Mansi_Waghmare_Clustering.ipynb

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      "authorship_tag": "ABX9TyPF7O8sItsplt5FMo47OHwF",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/manasiwaghmare18/eCommerce-Transactions-Dataset-Analysis/blob/main/Mansi_Waghmare_Clustering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N1AKNN0NRJO2",
        "outputId": "6fea69a5-6317-4123-9a32-77e1bb120fb6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.6.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (3.10.0)\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.11/dist-packages (0.13.2)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (2.2.2)\n",
            "Requirement already satisfied: reportlab in /usr/local/lib/python3.11/dist-packages (4.2.5)\n",
            "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.26.4)\n",
            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.13.1)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (3.5.0)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.3.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (4.55.5)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.4.8)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (24.2)\n",
            "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (11.1.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (3.2.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas) (2025.1)\n",
            "Requirement already satisfied: chardet in /usr/local/lib/python3.11/dist-packages (from reportlab) (5.2.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.7->matplotlib) (1.17.0)\n"
          ]
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      ],
      "source": [
        "!pip install scikit-learn matplotlib seaborn pandas reportlab\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import davies_bouldin_score\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from reportlab.lib.pagesizes import letter\n",
        "from reportlab.pdfgen import canvas"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from mpl_toolkits.mplot3d import Axes3D  # For 3D scatter plot (optional)\n"
      ],
      "metadata": {
        "id": "gvP3yuCxYelf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load data\n",
        "customers_df = pd.read_csv('/content/Customers.csv')\n",
        "transactions_df = pd.read_csv('/content/Transactions.csv')"
      ],
      "metadata": {
        "id": "ZuHo-RUbSGJA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature Engineering\n",
        "# Create customer features\n",
        "customer_features = transactions_df.groupby('CustomerID').agg({\n",
        "    'TransactionID': 'count',\n",
        "    'Quantity': 'sum',\n",
        "    'TotalValue': 'sum'\n",
        "}).rename(columns={\n",
        "    'TransactionID': 'total_transactions',\n",
        "    'Quantity': 'total_items',\n",
        "    'TotalValue': 'total_spend'\n",
        "})\n",
        "\n",
        "customer_features['avg_transaction_value'] = (\n",
        "    customer_features['total_spend'] / customer_features['total_transactions']\n",
        ")"
      ],
      "metadata": {
        "id": "myL45GvOSAkz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Merge with customer data\n",
        "customer_data = customers_df.merge(\n",
        "    customer_features,\n",
        "    how='left',\n",
        "    left_on='CustomerID',\n",
        "    right_index=True\n",
        ").fillna(0)"
      ],
      "metadata": {
        "id": "a1FZqd7fR2Pk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Select features for clustering\n",
        "features = ['total_transactions', 'total_items', 'total_spend', 'avg_transaction_value']\n",
        "X = customer_data[features]\n",
        "\n",
        "# Scale features\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)"
      ],
      "metadata": {
        "id": "qfMhNqi-RvCj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Elbow Method:**\n",
        "\n",
        "\n",
        "1.   **Visualizes Cluster Inertia: The Elbow method uses a plot to show how \"inertia\" (within-cluster variance) changes as the number of clusters increases.**\n",
        "2.  **Identifies Optimal Clusters: The \"elbow\" point on this plot suggests the optimal number of clusters, balancing model complexity with data variance.**\n",
        "\n"
      ],
      "metadata": {
        "id": "MYOf1jkcVx_5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Determine optimal number of clusters (using Elbow method)\n",
        "\n",
        "inertia = []\n",
        "cluster_values = []  # To store the cluster assignments for each k\n",
        "\n",
        "for i in range(2, 11):\n",
        "    kmeans = KMeans(n_clusters=i, random_state=42)\n",
        "    kmeans.fit(X_scaled)\n",
        "    inertia.append(kmeans.inertia_)\n",
        "\n",
        "    # Get cluster assignments for current k\n",
        "    cluster_values.append(kmeans.labels_)\n",
        "\n",
        "# Print inertia values\n",
        "print(\"Inertia Values:\", inertia)\n",
        "\n",
        "# Print cluster values for each k (optional - might be lengthy)\n",
        "for k_clusters, clusters in enumerate(cluster_values, start=2):\n",
        "    print(f\"Cluster Assignments for k={k_clusters}:\\n{clusters}\\n\")\n",
        "\n",
        "plt.plot(range(2, 11), inertia, marker='o')\n",
        "plt.title('Elbow Method for Optimal k')\n",
        "plt.xlabel('Number of Clusters (k)')\n",
        "plt.ylabel('Inertia')\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "dDWXzuCDRrHr",
        "outputId": "aba1e670-a218-4b21-c750-a1cddf210a05"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Inertia Values: [431.62877128255735, 328.84358919733916, 245.3556059241585, 209.19651325617477, 171.7351090962742, 154.12021744994266, 134.45918698937538, 122.26820199506979, 113.49671095150386]\n",
            "Cluster Assignments for k=2:\n",
            "[1 1 1 0 1 1 1 0 1 1 1 0 0 1 1 1 0 0 1 1 0 0 0 0 1 1 1 0 1 1 1 1 1 0 1 1 0\n",
            " 1 0 1 0 1 1 1 0 0 1 1 0 1 0 1 0 0 1 1 0 1 0 1 1 1 1 0 0 1 0 0 1 1 1 1 1 1\n",
            " 0 1 1 1 1 1 0 0 1 0 1 1 0 1 1 0 0 1 0 1 1 0 1 0 0 0 0 0 1 0 0 1 1 0 0 1 1\n",
            " 1 0 0 1 1 1 1 1 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 0 0 1 0 1 0 1 0 1 0 0\n",
            " 0 1 1 1 0 1 0 0 1 1 1 1 0 0 0 1 0 1 1 0 0 0 0 1 0 1 0 1 1 1 0 1 0 0 1 1 1\n",
            " 1 0 0 1 1 1 1 1 0 0 0 1 1 1 0]\n",
            "\n",
            "Cluster Assignments for k=3:\n",
            "[2 1 2 0 1 2 2 0 1 1 2 0 0 1 1 2 0 2 1 1 0 2 2 2 1 2 2 0 1 2 1 1 1 2 2 1 2\n",
            " 2 2 2 0 1 1 2 0 0 2 2 2 2 0 2 0 0 2 2 2 1 0 1 1 1 1 2 0 2 2 0 2 2 1 2 1 2\n",
            " 0 1 1 1 2 1 2 0 1 0 2 2 0 1 2 2 2 2 0 1 1 2 1 2 0 2 0 0 1 0 2 2 2 0 0 1 2\n",
            " 1 0 0 2 2 2 2 1 2 1 0 1 0 2 2 2 1 1 1 1 1 2 2 2 0 2 2 2 1 0 1 0 1 0 1 2 2\n",
            " 2 1 1 2 2 2 0 0 1 2 1 2 2 0 0 2 0 2 1 2 0 2 2 1 0 2 0 2 1 2 2 1 2 2 2 2 2\n",
            " 1 2 0 2 2 2 1 2 2 2 2 1 1 1 2]\n",
            "\n",
            "Cluster Assignments for k=4:\n",
            "[2 1 2 0 1 3 1 0 1 2 2 0 0 1 1 2 0 3 2 1 0 2 3 2 1 3 2 0 1 2 2 1 1 2 2 1 3\n",
            " 2 2 3 0 1 1 3 0 0 2 2 2 3 0 2 0 0 2 2 2 1 0 1 1 1 1 2 0 3 2 0 2 2 1 2 1 3\n",
            " 0 2 1 1 3 1 2 0 1 0 1 2 0 2 3 2 2 2 0 1 1 3 1 2 0 3 0 0 2 0 0 2 2 0 0 1 2\n",
            " 1 0 3 3 2 3 2 1 1 1 0 1 0 2 2 2 1 1 1 1 1 2 2 2 0 2 3 2 1 0 2 0 1 0 2 2 3\n",
            " 3 1 1 2 0 2 0 0 2 3 1 2 2 0 0 2 0 2 1 3 0 3 3 2 0 2 0 2 2 2 2 1 2 3 2 2 3\n",
            " 1 3 0 2 2 2 1 2 2 2 3 1 1 1 3]\n",
            "\n",
            "Cluster Assignments for k=5:\n",
            "[1 1 1 0 1 3 1 2 4 1 1 2 0 4 4 1 2 3 1 1 2 2 3 2 1 3 1 0 1 1 1 4 4 2 1 1 3\n",
            " 1 2 3 0 1 4 3 0 0 2 1 2 3 2 1 2 0 1 1 2 4 0 4 1 1 1 3 0 3 2 0 1 1 4 1 1 3\n",
            " 0 1 4 1 3 1 2 0 4 2 1 1 0 1 3 2 2 1 2 4 1 3 4 2 0 2 0 0 1 0 2 1 1 2 0 1 1\n",
            " 1 2 3 3 1 3 1 4 1 4 2 1 2 1 2 1 4 1 4 1 1 1 1 1 2 1 3 2 1 0 1 0 1 0 1 2 3\n",
            " 3 4 4 1 2 2 2 0 1 3 1 1 2 2 0 1 0 1 1 3 2 3 3 1 0 1 0 1 1 1 2 4 2 3 1 2 3\n",
            " 1 3 0 1 1 1 1 1 2 2 3 1 4 1 3]\n",
            "\n",
            "Cluster Assignments for k=6:\n",
            "[1 1 1 0 1 3 5 2 4 1 1 2 0 4 4 1 2 3 1 5 2 2 3 2 1 3 1 0 1 1 1 4 4 2 1 1 3\n",
            " 1 2 5 0 5 4 5 0 0 2 1 2 5 2 1 3 0 1 1 2 4 0 4 5 1 5 3 0 5 2 0 1 1 4 1 1 5\n",
            " 0 1 4 5 3 5 2 0 4 2 5 1 0 1 5 2 2 1 2 4 4 3 4 2 0 3 0 0 1 0 3 1 1 2 0 5 1\n",
            " 1 2 3 5 1 3 1 4 5 4 2 5 2 1 2 1 4 5 4 1 1 1 1 1 2 1 3 2 5 0 1 0 1 0 1 2 3\n",
            " 3 4 4 1 2 1 2 0 1 5 1 1 2 2 3 1 0 1 5 3 2 3 3 1 0 1 0 1 1 1 2 4 2 3 1 2 5\n",
            " 1 3 0 1 1 1 1 1 2 2 3 1 4 1 3]\n",
            "\n",
            "Cluster Assignments for k=7:\n",
            "[1 1 1 6 1 3 5 2 4 1 1 6 6 4 4 1 6 3 2 5 6 3 3 2 1 3 1 6 1 1 1 4 4 2 1 1 3\n",
            " 1 2 5 6 5 4 5 6 6 2 1 2 5 6 1 3 6 1 1 3 4 6 4 1 1 5 3 0 5 2 6 1 1 4 1 1 5\n",
            " 0 1 4 5 3 5 2 6 4 2 5 1 6 1 5 2 2 1 6 4 4 3 4 2 6 3 6 6 1 6 3 1 1 6 0 5 2\n",
            " 1 6 3 5 2 3 2 4 5 4 6 5 2 1 3 2 4 1 4 1 1 1 2 1 6 1 3 2 5 0 1 6 1 6 1 2 3\n",
            " 3 4 4 1 3 2 6 0 1 5 1 1 2 6 3 1 0 1 1 3 6 3 3 2 6 1 0 1 1 1 2 4 2 3 1 2 5\n",
            " 1 3 6 1 1 1 1 1 2 2 3 1 4 1 3]\n",
            "\n",
            "Cluster Assignments for k=8:\n",
            "[1 1 1 0 5 7 5 0 4 1 1 2 6 4 5 1 0 2 1 5 6 2 2 3 1 7 1 6 1 1 1 4 4 3 1 5 2\n",
            " 1 2 7 6 5 4 7 6 6 3 1 3 7 2 1 2 6 1 1 2 4 6 4 5 5 5 2 0 7 3 6 1 1 5 1 5 7\n",
            " 0 1 4 5 7 5 3 6 4 0 5 1 6 1 7 3 3 1 0 4 5 2 4 3 6 2 6 6 1 6 2 1 1 2 0 5 1\n",
            " 5 0 2 7 1 7 1 4 5 4 2 5 3 1 2 1 4 5 5 5 5 1 1 1 2 1 2 3 5 6 1 6 5 6 1 3 2\n",
            " 2 5 4 1 2 1 2 0 1 7 5 1 3 0 2 1 0 1 5 7 2 2 2 1 6 1 0 5 1 1 3 4 3 2 1 3 7\n",
            " 5 2 6 1 1 1 1 1 3 3 7 5 4 1 2]\n",
            "\n",
            "Cluster Assignments for k=9:\n",
            "[1 1 1 8 5 2 5 0 4 1 1 8 8 4 5 1 8 2 3 5 8 8 2 3 1 2 1 6 1 1 1 4 4 3 1 5 2\n",
            " 1 8 7 6 5 4 7 6 8 3 1 3 7 8 1 8 6 1 1 8 4 6 4 5 5 5 2 0 7 3 8 1 1 5 1 5 7\n",
            " 0 1 4 5 2 5 3 6 4 8 5 1 6 1 7 3 3 1 8 4 5 2 4 3 6 2 8 8 1 6 8 1 1 8 0 5 3\n",
            " 5 8 2 7 3 2 3 4 5 4 8 5 8 1 8 3 4 5 5 5 5 1 1 1 8 1 2 3 5 6 1 6 5 8 1 3 2\n",
            " 2 5 4 1 8 3 8 0 1 7 5 1 3 8 2 1 0 1 5 2 8 2 2 3 8 1 0 1 1 1 3 4 3 2 1 3 7\n",
            " 5 2 6 1 1 1 1 1 3 8 2 5 4 1 2]\n",
            "\n",
            "Cluster Assignments for k=10:\n",
            "[3 1 1 8 5 2 5 9 4 1 3 8 8 4 5 3 8 2 1 5 8 8 2 3 1 7 1 6 1 1 1 4 4 3 1 5 2\n",
            " 1 8 7 6 5 4 7 6 8 9 3 9 7 8 1 8 6 1 3 8 4 6 4 5 5 5 3 0 7 3 8 1 3 5 3 5 7\n",
            " 0 1 4 5 2 5 3 6 4 9 5 1 6 1 7 8 3 1 8 4 5 2 4 9 6 2 8 8 1 6 8 3 3 8 0 5 1\n",
            " 5 8 2 7 1 2 3 4 5 4 8 5 8 1 3 3 4 5 5 5 5 1 3 1 8 3 2 9 5 6 1 6 5 8 1 9 2\n",
            " 2 5 4 3 8 3 8 0 1 7 5 3 9 8 2 1 0 1 5 2 8 2 2 1 8 1 0 5 1 1 3 4 3 2 3 3 7\n",
            " 5 2 6 1 3 1 1 1 3 3 2 5 4 1 2]\n",
            "\n"
          ]
        },
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          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. **At k=3, we can see a sharp change in inertia, indicating a significant improvement in cluster quality.**\n",
        "\n",
        "2.   **Three distinct customer segments emerged, differentiated by purchase frequency and spending habits.This means dividing your customers into 3 distinct groups based on their purchasing behavior is likely to provide the most meaningful and actionable insights.**\n",
        "3.   **These segments enable targeted marketing, allowing for personalized offers and improved customer relationships.**\n",
        "\n"
      ],
      "metadata": {
        "id": "NZfziX4IVeUi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply KMeans clustering (using optimal k)\n",
        "optimal_k = 3  # Based on the elbow method visualization\n",
        "kmeans = KMeans(n_clusters=optimal_k, random_state=42)\n",
        "customer_data['cluster'] = kmeans.fit_predict(X_scaled)"
      ],
      "metadata": {
        "id": "UkKL5uCGRoue"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate clustering metrics\n",
        "from sklearn.metrics import silhouette_score, calinski_harabasz_score #importing the modules\n",
        "\n",
        "# Calculate clustering metrics\n",
        "db_index = davies_bouldin_score(X_scaled, customer_data['cluster'])\n",
        "silhouette_avg = silhouette_score(X_scaled, customer_data['cluster'])\n",
        "ch_score = calinski_harabasz_score(X_scaled, customer_data['cluster'])\n"
      ],
      "metadata": {
        "id": "r0VtWEMaRipv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visualize clusters**"
      ],
      "metadata": {
        "id": "GDQBtzDAahzQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Scatter plot\n",
        "sns.scatterplot(x='total_spend', y='total_transactions', hue='cluster', data=customer_data)\n",
        "plt.title('Customer Segmentation - Pair Plot')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Aa95l_4ARd6z",
        "outputId": "188d8328-b554-43c8-e664-ab755be8a352"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Customer Value Tiers The data naturally forms into three distinct spending and transaction patterns:**\n",
        "\n",
        "\n",
        "- 1. Low-value segment (Cluster 1, purple): Makes 0-3 transactions with total spend under 2,000\n",
        "- 2. Mid-value segment (Cluster 2, navy): Makes 3-8 transactions with spend between 2,000-5,000\n",
        "-3.  High-value segment (Cluster 0, pink): Makes 6-11 transactions with spend ranging up to 10,000\n",
        "\n",
        "\n",
        "**Transaction-Spend Relationship**\n",
        "There's a **strong positive correlation** between spending and transaction frequency - customers who spend more tend to make more purchases. However, there's significant variation in total spend even among customers with the same number of transactions, suggesting different purchasing behaviors within each segment.\n",
        "\n",
        "**Distribution Pattern**\n",
        "The data points form distinct horizontal bands corresponding to whole number transaction counts, with spending spread out along each band. This suggests customers tend to fall into regular transaction frequency patterns (like monthly shoppers, quarterly shoppers, etc.) while their total spend per transaction varies considerably."
      ],
      "metadata": {
        "id": "zdS5YQwteA2y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "ay8L3mD9etGQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Optional: 3D scatter plot\n",
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(111, projection='3d')\n",
        "ax.scatter(customer_data['total_spend'], customer_data['total_transactions'], customer_data['total_items'], c=customer_data['cluster'])  # Replace 'feature1', 'feature2', 'feature3' with actual feature names\n",
        "plt.title('Customer Segmentation - 3D Scatter Plot')\n",
        "ax.set_xlabel('Total Spend')  # Set x-axis label\n",
        "ax.set_ylabel('Total Transactions')  # Set y-axis label\n",
        "ax.set_zlabel('Total Items')  # Set z-axis label\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "ZQ9UV96Ca135",
        "outputId": "08f00531-7258-4cc6-a1fc-5cfa810c7f02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Interpreting the 3D scatter plot:**\n",
        "\n",
        "- Each point represents a customer, and its position in the 3D space is determined by the values of the three features (total spend, total transactions, total items).\n",
        "- Different colors represent different clusters.\n",
        "- By rotating and viewing the plot from different angles, you can gain a better understanding of the cluster separation and the relationships between the features in 3D space."
      ],
      "metadata": {
        "id": "FgYOtkwCbl5W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Box plots\n",
        "for feature in features:\n",
        "    sns.boxplot(x='cluster', y=feature, data=customer_data)\n",
        "    plt.title(f'Customer Segmentation - Box Plot ({feature})')\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "69y4_wH0fMFy",
        "outputId": "5d257cfa-6883-4117-bc0d-873a150d3f26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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1a5WVlaWBAwfK3f3/v9769OkjPz8/U+MxPTw8ZLFYJEk5OTlKT0/XxYsX1bBhQ/twgoLy9/fXmTNntGbNmn/s27dvX4f3a79+/VSsWDH761mQz6vnnnvO4XHz5s0d3pcrV65UsWLFHD7XPDw8NGDAgKvWWapUKf3111//uD63Cg7Pwc7Pz0/SpfFHhS0uLk7du3dXcHCwGjRooLZt26pbt26mBg8mJSXp9ttvd/gQlP5/d/yVX2QhISEOj3MDVHBwcL7tueMEDh06JOn/x3BcKff1yVWsWLF8D4NdTaNGjbR06VJlZWVpz549WrZsmaZMmaLHHntMu3fvVs2aNXXo0CEZhqHbb78933nkfojmrvPlZxTl1nT5LvnLXcvrkpOTI5vNpjJlypiuKVelSpUcAod06YN17969+T7/SsnJyRo9erS+/PLLPGM4bDabqXlc7s8//9TZs2d1xx135JlWo0YN5eTk6MiRI6pVq5a9/crXKveLO78xJbnOnTuXp77AwMB/rK9x48YOgaRr166qX7++YmJi9NBDD8lisSgpKUlBQUF5/qDJ7z1gsVj03nvvqVGjRvLy8tK8efPy/D7+zujRo9W8eXN5eHiobNmyqlGjRp7xVZfLXfbVXt+vv/76b0+S+CfG/43ZMuNqtVgsFlWpUsV06H3//ff1xhtv6KefftKFCxfs7WFhYaZryU///v31ySefqE2bNqpYsaJat26tzp0768EHH8zT98r3m4+PjypUqGAfA3atn1deXl55DoWWKlXKYZtOSkpShQoV7Icqc+X3u81lGMY1bV83O0IT7Pz8/BQUFKQffvjBVP+rvVHyG2zauXNnNW/eXMuWLdPq1as1efJkTZo0SUuXLlWbNm3+Vd1Xutq4jau1534o5+TkSLo0TiC/L7srvzg8PT3zBDkzLBaLGjVqpEaNGqlatWrq2bOnlixZojFjxignJ0dubm5atWpVvvVe+WF2Lf7N63ItNf3T/P5Odna2HnjgAaWnp+vll19W9erVVbJkSf3xxx/q0aOH/Xd0vRVkHRYvXqyePXua7n817u7uatmypaZNm6ZDhw45hDmzvv76a0nS+fPndejQoWv6sq9du7bDwGtnKlOmzN8G1ethwYIF6tGjhzp06KCXXnpJ5cuXl4eHhyZOnGgf31VQ5cuX1+7du/X1119r1apVWrVqlebNm6du3brlObHln1zr51VBxrOZceLECfv4tKKA0AQHDz30kOLj45WQkKDw8PC/7Zv71/eVZ1Zc7a+5ChUqqH///urfv7/S0tJ01113acKECfbQdLUQFhoaqr179yonJ8chpPz000/26YWhatWqki59sN2oL43cPQzHjh2z12AYhsLCwlStWrWrPi93nX/55Re1bNnS3n7x4kUdPnxYderUKbQazdZ0La72u963b59+/vlnvf/+++rWrZu9Pb/DGWb/ui1XrpxKlCihgwcP5pn2008/yd3dPc/etoKIjIw0ddjFjNxDo6dPn5Z06fe9du1anTp1ymFvU37vgb1792rcuHHq2bOndu/erd69e2vfvn0Oh6wLU+6yr/b6li1b1r6X6Vr3SFSvXl2fffZZnva/+6zIreXyvdhZWVlKTEx0eF9fbR6ffvqpqlSpoqVLlzr0GTNmzDXVfjUWi0Xt27dX+/btlZOTo/79++utt97SqFGjHPYcHzp0yOG9ffr0aR07dkxt27aVdH0+r0JDQ7Vu3TqdPn3a4Y+h/H63uRITE1W3bt1CWf7NgDFNcDB06FCVLFlSvXv3Vmpqap7pv/76q/30WD8/P5UtW1YbN2506DN79myHx9nZ2XkOW5QvX15BQUEOp7aXLFky38Mvbdu2VUpKisOZSxcvXtSMGTPk4+Oje++999pXNB+RkZHy8/PTq6++6rBLPte/OeX822+/zXevQ+74hNzd3506dZKHh4fGjh2bp79hGDp+/LikS2GrTJkyevvtt+1fsNKlMwcL+y9zszVdi9wv0SsDd+5fw5cvxzCMfC8AerV5XMnDw0OtW7fWF1984XB6e2pqqhYtWqRmzZrlOZRREBUqVFBERITDT0FcuHBBq1evlsVisR9+a9u2rbKzszVz5kyHvlOmTJGbm5v9D48LFy6oR48eCgoK0rRp0zR//nylpqZe1zObKlSooHr16un99993+F388MMPWr16tf1LXjL/O8sVHh6uEydO5BkPd7X5REREyGKxaPr06Q7b0LvvviubzaZ27do5zCO/z5v8tsFt27YpISHBVM1/58r3iru7u/0PnCsv8xEfH+/wOTRnzhxdvHjR/ru+Hp9Xbdu21cWLFzVnzhx7W3Z29lXvWmCz2fTrr78W+tnWrow9TXBQtWpVLVq0SE888YRq1Kihbt266c4771RWVpa2bNliP9U/V+/evfXaa6+pd+/eatiwoTZu3Kiff/7ZYZ6nTp1SpUqV9Nhjj6lu3bry8fHR2rVrtX37dr3xxhv2fg0aNNDixYs1ePBgNWrUSD4+Pmrfvr369u2rt956Sz169NCOHTtUuXJlffrpp9q8ebOmTp1aaAPX/fz8NGfOHD399NO666671KVLF5UrV07JyclasWKFmjZtmudLy6wBAwbo7Nmz6tixo6pXr25/PRcvXqzKlSvbD+tUrVpV48eP1/Dhw+2XEPD19VViYqKWLVumvn37asiQIbJYLIqNjdWAAQN0//33q3Pnzjp8+LDmz5+vqlWrFuoYA7M1Xes8/f39NXfuXPn6+qpkyZJq0qSJqlevrqpVq2rIkCH6448/5Ofnp88++yzfINigQQNJl65gHRkZKQ8PD3Xp0iXf5Y0fP15r1qxRs2bN1L9/fxUrVkxvvfWWMjMzHa4h5QyrVq2y7zFKS0vTokWLdOjQIQ0bNswe5tq3b6+WLVtqxIgROnz4sOrWravVq1friy++0MCBA+17HcaPH6/du3dr3bp18vX1VZ06dTR69GiNHDlSjz32mEOAKUyTJ09WmzZtFB4erl69etkvOWC1Wh2ul5X7OxsxYoS6dOmi4sWLq3379lcd79SuXTsVK1ZMa9euVd++fe3t9erVk4eHhyZNmiSbzSZPT0/df//9Kl++vIYPH66xY8fqwQcf1MMPP6yDBw9q9uzZatSokcMJClf7vHnooYe0dOlSdezYUe3atVNiYqLmzp2rmjVr2vf8FVTv3r2Vnp6u+++/X5UqVVJSUpJmzJihevXq2QNyrqysLLVq1UqdO3e2r0OzZs308MMPS7o+n1ft27dX06ZNNWzYMB0+fFg1a9bU0qVLrzqWcO3atfbLFBQZN+5EPdxMfv75Z6NPnz5G5cqVDYvFYvj6+hpNmzY1ZsyYYT9F3zAunR7eq1cvw2q1Gr6+vkbnzp2NtLQ0h1NxMzMzjZdeesmoW7eu4evra5QsWdKoW7euMXv2bIdlnj592njyyScNf39/Q5LD6cCpqalGz549jbJlyxoWi8WoXbt2ntNfc0+Fnjx5skN77qnUS5YscWjPPbU391Tqy/tHRkYaVqvV8PLyMqpWrWr06NHD+P777+19unfvbpQsWdL067lq1SrjmWeeMapXr274+PgYFovFuO2224wBAwYYqampefp/9tlnRrNmzYySJUsaJUuWNKpXr25ER0cbBw8edOg3ffp0IzQ01PD09DQaN25sbN682WjQoIHx4IMPFnj9x4wZY0gy/vzzz2uu6d577zVq1aqVZ326d++e5/TuL774wqhZs6ZRrFgxh9OZDxw4YERERBg+Pj5G2bJljT59+hh79uzJc8rzxYsXjQEDBhjlypUz3NzcHC4/oHxOBd+5c6cRGRlp+Pj4GCVKlDBatmxpbNmyxdRrkvsaXnmK+7+R3yUHvLy8jHr16hlz5sxxuOSBYVw6xXzQoEFGUFCQUbx4ceP22283Jk+ebO+3Y8cOo1ixYsaAAQMcnnfx4kWjUaNGRlBQkHHixImr1nO17eRKV7vUyNq1a42mTZsa3t7ehp+fn9G+fXvjwIEDeZ7/yiuvGBUrVjTc3d1NXX7g4YcfNlq1apWn/e233zaqVKlieHh45PndzJw506hevbpRvHhxIyAgwOjXr1+edb/a501OTo7x6quv2t9X9evXN5YvX57vNpzfdvZ3Pv30U6N169ZG+fLlDYvFYoSEhBjPPvuscezYMXuf3O1iw4YNRt++fY1SpUoZPj4+RlRUlMMlHXL9m8+r3Pf65Y4fP248/fTThp+fn2G1Wo2nn37a2LVrV76/8yeeeMJo1qyZ6fW/FbgZRgFGKgJwSTk5OSpXrpw6deqkt99+29nlAP9a7lXuf/rpp6uewXkrmT9/vnr27Knt27df9TIPriAlJUVhYWH6+OOPi9SeJsY0ATep8+fP5xlj9MEHHyg9Pd3hNirAzax58+Zq3bq10w+jwtHUqVNVu3btIhWYJMY0ATetrVu3atCgQXr88cdVpkwZ7dy5U++++67uvPNO+725gFvB5RfvdEXZ2dn/OPDax8fnX10yxNW89tprzi7BKQhNwE2qcuXKCg4O1vTp05Wenq7SpUurW7dueu211+xXNAZw/R05cuQfr4U1ZsyYPDeRxs2HMU0AAPwL58+f16ZNm/62T5UqVUzdAQGujdAEAABgAgPBAQAATGBMUyHJycnR0aNH5evrW6RuXggAwM3MMAydOnVKQUFB/3g/UUJTITl69Gih3L8KAADceEeOHFGlSpX+tg+hqZDk3srjyJEjhXIfKwAAcP1lZGQoODjY1C25CE2FJPeQnJ+fH6EJAICbjJmhNQwEBwAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCBK4LDpWRnZ2vv3r1KT09X6dKlVadOHXl4eDi7LAAACE1wHRs3btTs2bOVkpJibwsMDFT//v3VokULJ1YGAACH5+AiNm7cqDFjxqhKlSqaNWuWVq5cqVmzZqlKlSoaM2aMNm7c6OwSAQBFnJthGIazi7gVZGRkyGq1ymazccPea5Sdna2oqChVqVJF48ePl7v7/2f5nJwcjRw5UomJiVqwYAGH6gAAhepavr/Z0wSn27t3r1JSUhQVFeUQmCTJ3d1dUVFROnbsmPbu3eukCgEAIDTBBaSnp0uSwsLC8p2e257bDwAAZyA0welKly4tSUpMTMx3em57bj8AAJyB0ASnq1OnjgIDA7Vw4ULl5OQ4TMvJydHChQtVoUIF1alTx0kVAgBAaIIL8PDwUP/+/ZWQkKCRI0dq//79Onv2rPbv36+RI0cqISFB/fr1YxA4AMCpOHuukHD23L+X33WaKlSooH79+nGdJgDAdXEt39+EpkJCaCocXBEcAHAjXcv3N1cEh0vx8PBQ/fr1nV0GAAB5MKYJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAlODU0bN25U+/btFRQUJDc3N33++ecO0w3D0OjRo1WhQgV5e3srIiJChw4dcuiTnp6uqKgo+fn5yd/fX7169dLp06cd+uzdu1fNmzeXl5eXgoODFRcXl6eWJUuWqHr16vLy8lLt2rW1cuXKQl9fAABw83JqaDpz5ozq1q2rWbNm5Ts9Li5O06dP19y5c7Vt2zaVLFlSkZGROn/+vL1PVFSU9u/frzVr1mj58uXauHGj+vbta5+ekZGh1q1bKzQ0VDt27NDkyZMVGxur+Ph4e58tW7aoa9eu6tWrl3bt2qUOHTqoQ4cO+uGHH67fygMAgJuL4SIkGcuWLbM/zsnJMQIDA43Jkyfb206ePGl4enoaH330kWEYhnHgwAFDkrF9+3Z7n1WrVhlubm7GH3/8YRiGYcyePdsoVaqUkZmZae/z8ssvG3fccYf9cefOnY127do51NOkSRPj2WefNV2/zWYzJBk2m830cwAAgHNdy/e3y45pSkxMVEpKiiIiIuxtVqtVTZo0UUJCgiQpISFB/v7+atiwob1PRESE3N3dtW3bNnufFi1ayGKx2PtERkbq4MGDOnHihL3P5cvJ7ZO7nPxkZmYqIyPD4QcAANy6XDY0paSkSJICAgIc2gMCAuzTUlJSVL58eYfpxYoVU+nSpR365DePy5dxtT650/MzceJEWa1W+09wcPC1riIAALiJuGxocnXDhw+XzWaz/xw5csTZJQEAgOvIZUNTYGCgJCk1NdWhPTU11T4tMDBQaWlpDtMvXryo9PR0hz75zePyZVytT+70/Hh6esrPz8/hBwAA3LpcNjSFhYUpMDBQ69ats7dlZGRo27ZtCg8PlySFh4fr5MmT2rFjh73PN998o5ycHDVp0sTeZ+PGjbpw4YK9z5o1a3THHXeoVKlS9j6XLye3T+5yAAAAnBqaTp8+rd27d2v37t2SLg3+3r17t5KTk+Xm5qaBAwdq/Pjx+vLLL7Vv3z5169ZNQUFB6tChgySpRo0aevDBB9WnTx9999132rx5s2JiYtSlSxcFBQVJkp588klZLBb16tVL+/fv1+LFizVt2jQNHjzYXscLL7ygr776Sm+88YZ++uknxcbG6vvvv1dMTMyNfkkAAICrugFn813Vt99+a0jK89O9e3fDMC5ddmDUqFFGQECA4enpabRq1co4ePCgwzyOHz9udO3a1fDx8TH8/PyMnj17GqdOnXLos2fPHqNZs2aGp6enUbFiReO1117LU8snn3xiVKtWzbBYLEatWrWMFStWXNO6cMkBAABuPtfy/e1mGIbhxMx2y8jIyJDVapXNZmN8EwAAN4lr+f522TFNAAAAroTQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAkuHZqys7M1atQohYWFydvbW1WrVtUrr7wiwzDsfQzD0OjRo1WhQgV5e3srIiJChw4dcphPenq6oqKi5OfnJ39/f/Xq1UunT5926LN37141b95cXl5eCg4OVlxc3A1ZRwAAcHNw6dA0adIkzZkzRzNnztSPP/6oSZMmKS4uTjNmzLD3iYuL0/Tp0zV37lxt27ZNJUuWVGRkpM6fP2/vExUVpf3792vNmjVavny5Nm7cqL59+9qnZ2RkqHXr1goNDdWOHTs0efJkxcbGKj4+/oauLwAAcF1uxuW7bVzMQw89pICAAL377rv2tkcffVTe3t5asGCBDMNQUFCQXnzxRQ0ZMkSSZLPZFBAQoPnz56tLly768ccfVbNmTW3fvl0NGzaUJH311Vdq27atfv/9dwUFBWnOnDkaMWKEUlJSZLFYJEnDhg3T559/rp9++slUrRkZGbJarbLZbPLz8yvkV+LGOX/+vJKTk51dhssICQmRl5eXs8sAAFwn1/L9XewG1VQg99xzj+Lj4/Xzzz+rWrVq2rNnjzZt2qQ333xTkpSYmKiUlBRFRETYn2O1WtWkSRMlJCSoS5cuSkhIkL+/vz0wSVJERITc3d21bds2dezYUQkJCWrRooU9MElSZGSkJk2apBMnTqhUqVJ5asvMzFRmZqb9cUZGxvV4CW645ORkh71wRV18fLyqVavm7DIAAC7ApUPTsGHDlJGRoerVq8vDw0PZ2dmaMGGCoqKiJEkpKSmSpICAAIfnBQQE2KelpKSofPnyDtOLFSum0qVLO/QJCwvLM4/cafmFpokTJ2rs2LGFsJauJSQkxOmHJZOSkjRhwgSNGDFCoaGhTq0lJCTEqcsHALgOlw5Nn3zyiRYuXKhFixapVq1a2r17twYOHKigoCB1797dqbUNHz5cgwcPtj/OyMhQcHCwEysqHF5eXi6zZyU0NNRlagEAwKVD00svvaRhw4apS5cukqTatWsrKSlJEydOVPfu3RUYGChJSk1NVYUKFezPS01NVb169SRJgYGBSktLc5jvxYsXlZ6ebn9+YGCgUlNTHfrkPs7tcyVPT095enr++5UEAAA3BZc+e+7s2bNyd3cs0cPDQzk5OZKksLAwBQYGat26dfbpGRkZ2rZtm8LDwyVJ4eHhOnnypHbs2GHv88033ygnJ0dNmjSx99m4caMuXLhg77NmzRrdcccd+R6aAwAARY9Lh6b27dtrwoQJWrFihQ4fPqxly5bpzTffVMeOHSVJbm5uGjhwoMaPH68vv/xS+/btU7du3RQUFKQOHTpIkmrUqKEHH3xQffr00XfffafNmzcrJiZGXbp0UVBQkCTpySeflMViUa9evbR//34tXrxY06ZNczj8BgAAijaXPjw3Y8YMjRo1Sv3791daWpqCgoL07LPPavTo0fY+Q4cO1ZkzZ9S3b1+dPHlSzZo101dffeVwmvjChQsVExOjVq1ayd3dXY8++qimT59un261WrV69WpFR0erQYMGKlu2rEaPHs1ZZAAAwM6lr9N0M7lVrtPkCn7++Wf17duX0/0BANfdtXx/u/ThOQAAAFdBaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACcWcXQAcpaamymazObsMp0pKSnL4tyizWq0KCAhwdhkAAEluhmEYzi7iVpCRkSGr1SqbzSY/P78CzSM1NVVPPd1NF7IyC7k63KyKWzy14MMPCE4AcJ1cy/c3e5pciM1m04WsTJ2rcq9yvKzOLgdO5n7eJv22QTabjdAEAC7AdGiqX7++3NzcTPXduXNngQuClONlVU7Jss4uAwAAXMZ0aOrQoYP9/+fPn9fs2bNVs2ZNhYeHS5K2bt2q/fv3q3///oVeJAAAgLOZDk1jxoyx/7937956/vnn9corr+Tpc+TIkcKrDgAAwEUU6JIDS5YsUbdu3fK0P/XUU/rss8/+dVEAAACupkChydvbW5s3b87TvnnzZnl5ef3rogAAAFxNgc6eGzhwoPr166edO3eqcePGkqRt27bpvffe06hRowq1QAAAAFdQoNA0bNgwValSRdOmTdOCBQskSTVq1NC8efPUuXPnQi0QAADAFRT4Ok2dO3cmIAEAgCLjX13cMisrS2lpacrJyXFoDwkJ+VdFAQAAuJoChaZDhw7pmWee0ZYtWxzaDcOQm5ubsrOzC6U4AACQV3Z2tvbu3av09HSVLl1aderUkYeHh7PLuuUVKDT16NFDxYoV0/Lly1WhQgXTVwoHAAD/zsaNGzV79mylpKTY2wIDA9W/f3+1aNHCiZXd+goUmnbv3q0dO3aoevXqhV0PAAC4io0bN2rMmDEKDw/XqFGjFBYWpsTERC1cuFBjxozR2LFjCU7XUYGu01SzZk399ddfhV1Lvv744w899dRTKlOmjLy9vVW7dm19//339umGYWj06NGqUKGCvL29FRERoUOHDjnMIz09XVFRUfLz85O/v7969eql06dPO/TZu3evmjdvLi8vLwUHBysuLu6GrB8AAGZkZ2dr9uzZCg8P1/jx41WrVi2VKFFCtWrV0vjx4xUeHq45c+YwROY6KlBomjRpkoYOHar169fr+PHjysjIcPgpLCdOnFDTpk1VvHhxrVq1SgcOHNAbb7yhUqVK2fvExcVp+vTpmjt3rrZt26aSJUsqMjJS58+ft/eJiorS/v37tWbNGi1fvlwbN25U37597dMzMjLUunVrhYaGaseOHZo8ebJiY2MVHx9faOsCAMC/sXfvXqWkpCgqKkru7o5f3+7u7oqKitKxY8e0d+9eJ1V46yvQ4bmIiAhJUqtWrRzaC3sg+KRJkxQcHKx58+bZ28LCwhyWN3XqVI0cOVKPPPKIJOmDDz5QQECAPv/8c3Xp0kU//vijvvrqK23fvl0NGzaUJM2YMUNt27bV66+/rqCgIC1cuFBZWVl67733ZLFYVKtWLe3evVtvvvmmQ7gCAMBZ0tPTJTl+D14utz23HwpfgULTt99+W9h15OvLL79UZGSkHn/8cW3YsEEVK1ZU//791adPH0lSYmKiUlJS7CFOkqxWq5o0aaKEhAR16dJFCQkJ8vf3twcm6VLoc3d317Zt29SxY0clJCSoRYsWslgs9j6RkZGaNGmSTpw44bBnK1dmZqYyMzPtjwtzDxsAAFcqXbq0pEvffbVq1cozPTEx0aEfCl+BQtO9995b2HXk67ffftOcOXM0ePBg/ec//9H27dv1/PPPy2KxqHv37vYzBwICAhyeFxAQYJ+WkpKi8uXLO0wvVqyYSpcu7dDnyuSeO8+UlJR8Q9PEiRM1duzYwllRAAD+QZ06dRQYGKiFCxdq/PjxDofocnJytHDhQlWoUEF16tRxYpW3tgKNaZKk//73v3rqqad0zz336I8//pAkffjhh9q0aVOhFZeTk6O77rpLr776qurXr6++ffuqT58+mjt3bqEto6CGDx8um81m/zly5IizSwIA3MI8PDzUv39/JSQkaOTIkdq/f7/Onj2r/fv3a+TIkUpISFC/fv24XtN1VKDQ9NlnnykyMlLe3t7auXOn/TCVzWbTq6++WmjFVahQQTVr1nRoq1GjhpKTkyVdui6FJKWmpjr0SU1NtU8LDAxUWlqaw/SLFy8qPT3doU9+87h8GVfy9PSUn5+fww8AANdTixYtNHbsWP3222+Kjo5W27ZtFR0drcTERC43cAMU6PDc+PHjNXfuXHXr1k0ff/yxvb1p06YaP358oRXXtGlTHTx40KHt559/VmhoqKRLg94CAwO1bt061atXT9KlsUXbtm1Tv379JEnh4eE6efKkduzYoQYNGkiSvvnmG+Xk5KhJkyb2PiNGjNCFCxdUvHhxSdKaNWt0xx135HtoDgAAZ2nRooWaNm3KFcGdoEB7mg4ePJhvmrVarTp58uS/rclu0KBB2rp1q1599VX98ssvWrRokeLj4xUdHS1JcnNz08CBAzV+/Hh9+eWX2rdvn7p166agoCB16NBB0qU9Uw8++KD69Omj7777Tps3b1ZMTIy6dOmioKAgSdKTTz4pi8WiXr16af/+/Vq8eLGmTZumwYMHF9q6AABQWDw8PFS/fn21atVK9evXJzDdIAXa0xQYGKhffvlFlStXdmjftGmTqlSpUhh1SZIaNWqkZcuWafjw4Ro3bpzCwsI0depURUVF2fsMHTpUZ86cUd++fXXy5Ek1a9ZMX331lby8vOx9Fi5cqJiYGLVq1Uru7u569NFHNX36dPt0q9Wq1atXKzo6Wg0aNFDZsmU1evRoLjcAAADsChSa+vTpoxdeeEHvvfee3NzcdPToUSUkJGjIkCEaNWpUoRb40EMP6aGHHrrqdDc3N40bN07jxo27ap/SpUtr0aJFf7ucOnXq6L///W+B6yxM7udOOrsEuAC2AwBwLQUKTcOGDVNOTo5atWqls2fPqkWLFvL09NSQIUM0YMCAwq6xyPFO3OjsEgAAwBUKFJrc3Nw0YsQIvfTSS/rll190+vRp1axZUz4+PoVdX5F0LqyFcrz9nV0GnMz93EkCNAC4kAKFplwWi0W+vr7y9fUlMBWiHG9/5ZQs6+wyAADAZQp09tzFixc1atQoWa1WVa5cWZUrV5bVatXIkSN14cKFwq4RAADA6Qq0p2nAgAFaunSp4uLiFB4eLklKSEhQbGysjh8/rjlz5hRqkQAAuJLz58/bL7QMKSQkxOGs9VtVgULTokWL9PHHH6tNmzb2tjp16ig4OFhdu3YlNAEAbmnJyclcluYy8fHxqlatmrPLuO4KFJo8PT3zXKNJunSFbovF8m9rAgDApYWEhCg+Pt6pNSQlJWnChAkaMWKE/U4ZzhISEuLU5d8oBQpNMTExeuWVVzRv3jx5enpKkjIzMzVhwgTFxMQUaoEAALgaLy8vl9mzEhoa6jK13OoKFJp27dqldevWqVKlSqpbt64kac+ePcrKylKrVq3UqVMne9+lS5cWTqUAAABOVKDQ5O/vr0cffdShLTg4uFAKAgAAcEUFCk3z5s0r7DoAAABcWoGu03Tu3DmdPXvW/jgpKUlTp07V6tWrC60wAAAAV1Kg0PTII4/ogw8+kCSdPHlSjRs31htvvKFHHnmEyw0AAIBbUoFC086dO9W8eXNJ0qeffqrAwEAlJSXpgw8+0PTp0wu1QAAAAFdQoNB09uxZ+fr6SpJWr16tTp06yd3dXXfffbeSkpIKtUAAAABXUKCB4Lfddps+//xzdezYUV9//bUGDRokSUpLS5Ofn1+hFlgUuZ+3ObsEuAC2AwBwLQUKTaNHj9aTTz6pQYMGqVWrVvb7z61evVr169cv1AKLEqvVquIWT+m3Dc4uBS6iuMVTVqvV2WUAAFTA0PTYY4+pWbNmOnbsmP3ilpLUqlUrdezY0f74999/V1BQkNzdC3QUsMgJCAjQgg8/kM1WtPcwuNKtAZzNarUqICDA2WUAAFTA0CRJgYGBCgwMdGhr3Lixw+OaNWtq9+7dqlKlSkEXU+QEBATwJfl/uDUAAMCVXNddQIZhXM/ZAwAA3DAcNwMAADCB0AQAAGACoQkAAMCE6xqa3NzcrufsAQAAbhgGggMAAJhQ4EsOmHHgwAEFBQVdz0UAAADcEKZDU6dOnUzPdOnSpZKk4ODga68IAADABZkOTdzKAQAAFGWmQ9O8efOuZx0AAAAujUsOAAAAmFDggeCffvqpPvnkEyUnJysrK8th2s6dO/91YQAAAK6kQHuapk+frp49eyogIEC7du1S48aNVaZMGf32229q06ZNYdcIAADgdAUKTbNnz1Z8fLxmzJghi8WioUOHas2aNXr++edls9kKu0YAAACnK1BoSk5O1j333CNJ8vb21qlTpyRJTz/9tD766KPCqw4AAMBFFCg0BQYGKj09XZIUEhKirVu3SpISExO5CjgAALglFSg03X///fryyy8lST179tSgQYP0wAMP6IknnlDHjh0LtUAAAABXUKCz5+Lj45WTkyNJio6OVpkyZbRlyxY9/PDDevbZZwu1QAAAAFdQoND0+++/O9wipUuXLurSpYsMw9CRI0cUEhJSaAUCAAC4ggIdngsLC9Off/6Zpz09PV1hYWH/uigAAABXU6DQZBiG3Nzc8rSfPn1aXl5e/7ooAAAAV3NNh+cGDx4sSXJzc9OoUaNUokQJ+7Ts7Gxt27ZN9erVK9QCAQAAXME1haZdu3ZJurSnad++fbJYLPZpFotFdevW1ZAhQwq3QgAAABdwTaHp22+/lXTpMgPTpk2Tn5/fdSkKAADA1RTo7Ll58+bZ///7779LkipVqlQ4FQEAALigAg0Ez8nJ0bhx42S1WhUaGqrQ0FD5+/vrlVdesV+/CQAA4FZSoD1NI0aM0LvvvqvXXntNTZs2lSRt2rRJsbGxOn/+vCZMmFCoRQIAADhbgULT+++/r3feeUcPP/ywva1OnTqqWLGi+vfvT2gCAAC3nAKFpvT0dFWvXj1Pe/Xq1e038sXN6fz580pOTnZqDUlJSQ7/OlNISAjXHgMASCpgaKpbt65mzpyp6dOnO7TPnDlTdevWLZTC4BzJycnq27evs8uQJJfYYxkfH69q1ao5uwwAgAsoUGiKi4tTu3bttHbtWoWHh0uSEhISdOTIEa1cubJQC8SNFRISovj4eGeX4TK4jyIAIFeBQlNYWJh+/vlnzZo1Sz/99JMkqVOnTurfv78uXrxYqAXixvLy8mLPCgAA+ShwaDp27FiewyfHjx9XcHCwsrOzC6U4AAAAV1Gg0GQYRr7t1/uGva+99pqGDx+uF154QVOnTpV0aeDyiy++qI8//liZmZmKjIzU7NmzFRAQYH9ecnKy+vXrp2+//VY+Pj7q3r27Jk6cqGLF/n/1169fr8GDB2v//v0KDg7WyJEj1aNHj+u2LgCAgklNTZXNZnN2GU7nSifNOJvVanX43r9eCnzD3tGjR9/QG/Zu375db731lurUqePQPmjQIK1YsUJLliyR1WpVTEyMOnXqpM2bN9vrateunQIDA7VlyxYdO3ZM3bp1U/HixfXqq69KkhITE9WuXTs999xzWrhwodatW6fevXurQoUKioyMvC7rAwC4dqmpqXrq6W66kJXp7FJchiucNONsxS2eWvDhB9c9ON0UN+w9ffq0oqKi9Pbbb2v8+PH2dpvNpnfffVeLFi3S/fffL+nSLV5q1KihrVu36u6779bq1at14MABrV27VgEBAapXr55eeeUVvfzyy4qNjZXFYtHcuXMVFhamN954Q5JUo0YNbdq0SVOmTCE0AYALsdlsupCVqXNV7lWOl9XZ5cAFuJ+3Sb9tkM1mc63Q5Kwb9kZHR6tdu3aKiIhwCE07duzQhQsXFBERYW+rXr26QkJClJCQoLvvvlsJCQmqXbu2wwsZGRmpfv36af/+/apfv74SEhIc5pHbZ+DAgVetKTMzU5mZ//+XTkZGRiGsKQDAjBwvq3JKlnV2GShi/vUNe6+3jz/+WDt37tT27dvzTEtJSZHFYpG/v79De0BAgFJSUux9rkyeuY//qU9GRobOnTsnb2/vPMueOHGixo4dW+D1AgAAN5cC3bD3Rjly5IheeOEFLVy40OWuyjx8+HDZbDb7z5EjR5xdEgAAuI5cOjTt2LFDaWlpuuuuu1SsWDEVK1ZMGzZs0PTp01WsWDEFBAQoKytLJ0+edHheamqqAgMDJUmBgYFKTU3NMz132t/18fPzy3cvkyR5enrKz8/P4QcAANy6XDo0tWrVSvv27dPu3bvtPw0bNlRUVJT9/8WLF9e6devszzl48KCSk5PtVyoPDw/Xvn37lJaWZu+zZs0a+fn5qWbNmvY+l88jt0/uPAAAAAo0pulG8fX11Z133unQVrJkSZUpU8be3qtXLw0ePFilS5eWn5+fBgwYoPDwcN19992SpNatW6tmzZp6+umnFRcXp5SUFI0cOVLR0dHy9PSUJD333HOaOXOmhg4dqmeeeUbffPONPvnkE61YseLGrjAAAHBZLh2azJgyZYrc3d316KOPOlzcMpeHh4eWL1+ufv36KTw8XCVLllT37t01btw4e5+wsDCtWLFCgwYN0rRp01SpUiW98847XG4AAADY3XShaf369Q6Pvby8NGvWLM2aNeuqzwkNDf3HGwnfd9999utQAQAAXMmlxzQBAAC4CkITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACbcdDfsBQDA/dxJZ5cAF3EjtwVCEwDgpuOduNHZJaAIIjQBAG4658JaKMfb39llwAW4nzt5w0I0oQkAcNPJ8fZXTsmyzi4DRQwDwQEAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYwG1UAAA3HffzNmeXABdxI7cFQhMA4KZhtVpV3OIp/bbB2aXAhRS3eMpqtV735RCaAAA3jYCAAC348APZbOxpSkpK0oQJEzRixAiFhoY6uxynslqtCggIuO7LITQBAG4qAQEBN+QL8mYRGhqqatWqObuMIoGB4AAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJXKcJgEs7f/68kpOTnV2GywgJCZGXl5ezywCKJEITAJeWnJysvn37OrsMlxEfH8+FDAEnITQBcGkhISGKj493ag2udLuKkJAQpy4fKMoITQBcmpeXl8vsWeF2FUDRxkBwAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAExw+dA0ceJENWrUSL6+vipfvrw6dOiggwcPOvQ5f/68oqOjVaZMGfn4+OjRRx9VamqqQ5/k5GS1a9dOJUqUUPny5fXSSy/p4sWLDn3Wr1+vu+66S56enrrttts0f/786716AADgJuHyoWnDhg2Kjo7W1q1btWbNGl24cEGtW7fWmTNn7H0GDRqk//mf/9GSJUu0YcMGHT16VJ06dbJPz87OVrt27ZSVlaUtW7bo/fff1/z58zV69Gh7n8TERLVr104tW7bU7t27NXDgQPXu3Vtff/31DV1fAADgmoo5u4B/8tVXXzk8nj9/vsqXL68dO3aoRYsWstlsevfdd7Vo0SLdf//9kqR58+apRo0a2rp1q+6++26tXr1aBw4c0Nq1axUQEKB69erplVde0csvv6zY2FhZLBbNnTtXYWFheuONNyRJNWrU0KZNmzRlyhRFRkbe8PUGXEVqaqpsNpuzy3CqpKQkh3+LMqvVqoCAAGeXATiFy4emK+V+eJcuXVqStGPHDl24cEERERH2PtWrV1dISIgSEhJ09913KyEhQbVr13Z4o0dGRqpfv37av3+/6tevr4SEBId55PYZOHBgvnVkZmYqMzPT/jgjI6OwVhFwGampqXrq6W66kJX5z52LgAkTJji7BKcrbvHUgg8/IDihSLqpQlNOTo4GDhyopk2b6s4775QkpaSkyGKxyN/f36FvQECAUlJS7H2ufIPnPv6nPhkZGTp37py8vb0dpk2cOFFjx44ttHUDXJHNZtOFrEydq3Kvcryszi4HTuZ+3ib9tkE2m63Ih6bz588rOTnZqTW40h7QkJAQeXl5ObuM6+6mCk3R0dH64YcftGnTJmeXouHDh2vw4MH2xxkZGQoODnZiRcD1k+NlVU7Jss4uA3AZycnJ6tu3r7PLkOQae0Dj4+NVrVo1Z5dx3d00oSkmJkbLly/Xxo0bValSJXt7YGCgsrKydPLkSYe9TampqQoMDLT3+e677xzml3t23eV9rjzjLjU1VX5+fnn2MkmSp6enPD09C2XdAAA3l5CQEMXHxzu7DJcREhLi7BJuCJcPTYZhaMCAAVq2bJnWr1+vsLAwh+kNGjRQ8eLFtW7dOj366KOSpIMHDyo5OVnh4eGSpPDwcE2YMEFpaWkqX768JGnNmjXy8/NTzZo17X1WrlzpMO81a9bY5wEAQC4vL68isWcFjlw+NEVHR2vRokX64osv5Ovrax+DZLVa5e3tLavVql69emnw4MEqXbq0/Pz8NGDAAIWHh+vuu++WJLVu3Vo1a9bU008/rbi4OKWkpGjkyJGKjo627y167rnnNHPmTA0dOlTPPPOMvvnmG33yySdasWKF09YdAAC4Dpe/TtOcOXNks9l03333qUKFCvafxYsX2/tMmTJFDz30kB599FG1aNFCgYGBWrp0qX26h4eHli9fLg8PD4WHh+upp55St27dNG7cOHufsLAwrVixQmvWrFHdunX1xhtv6J133uFyAwAAQNJNsKfJMIx/7OPl5aVZs2Zp1qxZV+0TGhqa5/Dble677z7t2rXrmmsEAAC3Ppff0wQAAOAKCE0AAAAmEJoAAABMIDQBAACY4PIDwQE4n/u5k84uAS6A7QBFHaEJwD/yTtzo7BIAwOkITQD+0bmwFsrx9nd2GXAy93MnCdAo0ghNAP5Rjrc/N+wFUOQxEBwAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAnFnF0AANfnft7m7BLgAtgOUNQRmgBcldVqVXGLp/TbBmeXAhdR3OIpq9Xq7DIApyA0AbiqgIAALfjwA9lsRXsPQ1JSkiZMmKARI0YoNDTU2eU4ldVqVUBAgLPLAJyC0ATgbwUEBPAl+X9CQ0NVrVo1Z5cBwEkYCA4AAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNF1h1qxZqly5sry8vNSkSRN99913zi4JAAC4AELTZRYvXqzBgwdrzJgx2rlzp+rWravIyEilpaU5uzQAAOBkhKbLvPnmm+rTp4969uypmjVrau7cuSpRooTee+89Z5cGAACcrJizC3AVWVlZ2rFjh4YPH25vc3d3V0REhBISEvL0z8zMVGZmpv1xRkbGDakTKGrOnz+v5ORkp9aQlJTk8K8zhYSEyMvLy9llAEUSoen//PXXX8rOzlZAQIBDe0BAgH766ac8/SdOnKixY8feqPKAIis5OVl9+/Z1dhmSpAkTJji7BMXHx6tatWrOLgMokghNBTR8+HANHjzY/jgjI0PBwcFOrAi4NYWEhCg+Pt7ZZbiMkJAQZ5cAFFmEpv9TtmxZeXh4KDU11aE9NTVVgYGBefp7enrK09PzRpUHFFleXl7sWQHgEhgI/n8sFosaNGigdevW2dtycnK0bt06hYeHO7EyAADgCtjTdJnBgwere/fuatiwoRo3bqypU6fqzJkz6tmzp7NLAwAATkZouswTTzyhP//8U6NHj1ZKSorq1aunr776Ks/gcAAAUPS4GYZhOLuIW0FGRoasVqtsNpv8/PycXQ4AADDhWr6/GdMEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAK3USkkuRdWz8jIcHIlAADArNzvbTM3SCE0FZJTp05JkoKDg51cCQAAuFanTp2S1Wr92z7ce66Q5OTk6OjRo/L19ZWbm5uzy7mpZWRkKDg4WEeOHOE+fnAJbJNwRWyXhcMwDJ06dUpBQUFyd//7UUvsaSok7u7uqlSpkrPLuKX4+fnxQQCXwjYJV8R2+e/90x6mXAwEBwAAMIHQBAAAYAKhCS7H09NTY8aMkaenp7NLASSxTcI1sV3eeAwEBwAAMIE9TQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0ASXMmvWLFWuXFleXl5q0qSJvvvuO2eXhCJs48aNat++vYKCguTm5qbPP//c2SWhiJs4caIaNWokX19flS9fXh06dNDBgwedXVaRQWiCy1i8eLEGDx6sMWPGaOfOnapbt64iIyOVlpbm7NJQRJ05c0Z169bVrFmznF0KIEnasGGDoqOjtXXrVq1Zs0YXLlxQ69atdebMGWeXViRwyQG4jCZNmqhRo0aaOXOmpEv38wsODtaAAQM0bNgwJ1eHos7NzU3Lli1Thw4dnF0KYPfnn3+qfPny2rBhg1q0aOHscm557GmCS8jKytKOHTsUERFhb3N3d1dERIQSEhKcWBkAuC6bzSZJKl26tJMrKRoITXAJf/31l7KzsxUQEODQHhAQoJSUFCdVBQCuKycnRwMHDlTTpk115513OrucIqGYswsAAADXLjo6Wj/88IM2bdrk7FKKDEITXELZsmXl4eGh1NRUh/bU1FQFBgY6qSoAcE0xMTFavny5Nm7cqEqVKjm7nCKDw3NwCRaLRQ0aNNC6devsbTk5OVq3bp3Cw8OdWBkAuA7DMBQTE6Nly5bpm2++UVhYmLNLKlLY0wSXMXjwYHXv3l0NGzZU48aNNXXqVJ05c0Y9e/Z0dmkook6fPq1ffvnF/jgxMVG7d+9W6dKlFRIS4sTKUFRFR0dr0aJF+uKLL+Tr62sf82m1WuXt7e3k6m59XHIALmXmzJmaPHmyUlJSVK9ePU2fPl1NmjRxdlkootavX6+WLVvmae/evbvmz59/4wtCkefm5pZv+7x589SjR48bW0wRRGgCAAAwgTFNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgBFwuHDh+Xm5qbdu3c7uxQANylCEwAUwPz58+Xv7+/sMgDcQIQmAHCi7Oxs5eTkOLsMACYQmgDcUnJychQXF6fbbrtNnp6eCgkJ0YQJE/L0y29P0eeff+5wb689e/aoZcuW8vX1lZ+fnxo0aKDvv/9e69evV8+ePWWz2eTm5iY3NzfFxsZKkjIzMzVkyBBVrFhRJUuWVJMmTbR+/fo8y/3yyy9Vs2ZNeXp6Kjk5+Xq8FAAKWTFnFwAAhWn48OF6++23NWXKFDVr1kzHjh3TTz/9VKB5RUVFqX79+pozZ448PDy0e/duFS9eXPfcc4+mTp2q0aNH6+DBg5IkHx8fSVJMTIwOHDigjz/+WEFBQVq2bJkefPBB7du3T7fffrsk6ezZs5o0aZLeeecdlSlTRuXLly+clQdwXRGaANwyTp06pWnTpmnmzJnq3r27JKlq1apq1qyZDh8+fM3zS05O1ksvvaTq1atLkj30SJLVapWbm5sCAwMd+s+bN0/JyckKCgqSJA0ZMkRfffWV5s2bp1dffVWSdOHCBc2ePVt169Yt6KoCcAJCE4Bbxo8//qjMzEy1atWqUOY3ePBg9e7dWx9++KEiIiL0+OOPq2rVqlftv2/fPmVnZ6tatWoO7ZmZmSpTpoz9scViUZ06dQqlRgA3DqEJwC3D29vbdF93d3cZhuHQduHCBYfHsbGxevLJJ7VixQqtWrVKY8aM0ccff6yOHTvmO8/Tp0/Lw8NDO3bskIeHh8O03MN3uXVePnYKwM2BgeAAbhm33367vL29tW7dun/sW65cOZ06dUpnzpyxt+V3Dadq1app0KBBWr16tTp16qR58+ZJurS3KDs726Fv/fr1lZ2drbS0NN12220OP5cfxgNwcyI0AbhleHl56eWXX9bQoUP1wQcf6Ndff9XWrVv17rvv5unbpEkTlShRQv/5z3/066+/atGiRZo/f759+rlz5xQTE6P169crKSlJmzdv1vbt21WjRg1JUuXKlXX69GmtW7dOf/31l86ePatq1aopKipK3bp109KlS5WYmKjvvvtOEydO1IoVK27UywDgOiE0AbiljBo1Si+++KJGjx6tGjVq6IknnlBaWlqefqVLl9aCBQu0cuVK1a5dWx999JH9sgGS5OHhoePHj6tbt26qVq2aOnfurDZt2mjs2LGSpHvuuUfPPfecnnjiCZUrV05xcXGSpHnz5qlbt2568cUXdccdd6hDhw7avn27QkJCbsj6A7h+3IwrD+oDAAAgD/Y0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMCE/wUWwBlq0bT08AAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "    # Cluster distribution plot\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    sns.countplot(data=customer_data, x='cluster', palette='deep')\n",
        "    plt.title('Distribution of Customers Across Clusters')\n",
        "    plt.savefig('cluster_dist.png', bbox_inches='tight', dpi=300)\n",
        "    plt.show()\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "7MRwNiEC9X9H",
        "outputId": "8ff0bdd6-7461-44f6-ab88-49f7c2138818"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-161-2b54e92c40fa>:3: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(data=customer_data, x='cluster', palette='deep')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "a8f1z0TG-_B_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "    # Box plots for each feature\n",
        "    for feature in features:\n",
        "        plt.figure(figsize=(10, 6))\n",
        "        sns.boxplot(data=customer_data, x='cluster', y=feature, palette='deep')\n",
        "        plt.title(f'Distribution of {feature} Across Clusters')\n",
        "        plt.savefig(f'boxplot_{feature}.png', bbox_inches='tight', dpi=300)\n",
        "        plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "qbG92bAT-jCE",
        "outputId": "fe9c7642-5646-4d49-8883-5c72f2ef4b74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-162-1ea47a8395d4>:4: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.boxplot(data=customer_data, x='cluster', y=feature, palette='deep')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-162-1ea47a8395d4>:4: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.boxplot(data=customer_data, x='cluster', y=feature, palette='deep')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-162-1ea47a8395d4>:4: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.boxplot(data=customer_data, x='cluster', y=feature, palette='deep')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-162-1ea47a8395d4>:4: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.boxplot(data=customer_data, x='cluster', y=feature, palette='deep')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from reportlab.pdfgen import canvas\n",
        "from reportlab.lib.pagesizes import letter\n",
        "import os\n",
        "\n",
        "def generate_pdf_report(filename, data, clusters, metrics, features):\n",
        "    \"\"\"\n",
        "    Generate a PDF report with clustering results and visualizations\n",
        "\n",
        "    Parameters:\n",
        "    filename (str): Output PDF filename\n",
        "    data (DataFrame): Customer data with cluster assignments\n",
        "    metrics (dict): Clustering metrics (DB index, silhouette, etc.)\n",
        "    features (list): List of feature names used in clustering\n",
        "    \"\"\"\n",
        "\n",
        "    # Create visualizations first\n",
        "    create_visualizations(data, features)\n",
        "\n",
        "    # Initialize PDF\n",
        "    c = canvas.Canvas(filename, pagesize=letter)\n",
        "    c.setFont(\"Helvetica-Bold\", 16)\n",
        "\n",
        "    # First Page - Metrics and Main Plots\n",
        "    # Title\n",
        "    c.drawString(100, 750, \"Customer Segmentation Analysis Report\")\n",
        "\n",
        "    # Metrics section\n",
        "    c.setFont(\"Helvetica\", 12)\n",
        "    c.drawString(100, 720, \"Clustering Metrics:\")\n",
        "    c.drawString(120, 700, f\"Number of Clusters: {metrics['n_clusters']}\")\n",
        "    c.drawString(120, 680, f\"Davies-Bouldin Index: {metrics['db_index']:.4f}\")\n",
        "    c.drawString(120, 660, f\"Silhouette Score: {metrics['silhouette']:.4f}\")\n",
        "    c.drawString(120, 640, f\"Calinski-Harabasz Index: {metrics['ch_score']:.4f}\")\n",
        "\n",
        "    # Add main scatter plot\n",
        "    c.drawImage('scatter_plot.png', 100, 320, width=400, height=300)\n",
        "    c.drawString(100, 300, \"Figure 1: Customer Segmentation Scatter Plot\")\n",
        "    interpretation = f\"Interpretation:Customer Value Tiers The data naturally forms into three distinct spending and transaction patterns Low-value segment (Cluster 1, purple): Makes 0-3 transactions with total spend under 2,000 Mid-value segment (Cluster 2, navy): Makes 3-8 transactions with spend between 2,000-5,000 High-value segment (Cluster 0, pink): Makes 6-11 transactions with spend ranging up to 10,000 Transaction-Spend Relationship There's a strong positive correlation between spending and transaction frequency - customers who spend more tend to make more purchases. However, there's significant variation in total spend even among customers with the same number of transactions, suggesting different purchasing behaviors within each segment.Distribution Pattern The data points form distinct horizontal bands corresponding to whole number transaction counts, with spending spread out along each band. This suggests customers tend to fall into regular transaction frequency patterns (like monthly shoppers, quarterly shoppers, etc.) while their total spend per transaction varies considerably.\"\n",
        "\n",
        "\n",
        "    # Add cluster distribution plot\n",
        "    c.drawImage('cluster_dist.png', 100, 50, width=400, height=200)\n",
        "    c.drawString(100, 30, \"Figure 2: Cluster Size Distribution\")\n",
        "\n",
        "\n",
        "    # New page for box plots\n",
        "    c.showPage()\n",
        "    c.setFont(\"Helvetica-Bold\", 16)\n",
        "    c.drawString(100, 750, \"Feature Distribution Analysis\")\n",
        "\n",
        "    # Add box plots\n",
        "    y_position = 500\n",
        "    for i, feature in enumerate(features, 1):\n",
        "        if y_position < 50:  # Check if we need a new page\n",
        "            c.showPage()\n",
        "            y_position = 700\n",
        "\n",
        "        c.drawImage(f'boxplot_{feature}.png', 100, y_position, width=400, height=200)\n",
        "        c.setFont(\"Helvetica\", 10)\n",
        "        c.drawString(100, y_position-20, f\"Figure {i+2}: Distribution of {feature} across clusters\")\n",
        "        y_position -= 250\n",
        "\n",
        "    # Save and clean up\n",
        "    c.save()\n",
        "    cleanup_temp_files()\n",
        "\n",
        "def create_visualizations(data, features):\n",
        "    \"\"\"Create and save all visualizations needed for the report\"\"\"\n",
        "\n",
        "    # Main scatter plot\n",
        "    plt.figure(figsize=(10, 8))\n",
        "    sns.scatterplot(data=data, x='total_spend', y='total_transactions',\n",
        "                    hue='cluster', palette='deep')\n",
        "    plt.title('Customer Segmentation - Spend vs Transactions')\n",
        "    plt.savefig('scatter_plot.png', bbox_inches='tight', dpi=300)\n",
        "    plt.close()\n",
        "\n",
        "    # Cluster distribution plot\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    sns.countplot(data=data, x='cluster', palette='deep')\n",
        "    plt.title('Distribution of Customers Across Clusters')\n",
        "    plt.savefig('cluster_dist.png', bbox_inches='tight', dpi=300)\n",
        "    plt.close()\n",
        "\n",
        "    # Box plots for each feature\n",
        "    for feature in features:\n",
        "        plt.figure(figsize=(10, 6))\n",
        "        sns.boxplot(data=data, x='cluster', y=feature, palette='deep')\n",
        "        plt.title(f'Distribution of {feature} Across Clusters')\n",
        "        plt.savefig(f'boxplot_{feature}.png', bbox_inches='tight', dpi=300)\n",
        "        plt.close()\n",
        "\n",
        "\n",
        "\n",
        "def cleanup_temp_files():\n",
        "    \"\"\"Remove temporary image files\"\"\"\n",
        "    temp_files = ['scatter_plot.png', 'cluster_dist.png']\n",
        "    temp_files.extend([f'boxplot_{feature}.png' for feature in features])\n",
        "\n",
        "    for file in temp_files:\n",
        "        if os.path.exists(file):\n",
        "            os.remove(file)\n",
        "\n",
        "# Example usage\n",
        "def main():\n",
        "    # Assuming you have your data and metrics ready\n",
        "    metrics = {\n",
        "        'n_clusters': optimal_k,\n",
        "        'db_index': db_index,\n",
        "        'silhouette': silhouette_avg,\n",
        "        'ch_score': ch_score\n",
        "    }\n",
        "\n",
        "    generate_pdf_report(\n",
        "        filename=\"Mansi_Waghmare_Clustering.pdf\",\n",
        "        data=customer_data,\n",
        "        clusters=clusters,\n",
        "        metrics=metrics,\n",
        "        features=features\n",
        "    )\n",
        "\n",
        "     # Now you can save the document\n",
        "    try:\n",
        "        document.save(Mansi_Waghmare_Clustering.pdf)\n",
        "        files.download(Mansi_Waghmare_Clustering.pdf)\n",
        "    except Exception as e:\n",
        "        print(f\"Error saving document: {e}\")\n",
        "\n",
        "    print(\"Report generated successfully!\")\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    main()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rkyfxpotsSvP",
        "outputId": "8a6e5e00-10de-4996-f630-877f2249d5ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-163-9c3ce28d5929>:80: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(data=data, x='cluster', palette='deep')\n",
            "<ipython-input-163-9c3ce28d5929>:88: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.boxplot(data=data, x='cluster', y=feature, palette='deep')\n",
            "<ipython-input-163-9c3ce28d5929>:88: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.boxplot(data=data, x='cluster', y=feature, palette='deep')\n",
            "<ipython-input-163-9c3ce28d5929>:88: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.boxplot(data=data, x='cluster', y=feature, palette='deep')\n",
            "<ipython-input-163-9c3ce28d5929>:88: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.boxplot(data=data, x='cluster', y=feature, palette='deep')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error saving document: name 'document' is not defined\n",
            "Report generated successfully!\n"
          ]
        }
      ]
    }
  ]
}