Clustering.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Edu963/python_projects/blob/main/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": 4,
      "metadata": {
        "id": "2Nc5b6CT9rRO"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline\n",
        "from matplotlib import rcParams"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "BdxfuQDE9rRS"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8BCSG71y9rRT"
      },
      "source": [
        "# Introduction to clustering\n",
        "\n",
        "Clustering is a machine learning technique used to group unlabeled datapoints in groups called *clusters*. The grouping is done such that similar items are together in the same cluster and items in different clusters are dissimilar from each-other. Typically similarity is defined using a distance measure (Euclidean, cosine, etc). Common practical applications for clustering include: market segmentation, social network analysis, search result grouping, anomaly detection etc.\n",
        "\n",
        "\n",
        "Two clustering methods will be explored in this notebook:\n",
        "\n",
        "- K-means.\n",
        "- Hierarchical clustering.\n",
        "\n",
        "More methods can be found [here](https://scikit-learn.org/stable/modules/classes.html?highlight=cluster#module-sklearn.cluster).\n",
        "\n",
        "#### To begin with\n",
        "Get familiar with how clustering works by watching the videos in Resources A and reading the article in Resources B.\n",
        "\n",
        "\n",
        "#### Resources:\n",
        "\n",
        "* A. Introduction to clustering:\n",
        "    * https://www.youtube.com/watch?v=4cxVDUybHrI\n",
        "    \n",
        "    \n",
        "* B. A Comprehensive Survey of Clustering Algorithms\n",
        "    * https://link.springer.com/article/10.1007/s40745-015-0040-1 (Read everything until the section Traditional Clustering Algorithms. From the section Traditional Clustering Algorithms, read the first 2 subsections. You can skip Modern Traditional Clustering Algorithms.)\n",
        "\n",
        "\n",
        "\n",
        "* C. K-means clustering:\n",
        "     * https://stanford.edu/~cpiech/cs221/handouts/kmeans.html\n",
        "     \n",
        "     * https://www.youtube.com/watch?v=QXOkPvFM6NU&t=1s (up to minute 11:34)\n",
        "     \n",
        "     \n",
        "* D. Hierarchical clustering:\n",
        "    * https://www.youtube.com/watch?v=QXOkPvFM6NU&t=1s (from minute 11:34 onwards)\n",
        "    * https://stackabuse.com/hierarchical-clustering-with-python-and-scikit-learn/ (example on how to do hierarchical clustering with scikit-learn)\n",
        "    * https://scikit-learn.org/stable/modules/clustering.html#hierarchical-clustering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ouMMlAFP9rRW"
      },
      "source": [
        "### Subject comprehension\n",
        "\n",
        "To check your understanding, before moving further, answer the questions in your own words in the space below.\n",
        "\n",
        "1. What is clustering?\n",
        "2. What are the main steps in the standard process of clustering?\n",
        "3. What is the Silhouette coefficient?\n",
        "4. What are the two types of evaluation for a clustering algorithm and what is the difference between them?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UzcuaOvA9rRW"
      },
      "source": [
        "**Answer here**\n",
        "1. *Clustering* is like organizing legos by color\n",
        "\n",
        "2. *What are the main steps in the standard process of clustering?*\n",
        "- Choose the number of clusters (K): Decide how many groups you want to divide your data into.\n",
        "- Initialize centroids: Select initial central points for each cluster. These can be chosen randomly.\n",
        "- Assign points to clusters: For each data point, find the nearest centroid and assign the point to that cluster.\n",
        "- Update centroids: Calculate the new centroids by finding the average position of all points in each cluster.\n",
        "- Repeat: Reassign points to clusters based on the new centroids and update the centroids again. Keep repeating until the centroids don't change much.\n",
        "\n",
        "3. *What is the Silhouette coefficient?*\n",
        "\n",
        "The Silhouette coefficient is a way to measure how well your data points are clustered. It looks at how close each point is to the points in its own cluster compared to the points in other clusters. itacts like a score that tells you how well each item fits into its group. The score ranges from -1 to 1, where 1 means the point is well clustered, 0 means it's on or very close to the border between clusters, and negative values mean the point might be in the wrong cluster.\n",
        "\n",
        "4. *What are the two types of evaluation for a clustering algorithm and what is the difference between them?*\n",
        "\n",
        "- Internal Evaluation: This method looks at how well the clusters are formed using only the data you have. It measures things like compactness (how close the points in a cluster are to each other) and separation (how far apart the clusters are from each other). The Silhouette coefficient is an example of an internal evaluation metric.\n",
        "\n",
        "- External Evaluation: This method compares the clusters formed by your algorithm to a pre-defined structure or labels you already have. For example, if you know in advance which fruits should be grouped together, you can see how well your clustering matches this known grouping. It's like having a teacher grade your grouping against an answer key"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IyHc0nFU9rRX"
      },
      "source": [
        "### Data acquisition\n",
        "\n",
        "Load the data about bank clients from the *bank-full.csv* file into a pandas dataframe. Make the necessary imports."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "EuuFursA9rRX"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('bank-full.csv', sep=';')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "14LQSdG69rRY"
      },
      "source": [
        "Print a summary of the data as well as the first 5 rows."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 643
        },
        "id": "iMd-elN39rRY",
        "outputId": "522c7a83-7b9f-4910-84a6-3488b20a257f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 45211 entries, 0 to 45210\n",
            "Data columns (total 17 columns):\n",
            " #   Column     Non-Null Count  Dtype \n",
            "---  ------     --------------  ----- \n",
            " 0   age        45211 non-null  int64 \n",
            " 1   job        45211 non-null  object\n",
            " 2   marital    45211 non-null  object\n",
            " 3   education  45211 non-null  object\n",
            " 4   default    45211 non-null  object\n",
            " 5   balance    45211 non-null  int64 \n",
            " 6   housing    45211 non-null  object\n",
            " 7   loan       45211 non-null  object\n",
            " 8   contact    45211 non-null  object\n",
            " 9   day        45211 non-null  int64 \n",
            " 10  month      45211 non-null  object\n",
            " 11  duration   45211 non-null  int64 \n",
            " 12  campaign   45211 non-null  int64 \n",
            " 13  pdays      45211 non-null  int64 \n",
            " 14  previous   45211 non-null  int64 \n",
            " 15  poutcome   45211 non-null  object\n",
            " 16  y          45211 non-null  object\n",
            "dtypes: int64(7), object(10)\n",
            "memory usage: 5.9+ MB\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age           job  marital  education default  balance housing loan  \\\n",
              "0   58    management  married   tertiary      no     2143     yes   no   \n",
              "1   44    technician   single  secondary      no       29     yes   no   \n",
              "2   33  entrepreneur  married  secondary      no        2     yes  yes   \n",
              "3   47   blue-collar  married    unknown      no     1506     yes   no   \n",
              "4   33       unknown   single    unknown      no        1      no   no   \n",
              "\n",
              "   contact  day month  duration  campaign  pdays  previous poutcome   y  \n",
              "0  unknown    5   may       261         1     -1         0  unknown  no  \n",
              "1  unknown    5   may       151         1     -1         0  unknown  no  \n",
              "2  unknown    5   may        76         1     -1         0  unknown  no  \n",
              "3  unknown    5   may        92         1     -1         0  unknown  no  \n",
              "4  unknown    5   may       198         1     -1         0  unknown  no  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1f372e32-c1ff-4838-ad37-6b6ddb9e3be0\" 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>age</th>\n",
              "      <th>job</th>\n",
              "      <th>marital</th>\n",
              "      <th>education</th>\n",
              "      <th>default</th>\n",
              "      <th>balance</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>contact</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>poutcome</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>58</td>\n",
              "      <td>management</td>\n",
              "      <td>married</td>\n",
              "      <td>tertiary</td>\n",
              "      <td>no</td>\n",
              "      <td>2143</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>261</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44</td>\n",
              "      <td>technician</td>\n",
              "      <td>single</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>29</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>151</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33</td>\n",
              "      <td>entrepreneur</td>\n",
              "      <td>married</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>76</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47</td>\n",
              "      <td>blue-collar</td>\n",
              "      <td>married</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>1506</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>92</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33</td>\n",
              "      <td>unknown</td>\n",
              "      <td>single</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>198</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</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-1f372e32-c1ff-4838-ad37-6b6ddb9e3be0')\"\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-1f372e32-c1ff-4838-ad37-6b6ddb9e3be0 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-1f372e32-c1ff-4838-ad37-6b6ddb9e3be0');\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-feabf621-4c64-4056-8684-5f927209c122\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-feabf621-4c64-4056-8684-5f927209c122')\"\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-feabf621-4c64-4056-8684-5f927209c122 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",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 45211,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 18,\n        \"max\": 95,\n        \"num_unique_values\": 77,\n        \"samples\": [\n          35,\n          34,\n          53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"job\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"housemaid\",\n          \"unemployed\",\n          \"management\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"marital\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"married\",\n          \"single\",\n          \"divorced\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"education\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"secondary\",\n          \"primary\",\n          \"tertiary\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"default\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"balance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3044,\n        \"min\": -8019,\n        \"max\": 102127,\n        \"num_unique_values\": 7168,\n        \"samples\": [\n          3276,\n          43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"loan\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"contact\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"unknown\",\n          \"cellular\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 1,\n        \"max\": 31,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          1,\n          27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"apr\",\n          \"mar\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 0,\n        \"max\": 4918,\n        \"num_unique_values\": 1573,\n        \"samples\": [\n          835,\n          1135\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"campaign\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 63,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          41,\n          27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pdays\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 100,\n        \"min\": -1,\n        \"max\": 871,\n        \"num_unique_values\": 559,\n        \"samples\": [\n          249,\n          551\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"previous\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 275,\n        \"num_unique_values\": 41,\n        \"samples\": [\n          17,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"poutcome\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"failure\",\n          \"success\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df.info()\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dlJ4lOyM9rRY"
      },
      "source": [
        "### Preprocessing\n",
        "\n",
        "To facilitate the computation of distances, keep only the numerical features in the dataset.\n",
        "\n",
        "- Remove all non-numerical features, as well as the features related to the date.\n",
        "- Make sure there are no missing values - if they exist, remove them.\n",
        "- Normalize the new dataset (transform the data for multiple features to the same scale).\n",
        "- Save the result in a dataframe *df_norm*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "hpk3Igq-9rRZ",
        "outputId": "76a9706a-ff37-4d5b-af71-b9d1d56799ed"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-7-908328d56f3b>:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df.dropna(inplace=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  balance  duration  campaign  pdays  previous\n",
              "0   58     2143       261         1     -1         0\n",
              "1   44       29       151         1     -1         0\n",
              "2   33        2        76         1     -1         0\n",
              "3   47     1506        92         1     -1         0\n",
              "4   33        1       198         1     -1         0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f95ef054-d7e9-48de-a62a-98645ee9f69e\" 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>age</th>\n",
              "      <th>balance</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>58</td>\n",
              "      <td>2143</td>\n",
              "      <td>261</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44</td>\n",
              "      <td>29</td>\n",
              "      <td>151</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33</td>\n",
              "      <td>2</td>\n",
              "      <td>76</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47</td>\n",
              "      <td>1506</td>\n",
              "      <td>92</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "      <td>198</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</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-f95ef054-d7e9-48de-a62a-98645ee9f69e')\"\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-f95ef054-d7e9-48de-a62a-98645ee9f69e 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-f95ef054-d7e9-48de-a62a-98645ee9f69e');\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-9e51e0c4-c7ef-4f85-993b-d1ee7956950c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9e51e0c4-c7ef-4f85-993b-d1ee7956950c')\"\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-9e51e0c4-c7ef-4f85-993b-d1ee7956950c 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",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 45211,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 18,\n        \"max\": 95,\n        \"num_unique_values\": 77,\n        \"samples\": [\n          35,\n          34,\n          53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"balance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3044,\n        \"min\": -8019,\n        \"max\": 102127,\n        \"num_unique_values\": 7168,\n        \"samples\": [\n          3276,\n          43,\n          7003\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 0,\n        \"max\": 4918,\n        \"num_unique_values\": 1573,\n        \"samples\": [\n          835,\n          1135,\n          1735\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"campaign\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 63,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          41,\n          27,\n          63\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pdays\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 100,\n        \"min\": -1,\n        \"max\": 871,\n        \"num_unique_values\": 559,\n        \"samples\": [\n          249,\n          551,\n          127\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"previous\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 275,\n        \"num_unique_values\": 41,\n        \"samples\": [\n          17,\n          9,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "numeric_columns = ['age', 'balance', 'duration', 'campaign', 'pdays', 'previous']\n",
        "df = df[numeric_columns]\n",
        "\n",
        "df.dropna(inplace=True)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "6Y1_bQ90Od97",
        "outputId": "0084a233-8a48-433f-ec7d-8b1db5332876"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                age        balance      duration      campaign         pdays  \\\n",
              "count  45211.000000   45211.000000  45211.000000  45211.000000  45211.000000   \n",
              "mean      40.936210    1362.272058    258.163080      2.763841     40.197828   \n",
              "std       10.618762    3044.765829    257.527812      3.098021    100.128746   \n",
              "min       18.000000   -8019.000000      0.000000      1.000000     -1.000000   \n",
              "25%       33.000000      72.000000    103.000000      1.000000     -1.000000   \n",
              "50%       39.000000     448.000000    180.000000      2.000000     -1.000000   \n",
              "75%       48.000000    1428.000000    319.000000      3.000000     -1.000000   \n",
              "max       95.000000  102127.000000   4918.000000     63.000000    871.000000   \n",
              "\n",
              "           previous  \n",
              "count  45211.000000  \n",
              "mean       0.580323  \n",
              "std        2.303441  \n",
              "min        0.000000  \n",
              "25%        0.000000  \n",
              "50%        0.000000  \n",
              "75%        0.000000  \n",
              "max      275.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5264a926-ffd2-46df-bd1d-c92958aa11f2\" 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>age</th>\n",
              "      <th>balance</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>45211.000000</td>\n",
              "      <td>45211.000000</td>\n",
              "      <td>45211.000000</td>\n",
              "      <td>45211.000000</td>\n",
              "      <td>45211.000000</td>\n",
              "      <td>45211.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>40.936210</td>\n",
              "      <td>1362.272058</td>\n",
              "      <td>258.163080</td>\n",
              "      <td>2.763841</td>\n",
              "      <td>40.197828</td>\n",
              "      <td>0.580323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>10.618762</td>\n",
              "      <td>3044.765829</td>\n",
              "      <td>257.527812</td>\n",
              "      <td>3.098021</td>\n",
              "      <td>100.128746</td>\n",
              "      <td>2.303441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>-8019.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>33.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>103.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>39.000000</td>\n",
              "      <td>448.000000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>48.000000</td>\n",
              "      <td>1428.000000</td>\n",
              "      <td>319.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>95.000000</td>\n",
              "      <td>102127.000000</td>\n",
              "      <td>4918.000000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>871.000000</td>\n",
              "      <td>275.000000</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-5264a926-ffd2-46df-bd1d-c92958aa11f2')\"\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-5264a926-ffd2-46df-bd1d-c92958aa11f2 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-5264a926-ffd2-46df-bd1d-c92958aa11f2');\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-3e07a296-9163-4bf5-8c64-c22fe1ea6b59\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3e07a296-9163-4bf5-8c64-c22fe1ea6b59')\"\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-3e07a296-9163-4bf5-8c64-c22fe1ea6b59 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\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15970.15020169202,\n        \"min\": 10.61876204097542,\n        \"max\": 45211.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          40.93621021432837,\n          39.0,\n          45211.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"balance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37605.12655558522,\n        \"min\": -8019.0,\n        \"max\": 102127.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1362.2720576850766,\n          448.0,\n          45211.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15767.149700866448,\n        \"min\": 0.0,\n        \"max\": 45211.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          258.1630797814691,\n          180.0,\n          45211.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"campaign\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15980.684949017386,\n        \"min\": 1.0,\n        \"max\": 45211.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          45211.0,\n          2.763840658246887,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pdays\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15936.427694880493,\n        \"min\": -1.0,\n        \"max\": 45211.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          40.19782796222158,\n          871.0,\n          100.12874599059835\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"previous\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15970.756012949014,\n        \"min\": 0.0,\n        \"max\": 45211.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.5803233726305546,\n          275.0,\n          2.3034410449312213\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BXWmMDf99rRZ"
      },
      "source": [
        "Plot histograms of features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 957
        },
        "id": "WpVRq3yC9rRZ",
        "outputId": "abf3650f-d7a5-4dd0-8277-e213ad4c02af"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'age'}>,\n",
              "        <Axes: title={'center': 'balance'}>],\n",
              "       [<Axes: title={'center': 'duration'}>,\n",
              "        <Axes: title={'center': 'campaign'}>],\n",
              "       [<Axes: title={'center': 'pdays'}>,\n",
              "        <Axes: title={'center': 'previous'}>]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 6 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df.hist(figsize=(10,10))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sS59im7Y9rRZ"
      },
      "source": [
        "## KMeans\n",
        "\n",
        "\n",
        "Let's warm up with the well known algorithm named KMeans!\n",
        "\n",
        "Centroid-based clustering organizes the data into non-hierarchical clusters, contrarily to hierarchical clustering. KMeans is the most widely-used centroid-based clustering algorithm. Centroid-based algorithms are efficient but sensitive to initial conditions and outliers.\n",
        "\n",
        "\n",
        "To better understand the way the algorithm works, read the documents and watch the video in Resources C:\n",
        "* https://stanford.edu/~cpiech/cs221/handouts/kmeans.html\n",
        "* https://www.youtube.com/watch?v=QXOkPvFM6NU&t=1s (up to minute 11:34)\n",
        "\n",
        "\n",
        "Then answer the below questions (in your own words):\n",
        "1. What is a centroid?\n",
        "2. How can you compute a centroid?\n",
        "3. Are you supposed to know in advance the number of clusters in order to use the KMeans algorithm?\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uV_1gk_q9rRZ"
      },
      "source": [
        "***ANSWER***\n",
        "1. A centroid is the central point of a cluster.Kind off like  the \"average\" position of all the points in a group.\n",
        "2. To compute a centroid, you take the average of all the data points in the cluster. For each dimension (or feature) of the data, you calculate the mean value. For example, if your data points have two dimensions (like coordinates on a map), the centroid's coordinates would be the average of all the x-coordinates and the average of all the y-coordinates of the points in the cluster.\n",
        "3.Yes, we need to specify the number of clusters in advance when using the KMeans algorithm."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oyyMh9Oq9rRZ"
      },
      "source": [
        "#### Make the necessary imports from scikit learn in order to perform KMeans clustering"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "ca16YoIW9rRa"
      },
      "outputs": [],
      "source": [
        "from sklearn.cluster import KMeans\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.metrics import silhouette_score\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ODPLPHq9rRa"
      },
      "source": [
        "#### Exploring KMeans with sklearn\n",
        "\n",
        "The goal of this step is to become familiar with the sklearn clustering package and the silhouette analysis concepts.\n",
        "\n",
        "Start by peforming a clustering of the data in *df_norm* into 5 clusters."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Normalize the Data: Since KMeans is sensitive to the scale of the data, we'll first normalize the data.\n",
        "2. Apply KMeans Clustering: We'll cluster the data into 5 clusters.\n",
        "3. Calculate Silhouette Score: To evaluate the quality of the clustering"
      ],
      "metadata": {
        "id": "eWXjtvE9YIEP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalize the data\n",
        "numeriq_pipline = Pipeline([\n",
        "        ('std_scaler', StandardScaler()),\n",
        "    ])\n",
        "df_norm = numeriq_pipline.fit_transform(df)\n",
        "df_norm = pd.DataFrame(df_norm, index=df.index,columns=df.columns)\n",
        "print(df_norm)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LcX9aSbwXWkd",
        "outputId": "e9a7fdc1-010e-4abd-b223-cfc75eda3f49"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            age   balance  duration  campaign     pdays  previous\n",
            "0      1.606965  0.256419  0.011016 -0.569351 -0.411453 -0.251940\n",
            "1      0.288529 -0.437895 -0.416127 -0.569351 -0.411453 -0.251940\n",
            "2     -0.747384 -0.446762 -0.707361 -0.569351 -0.411453 -0.251940\n",
            "3      0.571051  0.047205 -0.645231 -0.569351 -0.411453 -0.251940\n",
            "4     -0.747384 -0.447091 -0.233620 -0.569351 -0.411453 -0.251940\n",
            "...         ...       ...       ...       ...       ...       ...\n",
            "45206  0.947747 -0.176460  2.791329  0.076230 -0.411453 -0.251940\n",
            "45207  2.831227  0.120447  0.768224 -0.246560 -0.411453 -0.251940\n",
            "45208  2.925401  1.429593  3.373797  0.721811  1.436189  1.050473\n",
            "45209  1.512791 -0.228024  0.970146  0.399020 -0.411453 -0.251940\n",
            "45210 -0.370689  0.528364  0.399328 -0.246560  1.476138  4.523577\n",
            "\n",
            "[45211 rows x 6 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_norm.describe()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "nXHr1GBUztLy",
        "outputId": "3b5f8af4-4c4f-4f7e-d9e0-86c875fa2979"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                age       balance      duration      campaign         pdays  \\\n",
              "count  4.521100e+04  4.521100e+04  4.521100e+04  4.521100e+04  4.521100e+04   \n",
              "mean   2.112250e-16  1.760208e-17  6.035001e-17  3.017500e-17  2.011667e-17   \n",
              "std    1.000011e+00  1.000011e+00  1.000011e+00  1.000011e+00  1.000011e+00   \n",
              "min   -2.159994e+00 -3.081149e+00 -1.002478e+00 -5.693506e-01 -4.114531e-01   \n",
              "25%   -7.473845e-01 -4.237719e-01 -6.025167e-01 -5.693506e-01 -4.114531e-01   \n",
              "50%   -1.823406e-01 -3.002800e-01 -3.035165e-01 -2.465603e-01 -4.114531e-01   \n",
              "75%    6.652252e-01  2.158743e-02  2.362370e-01  7.622994e-02 -4.114531e-01   \n",
              "max    5.091402e+00  3.309478e+01  1.809470e+01  1.944365e+01  8.297431e+00   \n",
              "\n",
              "           previous       cluster  \n",
              "count  4.521100e+04  4.521100e+04  \n",
              "mean   4.023334e-17  1.810500e-16  \n",
              "std    1.000011e+00  1.000011e+00  \n",
              "min   -2.519404e-01 -1.941696e+00  \n",
              "25%   -2.519404e-01 -4.821715e-01  \n",
              "50%   -2.519404e-01 -4.821715e-01  \n",
              "75%   -2.519404e-01  9.773529e-01  \n",
              "max    1.191360e+02  9.773529e-01  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-861374a0-2404-438a-8303-3760aeb9fab9\" 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>age</th>\n",
              "      <th>balance</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4.521100e+04</td>\n",
              "      <td>4.521100e+04</td>\n",
              "      <td>4.521100e+04</td>\n",
              "      <td>4.521100e+04</td>\n",
              "      <td>4.521100e+04</td>\n",
              "      <td>4.521100e+04</td>\n",
              "      <td>4.521100e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.112250e-16</td>\n",
              "      <td>1.760208e-17</td>\n",
              "      <td>6.035001e-17</td>\n",
              "      <td>3.017500e-17</td>\n",
              "      <td>2.011667e-17</td>\n",
              "      <td>4.023334e-17</td>\n",
              "      <td>1.810500e-16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.000011e+00</td>\n",
              "      <td>1.000011e+00</td>\n",
              "      <td>1.000011e+00</td>\n",
              "      <td>1.000011e+00</td>\n",
              "      <td>1.000011e+00</td>\n",
              "      <td>1.000011e+00</td>\n",
              "      <td>1.000011e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-2.159994e+00</td>\n",
              "      <td>-3.081149e+00</td>\n",
              "      <td>-1.002478e+00</td>\n",
              "      <td>-5.693506e-01</td>\n",
              "      <td>-4.114531e-01</td>\n",
              "      <td>-2.519404e-01</td>\n",
              "      <td>-1.941696e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-7.473845e-01</td>\n",
              "      <td>-4.237719e-01</td>\n",
              "      <td>-6.025167e-01</td>\n",
              "      <td>-5.693506e-01</td>\n",
              "      <td>-4.114531e-01</td>\n",
              "      <td>-2.519404e-01</td>\n",
              "      <td>-4.821715e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-1.823406e-01</td>\n",
              "      <td>-3.002800e-01</td>\n",
              "      <td>-3.035165e-01</td>\n",
              "      <td>-2.465603e-01</td>\n",
              "      <td>-4.114531e-01</td>\n",
              "      <td>-2.519404e-01</td>\n",
              "      <td>-4.821715e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.652252e-01</td>\n",
              "      <td>2.158743e-02</td>\n",
              "      <td>2.362370e-01</td>\n",
              "      <td>7.622994e-02</td>\n",
              "      <td>-4.114531e-01</td>\n",
              "      <td>-2.519404e-01</td>\n",
              "      <td>9.773529e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.091402e+00</td>\n",
              "      <td>3.309478e+01</td>\n",
              "      <td>1.809470e+01</td>\n",
              "      <td>1.944365e+01</td>\n",
              "      <td>8.297431e+00</td>\n",
              "      <td>1.191360e+02</td>\n",
              "      <td>9.773529e-01</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-861374a0-2404-438a-8303-3760aeb9fab9')\"\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-861374a0-2404-438a-8303-3760aeb9fab9 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-861374a0-2404-438a-8303-3760aeb9fab9');\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-9db64ecd-c3eb-47d5-93c2-7807559ee56c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9db64ecd-c3eb-47d5-93c2-7807559ee56c')\"\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-9db64ecd-c3eb-47d5-93c2-7807559ee56c 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_norm\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15984.317271922751,\n        \"min\": -2.159994148794221,\n        \"max\": 45211.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.112250197654497e-16,\n          -0.18234061750847744,\n          45211.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"balance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15982.975752957496,\n        \"min\": -3.0811485466035458,\n        \"max\": 45211.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.7602084980454143e-17,\n          -0.30027996250561056,\n          45211.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15983.623656815023,\n        \"min\": -1.0024778786007835,\n        \"max\": 45211.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.035000564727134e-17,\n          -0.30351651549307,\n          45211.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"campaign\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15983.537360924245,\n        \"min\": -0.5693506376457914,\n        \"max\": 45211.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          45211.0,\n          3.017500282363567e-17,\n          0.07622994241825963\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pdays\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15984.116154234272,\n        \"min\": -0.4114531064930479,\n        \"max\": 45211.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.0116668549090448e-17,\n          8.297430992711057,\n          1.000011059438955\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"previous\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15978.539849017188,\n        \"min\": -0.25194037067217256,\n        \"max\": 45211.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4.0233337098180897e-17,\n          119.13598229870267,\n          1.000011059438955\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cluster\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15984.499915440769,\n        \"min\": -1.9416960289519671,\n        \"max\": 45211.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          45211.0,\n          1.8105001694181403e-16,\n          0.9773529399059109\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_norm.hist(figsize=(10,10))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 992
        },
        "id": "IYTPtmqYywSF",
        "outputId": "e1ab2b51-c0cf-438b-e20c-881470326ab2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'age'}>,\n",
              "        <Axes: title={'center': 'balance'}>,\n",
              "        <Axes: title={'center': 'duration'}>],\n",
              "       [<Axes: title={'center': 'campaign'}>,\n",
              "        <Axes: title={'center': 'pdays'}>,\n",
              "        <Axes: title={'center': 'previous'}>],\n",
              "       [<Axes: title={'center': 'cluster'}>, <Axes: >, <Axes: >]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 9 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1AAAANECAYAAAC+XM68AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADEeklEQVR4nOzde1xUdf4/8BcgM4AyICggCwKlq+ANhYSpvCOjkWmSaVkSoq4GbsB3tdhVRM00SvGGkqViq5aX3dyUEiZMrRhvKOWVbphtNmApjKIMI3N+f/Sbs47cBhhgwNfz8eBh53Pe55z3Z2A+nfe5WgmCIICIiIiIiIjqZd3aCRAREREREbUVLKCIiIiIiIhMxAKKiIiIiIjIRCygiIiIiIiITMQCioiIiIiIyEQsoIiIiIiIiEzEAoqIiIiIiMhELKCIiIiIiIhMxAKKiIiIiIjIRCygiIjIIqSkpMDKygq//fabWdY3fPhwDB8+3CzrIqL2xTDeWBpfX1+89NJLrZ0G1YMFFBERERFRC8nLy0NKSgpKS0tbOxVqpA6tnQARERER0YMiLy8PixcvxksvvQRnZ2ejeYWFhbC25vkNS8ffEBERERFRI5WXl5ttXVKpFLa2tmZbHzUPFlDU4n766Se8/PLL6NWrF+zt7eHq6opJkybh8uXL1WK/+eYbDBs2DPb29vDy8sLrr7+OrVu3wsrKqlr8p59+iiFDhqBjx45wdHREREQEzp8/3zKdIiKz+e233/Dss89CJpPB1dUVr7zyCioqKsT5W7duxciRI+Hm5gapVIqAgABs3Lix3vVWVlYiOTkZQUFBcHJyQseOHTFkyBB8/vnnRnGXL1+GlZUV3n77bWzatAkPP/wwpFIpHnnkEZw8ebLaei9duoRnn30WXbt2hb29PXr16oV//OMfRjG//PILpk+fDnd3d0ilUvTp0wdbtmxp5CdERA3x5Zdf4pFHHoGdnR0efvhhvPPOO0bzDd/5zMzMastaWVkhJSVFnDbcO3XhwgU8//zz6Ny5Mx5//HEAf+yzvPTSS3jooYdgZ2cHDw8PTJ8+Hb///rvR8vPmzQMA+Pn5wcrKymifpqZ7oH788UdMmjQJLi4ucHBwQGhoKLKysoxiDh8+DCsrK+zevRvLli2Dl5cX7OzsMGrUKHz//feN/OSoNryEj1rcyZMnkZeXhylTpsDLywuXL1/Gxo0bMXz4cFy4cAEODg4A/tjhGDFiBKysrJCUlISOHTvivffeg1QqrbbOf/7zn4iKioJCocCbb76J27dvY+PGjXj88cdx5swZ+Pr6tnAviaixnn32Wfj6+mL58uU4duwY1q5dixs3buD9998HAGzcuBF9+vTBU089hQ4dOmD//v14+eWXodfrERsbW+t6NRoN3nvvPTz33HOYOXMmbt68ic2bN0OhUODEiRMIDAw0it+5cydu3ryJv/zlL7CyskJqaiomTpyIH3/8UTxC/M0332DIkCGwtbXFrFmz4Ovrix9++AH79+/HsmXLAADFxcUIDQ2FlZUV4uLi0LVrV3z66aeIiYmBRqNBfHx8s3yORAScPXsW4eHh6Nq1K1JSUnD37l0sWrQI7u7uTVrvpEmT0LNnT7zxxhsQBAEAoFQq8eOPPyI6OhoeHh44f/48Nm3ahPPnz+PYsWOwsrLCxIkT8e233+KDDz5AWloaunTpAgDo2rVrjdspLi7Go48+itu3b+Ovf/0rXF1dsW3bNjz11FPYu3cvnn76aaP4FStWwNraGn/7299QVlaG1NRUTJ06FcePH29Sf+k+AlELu337drU2lUolABDef/99sW3u3LmClZWVcObMGbHt999/F1xcXAQAQlFRkSAIgnDz5k3B2dlZmDlzptE61Wq14OTkVK2diCzTokWLBADCU089ZdT+8ssvCwCEr7/+WhCEmscQhUIhPPTQQ0Ztw4YNE4YNGyZO3717V9BqtUYxN27cENzd3YXp06eLbUVFRQIAwdXVVbh+/brY/p///EcAIOzfv19sGzp0qODo6Cj89NNPRuvV6/Xif8fExAjdunUTfvvtN6OYKVOmCE5OTjX2h4jMY8KECYKdnZ3Rd/TChQuCjY2NYNgNNnznt27dWm15AMKiRYvEacM49dxzz1WLrem7/MEHHwgAhKNHj4ptb731ltF+zL18fHyEqKgocTo+Pl4AIHzxxRdi282bNwU/Pz/B19dXqKqqEgRBED7//HMBgODv7280zq1Zs0YAIJw9e7b6h0ONxkv4qMXZ29uL/63T6fD777+jR48ecHZ2xunTp8V5Bw8ehFwuNzoq7OLigqlTpxqtT6lUorS0FM899xx+++038cfGxgYhISHVLs8hIst2/1mkuXPnAgA++eQTAMZjSFlZGX777TcMGzYMP/74I8rKympdr42NDSQSCQBAr9fj+vXruHv3LoKDg43GHoPJkyejc+fO4vSQIUMA/HE5DQBcu3YNR48exfTp09G9e3ejZQ2PRxYEAf/6178wbtw4CIJgNEYpFAqUlZXVuG0iarqqqipkZ2djwoQJRt9Rf39/KBSKJq179uzZ1druHZsqKirw22+/ITQ0FAAa/T3/5JNPMHjwYPEyQQDo1KkTZs2ahcuXL+PChQtG8dHR0eI4B1Qft8g8eAkftbg7d+5g+fLl2Lp1K3755Rfx1DcAo52fn376CXK5vNryPXr0MJr+7rvvAAAjR46scXsymcwcaRNRC+nZs6fR9MMPPwxra2vxHoGvvvoKixYtgkqlwu3bt41iy8rK4OTkVOu6t23bhpUrV+LSpUvQ6XRiu5+fX7XY+4siQzF148YNAP/bIenbt2+t27t27RpKS0uxadMmbNq0qcaYkpKSWpcnosa7du0a7ty5U21MAYBevXqJB2Uao6Yx4/r161i8eDE+/PDDat/rug7u1OWnn35CSEhItXZ/f39x/r1jUH3jFpkHCyhqcXPnzsXWrVsRHx8PuVwOJycnWFlZYcqUKdDr9Q1en2GZf/7zn/Dw8Kg2v0MH/pkTtWX3vuzyhx9+wKhRo9C7d2+sWrUK3t7ekEgk+OSTT5CWllbnGLJ9+3a89NJLmDBhAubNmwc3NzfY2Nhg+fLl+OGHH6rF29jY1Lieew/61MeQzwsvvICoqKgaY/r372/y+ojI/Gp7oW5VVVWty9x7tsng2WefRV5eHubNm4fAwEB06tQJer0eY8aMadT+TWOYY9yi+nHPklrc3r17ERUVhZUrV4ptFRUV1V4o5+PjU+OTY+5ve/jhhwEAbm5uCAsLM3/CRNSivvvuO6Oju99//z30ej18fX2xf/9+aLVafPzxx0ZHWk25VHfv3r146KGH8O9//9toh2nRokWNyvOhhx4CAJw7d67WmK5du8LR0RFVVVUcn4hamOHJmIYrVe5VWFgo/rfhLM39+yE//fSTydu6ceMGcnNzsXjxYiQnJ4vtNW27toKtJj4+Pka5Gly6dEmcTy2P90BRi7Oxsal2JGTdunXVjvQoFAqoVCoUFBSIbdevX8eOHTuqxclkMrzxxhtGl+QYXLt2zXzJE1GzS09PN5pet24dAGDs2LHi0dX7L/3dunVrveutadnjx49DpVI1Ks+uXbti6NCh2LJlC65cuWI0z7ANGxsbREZG4l//+leNhRbHJ6LmY2NjA4VCgX379hl9Ry9evIjs7GxxWiaToUuXLjh69KjR8hs2bGjQtoDqZ3pWr15dLbZjx44AqhdsNXniiSdw4sQJo3GqvLwcmzZtgq+vLwICAkzOkcyHZ6CoxT355JP45z//CScnJwQEBEClUuGzzz6Dq6urUdz8+fOxfft2jB49GnPnzhUfY969e3dcv35dPIIjk8mwceNGvPjiixg0aBCmTJmCrl274sqVK8jKysJjjz2G9evXt0ZXiagRioqK8NRTT2HMmDFQqVTYvn07nn/+eQwYMAB2dnaQSCQYN24c/vKXv+DWrVt499134ebmhl9//bXO9T755JP497//jaeffhoREREoKipCRkYGAgICcOvWrUblunbtWjz++OMYNGgQZs2aBT8/P1y+fBlZWVniwZ8VK1bg888/R0hICGbOnImAgABcv34dp0+fxmeffYbr1683attEVL/Fixfj4MGDGDJkCF5++WXcvXsX69atQ58+ffDNN9+IcTNmzMCKFSswY8YMBAcH4+jRo/j2229N3o5MJsPQoUORmpoKnU6HP/3pT8jJyUFRUVG12KCgIADAP/7xD0yZMgW2trYYN26cWFjd67XXXsMHH3yAsWPH4q9//StcXFywbds2FBUV4V//+hesrXkupFW01uP/6MF148YNITo6WujSpYvQqVMnQaFQCJcuXar26E5BEIQzZ84IQ4YMEaRSqeDl5SUsX75cWLt2rQBAUKvVRrGff/65oFAoBCcnJ8HOzk54+OGHhZdeekk4depUC/aOiBrL8HjgCxcuCM8884zg6OgodO7cWYiLixPu3Lkjxn388cdC//79BTs7O8HX11d48803hS1btlR7LPD9jzHX6/XCG2+8Ifj4+AhSqVQYOHCgcODAASEqKkrw8fER4wyPNH7rrbeq5Yj7HmksCIJw7tw54emnnxacnZ0FOzs7oVevXsLChQuNYoqLi4XY2FjB29tbsLW1FTw8PIRRo0YJmzZtatJnRkT1O3LkiBAUFCRIJBLhoYceEjIyMsTxxuD27dtCTEyM4OTkJDg6OgrPPvusUFJSUutjzK9du1ZtO//973/FscDJyUmYNGmScPXq1RrHjaVLlwp/+tOfBGtra6Oxq6Z9oR9++EF45plnxDFm8ODBwoEDB4xiDI8x37Nnj1F7XY9op8azEgTeVUZtS3x8PN555x3cunWr1psliYiIiIiaA8/7kUW7c+eO0fTvv/+Of/7zn3j88cdZPBERERFRi+M9UGTR5HI5hg8fDn9/fxQXF2Pz5s3QaDRYuHBha6dGRERERA8gFlBk0Z544gns3bsXmzZtgpWVFQYNGoTNmzdj6NChrZ0aERERET2AeA8UERERERGRiXgPFBERERERkYlYQBEREREREZnogb4HSq/X4+rVq3B0dBRfykpETScIAm7evAlPT88H8iV/HFuImg/HF44vRM3F1PHlgS6grl69Cm9v79ZOg6jd+vnnn+Hl5dXaabQ4ji1EzY/jCxE1l/rGlwe6gHJ0dATwx4ckk8lMXk6n0yEnJwfh4eGwtbVtrvRaHPvVtlhyvzQaDby9vcXv2IOmsWNLY1jy30FTsF9tS0v2i+OLeceX9vY32Z76w760PFPHlwe6gDKc+pbJZA0uoBwcHCCTySz6j6Ch2K+2pS3060G9vKSxY0tjtIW/g8Zgv9qW1ugXxxfzjC/t7W+yPfWHfWk99Y0vD97Fw0RERERERI3EAoqILNKKFStgZWWF+Ph4sa2iogKxsbFwdXVFp06dEBkZieLiYqPlrly5goiICDg4OMDNzQ3z5s3D3bt3jWIOHz6MQYMGQSqVokePHsjMzKy2/fT0dPj6+sLOzg4hISE4ceJEc3STiFoBxxciagoWUERkcU6ePIl33nkH/fv3N2pPSEjA/v37sWfPHhw5cgRXr17FxIkTxflVVVWIiIhAZWUl8vLysG3bNmRmZiI5OVmMKSoqQkREBEaMGIGCggLEx8djxowZyM7OFmN27dqFxMRELFq0CKdPn8aAAQOgUChQUlLS/J0nombF8YWImooFFBFZlFu3bmHq1Kl499130blzZ7G9rKwMmzdvxqpVqzBy5EgEBQVh69atyMvLw7FjxwAAOTk5uHDhArZv347AwECMHTsWS5cuRXp6OiorKwEAGRkZ8PPzw8qVK+Hv74+4uDg888wzSEtLE7e1atUqzJw5E9HR0QgICEBGRgYcHBywZcuWlv0wiMisOL4QkTk80A+RICLLExsbi4iICISFheH1118X2/Pz86HT6RAWFia29e7dG927d4dKpUJoaChUKhX69esHd3d3MUahUGDOnDk4f/48Bg4cCJVKZbQOQ4zhUp7Kykrk5+cjKSlJnG9tbY2wsDCoVKoac9ZqtdBqteK0RqMB8MdNszqdrvEfhgkM62/u7bQ09qttacl+NWUbbXF8ISLLwwKKiCzGhx9+iNOnT+PkyZPV5qnVakgkEjg7Oxu1u7u7Q61WizH37twY5hvm1RWj0Whw584d3LhxA1VVVTXGXLp0qca8ly9fjsWLF1drz8nJgYODQx09Nh+lUtki22lp7Ffb0hL9un37dqOWa6vjS3MfoGlvRX176g/70vJMzY8FFBFZhJ9//hmvvPIKlEol7OzsWjudBklKSkJiYqI4bXiPRHh4eIs8xlypVGL06NFt4tGwpmK/2paW7JehgGiItjy+tNQBmvZW1Len/rAvLcfUAzQsoNoB39eyzLIeqY2A1MFmWRVRg+Xn56OkpASDBg0S26qqqnD06FGsX78e2dnZqKysRGlpqdFR4uLiYnh4eAAAPDw8qj3NyvAUrXtj7n+yVnFxMWQyGezt7WFjYwMbG5saYwzruJ9UKoVUKq3WbmtrW+/OZFO/v4bv7cBlh6CtssLlFRFNWp+lMeUzbIvYr6Zto6Ha8vjSlAM0fVOy65wPAFJrAUuD9Vh4yhpafd3vvjmXoqh3fa2tPR2kYF9anqkHaFhAEZFFGDVqFM6ePWvUFh0djd69e+PVV1+Ft7c3bG1tkZubi8jISABAYWEhrly5ArlcDgCQy+VYtmwZSkpK4ObmBuCPo10ymQwBAQFizCeffGK0HaVSKa5DIpEgKCgIubm5mDBhAgBAr9cjNzcXcXFxzdZ/Imo+bXl8acoBGm2V6S8b1uqt6o235B3f+7WngxTsS8sxNTcWUERkERwdHdG3b1+jto4dO8LV1VVsj4mJQWJiIlxcXCCTyTB37lzI5XKEhoYCAMLDwxEQEIAXX3wRqampUKvVWLBgAWJjY8UdkNmzZ2P9+vWYP38+pk+fjkOHDmH37t3IyvrfmaDExERERUUhODgYgwcPxurVq1FeXo7o6OgW+jSIyJw4vhCRObGAIqI2Iy0tDdbW1oiMjIRWq4VCocCGDRvE+TY2Njhw4ADmzJkDuVyOjh07IioqCkuWLBFj/Pz8kJWVhYSEBKxZswZeXl547733oFD879KUyZMn49q1a0hOToZarUZgYCAOHjxY7cZvImo/OL4QkalYQBGRxTp8+LDRtJ2dHdLT05Genl7rMj4+PtUuobnf8OHDcebMmTpj4uLieMkeUTvG8YWIGosv0iUiIiIiIjIRCygiIiIiIiITsYAiIiIiIiIyEQsoIiIiIiIiE7GAIiIiIiIiMhELKCIiIiIiIhM1uIA6evQoxo0bB09PT1hZWWHfvn3iPJ1Oh1dffRX9+vVDx44d4enpiWnTpuHq1atG67h+/TqmTp0KmUwGZ2dnxMTE4NatW0Yx33zzDYYMGQI7Ozt4e3sjNTW1Wi579uxB7969YWdnh379+tX7aFEiIiIiIqKmaHABVV5ejgEDBtT4noTbt2/j9OnTWLhwIU6fPo1///vfKCwsxFNPPWUUN3XqVJw/fx5KpRIHDhzA0aNHMWvWLHG+RqNBeHg4fHx8kJ+fj7feegspKSnYtGmTGJOXl4fnnnsOMTExOHPmDCZMmIAJEybg3LlzDe0SERERERGRSRr8It2xY8di7NixNc5zcnKCUqk0alu/fj0GDx6MK1euoHv37rh48SIOHjyIkydPIjg4GACwbt06PPHEE3j77bfh6emJHTt2oLKyElu2bIFEIkGfPn1QUFCAVatWiYXWmjVrMGbMGMybNw8AsHTpUiiVSqxfvx4ZGRkN7RYREREREVG9GlxANVRZWRmsrKzg7OwMAFCpVHB2dhaLJwAICwuDtbU1jh8/jqeffhoqlQpDhw6FRCIRYxQKBd58803cuHEDnTt3hkqlQmJiotG2FAqF0SWF99NqtdBqteK0RqMB8MelhzqdzuQ+GWIbskxzktoI5lmP9R/rsZR+mYul/b7MxZL7ZYk5EREREZlDsxZQFRUVePXVV/Hcc89BJpMBANRqNdzc3IyT6NABLi4uUKvVYoyfn59RjLu7uzivc+fOUKvVYtu9MYZ11GT58uVYvHhxtfacnBw4ODg0uH/3n21rLamDzbs+S+mXubFfLef27dutnQIRERFRs2i2Akqn0+HZZ5+FIAjYuHFjc22mQZKSkozOWmk0Gnh7eyM8PFws8Eyh0+mgVCoxevRo2NraNkeqDdI3Jdss65FaC1garLeYfpmLpf2+zMWS+2U4u0tERETU3jRLAWUonn766SccOnTIqDjx8PBASUmJUfzdu3dx/fp1eHh4iDHFxcVGMYbp+mIM82silUohlUqrtdva2jZqB7Sxy5mbtsrKrOuzlH6ZG/vVciwtHyIiIiJzMft7oAzF03fffYfPPvsMrq6uRvPlcjlKS0uRn58vth06dAh6vR4hISFizNGjR43uo1AqlejVqxc6d+4sxuTm5hqtW6lUQi6Xm7tLREREREREABpxBurWrVv4/vvvxemioiIUFBTAxcUF3bp1wzPPPIPTp0/jwIEDqKqqEu9JcnFxgUQigb+/P8aMGYOZM2ciIyMDOp0OcXFxmDJlCjw9PQEAzz//PBYvXoyYmBi8+uqrOHfuHNasWYO0tDRxu6+88gqGDRuGlStXIiIiAh9++CFOnTpl9KhzS+X7WlZrp0BERERERI3Q4DNQp06dwsCBAzFw4EAAQGJiIgYOHIjk5GT88ssv+Pjjj/Hf//4XgYGB6Natm/iTl5cnrmPHjh3o3bs3Ro0ahSeeeAKPP/64UeHj5OSEnJwcFBUVISgoCP/3f/+H5ORko3dFPfroo9i5cyc2bdqEAQMGYO/evdi3bx/69u3blM+DiIiIiIioVg0+AzV8+HAIQu2Pza5rnoGLiwt27txZZ0z//v3xxRdf1BkzadIkTJo0qd7tERERERERmYPZ74EiIiIiIiJqr1hAERERERERmYgFFBERERERkYlYQBEREREREZmIBRQREREREZGJWEARERERERGZiAUUERERERGRiVhAERERERERmYgFFBERERERkYlYQBEREREREZmIBRQREREREZGJWEARERERERGZiAUUERERERGRiVhAERERERERmYgFFBERERERkYlYQBEREREREZmIBRQREREREZGJWEARERERERGZiAUUERERERGRiVhAERERERERmajBBdTRo0cxbtw4eHp6wsrKCvv27TOaLwgCkpOT0a1bN9jb2yMsLAzfffedUcz169cxdepUyGQyODs7IyYmBrdu3TKK+eabbzBkyBDY2dnB29sbqamp1XLZs2cPevfuDTs7O/Tr1w+ffPJJQ7tDRERERERksgYXUOXl5RgwYADS09NrnJ+amoq1a9ciIyMDx48fR8eOHaFQKFBRUSHGTJ06FefPn4dSqcSBAwdw9OhRzJo1S5yv0WgQHh4OHx8f5Ofn46233kJKSgo2bdokxuTl5eG5555DTEwMzpw5gwkTJmDChAk4d+5cQ7tERERERERkkg4NXWDs2LEYO3ZsjfMEQcDq1auxYMECjB8/HgDw/vvvw93dHfv27cOUKVNw8eJFHDx4ECdPnkRwcDAAYN26dXjiiSfw9ttvw9PTEzt27EBlZSW2bNkCiUSCPn36oKCgAKtWrRILrTVr1mDMmDGYN28eAGDp0qVQKpVYv349MjIyGvVhEBERERER1cWs90AVFRVBrVYjLCxMbHNyckJISAhUKhUAQKVSwdnZWSyeACAsLAzW1tY4fvy4GDN06FBIJBIxRqFQoLCwEDdu3BBj7t2OIcawHSJqWzZu3Ij+/ftDJpNBJpNBLpfj008/FedXVFQgNjYWrq6u6NSpEyIjI1FcXGy0jitXriAiIgIODg5wc3PDvHnzcPfuXaOYw4cPY9CgQZBKpejRowcyMzOr5ZKeng5fX1/Y2dkhJCQEJ06caJY+ExERUdtj1gJKrVYDANzd3Y3a3d3dxXlqtRpubm5G8zt06AAXFxejmJrWce82aosxzK+JVquFRqMx+gEAnU7X4J/GLqfT6SC1ESzzx1poUr8s+Yf9ap3cGsLLywsrVqxAfn4+Tp06hZEjR2L8+PE4f/48ACAhIQH79+/Hnj17cOTIEVy9ehUTJ04Ul6+qqkJERAQqKyuRl5eHbdu2ITMzE8nJyWJMUVERIiIiMGLECBQUFCA+Ph4zZsxAdna2GLNr1y4kJiZi0aJFOH36NAYMGACFQoGSkpIG94mILAMP0BCROTX4Er62bPny5Vi8eHG19pycHDg4ODR4fUqlslF5pA5u1GItprH9snTsV8u5fft2g5cZN26c0fSyZcuwceNGHDt2DF5eXti8eTN27tyJkSNHAgC2bt0Kf39/HDt2DKGhocjJycGFCxfw2Wefwd3dHYGBgVi6dCleffVVpKSkQCKRICMjA35+fli5ciUAwN/fH19++SXS0tKgUCgAAKtWrcLMmTMRHR0NAMjIyEBWVha2bNmC1157rSkfCxG1EsMBmp49e0IQBGzbtg3jx4/HmTNn0KdPHyQkJCArKwt79uyBk5MT4uLiMHHiRHz11VcA/neAxsPDA3l5efj1118xbdo02Nra4o033gDwvwM0s2fPxo4dO5Cbm4sZM2agW7du4vhiOECTkZGBkJAQrF69WrzC5v6Dy0RkucxaQHl4eAAAiouL0a1bN7G9uLgYgYGBYsz9R3Lv3r2L69evi8t7eHhUO/JjmK4vxjC/JklJSUhMTBSnNRoNvL29ER4eDplMZnI/dTodlEolRo8eDVtbW5OXM+ibkl1/UCuQWgtYGqxvdL8sVVN/X5bKkvtlOLvbWFVVVdizZw/Ky8shl8uRn58PnU5ndNlu79690b17d6hUKoSGhkKlUqFfv35GZ6YVCgXmzJmD8+fPY+DAgbVe+hsfHw8AqKysRH5+PpKSksT51tbWCAsL4+XBRG0YD9AQkTmZtYDy8/ODh4cHcnNzxYJJo9Hg+PHjmDNnDgBALpejtLQU+fn5CAoKAgAcOnQIer0eISEhYsw//vEP6HQ6ccdQqVSiV69e6Ny5sxiTm5sr7vgYYuRyea35SaVSSKXSau22traN2gFt7HLaKqsGL9OSGtsvS8d+tZzG5nP27FnI5XJUVFSgU6dO+OijjxAQEICCggJIJBI4Ozsbxd9/eXBjL/3VaDS4c+cObty4gaqqqhpjLl26VGveWq0WWq1WnL7/8uC6SG2EOufXx3Dp7b2X4LYH916i2p6wX+bbVmO1tQM0zT2+3D+G1KUt/N22p+8Y+9LyTM2vwQXUrVu38P3334vTRUVFKCgogIuLC7p37474+Hi8/vrr6NmzJ/z8/LBw4UJ4enpiwoQJAP44IjNmzBjMnDkTGRkZ0Ol0iIuLw5QpU+Dp6QkAeP7557F48WLExMTg1Vdfxblz57BmzRqkpaWJ233llVcwbNgwrFy5EhEREfjwww9x6tQpo0edE1Hb0qtXLxQUFKCsrAx79+5FVFQUjhw50tpp1asplweb65LepcF6AGh378OzxEtUzYH9arzGXCIMtN0DNC01vhjGkLq0pfGlPX3H2JeWY+r40uAC6tSpUxgxYoQ4bbgkLioqCpmZmZg/fz7Ky8sxa9YslJaW4vHHH8fBgwdhZ2cnLrNjxw7ExcVh1KhRsLa2RmRkJNauXSvOd3JyQk5ODmJjYxEUFIQuXbogOTnZ6F1Rjz76KHbu3IkFCxbg73//O3r27Il9+/ahb9++De0SEVkIiUSCHj16AACCgoJw8uRJrFmzBpMnT0ZlZSVKS0uNdnLuvWzXw8Oj2s3Ypl76K5PJYG9vDxsbG9jY2LTo5cFNvaTXcOntwlPW0OqtcC5F0aT1WQpLvkS1KdivpmvsJcJt9QBNc48v948hdWkL40t7+o6xLy3P1PGlwQXU8OHDIQi1n+a1srLCkiVLsGTJklpjXFxcsHPnzjq3079/f3zxxRd1xkyaNAmTJk2qO2EiarP0ej20Wi2CgoJga2uL3NxcREZGAgAKCwtx5coV8bJduVyOZcuWoaSkRLwZW6lUQiaTISAgQIy5/wjqvZf+SiQSBAUFITc3VzxrrtfrkZubi7i4uFrzbMrlwea6pFert4K2ysqi/8fUGJZ4iao5sF9N20ZjtNUDNC01vhjGkLq0pb/Z9vQdY19ajqm5mfUx5kREjZWUlISjR4/i8uXLOHv2LJKSknD48GFMnToVTk5OiImJQWJiIj7//HPk5+cjOjoacrkcoaGhAIDw8HAEBATgxRdfxNdff43s7GwsWLAAsbGx4s7H7Nmz8eOPP2L+/Pm4dOkSNmzYgN27dyMhIUHMIzExEe+++y62bduGixcvYs6cOSgvLxdv+iai9qGmAzQGNR2gOXv2rNFDsGo6QHPvOgwxNR2guTeH3NzcOu/fJiLL80A9xpyILFdJSQmmTZuGX3/9FU5OTujfvz+ys7MxevRoAEBaWpp4ya9Wq4VCocCGDRvE5W1sbHDgwAHMmTMHcrkcHTt2RFRUlNHZcD8/P2RlZSEhIQFr1qyBl5cX3nvvPfEJWQAwefJkXLt2DcnJyVCr1QgMDMTBgwer3bdARG1HUlISxo4di+7du+PmzZvYuXMnDh8+jOzsbKMDNC4uLpDJZJg7d26tB2hSU1OhVqtrPECzfv16zJ8/H9OnT8ehQ4ewe/duZGVliXkkJiYiKioKwcHBGDx4MFavXs0DNERtEAsoIrIImzdvrnO+nZ0d0tPTkZ6eXmuMj49PvTc5Dx8+HGfOnKkzJi4urs5L9oiobeEBGiIyJxZQRERE1K7xAA0RmRPvgSIiIiIiIjIRCygiIiIiIiITsYAiIiIiIiIyEQsoIiIiIiIiE7GAIiIiIiIiMhELKCIiIiIiIhOxgCIiIiIiIjIRCygiIiIiIiITsYAiIiIiIiIyEQsoIiIiIiIiE7GAIiIiIiIiMhELKCIiIiIiIhOxgCIiIiIiIjIRCygiIiIiIiITsYAiIiIiIiIyEQsoIiIiIiIiE7GAIiIiIiIiMpHZC6iqqiosXLgQfn5+sLe3x8MPP4ylS5dCEAQxRhAEJCcno1u3brC3t0dYWBi+++47o/Vcv34dU6dOhUwmg7OzM2JiYnDr1i2jmG+++QZDhgyBnZ0dvL29kZqaau7uEBERERERicxeQL355pvYuHEj1q9fj4sXL+LNN99Eamoq1q1bJ8akpqZi7dq1yMjIwPHjx9GxY0coFApUVFSIMVOnTsX58+ehVCpx4MABHD16FLNmzRLnazQahIeHw8fHB/n5+XjrrbeQkpKCTZs2mbtLREREREREAIAO5l5hXl4exo8fj4iICACAr68vPvjgA5w4cQLAH2efVq9ejQULFmD8+PEAgPfffx/u7u7Yt28fpkyZgosXL+LgwYM4efIkgoODAQDr1q3DE088gbfffhuenp7YsWMHKisrsWXLFkgkEvTp0wcFBQVYtWqVUaFFRERERERkLmYvoB599FFs2rQJ3377Lf785z/j66+/xpdffolVq1YBAIqKiqBWqxEWFiYu4+TkhJCQEKhUKkyZMgUqlQrOzs5i8QQAYWFhsLa2xvHjx/H0009DpVJh6NChkEgkYoxCocCbb76JGzduoHPnztVy02q10Gq14rRGowEA6HQ66HQ6k/toiG3IMveS2gj1B7UCqfUfeTW2X5aqqb8vS2XJ/bLEnIiIiIjMwewF1GuvvQaNRoPevXvDxsYGVVVVWLZsGaZOnQoAUKvVAAB3d3ej5dzd3cV5arUabm5uxol26AAXFxejGD8/v2rrMMyrqYBavnw5Fi9eXK09JycHDg4ODe6rUqls8DIAkDq4UYu1mMb2y9KxXy3n9u3brZ0CERERUbMwewG1e/du7NixAzt37hQvq4uPj4enpyeioqLMvbkGSUpKQmJiojit0Wjg7e2N8PBwyGQyk9ej0+mgVCoxevRo2NraNjiPvinZDV6mJUitBSwN1mPhKWto9VZmWee5FIVZ1tMUTf19WSpL7pfh7C4RERFRe2P2AmrevHl47bXXMGXKFABAv3798NNPP2H58uWIioqCh4cHAKC4uBjdunUTlysuLkZgYCAAwMPDAyUlJUbrvXv3Lq5fvy4u7+HhgeLiYqMYw7Qh5n5SqRRSqbRau62tbaN2QBu7nLbKPMVJc9HqrcyWoyXt2Df292XpLLFflpYPERERkbmY/Sl8t2/fhrW18WptbGyg1+sBAH5+fvDw8EBubq44X6PR4Pjx45DL5QAAuVyO0tJS5OfnizGHDh2CXq9HSEiIGHP06FGjey2USiV69epV4+V7RERERERETWX2AmrcuHFYtmwZsrKycPnyZXz00UdYtWoVnn76aQCAlZUV4uPj8frrr+Pjjz/G2bNnMW3aNHh6emLChAkAAH9/f4wZMwYzZ87EiRMn8NVXXyEuLg5TpkyBp6cnAOD555+HRCJBTEwMzp8/j127dmHNmjVGl+gRERERERGZk9kv4Vu3bh0WLlyIl19+GSUlJfD09MRf/vIXJCcnizHz589HeXk5Zs2ahdLSUjz++OM4ePAg7OzsxJgdO3YgLi4Oo0aNgrW1NSIjI7F27VpxvpOTE3JychAbG4ugoCB06dIFycnJfIQ5ERERERE1G7MXUI6Ojli9ejVWr15da4yVlRWWLFmCJUuW1Brj4uKCnTt31rmt/v3744svvmhsqkRERERERA1i9kv4iIiIiIiI2isWUERERERERCZiAUVERERERGQiFlBEREREREQmYgFFRERERERkIhZQREREREREJmIBRUREREREZCIWUERERERERCYy+4t0ie7l+1qW2dZ1eUWE2dZFRERERNQYPANFRERERERkIhZQREREREREJmIBRUREREREZCIWUERkEZYvX45HHnkEjo6OcHNzw4QJE1BYWGgUU1FRgdjYWLi6uqJTp06IjIxEcXGxUcyVK1cQEREBBwcHuLm5Yd68ebh7965RzOHDhzFo0CBIpVL06NEDmZmZ1fJJT0+Hr68v7OzsEBISghMnTpi9z0TUMji+EJE5sYAiIotw5MgRxMbG4tixY1AqldDpdAgPD0d5ebkYk5CQgP3792PPnj04cuQIrl69iokTJ4rzq6qqEBERgcrKSuTl5WHbtm3IzMxEcnKyGFNUVISIiAiMGDECBQUFiI+Px4wZM5CdnS3G7Nq1C4mJiVi0aBFOnz6NAQMGQKFQoKSkpGU+DCIyK44vRGROfAofEVmEgwcPGk1nZmbCzc0N+fn5GDp0KMrKyrB582bs3LkTI0eOBABs3boV/v7+OHbsGEJDQ5GTk4MLFy7gs88+g7u7OwIDA7F06VK8+uqrSElJgUQiQUZGBvz8/LBy5UoAgL+/P7788kukpaVBoVAAAFatWoWZM2ciOjoaAJCRkYGsrCxs2bIFr732Wgt+KkRkDhxfiMicWEARkUUqKysDALi4uAAA8vPzodPpEBYWJsb07t0b3bt3h0qlQmhoKFQqFfr16wd3d3cxRqFQYM6cOTh//jwGDhwIlUpltA5DTHx8PACgsrIS+fn5SEpKEudbW1sjLCwMKpWqxly1Wi20Wq04rdFoAAA6nQ46na7OfkpthPo+irqXtxaM/q1ve22FoR/tpT8G7Jf5ttUUHF/uiblvDKlLW/i7bU/fMfal5ZmaHwsoIrI4er0e8fHxeOyxx9C3b18AgFqthkQigbOzs1Gsu7s71Gq1GHPvzo1hvmFeXTEajQZ37tzBjRs3UFVVVWPMpUuXasx3+fLlWLx4cbX2nJwcODg41NnX1MF1zjbZ0mA9AOCTTz4xzwothFKpbO0UmgX71Xi3b99u0vIcX2pmGEPq0pbGl/b0HWNfWo6p4wsLKCKyOLGxsTh37hy+/PLL1k7FJElJSUhMTBSnNRoNvL29ER4eDplMVueyfVOy65xfH6m1gKXBeiw8ZQ2t3grnUhRNWp+l0Ol0UCqVGD16NGxtbVs7HbNhv5rOcAamsTi+GLt/DKlLWxhf2tN3jH1peaaOLyygiMiixMXF4cCBAzh69Ci8vLzEdg8PD1RWVqK0tNToKHFxcTE8PDzEmPufZmV4ita9Mfc/Wau4uBgymQz29vawsbGBjY1NjTGGddxPKpVCKpVWa7e1ta33fxTaqrp3WEyl1VtBW2Vl0f9jagxTPsO2iP1q2jYai+NLHbH/fwypS1v6m21P3zH2peWYmhufwkdEFkEQBMTFxeGjjz7CoUOH4OfnZzQ/KCgItra2yM3NFdsKCwtx5coVyOVyAIBcLsfZs2eNnmalVCohk8kQEBAgxty7DkOMYR0SiQRBQUFGMXq9Hrm5uWIMEbUtHF+IyJyapYD65Zdf8MILL8DV1RX29vbo168fTp06Jc4XBAHJycno1q0b7O3tERYWhu+++85oHdevX8fUqVMhk8ng7OyMmJgY3Lp1yyjmm2++wZAhQ2BnZwdvb2+kpqY2R3eIqAXExsZi+/bt2LlzJxwdHaFWq6FWq3Hnzh0AgJOTE2JiYpCYmIjPP/8c+fn5iI6OhlwuR2hoKAAgPDwcAQEBePHFF/H1118jOzsbCxYsQGxsrHgEd/bs2fjxxx8xf/58XLp0CRs2bMDu3buRkJAg5pKYmIh3330X27Ztw8WLFzFnzhyUl5eLT80ioraF4wsRmZPZL+G7ceMGHnvsMYwYMQKffvopunbtiu+++w6dO3cWY1JTU7F27Vps27YNfn5+WLhwIRQKBS5cuAA7OzsAwNSpU/Hrr7+K72uIjo7GrFmzsHPnTgB/XKMYHh6OsLAwZGRk4OzZs5g+fTqcnZ0xa9Ysc3eLiJrZxo0bAQDDhw83at+6dSteeuklAEBaWhqsra0RGRkJrVYLhUKBDRs2iLE2NjY4cOAA5syZA7lcjo4dOyIqKgpLliwRY/z8/JCVlYWEhASsWbMGXl5eeO+998RHDAPA5MmTce3aNSQnJ0OtViMwMBAHDx6sduM3EbUNHF+IyJzMXkC9+eab8Pb2xtatW8W2e0+VC4KA1atXY8GCBRg/fjwA4P3334e7uzv27duHKVOm4OLFizh48CBOnjyJ4OBgAMC6devwxBNP4O2334anpyd27NiByspKbNmyBRKJBH369EFBQQFWrVrFAoqoDRKE+h+ha2dnh/T0dKSnp9ca4+PjU++TooYPH44zZ87UGRMXF4e4uLh6cyIiy8fxhYjMyeyX8H388ccIDg7GpEmT4ObmhoEDB+Ldd98V5xcVFUGtVhu9J8HJyQkhISHiOxBUKhWcnZ3F4gkAwsLCYG1tjePHj4sxQ4cOhUQiEWMUCgUKCwtx48YNc3eLiIiIiIjI/GegfvzxR2zcuBGJiYn4+9//jpMnT+Kvf/0rJBIJoqKixHcl1PQOhHvfo+Dm5macaIcOcHFxMYq5/ybQe9/HcO8lgwZNeRndvZr6MrCmvjizuTTkZXqtobGfd1t5eVtDWXK/LDEnIiIiInMwewGl1+sRHByMN954AwAwcOBAnDt3DhkZGYiKijL35hqkKS+jq0ljXwZmrhdnNhdTXqbXGpr6Aj9Lf3lbY1liv5r6oksiIiIiS2X2Aqpbt27i4zwN/P398a9//QvA/96VUFxcjG7duokxxcXFCAwMFGPufUwoANy9exfXr1+v910L927jfk15Gd29mvoysKa+OLO5NORleq2hsS/waysvb2soS+5XU190SURERGSpzF5APfbYYygsLDRq+/bbb+Hj4wPgjwdKeHh4IDc3VyyYNBoNjh8/jjlz5gD44z0KpaWlyM/PR1BQEADg0KFD0Ov1CAkJEWP+8Y9/QKfTiTuPSqUSvXr1qvHyPaBpL6OrSWOXM9eLM5uLKS/Taw1NLRIs/eVtjWWJ/bK0fIiIiIjMxewPkUhISMCxY8fwxhtv4Pvvv8fOnTuxadMmxMbGAgCsrKwQHx+P119/HR9//DHOnj2LadOmwdPTExMmTADwxxmrMWPGYObMmThx4gS++uorxMXFYcqUKfD09AQAPP/885BIJIiJicH58+exa9curFmzxugMExERERERkTmZ/QzUI488go8++ghJSUlYsmQJ/Pz8sHr1akydOlWMmT9/PsrLyzFr1iyUlpbi8ccfx8GDB8V3QAHAjh07EBcXh1GjRonvZVi7dq0438nJCTk5OYiNjUVQUBC6dOmC5ORkPsKciIiIiIiajdkLKAB48skn8eSTT9Y638rKCkuWLDF6+dz9XFxcxJfm1qZ///744osvGp0nERERERFRQ5j9Ej4iIiIiIqL2igUUERERERGRiVhAERERERERmYgFFBERERERkYlYQBEREREREZmIBRQREREREZGJWEARERERERGZiAUUERERERGRiVhAERERERERmYgFFBERERERkYlYQBEREREREZmIBRQREREREZGJWEARERERERGZiAUUERERERGRiVhAERERERERmYgFFBERERERkYlYQBEREREREZmIBRQREREREZGJWEARERERERGZiAUUERERERGRiVhAERERERERmajZC6gVK1bAysoK8fHxYltFRQViY2Ph6uqKTp06ITIyEsXFxUbLXblyBREREXBwcICbmxvmzZuHu3fvGsUcPnwYgwYNglQqRY8ePZCZmdnc3SEiIiIiogdYsxZQJ0+exDvvvIP+/fsbtSckJGD//v3Ys2cPjhw5gqtXr2LixIni/KqqKkRERKCyshJ5eXnYtm0bMjMzkZycLMYUFRUhIiICI0aMQEFBAeLj4zFjxgxkZ2c3Z5eIiIiIiOgB1mwF1K1btzB16lS8++676Ny5s9heVlaGzZs3Y9WqVRg5ciSCgoKwdetW5OXl4dixYwCAnJwcXLhwAdu3b0dgYCDGjh2LpUuXIj09HZWVlQCAjIwM+Pn5YeXKlfD390dcXByeeeYZpKWlNVeXiIiIiIjoAdehuVYcGxuLiIgIhIWF4fXXXxfb8/PzodPpEBYWJrb17t0b3bt3h0qlQmhoKFQqFfr16wd3d3cxRqFQYM6cOTh//jwGDhwIlUpltA5DzL2XCt5Pq9VCq9WK0xqNBgCg0+mg0+lM7pshtiHL3EtqIzRqueYmtRaM/rU0jf28m/r7slSW3C9LzImIiIjIHJqlgPrwww9x+vRpnDx5sto8tVoNiUQCZ2dno3Z3d3eo1Wox5t7iyTDfMK+uGI1Ggzt37sDe3r7atpcvX47FixdXa8/JyYGDg4PpHfz/lEplg5cBgNTBjVqsxSwN1rd2CjX65JNPmrR8Y39fls4S+3X79u3WToGIiIioWZi9gPr555/xyiuvQKlUws7Oztyrb5KkpCQkJiaK0xqNBt7e3ggPD4dMJjN5PTqdDkqlEqNHj4atrW2D8+ibYpn3aUmtBSwN1mPhKWto9VatnU4151IUjVquqb8vS2XJ/TKc3SUiIiJqb8xeQOXn56OkpASDBg0S26qqqnD06FGsX78e2dnZqKysRGlpqdFZqOLiYnh4eAAAPDw8cOLECaP1Gp7Sd2/M/U/uKy4uhkwmq/HsEwBIpVJIpdJq7ba2to3aAW3sctoqyytO7qXVW1lkjk0tEhr7+7J0ltgvS8uHiIiIyFzM/hCJUaNG4ezZsygoKBB/goODMXXqVPG/bW1tkZubKy5TWFiIK1euQC6XAwDkcjnOnj2LkpISMUapVEImkyEgIECMuXcdhhjDOoiIiIiIiMzN7GegHB0d0bdvX6O2jh07wtXVVWyPiYlBYmIiXFxcIJPJMHfuXMjlcoSGhgIAwsPDERAQgBdffBGpqalQq9VYsGABYmNjxTNIs2fPxvr16zF//nxMnz4dhw4dwu7du5GVlWXuLhEREREREQFoxqfw1SUtLQ3W1taIjIyEVquFQqHAhg0bxPk2NjY4cOAA5syZA7lcjo4dOyIqKgpLliwRY/z8/JCVlYWEhASsWbMGXl5eeO+996BQNO4+GSIiIiIiovq0SAF1+PBho2k7Ozukp6cjPT291mV8fHzqfera8OHDcebMGXOkSEREREREVK9me5EuEVFDHD16FOPGjYOnpyesrKywb98+o/mCICA5ORndunWDvb09wsLC8N133xnFXL9+HVOnToVMJoOzszNiYmJw69Yto5hvvvkGQ4YMgZ2dHby9vZGamlotlz179qB3796ws7NDv379mvwIfSJqXRxfiMicWEARkUUoLy/HgAEDaj0znZqairVr1yIjIwPHjx9Hx44doVAoUFFRIcZMnToV58+fh1KpxIEDB3D06FHMmjVLnK/RaBAeHg4fHx/k5+fjrbfeQkpKCjZt2iTG5OXl4bnnnkNMTAzOnDmDCRMmYMKECTh37lzzdZ6ImhXHFyIyp1a5B4qI6H5jx47F2LFja5wnCAJWr16NBQsWYPz48QCA999/H+7u7ti3bx+mTJmCixcv4uDBgzh58iSCg4MBAOvWrcMTTzyBt99+G56entixYwcqKyuxZcsWSCQS9OnTBwUFBVi1apW4I7RmzRqMGTMG8+bNAwAsXboUSqUS69evR0ZGRgt8EkRkbhxfiMiceAaKiCxeUVER1Go1wsLCxDYnJyeEhIRApVIBAFQqFZydncWdGwAICwuDtbU1jh8/LsYMHToUEolEjFEoFCgsLMSNGzfEmHu3Y4gxbIeI2heOL0TUUDwDRW2G72uNe0S91EZA6mCgb0q20QuCL6+IMFdq1MzUajUAwN3d3ajd3d1dnKdWq+Hm5mY0v0OHDnBxcTGK8fPzq7YOw7zOnTtDrVbXuZ2aaLVaaLVacVqj0QAAdDoddDpdnX2T2gh1zq+P1Fow+re+7bUVhn60l/4YsF/m25a5POjjy/1jSF3awt9te/qOsS8tz9T8WEARETXR8uXLsXjx4mrtOTk5cHBwqHPZ1MHmyWFpsB4A2t0N6UqlsrVTaBbsV+Pdvn272bdhSVpqfDGMIXVpS+NLe/qOsS8tx9TxhQUUEVk8Dw8PAEBxcTG6desmthcXFyMwMFCMKSkpMVru7t27uH79uri8h4cHiouLjWIM0/XFGObXJCkpCYmJieK0RqOBt7c3wsPDIZPJ6uxb35TsOufXR2otYGmwHgtPWUOrt8K5lPbxLjydTgelUonRo0fD1ta2tdMxG/ar6QxnYMzlQR9f7h9D6tIWxpf29B1jX1qeqeMLCygisnh+fn7w8PBAbm6uuEOj0Whw/PhxzJkzBwAgl8tRWlqK/Px8BAUFAQAOHToEvV6PkJAQMeYf//gHdDqdOIArlUr06tULnTt3FmNyc3MRHx8vbl+pVEIul9ean1QqhVQqrdZua2tb7/8o7r2stCm0eitoq6ws+n9MjWHKZ9gWsV9N24Y5cXz5/7H/fwypS1v6m21P3zH2peWYmhsfIkFEFuHWrVsoKChAQUEBgD9u7C4oKMCVK1dgZWWF+Ph4vP766/j4449x9uxZTJs2DZ6enpgwYQIAwN/fH2PGjMHMmTNx4sQJfPXVV4iLi8OUKVPg6ekJAHj++echkUgQExOD8+fPY9euXVizZo3R0d1XXnkFBw8exMqVK3Hp0iWkpKTg1KlTiIuLa+mPhIjMhOMLEZkTz0ARkUU4deoURowYIU4bdjqioqKQmZmJ+fPno7y8HLNmzUJpaSkef/xxHDx4EHZ2duIyO3bsQFxcHEaNGgVra2tERkZi7dq14nwnJyfk5OQgNjYWQUFB6NKlC5KTk43e5fLoo49i586dWLBgAf7+97+jZ8+e2LdvH/r27dsCnwIRNQeOL0RkTiygiMgiDB8+HIJQ+1OgrKyssGTJEixZsqTWGBcXF+zcubPO7fTv3x9ffPFFnTGTJk3CpEmT6k6YiNoMji9EZE68hI+IiIiIiMhELKCIiIiIiIhMxAKKiIiIiIjIRCygiIiIiIiITMQCioiIiIiIyEQsoIiIiIiIiEzEAoqIiIiIiMhELKCIiIiIiIhMxAKKiIiIiIjIRGYvoJYvX45HHnkEjo6OcHNzw4QJE1BYWGgUU1FRgdjYWLi6uqJTp06IjIxEcXGxUcyVK1cQEREBBwcHuLm5Yd68ebh7965RzOHDhzFo0CBIpVL06NEDmZmZ5u4OERERERGRyOwF1JEjRxAbG4tjx45BqVRCp9MhPDwc5eXlYkxCQgL279+PPXv24MiRI7h69SomTpwozq+qqkJERAQqKyuRl5eHbdu2ITMzE8nJyWJMUVERIiIiMGLECBQUFCA+Ph4zZsxAdna2ubtEREREREQEAOhg7hUePHjQaDozMxNubm7Iz8/H0KFDUVZWhs2bN2Pnzp0YOXIkAGDr1q3w9/fHsWPHEBoaipycHFy4cAGfffYZ3N3dERgYiKVLl+LVV19FSkoKJBIJMjIy4Ofnh5UrVwIA/P398eWXXyItLQ0KhcLc3SIiIiIiImr+e6DKysoAAC4uLgCA/Px86HQ6hIWFiTG9e/dG9+7doVKpAAAqlQr9+vWDu7u7GKNQKKDRaHD+/Hkx5t51GGIM6yAiIiIiIjI3s5+Bupder0d8fDwee+wx9O3bFwCgVqshkUjg7OxsFOvu7g61Wi3G3Fs8GeYb5tUVo9FocOfOHdjb21fLR6vVQqvVitMajQYAoNPpoNPpTO6XIbYhy9xLaiM0arnmJrUWjP5tL2rrV2N/f5aiqX+HzckScyIiIiIyh2YtoGJjY3Hu3Dl8+eWXzbkZky1fvhyLFy+u1p6TkwMHB4cGr0+pVDYqj9TBjVqsxSwN1rd2Cs3i/n598sknrZSJeTX277A53b59u7VTICIiImoWzVZAxcXF4cCBAzh69Ci8vLzEdg8PD1RWVqK0tNToLFRxcTE8PDzEmBMnThitz/CUvntj7n9yX3FxMWQyWY1nnwAgKSkJiYmJ4rRGo4G3tzfCw8Mhk8lM7ptOp4NSqcTo0aNha2tr8nIGfVMs80EXUmsBS4P1WHjKGlq9VWunYza19etcStu+V66pf4fNyXB2l4iIiKi9MXsBJQgC5s6di48++giHDx+Gn5+f0fygoCDY2toiNzcXkZGRAIDCwkJcuXIFcrkcACCXy7Fs2TKUlJTAzc0NwB9H2WUyGQICAsSY+88gKJVKcR01kUqlkEql1dptbW0btQPa2OW0VZZdnGj1VhafY2Pc3y9LKzoaq7F/h83J0vIhIiIiMhezF1CxsbHYuXMn/vOf/8DR0VG8Z8nJyQn29vZwcnJCTEwMEhMT4eLiAplMhrlz50IulyM0NBQAEB4ejoCAALz44otITU2FWq3GggULEBsbKxZAs2fPxvr16zF//nxMnz4dhw4dwu7du5GVlWXuLhEREREREQFohgJq48aNAIDhw4cbtW/duhUvvfQSACAtLQ3W1taIjIyEVquFQqHAhg0bxFgbGxscOHAAc+bMgVwuR8eOHREVFYUlS5aIMX5+fsjKykJCQgLWrFkDLy8vvPfee3yEORE90HxfM99BpMsrIsy2LiIiovaiWS7hq4+dnR3S09ORnp5ea4yPj0+9N/kPHz4cZ86caXCOREREREREjdHs74EiIiIiIiJqL1hAERERERERmahZ3wNFRERERO0D77Ek+gPPQBEREREREZmIZ6BMcP8RF6mNgNTBf7wQtz2+L4mIiIiIiGrGM1BEREREREQmYgFFRERERERkIhZQREREREREJmIBRUREREREZCIWUERERERERCZiAUVERERERGQiFlBEREREREQmYgFFRERERERkIhZQREREREREJmIBRUREREREZCIWUERERERERCZiAUVERERERGQiFlBEREREREQmYgFFRERERERkog6tnQBRa/F9Lcts67q8IsJs6yIiIiIiy8UCisgMzFmMASzIiIiIiCxVmy+g0tPT8dZbb0GtVmPAgAFYt24dBg8e3NppEVE7wPGFiJrLgz6+8MAjtWVt+h6oXbt2ITExEYsWLcLp06cxYMAAKBQKlJSUtHZqRNTGcXwhoubC8YWobWvTZ6BWrVqFmTNnIjo6GgCQkZGBrKwsbNmyBa+99lorZ0fUeKYcmZPaCEgdDPRNyYa2yqrOWB6ZaziOL0TUXDi+ELVtbbaAqqysRH5+PpKSksQ2a2trhIWFQaVS1biMVquFVqsVp8vKygAA169fh06nq3VbHe6WG0/rBdy+rUcHnTWq9HXvuLYl7Ffb0pB+/f777y2U1R9u3rwJABAEoUW3ay4NHV8aO7YA1ceXhmrOv+8ef9tt1vUdTxplcqxOp8Pt27fx+++/w9bW1qx5tCb2q+k4vph3fGkv/480jFdSawELBuoR+I9/Q9vI/jRkrGpO7Wm8aCt9MXV8abMF1G+//Yaqqiq4u7sbtbu7u+PSpUs1LrN8+XIsXry4Wrufn1+Dt/98g5doG9ivtsXUfnVZ2axp1OrmzZtwcnJqnY03QUPHF3OOLY3RVv6+W+vvkNonji/mG1/ayhhiqqb2h2MV1Te+tNkCqjGSkpKQmJgoTuv1ely/fh2urq6wsjL9KIVGo4G3tzd+/vlnyGSy5ki1VbBfbYsl90sQBNy8eROenp6tnUqLMNfY0hiW/HfQFOxX29KS/eL4Yt7xpb39Tban/rAvLc/U8aXNFlBdunSBjY0NiouLjdqLi4vh4eFR4zJSqRRSqdSozdnZudE5yGQyi/4jaCz2q22x1H61xSPDBg0dX8w9tjSGpf4dNBX71ba0VL84vjibPa/29jfZnvrDvrQsU8aXNvsUPolEgqCgIOTm5opter0eubm5kMvlrZgZEbV1HF+IqLlwfCFq+9rsGSgASExMRFRUFIKDgzF48GCsXr0a5eXl4lNtiIgai+MLETUXji9EbVubLqAmT56Ma9euITk5GWq1GoGBgTh48GC1GzPNTSqVYtGiRdVOqbd17Ffb0l77ZSlaa3xpqPb6d8B+tS3ttV/NxZLGl/b2u2tP/WFfLJeV0FafA0pERERERNTC2uw9UERERERERC2NBRQREREREZGJWEARERERERGZiAUUERERERGRiVhANcHly5cRExMDPz8/2Nvb4+GHH8aiRYtQWVnZ2qk1WHp6Onx9fWFnZ4eQkBCcOHGitVNqkuXLl+ORRx6Bo6Mj3NzcMGHCBBQWFrZ2Wma3YsUKWFlZIT4+vrVToVbQHr63R48exbhx4+Dp6QkrKyvs27fPaL4gCEhOTka3bt1gb2+PsLAwfPfdd62TrIlMGX8qKioQGxsLV1dXdOrUCZGRkdVerGqJNm7ciP79+4svw5TL5fj000/F+W21Xw+y9jCOpKSkwMrKyuind+/erZ2WydrTOFhfX1566aVqv6sxY8a0TrJNwAKqCS5dugS9Xo933nkH58+fR1paGjIyMvD3v/+9tVNrkF27diExMRGLFi3C6dOnMWDAACgUCpSUlLR2ao125MgRxMbG4tixY1AqldDpdAgPD0d5eXlrp2Y2J0+exDvvvIP+/fu3dirUCtrL97a8vBwDBgxAenp6jfNTU1Oxdu1aZGRk4Pjx4+jYsSMUCgUqKipaOFPTmTL+JCQkYP/+/dizZw+OHDmCq1evYuLEia2YtWm8vLywYsUK5Ofn49SpUxg5ciTGjx+P8+fPA2i7/XpQtZdxBAD69OmDX3/9Vfz58ssvWzslk7WncbC+vgDAmDFjjH5XH3zwQQtmaCYCmVVqaqrg5+fX2mk0yODBg4XY2FhxuqqqSvD09BSWL1/eilmZV0lJiQBAOHLkSGunYhY3b94UevbsKSiVSmHYsGHCK6+80topUQtrj99bAMJHH30kTuv1esHDw0N46623xLbS0lJBKpUKH3zwQStk2Dj3jz+lpaWCra2tsGfPHjHm4sWLAgBBpVK1VpqN1rlzZ+G9995rd/16ELSXcWTRokXCgAEDWjsNs2hP4+D9fREEQYiKihLGjx/fKvmYE89AmVlZWRlcXFxaOw2TVVZWIj8/H2FhYWKbtbU1wsLCoFKpWjEz8yorKwOANvW7qUtsbCwiIiKMfm/04HhQvrdFRUVQq9VG/XRyckJISEib6uf9409+fj50Op1Rv3r37o3u3bu3qX5VVVXhww8/RHl5OeRyebvp14OivY0j3333HTw9PfHQQw9h6tSpuHLlSmunZBbtZRy81+HDh+Hm5oZevXphzpw5+P3331s7pQbr0NoJtCfff/891q1bh7fffru1UzHZb7/9hqqqqmpvP3d3d8elS5daKSvz0uv1iI+Px2OPPYa+ffu2djpN9uGHH+L06dM4efJka6dCreRB+N4CgFqtBoAa+2mYZ+lqGn/UajUkEgmcnZ2NYttKv86ePQu5XI6Kigp06tQJH330EQICAlBQUNCm+/WgaU/jSEhICDIzM9GrVy/8+uuvWLx4MYYMGYJz587B0dGxtdNrkvYwDt5rzJgxmDhxIvz8/PDDDz/g73//O8aOHQuVSgUbG5vWTs9kLKBq8Nprr+HNN9+sM+bixYtGNyj+8ssvGDNmDCZNmoSZM2c2d4rUALGxsTh37lybuh66Nj///DNeeeUVKJVK2NnZtXY6RFSP9jT+GPTq1QsFBQUoKyvD3r17ERUVhSNHjrR2WvQAGzt2rPjf/fv3R0hICHx8fLB7927ExMS0YmZ0vylTpoj/3a9fP/Tv3x8PP/wwDh8+jFGjRrViZg3DAqoG//d//4eXXnqpzpiHHnpI/O+rV69ixIgRePTRR7Fp06Zmzs68unTpAhsbm2pPSSouLoaHh0crZWU+cXFxOHDgAI4ePQovL6/WTqfJ8vPzUVJSgkGDBoltVVVVOHr0KNavXw+tVtumjuBQ47T3762BoS/FxcXo1q2b2F5cXIzAwMBWysp0tY0/Hh4eqKysRGlpqdHZmrby+5NIJOjRowcAICgoCCdPnsSaNWswefLkNt2vB017HkecnZ3x5z//Gd9//31rp9JkbX0crM9DDz2ELl264Pvvv29TBRTvgapB165d0bt37zp/JBIJgD/OPA0fPhxBQUHYunUrrK3b1kcqkUgQFBSE3NxcsU2v1yM3NxdyubwVM2saQRAQFxeHjz76CIcOHYKfn19rp2QWo0aNwtmzZ1FQUCD+BAcHY+rUqSgoKGDx9IBor9/b+/n5+cHDw8OonxqNBsePH7foftY3/gQFBcHW1taoX4WFhbhy5YpF96s2er0eWq223fWrvWvP48itW7fwww8/GBUcbVVbHQdN9d///he///57m/td8QxUExiKJx8fH7z99tu4du2aOK8tHb1JTExEVFQUgoODMXjwYKxevRrl5eWIjo5u7dQaLTY2Fjt37sR//vMfODo6itcJOzk5wd7evpWzazxHR8dq93F17NgRrq6u7eL+LjJde/ne3rp1y+gocVFREQoKCuDi4oLu3bsjPj4er7/+Onr27Ak/Pz8sXLgQnp6emDBhQuslXY/6xh8nJyfExMQgMTERLi4ukMlkmDt3LuRyOUJDQ1s5+7olJSVh7Nix6N69O27evImdO3fi8OHDyM7ObtP9elC1l3Hkb3/7G8aNGwcfHx9cvXoVixYtgo2NDZ577rnWTs0k7WkcrKsvLi4uWLx4MSIjI+Hh4YEffvgB8+fPR48ePaBQKFox60Zo7ccAtmVbt24VANT409asW7dO6N69uyCRSITBgwcLx44da+2UmqS238vWrVtbOzWz42PMH1zt4Xv7+eef1/hdjYqKEgThj0f4Lly4UHB3dxekUqkwatQoobCwsHWTrocp48+dO3eEl19+WejcubPg4OAgPP3008Kvv/7aekmbaPr06YKPj48gkUiErl27CqNGjRJycnLE+W21Xw+y9jCOTJ48WejWrZsgkUiEP/3pT8LkyZOF77//vrXTMll7Ggfr6svt27eF8PBwoWvXroKtra3g4+MjzJw5U1Cr1a2ddoNZCYIgNHuVRkRERERE1A60rRt2iIiIiIiIWhELKCIiIiIiIhOxgCIiIiIiIjIRCygiIiIiIiITsYAiIiIiIiIyEQsoIiIiIiIiE7GAIiIiIiIiMhELKCIiIiIiIhOxgCIiIiIiIjIRCygiIiIiIiITsYAiIiIiIiIyEQsoIiIiIiIiE7GAIiIiIiIiMhELKCIiIiIiIhOxgCIiIiIiIjIRCygiIiIiIiITsYAiIiIiIiIyEQsoIiIiIiIiE7GAIiIiIiIiMhELKCIiIiIiIhOxgKIHxuHDh2FlZYXDhw+3dipE1AqsrKyQkpLS2mkQ0QOK+yHtBwsoIiIiIiIiE3Vo7QSIWsrQoUNx584dSCSS1k6FiIiIHjDcD2k/eAaKHhjW1taws7ODtTX/7ImIiKhmer0eFRUVZl8v90PaD/4GqZpffvkFMTEx8PT0hFQqhZ+fH+bMmYPKykpcv34df/vb39CvXz906tQJMpkMY8eOxddff220DsN1vrt378bixYvxpz/9CY6OjnjmmWdQVlYGrVaL+Ph4uLm5oVOnToiOjoZWqzVah5WVFeLi4rBjxw706tULdnZ2CAoKwtGjR43ifvrpJ7z88svo1asX7O3t4erqikmTJuHy5cs15nT/tcfp6el46KGHYG9vj8GDB+OLL77A8OHDMXz48Br7s2zZMnh5ecHOzg6jRo3C999/3+TPnIjql5KSAisrK1y6dAnPPvssZDIZXF1d8corrxjt7Gi1WiQkJKBr165wdHTEU089hf/+97/V1mfK2PHjjz/CysoKaWlp1ZbPy8uDlZUVPvjgAwDAzZs3ER8fD19fX0ilUri5uWH06NE4ffq0+T8MIqqXqWPGvfsbffr0gVQqxcGDBwH8sU80ffp0uLu7QyqVok+fPtiyZYu4bHFxMTp06IDFixdX235hYSGsrKywfv16ALXvh+zZswdBQUGwt7dHly5d8MILL+CXX34xirl/v8TgpZdegq+vr1Hbhx9+iKCgIDg6OkImk6Ffv35Ys2ZNQz46qgcv4SMjV69exeDBg1FaWopZs2ahd+/e+OWXX7B3717cvn0bP/74I/bt24dJkybBz88PxcXFeOeddzBs2DBcuHABnp6eRutbvnw57O3t8dprr+H777/HunXrYGtrC2tra9y4cQMpKSk4duwYMjMz4efnh+TkZKPljxw5gl27duGvf/0rpFIpNmzYgDFjxuDEiRPo27cvAODkyZPIy8vDlClT4OXlhcuXL2Pjxo0YPnw4Lly4AAcHh1r7u3HjRsTFxWHIkCFISEjA5cuXMWHCBHTu3BleXl7V4lesWAFra2v87W9/Q1lZGVJTUzF16lQcP37cDJ8+EZni2Wefha+vL5YvX45jx45h7dq1uHHjBt5//30AwIwZM7B9+3Y8//zzePTRR3Ho0CFERERUW48pY8dDDz2Exx57DDt27EBCQoLR8jt27ICjoyPGjx8PAJg9ezb27t2LuLg4BAQE4Pfff8eXX36JixcvYtCgQc3/wRBRjeobMwDg0KFD2L17N+Li4tClSxf4+vqiuLgYoaGhYoHVtWtXfPrpp4iJiYFGo0F8fDzc3d0xbNgw7N69G4sWLTLa7q5du2BjY4NJkybVmltmZiaio6PxyCOPYPny5SguLsaaNWvw1Vdf4cyZM3B2dm5QX5VKJZ577jmMGjUKb775JgDg4sWL+Oqrr/DKK680aF1UB4HoHtOmTROsra2FkydPVpun1+uFiooKoaqqyqi9qKhIkEqlwpIlS8S2zz//XAAg9O3bV6isrBTbn3vuOcHKykoYO3as0Trkcrng4+Nj1AZAACCcOnVKbPvpp58EOzs74emnnxbbbt++XS1XlUolABDef//9ajl9/vnngiAIglarFVxdXYVHHnlE0Ol0YlxmZqYAQBg2bFi1Zf39/QWtViu2r1mzRgAgnD17tloORGReixYtEgAITz31lFH7yy+/LAAQvv76a6GgoEAAILz88stGMc8//7wAQFi0aJHYZurY8c477wgAhIsXL4ptlZWVQpcuXYSoqCixzcnJSYiNjW1iL4nIXEwZMwThj/0Na2tr4fz580ZxMTExQrdu3YTffvvNqH3KlCmCk5OTOIYYxoj79wUCAgKEkSNHitP374dUVlYKbm5uQt++fYU7d+6IcQcOHBAACMnJyWLbsGHDjPZLDKKiooz2n1555RVBJpMJd+/erefToabgJXwk0uv12LdvH8aNG4fg4OBq862srCCVSsVrd6uqqvD777+jU6dO6NWrV42XqUybNg22trbidEhICARBwPTp043iQkJC8PPPP+Pu3btG7XK5HEFBQeJ09+7dMX78eGRnZ6OqqgoAYG9vL87X6XT4/fff0aNHDzg7O9d56cypU6fw+++/Y+bMmejQ4X8nY6dOnYrOnTvXuEx0dLTRzZ9DhgwB8MdlPkTUMmJjY42m586dCwD45JNP8MknnwAA/vrXvxrFxMfHV1uPqWPHs88+Czs7O+zYsUNsy87Oxm+//YYXXnhBbHN2dsbx48dx9erVxneOiMyurjHDYNiwYQgICBCnBUHAv/71L4wbNw6CIOC3334TfxQKBcrKysRxYuLEiejQoQN27dolLn/u3DlcuHABkydPrjWvU6dOoaSkBC+//DLs7OzE9oiICPTu3RtZWVkN7quzszPKy8uhVCobvCyZjgUUia5duwaNRiNeGlcTvV6PtLQ09OzZE1KpFF26dEHXrl3xzTffoKysrFp89+7djaadnJwAAN7e3tXa9Xp9tXX07Nmz2jr//Oc/4/bt27h27RoA4M6dO0hOToa3t7dRTqWlpTXmZPDTTz8BAHr06GHU3qFDh2rXE9fWH0OhdePGjVq3Q0Tmdf+48PDDD8Pa2hqXL1/GTz/9BGtrazz88MNGMb169aq2HlPHDmdnZ4wbNw47d+4U23bs2IE//elPGDlypNiWmpqKc+fOwdvbG4MHD0ZKSgoPrhBZgLrGDAM/Pz+jmGvXrqG0tBSbNm1C165djX6io6MBACUlJQCALl26YNSoUdi9e7e4/K5du9ChQwdMnDix1rwM+yE1jU+9e/cW5zfEyy+/jD//+c8YO3YsvLy8MH36dPF+LjIfFlDUIG+88QYSExMxdOhQbN++HdnZ2VAqlejTpw/0en21eBsbmxrXU1u7IAgNzmnu3LlYtmwZnn32WezevRs5OTlQKpVwdXWtMaemMGfeRGQeVlZWjVquIWPHtGnT8OOPPyIvLw83b97Exx9/jOeee87oaVrPPvssfvzxR6xbtw6enp5466230KdPH3z66adN6h8RmVdNY8a9Z6QBiGPACy+8AKVSWePPY489JsZPmTIF3377LQoKCgAAu3fvxqhRo9ClS5dmyxmAeDWOgZubGwoKCvDxxx/jqaeewueff46xY8ciKirKLHnQH/gQCRJ17doVMpkM586dqzVm7969GDFiBDZv3mzUXlpaarZB4l7fffddtbZvv/0WDg4O6Nq1q5hTVFQUVq5cKcZUVFSgtLS0znX7+PgAAL7//nuMGDFCbL979y4uX76M/v37m6EHRGRu3333ndHR4u+//x56vR6+vr4QBAF6vR4//PCD0VHdwsLCautpyNgxZswYdO3aFTt27EBISAhu376NF198sVpct27d8PLLL+Pll19GSUkJBg0ahGXLlmHs2LFN7DURNVZdY0ZtDE/xrKqqQlhYWL3bmDBhAv7yl7+Il/F9++23SEpKqnMZw35IYWGh0dlsQ5thPvDHFS81ndGu6SyVRCLBuHHjMG7cOOj1erz88st45513sHDhwmpX3VDj8AwUiaytrTFhwgTs378fp06dqjZfEATY2NhUO9uyZ8+eao/bNBeVSmV0L8LPP/+M//znPwgPDxfPBtWU07p166odlblfcHAwXF1d8e677xrde7Vjxw5ekkdkwdLT042m161bBwAYO3asWKisXbvWKGb16tXV1tOQsaNDhw547rnnsHv3bmRmZqJfv35GB1mqqqqqXTLs5uYGT0/Paq9oIKKWVdeYURsbGxtERkbiX//6V40Hlg23ERg4OztDoVBg9+7d+PDDDyGRSDBhwoQ68woODoabmxsyMjKMxolPP/0UFy9eNHp66MMPP4xLly4Zbffrr7/GV199ZbTO33//3Wja2tpaHKs4FpkPz0CRkTfeeAM5OTkYNmwYZs2aBX9/f/z666/Ys2cPvvzySzz55JNYsmQJoqOj8eijj+Ls2bPYsWMHHnrooWbJp2/fvlAoFEaPMQdg9L6FJ598Ev/85z/h5OSEgIAAqFQqfPbZZ3B1da1z3RKJBCkpKZg7dy5GjhyJZ599FpcvX0ZmZiYefvjhRl8WRETNq6ioCE899RTGjBkDlUolPrJ8wIABAIDnnnsOGzZsQFlZGR599FHk5ubW+L62ho4d06ZNw9q1a/H555+Ljwc2uHnzJry8vPDMM89gwIAB6NSpEz777DOcPHnS6AwXEbW8+saM2qxYsQKff/45QkJCMHPmTAQEBOD69es4ffo0PvvsM1y/ft0ofvLkyXjhhRewYcMGKBSKeh9BbmtrizfffBPR0dEYNmwYnnvuOfEx5r6+vkavTpg+fTpWrVoFhUKBmJgYlJSUICMjA3369IFGoxHjZsyYgevXr2PkyJHw8vLCTz/9hHXr1iEwMBD+/v4N//CoZq31+D+yXD/99JMwbdo0oWvXroJUKhUeeughITY2VtBqtUJFRYXwf//3f0K3bt0Ee3t74bHHHhNUKlW1x2saHtW5Z88eo3Vv3bpVAFDtMemGR41eu3ZNbAMgxMbGCtu3bxd69uwpSKVSYeDAgeLjPw1u3LghREdHC126dBE6deokKBQK4dKlS4KPj4/RI4bvf3yowdq1awUfHx9BKpUKgwcPFr766ishKChIGDNmTL39KSoqEgAIW7duNf0DJqJGMYwTFy5cEJ555hnB0dFR6Ny5sxAXF2f0COA7d+4If/3rXwVXV1ehY8eOwrhx44Sff/652mPMTR077tWnTx/B2tpa+O9//2vUrtVqhXnz5gkDBgwQHB0dhY4dOwoDBgwQNmzY0BwfBRGZwNQxw7C/UZPi4mIhNjZW8Pb2FmxtbQUPDw9h1KhRwqZNm6rFajQawd7eXgAgbN++vdr82vZDdu3aJQwcOFCQSqWCi4uLMHXq1GpjjCAIwvbt24WHHnpIkEgkQmBgoJCdnV3tMeZ79+4VwsPDBTc3N0EikQjdu3cX/vKXvwi//vqriZ8amcJKEHj3O1kmKysrxMbGim/wbil6vR5du3bFxIkT8e6777botomodikpKVi8eDGuXbvWLPdcmmLgwIFwcXFBbm5uq2yfiExnCWMGtU+8B4oeaBUVFdXugXj//fdx/fp1DB8+vHWSIiKLdOrUKRQUFGDatGmtnQoREbUi3gNFD7Rjx44hISEBkyZNgqurK06fPo3Nmzejb9++mDRpUmunR0QW4Ny5c8jPz8fKlSvRrVu3Ol+MSURE7R8LKHqg+fr6wtvbG2vXrsX169fh4uKCadOmYcWKFZBIJK2dHhFZgL1792LJkiXo1asXPvjgA9jZ2bV2SkRE1Ip4DxQREREREZGJeA8UERERERGRiVhAERERERERmeiBvgdKr9fj6tWrcHR05EtTicxIEATcvHkTnp6esLZ+8I7TcGwhaj4cXzi+EDUXU8eXB7qAunr1Kry9vVs7DaJ26+eff4aXl1drp9HiOLYQNT+OL0TUXOobXx7oAsrR0RHAHx+STCZr0LI6nQ45OTkIDw+Hra1tc6TX7NgHy9Ae+6DRaODt7S1+xx40TRlb7tcW/z6Yc8t4UHPm+GL6+NIW/0YsDT9D82grn6Op48sDXUAZTn3LZLJGFVAODg6QyWQW/YdQF/bBMrTnPjyol5c0ZWy5X1v8+2DOLeNBz5njS/3jS1v8G7E0/AzNo619jvWNLw/excNERERERESNxAKKiIiIiIjIRCygiIiIiIiITMQCioiIiIiIyEQsoIiIiIiIiEzEAoqIiIiIiMhELKCIiIiIiIhMxAKKiIiIiIjIRA/0i3RN5ftaVrU2qY2A1MFA35RsaKsa9jK/yysizJUaEbVxNY0v92roWMPxhYgM+qZkN3pfpSYcX4j+wDNQREREREREJmIBRUREREREZCIWUERERERERCZiAUVERERERGQiFlBEREREREQmYgFFRERERERkIhZQREREREREJmIBRUREREREZKImFVArVqyAlZUV4uPjxbaKigrExsbC1dUVnTp1QmRkJIqLi42Wu3LlCiIiIuDg4AA3NzfMmzcPd+/eNYo5fPgwBg0aBKlUih49eiAzM7Pa9tPT0+Hr6ws7OzuEhITgxIkTTekOERERERFRnRpdQJ08eRLvvPMO+vfvb9SekJCA/fv3Y8+ePThy5AiuXr2KiRMnivOrqqoQERGByspK5OXlYdu2bcjMzERycrIYU1RUhIiICIwYMQIFBQWIj4/HjBkzkJ2dLcbs2rULiYmJWLRoEU6fPo0BAwZAoVCgpKSksV0iIiIiIiKqU6MKqFu3bmHq1Kl499130blzZ7G9rKwMmzdvxqpVqzBy5EgEBQVh69atyMvLw7FjxwAAOTk5uHDhArZv347AwECMHTsWS5cuRXp6OiorKwEAGRkZ8PPzw8qVK+Hv74+4uDg888wzSEtLE7e1atUqzJw5E9HR0QgICEBGRgYcHBywZcuWpnweREREREREterQmIViY2MRERGBsLAwvP7662J7fn4+dDodwsLCxLbevXuje/fuUKlUCA0NhUqlQr9+/eDu7i7GKBQKzJkzB+fPn8fAgQOhUqmM1mGIMVwqWFlZifz8fCQlJYnzra2tERYWBpVKVWveWq0WWq1WnNZoNAAAnU4HnU5X63JSG6F6m7Vg9G9D1LWtlmTIw1LyaQz2wTLc34e23BciIiKiujS4gPrwww9x+vRpnDx5sto8tVoNiUQCZ2dno3Z3d3eo1Wox5t7iyTDfMK+uGI1Ggzt37uDGjRuoqqqqMebSpUu15r58+XIsXry4WntOTg4cHBxqXS51cK2zsDRYX/vMWnzyyScNXqY5KZXK1k6hydgHy2Dow+3bt1s5EyIiIqLm0aAC6ueff8Yrr7wCpVIJOzu75sqp2SQlJSExMVGc1mg08Pb2Rnh4OGQyWa3L9U3JrtYmtRawNFiPhaesodVbNSiPcymKBsU3F51OB6VSidGjR8PW1ra102kU9sEy3N8Hw9ldIiIiovamQQVUfn4+SkpKMGjQILGtqqoKR48exfr165GdnY3KykqUlpYanYUqLi6Gh4cHAMDDw6Pa0/IMT+m7N+b+J/cVFxdDJpPB3t4eNjY2sLGxqTHGsI6aSKVSSKXSau22trZ17rhqq2ovkLR6qzrn18TSdpLr639bwD5YBkMf2no/iIiIiGrToIdIjBo1CmfPnkVBQYH4ExwcjKlTp4r/bWtri9zcXHGZwsJCXLlyBXK5HAAgl8tx9uxZo6flKZVKyGQyBAQEiDH3rsMQY1iHRCJBUFCQUYxer0dubq4YQ0REREREZG4NOgPl6OiIvn37GrV17NgRrq6uYntMTAwSExPh4uICmUyGuXPnQi6XIzQ0FAAQHh6OgIAAvPjii0hNTYVarcaCBQsQGxsrnh2aPXs21q9fj/nz52P69Ok4dOgQdu/ejaysLHG7iYmJiIqKQnBwMAYPHozVq1ejvLwc0dHRTfpAiIiIiIiIatOkF+nWJC0tDU8++SQiIyMxdOhQeHh44N///rc438bGBgcOHICNjQ3kcjleeOEFTJs2DUuWLBFj/Pz8kJWVBaVSiQEDBmDlypV47733oFD8796hyZMn4+2330ZycjICAwNRUFCAgwcPVnuwBBEREdG9VqxYASsrK/HpvgBQUVGB2NhYuLq6olOnToiMjKx2q8CVK1cQEREBBwcHuLm5Yd68ebh7965RzOHDhzFo0CBIpVL06NEDmZmZ1bafnp4OX19f2NnZISQkpNqtDURk2Rr1GPN7HT582Gjazs4O6enpSE9Pr3UZHx+fep9EN3z4cJw5c6bOmLi4OMTFxZmcKxERET3YTp48iXfeeQf9+/c3ak9ISEBWVhb27NkDJycnxMXFYeLEifjqq68A/HHPd0REBDw8PJCXl4dff/0V06ZNg62tLd544w0AQFFRESIiIjB79mzs2LEDubm5mDFjBrp16yYeBN61axcSExORkZGBkJAQrF69GgqFAoWFhXBzc2vZD4OIGsXsZ6CIiIiILNGtW7cwdepUvPvuu+jcubPYXlZWhs2bN2PVqlUYOXIkgoKCsHXrVuTl5eHYsWMA/njlyYULF7B9+3YEBgZi7NixWLp0KdLT01FZWQkAyMjIgJ+fH1auXAl/f3/ExcXhmWeeQVpamritVatWYebMmYiOjkZAQAAyMjLg4OCALVu2tOyHQUSNxgKKiIiIHgixsbGIiIhAWFiYUXt+fj50Op1Re+/evdG9e3eoVCoAgEqlQr9+/YxuFVAoFNBoNDh//rwYc/+6FQqFuI7Kykrk5+cbxVhbWyMsLEyMISLL1+RL+IiIiIgs3YcffojTp0/j5MmT1eap1WpIJBKjV7AAgLu7O9RqtRhz/33Whun6YjQaDe7cuYMbN26gqqqqxphLly7VmLdWq4VWqxWnDe/Z0+l00Ol0dfZZai0Y/dtU9W2vPTL0+UHsuzm1lc/R1PxYQBEREVG79vPPP+OVV16BUqmEnZ1da6fTIMuXL8fixYurtefk5MDBwaHOZZcGG/7VmyWX+u5fb8+USmVrp9AuWPrnePv2bZPiWEARERFRu5afn4+SkhIMGjRIbKuqqsLRo0exfv16ZGdno7KyEqWlpUZnoYqLi+Hh4QEA8PDwqPa0PMNT+u6Nuf/JfcXFxZDJZLC3t4eNjQ1sbGxqjDGs435JSUlITEwUpzUaDby9vREeHg6ZTFZnv4OWHMTSYD0WnrKGVm9VZ6wpzqUo6g9qZ3Q6HZRKJUaPHs2XxDdBW/kcDWd468MCioiIiNq1UaNG4ezZs0Zt0dHR6N27N1599VV4e3vD1tYWubm5iIyMBAAUFhbiypUrkMvlAAC5XI5ly5ahpKREfFqeUqmETCZDQECAGHP/WRqlUimuQyKRICgoCLm5uZgwYQIAQK/XIzc3t9anCkulUvE9mfeytbWtd0fUUDRp9VbQVjW9gLLkHd/mZsrnTfWz9M/R1NxYQBEREVG75ujoiL59+xq1dezYEa6urmJ7TEwMEhMT4eLiAplMhrlz50IulyM0NBQAEB4ejoCAALz44otITU2FWq3GggULEBsbKxY4s2fPxvr16zF//nxMnz4dhw4dwu7du5GVlSVuNzExEVFRUQgODsbgwYOxevVqlJeXIzo6uoU+DSJqKhZQRERE9MBLS0uDtbU1IiMjodVqoVAosGHDBnG+jY0NDhw4gDlz5kAul6Njx46IiorCkiVLxBg/Pz9kZWUhISEBa9asgZeXF9577z3xHVAAMHnyZFy7dg3JyclQq9UIDAzEwYMHqz1YgogsFwsoIiIieuAcPnzYaNrOzg7p6elIT0+vdRkfH596H6QwfPhwnDlzps6YuLi4Wi/ZIyLLx/dAERERERERmYgFFBERERERkYlYQBEREREREZmIBRQREREREZGJWEARERERERGZiAUUERERERGRiVhAERERERERmYgFFBFZhI0bN6J///6QyWSQyWSQy+X49NNPxfkVFRWIjY2Fq6srOnXqhMjISBQXFxut48qVK4iIiICDgwPc3Nwwb9483L171yjm8OHDGDRoEKRSKXr06IHMzMxquaSnp8PX1xd2dnYICQnBiRMnmqXPRERE1PawgCIii+Dl5YUVK1YgPz8fp06dwsiRIzF+/HicP38eAJCQkID9+/djz549OHLkCK5evYqJEyeKy1dVVSEiIgKVlZXIy8vDtm3bkJmZieTkZDGmqKgIERERGDFiBAoKChAfH48ZM2YgOztbjNm1axcSExOxaNEinD59GgMGDIBCoUBJSUnLfRhERERksVhAEZFFGDduHJ544gn07NkTf/7zn7Fs2TJ06tQJx44dQ1lZGTZv3oxVq1Zh5MiRCAoKwtatW5GXl4djx44BAHJycnDhwgVs374dgYGBGDt2LJYuXYr09HRUVlYCADIyMuDn54eVK1fC398fcXFxeOaZZ5CWlibmsWrVKsycORPR0dEICAhARkYGHBwcsGXLllb5XIiIiMiydGjtBIiI7ldVVYU9e/agvLwccrkc+fn50Ol0CAsLE2N69+6N7t27Q6VSITQ0FCqVCv369YO7u7sYo1AoMGfOHJw/fx4DBw6ESqUyWochJj4+HgBQWVmJ/Px8JCUlifOtra0RFhYGlUpVa75arRZarVac1mg0AACdTgedTldnX6U2Qt3zrQWjf+tT3/ZagiEHS8jFVMy5ZZgj57bUXyJqn1hAEZHFOHv2LORyOSoqKtCpUyd89NFHCAgIQEFBASQSCZydnY3i3d3doVarAQBqtdqoeDLMN8yrK0aj0eDOnTu4ceMGqqqqaoy5dOlSrXkvX74cixcvrtaek5MDBweHOvucOrjO2aKlwXqT4j755BPTVtgClEpla6fQYMy5ZTQl59u3b5sxEyKihmMBRUQWo1evXigoKEBZWRn27t2LqKgoHDlypLXTqldSUhISExPFaY1GA29vb4SHh0Mmk9W5bN+U7DrnS60FLA3WY+Epa2j1VvXmci5FYVrSzUin00GpVGL06NGwtbVt7XRMwpxbhjlyNpzhJSJqLSygiMhiSCQS9OjRAwAQFBSEkydPYs2aNZg8eTIqKytRWlpqdBaquLgYHh4eAAAPD49qT8szPKXv3pj7n9xXXFwMmUwGe3t72NjYwMbGpsYYwzpqIpVKIZVKq7Xb2trWu5Ooraq/KAIArd7KpFhL2pE2pf+Whjm3jKbk3Nb6SkTtDx8iQUQWS6/XQ6vVIigoCLa2tsjNzRXnFRYW4sqVK5DL5QAAuVyOs2fPGj0tT6lUQiaTISAgQIy5dx2GGMM6JBIJgoKCjGL0ej1yc3PFGCIiInqw8QwUEVmEpKQkjB07Ft27d8fNmzexc+dOHD58GNnZ2XByckJMTAwSExPh4uICmUyGuXPnQi6XIzQ0FAAQHh6OgIAAvPjii0hNTYVarcaCBQsQGxsrnh2aPXs21q9fj/nz52P69Ok4dOgQdu/ejaysLDGPxMREREVFITg4GIMHD8bq1atRXl6O6OjoVvlciIiIyLKwgCIii1BSUoJp06bh119/hZOTE/r374/s7GyMHj0aAJCWlgZra2tERkZCq9VCoVBgw4YN4vI2NjY4cOAA5syZA7lcjo4dOyIqKgpLliwRY/z8/JCVlYWEhASsWbMGXl5eeO+996BQ/O++ocmTJ+PatWtITk6GWq1GYGAgDh48WO3BEkRERPRgYgFFRBZh8+bNdc63s7NDeno60tPTa43x8fGp9yl0w4cPx5kzZ+qMiYuLQ1xcXJ0xRERE9GDiPVBEREREREQmYgFFRERERERkIhZQREREREREJmIBRUREREREZCIWUERERERERCZiAUVERERERGQiFlBEREREREQmYgFFRERERERkIhZQREREREREJmpQAbVx40b0798fMpkMMpkMcrkcn376qTi/oqICsbGxcHV1RadOnRAZGYni4mKjdVy5cgURERFwcHCAm5sb5s2bh7t37xrFHD58GIMGDYJUKkWPHj2QmZlZLZf09HT4+vrCzs4OISEhOHHiREO6QkRERERE1GANKqC8vLywYsUK5Ofn49SpUxg5ciTGjx+P8+fPAwASEhKwf/9+7NmzB0eOHMHVq1cxceJEcfmqqipERESgsrISeXl52LZtGzIzM5GcnCzGFBUVISIiAiNGjEBBQQHi4+MxY8YMZGdnizG7du1CYmIiFi1ahNOnT2PAgAFQKBQoKSlp6udBRERERERUqwYVUOPGjcMTTzyBnj174s9//jOWLVuGTp064dixYygrK8PmzZuxatUqjBw5EkFBQdi6dSvy8vJw7NgxAEBOTg4uXLiA7du3IzAwEGPHjsXSpUuRnp6OyspKAEBGRgb8/PywcuVK+Pv7Iy4uDs888wzS0tLEPFatWoWZM2ciOjoaAQEByMjIgIODA7Zs2WLGj4aIiIiIiMhYo++Bqqqqwocffojy8nLI5XLk5+dDp9MhLCxMjOnduze6d+8OlUoFAFCpVOjXrx/c3d3FGIVCAY1GI57FUqlURuswxBjWUVlZifz8fKMYa2trhIWFiTFERERERETNoUNDFzh79izkcjkqKirQqVMnfPTRRwgICEBBQQEkEgmcnZ2N4t3d3aFWqwEAarXaqHgyzDfMqytGo9Hgzp07uHHjBqqqqmqMuXTpUp25a7VaaLVacVqj0QAAdDoddDpdrctJbYTqbdaC0b8NUde2WpIhD0vJpzHYB8twfx/acl+IiIiI6tLgAqpXr14oKChAWVkZ9u7di6ioKBw5cqQ5cjO75cuXY/HixdXac3Jy4ODgUOtyqYNrX+fSYH2D8/jkk08avExzUiqVrZ1Ck7EPlsHQh9u3b7dyJkRERETNo8EFlEQiQY8ePQAAQUFBOHnyJNasWYPJkyejsrISpaWlRmehiouL4eHhAQDw8PCo9rQ8w1P67o25/8l9xcXFkMlksLe3h42NDWxsbGqMMayjNklJSUhMTBSnNRoNvL29ER4eDplMVutyfVOyq7VJrQUsDdZj4SlraPVWdW73fudSFA2Kby46nQ5KpRKjR4+Gra1ta6fTKOyDZbi/D4azu0RERETtTYMLqPvp9XpotVoEBQXB1tYWubm5iIyMBAAUFhbiypUrkMvlAAC5XI5ly5ahpKQEbm5uAP44Yi2TyRAQECDG3H+GRqlUiuuQSCQICgpCbm4uJkyYIOaQm5uLuLi4OnOVSqWQSqXV2m1tbevccdVW1V4gafVWdc6viaXtJNfX/7aAfbAMhj609X4QERER1aZBD5FISkrC0aNHcfnyZZw9exZJSUk4fPgwpk6dCicnJ8TExCAxMRGff/458vPzER0dDblcjtDQUABAeHg4AgIC8OKLL+Lrr79GdnY2FixYgNjYWLGwmT17Nn788UfMnz8fly5dwoYNG7B7924kJCSIeSQmJuLdd9/Ftm3bcPHiRcyZMwfl5eWIjo4240dDRERE7QHfY0lE5tSgM1AlJSWYNm0afv31Vzg5OaF///7Izs7G6NGjAQBpaWmwtrZGZGQktFotFAoFNmzYIC5vY2ODAwcOYM6cOZDL5ejYsSOioqKwZMkSMcbPzw9ZWVlISEjAmjVr4OXlhffeew8Kxf8ue5s8eTKuXbuG5ORkqNVqBAYG4uDBg9UeLEFERERkeI9lz549IQgCtm3bhvHjx+PMmTPo06cPEhISkJWVhT179sDJyQlxcXGYOHEivvrqKwD/e4+lh4cH8vLy8Ouvv2LatGmwtbXFG2+8AeB/77GcPXs2duzYgdzcXMyYMQPdunUT92EM77HMyMhASEgIVq9eDYVCgcLCQvHKHCKyfA0qoDZv3lznfDs7O6SnpyM9Pb3WGB8fn3ofojB8+HCcOXOmzpi4uLh6L9kjIiIiGjdunNH0smXLsHHjRhw7dgxeXl7YvHkzdu7ciZEjRwIAtm7dCn9/fxw7dgyhoaHieyw/++wzuLu7IzAwEEuXLsWrr76KlJQUSCQSo/dYAoC/vz++/PJLpKWliQXUve+xBP5492VWVha2bNmC1157rQU/ESJqiibfA0VERETUVlRVVWHPnj0mv8cyNDS01vdYzpkzB+fPn8fAgQNrfY9lfHw8gP+9xzIpKUmcb8p7LBv7Chagaa9cqcmD+IoKvp7DPNrK52hqfiygiIiIqN1rq++xbOwrWABgabDh34a/cqUmlvYalpbUHl41Ygks/XM09TUsLKCIiIio3Wur77Fs7CtYACBoycFGv3KlJpbyGpaW1B5eNWIJ2srnaOprWFhAERERUbvXVt9j2dhXsAAQi6bGvHKlJpa849vc+IoO87D0z9HU3Br0GHMiIiKi9qCm91ga1PQey7Nnz6KkpESMqek9lveuwxBT03ss780hNzdXjCGitoFnoIiIiKhdS0pKwtixY9G9e3fcvHkTO3fuxOHDh5GdnW30HksXFxfIZDLMnTu31vdYpqamQq1W1/gey/Xr12P+/PmYPn06Dh06hN27dyMrK0vMIzExEVFRUQgODsbgwYOxevVqvseSqA1iAUVERETtGt9jSUTmxAKKiIiI2jW+x5KIzIn3QBEREREREZmIBRQREREREZGJWEARERERERGZiAUUERERERGRiVhAERERERERmYgFFBERERERkYlYQBEREREREZmIBRQREREREZGJWEARERERERGZiAUUERERERGRiVhAERERERERmYgFFBERERERkYlYQBEREREREZmIBRQREREREZGJWEARERERERGZiAUUEVmE5cuX45FHHoGjoyPc3NwwYcIEFBYWGsVUVFQgNjYWrq6u6NSpEyIjI1FcXGwUc+XKFURERMDBwQFubm6YN28e7t69axRz+PBhDBo0CFKpFD169EBmZma1fNLT0+Hr6ws7OzuEhITgxIkTZu8zERERtT0soIjIIhw5cgSxsbE4duwYlEoldDodwsPDUV5eLsYkJCRg//792LNnD44cOYKrV69i4sSJ4vyqqipERESgsrISeXl52LZtGzIzM5GcnCzGFBUVISIiAiNGjEBBQQHi4+MxY8YMZGdnizG7du1CYmIiFi1ahNOnT2PAgAFQKBQoKSlpmQ+DiIiILFaH1k6AiAgADh48aDSdmZkJNzc35OfnY+jQoSgrK8PmzZuxc+dOjBw5EgCwdetW+Pv749ixYwgNDUVOTg4uXLiAzz77DO7u7ggMDMTSpUvx6quvIiUlBRKJBBkZGfDz88PKlSsBAP7+/vjyyy+RlpYGhUIBAFi1ahVmzpyJ6OhoAEBGRgaysrKwZcsWvPbaay34qRAREZGl4RkoIrJIZWVlAAAXFxcAQH5+PnQ6HcLCwsSY3r17o3v37lCpVAAAlUqFfv36wd3dXYxRKBTQaDQ4f/68GHPvOgwxhnVUVlYiPz/fKMba2hphYWFiDBERET24eAaKiCyOXq9HfHw8HnvsMfTt2xcAoFarIZFI4OzsbBTr7u4OtVotxtxbPBnmG+bVFaPRaHDnzh3cuHEDVVVVNcZcunSpxny1Wi20Wq04rdFoAAA6nQ46na7OvkpthLrnWwtG/9anvu21BEMOlpCLqZhzyzBHzm2pv0TUPrGAIiKLExsbi3PnzuHLL79s7VRMsnz5cixevLhae05ODhwcHOpcNnWwadtYGqw3Ke6TTz4xbYUtQKlUtnYKDcacW0ZTcr59+7YZMyEiajgWUERkUeLi4nDgwAEcPXoUXl5eYruHhwcqKytRWlpqdBaquLgYHh4eYsz9T8szPKXv3pj7n9xXXFwMmUwGe3t72NjYwMbGpsYYwzrul5SUhMTERHFao9HA29sb4eHhkMlkdfa3b0p2nfOl1gKWBuux8JQ1tHqrOmMB4FyKot6Y5qbT6aBUKjF69GjY2tq2djomYc4twxw5G87wEhG1FhZQRGQRBEHA3Llz8dFHH+Hw4cPw8/Mzmh8UFARbW1vk5uYiMjISAFBYWIgrV65ALpcDAORyOZYtW4aSkhK4ubkB+ONIt0wmQ0BAgBhz/1kapVIprkMikSAoKAi5ubmYMGECgD8uKczNzUVcXFyNuUulUkil0mrttra29e4kaqvqL4oAQKu3MinWknakTem/pWHOLaMpObe1vhJR+8MCiogsQmxsLHbu3In//Oc/cHR0FO9ZcnJygr29PZycnBATE4PExES4uLhAJpNh7ty5kMvlCA0NBQCEh4cjICAAL774IlJTU6FWq7FgwQLExsaKBc7s2bOxfv16zJ8/H9OnT8ehQ4ewe/duZGVlibkkJiYiKioKwcHBGDx4MFavXo3y8nLxqXxERET04GIBRUQWYePGjQCA4cOHG7Vv3boVL730EgAgLS0N1tbWiIyMhFarhUKhwIYNG8RYGxsbHDhwAHPmzIFcLkfHjh0RFRWFJUuWiDF+fn7IyspCQkIC1qxZAy8vL7z33nviI8wBYPLkybh27RqSk5OhVqsRGBiIgwcPVnuwBBERET14WEARkUUQhPqfMmdnZ4f09HSkp6fXGuPj41PvgxSGDx+OM2fO1BkTFxdX6yV7RERE9ODie6CIiIiIiIhMxAKKiIiIiIjIRCygiIiIiIiITNSgAmr58uV45JFH4OjoCDc3N0yYMAGFhYVGMRUVFYiNjYWrqys6deqEyMjIau9TuXLlCiIiIuDg4AA3NzfMmzcPd+/eNYo5fPgwBg0aBKlUih49eiAzM7NaPunp6fD19YWdnR1CQkKqvf+FiIiIiIjInBpUQB05cgSxsbE4duwYlEoldDodwsPDUV5eLsYkJCRg//792LNnD44cOYKrV69i4sSJ4vyqqipERESgsrISeXl52LZtGzIzM5GcnCzGFBUVISIiAiNGjEBBQQHi4+MxY8YMZGf/74WTu3btQmJiIhYtWoTTp09jwIABUCgUKCkpacrnQUREREREVKsGPYXv4MGDRtOZmZlwc3NDfn4+hg4dirKyMmzevBk7d+7EyJEjAfzxCGJ/f38cO3YMoaGhyMnJwYULF/DZZ5/B3d0dgYGBWLp0KV599VWkpKRAIpEgIyMDfn5+WLlyJQDA398fX375JdLS0sRHDa9atQozZ84U38uSkZGBrKwsbNmyBa+99lqTPxgiIiIiIqL7Nekx5mVlZQAAFxcXAEB+fj50Oh3CwsLEmN69e6N79+5QqVQIDQ2FSqVCv379jN6nolAoMGfOHJw/fx4DBw6ESqUyWochJj7+/7V392FR1fn/x1+AMICKaCZIopKZ93dBEt2trQSa3zZW19VyC4108wutSGupmaLWUpZ3KcW2u2Z7pWXud3NLW3LC1Ey8I9nS0q3W1m4cLE1RTBjh/P7ox+jI3QEHZgafj+uaS+acz5zzPp8ZD+fFOecz6ZKksrIyFRQUaMaMGY75vr6+io+PV35+fo31lpaWqrS01PG8uLhYkmS322W322t8ncWv6vDKFl/D6d/6qG1dTamyDk+ppyHYBs9w8TZ487YAAADUpsEBqqKiQunp6brpppvUt29fSZLNZlNAQIBCQ0Od2oaFhclmsznaXPxllJXP62pTXFysH3/8UT/88IPKy8urbXPgwIEaa87KytLcuXOrTN+4caOCg4NrfN2CwTXO0vyYippn1qCu76hpalar1d0lXDK2wTNUbsOZM2fcXAkAAEDjaHCASk1N1b59+7Rt2zZX1tOoZsyYoYyMDMfz4uJiRUZGKiEhQSEhITW+rm/mO1WmWXwNzY+p0ON7fFVa4VOvOvZlJtarfWOx2+2yWq26/fbb5e/v7+5yGoRt8AwXb0Pl2V0A8ARZWVn6+9//rgMHDigoKEg33nijnn76afXo0cPR5uzZs3r44Yf12muvqbS0VImJiXr++eed/lh7+PBhTZ48We+9955atWql5ORkZWVlqUWL84dTmzdvVkZGhvbv36/IyEjNmjVL48ePd6onOztbzzzzjGw2mwYMGKBly5Zp8OBa/loLwKM0KEClpaVp/fr12rp1qzp16uSYHh4errKyMp04ccLpLFRRUZHCw8MdbS4eLa9ylL4L21w8cl9RUZFCQkIUFBQkPz8/+fn5VdumchnVsVgsslgsVab7+/vXeuBaWl5zQCqt8Kl1fnU87SC5ru33BmyDZ6jcBm/fDgDNS+UgWNdff73OnTunmTNnKiEhQZ988olatmwp6adBsDZs2KC1a9eqTZs2SktL08iRI/XBBx9IOj8IVnh4uLZv364jR47ovvvuk7+/v/7whz9IOj8I1oMPPqhVq1YpLy9PDzzwgDp27Oi4h7tyEKycnBzFxsZqyZIlSkxM1MGDB9WhQwf3dBCAeqnXKHyGYSgtLU1vvPGGNm3apKioKKf50dHR8vf3V15enmPawYMHdfjwYcXFxUmS4uLi9PHHHzuNlme1WhUSEqLevXs72ly4jMo2lcsICAhQdHS0U5uKigrl5eU52gAAAEg/DYI1fvx49enTRwMGDNDKlSt1+PBhFRQUSJJjEKxFixbp5z//uaKjo/XSSy9p+/bt2rFjhyQ5BsF65ZVXNHDgQA0fPlzz589Xdna2ysrKJMlpEKxevXopLS1Nv/rVr7R48WJHLRcOgtW7d2/l5OQoODhYK1asaPqOAdAg9ToDlZqaqtWrV+sf//iHWrdu7bhnqU2bNgoKClKbNm2UkpKijIwMtWvXTiEhIXrooYcUFxenG264QZKUkJCg3r17695779WCBQtks9k0a9YspaamOs4OPfjgg1q+fLkeeeQR3X///dq0aZNef/11bdiwwVFLRkaGkpOTFRMTo8GDB2vJkiUqKSlxjMoHAABQHW8aBKuhA2BJlzbgVXUuxwGCGBzJNbylH83WV68A9cILL0iShgwZ4jT9pZdeclzfu3jxYvn6+mrUqFFO1xBX8vPz0/r16zV58mTFxcWpZcuWSk5O1rx58xxtoqKitGHDBk2dOlVLly5Vp06d9Oc//9lx+luSxowZo++++06zZ8+WzWbTwIEDlZubW2VgCQAAgEreNghWQwfAkqT5MZX/1n/Aq+p42iBYTak5DPTkCTy9H80OglWvAGUYdf8FIzAwUNnZ2crOzq6xTZcuXer8TzhkyBDt3bu31jZpaWlKS0ursyYAAADJ+wbBaugAWJIUPS+3wQNeVcdTBsFqSs1hoCdP4C39aHYQrEv6HigAAABv4Y2DYDV0ACxJjtDUkAGvquPJB76NjQGSXMPT+9FsbfUaRAIAAMDbMAgWAFfiDBQAAGjWGAQLgCsRoAAAQLPGIFgAXIkABQAAmjUGwQLgStwDBQAAAAAmEaAAAAAAwCQCFAAAAACYRIACAAAAAJMIUAAAAABgEgEKAAAAAEwiQAEAAACASQQoAAAAADCJAAUAAAAAJhGgAAAAAMAkAhQAAAAAmESAAgAAAACTCFAAAAAAYBIBCgAAAABMIkABAAAAgEkEKAAAAAAwiQAFAAAAACYRoAAAAADAJAIUAAAAAJhEgAIAAAAAkwhQAAAAAGASAQoAAAAATCJAAQAAAIBJBCgAAAAAMIkABcAjbN26VXfeeaciIiLk4+OjdevWOc03DEOzZ89Wx44dFRQUpPj4eH322WdObY4fP65x48YpJCREoaGhSklJ0enTp53afPTRR7rlllsUGBioyMhILViwoEota9euVc+ePRUYGKh+/frp7bffdvn2AgAA70SAAuARSkpKNGDAAGVnZ1c7f8GCBXruueeUk5OjnTt3qmXLlkpMTNTZs2cdbcaNG6f9+/fLarVq/fr12rp1qyZNmuSYX1xcrISEBHXp0kUFBQV65plnlJmZqRdffNHRZvv27br77ruVkpKivXv3KikpSUlJSdq3b1/jbTwAAPAaLdxdAABI0vDhwzV8+PBq5xmGoSVLlmjWrFm66667JEl//etfFRYWpnXr1mns2LH69NNPlZubq927dysmJkaStGzZMt1xxx169tlnFRERoVWrVqmsrEwrVqxQQECA+vTpo8LCQi1atMgRtJYuXaphw4Zp2rRpkqT58+fLarVq+fLlysnJaYKeAAAAnowzUAA83qFDh2Sz2RQfH++Y1qZNG8XGxio/P1+SlJ+fr9DQUEd4kqT4+Hj5+vpq586djja33nqrAgICHG0SExN18OBB/fDDD442F66nsk3legAAwOWNM1AAPJ7NZpMkhYWFOU0PCwtzzLPZbOrQoYPT/BYtWqhdu3ZObaKioqoso3Je27ZtZbPZal1PdUpLS1VaWup4XlxcLEmy2+2y2+21bpvFz6h9vq/h9G9d6lpfU6iswRNqMYuam4Yravam7QXQPBGgAOASZWVlae7cuVWmb9y4UcHBwbW+dsFgc+uYH1Nhqp0nDXhhtVrdXUK9UXPTuJSaz5w548JKAKD+CFAAPF54eLgkqaioSB07dnRMLyoq0sCBAx1tjh496vS6c+fO6fjx447Xh4eHq6ioyKlN5fO62lTOr86MGTOUkZHheF5cXKzIyEglJCQoJCSk1m3rm/lOrfMtvobmx1To8T2+Kq3wqbWtJO3LTKyzTWOz2+2yWq26/fbb5e/v7+5yTKHmpuGKmivP8AKAu9T7HiiGGgbQ1KKiohQeHq68vDzHtOLiYu3cuVNxcXGSpLi4OJ04cUIFBQWONps2bVJFRYViY2MdbbZu3ep0CZDValWPHj3Utm1bR5sL11PZpnI91bFYLAoJCXF6SJK/v3+dj9Jyn9of/z80lVbU0e7/P8yssykeZrffkx7U7D01A4A71TtAMdQwgMZw+vRpFRYWqrCwUNJPA0cUFhbq8OHD8vHxUXp6up544gm9+eab+vjjj3XfffcpIiJCSUlJkqRevXpp2LBhmjhxonbt2qUPPvhAaWlpGjt2rCIiIiRJ99xzjwICApSSkqL9+/drzZo1Wrp0qdPZoylTpig3N1cLFy7UgQMHlJmZqT179igtLa2puwQAAHigel/Cx1DDABrDnj17dNtttzmeV4aa5ORkrVy5Uo888ohKSko0adIknThxQjfffLNyc3MVGBjoeM2qVauUlpamoUOHytfXV6NGjdJzzz3nmN+mTRtt3LhRqampio6OVvv27TV79mynP+DceOONWr16tWbNmqWZM2eqe/fuWrdunfr27dsEvQAAADydS4cxZ6hhAA01ZMgQGYZR5bFy5UpJko+Pj+bNmyebzaazZ8/q3Xff1bXXXuu0jHbt2mn16tU6deqUTp48qRUrVqhVq1ZObfr376/3339fZ8+e1ddff61HH320Si2jR4/WwYMHVVpaqn379umOO+5otO0G0Pi4/QCAK7l0EInmOtRwdcMM13do4Qt5yhCs3jgE7sXYBs9w8TZ487YAaH4qbz+4//77NXLkyCrzK28/ePnllxUVFaXHH39ciYmJ+uSTTxxnuceNG6cjR47IarXKbrdrwoQJmjRpklavXi3p/O0H8fHxysnJ0ccff6z7779foaGhjrPclbcfZGVl6X/+53+0evVqJSUl6cMPP+QsN+BFLqtR+Bo61HBtwwybHVr4Qp721yZvHAL3YmyDZ6jcBoYZBuBJuP0AgCu5NEA116GGqxtmuL5DC1/IE4YZlrxzCNyLsQ2e4eJtYJhhAN6irtsPxo4dW+ftB7/85S9rvP3g6aef1g8//KC2bdsqPz/f6Tikss3FlxRe6JK+qPsSrpapzuV4dQFXVriGt/Sj2fpcGqAuHGq4MjBVDjU8efJkSc5DDUdHR0uqfqjhxx57THa73XFAWdNQw+np6Y71mxlq2GKxVJle17CopeU1B6TKoYXrw9MOkpvDsLBsg2dgmGEA3sbTbz+4lC/qnh9T+W/9r5apjqddQdOUmsNVIp7A0/vR7BU09Q5Qp0+f1ueff+54XjnUcLt27dS5c2fHUMPdu3d3XEdc01DDOTk5stvt1Q41PHfuXKWkpOjRRx/Vvn37tHTpUi1evNix3ilTpuhnP/uZFi5cqBEjRui1117Tnj17nIY6BwAA8GaX8kXd0fNyG3y1THU85QqaptQcrhLxBN7Sj2avoKl3gGKoYQAA0Fx4+u0HDb16RlKVL+K+VJ584NvYuLrCNTy9H83WVu8AVTnUcE0qhxqeN29ejW0qhxquTeVQw7UZPXq0Ro8eXXvBAAAANfD02w8AeB6Xfg8UAACApzl9+rQKCwtVWFgo6fztB4cPH5aPj4/j9oM333xTH3/8se67774abz/YtWuXPvjgg2pvPwgICFBKSor279+vNWvWaOnSpU6X302ZMkW5ublauHChDhw4oMzMTO3Zs0dpaWlN3SUALsFlNYw5AAC4/HD7AQBXIkABAIBmjdsPALgSl/ABAAAAgEkEKAAAAAAwiQAFAAAAACYRoAAAAADAJAIUAAAAAJhEgAIAAAAAkwhQAAAAAGASAQoAAAAATCJAAQAAAIBJBCgAAAAAMIkABQAAAAAmEaAAAAAAwCQCFAAAAACYRIACAAAAAJMIUAAAAABgEgEKAAAAAEwiQAEAAACASQQoAAAAADCJAAUAAAAAJhGgAAAAAMAkAhQAAAAAmESAAgAAAACTCFAAAAAAYBIBCgAAAABMIkABAAAAgEkEKAAAAAAwqYW7CwAAuE7X6RtctqwvnxrhsmUBANBcEKAAANVqaBiz+BlaMFjqm/mOSst9HNMJZACA5oAABQBoEq48OyYRyAAA7sE9UAAAAABgEgEKAAAAAEziEj434DIWAAAAwDsRoAAAXsmVf4z6bH6Cy5YFAGjeuIQPAAAAAEzy+gCVnZ2trl27KjAwULGxsdq1a5e7SwLQTLB/AdBY2L8A3surA9SaNWuUkZGhOXPm6MMPP9SAAQOUmJioo0ePurs0AF6O/QuAxsL+BfBuXn0P1KJFizRx4kRNmDBBkpSTk6MNGzZoxYoVmj59upurazqu/LJLBqQAfsL+BUBjYf8CeDevDVBlZWUqKCjQjBkzHNN8fX0VHx+v/Pz8al9TWlqq0tJSx/OTJ09Kko4fPy673V7julqcK6k6rcLQmTMVamH3VXmFT0M3w62q24Zrfv+6S9exc8ZQly7vYna7XWfOnNGxY8fk7+/fqOtqLM1xG06dOiVJMgzDzZU1TH33Lw3dt0jV71+c5nvhvsYbaz527JjX/T/0xn2HK2pm/1KP/Yu9xKX/F48dO3bJy/A23vj/zBN5Sz+a3b94bYD6/vvvVV5errCwMKfpYWFhOnDgQLWvycrK0ty5c6tMj4qKalAN9zToVZ6lsbeh/cJGXgE82qlTp9SmTRt3l1Fv9d2/uHrfcjFv3Nd4W80d2Vd5HfYv5vYvrvy/yO90XC7q2r94bYBqiBkzZigjI8PxvKKiQsePH9cVV1whH5/6/WWmuLhYkZGR+uqrrxQSEuLqUpsE2+AZmuM2GIahU6dOKSIiwt2lNQlX7lsu5o2fD2puGpdrzexfzO9fvPEz4mnoQ9fwln40u3/x2gDVvn17+fn5qaioyGl6UVGRwsPDq32NxWKRxWJxmhYaGnpJdYSEhHj0B8EMtsEzNLdt8Ma/DFeq7/6lMfYtF/PGzwc1N43LsWb2L6H1Wqc3fkY8DX3oGt7Qj2b2L147Cl9AQICio6OVl5fnmFZRUaG8vDzFxcW5sTIA3o79C4DGwv4F8H5eewZKkjIyMpScnKyYmBgNHjxYS5YsUUlJiWNUGwBoKPYvABoL+xfAu3l1gBozZoy+++47zZ49WzabTQMHDlRubm6VGzMbg8Vi0Zw5c6qcVvcmbINnYBs8kzv3Lxfyxr6l5qZBzd6rqfYv9Pelow9do7n1o4/hreOAAgAAAEAT89p7oAAAAACgqRGgAAAAAMAkAhQAAAAAmESAAgAAAACTCFANkJ2dra5duyowMFCxsbHatWuXu0uql8zMTPn4+Dg9evbs6e6yarR161bdeeedioiIkI+Pj9atW+c03zAMzZ49Wx07dlRQUJDi4+P12WefuafYGtS1DePHj6/yngwbNsw9xdYgKytL119/vVq3bq0OHTooKSlJBw8edGpz9uxZpaam6oorrlCrVq00atSoKl8Wifrxpv2Nmc+Ip3vqqafk4+Oj9PR0d5dSq2+++Ua/+c1vdMUVVygoKEj9+vXTnj173F1WjcrLy/X4448rKipKQUFB6tatm+bPny/GsWo83rTv8AR1HRvx+616rjhGO378uMaNG6eQkBCFhoYqJSVFp0+fbsKtqD8CVD2tWbNGGRkZmjNnjj788EMNGDBAiYmJOnr0qLtLq5c+ffroyJEjjse2bdvcXVKNSkpKNGDAAGVnZ1c7f8GCBXruueeUk5OjnTt3qmXLlkpMTNTZs2ebuNKa1bUNkjRs2DCn9+TVV19twgrrtmXLFqWmpmrHjh2yWq2y2+1KSEhQSUmJo83UqVP11ltvae3atdqyZYu+/fZbjRw50o1Vezdv29+Y+Yx4st27d+uPf/yj+vfv7+5SavXDDz/opptukr+/v/75z3/qk08+0cKFC9W2bVt3l1ajp59+Wi+88IKWL1+uTz/9VE8//bQWLFigZcuWubu0Zsnb9h2eorZjI36/Vc8Vx2jjxo3T/v37ZbVatX79em3dulWTJk1qqk1oGAP1MnjwYCM1NdXxvLy83IiIiDCysrLcWFX9zJkzxxgwYIC7y2gQScYbb7zheF5RUWGEh4cbzzzzjGPaiRMnDIvFYrz66qtuqLBuF2+DYRhGcnKycdddd7mlnoY6evSoIcnYsmWLYRg/9bu/v7+xdu1aR5tPP/3UkGTk5+e7q0yv5u37m4s/I57s1KlTRvfu3Q2r1Wr87Gc/M6ZMmeLukmr06KOPGjfffLO7y6iXESNGGPfff7/TtJEjRxrjxo1zU0XNm7fvO9yhtmMjfr+Z05BjtE8++cSQZOzevdvR5p///Kfh4+NjfPPNN01We31xBqoeysrKVFBQoPj4eMc0X19fxcfHKz8/342V1d9nn32miIgIXX311Ro3bpwOHz7s7pIa5NChQ7LZbE7vSZs2bRQbG+t178nmzZvVoUMH9ejRQ5MnT9axY8fcXVKtTp48KUlq166dJKmgoEB2u93pvejZs6c6d+7sde+FJ2gO+5uLPyOeLDU1VSNGjHDqb0/15ptvKiYmRqNHj1aHDh00aNAg/elPf3J3WbW68cYblZeXp3//+9+SpH/961/atm2bhg8f7ubKmp/msO9wl5qOjfj91jBmjtHy8/MVGhqqmJgYR5v4+Hj5+vpq586dTV6zWS3cXYA3+f7771VeXl7lm8LDwsJ04MABN1VVf7GxsVq5cqV69OihI0eOaO7cubrlllu0b98+tW7d2t3l1YvNZpOkat+TynneYNiwYRo5cqSioqL0xRdfaObMmRo+fLjy8/Pl5+fn7vKqqKioUHp6um666Sb17dtX0k/vRUBAgEJDQ53aett74Sm8fX9T3WfEU7322mv68MMPtXv3bneXYsp//vMfvfDCC8rIyNDMmTO1e/du/e53v1NAQICSk5PdXV61pk+fruLiYvXs2VN+fn4qLy/Xk08+qXHjxrm7tGbH2/cd7lLbsRG/3xrGzDGazWZThw4dnOa3aNFC7dq18+i+JUBdhi78i1///v0VGxurLl266PXXX1dKSoobK7t8jR071vFzv3791L9/f3Xr1k2bN2/W0KFD3VhZ9VJTU7Vv3z6PvncO7uUtn5GvvvpKU6ZMkdVqVWBgoLvLMaWiokIxMTH6wx/+IEkaNGiQ9u3bp5ycHI8NUK+//rpWrVql1atXq0+fPiosLFR6eroiIiI8tmZcXmo7NgoKCnJjZfBEXMJXD+3bt5efn1+VUVeKiooUHh7upqouXWhoqK699lp9/vnn7i6l3ir7vbm9J1dffbXat2/vke9JWlqa1q9fr/fee0+dOnVyTA8PD1dZWZlOnDjh1N7b3wt38eb9TU2fEU9UUFCgo0eP6rrrrlOLFi3UokULbdmyRc8995xatGih8vJyd5dYRceOHdW7d2+nab169fLoS7GnTZum6dOna+zYserXr5/uvfdeTZ06VVlZWe4urdnx5n2HJ7nw2Ijfbw1j5hgtPDy8yuAm586d0/Hjxz26bwlQ9RAQEKDo6Gjl5eU5plVUVCgvL09xcXFurOzSnD59Wl988YU6duzo7lLqLSoqSuHh4U7vSXFxsXbu3OnV78nXX3+tY8eOedR7YhiG0tLS9MYbb2jTpk2Kiopymh8dHS1/f3+n9+LgwYM6fPiwV78X7uKN+5u6PiOeaOjQofr4449VWFjoeMTExGjcuHEqLCz0yEtob7rppirDw//73/9Wly5d3FRR3c6cOSNfX+dDDj8/P1VUVLipoubLG/cdnujCYyN+vzWMmWO0uLg4nThxQgUFBY42mzZtUkVFhWJjY5u8ZtPcPYqFt3nttdcMi8VirFy50vjkk0+MSZMmGaGhoYbNZnN3aaY9/PDDxubNm41Dhw4ZH3zwgREfH2+0b9/eOHr0qLtLq9apU6eMvXv3Gnv37jUkGYsWLTL27t1r/Pe//zUMwzCeeuopIzQ01PjHP/5hfPTRR8Zdd91lREVFGT/++KObKz+vtm04deqU8fvf/97Iz883Dh06ZLz77rvGddddZ3Tv3t04e/asu0t3mDx5stGmTRtj8+bNxpEjRxyPM2fOONo8+OCDRufOnY1NmzYZe/bsMeLi4oy4uDg3Vu3dvG1/Y+Yz4g08fRS+Xbt2GS1atDCefPJJ47PPPjNWrVplBAcHG6+88oq7S6tRcnKycdVVVxnr1683Dh06ZPz973832rdvbzzyyCPuLq1Z8rZ9hyeo69iI32/Vc8Ux2rBhw4xBgwYZO3fuNLZt22Z0797duPvuu921SaYQoBpg2bJlRufOnY2AgABj8ODBxo4dO9xdUr2MGTPG6NixoxEQEGBcddVVxpgxY4zPP//c3WXV6L333jMkVXkkJycbhvHTMJmPP/64ERYWZlgsFmPo0KHGwYMH3Vv0RWrbhjNnzhgJCQnGlVdeafj7+xtdunQxJk6c6HG/6KqrX5Lx0ksvOdr8+OOPxv/+7/8abdu2NYKDg41f/vKXxpEjR9xXdDPgTfsbM58Rb+DpAcowDOOtt94y+vbta1gsFqNnz57Giy++6O6SalVcXGxMmTLF6Ny5sxEYGGhcffXVxmOPPWaUlpa6u7Rmy5v2HZ6grmMjfr9VzxXHaMeOHTPuvvtuo1WrVkZISIgxYcIE49SpU27YGvN8DIOvAQcAAAAAM7gHCgAAAABMIkABAAAAgEkEKAAAAAAwiQAFAAAAACYRoAAAAADAJAIUAAAAAJhEgAIAAAAAkwhQAAAAAGASAQoAAAAATCJAAQAAAIBJBCgAAAAAMIkABQAAAAAmEaAAAAAAwCQCFAAAAACYRIACAAAAAJMIUAAAAABgEgEKAAAAAEwiQAEAAACASQQoAAAAADCJAAUAAAAAJhGg0Og2b94sHx8fbd682d2lAAAAAJeEAAWvt337dmVmZurEiRPuLgUAAADNHAEKXm/79u2aO3cuAQoAAACNjgAF1ODMmTPuLgEAAAAehgAFl/jmm2+UkpKiiIgIWSwWRUVFafLkySorK6u2fdeuXTV+/Pgq04cMGaIhQ4Y4TVu2bJn69Omj4OBgtW3bVjExMVq9erUkKTMzU9OmTZMkRUVFycfHRz4+Pvryyy8dr3/llVcUHR2toKAgtWvXTmPHjtVXX31VZb19+/ZVQUGBbr31VgUHB2vmzJkN7xAAAAA0Sy3cXQC837fffqvBgwfrxIkTmjRpknr27KlvvvlGf/vb3y75LM6f/vQn/e53v9OvfvUrTZkyRWfPntVHH32knTt36p577tHIkSP173//W6+++qoWL16s9u3bS5KuvPJKSdKTTz6pxx9/XL/+9a/1wAMP6LvvvtOyZct06623au/evQoNDXWs69ixYxo+fLjGjh2r3/zmNwoLC7uk2gEAAND8EKBwyWbMmCGbzaadO3cqJibGMX3evHkyDOOSlr1hwwb16dNHa9eurXZ+//79dd111+nVV19VUlKSunbt6pj33//+V3PmzNETTzzhdDZp5MiRGjRokJ5//nmn6TabTTk5Ofrtb397STUDAACg+eISPlySiooKrVu3TnfeeadTeKrk4+NzScsPDQ3V119/rd27d9f7tX//+99VUVGhX//61/r+++8dj/DwcHXv3l3vvfeeU3uLxaIJEyZcUr0AAABo3jgDhUvy3Xffqbi4WH379m2U5T/66KN69913NXjwYF1zzTVKSEjQPffco5tuuqnO13722WcyDEPdu3evdr6/v7/T86uuukoBAQEuqRsAAADNEwEKblHTmany8nL5+fk5nvfq1UsHDx7U+vXrlZubq//7v//T888/r9mzZ2vu3Lm1rqOiokI+Pj765z//6bTMSq1atXJ6HhQU1IAtAQAAwOWEAIVLcuWVVyokJET79u2r1+vatm1b7fc2/fe//9XVV1/tNK1ly5YaM2aMxowZo7KyMo0cOVJPPvmkZsyYocDAwBrDWLdu3WQYhqKionTttdfWqz4AAACgOtwDhUvi6+urpKQkvfXWW9qzZ0+V+TUNItGtWzft2LHDaZjz9evXVxle/NixY07PAwIC1Lt3bxmGIbvdLumngCWpSiAbOXKk/Pz8NHfu3Cp1GIZRZdkAAABAXTgDhUv2hz/8QRs3btTPfvYzTZo0Sb169dKRI0e0du1abdu2rdrXPPDAA/rb3/6mYcOG6de//rW++OILvfLKK+rWrZtTu4SEBIWHh+umm25SWFiYPv30Uy1fvlwjRoxQ69atJUnR0dGSpMcee0xjx46Vv7+/7rzzTnXr1k1PPPGEZsyYoS+//FJJSUlq3bq1Dh06pDfeeEOTJk3S73//+8btHAAAADQrBChcsquuuko7d+7U448/rlWrVqm4uFhXXXWVhg8fruDg4Gpfk5iYqIULF2rRokVKT09XTEyM1q9fr4cfftip3W9/+1utWrVKixYt0unTp9WpUyf97ne/06xZsxxtrr/+es2fP185OTnKzc1VRUWFDh06pJYtW2r69Om69tprtXjxYsc9U5GRkUpISNAvfvGLxusUAAAANEs+xqV+UQ8AAAAAXCa4BwoAAAAATCJAAQAAAIBJBCgAAAAAMIkABQAAAAAmEaAAAAAAwCQCFAAAAACYdFl/D1RFRYW+/fZbtW7dWj4+Pu4uB2g2DMPQqVOnFBERIV9f/k4DAACaj8s6QH377beKjIx0dxlAs/XVV1+pU6dO7i4DAADAZS7rANW6dWtJPx3khYSESJLsdrs2btyohIQE+fv7u7M8j0B/nEdfnFdXXxQXFysyMtLxfwwAAKC5uKwDVOVleyEhIU4BKjg4WCEhIZf9QbJEf1yIvjjPbF9waSwAAGhuuDkBAAAAAEwiQAEAAACASQQoAAAAADCJAAUAAAAAJhGgAAAAAMAkAhQAAAAAmESAAgAAAACTCFAAAAAAYNJl/UW6wOWi6/QNLlvWl0+NcNmyAAAAvA1noAAAAADApHoFqKysLF1//fVq3bq1OnTooKSkJB08eNCpzdmzZ5WamqorrrhCrVq10qhRo1RUVOTU5vDhwxoxYoSCg4PVoUMHTZs2TefOnXNqs3nzZl133XWyWCy65pprtHLlyir1ZGdnq2vXrgoMDFRsbKx27dpVn80BAAAAgHqpV4DasmWLUlNTtWPHDlmtVtntdiUkJKikpMTRZurUqXrrrbe0du1abdmyRd9++61GjhzpmF9eXq4RI0aorKxM27dv18svv6yVK1dq9uzZjjaHDh3SiBEjdNttt6mwsFDp6el64IEH9M477zjarFmzRhkZGZozZ44+/PBDDRgwQImJiTp69Oil9AcAAAAA1Khe90Dl5uY6PV+5cqU6dOiggoIC3XrrrTp58qT+8pe/aPXq1fr5z38uSXrppZfUq1cv7dixQzfccIM2btyoTz75RO+++67CwsI0cOBAzZ8/X48++qgyMzMVEBCgnJwcRUVFaeHChZKkXr16adu2bVq8eLESExMlSYsWLdLEiRM1YcIESVJOTo42bNigFStWaPr06ZfcMQAAAABwsUu6B+rkyZOSpHbt2kmSCgoKZLfbFR8f72jTs2dPde7cWfn5+ZKk/Px89evXT2FhYY42iYmJKi4u1v79+x1tLlxGZZvKZZSVlamgoMCpja+vr+Lj4x1tAAAAAMDVGjwKX0VFhdLT03XTTTepb9++kiSbzaaAgACFhoY6tQ0LC5PNZnO0uTA8Vc6vnFdbm+LiYv3444/64YcfVF5eXm2bAwcO1FhzaWmpSktLHc+Li4slSXa7XXa73fHzhf9e7uiP87y5Lyx+hsuWVd3/l+raAAAANEcNDlCpqanat2+ftm3b5sp6GlVWVpbmzp1bZfrGjRsVHBzsNM1qtTZVWV6B/jjPG/tiwWDXLevtt992/FxTX5w5c8Z1KwQAAPAgDQpQaWlpWr9+vbZu3apOnTo5poeHh6usrEwnTpxwOgtVVFSk8PBwR5uLR8urHKXvwjYXj9xXVFSkkJAQBQUFyc/PT35+ftW2qVxGdWbMmKGMjAzH8+LiYkVGRiohIUEhISGSfvrLudVq1e233y5/f3+zXdJs0R/neXNf9M18p+5GJu3LTKyzLyrP7gIAADQ39QpQhmHooYce0htvvKHNmzcrKirKaX50dLT8/f2Vl5enUaNGSZIOHjyow4cPKy4uTpIUFxenJ598UkePHlWHDh0k/fRX7JCQEPXu3dvR5sK/cle2qVxGQECAoqOjlZeXp6SkJEk/XVKYl5entLS0Guu3WCyyWCxVpvv7+1c5CKxu2uWM/jjPG/uitNzHZcu6cNtr6gtv6x8AAACz6hWgUlNTtXr1av3jH/9Q69atHfcstWnTRkFBQWrTpo1SUlKUkZGhdu3aKSQkRA899JDi4uJ0ww03SJISEhLUu3dv3XvvvVqwYIFsNptmzZql1NRUR7h58MEHtXz5cj3yyCO6//77tWnTJr3++uvasGGDo5aMjAwlJycrJiZGgwcP1pIlS1RSUuIYlQ8AAAAAXK1eAeqFF16QJA0ZMsRp+ksvvaTx48dLkhYvXixfX1+NGjVKpaWlSkxM1PPPP+9o6+fnp/Xr12vy5MmKi4tTy5YtlZycrHnz5jnaREVFacOGDZo6daqWLl2qTp066c9//rNjCHNJGjNmjL777jvNnj1bNptNAwcOVG5ubpWBJQAAAADAVep9CV9dAgMDlZ2drezs7BrbdOnSpcolehcbMmSI9u7dW2ubtLS0Wi/ZAwAAAABXuqTvgQIAAACAywkBCgAAAABMIkABAAAAgEkEKAAAAAAwiQAFAAAAACYRoAAAAADAJAIUAAAAAJhEgAIAAAAAkwhQAAAAAGASAQoAAAAATCJAAQAAAIBJBCgAAAAAMIkABQAAAAAmEaAAAAAAwCQCFAAAAACYRIACAAAAAJMIUAAAAABgEgEKAAAAAEwiQAEAAACASQQoAAAAADCJAAUAAAAAJhGgAAAAAMAkAhQAAAAAmESAAgAAAACTCFAAAAAAYBIBCgAAAABMIkABAAAAgEkEKAAAAAAwiQAFAAAAACYRoAAAAADAJAIUAAAAAJhEgAIAAAAAkwhQAAAAAGASAQoAAAAATCJAAQAAAIBJBCgAAAAAMIkABQAAAAAmEaAAAAAAwKR6B6itW7fqzjvvVEREhHx8fLRu3Tqn+ePHj5ePj4/TY9iwYU5tjh8/rnHjxikkJEShoaFKSUnR6dOnndp89NFHuuWWWxQYGKjIyEgtWLCgSi1r165Vz549FRgYqH79+untt9+u7+YAAAAAgGn1DlAlJSUaMGCAsrOza2wzbNgwHTlyxPF49dVXneaPGzdO+/fvl9Vq1fr167V161ZNmjTJMb+4uFgJCQnq0qWLCgoK9MwzzygzM1Mvvviio8327dt19913KyUlRXv37lVSUpKSkpK0b9+++m4SAAAAAJjSor4vGD58uIYPH15rG4vFovDw8Grnffrpp8rNzdXu3bsVExMjSVq2bJnuuOMOPfvss4qIiNCqVatUVlamFStWKCAgQH369FFhYaEWLVrkCFpLly7VsGHDNG3aNEnS/PnzZbVatXz5cuXk5NR3swAAAACgTo1yD9TmzZvVoUMH9ejRQ5MnT9axY8cc8/Lz8xUaGuoIT5IUHx8vX19f7dy509Hm1ltvVUBAgKNNYmKiDh48qB9++MHRJj4+3mm9iYmJys/Pb4xNAgAAAID6n4Gqy7BhwzRy5EhFRUXpiy++0MyZMzV8+HDl5+fLz89PNptNHTp0cC6iRQu1a9dONptNkmSz2RQVFeXUJiwszDGvbdu2stlsjmkXtqlcRnVKS0tVWlrqeF5cXCxJstvtstvtjp8v/PdyR3+c5819YfEzXLas6v6/VNcGAACgOXJ5gBo7dqzj5379+ql///7q1q2bNm/erKFDh7p6dfWSlZWluXPnVpm+ceNGBQcHO02zWq1NVZZXoD/O88a+WDDYdcu6cLCWmvrizJkzrlshAACAB3F5gLrY1Vdfrfbt2+vzzz/X0KFDFR4erqNHjzq1OXfunI4fP+64byo8PFxFRUVObSqf19WmpnuvJGnGjBnKyMhwPC8uLlZkZKQSEhIUEhIi6ae/nFutVt1+++3y9/dv4FY3H/THed7cF30z33HZsvZlJtbZF5VndwEAAJqbRg9QX3/9tY4dO6aOHTtKkuLi4nTixAkVFBQoOjpakrRp0yZVVFQoNjbW0eaxxx6T3W53HJxZrVb16NFDbdu2dbTJy8tTenq6Y11Wq1VxcXE11mKxWGSxWKpM9/f3r3IQWN20yxn9cZ439kVpuY/LlnXhttfUF97WPwAAAGbVexCJ06dPq7CwUIWFhZKkQ4cOqbCwUIcPH9bp06c1bdo07dixQ19++aXy8vJ011136ZprrlFiYqIkqVevXho2bJgmTpyoXbt26YMPPlBaWprGjh2riIgISdI999yjgIAApaSkaP/+/VqzZo2WLl3qdPZoypQpys3N1cKFC3XgwAFlZmZqz549SktLc0G3AAAAAEBV9Q5Qe/bs0aBBgzRo0CBJUkZGhgYNGqTZs2fLz89PH330kX7xi1/o2muvVUpKiqKjo/X+++87nflZtWqVevbsqaFDh+qOO+7QzTff7PQdT23atNHGjRt16NAhRUdH6+GHH9bs2bOdvivqxhtv1OrVq/Xiiy9qwIAB+tvf/qZ169apb9++l9IfAAAAAFCjel/CN2TIEBlGzSN6vfNO3fdatGvXTqtXr661Tf/+/fX+++/X2mb06NEaPXp0nesDAAAAAFdolO+BAgAAAIDmiAAFAAAAACYRoAAAAADAJAIUAAAAAJhEgAIAAAAAkwhQAAAAAGASAQoAAAAATCJAAQAAAIBJBCgAAAAAMIkABQAAAAAmEaAAAAAAwKQW7i4AzVvX6RtctqwvnxrhsmUBAAAADcEZKAAAAAAwiQAFAAAAACYRoAAAAADAJAIUAAAAAJhEgAIAAAAAkwhQAAAAAGASAQoAAAAATCJAAQAAAIBJBCgAAAAAMIkABQAAAAAmEaAAAAAAwCQCFAAAAACYRIACAAAAAJMIUAAAAABgEgEKAAAAAEwiQAEAAACASQQoAAAAADCJAAUAAAAAJhGgAAAAAMAkAhQAAAAAmESAAgAAAACTCFAAAAAAYBIBCgAAAABMIkABAAAAgEkEKAAAAAAwiQAFAAAAACbVO0Bt3bpVd955pyIiIuTj46N169Y5zTcMQ7Nnz1bHjh0VFBSk+Ph4ffbZZ05tjh8/rnHjxikkJEShoaFKSUnR6dOnndp89NFHuuWWWxQYGKjIyEgtWLCgSi1r165Vz549FRgYqH79+untt9+u7+YAAAAAgGn1DlAlJSUaMGCAsrOzq52/YMECPffcc8rJydHOnTvVsmVLJSYm6uzZs44248aN0/79+2W1WrV+/Xpt3bpVkyZNcswvLi5WQkKCunTpooKCAj3zzDPKzMzUiy++6Gizfft23X333UpJSdHevXuVlJSkpKQk7du3r76bBAAAAACmtKjvC4YPH67hw4dXO88wDC1ZskSzZs3SXXfdJUn661//qrCwMK1bt05jx47Vp59+qtzcXO3evVsxMTGSpGXLlumOO+7Qs88+q4iICK1atUplZWVasWKFAgIC1KdPHxUWFmrRokWOoLV06VINGzZM06ZNkyTNnz9fVqtVy5cvV05OToM6AwAAAABq49J7oA4dOiSbzab4+HjHtDZt2ig2Nlb5+fmSpPz8fIWGhjrCkyTFx8fL19dXO3fudLS59dZbFRAQ4GiTmJiogwcP6ocffnC0uXA9lW0q1wMAAAAArlbvM1C1sdlskqSwsDCn6WFhYY55NptNHTp0cC6iRQu1a9fOqU1UVFSVZVTOa9u2rWw2W63rqU5paalKS0sdz4uLiyVJdrtddrvd8fOF/17uLrU/LH6Gy2txF2/+bLj6fairL7yxjwAAAMxwaYDydFlZWZo7d26V6Rs3blRwcLDTNKvV2lRleYWG9seCwa6rwVMGCfHGz0ZjvQ819cWZM2dct0IAAAAP4tIAFR4eLkkqKipSx44dHdOLioo0cOBAR5ujR486ve7cuXM6fvy44/Xh4eEqKipyalP5vK42lfOrM2PGDGVkZDieFxcXKzIyUgkJCQoJCZH001/OrVarbr/9dvn7+5ve9ubqUvujb+Y7LqtlX2aiy5bVEN782XD1+1BXX1Se3QUAAGhuXBqgoqKiFB4erry8PEdgKi4u1s6dOzV58mRJUlxcnE6cOKGCggJFR0dLkjZt2qSKigrFxsY62jz22GOy2+2OgzOr1aoePXqobdu2jjZ5eXlKT093rN9qtSouLq7G+iwWiywWS5Xp/v7+VQ4Cq5t2OWtof5SW+7i0Bk/gjZ+NxnofauoLb+sfAAAAs+o9iMTp06dVWFiowsJCST8NHFFYWKjDhw/Lx8dH6enpeuKJJ/Tmm2/q448/1n333aeIiAglJSVJknr16qVhw4Zp4sSJ2rVrlz744AOlpaVp7NixioiIkCTdc889CggIUEpKivbv3681a9Zo6dKlTmePpkyZotzcXC1cuFAHDhxQZmam9uzZo7S0tEvvFQAAAACoRr3PQO3Zs0e33Xab43llqElOTtbKlSv1yCOPqKSkRJMmTdKJEyd08803Kzc3V4GBgY7XrFq1SmlpaRo6dKh8fX01atQoPffcc475bdq00caNG5Wamqro6Gi1b99es2fPdvquqBtvvFGrV6/WrFmzNHPmTHXv3l3r1q1T3759G9QRAAAAAFCXegeoIUOGyDBqHtHLx8dH8+bN07x582ps065dO61evbrW9fTv31/vv/9+rW1Gjx6t0aNH114wAAAAALiIS78HCgAAAACaMwIUAAAAAJhEgAIAAAAAkwhQAAAAAGASAQoAAAAATCJAAQAAAIBJBCgAAAAAMIkABQAAAAAmEaAAAAAAwCQCFAAAAACYRIACAAAAAJMIUAAAAABgEgEKAAAAAEwiQAEAAACASQQoAAAAADCJAAUAAAAAJhGgAAAAAMAkAhQAAAAAmESAAgAAAACTCFAAAAAAYBIBCgAAAABMIkABAAAAgEkEKAAAAAAwiQAFAAAAACYRoAAAAADAJAIUAAAAAJhEgAIAAAAAkwhQAAAAAGASAQoAAAAATCJAAQAAAIBJBCgAAAAAMIkABQAAAAAmtXB3Abh0XadvcNmyvnxqhMuWBQAAADQ3nIECAAAAAJMIUAAAAABgEgEKAAAAAEwiQAEAAACASQQoAAAAADCJAAUAAAAAJrk8QGVmZsrHx8fp0bNnT8f8s2fPKjU1VVdccYVatWqlUaNGqaioyGkZhw8f1ogRIxQcHKwOHTpo2rRpOnfunFObzZs367rrrpPFYtE111yjlStXunpTAAAAAMBJo5yB6tOnj44cOeJ4bNu2zTFv6tSpeuutt7R27Vpt2bJF3377rUaOHOmYX15erhEjRqisrEzbt2/Xyy+/rJUrV2r27NmONocOHdKIESN02223qbCwUOnp6XrggQf0zjvvNMbmAAAAAICkRvoi3RYtWig8PLzK9JMnT+ovf/mLVq9erZ///OeSpJdeekm9evXSjh07dMMNN2jjxo365JNP9O677yosLEwDBw7U/Pnz9eijjyozM1MBAQHKyclRVFSUFi5cKEnq1auXtm3bpsWLFysxMbExNgkAAAAAGidAffbZZ4qIiFBgYKDi4uKUlZWlzp07q6CgQHa7XfHx8Y62PXv2VOfOnZWfn68bbrhB+fn56tevn8LCwhxtEhMTNXnyZO3fv1+DBg1Sfn6+0zIq26Snp9daV2lpqUpLSx3Pi4uLJUl2u112u93x84X/egOLn+GyZV283ZfaH41ZW1Pzxs9GJVe/D3X1hTf2EQAAgBkuD1CxsbFauXKlevTooSNHjmju3Lm65ZZbtG/fPtlsNgUEBCg0NNTpNWFhYbLZbJIkm83mFJ4q51fOq61NcXGxfvzxRwUFBVVbW1ZWlubOnVtl+saNGxUcHOw0zWq1mt9oN1sw2HXLevvtt6ud3tD+aIrampo3fTYqNdb7UFNfnDlzxnUrBAAA8CAuD1DDhw93/Ny/f3/FxsaqS5cuev3112sMNk1lxowZysjIcDwvLi5WZGSkEhISFBISIumnv5xbrVbdfvvt8vf3d1ep9dI303X3fu3LdL4E8lL7ozFra2re+Nmo5Or3oa6+qDy7CwAA0Nw0yiV8FwoNDdW1116rzz//XLfffrvKysp04sQJp7NQRUVFjnumwsPDtWvXLqdlVI7Sd2Gbi0fuKyoqUkhISK0hzWKxyGKxVJnu7+9f5SCwummeqrTcx2XLqmmbG9ofTVFbU/Omz0alxnofauoLb+sfAAAAsxr9e6BOnz6tL774Qh07dlR0dLT8/f2Vl5fnmH/w4EEdPnxYcXFxkqS4uDh9/PHHOnr0qKON1WpVSEiIevfu7Whz4TIq21QuAwAAAAAag8sD1O9//3tt2bJFX375pbZv365f/vKX8vPz09133602bdooJSVFGRkZeu+991RQUKAJEyYoLi5ON9xwgyQpISFBvXv31r333qt//etfeueddzRr1iylpqY6zh49+OCD+s9//qNHHnlEBw4c0PPPP6/XX39dU6dOdfXmAAAAAICDyy/h+/rrr3X33Xfr2LFjuvLKK3XzzTdrx44duvLKKyVJixcvlq+vr0aNGqXS0lIlJibq+eefd7zez89P69ev1+TJkxUXF6eWLVsqOTlZ8+bNc7SJiorShg0bNHXqVC1dulSdOnXSn//8Z4YwBwAAANCoXB6gXnvttVrnBwYGKjs7W9nZ2TW26dKlS50jrg0ZMkR79+5tUI0AAAAA0BCNfg8UAAAAADQXBCgAAAAAMIkABQAAAAAmEaAAAAAAwCQCFAAAAACYRIACAAAAAJMIUAAAAABgEgEKAAAAAExy+RfpNkddp29w6fK+fGqES5cHAAAAoGlwBgoAAAAATCJAAQAAAIBJBCgAAAAAMIkABQAAAAAmEaAAAAAAwCQCFAAAAACYRIACAAAAAJMIUAAAAABgEgEKAAAAAEwiQAEAAACASQQoAAAAADCJAAUAAAAAJhGgAAAAAMAkAhQAAAAAmESAAgAAAACTCFAAAAAAYBIBCgAAAABMauHuAoDmoOv0DS5d3pdPjXDp8gAAAOAanIECAAAAAJMIUAAAAABgEgEKAAAAAEwiQAEAAACASQQoAAAAADCJAAUAAAAAJhGgAAAAAMAkAhQAAAAAmESAAgAAAACTCFAAAAAAYBIBCgAAAABM8voAlZ2dra5duyowMFCxsbHatWuXu0sCAAAA0Ex5dYBas2aNMjIyNGfOHH344YcaMGCAEhMTdfToUXeXBgAAAKAZ8uoAtWjRIk2cOFETJkxQ7969lZOTo+DgYK1YscLdpQEAAABohlq4u4CGKisrU0FBgWbMmOGY5uvrq/j4eOXn51f7mtLSUpWWljqenzx5UpJ0/Phx2e12SZLdbteZM2d07Ngx+fv7S5JanCtxae3Hjh1z6fJcWd/FtVXXH/XRmLU1tdr64nL7jNT1uTh16pQkyTAMl60XAADAE3htgPr+++9VXl6usLAwp+lhYWE6cOBAta/JysrS3Llzq0yPiopqlBpr0n5hk66uXqjNM3jyttantlOnTqlNmzaNVwwAAEAT89oA1RAzZsxQRkaG43lFRYWOHz+uK664Qj4+PpKk4uJiRUZG6quvvlJISIi7SvUY9Md59MV5dfWFYRg6deqUIiIi3FAdAABA4/HaANW+fXv5+fmpqKjIaXpRUZHCw8OrfY3FYpHFYnGaFhoaWm3bkJCQy/4g+UL0x3n0xXm19QVnngAAQHPktYNIBAQEKDo6Wnl5eY5pFRUVysvLU1xcnBsrAwAAANBcee0ZKEnKyMhQcnKyYmJiNHjwYC1ZskQlJSWaMGGCu0sDAAAA0Ax5dYAaM2aMvvvuO82ePVs2m00DBw5Ubm5ulYEl6sNisWjOnDlVLvW7XNEf59EX59EXAADgcuVjMM4wAAAAAJjitfdAAQAAAEBTI0ABAAAAgEkEKAAAAAAwiQAFAAAAACYRoGrx5ZdfKiUlRVFRUQoKClK3bt00Z84clZWVubs0t3jyySd14403Kjg4uMYvIG6usrOz1bVrVwUGBio2Nla7du1yd0lusXXrVt15552KiIiQj4+P1q1b5+6SAAAAmhQBqhYHDhxQRUWF/vjHP2r//v1avHixcnJyNHPmTHeX5hZlZWUaPXq0Jk+e7O5SmtSaNWuUkZGhOXPm6MMPP9SAAQOUmJioo0ePuru0JldSUqIBAwYoOzvb3aUAAAC4BcOY19MzzzyjF154Qf/5z3/cXYrbrFy5Uunp6Tpx4oS7S2kSsbGxuv7667V8+XJJUkVFhSIjI/XQQw9p+vTpbq7OfXx8fPTGG28oKSnJ3aUAAAA0Gc5A1dPJkyfVrl07d5eBJlJWVqaCggLFx8c7pvn6+io+Pl75+flurAwAAADuQICqh88//1zLli3Tb3/7W3eXgiby/fffq7y8XGFhYU7Tw8LCZLPZ3FQVAAAA3OWyDFDTp0+Xj49PrY8DBw44veabb77RsGHDNHr0aE2cONFNlbteQ/oCAAAAuFy1cHcB7vDwww9r/Pjxtba5+uqrHT9/++23uu2223TjjTfqxRdfbOTqmlZ9++Jy0759e/n5+amoqMhpelFRkcLDw91UFQAAANzlsgxQV155pa688kpTbb/55hvddtttio6O1ksvvSRf3+Z10q4+fXE5CggIUHR0tPLy8hyDJVRUVCgvL09paWnuLQ4AAABN7rIMUGZ98803GjJkiLp06aJnn31W3333nWPe5Xj24fDhwzp+/LgOHz6s8vJyFRYWSpKuueYatWrVyr3FNaKMjAwlJycrJiZGgwcP1pIlS1RSUqIJEya4u7Qmd/r0aX3++eeO54cOHVJhYaHatWunzp07u7EyAACApsEw5rVYuXJljQfJl2O3jR8/Xi+//HKV6e+9956GDBnS9AU1oeXLl+uZZ56RzWbTwIED9dxzzyk2NtbdZTW5zZs367bbbqsyPTk5WStXrmz6ggAAAJoYAQoAAAAATGpeN/QAAAAAQCMiQAEAAACASQQoAAAAADCJAAUAAAAAJhGgAAAAAMAkAhQAAAAAmESAAgAAAACTCFAAAAAAYBIBCgAAAABMIkABAAAAgEkEKAAAAAAwiQAFAAAAACb9P00ZJQByiQyyAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_norm = df_norm.sample(frac=0.1).reset_index(drop=True)\n",
        "df_norm.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "130WQAVe15_w",
        "outputId": "b8e427ab-801c-43d9-99fb-0b90db516deb"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        age   balance  duration  campaign     pdays  previous\n",
              "0  0.382703 -0.437238  0.523588 -0.246560  2.694582  0.182198\n",
              "1  1.512791  1.447000  0.985679 -0.569351 -0.411453 -0.251940\n",
              "2  0.476877 -0.056580  0.224588 -0.246560 -0.411453 -0.251940\n",
              "3 -0.841558 -0.404394 -0.567569 -0.569351  0.407502  0.182198\n",
              "4  0.100181  0.202227 -0.738426  0.399020 -0.411453 -0.251940"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ab2204b1-b65c-49ee-aeb4-d771c5866672\" 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>age</th>\n",
              "      <th>balance</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.382703</td>\n",
              "      <td>-0.437238</td>\n",
              "      <td>0.523588</td>\n",
              "      <td>-0.246560</td>\n",
              "      <td>2.694582</td>\n",
              "      <td>0.182198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.512791</td>\n",
              "      <td>1.447000</td>\n",
              "      <td>0.985679</td>\n",
              "      <td>-0.569351</td>\n",
              "      <td>-0.411453</td>\n",
              "      <td>-0.251940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.476877</td>\n",
              "      <td>-0.056580</td>\n",
              "      <td>0.224588</td>\n",
              "      <td>-0.246560</td>\n",
              "      <td>-0.411453</td>\n",
              "      <td>-0.251940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.841558</td>\n",
              "      <td>-0.404394</td>\n",
              "      <td>-0.567569</td>\n",
              "      <td>-0.569351</td>\n",
              "      <td>0.407502</td>\n",
              "      <td>0.182198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.100181</td>\n",
              "      <td>0.202227</td>\n",
              "      <td>-0.738426</td>\n",
              "      <td>0.399020</td>\n",
              "      <td>-0.411453</td>\n",
              "      <td>-0.251940</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-ab2204b1-b65c-49ee-aeb4-d771c5866672')\"\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-ab2204b1-b65c-49ee-aeb4-d771c5866672 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-ab2204b1-b65c-49ee-aeb4-d771c5866672');\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-0ae89cc3-d9bc-494c-a353-333c4ee12f59\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0ae89cc3-d9bc-494c-a353-333c4ee12f59')\"\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-0ae89cc3-d9bc-494c-a353-333c4ee12f59 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_norm",
              "summary": "{\n  \"name\": \"df_norm\",\n  \"rows\": 4521,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9925620650455773,\n        \"min\": -2.065820171113948,\n        \"max\": 4.808880199546019,\n        \"num_unique_values\": 68,\n        \"samples\": [\n          2.7370526905800014,\n          0.8535731369745312,\n          0.1001813155323431\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"balance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1398695240560959,\n        \"min\": -3.0811485466035458,\n        \"max\": 21.466166674310553,\n        \"num_unique_values\": 2356,\n        \"samples\": [\n          0.8571289060855225,\n          -0.2474017480211689,\n          0.15033438701397928\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9634581844815158,\n        \"min\": -0.9908285225489883,\n        \"max\": 12.06809961151346,\n        \"num_unique_values\": 845,\n        \"samples\": [\n          2.8767576866470264,\n          0.5197046455004593,\n          0.5585358323397768\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"campaign\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0334235783488275,\n        \"min\": -0.5693506376457914,\n        \"max\": 15.247373573923461,\n        \"num_unique_values\": 34,\n        \"samples\": [\n          6.209245453026744,\n          6.53203574305877,\n          11.373890093539154\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pdays\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0430352203816409,\n        \"min\": -0.4114531064930479,\n        \"max\": 8.127647701786206,\n        \"num_unique_values\": 302,\n        \"samples\": [\n          6.419827540130355,\n          4.17269574847792,\n          1.5959846273831275\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"previous\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8749531476100587,\n        \"min\": -0.25194037067217256,\n        \"max\": 16.24529985273235,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          4.089438635486913,\n          5.3918523373346385,\n          0.18219752994373598\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nb_clusters = 5\n",
        "\n",
        "# Apply KMeans clustering\n",
        "kmeans = KMeans(n_clusters=nb_clusters,random_state=42)\n",
        "clusters = kmeans.fit_predict(df_norm)\n",
        "\n",
        "# Add the cluster assignments to the original dataframe\n",
        "df['cluster'] = clusters\n",
        "\n",
        "# Calculate the silhouette score\n",
        "sil_score = silhouette_score(df_norm, clusters)\n",
        "\n",
        "df.head(), sil_score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "6eSvPFYPCGIw",
        "outputId": "417db951-c8e1-4cef-c935-ec64958eb012"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "Length of values (4521) does not match length of index (45211)",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-11-7d47d82adbe0>\u001b[0m in \u001b[0;36m<cell line: 8>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# Add the cluster assignments to the original dataframe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cluster'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclusters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;31m# Calculate the silhouette score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   3948\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3949\u001b[0m             \u001b[0;31m# set column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3950\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3951\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3952\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_setitem_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_set_item\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   4141\u001b[0m         \u001b[0mensure\u001b[0m \u001b[0mhomogeneity\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4142\u001b[0m         \"\"\"\n\u001b[0;32m-> 4143\u001b[0;31m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sanitize_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4144\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4145\u001b[0m         if (\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_sanitize_column\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m   4868\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4869\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4870\u001b[0;31m             \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequire_length_match\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4871\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0msanitize_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4872\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/common.py\u001b[0m in \u001b[0;36mrequire_length_match\u001b[0;34m(data, index)\u001b[0m\n\u001b[1;32m    574\u001b[0m     \"\"\"\n\u001b[1;32m    575\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 576\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m    577\u001b[0m             \u001b[0;34m\"Length of values \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    578\u001b[0m             \u001b[0;34mf\"({len(data)}) \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Length of values (4521) does not match length of index (45211)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.hist(figsize=(10,10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 992
        },
        "id": "HMHsOa-nQH0C",
        "outputId": "acc82264-db65-49f6-c9b4-30aa137b4538"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'age'}>,\n",
              "        <Axes: title={'center': 'balance'}>,\n",
              "        <Axes: title={'center': 'duration'}>],\n",
              "       [<Axes: title={'center': 'campaign'}>,\n",
              "        <Axes: title={'center': 'pdays'}>,\n",
              "        <Axes: title={'center': 'previous'}>],\n",
              "       [<Axes: title={'center': 'cluster'}>, <Axes: >, <Axes: >]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 9 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pXTqrVmw9rRa"
      },
      "source": [
        "Print the centroids obtained"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-rKjq_c_9rRa",
        "outputId": "16bd3016-b40e-420b-b061-72603477be42"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.50151181e-01,  5.68419470e+00, -9.37884829e-02,\n",
              "         6.96423855e-02, -1.84852975e-02, -8.29154890e-03],\n",
              "       [-6.41130488e-01, -1.57917557e-01,  2.52766057e-02,\n",
              "        -1.59308112e-01, -3.72403969e-01, -2.22458303e-01],\n",
              "       [-1.09380869e-01, -1.99284946e-02,  1.75647973e-02,\n",
              "        -2.01851190e-01,  2.23469754e+00,  1.33593387e+00],\n",
              "       [ 1.08179656e+00, -1.34022175e-03, -8.66624116e-02,\n",
              "        -1.24423481e-01, -3.29358192e-01, -1.89361935e-01],\n",
              "       [-1.11580060e-01, -1.32427333e-01, -3.92463532e-01,\n",
              "         3.81952728e+00, -4.11453106e-01, -2.51940371e-01]])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "kmeans.cluster_centers_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CLmD3lpY9rRa"
      },
      "source": [
        "Print the cluster labels assigned to each point"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ImiSDzUW9rRb",
        "outputId": "be04349c-0dda-4dc5-8067-d5ae66c9320a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 3, 3, ..., 1, 3, 3], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "kmeans.labels_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7WmMjisF9rRb"
      },
      "source": [
        "### **Visualization**\n",
        "\n",
        "Let's try to visualize the created clusters. As it is easier to visualize 2-dimensional data, first run PCA on the data. Then create a scatterplot to visualize the clustered points, each coloured differently depending on the cluster they belong to."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "7BRItE8z9rRb",
        "outputId": "28525384-cc02-47fd-b777-a6dbfb820cec"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        PC1       PC2\n",
              "0  2.153382 -0.826021\n",
              "1  0.095423  1.828671\n",
              "2 -0.407388  0.149706\n",
              "3  0.348134 -0.832022\n",
              "4 -0.586188  0.338798"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-713583f2-371f-461e-b159-a248b6244f9b\" 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>PC1</th>\n",
              "      <th>PC2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.153382</td>\n",
              "      <td>-0.826021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.095423</td>\n",
              "      <td>1.828671</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.407388</td>\n",
              "      <td>0.149706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.348134</td>\n",
              "      <td>-0.832022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.586188</td>\n",
              "      <td>0.338798</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-713583f2-371f-461e-b159-a248b6244f9b')\"\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-713583f2-371f-461e-b159-a248b6244f9b 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-713583f2-371f-461e-b159-a248b6244f9b');\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-f81d2e2d-ac75-4aed-9d30-974d0377cbc0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f81d2e2d-ac75-4aed-9d30-974d0377cbc0')\"\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-f81d2e2d-ac75-4aed-9d30-974d0377cbc0 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_pca",
              "summary": "{\n  \"name\": \"df_pca\",\n  \"rows\": 4521,\n  \"fields\": [\n    {\n      \"column\": \"PC1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2086979577215218,\n        \"min\": -4.1090971735035655,\n        \"max\": 9.480197547688434,\n        \"num_unique_values\": 4520,\n        \"samples\": [\n          3.034650757509688,\n          1.8166239852592787,\n          1.1147832243240516\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PC2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1569971251095552,\n        \"min\": -3.1965026071976195,\n        \"max\": 20.13472910892902,\n        \"num_unique_values\": 4520,\n        \"samples\": [\n          -0.7124262572882895,\n          -0.5592769584036212,\n          -0.8763875007721993\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "from sklearn.decomposition import PCA\n",
        "# Perform PCA to reduce to 2 dimensions\n",
        "pca = PCA(n_components=2)\n",
        "df_pca =pd.DataFrame (data = pca.fit_transform(df_norm) , columns = ['PC1', 'PC2'])\n",
        "df_pca.head()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_pca['cluster'] = kmeans.labels_"
      ],
      "metadata": {
        "id": "ER5Nnm0dfByj"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_pca)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uOsSJMSMfH5Z",
        "outputId": "f2e05a19-fabf-47f6-aec0-97ac96139442"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "           PC1       PC2  cluster\n",
            "0     2.153382 -0.826021        2\n",
            "1     0.095423  1.828671        3\n",
            "2    -0.407388  0.149706        3\n",
            "3     0.348134 -0.832022        1\n",
            "4    -0.586188  0.338798        1\n",
            "...        ...       ...      ...\n",
            "4516  0.794950  0.601187        1\n",
            "4517 -0.632594  0.061944        1\n",
            "4518 -0.437943  0.100733        1\n",
            "4519 -0.315660  0.507639        3\n",
            "4520 -0.810286  0.296006        3\n",
            "\n",
            "[4521 rows x 3 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the scatter plot\n",
        "plt.figure(figsize=(8, 7))\n",
        "#ax=fig.add_subplot(111)\n",
        "plt.scatter(df_pca['PC1'], df_pca['PC2'], c=df_pca['cluster'], cmap='viridis')\n",
        "plt.xlabel('PCA Component 1')\n",
        "plt.ylabel('PCA Component 2')\n",
        "plt.title('KMeans Clustering with 5 Clusters')\n",
        "plt.colorbar(label='Cluster')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "collapsed": true,
        "id": "ORL_mQdCe-2N",
        "outputId": "185afbba-6453-4226-84d0-0ee0b4eb3cda"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x700 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IR_Be8629rRb"
      },
      "source": [
        "### Evaluation\n",
        "\n",
        "**Inertia** can be a measure of how internally coherent clusters are. It represents the within-cluster sum-of-squares.\n",
        "\n",
        "Read more about it here:\n",
        "https://scikit-learn.org/stable/modules/clustering.html#k-means\n",
        "\n",
        "Print the inertia obtained after the clustering."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TomfC8Kc9rRb",
        "outputId": "f1e12bd7-2267-4534-cdb7-87585b867737"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14120.714219574555"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "inertia = kmeans.inertia_\n",
        "inertia"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p3y-BMzO9rRb"
      },
      "source": [
        "**The silhouette score**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Mw__WJW9rRb"
      },
      "source": [
        "Make the necessary imports from scikit learn and compute the silhoutte score.\n",
        "\n",
        "Read what this score represents and how it is computed here: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html and here https://scikit-learn.org/stable/modules/clustering.html#silhouette-coefficient"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n4VcpqmL9rRc"
      },
      "source": [
        "You can read more about other clustering evaluation methods here: https://scikit-learn.org/stable/modules/clustering.html#clustering-performance-evaluation"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the silhouette score\n",
        "sil_score = silhouette_score(df_norm, clusters)\n",
        "\n",
        "sil_score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lp3NF16KOn1-",
        "outputId": "67e694dd-bd95-4c37-8bbc-abfe22e536b7"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.277638221472209"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EAk-gI1r9rRc"
      },
      "source": [
        "### Finding the optimal number of clusters\n",
        "\n",
        "In the following article you will find several [methods](https://en.wikipedia.org/wiki/Determining_the_number_of_clusters_in_a_data_set) for determining the number of clusters.\n",
        "\n",
        "We will first use the silhouette score for obtaining the optimal number of clusters. Optional: another method you can try is the [elbow method](https://en.wikipedia.org/wiki/Elbow_method_(clustering)) ).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z5nVI_b49rRc"
      },
      "source": [
        "**Use the silhouette analysis to find an optimal number of clusters for the bank clients data.** <u><i>Hint:</u></i> Feel free to use the `n_jobs` hyperparameter to speed up your computation.\n",
        "\n",
        "An example of silhouette analysis is presented here: https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html\n",
        "\n",
        "Plot the silhouette scores for 20 different numbers of clusters of your choice."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Range for the number of clusters\n",
        "range_n_clusters = list(range(2, 21))\n",
        "\n",
        "# List to store silhouette scores\n",
        "silhouette_scores = []\n",
        "\n",
        "# Compute silhouette scores for different numbers of clusters\n",
        "for n_clusters in range_n_clusters:\n",
        "    kmeans = KMeans(n_clusters=n_clusters, random_state=42)\n",
        "    clusters = kmeans.fit_predict(df_norm)\n",
        "    silhouette_avg = silhouette_score(df_norm, clusters)\n",
        "    silhouette_scores.append(silhouette_avg)\n",
        "    print(f\"For n_clusters = {n_clusters}, the average silhouette_score is {silhouette_avg}\")\n",
        "\n",
        "# Plot the silhouette scores\n",
        "plt.figure(figsize=(10, 7))\n",
        "plt.plot(range_n_clusters, silhouette_scores, marker='o')\n",
        "plt.xlabel(\"Number of Clusters\")\n",
        "plt.ylabel(\"Silhouette Score\")\n",
        "plt.title(\"Silhouette Analysis for Optimal Number of Clusters\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "hxGgkfj5PJ5b",
        "outputId": "dd00f623-c97e-44be-d8eb-d777430b921d"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For n_clusters = 2, the average silhouette_score is 0.4035563141882848\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For n_clusters = 3, the average silhouette_score is 0.41691558091540165\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For n_clusters = 4, the average silhouette_score is 0.25834165677793525\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For n_clusters = 5, the average silhouette_score is 0.277638221472209\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For n_clusters = 6, the average silhouette_score is 0.3008465117518495\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For n_clusters = 7, the average silhouette_score is 0.31218028421091143\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For n_clusters = 8, the average silhouette_score is 0.3206708150468176\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For n_clusters = 9, the average silhouette_score is 0.32327653520912225\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For n_clusters = 10, the average silhouette_score is 0.31473634440491766\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For n_clusters = 11, the average silhouette_score is 0.3005729608147903\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For n_clusters = 12, the average silhouette_score is 0.24616408729663095\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For n_clusters = 13, the average silhouette_score is 0.24743575060404827\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For n_clusters = 14, the average silhouette_score is 0.2431595688776526\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For n_clusters = 15, the average silhouette_score is 0.2436776917552514\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For n_clusters = 16, the average silhouette_score is 0.2461786890229904\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For n_clusters = 17, the average silhouette_score is 0.24737346293866924\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For n_clusters = 18, the average silhouette_score is 0.23214879628995666\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For n_clusters = 19, the average silhouette_score is 0.23080693016089407\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For n_clusters = 20, the average silhouette_score is 0.22359368160419593\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "od-9482a9rRc"
      },
      "source": [
        "What is the optimal number of clusters that you obtained?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pEcrCQzT9rRd"
      },
      "source": [
        "**Answer here** Not sure.. I think around 3.5"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rW5wpwue9rRd"
      },
      "source": [
        "## Hierarchical Clustering\n",
        "\n",
        "Let's try to apply now a different method on the same data. You can find more explanations on hierarchical clustering in the links in section Resources D. Hierarhchical clustering:\n",
        " * https://www.youtube.com/watch?v=QXOkPvFM6NU&t=1s (from minute 11:34 onwards)\n",
        " * https://stackabuse.com/hierarchical-clustering-with-python-and-scikit-learn/ (example on how to do hierarchical clustering with scikit-learn)\n",
        " * https://scikit-learn.org/stable/modules/clustering.html#hierarchical-clustering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cjTiSCMq9rRd"
      },
      "source": [
        "Make the necessary imports from scikit learn to perform hierarchical clustering.\n",
        "\n",
        "Start by plotting the dendrogram and deciding the number of clusters. Then apply hierarchical clustering.\n",
        "\n",
        "You might find this useful: https://docs.scipy.org/doc/scipy/reference/cluster.hierarchy.html for creating the dendrogram."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "qn98-Ywm9rRd"
      },
      "outputs": [],
      "source": [
        "from scipy.cluster.hierarchy import dendrogram, linkage\n",
        "from sklearn.cluster import AgglomerativeClustering"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create and plot the dendrogram\n",
        "linked = linkage(df_norm, method='ward')\n",
        "plt.figure(figsize=(10, 7))\n",
        "dendrogram(linked, orientation='top', distance_sort='descending', show_leaf_counts=True)\n",
        "plt.xlabel(\"Samples\")\n",
        "plt.ylabel(\"Distance\")\n",
        "plt.title(\"Dendrogram\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "lHbaWdXrkqwV",
        "outputId": "38b9e651-342f-4ff0-8cc2-1d2650641d46"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zhaLxyQx9rRi"
      },
      "source": [
        "How many clusters did you find?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3TRZYOpJ9rRj"
      },
      "source": [
        "*Answer here*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WMyAsayQ9rRj"
      },
      "source": [
        "Compare the kmeans labels with the labels obtained by the hierarchical clustering."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rf4ngr6S9rRj"
      },
      "outputs": [],
      "source": [
        "# code here"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QCOPfhjw9rRk"
      },
      "source": [
        "### Application: clustering images"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0fn5fTwx9rRk"
      },
      "source": [
        "Theses techniques can be use to reduce the number of colours in a picture\n",
        "\n",
        "- find a picture you want to reduce the number of colours.\n",
        "- use `skimages` and `sklearn` to reduce the number of colours in this picture\n",
        "- from your picture create two pictures with 16 colours what do you notice?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zkbIRyD39rRk"
      },
      "outputs": [],
      "source": [
        "# install skimage\n",
        "\n",
        "#Code here"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1CbMsh_c9rRk"
      },
      "source": [
        "Now let's try to apply KMeans clustering on the simple digits data that can be loaded from sklearn (https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html)\n",
        "\n",
        "The goal is to use KMeans to try to identify similar digits without using the original label information.\n",
        "\n",
        "Start by loading the digits and then finding the KMeans clusters. The digits data consists of 1,797 samples with 64 features, where each of the 64 features is the brightness of one pixel in an 8×8 image."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5Pws9BbP9rRl"
      },
      "outputs": [],
      "source": [
        "# code here"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "30maEkY49rRl"
      },
      "source": [
        "The cluster centroids themselves are 64-dimensional points, and can thus be interpreted as the \"typical\" digit within the cluster. Plot each cluster center. Hint: use *imshow*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RIM_UxHZ9rRl"
      },
      "outputs": [],
      "source": [
        "# code here"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "19XshRV09rRl"
      },
      "source": [
        "For each of the data points we also have access to their real labels (using *digits.target*). This means we can verify if the clustering algorithm split the data in such a way as to predict the original classes.\n",
        "\n",
        "Note that the labels of the discovered clusters may not directly match the initial labels in the dataset (they could be permuted). Write code to match each learned cluster label with the true labels of the data found in them."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N960m1YZ9rRl"
      },
      "outputs": [],
      "source": [
        "# code here"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bY4UcagS9rRl"
      },
      "source": [
        "Now compute the accuracy score between the true labels from the data and the predicted ones."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fJPmQRIN9rRm"
      },
      "outputs": [],
      "source": [
        "# code here"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7nJ8korT9rRm"
      },
      "source": [
        "Plot the confusion matrix between the predicted labels and the actual labels. Which were the digits that were most often confused with other digits?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RA9mUK479rRm"
      },
      "outputs": [],
      "source": [
        "# code here"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JPD-76kI9rRm"
      },
      "source": [
        "What if instead of keeping the original 64 features, we perform a projection into 2 dimensions?\n",
        "Use the t-distributed stochastic neighbor embedding (t-SNE) algorithm to pre-process the data before performing KMeans. t-SNE is a nonlinear embedding algorithm that is particularly adept at preserving points within clusters.\n",
        "\n",
        "Compute the new accuracy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vz6uVGDC9rRm"
      },
      "outputs": [],
      "source": [
        "# code here"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "orQ2q5ka9rRm"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.12"
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}