status.idle": "2025-01-16T16:53:15.180196Z",
          "shell.execute_reply": "2025-01-16T16:53:15.179074Z",
          "shell.execute_reply.started": "2025-01-16T16:53:15.030179Z"
        },
        "id": "yzoE5PDA_NUT",
        "outputId": "df1a3b4d-b701-4745-bc86-fb0bacba913e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [InvoiceNo, StockCode, Description, Quantity, InvoiceDate, UnitPrice, CustomerID, Country]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ea9a074a-5b31-42c0-a2c9-e49ed97adffd\" class=\"colab-df-container\">\n",
              "    <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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ea9a074a-5b31-42c0-a2c9-e49ed97adffd')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ea9a074a-5b31-42c0-a2c9-e49ed97adffd button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ea9a074a-5b31-42c0-a2c9-e49ed97adffd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "Out of range float values are not JSON compliant: nan"
            }
          },
          "metadata": {},
          "execution_count": 81
        }
      ],
      "source": [
        "df[df['InvoiceNo'].str.contains('[a-zA-Z]',regex=True)] # to check if there are any values that starts with characters"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WJs91tE48F8a"
      },
      "source": [
        "### StockCode Anomalies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:15.181667Z",
          "iopub.status.busy": "2025-01-16T16:53:15.181331Z",
          "iopub.status.idle": "2025-01-16T16:53:15.575242Z",
          "shell.execute_reply": "2025-01-16T16:53:15.573821Z",
          "shell.execute_reply.started": "2025-01-16T16:53:15.181641Z"
        },
        "id": "JA-Y13-ztspb",
        "outputId": "d1ec3e6b-59e9-4953-c173-75b17b1eda8d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Anomaly Stock Codes : 6 \n",
            "\n",
            "POST ---> POSTAGE\n",
            "C2 ---> CARRIAGE\n",
            "M ---> Manual\n",
            "BANK CHARGES ---> Bank Charges\n",
            "PADS ---> PADS TO MATCH ALL CUSHIONS\n",
            "DOT ---> DOTCOM POSTAGE\n",
            "\n",
            " Total Number of Orders with Anomaly Stock Codes : 1549\n"
          ]
        }
      ],
      "source": [
        "# Anomalies in Stock code\n",
        "anomaly_stock_codes = df[df['StockCode'].str.contains('^[a-zA-Z]',regex=True)]['StockCode']\n",
        "print(f\"Number of Anomaly Stock Codes : {anomaly_stock_codes.nunique()} \\n\")\n",
        "\n",
        "for stock_code in anomaly_stock_codes.unique():\n",
        "  desc = df[df['StockCode'] == stock_code]\n",
        "  print(f\"{stock_code} ---> {desc['Description'].iloc[0]}\")\n",
        "\n",
        "print(f\"\\n Total Number of Orders with Anomaly Stock Codes : {anomaly_stock_codes.shape[0]}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:15.576791Z",
          "iopub.status.busy": "2025-01-16T16:53:15.576434Z",
          "iopub.status.idle": "2025-01-16T16:53:15.798226Z",
          "shell.execute_reply": "2025-01-16T16:53:15.797058Z",
          "shell.execute_reply.started": "2025-01-16T16:53:15.576761Z"
        },
        "id": "A3bVlOwi-LCN",
        "outputId": "ffce9917-1d90-4f42-c7c3-b94a3c08031a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of the Dataset Before removing Anomaly Stock Codes (392732, 8)\n",
            "Shape of the Dataset After removing Anomaly Stock Codes (391183, 8)\n"
          ]
        }
      ],
      "source": [
        "# Removing all the Anomaly Stock Codes\n",
        "print(f\"Shape of the Dataset Before removing Anomaly Stock Codes {df.shape}\")\n",
        "df = df[~df['StockCode'].str.contains('^[a-zA-Z]',regex=True)]\n",
        "print(f\"Shape of the Dataset After removing Anomaly Stock Codes {df.shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TqB07v2h8ifT"
      },
      "source": [
        "### Description Anomalies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:15.799586Z",
          "iopub.status.busy": "2025-01-16T16:53:15.799297Z",
          "iopub.status.idle": "2025-01-16T16:53:16.241148Z",
          "shell.execute_reply": "2025-01-16T16:53:16.239743Z",
          "shell.execute_reply.started": "2025-01-16T16:53:15.799551Z"
        },
        "id": "Z6ZjCpUMB-VF",
        "outputId": "fba723bf-7705-4b1e-d7d2-2d542dbdde14"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "description = df['Description'].value_counts()[:10].reset_index()\n",
        "description.columns = ['Description','Count']\n",
        "\n",
        "plt.figure(figsize=(8,4))\n",
        "sns.barplot(data=description,x='Count',y='Description',color='#5a189a')\n",
        "plt.xlabel('Count',fontsize = 12)\n",
        "plt.ylabel('Description',fontsize = 12)\n",
        "plt.title('Value Counts of Top 10 Descriptions')\n",
        "plt.yticks(fontsize = 8)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-h6FVhaF8qCb"
      },
      "source": [
        "From the bar graph, we can observe that product descriptions are typically entered in uppercase. Any lowercase entries might represent anomalies or errors. Therefore, it is essential to check for and handle such entries appropriately to maintain consistency in the dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:16.242461Z",
          "iopub.status.busy": "2025-01-16T16:53:16.242190Z",
          "iopub.status.idle": "2025-01-16T16:53:16.446129Z",
          "shell.execute_reply": "2025-01-16T16:53:16.444744Z",
          "shell.execute_reply.started": "2025-01-16T16:53:16.242438Z"
        },
        "id": "ITe2BfLLDe1l",
        "outputId": "ddb71792-5399-4e05-bdb3-b74320607eac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BAG 500g SWIRLY MARBLES\n",
            "POLYESTER FILLER PAD 45x45cm\n",
            "POLYESTER FILLER PAD 45x30cm\n",
            "POLYESTER FILLER PAD 40x40cm\n",
            "FRENCH BLUE METAL DOOR SIGN No\n",
            "BAG 250g SWIRLY MARBLES\n",
            "BAG 125g SWIRLY MARBLES\n",
            "3 TRADITIONAl BISCUIT CUTTERS  SET\n",
            "FOLK ART GREETING CARD,pack/12\n",
            "ESSENTIAL BALM 3.5g TIN IN ENVELOPE\n",
            "POLYESTER FILLER PAD 65CMx65CM\n",
            "NUMBER TILE VINTAGE FONT No \n",
            "NUMBER TILE COTTAGE GARDEN No\n",
            "POLYESTER FILLER PAD 30CMx30CM\n",
            "POLYESTER FILLER PAD 60x40cm\n",
            "FLOWERS HANDBAG blue and orange\n",
            "Next Day Carriage\n",
            "THE KING GIFT BAG 25x24x12cm\n",
            "High Resolution Image\n"
          ]
        }
      ],
      "source": [
        "anomaly_desc = df[df['Description'].str.contains('[a-z]',regex=True)]['Description']\n",
        "for desc in anomaly_desc.unique():\n",
        "  print(desc)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "guSH_ESj9NYz"
      },
      "source": [
        "The descriptions **Next Day Carriage** and **High Resolution Image** are identified as anomalies. Calculating the number of records with these descriptions is necessary, followed by appropriate handling to ensure data consistency and accuracy in the analysis."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:16.447258Z",
          "iopub.status.busy": "2025-01-16T16:53:16.446940Z",
          "iopub.status.idle": "2025-01-16T16:53:16.515417Z",
          "shell.execute_reply": "2025-01-16T16:53:16.514136Z",
          "shell.execute_reply.started": "2025-01-16T16:53:16.447232Z"
        },
        "id": "4SS51sV7Eume",
        "outputId": "0e36312d-b49a-4455-8d48-c3f8222914b6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "82"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ],
      "source": [
        "df[(df['Description'] == 'Next Day Carriage') | (df['Description'] == 'High Resolution Image')].shape[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:16.516716Z",
          "iopub.status.busy": "2025-01-16T16:53:16.516398Z",
          "iopub.status.idle": "2025-01-16T16:53:16.657027Z",
          "shell.execute_reply": "2025-01-16T16:53:16.655803Z",
          "shell.execute_reply.started": "2025-01-16T16:53:16.516688Z"
        },
        "id": "m6c6dlAiFbHy"
      },
      "outputs": [],
      "source": [
        "df = df[~((df['Description'] == 'Next Day Carriage') | (df['Description'] == 'High Resolution Image'))]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MFG9O5v2-HvB"
      },
      "source": [
        "\n",
        "There are only **82 records** with the anomalies \"Next Day Carriage\" and \"High Resolution Image\". These records have been removed to maintain the quality and consistency of the dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:16.658712Z",
          "iopub.status.busy": "2025-01-16T16:53:16.658294Z",
          "iopub.status.idle": "2025-01-16T16:53:16.673776Z",
          "shell.execute_reply": "2025-01-16T16:53:16.671969Z",
          "shell.execute_reply.started": "2025-01-16T16:53:16.658672Z"
        },
        "id": "MK0xaMCJFtV-",
        "outputId": "f2aa56a9-ddf7-4406-e965-68b5238be6ec"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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",
              "\n",
              "      InvoiceDate  UnitPrice  CustomerID         Country  \n",
              "0  12/1/2010 8:26       2.55     17850.0  United Kingdom  \n",
              "1  12/1/2010 8:26       3.39     17850.0  United Kingdom  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6e01a4ca-a91a-4848-8f3f-327a3e867289\" class=\"colab-df-container\">\n",
              "    <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",
              "    </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>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</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",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6e01a4ca-a91a-4848-8f3f-327a3e867289')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6e01a4ca-a91a-4848-8f3f-327a3e867289 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6e01a4ca-a91a-4848-8f3f-327a3e867289');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a4b08776-9428-4c8e-a4a6-1d4658df1d11\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a4b08776-9428-4c8e-a4a6-1d4658df1d11')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a4b08776-9428-4c8e-a4a6-1d4658df1d11 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 88
        }
      ],
      "source": [
        "df.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:16.675287Z",
          "iopub.status.busy": "2025-01-16T16:53:16.674952Z",
          "iopub.status.idle": "2025-01-16T16:53:16.733262Z",
          "shell.execute_reply": "2025-01-16T16:53:16.732130Z",
          "shell.execute_reply.started": "2025-01-16T16:53:16.675259Z"
        },
        "id": "hTsy5CN4GsSO",
        "outputId": "1fcbe2cd-b727-43cc-bd0c-2fc9fa89890f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Quantity      UnitPrice\n",
              "count  391101.000000  391101.000000\n",
              "mean       13.182216       2.871599\n",
              "std       181.926386       4.281346\n",
              "min         1.000000       0.000000\n",
              "25%         2.000000       1.250000\n",
              "50%         6.000000       1.950000\n",
              "75%        12.000000       3.750000\n",
              "max     80995.000000     649.500000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5aae0b32-10ac-41c6-a6cd-f0b0252ef1b8\" class=\"colab-df-container\">\n",
              "    <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>Quantity</th>\n",
              "      <th>UnitPrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>391101.000000</td>\n",
              "      <td>391101.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>13.182216</td>\n",
              "      <td>2.871599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>181.926386</td>\n",
              "      <td>4.281346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>1.950000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>12.000000</td>\n",
              "      <td>3.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>80995.000000</td>\n",
              "      <td>649.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5aae0b32-10ac-41c6-a6cd-f0b0252ef1b8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5aae0b32-10ac-41c6-a6cd-f0b0252ef1b8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5aae0b32-10ac-41c6-a6cd-f0b0252ef1b8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-04c98f66-5a7c-44ba-9f4e-78e3989c3370\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-04c98f66-5a7c-44ba-9f4e-78e3989c3370')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-04c98f66-5a7c-44ba-9f4e-78e3989c3370 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[['Quantity','UnitPrice']]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 137131.51908733364,\n        \"min\": 1.0,\n        \"max\": 391101.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          13.182216358434266,\n          6.0,\n          391101.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"UnitPrice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 138241.75309021297,\n        \"min\": 0.0,\n        \"max\": 391101.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.8715990242929577,\n          1.95,\n          391101.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 89
        }
      ],
      "source": [
        "df[['Quantity','UnitPrice']].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:16.734742Z",
          "iopub.status.busy": "2025-01-16T16:53:16.734360Z",
          "iopub.status.idle": "2025-01-16T16:53:16.759982Z",
          "shell.execute_reply": "2025-01-16T16:53:16.758744Z",
          "shell.execute_reply.started": "2025-01-16T16:53:16.734704Z"
        },
        "id": "094vC_MtHmIM"
      },
      "outputs": [],
      "source": [
        "df = df[df['UnitPrice']>0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ug-H31CW-wGq"
      },
      "source": [
        "\n",
        "The dataset contains records with a UnitPrice of 0, which is invalid. Therefore, only records with a UnitPrice greater than zero have been retained. Additionally, upon examining the descriptive statistics, it is evident that there are outliers in the data that need to be addressed."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fm4YZMHgm2LY"
      },
      "source": [
        "# **RFM Analysis**\n",
        "RFM (Recency, Frequency, Monetary) analysis is a customer segmentation technique that helps businesses understand customer behavior and categorize them into different groups based on their purchasing patterns. It is widely used in marketing and customer relationship management to target specific customer segments effectively.\n",
        "\n",
        "**Recency (R)** – How recently a customer made a purchase.\n",
        "* Customers who purchased recently are more likely to respond to promotions.\n",
        "* **Example**: A customer who bought something last week is more valuable than one who bought a year ago.\n",
        "\n",
        "**Frequency (F)** – How often a customer makes purchases.\n",
        "* Frequent buyers are more engaged and loyal to the business.\n",
        "* **Example**: A customer who makes frequent purchases is more valuable than one who buys once a year.\n",
        "\n",
        "**Monetary (M)** – How much money a customer spends.\n",
        "\n",
        "* Customers who spend more are more valuable.\n",
        "* **Example:** A customer who has spent ₹50,000 in total is more valuable than one who has spent ₹5,000.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S5GfJJu5nvsB"
      },
      "source": [
        "### Recency"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:16.761380Z",
          "iopub.status.busy": "2025-01-16T16:53:16.760966Z",
          "iopub.status.idle": "2025-01-16T16:53:18.117421Z",
          "shell.execute_reply": "2025-01-16T16:53:18.116144Z",
          "shell.execute_reply.started": "2025-01-16T16:53:16.761332Z"
        },
        "id": "ZFrP9Lv7ICOl",
        "outputId": "af84ebe7-82aa-4dba-eb97-207ec86adbb9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerID\n",
              "12346.0    326\n",
              "12347.0      2\n",
              "Name: Recency, dtype: int64"
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Recency</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CustomerID</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12346.0</th>\n",
              "      <td>326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12347.0</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ],
      "source": [
        "df[\"Date\"] = pd.to_datetime(df[\"InvoiceDate\"]) # Converting object column to datetime\n",
        "reference_date = max(df[\"Date\"]) + pd.DateOffset(days=1) # fetching the reference date\n",
        "recency = (reference_date - df.groupby('CustomerID')[\"Date\"].max()).dt.days # finding recency\n",
        "recency.name = \"Recency\"\n",
        "recency.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aIAE_wiYolNu"
      },
      "source": [
        "### Frequency"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:18.119316Z",
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          "iopub.status.idle": "2025-01-16T16:53:18.139413Z",
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          "shell.execute_reply.started": "2025-01-16T16:53:18.119286Z"
        },
        "id": "zf221ed1RI2o",
        "outputId": "3438cc05-bed3-4abf-cde4-5fc195371f5d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerID\n",
              "12346.0      1\n",
              "12347.0    182\n",
              "Name: Freq, dtype: int64"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Freq</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CustomerID</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>12346.0</th>\n",
              "      <td>1</td>\n",
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              "      <td>182</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
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          },
          "metadata": {},
          "execution_count": 92
        }
      ],
      "source": [
        "freq = df.groupby('CustomerID')['Date'].count() # calculating frequency\n",
        "freq.name = \"Freq\"\n",
        "freq.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qq3XdFRPooAc"
      },
      "source": [
        "### Monetary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:18.140758Z",
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          "shell.execute_reply.started": "2025-01-16T16:53:18.140731Z"
        },
        "id": "B2c6B18pRPUY",
        "outputId": "bacab75e-a19d-402e-e6a6-88b4ceeebb93"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerID\n",
              "12346.0    77183.6\n",
              "12347.0     4310.0\n",
              "Name: Monetary, dtype: float64"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Monetary</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CustomerID</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12346.0</th>\n",
              "      <td>77183.6</td>\n",
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              "      <th>12347.0</th>\n",
              "      <td>4310.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ],
      "source": [
        "df[\"Total_Price\"] = df[\"Quantity\"]*df[\"UnitPrice\"] # calculating total price\n",
        "monetary = df.groupby('CustomerID')['Total_Price'].sum() # monetary --> total amount spent by each customer\n",
        "monetary.name = \"Monetary\"\n",
        "monetary.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:18.174443Z",
          "iopub.status.busy": "2025-01-16T16:53:18.174151Z",
          "iopub.status.idle": "2025-01-16T16:53:18.189288Z",
          "shell.execute_reply": "2025-01-16T16:53:18.188134Z",
          "shell.execute_reply.started": "2025-01-16T16:53:18.174419Z"
        },
        "id": "jndIztzKRVuQ"
      },
      "outputs": [],
      "source": [
        "# converting all the pd.Series into data frame and merging them\n",
        "recency_df = recency.reset_index()\n",
        "recency_df.columns = ['CustomerID', 'Recency']\n",
        "\n",
        "frequency_df = freq.reset_index()\n",
        "frequency_df.columns = ['CustomerID', 'Frequency']\n",
        "\n",
        "monetary_df = monetary.reset_index()\n",
        "monetary_df.columns = ['CustomerID', 'Monetary']\n",
        "\n",
        "rfm = recency_df.merge(frequency_df, on=\"CustomerID\").merge(monetary_df, on=\"CustomerID\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:18.190727Z",
          "iopub.status.busy": "2025-01-16T16:53:18.190403Z",
          "iopub.status.idle": "2025-01-16T16:53:18.213315Z",
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        },
        "id": "0QW83eUYReA7",
        "outputId": "be64d08b-bc96-4707-f5dd-4e4f4a7effc4"
      },
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        {
          "output_type": "execute_result",
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            "text/plain": [
              "   CustomerID  Recency  Frequency  Monetary\n",
              "0     12346.0      326          1  77183.60\n",
              "1     12347.0        2        182   4310.00\n",
              "2     12348.0       75         27   1437.24\n",
              "3     12349.0       19         72   1457.55\n",
              "4     12350.0      310         16    294.40"
            ],
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              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c0358cda-53a3-43e1-9672-3d9140b93cee button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "rfm",
              "summary": "{\n  \"name\": \"rfm\",\n  \"rows\": 4334,\n  \"fields\": [\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1721.9941090491063,\n        \"min\": 12346.0,\n        \"max\": 18287.0,\n        \"num_unique_values\": 4334,\n        \"samples\": [\n          15664.0,\n          13991.0,\n          15976.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 100,\n        \"min\": 1,\n        \"max\": 374,\n        \"num_unique_values\": 349,\n        \"samples\": [\n          37,\n          118,\n          334\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Frequency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 224,\n        \"min\": 1,\n        \"max\": 7667,\n        \"num_unique_values\": 458,\n        \"samples\": [\n          213,\n          105,\n          444\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Monetary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8903.655719570708,\n        \"min\": 3.75,\n        \"max\": 279138.02,\n        \"num_unique_values\": 4253,\n        \"samples\": [\n          2402.51,\n          349.35,\n          2222.65\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 95
        }
      ],
      "source": [
        "rfm.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:18.214669Z",
          "iopub.status.busy": "2025-01-16T16:53:18.214148Z",
          "iopub.status.idle": "2025-01-16T16:53:18.245052Z",
          "shell.execute_reply": "2025-01-16T16:53:18.244132Z",
          "shell.execute_reply.started": "2025-01-16T16:53:18.214639Z"
        },
        "id": "8fmHn55ihBp8",
        "outputId": "18a8e263-c5b6-470e-e987-9c8c2641688c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Recency    Frequency       Monetary\n",
              "count  4334.000000  4334.000000    4334.000000\n",
              "mean     92.703046    90.232580    2015.690042\n",
              "std     100.177047   224.707914    8903.655720\n",
              "min       1.000000     1.000000       3.750000\n",
              "25%      18.000000    17.000000     304.240000\n",
              "50%      51.000000    41.000000     661.420000\n",
              "75%     143.000000    98.000000    1631.622500\n",
              "max     374.000000  7667.000000  279138.020000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8d706b2c-0d88-4044-9fa8-a2575077434a\" class=\"colab-df-container\">\n",
              "    <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>Recency</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>Monetary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4334.000000</td>\n",
              "      <td>4334.000000</td>\n",
              "      <td>4334.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>92.703046</td>\n",
              "      <td>90.232580</td>\n",
              "      <td>2015.690042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>100.177047</td>\n",
              "      <td>224.707914</td>\n",
              "      <td>8903.655720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>304.240000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>51.000000</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>661.420000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>143.000000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>1631.622500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>374.000000</td>\n",
              "      <td>7667.000000</td>\n",
              "      <td>279138.020000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8d706b2c-0d88-4044-9fa8-a2575077434a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8d706b2c-0d88-4044-9fa8-a2575077434a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8d706b2c-0d88-4044-9fa8-a2575077434a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-9a8755ac-ebf9-4caf-aaad-796298e54f00\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9a8755ac-ebf9-4caf-aaad-796298e54f00')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9a8755ac-ebf9-4caf-aaad-796298e54f00 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"rfm\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Recency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1497.448548843952,\n        \"min\": 1.0,\n        \"max\": 4334.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          92.70304568527919,\n          51.0,\n          4334.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Frequency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2883.1994875323107,\n        \"min\": 1.0,\n        \"max\": 7667.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          90.23257960313798,\n          41.0,\n          4334.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Monetary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 97832.062757247,\n        \"min\": 3.75,\n        \"max\": 279138.02,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2015.6900415320722,\n          661.42,\n          4334.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 96
        }
      ],
      "source": [
        "rfm.describe().drop(columns=['CustomerID'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JZc_CrC0o1o-"
      },
      "source": [
        "From `rfm.describe()`, we notice some extreme values (outliers) in the dataset. These outliers can affect our analysis, so we need to identify and handle them using methods like visualization (boxplots) or statistical techniques (IQR, Z-score) for accurate segmentation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:18.246322Z",
          "iopub.status.busy": "2025-01-16T16:53:18.246061Z",
          "iopub.status.idle": "2025-01-16T16:53:18.252434Z",
          "shell.execute_reply": "2025-01-16T16:53:18.251359Z",
          "shell.execute_reply.started": "2025-01-16T16:53:18.246300Z"
        },
        "id": "fgUs83gJSBpx"
      },
      "outputs": [],
      "source": [
        "# function to plot boxplots\n",
        "def plot_boxplot(data):\n",
        "  fig,axes = plt.subplots(nrows=1,ncols=3,figsize=(10,4))\n",
        "  for idx,feature in enumerate(data.columns):\n",
        "    sns.boxplot(data=data,x=feature,color='#5a189a',ax=axes[idx])\n",
        "    axes[idx].set_xlabel(f'{feature}',fontsize = 10)\n",
        "    axes[idx].set_title(f'Box Plot of {feature}')\n",
        "\n",
        "  plt.tight_layout()\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:18.254194Z",
          "iopub.status.busy": "2025-01-16T16:53:18.253796Z",
          "iopub.status.idle": "2025-01-16T16:53:18.844577Z",
          "shell.execute_reply": "2025-01-16T16:53:18.843369Z",
          "shell.execute_reply.started": "2025-01-16T16:53:18.254165Z"
        },
        "id": "m_KEJ4qNJRfE",
        "outputId": "2ca94b95-e35e-4d77-e7c9-ad99404dab46"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 3 Axes>"
            ],
            "image/png": 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rLwUFBSkgIOCW60quTJkyKdoULlzY/luNly9fVmRkpB5++OHbbtfDw0OBgYFavny5Xn31VUmJhbBUqVL2cA9kJWpg+tdrRQ28edxvdvMdxk+fPi1J9usXUxMREaHY2FhFR0frgQceSDG/UqVKd3UAffnyZYWHh2vevHmaN2/eLdsklzzcS/9e/pNUQ5MOXO905/SGDRuqRIkS+vnnnxUQEKCEhAQtX75cjz76aIq/RyCjUS/Tv14r6qWUeHp6QECAli9frujoaMXHx6t169aptk3PPpDSdsyX3hqYmvPnz2vy5MnauHGjrl696jAv6T4bSZJ+eu1mjz/+uN577z39/PPPevnllxUREaFNmzbp2WefTdffDhwRsDOYs7Oz6tevr2+++UZ//fXXHQNUarZt2yYp8c6vf/31V7p+j9Bms6X5ZwtysuRvUi1atNDTTz+t119/XatXr1aBAgWUkJAgKfHTxsaNG6e6jvRc652QkCCbzXbL35+9uYjd6tonc9PNMdKiY8eOWr16tfbv3y+bzaaNGzeqR48e3BEY2RI1MHtI/k2H9G/tGT58+C1/+iV//vwpbtp4O7c6GLv55kpJ9fiJJ55Qp06dUl0m+c3TJN2yvqW3hrq4uKh9+/aaP3++3nnnHe3fv18XL17kDCBkC9TLzNWuXTuNGTNG//zzj5o0aWLZz4fd6ZjvbmrgzeLj4/Xcc8/p6tWr6tevnypXrqz8+fMrJCREI0eOtG8jiZubW6p1tHDhwgoMDNSyZcv08ssva/Xq1YqNjaUm3iMCdiZIOriIioqSlHhzhZ07dyoyMtLh07CTJ0/a5yc5duyYpk6dqs6dO+vYsWN66623tGzZsnT9HEF6JH1LcOrUqRTzTp48qSJFitg/iUzPJ1vJ13vzt76nTp1K8e3EvXBxcdHQoUPVu3dvfffddxowYID9DcbV1fWObx4VK1bUn3/+ecc2x44dU0BAgCWf8BUtWlQFCxa843YlqXHjxipatKiWLVumGjVq6Pr16+rQocM99wHIKNTAzK2BaZFUEwsWLHjbmli0aFHly5cv1Tt23zxGhQsXlvTvt8pJkr6NSr7OpA8+rTqYT3o+x48fv+M6O3TooFmzZmnjxo3asmWLihYtetenuwNWo15mXr1s2bKlxo4dq99++00TJ068Zbv07IO0SE8NvNW4HT9+XKdPn9aHH36ojh072qcnvzN6WnXo0EEvvfSSDh06ZL8J5N18uIN/8ZVXBouLi9P27dvl6upqP5WkSZMmio+P13fffefQdvbs2XJyclKTJk3sy44aNUolS5bUm2++qffff1///POP3nvvvQzrb8mSJVW1alUtXbrU4SDp+PHj2r59u/1ui5Lk7u4uKfE0wjvx9fVVsWLF9OOPPzp8I/LLL78oKChIzZo1s+5JSKpfv778/Pw0Z84cxcTEqFixYqpXr57mzZunixcvpmif/FScVq1a6dixY1q3bl2KdkmfPj722GMKCQnR/PnzU7SJjo62vzGmlbOzs1q0aKFNmzbp999/v+V2pcTTfNq2batVq1Zp8eLFstls8vb2Ttf2gMxCDUyU2TUwLf2pWLGiZs2apWvXrqWYn1QTXVxc1KhRI61fv94hKAcFBdm/KUtSsGBBFSlSRHv37nWY/v333zs8dnFxUevWrbVmzRodP378lttODx8fH5UvX17ffPNNioB/87fc3t7e8vLy0sKFC7V27Vq1bdv2tqfBApmFepkos+plgQIF9M4772jw4MFq3rz5LduldR+kVXpqYNK43VzXkr6NTl7fjDH65ptv0tUXKfH5FSlSRDNnztSePXv49toCvKNYbMuWLfZPtC5fvqxly5bp9OnTGjBggP1Tr+bNm6t+/fqaOHGizp07Jy8vL23fvl0bNmxQnz597Kcqf/755zp69Khmz56tggULytvbW4MGDdJ///tftWnTxqFwWWn48OHq37+/unfvrieffNL+kwuFChXSyy+/bG/n4+MjSZo4caIef/xxubq6KjAwMNVrbVxdXTVs2DCNGjVKPXv2VNu2be0/uVCuXLkUP8liheeff16vvPKKFi9erB49emjs2LF6+umn1b59e3Xr1k0VKlTQP//8o99++00XLlzQzz//bF9uzZo1euWVV9SlSxf5+Pjo6tWr2rhxo8aNGydvb2916NBBq1at0tixY7V7927VqlVL8fHxOnnypFavXq2ZM2fe9nrI1AwdOlTbt29Xr1697D/9denSJa1evVrff/+9w6lLHTt21Lfffqvdu3dr2LBhlo4bcC+ogdmnBt6Os7Ozxo8fr/79+6tdu3bq3LmzSpUqpZCQEO3evVsFCxbU9OnTJUmDBw/W1q1b9cwzz6hHjx6Kj4/X3Llz9dBDD+mPP/5wWG/Xrl31xRdf6M0335Svr6/27t2b6rdbr7/+unbv3q1u3bqpa9eueuihh3T16lUdPnxYO3fu1K+//pru5/POO+9o4MCB6tixozp37qwSJUro5MmTOnHihL766iuH9h07dtSHH34oSRxMIstQL7O+Xt7qFO3k0roP0iOtNbBixYry8PDQjz/+qAIFCih//vzy8/NT5cqVVbFiRX344YcKCQlRwYIFtWbNmhRBPC1cXV3Vtm1bzZ07Vy4uLmrbtm261wFHBGyLJf/dubx586py5cp655139NRTT9mnOzs76/PPP9fkyZO1cuVKLV68WOXKldPw4cPVt29fSdLhw4c1Y8YM9ezZ0+HmVQMGDNCGDRv01ltvacWKFZZdL5JcgwYNNHPmTE2ePFmTJ09Wnjx5VLduXb3xxhsO1/L4+fnplVde0Y8//qitW7cqISFBGzZsuOXNLDp37qx8+fLpyy+/1Mcff6z8+fOrRYsWeuONNzLkebRq1cr+DU23bt300EMPadGiRfrss8+0ZMkShYWFqWjRoqpWrZoGDRpkX65AgQL67rvvNGXKFK1bt05LlixRsWLFFBAQYP+NVGdnZ02dOlWzZ8/WTz/9pHXr1snd3V3ly5dXr169Ur0xx52UKlVK8+fP16RJk7Rs2TJFRkaqVKlSatKkSYrrJ319ffXwww8rKCiIg0NkK9TA7FMD76R+/fqaN2+epk2bprlz5yoqKkolSpSQn5+funfvbm/n7e2tr776Su+//74mT56s0qVLa/Dgwbp06VKKgD1o0CBdvnxZa9as0apVq9SkSRPNnDkzxWmexYsX14IFCzR16lStW7dOP/zwgzw9PfXQQw/d9YeGjRs31pw5czR16lTNmjVLxhhVqFBB3bp1S9G2ffv2+vjjj1WhQgX7730DmY16mTPqZVr2QXqltQa6urrqgw8+0Keffqp33nlHN27c0Pvvv6/OnTtr+vTpGj9+vGbMmKG8efOqZcuWeuaZZ+7qssEOHTpo7ty5CggIUMmSJe/qOeFfTuZu7rIEIMt17NhRhQsX1pw5c7K6KwByoSlTpuizzz5LEbJzgsuXL6tx48Z66aWXHD5gBYDc6NixY+rQoUOKa7pxd7gGG8iBfv/9dx09epQiCAB3YcmSJYqPj+cGkQAgaf78+cqfP/8df6YWacMp4kAOcvz4cR0+fFizZs1SiRIl9Pjjj2d1lwAgx9i5c6eCgoI0ffp0tWjRIsXvgwNAbrJx40adOHFC8+fP1zPPPJPu3yxH6gjYQA6yZs0aTZ06VZUqVdKnn36qvHnzZnWXACDHmDZtmg4cOCB/f3+NGTMmq7sDAFlq/Pjx9t8BHzx4cFZ3577BNdgAAAAAAFiAa7ABAAAAALAAARsAAAAAAAsQsAEAAAAAsECab3JmEqQbUWlfsZOz5OIuxV9PXBa3xlilHWOVNoxTSnnyJ45LRklvjZTYT3fC+NwZY3RnjNHtJY2PDDUSt8a+yF7YH5kvPceRaQ7YN6Kk86vS3gk3T6lMS+nSdik2LO3L5UaMVdoxVmnDOKVU9jHJtWDGrT+9NVJiP90J43NnjNGdMUa3lzQ+N64nHkBmFGpkzsa+yF7YH5kvPceRnCIOAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAXyZHUHUnMp/IIirodldTcyjes16eox6Z9zUlxk1vShkLunSniUzpqNA7nYhQsXFHQuTHGRvA4B4GYXLlzQpTCpsKiNAHKGbBewL4Vf0CszuyouPiaru5K5pmXt5l1d8mpSvwUc3AOZ6FLYBQ3s8qTi4mIl8ToEgOT+PHtEw8c8LxcnF03qt5DaCCBHyHYBO+J6mOLiY1Tqej25JXhYvv5Y53CFuP+aYevPiZLGJOJ6GG9eQCYKvxZmD9dFo310Od9hXocA8D/n/wlWQkK8EhRPbQSQY2S7gJ3ELcFD+RKK5Nj1A0B6uJoCWd0FAAAA3CNucgYAAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUyPGCHXwvL6E0AyGThUWFZ3YVcg7EGgETUQwA5QYYG7HPnzqnP+20UEnYuIzcDIBOFhJ1Tv2m8rq0QevXibecz1gBys7DIy/Z/h0ZcpB4CyBEyNGBHREQowSToWkxERm4GQCa6FhMhw+vaEtdjo247n7EGkJvFxEXb/x0dG0U9BJAjcA02AAAAAAAWIGADAAAAAGABAjYAAAAAABYgYAMAAAAAYAECNgAAAAAAFiBgAwAAAABgAQI2AAAAAAAWIGADAAAAAGABAjYAAAAAABYgYAMAAAAAYAECNgAAAAAAFiBgAwAAAABgAQI2AAAAAAAWIGADAAAAAGABAjYAAAAAABYgYAMAAAAAYAECNgAAAAAAFiBgAwAAAABgAQI2AAAAAAAWIGADAAAAAGABAjYAAAAAABYgYAMAAAAAYAECNgAAAAAAFiBgAwAAAABgAQI2AAAAAAAWIGADAAAAAGABAjYAAAAAABYgYAMAAAAAYAECNgAAAAAAFiBgAwAAAABgAQI2AAAAAAAWIGADAAAAAGABAjYAAAAAABYgYAMAAAAAYAECNgAAAAAAFiBgAwAAAABgAQI2AAAAAAAWIGADAAAAAGABAjYAAAAAABYgYAMAAAAAYAECNgAAAAAAFiBgAwAAAABgAQI2AAAAAAAWIGADAAAAAGABAjYAAAAAABYgYAMAAAAAYAECNgAAAAAAFiBgAwAAAABgAQI2AAAAAAAWIGADAAAAAGABAjYAAAAAABbIk9UdAJBzXA2/pPcWvCpJGvFN7xTz87nmV5XSVdWhXi/5PVhfLs4u9nnxCfH6v7/2auPvy/THuYOKiL4qV2dXyTjpWlz4XfVneNuPVLda07taNrubu3mqftozR1LqY51eTnJWCY8yqv9wExUpWFIF3T10/PxhBV/6U1Exkcrj4qpiHiVVpEAJFchXUKERl5SQEK/wqDDFxceoZOEyaurTTtUq+Ov4+d915do/KlKguGxlq9sfF3YvKuMkhUddVpECxeVdvqZcnF0UnxCvw2f260jwPkmST4Xaqlaxln3esbO/2deXtExy8QnxOhx8+zbJ295pfXfT9n6SWc/bqu3k1v10N+7nsZq84m1Jd18PXZRHZYs9IKMEFchbSHUebqrWfp20/tBPOnbuN+VxcdWl8BBdj72mEh5lVaRgcf0Tfl6uefKpfNGKyuPiJq9yfjoTekoXw87JGCN3t/y6fO0fFfcoJZ/yteXk7Jxq/TsSvF+Hz+xLXCZfQUXFRMjJSO75Cuna9XCFRl5UsUIlVcjdU575i6pYoZL22hoaeVHhUWHycPdUsUIlHdZ7876WlO79n13qQU6r3ZnRh+zwPLMDK8chs8aUgA0gTfpMaq6o2MjbtomOi9LhM/t0+Mw+ueXJpyFtx6m+LVC7j2/S1FXjdD02yqF9rKLvqU8TVrwhrZAWvPHrPa0nu+n6UT3L12mUoIvh57Rs3w+3bHP60vFbzvvj/CFtPbpGTnKSkbFPd3ZyUYKJT3WZEoXLKMDWQpsPL1d41BX79EW7Zskjv6ea+bTXzuPrdenq3w7L9Gn2qurbAiVJmzZt0ief/lcXr9y6TZLdxzdpzub/3nZ9d9P2fpJZz9uq7eTW/XQ3GKvbi9cNnQkNsj/+4/whfffLlFTbng096fB4X1CqzRws0WyHx//Wv2UKjwpLb3dvWVuT1ntz7fTIX0SScdjWnfZ/dqkHOa12Z0YfssPzzA6sHIfMHFNOEQdwR2kJ1zeLvRGtj38aoW83T9HHP41IEa6tlBGBNKtY8W11RkoK14/XekqSVMi9sH2ed7ka8ipXQ5L0dOOX5OHuqZ/3fKvwqCvyKldDb3ebqre7TZV3uRoKjwrTz3u+VaF8nvrPM1/pm1c26z/PfKWKxR/SJz+N1O7jm7Tz8CaNHDlSD5R66JZtkuw+vkmf/DRSFYtb2/Z+klnP26rt5Nb9dDfu17H6bv30TN2ek5xSTHNxdkl1uiS5OKX+zZdj/QtTuaIP2ue55XFLdZm8efI5PM7j4ipJqlTSSy+0Hq2alRpISgzeN9fOpxu/pPCoKwqPCtPTjV9K0/7PLvXg281TclTtzow+ZIfnmR1YOQ6ZPaYEbAC3dTX8UrrDdXLL935305TUD1Tu1Z4jv2TIenMDV5fUD/gkqcYDAfb5iQeZiftv5f55qlW5kaa98JPc8uSVW558Gtt9mt7tMUO1qzTWuoNLdPXaFTk5OcstT169032aqj9QV9UfqKu3u0+TW568cnJyVsT1MFUpXU3ubvllK1tdwzt9pFpVGmnOpv9q1sr/qlGjRhr9zEeyla2eos03mycpPiFe8QnxmrP5v6pVpZGGd7Ku7f0ks563VdvJrfvpbtyvYzV9+fjM3d4LyxzOzkkSnxDvMD0pbLu6uGnW4HVycko8lK75QMD/amFerf1tscIiL9vr3/XYKLnlyatalRvKw72ofZk8zq72WljI3VP+/wvRTk7OirsRq8L5iyoyOlyBvu01qsunqlW5kS6Gnf/f9KuqUrqa3PLk1bpDS1SrciPVrtJI6w8tlVuevLfd/9mmHlRuqOV7v5d/5YY5onZnRh+yw/PMDqwch6wY00w5Rfxc6OkMaQtr5YSxd70mXT0m/XNOirv7zHffs3Kc3l849J6WTzAJDo9Le1bQhbDge1pnaiaseEMLqt1fp4pnlrj42FvOK1usgg7+tVOSbjr4NPKvHKA/z/+fYm/ESJL+OHdIPhVrq1P9Pnrr+372lrE3YuzzJOn4uUP2ZS6F/61jZ3+zz3N2cnZY/v1nx8v5guNnwcnbHDv7W+J6rv6tV9uNl7OTdW2T+nQ/OHb2t0x53lZtJ7P6ez+4X8dqw9GfM3V7E5e9KUmqUrqqgi4cTfx3qaoKCjnqMD2pDsbFx2rj78tk/vceV7ZYRf32v1r5T8QF+3pjb8TocuRFSZJ/5Qbaf3K7fd6NhDjpf2+R/0RcUN2Hm0qnZF9nk2qPadne7+z7rmalR7T/5DY1rtZGy/d+n6KmGWMc9vWt9n92qQc1KwVo38lt8q8ckCNqd2aMW1q3ceT0byqvnPN6Ti8rxzoramSmBOykG1Mge8sx+2laVncgh8im41QobyFduHMzZBNxN24dvt3y5NOVa//YHyf9u2KJKinaptbuVo+TL1+lShVdTeUPJqlN8mUrFE+5XSva3g+Snk9GP2+rtpNZ/b0fMFbWuPS/UOz/YEN7wPav3NAesGslm54kJOys/d9JHxrejttNp4HfLO6mdZTyLCfp332X1zVfqtOlpP1vUkxPbf9nl3rg5po38f+3GJfsVrszY9zSvI2I+/v1bOVYZ0WNzJSAPaTtuypX7ME0tT0XejrnBL37THr2U1ZxLSgVf0T6ZxffYN+OleP0/sKhCouyruhExERYti5kPNdbXC8oJV5nX6pwWfvjIgWKS5KCL6W8I1DSvJv/ndrj5MsHBQWpuKqnWF9Sm+TLnvknSLay1re9HyQ9n4x+3lZtJ7P6ez9grKxRolBpXY64qAOn//2G+UCyb5v3J5uepJRnefu/3fLkveM2Ym/c/saerjetIyTsnKR/911MXHSq06XE/W+MSTE9tf2fXepBbFziBwq3GpfsVrszY9zSvI1C9/fr2cqxzooamSkBu1yxB1W5lHdmbAr3ICfsJzdPqYy39PcZKTYsq3uTfVk5Th/3mqN+M9re9fLOTs4Op4lfCDtzbx26heFtP8qQ9eYGri5utzxN/HzoGfv8xDuIS4nfkjjpwMmdal79if8dWDrJq5yfEkyCluyeoxIeZWSMUWjkRbm6uMqrnJ99nbZyfnLLk1dx8XEqXqiU/edlJDks7+QszZ49W0NbfaTktwxJalOycFn7siUKl9HiXbM1vNNHDqeA3Wvb+4V3+ZqZ8ryt2k5m9fd+cL+O1aNVn8jU08Rfa/8fvTijvcO31EnfXkuyT0/6JQVXFzc1r95e32yeJGMSdD402B6yPdyLyBijy9cuydXFVQXzFVZk9FUdOLlDxQuVVmjkRRmToDzOrnJ2dlZcfJyKFSypC1cS3x+dnJwlI205skolPMrIu3xNJZgE/XZql5zkrK1HVqeoaYt2fi0nJzlMv9X+zy714LdTO+Xs5KIDJ3eqVc0u2b52Z8a4pXUb1R68+23kBFaOdVbUSG5yBuC2CnuUUH63gne9fLs6z9w0JeVNZKxwv/4edma43TXYB//aaZ+feO1h0l3Eu2v/yW16aUYHxd6IUeyNaI2b95Le/uEF7QvaqpY1OqlwgSIyJkGxN2L0zryXdOj0rzr01696d95Lir0RI2MSVMjdUyf+Pqzrsdf0x7lDmrDkDe0P2qY+ga+q7+Ovatu2bXrvuzf0x7lDKdr0bvaKXJxd5OLsoj7NXtX+oG2asMS6tveTzHreVm0nt+6nu3G/jtWL7d7K3O3NaJ/6XcSdHO8invwa7L5TWtqvl/7tr53/q4UxalWzszwLFrXXP3c3d8XeiNH+k9sVfv2yfZkbCXH2WhhxPUwHTu1I3IZJkGseN12NuqyC+Ty08dDPen/Ra9p/cptKepb93/TCOvH3YcXeiFZLv07af3Kb9gVt06N+HRR7I/q2+z/b1IOT29WuztM6cHJ7jqjdmdGH7PA8swMrxyErxtTJJJ1PcgdxkdL5VWlfsZundLXCMfXu3Vsf9v4mzd+Mngw5phHf9FaFay2UL6FI2jeYRtHOV3SmwPoMW39OlDQm6dlPWcXNUyrTUvp7Hd9g305GjFN6f6orLb+DbZW0/A522ccST53PKOmtkZK08/Rqfbog8ZKYUtfrKcT9V33Y+5ts/VNd6fkd7JKFy+oR26MpfgdbSvzN1mY+7VL8lmvJwmXVu9krqm8LlJundCzPJn3ygePvYCdvk1xqv3FpRdvsLL2v9cx63lZtx4r15Jb3jbsdq6TxuREl5cmfcf27mxq59MCsTP+pLqv8W/+s/R3spPXeXDsL5y8qowSHbd1p/6fnb+ZeXkd32k5Oq92Z0Yc7bYO6lv6xvtd1pec4koANAvZ9KKPG6Wr4Jb3+TS9dvX451fn5XPOrSumq6lCvl/werO/waWB8Qrz+76+92vj7Mv1x7qAioq/K1dlVMk66Fhd+V/0Z3vajNH9znZMCduVS3pq7eap+2jPHsv45yVklPMqo/sNNVKRgSRV099Dx84cVfOlPRcVEKo+Lq4p5lFSRAiVUIF9BhUZcUkJCvMKjwhQXH6OShcuoqU87Vavgr+Pnf9eVa/+oSIHispWtbn9c2L2ojJMUHnVZRQoUl3f5mnJxdlF8QrwOn9mvI8H7JEk+FWqrWsVa9nnHzv5mX1/SMtK/f8dn18Tr0P+l3uZmt1vfvbTNru7mtZ5Zz9uq7dzrenLT+8bdjFVuCdguyqOyxR6QUYIK5C2kOg83VWu/Tlp/6CcdO/eb8ri46lJ4iK7HXlMJj7IqUrC4/gk/L9c8+VS+aEXlcXGTVzk/nQk9pYth52SMkbtbfl2+9o+Ke5SST/nacnJ2TrX+HQner8Nn9iUuk6+gomIi5GQk93yFdO16uEIjL6pYoZIq5O4pz/xFVaxQSXttDY28qPCoMHm4e6pYoZIO6715X0tK9/5P69/Mvb6O7rSdnFa7M6MPaXl/pK5l3rrScxyZKddgA7g/FPYoodFd/6sR36TvgzMp8RSdGpXqq0al+hnYw/tHz2aD1KDqo3c11mkVWL39XS13889Y3OlnLVycXeT3QF35PVA31XlpWT6tP52RUW3vJ5n1vK3aTm7dT3fjfh6rIW3f1eQVb1teD9vXe0btdfOlTLfmX7lButbv4uyi6g/WVfUHU9a/O7ndvrzVvk7v/s8u9SCn1e7M6EN2eJ7ZgZXjkFljyjXYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUyNGAXKlRIzk7OKpC3UEZuBkAmKpC3kJx4XVvC3S3/becz1gBys7yu+ez/zueWn3oIIEfIk5ErL1eunOaMWq18cZ4ZuRkAmaiUZznNfGm1PPJ7ZnVXcrxihUvedj5jDSA38yxY1P7vYoVKUg8B5AgZfoq4RwHPjN4EgEzGAU7mYawBIBH1EEBOwDXYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYIE9Wd+BWYp3DM3S9GbX+nIixALJenNO1rO4CAAAA7lG2C9iF3D3l6pJXIe6/Zuh2Mnr9OY2rS14VcvfM6m4AuYpHAU+5uropLi5Wl/Md5nUIAMmULV5Rzs4ucnFyoTYCyDGyXcAu4VFak/otUMT1sKzuSqZxLSgVf0T6Z5cUF5k1fSjk7qkSHqWzZuNALlXCs7QWLVqoE6vDFBfJ6xAAknu4fDUtXbpEF7dKhUVtBJAzZLuALSWG7Nx0kOnmKZXxlv4+I8WGZXVvAGSm0qVLy5QrzWsfAFJRunRpGU+OjwDkHNzkDAAAAAAACxCwAQAAAACwAAEbAAAAAAALELABAAAAALAAARsAAAAAAAsQsAEAAAAAsAABGwAAAAAACxCwAQAAAACwAAEbAAAAAAALELABAAAAALAAARsAAAAAAAsQsAEAAAAAsAABGwAAAAAACxCwAQAAAACwAAEbAAAAAAALELABAAAAALAAARsAAAAAAAsQsAEAAAAAsAABGwAAAAAACxCwAQAAAACwAAEbAAAAAAALELABAAAAALAAARsAAAAAAAsQsAEAAAAAsAABGwAAAAAACzgZY0xaGpoE6UZUOlbsLOXJn7iMSbjb7uUOjFXaMVZpwzillCd/4rhklPTWSIn9dCeMz50xRnfGGN1e0viYBGokbo19kb2wPzJfeo4j0xywAQAAAADArXGKOAAAAAAAFiBgAwAAAABgAQI2AAAAAAAWIGADAAAAAGABAjYAAAAAABYgYAMAAAAAYAECNgAAAAAAFiBgAwAAAABgAQI2AAAAAAAWIGADAAAAAGABAjYAAAAAABYgYAMAAAAAYAHLA3ZQUJCee+451axZUw0bNtSECRMUGxtr9Waytb/++ktvv/22OnTooGrVqqldu3aptluwYIFat26t6tWr64knntCmTZtStImIiNDo0aNVr149+fv7a8iQIbp48WJGP4VMsWrVKg0cOFBNmjRRzZo11aFDBy1cuFDGGId2uX2cJOmXX35Rz5499cgjj8jX11ePPvqo3n//fUVERDi027hxo5544glVr15drVu31qJFi1KsKzY2Vh9++KEaNmyomjVr6rnnntPJkycz66lAuaNOZsXre//+/erevbv8/PwUGBioL774IsX2srNr166pSZMm8vLy0u+//+4wL7eP05IlS9SxY0dVr15d9evXV79+/RQdHW2fb2Xty2mvzw0bNqhr167y9/dXo0aN9Morr+jMmTMp2uWkv6Gctg+yUlYcc6Zl/xtj9MUXX6hZs2by8/NT9+7d9dtvv6VYV0hIiAYPHix/f3/Vq1dPb775piIjI+9uMLJYdn3fy437IssZC4WFhZmGDRuaZ555xmzZssUsWLDA1K5d24wbN87KzWR769atM02aNDGDBw827dq1M23btk3RZvny5cbLy8tMnDjR7Ny504wZM8ZUq1bNHDhwwKFd3759TZMmTcyKFSvM+vXrTbt27cwTTzxh4uLiMunZZJxu3bqZ1157zaxYscLs2LHDfPzxx8bb29tMmTLF3oZxSrR06VLz4YcfmtWrV5tdu3aZb7/91tSrV88899xz9jZ79uwxVatWNWPGjDE7d+40EydONF5eXmbVqlUO6xozZoypXbu2WbBggdmyZYt5+umnTePGjU14eHhmP61cKbfUycx+fZ8+fdrUrFnTDBo0yOzYscN8/fXXxsfHx8ycOTOznvI9mzBhgmnQoIGx2Wzm0KFD9um5fZymTZtm/P39zYwZM8zu3bvN6tWrzdixY01kZKQxxtral9Nen7t27TLe3t5m5MiRZvv27WbFihWmVatWpkWLFub69ev2djnpbyin7YOsltnHnGnd/zNmzDA+Pj7m66+/Njt27DCDBg0y/v7+Jjg42N4mNjbWtGvXzrRr185s2LDBrFixwjRp0sQMGDDA2kHKJNn1fS837ousZmnAnj59uqlZs6a5cuWKfdqPP/5oqlatai5cuGDlprK1+Ph4+79HjBiRarFr1aqVGTp0qMO07t27m379+tkf79+/39hsNrN161b7tKCgIOPl5WVWrFiRAT3PXKGhoSmmvfXWW6ZWrVr2MWScbm3evHnGZrPZX1t9+/Y13bt3d2gzdOhQ89hjj9kf//3336Zq1armxx9/tE+7cuWKqVmzpvniiy8yp+O5XG6pk5n9+h4zZowJDAw0MTEx9mmffPKJqVOnjsO07OrEiROmZs2a5ocffkgRsHPzOAUFBZlq1aqZzZs337KNlbUvp70+x4wZY5o3b24SEhLs03bu3GlsNpvZs2ePfVpO+hvKafsgq2X2MWda9n90dLSpVauW+eSTT+xtYmJiTGBgoBk7dqx92rJly4yXl5cJCgqyT9u6daux2Wzm4MGD6RmGbCE7vu/l1n2R1Sw9RXzLli0KCAiQp6enfdpjjz2mhIQEbd++3cpNZWvOzrcf1jNnzuj06dN67LHHHKY//vjj2rlzp/00qC1btsjDw0MNGza0t6lcubKqVq2qLVu2WN/xTFa0aNEU06pWrarIyEhFRUUxTneQ9DqLi4tTbGysdu/erTZt2ji0efzxxxUUFKSzZ89KkrZt26aEhASHdp6enmrYsOF9PVbZSW6pk5n9+t6yZYseffRRubm5OawrPDxcBw4csPrpWW78+PF66qmnVKlSJYfpuX2cFi9erPLly6tp06apzre69uW01+eNGzdUoEABOTk52acVKlRIkuyniea0v6Gctg+yWmYfc6Zl/+/fv1+RkZEO23Rzc1PLli1TrMvLy0uVK1e2T2vYsKE8PT31yy+/pGcYsoXs+L6XW/dFVrM0YJ88edJhx0iSh4eHSpQowTWeySSNxc0HUlWqVFFcXJz92qmTJ0+qUqVKDm+cUuKL7H4dz3379qlUqVIqWLAg45SK+Ph4xcTE6PDhw5o6daqaN2+u8uXLKzg4WHFxcSlef1WqVJH079/cyZMnVaxYMRUuXDhFu/ttrLKr3FwnM+r1HRUVpb///jvFuFauXFlOTk7ZflxXr16t48ePa9CgQSnm5fZxOnjwoGw2m6ZNm6aAgAD5+vrqqaee0sGDByXJ8tqX016fnTt3VlBQkL777jtFRETozJkz+vTTT1WtWjXVqlVLUs77G8pp+yC7y4r9n/T/1F6X58+ft98/IbV97eTkpEqVKt03+zqr3/fYF1nD0oAdHh4uDw+PFNMLFy6sq1evWrmpHC1pLG4eq6THSfPDw8Ptn0Qnd7+O5969e7Vy5Ur17dtXEuOUmsDAQPn5+alz584qUaKEPvnkE0n3PlYeHh733VhlV7m1Tmbk6zvpZn83r8vNzU3u7u7ZelyvX7+uDz74QK+99poKFiyYYn5uH6dLly5p27Zt+umnnzR27FhNnTpVTk5O6tu3r0JDQy2vfTnt9VmnTh199tln+uSTT1SnTh21aNFCoaGh+vLLL+Xi4iIp5/0N5bR9kN1lxf4PDw+Xm5ub8ubNm2Kbxphcc/yWHd732BdZg5/pQrZw4cIFvfbaa6pfv7569+6d1d3Jtr744gv9+OOPGj9+vE6ePKkXX3xR8fHxWd0t4LZ4fd/a559/rmLFiqlLly5Z3ZVsyRijqKgoTZo0SW3atFHTpk31+eefyxijuXPnZnX3stz+/fs1fPhwdevWTXPmzNGkSZOUkJCgAQMGONxlHUDm4n0vd7M0YHt4eKT42SAp8ROZm0/Lys2SxuLmsQoPD3eY7+Hhkert8e+38QwPD1f//v3l6empKVOm2K8nYpxS8vb2lr+/v7p27app06Zp9+7dWrdu3T2PVXh4+H03VtlVbquTmfH6TvrU/eZ1xcbG6vr169l2XM+dO6dZs2ZpyJAhioiIUHh4uKKioiQlnv537dq1XD9OHh4e8vT0lLe3t32ap6enqlWrphMnTlhe+3La63P8+PF65JFHNHLkSD3yyCNq06aNvvjiCx05ckQ//fSTpJz3Wstp+yC7y4r97+HhodjYWMXExKTYppOT031//Jad3vdy+77IKpYG7NSueY2IiNClS5dSnNefmyWNxc1jdfLkSbm6uqpChQr2dqdOnUrxe3anTp26b8YzOjpaL7zwgiIiIjRz5kyH01MYp9vz8vKSq6urgoODVbFiRbm6uqY6VtK/Y1m5cmX9888/KU73Se3aG2SM3FQnM+v1nT9/fpUpUybFupKWy67jevbsWcXFxWnAgAGqW7eu6tatqxdffFGS1Lt3bz333HO5fpweeuihW86LiYmxvPbltNdnUFCQw4cPklS6dGkVKVJEwcHBknLeay2n7YPsLiv2f9L/T506lWKbZcuWVb58+eztbl6XMSZHH79lt/e93LwvspKlAbtJkybasWOH/ZMYKfHmLc7Ozg53wsvtKlSooAcffFCrV692mL5y5UoFBATY7wbYpEkTXb16VTt37rS3OXXqlI4cOaImTZpkap8zwo0bN/Tqq6/q5MmTmjlzpkqVKuUwn3G6vYMHDyouLk7ly5eXm5ub6tevrzVr1ji0WblypapUqaLy5ctLkho1aiRnZ2etXbvW3ubq1avatm3bfT1W2UluqZOZ/fpu0qSJNmzYoLi4OId1eXh4yN/fPyOe4j2rWrWqvvnmG4f/Ro0aJUkaN26cxo4dm+vHKTAwUGFhYTp69Kh92pUrV3T48GH5+PhYXvty2uuzbNmyOnLkiMO0c+fO6cqVKypXrpyknPday2n7ILvLiv1fq1YtFSxYUKtWrbK3iYuL09q1a1Os69ixYzp9+rR92s6dOxUWFnbLXw7IzrLj+15u3RdZzsrf/AoLCzMNGzY0PXv2NFu3bjULFy40derUMePGjbNyM9leVFSUWbVqlVm1apXp2bOnadq0qf1x0m/kJf3e3KRJk8yuXbvM22+/bapVq2b279/vsK6+ffuapk2bmpUrV5oNGzak+kPzOdVbb71lbDabmTVrljlw4IDDf0m/38c4JRo0aJD5/PPPzcaNG82OHTvMrFmzTMOGDU379u3tY7Vnzx5TtWpVM3bsWLNr1y4zadIk4+XlZVauXOmwrjFjxpg6deqYhQsXmq1bt5qePXuaxo0bm/Dw8Kx4arlObqmTmf36Pn36tKlZs6YZPHiw2bFjh5k9e7bx8fExM2fOzNTnfa927dqV4newc/M4xcfHmy5dupgWLVqYFStWmPXr15tu3bqZevXqmYsXLxpjrK19Oe31OXv2bGOz2cz/+3//z2zfvt2sWLHCtGvXzjRo0MBcvnzZ3i4n/Q3ltH2Q1TL7mDOt+3/GjBnG19fXzJ492+zYscMMHjzY+Pv7m+DgYHub2NhY065dO9OuXTuzceNGs2LFCtO0aVMzYMCADByxjJNd3/dy477IapYGbGOMOXHihOnTp4/x8/MzAQEB5oMPPnD4AfTc4MyZM8Zms6X6365du+zt5s+fb1q2bGl8fHzsf9A3Cw8PN6NGjTJ16tQxNWvWNC+//LK5cOFCZj6dDBMYGHjLcTpz5oy9XW4fJ2MSi2OHDh2Mv7+/qVmzpmnbtq3573//ayIiIhzarV+/3rRr1874+PiYli1bmgULFqRYV0xMjPnggw9MQECA8fPzM88++6w5ceJEZj0VmNxRJ7Pi9b1v3z7TtWtX4+vra5o0aWJmzJhhEhISMvR5Wi21gG1M7h6n0NBQM2zYMFO7dm3j5+dn+vbta/7880+HNlbWvpz0+kxISDDff/+9ad++valZs6Zp2LChGTRoUKrPKyf9DeWkfZDVsuKYMy37PyEhwUyfPt00adLE+Pr6mq5du6YIkcYYc+HCBfPyyy+bmjVrmjp16phRo0alOLbJKbLr+15u3BdZzcmYm07wBwAAAAAA6cbPdAEAAAAAYAECNgAAAAAAFiBgAwAAAABgAQI2AAAAAAAWIGADAAAAAGABAjYAAAAAABYgYAMAAAAAYAECNgAAAAAAFiBgAwAAAABgAQI2bmnkyJHy8vKSl5eXfHx81Lx5c02YMEExMTFZ3TUAyDTJa2Hy//7666+s7hoAZBtJtfLtt99OMW/cuHHy8vLSyJEjM7VPXl5eWr9+faZuE8iT1R1A9ta4cWO9//77unHjhg4fPqwRI0bIyclJb7zxRlZ3DQAyTVItTK5o0aIOj2NjY+Xm5paZ3QKAbKVMmTJauXKlRo8erXz58kmSYmJitHz5cpUtWzaLe3f3qO9ID77Bxm25ubmpRIkSKlOmjFq0aKEGDRpox44dkqSEhATNmDFDzZs3l5+fn5544gmtXr3aYfk///xTL7zwgmrVqiV/f389/fTTCg4Ots9fsGCBHnvsMVWvXl1t2rTRd999Z5939uxZeXl5ae3aterVq5dq1KihJ554QgcOHHDYxr59++zz69atq+eff15Xr17V0qVLVb9+fcXGxjq0f+mll/iAAEC6JNXC5P89++yzevfdd/Wf//xH9evX1/PPPy9JOn78uPr16yd/f381aNBAb7zxhi5fvmxfV1RUlIYPHy5/f381atRIs2bNUq9evfSf//zH3ia1b13q1KmjxYsX2x///fffeuWVV1SnTh3Vq1dPAwcO1NmzZ+3zR44cqZdeeklfffWVGjVqpPr162vcuHGKi4uzt4mNjdVHH32kpk2bytfXVy1bttSCBQtkjFHLli311VdfOfTh6NGjfHsP4JaqVaumMmXKaO3atfZpa9euVZkyZVS1alX7tNjYWI0fP14BAQGqXr26evTooUOHDtnn7969W15eXtq5c6c6d+6sGjVq6KmnntLJkycdtrd+/Xp16tRJ1atX16OPPqrPPvtMN27ckCQ1b95ckjRo0CB5eXnZHwcHB2vgwIFq0KCB/P391aVLF/uxbZLmzZtr6tSpGj58uGrVqqW3335bvXv31rvvvuvQ7vLly/L19dXOnTstGD3cLwjYSLPjx4/rwIEDcnV1lSTNmDFDS5cu1bhx47RixQo9++yzeuONN/Trr79KkkJCQtSzZ0+5ublpzpw5Wrx4sbp06WIvfD///LMmTZqk1157TStXrtTQoUM1efJkLVmyxGG7EydO1PPPP6+lS5fqwQcf1Ouvv25fx9GjR/Xss8+qSpUqmjdvnr7//nsFBgYqPj5ebdq0UXx8vDZs2GBfV2hoqH755Rd16dIlM4YMwH1uyZIlcnV11Q8//KBx48YpPDxcffr0UbVq1bRw4ULNnDlToaGhevXVV+3LTJgwQXv27NG0adP01Vdf6ddff9Xhw4fTtd24uDg9//zzKlCggL777jv98MMPyp8/v/r16+fwoeLu3bsVHBysOXPm6IMPPtCSJUscauzw4cO1YsUKvfXWW1q1apXeffddFShQQE5OTurSpYtDoJekRYsWqW7dunrggQfubsAA3Pdurh2LFi1S586dHdpMmDBBa9assdelBx54QP369VNYWJhDu4kTJ2rkyJFatGiRXFxcNHr0aPu8vXv3asSIEerdu7dWrlypd999V4sXL9b06dMlSQsXLpQkvf/++9q2bZv9cVRUlJo2barZs2dryZIlaty4sV588UWdP3/eYduzZs2St7e3li5dqpdeekldu3bV8uXLHWrszz//rJIlS+qRRx6594HD/cMAtzBixAhTtWpVU7NmTePr62tsNpvx9vY2q1evNjExMaZGjRpm//79DsuMHj3aDB061BhjzCeffGKaN29uYmNjU11/ixYtzLJlyxymTZ061XTv3t0YY8yZM2eMzWYz8+fPt8//888/jc1mMydOnDDGGDN06FDz1FNP3fI5jB071vTr18/+eNasWebRRx81CQkJ6RgJALlZ8lqY9N/gwYNNz549TceOHR3aTp061fTt29dh2t9//21sNps5efKkiYyMND4+PmblypX2+VeuXDF+fn5m/Pjx9mk2m82sW7fOYT21a9c2ixYtMsYYs3TpUtO6dWuHWhYTE2P8/PzM1q1b7f0ODAw0N27csLcZMmSIefXVV40xxpw8edLYbDazffv2VJ/3hQsXTNWqVc3BgweNMcbExsaa+vXrm8WLF6dt4ADkKiNGjDADBw40oaGhxtfX15w9e9acPXvWVK9e3YSGhpqBAweaESNGmGvXrhkfHx/z888/25eNjY01jRo1Ml9++aUxxphdu3YZm81mduzYYW+zefNmY7PZTHR0tDHGmD59+pjp06c79GHp0qWmYcOG9sep1dLUtG3b1nz77bf2x4GBgeall15yaBMdHW3q1q1rVqxYYZ/Wvn17M2XKlLQMD3IRrsHGbdWvX1/vvPOOrl+/rtmzZ8vFxUWtW7fWn3/+qevXr6tv374O7ePi4uynAB09elR16tSxf+OdXFRUlIKDg/Xmm29qzJgx9uk3btxQoUKFHNp6eXnZ/12iRAlJiafkVKlSRUePHlWbNm1u2f9u3brpySefVEhIiEqVKqXFixerU6dOcnJySv9gAMi1kmphEnd3d73++uvy8fFxaHfs2DHt3r1b/v7+KdYRHBysmJgYxcXFqUaNGvbpnp6eqlSpUrr6c+zYMQUHB6tWrVoO02NiYhwuw3nooYfk4uJif1yiRAkdP35cUmKNdnFxUd26dVPdRqlSpdS0aVMtXLhQfn5+2rRpk2JjY29bcwGgaNGiatasmZYsWSJjjJo1a+Zwz4rg4GDFxcU51C9XV1f5+fkpKCjIYV2pHQOGhoaqbNmyOnbsmPbv32//xlqS4uPjFRMTo+vXr8vd3T3V/l27dk2fffaZNm/erEuXLik+Pl7R0dEpvsH29fV1eJw3b1498cQTWrRokR5//HEdPnxYf/75pz7//PN0jhDudwRs3Ja7u7v9VMD33ntPHTp00IIFC2Sz2SQlniZeqlQph2WSbgKRdHOL1ERFRUmS/t//+38OB5qS5OzseOVC8oCeFIwTEhLuuA0p8VqgpNN7GjZsqBMnTqQ4TQkA7iR5Lbx5enJRUVEKDAzUsGHDUrQtUaKEQ/i9HScnJxljHKYlXRqTtB0fHx99/PHHKZZNfiCbJ4/j23zy9d6pfkpS165dNXz4cI0ePVqLFy/W448/fsuDVgBI0qVLF/v1ymPHjr3r9SSvYTcfA0ZFRWnw4MFq1apViuXy5s17y3V++OGH2rFjh0aMGKGKFSsqX758GjJkiMP9KaSU9V1KrIkdO3bUhQsXtHjxYj3yyCMqV67cXT033L8I2EgzZ2dnvfDCC/rggw+0evVqubm56fz586pXr16q7b28vLRkyRLFxcWl+Ba7ePHiKlmypM6cOaMnnnjirvuUdAOMIUOG3LLNk08+qTlz5igkJEQNGjRQmTJl7np7AHA7Pj4+WrNmjcqVK5ci3EpShQoV5OrqqoMHD9rvqHv16lWdPn3a4ZvkokWL6uLFi/bHp0+f1vXr1x22s2rVKhUrVkwFCxa8q77abDYlJCRoz549atCgQaptmjZtKnd3d/3www/aunWr5s6de1fbApC7NG7cWHFxcXJyclKjRo0c5lWsWFGurq7av3+/PZzGxcXp999/V58+fdK8jWrVqunUqVO3vSeEq6ur4uPjHaYdOHBAnTp1UsuWLSUlfqN97ty5NG3Ty8tLvr6+mj9/vpYvX+5wFiaQhJucIV3atGkjZ2dnzZs3T3379tX777+vJUuWKDg4WIcPH9a3335rv4HOM888o8jISA0dOlS///67Tp8+raVLl9rvADlkyBB98cUX+uabb3Tq1Cn98ccfWrRokb7++us092fAgAH6/fff9c477+jYsWMKCgrS999/73DH3vbt2yskJETz58/n5mYAMtTTTz+tq1evaujQoTp06JCCg4O1detWjRo1SvHx8SpQoIC6dOmijz76SDt37tTx48c1cuTIFJetPPLII/ruu+905MgR/f777xo7dqzDB5Xt27dXkSJFNHDgQO3du1dnzpzR7t27NX78eF24cCFNfS1fvrw6deqk0aNHa/369fZ1rFy50t7GxcVFnTt31ieffKIHHngg1VPfAeBmLi4uWrVqlVauXOlwmYok5c+fXz169NCECRO0ZcsWnThxQmPGjFF0dLSefPLJNG9j0KBB+umnn/TZZ5/pzz//VFBQkFasWKGJEyfa25QrV047d+7UpUuXdPXqVUnSAw88oHXr1uno0aM6duyYXn/9dfu34mnRtWtXffHFF/ZfWwBuxjfYSJc8efKoZ8+emjlzpjZs2KCiRYtqxowZOnv2rAoVKqRq1arpxRdflCQVKVJEc+bM0UcffaRevXrJ2dlZVatWVe3atSUlFqh8+fLpq6++0oQJE5Q/f37ZbLZ0fXpZqVIlzZo1S59++ql9fX5+fmrXrp29TaFChdSqVSv98ssvatGihbUDAgDJlCpVSj/88IM+/vhjPf/884qNjVXZsmXVuHFj++Uvw4cPV1RUlAYOHKgCBQroueeeU2RkpMN6RowYodGjR+uZZ55RyZIlNXr0aIc7jbu7u2vu3Ln6+OOP9fLLL+vatWsqVaqUAgIC0vWN9jvvvKNPP/1U77zzjsLCwlS2bFm98MILDm2efPJJTZ8+nctrAKTL7WrRsGHDZIzR8OHDde3aNfn6+mrmzJkqXLhwmtffuHFjTZ8+XVOnTtWXX36pPHnyqHLlyuratau9zYgRI/TBBx9owYIFKlWqlDZu3KiRI0dq9OjReuqpp1SkSBH1799f165dS/N227Ztq/fee09t27a97anoyL2czM0XeQH3oT59+ujhhx/WW2+9ldVdAYAUevXqJW9vb7355ptZ3ZUU9u7dq2effVabN29W8eLFs7o7AJClzp49q5YtW2rhwoUpbnQJSJwijvvc1atXtW7dOv366696+umns7o7AJBjxMbG6sKFC5oyZYpat25NuAaQq8XFxenSpUv673//qxo1ahCucUucIo77WqdOnXT16lUNGzZMlStXzuruAECOsXz5cr355puqWrWqJkyYkNXdAYAstX//fvXu3VsPPvigJk+enNXdQTbGKeIAAAAAAFiAU8QBAAAAALAAARsAAAAAAAsQsAEAAAAAsAABGwAAAAAACxCwAQAAAACwAAEbAAAAAAALELABAAAAALAAARsAAAAAAAsQsAEAAAAAsMD/BzVpzOxsIZnGAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "data = rfm.drop(columns=['CustomerID'])\n",
        "plot_boxplot(data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NYHfX1XiplcZ"
      },
      "source": [
        "From the boxplots, we observe that Recency has a few outliers, while Frequency and Monetary have many. To understand their distribution better, we will perform further analysis using KDE plots."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:18.846306Z",
          "iopub.status.busy": "2025-01-16T16:53:18.845868Z",
          "iopub.status.idle": "2025-01-16T16:53:18.852830Z",
          "shell.execute_reply": "2025-01-16T16:53:18.851478Z",
          "shell.execute_reply.started": "2025-01-16T16:53:18.846265Z"
        },
        "id": "7lsSInY4TYI4"
      },
      "outputs": [],
      "source": [
        "# function to plot histplots\n",
        "def plot_distribution(data):\n",
        "  fig,axes = plt.subplots(nrows=1,ncols=3,figsize=(10,4))\n",
        "  for idx,feature in enumerate(data.columns):\n",
        "    sns.histplot(data=data,x=feature,color='#5a189a',ax=axes[idx],kde=True)\n",
        "    axes[idx].set_xlabel(f'{feature}',fontsize = 10)\n",
        "    axes[idx].set_title(f'Box Plot of {feature}')\n",
        "\n",
        "  plt.tight_layout()\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:18.853767Z",
          "iopub.status.busy": "2025-01-16T16:53:18.853512Z",
          "iopub.status.idle": "2025-01-16T16:53:26.037873Z",
          "shell.execute_reply": "2025-01-16T16:53:26.036606Z",
          "shell.execute_reply.started": "2025-01-16T16:53:18.853746Z"
        },
        "id": "kg--BDqZVppG",
        "outputId": "262d1767-20c0-405e-d024-b7ea632aa01a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_distribution(data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k2HCOKXrqK2t"
      },
      "source": [
        "Recency, Frequency, and Monetary are right-skewed, so applying a log-normal transformation can help reduce outliers and make the distribution more balanced."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:26.044211Z",
          "iopub.status.busy": "2025-01-16T16:53:26.043863Z",
          "iopub.status.idle": "2025-01-16T16:53:27.499429Z",
          "shell.execute_reply": "2025-01-16T16:53:27.498120Z",
          "shell.execute_reply.started": "2025-01-16T16:53:26.044185Z"
        },
        "id": "4E5be62SeR8-",
        "outputId": "2e79ba5c-f1b0-4084-c597-bfb03f42ccf0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 3 Axes>"
            ],
            "image/png": 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ByMnJGfBcXl4e69evH9VCDEc7b25u7qBzCUfi7rvvJicnB6vVyhtvvMGWLVv63aiVl5ejqiqPPPIIjzzyyKDnaGpqIiUlhfLy8iFvwsrKyigqKmLRokVHPNeh0tLSBpSJiYnx7ivZ3NxMR0cHU6dOPer7WiwWTj31VN59911uuukmwF1RpqSkeBN+IbQgdd/Iz+uLuu/wz/1wh69sXlpaCuCdEzkYq9WKzWajp6eHSZMmDXg+JydnVDfWzc3NtLe388orr/DKK68cscyhDk344eCUIU/d6bmhHWrF9sWLF5OUlMTbb7/NokWLcLlcvPvuuyxbtmzA91EIf5K6cuTn9UVdCe6h7YsWLeLdd9+lp6cHp9PJmWeeOWjZkfwMYHj3eSOt/wZTXV3No48+yqeffkpbW1u/5zxrdnh4toE73IoVK3jggQd4++23ue6667BarXz22WdcddVVI/ruBANJuicQnU7HggULeP755ykrKxsyqRrM+vXrAffKs2VlZSPaOzE/P3/Y2ykEs0P/iC1fvpxLL72U//iP/+CDDz4gKioKl8sFuFsoTzrppEHPMZK54y6Xi/z8/CPuk3t4JXekeVXqYYtxDMf555/PBx98wLfffkt+fj6ffvopl1xyiaxILAKK1H2B4dBeEThY59xyyy1H3IomMjJywIKQR3Okm7TDF3Hy1MPnnXceF1xwwaCvOXSBNuCI9dpI686wsDDOPfdcXn31Ve655x6+/fZb6uvrZYSQ0JzUlePrnHPO4a677qKxsZGlS5f6bCuzoe7zRlP/Hc7pdPKTn/yEtrY2rr76anJzc4mMjKSuro7bbrvN+x4eRqNx0Do0JiaGU089lXfeeYfrrruODz74AJvNFpL1oSTdE4znxqOrqwtwL+awceNGOjo6+rWgFRcXe5/32Lt3L48//jgXXnghe/fu5c477+Sdd94Z0TYJI+HpVSgpKRnwXHFxMXFxcd7Wy5G0hh163sN7h0tKSgb0ZoxFWFgYv/nNb7jiiit46aWXuOaaa7x/gAwGw5B/XLKzs9m/f/+QZfbu3cuiRYt80ioYHx9PdHT0kO8LcNJJJxEfH88777zDMcccQ3d3NytXrhxzDEL4mtR941v3DYenLoyOjj5qXRgfH094ePigK4Uf/hnFxMQAB3ufPTw9V4ee09MI6qubfM/1FBYWDnnOlStX8ve//51PP/2UdevWER8fP+qh8kL4ktSV41dXnn766fzud79j27ZtPPTQQ0csN5KfwXCMpP470udWWFhIaWkpf/jDHzj//PO9xw9dkX24Vq5cyS9/+Uu2b9/uXWRyNA0+gU66oyYQu93Ohg0bMBgM3iEpS5cuxel08tJLL/Ur++yzz6IoCkuXLvW+9vbbbyc5OZnf/va3PPjggzQ2NvLAAw/4Ld7k5GSmT5/OP//5z343UIWFhWzYsMG74iNAREQE4B6KOJRZs2aRkJDAyy+/3K8H5YsvvqCoqIhTTjnFdxcBLFiwgDlz5vDcc8/R29tLQkIC8+fP55VXXqG+vn5A+UOH9Jxxxhns3buXjz76aEA5T4vlWWedRV1dHa+++uqAMj09Pd4/nMOl0+lYvnw5n332GTt27Dji+4J7uNDZZ5/N+++/z5tvvkl+fj7Tpk0b0fsJ4W9S97mNd903nHiys7P5+9//Tmdn54DnPXVhWFgYS5Ys4eOPP+6XPBcVFXl71Tyio6OJi4vj66+/7nf8//7v//o9DgsL48wzz+TDDz+ksLDwiO89EjNnziQzM5Pnn39+QNJ/eG/4tGnTKCgo4PXXX2ft2rWcffbZRx1GK8R4kLrSbbzqyqioKO655x6uv/56TjvttCOWG+7PYLhGUv95PrfD6zRPr/WhdZuqqjz//PMjigXc1xcXF8dTTz3Fli1bQrKXG6SnO6StW7fO2wrW3NzMO++8Q2lpKddcc423pey0005jwYIFPPTQQ1RVVVFQUMCGDRv45JNPuPLKK73DnJ988kn27NnDs88+S3R0NNOmTeNXv/oVDz/8MN/73vf6VWy+dMstt7Bq1Sp++MMfctFFF3m3gjCbzVx33XXecjNnzgTgoYceYsWKFRgMBk499dRB5/EYDAZuvvlmbr/9di677DLOPvts71YQGRkZA7aJ8YWf/exn3Hjjjbz55ptccskl/O53v+PSSy/l3HPP5Qc/+AFZWVk0Njaybds2amtrefvtt72v+/DDD7nxxhv5/ve/z8yZM2lra+PTTz/l3nvvZdq0aaxcuZL333+f3/3ud2zatIljjz0Wp9NJcXExH3zwAU899dRR51kO5je/+Q0bNmzg8ssv925D1tDQwAcffMD//d//9RsCdf755/PCCy+wadMmbr75Zp9+bkKMhtR9gVP3HY1Op+P+++9n1apVnHPOOVx44YWkpKRQV1fHpk2biI6O5i9/+QsA119/Pf/+97/58Y9/zCWXXILT6eTFF19kypQp7Nu3r995L774Yv72t7/x29/+llmzZvH1118P2hP2H//xH2zatIkf/OAHXHzxxUyZMoW2tjZ27drFxo0b2bx584iv55577uEXv/gF559/PhdeeCFJSUkUFxdz4MABnn766X7lzz//fP7whz8AhOxNpghsUldqX1ceaXj3oYb7MxiJ4dZ/2dnZWCwWXn75ZaKiooiMjGTOnDnk5uaSnZ3NH/7wB+rq6oiOjubDDz8ckJwPh8Fg4Oyzz+bFF18kLCyMs88+e8TnCAaSdIewQ/fIM5lM5Obmcs899/CjH/3Ie1yn0/Hkk0/y6KOPsmbNGt58800yMjK45ZZb+OlPfwrArl27+Otf/8pll13Wb4Gsa665hk8++YQ777yT9957z2dzUQ514okn8tRTT/Hoo4/y6KOPotfrOeGEE/jP//zPfvOE5syZw4033sjLL7/Mv//9b1wuF5988skRF8+48MILCQ8PZ/Xq1fzpT38iMjKS5cuX85//+Z9+uY4zzjjD26Pzgx/8gClTpvDGG2/w5z//mbfeeovW1lbi4+OZMWMGv/rVr7yvi4qK4qWXXuKxxx7jo48+4q233iIhIYFFixZ593LV6XQ8/vjjPPvss/zrX//io48+IiIigszMTC6//PJBFwIZSkpKCq+++iqPPPII77zzDh0dHaSkpLB06dIB8zJnzZrF1KlTKSoqkhtHERCk7gucum8oCxYs4JVXXuGJJ57gxRdfpKuri6SkJObMmcMPf/hDb7lp06bx9NNP8+CDD/Loo4+SmprK9ddfT0NDw4Ck+1e/+hXNzc18+OGHvP/++yxdupSnnnpqwDDRxMREXnvtNR5//HE++ugj/vGPfxAbG8uUKVNG3YB40kkn8dxzz/H444/z97//HVVVycrK4gc/+MGAsueeey5/+tOfyMrK8u5HLsR4kroyOOrK4fwMRmq49Z/BYOD3v/89//u//8s999yDw+HgwQcf5MILL+Qvf/kL999/P3/9618xmUycfvrp/PjHPx7VNMOVK1fy4osvsmjRIpKTk0d1TYFOUUezepIQQhzi/PPPJyYmhueee07rUIQQE8hjjz3Gn//85wGJdzBobm7mpJNO4pe//GW/xlYhhJho9u7dy8qVKwfMEQ8lMqdbCDEmO3bsYM+ePSFbSQohhD+89dZbOJ1OWXxSCDHhvfrqq0RGRg65TW4wk+HlQohRKSwsZNeuXfz9738nKSmJFStWaB2SEEIEvI0bN1JUVMRf/vIXli9fPmD/ciGEmCg+/fRTDhw4wKuvvsqPf/zjEe+pHkwk6RZCjMqHH37I448/Tk5ODv/7v/+LyWTSOiQhhAh4TzzxBFu3bmXevHncddddWocjhBCauf/++737lF9//fVah+NXMqdbCCGEEEIIIYTwE5nTLYQQQgghhBBC+Ikk3UIIIYQQQgghhJ9I0i2EEEIIIYQQQviJLKQ2QqoLHF1aR6EtRQdhEeDsdn8eYuzkM/WtoT5PfaS7jPCdYKwbQ/X3Tq4reATiNUn96Fta1I2B+L0arVC5llC5DgidaxnpdYy1bpSke4QcXVD9vtZRaMsYC2mnQ8MGsLVqHU1okM/Ut4b6PNPPAkP0eEcV2oKxbgzV3zu5ruARiNck9aNvaVE3BuL3arRC5VpC5TogdK5lpNcx1rpR2jKFEEIIIYQQQgg/kaRbCCGEEEIIIYTwE0m6hRBCCCGEEEIIP5GkWwghhBBCCCGE8BNJuoUQQgghhBBCCD+RpFsIIYQQQgghhPATSbqFEEIIIYQQQgg/kaRbCCGEEEIIIYTwE0m6hRBCCCGEEEIIP5GkWwghhBBCCCGE8BNJuoUQQgghhBBCCD/Rax2AEEIIIYQQQviEAoaY/ofsbYCqSTRCAJJ0CyE0lLLMiT5KGbKco1Ol7pOwcYhIiPFlnNRLr93W75jJYMRWZtIoIiGECG6GGNhXVExbTQ8AMWnhFOTlYm/VNi4xsUnSLYTQjD5K4blrtw5Z7sq/zBuHaIQYf712G588Vdjv2LKr81GQpFsIIUarraaHpvLugwfytItFCJA53UIIIYQQQgghhN9I0i2EEAHuiy++4LLLLmPhwoXMmjWLZcuW8eCDD2K1WvuV+/TTTznvvPOYPXs2Z555Jm+88caAc9lsNv7whz+wePFi5s6dy09+8hOKi4vH61KEEEIIISacgBpe/sUXX7B69WoOHDhAR0cHKSkpLF++nOuuuw6z2ewt9+mnn/Lwww9TUlJCeno611xzDd///vf7nctms/HQQw/x9ttv09nZybx587jrrrvIzc0d78sSwudkLvTE0traypw5c7j88suJjY1l//79PPbYY+zfv5+///3vAHz99ddcd911XHTRRdxxxx189dVX/Pa3vyUqKorvfe973nPdf//9rFmzhttuu42UlBT+8pe/cNVVV/Hee+/1q2eFEEIIIYRvBFTSLTeWQgyPzIWeWFauXNnv8YIFCzAajdx1113U1dWRkpLCk08+yZw5c7jvvvsAWLhwIRUVFTz66KPeurG2tpbXX3+d3/3ud1x00UUAzJ49m1NPPZWXX36ZVatWje+FCSGEEEJMAAGVdMuNpRBCDE9sbCwAdrsdm83Gpk2buPnmm/uVWbFiBe+++y6VlZVkZmayfv16XC5XvwbK2NhYFi9ezLp166RuFEIEFRkhKYQIFgE/p3uwG8tDbxjBfWNZVFREZWUlwJA3lkIIEYycTie9vb3s2rWLxx9/nNNOO43MzEzKy8ux2+0Dbg7z8tzLtXrmbBcXF5OQkEBMTMyAcjKvWwgRbDwjJO+9916efvppfvKTn/DPf/6TG2+80VvGM0Jy7ty5rF69mrPOOovf/va3fPDBB/3Odf/99/Paa6/x61//msceewybzcZVV101YO0MIYQYjYDq6fZwOp04HA4OHDjQ78bywIEDQ95YZmZmHvXG8vXXXx+36xBCCF869dRTqaurA+Ckk07if/7nfwBoa2sDwGKx9Cvveex5vr29fdDpNRaLxVtmtBQdGGPHdIpxpzf3/68W7AYIC+9/TGcAQ+zozxkI1+UPoXhdgXhNSsB3xxwkIySFEMEiIJNuubEMbIF4kxDsRvyZKgNv1I9ULqC/r366jqE+z2C6qTzU3/72N7q7uzlw4ABPPvkk1157Lc8884zWYQEQFgFpp2sdxegkLdTuvRsqwHzY/rERaZA0f+zn1vK6/CkUrysUr0krMvVGCBGIAjLplhvL4CA3Cb433M/U6Rh4oz4YRRfY31d/X0eofUenTZsGwLx585g9ezYrV67ko48+YsqUKQADhkG2t7cDeEf9WCwWOjo6Bpy3vb19wMigkXJ2Q8OGMZ1i3OnN7u9Iw1fg0GgEqT0GrEX9j3XXQM3e0Z8zEK7LH0LxugLxmpIWgz5S6yhGRkZICiECXUAm3XJjGdgC8SYh2I30M006eeCN+mBUF9R8NPb4/MVf1zHU5xmMN5WHKygowGAwUF5ezmmnnYbBYKC4uJiTTjrJW8YzT9tzw5mbm0tjYyNtbW396sLi4uIxLxakusDWOqZTaMZh1S52NRKcPf2Puey+iUfL6/KnULyuQLom1aV1BCMnIyT7C6URiaO5Fr0ZdMaDI+l0RjCYYeiNVv1nov9MAtFIr2OsoyQDMuk+lNxYBq5AukkIFcP+TNWBN+pHKhfQPyM/X8eRPs9gvKk83HfffYfdbiczMxOj0ciCBQv48MMPufLKK71l1qxZQ15eHpmZmQAsWbIEnU7H2rVrufjiiwH3Tef69ev55S9/qcl1CCHEWMkIycGF0mivkV5LZCHYTX3/nxw4n0WgxOELoXIt43UdAZ90y42lmEji5jvRhw/dFquEjUMwImBcd911zJo1i4KCAsLDw9m7dy9PP/00BQUFLF++HIBf/OIXXHHFFdxzzz2cddZZbNq0iXfffZeHHnrIe57U1FQuuugi/vjHP6LT6UhJSeGvf/0rZrOZH/3oR1pdnhBCjImMkOwvlEYkjuZa9GboqgCre1MjDL3afxYT/WcSiEY8ynSMoyQDKumWG0sx0ekjFJ77+dYhy121et44RCMCxZw5c1izZg1/+9vfUFWVjIwMLr74Yn72s59hNBoBOP7443nsscd4+OGHef3110lPT+f+++/nrLPO6neuO++8k6ioKP7nf/6Hzs5Ojj32WJ555plBh1YKIUSwkRGSB4XSiMSRXIsKuGwHR9K5bGC3gn2Yr/enifozCWTDvY6xjpIMqKRbbiyFP6Usc6KPGt6MHkenSt0n0p0sAsM111zDNddcM2S5ZcuWsWzZsqOWMRqN3Hrrrdx6662+Ck8IIQKGjJAUY6aA4bABDvY23Nm8EKMUUEm33FgKf9JHKTx37dC9yABX/kV6koUQQohAJiMkhT8YYmBfUTFtNe6u8pi0cArycgOip1wEr4BKuoUQQggxOOOkXnrttn7HTAYjtjKTRhEJoS0ZISn8pa2mh6by7oMHhrG9qRBHI0m3EEIIEQR67TY+eaqw37FlV+ejIEm3mJhkhKQQIlhI0i2ECHiqopJx3tArWMhcfCGEEEIIEWgk6RZCBDydTuHZVUPPx5e5+EIIIYQQItDotA5ACCGEEEIIIYQIVdLTLUQAampoo6ykjvbWTvQGPckpsUyekorRaNA6NCGEEEIIIcQISNItRABxuVQKd1dQWd7gPWbrtVNZ3kBDfSvzTphKZFS4hhEKIYQQQgghRkKSbiEChKqqPHvHBm/CnTUpmbTMBHp7bOzfU0lXVy9bt+xn/onTNY5UCCGEECI4KDrQH7bzm70NUDUJR0xQknQLESA+WL2TL/6xD4AZcyaTmh4PQLQ5ArMlkm82FdLd1cveXeWo6vFahiqEEEIIERQsyeHs2VVMS3kPADFp4RTk5WJv1TYuMbHIQmpCBICaymb+cf9mAKZOy/Qm3B5Gk4FZx+SgKAoNda1s/7xSizCFEEIIIYJOW20PTeXdNJV301bTo3U4YgKSpFsIjTmdLl59ah1Ou4u5y7PJnJQ0aDlzTCRZfc+9fP9mVFXGRQkhhBBCCBHoJOkWQmMbPt9BRXEDkRYjVz1wIoqiHLHspLxU9IYwqgpbaahrHb8ghRBCCCGEEKMiSbcQGmppsvLxmm8AuOzehcSlRB21vMGgJzPb3dtdXlInvd1ChLDeXjsul0vrMIQQQggxRrKQmhAa+uCdzTjsTvKmp7H0h/k47UMn0ZnZSVRVNtDe1kV7aycxcdHjEKkQYrwUba9lzVNrqa1uxmjUM2POZE4/+3hi07WOTAghhBCjIT3dQmikrLiOHd8Woyiw8seLjjqs/FBGk4GFK3MBqKps9GeIQohxVlPVxDP/73Nqq5sBsNkcbPv6AI/+/g32fF2lbXBCCCGEGBVJuoXQgMul8t5bGwE4bmEB6dkJI3r9KZcUAFBf24rD4fR5fEKI8ddh7WbfrnJUFY5dkM/t9/+YX/xmJdk5KfT22nnpj+uprW7SOkwhhBBCjJAk3UJoYPu3RVSWNWA0GTj97JHvuT3luGQio0y4nC7qa1v8EKEQYjypqsreXeW4XCpT56Zx4SVLMVsiyZqczKobzuG4BfmoLpXd28uoqZLEWwghhAgmknQLMc5sNgcfvuPek/uU04/BbIkc8TkURSE13d07Xl/b6svwhBAaaGpop721E51O4cJfzkenOzjdJCxMxwWXLOWE0/MA2LNDEm8hhBAimEjSLcQ4W//pdtpaOomNi2bxKbNHfZ7k1FgAWprasdsdPopOCKGFkqIaADInJWOJjxjwvE6ncN6q48nISgQk8RZCCH9oa+lg7Zvf8O36ItpaOrQOR4QQWb1ciHHU2tLBFx9tA+B7K+djMI7+VzAyKpyo6HA6O3porG8jLWNk88KFEIGhva0Ta1sXiqKQnZN8xHI6nUL+jCwAqioa2bOjjG3r0pg3ZfSNd0IIMRG0N3exe0cZToeTAjIGLVNRVs/zf1tLp7W770gtU6dlkpA9afwCFSFLerqFGEcf/GszdruTyXmpzJ6XO+bzJafGAdAgQ8yFCFpVFe5dCJJTYzEaDUctqyjuxDu9r8f79T9vYuvm/X6PUQghglVPl53P395FbVUzDXVtrP9gN2+/tBGXy+Uts3NbCasffZdOazdpWfFk5bnr2AP7qmhr7tQqdBFCJOkWYpyUFNWw/dsiFEXhnO+fOOwtwo4mKSUWgKbGdlnFXIggZOt1UF/jXgwxIytpWK9RFIWCGVmkZyaiulRef+lzNm/Y488whRAiaG36aD/dHTYMRj3pme5Rgf/+cCfP//VDykvq+Oi9r/nHMx/jsDspmJnFL+88lwWn5ZOYHIOqqny7oVjjKxChQIaXCzEO7HYH/3plPQAnnFjgrfTHKio6nMgoE12dvTTWt5GaHu+T8wohxkfhtzU4nS7CI4zExEUN+3WKolAwM4vs2XF89f5+/vnKemw2B0tOlaHmQgjh4XA4Kd5VB8DsebnExkWTlZ/A1+sOULinksI9ld6yi5fP4NwfL8IUq0Ppm87T1NBOY007FcUNpMYPr2FUiMFIT7cQ4+DjNd9QX9uK2RLBGeec4LPzKori7e1uqGv12XmFEONjx4ZywD1VZKSjXxRF4ZyfHsvSZXMAWPPWV3z24VafxyiEEMGqqaENp8NFdEw4MbHuhs3sKUn8+v9dSMHMLKLNEWRMTuTUC2eSnhvHNxv3UlZRic4A4eFGktPc0/j+vXanlpchQoD0dAvhZ2XFtaz/dDsA5//wJCKjwn16/sTkWMqK62hpsuJyqf22GhJCBC6bzcG+b6sBSOlbn2GkFEXhzPPmYzQZ+HjNN3z03tc4dQ5+uvx4QOoCIcTE5tlWNWtKYr+GzZSMOK78+fcAMMTC5vW7aSp3L6AWk3rwPi1rUhJ11c3s2FLCuSsXEx5hHLfYRWiRnm4h/Kirs4fXXvgcVYVj509l+mzfr4BpiYnEYAjD4XDS3hYci33Y7Q62fX2Alx/YTHlpnWx5Jiakon1V2G1OwsONRFsGbhM2XIqicNr3jmXF+QsA+PSdbbz7+He+ClMIIYKSqqo0N7UDkJk7uul3Zksk5tgIHHYnu3eU+jA6MdFIT7cQfuKwO/nHM5/Q3GQlLj6asy9c5Jf3URSF+EQLdTUtNDW0ExsX7Zf38ZX62hZeWL2WpoZ277Gyolpmzc0lLsGsYWRCjK+9u9xDyxOTY3yysOKS0+aAorDmra94+b+2oK6K5JjZ+WM+rxBCBKOuzh6cDhd6g47YpGiarD0jPoeiKGTlJbL7mwq2f1vMsfOlThWjIz3dQviB3e7gH89+QlFhNUajnstWnUFEpMlv7xefaAGgubHNb+/hC431rfz14XdoamjHbIlg2RXTiYoOx253sn1rER3evTGFCG0ul8rene6kOyEpxmfnXXLqbE5Z4Z7j/drT6ygqrPbZuYUQIpi0t3UBkJhmGdPUu2zP9mF7K+nu6vVJbGLikZ5uIQ5j67Xz+cv72Pl2Ix3t3ejCdCQkmklKiSVzUtKQCx41NbTz6gufUVFaj14fxmWrziAtwzerlR9JQl/SbW3vprfXjsl09L1+tdDbY+O5v35Id1cvmdlJXPHzMyn4sQlHjYltXx+graWDvTvLOG5hgU96/YQIZLXVzVjbuzCG64lL6D86xRgHNqwDXqOLcg04NljZ01dNp0tnZfO7Jbz83Kdcf8sFWGKGvzK6EEKEgvZW95S7pAzLmM5jjo0kOS2W+ppW9u+tZM6xeb4IT0wwknQL0cfhcFK8v5rqikbWf6YesVxkVDg5U1LJmZJG1qRkLLFRoKo01Lex67sSvtlUiMPuJDzCyI9/tpy8/Ay/x240GTBbIrG2d9Hc2O73JH803v/XJu/w98uvOYNocwTgIixMx6y5OXy1bhftbV3U1TSTmh548QvhS0WFVQDkzEhGp+s/6MzWY+eTp0oGvGbZ9TkDjg1WNiwcLrrlOCq+a6Omopl/PPMJV19/DmFhMrhNCDFxeHq6x5p0A0ybm0V9TSv7dldI0i1GRZJuIYDOjm6++6aInm4bAJNmJjDnlEwS0qOx25zUlbZTua+F4m31dHX2sOu7UnZ9V3rE8+Xlp3PBj07yDvseD/GJFqztXTQ1BF7SXVJUw+YNewH4/o9PxmyJ7Pe8yWRgUm4qxfurKS2qJSUtXnq7RUgr2udOuvNmp9BT5/vzG8P1XHH9ch6++y3Kiuv4eM03nHmu77YrFEKIQOZyubxT1pLSLTBwoNCITD8mm3Xv76Bwd4XsFCNGRZJuMeFZ27vYumW/u3c63EjBzGx+89ZSnrtmG51FnjnG4aTHppG6NAVrWxctLR20NnfgCnPQVt+NokBMbBST81I5dkEBuVPTxj1pTEiyUFZcS3NTO6qqapq0pixzoo9yv7/LpfK3lV8BcPKP8ll6cyqev35K2MHXZE5Koqy4lq7OXlqarOPaYCHEeHI4nJQU1QLupHtXXb1f3icpNYYLL1nKP575hHUff8f0Wdlk56T45b2EECKQdLT1oKoqYWE6omPC6WgZ21zsnPxUTOEGOjt6qCpvIGtyso8iFRNFQCXd77//Pm+//Ta7du2ivb2dSZMmcfnll/P973/fm0BcfvnlbN68ecBr16xZQ17eweEeVquVBx98kI8//hi73c5JJ53EnXfeSXKy/JKIg3q6bXz3TREOuxNLTCRzjpuC0ag/YsKq0+mIiYsmJi4acuHKv8yj6m2d5kkugCUmijC9DofdibW9S9M5nPooheeu3QpAXU0zpTsaCQvT4awzeY8DXLV63sHX6MNIzUigqryByvIGSboPIXVjaKkorcducxBljiA5O4Zd+CfpBpg9L5c9O8rY9vUBXn/pC66/5UIMxoD60y+EED7X3uIeWh4ZHe6T+7MwvY6p0zLZua2EvbvKJekWIxZQf3mfffZZMjIyuO2224iLi+PLL7/krrvuora2luuuu85b7thjj+XWW2/t99rMzMx+j2+66SYOHDjAPffcg8lk4uGHH2bVqlW88cYb6PUBddlCIy6Xyq7vSrD12omKDueY46dgMIzuu6F1wg2g0ynExZtprG+judEaEAsnqapKaV+PXnZOCsYhFnjLyEqkqryBpoZ2HHYnekPYUctPFFI3hpYDnqHlU9PHZYjiuRedSPH+ahrr2/jova9ZccFCv7+nEEJoqb3VPVIxKircZ+csmJnNzm0l7NtVwelnH++z84qJIaDusJ588kni4w9uXr9o0SJaW1t55pln+OUvf+ldbMZisTB37twjnmfr1q2sX7+ep59+miVLlgCQk5PDihUrWLt2LStWrPDrdYjgUFZSS1trJ2F6HXOOzRt1wj0chw63HpQCTgcoY1znKD7R4k66m9qZnJc6tpP5QFNDG50dPYSF6ciclDRk+WhzBJFRJro6e2lsaCM1PX7I10wEUjeGFs82XlMK0sfl/SIiTVzwo5N47q8fsuHzncw9fgrpWYnj8t5C+JOMAhJHYm092NPtK/nT3Y3Y1ZWNtLd1YYmJHOIVQhwUUEn3oTeVHtOnT+fVV1+lq6uL6OjoQV410Lp167BYLCxevNh7LDc3l+nTp7Nu3Tq5sRR0dfZQ1tcDWzAjy697aEP/4daDCQsHcx6cf9O8I5YZjvgEMwBtLZ04Hc4xnWusDu3lzshOGnajRnJqHKVFtTTUtUjS3UfqxtDR022jssw9nHw8djbwKJiZzexjc9nxbTH/fHU91/76PG9jjXFSL712W7/yJoMRW5l/60UhxkpGAYkjaW/xXU+3ogO9GeKzI8nMSaSypJH9eys4bkHBmM8tJo6Ar0W++eYbUlJS+t1Ubt68mblz5+J0OjnmmGO48cYbOeGEg6uyFhcXk5OTM2DIb25uLsXFxeMWuwhcB/ZW4XKpxCWYSUkLncQuItJEeISRnm4bLS0dmsbS0txBe1sXOp1C9gjmPiWlxFJaVEtTQzsul2vAdkrCTerG4FRaVIPLpRKfaCEuwYw6yH7c/nL2BQsp3FVBZVkDW77cy4IlMwDotdv45KnCfmWXXZ2PgiTdIrDJKCAxGJdLxeoZXu6Dnm5Lcjh7dhXTUt6DJS4CSmB/oSTdYmQCOun++uuvWbNmTb/WyRNOOIGVK1cyefJk6uvrefrpp/nJT37CCy+8wLx57l7C9vZ2zGbzgPPFxMSwc+fOMcWk6MAYO6ZTBD29uf9/g4bi7lFuaeqgsaENRYHpc7PQRww+7DtsOPW0Mszvg3L08+kOubcd1vsesZxCQoqFqtJGWlutw4/P1xSoLHfvg5QxKZGImCPP5T78OmJMERhNemy9DqydncQnmQctd6T3NcYO/R0d6zB+rUnd6Bta1GXlle7RH1NmpmGMBbth4Hdb0Q3+fR/s+GDHPPXJ4deVGBvFWRcfzz9f3Mjad7dw7Mm5RJnDB41BZwBD7Iguze+C9m/PUQTiNQVT/SijgMRg2po7cTpdKIpCeIRvGg/bantoKu8mwugeUl64sxKn00VYWBD9wghNBWzSXVtby69//WsWLFjAFVdc4T1+ww039Ct3yimncM455/DEE0+wevVqv8cVFgFpp/v9bYJCUpCtxeN0uIdwb/vGPZ9y8uxEUuceOZMz5x3xKS9FN7zvg+e9h2Os5TKdZqpKG2lpbR92fL5WW2ylsa4dgOmnJhEdd+SyA69DISXXQsWeZjocViblmY9QbqDDrzfYvqPDIXWj743n96TqUXfSPe/CVNJOh4aKgd9tXcTg3/fBjh+pLAx+Xd8/bQbfbt1H+a5mNnzzDVf+1+JBY4hIg6T5w7yocRaKv9eheE1akVFAornRPYIoPMLo88UqLTGRGE16errtlJfUkTMlzafnF6ErIJPu9vZ2Vq1aRWxsLI899thRh5dGRkZy8skn8+GHH3qPWSwWamtrB5Rta2sjJiZmTLE5u6Fhw5hOEfT0ZvcNQsNX4Bi/kZFjlnQyVH3TRUNFB4oCmampWIuOXP5oz3moLqj5aHjvfbTz6UwQlTn89z1auQinO0m1NvXQVNWJs3D8u1A++2ovAPFJZtTmcKzNRy472HVYIsxAM7WF7WSnpR+x3OE8P4+hvqNJi0EfhOufSN3oW+Ndl9ltDoq3NgIQb0+l5iOwxwz8bru6B/++D3Z8sGOenu7Br0vHWecu5K+71vDxs3s4ZuoM4mfoB5yjuwZq9o7o8vwuWP/2HE0gXlOw1o8go4A8AnEExWiN5lra9rt/mSKijISFg2IAnfHgiB6dEQxm8KTjenP/5w8v3/+xQtrkOMr2NVBcUcHMU91Jt6MDUH17HYEqVK5lpNcx1lFAAZd09/T08POf/xyr1corr7wyaCU4lNzcXDZu3Dhg7+SSkhLy8/PHFJ/qAlvrmE4RMhzWIPssVCjd5x7ynJwah0FnxNlz5OJHe+7Qcw7rM1CHeb7hvu9RyunQY4mJpL2ti13/riLXNG14J/QRh93Julfc80MzMpOGvJ7Bno+xeBaE66LX6jxiuQEO+3kc6TuquoZxrgAjdaP/jFddVry/HqfThdkSSbTBjK0V1MiB323VNfj3fbDjRyoL0K2z4jokefIsjjYpPYPpsyaxZ2cZb7+wiSvuWzzgHC574P48g+5vzzAE0jUFY/0IMgpoMKE0gmIk19KzzZ10W9JMmPMgIhUcRjD3/dmLTB54vshCsPc1WB5e/vDHBQnJlO1r4Nuv9pO9MBpzfDgLzsn1+XUEulC5lvG6joBKuh0OBzfddBPFxcW89NJLpKSkDPmarq4uPv/8c2bPnu09tnTpUp544gk2btzIiSeeCLhvKnfv3s3VV1/tt/hFYGuu6aS+tgWArMlDf7eCWXyihfa2Lnb+u4rc5eObdO/8rgRrUw8mk4GEpNH1noaHG70LwrW3dfo4wuAjdWNoKC12jzKYnJc6YBirr/V02fn0mZJ+yfRZ/5mPmu5epfx718xk36/L2be7gpL9NX6NRQh/k1FA/QXiCIrRGs21VHzrnt6mdxixFkF3LPS0gLVvpoCht//59GboqgBrpfvx4eUPf5w6zb2WQEtNFwc+bSN9KjQkHj2+if4zCUQjvY6xjgIKqKT73nvv5bPPPuO2226jo6ODbdu2eZ+bMWMG27dv56mnnuL0008nIyOD+vp6nnnmGRoaGnjkkUe8ZefNm8eSJUu44447uPXWWzGZTDz00EMUFBRwxhlnaHBlIhB89OwuVBVi46JDfm/FuAQzpUW17NpQzTmnqT6f03Q0m9bvBiA9K3FM7xsTG0VPt422Vkm6pW4MDZ5tCifnpWry/rYeO588VeJ9nJ6RSGV5Ax+9uJPczGy/NwQI4Q8yCujIAmkExViN5Fqaqt0ZlMngHtGo2sFlOzgqyGUDuxXsfedT6f/84eUPf2wyGklIMdNUZ6Whsp3USTH9zuer6wh0oXItw72OsY4CCqike8MGd1Pg73//+wHPffLJJyQlJWG323nooYdobW0lIiKCefPmce+99zJnzpx+5R9++GEefPBB7r77bhwOB0uWLOHOO++UvRYnKLvdwef/2AdAVs7wt68KVjGxUYSF6bA29VBb1UR6VuK4vG9tdTNlxXXowhTSMxPGdK6Y2Gjqalok6UbqxmAz2L7Xep2eshL3/tyTcrVJug83KTeF6spGynY1ERcRR3yiReuQhBgRGQUkBtPc4N4y1Vcrlw8mbVIsTXVWmhrbgCy/vY8IHQF1l/Xpp58OWebpp58e1rnMZjMPPPAADzzwwFjDEiFg785yutptmMINJI5yyHMw0el0xMWbaWxo48C+qnFLujet3wPAsWdMwqQax3SumNgoANpbO3G5jrI6yQQgdWNwGWzf62mnJWLrtWMKN5CafpTl/MeRKdxIRlYSFWX1FO+vIS7BLL3dIqjIKCBxOIfdSXtfY31ExNjuQ44mbVIcOzdX0NxoxeUM0oUQxLgKqKRbCH/Zunk/AKnp8RPmpjIu0Z10799bydLlx/j9/Xp77Wzb4v6cT7tsOl+/UDem80WZI9CF6XA4nNQUtfkiRCE0U7anAXD3ch9tvul4y85Noba2ifa2Tpob20e9DoMQWpBRQOJwrS1WVBXC9DoMRv/97OKSozEY9dhtDu8WqUIcjdQkIuR1WLsp3FMBuJPuiSI+wT1UtKy4DpvNgdGPf3wAdnxbRG+vnYQkCzNOTBtz0q3TKZjNEbS1dlK2s9FHUQqhjdK+pHtybmAt4mgyGZh/Vg4b/nmA8tJ6SbpFUJFRQOJwzU3u+dxR5nC/drIoikJCooXa6mZqylv89j4idAROc7sQfrL92yJcLpWcYxKJio7QOpxxExllIj49CofDSWmR/1cn3vKle878CYum+ewPndniXvCudEeTT84nhBZUVaVsr7vhaJJGi6gdzaKVUwBoabLSYe3WOBohhBi9Fm/SfeT53IrOvXK1Idb9T2/m4KbdI5CQ5O7cqK1oHfmLxYQjSbcIeZ6h5YsvnKpxJONLURRmnZQBwIF9VX59r5qqJirK6tHpFObN993nHG1xN5KU7ZKkWwSv7q5eOlp7CAvTkZmdpHU4A8QlR5GUEgtAZVmDtsEIIcQYtBzS030kluRw9uwqZvP63Wxev5uyikp0hpG/l2dEYXtLFy2NHaOKV0wcMrxchLT62haqKhrR6RQWnpvLmxv3+PT8qqKScd7QC2goYT5922GbtSSDda8UcmCvf5PuLRv3AjB99uS+3mnfLCri6eku29VE6kL/720shD+0trhvxjInJWEwBOaf3cxJSTTUtVJb3URefrrW4QghxKg0D6OnG6CttoemcvfInpjUIyfoR2Mw6omJjaKttZO92ys4/tjpozqPmBgC86+/ED6y67tSAKZMy8QcP7pK9Wh0OoVnV20dstxVq+f5/L2HY8bidBTFvZVXe1uXX/Ynt9scbNtyAIATTizw6bmjot1zsrrabPR024iI9N/2H0L4S1uLeyXdyQGyVdhgYuOiiTZH0GHtprpS1lAQQgSnlua+pNvi+3u+wSQkWdxJ93flknSLo5Lh5SKk7d5eCsDMOZM1jUMr5vhw0jPd24Ud2Ffpl/fYua2Enm4bsfHRTCnI9Om5dTqdd4iYtb3Lp+cWYrx4eroDcT63h6IoZE5KBqC6sglVndjb9AkhgtNwhpf7kmfxyf27qrDbHOPyniI4SdItQlZrcwdVFY0oCkybla11OJrJn5EFuPcq9wfP0PLjFxWg0/l++Hd03+J3nR09Pj+3EP7W22unu6sXRYFJOYG1cvnhUlJjCQvT0d3V6134TQghgkVvj42uzl5g6OHlvhJtjiAiyojd5qSo0L9T+URwk6RbhKzdO0oByM5J8c4NnohmzJ4EQOHuCux237bC1te2UFpUi6IoHLfAt0PLPaLMknSL4NXW18udkh0b8NMjwvRhJKfGAfDt5yUaRyOEECPjmc8dGWXy6x7dh1IUhfRJ7u1o9/ipc0OEBkm6RcjyzOeeqEPLPdKzEomJjcJmc1BUWO3Tc3+90b1NWMHMLGJio3x6bo+oaPcQsc4O2cpIBB/v0PJpiRpHMjypGe6bx10bK3A4nBpHI4QQw+cZWh6fZB7X9/Uk3ft2lcvUHHFEknSLkNTZ0UNpUS0AM46ZrG0wGlMUhelz3L3dnjnuvuCwO/l2cyEA80+c5rPzHs4zvLyrsxeXS/6YieDi6emePD3wtgobTGxcNEaTgZ4uO/vr96OmW/v9M07q1TpEIYQYlFZJd1JaDEaTnva2LqorZGqOGJysXi5Ckqe1MS0j3ruP4kQ2Y/Zkvlq3mz07y3G5XOh0Y29v27W9hK7OXiwxUUydnuWDKAdnCjcQHm2gp8NOd1cPUX1JuBCBzuFwYm13j9CYND0JgiBfVRSF5NRYKssa+Oj5XVRt6+z3/LKr81EI7GHyQoiJqaXZ3cgZlzi+SXeYXkf+rEx2flPKnp3lZGQHRyOrGF/S0y1CUuFe90rdBTMn7gJqh8qZkkZ4hJFOazcVpfU+OeeXX+wC3NuEhYX5rypRFIWM/FgAOqwyr1sED89WYeERRmISgmddCc+87ob6Vlwul8bRCCHE8GjV0w0wY577ftNfi9aK4CdJtwg5LpfKgb3uFSSnTvftFlbBKixMx7S+Bojd28vGfL6KsnoqSusJC9Mxf7H/96VMnxILQFenJN0ieLS1untdYuOiMcYxYKi2mm5FFxV4SW1MbBRRsSacDhetfT1HQggR6Dx7dGuRdE+fm42iQHVlI22tnUO/QEw4knSLkFNT2UhXZw9Gk4HsyYG9Rc94mtk3t337t0Vjnhv95ec7AZhzXN64rAyfmuPeB7O7KwjG5wrRx5OwxsRFY+ux88lThQP+uVyBt1iZoihMm58GQGN9m8bRCCHE0FRV9a5erkXSHW2JIHNSMgB7d0lvtxhIkm4Rcvb39XLnTU3367DnYJM/I4vwCCNtrZ2UFtWM+jztbZ3s2FoMwIknz/JVeEeVmuuely893SJYuFwu2tvcvR2xcf5Z2d+fDk26ZTVeIUSg6+rsxdZrByAuMVqTGKbP8gwxH/uIQhF6JCMRIWd/33xuGVren8GgZ9YxOQBs23Jg1Of56t+7cblUJuWmkJE1PtsgpfT1dHdJT7cIEu1tXbhcKgajnsiocK3DGbEp85JRFIWeHhtdnfJ7J4QIbJ753GZL5Ljt0X24abPcO8UUFVZjszk0iUEELkm6RUjp7bVTXlIHwNRpGRpHE3jmnjAVgO1bi+ntsY349V2dPWxc515AbfGps30a29GkTHb3dDvsTuzyh0wEAc/Q8ti4aBRF0TiakTOG64np66H3zJMUQohA1dzUDkBcwvgPLfdISYsjNj4ah91J0b4qzeIQgUmSbhFSSvZX43S6iEswE58oW4UdLmdKKonJMdh67Wz7pmjEr9/w+U56e+ykpsczY/Zk3wd4BKYIPSaTAZDebhEcWlsOJt3BynPz6ulBEkKIQOVduVzDpFtRFKb1DTHfI0PMxWEk6RYhxTOfe+q0jKDsXfI3RVG8q41v+vfuEc3V7Ors8S6gdtpZx6LTje/nGxFl8sYhRCBzOl20eZLu+OBNuuPj+5LuZqvM6xZCBLSmRndPt9YdLtP7hpjv21U+5kVrRWjRZtKDEH7inc89TeZzH8mx86fy8XtfU1vdzP49leTPyBrW69Z/toPeXjvZ0+M5/bfZ6HRH3upICfNVtAdFRobT2twhK5iLgFdT0orT6UKvDyPaHKF1OKNmjokiLEyHw+7E2t6NJSZ49hoXQkwszX1Jd4LGSXfOlDRMJgPW9m6qKxrJnJSkaTwicEjSLUJGS7OVxvo2dDqF3KnpWocTsCKjwjnhxGls+Hwnn3+0janTM4ccFdDSbGXDZzsAiAmP5YVfbjtq+atWz/NVuF6R3p5uSbpFYCvdUw9ATFxUUI+40ekUYuOjaWpop6WpXZJuIUTAam7sG16ucdKt14cxdXomO7eVUHigjJxj+pLu4P1TIHxEhpeLkHGgb2h55qRkIiJNGkcT2JacOpuwMB2lRbUU7q44allVVXnvza+w250UzHfPCdeC52fa3SXDy0VgK93dAEBsvHZzC30lPsF9AyvzuoUQgcphd9LW6p7SE5+ofb07c757XvfX6wvZvH43+4qK0QfvTCPhI5J0i5AhQ8uHLyYumhNPce+x/d5bX2G3H3lF8O++LmL39lJ0OoXL71ukWc/doT3dMr9UBCqXS6V0T1/SHcSLqHl4FlNrbenA5TrylBIhhNBKc7MVVQWjUR8QU3qmH5MNCrQ2dVK1r422GuksEJJ0ixDhcrk4sO/gImpiaKeeMY8ocwSN9W18+PaWQcvUVjfzr1fXA3Da944la1r8eIbYj6en2+l0yf6XImDV17bQ3WFDF6bDbAn+4dhR0eEYjXpcLpW21k6twxFCiAGaD1lELRCm9ESZw0lIdjdYNja0aRyNCBSSdIuQUFXeSE+3jfAIIxnZsmjFcIRHGPn+pUsB+PKLnd79tz1qq5t59skP6O21kzMljZNPn6tBlAfpdDrCI4wAdMu8bhGgSg7UABATGzXuK/z7g6IoxPT12LdL0i2ECEDeRdSStJnPrehAbwZDrPuf3gzpk92dFI31knQLN1lITYSEwj3uoeV5+RmEhUlb0nBNm5nNqWfO47MPt/LO619SWlTL9NmTqKtpYeMXO7HZHCSlxHLZ1af3fa7aDi+NiDTR022jq6snqLdiEqGrtKgWCO6twg4XExtFQ12r9HQLIQKS1tuFWZLD2bOrmJZy9zDyjFkWMvLi2bGpjJYmKw67U5O4RGCRpFsEvZRlTsqfdyfd83+UTsZ5gyeG/tjGKhQsX3EciqLw2YffsmNrMTu2Fnufy52axqU/XR4wC9NFRoXT0mSVFcxFQFJVldIid093KMzn9oiJjQKgrbUTVVVlEV4hREBpbtB+j+622h6ayrsBiEkNxxIXQXiEkZ5uG3WVrZrFJQKHJN0i6Nmcdg58UwfA3vdbKfti66Dl/LGNVShQFIXlK45jxuxJbNqwh/raFsyWSOYcm8eMOZMDaohspHcFc0m6ReBpamjH2t5NmF6HJSZK63B8JtoSiaIo2G0Omus6SETbLXmEEOJQTQGyR/ehFEUhMTmGyrIGqsuatQ5HBABJukXQ27OxGlV1J2SB0iMbjNKzErngRydpHcZReeZ09/TYNI5EiIE8vdxZUxNCappLWJgOsyWC9rYuKgqbSJySrnVIQggBuBfS9WxpGAjbhR3Kk3TXVLTgcqnIZt0TW+jcFYgJa8c696rlWg4rEuPDm3R3S9ItAk9J33zuyTNCbzHHmFj3cPnyfY0aRyKEEAe1t3bhdLoIC9N566lAERsXTZheR2+3naqKeqD/gmuSg08s0tMtgt7Of0vSPVF4km67zYHT4SRMLxP1ReDwrFyeMyOZsi3tGkfjWzFxUVSUQXlhE5ytdTRCCOHmGVoeGx8dcCOMdDodCYkW6mtb+eydHTT3NtFVAS4bxKSFU5CXi71V6yjFeAmob+f777/PL37xC5YuXcrcuXNZuXIlr7/+Oqqq9iv32muvceaZZzJ79mzOO+88PvvsswHnslqt3HHHHcyfP5958+Zxww03UF9fP16XIsZJU0M7DeVWFEUJqdWCxeAMBj36vkR7Ig0xl7ox8LU0W2lt7kCnU8jKT9A6HJ+z9C2mVlfWiq3XrnE0Qgjh1hyA87kPFZ/gjqtibxNt9d00V3bTVN5NW02PxpGJ8RZQSfezzz5LREQEt912G08++SRLly7lrrvu4vHHH/eWee+997jrrrs466yzWL16NXPnzuW6665j27Zt/c510003sWHDBu655x7+9Kc/UVJSwqpVq3A4HON8VcKf9u91r1oeExvlTcZEaPPu1T2BhphL3Rj4PFuFpWclYoowaByN74WHGzGFG3C5VCrLG7QORwhAGiSF9tuFDcXTIdRUa8Vh13bbVaGtgBpe/uSTTxIfH+99vGjRIlpbW3nmmWf45S9/iU6n49FHH+Xss8/mpptuAmDhwoUUFhby+OOPs3r1agC2bt3K+vXrefrpp1myZAkAOTk5rFixgrVr17JixYpxvzbhH56kO1ArW+F74RFGOqzdE2pet9SNgc+TdOfkpWkcif/ExEZRX9tKRWk9uVNlMTWhvWeffZaMjAxuu+024uLi+PLLL7nrrruora3luuuuAw42SF577bUsXLiQNWvWcN111/HSSy8xd+5c77luuukmDhw4wD333IPJZOLhhx9m1apVvPHGG+j1AXW7LA7RFADbhR1NRKSJSLOJLmsvzTWdRBBYi72J8RNQPd2H3lR6TJ8+nY6ODrq6uqioqKC0tJSzzjqrX5kVK1awceNGbDb3Tfi6deuwWCwsXrzYWyY3N5fp06ezbt06/16EGDdOp4viwmogcCtb4XsREe4V6nu6J862YVI3Bj7PfO7JeakaR+I/5r5t0KoqZDE1ERiefPJJ/vd//5cVK1awaNEi/uM//oOLLrqIZ555BpfL3at4aIPkwoULue+++5g9e3a/kUKeBsn/+q//YsWKFSxbtoxHHnmEffv2sXbtWq0uTwxDQ10LAMkpsdoGcgSKopA2OQ6AxgqrxtEILQVU0j2Yb775hpSUFKKjoykuLgbcPTOHysvLw263U1FRAUBxcTE5OTkoSv9lAXNzc73nEMGvorSe3l470XEmzJYIrcMJSKqiknGea8h/KcucWoc6bLKCuZvUjYHD2t5FY30bihLiSbclEpCkWwQOaZCc2JxOl7enOyk1VttgjiJtUiwADRUd2gYiNBXQ42W+/vpr1qxZw6233gpAW1sbABZL/15Nz2PP8+3t7ZjNA4dvxMTEsHPnzjHFpOjAGDumUwQ9vbn/f7VSXOoeWj7zpAz0uuHtuxAWPvzzD7esL8rpTMMrN9L31ekUXrh+65DlLntk3vC+18r4fi6DlYuMObhX94DXKu7fz6G+o0rANzcendSNvuGruqyy0D20PC0rnph0E3bDwO+mohv8uz7Y8bGW9dQnh9YrvjhvbJK7cbOlyYo9rIco8wgqVB8IlL89vhSI1xTs9eOhDZLffPMNcPQGyby8PGmQDFJNje04nS4MRn3AbRd2qPS+nu6W2k6cDhdB0Ocp/CBgk+7a2lp+/etfs2DBAq644gqtw/EKi4C007WOIjAkLRzd6xx254A/bINRVRW94ciLo5U87N4qbM7JGTRVdw7rvc15w4txJGVDoZyiG9732unQ/jqcZiNsgt5e24DXHn4do/2OBjKpG31vrN+TtZ+7h5bPOj2NtNOhoWLg91oXMfh3fbDjvigLEJXp6/PqSdweTWNlB90JjUw5OfPwAuMiFH+vQ/GatCANkm6B2JgzWke7lpYDfUPL02MJj1f6vUZnPNhwqBi0fWxJjiDSbKTLaqO9o4O4WAs6IxjMwblVd6h8v0Z6HWNtkAzIpLu9vZ1Vq1YRGxvLY489hk7nvsqYmBjAvcJkUlJSv/KHPm+xWKitrR1w3ra2Nm+Z0XJ2Q8OGMZ0i6OnN7huEhq/AMYrpKUknK7xww/B6X2s+Gvy5ro4eire6V9CdsSSDNQ8WDuu9rUXDDnPYZX1RTmc6eIM8nu/robo44md9qKSTtYnv0HIum7unu7fbQeu+/nt1e65jqO9o0mLQRw7vfQOJ1I2+Nda6zGPHR+7PNCUilZqPwB4z8Hvt6h78uz7Y8bGW9fRwd1aCq/foZUcaQ9qkOBorO9j+RiOJtvFNun318wokgXhNwVo/SoPkQKHUmDPYtWze3QrA5ONiB3zGkYVg76sLI1LBYQSzos3jyDSFtCkxFG1twNrTQXaehcjk4P/5BHv8HuN1HQGXdPf09PDzn/8cq9XKK6+80q/lMTc3F3DPS/T8v+exwWAgKyvLW27jxo2oqtqvR7WkpIT8/Pwxxae6wNY6plOEDId1lJ+FCs7hbE+oHvn8u7+tQlVVktPiiE+JGt75GOb7jrBsSJQ7ymd9eDmtr0OHe69uh8NJZ6uNqOhD5vMfdh1H+o6qQbhrh9SN/jPqugzo6uyhtrIZgMzUVGytoEYO/F6rrsG/64Md90VZcCfchz7ni/Nmz4xnx4YKyitq6Y10Z4kmgxFbmWlgYT8Zy88rUAXSNQVj/SgNkv0FYmPOaB3tWg6sawXAosT26zjQm6GrAqzuWYh0x0JPC1iLtXuckGyhiAbqizrITgVDb/D+fELl+zXS6xhrg2RAJd0Oh4ObbrqJ4uJiXnrpJVJSUvo9n5WVxeTJk/nggw9Yvny59/iaNWtYtGgRRqO7B2zp0qU88cQTbNy4kRNPPBFw31Tu3r2bq6++evwuSPjNvt3uhaEKZmRpHInQgmfbsO7uw5LuECV1Y+AqK65DVSEpJda70FgoS5nsHpZ74Ls6PnnKPcJo2dX5KIxf0i3EoaRB8sgCqTFnrAa7ltpy94H4mNh+z6mAy3aw4VC1a/84MbVvSkNzJ/YuFZcN7FawH3ZNwSRUvl/DvY6xNkgGVNJ977338tlnn3HbbbfR0dHBtm3bvM/NmDEDo9HI9ddfz80330x2djYLFixgzZo1bN++nRdffNFbdt68eSxZsoQ77riDW2+9FZPJxEMPPURBQQFnnHGGBlcmfMnlUimUpHtCm2h7dUvdGLg8+3OH8qrlh0rPiwXcuwfYbQ4MxoC6jRATjDRITlxOp4v6Wvec7tT0BI2jGZolPgKDKQx7r5MOazdJhH4jregvoP5abtjgHn/z+9//fsBzn3zyCZmZmZxzzjl0d3ezevVq/va3v5GTk8Of//xn5s2b16/8ww8/zIMPPsjdd9+Nw+FgyZIl3Hnnnej1AXXJYhSqKxvp7OjBZDIwKXdi3OiK/ibaXt1SNwYuz/7cOXlpGkcyPiKijUREmuju6sXa3kV8omXoFwnhJ9IgOXE1NbThcDgxGvXEJQT+il6KohCfFkVdaTvtrR1A4DcUCN8KqLusTz/9dFjlLr74Yi6++OKjljGbzTzwwAM88MADvghNBJB9u9y93FOmZRAWpgOCcAKaGJOJtle31I2BqbfHRnWle8/qyVMmTgOg2RJBd1cv7ZJ0C41Jg+TEVVvtXksjJS0e3TC3jdVafLo76W5rHd6OOyK0SE0igo4MLReepLu7a2Ik3SIwVXdV4XKpxCVHETNTRcW9EosuKrQbAs2WSOprW7G2dWkdipjgpEFy4vIk3akZ8RpHMnzxaVEAknRPUJJ0i6DSYe2msrwegHxJuicsb093jyTdQjv7d1QDYNKbvIuKASy7PkerkMaFZ8G4Dmu3xpEIIUJeXye23uxeIA3A3nZI0p0efEl3T7eNHuk0mHAk6RZBZf+eSlQV0jISsMREaR2O0Eh4uDvpttscuFwu7/YwQoyn0t0NAMTGR2scyfjyJN3dXb047E6NoxFChDJ9NGx6t5i673pw2SAmLZyCvFxqqw4OLw8WBlMY0ZZwOtp7aKoP4r22xKhI0i2Cyu4dpQBMm5WtbSBCU3pDGDqdgsul0ttjJyJStisS48tud1CxvwmA2LiJlXQbjHpM4QZ6e+xYrTLEXAjhX9bmHporu71bcHUm99Da0gFAWmY8htj+5fVmvD3kgSYmLsqddNdJ0j3RjLp76IorrmDjxo1HfP6rr77iiiuuGO3phRjAZnN453PPnDNZ22CEphRFwRQemEPMpW6cGCpKG3A6XBhNhgnZ6OMdYt4uQ8zF8En9KHyhutzd4BmfaMGSZmJfUTGb1+/2/iurqERn0DjII4jpGxklSffEM+qe7s2bNx91UYrm5ma2bNky2tMLMcD+PRXY7U7i4qNJy5StFnxJVVQyzht68SclbByCGabwcCPdXb30BljSLXXjxFBa5N4qLDYuGkUJ0C4VPzJbImmsb8PaLj3dYvikfhS+UFXq3jUive9esK2mh6bygw2AManhmsQ1HLHx7qmRLQ0dOJ0uxtD/KYLMmIaXH+1Go6ysjKgomXMrfGfXd6UAzDwmZ0Le5PqTTqfw7KqtQ5a7avW8IcuMF1O4uxm7p9uucSQDSd0Y+kqLaoGJN5/bI9oSASBJtxgxqR/FWFWVuXu6M7ISNY5k5CKjTYSF6XA6XdRXt5JoDp456WJsRpR0v/XWW7z11lvex08++SSvvvrqgHJWq5V9+/axdOnSsUcoBOBwONm7qxyAmcdM1jYYERA8K5gHQk+31I0Ti9PpoqykDph487k9zGb38PKuzh7svQ6MGscjApfUj8LXPD3dwTjqUVEUzJZIWls6qCxtJHG2JN0TxYiS7u7ublpaWryPOzs7B101ODIykh/96Ef86le/GnuEQgDF+6vp6bZhtkSQNTlF63BEAAikOd1SN04s1RWN2G0OIs1GoqIDdxijP5nCDRgMeux2B3UVbWRFxmkdkghQUj8KX3I4HDTUtgEwaWZiQC+adiTmmL6ku6SBubPztQ5HjJMRJd2XXnopl156KQCnnXYav/3tb1m2bJlfAhPiUDu3lQAwY85kdLogq12FX4T3DS/v7dF+eLnUjRNLSd987knTkybsVBdFUYi2RNDSZKW6pIWsmZO1DkkEKKkfhS/ZXQ4AIqKM7N5eQobLErCLph2JJcY9UqiypFHjSMR4GvWc7k8//dSXcQhxRHabgx1biwGYNTdX42hEoAiknu5DSd0Y+koOuJPunOnJ9NRpHIyGzJZId9Jd3AIztY5GBAOpH8VYNda4V/2Oioqgqbw7oBdNOxLP7g/V5U04nS7CwmQxtYlgzPt0d3R0UF1dTXt7O6qqDnj+hBNOGOtbiAluz84yenvsxMZHkzMlTetwRIDwLKTmsDsD8o+W1I2hyeVSKSt2Z9qTZySxt27i9lSY+xZTqylpGaKkEP1J/ShGq6nWnXRHmyM0jmT0IiJNGIxh2G1O6mtbSMsIvrnpYuRGnXQ3Nzdz//33s3btWpxO54DnVVVFURT27NkzpgCF+HbzfgDmnTBVhpYLL70+zLsCaE+3LWDm1krdGNrqa1vo6bZhNBlInRzLXiZy0u3uraktbwvIhi8ReKR+FGPl6ek29w3RDkaKohCXGE19dRtV5Q2SdE8Qo0667777bj777DMuv/xyjj/+eCwWiy/jEgKA9rYu9u+pBNxJtxAeiqJgCjfS1dlDb0/gJN1SN4Y2z1Zh2ZOTJ3ySGRHp3vrGYXPSWN9KSpqswiuOTupHMRZOp4vWhk7gYKNfsPIm3RWNHL9I62jEeBh10r1hwwauvPJKbrnlFl/GI0Q/3319AFVVyZ6cTGJyjNbhiAATHm7oS7q1X0zNQ+rG0FZW7E66J+WmahyJ9hRFIdocQVtrJ9UVTZJ0iyFJ/SjGorOjG1VVMYXrMZmCbPW0w8QlubebrCqfuKOlJppRN9OHh4eTkZHhy1iE6EdVVb7ZXAjAvAWypYIYyBQReIupSd0Y2rzzufNk60I4ZEGgSrlxFEOT+lGMRUd7NwCxSVFBv3NEXFIUADVVTTgcA6daiNAz6qT7vPPO4+OPP/ZlLEL0s2djDfU1LRiNeubMk1XLxUDhfSuY9wZQ0i11Y+hqbe6gtaUDnU4ha1Ky1uEEhGhv0t2kcSQiGEj9KMaiw9qXdCdEaRzJ2EWZw4mINOJ0uqirkcUoJ4JRDy8/88wz2bJlCz/72c/44Q9/SGpqKmFhYQPKzZwp+4iI0fnw77sAOHZBPhGRJo2jEYHIs4J5TwANL5e6MXSVlbiHlqdlJmA0GVDp0Tgi7XlXMK9q8i6CJcSRSP0oxsJq7QLcPd3BTlEUMnOS2L+riuqKRjKyErUOSfjZqJPuSy+91Pv/X3755YDnZQVKMRZdnT18t74cgEVL5Y+vGFwg9nRL3Ri6Sov6hpbLfG6vqOhwwvQ6erpttDRZiU+UhbHEkUn9KEZLVdWDw8sTo7CHQOdw5uRE9u+qorK8gRNOnKZ1OMLPRp10P/jgg76MQ4h+KssbUFXIn5FFUkqs1uGIAHWwpztwkm6pG0OXLKI2kE6nIyU7huriFqormyTpFkcl9aMYre4uG06nC12YgiU2gqaWXq1DGrPMHHfvtiymNjGMOum+4IILfBmHEF52u4OavvmBi0+epXE0IpCZ+nq6nQ5XwCxEInVjaOru6qWuphmASbmyiNqh0nLi+pLuRmbNzdE6HBHApH4Uo9XR5u7ljkuORhci2zV6ku66mmYcdid6w8CpFiJ0hMa3VoSUsuI6nE4XmdPimDJNVjkVR6bXh6HXu/9IBdIQcxF6ykvrUVVISLIE/f6wvpY+OQ7A21gqhBC+Zm1zz+dOSInWOBLfiUs0ExFpwul0UdvXqCtC16h7um+//fYhyyiKwgMPPDDatxATUG+PncqyegAuuvl4lC5ZlEccnSncgKPDGTB7dUvdGJpkaPmRpeW4k27ZNkwMRepHMVrWvp7u+BSzxpH4jqIoZGQlcmBfFTWVTWRmJ2kdkvCjUSfdmzZtGnDM5XLR0NCA0+kkPj6eiIiIMQUnJp7SohpcLhVLbBRzl2VR/Y7WEYlAZzIZ6OzoCZikW+rG0FRa5E66J8vQ8gHSJseiKGBt78ba3iUjAcQRSf0oRsuTdCekhk5PN7h3wziwr0q2XZwARp10f/rpp4Met9vtvPLKKzz33HP8/e9/H3VgYuLp6ur19pTk5afL1jNiWDzzunt6A2N4udSNocdhd1JZ3gBIT/dgjOF6EpNjaahrpbqyiYIZknSLwUn9KEajt8tBT7f7b3x8SjS2rsBYw8UX0jISAPe2iyK0+XxOt8Fg4LLLLmPx4sX8v//3/3x9ehHCSvZXo6oQn2ghLj50hg8J//KsYG4LkJ7uI5G6MXhVVzbisDuJjAonMTlG63ACUlpm342jDDEXoyD1ozialtpOAMIjjN6/+aHCk3TXVjXhcrk0jkb4k98WUps2bRpbtmzx1+lFiOmwdlFX4950MS8/XeNoRDAxeffqDuyk20PqxuBTUepeZyJ7crKMwDmC9L6ku6pCemvE6En9KAbTUuteRC06OvSmHiQmx6A3hGGzOWhubNc6HOFHfku6v/zyS5mXI4atqLAagOTUWJkPKEbEZOrbqztAhpcPRerG4FNR5h5anjlZFrk5kvRM99Y30tMtxkLqRzGY1np30h0ZHa5xJL4XFqYjNT0eQOZ1h7hRz+n+85//POhxq9XKli1b2L17N9dcc82oAxMTR2tLB00N7SgK5E6VXm4xMp6hZoHS0y11Y+jx7KiQNSlZ40gCl6enu7nJSk+3jfAIo8YRiUAk9aMYjdZ69yJqUSGYdIN7iHllWQM1Vc3MOTZP63CEn/g86Y6JiSErK4t7772XH/zgB6MOTEwMqqp6e7nTMhKIjArNClX4j2d4ud3mwN7rxI8DeIZF6sbQ0mHtprnJCiDbuRxFZFQ4sXHRtLZ0UF3ZRO7UNK1DEgFI6kcxGm19Pd1RIXSPqOhA37d8UebUBLZ8KSOFQt2ok+69e/f6Mg4xQTU3ttPW0oFOpzB5itykiZEzGMLQ6RRcLrVvCJq2C11J3RhaPKuWJybHEBFp0jiawJaWmdCXdDdK0i0GJfWjGKmuzl66re6RbKE0vNySHM6eXcW0lPfQ1uqey11TLcPLQ5m2XUJiQju0lzsjO4nwcBmOKEZOURSMffO6W+q6NI5GhJrKvvncWZNlaPlQZF63EMLX6ipaATBFGNDrw7QNxsfaantoKu/G2elOx6xt3Vjb5T4mVI26p9tj8+bNfP7551RXu5On9PR0TjnlFObPnz/ic5WVlfH000/z3XffsX//fnJzc3n33Xf7lbn88svZvHnzgNeuWbOGvLyD8yCsVisPPvggH3/8MXa7nZNOOok777yT5GS5cQoU9bUtdFi7CdPrmCx734oxMIUb6em20VLTSZTWwfTxZd0IUj9qxbNyucznHppnXrcsBiSG4uv6UYSuukr3zjahOp8bIEwfhjkmAmtbNzVVTbKgcIgaddJts9n4j//4Dz7++GNUVcVisQDQ3t7OM888w+mnn87//M//YDAMfz+9/fv388UXX3DMMcfgcrlQVXXQcsceeyy33nprv2OZmZn9Ht90000cOHCAe+65B5PJxMMPP8yqVat444030OvH3NYgxkhVVUoO1ACQPTkFg1F+JmL0TN6e7k4yNc6N/FE3gtSPWlBV1Tu8POO4KNR0a7/ndVGyp+qhPEl3Q10rdptD6nUxgD/qR2mQDG11la0ARJlDN+kGiE2IcifdlU3kT8/SOhzhB6P+i/j444/z0Ucf8dOf/pSf/vSnJCa6h5U1NTXx97//naeffprHH3+cm266adjnPO2001i+fDkAt912Gzt37hy0nMViYe7cuUc8z9atW1m/fj1PP/00S5YsASAnJ4cVK1awdu1aVqxYMeyYhH/U17bQ1dmL3hB2xGGbqqKScd7QN7VKaI02EqMQ3reCeXNNJ2h8b+SPuhGkftRCU0M73V296A069nxaz77P+w+bXnZ9jkaRBSZLbBSRUeF0dfZQW9MsowPEAP6oH6VBMrRNmKQ7MYqK4kaqq2SkUKgadQ3yzjvvcMEFF3DLLbf0O56QkMB//ud/0tTUxNtvvz2iilOn880U83Xr1mGxWFi8eLH3WG5uLtOnT2fdunVyU6kxl0ultKgWcA/ZPNIcHZ1O4dlVW4c831Wr5/k0PhF8PCuYt9R2wTHaxuKPuhGkftRCRd9WYWk5cT77/EOZoihkZCWwf28V1ZVNknSLAfxRP0qDZGirregbXh7iSXdckntyXG1NE4ZY9zF7GzB4G5IIQqO+i2hoaGDOnDlHfH7OnDk0NDSM9vRHtXnzZubOncvs2bO57LLL2LJlS7/ni4uLycnJQVGUfsdzc3MpLi72S0xi+L75sJTOjh7C9DoyJ8kWPGLsPHt1B8JCalrWjSD1oy95ku6sqQkaRxI80mQxNXEU/qgfx6tBUow/m81BS0MHANEhnnRnFsQD0FDTxpef7WBfUTEGbTdjET426p7u1NRUNm/ezCWXXDLo81u2bCE11feLY51wwgmsXLmSyZMnU19fz9NPP81PfvITXnjhBebNc/d4tre3YzabB7w2JibmiC2gw6XowBg7plMEPc++gvqBH/GQVFXl7T9sAyA7L5lw89G/gmHDrGN9XW6831tnGl45X79vqJSLsPQl3bWdGGOH/o4qfuy01KpuBG3rx2CsG4f6nlRV9q1cPi2B5qLeAc8rusG/l4MdH8+ynvpEZxq6rC9i0Bnw9sxk5yfAx1BT0+Tz78NY/vYEqkC8plCtHz0Nkk6nk2OOOYYbb7yRE044wfu8NEgGnsa6VlQVTJF6jCYDzh6tI/KfyGgT4ZEGerrslO9pQW8Ig7yhXyeCx6iT7vPPP5/HHnsMs9nMVVddxaRJk1AUhdLSUp577jk++OADrr/+el/GCsANN9zQ7/Epp5zCOeecwxNPPMHq1at9/n6HC4uAtNP9/jZBIWnhyF+z+8saync3E6bXMWNZMqaIo5c3D7PC8XU5Ld9byo28XFiSEda5e7pTlqnodO6bptF8R8dKq7oRtK0fg7luHOx7Yu91UtO3CvfUxfHspmZAGV3E4N/LwY6Pd1mAqMzhlR1rDBFpkORZdDo3gRefgNrqZpJPdRGm930Gp8Xvtb+F4jUNRqv6caI1SAZiY85oNO9uBSA2OdLbiKgYQGc82Ph3+OPhlNHisaIHnBz1OuKSo6gpbaWzuwudEQxm6N8EFBhC5fs10usYa4PkqJPua6+9loqKCl599VVee+017/AezyIWF1xwAddee+3YohuGyMhITj75ZD788EPvMYvFQm1t7YCybW1txMSMbayGsxsaNozpFEFPb3bfIDR8BQ7r0OUP9a9HdgGQlhmPrVqPbYjy1qLhndfX5cb7vXWmgzfIWl1zMJdzudw93U67iwNvdhOXGXnU72jSYtD7aUeOQKkbYXzrx2CsG49Wl5UXNeGwuYgyhxOpix70++fqHvx7Odjx8SzruanrrARX79HL+iKG7hqo2dv3vCsGU7iB3h47373QSlpm/MCTjNJY/vYEqkC8plCsHydqg2SwN+Z0fdsKQExyhPceKSIVHEYwK4M/Hk4ZrR731HPU60jKiaamtJUetZvIrMD/+QV6fMM1Xtcx6qQ7LCyM3//+91x11VWsW7eOqqoqADIyMli6dCnTpk3zWZAjlZuby8aNG1FVtd8woZKSEvLz88d0btUFttYxBhgiHNaRfRYtzVZ2fVsGQHpG0rCGCQ13KJGvy2n53lJuNOUUjCY9tl4HjeVdmGPcd4xH+o6qftzpKZDrRvBf/RjMdeNg35Pine753JlZSagOZdDvn+oa/Hs52PHxLgvuhPvQ5/wVg8t+6OenkJoeT1lxHeW7m0iI9l3S7THSvz3BIJCuaSLUj6HeIBmIjTmjUfpVOwCWxAhvI2J3LPS0gLVvxP/hjwc7FgiPe/t+Dke7DnOkezG15oouuioC9+cXKt+vkV7HWBskR5R09/b28l//9V9MnTqVyy+/HIBp06YNqCSff/55Xn75ZX7729+OeC/akerq6uLzzz9n9uzZ3mNLly7liSeeYOPGjZx44omA+4Zy9+7dXH311X6NRxzZpvV7UFWVGSemEW0eYly5ECMUEWHC1uvA1msf9/cOxLoRpH4ci8oy93xuWexx5NKzEikrrqOyooF586dqHY7QWKDWj4cLxQbJQGrMGY3G6r6kOyEcl9Xd4KfawWU72Ph3+OPBjgXCY9UBKAcbQwcrExPrTro72rpx9KjYrWBv9c1n6Q/B/v3yGO51jLVBckRJ9yuvvMJbb73FmjVrjlrulFNO4b//+7/Jz8/n0ksvHfb5u7u7+eKLLwCoqqqio6ODDz74AID58+dTXFzMU089xemnn05GRgb19fU888wzNDQ08Mgjj3jPM2/ePJYsWcIdd9zBrbfeislk4qGHHqKgoIAzzjhjJJcsfMRuc7DlS/f4w+VXzmDnWy0aRyRCzdTpmeSdYiErafy3KfJ33QhSP44378rlsu3VkIxxYONgN0HmMWb4Aqpq6jSMSgSK8agfR0oaJINDc5M76TbHh9MVxD2qwxUVE05YmA6n00VHW7fW4QgfG1HS/f7773PGGWeQlZV11HLZ2dl873vf47333htRxdnU1MSNN97Y75jn8fPPP09qaip2u52HHnqI1tZWIiIimDdvHvfee++ALSgefvhhHnzwQe6++24cDgdLlizhzjvvRK8f9Yh6MQa7viulu6uX2Lho5i3PlqRb+JwlJooVP59D1dvjv5+yv+tGkPpxPHV19tDU4L7Zc/d0j//oiWBi67HzyVMl3sddne6um6riZhx2p3sVXjFh+bt+lAbJ0NTTbaOr070ohTnBRFdZ6NfDOp1ClDmC9tZOWps6tQ5H+NiI7rAKCws599xzh1V23rx5fPbZZyMKJjMzk3379h21zNNPPz2sc5nNZh544AEeeOCBEcUg/OPbzYUAHLcwH13Y+CdFQviTv+tGkPpxPHmGlickWYiMCkeVpHtEIiJNGAxh2O1OaqqbZLTABOfv+lEaJENTc6O74TPaEo7BpGeiNH6aLX1Jd6Mk3aFmRLWI3W4f9jwbg8GAzTbU2tQilKQsc6KPGri5QVN1B0WF7sVSzrpjCop0eogQI3Vj6DBO6qViQyUAmdPiUNOt6KL8uLJUCFIUBUtMFE2N7VSU1kvSPcH5u36UBsnQ1NzkHk+ekGrROJLxFW12r9TV0tShcSTC10aUdCcnJ7N///5hld2/fz/JyfKHdiLRRyk8d+3WAcdLi2pRVYiNi2bNfx3gqtXzNIhOCP+RujF09NptbPu0HICuBiefPFXIsutzNI4q+Fhi3Um3Z9SAmLikfhSj4ZnPnZga5JtBj5BnoeG25i6NIxG+NqJxvieeeCL/+te/aGpqOmq5pqYm/vWvf3kXohATl6qq1FS5vy9pGQkaRyOEf0jdGHyMk3qxx1hpqLBij7Giprv/KZFO2lvdw/osfSvJipGz9G3b51mQTkxcUj+K0fAML09ImVg93VHR4QD0dtvpaJfF1ELJiJLuVatW0dvby5VXXsl33303aJnvvvuOq666it7eXlntUdDe2kl3Vy9hYTqSUmO1DkcIv5C6Mfj02m18+kwh614p5NNnCvnkKfe/pup27HYniqJgtsjWhqNliXE3WDQ1tHsXVhMTk9SPYjSaG/uGl6dMrJ5uvT6MiEgTADUVzRpHI3xpRMPLs7KyePjhh/nNb37Dj370I7KyssjPzycqKorOzk72799PeXk54eHh/O///i/Z2dn+ilsEiboa9yrlSSmx6PUymVuEJqkbQ0fFPnedFW2JQKeTRR9Hy2DUk5BmpqnGSmVZA/kzjr5ytQhdUj+K0fDO6U6x0MnE2vEm2hxBd1cvNRXNTM7I0Doc4SMjXo7xlFNO4e2332b16tV8/vnnfPzxx97nkpOTufjii1m1atWQW0OI0KeqKvV1rQAkp8VpG4wQfiZ1Y2ioLHT3LMTEyNDyscqaGk9TjZWKsnpJuic4qR/FSDidLlqb3Ul3YqqZzuaJl3Q31LW6e7oXah2N8JVR7YGQmZnJvffeC0BHRwednZ1ERUURHR3t0+BEcGtr6cTWa0evDyM+YWINDxITk9SNwa9ynzvpNvfNSRajN/mYBLatK6O8pgY13X0DbTIYsZWZNI5MaEHqRzFcba0duFwqYWE6LPFRMMFGWXvmdcvw8tAy5o0Ho6OjpcIUg6qvc7dMJibHyDBNMeFI3Rh8XC4X1UWtAMTIImpjlj4lFoCSnfV8vHofiqKw7Op8FCTpnuikfhRH45nPHZdgRqcbuBVtqPOsYF5X1YLT6SIsTO6hQ4H8FIVfqKpKfW0rAMmpMrRcCBH4Oqw9OOyufgvZiNFLzYkhLEyHw+Gks0MWUxNCDE+LZz534sRaudwjItJEmF6Hw+6kqW8VdxH8JOkWftHW6h5aHqbXEZ8oQ8uFEIGvva1vq7CYSBRl4vWu+FpYmM677VpbS4fG0QghgoUn0YyboPePiqIQE++e4lRbdfSt9kTwkKRb+EVDXy93YnKsDC0XQgQF2Z/b9zzD9Fsl6RZCDJNnj+74hInZ0w0QE++uO2urZV53qJBsSPice9Vy93zuZNmbWwgRJA72dEvS7Suxce55u219DRpCCDGUiT68HDjY0y1Jd8iQpFv4XHtrJ709dsLCdBO6lVIIETzsdgddnb0AWGJl5XJf8Ywa6Om20dtj0zgaIUQw8A4vn8A738T29XTXSdIdMiTpFj7n2Zs7MTlGVlwUQgSF9tYuAOLTojAaDRpHEzr0+jCiLe6VeFtbpLdbCHF03V299HS7G+gm8naznp7uluYO7+chgptkRMKnZNVyIUQw8gwtzyqI1ziS0BMb6xliLvO6hRBH55nPbbZEYDRN3AZQY7jBO6+7rkZ6u0OBJN3Cp9rbuujtsbmHlk/guThCiODimXOcPT1B40hCT0ycrGAuhBieg0PL5R4yLcvdCFxTJUl3KJCkW/hUfa17AbWEJBlaLoQIDqqqelcul55u3/MspmZt76any65xNEKIQOZZRG0iDy33SMt0/z2SxdRCg2RFwmdUVfVuFSarlgshgkVXRy8OhxOdTiE1J0brcEKOKdxIRKQJgNI9DRpHI4QIZM19SXdSpgVDLOgn8LqWnp5uGV4eGiTpFj5Tsr2Rnh4bujAdCUly4yqECA6tze5ebnNMJGF6+bPoD3Hx7t7ukp11GkcihAhkLa3u4eWtrVY2r99NcXGlxhFpQ9FB5rSDPd36GBUUjYMSYyJ3F8JntqwpASAxySJDy4UQQaOtb1XtmFjZn9tfYuPdQ0WLd9VrHIkQIpA11buTbme3QlN5N9bGiblytyU5nOa2ZhSdQm+PnW++3Y1B+rOCmmRGwidUVWXLmlJAVi0XQgSXtr6ebkuMJN3+4unprilpoburV+NohBABQwFDrPufLtrlHXkUEWHSNKxAYG2wERUVDkDlHhliHuz0WgcgQkNVeSMNFVb30HJZtVwIESTsNicd7d2A9HT7kyncSGSkia6uXkqLapk+e5LWIQkhAoAhBvYVFdNW00NHezeqSyUsTIfRJCkKQJQ5gg5rN63NXVqHIsZIerqFT2z/tgiAxKQYwvRhGkcjhBDD01LrvpExhRsxhRs1jia0xfatRly8v1rjSIQQgaStpoem8m6qD7iHlkfFmFAUmcAMEG2OAA6OyBLBS5JuMWYul8r2b4sBSEmToeVCiODRXOOZzz2Bl8gdJ54h5pJ0CyEG09M39STaEq5xJIEjOtqTdEtPd7CTpFuMWXlJHe1tnUSYDcTL0HIhRBBpru6bzy1Dy/3Os5habXUzXZ09GkcjhAg03d3upDsqRpJuD09Pt7WtG7vNoXE0Yiwk6RZj5hlaftwZk2XVciFE0FBV9WBPtyyi5ncmk4GkTAuqCiUHarQORwgRYLq73CuVS0/3QUaTHoNBDyrUVrZoHY4YA8mQxJg4nS52bHNvFbbg3FyNoxFCiOHr7rJh63ag6BTMMTK8fDzkzkwGoHi/JN1CiP48OxtES0+3l6Io3t7u2kpZwTyYSdItxqSosIpOazeRUSZmLE7XOhwhhBi21qYOACwxEeh08udwPOT0Jd3S0y2EOJSqqt7h5dLT3Z8n6a6pkKQ7mMl6/CEoZZkTfdTQqz46OlXqPhnbSuNfb9wHwDHHTUFvkJtWIUTw8OwHG5sQrXEkE4enp7u2upkOa7f3ZlIIMbHZ7U6cDhcAkRYTvc02jSMKHFGSdIcESbpDkD5K4blrtw5Z7sq/zBvT+1jbu9mzowyA4xcVjOlcQggx3jw93bHxknSPl6iYcFLS4qiraaF4fzVzjs3TOiQhRADwrFweaTahl61n+/E0TlaXN6GqqmynFqSka1KM2jfr9+N0usiclERaRoLW4QghxLB1WXvptLpX0I5NkEXUxtOUggwADuyt0jgSIUSg8Awtt8TJ6JfDRUWHgwJdHb1Y27u1DkeMkiTdYlRUVWXTF3sBOGHRNI2jEUKIkSnf1whAdJwJo8mgcTQTy9RpmQDs31eJqqoaRyOECASelcvNknQPEBamwxzTt5hadZPG0YjRCqiku6ysjLvvvpuVK1cyY8YMzjnnnEHLvfbaa5x55pnMnj2b8847j88++2xAGavVyh133MH8+fOZN28eN9xwA/X19f6+hAlj36ZaGmraMBr1zDlWVi0Xwt+kfvStsr6kOyFDhpaPt8lT0tDrw2hr6aShvk3rcIQQAcCzcrk5VhZRG0xMvHuHjdpqmdcdrAIq6d6/fz9ffPEFkyZNIi9v8Hle7733HnfddRdnnXUWq1evZu7cuVx33XVs27atX7mbbrqJDRs2cM899/CnP/2JkpISVq1ahcMhG8v7wodP7wJgzrF5mMKNGkcjROiT+tG3yvY0AJJ0a8Fo1DM5LxWAA3srNY5GBDtpkAwN3uHl8bJ942Bi493ToGqrJOkOVgGVdJ922ml88cUXPProo8ycOXPQMo8++ihnn302N910EwsXLuS+++5j9uzZPP74494yW7duZf369fzXf/0XK1asYNmyZTzyyCPs27ePtWvXjtflhKzGuja2vOfem/vEU2ZpHI0QE4PUj75jtzuoPOC+cUnMkPncWpgyzT2ve/8eSbrF2EiDZGjwDi+PleHlg4npW3tEerqDV0Al3UPtk1pRUUFpaSlnnXVWv+MrVqxg48aN2GzuX9h169ZhsVhYvHixt0xubi7Tp09n3bp1vg98gvns3e9QVZh2TBap6fFahyPEhCD1o+9UVzTidLgwmvRExZq0DmdC8szrLj5Qg8Pu1DgaEcykQTL4OZ0uens8c7plePlgPMPLG+pacTikzgxGAZV0D6W4uBiAnJycfsfz8vKw2+1UVFR4y+Xk5AxYUj83N9d7DjE6DXWtbPl3IQDLzxvblmNCCN+R+nH4yorrAPdWYbL1ijZS0uKJNkdgtzkoK6nTOhwRxKRBMvh1Wd1Dy8PCdEREyZTFwURGmwiPMOB0umiUtTCCUlDt093W5v6SWSyWfsc9jz3Pt7e3YzabB7w+JiaGnTt3jikGRQfG2DGdwv8UCBtOQ6Ey8mtZ+8xmXC6VY8/IZsqxKTiso3hfAr/ceL+3zjS8cr5+35Ar1/ed1vf9+usHVgPuYkHV3Dg8WtePQVE39imvqAUgLsU9XO/Q3z9FN/C7NtixkR4fz7Ke69GZhi473vGaEsBusBIGTDkumW2fl3GgppiZ5yTgqj/6qIOhfq+DUSBeU6jVj8NpkMzLy5MGSQ15tm8MjzBKQ+gRKIpCWlY8JYV11FY1y0jTIBRUSXcgCIuAtNO1juLonA4wDz6tqR9FN7Jr2bKmhF1bywjTK/zwjhNIKhjd+0Lgl9PyvaXc6Msd/p1OWji8c4uxC4a6EcDlUim/0d2zmjbDvYhaVObB53URA79rgx0b6fHxLgv9rytQ4nUodrasc68Jou+burnt36VccOsxpBwzvKH+ofh7HYrXFCgmYoNkIDbmHIneDJ2d7qQ7MtqEYgCd8WCDna5vR0dPI+Lhzx/+eDhltHis6AHn8K9jwOdghIzcBEoK66hvbsIYO2XkH7aPBNP362hGeh1jbZAMqqQ7JiYGcK8umZSU5D3e3t7e73mLxUJtbe2A17e1tXnLjJazGxo2jOkUfpd0MliLhi6nuqDmo+Gds6Wxg9W/Ww/AsgvmklkQT8NX9OvpHu77QuCXG+/31pkO3iAH+mcTyOU832m92X0Te/h31CNpMehDbIFUrevHYKgbAeqqWulo6cVgDMNgd38JOivB5R7diKt74HdtsGMjPT6eZT03dYdeVyDGG6n2JT313TTs6cGx9+h3PkP9XgejQLymUKwftaRlg2SwNOb0qu6kOybDREQqOIxg7uvwNiWArfngPdLhzx/+eDhltHrcUz/86zj8cWQy5GfFs/4DaO5sDohG7mD5fg1lvK4jqJLu3Fz3ftDFxcXe//c8NhgMZGVlectt3LgRVVX7DVMpKSkhPz9/TDGoLrC1jukU/qeCs2d45YZzLV2dPfz9zx/Sae0hPTOBU8+cC7hvEPq9frjvS+CX0/K9pdwYyh32nR7wHfUUcw3vPYOJ1vVjUNSNwIHv3L3cWfkJYHdfv6v34PdLdQ38rg12bKTHx7ss9L+uQIxXj4FocwQd1m72f1vL3LykgScZxJF+r4NZIF1TqNWPE7FBMhAbc45Eb4bWKneloLeb6K6Fnhaw9o3o700ClIONiN2x/Z8//PFgxwLhcW/fz2G413H4Y6Md0nPcQ8pLv2t2/2w7AHWYH7QPBdP362hGeh1jbZAMqqQ7KyuLyZMn88EHH7B8+XLv8TVr1rBo0SKMRvfiC0uXLuWJJ55g48aNnHjiiYD7hnL37t1cffXVmsSuNafTRX1tC431bXRau3E4XRR+r4TY8FhSM+LJmZJGVnYyekNYv9dVljfw2guf01DXSlR0OD/+2ekYjEH1tRFiQpD6cXhKi2oAyC5IBFkAVnPxiWY6rN0Ubq1lbt5srcMRIWgiN0gGUmPOkahAR6s76TYZjKh2cNkONs657IDxYCPi4c8f/niwY4HwWHUAyvCv4/DH0bHhNDf3TYVo6WLLhr3MnTMNe+toP/mxC4bv13AM9zrG2iAZUNlTd3c3X3zxBQBVVVV0dHTwwQcfADB//nzi4+O5/vrrufnmm8nOzmbBggWsWbOG7du38+KLL3rPM2/ePJYsWcIdd9zBrbfeislk4qGHHqKgoIAzzjhDk2vTUn1tC/v3VNLba+93vHJvC5W0sHNb3/w6QxhZk5JJSolBUXTUVjd5V/m1xETy01+tIC4hyCdwCBGkpH70jZID7p6snBnJVO5o1zgakZAYQ3lJPfu31eC6UEWnk0WUhG9Jg2RgU1WVjnZ3ZhkRKVs4Hk13i4PwCCM93TYq9zQzd47WEYmRCKiku6mpiRtvvLHfMc/j559/ngULFnDOOefQ3d3N6tWr+dvf/kZOTg5//vOfmTev//ZVDz/8MA8++CB33303DoeDJUuWcOedd6LXB9Ql+5XLpbJ/TwVVFY0AmMINpGcmEhMXjcEQxkmrstn7rzYqyhso3l9Dp7WbkgM1lByo8Z5DURSOOS6Psy9cRFT0IMvTCiHGhdSPY9fSZKW1pQOdTiG7IFGS7gAQExdFWJiOzvZeqisbycwe3hBzITykQTK4dVp7cDrcXYjhEbJd2FCizRH0dNtoberUOhQxQgF1h5WZmcm+ffuGLHfxxRdz8cUXH7WM2WzmgQce4IEHHvBVeEHF5VLZua3Yu5ffpNxUJuelEhZ2cOm9Y07NItE6CXC3NDbUtVJeUk9rSwcul4v4BDNTCjKJjY/W5BqEEAdJ/Th2ngbFjOwkjOEB9edvwtLpdMQnmmmoa2PfrgpJusWISYNkcGuqdzd+msINQ+65LtxJd2N9myTdQUhqET9KWeZEHzX0UDlHp0rdJ2FDlhsuVVXZs6OUxvo2dDqFmcfkkJQSe9TXKIpCcmocyalxPotDCCECiSfpzpmSpnEk4lAJiTE01LVRuLuCZWcdq3U4IshIg2Rwa6p3r2AlQ8uHxxzjXsmrpVGS7mAjSbcf6aMUnrt265DlrvzLvCHLjMSav2ynrqYFRVGYPS+XhKSxbZMmhBChQJLuwJSQ5N46rLK8ng5rN9HmCI0jEkKMF09Pd0SEJN3DYbG4k+721i5svQ4USeWChozjCDH791by2n9/A0D+9ExJuIUQAmhr6aC5yYqiKEzKSdE6HHEIU7iR1MmxqKr7b5gQYuJobvD0dMt87uEwmgzuXYRUqKlo0jocMQKSdIeQttZOXn72U1SXSlpGAulZiVqHJIQQAcGzanl6VoIs1hOACua5Rx8U7q7QOBIhxHjy9nTL8PJhURTF29tdVSpJdzCRMQlB5GhzxFVV5ZWfrqO7q5dJsxKYnJbVb59JIYSYyEqKZGh5IMs/Np0v3tpD4Z5KXC6XLKgkxATR7JnTLcPLhy3aEklTYzuVpQ2ccLzW0YjhkqQ7iBxtjnhNZRN7dlaiKAo/f/hkPv5T6fgGJ4QQAcw7nztPku5AlJXvHoHQ3dVLZVkD2TIFQIiQZ7c7aGtxLwgWLj3dw2aJkZ7uYCRJdwBQFZWM81xDllOOsMB5T4/NOw8ud2oamQVxQKnvAhRCiCBmbe+isb4NRYHJealahyMGEZGoY+q8FHZ8WcHe8gNkLXbfVJoMRmxlcjMuRChqaXL3cusNYRgMvtvFJ9RFW9yLTdZWNeOwO9HLZxcUJOkOADqdwrOrhl7l/KrVg69yvn9PJQ6HE0tMJFmTpXdACCEO5enlTk2Pl3mDAcrWY8dhdQ8p/+ajUnSd4QAsuzofBfmZCRGKmvuS7mhLuEyJHIHwcCNGkx5br4PammYys5O0DkkMg0yaCnJNDe001LWiKDBtVjY6nVRaQghxKM8iapNlPndAS0h0bx1mbe+it9eucTRCCH9ranAvohZlDtc4kuCiKApxidEAVFc0ahyNGC5JuoOY0+micI97pdfM7GSizZEaRySEEIFHFlELDkaTAXPfqrzNfTfjQojQ1dTYBkB0jCTdIxWbGAVAdaUk3cFCku4gVl5SR3dXL0aTgZypcjMphBCHs7Z3UV/TAsgiasEgIcnd2+25GRdChC5P45ok3SMXl+Tu6a4sa9A4EjFcknQHqe6uXsqK3UMmp07LQK+XRRSEEOJwxfurAUjLSCAqWm7sAl1CUgwAzY1WXC5V42iEEP7U2Jd0m/sWBhPDl5BsBqC2uhmbTMcJCpJ0B6nCPRW4XCpx8WaSU+O0DkcIIQLSgX1VAOQVpGsciRgOS0wkBoMeh8NJW0uH1uEIIfzE6XTR2ty3kJr0dI9YZLSJmLgoXC6VynIZYh4MJOkOQo31rTQ1tKMoCvkzMmXFRyGEGISqqhTtc/d0T8nP0DgaMRyKoniHmDfWyxBzIUJVa7N7NIvBGEZ4pFHrcILSpCnJAFSU1mkciRgOSbqDjHvxNPee3FmTk4mKliE5QggxmObGdlpbOggL08n+3EEkMdk9xLyxoQ1VlSHmQoQiz9DyhJQY6Twapey+pLu8pF7jSMRwyD7dQaasuJaebhumcIPcRAohgpJxUi+9dtuA4yaDEVuZ7/ZkPlDo7uXOmpyM0WTw2XmFf8UnWlAUhe6uXhqq2knRWbQOSQjhY00N7pEsicny+z1ak6akAFBeWoeqqtJ4EeAk6Q4idSXtlJe4h5BMnZYpi6cJIYJSr93GJ08VDji+7Op8FHyXdBd55nPL0PKgoteHEZdgprmxnb1fV5MyP1PrkIQQPtbU6OnplqR7tDImJRAWpqOzo4fmRqt3ao4ITDK8PEioqsoLv9uIy6USn2AmKSVW65CEECJguVyqd+XyKQWSdAcbzxDzvV9XaRyJEMIfmvqGlyelxGgcSfAyGPWkZyYC7t5uEdgk6Q4SjfVtbP+ssm/xtCwZQiKEEEdRU9lIV2cvRpOB3JMsqOnWfv90US6tQxRHkdi3dVh5YRMd1m6NoxFC+JpneLn0dI9Ndo7M6w4WMrw8CDgdTvb3LZ6WnZNMZJRsrSCEEEezd1cFAFMK0nGojgHD2Zddn6NFWGKYwiOMRJsj6LB2U7i7gmMX5GsdkhDCR5xOFy1N7u3CElMsNDe3ahtQEMua7FnBXJLuQCc93UGgeH8NPT02EjKimJwri6cJIcRQ9u0uB6BgRrbGkYjR8gwx37OzXONIhBC+1NrcgculojeEYYmL0jqcoKToQG+GvGPci6nVVjfR0zNwgVIROCTpDnBtLR1UlLlbr37y4BLCZPE0IYQ4qg5rN1XlDQAUzMzSOBoxWp6ke//eShx2p8bRCCF8panRPbQ8PtGCTifTJUfDkhzOnl3FFO4pJ8pswuVSqaqTed2BTJLuAOZyubwt/Knp8cw5VVZwFUKIoRTuqUBVIT0zAUuM9KIEK7MlEnN8BLZeO8UHqrUORwjhI55F1BISZT73WLTV9tBU3o3FEg1A0V6pJwOZJN0BrKiwmq7OHoxGPVOnScIthBDDsa9vPnf+DOnlDmaKojDtuHQA9uyQIeZChIpGzyJqssWVT8TG9yXde2o0jkQcjSTdAaqpoc27KELBrGwMRlnzTgghhuJ0uti/173w5LSZMp872E073r3d295dZaiqqnE0QghfaKhtBSApOVbTOEKFJ+muKm2kV+Z1ByxJugOQrdfO7h1lAGRkJ0mlJIQQw1RWXEdPt43IqHAyJyVpHY4Yo7xZyRgMYbS1dFJd3qx1OEIIH2ioawUgOTVO20BCRESEyTuvu6xY5nUHKuk+DTAul8rO70qw2xxERYczpSBD65BIWeZEH3XIQhcKOB2QdDJwSMeDImu8CSE0tnt7KQAFMzLR6aRdOdgZTHqmTMtkz44ydn1byvEkaB2SEGIMerpttLV2ApCcGqttMCEkKS2GTms9xQdqZGpVgJKkO8Ds31tJa3MHYWE6Zh6TQ1iY/24aVUUl4zzXkOWUMIVnV231Pg4LB3MeWIvA2XOw3FWr5/kjTCGEGBaXS2XntmIAZs3N1Tga4Ssz5kxmz44ydnxdChyndThCiDHw9HKbLZFERJq0DSaEJKXHUFpYT8l+mdcdqCTpDiBV5Q3ebW5mHDOZaHOEX99Pp+ufTB+JJNNCiGBQXlJHe1sX4RFGWXwyhEyflY1Op1BT0UxtcRsQo3VIQohRqq9tAaSX29eS0tyL0lVVNNDbY8MUbtQ4InE4GXsXIOprW9i3273ibu7UdJnHLYQQI7R9axEAM2ZPQm+Q+S6hIjIqnNyp7lXMN79XonE0QoixqO9bRE3mc/tWdEw48SlmXC6V8poaDLGAbIEeUCTpDgDbPiln13fuG4m0zAQm5aZoHJEQQgQXl8vFrm3uenT2PBlaHiqMcaCmW5l5choAX75VhC65V+OohBCjJT3d/mFJDicl293b/e+1O9hXVIxBBgUFFEm6NdZQ18pjP/8UVYWUtDimzcxGUaRpSgghRqK0qA5rezcRkSbyAmABSuEbth47nzxVSNN+d6JdsaeZ2ooWjaMSQozWwaRberp9LT7RDEB1STNtNT1DlBbjLeiS7jfffJOCgoIB//70pz/1K/faa69x5plnMnv2bM477zw+++wzjSI+sqqKRnZsLcbe6yQpJYbpsydLwi2EGLVQqh+HYpzUi5pu9f77bu8eAGbMmYReL0PLQ43RZCAuwb0X7c6NFRpHI4QYDVuvnZbmDkCSbn9IzoxBUaCrq5dOqyTdgSZoF1J76qmnMJvN3scpKQeHZL/33nvcddddXHvttSxcuJA1a9Zw3XXX8dJLLzF37lwNou1PVVVKi2u9KwyefEk+SlMUOp0k3EKIsQvm+nG4eu02PnmqEACHw8m3n7mHls89foqWYQk/Sk6PpaWpg11fVrJk7glahyOCyJtvvsntt98+4PiqVau4+eabvY9fe+01nnrqKaqrq8nJyeHXv/41p5566niGGtI8K5dHRYcTFR2ubTAhyGjSY4mJoq21k7rKVq3DEYcJ2qR75syZxMfHD/rco48+ytlnn81NN90EwMKFCyksLOTxxx9n9erV4xjlQKqqUrin0rtK+aTcVH76hyU8d802TeMSQoSOYK0fR6u+pgWn00VCmtm74JYIPcnpsezbUUnZ3kba2zqxxERpHZIIMhOhQTKQySJq/hefaKGttZNaSboDTtANLx9KRUUFpaWlnHXWWf2Or1ixgo0bN2Kz2TSKDJxOFzu3lXgT7qnTMsnLT5ch5UKIcRHI9eNYVFU0AnDC8jypT0NYeISR+DR3or3ru1JtgxFBaebMmcydO9f7Ly0tzfvcoQ2SCxcu5L777mP27Nk8/vjjGkYcWmQRNf+LT3QvplZf1YrT6dI4GnGooE26zznnHKZPn86yZcv461//itPpBKC4uBiAnJycfuXz8vKw2+1UVGgzF8xud7Dt6wM01LWiKAozj8kha3KyJrEIIUJbsNWPY2Ft68La3oWiKBx7ymStwxF+lpEfC8DObbJ1mPCdUG2QDDTS0+1/lphI9Pow7DYnFcUNWocjDhF0w8uTkpK4/vrrOeaYY1AUhf/f3n3HR1GnDxz/bE02PYFQQgkJkJDQa6giTUSKBQvnCfgDFCygIAfIKQjHKYdnOUVF2omKnChFSkAUBWmCSIfQEiAJkAAhve1md35/xKwsCZAAyWw2z/v1ygvmO9+ZeWY3+2SfKd/56aefeP/990lOTmbatGmkp6cD4OPj47Bc0XTR/Nul0YLRr7SdQecOFnMBB/aeIjM9F71eS8uohgQEejt01ZXy1hZn6Kd1c/y3orZbkf0qetvXvpbO/to4dT9N4edT/8fHS+99g26V9nDjzamZH8uSGy2Gkt9PrYHCZ4uWYR0Xjhee5a4R5Id3dXcMhtJtT6MtOZdptCX3LSnesrRXZN8b5Whnjbe07Vq3wqL78NbznI29SK4tG9+Ayn2J+a1ylRpcNT9C4QHJ1NRUgoKCePzxxxk1ahQ6na5UByQbNmyoRsguJeniVUCK7vKk0Wjwr+bN5eQ0jh+Mp05veQyxs6h0RXe3bt3o1q2bfbpr1664ubmxZMkSxowZU+7b15mgdp/S9bUWgHs9K3u/PU1mei5uHnq6PtoI30CPYn29S5nLnamfZ111tlsR/dTctvS7/X4arePnM7Bj6dbtKtTMj2XJjZcTSn4/TbUhsEPp12GobSFpXQoAYV2q33D5kranNf2Zw67NZVpTyX1Lircs7RXdF4rnaGeOt/TtboS0rM6Zg1eIS43lgSdaFF+oEqpquaqiVZYDkneLMx7Myc3OJzUlE4DgyACMhQ8jQO8NWuOfB9c0Bsdp7R8HUosOIl4///rp0vRRY1qjB6yl3487ma5Rx5fLyWnEHIqn36N3f9BJZ/z9uh1l3Y87PSBZ6YrukvTr14/FixcTExODr2/hk+AzMzMJDAy098nIyACwz79d1ly4vKN0ff0729i+LJbUKzkYDDpaRzVGm2EiM6N438zY0q3TGfpp3Qq/zGUngi3f+eK7G/0qettFr2lFb9fV+ik2uPhDYQIN7AiXf4WCzOL9AruAvvixL5dUUfmxLLnR4lvy+5l7ES4eL/06jm2+hLVAwcfPA7d8rxsuX9L2bLmFOez6XGbLLblvSfGWpb0i+xZ9qbs+RztrvKVtL8qTLboEc+bgFbYsiaV1w8pddN8qV6nBFfNjZTkgebc508GcmJ2FZ7mr1/Wi4cOOl7V4nATLH3nLVAsKjOD9x/AcbtXAfPXP70jXz79+ujR91JrOu1T6/biT6QZBvhzbDxfOXkXfJJPAeuVTHTvT79edqKj9cImi+1qhoaFA4b2LRf8vmjYYDNSrV++O1q/YwJxWur7/m7Wb1CtZ6HRaWrZrhIebCesNHpt3o3Zn7mfLL97uTPHdST81ty397qCf4vj5LMgs+fOqVNGxRcozP5YlNyoeN8gpltKvI0cx2+9XCw6phS1fg94L8i3FKxet0VZse4rtmkL7mlym2IrHVlJbWdsrui8Uz9HOHG9Z2pt3qs/aefs4f/YKiSfSqFHTr/iClcyNcpUaqkp+dMYDkneLMx7MObSx8Fag2nWqsXVRHOlJhR/sOk19yEuGzMIr/Mn1g7zUP6fzAwHNnwcRr59//XRJbc4wnf/H+1Da/bizaT3Va/tw+UIGP39wjm73NSv5TblNzvj7dTvKuh93ekDSJYru6OhodDodkZGRBAYG0qBBAzZu3Ejv3r0d+nTq1Amj0VghMe3/7RQ/fHEMgMgWDeTRJkIIVThjfrwRoz+YcfzL52YwYj5XfACJXzecwlpgw9PLneo1Cr8Qm/MsbF5YfICtXmNDirWJysvTx43GEXU5cTSBg3tP06d/O7VDEpWcsxyQvNuc4mCOBgy+cP5c4a1AQSHVSE3II+VcLgDeAe7YzNcc+LTgMG2zAMY/DyJeP//66ZLanGFaKSh8LUq7H3c6HVQ/gMsXMjiy5xxRHe5u0V3EKX6/7oLS7sedHpCsdEX3yJEjiYqKIjw8HIDNmzezfPlyhg0bZj86OXbsWCZOnEj9+vWJiooiOjqaQ4cO8eWXX1ZIjLk5+az+3zYAGjSsRaALHIUXQji/ypAfb6akornXqDA0OBbdeblmdq4/AUBwaC15TFgV1LJto8Ki+/dYej/QVn4HRJlVpgOSlZnBF07ExnE65jwARu8/79MW5ScoOICDv57lzOmL5ObkY/IoYfRjUaEqXdEdEhLCihUrSEpKwmaz0aBBA6ZOncrQoUPtfQYMGEBubi4LFixg/vz5hISEMHfuXFq3bl0hMWo0Gnz9vWjcuTruuT63XkAIIe6CypAf74Ytm/aTk2nGw8NNRsGtoiKbB2Mw6rl6JYPEc5flEZzipir7AcnKLiUhm4zUHAC8PE3YClQOqArw8jVRs44fyefTOBmTQMu2jdQOqcqrdEX3a6+9Vqp+jz32GI899lg5R1Myd5ORCa89Tp1BNpaM2a9KDEKIqqcy5Mc7lXI5gx1bjgDQqEldtFo5w1nVGHzAFpJHZIc6HNx+jgPHY6jb2XTDWxGEqCoHJJ1V6uUsFAWMbgY8fdzJvFqGAXbEbYtsHUzy+TSOHjwrRbcTqHRFtxBCiKpr45rdWK02GrWoSbVAuZKoKiq6DUHJLfwKs/fHONwKPOnzbHixWxGEgKpxQNKZXb2cBYCPr4sNi+/kWnYI5ed1Bzl+JJ68XDPuJrlNQk13+MQxIYQQomKcjEng6MGzaDQaHni6tdzHW8UFVPPBYNBjMRdwNaWEZ3EKIZzC1UuFA2T6+MmgwhUpKLgaNWr5UVBg5ciB4oOMioolRbcQQginl5Odx8qvfgGgY7dIatb3UzcgoTqtVkOtoAAALiamqByNEOJGrl4qPNPtK0/yqVAajYZW7RoDcGDvKZWjEVJ0CyGEcHprvtlJRnoO1Wv40ndQB7XDEU6idt1qAFy5lEZWmtwnKoSzSU/NJicrHwBvuby8wrVs1xCAM6cvkp6apXI0VZsU3UIIIZza/j2nOLQvFq1Ww+NDe2A0ynAkopCXtwkfXw8UBfZvPat2OEKI65w5kQSAt48JvV6ncjRVj3+ANw0a1kJR4OC+WLXDqdKk6BZCCOG0zh2/zKr/bQOgR9/W1A0OVDki4Wxq160OwN7NsSiKonI0QohrxR2/CICfv7fKkVRdrdoVjly+b/dJyZEqkqJbCCGEU0pLzWLJP7dSUGAlonkwPfq2UTsk4YRq1vZHq9Ny5UIm5+KS1Q5HCHGNuD/OdPsFeKkcSdWi0YLeGwx+0ObeUIxuei4lpRF78oLaoVVZUnQLIYRQTUlH3a1WG2djk9i/5yT5uQWENKrNE8N6yDO5RYn0eh01a/kD8Ov2YypHI4QokpWZS/L5VAB8/aXorkg+NdyJORrHnu3HOLw/lkYtagGwc8sRlSOruuTGOCGEEBUq5WImMYfPkXI5HbO5AK1Wg06v48CBE5izbeTm5GO12gBo1qkejz7SC6ObQeWohTOrWz+Qi+dTOLI/joyHOsrzgIVwArEnzwPgG+AhY3GoID0pj5T4XADqh9bg2G+JHD8az5VLaVSv4aducFWQnOkWQghRYU4dT+SjSd9z8XwKZnMBADabUvis5aRssjJzsVptuJuMRDQPZsiEzlJwi1vy9vUguEl1bDaFPTti1A5HCAGcikkEoFZdf5UjEd6+JiJa1Qdg59ajKkdTNclhJyGEEBXmh/V7yc8twNffi9BGtfHwcsdms2EtsNHqoZrs/l8CRjcDXt4mNBoNGo1cUi5Kp9MDYZw7foU9O2K4975WMlKyECqy2RROFhXd9fzAqm48Au65vxkxB+L5ffdJet7fBi9vk9ohVSlyplsIIUSFeeChjgwc1ZbW7RvjX80bNzcDJpMbXt4mgiOrUS3QF28fDym2RZlFdqiLj68nWZm5HN4fp3Y4QlRpSedTyMrMxWDUU62Wj9rhCKBhRBB16lXHYi5g6w8H1A6nypGiWwghRIVp0LAWHe9vLIOiibtOp9cS1TUCgO0/HZJH4wihoqOHzgIQ1qwOOp2UG85Ao9Fw38D2APy67RipVzNVjqhqkU+BEEIIIVxCVNcIjG4GLp6/yomj8WqHI0SVdfTgGQCatw9RORIBfz5CLCKqDo0ig7BabfwY/bvaYVUpUnQLIYQQwiV4eLrT8Y+z3T9/f0DOdguhgkvJaVxKSkOn0xL5x+BdQl1FjxD7bUcMwWHVATjw2ykunk9RObKqQ4puIYQQQriMLj2aozfoSDh3idiTF9QOR4gq5+De0wA0DK+DydNN5WhEkaJHiCl5euqGVkNR4Luvt2OzycHJiiBFtxBCCCEqPaM/KEGZeDWx0r5PQwB+/nmvnO0WogLZbDb27TkJQJsOjVWORtxIq84huLkbiD97iQMHT2DwA2SolXIlRbcQQgghKj1znoXNC0+yeeFJNDlGtFoNZ45d4vgRubdbiIoSe+IC6anZmDzciGgerHY44gZqhfrSpmcDAL77ahcHDh3H4KtuTK5Oim4hhBBCuBR3dyP1GtQAYMN3u7FabSpHJETVsPOXIwC0bNcQg0GvcjTiZuo2CMTL24Qlv4CtK4/JVUHlTIpuIYQQQric4NBaePq6ceVSOru3H1M7HCFcXvLFVE4cTUCjgc73NFM7HHELWq2GJs2C0WggMS6FA7ti1Q7JpUnRLYQQQgiXo9fr6P1EcwA2b9hHVmauyhEJ4dq2bNoPQGSLBlSvIdcqVwY+vh40aFgbgFWf7yA9NUvliFyXFN1CCCGEcElte4VSKyiA3Jx81nyzQ+1whHBZifGXOfh7LBoN9LivtdrhiDIIDq1FQKAXuTlmln+xRW7HKSdSdAshhBDCJel0Wgb/tTtarYYjB85waJ9cPinE3Waz2Vj77U4AWrVrRHDz6hj8wOAHem9kVGwnp9Vq6NCjMW7uBs6cvsimdb+pHZJLkqJbCCGEEC7J6A9BUW7cOzgSgDUrtpPufgljcL7KkQnhIjSw69cjJJy9hLvJwKBn23MiLo4924+xZ/sxziUkojWoHaS4FW8/Dx4bdQ8A2zYf4siBMypH5Hqk6BZCCCGESyp6jJgt3YCXt4mcTDMf/+0HsrJy1A5NCJeQkHSR6OV7AGjavj7puWlkXskjJT6XlPhcsq6YVY5QlFbLDqF07Vk4Dsa3X24h8dxllSNyLVJ0CyGEEMKlabVamrUKQW/QkZmew4qPdmOzyeNxhCgTDfbLxg1+kJJ1lc8//BHFplCjlj8+Jh8psiu5vgM70Ci8DmZzAZ99upG0vHSH91xuFbh98gA9IYQQQrg8D093mrcO5cBvpzm8MwFP3U4GDO6MVivfIoUoDYMvnIiNI/1iHhmpOWxZd4T8XAv+NbyIaFYfjUY+S5WdTqflryN7s+CD9VxIvMInb67hngea4eHlhm9td8IbhqodYqUlZ7qFEEIIUSX4B3jTpFl9AH7ddozlX/xMQYFV5aiEqDzSL+Zx9kgKP685TH6uhWq1vOg+KBKdXqd2aOIucXM3MnxMX6rV8CEzLY8fVx4iISaV9It5aodWqUnRLYQQQogqo3adajz+cie0Wg2Hfo9l0dz1XE3JUDssISqFi/Gp7Nt9EnN+AV7eJvo91QY3dxkprbLTaAtHmi+6jDygvgcv/LM/Xr7u5OeZ2bfnJKmX5Rned0KKbiGEEEJUKS27BjPs2b4Y3Qyci0vmg9kr2b0jBptNnk8rREkURWHb90fY8f0xrFYb/gHetO7QGHcPKbhdgU8Nd2KO/jnq/J7tx8jITaPX4OZ4eZsw5xfw03eH+PXnGBRFxsO4HVJ0CyGEEKLKCYusx7jJjxAcWhNzvoXvvt7OB7NXcOzQWRlkTYhrmM0FfPPFFtYs3YWiQK2gAFq2a4jBIENDuZL0pD9HnS8aed7dw0jrDo2pXsMXm01hxX+3s+SDH0k+K1cHlZV8WoQQQghRpRj9wUwm/kEaRr3VnV0bTvHzt0e5lJTGlwt/oFqgDx27NaVtVBjuJqPa4QqhmqtXMli66Ecunk9Bq9XQPKoBAT5+MmhaFWIw6GneOpQrqVc5ujeeI7+f5ZUu54hsVZ+IVvWpE1yNGkH+GN30WNIBOWZZIpcuumNjY5k1axb79+/H09OTBx98kJdffhmjUf6ACiGqLsmNoqorfH73mWtaNLy6dACbP4th9/enSbmcwfqVu/gh+jdadA6mWXhjGobVQaeTCwRdneTHQjabjV2/HGPTut+wmAvw9HJn6NhepKSkkRKfq3Z4ooJpNBqatKrLfQ+1Y93/dnPiUCJH953j6L5z9j4Gow4fP088PU14ebvj6W3Cp5oJLx93vHxMePmYcNe64+lpwsPTDa1Oi8HXcTuuXLS7bNGdnp7O8OHDadCgAR9++CHJycnMnj2bvLw8pk2bpnZ4QgihCsmNQpRMp9egyzXRoXMkyReuknDuMjnZeez9KZa9P8Xi6W0ioll9GoXXoVF4HTw83dUOWdxlkh/BarVxeF8c2346yMXzVwEIDa/FkNE9CAzxYufGNHUDFKqqVTeAZyb2w1r/Kps/iuPAzjhSL2VjsRRgMVtJuZRBCre+9FyjAZOnG0Z3HR6e7vhW86B+RHW692gNOa5ZnrrmXgH/+9//yM7OZu7cufj5+QFgtVqZMWMGo0ePpmbNmuoGKIQQKpDcKMTN6fU66tQPJKheddJTs9H72ziyPZHszFz27jrB3l0n0GgKR0EPqledoLrVCapbjcCafpg83NQOX9wBl8uPmsKRqK917ZlEi7mAjPRs0tOyuXolk7hTFzh14jzZmYVnst1NBtr2DKV2UDVOHY8nR++DVsZNq7KKRjjXALXDAxg0KgDfAA+unM2hoMBKrSae5OebuXwum/xcM0ZvLVcSs8i4nIc5vwAbVqwFNrIz81EUyMnKJycL0q7kcOHcVWL2JbJ5+WFCGtamcURdGoYFUbN2AFqta9zK4LJF9y+//EKnTp3sSROgX79+TJ8+nR07dvDII4+oF5wQQqhEcqMQpaPRaPAL8KLXqDAG3t+d2JPnORWTyKkT57l0MZULiSlcSEwBTtiXMXm4Ua26DwHVffCv5o2Prwe+fp74+Hni4+uJl7c7Wq1cou6sXC0/ak02dv8Sw4VTaWSm5ZJvMaNYtKRdLiy0c3PyS1zOy9dE1z5N6TowgoM740g5V1iE+9aSqzuqsqIRztOT8vA4Cf4+hQdhNBoNBoMe32oeZF7VYtIpmLw8CG3vT2bDPC6fLvz9Ce3gj9YIKWdzMedZ8K3rRkJMKhdOpJOZkUNGRhY5WWZOHU/k1PFEADy93AltHETtOgFUC/TFy9uEm5sBN3cDBqMevUGHe4AOvV6HVqdBo9Hc/BJ1DfZL2vXef7ZVBJctuuPi4hg8eLBDm4+PD4GBgcTFxakUlRBCqEtyoxBlUzjoWjaN6/nRuJcf0Ayzks/J3Ze4EJfKhbhUks6lkpmaR25OPonxl0mMv1ziurRaDd4BJnwCTHj7mjAZTQRuc8OW4oZeMaDVFn5p1Gg02Gw2rFYbNqsNq82Gzar8Of3HDx4WLJYCrAWF7VqdFg9PN/QW98Ivpn98OfXwdCcsoh56g65CX7vKRs38qKCQlHKZ7Mw8zOYCLH/8mM0FFJitWK02DEYdeoMevV5b+K9Oi2LWotNqsVis5Obkk5GezdXUdK6+mUZSbAYFFutNt6s3aDF5uGHyNBLctBo16/nipnFHq9NyJf2KnNkWDtKT8riamIvFDfTmsh+ESU/KIzUxD4CaoW7UqueHLr/wCqGQ9n5kpGdzYk8yyYlppCRnkJ2Vx+H9cRzeX7rPn06vLfyc6PUYjDoMRj0Gox6jUYfeqMfNpCcnNw+bWUHvpsWntpFO3ZpTxzeozPtSVhrFRR+21rRpU1566SWeffZZh/YBAwbQunVr/vGPf9zWehUbWPNK11dnUshKMd+yn1c1Y6Xrp9WDrcB547uTfmptW6sHD1/nfm0qQz9rrgaNFnTuhZ9VpYTH7urcCy+TqoqcITeitZGbZSnW7O6tJy/TMbGYvAxgK+HNKmEdJS1/o/aitutz2c36lma9d2Mdd6OvyUdPboa6MZTHa6nVg9GkfmwltSmKgtFDR15mAYqiFD52TFFAC7YCRfVn25q8jJhMt/6SLPnx7ubH0ubG/Dwz2ZmlTaJlo9Vq0Gi16A1atHoNGjRotBr0Ri02q4KtoPB3U2/UYrOVfvp2lrmdaQUFq0UBxXliKvM+2BRsKChWSrUfak/ftI9VQaMDnbZ8Y9AZtFitNgryrdhsNmw2BcVWmGsVhcL8ehdodVr8Arxu2e9Oc6PLnukuLxot6D1K3Rvv6qW7v0v6OVc/Nbct/e6s37WfT51cCVdhypYbtXi7l/x+GtxKeyau5HXcaPmS2surb0Vv70Z9vaurH4OrvJZl6evuKacGxZ9Kmxv1HkY8A6rWCOlCVCUueyzTx8eHzMzMYu3p6en4+vqWsIQQQrg+yY1CCFEyyY9CiPLiskV3aGhosftvMjMzuXz5MqGhoSpFJYQQ6pLcKIQQJZP8KIQoLy5bdN9zzz3s3LmTjIw/nxW3ceNGtFotXbp0UTEyIYRQj+RGIYQomeRHIUR5cdmB1NLT0+nfvz8hISGMHj2a5ORkZs+ezcCBA5k2bZra4QkhhCokNwohRMkkPwohyovLFt0AsbGx/OMf/2D//v14enry4IMPMn78eIxGGahCCFF1SW4UQoiSSX4UQpQHly66hRBCCCGEEEIINbnsPd1CCCGEEEIIIYTapOgWQgghhBBCCCHKiRTdQgghhBBCCCFEOZGiWwghhBBCCCGEKCdSdAshhBBCCCGEEOVEim4hhBBCCCGEEKKc6NUOQFQOGzZsYM2aNRw9epSMjAyCg4MZOnQogwcPRqPRqB2eS8jOzqZfv34kJyfz7bff0rx5c7VDqrRWrVrFkiVLiI2NxcPDg+bNmzN37lzc3d3VDk04gaqQz1wtn7jaZ3rz5s3MmzeP06dP4+npSdu2bZk4cSL16tVTOzTh5G43f/Xs2ZPz588Xaz906BBubm7lGXKJVq5cyauvvlqs/ZlnnmHixIk3XE5RFBYsWMBXX33F1atXiYiI4NVXX6VVq1blGO2NDR06lD179pQ4791336V///4lznOG9+PcuXMsWrSIgwcPcurUKUJDQ1m3bl2xft988w0LFy7kwoULhISEMH78eHr06HHL9ScnJzNr1iy2b9+OwWCgT58+vPrqq3h5eVXofmRlZfHf//6XrVu3cvbsWYxGIy1atGD8+PGEh4ffdN27d+9m2LBhxdofeOAB3nvvvTLFKUW3KJXPPvuMOnXqMGXKFPz9/dm5cyevv/46SUlJvPjii2qH5xI+/vhjrFar2mFUep988gkLFixgzJgxtGrVitTUVHbt2iWvrbCrCvnMlfKJq32md+/ezYsvvshDDz3E+PHjSUtL4z//+Q8jRoxg7dq1lfZAgqgYd5K/+vbty4gRIxzajEZjeYZ7SwsXLsTb29s+XbNmzZv2X7BgAR988AETJ04kPDycpUuXMmLECL777jtVDlpNnz6drKwsh7YlS5awadMmOnXqdNNl1X4/Tp06xdatW2nZsiU2mw1FUYr1Wb9+Pa+//jpjxoyhY8eOREdH8+KLL7J06dKbHuiwWCyMGjUKgHfeeYe8vDz+9a9/8corr/Dpp59W6H5cuHCBr7/+msGDB/Pyyy+Tn5/P4sWLeeKJJ1ixYgUNGza85TbeeustQkND7dP+/v5lD1QRohRSUlKKtb322mtKmzZtFKvVqkJEruX06dNKq1atlGXLlilhYWHKoUOH1A6pUoqNjVUiIyOVLVu2qB2KcGKuns9cKZ+44mf69ddfV3r27KnYbDZ7265du5SwsDDlt99+UzEyURncbv7q0aOHMmPGjPIMrUxWrFihhIWFlbg/N5KXl6e0adNGeeedd+xt+fn5So8ePZTp06eXQ5S3p2fPnsozzzxz0z7O8H5c+/syefJkpX///sX63HfffcqECRMc2p544gll1KhRN1332rVrlfDwcCU2Ntbetm3bNiUsLEw5ePDgHUbu6Fb7kZ2dreTk5Di0ZWVlKR06dFBmzpx503X/+uuvd+3vqNzTLUolICCgWFtERARZWVnk5OSoEJFrmTVrFkOGDCEkJETtUCq1lStXUrduXbp37652KMKJuXo+c6V84oqf6YKCAjw9PR0uBS4606eUcKZJiGu5ev66mX379pGVlUW/fv3sbUajkT59+vDLL7+oGNmf9u3bR2JiIgMHDlQ7lFvSam9eBiYkJHD27FmH1xsKL63etWsXZrP5hsv+8ssvhIeHO5wd7tKlC35+fmzduvXOAr/OrfbDw8MDk8nk0Obp6Un9+vW5dOnSXY3lZqToFrft999/p2bNmnf93oyqZuPGjZw8eZIXXnhB7VAqvYMHDxIWFsbHH39Mp06daNasGUOGDOHgwYNqhyacnKvkM1fLJ674mX7kkUeIjY1l6dKlZGZmkpCQwLvvvktkZCRt2rRROzxRCZU2f61du5ZmzZrRunVrnnnmGU6cOFFBEd7YgAEDiIiIoFevXnz66ac3vW0kLi4OwKGQA2jYsCEXLlwgLy+vXGMtjXXr1uHh4UGvXr1u2dcZ349rFb3e1x/AbdiwIRaLhYSEhJsue/37pNFoCAkJsa9XTRkZGfb7v0vj2WefJSIignvuuYd//etft/W7Jvd0i9uyd+9eoqOjmTx5stqhVGq5ubnMnj2b8ePHV/ov+87g8uXLHDlyhJMnTzJ9+nRMJhPz5s1jxIgRbNq0iWrVqqkdonBCrpLPXDGfuOJnul27dsydO5dXXnmFmTNnAoVnKhcuXIhOp1M5OlHZlDZ/9ezZkxYtWhAUFERCQgLz5s3jySefZPXq1arcCx0YGMjYsWNp2bIlGo2Gn376iffff5/k5GSmTZtW4jIZGRkYjcZiA435+PigKArp6emqjolQUFDAhg0b6NmzJx4eHjft62zvR0nS09OBwtf3WkXTRfNLkpGR4XCvfhFfX9+bLldR3n77bTQaDX/5y19u2s/b25tRo0bRvn173Nzc+PXXX1m8eDFxcXFlvjddim5RZklJSYwfP56oqKgSR/QTpffJJ59QrVo1Bg8erHYoLkFRFHJycvjPf/5DkyZNAGjZsiU9e/bkyy+/5KWXXlI5QuFsXCmfuWI+ccXP9L59+5g0aRKPP/449957L2lpaXz88cc8++yzfPXVVzKQmii1suSv1157zf7/du3a0aVLF/r168eiRYt44403yjnS4rp160a3bt3s0127dsXNzY0lS5YwZswYatSoUeEx3akdO3Zw9epVBgwYcMu+zvZ+VCUrVqxg+fLlzJ49m1q1at20b2RkJJGRkfbpTp06UaNGDWbOnMmhQ4do0aJFqbcrl5eLMsnIyOCZZ57Bz8+PDz/88Jb3UYgbO3/+PIsXL2bcuHFkZmaSkZFhvx8rJyeH7OxslSOsfHx8fPDz87N/OQfw8/MjMjKS06dPqxiZcEaulM9cNZ+44md61qxZdOzYkSlTptCxY0fuv/9+5s+fz7Fjx/juu+/UDk9UEneav2rUqEHbtm05evRoOUVYdv369cNqtRITE1PifB8fH8xmM/n5+Q7tGRkZaDQafH19KyLMG1q3bh1+fn507dq1zMs64/tR9HpmZmY6tGdkZDjML4mPj0+xUd2h8Oy4mu/T1q1bmTZtGs8//zwPP/zwba2j6B73I0eOlGk5OdMtSi0vL4/Ro0eTmZnJ119/XeJlI6L0EhMTsVgsPPvss8XmDRs2jJYtW7J8+XIVIqu8GjVqRHx8fInzrv8jLao2V8tnrppPXPEzHRsbW+x+z1q1auHv73/DfRXiWq6Wv0qr6P7bM2fOOByIi4uLIygoSNWrRPLy8vjxxx8ZNGgQBoNBtTjupqLX+/r7s+Pi4jAYDDe9DD40NJSTJ086tCmKwpkzZ+jSpUv5BHwLBw4c4KWXXuKhhx5S5SopKbpFqRQUFPDyyy8TFxfH0qVLb/kcRXFrERERfP755w5tMTExvPXWW8yYMYPmzZurFFnl1aNHD1auXElMTAwREREApKamcvToUZ5++ml1gxNOwxXzmavmE1f8TAcFBXHs2DGHtvPnz5OamkqdOnVUikpUFncrfyUnJ/P777/z4IMP3uUIb190dDQ6nc7hct5rtWnTBi8vLzZs2GAvui0WC5s2beKee+6pyFCL+emnn8jJybntUcud8f2oV68eDRo0YOPGjfTu3dveHh0dTadOnW76TPF77rmHNWvWcPbsWRo0aADArl27SEtLU+VpFKdPn2b06NF07NiRGTNm3NG61q9fD1Dmv6tSdItSmTFjBj///DNTpkwhKyuLAwcO2OdFRkbe9IMnSubj40NUVFSJ85o2bUrTpk0rOKLKr3fv3jRv3pxx48Yxfvx43NzcmD9/PkajkSeffFLt8ISTcMV85qr5xBU/00OGDOHNN99k1qxZ9OzZk7S0NPv9+Nc/mkeI65Umfw0fPpwLFy7www8/AIWXPf/88890796dGjVqkJCQwPz589HpdPzf//2fKvsxcuRIoqKiCA8PB2Dz5s0sX76cYcOGERgYCFBsP9zc3Bg9ejQffvghAQEBhIWFsWzZMtLS0hg5cqQq+1Fk7dq1BAUF0bZt22LznPX9yM3NtT++6/z582RlZbFx40YAOnToQEBAAGPHjmXixInUr1+fqKgooqOjOXToEF9++aV9PefPn6dPnz48//zzvPjiiwD07duXTz/9lLFjxzJhwgRyc3OZM2cO9957b5nug74b+6EoCiNHjsTNzY3hw4c7XBbu5eVFo0aNbrgfEydOJDg4mMjISPtAap999pn9b1NZSNEtSmXHjh0AzJ49u9i8zZs3U7du3YoOSYhitFot8+fP56233mLatGlYLBbatWvH0qVL7X/EhZB8Vnm44md62LBhGI1Gli1bxooVK/D09KRVq1a8//77+Pv7qx2ecHKlyV82m83h0Vt169bl0qVLvPnmm2RmZuLt7U3Hjh0ZN26caiNlh4SEsGLFCpKSkrDZbDRo0ICpU6cydOhQe5/r9wPgmWeeQVEUFi9ezNWrV4mIiGDRokWqjvidnp7Otm3bGD58OBqNpth8Z30/UlJSil1mXTT9+eefExUVxYABA8jNzWXBggXMnz+fkJAQ5s6dS+vWre3LKIqC1WpFURR7m8FgYOHChcyaNYsJEyag1+vp06cPU6dOrfD9gMJBB4FiV0h16NCBL7744ob70bhxY9auXcvixYuxWCzUqVOHMWPGlHgr161olGvXLIQQQgghhBBCiLum8g7VKoQQQgghhBBCODkpuoUQQgghhBBCiHIiRbcQQgghhBBCCFFOpOgWQgghhBBCCCHKiRTdQgghhBBCCCFEOZGiWwghhBBCCCGEKCdSdAshhBBCCCGEEOVEim4hhBBCCCGEEKKcSNEthBBCCCGEEEKUEym6RaUwZcoUwsPDCQ8Pp2nTpvTs2ZM5c+aQn5+vdmhCCFGurs1/1/6cO3dO7dCEEMJpFOXKadOmFZs3Y8YMwsPDmTJlSoXGFB4ezo8//lih2xTOSa92AEKUVrdu3XjrrbcoKCjg6NGjTJ48GY1Gw9/+9je1QxNCiHJVlP+uFRAQ4DBtNpsxGo0VGZYQQjiV2rVrEx0dzdSpU3F3dwcgPz+fdevWERQUpHJ0t0/ye+UnZ7pFpWE0GgkMDKR27dr07t2bzp07s3PnTgBsNhuffvopPXv2pEWLFgwaNIiNGzc6LH/q1ClGjx5NmzZtaN26NU8++STx8fH2+d988w39+vWjefPm3H///SxdutQ+LzExkfDwcDZt2sTQoUNp2bIlgwYNYv/+/Q7b+P333+3z27dvz8iRI0lPT2f16tVERUVhNpsd+j///PNy0EAIcUtF+e/an6effpqZM2fyz3/+k6ioKEaOHAnAyZMnGTVqFK1bt6Zz58787W9/4+rVq/Z15eTkMGnSJFq3bk3Xrl1ZvHgxQ4cO5Z///Ke9T0lnZ9q1a8fKlSvt0xcvXuSll16iXbt2dOjQgeeee47ExET7/ClTpvD888+zaNEiunbtSlRUFDNmzMBisdj7mM1m3n77bbp3706zZs3o06cP33zzDYqi0KdPHxYtWuQQQ0xMjJzlF0LcUGRkJLVr12bTpk32tk2bNlG7dm0iIiLsbWazmVmzZtGpUyeaN2/OX/7yFw4dOmSfv3v3bsLDw9m1axePPPIILVu2ZMiQIcTFxTls78cff+Thhx+mefPm9OrVi7lz51JQUABAz549AXjhhRcIDw+3T8fHx/Pcc8/RuXNnWrduzeDBg+3fZ4v07NmTjz76iEmTJtGmTRumTZvGsGHDmDlzpkO/q1ev0qxZM3bt2nUXXj1RnqToFpXSyZMn2b9/PwaDAYBPP/2U1atXM2PGDNavX8/TTz/N3/72N/bs2QNAcnIyTz31FEajkSVLlrBy5UoGDx5sT4xr1qzhP//5D+PHjyc6OpoJEybwwQcfsGrVKoftvvfee4wcOZLVq1fToEEDXnnlFfs6YmJiePrpp2nYsCFff/01X331FT169MBqtXL//fdjtVrZvHmzfV0pKSls3bqVwYMHV8RLJoRwQatWrcJgMLBs2TJmzJhBRkYGw4cPJzIykm+//ZaFCxeSkpLCyy+/bF9mzpw5/Pbbb3z88ccsWrSIPXv2cPTo0TJt12KxMHLkSDw9PVm6dCnLli3Dw8ODUaNGORxc3L17N/Hx8SxZsoTZs2ezatUqh7w6adIk1q9fz2uvvcaGDRuYOXMmnp6eaDQaBg8e7FDkA6xYsYL27dsTHBx8ey+YEMLlXZ87VqxYwSOPPOLQZ86cOXz//ff2vBQcHMyoUaNIS0tz6Pfee+8xZcoUVqxYgU6nY+rUqfZ5e/fuZfLkyQwbNozo6GhmzpzJypUrmTdvHgDffvstAG+99Rbbt2+3T+fk5NC9e3c+++wzVq1aRbdu3RgzZgwXLlxw2PbixYtp0qQJq1ev5vnnn+exxx5j3bp1Djl2zZo11KhRg44dO975CyfKlyJEJTB58mQlIiJCadWqldKsWTMlLCxMadKkibJx40YlPz9fadmypbJv3z6HZaZOnapMmDBBURRFeeedd5SePXsqZrO5xPX37t1bWbt2rUPbRx99pDzxxBOKoihKQkKCEhYWpixfvtw+/9SpU0pYWJhy+vRpRVEUZcKECcqQIUNuuA/Tp09XRo0aZZ9evHix0qtXL8Vms5XhlRBCVDXX5r+in7FjxypPPfWU8tBDDzn0/eijj5QRI0Y4tF28eFEJCwtT4uLilKysLKVp06ZKdHS0fX5qaqrSokULZdasWfa2sLAw5YcffnBYT9u2bZUVK1YoiqIoq1evVvr27euQv/Lz85UWLVoo27Zts8fdo0cPpaCgwN5n3Lhxyssvv6woiqLExcUpYWFhyo4dO0rc76SkJCUiIkI5ePCgoiiKYjablaioKGXlypWle+GEEFXK5MmTleeee05JSUlRmjVrpiQmJiqJiYlK8+bNlZSUFOW5555TJk+erGRnZytNmzZV1qxZY1/WbDYrXbt2VRYsWKAoiqL8+uuvSlhYmLJz5057ny1btihhYWFKXl6eoiiKMnz4cGXevHkOMaxevVrp0qWLfbqkXFqS/v37K1988YV9ukePHsrzzz/v0CcvL09p3769sn79envbwIEDlQ8//LA0L49QmdzTLSqNqKgo3njjDXJzc/nss8/Q6XT07duXU6dOkZuby4gRIxz6WywW+6VEMTExtGvXzn5m/Fo5OTnEx8fz97//nddff93eXlBQgLe3t0Pf8PBw+/8DAwOBwkt7GjZsSExMDPfff/8N43/88cd59NFHSU5OpmbNmqxcuZKHH34YjUZT9hdDCFGlFOW/IiaTiVdeeYWmTZs69Dt+/Di7d++mdevWxdYRHx9Pfn4+FouFli1b2tv9/PwICQkpUzzHjx8nPj6eNm3aOLTn5+c73LbTqFEjdDqdfTowMJCTJ08ChXlZp9PRvn37ErdRs2ZNunfvzrfffkuLFi34+eefMZvNN82zQggREBDAvffey6pVq1AUhXvvvddhDIz4+HgsFotD/jIYDLRo0YLY2FiHdZX0vS8lJYWgoCCOHz/Ovn377Ge2AaxWK/n5+eTm5mIymUqMLzs7m7lz57JlyxYuX76M1WolLy+v2JnuZs2aOUy7ubkxaNAgVqxYwQMPPMDRo0c5deoUn3zySRlfIaEGKbpFpWEymeyXFL755ps8+OCDfPPNN4SFhQGFl5jXrFnTYZmiQSeKBtMoSU5ODgD/+Mc/HL6IAmi1jndgXFu0FxXLNpvtltuAwvuMii4T6tKlC6dPny52uZMQQpTk2vx3ffu1cnJy6NGjBxMnTizWNzAw0KEgvhmNRoOiKA5tRbfSFG2nadOm/Pvf/y627LVfbvV6x68Z1673VjkT4LHHHmPSpElMnTqVlStX8sADD9zwi6wQQhQZPHiw/f7n6dOn3/Z6rs1h13/vy8nJYezYsdx3333FlnNzc7vhOv/1r3+xc+dOJk+eTP369XF3d2fcuHEO411A8fwOhTnxoYceIikpiZUrV9KxY0fq1KlzW/smKpYU3aJS0mq1jB49mtmzZ7Nx40aMRiMXLlygQ4cOJfYPDw9n1apVWCyWYme7q1evTo0aNUhISGDQoEG3HVPRgBvjxo27YZ9HH32UJUuWkJycTOfOnaldu/Ztb08IIa7XtGlTvv/+e+rUqVOs4AWoV68eBoOBgwcP2kfyTU9P5+zZsw5nnAMCArh06ZJ9+uzZs+Tm5jpsZ8OGDVSrVg0vL6/bijUsLAybzcZvv/1G586dS+zTvXt3TCYTy5YtY9u2bXz55Ze3tS0hRNXSrVs3LBYLGo2Grl27OsyrX78+BoOBffv22QtWi8XC4cOHGT58eKm3ERkZyZkzZ246xoTBYMBqtTq07d+/n4cffpg+ffoAhWe+z58/X6pthoeH06xZM5YvX866descrtAUzk0GUhOV1v33349Wq+Xrr79mxIgRvPXWW6xatYr4+HiOHj3KF198YR+w569//StZWVlMmDCBw4cPc/bsWVavXm0fhXLcuHHMnz+fzz//nDNnznDixAlWrFjBf//731LH8+yzz3L48GHeeOMNjh8/TmxsLF999ZXDqMEDBw4kOTmZ5cuXywBqQoi77sknnyQ9PZ0JEyZw6NAh4uPj2bZtG6+++ipWqxVPT08GDx7M22+/za5duzh58iRTpkwpdptLx44dWbp0KceOHePw4cNMnz7d4YDlwIED8ff357nnnmPv3r0kJCSwe/duZs2aRVJSUqlirVu3Lg8//DBTp07lxx9/tK8jOjra3ken0/HII4/wzjvvEBwcXOJl80IIcT2dTseGDRuIjo52uMUFwMPDg7/85S/MmTOHX375hdOnT/P666+Tl5fHo48+WuptvPDCC3z33XfMnTuXU6dOERsby/r163nvvffsferUqcOuXbu4fPky6enpAAQHB/PDDz8QExPD8ePHeeWVV+xnz0vjscceY/78+fanPIjKQc50i0pLr9fz1FNPsXDhQjZv3kxAQACffvopiYmJeHt7ExkZyZgxYwDw9/dnyZIlvP322wwdOhStVktERARt27YFChOYu7s7ixYtYs6cOXh4eBAWFlamI54hISEsXryYd999176+Fi1aMGDAAHsfb29v7rvvPrZu3Urv3r3v7gsihKjyatasybJly/j3v//NyJEjMZvNBAUF0a1bN/vtMpMmTSInJ4fnnnsOT09P/u///o+srCyH9UyePJmpU6fy17/+lRo1ajB16lSHEc5NJhNffvkl//73v3nxxRfJzs6mZs2adOrUqUxnvt944w3effdd3njjDdLS0ggKCmL06NEOfR599FHmzZsnt+MIIcrkZrlo4sSJKIrCpEmTyM7OplmzZixcuBBfX99Sr79bt27MmzePjz76iAULFqDX6wkNDeWxxx6z95k8eTKzZ8/mm2++oWbNmvz0009MmTKFqVOnMmTIEPz9/XnmmWfIzs4u9Xb79+/Pm2++Sf/+/W96GbtwLhrl+pu2hBDlavjw4TRu3JjXXntN7VCEEAKAoUOH0qRJE/7+97+rHUoxe/fu5emnn2bLli1Ur15d7XCEEEJViYmJ9OnTh2+//bbYYJrCecnl5UJUkPT0dH744Qf27NnDk08+qXY4Qgjh1MxmM0lJSXz44Yf07dtXCm4hRJVmsVi4fPky77//Pi1btpSCu5KRy8uFqCAPP/ww6enpTJw4kdDQULXDEUIIp7Zu3Tr+/ve/ExERwZw5c9QORwghVLVv3z6GDRtGgwYN+OCDD9QOR5SRXF4uhBBCCCGEEEKUE7m8XAghhBBCCCGEKCdSdAshhBBCCCGEEOVEim4hhBBCCCGEEKKcSNEthBBCCCGEEEKUEym6hRBCCCGEEEKIciJFtxBCCCGEEEIIUU6k6BZCCCGEEEIIIcqJFN1CCCGEEEIIIUQ5kaJbCCGEEEIIIYQoJ/8PdXwFsoEZQVMAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# applying log to recency, frequency and monetary\n",
        "transformation = pd.DataFrame()\n",
        "\n",
        "transformation['Recency'] = np.log1p(rfm['Recency'])\n",
        "transformation['Frequency'] = np.log1p(rfm['Frequency'])\n",
        "transformation['Monetary'] = np.log1p(rfm['Monetary'])\n",
        "\n",
        "plot_distribution(transformation)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VGVzEz4NqsUx"
      },
      "source": [
        "\n",
        "After applying the log transformation, we can see that Frequency and Monetary now follow a normal distribution, making the data more balanced and suitable for analysis."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:27.502460Z",
          "iopub.status.busy": "2025-01-16T16:53:27.502037Z",
          "iopub.status.idle": "2025-01-16T16:53:28.118507Z",
          "shell.execute_reply": "2025-01-16T16:53:28.117092Z",
          "shell.execute_reply.started": "2025-01-16T16:53:27.502419Z"
        },
        "id": "LAxqlUu-JaFy",
        "outputId": "1f8ed629-f57b-4d8b-a1a8-dc677346aa77"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_boxplot(transformation)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_OZNg9FArDkx"
      },
      "source": [
        "From the boxplots, we observe that after applying the log transformation, Frequency and Monetary still have a few outliers. These can be easily handled using the IQR method for better accuracy in segmentation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:28.120306Z",
          "iopub.status.busy": "2025-01-16T16:53:28.119874Z",
          "iopub.status.idle": "2025-01-16T16:53:28.140769Z",
          "shell.execute_reply": "2025-01-16T16:53:28.139686Z",
          "shell.execute_reply.started": "2025-01-16T16:53:28.120266Z"
        },
        "id": "ATeSSzPRe_Q8",
        "outputId": "fe4c1138-b737-43d9-b9dd-44522e623abb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Recency    Frequency     Monetary\n",
              "count  4334.000000  4334.000000  4334.000000\n",
              "mean      3.832072     3.724842     6.575070\n",
              "std       1.340772     1.246148     1.255304\n",
              "min       0.693147     0.693147     1.558145\n",
              "25%       2.944439     2.890372     5.721098\n",
              "50%       3.951244     3.737670     6.495900\n",
              "75%       4.969813     4.595120     7.397943\n",
              "max       5.926926     8.944811    12.539465"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c75f199e-5003-43d4-abcd-0b2204b34fc2\" class=\"colab-df-container\">\n",
              "    <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>Recency</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>Monetary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4334.000000</td>\n",
              "      <td>4334.000000</td>\n",
              "      <td>4334.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.832072</td>\n",
              "      <td>3.724842</td>\n",
              "      <td>6.575070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.340772</td>\n",
              "      <td>1.246148</td>\n",
              "      <td>1.255304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.693147</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>1.558145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.944439</td>\n",
              "      <td>2.890372</td>\n",
              "      <td>5.721098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.951244</td>\n",
              "      <td>3.737670</td>\n",
              "      <td>6.495900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.969813</td>\n",
              "      <td>4.595120</td>\n",
              "      <td>7.397943</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.926926</td>\n",
              "      <td>8.944811</td>\n",
              "      <td>12.539465</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c75f199e-5003-43d4-abcd-0b2204b34fc2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c75f199e-5003-43d4-abcd-0b2204b34fc2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c75f199e-5003-43d4-abcd-0b2204b34fc2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-dc0fe966-f297-444d-b3ae-900f4ee6d6c2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dc0fe966-f297-444d-b3ae-900f4ee6d6c2')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-dc0fe966-f297-444d-b3ae-900f4ee6d6c2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"transformation\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Recency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1531.1064494489572,\n        \"min\": 0.6931471805599453,\n        \"max\": 4334.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.8320720218015487,\n          3.9512437185814275,\n          4334.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Frequency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1530.9977304258573,\n        \"min\": 0.6931471805599453,\n        \"max\": 4334.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.7248422985953487,\n          3.7376696182833684,\n          4334.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Monetary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1530.2062486399986,\n        \"min\": 1.255304488718732,\n        \"max\": 4334.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.575070459984023,\n          6.495899784457252,\n          4334.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 103
        }
      ],
      "source": [
        "transformation.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:28.142438Z",
          "iopub.status.busy": "2025-01-16T16:53:28.142082Z",
          "iopub.status.idle": "2025-01-16T16:53:28.418252Z",
          "shell.execute_reply": "2025-01-16T16:53:28.417121Z",
          "shell.execute_reply.started": "2025-01-16T16:53:28.142397Z"
        },
        "id": "ObuEIjFJlQsB",
        "outputId": "52b4635d-e93e-4567-c10f-4117d854412e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.heatmap(transformation.corr(),annot=True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CPYeu0D8r7ox"
      },
      "source": [
        "From the correlation analysis, we observe that Recency has a negative correlation with Frequency and Monetary, which makes sense—customers who purchase frequently tend to have lower recency values. Meanwhile, Frequency and Monetary show a positive correlation, indicating that frequent buyers also tend to spend more"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:28.420093Z",
          "iopub.status.busy": "2025-01-16T16:53:28.419541Z",
          "iopub.status.idle": "2025-01-16T16:53:28.425747Z",
          "shell.execute_reply": "2025-01-16T16:53:28.424542Z",
          "shell.execute_reply.started": "2025-01-16T16:53:28.420055Z"
        },
        "id": "Tk3HmYiF9ui0"
      },
      "outputs": [],
      "source": [
        "# IQR function to calculate the outliers\n",
        "def IQR_outlier(data,col):\n",
        "  Q1 = data[col].quantile(0.25)\n",
        "  Q3 = data[col].quantile(0.75)\n",
        "  IQR = Q3 - Q1\n",
        "\n",
        "  lower_bound = Q1 - 1.5*IQR\n",
        "  upper_bound = Q3 + 1.5*IQR\n",
        "\n",
        "  outliers = data[(data[col] < lower_bound) | (data[col] > upper_bound)]\n",
        "  print(f\"Number of Outliers in {col} : {outliers.shape[0]}\")\n",
        "  return lower_bound,upper_bound"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:28.427498Z",
          "iopub.status.busy": "2025-01-16T16:53:28.427064Z",
          "iopub.status.idle": "2025-01-16T16:53:28.457226Z",
          "shell.execute_reply": "2025-01-16T16:53:28.456151Z",
          "shell.execute_reply.started": "2025-01-16T16:53:28.427452Z"
        },
        "id": "vSPzblz_-zj1",
        "outputId": "6a79fec6-188f-4006-9c86-f4a98fd9c0f1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Outliers in Frequency : 12\n"
          ]
        }
      ],
      "source": [
        "lower_bound,upper_bound = IQR_outlier(transformation,'Frequency')\n",
        "transformation = transformation[(transformation['Frequency'] > lower_bound) & (transformation['Frequency'] < upper_bound)]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QL8HrJJzsHaz"
      },
      "source": [
        "There are 12 outliers in Frequency, which is a small number, so we can remove them to ensure a more balanced dataset for analysis."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:28.459116Z",
          "iopub.status.busy": "2025-01-16T16:53:28.458666Z",
          "iopub.status.idle": "2025-01-16T16:53:28.477410Z",
          "shell.execute_reply": "2025-01-16T16:53:28.476160Z",
          "shell.execute_reply.started": "2025-01-16T16:53:28.459075Z"
        },
        "id": "hyvfwiEZ_ck5",
        "outputId": "0139a22b-6b27-443b-d5d9-d5a22015e0fe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Outliers in Monetary : 44\n"
          ]
        }
      ],
      "source": [
        "lower_bound,upper_bound = IQR_outlier(transformation,'Monetary')\n",
        "transformation = transformation[(transformation['Monetary'] > lower_bound) & (transformation['Monetary'] < upper_bound)]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hJZNVpCbseLU"
      },
      "source": [
        "There are 44 outliers in Monetary, which is a small number, so we can remove them to maintain a more balanced dataset for analysis."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:28.479002Z",
          "iopub.status.busy": "2025-01-16T16:53:28.478679Z",
          "iopub.status.idle": "2025-01-16T16:53:28.513835Z",
          "shell.execute_reply": "2025-01-16T16:53:28.512664Z",
          "shell.execute_reply.started": "2025-01-16T16:53:28.478973Z"
        },
        "id": "dKTEMea9CVwa",
        "outputId": "e89f869f-dc14-41fc-ea16-4dc7238ab7e7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Recency    Frequency     Monetary\n",
              "count  4278.000000  4278.000000  4278.000000\n",
              "mean      3.849925     3.710535     6.541597\n",
              "std       1.327380     1.212116     1.168768\n",
              "min       0.693147     0.693147     3.277145\n",
              "25%       2.944439     2.890372     5.719639\n",
              "50%       3.960768     3.713572     6.487684\n",
              "75%       4.976734     4.584967     7.367336\n",
              "max       5.926926     7.141245     9.839104"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0cac604d-3b73-44ca-9141-ad3df39fc609\" class=\"colab-df-container\">\n",
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              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Recency</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>Monetary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4278.000000</td>\n",
              "      <td>4278.000000</td>\n",
              "      <td>4278.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.849925</td>\n",
              "      <td>3.710535</td>\n",
              "      <td>6.541597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.327380</td>\n",
              "      <td>1.212116</td>\n",
              "      <td>1.168768</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.693147</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>3.277145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.944439</td>\n",
              "      <td>2.890372</td>\n",
              "      <td>5.719639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.960768</td>\n",
              "      <td>3.713572</td>\n",
              "      <td>6.487684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.976734</td>\n",
              "      <td>4.584967</td>\n",
              "      <td>7.367336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.926926</td>\n",
              "      <td>7.141245</td>\n",
              "      <td>9.839104</td>\n",
              "    </tr>\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0cac604d-3b73-44ca-9141-ad3df39fc609')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0cac604d-3b73-44ca-9141-ad3df39fc609 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0cac604d-3b73-44ca-9141-ad3df39fc609');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f49e6e40-8624-4c78-80f6-b1014c70da7f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f49e6e40-8624-4c78-80f6-b1014c70da7f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f49e6e40-8624-4c78-80f6-b1014c70da7f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"transformation\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Recency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1511.3064214910828,\n        \"min\": 0.6931471805599453,\n        \"max\": 4278.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.849925154452642,\n          3.9607678160667747,\n          4278.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Frequency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1511.2932733877433,\n        \"min\": 0.6931471805599453,\n        \"max\": 4278.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.710535324813659,\n          3.713572066704308,\n          4278.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Monetary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1510.4630838006838,\n        \"min\": 1.168768249030403,\n        \"max\": 4278.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.54159749680743,\n          6.48768394608782,\n          4278.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 108
        }
      ],
      "source": [
        "transformation.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:28.515193Z",
          "iopub.status.busy": "2025-01-16T16:53:28.514854Z",
          "iopub.status.idle": "2025-01-16T16:53:29.139083Z",
          "shell.execute_reply": "2025-01-16T16:53:29.138068Z",
          "shell.execute_reply.started": "2025-01-16T16:53:28.515167Z"
        },
        "id": "5I-jNDBsCbHr",
        "outputId": "ea9a338f-ae58-4f3b-aed4-48d98f01a586"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_boxplot(transformation)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:29.140314Z",
          "iopub.status.busy": "2025-01-16T16:53:29.139976Z",
          "iopub.status.idle": "2025-01-16T16:53:29.197374Z",
          "shell.execute_reply": "2025-01-16T16:53:29.196356Z",
          "shell.execute_reply.started": "2025-01-16T16:53:29.140284Z"
        },
        "id": "4OZGNQVBfoUy",
        "outputId": "caaedf62-8376-4f8a-cf6f-f6dbc6dcac63"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
        }
      ],
      "source": [
        "fig = px.scatter_3d(transformation, x='Recency', y='Frequency', z='Monetary') # 3d plot for RFM\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:29.198649Z",
          "iopub.status.busy": "2025-01-16T16:53:29.198381Z",
          "iopub.status.idle": "2025-01-16T16:53:29.208675Z",
          "shell.execute_reply": "2025-01-16T16:53:29.207419Z",
          "shell.execute_reply.started": "2025-01-16T16:53:29.198626Z"
        },
        "id": "oB_tAWfnf0ps"
      },
      "outputs": [],
      "source": [
        "# Standardizing the columns\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(transformation)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:29.210114Z",
          "iopub.status.busy": "2025-01-16T16:53:29.209762Z",
          "iopub.status.idle": "2025-01-16T16:53:33.893651Z",
          "shell.execute_reply": "2025-01-16T16:53:33.892458Z",
          "shell.execute_reply.started": "2025-01-16T16:53:29.210087Z"
        },
        "id": "TowlaI74gApy"
      },
      "outputs": [],
      "source": [
        "inertia = []\n",
        "score = []\n",
        "for i in range(2,15):\n",
        "  kmeans = KMeans(n_clusters=i)\n",
        "  kmeans.fit(X_scaled)\n",
        "  inertia.append(kmeans.inertia_)\n",
        "  score.append(silhouette_score(X_scaled,kmeans.labels_))"
      ]
    },
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      "execution_count": 113,
      "metadata": {
        "colab": {
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          "height": 419
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        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:33.895277Z",
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          "iopub.status.idle": "2025-01-16T16:53:34.171174Z",
          "shell.execute_reply": "2025-01-16T16:53:34.170020Z",
          "shell.execute_reply.started": "2025-01-16T16:53:33.895244Z"
        },
        "id": "eI4-ZMwrmX6l",
        "outputId": "b67e627d-e926-492b-d1f3-da570832a13b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(6, 4))\n",
        "plt.plot(range(2, 15), inertia, marker='o',color='#5a189a')\n",
        "plt.xlabel('Number of Clusters (k)')\n",
        "plt.ylabel('Inertia')\n",
        "plt.title('Elbow Curve for K-means Clustering')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DbVENJdUtkYX"
      },
      "source": [
        "From the elbow curve, we notice that the inertia starts to dip at the 4th cluster, indicating that 4 clusters might be the optimal choice. Meanwhile, when plotting the silhouette score, we can evaluate how well the data points fit into their respective clusters. A higher silhouette score indicates better-defined clusters. Both methods help in confirming the ideal number of clusters."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:34.172522Z",
          "iopub.status.busy": "2025-01-16T16:53:34.172216Z",
          "iopub.status.idle": "2025-01-16T16:53:34.469865Z",
          "shell.execute_reply": "2025-01-16T16:53:34.468425Z",
          "shell.execute_reply.started": "2025-01-16T16:53:34.172490Z"
        },
        "id": "-SerwzjIokU1",
        "outputId": "39e85031-a741-4602-8b0f-2346e99bd9e2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(6, 4))\n",
        "plt.plot(range(2, 15), score, marker='o',color='#5a189a')\n",
        "plt.xlabel('Number of Clusters (k)')\n",
        "plt.ylabel('silhouette_score')\n",
        "plt.title('silhouette_score for K-means Clustering')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uuw1QAFituPj"
      },
      "source": [
        "\n",
        "From the silhouette score, we can see that the 4th cluster has the highest score, indicating that it has the best-defined clusters and is the optimal choice for segmentation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:34.472334Z",
          "iopub.status.busy": "2025-01-16T16:53:34.471627Z",
          "iopub.status.idle": "2025-01-16T16:53:34.552922Z",
          "shell.execute_reply": "2025-01-16T16:53:34.552119Z",
          "shell.execute_reply.started": "2025-01-16T16:53:34.472284Z"
        },
        "id": "MchNaojxooHV"
      },
      "outputs": [],
      "source": [
        "# applying kmeans with 4 clusters\n",
        "kmeans = KMeans(n_clusters=4,random_state=14)\n",
        "kmeans.fit(X_scaled)\n",
        "pred = kmeans.predict(X_scaled)\n",
        "transformation['Cluster'] = pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:34.553806Z",
          "iopub.status.busy": "2025-01-16T16:53:34.553553Z",
          "iopub.status.idle": "2025-01-16T16:53:34.566435Z",
          "shell.execute_reply": "2025-01-16T16:53:34.563710Z",
          "shell.execute_reply.started": "2025-01-16T16:53:34.553783Z"
        },
        "id": "yBrkSNoOJD1K",
        "outputId": "e56ea5ae-371e-430b-fac4-8d0a0cc1c211"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Recency  Frequency  Monetary  Cluster\n",
              "1  1.098612   5.209486  8.368925        2\n",
              "2  4.330733   3.332205  7.271175        0\n",
              "3  2.995732   4.290459  7.285198        0\n",
              "4  5.739793   2.833213  5.688330        1\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "transformation",
              "summary": "{\n  \"name\": \"transformation\",\n  \"rows\": 4278,\n  \"fields\": [\n    {\n      \"column\": \"Recency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3273802204790353,\n        \"min\": 0.6931471805599453,\n        \"max\": 5.926926025970411,\n        \"num_unique_values\": 348,\n        \"samples\": [\n          5.093750200806762,\n          5.6240175061873385,\n          5.814130531825066\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Frequency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2121157418631063,\n        \"min\": 0.6931471805599453,\n        \"max\": 7.141245122350491,\n        \"num_unique_values\": 436,\n        \"samples\": [\n          6.498282149476434,\n          5.707110264748875,\n          6.257667587882639\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Monetary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.168768249030403,\n        \"min\": 3.2771447329921766,\n        \"max\": 9.839104219356283,\n        \"num_unique_values\": 4179,\n        \"samples\": [\n          5.073610093257644,\n          6.5153791354156905,\n          7.141403462941432\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cluster\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0,\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 116
        }
      ],
      "source": [
        "transformation.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 117,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:34.567490Z",
          "iopub.status.busy": "2025-01-16T16:53:34.567199Z",
          "iopub.status.idle": "2025-01-16T16:53:34.797930Z",
          "shell.execute_reply": "2025-01-16T16:53:34.796695Z",
          "shell.execute_reply.started": "2025-01-16T16:53:34.567455Z"
        },
        "id": "yI4ckEQ9JGDw",
        "outputId": "b3bb4804-6408-4d85-dcf6-a3de38e273ba"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# number of customers in each cluster\n",
        "cluster_counts = transformation['Cluster'].value_counts().reset_index()\n",
        "cluster_counts.columns = ['Cluster','Count']\n",
        "\n",
        "sns.barplot(data=cluster_counts,x='Cluster',y='Count',color='#5a189a')\n",
        "plt.xlabel('Cluster',fontsize = 10)\n",
        "plt.ylabel('Count',fontsize = 10)\n",
        "plt.title('Number of Customers in each Cluster')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:34.799372Z",
          "iopub.status.busy": "2025-01-16T16:53:34.799081Z",
          "iopub.status.idle": "2025-01-16T16:53:34.815087Z",
          "shell.execute_reply": "2025-01-16T16:53:34.813597Z",
          "shell.execute_reply.started": "2025-01-16T16:53:34.799345Z"
        },
        "id": "x44EuUZdJQjP",
        "outputId": "bbd2e2d3-f724-4fd4-a544-c67c77371612"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Cluster   Recency  Frequency  Monetary\n",
              "0        0  4.289976   4.184950  6.987841\n",
              "1        1  5.150869   2.521523  5.446587\n",
              "2        2  2.322433   5.127515  7.963416\n",
              "3        3  2.994559   3.196978  5.925597"
            ],
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Cluster</th>\n",
              "      <th>Recency</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>Monetary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>4.289976</td>\n",
              "      <td>4.184950</td>\n",
              "      <td>6.987841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>5.150869</td>\n",
              "      <td>2.521523</td>\n",
              "      <td>5.446587</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2.322433</td>\n",
              "      <td>5.127515</td>\n",
              "      <td>7.963416</td>\n",
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              "      <th>3</th>\n",
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              "      <td>2.994559</td>\n",
              "      <td>3.196978</td>\n",
              "      <td>5.925597</td>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "        document.querySelector('#df-7236d3f6-b1e8-452d-b476-da7b5325e763 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "        const element = document.querySelector('#df-7236d3f6-b1e8-452d-b476-da7b5325e763');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "avg_clusters",
              "summary": "{\n  \"name\": \"avg_clusters\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Cluster\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          3,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2712183077715677,\n        \"min\": 2.322432801600238,\n        \"max\": 5.150869363109707,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5.150869363109707,\n          2.994558719156329,\n          4.289976011589731\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Frequency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1403915060840344,\n        \"min\": 2.5215227839580434,\n        \"max\": 5.127514802874189,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2.5215227839580434,\n          3.1969781054694066,\n          4.184950069250536\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Monetary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1244316789764077,\n        \"min\": 5.446586925632063,\n        \"max\": 7.963416008676762,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5.446586925632063,\n          5.925596585941129,\n          6.987840510453158\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 118
        }
      ],
      "source": [
        "avg_clusters = transformation.groupby('Cluster').mean()\n",
        "avg_clusters = avg_clusters.reset_index()\n",
        "avg_clusters"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oA7Vcp0ouFGf"
      },
      "source": [
        "This will group the data by the Cluster column and compute the mean of all features for each cluster. The resulting `avg_clusters` DataFrame will show the average values of the features (Recency, Frequency, Monetary, etc.) for each cluster, helping to understand the characteristics of each segment."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 119,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:34.817053Z",
          "iopub.status.busy": "2025-01-16T16:53:34.816595Z",
          "iopub.status.idle": "2025-01-16T16:53:34.836096Z",
          "shell.execute_reply": "2025-01-16T16:53:34.834683Z",
          "shell.execute_reply.started": "2025-01-16T16:53:34.816992Z"
        },
        "id": "aTF7zJbwRzib"
      },
      "outputs": [],
      "source": [
        "# labeling the clusters\n",
        "cluster_label = {0: 'Loyal Customers', 1: 'At Risk', 2: 'Champions', 3: 'New Customers'}\n",
        "transformation['Cluster Labels'] = transformation['Cluster'].map(cluster_label)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:34.837686Z",
          "iopub.status.busy": "2025-01-16T16:53:34.837383Z",
          "iopub.status.idle": "2025-01-16T16:53:34.865940Z",
          "shell.execute_reply": "2025-01-16T16:53:34.864650Z",
          "shell.execute_reply.started": "2025-01-16T16:53:34.837660Z"
        },
        "id": "_Pje20f6SDuA",
        "outputId": "5bde925c-5348-4524-a2e3-41d1b0932747"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Recency  Frequency  Monetary  Cluster   Cluster Labels\n",
              "1  1.098612   5.209486  8.368925        2        Champions\n",
              "2  4.330733   3.332205  7.271175        0  Loyal Customers\n",
              "3  2.995732   4.290459  7.285198        0  Loyal Customers\n",
              "4  5.739793   2.833213  5.688330        1          At Risk\n",
              "5  3.610918   4.356709  7.234711        0  Loyal Customers"
            ],
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fdcd1b74-daf4-4339-8342-91f9442c5445 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "transformation",
              "summary": "{\n  \"name\": \"transformation\",\n  \"rows\": 4278,\n  \"fields\": [\n    {\n      \"column\": \"Recency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3273802204790353,\n        \"min\": 0.6931471805599453,\n        \"max\": 5.926926025970411,\n        \"num_unique_values\": 348,\n        \"samples\": [\n          5.093750200806762,\n          5.6240175061873385,\n          5.814130531825066\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Frequency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2121157418631063,\n        \"min\": 0.6931471805599453,\n        \"max\": 7.141245122350491,\n        \"num_unique_values\": 436,\n        \"samples\": [\n          6.498282149476434,\n          5.707110264748875,\n          6.257667587882639\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Monetary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.168768249030403,\n        \"min\": 3.2771447329921766,\n        \"max\": 9.839104219356283,\n        \"num_unique_values\": 4179,\n        \"samples\": [\n          5.073610093257644,\n          6.5153791354156905,\n          7.141403462941432\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cluster\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0,\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cluster Labels\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Loyal Customers\",\n          \"New Customers\",\n          \"Champions\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 120
        }
      ],
      "source": [
        "transformation.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 869
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:34.867809Z",
          "iopub.status.busy": "2025-01-16T16:53:34.867423Z",
          "iopub.status.idle": "2025-01-16T16:53:35.852975Z",
          "shell.execute_reply": "2025-01-16T16:53:35.851549Z",
          "shell.execute_reply.started": "2025-01-16T16:53:34.867777Z"
        },
        "id": "Nvlz8aOrJ7RP",
        "outputId": "64910d78-f633-4c01-826a-98c3552624dc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# bar chart of rfm\n",
        "sns.set(style=\"whitegrid\")\n",
        "lst = avg_clusters.columns\n",
        "colors = ['#99ff99', '#ff9999', '#66b3ff', '#ffcc99']\n",
        "for i in range(1,4):\n",
        "  plt.figure(figsize=(8,3))\n",
        "  sns.barplot(data=avg_clusters,x='Cluster',y=lst[i],hue='Cluster',palette=colors)\n",
        "  plt.tight_layout()\n",
        "  plt.legend(loc='best')\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 122,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:35.854166Z",
          "iopub.status.busy": "2025-01-16T16:53:35.853846Z",
          "iopub.status.idle": "2025-01-16T16:53:36.053196Z",
          "shell.execute_reply": "2025-01-16T16:53:36.051855Z",
          "shell.execute_reply.started": "2025-01-16T16:53:35.854139Z"
        },
        "id": "8HY0f0obNIFg",
        "outputId": "e932db1d-e3ff-438d-e842-ec1479bec73b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "customers = transformation.shape[0]\n",
        "labels = ['Loyal Customers','At Risk','Champions','New Customers']\n",
        "sizes = (transformation[\"Cluster\"].value_counts()/customers)*100\n",
        "colors = ['#99ff99', '#ff9999', '#66b3ff', '#ffcc99']\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.pie(\n",
        "    sizes, labels=labels, colors=colors, autopct='%1.1f%%',\n",
        "    startangle=120, wedgeprops={'edgecolor': 'black'}\n",
        ")\n",
        "plt.title('Customer Segmentation', fontsize=14)\n",
        "plt.legend([0,1,2,3],title='Clusters',loc='best',)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "we6ry7O-vqd4"
      },
      "source": [
        "### **Customer Segmentation using RFM Analysis and K-means Clustering**\n",
        "\n",
        "The pie chart illustrates the customer segmentation results obtained by combining RFM analysis and K-means clustering. This approach categorizes customers into four distinct groups:\n",
        "\n",
        "* **Loyal Customers (29.2%):** This segment represents the largest portion of customer base. They are characterized by frequent purchases, high monetary value, and recent interactions. **Averange Recency, Average Frequency, Average Monetary**\n",
        "\n",
        "* **Champions (22.0%):** These customers are brand advocates. Similar to loyal customers, they make frequent purchases and contribute significantly to revenue. **Low Recency, High Frequency, High Monetary**\n",
        "\n",
        "* **New Customers (19.9%):** This group consists of recent additions to  customer base. **High Recency, High Frequency, High Monetary**\n",
        "\n",
        "* **At Risk (28.9%):** This segment might require further analysis to understand their specific characteristics and behaviors. **High Recency, Average Frequency, Average Monetary**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 129,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:36.054731Z",
          "iopub.status.busy": "2025-01-16T16:53:36.054430Z",
          "iopub.status.idle": "2025-01-16T16:53:36.129450Z",
          "shell.execute_reply": "2025-01-16T16:53:36.128083Z",
          "shell.execute_reply.started": "2025-01-16T16:53:36.054704Z"
        },
        "id": "KhRGNPjFQSV5",
        "outputId": "59b1fc52-08d4-46dd-f009-f84445f07a97"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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      ],
      "source": [
        "custom_colors = {\n",
        "    'Loyal Customers': '#99ff99',\n",
        "    'Champions': '#66b3ff',\n",
        "    'At Risk': '#ff9999',\n",
        "    'New Customers': '#ffcc99'\n",
        "}\n",
        "\n",
        "fig = px.scatter_3d(\n",
        "    transformation,\n",
        "    x='Recency',\n",
        "    y='Frequency',\n",
        "    z='Monetary',\n",
        "    color='Cluster Labels',\n",
        "    color_discrete_map=custom_colors,\n",
        "    labels={'Recency': 'Recency', 'Frequency': 'Frequency', 'Monetary': 'Monetary'},\n",
        "    title='Customer Segmentation Visualization'\n",
        ")\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7XIJmxdAxrgB"
      },
      "source": [
        "### Predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 124,
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        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:36.131376Z",
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          "shell.execute_reply.started": "2025-01-16T16:53:36.131329Z"
        },
        "id": "PY3ana3sWrWL"
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      "outputs": [],
      "source": [
        "def predictions(data):\n",
        "  X_data = scaler.transform(data)\n",
        "  pred = kmeans.predict(X_data)\n",
        "  return pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 125,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-16T16:53:36.138784Z",
          "iopub.status.busy": "2025-01-16T16:53:36.138371Z",
          "iopub.status.idle": "2025-01-16T16:53:36.169244Z",
          "shell.execute_reply": "2025-01-16T16:53:36.168207Z",
          "shell.execute_reply.started": "2025-01-16T16:53:36.138743Z"
        },
        "id": "laUlRpdbXBMI"
      },
      "outputs": [],
      "source": [
        "def customer_segmentation():\n",
        "  print(\"Customer Segmentation\")\n",
        "  data_recency = np.log1p(int(input(\"Enter Recency : \")))\n",
        "  data_frequency = np.log1p(int(input(\"Enter Frequency : \")))\n",
        "  data_monetary = np.log1p(int(input(\"Enter Monetary : \")))\n",
        "  data = pd.DataFrame({'Recency': [data_recency], 'Frequency': [data_frequency], 'Monetary': [data_monetary]})\n",
        "  pred = predictions(data)\n",
        "  print(f\"The Customer belongs to {cluster_label[pred[0]]}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 126,
      "metadata": {
        "colab": {
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        },
        "execution": {
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        "id": "WFWG-tNSnE-r",
        "outputId": "0b18ed6f-efa0-40f5-cbc8-40bc39f8a3bc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Customer Segmentation\n",
            "Enter Recency : 5\n",
            "Enter Frequency : 7\n",
            "Enter Monetary : 100\n",
            "The Customer belongs to New Customers\n"
          ]
        }
      ],
      "source": [
        "customer_segmentation()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 127,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2025-01-16T16:54:45.456471Z",
          "iopub.status.busy": "2025-01-16T16:54:45.456159Z",
          "iopub.status.idle": "2025-01-16T16:55:04.655333Z",
          "shell.execute_reply": "2025-01-16T16:55:04.653975Z",
          "shell.execute_reply.started": "2025-01-16T16:54:45.456444Z"
        },
        "id": "1G-rsNicnHG2",
        "outputId": "d500b663-284e-475f-83c5-f90a88400cad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Customer Segmentation\n",
            "Enter Recency : 3\n",
            "Enter Frequency : 56\n",
            "Enter Monetary : 6000\n",
            "The Customer belongs to Champions\n"
          ]
        }
      ],
      "source": [
        "customer_segmentation()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ALbTNU7vxz0T"
      },
      "source": [
        "### **Conclusion:**\n",
        "This project utilized RFM Analysis and K-means Clustering to segment customers into distinct groups, allowing us to gain deeper insights into customer behavior and preferences. By leveraging the three key RFM metrics—Recency, Frequency, and Monetary—we effectively identified four segments that can be targeted with personalized marketing strategies:\n",
        "\n",
        "* **Loyal Customers:** High-frequency, high-spending customers who have recently interacted with the brand. This group can be nurtured with loyalty programs to maintain engagement.\n",
        "* **Champions:** These customers are brand advocates, with high-frequency and high-spending behavior. Offering them exclusive offers and rewards can further solidify their loyalty.\n",
        "* **New Customers:** Recently acquired customers with low frequency and monetary value. Engaging them with onboarding campaigns or personalized offers can help convert them into repeat buyers.\n",
        "* **At Risk:** Customers who have not engaged recently, despite having a history of frequent purchases. Targeting them with re-engagement campaigns and special discounts may help retain them.\n",
        "\n",
        "The use of K-means clustering alongside RFM analysis proved to be an effective method for understanding customer behavior and creating tailored strategies for each segment.\n",
        "\n",
        "### **Made with ❤️ by Syed Afnan**"
      ]
    }
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