Customer_Segmentation_using_K_Means.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Customer Segmentation using K-Means.ipynb",
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyNoc7AQghSyrZXoE96G34Vq",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Michaelzats/Customer-Segmentation-using-K-Means/blob/main/Customer_Segmentation_using_K_Means.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Customer Segmentation using K-Means"
      ],
      "metadata": {
        "id": "x1W0_im7lufF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In marketing, the only right people must be targeted so as to cut the costs at SEO, PPC etc. So it is important to be able to cluster different groups of people. Therefore the following study is produced to show how it can be accomplished by implementing  K-Means Clustering"
      ],
      "metadata": {
        "id": "AmpeglT_lUCm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#1. Reading the Dataset\n"
      ],
      "metadata": {
        "id": "ZBSQr_w5fGgn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We're going to begin by importing all the packages we'll use in this project.\n",
        "\n"
      ],
      "metadata": {
        "id": "4GSZmY56eOBM"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "way_RmTYdgdI"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import scipy\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set()\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from scipy.cluster.hierarchy import dendrogram, linkage\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.decomposition import PCA\n",
        "import pickle\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df= pd.read_csv('segmentation data.csv', index_col = 0)\n"
      ],
      "metadata": {
        "id": "GDOjsnoFeSNb"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "xS1CVbYieYj6",
        "outputId": "e1e6b163-9bdd-4495-c1aa-c07f2876754e"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Sex  Marital status  Age  Education  Income  Occupation  \\\n",
              "ID                                                                   \n",
              "100000001    0               0   67          2  124670           1   \n",
              "100000002    1               1   22          1  150773           1   \n",
              "100000003    0               0   49          1   89210           0   \n",
              "100000004    0               0   45          1  171565           1   \n",
              "100000005    0               0   53          1  149031           1   \n",
              "\n",
              "           Settlement size  \n",
              "ID                          \n",
              "100000001                2  \n",
              "100000002                2  \n",
              "100000003                0  \n",
              "100000004                1  \n",
              "100000005                1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ddf39309-c6cb-4298-a4ab-0c63a31b81a5\">\n",
              "    <div 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>Sex</th>\n",
              "      <th>Marital status</th>\n",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Income</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Settlement size</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>100000001</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>67</td>\n",
              "      <td>2</td>\n",
              "      <td>124670</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100000002</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>22</td>\n",
              "      <td>1</td>\n",
              "      <td>150773</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100000003</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>49</td>\n",
              "      <td>1</td>\n",
              "      <td>89210</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100000004</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "      <td>171565</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100000005</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>53</td>\n",
              "      <td>1</td>\n",
              "      <td>149031</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ddf39309-c6cb-4298-a4ab-0c63a31b81a5')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-ddf39309-c6cb-4298-a4ab-0c63a31b81a5 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-ddf39309-c6cb-4298-a4ab-0c63a31b81a5');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "8Hwdme3NebPE",
        "outputId": "1f65b6ab-98b9-46bd-ef27-170510a37e2d"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Sex  Marital status          Age   Education         Income  \\\n",
              "count  2000.000000     2000.000000  2000.000000  2000.00000    2000.000000   \n",
              "mean      0.457000        0.496500    35.909000     1.03800  120954.419000   \n",
              "std       0.498272        0.500113    11.719402     0.59978   38108.824679   \n",
              "min       0.000000        0.000000    18.000000     0.00000   35832.000000   \n",
              "25%       0.000000        0.000000    27.000000     1.00000   97663.250000   \n",
              "50%       0.000000        0.000000    33.000000     1.00000  115548.500000   \n",
              "75%       1.000000        1.000000    42.000000     1.00000  138072.250000   \n",
              "max       1.000000        1.000000    76.000000     3.00000  309364.000000   \n",
              "\n",
              "        Occupation  Settlement size  \n",
              "count  2000.000000      2000.000000  \n",
              "mean      0.810500         0.739000  \n",
              "std       0.638587         0.812533  \n",
              "min       0.000000         0.000000  \n",
              "25%       0.000000         0.000000  \n",
              "50%       1.000000         1.000000  \n",
              "75%       1.000000         1.000000  \n",
              "max       2.000000         2.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-833a4a2c-467b-43e3-8b25-f68c498e7562\">\n",
              "    <div 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>Sex</th>\n",
              "      <th>Marital status</th>\n",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Income</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Settlement size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.00000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.457000</td>\n",
              "      <td>0.496500</td>\n",
              "      <td>35.909000</td>\n",
              "      <td>1.03800</td>\n",
              "      <td>120954.419000</td>\n",
              "      <td>0.810500</td>\n",
              "      <td>0.739000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.498272</td>\n",
              "      <td>0.500113</td>\n",
              "      <td>11.719402</td>\n",
              "      <td>0.59978</td>\n",
              "      <td>38108.824679</td>\n",
              "      <td>0.638587</td>\n",
              "      <td>0.812533</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>35832.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>97663.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>115548.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>138072.250000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>76.000000</td>\n",
              "      <td>3.00000</td>\n",
              "      <td>309364.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-833a4a2c-467b-43e3-8b25-f68c498e7562')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-833a4a2c-467b-43e3-8b25-f68c498e7562 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-833a4a2c-467b-43e3-8b25-f68c498e7562');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hm7TqxQXebg9",
        "outputId": "0169797f-e44d-489b-e06c-1d560e3ebb87"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 2000 entries, 100000001 to 100002000\n",
            "Data columns (total 7 columns):\n",
            " #   Column           Non-Null Count  Dtype\n",
            "---  ------           --------------  -----\n",
            " 0   Sex              2000 non-null   int64\n",
            " 1   Marital status   2000 non-null   int64\n",
            " 2   Age              2000 non-null   int64\n",
            " 3   Education        2000 non-null   int64\n",
            " 4   Income           2000 non-null   int64\n",
            " 5   Occupation       2000 non-null   int64\n",
            " 6   Settlement size  2000 non-null   int64\n",
            "dtypes: int64(7)\n",
            "memory usage: 125.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#2.Exploratory Data Analysis (EDA) and Visualization. Quantitative and qualitative analysis"
      ],
      "metadata": {
        "id": "oidgVy_UfBA_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute Pearson correlation coefficient for the features in our data set.\n",
        "# The correlation method in pandas, it has the Pearson correlation set as default.\n",
        "df.corr()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "VgZV47aefR8W",
        "outputId": "887f881e-72a1-4e32-af6f-562e4743cbb7"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      Sex  Marital status       Age  Education    Income  \\\n",
              "Sex              1.000000        0.566511 -0.182885   0.244838 -0.195146   \n",
              "Marital status   0.566511        1.000000 -0.213178   0.374017 -0.073528   \n",
              "Age             -0.182885       -0.213178  1.000000   0.654605  0.340610   \n",
              "Education        0.244838        0.374017  0.654605   1.000000  0.233459   \n",
              "Income          -0.195146       -0.073528  0.340610   0.233459  1.000000   \n",
              "Occupation      -0.202491       -0.029490  0.108388   0.064524  0.680357   \n",
              "Settlement size -0.300803       -0.097041  0.119751   0.034732  0.490881   \n",
              "\n",
              "                 Occupation  Settlement size  \n",
              "Sex               -0.202491        -0.300803  \n",
              "Marital status    -0.029490        -0.097041  \n",
              "Age                0.108388         0.119751  \n",
              "Education          0.064524         0.034732  \n",
              "Income             0.680357         0.490881  \n",
              "Occupation         1.000000         0.571795  \n",
              "Settlement size    0.571795         1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dbddd6e5-f3e8-45aa-8a96-8620b0b1c771\">\n",
              "    <div 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>Sex</th>\n",
              "      <th>Marital status</th>\n",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Income</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Settlement size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.566511</td>\n",
              "      <td>-0.182885</td>\n",
              "      <td>0.244838</td>\n",
              "      <td>-0.195146</td>\n",
              "      <td>-0.202491</td>\n",
              "      <td>-0.300803</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Marital status</th>\n",
              "      <td>0.566511</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.213178</td>\n",
              "      <td>0.374017</td>\n",
              "      <td>-0.073528</td>\n",
              "      <td>-0.029490</td>\n",
              "      <td>-0.097041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>-0.182885</td>\n",
              "      <td>-0.213178</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.654605</td>\n",
              "      <td>0.340610</td>\n",
              "      <td>0.108388</td>\n",
              "      <td>0.119751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Education</th>\n",
              "      <td>0.244838</td>\n",
              "      <td>0.374017</td>\n",
              "      <td>0.654605</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.233459</td>\n",
              "      <td>0.064524</td>\n",
              "      <td>0.034732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Income</th>\n",
              "      <td>-0.195146</td>\n",
              "      <td>-0.073528</td>\n",
              "      <td>0.340610</td>\n",
              "      <td>0.233459</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.680357</td>\n",
              "      <td>0.490881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Occupation</th>\n",
              "      <td>-0.202491</td>\n",
              "      <td>-0.029490</td>\n",
              "      <td>0.108388</td>\n",
              "      <td>0.064524</td>\n",
              "      <td>0.680357</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.571795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Settlement size</th>\n",
              "      <td>-0.300803</td>\n",
              "      <td>-0.097041</td>\n",
              "      <td>0.119751</td>\n",
              "      <td>0.034732</td>\n",
              "      <td>0.490881</td>\n",
              "      <td>0.571795</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dbddd6e5-f3e8-45aa-8a96-8620b0b1c771')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-dbddd6e5-f3e8-45aa-8a96-8620b0b1c771 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-dbddd6e5-f3e8-45aa-8a96-8620b0b1c771');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,9))\n",
        "sns.heatmap(df.corr(),annot=True,cmap='RdBu')\n",
        "plt.title('Correlation Heatmap',fontsize=16)\n",
        "plt.yticks(rotation =0)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558
        },
        "id": "-rQ9jwL8fUM2",
        "outputId": "be564f31-deaa-4193-efe8-f511a22461d8"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x648 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3IAAAIdCAYAAAB4Ak/aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdd3xV9f3H8VcSCDvsDQKifN0UQVQqCCKC2DprnbVWbd27rbPOOupotRW34h6tWrU/xQGKigsHDkS/OAGREfYKK8nvj3uzICAIyeWE1/PxuA9yz/necz73JEA+9/0952QVFxcjSZIkSUqO7EwXIEmSJElaPzZykiRJkpQwNnKSJEmSlDA2cpIkSZKUMDZykiRJkpQwNnKSJEmSlDC1Ml2AJKlMCGF34GxgD6AFsBD4EHgIeCjGWJjB8kqFEL4DRscYj13P1x0IbBlj/Psqy/sDrwIDYoyjN0qR61bPZcClQO0Y48pV1m0FfAn8LsZ4XxXt/yxgcozxqarYviSp5jKRk6RNRPqX+jeBZsB5wN7AccBE4DbgF5mrbqM5EDinkuUfArun/9ycnAUcnOkiJEnJYyInSZuAEEI/4O/ALTHGM1ZZ/UwI4e9Ag42wnzoxxmWVLK8NrIwxFm/oPn6KGOMC4J1M7FuSpCSykZOkTcN5wBzgz5WtjDF+Xf55CKE3cDWwG5BFqgm6IMY4ttyY+0ileocCNwI9gDtDCP8AvgVOBToDRwNtgObA3BDCwek6dgKWAy8D58YYJ6+p+BBCS+CvwACgAzAbeAP4U4xxarl6fpv+uqRhnBRj7FzZ1MoQQhapxOokoEt6m08CF6YbP8pt6ypgJqlpqS1IJXunxBg/W1PNP1UIoTtwJdAXqJve1/kxxjfKjdmF1Pd0N1LHdXK69itjjAXpMd8BnYBOIYSj0i+9P8Z4bLkpn9sCN5OaajsbuDTGODyE8BvgIlLH+j3ghPI/IyGEw4E/ADsC9UhNEb0pxnj/Ku+lmNTP0RLg5HSt7wFnxBg/2tBjJUmqOk6tlKQMCyHkkGqAXooxLl2H8TsBrwFNgWOBY4A84LV0k1FeY+Ax4FFgX+CRcusuArqR+oX/IGBpCOEkUg3HBOBXwInADultN1pLWc2ApcAFwBDgT8DWwJshhLrpMVcCzwP5pKZR7p7e75pcRSqlfBn4JXBd+v0+F0JY9f+vo4H9gDOB3wFbkEoy1/UDy5wQQq3yDyBn1UEhhJ2Bt9Lv9/fAIaQarJEhhJ7lhm4BfESqCR1Cqhk7DhhebsxBwHTgRcqOx5Wr7PI/wHOkpqR+ANwbQriaVNN1fvq9Bip+XwG2BJ4Ajkq/9n/A3env76qOAYYCp5E6vq2BUSGEZpWMlSRtIkzkJCnzWpBKTSat4/hLgGXAwBjjPIAQwsvAd6RSnPLnXDUEjo4xPlOyIITQOf3lDOCgkumUIYSGwN+A4THG48qNHwtE4HjgpsoKijFGUk1UyWtySJ3vN5lUA/nfGOPXIYR8YHmMca3TKNNNxLmkEqrT0otfTL/+QVLnCz5b7iUrgF/EGFekXw+pJqg3qcbrx/xoA512ffo97RVjXJ7e14vAeOAvpJomYoxPlnsvWaSOxQLggRDCqTHG2THGcSGEZcCstRyP62OMD6S38z6phvZEoEtJKhlCaAvcHELoFGOclN7/1eX2nw2MBtqSagBvX2Uf9YB9YoyL0+PfJZXgnZ1+T5KkTZCNnCQlTz/g/0qaOEidYxZCeJbUL/rlrQD+bw3beXqVc+J2J5XsPbxKkjUF+CK930obOYAQwsmkEqiuVDyfL6z97VRqNyCX1NU6y3uMVKq1JxUbuZdLmri0T9N/bsG6NXK7AateEbQD8N+SJyGEeun9Xg0UrXKMRpJKv0rG5pFKPH8FdARqlxu7NakUb12MKPkixjg3hDATGFd+aimp7w3p/UxK739r4ApS37M2lM3AWe38SOD5kiYuvZ/vQgjvkPp5kCRtomzkJCnzZgMFpM6XWhfNgGmVLJ9OarpleflruWXBqttolf5z5BrGz11TQSGE04F/kpoK+af02GxS5+7VXdPr1qJkWl+FGmOMK0MIs8utLzFnleclDcu67vuDSm4/MG+VMc1ITbf8C2tIqkII2THGIlLN5t6k0tOPgMWk0sFh61ETrH7Ml69hGSXbTSerL5M67+184Ov0mJNJTe9c1Yw1LNt+PeqUJFUzGzlJyrB0czIaGLSmq0quYg6plGVVbVj9l/y1XYVy1XUlKdGxQGUXCVm4lm0dDoyKMZ5bsiCE0GUt439MSWPWpnwt6RSsOas3btVhHlBEqhl7oLIBMcai9DmBBwCXxRhvLlkXQtixWqpMJWmdgL4xxjHl9r+m//Nbr2HZ1CqoTZK0kdjISdKm4VpS5zFdR7lzzUqkm6JGMcZPSF3oZGgIoVGMcWF6fSNS0ypHb0ANb5Fq1rZa9eqG66A+qXPAyvtdJeOWkTon68e8QypFOhwYVW75YaT+7xq9nvVtsBjj4hDCG0B34MN08laZOqSSuxWrLD+2krHrejzWR/30n6X7DyE0JdVcVmZoCKFBuXPkOpOaanrtRq5LkrQR2chJ0iYgxvh6COEc4O8hhO2A+0hdVKMpMBA4ATgS+ITUlQ1/QerKgn8jlaydR+oX+Cs2oIYFIYQ/AcPStxMYAcwH2pM6N2x0jHHVqyOWeAE4L4RwITAW2IvU+WGrmgA0S59P9z6wNMb46aqDYoxzQgg3AheEEBaTutrltqRucTCG1JUcM+Ec4HVSF165h9TUzxbAzkBOjPH8GOP89Dlm54YQpgGzSE1pbF/J9iYAfUMIvyA1NXZWjPG7DazxLVJN9bAQwqWkzle8OF1H40rGFwAvhRCuJ9WEXp5+/T82sA5JUhXy9gOStImIMd5E6n5h84AbgFdINXTbkrpS4f/S4z4B+pP6Zft+UldxXATsGWP8eANruAPYn9QFSh4k1UBdRuqDv7XdV+wK4A5SVzr8L6l70A2uZNzdpC5YcjWphu9/a9nmRaQap31JXbDlfFJTGvdbSxpWpWKMHwK7kJqG+k/gJVK3FtiRVINX4ghStwsYRup7OJ1KklZSt2uIwL9J3b/tso1QYz6pWxvkkLoFwTWkjvuqF44p8QCpxvgWUj9P+aSuiJqJ6auSpHWUVVy8ttMnJElSTVVyM/UY48WZrkWStH5M5CRJkiQpYTxHTpIkSZLWUwjhBuAQoDOwY4xxfCVjckhNxR9C6pz2a2OMd2+M/dvISZK0mYoxZmW6BklKsKdJnSf9xlrGHAVsBWxN6vY540IIIzfCha2cWilJkiRJ6yvGOCbGOOVHhh0G3BVjLEpfjOpp4NCNsX8TOUmSJEkCQghNgCaVrJoXY5z3Eza5BTCp3PPJQMefUtuqbOQ2Ybk9jvOSohnywH1XZrqEzVqtffbJdAmbtYPGrulWcapqj/Q8PNMlbNYK/V83o96ZU5DpEjZrtxd/l4ip1lX9+3GX1L00L61k1eVshFvEbEw2cpIkSZKUchOp+3+u6qekcZBK4DqRulcorJ7Q/WQ2cpIkSZISISs7p0q3n54++VObtsr8B/h9COEpUhc7ORDouzE27MVOJEmSJGk9hRD+GUL4HugAjAwhfJZe/nwIoVd62IPAN8CXwDvAFTHGbzfG/k3kJEmSJCVCVSdy6yPGeAZwRiXLh5b7uhA4uSr2byInSZIkSQljIidJkiQpETalRC7TTOQkSZIkKWFM5CRJkiQlgolcGRM5SZIkSUoYEzlJkiRJiZCVYyJXwkROkiRJkhLGRE6SJElSImR7jlwpEzlJkiRJShgTOUmSJEmJ4FUry5jISZIkSVLCmMhJkiRJSgQTuTImcpIkSZKUMCZykiRJkhIhK9scqoRHQpIkSZISxkROkiRJUiJ4jlwZEzlJkiRJShgTOUmSJEmJYCJXxkZOkiRJUiLYyJVxaqUkSZIkJYyJnCRJkqREyMoxkSthIidJkiRJCWMiJ0mSJCkRPEeujImcJEmSJCWMiZwkSZKkRDCRK2Mjtx5CCIcCFwJZQF3gwxjjkZmtatNz56W/Y2i/7uTPWUCPQy/JdDk1zpcfjWXE8FsoLipi54FD6XtgxR/BcaNf4KUH7yCvWQsAeg85kJ4D9+Pb8eN44f5bS8fN+mEyvzrzL2zbe49qrb8m6H7VBbQd2I+VBQW8f8ZFzPv089XGbH/BGXQ6dH9ymzTm6S13KV1er31bdvnX1dTOa0RWTjbj//oPpo96ozrLT7Q33vuIa24bTmFREb8aMpDfH35ghfX3PfF/PPHCKGrl5NC0cR5/Pfdk2rduWbp+0eIl/PL35zCwzy5cfNrx1V1+jbPL1RfSbu9+FBYs5a3TL2TOJxMqrM+pV5d+995Eo84dKS4s4vsXX2XclX/PULU1S+9rLqTD3v1YWbCUMadVfuz733sTeV06UpQ+9h9c4bHfWH5986XsMHQAy5cUcP+xf2TKuM9WG3P6iPtp3LYV2bVy+OqN93j01L9QXFSUgWpVU9nIraMQQlvgVmDnGOOUEEIW8LMMl7VJeuB/b3Lr46MYfuUJmS6lxikqKuS5e27mmIuvJ695S+684GRCrz606tC5wrgd+vRnv+PPrLCsyw49OPn6uwBYsmgB/zz9N3Tt3qu6Sq8x2gzsS6MunXhht31p1nMndr7uEl7Z94jVxk17aTRf3/MIQ94ZUWH5tmefyPfPvMA39z9Oo25d2ePh2xixyz7VVX6iFRYW8ddb7uHuay+mdYvmHHb6BQzYvRdbdepQOmbbrTrzn1uupV7dOjz2v5e48e6H+PtFZ5eu/+f9j9Nrx20zUX6N027vfjTashPP9B5Ci57d2fX6Sxgx+PDVxk0Ydi8zxowlu3Zt9n7qXtoN7MsPfnixQdrv3Y+8LTvx1C5DaNmrO7vfcAnP7bP6sf9s2L1MTx/7wf+9l/YD+zLVY7/Bdti3P6227sIlW/eny649OPK2q/jbbgeuNu6uX5/K0oWLAPjDE7fR89D9eP/x/1V3uTVOtolcKc+RW3dtgBXAbIAYY3GMcRxACGHXEMKrIYQP0o/90ssvDiE8lf66fgjh0xDC0Ey9geoy5sOJzJ2/ONNl1EhTv/qCZm3a06x1O2rVqs0Offbii/feWu/tTHjndbbq0ZvcOnWroMqard2QvZj0n2cBmPPBJ9TOa0TdVi1WGzfng09YOnPW6hsoLqZ2o4YA1M5ryNIZM6u03prk0/gVW7RrQ8e2rcmtXYt99+zDK2+9V2HMrj/bgXp16wCw07ZbMyN/Tum6zyZ+w+y58+nTs3u11l1Tddx3L7759zMAzPrgY2o3zqNeufQToLBgKTPGjAWgaMUK5nwygfrt2lR7rTXNFvvuxdePp459/vsfk7uGYz+93LGf7bHfaHY6YB/eeeApAL59dxz1mjQir03L1caVNHHZtWpRK7c2xcXF1Vqnaj4TuXX3MTAWmBxCGA2MAR4ECoHbgaExxmnp5O69EMIOwNXACyGE04EewIgY4/MZqV41woI5s2jcvFXp88bNW/D9l6tP65vw7htM+vxTmrftwJDfnkLjFq0qrB//5ivs/otDq7zemqhe21YsmTq99HnBtBnUa9u68qatEhOuH0bff99F1+OPpFb9erx+qMn1upoxaw5tWjYvfd6mZXM++eLLNY5/6oVX6LtLauJEUVER1935AH8773TeHvdplde6OajftjWLy/1dWPLDdOq1bUXBjPxKx9fOa0SHwQP44s4Hq6vEGmvVY7/4h+nUX8uxz81rRMfBA5hwh8d+Y2jSvjVzp/xQ+nze99Np0r4NC6avfvxPf+EBOvfuzmcjRvPhE/4KuDF4jlwZE7l1FGMsijEeCPQHXgX2Az4BhgJdgBEhhI+AEUAxsFWMsQg4GrgA2I7U+XVSlQo9d+fsYY9wyg13s+VOPfnvsGsrrF84dzYzJn/LVt13WcMWVJU6HrQf3z32NM/3GMiYo06m9y3XQlZWpsuqcZ4d+TrjJ37DcYfuD8Cj/3uJfr17VGgEVX2ycnLoe+cNfHHXQyya9H2my9msZOXk0O+uG/j8To99JvxryDGc17Y3terkss1efTJdjmoYE7n1FGMcD4wHhoUQJpC68MknMcZ+a3hJF6AIaALUAxZWS6GqkfKatWD+7LKpePNnz6JRs4rTOeo3alz6dc+BQ3n5oTsrrB//9mi27b0HObX867+uuv7uCLoc/SsA5nw0nvrt26TmWAP12ramYNqMdd5W5yMPZswRJ6a29f7HZNfNpU7zpiybNedHXqnWLZoxPX926fPp+bNp1bzZauPe+vAT7nz0v9x/w2Xk5tYG4KMJE/lg/Oc8+r+XWFKwlBUrV1K/Xl3OOf6oaqu/Juh23JFs/ZvU34XZH42nQfs2lGQQ9du1oWBa5VOFd/v75Sz8ZhJf3PFANVVa82xz/JF0Sx/7WeNSx75Eg3ZtWLKGY9/nH5ez4JtJTPDYb5A9T/kNe/w+dT70pPc+pmnHdqXrmnRow7xyCemqVi5bxsfPvEz3Awbx+cgxVV5rTWciV8ZEbh2FENqHEHYv97wD0BKYAGwdQhhQbt0uIYSsEEJT4GHgcOBx4K5qLls1TLuu2zBn2lTmzpzGypUrGP/WK2zTa/cKYxbOLftFN77/Fi07bFFh/fg3X2HHn+9VLfXWFF8Pf5SRAw9h5MBD+GHEKDqlU55mPXdixcJF6zytEqBg6jRa9d0NgEZbb0lOnTo2cetoh9CVSVOn8f20mSxfsZIRr73FgN0rXrBnwlffcvnNd3HLFX+medOyDzWuv+AMXnn4NkY+OIw//eE3HLB3P5u4n2DivY/w3ICDeW7AwUx5fhRb/voAAFr07M6KBQsrndr3swvOpHZeI9676JrqLrdG+eKeR3i2/8E82/9gJj8/iq6HpY59y17dWb6GY9/jwtSxH3uhx35DvXbrg1zVYyhX9RjKR0+/xG7HHAxAl117sHT+wtWmVdZpUL/0vLnsnBx23G8vpn/xdbXXrZrNj+TXXS3g8hBCJ6CAVBN8cYxxXAhhf+D6EMJNQC7wDfBL4F7g3hjjmBDC28CoEMJJMcbbM/QeqsWD15xIv56BFk0a8s0LN3DF7c9w39NeJWtjyMnJYehxp/PgVedRVFRIjwH70qpjF155fDjtunZjm14/550RTxHff4vsnBzqNczjwFPOK3393JnTmT9rJp2282IPP9X0ka/TZmA/hrw7gsKCpbx/5sWl6/Ye9SQjBx4CwI5/OZeOBw8lp15dho4bxXcPP8mEG27l48uup+eNl7P1icdAcTHvn3FRpt5K4tTKyeGi047j9xdeRVFREQcNHsDWnTvyr/sfZ/tuXdlr917ccNdDLClYytnpS9y3a9WCYVec9yNb1k8x9eXXaL93Pw5870VWFizlrTPKzh7Y79WneG7AwdRv25odzz2J+RO/Zr9XngQg3vMIXz30RKbKrhG+f/k12g/qx8Hvv0hhwVLGnF527Pcf/RTP9j+Y+u1a0/3ck5g38Wv2fzV17D+/+xG+9NhvsPHPv8oOQwdw5VevpW4/8Ls/la67aNzzXNVjKLkN6nPKs3dTq04uWdnZTHz1bV6//eEMVl1zmMiVyfIKOpuu3B7H+c3JkAfuuzLTJWzWau3j5fgz6aCxj2S6hM3WIz1Xv4S8qk+h/+tm1DtzCjJdwmbt9uLvEnHCdvsj7qzSv6lTH/1DIo4DmMhJkiRJSggTuTKeIydJkiRJCWMiJ0mSJCkRTOTKmMhJkiRJUsKYyEmSJElKhKwcE7kSJnKSJEmSlDAmcpIkSZISwXPkytjISZIkSUoEG7kyTq2UJEmSpIQxkZMkSZKUCCZyZUzkJEmSJClhTOQkSZIkJUJ2dlamS9hkmMhJkiRJUsKYyEmSJElKhCwTuVImcpIkSZKUMCZykiRJkhIhK8tEroSJnCRJkiQljImcJEmSpETwqpVlTOQkSZIkKWFM5CRJkiQlgletLGMiJ0mSJEkJYyInSZIkKRFM5MqYyEmSJElSwpjISZIkSUqEbO8jV8pETpIkSZISxkROkiRJUiJ4jlwZEzlJkiRJShgTOUmSJEmJYCJXxkZOkiRJUiJk28iVcmqlJEmSJCWMidwm7IH7rsx0CZu1Y479S6ZL2Gzdsrww0yVs1ha16JbpEjZb+0/6kK8O3z/TZWy2GrZtlOkSNmvhq9mZLkEJkGUMVcpDIVXCJk5SJtjESZLWlYmcJEmSpETI2oRuCB5C6AbcDzQHZgPHxBi/XGVMK2A40BGoDbwKnBFjXLmh+zeRkyRJkqT1dzswLMbYDRgG3FHJmAuBz2OMOwE7AT2BgzfGzm3kJEmSJCVCdnZWlT7WVTpp2xl4NL3oUWDnEELLVYYWA41CCNlAHSAXmLrhR8KplZIkSZIEQAihCdCkklXzYozzyj3vCEyNMRYCxBgLQwg/pJfnlxt3JfAkMA1oANwSY3xzY9RqIidJkiQpEbKys6r0AZwFfFvJ46yfWPKhwCdAW6A90C+E8KsNPhDYyEmSJElSiZuALpU8blpl3BSgfQghByD9Z7v08vJOBx6OMRbFGOcDzwADNkahTq2UJEmSlAhZ63Ee20+Rnj45bx3GzQwhfAQcATyU/nNcjDF/laHfAkOAsSGEXGBv4KmNUauJnCRJkiStv5OA00MIE0klbycBhBCeDyH0So85C+gbQvgU+AiYCNy1MXZuIidJkiQpEbI3ofvIxRi/AHatZPnQcl9/DQyqiv2byEmSJElSwpjISZIkSUqEqj5HLklM5CRJkiQpYUzkJEmSJCWCiVwZEzlJkiRJShgTOUmSJEmJkG0iV8pETpIkSZISxkROkiRJUiJkbUL3kcs0GzlJkiRJiZDlfMJSHgpJkiRJShgTOUmSJEmJ4MVOypjISZIkSVLCmMhJkiRJSgRvCF7GRE6SJEmSEsZETpIkSVIiePuBMiZykiRJkpQwJnKSJEmSEsGrVpYxkZMkSZKkhDGRkyRJkpQIXrWyjImcJEmSJCXMRk3kQgjfAXWADjHGwvSyY4HhwOkxxlvWc3vtgIdjjAPSzy8Dro4xLl/HWn4RYxy/jvvqD+TGGF9ah7E/A7rFGP+9Ltuuib78aCwjht9CcVEROw8cSt8Dj6ywftzoF3jpwTvIa9YCgN5DDqTnwP34dvw4Xrj/1tJxs36YzK/O/Avb9t6jWuuvye689HcM7ded/DkL6HHoJZkup0bqc+1FdBzUj5UFSxl9ygXM/mRChfU59eoy6L6byOu8BUWFhUx+8VXGXv53ANr06UWfqy+g2faBUcefy7fPvpiJt5BYb7/5JjfecB1FhUUccNBB/PZ3x1VY/+QT/+GJfz9OdnY29evX54KL/8KWW3blheef48EH7i8d99WXX/LgI4/SLWxT3W8hcTr+4RTyevamaNkyvrv5egq+/mq1MfW7bk3ns/5EVm4uCz4Yy5Q7U//Od/nzRdRt3xGAnAYNKFy8mM/PPKn0dbVbtmT7Yfcw7dEHmPHfJ6rnDSVMq8OOp8GOO1O8fBnT7ruFZZO/WW1MnS22pO3vTierdi6LP/2QmY/fA0DLQ46hQfdesHIly/NnMP2+f1FUsARyatHm6JOo27krxUXFzHz8Hgomflbdby0ROp92Bk133ZXCpcv4+rprWPzll6uNabB1N7Y67wKy6+Qy9913+e6WfwJQf8uubHn2ueTUq8fSGdP56qorKVyyhBYD96bdYYeXvr7+ll355MTfs6SSv1taXY6JXKmqmFr5AzAYeD79/Fjgw/XdSAihVozxB2BAucWXAjcAP9rI/QT9gYbAjzZywM+AXwCbZSNXVFTIc/fczDEXX09e85bcecHJhF59aNWhc4VxO/Tpz37Hn1lhWZcdenDy9XcBsGTRAv55+m/o2r1XdZW+WXjgf29y6+OjGH7lCZkupUbqOKgfeV078XjPwbTq1Z2+N17K04MOW23cx/8azrQx75Jduzb7PTOcjnv3ZcrIN1g0ZRqjT72AnU47rpKta20KCwu57m/XcMutt9OqdWt+e/RR9N1zT7bcsmvpmMFD9uWQXx0KwOuvjeamG2/kn8NuZcjQ/RgydD8g1cT96dyzbeLWQV7P3tRp157PTjyWBmFbOp18Bl/88YzVxm1xyhlMuuUfLI6fs9VlV5HXcxcWfPAe3153VemYDsedSOGSxRVe1/H4k1jwwXtV/j6SqsEOO1O7dVu+vfhU6nbpRuuj/sDka85fbVzro05k+gO3sfTbibQ/42Ia7NCDxePHsfjzj8n/70NQVESLg39Ds30PYdZTD9Kk794AfHf52eQ0akyHMy5m0tV/huLi6n6Lm7Qmu+5K3fYdGPebo2i47XZ0Oescxp968mrjtjz7HL6+8XoWfT6Bba65jia9d2Xe2Hfp+sc/M+n2W1nwyce0HDKUdocdzpTh9zJr1EhmjRoJQP0uWxKu/KtNnH6Sqmjk7iPVvD0fQtgSaAB8WrIyhDAQ+CtQN73/q2KMj6XXjQY+AnYD5oQQTgHejzG2CCEMS2/irRBCEanGayhwJpCbXvfHGOOotRUXQgjpGusDOemvXwROArJDCHsDj5FqGJ8DmgP1gLHAiUAj4AogL4TwEfA68PeSOtP76Fyu7lbAI0DrdAkjY4xn/+hR3IRN/eoLmrVpT7PW7QDYoc9efPHeW6s1cj9mwjuvs1WP3uTWqVsFVW6+xnw4kU5tm2e6jBqr89CBfPnYMwDMfP9jchvnUa91Swpm5JeOKSxYyrQx7wJQtGIFsz6eQIN2bQBYNGUqAMVF/sK0vj4bP54OHTrSvkMHAPYZPJjXR4+u0Mg1bNiw9OuCgoJK7zf00gsjGLTP4KovuAZostvuzH4l9Qvn4vg5OQ0aUqtpM1bOnVM6plbTZuTUr8/i+DkAs18ZSZPd+qzWoDXdox8TL/pz6fPGu/Vh2YzpFC1dWg3vJJka/qw3C94eDcDSbyeSU68BOY2bUjh/bumYnMZNya5Xj6XfTgRgwdujafizXVk8fhxLJnxcOm7pNxNp1HN3AHLbdmRJTP1qVrhwPoVLFlO3U1eWfmczUV6zPnuQ/3Jq1sSizydQq2FDajdrxoo5ZT//tZulfv4XfZ6amZH/8os0+/kezBv7LqPZ7MkAACAASURBVHU7dGDBJ6nvwfwP3qPd325gyvB7K+yj+V4DmfXKK9X0jmoGE7kyVXGO3GhgxxBCU+C3wAOrrP8Q2CPG2APYG7ghPbbElun1Q8u/KMZ4avrLPjHGn8UY55FqwHZLb+tw4H5+3CnAszHG7jHGHYB7YoyfArcDD6S3fS1QCBwZY+wF7ECq6TsuxjgbuIRUQ/azGOPqH01WdBTwdYxxxxjjjqSawERbMGcWjZu3Kn3euHkLFs7JX23chHff4NY/nsDjN17G/FkzV1s//s1X2PHne1VprdLGVr9taxZNnVb6fPEP02nQtvUax+fmNaLTkAFMfe3t6iivRsvPn0nrNm1Kn7dq1Zr8mav/2/Kfxx/joP1/wb9uvolz//zn1da//PJLDB6yb5XWWlPUbt6C5eX+/V4+exa5zVtUGJPbvAXLZ80qfb5iVj61VxnTcPsdWTFvHsumpT7IyK5blzaHHMa0Rx+swuqTr1aTZqycW+7Yzp1NrSbNKhkzu/T5ykrGADT++V4sHp+aILXs++9o2H0XyM6mdvNW1O3UlVrNWqz2ms1dbosWLC/3b8zy/HxyW7RcZUxLluXnrzImdSwLJn1H05+nTh1pvucA6rRqxapaDBjArFfWmkFIa1QViVwxqSmHh6cffYCe5da3BO4NIWwNrASaAQF4J73+kRjjynXcV1fg0RBCe2AF0CaE0CbGOH0tr3kduC6EUB94Nf2oTDbwxxDCvqSauKbAknWsq7x3gLNDCNcDr5FqPmu80HN3dvz5XtSqnct7L/+P/w67lmMv/Xvp+oVzZzNj8rds1X2XDFYpVa2snBz2uudGxt/xIAsnfZ/pcjYbhx52OIcedjgvjHiee+++i8uu+GvpuvGffkrdunXputVWGaxw89Os3wDmvF72323bI49h5jNPmsZVk2ZDD6G4qIgF774OwPw3R5HbtgOdLrqeFbPzKfj6CygqynCVNc9X1/2NLqefQYffHMPct96kaMWKCusbbrMtRUuXUfDdtxmqMJlM5MpU1e0H7gfeBV6PMc5OzWYsdRvwLHBwjLE4hDCR1DTLEovWYz+PAufGGJ8OIWSTarTWOk8vxvhkCOFtYB/gfOA44OhKhh4J7AH0jTEuDCFcCHRbw2ZXUjHdLK0hxvh2CKEHMAj4TXqfib6yR16zFsyfXfYJ1fzZs2jUrOInVPUbNS79uufAobz80J0V1o9/ezTb9t6DnFreAUObvu1OOJJtjkmdd5X/4ac0bN+WGel1Ddq1YfG0GZW+rt9NV7Dg60mMv33ViQn6KVq2bMWM6WWf082cOYOWlXzCXWKfwUP42zVXV1j20osvsM/gIVVWY03Qcuj+tBicmhSz+MtIbotWLCZ1IYzc5i1YPntWhfHLZ88qTSAAardoyYryY7KzabL7Hnx+9imlixp024amffrS/tjfk9OgIRQXUbR8BfnPPVOF7ywZmvQfQuO+gwBY+t1X1Gpa7tg2bc7KeXMqjF85bw61mpZNp6+1ypi83QfQcMdeTPnHpWUvKioi/9/DS59ucd7VLJ/xw8Z+K4nU+oADab3fLwBYFCO55f6NyW3ZkuWzKs5AWj4rnzotW64yJvXzv3TKZD7/8x8BqNuhA013273Ca5vvtZdpnDZIldx+IMb4DXARcGUlq5sA36WbuEHA+nwsuhBoXO55E6DkY4zjSF0xc61CCFsB02OM9wGXA73TqxZUsu1Z6SauManGjjWMnQ7UTm+b8mNDCF2ABenzAM8BeqabzsRq13Ub5kybytyZ01i5cgXj33qFbXpV/MdpYblpHvH9t2jZYYsK651WqSSZcPcjPNXvIJ7qdxDfPT+KrQ8/AIBWvbqzfMHCCufHleh10Znk5jXirQuuXm2dfprttt+eKVMmM3XqVFasWMFLL75I3z33rDBm8uRJpV+/+cYbdOxY9m9PUVERo15+yUbuR+Q//yyfn3kSn595EvPeeZPme6UujNEgbEvhksUVzo8DWDl3DoVLltAgbAtA8732Zt47ZVOJ8362M0unTqnQ3E08/xzGn/Abxp/wG2Y++xTT//OoTVzavNEvMOnKc5l05bks+mgsebv3B6Bul24UFiypcH4cQOH8uRQVFFC3S+qz5rzd+7Poo7EA1N++B80GH8jUYddQvLzsOnFZublk5aZ+Zaq/bXeKCwtZPs1ZAwAznnmaT/5wAp/84QTmjHmDloNS59M23HY7ChcvrnB+HMCKOamf/4bbbgdAy0GDmfPWGABqNWmSGpSVRYejj2H6s8+WvTArixb9BzDrVRu59ZWTnVWljySpsjgkxnjnGladD9waQrgceA/4ZD02eyPwSgihgNTFTs4Cng4hzAVeAGav5bUlfg0cFUJYTmoaaMllFf8LHJO+gMljpJLDA0IIXwAzgTdIXfQEYBSpaZcfA6/FGM8IIZwJvBxCyCd1kZQS/YFzQgiFpBrnk2KMiZ6/kJOTw9DjTufBq86jqKiQHgP2pVXHLrzy+HDade3GNr1+zjsjniK+/xbZOTnUa5jHgaecV/r6uTOnM3/WTDpt1z2D76LmevCaE+nXM9CiSUO+eeEGrrj9Ge57+o1Ml1VjTHnpNbYY1I/DP3wpdfuBUy8sXXfw6//lqX4H0aBda3b+48nMjV9z8GtPAfDZXQ8TH3yClj12YNCDt1CnSR6dhgyg5/mn8USfX2bq7SRKrVq1+NN553PGqSdTVFTEL/c/gK5dt+KO225l2+22o9+e/fnP448x9t13qVWrFnl5eVx6RdlpyeM+/IDWrduUXixFP27B+2Np3GtXdrjz/vTtB24oXbftzbeX3kpg8m3/ovNZfyQ7tw7zP3iPBR+MLR3XtN8A5ry2prMYtDaLP/2ABjvsTJerbi29/UCJTn+5kUlXngvAjEfupO2xp5OVm8vi8R+WngvX+ogTyKpVmw5np9K4pd9MZMbDd5DTqDEdz7yE4uJiVs6bzbR7/1n9by4B5r37Dk133Y0eDz1C0dJlfHXdtaXrdrrzbj75Q+rq0N/c9A+2Ou98suvUYd7Yd5n3bupiVy32GkibAw4CYM6Y18l/4fnS1+ft1J1lM2eybNo0pJ8qq9hLzW6yHvt4qt+cDDnm2L9kuoTN2i3fvZXpEjZrh30/LtMlbLa+Onz/TJewWWvYtlGmS9iszflqXT6PV1XZ/ZXXEhFHHXLvu1X6+/GTx+2aiOMAVZjISZIkSdLGVCth0x+rUqLP1ZIkSZKkzZGJnCRJkqRESNoFSaqSiZwkSZIkJYyJnCRJkqREMJErYyInSZIkSQljIidJkiQpEXKyzaFKeCQkSZIkKWFM5CRJkiQlgufIlTGRkyRJkqSEMZGTJEmSlAgmcmVM5CRJkiQpYUzkJEmSJCWCiVwZEzlJkiRJShgTOUmSJEmJkJNlIlfCRE6SJEmSEsZETpIkSVIieI5cGRM5SZIkSUoYEzlJkiRJiWAiV8ZETpIkSZISxkROkiRJUiLUMpErZSInSZIkSQljIidJkiQpETxHroyNnCRJkqREsJEr49RKSZIkSUoYEzlJkiRJiWAiV8ZETpIkSZISxkROkiRJUiKYyJUxkZMkSZKkhDGRkyRJkpQIJnJlbOQ2YbX22SfTJWy2HgHmLC/MdBmbrdM698l0CZu1r175JtMlbLauePzpTJewWSsacVumS9isbfGL3EyXICWKjZxUCZs4SZKkTY+JXBnPkZMkSZKkhDGRkyRJkpQIJnJlTOQkSZIkKWFM5CRJkiQlgolcGRM5SZIkSUoYEzlJkiRJiWAiV8ZGTpIkSZLWUwihG3A/0ByYDRwTY/yyknG/Bv4CZAHFwN4xxhkbun+nVkqSJElKhJzsrCp9rKfbgWExxm7AMOCOVQeEEHoBlwGDYow7AHsA8zfsKKTYyEmSJEnSegghtAJ2Bh5NL3oU2DmE0HKVoWcDN8QYpwPEGOfHGJdujBqcWilJkiQpEXKyqvYcuRBCE6BJJavmxRjnlXveEZgaYywEiDEWhhB+SC/PLzduO+DbEMLrQEPgKeCqGGPxhtZqIidJkiRJKWcB31byOOsnbi8H2AkYBOwJ7Av8ZsPLNJGTJEmSlBDZVZzIATcB91WyfN4qz6cA7UMIOek0Lgdol15e3mTgiRjjMmBZCOEZoDfwwIYWaiMnSZIkKRFyqriPS0+fXLVpq2zczBDCR8ARwEPpP8fFGPNXGfoIMDSE8CCp3msg8MTGqNWplZIkSZK0/k4CTg8hTAROTz8nhPB8+mqVAI8BM4EJwEfAZ8A9G2PnJnKSJEmSEiF7E7oheIzxC2DXSpYPLfd1EXBO+rFRmchJkiRJUsKYyEmSJElKhKq+/UCSmMhJkiRJUsKYyEmSJElKhGq4/UBimMhJkiRJUsKYyEmSJElKhKq+j1ySmMhJkiRJUsKYyEmSJElKhE3pPnKZZiInSZIkSQljIidJkiQpEbxqZRkTOUmSJElKGBM5SZIkSYngVSvLmMhJkiRJUsKYyEmSJElKBM+RK2Mjtx5CCE2BH4A7Y4xnZrqeTOt+1QW0HdiPlQUFvH/GRcz79PPVxmx/wRl0OnR/cps05uktdyldXq99W3b519XUzmtEVk424//6D6aPeqM6y0+0PtdeRMdB/VhZsJTRp1zA7E8mVFifU68ug+67ibzOW1BUWMjkF19l7OV/B6BNn170ufoCmm0fGHX8uXz77IuZeAs10p2X/o6h/bqTP2cBPQ69JNPl1EjdWjbkgB3akJUFYyfPY/RXs1Ybs1PbPAaFlhQD0+Yv5dFxUwG49hfbMX3BUgDmFazgvvemVGfpiffmm2/yt+tvoKiokIMOPIjjj/tdhfX//s8TPP7vf5OTnU29+vW55OKL6dp1y9L106ZN46BDfsXJJ53Ib485prrLT7w3J07h+v97m6KiYg7cJXDcnj+rsP6Db6dxw3Nv8+X0OVxz2F4M2rHs2J86fASfTJlJj06t+edvh1R36TXCm19M4rpnx1BUVMRBvbfjuL16Vlj/wTc/cP2zb/DltNlce9Q+DNppKwC+mJrP1U+9xqJly8nJyuaEgT0Z/LOtM/EWVAPZyK2fI4F3gCNCCH+KMS7PdEGZ0mZgXxp16cQLu+1Ls547sfN1l/DKvkesNm7aS6P5+p5HGPLOiArLtz37RL5/5gW+uf9xGnXryh4P38aIXfaprvITreOgfuR17cTjPQfTqld3+t54KU8POmy1cR//azjTxrxLdu3a7PfMcDru3ZcpI99g0ZRpjD71AnY67bgMVF+zPfC/N7n18VEMv/KETJdSI2UBB+3Ylrve+Y75BSs5ve+WTJi+kJmLlpWOadEglwFbt+DWN7+lYEURDXJzStetKCzipte/yUDlyVdYWMjV1/6NO267ldatW3PkUUfTf889KzRqQ/cdwq8P/RUAo0e/xg1/v5Hbhg0rXX/DjX9nj5//vNprrwkKi4q49tk3ue24obTOa8BRtz7Nntt0omvrpqVj2jZpyOWH7MkDYz5Z7fXH9N2JpStW8uTY1T9w1Y8rLCrimv++zu1/2J/WjRty1D//w57bd6Fr62alY9o0acgVvx7IA699VOG19XJrc+Xhe9OpZRNmzl/MkTf/m93DFuTVq1Pdb6PGyPE+cqVs5NbPccCfgQuAA4D/hBAaA/cC2wNT04+ZMcY/hhBygauAPYE6wCfAyTHGRZkofmNqN2QvJv3nWQDmfPAJtfMaUbdVC5bOrPjp+JwPVv8PBYDiYmo3aghA7byGLJ0xs0rrrUk6Dx3Il489A8DM9z8mt3Ee9Vq3pGBGfumYwoKlTBvzLgBFK1Yw6+MJNGjXBoBFU1LpRHFRcTVXXvON+XAindo2z3QZNVbHpvWYtXg5c5asAODjH+azfZtGzPyqrJHrvUVT3v5uDgUrigBYvLwwI7XWNOPHj6djxw506NABgCGDBzN69OgKjVzDhg1Lvy4oKCCLsl+2Xnn1Vdq3b0e9evWqr+gaZPz3+XRsnkeHZnkADN6pK6M/n1ShkWvXtBFQ+bSzXbdqz/vf/FA9xdZA4yfPpGOLxnRo3hiAwT/bmtGffVuhkWuf/t5krXL8O7VsUvp1q8YNaNawHnMXFdjIaaPwYifrKISwE9AceAUYTqqpA7gEmBtj3AY4FOhb7mV/BubHGHvHGLuTmpZ5QfVVXXXqtW3FkqnTS58XTJtBvbat1/n1E64fxha/+gVDx41ij4dvY9yFV1dFmTVS/batWTR1WunzxT9Mp8Fajn1uXiM6DRnA1Nfero7ypCrTuG5t5hesKH0+f+kK8upW/DyyRcNcWjSowyk/78Kpe3ShW8uy5qJWdjZn9N2SU/fowvZtGlVb3TXBzJn5tGndpvR5q9atmJG/+gdwjz3+OPv9cn/+cfPNnPfnPwOwZMkShg+/j5NOPLHa6q1pZs5fTOvGZT/LrRs3IH/B4gxWtHmZuWARbZqUP/4NmTl//Y//p5NnsKKwiI7phlA/TXZWVpU+ksRGbt0dDzwQYywGngJ2DSG0BwaQauyIMc4Bni73mv2Bo0MIH4UQPko/71q9ZW+aOh60H9899jTP9xjImKNOpvct10LC/vIkQVZODnvdcyPj73iQhZO+z3Q5UpXLycqiRYNcbn/rWx754Ht+1b0ddWul/qu7ZtRE/vnGNzz64ff8cvs2NKtfO8PV1jyHH3YYz/3vWc468wzuuvtuAG67/Q6OPvoo6tevn+HqpMzJX7CYix8byeW/3otspwZukJysqn0kiVMr10F6iuSRwLIQQskZ2rWBY3/kpVnAKTHGV6qwvGrT9XdH0OXo1PkPcz4aT/32bZidXlevbWsKps1Y5211PvJgxhyR+nR2zvsfk103lzrNm7Js1pyNXXaNsN0JR7LNMYcCkP/hpzRs35aSo92gXRsWr+HY97vpChZ8PYnxtz9QTZVKVWf+0hU0rlfWfDWuW5sFS1dWHFOwgsnzCigqhrkFK8hftIwWDXL5fv7S0rFzlqzgm9mLad+4Xuk0Ta1dq1YtmT6jbBbGzBkzad2y1RrHDxk8mKuuvgaAT8d/ysiRI7nppptZuHAhWdnZ5ObmcsThh1d53TVFq8YNmDG/7KyMGfMX0zKvQQYr2ry0ymvI9Hnlj/8iWjVe9+O/aOlyTr/3/zhtyK7s1KnNj79AWkcmcuvmACDGGDvEGDvHGDsD+5Bq5EYDxwCEEJqkx5Z4FjgnhFAvvb5RCGHbaqx7o/p6+KOMHHgIIwcewg8jRtHp0P0BaNZzJ1YsXLTa+XFrUzB1Gq367gZAo623JKdOHZu4tZhw9yM81e8gnup3EN89P4qtD0/9mLXq1Z3lCxZWOD+uRK+LziQ3rxFvXeC0VdUM388roEWDXJrWq01OVhbd2zVmwvSFFcaMn76QLZunfsGqn5tDy4Z1mLNkBfVqZ5eeIF8/N4fOTeszY+Gy1fahym2//fZMnjyF76dOZcWKFbzw4ovs2X/PCmMmTZpc+vXrb7zBFh07AnDfvfcy4vnnGPH8cxx11JGccPxxNnHrafv2LZk8awFT5yxgxcpCXvzka/pvu0Wmy9psbN+xFZNnzS87/h99yZ7bdV6n165YWcg59z/PL3puU3olS20Yp1aWMZFbN8cBD5dfEGN8O4SQDTwDnBVC+AKYBrwPzE8Puxa4DHgvhFAEFAOXA4m/bNT0ka/TZmA/hrw7gsKCpbx/5sWl6/Ye9SQjBx4CwI5/OZeOBw8lp15dho4bxXcPP8mEG27l48uup+eNl7P1icdAcTHvn3FRpt5K4kx56TW2GNSPwz98KXX7gVMvLF138Ov/5al+B9GgXWt2/uPJzI1fc/BrTwHw2V0PEx98gpY9dmDQg7dQp0kenYYMoOf5p/FEn19m6u3UKA9ecyL9egZaNGnINy/cwBW3P8N9T3tbjY2lqBieGT+NE3brRHZWFu9NmcuMRcvYJ7Tk+3lLmTBjIRPzF9GtZQPO7d+VomJ4bsJ0lqwopFPTehy8UzuKi1OzuF/9alaFq11q7WrVqsUF553HyaecSlFREQcesD9bde3KsFtvY/vttqN//z157PHHeefdd6ldqxaN8vK48sorMl12jVErJ5vz9u/DKcNHUFRczAE9A11bN+PWl99nuw4t6b9tJz77Pp9zHnqZBQXLeP3zydw+6gOePCs1k+O4O57l2/z5FCxfweBrH+HSg/vSp1vHDL+r5KiVk835B/bl5LuepaiomAN6b8tWbZpz64vvsl2HVvTfvgvjp8zgnPtHsGDJMl7//Ftue2ksT/3xSF76+Cs+/GYa8xYv5dn3Ur/+XXHYQLZp3zLD70o1QVZxsVeu2xAhhNpAToxxaQghDxgDnBNjHLmh236i9fZ+czJkjle6y6jTOvfJdAmbtbOuODfTJWy2rhjYOdMlbNaKRtyW6RI2a1m1czNdwmat3v5nJCKOGvllfpX+frz31i0TcRzARG5jaAqMCCHkAHWBRzZGEydJkiRJa2Ijt4FijDOBnpmuQ5IkSarpvOhnGS92IkmSJEkJYyInSZIkKRFyEnZlyapkIidJkiRJCWMiJ0mSJCkRknavt6pkIidJkiRJCWMiJ0mSJCkRcoyhSnkoJEmSJClhTOQkSZIkJYLnyJUxkZMkSZKkhDGRkyRJkpQI3keujImcJEmSJCWMiZwkSZKkRPAcuTImcpIkSZKUMCZykiRJkhLB+8iVsZGTJEmSlAhOrSxjTytJkiRJCWMiJ0mSJCkRDOTKmMhJkiRJUsKYyEmSJElKhGyM5EqYyEmSJElSwpjISZIkSUoEz5ErYyInSZIkSQljIidJkiQpEbJN5EqZyEmSJElSwpjISZIkSUoEz5ErYyInSZIkSQljIidJkiQpEbyPXBkTOUmSJElKGBO5TdhBYx/JdAmbrUUtumW6hM3aV698k+kSNms3XXJjpkvYbF2zw2mZLmGztmDmrEyXsFlbvnBxpkvYrNXbP9MVrBvPkStjIidJkiRJCWMiJ0mSJCkRvI9cGRM5SZIkSUoYEzlJkiRJiWAgV8ZETpIkSZISxkROkiRJUiJke9nKUiZykiRJkpQwJnKSJEmSEsFAroyNnCRJkqREcDphGY+FJEmSJCWMiZwkSZKkRMhybmUpEzlJkiRJShgTOUmSJEmJkL0JBXIhhG7A/UBzYDZwTIzxyzWMDcA44NYY4x83xv5N5CRJkiRp/d0ODIsxdgOGAXdUNiiEkJNe9/TG3LmNnCRJkqREyMqq2se6CiG0AnYGHk0vehTYOYTQspLh5wP/B0zcwLdfgVMrJUmSJAkIITQBmlSyal6McV655x2BqTHGQoAYY2EI4Yf08vxy2+sODAYGAH/ZmLWayEmSJElKhOwqfgBnAd9W8jhrfWsNIdQG7gROKmn4NiYTOUmSJElKuQm4r5Ll81Z5PgVoH0LISadxOUC79PISbYGuwPOpa53QBMgKIeTFGP+woYXayEmSJElKhKq+j1x6+uSqTVtl42aGED4CjgAeSv85LsaYX27MZKBFyfMQwmVAQ69aKUmSJEmZcxJweghhInB6+jkhhOdDCL2qeucmcpIkSZISYVO6j1yM8Qtg10qWD13D+Ms25v5N5CRJkiQpYUzkJEmSJCXCJhTIZZyJnCRJkiQljImcJEmSpETYlM6RyzQTOUmSJElKGBM5SZIkSYlQ1feRSxITOUmSJElKGBM5SZIkSYngOXJlbOQkSZKk/2fvvsOjqvI/jr8nPaR3Qu8HBAUFVFAQbCgWdi1rW+vq2lbUXSv6U8GCBbtYUAGxYq8INhBEFAGlcyCC9HRSSM/M/P6YYZIQXHSVDDf5vJ4nj3PvPffO9w5xMmc+95wrjqB+XJ39viNnjPkFqPT/7PIXa+0vu7XzAnHW2p1/8vN3Ao631k6qt24GcI219uc/87mcZN4PPzH+mSm4PR7OOOEYLjv7Lw22T337Y96e+SVhoaEkJcRzz3+upG1GWmD7zrJyTrns3xwzeCC3/+sfTV2+oy2YP5+HJzyIx+1h1F//yoUXX9Jg+ztvv8Xbb04nJCSEVq1acevt/0eXLl2ZOeMTXp72UqBd1rp1vPza6/QwPZv6FBytR1oso/q0xuWChZuKmJOV36jNQZnxHGfS8ALbiyt5/cetANx/8gFkl/jeyooqapj6w+amLL3Zm3TnxYwc2pe8whIOPvOOYJfT7Mxb9BPjn5nmf98fzmVnjWqwfeo7n/D2rNmEhYSQlBjPPddfTtuMNLbm5DF63CN4vF5qa2s5b9QIzj7puCCdhbMs+CWHR79ehsfj5dQ+HblgoGmwvbrWzdhZi7G5RcRHRXDPyIG0SYgBYF1eMQ98+SNl1bWEuFxMPmcYkWGhXPnWPArKK4kMDQXg8dOOILlVZJOfmxNEdO5F3DGngyuEimULKP/+84YNQsNIOOl8wjLa460oo+jDKXhKCglr3ZH4EWf72rhclM2fQdW6ZQCkXn4Xnuoq8HjA66Fw2kNNfFbSnOz3HTm/M6y1K4L03J2AfwKBjpy1dmSQatkvuN0e7nnqRV64/3YyUlM465pbGT5oAN06tgu06dWtE289dT/RUZG88dFnPPzCKzxy2/WB7U+8NJ0BB/YKRvmO5na7efCB8Tz19LOkZ2Rw4d/PY8hRR9GlS9dAmxEnnMjpZ5wJwNyv5/DYww/zxMSnOWHkSZww8iTA14m78T/XqxP3O7mAvx6YyfPf/UJxRS3XDOnCquxScndWBdqkxkQwvHsqT8/fQEWNh5iI0MC2GreHx+auD0LlLcO0j+bz9PQvmXL3pcEupdlxuz3cM3EKL9w3xve+P/o2hh/ev/H7/kn3+t73P/6ch198jUfGXEtachKvPzqOiIhwyioqGXX5jRx9eH/SU5KDeEb7P7fHy4TZS3nitCNIj43m4tdnM6RLJp1T4gNtPly5kfiocN6++Hg+t1uY+M1K7j3pUGo9Hu6atYi7Rgyge1oCxRVVhIXUTYsw9oQB9MpInU4+gwAAIABJREFUCsZpOYfLRdyxZ1L05kTcpUUkX3AjVVnLcRdkB5pEHzgIT2U5Bc+PI7LnIcQNG0Xxh1Oozd/m66B5PYTExJNy0S3kZa0ArweAHW88gbeiLFhn5nghmuwkwCkduUaMMacB9+FL6t6pt74TsMham/oryycDdwHhgAe40Fq7zBjzKmCASCALuMRauwOYCHQ2xvwEZFlrz/CnhCdba1cYY7oBzwFpQC0wxlo70/9cXuA24K9ACnCjtTZQq1Mtt1l0aNOa9pkZAJx41GC++vaHBn/QD+vXJ/D4oF7d+ejLeYHllWvXU7CjmCMH9mPl2hYbav5PVq5YQbt27WnbzvdaHz9iBHPnzGnQkYuNjQ08rqio2OPsTp/N/JTjjh+x7wtuZtonRZNfVk1heQ0AS7cV07t1HLlZdR25QzskseCXQipqfH+wy6rdQam1JfpmyVo6ZqYEu4xmabnNokNm/ff9QXy1YFHD9/2+vQOPD+rZjY+++gaAiPC6jxo1NTV4vN4mqtrZVmUX0i4hhrb+hO24Hu2Y+/P2Bh25eT9v59LDfV/IDe/ehgmzl+L1elm4MZduqQl0T0sAICFaidvvFZ7ZEXdRPu7iAgAqVy8mstuBlNfryEV2P5Cy+TMAqLI/EX+s70tUamvqDhQWjhf9zsu+4ZSO3NvGmF2XVtYCJwHPA4OttdYYc9NvOYgxpgfwAjDEWrvOGBMJRPg3X2utzfe3uwe4GbgFuBqYYK0d8CuHfRWYZK190RhzADDXGNPLWpvn315irR1ojDkCeJN6nU6nyskvpHVa3Yel1mkpLFuz7lfbvzvzK4YM7AeAx+PhwUnTeODma1jw4/J9Xmtzk5eXS0br1oHl9PQMVq5o/Dq+Nf0NXnv1FWpqanj6uUmNtn/++WdMeOSxfVprc5QQFU5xRd0f6OLKGtonRjdokxrre0u56ojOuFzwuc1jbZ7viu+wkBBGD+mC2+tlTlY+K7NLm654kT8gp2BHw/f91BSW2axfbf/urDkMGdA3sLw9r4Ar/+8BNm3P4YZ/nKc07jfIK6skPa7u/SU9LpqV2Tt2a1NBRlwrwPf+EhsZTnFlNZt27MQFXPvufHZUVHGcacf5A3oE9rvnsyWEuFwM796Giw81ms59D0JiE/GU1r3entIiwtt0atAmNDYBd0mRb8HrwVNVgSs6Bm9FGWGZHUk48TxC4pMp+WRaII3DC0l/uxq8XiqWzqdi6bdNdEbNh35d6zilI9fg0kpjzKnAEmut9a+aBDzwG45zHDDDWrsOwFpbBez6Kv0CY8x5+Dp2McDavR3MGBMH9AOm+I+3yp/cHQ585G/2hv+/3wFtjDFR1trKRgdrpj78Yi4r1q5n2oS7AHj9o88YeujBDT4QyJ/vzLPO5syzzmbmpzOY/MLz3DXunsC2FcuXExUVRddu3YJYYfMV6nKRGhPBs99uICEqnCuP6Mwjc7KorPUw/su1lFTWktwqnH8O6sT2kspAuifSXHz45TxWrFvPtAfrxilmpqXw/rMPkltQyDVjH+H4IYeSmpQYxCqbN7fXy9JtBUw5ZxhRYaH8651v6JmeyMAO6Yw9cQDpsdGUVddw68cL+XT1ZkYe0CHYJTc7tds3UjD5PkKTM0g46Xyq1q8Cdy2Frz2KZ2cxrlaxJP3tX9QW5FCzRVcnyf+mOd5HrpaG5xW1tx2MMUOAK4ETrLUHArf/lv1+o0oAa+2u66uc0nn+VRmpyWTnFQSWs/MK9vjt6rdLljHp9feYOPYmIiLCAfhp1Vpe/WAmx55/NQ9NepkPvpjLIy++2mS1O11aWjo52XWXdeTm5pCWnv6r7Y8fcQJfz5nTYN1ns2Zy/IgT9lWJzVpxZQ0J0eGB5YSocEoqaxu2qahhVU4pHi/sqKghb2cVqTG+lG5X28LyGtYXlNE2oWGaJ7K/ykhJavi+n19AekrjMVbfLlnOpDfeZ+JdNwTe9+tLT0mmW6d2LF5hG22ThtJiosgtrQgs55ZWkBYTtVubaHJKywGo9XjYWVVDQlQE6bHRHNw2hcToSKLCwxjcuTU215ccpcf63ndiIsI53rRjVU7DlE98PDuLCImr+x0PiUvEXVrUoI17ZzGh8f4vJFwhhERGNxr75i7MwVtdRVhapv+4xQB4y3dStW4p4Zkd9+FZNE8ur3ef/jiJUzty3wEHG2O6+5frj2zPBsL9Y9cAzq237TNg5K79jDGR/lQtESgGCvyXW9afBrAESNhTEdbaUuAn4EL/8XoBff31NVt9TFc2bt3Olu25VNfU8unX3zJ8UMMrT1dlbWDs48/z1LibSEmqe/keunU0X736DF+8PJEb/3k+o44dyr//cV5Tn4JjHdC7N5s3b2Lr1q3U1NTw2axZDDnqqAZtNm3aGHg8f9482rev+6bV4/Hw5eefqSP3P9pSVEFqTARJ0eGEulz0bZPAqt0uj1yRXUqXFN+YllYRoaTFRlJYXkN0eAih/pvftIoIpVNSK3JKqxo9h8j+qI/pysZt2WzJ3vW+v4Dhh/dv0GZV1gbGPvkCT911AymJde/72XkFVFZVA1BcupMlKy2d22U2af1O1Kt1EpuLdrKtuIwat4fP125hSNeGr9uQrpnMWL0JgNnrtjGgfRoul4vDOqaTlV9CZU0ttR4PS7bk0zklnlqPh6IK3/tOrdvD/A3ZdKk35k7q1GzfRGhSGiEJKRASSlSv/lRlNRzKUJW1nKg+hwEQafpRvcl3MVdIQgq4fB+xQ+KTCE3JwF1cCOERuCL84xXDI4jo1JPa/O1Nd1LS7DglHao/Rg58Hbd/Ah8ZYyqoN+7MWltrjLkW+NwYkwd8Um/bOmPMZcB0Y0wo4MbXCZsJ/B3f5ZT5wFzgUP9uywBrjFkBrLHWnrFbbecBzxljrseXBp5fb3xcsxQWGspt/7qEy8bci8fj4a8jhtO9U3uefGk6vXt05ehBA5jw/CuUV1Ry/d2PANAmPZWJ424OcuXOFxYWxo0338Loq6/E4/Fwyqmj6Nq1G8898zS9DjiAoUcN463pb7Dw++8JCwsjPj6eO8eNC+z/45LFZGS0DkyWIr+PxwsfrNjOpYd3JMTl4ofNO8jZWcXxJo0tRZWsyillbd5OeqTF8J9hXfF44ZNV2ZTXuOmYFM1pB7XB6/Vd3z87K7/BbJfyx708/nKG9jekJsayfuYExj37AVPfn7f3HWWvwkJDue2qi7jstvG+9/3jh/ne96e9Re/unX3v+y+85nvfv/dxANqkpTBx7I2s37yVBye9gsvlwuv1cvHpJ9Ojsy7l25uwkBBuGN6Xa9+bj8cLJ/fuSJeUeCYtWEXP9CSGds3klN4dGTtrEWdM+Yz4qAjuHjkQgPioCM45pBsXvz4HlwsGdWrNEZ1bU1FTy7XvfUutx4PH42Vgh3RG9ekU3BPdX3k9lH7xFklnXgUuF5XLv8NdkE3MkSOpzd5EVdYKKpYtIOGkC0i57A68leUUfzgFgIi2XYg5/Ti8bjfgpfSzN/FWlBGakELCXy8DwBUSQuWqRVRvWB3Ek3SoXeMNBZfXYRFiS+LeuFT/OEGyM7XH3hvJPnPvV5qiP5geu+PhYJfQYlW8+69gl9Cilcx8M9gltGjVpZqSP5gybnrSEdOIVJaX7dPPx1GtYhzxOoBzEjkREREREWnhXErkApw6Rk5ERERERKTFUiInIiIiIiLOoEQuQImciIiIiIiIwyiRExERERERZ9BEjQFK5ERERERERBxGiZyIiIiIiDiDxsgFKJETERERERFxGCVyIiIiIiLiCLqPXB0lciIiIiIiIg6jRE5ERERERJxBiVyAOnIiIiIiIuIM6sgF6NJKERERERERh1EiJyIiIiIizqBELkCJnIiIiIiIiMMokRMREREREWfwKJHbRYmciIiIiIiIwyiRExERERERR9ANwesokRMREREREXEYJXIiIiIiIuIMSuQClMiJiIiIiIg4jBI5ERERERFxBq832BXsN5TIiYiIiIiIOIwSORERERERcQaNkQtQIiciIiIiIuIwSuRERERERMQRdB+5OkrkREREREREHEaJnIiIiIiIOIMSuQAlciIiIiIiIg6jRE5ERERERJxBiVyAy6ub6u23Xk7tpX+cIDlgULtgl9Ci9Z7+frBLaNHCc2ywS2ixok97KtgltGgX3To62CW0aJcP6hTsElq0/u0TXcGu4beo3bp6n34+DmvbyxGvAyiRExERERERp1AiF6COnIiIiIiIOIJuP1BHk52IiIiIiIg4jBI5ERERERFxBo8SuV2UyImIiIiIiDiMEjkREREREXEGzbgfoERORERERETEYZTIiYiIiIiIM2jWygAlciIiIiIiIg6jRE5ERERERBxB95Gro0RORERERETEYZTIiYiIiIiIMyiRC1AiJyIiIiIi4jBK5ERERERExBmUyAUokRMREREREXEYJXIiIiIiIuIMHnewK9hvKJETERERERFxGCVyIiIiIiLiCF7P/jNGzhjTA3gJSAEKgAustet2a/N/wNmAG6gBxlhrZ/0Zz69ETkRERERE5Pd7Fphore0BTASe20ObhcBAa+1BwCXAdGNM9J/x5ErkRERERETEGfaTMXLGmHTgEOA4/6rXgaeMMWnW2rxd7XZL35YBLnwJ3pY/WoM6ciIiIiIiIoAxJhFI3MOmImttUb3l9sBWa60bwFrrNsZs86/P28P+ABcAP1tr/3AnDtSRExERERERp9j3idx1wJ17WD8WuOt/Pagx5ijgbuoSvD9MHTkRERERERGfx4Cpe1hftNvyZqCtMSbUn8aFAm386xswxgwCXgFGWWvtn1WoOnIiIiIiIuIIXve+TeT8l0/u3mnbU7tcY8xPwDn4OmnnAD/WHx8HYIwZCEwHzrDWLvkza1VHTkREREREnGE/uv0AcAXwkjHmDmAHvjFwGGNmAHdYaxcBTwPRwHPGmF37nW+tXf5Hn1wdORERERERkd/JWrsGOGwP60fWezxwXz2/OnIiIiIiIuIM+8ntB/YHuiG4iIiIiIiIwyiRExERERERR/AqkQtQIiciIiIiIuIwzTaRM8b8ApxsrV0R5FJahIH3jaHNsUNxV1Ty7TVjKFy2qsH20Ogohk5+jLhO7fG6PWyZNZsf734kSNU6U/t/XkV8/0PxVFXxy+MPUfFzVqM2rbp2p9N1N+KKiKBk8UI2T3oagM433UZU2/YAhMbE4C4rY/W1VwT2C09Lo/fEF9n++jRy3nu7aU7IoebPn88DD03A43Hz17/8lX9ccnGD7W++9TbT33yT0JAQolu14o7bb6dr1y6B7du3b+evp5/BlVdczoUXXNDU5TvevEU/Mf6Zabg9Hs44YTiXnTWqwfap73zC27NmExYSQlJiPPdcfzltM9LYmpPH6HGP4PF6qa2t5bxRIzj7pD/tnqwCTLrzYkYO7UteYQkHn3lHsMtpdnq3juOsfm0Jcbn4ZkMBM9fkNmrTv10ip/RuDXjZXFTJi99vBOD0gzI5MDMel8vFqpxSpv+4tYmrd76lCxcw7elH8Hg8DD/xVE4958I9tls49yseG3cr90ycShfTi9qaGl54bDwb7BpcIS4uuOrfHNCvfxNX38zsX7NWBlWz7chJ02lz7FDiunTkg0NPILV/Xw576A4+HXF2o3arJk4m55uFhISHc+y7k2lzzBC2fTkvCBU7T3z/Q4ls05aVl19EjOlFxytHs+aG0Y3adbhqNBufepQyu5pud91LfP+BlCz+gQ0P3hto0+6Sy3GXlzXYr/0/rqBk8Q/7/Dyczu12c9/9D/DcM0+TkZHBuef9nWFHHdWgozbyxBP425lnADBnztdMeORhnpk4MbB9wsOPcOQRRzR57c2B2+3hnolTeOG+MWSkpnDW6NsYfnh/unVsF2jTq1sn3jrpXqKjInnj4895+MXXeGTMtaQlJ/H6o+OIiAinrKKSUZffyNGH9yc9JTmIZ9S8TPtoPk9P/5Ipd18a7FKaHZcLzj2kHY9+/TM7KmoYc2wPlm4rZntJVaBNemwEJ/ZK58Gv1lFe4yYu0vcRr0tKK7qmxjD2M989iG8a3p0eabGszdsZlHNxIo/bzZQnH+LWB54kJS2d26++iEMGD6Fdxy4N2lWUlzHzvel069k7sO6rGe8D8MALr1G8o5AHxlzHPROnEhKii+Lkj2v2HTljzBzgB2AQvrutv2mtvcW/rS3wBNDd3/x1a+14Y0wG8CzQFXABD1lrp/n3+QXfTf+OAdoCtwDpwLlAMnCJtXauv+1I4DYgCqgGrrfWfrdvz7jptT/xaNa/+QEA+YuXEp4QT3RGGhU5dfdDdFdUkvPNQgA8NTUULltFqzatg1KvEyUePoiCr74AoMyuJjQmlrCkZGp3FAbahCUlE9qqFWV2NQAFX31B4uGDG3XQko4cytrbbgosJxw+mKqcbDyVlU1wJs62YsUK2rdvR7t2vo7DCSNGMGfOnAYdudjY2MDjiooKXLgCy1/Nnk3btm2Ijo5uuqKbkeU2iw6ZrWmfmQHAiUcN4qsFixp05A7rW/cB6qCe3fjoq28AiAiv+3NXU1ODx+ttoqpbjm+WrKVjZkqwy2iWOie3IndnFfll1QD8sGkHfdsksL2kLpUb0iWFOVn5lNf4xg+VVtUGtoWHhBAW4ns3Cg1xUVJZ06T1O12WXUVGm3ZktGkLwKBhx7F4/txGHbm3pj7HKWedz8dvvhpYt3XjBnr3GwBAQlIyMbFxrF+7ukFnT34fjZGr01K+DugADAUOBi41xuzquL0CfGetPchaexDwvH/9E8AK/7rjgfuNMX3qHS/SWjsION2/T4219lBgDHAfgDGmK/B/wInW2v7ApcCb+/Ikg6VVZgZlW7MDy+XbsonOTP/V9uHxcbQbMZzsuQuaorxmITwller8uj/Y1QX5RKSkNmgTkZJKdX5+YLkmP4/w3drE9j6QmqIiqrb7LqsJiYqi9elnsf31l/dh9c1Hbm4erTPqvoBIz0gnJ6/x5U1vTJ/OSaecyqOPP87NN/k6zeXl5UyZMpUrLr+8yeptbnIKdtA6ra6j0Do1hdyCHb/a/t1ZcxgyoG9geXteAX+54iaOPv9fXHrmqUrjxDESo8MpLK/rfBVV1JAUHd6gTUZcFBlxkdx0dDduOaY7vVvHAbC+oBybt5OHTunDQ6f0YVV2CdmlVchvtyM/l5T0jMByclo6hQV5DdpsWLeGgtwcDj78yAbrO3TpzuIF83C7a8ndvo0Na9dQmJvTJHVL89dSOnJvWWs91tpiYDXQ1RgTCwwGHt3VyFq761PwscBz/nXbgRnA8HrHm+7/7xKgVb3lxUA3/+MR+BK9ucaYn4BXgTB/2tdiuUJDGTJpAmuef4WdG7cEu5wWJ3nocArnzg4sZ557AbkfvKM07k929lln8clHH3LdtaN5/oUXAHjm2ef4+9/Po1WrVkGurmX48Mt5rFi3nkvOOCWwLjMthfeffZCZkx/lgy/mkr+jKIgVivy5QlyQHhvJw7OzeP67jZw/oD3R4aGkxUaQGRfJzR+v5KaPV2LS4+iWGhPscpsVj8fDK888zt+vuLbRtmEnnkJKajq3X3URLz/9CN17H4grNDQIVTYjHve+/XGQZn9ppV/9T6lu/vh5VwJYa93GmPrHr39sFzDTWtssZzPoccm5dD/fNw6o4KcVxLRtza7vplq1aU3F9sYpBcDhj4yldP1G1jw3rYkqda60kaeSOmIkAGXrLBGp6ZSxEvCnbwX5DdpXF+QTkVqXwIWnplFTv01ICImDjmT19VcFVsX06EnS4CG0vegyQmNiwevBU11D3icf7MMzc6709DSyc+rS59ycXDLSfj19PmHECO69bzwAy1cs54svvuCxxx6ntLQUV0gIERERnHN24/GksmcZKUlk5xUElrPzC0hPSWrU7tsly5n0xvu89NAdRESEN9qenpJMt07tWLzCMmLIYfu0ZpE/Q1FFDcmt6n6XE6PD2VHR8PLIHRU1bCgox+2FgrJqckqrSI+NwKTHsr6wnKpa3wQRK7JL6JoSQ1Z+w7HS8uuSUtMpqJeiFeblkpySFliuLC9n8y8/c/d/fH9fiwsLmHDHDdwwbgJdTC/Ov+r6QNs7R19KZrv2TVe8NGstJZFrxFq7E/gWCPzfZYzZ9Sn4C+Ay/7rWwEjgq9/5FJ8BJxhjAhdBG2MG/pGa9ydrJ7/GJ8NP45Php7F5xpd0+Ztv5rjU/n2pKSltMD5ul363Xkt4fBw/3Da+qct1pLwZH7L62itYfe0VFH03n5SjjwUgxvTCXV7WYHwcQO2OQtzl5cSYXgCkHH0sRd/VXb4a3+8QKrdubtC5W3vLv1lx6fmsuPR8cj98l+y3Xlcn7r/o3bs3mzZtZsvWrdTU1DBz1iyOGnZUgzYbN24KPJ47bx4d2vv+YE+dPJlPZ3zCpzM+4bzzzuXSf1yiTtzv1Md0ZeO2bLZk51JdU8unXy9g+OENZ39blbWBsU++wFN33UBKYkJgfXZeAZVVvvFFxaU7WbLS0rldZpPWL/K/+qWwnPTYSFJiIggNcTGwQxJLt5U0aPPT1mJ6pPvG6MZGhJIRF0l+WTWF5TX0SIslxAWhLuiRFsv2El2F8Xt0Nb3I3rqZ3O3bqK2pYcGcz+k/eGhge6vYWCa9+xlPvPo+T7z6Pt169Ql04qoqK6msqABg+eLvCQ0NbTS2Tn4nj2ff/jhIS0nkfs3fgYnGmAvxpWmvAQ8Ao4HnjDHL8CVrt1hrV/6eA1tr1xlj/g68aIyJBiKA+fgmXmlWtn7+NW2PHcpffphFbUUl344eE9h20ux3+WT4abTKzODA/1xB8dqfOemrdwCwL75G1iua6v63KFm0kIQBh9Fn0kv+2w9MCGzr9fizgVsJbHrmSTpddwMhEZEUL/6BksULA+2Shg6n8OvZjY4tv11YWBi33nwzV151NR6Ph7+MOpVuXbsy8eln6H3AAQwbdhRvTJ/Od99/T3hYGHHx8dx997hgl91shIWGcttVF3HZbePxeDz89fhhdO/UnienvUXv7p05etAAJrzwGuUVlVx/7+MAtElLYeLYG1m/eSsPTnoFl8uF1+vl4tNPpkfnDkE+o+bl5fGXM7S/ITUxlvUzJzDu2Q+Y+r5mJv4zeLzw+pItXDe0CyEuF/M3FLK9pJJTe7dm445ylm4rYWV2KQdkxHHXiJ54vV7eWbqNsmo3i7cU0TM9ljtH9MTrhZXZJSzbXrL3J5WA0NAwLrrmBu6/ZTQej4dhJ5xCu05deGvqc3Tp0atBp253JUWF3H/LtbhCQkhKSePKW+5qusKl2XN5NXPXfuvl1F76xwmSAwa123sj2Wd6T38/2CW0aOE5NtgltFjRpz0V7BJatItubXxbF2k6lw/qFOwSWrT+7RNde28VfFVzXt2nn48jh53niNcBWvCllSIiIiIiIk7V0i+tFBERERERp3DYzJL7khI5ERERERERh1EiJyIiIiIizqBELkCJnIiIiIiIiMMokRMREREREUfwOuxeb/uSOnIiIiIiIuIMurQyQJdWioiIiIiIOIwSORERERERcQYlcgFK5ERERERERBxGiZyIiIiIiDiCJjupo0RORERERETEYZTIiYiIiIiIM2iMXIASOREREREREYdRIiciIiIiIs6gRC5AiZyIiIiIiIjDKJETERERERFH8LqVyO2iRE5ERERERMRhlMiJiIiIiIgz6D5yAUrkREREREREHEaJnIiIiIiIOINmrQxQIiciIiIiIuIwSuRERERERMQRvErkApTIiYiIiIiIOIwSORERERERcQSvZq0MUCInIiIiIiLiMErkRERERETEEbxuJXK7qCMnIiIiIiKOoI5cHV1aKSIiIiIi4jBK5ERERERExBE02UkddeT2Y25vsCtouWIz44JdQovm+fSZYJfQopXk5ge7hBbroltHB7uEFm3q+CeCXUKLNmzyuGCX0KL1b58Y7BLkd1JHTkREREREHEFj5OpojJyIiIiIiIjDKJETERERERFHUCJXR4mciIiIiIiIwyiRExERERERR/C43cEuYb+hRE5ERERERMRhlMiJiIiIiIgj6D5ydZTIiYiIiIiIOIwSORERERERcQTNWllHiZyIiIiIiIjDKJETERERERFHUCJXR4mciIiIiIiIwyiRExERERERR9CslXWUyImIiIiIiDiMEjkREREREXEEj8bIBSiRExERERERcRglciIiIiIi4giatbKOOnIiIiIiIuII6sjV0aWVIiIiIiIiDqNETkREREREHEG3H6ijRE5ERERERMRhlMiJiIiIiIgjaIxcHXXkREREREREfidjTA/gJSAFKAAusNau261NKPAEcALgBe631r7wZzy/Lq0UERERERFH8Lo9+/Tnd3oWmGit7QFMBJ7bQ5vzgG5Ad2AQcJcxptMfeAkC1JETERERERH5HYwx6cAhwOv+Va8Dhxhj0nZrehbwvLXWY63NA94HzvwzatCllSIiIiIi4giefTxrpTEmEUjcw6Yia21RveX2wFZrrRvAWus2xmzzr8+r164DsLHe8iZ/mz9MiZyIiIiIiIjPdcCGPfxcF8yi9kSJnIiIiIiIOEITzFr5GDB1D+uLdlveDLQ1xoT607hQoI1/fX2bgI7AD/7l3RO6/5k6ciIiIiIiIoD/8sndO217apdrjPkJOAd4xf/fH/3j4Op7C7jMGPMuvtkt/wIM+TNqVUdOREREREQcwet2B7uE+q4AXjLG3AHsAC4AMMbMAO6w1i4CXgYOA3bdlmCctXbDn/Hk+0VHzhgTCdyHr4daA1QAY6217we1MD9jzDAgwlr7mX+5DfCqtXZ4UAvbjxw6fgztjh1KbUUl3/xrDIXLVjXYHhodxbDJjxHfuT0et4cts2azeNwjQarWmdLP+gcxBx6Ct7qK7VOfomrT+kZtIjt0IfPia3CFR1C2fAm501+JWVQyAAAgAElEQVQEIO30C4jpOwBqa6nOyyF76pN4KsohNIzWf7+CqE5d8Xq85E5/kYq1K5v61Bxl/trNPPTxAjweL38ZaLjkqH4Nti/esJ0JnyxgXXYh4886muMO7BLYdvWUT1m2OZeDO2bwxIUnNHXpjrXglxwe/XoZHo+XU/t05IKBpsH26lo3Y2ctxuYWER8VwT0jB9ImIQaAdXnFPPDlj5RV1xLicjH5nGFEhoVy5VvzKCivJDI0FIDHTzuC5FaRTX5uTtK7dRxn9WtLiMvFNxsKmLkmt1Gb/u0SOaV3a8DL5qJKXvzed/XQ6QdlcmBmPC6Xi1U5pUz/cWsTV9+8TbrzYkYO7UteYQkHn3lHsMtplrJ+WsjMlybi8Xg45OiRHDnqnAbbf5ozk89fnURccioAh44YxSFHn8SGlT8ya9ozgXb52zZxxujb6TnwyCatX/YNa+0afJ203dePrPfYDVy5L55/v+jIAU8DsUBva22lMaYPMNMYU2itnRvk2gCG4avvMwBr7TZAnTi/tscOJb5LR94deAJpA/oyaMIdfHL82Y3arZw4mexvFhISHs6I9ybT9pghbP1yXhAqdp6YPocQnpHJhtuvJqpzDzLO+yebxt/SqF3GeZeTPe0ZKjespe3o24npczBlK36kbPVS8t57BTweUk87n+QTTyf/3ZdJHHIsAL+MvZ7QuATajb6djffdBF5vU5+iI7g9Hu7/cD7PXDKSjPgYznv6fY7q2ZGuGUmBNpmJsYw9/SimfbOs0f4XDDmIyppa3lm4uinLdjS3x8uE2Ut54rQjSI+N5uLXZzOkSyadU+IDbT5cuZH4qHDevvh4PrdbmPjNSu496VBqPR7umrWIu0YMoHtaAsUVVYSF1M3xNfaEAfSq928nv87lgnMPacejX//Mjooaxhzbg6XbitleUhVokx4bwYm90nnwq3WU17iJi/R9xOiS0oquqTGM/cwCcNPw7vRIi2Vt3s6gnEtzNO2j+Tw9/Uum3H1psEtpljweNzMmP8H5tz1IfEoaz4+5CtN/EGntOjVo13vQMEZeMrrBus69D+aKByYBULGzhCeuvYCuBw1oqtKbJe8+nrXSSYLekTPGdMR3f4UO1tpKAGvtCmPMvcCdwDHGmFuBcwEPUAYcaa31GGMuAa71H6oaOBnoBUyw1g7wH3/YrmX/48eBpUB//7EustauMsa0xnf/h3ggCvjEWnuTMeZAfLFpiDHmWOAN/88ia22q/zlOAMYDofimG73cWpvlf77HgO/x3QDQC5xtrW1Wn+I6nHg0P0//AIC8RUuJSIgnOiONipy6S4TdFZVkf7MQAE9NDQXLVtGqTeug1OtEsf0OpWTBHAAqN6wlNDqG0IQk3MU7Am1CE5IIiY6mcsNaAEoWzCG232GUrfiR8lVLA+0q168lrv8gACIy21NulwPgLi3GXV5GVMeuVP6S1URn5iwrtuTRPiWedsm+TsSIg7oyZ/XGBh25NklxAIS4XI32P6xbWxat39Y0xTYTq7ILaZcQQ1t/wnZcj3bM/Xl7g47cvJ+3c+nhPQEY3r0NE2Yvxev1snBjLt1SE+ielgBAQrQSt/9V5+RW5O6sIr+sGoAfNu2gb5sEtpfUpXJDuqQwJyuf8hrfZU+lVbWBbeEhIYSFuHABoSEuSiprmrT+5u6bJWvpmJkS7DKara1Za0hu3ZakjDYA9B48nDWLvm3UkdubVd/NpXu/QwmPjNoHVUpLtD/cfuBAIMtaW7jb+u+AvsaYC4FTgcHW2r7AKf5O3DBgDDDCv344UPwbnu8g4EVrbW98d2Cf5l9f5D92f6AfMMAYc4K1djm+u7ZPs9b2s9beX/9g/psBvgycZ609CHgNeLVek97As/5tbwK3/4YaHaVVZgZlW7MDy2XbsmmVmf6r7SPi42g/Yjjb5y5oivKahbDEZGp35AeWa3YUEJaYvIc2BYHl2j20AUg44mjKViwBoGrLL8T2HQghIYSnpBPVsSth/stCpLHc4jIyEmIDyxkJMeSVlAWxouYvr6yS9LjowHJ6XDR5ZZW7takgI64VAGEhIcRGhlNcWc2mHTtxAde+O58LXv2KlxetbbDfPZ8t4fxXvmLy92vwKoX+rxKjwyksr+t8FVXUkBQd3qBNRlwUGXGR3HR0N245pju9W/u+1FhfUI7N28lDp/ThoVP6sCq7hOzSKkScorQwn/iUuns8xyenUVqY36jd6oXzeOamS3nzkbsozm986fGKBbPpM1gXdP1RXrdnn/44SdATOaDx19YNnQw8Y60tBbDW7vqkehK+zlW2f/1OAGPMHg9ST5a19mv/45eBScaYeMANPGSMGeyvqTW+Dt3MvRzvMGCptXbXoLApwNPGmDj/srXW/uh//B1wyt4KbM5coaEMfX4Cqye9ws6NW4JdTouTPPJ0vB4PJd/7rlgunv8lEZnt6HjbQ9QU5FHx8xrQJQvSTLi9XpZuK2DKOcOICgvlX+98Q8/0RAZ2SGfsiQNIj42mrLqGWz9eyKerNzPygA7BLtnRQlyQHhvJw7OzSGwVwY3DuzF2liU2MpTMuEhu/tg3/va6oV3pllpKVr6+BJHmo0f/QfQ54mjCwiNY9MVHvP/MA1z4fw8HtpfuKCB30wa69h0YxCqludkfOnLLgW7GmOTdUrnDgcaDTPauloZJ42/Nr/8NJAGH+cfpTfod+/439b86drN/vOZ/WM9/nEuP888AIP/HFcS0rbtMMqZNa8q3N/4mCmDwo2MpWb+RVc9N2+N2qZM47AQShhwHQOUvWYQl1SVl4Ukp1BY1DLFriwoJS6q7tCZstzbxg4YTe+AANj96Z91OHg95b04JLHa4+T6qc3Tp369JT4ghp7huXE9OcRlp8TFBrKj5S4uJIre0IrCcW1pBWkzUbm2iySktJz0umlqPh51VNSRERZAeG83BbVNI9F9SObhza2xuEQM7pJMe60v5YiLCOd60Y1XODnXk/ouiihqSW9UlcInR4eyoaHh55I6KGjYUlOP2QkFZNTmlVaTHRmDSY1lfWE5Vre9LohXZJXRNiVFHThwjLjmVkoK64SIlhXmBSU12aRWXEHh8yNEj+eLV5xtsX7lgDj0HHkloWLP4GBhUTkvN9qWgX1pprf0F3/0VnjHGRAH4Jzu5DRgLfAxcuSvhMsbs+qT6CXCBMSbDvz7Wv/96oIsxJskY48J3T4f6uhpjdt274VxgubW2BEgEtvs7cW2BUfX2KQES2LNdl4D29C9fiO8eEqW/97VwkjUvvsaHw07jw2GnsWnGl3Q9y/dypQ3oS3VJaYPxcbscPOZawuPjWDhmfFOX60hFc2ay8e7/sPHu/7Dzp4XEDxoGQFTnHrgryhuMjwNwF+/AU1FBVOceAMQPGsbOn3zjElv1PpjkEX9h68TxeKurA/u4IiJwRfg+5Lbq1Rev2031diWlv6Z32zQ25ZewtbCEmlo3s5b9zLBe+vC/L/VqncTmop1sKy6jxu3h87VbGNI1s0GbIV0zmbF6EwCz121jQPs0XC4Xh3VMJyu/hMqaWmo9HpZsyadzSjy1Hg9FFb5L+2rdHuZvyKZLvTF30tgvheWkx0aSEhNBaIiLgR2SWLqtpEGbn7YW0yPdd+lxbEQoGXGR5JdVU1heQ4+0WEJcEOqCHmmxbC+p3NPTiOyX2nbtSUH2VnbkbsddW8PKb2dj+g9u0Ka03tAGu2gBqW0b/m1Y8e1s+hyhyyrlz7W/fC1wFb7bD6wyxlTjS7GutdZ+bYyZC7QFvjPG1AA7jTFDrbVzjDHjgS+MMR6gCt8Yt23GmIeBxUAO8DW+cWq7LAcuNcY8A5Tjv98D8ATwljFmBbAF+LLePu/h6zT+RN1kJwBYa/OMMecDrxljwvBNdvL3P/G12e9t+fxr2h43lNMWzcJdUck314wJbDt1zrt8OOw0WrXJoO9/rqBo7c+cOvsdAFa/8BrrXnk7WGU7StnyxcT0OYTO9z4duP3ALh3/72E23v0fAHJem0TmRdfgioigbMWSwFi4jHMuxRUWTrvrfWlc5fq15Lz6HKFxCbS/9g68Xi+1RQVsn/xE05+cg4SFhnDzqYO5asqneLxeRvU3dM1I5unPF3FAuzSG9erIyi15/PuVzympqGLu6k08++Vi3rnuTAAuee5DNuQVU1Fdw4j7X+PO04YwuEf7IJ/V/i0sJIQbhvfl2vfm4/HCyb070iUlnkkLVtEzPYmhXTM5pXdHxs5axBlTPiM+KoK7R/ouXYqPiuCcQ7px8etzcLlgUKfWHNG5NRU1tVz73rfUejx4PF4GdkhnVJ9OwT3R/ZzHC68v2cJ1Q7sQ4nIxf0Mh20sqObV3azbuKGfpthJWZpdyQEYcd43oidfr5Z2l2yirdrN4SxE902O5c0RPvF5YmV3Csu0le39S+c1eHn85Q/sbUhNjWT9zAuOe/YCp72tW6D9LSGgoIy++hlfuuxmvx0O/4SeS3r4Ts9+cQpsuBjNgMN/PfI+1i78lJCSU6Ng4/nLlTYH9i3KzKSnIpVOvvkE8i+bDo0QuwNWSBnjXn8Ey2LX8FlNTerWcf5z9zKDTewW7hBat/YjBe28k+0xVbuNB/NI0bk5pfOsWaTpTx+vLrGCaOnlcsEto0c49uN3e5q3YL9h/nrZPPx+bSe864nWA/SeRExERERER+a90H7k6LaojZ62dAzgijRMRERERkYY02UmdoE92IiIiIiIiIr9Pi0rkRERERETEubxuTSGxixI5ERERERERh1EiJyIiIiIijqDbD9RRIiciIiIiIuIwSuRERERERMQRvB6NkdtFiZyIiIiIiIjDKJETERERERFH8GjWygAlciIiIiIiIg6jRE5ERERERBzBq1krA5TIiYiIiIiIOIwSORERERERcQSvxsgFKJETERERERFxGCVyIiIiIiLiCJq1so4SOREREREREYdRIiciIiIiIo6gWSvrKJETERERERFxGCVyIiIiIiLiCB6PxsjtokRORERERETEYZTIiYiIiIiII+g+cnXUkRMREREREUfwaLKTAF1aKSIiIiIi4jBK5ERERERExBF0aWUdJXIiIiIiIiIOo0ROREREREQcQYlcHSVyIiIiIiIiDqNETkREREREHEGzVtZRIiciIiIiIuIwSuT2Y98VVgS7hBbLZBUEu4QWrcPJEcEuoUWrLi0Ldgkt1uUndwp2CS3asMnjgl1Ci3bRJXcEu4QW7dwfJwe7hN/E69EYuV2UyImIiIiIiDiMEjkREREREXEEj2atDFAiJyIiIiIi4jBK5ERERERExBG8mrUyQImciIiIiIiIwyiRExERERERR/BqjFyAEjkRERERERGHUSInIiIiIiKOoFkr6yiRExERERERcRglciIiIiIi4ghej2at3EWJnIiIiIiIiMMokRMREREREUfQGLk66siJiIiIiIgj6PYDdXRppYiIiIiIiMMokRMREREREUfwujXZyS5K5ERERERERBxGiZyIiIiIiDiCJjupo0RORERERETEYZTIiYiIiIiII2jWyjpK5ERERERERBxGiZyIiIiIiDiCx6tEbhclciIiIiIiIg6jRE5ERERERBzBrUQuQImciIiIiIiIwyiRExERERERR9CklXWUyImIiIiIiDiMEjkREREREXEEjZGro0RORERERETEYZTIiYiIiIiIIzhpjJwxphUwBegP1AI3WGs/3kO7UcAdQCTgAiZbax/e2/H32pEzxpwJjPEfNApYYq09dy/7JAL/tNY+WG/dRcC31tq19ZZPttaesbca9rXda/uN+7QBXrXWDt9nhTnI3x6/kz4jh1NdXsFLF93A5h9XNmpzzacvkZCZTkhYKFnzfuD1q/8Pr8cThGqdqdO/RpN02GG4K6v4+cHxlK1b16hNTPcedLv5VkIiI9jx/ff88tQTALTq0pUu1/+H0OhoKnOyybr3btzl5aQecyxtzjo7sH+rLl1ZdvlllP+c1WTn5TTz12zkwQ+/wePx8NdDD+CSo/s32L54/TYe+nAe67YXcP95x3PcQd0AWLM1j/ve/ZqdVdWEukK49Jj+jOjXPRin4DgRnXsRd8zp4AqhYtkCyr//vGGD0DASTjqfsIz2eCvKKPpwCp6SQsJadyR+hP/32+WibP4MqtYtAyD18rvwVFeBxwNeD4XTHmris3KepQsXMO3pR/B4PAw/8VROPefCPbZbOPcrHht3K/dMnEoX04vamhpeeGw8G+waXCEuLrjq3xzQr/8e95Vfl/XTQma+NBGPx8MhR4/kyFHnNNj+05yZfP7qJOKSUwE4dMQoDjn6JDas/JFZ054JtMvftokzRt9Oz4FHNmn9zdmkOy9m5NC+5BWWcPCZdwS7HNm/3ACUWGu7GWO6A/OMMd2stTt3a5cNnGKt3WaMSQAWG2MWWmvn/beD/9eOnDEmE3gaOMRau9kY4wL6/YaiE4GbgAfrrbsIyAd+c2epCV3E76zNWrsNUCcO6HPiMNK7d+aO7sPofNjBnPvMvTxw+F8atXv+b1dTWer7vf3n28/Q/8yTWDT9o6Yu15ESDzuMqLbt+PH884jtdQCdr/s3K66+slG7Ltf/m58ffoidq1fRc/yDJB56GEULv6frDTex8dmnKVm2lLQTRtLmrLPZPGUy+V9+Qf6XXwDQqnMXzN33qBP3X7g9Hsa/N5dn/3kqGQmxnPfEWxzVuzNdM5IDbVonxjLub8cw7eufGuwbHRHO3WcfS8e0RHKLyzj38TcZZDoQHx3Z1KfhLC4XcceeSdGbE3GXFpF8wY1UZS3HXZAdaBJ94CA8leUUPD+OyJ6HEDdsFMUfTqE2f5uvg+b1EBITT8pFt5CXtQK8vi+QdrzxBN6KsmCdmaN43G6mPPkQtz7wJClp6dx+9UUcMngI7Tp2adCuoryMme9Np1vP3oF1X814H4AHXniN4h2FPDDmOu6ZOJWQEI3u+K08HjczJj/B+bc9SHxKGs+PuQrTfxBp7To1aNd70DBGXjK6wbrOvQ/migcmAVCxs4Qnrr2ArgcNaKrSW4RpH83n6elfMuXuS4NdSouwr8fI+QOpxD1sKrLWFv3Ow50FXAhgrV1njFkEnAi8Vb+Rtfb7eo+LjTGrgY7A/96RA1oDNUCB/8Be4MddG40xhwH3A/H+VXdYaz8BJgKJxpifgHLgeWAA8IQx5h58vdMGjDEXAlf5ayoGrrTWWn9adi5QBBwEbAWuASYA3YAfgL9ba73GmHjgEX+7KGA28G9rrdsYM8ffdhDQBnjTWnuLMebi3Wuz1n5Rr64Q4CngaKAK2GmtPcIY0wlYZK1NNcaMBO6rdzoHAGdaaz/4tfPay+vuKAeNOp7vpr0LwIbvfyQ6MY741mmUZOc1aLerExcSFkZYRDheDVb9zZIHH0ne57MA2Ll6FWGxsYQnJ1NTWBhoE56cTGirVuxcvQqAvM9nkXzEkRQt/J6odu0oWbYUgOLFP9DmgQlsnjK5wXOkHH0M+V991URn5EwrNuXSPjWBdikJAIzo1505Kzc06Mi1Tfa9Hbpcrgb7dkyr+5uQnhBDcmw0O3ZWqCO3F+GZHXEX5eMuLgCgcvViIrsdSHm9jlxk9wMpmz8DgCr7E/HHnunbUFtTd6CwcLzoPed/lWVXkdGmHRlt2gIwaNhxLJ4/t1FH7q2pz3HKWefz8ZuvBtZt3biB3v18HYeEpGRiYuNYv3Z1g86e/Hdbs9aQ3LotSRltAOg9eDhrFn3bqCO3N6u+m0v3focSHhm1D6psub5ZspaOmSnBLkP+PNcBd+5h/Vjgrt95rA7AxnrLm4D2/20HY0xP4HDg8r0dfG8duaXAQmCTvyP0DfCytbbA31t9Fhhprd3uT+9+MMb0Aa7G18kJpHf+Ds2EXdeF+jtou7YNAf4GDLXWVhljTgQmA0f4mwwEDrTWbjHGfAy8BgwDdgJLgGOAL/B14r621l7q74C9ClyCryMJvhdzKBAH/GyMedFaO2X32nbTF1/ydoC11mOMSdq9gbV2BjDDfy6XARcDs37DeTULiW0z2LF5W2C5aEs2iW1bN+rIAVwzcxqdDu3Lyk/nsOTtGU1ZpqNFpKZSnZsbWK7OyyMiNa1BRy4iNY2qvLzd2vgusanY+AtJRxzJjvnfkHLUcCLT0xs9R+rw4ay5/bZ9eBbOl1uyk9aJsYHljIRYlm/K+d3HWb4phxq3h/b+DqH8upDYRDylOwLLntIiwtt0atAmNDYBd4n/S1KvB09VBa7oGLwVZYRldiThxPMIiU+m5JNpgTQOLyT97WrweqlYOp+Kpd820Rk50478XFLSMwLLyWnpZK1peAn9hnVrKMjN4eDDj2zQkevQpTuLF8xj8NHHU5Cby4a1/9/efcdZUZ1/HP/sLkWRKiyikEhRHxCliChiA0tQRCEmJlERkZ+VxIaNRGMs0diSGBNLMLYoaDQFMcFGRImhKEoVfRSRYAERVpq4C+zu749z7u7dyzYQdpnl+3699sW9d2bOPVPuzDznOWd4j7zln4MCuWpbm7eCpi1zS9433T2XTxe+u9l8777xH/733lxatmnHgGEjadaq7Ll+/rTJHDaw1ke0iHwjNTBG7m7g0XI+3ywbZ2ZvE+KL8uxRwecVivHUs8DI2PuvUpUGcu5eBAyJwdnRwBDgKjM7kBApdgCeN7PUIsWELNmKLaz3yYSAaUYsKwtID5j+6+6fxNezgMWp1KaZzYnfOQk4BTjEzK6I8zYCPkkr55m4TqmUZSdg84FGZS0C6gMPmdkrQHnBHrEuA4BRwJHunm9mVa3XTuf3JwyjXsOGjBh7N52P6cu7k16v7SrtFBbecTsdLr6EdmcN48up/6Vo48Yy0xt37kJRfgFfL/6olmq48/hizVdc99Qkbv7hsWRnZ1W9gHwjm5b+j5UP30rO7nvQ7KSzKFi0AAo3kTfutxStW01Wo8a0+MFP2LTyczZ+8mFtVzexioqKeOL+33Hh1T/fbFq/E0/msyWLuW7kcFq1bsO+XQ8kKyenFmpZt+3X6zAOOPwY6tVvwMxJzzH+/ts5++elz0pY++VKli/5iE7de9diLUW+ue3dtTLGGNXqQunuB1U23cyWELpIplrav03oMVjevK0J8cwd7v5MefNkqtZTK919PjAfuNfMFhCyYQXAXHc/qpyKtK9OuWlST2epaIRoftrrwnLep9YjCxji7ouqWU6V6x/7qXYlrPNxwO1mttlOM7PuhAzlAHdPBbJVrVdiHT3yLI44Lwy0/t+bc2jxrb1KpjVv14ZVny6raFE2FRQw59mX6T74eAVyldhj8BD2OGkQAOvcaZCWRWuQm8uGFWUznhtWfEHD3NyMecKhmP/xEt69OvRo3qVdO1r0OazMsi2POYYVr/x7u6xHXdK6aWOWrSodn/z56nW0brZbtZdfl7+Bix/+Jz854VC67d1me1Sxzilat4rsJqXtX9lNmlO4tuz1tXDdanKaNqdo3SrIyia74a6bjX0rzPuc4g0F1Mvdk03LPqZo3WoAitevo+CDOdTfc28FcpVo0ao1K5eXZp/zvljO7mkZovz16/l48YfcfMVIAFbnreSu66/kypvuoqN14ayRl5fM+4tLzmXPdpX2LJIMTXZvxZqVpef8NXlflDzUJKVRk9IM/0HHDGTS2AfLTH9n2qt07n0EOfX0wHKRGvQMoYvkzPiwk97A6ZkzmVlL4GXgD+7+UHULr3SksZm1NbPD0t63A3KBj4CpwL5m1j9teu/4QJQ1QCMzSz9brAEq6kf0HDAslo+Z5ZjZ1jzSagIw2sxyYjmtzKxDNZarsG5mlgs0cvcXgdGEcW4dM+ZpC/yNMFYv/YEp22q9djiv3fc4t/QcyC09BzJ7/Ev0GXYqAB0O7Un+6rWbdatsuFsjmrYJF/3snBwOPOkYlr2nm6bKfP7seOaefy5zzz+XvNf/Q+7xAwBo3GV/Cr/6qky3SoCNeXkUrl9P4y77A5B7/ADypoZAuV7zOD4rK4t2Q4exbMKE0gWzsmjVrz8rJiuQq0rXb7VmyYrVfJq3ho2bCnlx9gccvX/7ai27cVMhox6byKBenUueZClV27h0CTktcslu1hKyc9ilSy8KFs4rM0/BwnnscsChADS0HmxYEk7D2c1aQla4zGU3bUFOyz0oXJ0H9RuQ1SCOTazfgAbtO7NpxdKaW6kE6mRdWPbpxyxf+hmbNm5k2qsv06tvaTtuo8aNGfP3l7hn7HjuGTuefbocUBLEFeTnk//11wDMe2sGOTk5m42tk8q17dSZlcs+5cvlSynctJF3pk7GevUtM8/aL1eWvPaZ02jVtmxvr/lTJ3PA4XpGmyRfYfH2/dvG7iQ8N2QhoVff+e6+FsDMbjKzC+N8o4H9gAvMbHb8O6eqwqtqlqkH3GhmewNfEwK/69x9VqzAKcCdZnY30IDQDfFkd88zs7HAPDP70t37AmOAX5vZVWQ87MTdp5jZtcCEGIQ1IESwb1W1AhkuIzwpc46ZFROyhpcRAs/KlKlb+sNOCAMSH4xBaT3geWA6ZfvDnksIcO9N62Z6ubtP3kbrtUObP3EyBwzsz80LXwv//cA5V5VMu3bWRG7pOZAGuzVi5IQ/Ua9hA7Kys3l/8jSmPDC2klIl3aoZ02lxaB96PjGOovwCFt5xW8m0bmP+xNzzw5OyFt39W/a5ZjTZDRuy6o0ZrJoRHoLU6phjaTP4uwDkvT6FL14oHZ/YtFt3CpYvp2CpbmSrUi8nm9FDjuSiBydQVFTM4EO6sE+bltz34gz2b9eafl07MP/jzxn12POsWV/AlHc/4v6X3uDvV57BS3MW8vaipaz6Kp8Jb4axLTf98Fg6t82t4lt3csVFrJ30DC1OGwlZWeTPm07hymXsdsRANi1bQsHC+Xw9dxrNThpGy/Oupzh/PasnPAJAg7Yd2e17x1NcWAgUs/alpyn++itymrWk2XfPAyArO5v8BTPZ8FS4AyUAAA/wSURBVNHm442kVE5OPYZffCW3jb6EoqIi+p1wMu3ad+SZR/9Ix/26lAnqMq1Zlcdtoy8lKzubFi1zuWj0DTVX8ToiOyeHgedczBO3XkNxURE9+p9I62+1Z/LTj7BXR8MO7suMF/7B+29NJTs7h10bN2HIRVeXLL9q+TLWrFxO+y7da3Et6q7Hf3UBR/UyWjVvzKIX7uKmB57l0fGVPmxQdhLu/hVwWgXTrk97fRVwVXnzVSZLTw7ccV2Y1V47p5ac3X/v2q7CTq3HZd+r7Srs1Na8V9XQYdlePjn95tquwk7NV2T+105Sk4aPqHMjURJlw6yHEzFw+/7mnbfr/fFFq95LxHaAKrpWioiIiIiIyI5HI15FRERERCQRtvdTK5NEGTkREREREZGEUUZOREREREQSoQb+Q/DEUEZOREREREQkYZSRExERERGRRNAYuVLKyImIiIiIiCSMMnIiIiIiIpIIGiNXShk5ERERERGRhFFGTkREREREEkFj5EopIyciIiIiIpIwysiJiIiIiEgiaIxcKWXkREREREREEkYZORERERERSQSNkSuljJyIiIiIiEjCKCMnIiIiIiKJUFTbFdiBKCMnIiIiIiKSMMrIiYiIiIhIImiMXCkFciIiIiIikgj67wdKqWuliIiIiIhIwigjJyIiIiIiiaCulaWUkRMREREREUkYZeRERERERCQRNEaulDJyIiIiIiIiCaOMnIiIiIiIJILGyJVSRk5ERERERCRhlJETEREREZFE0Bi5UsrIiYiIiIiIJIwyciIiIiIikggaI1dKGTkREREREZGEUUZOREREREQSQWPkSmUVKz0pIiIiIiKSKOpaKSIiIiIikjAK5ERERERERBJGgZyIiIiIiEjCKJATERERERFJGAVyIiIiIiIiCaNATkREREREJGEUyImIiIiIiCSMAjkREREREZGEUSAnIiIiIiKSMPVquwKSXGZ2GvAzIAvYBXjb3c+o3VrVDjNbDDQE2rl7YfxsOPAIcLG7/2ELy9sLGOvu/eP7G4Bb3X1DNesyyN3nV/O7+gEN3P2laszbA9jP3Z+uTtl1lZm1AD4Dxrj7pbVdn6SIx2Z+/EsZ4u6LM+YrBpq4+7pt/P3tge+4+5i0zyYSfqMfbsvvSpotPW9I1cysIXArMATYCHwN3Oju42u1YlHmuT/zurMj2Zr7DTNrDpzv7nekfTYcmOru76e9H+Tu399OVa+2zLpVc5kddp9JzVAgJ1vFzPYE7gMOcvePzSwL6FHL1aptnwEDgInx/XDg7S0txMzquftnQPqJ+RfAXUCVgdxW6Ac0BqoM5Aj7eBCwUwdywBnAdOB0M7uqOgG2lPh+LQYL7YHzgZJAzt0H1lJdpO67j3Bu7eru+WZ2APCCmeW5+5RarhtknPvLue7sEL7B/UZz4GrgjrTPhgMrgGoHSzVoOFtYtx11n0nNUSAnW6sNoYVxJYC7FwOzAMzsUOA2oGmc93p3/5eZXUc4EZ9qZo2AGcA17j5xs9KT6VHCiXiimXUEdgPmpSaa2bHALwmtifWAW9z9qTjtVWA20AfIM7ORwEx3b2Vm98YipppZEeHiOxC4FGgQp13p7v+urHJmZrGOjYCc+PpF4EIg28yOA54iBIz/AloCuwJvABcATYCbgKZmNhuYAvwmVc/4He3T6t0aGAfsEaswyd0vr3IrJsMIwg3CT4HBwDNm1gx4GOgKfBr/lrv7lWbWALgFOJqQuZ0LXLStM05JZWanEjIX+cDf0j5vTwXHV3w/CLgBqA8UAWe7+1wzGwsYYVsvBEa4+5fAvUCHePwudPfvp2eizGwf4I9ALrAJ+Jm7vxC/qxi4Fvgu4bdxlbuX1LWuiOeiN4HDgL2Ap919dJzWFrgH2DfO/qS7/8rM9gAeADoRMiZ3uvuf4zKLgSeAY4G2wGigNaExZHfCvpkS5x1I2Ma7EBqtLnf36dt3jbcPM9sb+CHwbXfPB4jH2C2EhrljzeynhO1QBHwFHOHuRWY2gnB+h7AdBgFdgLvc/eBYfr/U+/j6d8AcoFcsa7i7LzCzNsCThOvxLsC/3P1qMzuQzc/9T1H293UC8CvC9eIL4AJ3Xxi/727CNfwwoBj4kbu/u623Y1Th/UasZ7n3HITfe/P4e18PPAgcDNxjZr8Ersz8IjM7GxhJuEavJpynPWbLzgBWAd0I5/eLCdfLfQi/maHuXmxmTQnXxm6EbT4ZGOXuhRX9vszsnMy6ufuktHplA38AjgEKgHXufnjGNXcg4Tyasj9wmrs/W9F6VWvryw5NY+Rka80h3OAvMbO/mtllZtYydmV4ADjD3XsRLkB/jJ/fCjQ2s4sJJ6Tn61AQB/AqcGDsdnc28OeM6W8TLtQ9geOAu+K8KR3j9DIZAnf/cXzZ1917uPsqQgDWJ5b1I+CxatRvJDDB3bu7+wHAQ+4+j7C//hzLvg0oJOy/g4EDCBfxEe6+ErieEJD1cPdLqvi+M4EP3f1Adz+QEAQmnpl1I9zIv0LoOjsiTroe+NLdOwOnAUemLXY1sNrdD3H37oTs7U9rrtY7lL+a2ez4NzMGAQ8Cg929B+EmpUpmth/wJ+D0uE37AB/FyZe6+8HxuHsHuCZ+/mNgQTx+y+tKNRYY5+7dgKHAE2aWmzZ9jbv3Bs4iBDR11beBo4CewLlmlgrcngCmu3u3uI0ejJ/fA8yPn30HuC1mn1IauvthwPfiMhvd/RBCV7lbAcysE/Bz4MR47TiXZGf+DyQ0FuRlfD4d6B5vrE8hnNe7AyfHIK4fYbsMiJ/3J9x4V6Ub4ZzelRDApK4/q2LZvQhZrIPN7IQKzv0lYkPc48CZcb+OI/w+UroCD8RpTwPXVaOOW6vc+41Yz8ruOX4MrIrr19fdHwFmApfEzyalf4mZHQn8ADgqlnUnoXEupTchIOtM6CY7jhDc7U/Y38fG+X4DvBaP8R6EhosRaeVs9vuqqm5A6ljYPx4XgzI3krtPjMv2IBwDM4EXq7FekmDKyMlWcfciYEi8WB9NGANwVfzrADwfEkBAaK3bx91nmtlQQuZpCXBEjVd8+yomXNB+FP/6ElpHU3KBh+NN0SZCa7QRLuwQbiA3VfO7OgFPxhbyjUAbM2vj7ssqWWYKcEfMhk6Of+XJBq40sxMJQVwLQmvmlpoOXG5mdwKvEYLPuuD/CDc/xWb2d+D3cT/0J7TQ4u55ZpY+DuYUQiYzFTw0JNyc7IzKdK00s1MI411SrcNjgNurUc7xwER3/wDA3QsoDQKHmdmZhIz1blSjq5KZNSHcdD0Sy1sQW/L7AM/F2Z6K/04H9jKzXVLZljrmmXiOX21m7wKdzGwp4Zx2fGomd18RXx4HXBE/WxrHHfYHUvv5L/Hftwk9AlLv3yJkMyB0S+8ETEm7dtQzsz3c/fNtvH41IauK6YOA+919LUBsKAM4iXB+WRY/XweQtk0qstDdX4uvHwfGxMxQIXCnmfWNdWpDOM5fqKK8Q4E57r4gvn8EuC/+TmLVPJUVmw6cXFUFt1ZF9xsxq9iHCu45CN0Ut8TJhIBpRiwri3D9S/mvu38SX88CFseGVcxsTvzOSYTz/SFmdkWctxHwSVo5m/2+gA+qqNsiQs+Dh8zsFeCfFc1oZgOAUcCRHrr0VrVekmAK5OQbiTdk84F7zWwB4QQx192PqmCRDoRuJM0J3fbW1khFa85jhO4mU9x9ZcbF935gAnBqDALeJ3S7SNmSbnZPAle4+/jY5WJ9Rlmbcfe/mdk0Qov5aEIL4dByZj2DEGQf6e5rzexnwH4VFLuJspn9kjq4+zQz60m48Tsrfmeig/fYRfIMoMDMhsWP6xO61FYmCxjp7q9sx+rVRRUeXxWJrc8XETIdX5jZGYRxcdtCqotcYfxt19VraHpwWsg3X8/M7ZYqP73sLOAFdx+2+eKJNA/Yx8x2z8jK9SF0rd5SW/xbiEYRbtoPjTf1Y7Zg2cps62OkSuXcb/QjNN6Ue88Rux1uiSzgYXe/voLpmetc0TbIIjzEaVE1y6ly27n7ajPrSljn44DbzeygzPnMrDshQzkgraGlqvWSBFPXStkqZtbWzA5Le9+OkHFaAOxrZv3TpvU2s6zYjXAsIVv1F0q75dQZ8cR9LXBzOZObE1rwis3seEpboqtjLdAso6xUN7IRhAxPpSyM/1nm7o8CNwKHxElryil7RQzimhECFyqYdxlQP5ZN+rxm1oHQFe0pws1Erxh0JtlgQkt0O3dv7+7tCYHxcELX2mFQ0t1ncNpyE4BRZrZrnN7EzLrUYL13ZNOBnmnd985Nm1bh8UV4QMPA1HJm1jBmC5oTuqKttPDUwPQuTZnHb4mYGZlN6BZN3D/dKc2Y79RiZmgqUDLO1cxaxZeTgPPiZ20IY3i3tNHiJeCEeLOaKr/3N6lzbfLwJNZngPvNbBeAmFG6lnD+/SdwUSrDleoqSBifPCx2OcbMGsflFwEdzayFhYd9nJ7xlZ1iIwaE38k8d19D+D0sjUFcW8qelyr8PVDaBbRzfH82MCuVQaxJldxvfEQ4Jsu95yCsXyMzSw+UKlvn5wjbvl0sJ8fMelUwb2UmAKPNLCeW0ypeD6tSYd0sdPFu5O4vEhpFVxOGY6TP05Ywxniol33y5bZaL9kBJf2mSmpPPeBGM/PY/WgicF3sanEK8AszmxO7DdxAbBEitAq9Thgv1cbMLqyd6m8/7j7G3WeXM2k0YVzcbEJ/9S1plf018IqFcUXNgcuA8Wb2NuFkvrLSpYMfAPPMbBbwe0oH0/8D6B3LHk0YW9HEzN4jXAD+k1bGv4Hd4r69J3YFvRR42czeILQupvQD3o7r+zxwYexOkmQjKDtOBHefRjiXPgu0jtvtH4TxCamxLbcRulK+aWZzgdcJDy/YGaWPkZtNGC9yPvBcPDbTs7oVHl+xS+V5wF9it6ZphKdSvgB8SOhO+Rplnxw7F3Azm29mfy2nbmcCQ+M+Gguc5e5fbKP1rguGAofH7TeH0M0Y4BLCTf9c4GVgtLu/syUFx/05lNB1LHXtuGAb1r02jCSMh10QzwtPEMZvvkY4zz4HTI+/g2fNLNvdXyU8YGRS3MavAM08PJ3w14TuqFOBpRnfNY8w3mo+YX+kMpv3EPcZ8BDhHJ6See4vEY/7s4Bxcb8OpfweHDWhwvsNDw8xKveeI2ZCxxKue1NjWWOA6+M6H5f+JR4eunMtMCFu+/mUDXyr6zLCuWqOmc0jnJPaVmO5CusGfIvSY2Iu4Zqa2ch0LiHAvTftHNt/G66X7ICyiouLa7sOIiKJZ2b1gZzY8t2UEKyNKmfQuojINmNpT7Cs7bqISM2qq/37RURqWgvCgPscQlZpnII4ERER2V6UkRMREREREUkYjZETERERERFJGAVyIiIiIiIiCaNATkREREREJGEUyImIiIiIiCSMAjkREREREZGEUSAnIiIiIiKSMP8Pv3GuGpE4z8kAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualization of raw data\n",
        "plt.figure(figsize = (12, 9))\n",
        "plt.scatter(df.iloc[:, 2], df.iloc[:, 4])\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Income')\n",
        "plt.title('Visualization of raw data')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "npXWqqjVfWeu",
        "outputId": "2e85aa4e-a08d-4dcf-c673-61094e782894"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Visualization of raw data')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Standardizing data, so that all features have equal weight. This is important for modelling.\n",
        "scaler = StandardScaler()\n",
        "df_std = scaler.fit_transform(df)\n"
      ],
      "metadata": {
        "id": "veIch6S8fjK7"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####K-Means Clustering"
      ],
      "metadata": {
        "id": "xIhoHbgQfnda"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Perform K-means clustering. We consider 1 to 10 clusters, so our for loop runs 10 iterations.\n",
        "wcss = []\n",
        "for i in range(1,11):\n",
        "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n",
        "    kmeans.fit(df_std)\n",
        "    wcss.append(kmeans.inertia_)\n"
      ],
      "metadata": {
        "id": "r0vKWOR_fqkK"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the Within Cluster Sum of Squares for the different number of clusters.\n",
        "\n",
        "plt.figure(figsize = (10,8))\n",
        "plt.plot(range(1, 11), wcss, marker = 'o', linestyle = '-.',color='red')\n",
        "plt.xlabel('Number of Clusters')\n",
        "plt.ylabel('WCSS')\n",
        "plt.title('K-means Clustering')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "4o1xhRlZftof"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# We run K-means with a fixed number of clusters. In our case 5.\n",
        "kmeans = KMeans(n_clusters = 5, init = 'k-means++', random_state = 42)\n"
      ],
      "metadata": {
        "id": "bRcJQaarf_sN"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# We divide our data into the five clusters.\n",
        "kmeans.fit(df_std)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oqGFGTthgEaC",
        "outputId": "11abf8b5-fe9e-4620-b119-3fcaea4e0dae"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(n_clusters=5, random_state=42)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Results"
      ],
      "metadata": {
        "id": "i6jIRcJpgJB4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# We create a new data frame with the original features and add a new column with the assigned clusters for each point.\n",
        "df_segm_kmeans= df_std.copy()\n",
        "df_segm_kmeans = pd.DataFrame(data = df_std,columns = df.columns)\n",
        "df_segm_kmeans['Segment K-means'] = kmeans.labels_\n"
      ],
      "metadata": {
        "id": "gz56fc6BgH_-"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_segm_kmeans.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "OgZyePONgOyG",
        "outputId": "280f9e16-bdfb-47b3-f8f6-20709e69978f"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Sex  Marital status       Age  Education    Income  Occupation  \\\n",
              "0 -0.917399       -0.993024  2.653614   1.604323  0.097524    0.296823   \n",
              "1  1.090038        1.007025 -1.187132  -0.063372  0.782654    0.296823   \n",
              "2 -0.917399       -0.993024  1.117316  -0.063372 -0.833202   -1.269525   \n",
              "3 -0.917399       -0.993024  0.775916  -0.063372  1.328386    0.296823   \n",
              "4 -0.917399       -0.993024  1.458716  -0.063372  0.736932    0.296823   \n",
              "\n",
              "   Settlement size  Segment K-means  \n",
              "0         1.552326                2  \n",
              "1         1.552326                4  \n",
              "2        -0.909730                3  \n",
              "3         0.321298                0  \n",
              "4         0.321298                0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9fc955e0-a73d-4d4f-a702-c82de48468a1\">\n",
              "    <div 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>Sex</th>\n",
              "      <th>Marital status</th>\n",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Income</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Settlement size</th>\n",
              "      <th>Segment K-means</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.917399</td>\n",
              "      <td>-0.993024</td>\n",
              "      <td>2.653614</td>\n",
              "      <td>1.604323</td>\n",
              "      <td>0.097524</td>\n",
              "      <td>0.296823</td>\n",
              "      <td>1.552326</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.090038</td>\n",
              "      <td>1.007025</td>\n",
              "      <td>-1.187132</td>\n",
              "      <td>-0.063372</td>\n",
              "      <td>0.782654</td>\n",
              "      <td>0.296823</td>\n",
              "      <td>1.552326</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.917399</td>\n",
              "      <td>-0.993024</td>\n",
              "      <td>1.117316</td>\n",
              "      <td>-0.063372</td>\n",
              "      <td>-0.833202</td>\n",
              "      <td>-1.269525</td>\n",
              "      <td>-0.909730</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.917399</td>\n",
              "      <td>-0.993024</td>\n",
              "      <td>0.775916</td>\n",
              "      <td>-0.063372</td>\n",
              "      <td>1.328386</td>\n",
              "      <td>0.296823</td>\n",
              "      <td>0.321298</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.917399</td>\n",
              "      <td>-0.993024</td>\n",
              "      <td>1.458716</td>\n",
              "      <td>-0.063372</td>\n",
              "      <td>0.736932</td>\n",
              "      <td>0.296823</td>\n",
              "      <td>0.321298</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9fc955e0-a73d-4d4f-a702-c82de48468a1')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-9fc955e0-a73d-4d4f-a702-c82de48468a1 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-9fc955e0-a73d-4d4f-a702-c82de48468a1');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate mean values for the clusters\n",
        "df_segm_analysis = df_segm_kmeans.groupby(['Segment K-means']).mean()\n",
        "df_segm_analysis.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "1MI0wGPngQoU",
        "outputId": "3cd365d3-a679-4f1a-bc24-199021cbc157"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      Sex  Marital status       Age  Education    Income  \\\n",
              "Segment K-means                                                            \n",
              "0               -0.908820       -0.993024  0.086916  -0.605017  0.547311   \n",
              "1                0.756871        1.007025 -0.441755   0.143338 -0.626163   \n",
              "2                0.029198        0.356602  1.716153   1.848376  1.038427   \n",
              "3               -0.190645       -0.993024 -0.032553  -0.490420 -0.598992   \n",
              "4                0.404023        0.973410 -0.666187  -0.049358  0.161911   \n",
              "\n",
              "                 Occupation  Settlement size  \n",
              "Segment K-means                               \n",
              "0                  0.691757         0.952595  \n",
              "1                 -0.644302        -0.904557  \n",
              "2                  0.570616         0.506453  \n",
              "3                 -0.743952        -0.858097  \n",
              "4                  0.503037         0.697158  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-eb3aef32-982f-4d65-9d0a-60bc527e100e\">\n",
              "    <div 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>Sex</th>\n",
              "      <th>Marital status</th>\n",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Income</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Settlement size</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Segment K-means</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.908820</td>\n",
              "      <td>-0.993024</td>\n",
              "      <td>0.086916</td>\n",
              "      <td>-0.605017</td>\n",
              "      <td>0.547311</td>\n",
              "      <td>0.691757</td>\n",
              "      <td>0.952595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.756871</td>\n",
              "      <td>1.007025</td>\n",
              "      <td>-0.441755</td>\n",
              "      <td>0.143338</td>\n",
              "      <td>-0.626163</td>\n",
              "      <td>-0.644302</td>\n",
              "      <td>-0.904557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.029198</td>\n",
              "      <td>0.356602</td>\n",
              "      <td>1.716153</td>\n",
              "      <td>1.848376</td>\n",
              "      <td>1.038427</td>\n",
              "      <td>0.570616</td>\n",
              "      <td>0.506453</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.190645</td>\n",
              "      <td>-0.993024</td>\n",
              "      <td>-0.032553</td>\n",
              "      <td>-0.490420</td>\n",
              "      <td>-0.598992</td>\n",
              "      <td>-0.743952</td>\n",
              "      <td>-0.858097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.404023</td>\n",
              "      <td>0.973410</td>\n",
              "      <td>-0.666187</td>\n",
              "      <td>-0.049358</td>\n",
              "      <td>0.161911</td>\n",
              "      <td>0.503037</td>\n",
              "      <td>0.697158</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb3aef32-982f-4d65-9d0a-60bc527e100e')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-eb3aef32-982f-4d65-9d0a-60bc527e100e 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-eb3aef32-982f-4d65-9d0a-60bc527e100e');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_segm_analysis.rename({0:'well-off',\n",
        "                         1:'fewer-opportunities',\n",
        "                         2:'standard',\n",
        "                         3:'career focused'})\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "wMArZix5gSfF",
        "outputId": "dcbbc4a4-9c95-45c4-e500-7e7eec180824"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          Sex  Marital status       Age  Education    Income  \\\n",
              "Segment K-means                                                                \n",
              "well-off            -0.908820       -0.993024  0.086916  -0.605017  0.547311   \n",
              "fewer-opportunities  0.756871        1.007025 -0.441755   0.143338 -0.626163   \n",
              "standard             0.029198        0.356602  1.716153   1.848376  1.038427   \n",
              "career focused      -0.190645       -0.993024 -0.032553  -0.490420 -0.598992   \n",
              "4                    0.404023        0.973410 -0.666187  -0.049358  0.161911   \n",
              "\n",
              "                     Occupation  Settlement size  \n",
              "Segment K-means                                   \n",
              "well-off               0.691757         0.952595  \n",
              "fewer-opportunities   -0.644302        -0.904557  \n",
              "standard               0.570616         0.506453  \n",
              "career focused        -0.743952        -0.858097  \n",
              "4                      0.503037         0.697158  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3aa08073-afb2-456b-b79c-8143c3300e2f\">\n",
              "    <div 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>Sex</th>\n",
              "      <th>Marital status</th>\n",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Income</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Settlement size</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Segment K-means</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>well-off</th>\n",
              "      <td>-0.908820</td>\n",
              "      <td>-0.993024</td>\n",
              "      <td>0.086916</td>\n",
              "      <td>-0.605017</td>\n",
              "      <td>0.547311</td>\n",
              "      <td>0.691757</td>\n",
              "      <td>0.952595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fewer-opportunities</th>\n",
              "      <td>0.756871</td>\n",
              "      <td>1.007025</td>\n",
              "      <td>-0.441755</td>\n",
              "      <td>0.143338</td>\n",
              "      <td>-0.626163</td>\n",
              "      <td>-0.644302</td>\n",
              "      <td>-0.904557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>standard</th>\n",
              "      <td>0.029198</td>\n",
              "      <td>0.356602</td>\n",
              "      <td>1.716153</td>\n",
              "      <td>1.848376</td>\n",
              "      <td>1.038427</td>\n",
              "      <td>0.570616</td>\n",
              "      <td>0.506453</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>career focused</th>\n",
              "      <td>-0.190645</td>\n",
              "      <td>-0.993024</td>\n",
              "      <td>-0.032553</td>\n",
              "      <td>-0.490420</td>\n",
              "      <td>-0.598992</td>\n",
              "      <td>-0.743952</td>\n",
              "      <td>-0.858097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.404023</td>\n",
              "      <td>0.973410</td>\n",
              "      <td>-0.666187</td>\n",
              "      <td>-0.049358</td>\n",
              "      <td>0.161911</td>\n",
              "      <td>0.503037</td>\n",
              "      <td>0.697158</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3aa08073-afb2-456b-b79c-8143c3300e2f')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-3aa08073-afb2-456b-b79c-8143c3300e2f 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-3aa08073-afb2-456b-b79c-8143c3300e2f');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Add the segment labels to our table\n",
        "df_segm_kmeans['Labels'] = df_segm_kmeans['Segment K-means'].map({0:'well-off', \n",
        "                                                                  1:'fewer opportunities',\n",
        "                                                                  2:'standard', \n",
        "                                                                  3:'career focused'})\n"
      ],
      "metadata": {
        "id": "4CodEsxcgT2U"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_segm_kmeans.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Rp4mDqXxgWBH",
        "outputId": "dcf20c6f-8da5-40f5-d76a-61f750895cd5"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Sex  Marital status       Age  Education    Income  Occupation  \\\n",
              "0 -0.917399       -0.993024  2.653614   1.604323  0.097524    0.296823   \n",
              "1  1.090038        1.007025 -1.187132  -0.063372  0.782654    0.296823   \n",
              "2 -0.917399       -0.993024  1.117316  -0.063372 -0.833202   -1.269525   \n",
              "3 -0.917399       -0.993024  0.775916  -0.063372  1.328386    0.296823   \n",
              "4 -0.917399       -0.993024  1.458716  -0.063372  0.736932    0.296823   \n",
              "\n",
              "   Settlement size  Segment K-means          Labels  \n",
              "0         1.552326                2        standard  \n",
              "1         1.552326                4             NaN  \n",
              "2        -0.909730                3  career focused  \n",
              "3         0.321298                0        well-off  \n",
              "4         0.321298                0        well-off  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a9bfc7ab-1ed4-4538-a914-12d75e361111\">\n",
              "    <div 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>Sex</th>\n",
              "      <th>Marital status</th>\n",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Income</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Settlement size</th>\n",
              "      <th>Segment K-means</th>\n",
              "      <th>Labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.917399</td>\n",
              "      <td>-0.993024</td>\n",
              "      <td>2.653614</td>\n",
              "      <td>1.604323</td>\n",
              "      <td>0.097524</td>\n",
              "      <td>0.296823</td>\n",
              "      <td>1.552326</td>\n",
              "      <td>2</td>\n",
              "      <td>standard</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.090038</td>\n",
              "      <td>1.007025</td>\n",
              "      <td>-1.187132</td>\n",
              "      <td>-0.063372</td>\n",
              "      <td>0.782654</td>\n",
              "      <td>0.296823</td>\n",
              "      <td>1.552326</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.917399</td>\n",
              "      <td>-0.993024</td>\n",
              "      <td>1.117316</td>\n",
              "      <td>-0.063372</td>\n",
              "      <td>-0.833202</td>\n",
              "      <td>-1.269525</td>\n",
              "      <td>-0.909730</td>\n",
              "      <td>3</td>\n",
              "      <td>career focused</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.917399</td>\n",
              "      <td>-0.993024</td>\n",
              "      <td>0.775916</td>\n",
              "      <td>-0.063372</td>\n",
              "      <td>1.328386</td>\n",
              "      <td>0.296823</td>\n",
              "      <td>0.321298</td>\n",
              "      <td>0</td>\n",
              "      <td>well-off</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.917399</td>\n",
              "      <td>-0.993024</td>\n",
              "      <td>1.458716</td>\n",
              "      <td>-0.063372</td>\n",
              "      <td>0.736932</td>\n",
              "      <td>0.296823</td>\n",
              "      <td>0.321298</td>\n",
              "      <td>0</td>\n",
              "      <td>well-off</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a9bfc7ab-1ed4-4538-a914-12d75e361111')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-a9bfc7ab-1ed4-4538-a914-12d75e361111 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-a9bfc7ab-1ed4-4538-a914-12d75e361111');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We plot the results from the K-means algorithm. \n",
        "# Each point in our data set is plotted with the color of the clusters it has been assigned to.\n",
        "x_axis = df_segm_kmeans['Age']\n",
        "y_axis = df_segm_kmeans['Income']\n",
        "plt.figure(figsize = (10, 8))\n",
        "sns.scatterplot(x_axis, y_axis, hue = df_segm_kmeans['Labels'], palette = ['g', 'r', 'c', 'm'])\n",
        "plt.title('Segmentation K-means')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "JU_RR9CEgXWC",
        "outputId": "f7e13c36-68b6-4109-c11d-2220284f88d3"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Principal component analysis (PCA)"
      ],
      "metadata": {
        "id": "V1SW1-t3gkWW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Principal component analysis, or PCA, is a statistical procedure that allows you to summarize the information content in large data tables by means of a smaller set of “summary indices” that can be more easily visualized and analyzed.\n"
      ],
      "metadata": {
        "id": "a0e2ve1jgsIw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pca = PCA()\n"
      ],
      "metadata": {
        "id": "csMnnEQSgoys"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit PCA with our standardized data.\n",
        "pca.fit(df_std)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mjl0dDUCgxSM",
        "outputId": "04f42221-ba09-464f-a8e4-1bc4f60190ce"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PCA()"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pca.explained_variance_ratio_\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xh4nOo7igzg6",
        "outputId": "33f7fb1e-9147-47cb-e1c2-d196e32481c0"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.35696328, 0.26250923, 0.18821114, 0.0755775 , 0.05716512,\n",
              "       0.03954794, 0.02002579])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the cumulative variance explained by total number of components.\n",
        "plt.figure(figsize = (12,9))\n",
        "plt.plot(range(1,8), pca.explained_variance_ratio_.cumsum(), marker = 'o', linestyle = '-.',color='red')\n",
        "plt.title('Explained Variance by Components',fontsize=16)\n",
        "plt.xlabel('Number of Components',fontsize=14)\n",
        "plt.ylabel('Cumulative Explained Variance',fontsize=14)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        },
        "id": "9lPdRnuKg0Rq",
        "outputId": "c9ee1b0e-8603-4b49-cf51-b102dbb87bde"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Cumulative Explained Variance')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We choose three components.4 seems the right choice according to the previous graph.\n",
        "pca = PCA(n_components = 4)\n"
      ],
      "metadata": {
        "id": "NYa1BDUBg9NO"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pca.fit(df_std)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7sDNeVVWg_fU",
        "outputId": "2788769e-c3ad-4d64-aaa8-eb85a3c7818e"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PCA(n_components=4)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### PCA Results"
      ],
      "metadata": {
        "id": "JIWRt-9xhCgC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pca.components_\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HVE72qu0hBEn",
        "outputId": "8c91857e-ba1d-4f31-b295-802cb82001d7"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.31469524, -0.19170439,  0.32609979,  0.15684089,  0.52452463,\n",
              "         0.49205868,  0.46478852],\n",
              "       [ 0.45800608,  0.51263492,  0.31220793,  0.63980683,  0.12468314,\n",
              "         0.01465779, -0.06963165],\n",
              "       [-0.29301261, -0.44197739,  0.60954372,  0.27560461, -0.16566231,\n",
              "        -0.39550539, -0.29568503],\n",
              "       [-0.31560091,  0.28045392, -0.00950602,  0.18147643, -0.48260023,\n",
              "        -0.28169039,  0.69026455]])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_pca_comp = pd.DataFrame(data = pca.components_,\n",
        "                           columns = df.columns,\n",
        "                           index = ['Component 1', 'Component 2', 'Component 3', 'Component 4'])\n",
        "df_pca_comp\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "3_YpyRRrhHjN",
        "outputId": "5b1511b1-696d-4a7e-c31e-b4186b4eb7ef"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Sex  Marital status       Age  Education    Income  \\\n",
              "Component 1 -0.314695       -0.191704  0.326100   0.156841  0.524525   \n",
              "Component 2  0.458006        0.512635  0.312208   0.639807  0.124683   \n",
              "Component 3 -0.293013       -0.441977  0.609544   0.275605 -0.165662   \n",
              "Component 4 -0.315601        0.280454 -0.009506   0.181476 -0.482600   \n",
              "\n",
              "             Occupation  Settlement size  \n",
              "Component 1    0.492059         0.464789  \n",
              "Component 2    0.014658        -0.069632  \n",
              "Component 3   -0.395505        -0.295685  \n",
              "Component 4   -0.281690         0.690265  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d688a29c-4717-4f0b-bb18-67ac7181a16a\">\n",
              "    <div 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>Sex</th>\n",
              "      <th>Marital status</th>\n",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Income</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Settlement size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Component 1</th>\n",
              "      <td>-0.314695</td>\n",
              "      <td>-0.191704</td>\n",
              "      <td>0.326100</td>\n",
              "      <td>0.156841</td>\n",
              "      <td>0.524525</td>\n",
              "      <td>0.492059</td>\n",
              "      <td>0.464789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Component 2</th>\n",
              "      <td>0.458006</td>\n",
              "      <td>0.512635</td>\n",
              "      <td>0.312208</td>\n",
              "      <td>0.639807</td>\n",
              "      <td>0.124683</td>\n",
              "      <td>0.014658</td>\n",
              "      <td>-0.069632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Component 3</th>\n",
              "      <td>-0.293013</td>\n",
              "      <td>-0.441977</td>\n",
              "      <td>0.609544</td>\n",
              "      <td>0.275605</td>\n",
              "      <td>-0.165662</td>\n",
              "      <td>-0.395505</td>\n",
              "      <td>-0.295685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Component 4</th>\n",
              "      <td>-0.315601</td>\n",
              "      <td>0.280454</td>\n",
              "      <td>-0.009506</td>\n",
              "      <td>0.181476</td>\n",
              "      <td>-0.482600</td>\n",
              "      <td>-0.281690</td>\n",
              "      <td>0.690265</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d688a29c-4717-4f0b-bb18-67ac7181a16a')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-d688a29c-4717-4f0b-bb18-67ac7181a16a 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-d688a29c-4717-4f0b-bb18-67ac7181a16a');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Heat Map for Principal Components against original features. Again we use the RdBu color scheme and set borders to -1 and 1.\n",
        "plt.figure(figsize=(12,9))\n",
        "sns.heatmap(df_pca_comp,\n",
        "            vmin = -1, \n",
        "            vmax = 1,\n",
        "            cmap = 'RdBu',\n",
        "            annot = True)\n",
        "plt.yticks([0, 1, 2, 3], \n",
        "           ['Component 1', 'Component 2', 'Component 3', 'Component 4'],\n",
        "           rotation = 45,\n",
        "           fontsize = 12)\n",
        "plt.title('Components vs Original Features',fontsize = 14)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "0RpgaMtohOBA",
        "outputId": "37e61916-def6-4597-a5b5-fb49ece19edb"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pca.transform(df_std)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RaaNptO0hb5U",
        "outputId": "7574cb9e-39e3-4ceb-d168-d383882d3b62"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 2.51474593,  0.83412239,  2.1748059 ,  1.21779432],\n",
              "       [ 0.34493528,  0.59814564, -2.21160279,  0.54838542],\n",
              "       [-0.65106267, -0.68009318,  2.2804186 ,  0.12067518],\n",
              "       ...,\n",
              "       [-1.45229829, -2.23593665,  0.89657125, -0.13177412],\n",
              "       [-2.24145254,  0.62710847, -0.53045631, -0.04260559],\n",
              "       [-1.86688505, -2.45467234,  0.66262172,  0.10089556]])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores_pca = pca.transform(df_std)\n"
      ],
      "metadata": {
        "id": "sl86cvAHhcXg"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##K-Means Clustering with PCA"
      ],
      "metadata": {
        "id": "Fe2LoI0Phe2n"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "It is a common practice to apply PCA (principal component analysis) before a clustering algorithm (such as k-means). It is believed that it improves the clustering results in practice (noise reduction).\n",
        "\n"
      ],
      "metadata": {
        "id": "noGPbaB0hrYM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# We fit K means using the transformed data from the PCA.\n",
        "wcss = []\n",
        "for i in range(1,11):\n",
        "    kmeans_pca = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n",
        "    kmeans_pca.fit(scores_pca)\n",
        "    wcss.append(kmeans_pca.inertia_)\n"
      ],
      "metadata": {
        "id": "873Ux81Qhxcc"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (10,8))\n",
        "plt.plot(range(1, 11), wcss, marker = 'o', linestyle = '-.',color='red')\n",
        "plt.xlabel('Number of Clusters')\n",
        "plt.ylabel('WCSS')\n",
        "plt.title('K-means with PCA Clustering',fontsize = 16)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "6EEMpPAUhzkJ",
        "outputId": "8b3712b1-61a7-44c9-f639-a80298043ccd"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We have chosen four clusters again, so we run K-means with number of clusters equals four. \n",
        "kmeans_pca = KMeans(n_clusters = 4, init = 'k-means++', random_state = 42)\n"
      ],
      "metadata": {
        "id": "SCExXrqqh2p3"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans_pca.fit(scores_pca)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EM08nwKeh8fx",
        "outputId": "3cbeecdc-577f-4687-8f01-0bb90761e757"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(n_clusters=4, random_state=42)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###K-Means Clustering with PCA Results"
      ],
      "metadata": {
        "id": "nAWULYV2iC9z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# We create a new data frame with the original features and add the PCA scores and assigned clusters.\n",
        "df_segm_pca_kmeans = pd.concat([df.reset_index(drop = True), pd.DataFrame(scores_pca)], axis = 1)\n",
        "df_segm_pca_kmeans.columns.values[-3: ] = ['Component 1', 'Component 2', 'Component 3', ]\n",
        "df_segm_pca_kmeans['Segment K-means PCA'] = kmeans_pca.labels_\n"
      ],
      "metadata": {
        "id": "_cI6NZ3tiEHg"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_segm_pca_kmeans.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "4ElAcdO7iGYi",
        "outputId": "a23bda4e-b0bc-43aa-a3cd-b425ff231cf9"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Sex  Marital status  Age  Education  Income  Occupation  Settlement size  \\\n",
              "0    0               0   67          2  124670           1                2   \n",
              "1    1               1   22          1  150773           1                2   \n",
              "2    0               0   49          1   89210           0                0   \n",
              "3    0               0   45          1  171565           1                1   \n",
              "4    0               0   53          1  149031           1                1   \n",
              "\n",
              "          0  Component 1  Component 2  Component 3  Segment K-means PCA  \n",
              "0  2.514746     0.834122     2.174806     1.217794                    3  \n",
              "1  0.344935     0.598146    -2.211603     0.548385                    0  \n",
              "2 -0.651063    -0.680093     2.280419     0.120675                    2  \n",
              "3  1.714316    -0.579927     0.730731    -0.510753                    1  \n",
              "4  1.626745    -0.440496     1.244909    -0.231808                    1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-399824a3-feb5-4be9-a477-1f8fea3d2af2\">\n",
              "    <div 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>Sex</th>\n",
              "      <th>Marital status</th>\n",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Income</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Settlement size</th>\n",
              "      <th>0</th>\n",
              "      <th>Component 1</th>\n",
              "      <th>Component 2</th>\n",
              "      <th>Component 3</th>\n",
              "      <th>Segment K-means PCA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>67</td>\n",
              "      <td>2</td>\n",
              "      <td>124670</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2.514746</td>\n",
              "      <td>0.834122</td>\n",
              "      <td>2.174806</td>\n",
              "      <td>1.217794</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>22</td>\n",
              "      <td>1</td>\n",
              "      <td>150773</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.344935</td>\n",
              "      <td>0.598146</td>\n",
              "      <td>-2.211603</td>\n",
              "      <td>0.548385</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>49</td>\n",
              "      <td>1</td>\n",
              "      <td>89210</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.651063</td>\n",
              "      <td>-0.680093</td>\n",
              "      <td>2.280419</td>\n",
              "      <td>0.120675</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "      <td>171565</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.714316</td>\n",
              "      <td>-0.579927</td>\n",
              "      <td>0.730731</td>\n",
              "      <td>-0.510753</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>53</td>\n",
              "      <td>1</td>\n",
              "      <td>149031</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.626745</td>\n",
              "      <td>-0.440496</td>\n",
              "      <td>1.244909</td>\n",
              "      <td>-0.231808</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-399824a3-feb5-4be9-a477-1f8fea3d2af2')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-399824a3-feb5-4be9-a477-1f8fea3d2af2 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-399824a3-feb5-4be9-a477-1f8fea3d2af2');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We calculate the means by segments.\n",
        "df_segm_pca_kmeans_freq = df_segm_pca_kmeans.groupby(['Segment K-means PCA']).mean()\n",
        "df_segm_pca_kmeans_freq\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "RNcQBgAGiI4X",
        "outputId": "e7af56b5-8781-4b42-ccac-6e7a99089087"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          Sex  Marital status        Age  Education  \\\n",
              "Segment K-means PCA                                                   \n",
              "0                    0.905045        0.986647  28.873887   1.063798   \n",
              "1                    0.026178        0.178010  35.624782   0.734729   \n",
              "2                    0.319018        0.089980  35.259714   0.768916   \n",
              "3                    0.503788        0.689394  55.689394   2.128788   \n",
              "\n",
              "                            Income  Occupation  Settlement size         0  \\\n",
              "Segment K-means PCA                                                         \n",
              "0                    107576.228487    0.672107         0.439169 -1.122432   \n",
              "1                    140950.319372    1.267016         1.520070  1.381089   \n",
              "2                     95850.155419    0.296524         0.038855 -0.987777   \n",
              "3                    158209.094697    1.125000         1.106061  1.697646   \n",
              "\n",
              "                     Component 1  Component 2  Component 3  \n",
              "Segment K-means PCA                                         \n",
              "0                       0.733291    -0.797918    -0.019662  \n",
              "1                      -1.044848    -0.273292     0.211736  \n",
              "2                      -0.882022     0.965476    -0.271684  \n",
              "3                       2.029427     0.841953     0.093869  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3df6222b-fb37-4222-a0fa-766af52bd5ea\">\n",
              "    <div 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>Sex</th>\n",
              "      <th>Marital status</th>\n",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Income</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Settlement size</th>\n",
              "      <th>0</th>\n",
              "      <th>Component 1</th>\n",
              "      <th>Component 2</th>\n",
              "      <th>Component 3</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Segment K-means PCA</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.905045</td>\n",
              "      <td>0.986647</td>\n",
              "      <td>28.873887</td>\n",
              "      <td>1.063798</td>\n",
              "      <td>107576.228487</td>\n",
              "      <td>0.672107</td>\n",
              "      <td>0.439169</td>\n",
              "      <td>-1.122432</td>\n",
              "      <td>0.733291</td>\n",
              "      <td>-0.797918</td>\n",
              "      <td>-0.019662</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.026178</td>\n",
              "      <td>0.178010</td>\n",
              "      <td>35.624782</td>\n",
              "      <td>0.734729</td>\n",
              "      <td>140950.319372</td>\n",
              "      <td>1.267016</td>\n",
              "      <td>1.520070</td>\n",
              "      <td>1.381089</td>\n",
              "      <td>-1.044848</td>\n",
              "      <td>-0.273292</td>\n",
              "      <td>0.211736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.319018</td>\n",
              "      <td>0.089980</td>\n",
              "      <td>35.259714</td>\n",
              "      <td>0.768916</td>\n",
              "      <td>95850.155419</td>\n",
              "      <td>0.296524</td>\n",
              "      <td>0.038855</td>\n",
              "      <td>-0.987777</td>\n",
              "      <td>-0.882022</td>\n",
              "      <td>0.965476</td>\n",
              "      <td>-0.271684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.503788</td>\n",
              "      <td>0.689394</td>\n",
              "      <td>55.689394</td>\n",
              "      <td>2.128788</td>\n",
              "      <td>158209.094697</td>\n",
              "      <td>1.125000</td>\n",
              "      <td>1.106061</td>\n",
              "      <td>1.697646</td>\n",
              "      <td>2.029427</td>\n",
              "      <td>0.841953</td>\n",
              "      <td>0.093869</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3df6222b-fb37-4222-a0fa-766af52bd5ea')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-3df6222b-fb37-4222-a0fa-766af52bd5ea 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-3df6222b-fb37-4222-a0fa-766af52bd5ea');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the size of each cluster and its proportion to the entire data set.\n",
        "df_segm_pca_kmeans_freq['N Obs'] = df_segm_pca_kmeans[['Segment K-means PCA','Sex']].groupby(['Segment K-means PCA']).count()\n",
        "df_segm_pca_kmeans_freq['Prop Obs'] = df_segm_pca_kmeans_freq['N Obs'] / df_segm_pca_kmeans_freq['N Obs'].sum()\n",
        "df_segm_pca_kmeans_freq = df_segm_pca_kmeans_freq.rename({0:'standard', \n",
        "                                                          1:'career focused',\n",
        "                                                          2:'fewer opportunities', \n",
        "                                                          3:'well-off'})\n",
        "df_segm_pca_kmeans_freq\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "UXVV4xShiLK6",
        "outputId": "f2af35af-df57-46ae-f6e2-30974e4e92b1"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          Sex  Marital status        Age  Education  \\\n",
              "Segment K-means PCA                                                   \n",
              "standard             0.905045        0.986647  28.873887   1.063798   \n",
              "career focused       0.026178        0.178010  35.624782   0.734729   \n",
              "fewer opportunities  0.319018        0.089980  35.259714   0.768916   \n",
              "well-off             0.503788        0.689394  55.689394   2.128788   \n",
              "\n",
              "                            Income  Occupation  Settlement size         0  \\\n",
              "Segment K-means PCA                                                         \n",
              "standard             107576.228487    0.672107         0.439169 -1.122432   \n",
              "career focused       140950.319372    1.267016         1.520070  1.381089   \n",
              "fewer opportunities   95850.155419    0.296524         0.038855 -0.987777   \n",
              "well-off             158209.094697    1.125000         1.106061  1.697646   \n",
              "\n",
              "                     Component 1  Component 2  Component 3  N Obs  Prop Obs  \n",
              "Segment K-means PCA                                                          \n",
              "standard                0.733291    -0.797918    -0.019662    674    0.3370  \n",
              "career focused         -1.044848    -0.273292     0.211736    573    0.2865  \n",
              "fewer opportunities    -0.882022     0.965476    -0.271684    489    0.2445  \n",
              "well-off                2.029427     0.841953     0.093869    264    0.1320  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ff2d58f4-dc38-4a7c-b672-0a41d1cd4529\">\n",
              "    <div 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>Sex</th>\n",
              "      <th>Marital status</th>\n",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Income</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Settlement size</th>\n",
              "      <th>0</th>\n",
              "      <th>Component 1</th>\n",
              "      <th>Component 2</th>\n",
              "      <th>Component 3</th>\n",
              "      <th>N Obs</th>\n",
              "      <th>Prop Obs</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Segment K-means PCA</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>standard</th>\n",
              "      <td>0.905045</td>\n",
              "      <td>0.986647</td>\n",
              "      <td>28.873887</td>\n",
              "      <td>1.063798</td>\n",
              "      <td>107576.228487</td>\n",
              "      <td>0.672107</td>\n",
              "      <td>0.439169</td>\n",
              "      <td>-1.122432</td>\n",
              "      <td>0.733291</td>\n",
              "      <td>-0.797918</td>\n",
              "      <td>-0.019662</td>\n",
              "      <td>674</td>\n",
              "      <td>0.3370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>career focused</th>\n",
              "      <td>0.026178</td>\n",
              "      <td>0.178010</td>\n",
              "      <td>35.624782</td>\n",
              "      <td>0.734729</td>\n",
              "      <td>140950.319372</td>\n",
              "      <td>1.267016</td>\n",
              "      <td>1.520070</td>\n",
              "      <td>1.381089</td>\n",
              "      <td>-1.044848</td>\n",
              "      <td>-0.273292</td>\n",
              "      <td>0.211736</td>\n",
              "      <td>573</td>\n",
              "      <td>0.2865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fewer opportunities</th>\n",
              "      <td>0.319018</td>\n",
              "      <td>0.089980</td>\n",
              "      <td>35.259714</td>\n",
              "      <td>0.768916</td>\n",
              "      <td>95850.155419</td>\n",
              "      <td>0.296524</td>\n",
              "      <td>0.038855</td>\n",
              "      <td>-0.987777</td>\n",
              "      <td>-0.882022</td>\n",
              "      <td>0.965476</td>\n",
              "      <td>-0.271684</td>\n",
              "      <td>489</td>\n",
              "      <td>0.2445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>well-off</th>\n",
              "      <td>0.503788</td>\n",
              "      <td>0.689394</td>\n",
              "      <td>55.689394</td>\n",
              "      <td>2.128788</td>\n",
              "      <td>158209.094697</td>\n",
              "      <td>1.125000</td>\n",
              "      <td>1.106061</td>\n",
              "      <td>1.697646</td>\n",
              "      <td>2.029427</td>\n",
              "      <td>0.841953</td>\n",
              "      <td>0.093869</td>\n",
              "      <td>264</td>\n",
              "      <td>0.1320</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ff2d58f4-dc38-4a7c-b672-0a41d1cd4529')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-ff2d58f4-dc38-4a7c-b672-0a41d1cd4529 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-ff2d58f4-dc38-4a7c-b672-0a41d1cd4529');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_segm_pca_kmeans['Legend'] = df_segm_pca_kmeans['Segment K-means PCA'].map({0:'standard', \n",
        "                                                          1:'career focused',\n",
        "                                                          2:'fewer opportunities', \n",
        "                                                          3:'well-off'})\n"
      ],
      "metadata": {
        "id": "o8wxHv05iL5w"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot data by PCA components. The Y axis is the first component, X axis is the second.\n",
        "x_axis = df_segm_pca_kmeans['Component 1']\n",
        "y_axis = df_segm_pca_kmeans['Component 2']\n",
        "plt.figure(figsize = (10, 8))\n",
        "sns.scatterplot(x_axis, y_axis, hue = df_segm_pca_kmeans['Legend'], palette = ['g', 'r', 'c', 'm'])\n",
        "plt.title('Clusters by PCA Components',fontsize=14)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "0blxU_M9iUBx",
        "outputId": "7d341b75-837a-4b5e-f519-e855abc5396c"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "When we plotted the K means clustering solution without PCA, we were only able to distinguish the green segment, but the division based on the components is much more pronounced.\n",
        "\n"
      ],
      "metadata": {
        "id": "4vQc8q6-kR9K"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#3.Conclusion\n",
        "\n"
      ],
      "metadata": {
        "id": "xES_W9jukVY9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We segmented our customers into 4 groups. We are ready to start to choose our groups based on our aims and marketing them!\n",
        "\n",
        "Segmentation helps marketers to be more efficient in terms of time, money and other resources.\n",
        "\n",
        "They gain a better understanding of customer's needs and wants and therefore can tailor campaigns to customer segments most likely to purchase products.\n"
      ],
      "metadata": {
        "id": "T77jsjgYkaD7"
      }
    }
  ]
}