01_Practica2_Clustering.ipynb

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        "<a href=\"https://colab.research.google.com/github/DanielIramain/aa-countries/blob/dev/01_Practica2_Clustering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "## Práctica 2\n",
        "En esta práctica vamos a trabajar con datos socioeconómicos de distintos paises con el objetivo de realizar un análisis de clustering que permita determinar si se pueden establecer grupos de paises a partir de esta clase de datos.\n",
        "\n",
        "El dataset tendrán que descargarlo del siguiente [link](https://www.kaggle.com/datasets/rohan0301/unsupervised-learning-on-country-data). Luego pueden importarlo a su sesión de Colab directamente desde sus PC locales o ubicarlo en su Drive.\n",
        "\n",
        "Tienen que probar por lo menos una de las técnicas de clustering vistas en clase, utilizando distintos criterios a la hora generar los clusters. Luego realizar una validación interna de los resultados.\n",
        "\n",
        "**EXTRA:** En el dataset no hay ninguna variable que agrupe a los paises en distintas categorias que nos permita realizar una validación cruzada. ¿Se les ocurre qué tipo de información externa podría usar para este problema? ¿Se animan a crear una variable de este tipo a partir del criterio elegido?"
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        "!pip install kneed"
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            "Requirement already satisfied: kneed in /usr/local/lib/python3.10/dist-packages (0.8.5)\n",
            "Requirement already satisfied: numpy>=1.14.2 in /usr/local/lib/python3.10/dist-packages (from kneed) (1.23.5)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from kneed) (1.11.2)\n"
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        "id": "EUDy5rj7BG54"
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      "source": [
        "#Librerias generales\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from random import sample\n",
        "from numpy.random import uniform\n",
        "%matplotlib inline\n",
        "\n",
        "#sklearn\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.neighbors import NearestNeighbors\n",
        "from sklearn.cluster import AgglomerativeClustering, KMeans, DBSCAN\n",
        "from sklearn.metrics import davies_bouldin_score, silhouette_score\n",
        "from sklearn.metrics.cluster import adjusted_rand_score\n",
        "\n",
        "#Visualizacion\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "#Otros\n",
        "import scipy.cluster.hierarchy as shc\n",
        "from yellowbrick.cluster import silhouette_visualizer\n",
        "from kneed import KneeLocator"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('Country-data.csv')"
      ],
      "metadata": {
        "id": "x6gvaqPjOeJ6"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "markdown",
      "source": [
        "###Preprocesamiento"
      ],
      "metadata": {
        "id": "9R4xDXJyXGn9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For the current 2024 fiscal year, low-income economies are defined as those with a GNI per capita (in dollars), calculated using the World Bank Atlas method, of 1,135 or less in 2022; lower middle-income economies are those with a GNI per capita between 1,136 and 4,465; upper middle-income economies are those with a GNI per capita between 4,466 and 13,845; high-income economies are those with a GNI per capita of 13,846 or more.\n",
        "\n",
        "Please note: Regions in this table include economies at all income levels. The term country, used interchangeably with economy, does not imply political independence but refers to any territory for which authorities report separate social or economic statistics. Click here for information about how the World Bank classifies countries.\n",
        "\n",
        "1. low-income = 0\n",
        "2. lower middle-income = 1\n",
        "3. upper middle-income = 2\n",
        "4. high-income = 3"
      ],
      "metadata": {
        "id": "5_a2wkPTQNnu"
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    {
      "cell_type": "code",
      "source": [
        "#¿por que tuve que estructurarlo asi?\n",
        "for i in range(0, len(data['income'] + 1), 1):\n",
        "  valor = data['income'][i]\n",
        "  if (valor <= 1135):\n",
        "    data.at[i, 'class'] = 0\n",
        "  elif (valor >= 1136) and (valor <= 4465):\n",
        "    data.at[i, 'class'] = 1\n",
        "  elif (valor >= 4466) and (valor <= 13845):\n",
        "    data.at[i, 'class'] = 2\n",
        "  else:\n",
        "    data.at[i, 'class'] = 3"
      ],
      "metadata": {
        "id": "wiu_pUFfOxw6"
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      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Columna clase en primer lugar (usando list comprehension)\n",
        "col_comienzo = ['class']\n",
        "data = data[[c for c in col_comienzo if c in data] + [c for c in data if c not in col_comienzo]]"
      ],
      "metadata": {
        "id": "cUMFhIwqX3My"
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      "execution_count": 8,
      "outputs": []
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    {
      "cell_type": "markdown",
      "source": [
        "Veamos la estructura del dataset. Nótese que todas las variables con numéricas continuas, esto nos permite recurrir a distancia euclidean."
      ],
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              "     class              country  child_mort  exports  health  imports  income  \\\n",
              "0      1.0          Afghanistan        90.2     10.0    7.58     44.9    1610   \n",
              "1      2.0              Albania        16.6     28.0    6.55     48.6    9930   \n",
              "2      2.0              Algeria        27.3     38.4    4.17     31.4   12900   \n",
              "3      2.0               Angola       119.0     62.3    2.85     42.9    5900   \n",
              "4      3.0  Antigua and Barbuda        10.3     45.5    6.03     58.9   19100   \n",
              "..     ...                  ...         ...      ...     ...      ...     ...   \n",
              "162    1.0              Vanuatu        29.2     46.6    5.25     52.7    2950   \n",
              "163    3.0            Venezuela        17.1     28.5    4.91     17.6   16500   \n",
              "164    2.0              Vietnam        23.3     72.0    6.84     80.2    4490   \n",
              "165    2.0                Yemen        56.3     30.0    5.18     34.4    4480   \n",
              "166    1.0               Zambia        83.1     37.0    5.89     30.9    3280   \n",
              "\n",
              "     inflation  life_expec  total_fer   gdpp  \n",
              "0         9.44        56.2       5.82    553  \n",
              "1         4.49        76.3       1.65   4090  \n",
              "2        16.10        76.5       2.89   4460  \n",
              "3        22.40        60.1       6.16   3530  \n",
              "4         1.44        76.8       2.13  12200  \n",
              "..         ...         ...        ...    ...  \n",
              "162       2.62        63.0       3.50   2970  \n",
              "163      45.90        75.4       2.47  13500  \n",
              "164      12.10        73.1       1.95   1310  \n",
              "165      23.60        67.5       4.67   1310  \n",
              "166      14.00        52.0       5.40   1460  \n",
              "\n",
              "[167 rows x 11 columns]"
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              "      <td>Afghanistan</td>\n",
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              "      <th>3</th>\n",
              "      <td>2.0</td>\n",
              "      <td>Angola</td>\n",
              "      <td>119.0</td>\n",
              "      <td>62.3</td>\n",
              "      <td>2.85</td>\n",
              "      <td>42.9</td>\n",
              "      <td>5900</td>\n",
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              "      <td>6.03</td>\n",
              "      <td>58.9</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>Vanuatu</td>\n",
              "      <td>29.2</td>\n",
              "      <td>46.6</td>\n",
              "      <td>5.25</td>\n",
              "      <td>52.7</td>\n",
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              "      <td>23.3</td>\n",
              "      <td>72.0</td>\n",
              "      <td>6.84</td>\n",
              "      <td>80.2</td>\n",
              "      <td>4490</td>\n",
              "      <td>12.10</td>\n",
              "      <td>73.1</td>\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
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              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-38e96337-c174-4990-bfb0-0c084cd5760e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#La cantidad de países y el número de atributos sin contar class que se\n",
        "#refiere a la clasificación del país según su ingreso per capita\n",
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YKCaA9s8XPuF",
        "outputId": "f53c47e8-5818-4142-b8c9-b17618361cbe"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(167, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Las diferencias notables entre las medias y desvíos de los atributos entre sí van a representar un problema a la hora de hacer clustering. Por este motivo, una primera aproximación es realizar una estandarización.  "
      ],
      "metadata": {
        "id": "VCW4kdyrX8Vl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "-m9PVn_8XSHc",
        "outputId": "84c784ce-1d1a-44db-92dd-d7e63cd9f2ab"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            class  child_mort     exports      health     imports  \\\n",
              "count  167.000000  167.000000  167.000000  167.000000  167.000000   \n",
              "mean     2.083832   38.270060   41.108976    6.815689   46.890215   \n",
              "std      0.914496   40.328931   27.412010    2.746837   24.209589   \n",
              "min      0.000000    2.600000    0.109000    1.810000    0.065900   \n",
              "25%      1.000000    8.250000   23.800000    4.920000   30.200000   \n",
              "50%      2.000000   19.300000   35.000000    6.320000   43.300000   \n",
              "75%      3.000000   62.100000   51.350000    8.600000   58.750000   \n",
              "max      3.000000  208.000000  200.000000   17.900000  174.000000   \n",
              "\n",
              "              income   inflation  life_expec   total_fer           gdpp  \n",
              "count     167.000000  167.000000  167.000000  167.000000     167.000000  \n",
              "mean    17144.688623    7.781832   70.555689    2.947964   12964.155689  \n",
              "std     19278.067698   10.570704    8.893172    1.513848   18328.704809  \n",
              "min       609.000000   -4.210000   32.100000    1.150000     231.000000  \n",
              "25%      3355.000000    1.810000   65.300000    1.795000    1330.000000  \n",
              "50%      9960.000000    5.390000   73.100000    2.410000    4660.000000  \n",
              "75%     22800.000000   10.750000   76.800000    3.880000   14050.000000  \n",
              "max    125000.000000  104.000000   82.800000    7.490000  105000.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d7e4459e-c4f7-4016-9eb3-9adfc939d8a3\" 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>class</th>\n",
              "      <th>child_mort</th>\n",
              "      <th>exports</th>\n",
              "      <th>health</th>\n",
              "      <th>imports</th>\n",
              "      <th>income</th>\n",
              "      <th>inflation</th>\n",
              "      <th>life_expec</th>\n",
              "      <th>total_fer</th>\n",
              "      <th>gdpp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>167.000000</td>\n",
              "      <td>167.000000</td>\n",
              "      <td>167.000000</td>\n",
              "      <td>167.000000</td>\n",
              "      <td>167.000000</td>\n",
              "      <td>167.000000</td>\n",
              "      <td>167.000000</td>\n",
              "      <td>167.000000</td>\n",
              "      <td>167.000000</td>\n",
              "      <td>167.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.083832</td>\n",
              "      <td>38.270060</td>\n",
              "      <td>41.108976</td>\n",
              "      <td>6.815689</td>\n",
              "      <td>46.890215</td>\n",
              "      <td>17144.688623</td>\n",
              "      <td>7.781832</td>\n",
              "      <td>70.555689</td>\n",
              "      <td>2.947964</td>\n",
              "      <td>12964.155689</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.914496</td>\n",
              "      <td>40.328931</td>\n",
              "      <td>27.412010</td>\n",
              "      <td>2.746837</td>\n",
              "      <td>24.209589</td>\n",
              "      <td>19278.067698</td>\n",
              "      <td>10.570704</td>\n",
              "      <td>8.893172</td>\n",
              "      <td>1.513848</td>\n",
              "      <td>18328.704809</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>0.109000</td>\n",
              "      <td>1.810000</td>\n",
              "      <td>0.065900</td>\n",
              "      <td>609.000000</td>\n",
              "      <td>-4.210000</td>\n",
              "      <td>32.100000</td>\n",
              "      <td>1.150000</td>\n",
              "      <td>231.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>8.250000</td>\n",
              "      <td>23.800000</td>\n",
              "      <td>4.920000</td>\n",
              "      <td>30.200000</td>\n",
              "      <td>3355.000000</td>\n",
              "      <td>1.810000</td>\n",
              "      <td>65.300000</td>\n",
              "      <td>1.795000</td>\n",
              "      <td>1330.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>19.300000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>6.320000</td>\n",
              "      <td>43.300000</td>\n",
              "      <td>9960.000000</td>\n",
              "      <td>5.390000</td>\n",
              "      <td>73.100000</td>\n",
              "      <td>2.410000</td>\n",
              "      <td>4660.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>62.100000</td>\n",
              "      <td>51.350000</td>\n",
              "      <td>8.600000</td>\n",
              "      <td>58.750000</td>\n",
              "      <td>22800.000000</td>\n",
              "      <td>10.750000</td>\n",
              "      <td>76.800000</td>\n",
              "      <td>3.880000</td>\n",
              "      <td>14050.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>208.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>17.900000</td>\n",
              "      <td>174.000000</td>\n",
              "      <td>125000.000000</td>\n",
              "      <td>104.000000</td>\n",
              "      <td>82.800000</td>\n",
              "      <td>7.490000</td>\n",
              "      <td>105000.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d7e4459e-c4f7-4016-9eb3-9adfc939d8a3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d7e4459e-c4f7-4016-9eb3-9adfc939d8a3 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-d7e4459e-c4f7-4016-9eb3-9adfc939d8a3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-05b3ed21-5e63-4600-8e45-748acc9e6f82\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-05b3ed21-5e63-4600-8e45-748acc9e6f82')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-05b3ed21-5e63-4600-8e45-748acc9e6f82 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "country_class = data['class']\n",
        "country_features = data.drop(['class'], axis = 1, inplace = False)"
      ],
      "metadata": {
        "id": "tlDdAZQIXu9E"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Antes de estandarizar los datos, el método con el que vamos a trabajar no acepta valores tipo string, por lo que dejaremos los nombres de los países en una Serie aparte."
      ],
      "metadata": {
        "id": "8f_j4CdbaWRr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "country_names = country_features['country']"
      ],
      "metadata": {
        "id": "bfUhLECaahTR"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "country_features.drop(columns='country', inplace=True)"
      ],
      "metadata": {
        "id": "-G0FapPPbpVh"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "country_features.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "SsaoJX0acBRx",
        "outputId": "9073bfce-2afb-41d5-ca3b-2c7177ac6d12"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   child_mort  exports  health  imports  income  inflation  life_expec  \\\n",
              "0        90.2     10.0    7.58     44.9    1610       9.44        56.2   \n",
              "1        16.6     28.0    6.55     48.6    9930       4.49        76.3   \n",
              "2        27.3     38.4    4.17     31.4   12900      16.10        76.5   \n",
              "3       119.0     62.3    2.85     42.9    5900      22.40        60.1   \n",
              "4        10.3     45.5    6.03     58.9   19100       1.44        76.8   \n",
              "\n",
              "   total_fer   gdpp  \n",
              "0       5.82    553  \n",
              "1       1.65   4090  \n",
              "2       2.89   4460  \n",
              "3       6.16   3530  \n",
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          },
          "metadata": {},
          "execution_count": 15
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      ]
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    {
      "cell_type": "code",
      "source": [
        "#Estandarizamos las variables (features) debido a que las diferentes medias y DS\n",
        "#representarían un problema para nuestro método de clustering\n",
        "columns_name = country_features.columns\n",
        "\n",
        "scaler = StandardScaler()\n",
        "scaled = scaler.fit_transform(country_features)\n",
        "country_features = pd.DataFrame(scaled, columns = columns_name)\n",
        "\n",
        "###Veamos el resultado\n",
        "country_features.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "8Y9NMUpjY7qy",
        "outputId": "b09be4ec-a166-4f2a-ed8f-9d095fef54ef"
      },
      "execution_count": 16,
      "outputs": [
        {
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              "   child_mort   exports    health   imports    income  inflation  life_expec  \\\n",
              "0    1.291532 -1.138280  0.279088 -0.082455 -0.808245   0.157336   -1.619092   \n",
              "1   -0.538949 -0.479658 -0.097016  0.070837 -0.375369  -0.312347    0.647866   \n",
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              "3    2.007808  0.775381 -1.448071 -0.165315 -0.585043   1.387054   -1.179234   \n",
              "4   -0.695634  0.160668 -0.286894  0.497568  0.101732  -0.601749    0.704258   \n",
              "\n",
              "   total_fer      gdpp  \n",
              "0   1.902882 -0.679180  \n",
              "1  -0.859973 -0.485623  \n",
              "2  -0.038404 -0.465376  \n",
              "3   2.128151 -0.516268  \n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6e10b2cd-4ca8-458f-b792-f95e878b6b84 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-6e10b2cd-4ca8-458f-b792-f95e878b6b84');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-1b8ddbeb-8865-4bd6-860d-80e768b71efc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1b8ddbeb-8865-4bd6-860d-80e768b71efc')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1b8ddbeb-8865-4bd6-860d-80e768b71efc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ahora la media es un número cercano a 0 y el desvío es un número cercano a 1 con esta estandarización."
      ],
      "metadata": {
        "id": "s_KnU1-xcnLn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "###Veamos ahora los estadisticos básicos estandarizados\n",
        "country_features.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "AiwtieliaMTO",
        "outputId": "86194b96-8a23-48c4-d967-278bf37d35cb"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         child_mort       exports        health       imports        income  \\\n",
              "count  1.670000e+02  1.670000e+02  1.670000e+02  1.670000e+02  1.670000e+02   \n",
              "mean  -3.722904e-17  2.127373e-16  5.504579e-16  2.765585e-16 -7.977650e-17   \n",
              "std    1.003008e+00  1.003008e+00  1.003008e+00  1.003008e+00  1.003008e+00   \n",
              "min   -8.871383e-01 -1.500192e+00 -1.827827e+00 -1.939940e+00 -8.603259e-01   \n",
              "25%   -7.466190e-01 -6.333367e-01 -6.922106e-01 -6.914785e-01 -7.174558e-01   \n",
              "50%   -4.717981e-01 -2.235279e-01 -1.810007e-01 -1.487432e-01 -3.738080e-01   \n",
              "75%    5.926666e-01  3.747198e-01  6.515412e-01  4.913530e-01  2.942370e-01   \n",
              "max    4.221297e+00  5.813835e+00  4.047436e+00  5.266181e+00  5.611542e+00   \n",
              "\n",
              "          inflation    life_expec     total_fer          gdpp  \n",
              "count  1.670000e+02  1.670000e+02  1.670000e+02  1.670000e+02  \n",
              "mean  -1.063687e-17  3.696311e-16  3.044803e-16  5.850277e-17  \n",
              "std    1.003008e+00  1.003008e+00  1.003008e+00  1.003008e+00  \n",
              "min   -1.137852e+00 -4.337186e+00 -1.191250e+00 -6.968005e-01  \n",
              "25%   -5.666409e-01 -5.927576e-01 -7.639023e-01 -6.366596e-01  \n",
              "50%   -2.269504e-01  2.869576e-01 -3.564309e-01 -4.544309e-01  \n",
              "75%    2.816364e-01  7.042584e-01  6.175252e-01  5.942100e-02  \n",
              "max    9.129718e+00  1.380962e+00  3.009349e+00  5.036507e+00  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c953271a-9915-41e8-a4f7-77dcfa9271bb\" 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>child_mort</th>\n",
              "      <th>exports</th>\n",
              "      <th>health</th>\n",
              "      <th>imports</th>\n",
              "      <th>income</th>\n",
              "      <th>inflation</th>\n",
              "      <th>life_expec</th>\n",
              "      <th>total_fer</th>\n",
              "      <th>gdpp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.670000e+02</td>\n",
              "      <td>1.670000e+02</td>\n",
              "      <td>1.670000e+02</td>\n",
              "      <td>1.670000e+02</td>\n",
              "      <td>1.670000e+02</td>\n",
              "      <td>1.670000e+02</td>\n",
              "      <td>1.670000e+02</td>\n",
              "      <td>1.670000e+02</td>\n",
              "      <td>1.670000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-3.722904e-17</td>\n",
              "      <td>2.127373e-16</td>\n",
              "      <td>5.504579e-16</td>\n",
              "      <td>2.765585e-16</td>\n",
              "      <td>-7.977650e-17</td>\n",
              "      <td>-1.063687e-17</td>\n",
              "      <td>3.696311e-16</td>\n",
              "      <td>3.044803e-16</td>\n",
              "      <td>5.850277e-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.003008e+00</td>\n",
              "      <td>1.003008e+00</td>\n",
              "      <td>1.003008e+00</td>\n",
              "      <td>1.003008e+00</td>\n",
              "      <td>1.003008e+00</td>\n",
              "      <td>1.003008e+00</td>\n",
              "      <td>1.003008e+00</td>\n",
              "      <td>1.003008e+00</td>\n",
              "      <td>1.003008e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-8.871383e-01</td>\n",
              "      <td>-1.500192e+00</td>\n",
              "      <td>-1.827827e+00</td>\n",
              "      <td>-1.939940e+00</td>\n",
              "      <td>-8.603259e-01</td>\n",
              "      <td>-1.137852e+00</td>\n",
              "      <td>-4.337186e+00</td>\n",
              "      <td>-1.191250e+00</td>\n",
              "      <td>-6.968005e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-7.466190e-01</td>\n",
              "      <td>-6.333367e-01</td>\n",
              "      <td>-6.922106e-01</td>\n",
              "      <td>-6.914785e-01</td>\n",
              "      <td>-7.174558e-01</td>\n",
              "      <td>-5.666409e-01</td>\n",
              "      <td>-5.927576e-01</td>\n",
              "      <td>-7.639023e-01</td>\n",
              "      <td>-6.366596e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-4.717981e-01</td>\n",
              "      <td>-2.235279e-01</td>\n",
              "      <td>-1.810007e-01</td>\n",
              "      <td>-1.487432e-01</td>\n",
              "      <td>-3.738080e-01</td>\n",
              "      <td>-2.269504e-01</td>\n",
              "      <td>2.869576e-01</td>\n",
              "      <td>-3.564309e-01</td>\n",
              "      <td>-4.544309e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5.926666e-01</td>\n",
              "      <td>3.747198e-01</td>\n",
              "      <td>6.515412e-01</td>\n",
              "      <td>4.913530e-01</td>\n",
              "      <td>2.942370e-01</td>\n",
              "      <td>2.816364e-01</td>\n",
              "      <td>7.042584e-01</td>\n",
              "      <td>6.175252e-01</td>\n",
              "      <td>5.942100e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.221297e+00</td>\n",
              "      <td>5.813835e+00</td>\n",
              "      <td>4.047436e+00</td>\n",
              "      <td>5.266181e+00</td>\n",
              "      <td>5.611542e+00</td>\n",
              "      <td>9.129718e+00</td>\n",
              "      <td>1.380962e+00</td>\n",
              "      <td>3.009349e+00</td>\n",
              "      <td>5.036507e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c953271a-9915-41e8-a4f7-77dcfa9271bb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c953271a-9915-41e8-a4f7-77dcfa9271bb 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-c953271a-9915-41e8-a4f7-77dcfa9271bb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b54fe651-8c96-45f6-8f58-10db57c69dc0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b54fe651-8c96-45f6-8f58-10db57c69dc0')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b54fe651-8c96-45f6-8f58-10db57c69dc0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Estadístico de Hopkins: permite evaluar la tendencia de clustering de un conjunto de datos mediante el cálculo de la probabilidad de que dichos datos procedan de una distribución uniforme, es decir, estudia la distribución espacial aleatoria de las observaciones.\n",
        "\n",
        "Therefore, we can interpret Hopkins' statistic in the following manner:\n",
        "\n",
        "If the value is between {0.01, ...,0.3}, the data is regularly spaced.\n",
        "\n",
        "If the value is around 0.5, it is random.\n",
        "\n",
        "If the value is between {0.7, ..., 0.99}, it has a high tendency to cluster.\n"
      ],
      "metadata": {
        "id": "ERimxEMJeUak"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# function to compute hopkins's statistic for the dataframe X\n",
        "def hopkins_statistic(X):\n",
        "\n",
        "    X=X.values  #convert dataframe to a numpy array\n",
        "    sample_size = int(X.shape[0]*0.05) #0.05 (5%) based on paper by Lawson and Jures\n",
        "\n",
        "\n",
        "    #a uniform random sample in the original data space\n",
        "    X_uniform_random_sample = uniform(X.min(axis=0), X.max(axis=0) ,(sample_size , X.shape[1]))\n",
        "\n",
        "\n",
        "\n",
        "    #a random sample of size sample_size from the original data X\n",
        "    random_indices=sample(range(0, X.shape[0], 1), sample_size)\n",
        "    X_sample = X[random_indices]\n",
        "\n",
        "\n",
        "    #initialise unsupervised learner for implementing neighbor searches\n",
        "    neigh = NearestNeighbors(n_neighbors=2)\n",
        "    nbrs=neigh.fit(X)\n",
        "\n",
        "    #u_distances = nearest neighbour distances from uniform random sample\n",
        "    u_distances , u_indices = nbrs.kneighbors(X_uniform_random_sample , n_neighbors=2)\n",
        "    u_distances = u_distances[: , 0] #distance to the first (nearest) neighbour\n",
        "\n",
        "    #w_distances = nearest neighbour distances from a sample of points from original data X\n",
        "    w_distances , w_indices = nbrs.kneighbors(X_sample , n_neighbors=2)\n",
        "    #distance to the second nearest neighbour (as the first neighbour will be the point itself, with distance = 0)\n",
        "    w_distances = w_distances[: , 1]\n",
        "\n",
        "\n",
        "\n",
        "    u_sum = np.sum(u_distances)\n",
        "    w_sum = np.sum(w_distances)\n",
        "\n",
        "    #compute and return hopkins' statistic\n",
        "    H = u_sum/ (u_sum + w_sum)\n",
        "    return H"
      ],
      "metadata": {
        "id": "f7O05yt6ciLP"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Como podemos ver al llamar a la función, pasándole como argumento el dataframe, este estadístico nos devuelve un valor que indica una alta tendencia a formar clusters."
      ],
      "metadata": {
        "id": "bT6KGwj8iQnF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hopkins_statistic(country_features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yi9GV-CVh6mH",
        "outputId": "d56a7ea3-5b5e-4150-b60c-c687ad483eca"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8892922262608488"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Cómo el valor de este estadístico depende de una comparación con una muestra aleatoria, lo vamos a calcular varias veces y promediar los distintos valores.\n",
        "#De esta forma obtenemos un valor más consistente\n",
        "l = []\n",
        "for i in range(20):\n",
        "    H=hopkins_statistic(country_features)\n",
        "    l.append(H)\n",
        "#Valor del estadístico Hopkins promedio\n",
        "np.mean(l)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pnM--FbLiMkn",
        "outputId": "0f379fe1-389f-4425-ae15-4590b359462f"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8573180690789048"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Realizaremos PCA para reducir la dimensionalidad de los datos y también poder graficarlos."
      ],
      "metadata": {
        "id": "ldiiWahFkiQD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Realizamos el PCA\n",
        "pca = PCA(n_components=2) #Vamos a representar nuestros datos en 2 dimensiones\n",
        "principalComponents = pca.fit_transform(country_features)\n",
        "principalDf = pd.DataFrame(data = principalComponents\n",
        "             , columns = ['principal component 1', 'principal component 2'])\n",
        "finalDf = pd.concat([principalDf, country_class], axis = 1)"
      ],
      "metadata": {
        "id": "o731jpKUk6Fk"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Graficamos la distribución de nuestros puntos en este nuevo espacio\n",
        "fig = plt.figure(figsize = (9,9))\n",
        "ax = fig.add_subplot(1,1,1)\n",
        "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
        "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
        "ax.set_title('PCA - Country Dataset', fontsize = 20)\n",
        "\n",
        "targets = [0, 1, 2, 3]\n",
        "colors = ['r', 'g', 'b', 'y']\n",
        "\n",
        "for target, color in zip(targets,colors):\n",
        "    indicesToKeep = finalDf['class'] == target\n",
        "    ax.scatter(finalDf.loc[indicesToKeep, 'principal component 1']\n",
        "               , finalDf.loc[indicesToKeep, 'principal component 2']\n",
        "               , c = color\n",
        "               , s = 50)\n",
        "ax.legend(targets)\n",
        "ax.grid()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 806
        },
        "id": "agGXsQujlD19",
        "outputId": "ec9b27ec-1904-4dcd-ce86-80bf7ce4b5ce"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Clustering jerarquico"
      ],
      "metadata": {
        "id": "mKSOxfMxo-MJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Grafiquemos algunos dendogramas para elegir el método de cálculo de similitudes entre clusters."
      ],
      "metadata": {
        "id": "jh62mLb2p-lm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Single linkage\n",
        "plt.figure(figsize=(15, 7))\n",
        "plt.title(\"Country Dendrogram\")\n",
        "\n",
        "#Con la funcion linkage estamos formando y calculando similaridades.\n",
        "#Con dendrogram vamos a poder visualizarlo.\n",
        "#El method es el criterio de similaridad, y es lo que iremos cambiando.\n",
        "clusters = shc.linkage(country_features,\n",
        "            method='single',\n",
        "            metric=\"euclidean\")\n",
        "shc.dendrogram(Z=clusters)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "FW--24Gcof1Z",
        "outputId": "47751648-00e3-4528-830d-6e7186f0cd4a"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Complete linkage\n",
        "plt.figure(figsize=(15, 7))\n",
        "plt.title(\"Country Dendrogram\")\n",
        "\n",
        "clusters = shc.linkage(country_features,\n",
        "            method='complete',\n",
        "            metric=\"euclidean\")\n",
        "shc.dendrogram(Z=clusters)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "Pd1bASZjqaWy",
        "outputId": "c30dbcc2-54b4-43b4-b1d4-f886495502fc"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 1 Axes>"
            ],
            "image/png": 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UzprNzQbm9sWq3G7js101pWFPt7Nmszqm91d6Rwb8BgDAX7S7mvnGQWntdD6Mn7JXXZVLu1dtlWROBgYbUDmerxv3CfagmchuH4mMuVwuZZSUhNhSAADEVtSSW7t371ZxcbGmT5+unJwc5eTkaOLEiXr22WejtYikVFfv0eLP56ku4A1861ZbTOR3Fxw4WLnVdOtWS+kdMnXOyJkJS3DVNZiTcL7knLT3TYSuRpcRy8vqRSIOACwEtjQKHFzaneWN+8O+U9mtqxnjp+xNbL1eeJm8lTWmz0MNqBwYi+XrxgEAgL1ELbnVuXNnzZ071/TZtm3b1KtXr7Dn0dTUJJfLJY9nX1P82tpauVz7bjY9Hk/ImCS5/9eqyff/wO/7x6ymC3eebZ2fq2Zns8RWLNU1eFS6a6O65+zfqnKGG7PaP3UNHr21/D55G2uDJuG+2PimpH0JurQOmTq1+DKld8hsdVmiuY/CXb9g07W1/lktr7VlCfa5p3HfPDwej1JT214fwp2nHY7l1sZ276nUzprNplhtba1cjTu1tWKdMmoyLGO+FpqSVL57i1yN3rCmCxXr0dBX6R32JYDjeTwHmy4R9T3ax0iszh2BMf/leDwepUQwT2+VS0sOvkb1Ve6gLX98g0ubH/azddKK21WfnhKV9Wu2z3Pjf1zGor4ffM+v1P3QA0zx2tpabdu2Tfn5+crIaH5cRiu2+4ct+mbqIiMe7rk9FrFIrzPRirlXbGuW2GoNb2WNyr5Zr6zi/KiWM9Hbxa6xYNvJt23sUsZ4xbxVLlWv2S5pbx1xb9umbTsV9Bzgi9WuLzc+L/t6nTKqqsKaLiMjQ50G7qe0//1w0dZ18F2/pX37sLXzTMR9eDSOy8B1j+TaHCrWlm0S6/VuL+fGtt6zRGM/ROP4CicWbDltmV80Ym25921qalJKSkqz7wWK2YDyy5cv11//+lc9/PDDYU/j9Xq1cuVKlZTvqwzbtm3VSm+l8bercadfbJuqSuuCzqvkf82iA7+fndrdiFlNF+48w50mVMx/Xn3TRigzNTbjk3kad2uL9wtjuWWp1a0qZ7gxq/3jatwpb2P4Y4h4GzxavuoLZad2b3VZormP/MW7/rW0PVtTlmCf+8+jpKRE2f+rF9Gq01bztMOx3JpYQ5NXS9c8rEZ5g8bXhZ5l0Ni325ZENJ1/LFVpGpx5qjqkmJslOLW+N/n9vW3bNm1fubLFmNU07pJtpljlvlDIcvhPE6yM0Yr5L6ekpERZubWtnqd7xTbVVzW/IbDirXRp+dJPjYf9tq5f4DbO6p4f1+MymjH/danOS1F9bsD1KlfK6p6vSkkKHA8rijF39r5zTLj1NlaxSI6haMb8l9971gRlHBheK7badeXGuE+RHl9WsURvFyfEfPHSgOPITmWMVaxhT63Wn7FQjdXmdW9N12T/N1+GM11qpwwVvj5FHXL3JcEiXYdt28zXJ/99GO48Y3UttTr2onFcBq57NM8dbdkmsV7vZD03NuypVV1JhVau2Fuu2nX7EsglHy1XxoE9jFh6QTfT8RNsntHYD9E4vsKJuS2WE+1lhRtr671venp6yO/5xCS59eWXX+pPf/qTpk+frjFjxoQ9XVpamoqKirRnU4WkEklSfn4fDSnKN76zs2az0bInPz9ffbqZB/F0u90qKdk7yGVWVlaz71eV1hkxq+nCnaekNs3P1djJmNfgwkOMh8G2ljEw9sP6L03l9W+5Fc3lWe0f/9ih+RPUo3Nf0zx9v2DndkszHvx9ZW1tWaK5j8Jdv2DTtbX+hbs9wylLsM/9619BQUHE9W/3nkr9+NOKva0PGrONu62uvbOVmZquiu01OvCAg1q1LU3zzMjYO9bc/+ab2y1Neyp2mlo7dMrsYbTyi9UxFCqxlSiN8ip/QLeEHM/BpmtrfXe59miD9k3fdciQFmNW01TsyTBu/PPz85XvFwtVDv9ppOidOwJjbr/lFBQUKKsgv9Xz9C9rSy2NateVG62BIl1esFjgNq6M4TaLdSyS+hKLmF3KYYey+B8ng04coazi8Oqte8U2I7kVzfpul+1i15jb7TYeFKW9277bkEJblTEeMfeKbVobkNiKtcbqWvVRZ3UbUtjmdcjPzzddnyKZZ6yupVbHXjSOy8B1j+a5oy3bJNbrnYznRv/W7cEEvvjC17I9LS87pvshGsdXOLFtO9VsOW2ZX6LvfdeuXatwRD259cEHH+jqq6/WDTfcoDPPPLNV06akpCg7O1uZmfuaoGdkZJgGq/QfLD0wJu0b08nV2EmNDZlyN1QZMU9jlVyNLiMWbEynrKysZvMMjAUO2B7ONKFijX7zyszMVFaHrDbPM1TMfzmB34nW8qz2j3+sR+e+6tdzoGleLpdLVaV16tG5k7QteFnDLUs091Fr6l/gdIHfl+pisj3DKUuwz6NR/+rqPXp/3UJ5GzzNWhr5upqmKk0D068Mu/xW85SCt3ryjSeXpdgfQ2OKzlbXnN6SEvOWqlXrvzNaYibqeA42XVvre2Nm6OWFillN47KIhSpH4NvsYlGPsrKy1JQZcOxltb7e+pe1+6EHaP9xxabvu1wuVa6U8ocMkStva5uXFywWuI2l2phts1jHIqkvsYjZpRx2KEukx0k0ji+rWKK3ixNiUmyvT3aO+de/0QunKKOoZ8zuE7q50owfLiK9Zw7k3wUy0nnG6lpqdexF47gMXPdonjvask1ivd7JeG4sX761Va3bvZUueTdWKm/0vhbCsdgP0Ti+wolZLSfaywo31pZrczhdEqUoJ7e++uorXXvttbrvvvt09NFHR3PWYamr9+itFffvfSBe3TweOKZTogdXB5JBlbtM3hbGjWuUV9WecnXp1C1q8wxU17D3raM5HWI/+HLXnN7q2am/JMnVwaWy1Gp1z9m/2Yk5VrHM1JLorxQAAIiqvOJ+yh7WJ2ZvrOy8x9yNCkBzhz0wWb0ON/c+CJYgjiXfC4B2u/cNJeJ7CZDH41FD4LAFiEjUklv19fW6/vrrNWPGjIQktqTWPxD7HoZ9D6kA2mZE/1+od9d+xt+7akqbvdGzrfOUzL9muhuq2rwMtE+BbxqUZHrbYO26clV8vp43DQIAADhU56F91WN0kemzeCaI66pcWlp8rbyVLlPXVP83/aZ2ytCglfOlFnqPwFrUklvffPON1q1bp9tuu0233XabKfbOO++ob9++IaaMDasH4q69c/TFxn/EtTyIHV9X1J01m41uUbtqSo24p7HKFJOkvKzw3+KJ8OVl9Yx6sjjYPP1bNgV2QU0Gweq0j8fjkadxX3drU133mKcL1vUae4W60fBXOnuJaUyGtC45OmP9/VJaiAkAhwqW6JXMyd7dK7eoPKBbTVr/LvEqIqKkrsql3av2dlP2eDxylzTf75HGPB6PacBm//oT7WV1HtyHczEAR9i9aqu8lS7L7zRW16p6zXbl5ce+B0oyi1pya8SIEVq9OkhfwASxeiDOy+qUoFIh2lrqiipJW7xfasvqL02fpXfI1CnFl8WhhEDrhFOn/QVrtUbX65aFc6MRyFtZo92rtip7WJ8YlQqIv3ASvZL57W0+aV2yNeCVi2NWNkRXXZVLrxdeJm9ljenz1rzFL9yYZG6VEO1lpXXJ0Ukr5rVQAgCwl8AuklUrNmnZlObnSkQmJm9LBOIlkrGZpL0tY6o95S1/MYL5+rec8W9VU+Uuk6vRRauaKPDfzv4vjdhVUypPB48amuz1VsPWiLROB0PX6/BYjcVQUFCg2rU7uPFA0ook0evjrXSprqRCGhnlQiEm9u7rmpa/6ADeyhpVr9ku5Sa6JAAQvmBdJBE9JLeQNFoamykzMzMqY0CFwgsN4sNqO/v2barSNLBhoLLl7H7r4dTpYDG6XreO1VgM3YYUNusm0574d2GSmncRCuy25s7yBu1aRPc1Z2gp0es75/BLs/PF4i1+8Xh7MHXPflo7fqVEt1IAsUFyC0mjpbGZrF5XHQ280CA+YvF2RrtqbZ2m6zWiyVvl0lvFM4O29AjWfcjXbS1YjO5rwbX0UBhsnKtYvlWppURvrK+jiJ9YvMWvrW/+a20MiRfJ+JUS3UoBxAbJLSAGeKFBfMTi7YwA9qpesz1qXZjovtZcOA+Fwca5knirEgB7iLRbc7J3K3Xy25h9LbYjedFDOD/OhDtPO20TOAfJLSAGeKFBfMTi7YwAmhu9cIryivu1umsRXYhCa8tYV7xVCYDdhNOtuT1cE5z8NuZgL52I9CUQoX6cCXeevm1CggutQXIrCdTVe7SzZrNpIHN/Ho9HnkbzoNv+sWDT5WX1in3BAQCOkFfcTz1GF9F9KEYCHwpDJRHbw4MhAGeiW/NeTn4bs51eOuHbJgy+jtYgueVwdfUeLf58nur+NwZRsIHMAwXrthU4XXqHTJ1SfFk0iggAACwEPhS2xwdCAEg2Tn4b82EPTFZFtjemL4gIFuvmStM3UxfFZJ2Q/EhuOVyVu8xIbEVTXYNH1Z7yqM8XsJu6+r0D+4dqxSjR+hGR83/boP94Ep51O4zvVH6/Keg4FIw3AQCAczn5bcydh/aVO44viPDFOu/JiM0KoV0guZVE+qaN0ODCQ9qUQWdAbrQngS0fJVo/InqCjV0hNR9r4rNLFgaN2WUMDgBA4gX7sWS3e98FwjeYNwNzt8x/W0qhtyc/PAHOQnIriWSmdlb3nP2bZ8L/N5B5a2NAsotVy0eJ1o9o+9gVdhmDAwCQWOH8WOL/Q4l/jIG5zbxVLr1VPDPo9TnU9uSHJ8AZSG6Foa7B3O3I1CWpscrUHSkvq5fSO9q3iSmA4Eb0/4V2lda0efwAWj8imNELpyijqGdY9cjOY3AAAOKvLT+WMDC3WfWa7fzwZBO0RkS0kdxqQV29R2+tuF/eBk/Q7kpbvF9qy+ovjb/TO2TqnJEz41hCANGQl9VTtamZtH5ETOQV91P2sD5hjUNh5zE4ACCa6qpcqvhmfbOuXz6BD7W+h11J2r1yi9xZ3rCmk5KnO1m4P5bYYWBub5VL7hXh7b9E7J/RC6cor7hfyB8x+eEpdmiNiFggudWCKneZvK3otlTXsHdw6pwOPWJYKgAAAMC5Ah9uA8cj9Bcs9s2li1o1XbJ0Jwv3x5JED8xdV+XS0uJr5a10hbX/EpGwyCvupx6ji0IOdM4PT7FDa8TklciXKZHcaoUR/X+h3l37GX/7Z/PdDVV0RYJt+d4IKJnf7uduML8B0NOh+Zv/6GoLIByBv9BLMv1KX7uuXBWfr2/2gJDWv0s8iwmEFGqQ6cCWQYGtT8rbUauhaGrruIStRXey+Nq7f11hf5+ERfvlpNaIsJbolymR3GqFvKye6tmpv/G3f3ckXyIAsJu6Bo9e+/z+ZgOnB3az9U/O+sfoamtf/klLaV/issrtNj7zjRHon9SsacgkaYmoaukXekkqnb1EpbOXNPs8rUu2BrxycWwLCLQg3EGmA/lanwSTjK2GYqX3rAkadOKINo15GSpGd7LEO+yByep1+IGmz0hYJEaoVjXhdvvtPLhP1M9jTmmNiJYl+mVKJLeAJFftKW/TGwHpamtPoZKWkkxPVIEtSn2JS1/SkgQXoqG1v9D781a6VFdSIY2McqGAVmjrINMtodWQtYwDe6jbSOvxCCON0Z0s8ToP7dusRRYJi/gLt1WNFLrbb1qXHJ20Yl5MyofkkoiXKZHcAtqRMUVnK6tDXlgnGbra2lu0kpb+rVGBaLD6hd7/vFO1YhOtKWBLLQ0yLdFqCIDzRKMrsLeyRtVrtku5USoUWs3X+i5U13lpb8s7H/8WeVbTRHuYiES8TInkFmyjrsG6S1VDkzdRRUsaXXN6K6dDj7De/EdXW+cYU3S2uub0Duthq2vvHH2x8R8JKinaA6tf6IPd3AB209Ig0xKthgA4W2ta1WRmZvKDlE0Ea31n1XVeav7WyVDTJMMwESS3YAt19R69teL+vW+mDNGlKlVpGtgwUNniwQj24UvK+say8iVkpb1vW3U1umI+zlXXnN7q2am/KTkZKnGZl9Up6stHcvENDL/bvW9QDd+vfh6PRw2qTVTRIhKrQcIZINxe/F9oEOyNTE6rtwAQS61pVZOMP0o59V4nli/iSIZhIkhuwRaq3GV7E1sWGuVVtadcXTp1C3u+dkg8IHn5J2UDB+iXpC82vikpece54viKHrs8mIcaGN7/V7/UThkatHK+5ICb3VgOEp6I19YjOKsXGvjqrpPqLQAgdpLlXuewByarItvbqq7zyT5MBMkt2M6I/r9Q7679jL931ZRGNPZTe088IPbCScr6S6Zxrji+osdOD+bhDAzfWF2r6jXblZdv/5dMxHKQcF5bbx/JVm+BRHBqSxagtZLlmtF5aF+5W9nyLtlb5JHcagP/Vgvuhirj8101pfJ0YIyoSOVl9YzKw7/dEg+MKZbcApOyUnKPc2W348vJ7HqTFTgwvNN/2YvWIOG8tt7eElFvrbq+BmuJGaxbbOfBfaQ0wUH893tr97ndujUnS0uWaLNLq2rETrLd67R3JLciZNVqwdfKiDGi7CPRiQfGFEt+wZKy7WWcq0QfX8nETjdZwQaGd7JoDRLOa+tjq60Pk/Gut63p+uqfKAiMpXXJ0Ukr5kW/gIiJYIM6S+Hvc1+3ZrskNO36I0s0tbZlmp1aVSN2ku1ep70juRWhWI0RFW+tGTNHkmPHzUl04iFZ6gsQTKKPr2TCTRbsxj/Z5Gt14j/4fu26clV8vj4qrZCc+DAZra6v3soaVa/ZLuW2bT7RaEUmRf+V8PEQz5ZUbR3U2detOXtYn4jnESt2+pElWiJpmdYeEn5of1p7TbdbK9OWtKvkVqy6hUVrjKh4a+2YOdK+cXMQOafWFwBA+2KVbPIpnb1EpbOXmD6LtBWS0x8mI+n6Gs3EQbRakUnOeyV8IltSjV44RRlFPcPa57Vrd9g+UZSMP7K09dySjAk/tD+RXNPt1sq0Je0muRXLbmHRGiMq3lo7Zo60b9ycnA72u6l0CjvXF8aRA4DEC/bLqmT+dXX3yi0qj3Frm3AeCIPxVtZoy+tfyp3ljbh1jBMfJsPp+lq1rVwqKTH+bkrZF9u9cotpm7X2F/NovkDBaa+ET2RLqrzifsoe1ies7s6BLeQQf5GcW5Ix4Yf2J5Jrup1bmQbTbpJbdAuzZjVmTkFBgdwNVbQuSnKMIwcgVuLZtc3pwvllVZK+uXRRs89i2dom8IHQu8ej/5w1Xw17go+B5StfpK1jkvFhsqV9G7jNfNsrki4hkb5AwQlJxJYkW0uqZJWotzMm47klFgKv24E/rrTlx4tIy8KbPKMn8JouJce5sd0kt/wluluYHVvHWI2Z0z1nf2PMLThHqG64oepYe0gAt5cx5gA7iXfXNqeLtLWUFNvWNoEPhOXL1oZMbIXitF+Ao621+9a3vSJ5EG/rCxScjJZU9sfbGe2ttYl4KXbd16grsREsyZsM58Z2mdxKZLewtrSOCXwwl2R6OPc0VvFgDkktd8NtqQVWohPAscAYc4gHXhveXFu6tkVjgG0na+mXVbu0tknWX4BjyWqbdXOl6ZupixJTsBhJ9kGM0TpOH18v2dmp+xp1Ba3RLpNbiRRp65iWHswlaYv3S21Z/aXxNw/m7VdL9aylFlh2HhcsUokYYy5WL7GAPTnxTW/xFk6yJtGJGjtp6ZdVu7S2SdZfgGPJapt13pORoFLFRnsYxBiRc+L4eu2JnX68aI91pTXdQ/lRgORWQrWmdQyDvyNS/vUsGVpgRUM8xpiL5UssYE/8utgypyRrAESPnVqBwH4YA8ve7PTjRXurK/EcpzFZkNxKoEhbxzD4O1ojGVthtVU8xpiz2xhm8W5F1t5brbXHXxcBoCV2aAVC93EkUjLUv2RYByeI5ziNiRCLekRyy4EY/B1wlmiOYRZJ0ijerchotdb+fl0E4DyJeItpoluBtIfu47xZzr6Sof4lwzo4UbKN0xirekRyCwBiLFqt5yJNGsW7FZndWq1Fgl8l44fxJID4a69vMU327uO8Wc7ekqH+JcM6OFGyjdMYq3pEcgsh+b+d0d1QZXy+q6ZUng7J360IsJtoJI3i/SZMJ755k18l44fxJJBoydDKJZJ14C2mydl9nMSDcyRD/WvtOvDDIYKJ5rFAcitJRDsRZfV2Rt+DabJ3KwLsLNKkUbzHYHPimG88HMRPJONJbHx5mboc3C/mN8J1VS7tXrXV+Nvj8chdEjqB4C7ZRgszh0mGVi7RWAenvsW0rQ/Kyd59PBmSJ8ksXvUvlgml1qwDPxwilGgeCyS3kkAsElHJ0K2oJe19wGs4mxOTRk7k5IcDp/1CGritvXs8+s9Z89Wwx1zOeCQevFUuvVU8U97KmmaxUAkE/xgtzJwhGRLZ0VgHJ77FlAflliV78g4ts9Nxkgzn22TntPvGYEhuJYFYJ6Kc2K2oJQx4DSAcTn04sNMNbbgCt3X5srXNEluBYnUjXL1me9DEVric9sYiODuR7ePUdYjkgSoRD8qRPvjFezrAx64JJaeeq5KZE+8bgyG5lWRikYhKxhYi7aFlGlqHMeaQTOx6QxupRN4Ij144RXnF/Zp10/IXzzcWtfYNd3SRDE9rE9l2HKvLicn4aDxQxeP8EGk54z0dEIqdEkpOPFclu2S5byS5lWSSMREVa8nYMg2twxhzSGZ2uqGNVCJvhPOK+6nH6CLLblrxemNRJG+483WRVFqICWIkmVudJMNYXXYRq26V0RZpOeM9HRAKCSWEy8kvCiC5hXaPhCBoyYdkxg1t8ojkDXe+LpLZw/rEqFTNJXurk1glHuzYGiyenJKIj7Sc8Z4OQHKL1TXDyS8KILkFAH5oyQc4X3tIErT0hrvatTsS9gDcnlqdRCvxYLfWYIHdX/27vu5euUXuLG/U3w7qlER8pOWM93QAkpddrhl2u96T3AIAP7TkA5wtETd8iUimtfSGO1fAuGCJkuytTqKVeLDTA0JL3V+/uXSRpOZvB41311cAaK/sdM3wscP1nuQWAABIGvG+4bPLr6d2RauT1kv0A0Jru78mousrAGCvRF8zfOxwvSe5BQAAklI8bvjs+OspnM0ODwg+Vt1f4/F2UACANTtdMxKN5BYAAEhK8b7hs8uvp0C0BDuGqraVSyUlUsq+z5LhLZgAAGcjuQUAABAF/HqKZNdSN9z23AUXAJBYqYkuAAAAAAD7a6kbrq8LLgAA8UbLLQAAACXmrYeAU/l3w6ULLgAg0UhuAQCAdo+3HgKtQzdcAICdkNwCAADtXjK89dDX8qxiT4Y863YYnzPYNwAASHYktwAAAPw48a2HoVqeSQz2DQAAkh/JLQAAAD9O7G6VDC3PAAAAIkVyCwAAIIk4seUZAABAW5DcAgAASCJObHkGAADQFqmJLgAAAAAAAAAQKZJbAAAAAAAAcCySWwAAAAAAAHCsqCa3tmzZoilTpmj06NE6/vjjdeedd6qxsTGaiwAAAAAAAAAMUR1Qftq0aTr44IP13nvvaefOnfrDH/6gHj166MILL4zmYgAAAAAAAABJUkpTU1NTNGa0fPlynXfeefrkk0+Ul5cnSXr++ef19NNP65133mlx+q+++kpNTU1KS0tTbX2jtlW7JUn75WYqM62D8b2Gxnp5vNWSpIy0XHVMTUtIzC7lIEasNTG7lIMYsWCxptpaebdvkyR17L2fUjMzW4xZTdNY65V7W6UkKXO/LuqQmUaMmO1jdikHMWKtidmlHMSIRStml3IQIxatmF3KEUnM6/UqJSVFhx9+uKxELbn197//XU888YTeffdd47PvvvtOkyZN0pdffqnc3FzL6b/++msjuQUAAAAAAID2zZfcGj58uOX3otYtsbKyUp07dzZ95mvBtWvXrhaTWy0VFAAAAAAAAAgU1QHlo9QIDAAAAAAAAAhL1JJb3bp1U2VlpemzyspKpaSkqFu3btFaDAAAAAAAAGCIWnKruLhY27ZtU0VFhfHZ8uXLVVRUpJycnGgtBgAAAAAAADBELbk1dOhQDRs2THfddZf27NmjdevW6amnntIvf/nLaC0CAAAAAAAAMIna2xIlafv27brhhhv02WefKTc3V+eff76mTp2qlJSUaC0CAAAAAAAAMEQ1uQUAAAAAAADEU1TflggAAAAAAADEE8ktAAAAAAAAOBbJLQAAAAAAADgWyS0AAAAAAAA4FsktIEls27ZN9913n2688UY98sgjKi0tTXSRour777/XU089pU2bNukvf/mLPvzww0QXCQCQQFu2bNF9992nm2++WY8//rh27doV0+WtWbNGFRUVuueeezRv3jytW7cu6st47733WvxOY2Oj8d8//vEPNTY2Rr0cQHv1ww8/6JlnntHu3bslSe+++25Y061atco4P9x+++366aefYllMICpKSkr06quvqrS0VAsWLNDXX38d0+UtXLhQLpcrZvO3VXKrpqZG33//verr67V06VLTw3lZWZk8Ho9eeuklPfvssyovLw8r5u+LL74w/V1fX68tW7ZIkj777DNVVVUZMbfbLUn65JNP9MEHH6iurk6StGfPHi1evFiPPfaY/vnPf8rr9QZd1n/+8x/T399++224m0E//vij8W+Xy6VVq1YZf69evdr499tvv61QL7ssLS3V+++/b5Tvs88+M2KVlZX67LPP5Ha79eqrr2rjxo1GbPv27aqtrdWLL76o5557znSjuG3bNi1dulTV1dV66qmntHLlyrDKYrV+gfz3kVU5rQ4Mq3Ja3RCG2ue+6T799FO9/fbb+u6770zLs9rWVvvPV/6PP/5Y//rXv0x1yWqeobb1ggULdNZZZ2nWrFk6+eSTdc8995jioZZntc/9BdZpq+POaltaHUNWJ9gnnnhCRxxxhG644QZNnTpVb7zxhhGzesCxehixWgerfW51PNfU1Gjx4sVauHCh/vGPfxjbQmo5Qffpp5/qrbfe0ldffWX63Or4CnWcWJVDst5HgfwftkLVaaubwdZcyPyX1dIF1+pY9+d/XrGapqVjIdT+sTqWra4zVuW0Ok6s6p/V+lmdV6zqptXxZTVPq+PL6iHA6l5ACr0frOq81bnYar+He82XzNe2cPZ74DnVah3CretS83udUGWM9P7C3zfffBNW+VuK+QvcLlbnqoceekgnn3yy+vTpo7Fjx+r22283LS9UPbK6F7A69z///PN67LHHNHHiRF100UV69NFHjVi496KB22zx4sXGfy+99JKef/55Sdb3QMcee6yuvvpqzZo1S0888YRmzZoVVjnCeWgPdXz5BO4fq+PHqixWx4hV3WzND3nh3m9ancOtprMqS7jHUOD2jPQaFel9v9W52GofWc3TX2B9t7ona+neKtTxHCoWzjknWH1/9tlnNWrUKM2fP19VVVVhX5tff/11PfLIIzrvvPP0//7f/9PChQtDrk+49S/ce/TA7WxVH6zWwaretnQ9DHXuiLTeWp2LA/kf65Heo1tda1q6noRTzsB9FGq/Wx1brbkXCFyeT+A5569//asOPPBA3XLLLTr//PP19ttvGzGrskSaE1m/fr0eeeQRLVq0qNn5uzX5hFBsldyaNWuWPvnkE02dOlVNTU2aM2eOEXvwwQf14IMPqk+fPiouLta8efNajH3yySem/5555hnT8ubOnau3335bV1xxhXbs2KG77rrLiN1555164IEHtGnTJrlcLt1www2SpHnz5ql79+7avXu30tLSdPPNNxvTnHLKKbr66qt19dVXa/78+brmmmuM2N133625c+dqxYoVzdZ706ZNxn8bN27UU089ZcRuuukmLVu2THPmzFFTU5NeeeUVI/bWW29pzpw5WrJkSbMKdeedd6qpqUl/+ctfVFtbq/fff9+I3XfffSovL9dVV12lAw44QE8++aQRe/rpp3XfffepoKBARxxxhO68804jNm/ePDU0NOiyyy7TyJEjTSdtq7JYrZ/VPrIqp9WBYVVOqxvCUPtckm699VatXbtWn332mb766ivddtttYW1rq/23YMECLViwQKWlpaqvr9df/vKXsOYZalunpKRov/32U0ZGhvLz85Waaj68Qy3Pap9b1WmrY9JqW/qOoerq6mbHkNUJtmPHjho8eLAuvPBCde3aVVlZWUbM6gHH6mHEah2s9rnV8Xzbbbdp//3314knnqju3bvr1ltvNWJWCbqbbrpJW7ZsUadOnbRu3TrddNNNRszq+Ap1nFiVo6V9FOphy1fOYHXa6mbQ6ni1WpZVfZBCH+tW5xWr84PVsWC1f6yOZavrTEvlDHWcWNU/q/WzOq9Y1U2r48tqnlbHl+8h4Nxzz9Wf/vQnUzmt7gWs9oNVnbc6F7d03Qu1H6yubaH2u9U51WodrParVT2Kxf3F5MmTdc011+jqq6/WTTfdZFoHq31gFbPaLlbnqszMTB1wwAGqqqpSv379VFtba8Ss6pHVvYDVub9Dhw7q3bu3MjIy5PV6TddZq/putc0++ugj9e3bV/3799eAAQOUk5Mjyfoe6NVXX9V+++2n888/X+PHj9fcuXPDKofVeVoKfXxZ7R+r48eqLFbnxpbunUL9kBfp/abVObyl6UKVxeoYstqekV6jIr3v9z8XByZkrPaR1Tyt6rvVPZnVtc3qeA4Va+k+KFR9T01NVWFhoa655hrdfffd2rlzp2n/hLomVFdXq7GxUT169FBOTo5pv0da/6z2udV2tqoPVs+yVvXWat2trs2R1lurc7HVsR7pPbrVtcaq/lmV02ofhdrvVseW1fnBanlW55yUlBT17dtXKSkp6t69uzp06GDEWipLqPpgtbyuXbvqqquu0nHHHaeXXnpJM2fONGJW9S9ctkpude3aVb///e915JFHasKECdpvv/2MWFpamnr16qWRI0fq4IMPVlpaWouxxx57TKWlpdq+fbu2b9+umpqaZsu75JJLlJeXp1NPPVU9evQwYunp6crKytK5556r0047TRkZGZKk3NxcHX/88ZKkE088UQ0NDcY09957r3r06KHLL79cJ510ku644w4jNnToUF199dVauXKlZs2aZdqR06dP1+uvv67XX39db7zxhtavX28q4wUXXKDzzjtPd999tymb2b9/f82aNUudO3fW3LlzTfPMzc3V+PHjNXXqVN15552mG75OnTrp5JNPVm5urg477DDl5eWZtkt6erpGjRqlwYMHmyp4bm6uTj75ZJ1xxhkqLi5W9+7dg5Zl3rx5Ya+f1T6yKqfVgWFVTt8N4S9/+ctmN4Sh9rkkZWdn6ze/+Y3y8vI0efJk0wHnv63vuOMO07a22n/S3l/FJk6cqPHjx5vqtNU8Q+338847TzfddJOuvfZazZ49W7/5zW8UKNTyQu1zX52+4oormtVpq2PSalv6jqHGxkadcMIJpmPI6gR7/vnn66efftK4ceP03Xff6Wc/+5kR8z3g7N69W/379zdtL6uHEat1sNrnVsdzRkaGjjzySB144IEaM2aMaZ5WCbqMjAydffbZOvbYYzVp0iTTdFbHeqjjxKocLe2jUA9bUug6bXUzaHW8+pY1YMCAZsvy1YfU1NRm9UEKfaxbnVeszg++7RLsWLDaP1LoY8vqOtNSOX3HSeC1xqr+Wa2f1XXBqm76JxACjy+rc5XV8eV7COjZs6c6depkOr6s7gWs9oNVnW/pXGx13Qt1vmrp2h1sv993330h7xOs1sFqv1rVo2jcXwRe03//+9+rT58+uvHGG/Wzn/3MtA5W+8AqZnX/ZHWuGj9+vObPn69zzjlH5eXluuSSS0zrF6oeWSWHrM79f/zjH+XxePTwww/rlVde0WWXXWbErOq71TabNWuWBg4cqFGjRmnUqFH65S9/Kcn6Hqhnz566+uqr9dNPP6myslL+rMphdZ727aNgx5fV/vHto2DHj1VZrM6NVnUzJSVF+fn5QX/IC+d+03esBD4ThDqHW01n9aOi1TFktT0jvUZZnYut6pL/uTgwIWO1j6zmaVXf/e/J6uvrTdvM6tpmdTyHirV0HxSqvk+aNEklJSXKzc3V1Vdfrb59+5q2c6hrwn777acuXbqoqqpKy5Yt0ymnnGLEWnomDVX/pND73Go7W9UHq2fZlp4tQ617S/dI4dTbwPsSq3Ox1bEe6T261bXGqv5ZldNqH0nB97vv2OrUqVOzY6ul80Oo5Vmdc4455hg98sgjuvDCC3XVVVdpyJAhRqylsoSqD1bPj8XFxZKkgoICTZ061fSjh1X9C1fHVk8RQwceeKCWL1+uyZMn69tvvzWdRCdNmqRFixZp+vTp6tSpk84991wj9stf/lKLFy/WVVddpW7duum3v/2tJGnq1Kn6/PPP9ctf/lKdO3c2VVJpbyW+4oorNHToUE2bNk0DBgwwYieeeKLefPNNXXLJJerevbtOPvlkSVJeXp5uueUWrVu3ThdddJFOOOEE0/wKCwv1+uuvq6SkRF9//bWGDx8uSercubM6duyoSZMmadKkSaZy/PnPf1afPn3Uu3dvSXtvWnwKCwv13Xff6ZBDDtGpp55qyhTn5eXpmWee0ZlnnqmjjjpKS5cuNWIHHHCAli9frvfee09ut1sej8eIDRgwQDNmzNDxxx+vadOm6dBDDzVimzdvVl1dna6++mqtW7dOEydONGIFBQV68skndeqpp+riiy/WqFGjjFh+fr5effVVjRkzRl26dNFpp50W1vpdc801+ve//60zzzxTL774og4//HAj1rdvXx1zzDGaMWOGpk6d2uxge+qppzR+/Hjt2rVLp556qhHr06ePvvvuO23YsEEXXHCBJkyYYMTq6uqUnp6uGTNmqKysTL/5zW/UpUsX0z6fMmWKunXrZuxzSerdu7duvvlmjRs3TitWrDAOTN/yvvjiC40YMUKXXHKJ5s+fH9b+27Bhgzp06KDNmzfrp59+Urdu3YzY0UcfreXLl2vYsGHN5rn//vtLko466igdddRRxueHHHKIDjnkEIUSankbNmzQV199pQkTJqiiosJ0HEjSJZdcouOPP17HHnus1q1bpwMPPFDS3qz8b37zG7333nsaMGCABg0aZExjtS2HDh2qW265RaeddpoeffRRnXTSSUasa9euGj9+vB577DFdeOGFpvrnXzcC13Pw4MG6/fbb9eOPP+r555/XhRdeaMROPvlkffLJJ7r++us1YMAAHXnkkUbs1FNP1YsvvmicV3wPFZJ5ny9fvty0z62O58LCQo0bN04FBQXq06ePCgoKjNj555+vq666Sj169NCPP/6oESNGGLGDDjpIM2bMUIcOHZSammo6vgL3uX/Xva5du+qwww7TnDlzNHXqVB122GGSpHHjxunqq69WY2Oj3nnnHT344IOmclrto9/85jf65ptvNGHCBC1atEhnn322af18dfqUU04xHgonTpyoxx9/XL/4xS9UV1fX7CH6/vvvV0VFhfbff39de+21Rmzy5Ml67bXXjPX0L0dhYaEefvhhVVdX6+CDDzY9gEp7W1+sWLFCZ555pr777jsNHTpUknT99dcrNzdXvXr1kiRToqZv37768ssvdeaZZ+rbb781ppFkPCRu3bpVGzZs0AEHHBDW/rE6lv2vM1OnTjXVh8ByZmdnGzHfA8MTTzyhSy+91HSetqp/vvP0Oeeco2+//daoO7519+0jt9utzZs3G7FJkybpiSee0GmnnaZLLrlEv/vd70zTjR49Wq+88oquvPJKHXHEEUZswIABeuKJJzRp0iT9/ve/18knn2z8ejdp0iS99tprmj59ulatWmX61b+8vFyfffaZTj31VM2aNcv0q57VvYDVfigsLNSUKVPUqVMn1dXVmc6PoeqttLeF4PHHH2/sd//zi+98dcYZZ2jNmjWm+ml1bQt1f/F///d/mjZtml544YWg3Xh8x21qaqpSU1N1zDHHSDLX9W+//Vb9+vUzprnuuuuUk5Oj3r1766233jLdH0V6f9GlSxdVVFTo008/VXp6uqk+HH300TrssMP0+OOPN2uJ2aNHDw0fPlxHHnmkunTporFjxzZbt5SUFHXo0MH04Ddw4EDNmDFDzzzzjDZu3Kj3339fJ554oiTrc1XgddCfVT0qKytT9+7d9dFHH2nXrl368MMPjW1tde7v0aOH/vjHPwZdXo8ePbR161ZNnz5dXbt2Nf3AZLXN/PeJb52kvefDK664Qscff7yGDBmiGTNmNFvm6aefrtNPP9302TnnnKMLLrjAuDb714dJkyYZ16CePXs2a+F90EEHmeqf7/iqr69Xz5499d577+mnn37Su+++a/zAVFZWpq+//lrHHHOM7rzzTmM7Svvuz33bxP8+wercaFU3zzvvPA0bNkynn3660tLSTPcsVvX92muvVadOnfTCCy/I4/GY7imszuGXX365evbsqRdeeKHZeeW8887TjTfeqNLSUn3yySd64YUXjFifPn2MY6hjx46mxGtgfffnu888/fTT9cgjj5i2mf81qqSkxHSuGjNmjJYvX66DDz5YhxxyiD766CMjFnjf77tP8O3bTp066bbbblP//v119NFHB91Hgc9J2dnZxoPy1KlTjecdybq+n3baafrNb36jDRs26PXXX9c///lPI2Z1bbM6nkPFevXqpcsuu0xZWVlKTU01nTukfdeT1NRUdejQQSNHjpQk0/bJzc3VlVdeaSrjddddp/POO0+rV682zXPq1KnGvwOP6yOPPFKjR49Wfn6+JPN9iVX927hxo+rr6zVjxgytX79e559/fljb2f854ve//71OOeUU49rcqVMn3XLLLdqwYYMmT55sek7q06ePGhsbNWrUKJWXl5uer3zXwzPPPFNr1qwx1ZVQ5w5p7/U+JSVFmzZt0ldffWWqtz179jSeV2tqarRnzx4j5jsXH3fccc2evayOdat61KtXL+M+wev1Gvtcsr7WWNU/q3Ja7aNQ+71nz5668MILtWnTJk2bNk2DBw82lcP3jHvhhRc2q9OhlldUVKQZM2boySef1COPPKJLL71UKSkpkvbeXxx77LGSZHoukaRu3brpwgsv1ObNm5udO3z3qU899ZRWr15tqg+NjY3G8+O4ceP0008/GecP/zoVqEuXLnK5XJbX9pbYquXWb37zGw0bNkySdOihh5pOlH//+9/VpUsXTZ8+XVdccYWp60p2draGDx+uOXPmqKCgwNhZL730ksaNG2c0vw7sg/urX/1KRx99tJqamvSzn/1Ml156qREbOXKkbrnlFj322GM65ZRTNG7cOEkymnDOnTtX9913n2lMgL/+9a8aNGiQvv/+e1177bWmLjQTJ04M2ac4Pz9fXbp0Mfoi+z/sn3DCCTrooIP00ksv6fPPP9fdd99txH766SdT8/Ivv/zSiG3fvl3/+Mc/dN5552nmzJnKzMw0Yk1NTTryyCPl8Xh07LHH6pNPPjFiPXr0UGFhoS699FI9/PDDWr58uRErLS3ViBEjdMstt2jevHmmsRM2btwYsvvQwIED9eGHHxr9m/1vxBYvXqyjjjrKmM7/F8gff/xRDz30kJYuXaqbbrrJFFu7dm3ILjTl5eX65z//qbPOOkt33HGHqc+xr3vNOeeco+eff96ULR4wYIBmzpypn/3sZxo6dKgOPvhgI3bBBRfo5ptv1vHHH6+KigrTyXL9+vX697//rUWLFiklJcWUiDr//PONk3hZWZmpGfItt9yi+fPn65NPPtH69et13nnnGbGDDz5Y27ZtU3V1tf75z3/q4osvNmLHH3982P2t/d10002666679PHHH6ukpES/+tWvJO09kTzzzDN6/vnnVVxcrB07dhjT+JqPv/7667rhhhtMzcdfeuklvfLKKxowYICuuuoqbdu2zYj1799fxxxzjB588EHNmzfPdBG/4447tHv3bi1evFhLliwxjfmxdu1aPf3003rggQd077336r///W9Y6/bNN9/o3HPP1dixYzV27Fj97W9/M2LPPfecjj32WNXX12vatGmmeS5dulSdOnXSzJkzNXPmTNON6YQJE3T00Ufr6KOP1rBhw0w3dX/605+Mfwf2Jd+4caMWLFignJwcXXXVVabj5OWXX9Zll11mdO/6+OOPjdgxxxyj2267TUcccYQOPvhgHXfccUYsPT09ZNe9NWvW6IUXXtCyZct02223GfvvhhtuUGNjo9LT03XQQQdpyZIlpnIOHTpUw4YN0xFHHKExY8aYbkT+9re/adSoUcbx9eabbxox/zo9ZMgQTZs2zZjm6KOP1g033KBrrrlGhYWFxjQ//fSTfv7znwft1vbSSy/pV7/6lRH797//bcRWrFhh7NdXXnlFn376qWkdhg0bpq1bt6q6ulpffPGFkQDPysrSN998Yxwj/vtu48aN+te//qVFixZpv/3201lnnWXEDj/8cB1++OH6+OOPtXXrVlNdmTRpkubPn6/bb79dc+fONSUXHnnkET344IPaf//9NXbsWI0ePdqInX766TrttNN04YUX6oEHHjA9+H311Vf6z3/+Y+zXF1980YhVVlbqmWee0RNPPKFbbrnFVI8mTpwYcvyK9evXa8yYMZo/f74KCgpMx2VpaalGjRqlW265RVdccYUOOuggI/b8889r7NixuuGGG3THHXeYzqkrVqzQokWLdPPNN+v222/X2rVrjdiaNWs0duxYzZ8/X9nZ2aYH6YsvvlhlZWXKzc1VTk6Oqd5mZWVp0aJFuu+++/Tcc8/prbfeMmLjx4/XQQcdpBdffFErVqzQOeecY8TGjRunv/zlLxoxYoSGDBli2p4bNmzQgQceqGnTpun66683nfuPO+447dixQ16vV0OGDDFd70899VQNGDBAS5cuVVFRken8PmLECONX3o8//thIRErS8OHDjRtrSUbiX9q73+fNm6cuXbpo9OjRxjnc10U3JSVF06dPV6CdO3dq1KhRGjFihA4//HC9/PLLkva2rli1apUWL16sNWvWmM47kydP1vz5840udv/617+MWJ8+fbRixQrj3sM/YXvaaadpzZo1WrhwoX788UfT+b2ysjLk2DE1NTV65513lJWVpSOPPNI0ZsmWLVv0/PPPq2fPnrr22mtN9xCDBw/W+PHjdf311+uggw4yPRwsXrxYr776qnJzczVy5EjTObxfv37685//rPHjx6u4uNjyRxx//veUVVVVpuTCE088oREjRujzzz9vdg/hf70fNmyYaZtZKSkpUceOHXXEEUfo0ksvNd1DlJaWatmyZbr00ks1c+ZM0z1gKPfff786duyoZcuWadCgQabjzopvDJScnBxNmzbNdD/20ksvGdegE088sdkYRCeffLJGjx6twsJCHXXUUfr5z38uaW93xjFjxmj9+vWaNWuWqTtj165dde+99+qJJ57QggULTPdqX3/9tQoLC3Xsscdq2LBhprH8pkyZonvvvVdTpkzRAw88YLrmHXfccRo4cKBefPFFffnll7rvvvuM2KJFi/TCCy9o+/btmj59umm7+B+T//nPf0z3VS+88IKeffZZnXXWWbr44otNY0vNnz9fxx9/vN5++21VVFTooosuMmLvvPOOFi1aZBwLGzZsMM3zwgsv1JFHHqlXXnnFVG/Xr1+vxx57zFie//G1bds2vf/++zrnnHM0ePBg033crbfeqqeeekqzZs3SO++8Yxq/aNq0aTruuOPUqVMnrVq1yvQQfeutt2rRokWaNWuWFi1apI4d97VfOOqoo3TSSSdp3LhxGj58uMaMGWPEfEkf3/XL/77Efx+dc845pgRrSUmJBgwYoPLyct1www2m+9SysjJ17NhRffv2Vd++fU3XL9+9Y9++ffX888+b9q3/vZVkHpfqzDPP1OrVq7Vw4UJt3LjRlDw+6aSTVFRUpJdeeknfffed8cPoxo0bNWDAAA0ZMkRXXHGF8QDv47uu33HHHTr55JNN9wOhbNy4Ud27d9fXX3+tbt26Gc+HLVm/fr2ef/55Y3gG37ElSTfffLMmTpyo3r17a+zYsaaEi68F/ZVXXqlFixaZnmW3b9+utLQ09enTR3379jXdC1RWVmr16tVavHixPvroI1Myt7y8XJmZmbr55pubPSc9+uijuvrqq3X99dfrySefNHXZrqur09ChQ/Xjjz9q+fLlevXVV43YMcccowkTJui2227T3LlzTa3dPvnkEzU0NOihhx7SU089ZarT5eXlevvttzVx4kRdeeWVpueFs88+W8XFxVq9erU2bNhgajjRt29f5eXlGeP5+X4MkaTf/va3IcfVqq6uVkFBga666irddtttpud437P/woULNW/ePFNPgvHjx6usrExer1eHHnqo6QfHCRMmaObMmSorK9O3336r8ePHy19ubq6uuOIKzZw501Sn/e8dGxoajPuS9evXa8iQIXriiSd0/fXXm85VO3bs0JtvvqnzzjtP9913X9DxxXzL8082HXvssbrmmmu0fv16FRUV6brrrms2XTClpaUaOHCgnn76ac2ePdv0w/WWLVv097//Xa+++qr69Oljut77uju//fbbmj17tuX4c/42bNgQchiTcNkquWXVH9mqa5Gv/+nll1+uUaNGGRuwpebXs2fPVr9+/TR+/Hj16NEj6PgP11xzje666y7jV2X/5ru5ubnNxjwK1aVq/vz5IfsUW/VFfuihh/TQQw8FHa/Aav2s+n1/9NFH2n///YN2ObJqLmy1flYxq/7Nkc7TqguNbx3S09ObrYPV+BwPPvigHnroIfXt21fDhg0zbWv/vsP+9UHae2M3ffr0oF2urPocP/zwwyH3rVWf6ttvv92yv3UozzzzjO6//34dcMABpnrWoUOHkPXI6rjzr3/33HOPaTrfvIPVd6vuIFb71YrVfvXN86KLLmo2T6vj2ZekDLYOkfZdtyrn008/beyfESNGmM4D//3vf0N2Ewy1H6y2s7T3uAx1/rPaD4Hn6WeffTboNP6tkKzWO9xYYHc4yXws+J/7rY4fq+P1v//9b8hzo9X1ySoWWI8++OADI2bV/dOqHlldM6yuC4Hz9H/4sdrnVrHA5VVUVBgxqzqYlpYWcp6+Y8HqmhjsPG11/Qq8BvvvB6s6cfvtt6uxsbFZHZOs93uo49mqi6607/oc2E3Xqq5YbWer8Ux890DBrs1W9xC+a/r48ePVs2dP03RW14V58+apsbGx2b2a//oF655sdW60YnXdbs05LnC81lCs6pH/fsjMzDTth1BaOoeHEuk1SAp9X2y1X62WF6o+S+EdP8HOAVbXdKtrc2pqqnFvGHg/E+mxYHWN8r8XDfXcEuy8YrXfrc5H/tOdeOKJYU+XlZUVsk74b8+777477Htf31hrffv21dixY03naattZlUnrPZRqGclqzL6r1/gs56VluZpNV2oc7/V/abVfarvXuCAAw7Q+PHjTceJ1TXD6lppVY+snh+t1sGqTls9Z/ifjwL3udV4frNnzw753Gm1PVu6l7N6jg/2fBU4T98LPXxCXaOs9o//+Siw/P7zvOaaa7Rw4UJjnpFeT6y2Vzh5iGDdna20dI8UDlt1S3zsscd0+umnG33r/fsj//GPf9TixYv18MMPq2fPnqZxDnz9T2tra039T319pgcOHKirr75ajz32mGl5GRkZxi/sBx54oN555x0jdu+99+rVV1/Vr3/9a7322mtGU1NfH9uqqiqtXbvW1Kw+sM+qrz+zr4zjx4/XIYccojvuuMPUL1Xa1xdZUrMkjm+8AkmmvsFW62dVzlmzZhlj2EgylcVqO1utn1XM17852HaOdJ5W4y9ZrYNvfI7zzz+/2fgcvrEhfE0yA8cDCVYfpOZ9h/1ZTWe1b0PV6ZZiLUlPTzeW56tnVvXIaltaTWdV331jhbzxxhvNxgqx2q9WrPar1TytjhOrdbDar1b11qqcUvD9I1kfs6H2g9V2lqzPf1bbLNR52moaq/UON7Zjx45m2yvUsWB1jFgdr1bb2er6ZBWzqkdWy7OqR1Loa4bVcRnpOdUqZrW8thzrLV0Tg52nW7pPCLUfrOqEVV2y2u++dQg8nq2WJYWuE1Z1xWo7W6231TnA6txodU1v6boQaltarV+obdkSq/N0JOe4lrRUj6zuAYNp6RweSluuQaHqRKTnlUjPqVLoc4DVvrPa51bnh0iPBatrVCTPLVLLx3O0p7OqE5He+1rdT1ttM6s6YbWPQt1Pt3S+tVq/UFqaZyTTWZ0frOqfFNl9o1XdtKpHVvOM9L4/0uPSfzw/39/+04W6Rlltz0jv5aTQ+8FqnqHqn9U2aak+hJpnpNcTq+VFmoewEunx5S+lqa3vW4yi9evXm8YfWbJkiakfcChLlixR3759VVxcrG+//VbffPONLrjgghan+7//+z+9/fbbprEt/Dd+Q0ODnnnmGa1cuVITJkwwNXlsrffee0+9e/fWsGHDtG3bNp1yyinGa+3//Oc/S9rb3HjDhg1avXq10SR63bp1Wrx4sTZv3qyuXbuqsLBQkydPjrgcidDSdraLlrZ1pPUh1HRWy7Oq05HWd6t6Fm1W9d0pWlqHaJ4fpPjuHyny4zLS83QshDoWIj1GrFitt1UsFsdCvOtKvMXimhjpfrCqS1b73S77yGq9Iz0HRDpdIq5dkZynY3GOc8o1MZ73a7E6fiLZ5/G+T430WIj3dFJk2zPS87RVnbDaR215Vor2vVwkIj0/2OU6I8XmHBfpPo/F/W2kz/EtXU9iUf/sUKcTyVYtt/z74DY1Nemdd94J64bCNxbFqlWr1NTUpP/85z9hnbR9Y1v4vPzyy0blX7x4sSSZxn9oS+WorKw0+j83NTWZ+j77/v3xxx+rqalJ//3vf42DwjdegW/8mnfeecdxyS2r7WwnVts60vpgNZ3V8qzqdKT13aqeRZtVfXcKq3WI9vlBiu/+kSI/LiM9T8dCqGMh0mPEitV6W8VicSzEu67EWyyuiZHuB6u6ZLXf7bKPrNY70nNApNPF+9oV6Xk6Fuc4p1wT43m/FovjJ9J9Hu/71EiPhXhPF+n2jPQ8bVUnrPZRpMuLxb1cJCI9P9jlOiPF5hwX6T6Pxf1tpM/xVvOMRf2zS51OJFuNuWXVPzhW04UaC8Bq/IdIWJXRapwXqzI6hVPWIRb1wWq6cJYXqk5EUt+t6lm0RVpGOwlnH0SzTsdz/0iRH5d22rehyhKLMkZ6TMaiLPGuK/EWi2tiLO4vIr2ux1NL5Y90W8ZzH0S6Ldt63Y7X+cNO4nm/Fovjpy37PJ73qYl43mnLdHY4R7R0z9yW5SX6+cQJ9/UtidV5M5J9Hqv6F+lzfDyfJexSpxOqyUbKysqaysvLjb8//vjjhE0X6TyjvaxolyMRnLIOsdgPsdjv8T5OIuGUfW4l3sdlvLeZE+pRpGWJ9/5J9roSb3Y6/9lpv0fCKdsyGaaL9zxjwS73Ccmwz2OxvPY8nZ3qS7Q5vfyxKoud6oOdnuciXYf2wlZjbgEAAAAAAACtYatuiQAAAAAAAEBrkNwCAAAAAACAY5HcAgAAAAAAgGOR3AIAAAAAAIBjkdwCAAAAAACAY5HcAgAAAPD/27EDEgAAAABB/1+3I9AZAsCW3AIAAABgK+NegtAi91HSAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Average linkage\n",
        "plt.figure(figsize=(15, 7))\n",
        "plt.title(\"Country Dendrogram\")\n",
        "\n",
        "clusters = shc.linkage(country_features,\n",
        "            method='average',\n",
        "            metric=\"euclidean\")\n",
        "shc.dendrogram(Z=clusters)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "pSbhHy30qpVa",
        "outputId": "30d840ab-508f-4d6e-a801-8e27d3898285"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Utilizaremos el método de Ward porque obtenemos agrupamientos con una buena distancia inter-clusters (se unen bastante arriba en el dendrograma).\n",
        "\n",
        "**Observacion:** hay cuatro grupos en nuestro dataset, pero este método nos devolvió solo tres."
      ],
      "metadata": {
        "id": "SMD_Z1JssYmR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Ward linkage\n",
        "plt.figure(figsize=(15, 7))\n",
        "plt.title(\"Country Dendrogram\")\n",
        "\n",
        "clusters = shc.linkage(country_features,\n",
        "            method='ward',\n",
        "            metric=\"euclidean\")\n",
        "shc.dendrogram(Z=clusters)\n",
        "plt.axhline(y = 18, color = 'r', linestyle = '--')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "WLm2iiAoqxRy",
        "outputId": "03c4df67-698a-44f7-dc05-c73ff78e8d69"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Clustering jerárquico aglomerativo"
      ],
      "metadata": {
        "id": "Foz-Q3hstF60"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Fijense que ya le estamos diciendo que forme tres grupos, decisión que viene de ver el dendrograma!\n",
        "#n_clusters define dónde vamos a cortar\n",
        "clustering_model = AgglomerativeClustering(n_clusters=3, metric='euclidean',\n",
        "                                           linkage='ward')\n",
        "clustering_model.fit(country_features)\n",
        "\n",
        "#Ver la asignación de las observaciones a los clusters?\n",
        "clustering_model.labels_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ADAnJnCfrV4a",
        "outputId": "fc22d3b9-1b21-4e6d-b60d-b0b64d4e3184"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 2, 1, 1, 1, 1,\n",
              "       1, 0, 1, 2, 2, 1, 2, 0, 1, 2, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 1,\n",
              "       0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 2, 1, 0, 1, 0, 1, 1, 2, 2, 1,\n",
              "       2, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 2, 1, 0, 1, 1, 1, 1, 1,\n",
              "       1, 0, 1, 0, 1, 2, 2, 1, 1, 2, 0, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1,\n",
              "       0, 0, 2, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 2, 1, 0, 2, 1, 1,\n",
              "       2, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 2, 1, 1, 2, 1, 1, 1,\n",
              "       1, 2, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Vamos a ir guardando los agrupamientos que formemos con cada uno de los métodos\n",
        "#en la misma tabla para compararlos al final\n",
        "finalDf['hclust'] = clustering_model.labels_"
      ],
      "metadata": {
        "id": "akXqDbAsuYei"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ahora podemos hacer la misma visualización que antes con nuestros datos en espacio de PCA pero los puntos no están pintados por la información externa o etiqueta que tenemos, si no que están pintados por cómo el método los asignó a diferentes clusters."
      ],
      "metadata": {
        "id": "TWE14IoOu-2E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Graficamos la distribución de nuestros puntos en este nuevo espacio\n",
        "fig = plt.figure(figsize = (9,9))\n",
        "ax = fig.add_subplot(1,1,1)\n",
        "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
        "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
        "ax.set_title('PCA - Country Dataset', fontsize = 20)\n",
        "\n",
        "targets = [0, 1, 2, 3]\n",
        "colors = ['r', 'g', 'b', 'y']\n",
        "\n",
        "for target, color in zip(targets,colors):\n",
        "    indicesToKeep = finalDf['hclust'] == target\n",
        "    ax.scatter(finalDf.loc[indicesToKeep, 'principal component 1']\n",
        "               , finalDf.loc[indicesToKeep, 'principal component 2']\n",
        "               , c = color\n",
        "               , s = 50)\n",
        "ax.legend(targets)\n",
        "ax.grid()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 806
        },
        "id": "vKye2Br7uvCq",
        "outputId": "332803fd-500a-45f5-e9ea-06494fe5dd12"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###K-Means"
      ],
      "metadata": {
        "id": "DN0mhvCk1te9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans_kwargs = {\n",
        "    \"init\": \"random\",\n",
        "    \"n_init\": 10,\n",
        "    \"max_iter\": 300,\n",
        "    \"random_state\": 42,\n",
        "}\n",
        "\n",
        "# SSE: Sum of Square Error\n",
        "###Hacemos un clustering donde vamos cambiando el valor de k\n",
        "sse = []\n",
        "for k in range(1, 11):\n",
        "    kmeans = KMeans(n_clusters=k, **kmeans_kwargs)\n",
        "    kmeans.fit(country_features)\n",
        "    #Calculamos la inercia (variabilidad del cluster)\n",
        "    sse.append(kmeans.inertia_)"
      ],
      "metadata": {
        "id": "ph17tjsY2FAn"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize = (4,4))\n",
        "plt.style.use(\"fivethirtyeight\")\n",
        "plt.plot(range(1, 11), sse)\n",
        "plt.xticks(range(1, 11))\n",
        "plt.xlabel(\"Number of Clusters\")\n",
        "plt.ylabel(\"SSE\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "X18NETHw2YRp",
        "outputId": "8f2434c6-5639-4cb7-f36f-07906f2bcdc1"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kl = KneeLocator(range(1, 11), sse, curve=\"convex\", direction=\"decreasing\")\n",
        "\n",
        "kl.elbow"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "05-9go5o2n7j",
        "outputId": "21f56e1b-7afd-460e-c8e1-3ca4c2e6839b"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans = KMeans(\n",
        "    init=\"random\",\n",
        "    n_clusters=4,\n",
        "    n_init=10,\n",
        "    max_iter=300,\n",
        "    random_state=42\n",
        ")"
      ],
      "metadata": {
        "id": "MbTLHUQNN_Zt"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans.fit(country_features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "--7SsikgOCJX",
        "outputId": "4d1f0e5d-2cef-4976-d563-8fb22bf2750b"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(init='random', n_clusters=4, n_init=10, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(init=&#x27;random&#x27;, n_clusters=4, n_init=10, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(init=&#x27;random&#x27;, n_clusters=4, n_init=10, random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#¿Donde estan los centroides de los clusters que generó?\n",
        "###El valor que tiene el centroide para cada atributo (9), y como tenemos 4\n",
        "###clusters tenemos 4 centroides\n",
        "kmeans.cluster_centers_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3FfMChPAOILe",
        "outputId": "e79e1240-9dbc-417a-f643-4f9e4f7116b3"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.84900324,  4.93567278, -0.00816303,  4.54805768,  2.4395424 ,\n",
              "        -0.50420614,  1.22682431, -1.03886271,  2.44079735],\n",
              "       [ 1.34541883, -0.45967186, -0.1895969 , -0.22567743, -0.68713515,\n",
              "         0.39226703, -1.26429235,  1.32908188, -0.60530769],\n",
              "       [-0.82787747,  0.16811209,  0.88737767, -0.30560645,  1.48891727,\n",
              "        -0.47955552,  1.1087252 , -0.75076544,  1.70196967],\n",
              "       [-0.43706491,  0.02737909, -0.19090588,  0.06955093, -0.20131886,\n",
              "        -0.03918493,  0.28566125, -0.44720856, -0.31752579]])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Asignación de las observaciones a cada cluster\n",
        "kmeans.labels_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "69y0cv6rOS_f",
        "outputId": "1f120325-52aa-4410-99fe-8a0106b2b2a3"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 3, 3, 1, 3, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 2, 3, 1, 3, 3, 3, 1,\n",
              "       3, 2, 3, 1, 1, 3, 1, 2, 3, 1, 1, 3, 3, 3, 1, 1, 1, 3, 1, 3, 2, 3,\n",
              "       2, 3, 3, 3, 3, 1, 1, 3, 3, 2, 2, 1, 1, 3, 2, 1, 2, 3, 3, 1, 1, 3,\n",
              "       1, 3, 2, 3, 3, 3, 1, 2, 2, 2, 3, 2, 3, 3, 1, 1, 2, 3, 1, 3, 3, 1,\n",
              "       1, 3, 3, 0, 3, 1, 1, 3, 3, 1, 0, 1, 3, 3, 3, 3, 3, 3, 1, 1, 1, 3,\n",
              "       2, 2, 1, 1, 2, 3, 1, 3, 3, 3, 3, 3, 2, 2, 3, 3, 1, 3, 3, 1, 3, 3,\n",
              "       1, 0, 3, 2, 3, 1, 3, 2, 3, 3, 1, 3, 2, 2, 3, 1, 3, 1, 1, 3, 3, 3,\n",
              "       3, 1, 3, 2, 2, 2, 3, 3, 3, 3, 3, 1, 1], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Lo agregamos a nuestra tabla comparativa\n",
        "finalDf['kmeans'] = kmeans.labels_"
      ],
      "metadata": {
        "id": "gNOo3KS3Obye"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Cómo se verán nuestros datos?\n",
        "fig = plt.figure(figsize = (8,8))\n",
        "ax = fig.add_subplot(1,1,1)\n",
        "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
        "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
        "ax.set_title('PCA - Country Dataset', fontsize = 20)\n",
        "targets = [0, 1, 2, 3]\n",
        "colors = ['r', 'g', 'b', 'y']\n",
        "for target, color in zip(targets,colors):\n",
        "    indicesToKeep = finalDf['kmeans'] == target\n",
        "    ax.scatter(finalDf.loc[indicesToKeep, 'principal component 1']\n",
        "               , finalDf.loc[indicesToKeep, 'principal component 2']\n",
        "               , c = color\n",
        "               , s = 50)\n",
        "ax.legend(targets)\n",
        "ax.grid()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 761
        },
        "id": "OTll8nrtOh9o",
        "outputId": "6fae3159-c32c-415d-85ee-370616607648"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###DBSCAN"
      ],
      "metadata": {
        "id": "WkBAVtxlQJAo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Calcularemos DBSCAN sobre la base de PCA\n",
        "pca_data = finalDf.loc[:,['principal component 1',\t'principal component 2']]"
      ],
      "metadata": {
        "id": "wdbu2sw0O3r_"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Ver qué valor de Eps nos puede servir\n",
        "k = 2\n",
        "data_nn = pca_data.copy()\n",
        "\n",
        "# Calculamos los vecinos más cercanos\n",
        "nearest_neighbors = NearestNeighbors(n_neighbors=k)\n",
        "neighbors = nearest_neighbors.fit(data_nn)\n",
        "distances, indices = neighbors.kneighbors(data_nn)\n",
        "distances = np.sort(distances, axis=0)\n",
        "\n",
        "# Obtenemos las distancias\n",
        "distances = distances[:,1]\n",
        "\n",
        "i = np.arange(len(distances))\n",
        "\n",
        "sns.lineplot(\n",
        "    x = i,\n",
        "    y = distances\n",
        ")\n",
        "plt.axhline(y = 0.5, color = 'r', linestyle = '--')\n",
        "plt.xlabel(\"Points\")\n",
        "plt.ylabel(\"Distance\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 552
        },
        "id": "SzLc8pFXQVEo",
        "outputId": "4446b244-9e31-4119-e3ec-d0afa0121557"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Distance')"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dbscan_clusters = DBSCAN(eps = 0.5, min_samples = 5).fit(pca_data)"
      ],
      "metadata": {
        "id": "WVC7Gx5SQmTX"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Mostramos las etiquetas asignadas\n",
        "dbscan_clusters.labels_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fcl00J2JQt2S",
        "outputId": "434964da-9a30-42de-aad7-f3e0d99299b2"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  1, -1,  1,  1,  1,  2,  4,  1,  1,  1,  1,  1,  1, -1,  1,\n",
              "        0,  1,  1,  1,  1,  1,  3,  1,  0,  0,  1,  0,  2,  1, -1,  0,  1,\n",
              "        1,  1,  0,  0, -1,  1,  0,  1,  3,  3,  4,  1,  1,  1,  1, -1,  0,\n",
              "        1,  1,  4,  2,  1,  0,  1,  2,  0,  2,  1,  1,  0,  0,  1, -1,  1,\n",
              "        4,  1,  1,  1,  1, -1,  1,  2,  1,  2,  1,  1,  0,  1,  3,  1,  1,\n",
              "        1,  1, -1, -1,  1,  1, -1,  1,  0,  0,  1,  1,  0, -1, -1,  1,  1,\n",
              "        1,  1,  1,  1,  0, -1,  1,  1, -1,  2,  0, -1, -1,  1,  0,  1,  1,\n",
              "        1,  1,  1,  2, -1,  1,  1,  1,  1,  1,  0,  1, -1,  0, -1,  1,  3,\n",
              "        1,  1,  3,  2,  1,  1,  0,  1,  4, -1,  1,  0,  1, -1, -1,  1,  1,\n",
              "        1,  1,  0,  1, -1,  2, -1,  1,  1,  1,  1, -1,  0,  0])"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Agregamos los valores obtenidos a nuestra tabla comparativa\n",
        "finalDf['dbscan_pca'] = pd.Series(dbscan_clusters.labels_)"
      ],
      "metadata": {
        "id": "lEBQcISiQvnH"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Mostramos los clusters\n",
        "fig = plt.figure(figsize = (8,8))\n",
        "ax = fig.add_subplot(1,1,1)\n",
        "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
        "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
        "ax.set_title('PCA - Country Dataset', fontsize = 20)\n",
        "targets = [-1, 0, 1, 2, 3]\n",
        "colors = ['gray', 'r', 'g', 'b', 'y']\n",
        "for target, color in zip(targets,colors):\n",
        "    indicesToKeep = finalDf['dbscan_pca'] == target\n",
        "    ax.scatter(finalDf.loc[indicesToKeep, 'principal component 1']\n",
        "               , finalDf.loc[indicesToKeep, 'principal component 2']\n",
        "               , c = color\n",
        "               , s = 50)\n",
        "ax.legend(targets)\n",
        "ax.grid()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 761
        },
        "id": "fWp6iYlyQyJf",
        "outputId": "2c8576c1-8f31-459c-e671-f5c3a770d071"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Comparación de métodos de clustering"
      ],
      "metadata": {
        "id": "VCnXgod9RObF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Validación externa"
      ],
      "metadata": {
        "id": "g6piFIXgRYQF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "ARI"
      ],
      "metadata": {
        "id": "RBvHkzSXRfyF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "labels_true = country_class"
      ],
      "metadata": {
        "id": "KFBp2SOgR3Hw"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Para el clustering jerarquico\n",
        "adjusted_rand_score(labels_true, finalDf['hclust'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8TmPTSZMRatV",
        "outputId": "d6181d00-e362-4322-aa14-1b73114eeb22"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.2028435895864485"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Para kmeans\n",
        "adjusted_rand_score(labels_true, finalDf['kmeans'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "61iehvAoRw2Q",
        "outputId": "20d17134-2c31-4722-8749-98cefc513de8"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.22233874189488412"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Para DBSCAN\n",
        "adjusted_rand_score(labels_true, finalDf['dbscan_pca'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jExb0kPMRyo_",
        "outputId": "af18a988-7a79-42a9-8af5-8a6028ef1c18"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.13897243095981643"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Validación Interna"
      ],
      "metadata": {
        "id": "cUHXbRjQTnAQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Davies-Bouldin Score"
      ],
      "metadata": {
        "id": "alii9BEpTrWC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Clustering Jerarquico\n",
        "davies_bouldin_score(country_features, finalDf['hclust'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KZXmr1WuTu16",
        "outputId": "1004eedd-12a2-493e-92e5-343a852716df"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.299788958694555"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Kmeans\n",
        "davies_bouldin_score(country_features, finalDf['kmeans'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dvuT3-z7T0kH",
        "outputId": "964e09da-7f97-42c5-d7f2-0ce9f540a423"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0419060938611253"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#DBScan\n",
        "davies_bouldin_score(country_features, finalDf['dbscan_pca'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-9hMnudlUAWQ",
        "outputId": "ca873645-330c-41d6-d205-cda73b04971c"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.1880926683328537"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Silhoutte Score"
      ],
      "metadata": {
        "id": "WzonjvW-VwZ2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Clustering jerárquico\n",
        "silhouette_score(country_features, finalDf['hclust'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0q03rn4SV4Sa",
        "outputId": "a546c654-115a-40a8-9a53-91c365f552ef"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.24563001303300652"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "silhouette_score(country_features, finalDf['kmeans'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ixq-srzhV74b",
        "outputId": "afa1c79d-2adc-4af4-f69d-52600d822b1c"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.303676957667828"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "silhouette_score(country_features, finalDf['dbscan_pca'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wIHuP0nyWChi",
        "outputId": "b8162731-3d64-4254-c2ea-9f7077061024"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.12212871903531475"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "silhouette_visualizer(KMeans(4, n_init=10, random_state=42), country_features, colors='yellowbrick')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 664
        },
        "id": "PUeiS3ugWbvq",
        "outputId": "bc9066fe-f889-4a49-b900-4a2712ee8e8f"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SilhouetteVisualizer(ax=<Axes: title={'center': 'Silhouette Plot of KMeans Clustering for 167 Samples in 4 Centers'}, xlabel='silhouette coefficient values', ylabel='cluster label'>,\n",
              "                     colors='yellowbrick',\n",
              "                     estimator=KMeans(n_clusters=4, n_init=10, random_state=42))"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SilhouetteVisualizer(ax=&lt;Axes: title={&#x27;center&#x27;: &#x27;Silhouette Plot of KMeans Clustering for 167 Samples in 4 Centers&#x27;}, xlabel=&#x27;silhouette coefficient values&#x27;, ylabel=&#x27;cluster label&#x27;&gt;,\n",
              "                     colors=&#x27;yellowbrick&#x27;,\n",
              "                     estimator=KMeans(n_clusters=4, n_init=10, random_state=42))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SilhouetteVisualizer</label><div class=\"sk-toggleable__content\"><pre>SilhouetteVisualizer(ax=&lt;Axes: title={&#x27;center&#x27;: &#x27;Silhouette Plot of KMeans Clustering for 167 Samples in 4 Centers&#x27;}, xlabel=&#x27;silhouette coefficient values&#x27;, ylabel=&#x27;cluster label&#x27;&gt;,\n",
              "                     colors=&#x27;yellowbrick&#x27;,\n",
              "                     estimator=KMeans(n_clusters=4, n_init=10, random_state=42))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=4, n_init=10, random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=4, n_init=10, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "silhouette_visualizer(KMeans(3, n_init=10, random_state=42), country_features, colors='yellowbrick')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 664
        },
        "id": "nrA3ks1RWoZ6",
        "outputId": "fd527b24-5c91-4e86-f04c-a835d7124278"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SilhouetteVisualizer(ax=<Axes: title={'center': 'Silhouette Plot of KMeans Clustering for 167 Samples in 3 Centers'}, xlabel='silhouette coefficient values', ylabel='cluster label'>,\n",
              "                     colors='yellowbrick',\n",
              "                     estimator=KMeans(n_clusters=3, n_init=10, random_state=42))"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SilhouetteVisualizer(ax=&lt;Axes: title={&#x27;center&#x27;: &#x27;Silhouette Plot of KMeans Clustering for 167 Samples in 3 Centers&#x27;}, xlabel=&#x27;silhouette coefficient values&#x27;, ylabel=&#x27;cluster label&#x27;&gt;,\n",
              "                     colors=&#x27;yellowbrick&#x27;,\n",
              "                     estimator=KMeans(n_clusters=3, n_init=10, random_state=42))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SilhouetteVisualizer</label><div class=\"sk-toggleable__content\"><pre>SilhouetteVisualizer(ax=&lt;Axes: title={&#x27;center&#x27;: &#x27;Silhouette Plot of KMeans Clustering for 167 Samples in 3 Centers&#x27;}, xlabel=&#x27;silhouette coefficient values&#x27;, ylabel=&#x27;cluster label&#x27;&gt;,\n",
              "                     colors=&#x27;yellowbrick&#x27;,\n",
              "                     estimator=KMeans(n_clusters=3, n_init=10, random_state=42))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=3, n_init=10, random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=3, n_init=10, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    }
  ]
}