Breast_Cancer.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LAcmP4kpD_gC"
      },
      "source": [
        "# **MACHINE LEARNING & DATA ANALYSIS - FINAL PROJECT 2021/2022**\n",
        "\n",
        "**Authors:**\n",
        "*   Patrone Paolo https://github.com/Buffless24\n",
        "*   Raffo Matteo https://github.com/Patpa99\n",
        "\n",
        "**Chosen dataset:** [Breast Cancer Wisconsin (Diagnostic) Data Set](https://www.kaggle.com/uciml/breast-cancer-wisconsin-data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k5Hx7BgnpaVW"
      },
      "source": [
        "## **1. Reading and correction of the dataset**\n",
        "In this section we read and acquire the dataset and replace the null values\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YWPuRRKtptq9"
      },
      "source": [
        "**1.1. Importing Libraries**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "Bi0RZm77oLWO"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt \n",
        "import seaborn as sns\n",
        "import plotly.express as px\n",
        "from plotly.subplots import make_subplots\n",
        "import plotly.graph_objects as go\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, roc_auc_score, precision_score, recall_score, f1_score, classification_report, roc_curve, auc\n",
        "from sklearn.model_selection import GridSearchCV, train_test_split\n",
        "from imblearn.over_sampling import SMOTE\n",
        "import missingno as msno\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "zJGXUN1wMlJi"
      },
      "outputs": [],
      "source": [
        "# Plot ROC-AUC\n",
        "def auc_plot(model, x, y, label):\n",
        "    fpr, tpr, thresholds = roc_curve(y, model.predict_proba(x)[:,1])\n",
        "    auc = roc_auc_score(y,model.predict(x))\n",
        "    print(label, auc)\n",
        "    plt.plot(fpr, tpr, label=label)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f8v2Jq7npwvl"
      },
      "source": [
        "**1.2. Importing Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "mroGCqywpxcR",
        "outputId": "386e17e1-0604-426f-f8b2-5149bbe65d58"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c535d77f-d3dc-481d-9fd1-6495ba941d69\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "      <th>Unnamed: 32</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>842302</td>\n",
              "      <td>M</td>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>1.0950</td>\n",
              "      <td>0.9053</td>\n",
              "      <td>8.589</td>\n",
              "      <td>153.40</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>0.04904</td>\n",
              "      <td>0.05373</td>\n",
              "      <td>0.01587</td>\n",
              "      <td>0.03003</td>\n",
              "      <td>0.006193</td>\n",
              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>842517</td>\n",
              "      <td>M</td>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.005225</td>\n",
              "      <td>0.01308</td>\n",
              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
              "      <td>0.01389</td>\n",
              "      <td>0.003532</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>84300903</td>\n",
              "      <td>M</td>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>0.7456</td>\n",
              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.04006</td>\n",
              "      <td>0.03832</td>\n",
              "      <td>0.02058</td>\n",
              "      <td>0.02250</td>\n",
              "      <td>0.004571</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>84348301</td>\n",
              "      <td>M</td>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>0.4956</td>\n",
              "      <td>1.1560</td>\n",
              "      <td>3.445</td>\n",
              "      <td>27.23</td>\n",
              "      <td>0.009110</td>\n",
              "      <td>0.07458</td>\n",
              "      <td>0.05661</td>\n",
              "      <td>0.01867</td>\n",
              "      <td>0.05963</td>\n",
              "      <td>0.009208</td>\n",
              "      <td>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>84358402</td>\n",
              "      <td>M</td>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.1980</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>0.7572</td>\n",
              "      <td>0.7813</td>\n",
              "      <td>5.438</td>\n",
              "      <td>94.44</td>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.02461</td>\n",
              "      <td>0.05688</td>\n",
              "      <td>0.01885</td>\n",
              "      <td>0.01756</td>\n",
              "      <td>0.005115</td>\n",
              "      <td>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c535d77f-d3dc-481d-9fd1-6495ba941d69')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c535d77f-d3dc-481d-9fd1-6495ba941d69 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-c535d77f-d3dc-481d-9fd1-6495ba941d69');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         id diagnosis  ...  fractal_dimension_worst  Unnamed: 32\n",
              "0    842302         M  ...                  0.11890          NaN\n",
              "1    842517         M  ...                  0.08902          NaN\n",
              "2  84300903         M  ...                  0.08758          NaN\n",
              "3  84348301         M  ...                  0.17300          NaN\n",
              "4  84358402         M  ...                  0.07678          NaN\n",
              "\n",
              "[5 rows x 33 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "df = pd.read_csv('data.csv')   # reading data into the dataframe\n",
        "df.head()   # displaying first five rows"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FJXMXCxdpziD",
        "outputId": "a44c92a7-e4f5-45f3-dfc3-3d6310b6b53e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset size (number of rows, number of columns): (569, 33)\n"
          ]
        }
      ],
      "source": [
        "print(\"Dataset size (number of rows, number of columns):\", df.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 832
        },
        "id": "B6xZzDPng-mQ",
        "outputId": "33692c65-828b-42c3-b919-a8a0afa61500"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABHcAAAMvCAYAAABY1TwPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOyddZgl1dGH31p3nMXdNUhwW1x3cVkcggR3C5CgCwsECAQNHtyCBdcQHIIECIGggUAgHx4IsvX9UdXMmd47y87tvrfnztTvec5zb+uRPl2nulRUlUAgEAgEAoFAIBAIBAKBQGuiV9UNCAQCgUAgEAgEAoFAIBAI1I8Q7gQCgUAgEAgEAoFAIBAItDBCuBMIBAKBQCAQCAQCgUAg0MII4U4gEAgEAoFAIBAIBAKBQAsjhDuBQCAQCAQCgUAgEAgEAi2MEO4EAoFAIBAIBAKBQCAQCLQwQrgTCAQCgUAgEAgEAoFAINDCCOFOIBAIBAKBQCAQCAQCgUALI4Q7gUAgEAgEAoFAIBAIBAItjBDuBAKBQCAQCAQCgUAgEAhUABEpRS4Twp1AIBAIBHo4ymIqAoFAIBAIBAKdg6qOg+L8WDBzgUAgEAj0YIhIr4SpmF9EFssdl2paFggEAoFAINB9ISIricgm/v9s4PhC91PVUhoWCAQCgUCgdSEiJwKbA1MBj2MMxn2q+r2IiAbDEAgEAoFAIFAYrjibBPgDMA74DFgZWFZVX6j3vmG5EwgEAoFAD0Rq+isiGwMbAHsCqwODgWOBkSLSW1U1LHgCgUAgEAgEikMNnwBbATMBI4GjMsFOvTxXWO4EAoFAINCDISKjgHmAL1X1TN83DLgJE/KMAW5W1e+ra2UgEAgEAoFA6yNzh3cBzgzAJcBAzHrnAlW9xs/r3VneKyx3AoFAIBDogRDDMOB6TIAzU3ZMVT8DRgFfAAcBm0XQ5UAgEAgEAoH6kcY5BEYAH6rqysCWmGxmFxHZFCAT7IhI74m9fzBqgUAgEAj0ULgQZybgDWB1EVkod2x9YAiwcsKMBAKBQCAQCAQ6gVwCi+OAc4CdRWSAqr4O7AN8D+woIlv6efcBh050HeGWFQgEAoFA90dOW5Q/NhPwFPACsJeqvpgcGwT8L9yyAoFAIBAIBIpBRI4FdsUUaC+q6sfJsfmAE4G5MEOcb4GfqOo3E3XvEO4EAoFAINC9kdMW7QzMi/l5nwa8oqoficjMwJOYgGdPVX0pd49O+34HAoFAIBAI9GSkGUdFZE7MHX5vVb1fRKbC+LEtsAyld4jILMBiwHDgPFX9TkT6qOp3P1pXCHcCgUAgEOgZEJETgO2BG4AZgfkws+DLVfVdF/A8BnwIjFTVN6tqayAQCAQCgUArI6dcmwv4CPgrsB/wMrAX8FNAgPmBUap6S+4eE61ci5g7gUAgEAj0AIjIjsDmwJqq+nPgeGAWYBdgexGZVlXfApYD3gbeqaqtgUAgEAgEAq0Mt9jJBDtjgAuA6YCbgZMwZdoXwOGquiDwALBC/j6dsZruU7zZgUAgEAgEujJEpD+WZvNkVf2LiGwAXATsCPwEOAQYJyKXq+o/gHX9unDFCgQCgUAgEOgEcq5YSwDLAvur6l9F5Ggs/fn3qvqEn9MX6EdBxVq4ZQUCgUAg0M2QYypEVVVE5gf+D+iPaY0uVNXTRGQGzET4G+AgVb04vT4QCAQCgUCgs8gURCIiAD2RrxCRXTHBTj9gS4+fk/JoA4HZsCDK0wM/nZjYOh0hLHcCgUAg0G1RK0PUhLJGdQfk/Lt7Y8Kc/2YZsERkFcwt+x6/ZBrgWuBvwGXQMxmwQCAQCAQC5UBEBqrqV745uar+p9IGVYfZgC2BN7DAyW8mgp0+wIZYMOUhwBIu/Knbajpi7gQCgUCgWyIn5FhFRDYSkbl7kGBnXywjw0MicqaIzOGnTQJMBczjKTePBHqr6imuYetdSeMDgUAgEAi0PERkXWBP/3828KiI9Ku2VY2HiIwnW1HVg4CDgcmAXURkuuTYd8DTWGKLVVT1W8+KVbc7fLhlBQKBQKBbwzNE7QZ8jAWy2w+4VFU/rbRhDYSIHA9sB/waeBG4DbgR2F5VPxORm7HAyV8CHwBLO1MR7liBQCAQCATqhoicDmyGZYNaAFhRVV+qtlWNRU65tgDQG/g267eIHAXsAJwLXKCq/6pxj8JxDsMtKxAIBALdCjlf5p8Ca3p5E9gKOB0YJiK/VdVPKmtogyAiCwMbAKNV9QERWQb4H/BHVf0MQFVHisjKwPfAw26x06eIn3cgEAgEAoGei4z/UtW9nf9aETgV+EfFTWsoamTFWh9TJr4vIk+r6mhV/aVb9uwMqIhcrKrvpvcpI4FFCHcCgUAg0K2QCHb2AyYF7lLVR/zwWBH5BrNoURE5qxsKeCYB/ueCnQ2AS4F9VfUCEZkEGKGqf1DV+7ILXFsUgp1AINBUdBQDrbvHRgsEuhtylit9MKvh17CYMv9yYcZHufO6xXue8J0HYMKbTYDvgFmBE0XkDlVdU1WPEJHvgV8B7wIXl92WEO4EAoFAoLtiESyI3e0i0k9VvwHwDFEAJwNDReQ4Vf2iwnaWjc+AfiJyGObnfaCqnuPH5gf2EZHXVfX57IJIdx4IBJqN3EfeGlhw917ATar6f+EmGgi0BnLv8ibAW6q6k2//GtjD/1+sqh/5/2lU9f2q2lw2RGQAsDRwSqY8E5GHMaulG0XkBFU9RFV/JSJv4wksykYIdwKBQCDQ8qiV+hvYHngf2AtYDwsuDPwg4BkCrAUcVkGTC2MC2q/3gb9jgZLPyAQ7ItIfOAT4D5b6PBAIBCpDQr/GYm4MH2NxwH4jIsup6nNVti8QCPw4ci5JJ2KWOmeJyD9V9T1V3c/dkXYD+ovIH4DTgMHAMpU1vCBqCJ/HAXMBP1iDq+o4EfkzcAWwYJZBTFUv9HsUjrGTRwh3AoFAINDSyAk5+mGpvz93N6MD3RXpEhH5TlVvyq5T1WPdakdbTUOcY6b2BeZ15ulwVX1fRM4CZgSWEpH9ga+xODzDgUWd4egW5tCBQKB1ISI/w4K/r6OqT4rItsBKWPrg5/yclqLPgUBPQqJYOxRTqo1S1Udz5+wjIl9j7/qOwL+BFZrc1FKR9HtDzFLpaRG5ARghIstk4QCcx/wAWAzQ3D1Kt5qOVOiBQCAQaFnkBDv7YxmhHhORY0RkegBV3Rm4Evi9iKyXXt+Kgh1ox1Qchlno9ANWxfq+oqreBRwEPA8cCIwC3gIWSVJthmAnEAhUjbmB37pgZ0PgTGAXVb1RRIaKyOBWo8+BQE9AmvZbRIYC6wAHqOqjIjKziKwlIteIyBluoXIIsC2WMWpZVf3GY/O0LERkdsz6eyXfdTcW93BXERnh50wKjABeU9WvG96moJeBQCAQaHVIW+rv32JCjAuA3wOnZ7FlRORcYCdgBVV9uKKmFkLe2kYs3ehNqnqfWCChu7C0o1uo6gN+zqRp0OjIihUIBKpALWtBEbkSeB14BLgKjxHm9GwXYHJgbNCsQFdGNrdbUVlUD3Ku8KsBz2I817vADcDPgCmADzDBxm2quk3uHqW7JDUaHdCwI7GYQour6tsisiZwFDAZFlT5v5gCbjFXrjV0joTlTiAQCLQ4Uu1Jsk+qaEsVEJH1scwEG6rqccCr2Po2GjhORBYAUNVdgEOBx6pqaxHkrJSWEJFVMebh/8CseVR1NeAF4HIRGSEifXOCHYmPpEAg0Gzk6NdsyaG/YPF2rgQOSYK/T4rFSusfNCvQlSEiA5IP/lkrbUwTkBPsHIOlOp8GuB+YHYtv+BJwmKpujFnj9c3zqq0m2IF2ccI2cqEWqno0Rscu8pg6d2DKxn2wsTgHc4fPrKYbKvwLy51AIBBoYcj42UYGAv9S1cerbVlz4MzCesD0qnqWiKyFBa7bFctQkAWy+62qPpVc17LWKyJyEmbW/CUwA6YxukBV/5eccwfmprWkqj5dSUMDgUCA8dapX2KB7PdR1cc8Jtq9wPSYy8bTmLXOb4ApgaVblVYHuj/EMkPNoqonichpwGrAEqr6ZcVNazhcSHsqZiF9n7tYTQEMUdV/JOfdDzyvqntX1NRSISJLAo8CbwC3q+oezn/vjQlzLqrl9t4sS6Ww3AkEAoEWRi7byLXAKcAjInJAq/sy10JmkZT9ev8fAW4RkSmw+DMnqOrVmKn/u9gHw4bpfVrpYyG1wnJN0Qgsxfs6mH/3QcDqHkwaAFVdEzgDM5UOBAKBypDLpLMrcDLwjh/7FFjdt38DvAlcAgwDllHV70SkdwXNDgQmBvMAJ7oAYxtg0x4i2NkPuB0TwL4Gxlep6geq+g8RGewWxndiwtr9K2xuIdSwjn8HuBoT8KzgfZwB6IvxZ/39unZ0q1mWSiHcCQQCgRZE7oN/bkwTOgJYA7PkOBE4QkT6VtPC8uHa38zcdFoRmVpEhqnqh6r6DhbEblLgmewS4BZgOeCI5re4HCTmz6OBdYG7VfUOVX1BVdfA0p6fBqyRE/Dsq6rfx4dRIBCoGiKyDLAZsLGqXgd8JCLTiMgo4HtgWezjeFdgPyw2WubG0HLuG4GeAVU9BngcWB64EHNH6gm4CRgKLI0JuIB2vOla2Hv8HRaLpmWFtIlwelnffg+4A5gFs5C+B8uENTkWDmB/P68SutXttLqBQCDQE5B88B+CaU7uTtxvXhNLOfk7QMXSfX9bUVNLgft4ZwvsLzBXrCFALxHZB7gPSzE5E7CWiAwAfo5pf/fxrFit7IqVBRddHrgjdXNQ1dVdc3QKMFBEbkj7GR9GgUCgC2AK4HtV/bOILIbFSVsfmBF4AthdVZ/w/8APAv2WpNmB7osaQXWfxWKu7Ae8LyLnqepn3SW4cq0gwm6dsxTwJHC4iPxTVV9K+nsL8DbwlAeabln+C8D7epOIPALsqaqXiMjKwKWquqaIzItZiy9CxSneI+ZOIBAItCjcSuPXwG7Anaq6lu8XF2ZsB5wLnI2lp2zZhTWDiByFCW12Al4ELsYC+C2rqq+7//tFGFPxH2DlZmQnaCSS59kLuAwT8BwBXK1JWk0ReQp4Q1U3qaipgUAgUBMiMhyzMnwVCzp7PeZW+hLm3rCtqt5YXQsDrYT8ml5LANGgetP4UZsA76vqn3z7MOBY4GDgXFX9zPcvoKp/bXTbGoFcf1cEpsPckt5znmt2zHLpWUzo8fKE7tGqEEtnPgfGUytmuXQbxn8/rqrn+3lLA0+41XQlfGdY7gQCgUAnkV+oko/vhhLy/P1V9RsXdnyEuWCNVtUrkuMXi8ggYAvM7L2lISJTYq5nO6jqrSIyEpgfy8jwugeru1ZEHsPcjt/pDhojn1u9nVnYGrgRM/v9TkSuywIpq+riNXzDA4FAoFL42vWBf/iMxqxzHlTVTz023IvYB1Mg8KMQkWlV9V/+f1fgclX9vAn1phbEJ2LWZyeIyOuq+q6qHu9r8Bign4jcChyNuess3+j2NQK5uI6jMV7ye+BrEdlTVe+VtgDDp4nIgar6fK17tDLUso4+BSwmIodj7v4/x4TT/UTkClX9UlUfhWqTdoTlTiAQCHQCqYDF3YEWxDJUHa6qrzew3lR7MhDokzEzIjIMCyS8D7Clql6da2dThE+NhojMiWmI5sT8m68HDlTVc0RkMLAvcI6qfpRc0/IaowyJgKcX8AdgZiy20g05C55u0+dAoFUxofew1WlxGRCR/phr7WXAVMBS4UIa+DGIyAjgOmBNYCvgZ8CCjeS/arThEIzf2lBVH6lx/DDgQOADLKvlUq3sGi8i2wMnYW6Uz2OuRzthcXU2UNWHRGQWzC3pDO0mWbHykCTblfOjO2IJLQC2U9VLK2tcghDuBAKBwEQiJ2A5BpPa3w3Mh8UN2FxV72pwvftjQZMnw3yd91fVr1y4cTSwFzBaVa/N3aOlPiZqtdetkK4F3gM2B/ZV1d/5sVmxGEOnq+rNzW5vs5AT8FyPBTPcphHzLhAI1IcczR4NLIRpu19Q1asqbVwXgFig/22AHTAryyx4clNSBQdaG2KZqRYC+gErquozjVRq5N7nwViWqCtV9WwRmRnjAbcGvlbVHfy8Jb19j/ia3XIWxCI/BEc+HZhEVbdNjs2FKZd6YTzIpyIyDfBhd36Ha7gDrgOsRhcKfRDm24FAIDCRSBb3ybDMTGup6hbAksCtwLUismYD6x2DuePcD4wFtgMuEJFZ1VJvHollTbpaLNBbeo9WEuz8kBVLLJ3mED/0LRZLZ3vgmkSwMxj4LTAO84HutnAmsbfPiY2xlMH3VtysQCCQIOfKcBIwtZffi0jLZu4rEb2BlzFh/fIaWbECPwIxZOFE/ogpuL7Ekgj0a6BgJ3XFWhkLDP4ZsKCIbI7xXAcBg4FVReRmAFV9XFX/lKzZXeLD/8eQCHSAdrzj3GKJKrL9f8d40UWxFOCo6vuZIKtZ7W02Uot4375NVfdRywbWJfodwp1AIBDoBERkW8zUdlngCwB3idkOc5W5SkTWaEC9I4ENsDSyY4D/80MjMQHPLC7gyax3Hiq7Dc1Cwkj9Eks3ea+IbOdmzfthWrMlRORmETndz5keWDuxamkZdLa9iRbwe1U92Le7Tcr7QKA7QETWpS319w5YRr/vsfWj26Aeeutr5mOqepp/FPVtlY/fQPORKXx8rvQHrgFmw1yErgZWrLUG5gUV9dbr/8cAZ2Lxap8E5sWSN7yIueWPAi7FBD/t0EpCy6S/W2AxhcCCJU8JrOsW1BlewhJXDMzdo6Xe5TppWJbkIhM8dpnMfi3FAAcCgUAXwOPAXVgg34Hwg6vMOEzAcyNwu5vklgJnUPoAZ6vqI24ddC2wO7AMZjk0RkTmVNXPVfXMrqRFqAcisjuW+vtujHm6UESOUdWvgE2BC4D/YbEa7gMWS7S/LRNvJmfuvYCIzCKWlSE73hFz2o5ZbGV//kCgm2JW4DlVfVRENgTOw7LJnCciQ8VS67Y06qVfqTUEBP0KdIzcHNsPcwUapKpvqurqWAa2i4Dlk4/tY0RkSFGL5aTeaTFh0l6q+rqqHoW5Yc2vqoer6p/9kqWAT4vU2RXgFjq7AdsCqOqFGB92IjBaROZwF6yDMGH1P6tqa1GUQcNc8Nhl+M6IuRMIBAIdoJYPtzMPc2DxXWbETMr/mZ3rxw8DTqhXip/36fV9gzCz/o8xy5Vb1TIzTAM8iAUZPktV96inzqqRH2sR2QXz3b7Bt3fEUlCeoKqHd3CPlo3XICInYG5WU2MpNn+vqnf6sbyPdxosez9gaY3054FAZehgrdgDCzz6R+BiLCbDuX5sPWBlYIyq/rvJzS0dQb8CjYaInIQJG/YC/qyq7yTHHsD4snOAFf3/HGXwAyKyG3AE8C6whaq+mjs+BJgHOA6YFli0q1hwTCxyAo4+rhycHbPYOVZVT/RjV2CxjmYHXsGUTEu5Yq2lEzl0JxrWslrdQCAQaCRyi92igABfqepLwN/FsgdcBjwkIiu4gCcTLhzr13U6gF6u3hmAb4Deamk/3xSROTDz2Mzt6hvM73kDbLFtOaQaEBFZHxiGZcE4OztHVS9wBco5IvIdcHSekWglwU7uOa+PpavfGWOaRgKHicgAVb3JzX/Hy3gmIjtjTOeeFXUjEOjxyL3LKwF/clr0OnAyFiNsj0SwMxizunwT+LCKNhdF0K9AMyHmDr85sJqqPuf7+mMCnBdVdSURuRJYAfgamEvdRbsEgcMdWODvnwDTAa/mjq+GWW1/g1kQf9dqiqbkXd4D+J+I3KOq/xBzjd9IRB5V1YdUdbSILIxZJX4F3KOtGyy629KwsNwJBAKBHHLE+xhgNBasd1rgUOACVf2vazYuxVyDVlPVt0qs9wgs1ecUGLNyrKpeJyJTYszFdcANWDrOIcByvvC0FFOR6/MYLKbOC1iQvguwVOefJOdv7/t3UtULKmhyqRCRVTBG4m+qerbvWxYLnD0VcLKq3uT70zScu2BBtbfPrJsCgUBzkftAOBZz1ThWVc/3fQdirgwHAE9jrqRHY9rhxf1DsKUyGaYI+hVoBGpY8h4IjFDVtcWyNK2JZSsdBlylqvv7eZMDHzsvVEi5lts/Mybk+RLYKM/richPgafdervlBB0AYhlHn8YSV7yIWSL9A7OYfggYqzXcJ4vwnF2B9nVHGhaWO4FAIJBDImw4EtgJE+48ApyKZUaYXEROcs3G1tiifzJtweeK1ns05u+8NWYKfCJwhYj8xevcFvMxXwYLZrdSolFoGcEOtOvzglha7+WwPq+BCXE+EJETVfVzP/8iEfkQG/O60RVMiEVkAYxxGg4cn+1X1T+LiGIfhPu69ujqhKnYGWMqduhqTEUg0JOQCHaOw9aKjbAYINnxk8Rin+0DTIpliPoY+GkravhTBP0KNArJe7UPZgEnwLQiciGwGCZ8uApLLHGSiFyiqs+r6v/5dVLQanolzErnbeA9VX1dLNbhvVjSjM1U9e2kvU8m92gJwU4NwcqHwPlYDMebvRyCxdTZF4sn+VKedypCvxL+r7+q/q/e+9SLbkvDVDVKlChRouQKsABwJ5buHGAUxkhcjlnx/BIY6semx1ynyqh3KuCBpN71vN5dfbuX/06JmY9m232qHrMCfT4Us0K6Iu0H5l8/DnNzG1Ljurr6nI2Z/58TmDnd18B+Svrr/zfFGNWHMU1+ev7SWDyls5J9O2F+7htU/dyiRImiAHMBfwFW9u0pgAUxC53lfd9MWKyKWRM60FI0O+hXdWPeU0pubd4VC048BzADMAaLX7UL5o4FFsD4MWDGEtswFvgXZiH9EfAosIkfmxkT3j4MzFb1eJXU35HAAv5/eiw48mgsjtClwIXOhz0OTFJSnSticWrAFKM7NWOu9xQaFm5ZgUCgJrqCZUOVEJHpsDg252MMxJVY8MszReQybEE4A/iFusahDC2siMwGPIX5d8+HZcU6UFXPEZGBmAb4Ok2C+rX6sxILlnw+8Bawiqq+nhzbBgtefQ5wsFq2rLLqHYMxNjNhTMyVqvpwWffP1ZVqBfsD32mbFmhzLOvEi8CpqvpMct2CwItq5t79sPgd/1bVGxvRzkCgs5gQ/Wl12jQxEJF5gSeAdbCP0d2wj4JJgaHAZqp6V+6alhqXoF/VQkSmVYu71yPgbjFLY3PlUt/XC+inql+LiGDZSq8C+mPKsMLvk4hshwl3NsT4sCWweDpLY/zHze6i9RxwtaruUrC+Si33RGQ+LL37gsD+qnqpiKyGKdR+hllRL4lZrH8MLFN0nEVkeuD3mIDkfWAzLFbR80XuOxH19hgaFsKdQCAwHnJEcCkscNqXqvpatS1rDCbgZz2Zqn4sImdjDMSuqvqNiJwKLO6nraB1EtJa/sYiMgVwCebrvD2WYeU8PzYH5hp2rqreWk+dST0d9bmpPtBZfWKpgq/DmIgTNMkgIyK7AltSYKz9Pum8Hg2cgJkbTw/siAU4/W3+Q6wocvXuC6yCuUW/jaVW/dqZiwOBl4Bfq+pfat2jVf35A90Tubm9NSaQ7g08parXVNq4JkFE+mLa7XWBvpg76b3+IfgMcLuq/qLKNhZB0K/mQ0Q2Aj5S1QdFZCxm0burVuC60my4sPRF39xHVX+TOz4EU7xtCUyDuTgWytaU8CFnAlOq6ubJsYWBX2F88LZe1zRYNs9SBDMishjwbBWCHhdWbYZleb0Bs0KcAvgvcIpawGTBZAbjyhBMi8jKWAbBaTHXpssaKfDuaTSsV9UNCAQCXQ8JERyLpQS8A7heRDautGENQI7orywim4rIFu4D/LFbyyyMCbe+8fgJswKHq+ryzhBInfVm/sZDxVKdo6r/wbQle2KpGDPBzlDgN5i26vaCfU6zU20gIjuKyJoi0s/70/S1Qc1veRvMMukAEZkqOXYOLtipZ6yT+2R9XgZ7poer6vXOPO4JTA7s6Zqr0pDUezzGQD2DmXyvCvxFROZU1aswwd3cwLFiQSPHu0dXZyoCPQu5tWIsFtx9JuBUETmlyrY1C2pBRvelLZvP3i7Y6Q18htHzlkXQr+bCeY6NgPtF5CrMEuzUniDYAVDVlzHhzRfAMiIyae6UPsC8WHbQxV3Y0qezgoEcL5HFoP0aGJ7xY96e5zAX/fUwHgFVfd+FHr07U2dS9yh/tojIaRjtHFrPvepF1n9VfUtVx2JzDkypuBfGj83t56gLNnoXEKClfOVHmOvb08AWIrJcQmfq5vE6Qo+jYdoFfMOiRInSNQrt/VCXwKxHlgHWx1yQvgBGV93OBvX9RIxZ+AvwZ2zhyfy6D8R8ji/HFoXn8XgJ6ZjVWe+RwJPAfZj7Vbb/D5jv87VYSvCHvN6+fryuGDG5Z3wyFizvn5i24lJgYJH7l9CmrXysTwCm6ei8euvBzI+/wrLW7Jc7vgLwJx/79Uru47z+Pq2V7BuO+fM/n+zbEbMCaNr4R4lSpGDuSG8AS/j25pjWd9vceT0ifggmgJ8XuAV4lhaLrdNBn4J+NXe8e2OWpN9glg1QUly/rlRoH2OnV+7YZljmplOAAbljAzN6UnRcfM6u4f9/hglkR6X3xSw9ngGmL6HPfbBkGZ84ffgUmLvCZ5DyX9MCq2NupuOA8xvwnBfN9nldd3lZNv+MS+5nj6FhlTcgSpQoXa8AOwC/Bo5M9s2ESbW/pJsJeLCUmh9i5r3ZAj8OGOnbfbC0iNdi/smZgKUoU7ErptU9FIs581/gzOT4wZjp6lXAEbQJlOoNJJwu4gsAd2OxfYZj2sEnsYwIVQt4tvTx361BdW2MCbVuAubLHVseE/KNLXD/+/Dgqsm+pbHA2LOmcwcLiv0vYOca92lZ5iJKzylOO+7w/xthH0e7+PZgYLmq29jEseiDZU28GwuMX8pa0eQ+BP2qZtzTD+BJgNtoS789IjuHbiIkzfV3dyxr0U1YwNrpff8WmIDnZHICHj9eeCywQLqPJduXYAKXrYH5nT+6y9/p0sben+844JZkX5egE8AAYO96ec0JPOejMUXApsm+9Xx8b6ctAP2NwFYF6uzRNCxSoQcCgXYQkRmxj9/lMYEDAKr6toj8GlDgHBEZpKq/q6iZZWNe4CRVfVJENsAEW7uomdYPAb5R1VMkSddYj99tDSwmSwoAACAASURBVJ9iBfZU1RtEZDDGZJwrIqjqHqp6Yj4GjpvF1mUWmt3H482Mxha0F9TMi3+HCZf2AC4XkS1V9auiMXhqtL/m/VR/SOWuqnq5iHyEpR2tGx35cKvqdWIB9cYCu4nIGar6ih/7k4hsCvy1QNX3YtZfKV7GLIY2Ak7WNt/6/2CBCgfXaGfLBFwNNBdF38sC9dZ6p/oD74nIWpgw+kBVPdePrYK5Vryiqh82samlYGLpVwa19OYvYJautzlt7fIxGnII+tVk5NzDR2DJBUZhMZzOBW4VkXVU9YHkmllU9c0KmlsKkv6eiCnULgWGYcLiLUVkB1W9Uiwl9UXAJCKyu6p+k9yjUzSwA/q1KfAnETlWVQ9X1W1F5BzMenggZtn8DZbdSeuNDSPjB/S9HeP59hKR36vqVk4v+qV9LILO0i8/p7eqfg2c7tuF6FfS5zGY4ng08Lfk+C0i8j3mGn+5iHyIZYPdtN466eE0LIQ7gUAPR57Yq+o7vth+A2wvIjep6oPJsV9jWUC2wLIYdQfMCnwgImtjDMaBqnq++/7uAPQRkdMSwY4UEeyIBUvsj6X6PhdAVb8Ukasxgc+5IvKdqu6TX4i1eDauAZjgbmHg/ex+avGErvDTfg7cISKrawE//1yf5wPe0Alku8oEPJh27E6/ri+W1aBuJk5EtsRSngv2wfWEC5B6Y+lVyQl4nvN9dWWyUNXj/PpDMNP6qzFf/puB9UTkPVXNxvorTKjWI+IpBIojN7cnxeb1f4u8q3XUuzLwtKp+ipnwn4JlldlBVS/2cwZhH2pvYzEWWgqdpV8ZVPVv+MeL05jKsuHUg6BfzYXzE2lMkE2Aw7GAvZ+LyP7YO36L8w6PYEG738esK1oWYvHvNsHcoB/xfRtgVs2/EZFtVfUqsSxFO2JWPHUjGed9sQ/6P6nqP8SSZqwuIkuq6uOququIXIjF2BHgriKCWhk/8Pw/Mf75W/9/ciLg+cbPWwl4qF4BQ730C7MmSq+vi36l3xZiQalHYSnlHxKRYWIJQtbFxvaPIvIBsBiW4OIYF5TXNd49noZpFzAfihIlSjWFnL8z7V1jlsMi57+ABbNNr5uaFjRXrNVm7/dRWKydT0lcgbCMAbdi6c6L1JuO6wnYYvI8tqBcnDven7a4M3uX0OfxzIiBybBUl//EYg2l86AfZiJ9QZFnnLvnUZiWaoMfu2duLGYoof8nAv/G4iU9h8XU2SM5vg2mJb0cmKnMscbSfX4LbODbc/s79Szm070HcL+/Yy0flyNK40vu/Tgcc9t419/XLRpYb/o+H4fF6NoDS02M04xvMRfTZX39uJMk5kwtWtRVSwH6lV43Y9X9qHdu+XbQr+aO/68wgc0IYEju2GDnFcb5OvYK7vLXSqXGHFvb1+c5kn29fF1+BViwxj0K8Z7AHJh7zr+BK7DYPsN8Xh89gevqcplifP7vfWAXYIrk2W6FuYpfA0zntPO6emlmAfpVCv+Vq38yzPXpM0yRuhCWHOQlfw5vY6nQC4930DDvd9UNiBIlSjUlR3x3Bi7DTF/3SfaP8AXmBdwXtqN7dPWS6+8ywKK0+d7OhEXOfwP7KBnii9DtmFa6FKIPzIYJFxb2+2+JCXpOy503AFizaL25Pk8BDAIG+/bkwEnev+Nz1/XJFskSGKkxWDyjtYGpJjR/cozFz/2ZTF2g7t0xrc1ivr05xhw/DeybnLcrFkS5LGHW0OT/mf6MN07mwP7OXNyHMZctF5cjSrUFOAazhtkAC2h8P+ZmOXOD6z3W610OmDx3bHc/9gHwFBZToqXndpX0q8n9DPpV0ZhjliEzYAF7N/F9UwOLA8eTxALBrB+2pS1eSEt+kNIWU2dZLMjtKtlY+G9/LODwLiXUlf/g74VZGr6F8WAfYBmUjsKsVMbjdUvq88Fe12LJ80vnwEZOw9/A4h8WFt5VQb9y9zkFuAezxrnH+/+p05VM4PIOcEAJfQ0alvW56gZEiRKl2oJZNvwLk6SfgknST0mOr4RpE/4NLFx1e0vo7wnONLzpZXXfPztmSv8i9oHyCBZFv6zgyYdgQeOuAQYl+zfFzEVP7eC6eoMn98rVfQ8mpDsX+Invn5I2Ac8xNe5RNDvVCOB1LF0pmP/6zJiQZdp0XHMMwS7+jDYuUPdA7EN0b9/eEDPDPgSzxnoLS/cpaf15hqeOsd4fy4A2V7LvLGcuNspdNyD535JMepTmF0zz/ASwkm+vimUy3NG3GzKXsNhkzyb1TgksglnyrOz7ZsaCtc+evFMtOberpF9N7mfQr+aPeTpfZvV59TeMH1gTU7Q9jvEjfwMOr3GPlvwQxeLr/MH/98KULY8DsyfnDHdaM7LEetfEg7tjwqOXMaHOdJgg+veY8ucRYHgJ9aXvVV9MgbSvb8+MuSTdiSUqWdb3T4Hx3JnQp+73qgr6lbvPsljcmyV9e0bMImsEbXx1HywT7DYljnWPp2GVNyBKlCjVFSe0rybEd2MngOOAC5Lz1sAY+JZjJnKLzeKYgGMp79N5mMnm+n58OBYA9GfAipSoHcP8yv+LaWWmzx3bFPs4u6gB/T8O09xs64v2wz4GWdriKTEB35vUyBZQsO4RGHM6F5Z1YiympXsHEyJO5+elC/MumGZnowL1ipf5gGm8/pdpY6yWcublVTwjQ3ZNwf6OxTRT2zN+GvdzsawnG+TrKVpvlJ5VsPhR/wCGAusDnwO7+rEBPv9mbUC9s/r83hAzrT/facnLvmasWeOalrHurNH2SuhXhf0N+tWccU55krMwi51B2Mf/m1i8w5MxoW0v4I/krGtbqeRpAMaHjcv4DedBXsOEOQdhwoc7fLsUntN5gT9hgrJ9fN9awPV+bBjGoz2GCXdKo1vAuv77AmYhvwlwC5Z96xav81Jyqb+L9r1K+oW5uv0e52lrzIEBvp7c4vO/LOv4oGEawp0oUXpscabhYDyeDJaO8BPMmmF3X3xPrnFdywh4aiwoSwMnJNtTOXP1LR1oiMrsrzMTXwO/JbHe8WPbYq4VZTIV62N+zVmK9zV9cXseE2xk7kpT+zOvu6/OIE3p/8cA82Dmx087U/UxJkzbGvs4fQPYLnePXfy8TjEWNZ5z39z2Fs5ATJM8h+sxrV0p4+3MxPskMQIwhn2GZPtsf69WKKPOKN2/1JqfmFvnK5gV2se0jxO2KOZKu2wD2jIZFgT0HUxQfTowyo89lNLWVitV0q+uUIJ+VTLmU2DxP0Yk+1bCLWuTffcBR1bd3hL6O4y2+FsHY3EOMyXTUKdbz2BCnRsp2VXG3/HdMB7sMmBfLFbZXn5cMEuSzGqmLt6A9sKSo/ydGQqsjMWX+Zfvz6yIfgncWkLfugT9wqyUrseUDk8m+3snx7fA+N0/lfWcg4YlY1F1A6JE6SmFiiXFterDpOdzYNYNz2NZogAWxNIDjgOOqHrsSuj7IZg71J990UkX36kwYcvXNMGMHhO4/A9zgxvUwTllCRyWxeP5YCbAH/kCPgLTbryYMRjJNfUEsVsIsw7aBxOWjQPm82OrOUO1Du4HDUziTMeo5B6j/Lq6BTtYcLzzMM3b9rhZrv9/2dswBXATiSa0KFPh9zgc+L3/nwsTlv0dMzc/NTlvf7qR+W+UxpXc3N6JJP4EZjUzDjg22TcYczn8Y1k0pEabZsSy7S2V7OuLWQTuXvWY1dmnyuhXVyk9kX6VQfcL1L0npni509ekPrnjQ7GP8NuwAMotPebAAZjVzI6YhfRwzHLlF7R3jRmG8WSluXQyPu+9ACY8ugWzYPk/LM15ek5h+onx0WNoL7ybivbCht5YbMfzCtRTKf3Kj6/vG+xteS99xslzXRRLiV6mdXzDaFit+VDGHGlUqbwBUaL0hEJ7Jr3pi3Su/sGY722/ZN9KvvDO4NvzYmaiq1bJAJXU30OweEEXYqa+35IT4mBmwZdjaSeb0b71MWHSaXiA4xLuWVNY6MzEAOBeXFCHWW39Gcuyc/WEru9E/b90Jum/KTOTO6cvJki8FTNF7p20ZzVgxQL1n+iMxDGYRc73wDne95l9gX8Lszr4C23aok73m/Zm9RmzMhbTyB2PMePXAEdjbnEvk2QD8fNbmlmP0tiSm2Njfe4egmd0w+JE3IRpR8dgsbPuxUz/s7ndUObT15H5/H3+SyvP6arpV4Vzq0fQL8wFJHsv9qmy/Zh1yHaY9d3bSbv6JudsjX2A300LBnvNzbEBwK99Tb4MU75kVjTvAXP6eXkL3NLpV/LOToW559yECTXOLbmeURjf+SYwT/75YcK7tTABU0qz682OVQn9oj2vPbuvSzP79mDMMupJYD+Sb45az6TA/GooDaPNkms6LDD1dkkfu6SAp/IGRInS3UuOCO2PCRFuwUzrG85g5IjvwZjG4m+YYGGE7/8JlqbwEGAWTPP7QxrGZrSzQX2fE/vwWcm3J8VcCf6HR+pPzp2kmYQaGOlMxb4l3Ct9xtNiJrmpNmw2TJCzkW8P94VvvaJ9ThiEjTFrr/d9IZ82OUewAIb7YwK2x0kC6mXnFGjDCljgwMz9bFEf262Sc6b3/m5KAW1Rbqz70p5huwjTxO4OzO37lsaY9FmaNbeidJ8C7I19JCxe41hvLH3yXcDNmIAze58aSrOxD9StMMuCB2jBD9C0vVXSryb3t8fRLyyz26uYUuV0XxvmrGLMk32T+Fr0Hzy4cDqf/P86RdaqrlCA/v47D8aDHItlp3wfs7T9wN+pAU1sU96SZ/eyxxezjr4ai580Ml8vxnPf5KUdLelkPZXRr1x/jsGEVP/EeLEskcUg2oKD71vGc24mDaNNsLMg9t30F5/HnwAL1Dt2jS6VNyBKlO5cckToCEyyfq4Tmo8xv9OBTWrL8Zjp5mjMTeUJZ3imdkbjaEwL/AaWxraQJqHqAqyOMXEf4hmxfP8gjMH7Gg+k3NEz60Rd9WpblivKVNRYYB/z53gLJtjqjQm17saEdpv54ndfsnAVyhDl25Ni2qhfYdYxhzF+QLvFnfEolWHFtCn3+//NvP8/9+1h5EyufX9R/+4DMHPqy4Ftk/3Dkv/9MS3ZbfWMcZSeXXz+XAMc6ttzOf3+MybQyWJm5eN3FdKEduKa+TCBact9gFZJvzqiBc1ca3sa/cKUWh/42pApARreJ9rzgAtjH5ozJ/s29XZdk4577h4tJTBN2r0VZj2bWRtujLmCz4DFn/kd9sE8joIu8XXSrzwNKPt9/inGh70FrFrj+Gy08WCF3ISaSb9qtOUwzOV/XX/GRwDfAWP8+BDMgucNYPMS51dTaBgWuuI9jJ8ejrkmX4PFmasZWqHqUnkDokTpCcUJwtkkH5lO7L7EmPWGai0wH+NnaQvgthpJ6lzf1wfTrowom/hXOO5HO+OwV47JGoilnywcWC1333mASeq4rnc9zEnufodjGv41fb7dhpmoLuLHt8IC2P0D+zCs23Uj1/afOtMwTbJvjDMYB9EWxPhKnMnL+lzic94cE1RujmV6SAPMrgdcC8xY4nM+DNOS/daZiw+AQ5Ljk2Cm/7f5e9cUN5ko3a9gmsjnMIH8/Zjm9WTM1P2pPN2oh47US8Py9RalYU0c08roV67uFZ1eb9jkPvcI+uVzMuNl9sQygb6GfYAOys5pZP3J/xMwF6z3vB1nAQv7sc18/5VVj1nJ/T/S6dX7mNXUvP5uHe7Hp8cUM1cX4QfqpV+0Waa3+y1Q97pYBtqfZ23ABHrXYvGVVknmpdS6Rx11Vsp/Ya5X9+CWOsn+LTH+enPfHuJ0p6zn3BQahrkUnoWFqUit6rbFXL361tufRpbKGxAlSncvmO/0N1jA4oVzx36HaZIaasGDuam8hgkRNqB96txB2Id/XsrfktqiGn0/GXPD2iy3fzAFfe9zi83RzsisSwe+xR1cN3vB/gnmP/4Q/pGApXNvJ7zz/QMxrVld2qIadY/FfMq/9kV0u+TY8Zim5jpv24dFF8LcuKX/pwYedGYiXeAHYO4qV1ISE4+ZUx+B+6djbnC/wAS1ByXPZCwmwG2Km0x3KhNiwMp6jq1SgDWwj4MPfZ4t7vs3xoS0Q8oa6wI0bI4ibahwbJtKv3J1n4DFXHkWE7g/TBNchaqgX1W8s7n5ORiYCdPAXwv8FePLxuO5JkR7CrRlL58/q2Bu79ti1ttXYtZ4fTALnnHAMc0eqwY/h6l8Lr2HfYhfi6V8n63GuUXdpDtDv1LhSmH6BZyCCbGew6zyX8atkTD++2rMbWntEse26fQr/y5jvNcHeHgB2gtUrwCuyr9nFLeabhoN8/scnN03nTOYe9aMRWhjo0rlDYgSpZGl1kLdbEYDkyD/wRfuNX1f6iN6nh9bvaT6agUaW8YXll0xX9GfJ+cs5UzGolU/rwY+g1N9Adysg+NFhRzH+wK3HjB5R8+jxvPZBfPhnaFg/ZNhwsPhWCyfVHg3ANgBt+BJrumsBkNoz0itgX2crOh1Xou5hO2enLMPZrFW+CMhN24/wwLI7g1M6vu2w9Ko3odZpm2FMXovJHUXjS+0NmZ+/DoeJNH3T+XMxefAwfk2000Epc0ouTk20p/1brSoAKHEcZk6+S+YYOfyEu/fNBpW9D2ss3+V0q9cW7KP/cytbieMB1ipwWPQdPpV0bNOn/MBwMW0ZQ/qhblo/RWzmu7n+08p49l2MO+uB36d2z/Kn8Mhvj0UE/50i7WiBs0YibloPe9z/cEy50Yz6VeNurfyuhfC+P0BmDvWX4HV/Jxli9DsrkS//D4b0sZ7/Q4TIM2Rjq8/75tKnlcNpWH5ueL7Jq2xby4svtA0SR0L0aQwGz/aj6obECVKo0qOEA4nCTDWjDpz+4dhmUzeAn5So32HlEF0c/fsmzt2ty+qByX7BmIS/5vLXGi7YsGyNXxJot0o6b7LY9qTJZMxnQXTrGcM5Xhmv85UfE7nU3/XWnwmw7Q0V2NxnVLh3ZzOVIzqTD25++fTtK6FaeEOTPbNjTHRj9PeLapfR/epsy1H+rhlWS7uoM31bGOfz19gcUmupMRAr8ASWNa1r4Gtc8em8vd4HLDNhJ5XlIka67GYZiwLAP8UsEPV7ap4TAZjTP1dmIa4lLhozaRhuTVqZ8yy8janKdM1aNy6DP3y+5wN7OX/N8YULrv4dsNiODSbfmHxZWb0/yeTfHg2o2AKgH9jltGzpn2iTcBzuq8hH5e0PuVjofTDeK8zasynMdi6XaplQ1cqOXoxHRYL8V9YxqxSeM5m0q8O6j8So8m9aZ/x7AHg0WR7/nr63AXp17zYery7b2/iz/Oc7D3zZ3APcGbJ86lhNIw2i/apgEWA5WvNZUxAvBDGnwzzfVm21sk726dGlMobECVKowsWZPZlJ/7n5RfSEuvJ+8GukC0svm+IE/s3qCHg8e2yiO/+GMP8GzwrlC92j/o4HAAc6sS3aalzqy6+KNxf8B55LdCymMXIfJi56CmYC9w/MDPdRfPXYUzFp3Qy1kJujk2PZQrINI/bYozKddm5Puduw6xZ6o0RcQGwf3LPWbGgiP8FTsudOzcWI+QR4ICSnlk+M8IV2aLrc/rvPo8XS86bDdOe1Z3traN3AWNsLvf3eJPcseGYz32XM9NtpeJj+G7y7myNBWhcr+q2VTwuywNnYMEcC5ma57abRsOS6zPh3anYh8o3mKtSITezGvVUTb/yY93Xx3pvYCXaW1n2Ao6iBAVElfTL+zEV9pF1OabZ/wSYv8xn+yNt2AKLO7JIsm8AiWu8z7sbMcuaUrO9YRaHQ/3/GH9Xpk/rwLJFPciPuBB1t4JZKf2QhruO6yunX7n6zwKeTbYH+u+SXsdCufMnus9V0y+/b+/cdl9v1wO08Z97eb3/xBJ3PI0JT4tk42oaDaNNsLMA9k30rNOv8zs4fy5MSDkMywD3BbBEWWNe+JlV3YAoUcoutP8Y3MlfwJ0xgcYHWPCt4SXXmS4ax2IWOn8HvsVMBWf2Y0OwoJiv4RkbGtD/AzBt1W8xKf7TwB5+bBhwGSbkuQf7UGi5mCCdWRw7elZFFxvaGLdFMIHZfRijfi4W2HchX9y2zN3j55iWsG5tEfYB8CymvT8A04j1xQSZ4zA3wOt8rj1PnYwrxgzvnr8eE1z+2duwRu6auTCrmvPrGeMJjPf8mO/6mTiTnNT3d0xztlxHz7tAvaN9jH+DMU+9MGuoC73eTTq4R8u8T12tOA09z/9vhjHIWfazQbRwWuakj/Vm7piVkoSWVdEwLIjwW7RZ3C1GEnyzxDHuSvRreNKOw73+L4GdknMmwzK7HFpivZXRL6/vKy9rlvlsJ9Rn394TeMD/z+Nj8DenJWck5w0q8j510JZ+WPDkq317MCbEedPHZBiWxecekixZrVQK0K/etf7XU29V9MvvcWj27jr9+hT4Ve6clX3OzVxnHZXSrxrtWZO2IOBDMUHOOcnxZfy9O4vEG6Ge96qZNCx5/+fDgjQf5+OYZdz9SY1rZsOCZF+CxfRcrDP9a3SpvAFRojSqYFqxbYFNk30LYtrCOyhZwOP3/wVmmreSb5+GmQ+eQpt58hBfcK4rqc48UzMWj9/jBPB0X2D2Ts6ZjPaLbMt8iOaI/kh/zj/KaNQYp04tfLl6D8Wi52fPdDmMYVgHGOz7BmKCtU1y7f2KAmk/sSwE//Tfy7CU9hfhboe+AF+JmcgeXO8Cmx8fYEdMA9vft1fCGIybcL/y5NyZaNOEFGYwMJP+tzHtyNfA6NzxOX2hfQpYsMS5NtbH+veYlurftJkiL4xpr14mMQGO0ukxHi8eAmaZ8itMmJdaNggWN2wPWljbXQ8Nq/UetTAN2wy41f9v4c84E94NpQQLj6rpV26sj8CE7dmH0XKYVcFjwFK+b2bMyvJxyhMyNJ1+kQhKMCuKTzGrrEvIuUaVVWeu/kMwzf223rdr/fcy7AMxy+KzWK12F33Wyb4NgT8B6/v2Qphi8UvMMuB52iteWsZ9tx76VauPLUy/emPWWE9iViS9vT1vYe45wzEhxC1YOIYyrJMq47/8Puv4e/MablnoY/k0HcSyzMaqYL1NoWHAlJj75KnpM8C+FVf1/qf0awkfj8+oIfypulTegChRGlEwCew4L9v5vozpWIA208HS4vBgkt5b8dgmWOrHjzGmZhwW8yX1Ry0jBki62K2M+bjfgvse+/45MAHPi8A+Ne7RSkxFPrXomxgTN1UnrluXJBBbHW3I3Al2oX1qyWx+DcBM0v+IMeqpEG09Opl6Pc8YYNZoeybbe2KWWJfgFg2M76NddIHt4/P3L5igMmMwVqGNwVjlx9pe53New+fuKC8v+SK8eu6aeTGhVll+/Js6nVjIt5f393jD5JwFgRuAq8qe6z2h5OjXgrRpYjfHtGHtrDkwDfidwElVt71An3sUDav1PgL7Yh8Fa2Ef/2mMiM0xjeyUJY55U+lX7h5ZLIatSfgNTAj/HCbkeR0T0j9GSe5BVdCv3Ps8f/J/YUwofwUlW93l6twOs86eD3Nb3hezYt2BtrVxfh/n0gO0Y8KFTIA3KebydXnaVswKYXfMbSuzxGgl5VqPol/5OZbsWwqzvNrPt2fAeLEPMeuP1zC+rHDIgyroFznhECb8+CNmgfZvTOG2H0arTwKmKFJfB21oGg3DMm4dkaNbhwPf+3P8DyasXduPDcRipy1QZp9LG7uqGxAlSiMKZha7CeZve2myPyNY8/tL++sS68x8PQdg5onv4B/hmJniZ5i56DTJNUWyUKSL5SmYT/uHmCvYYblzZ/fF4T90YMLYSgU4zJ/tsiSMUbIQ9c7v8/+7YpH2l6+z3g293jS+y1DMVWIy3/4lZh6cLuxlZIjaCmNWLwW2zZ23B+bvfDEJ00r9WudaVgJDsVSjT2JxMlIG4yGMyVi85Oe8LmZe/Itk3wLehjvoIMMc5XyU7Q1c4v9H0941aCge/BWzGurWsaoaUWj/UXY0JrDbBGNkp/Pn/gEmJB+KfbDdjgkFWuZjaAL97/Y0LPeMV6KNSZ8Fs7IbR3uL0oGYcuKSemlXfrxyfWw2/VoL+wjN0tcL9tG5OOZGO9zP2dd/S/vYbzb9qvE+348FtM3ijyyNCXguoy2rzvXA9iWN9Yo+b3fO7f/h4xqzmr7F53bZH6LL+Hx+C/voXRCzxvovHiy7g+taMnhyT6BfNeo+COO1sz7uiylwM+uhPphQbw2f73W9z12Ffvn9M4V0L+/vyf6ujcHSnH+MCXvWaUDdzaZhkyT/18YsvDb0uoZjFnepG1qXtR6uvAFRohQtHb3UmJBlM19cz8if74tBvUFmO6ozSw34G8yMMCPAY5343lMGEcrVOQvme7uYE93TsY+ivXPnze3EsiWZiaQfk/lilrlqzOgL3EW+6A1Mzs0H0PuEYua4+wC3+f+FMRPwVzEG/jcYw74kZv5diFGnPbN8ki+iL2IWDc+QS92JpYt+FTii4Pim9c7ii1oWCHIIFtfnKdozGGtjbmBlpjadBvuQ/xq4MHcsE/DcBowseX5ljNvZmEn/UphgNs1AtivG3PSvNW5ROjXeYzCh9Jok1hqYMOc8LJDyu5gLwwOUHPi0oj53exqWoyNjMIu7LWmLN3IQ5p58NeayMhIT3j0P5QThrJJ++X23BB72/4tgroavYZY8N5OY+SfXFLXYqZR+5d7nKXJtWg5zwXsUs1r6G7msnvX0F1NeZZbah9QYi8HYx+Hd2NpZhjVFLWuOP2IfoCdgvN5eXl4mF1S3yLOtutAD6FeNOTYc+9gfhwmfR2PC6Euxdal/B9d2Ns5hV6Jfq3l/j8QskwZgFobH+vH5MQHPOODmsuYoXYAHwzwe5kvviynn7y86n5pRKm9AlChFSo4QbopJln9JW3o6wcy8v6K9gCddcIoQ37Uxc+s9aNNQZX6aF9HGpP6BRFNRFhECDsSY4zMTgjizE713yQl46u3zBOpvOLORHytMM/IwpsXYzIn/A5im5iVMmu2ZFwAAIABJREFUqCa555RlRuhMyt5aqTM39oXsbMyU/nIsWPd+mPBlvtw9ynC9mx1jJhbFLNJ2xKx0biAJLOznblSkzlxfj8Y+tN7GgtZlwQMHYQzGE5gWa8CEnlfBvi+ABUp+Kf/sMMbiLeCUMudXsn8ZLBvDOGDHZP8gzP3yrGbM/+5caIs98lPfHowxVVvRxtAujrnjLUUbk9XlmatcP3ssDcPW4/eBEbT/6OvvtOwpTAHzJObGUrfwrgvSr8yN4Fofg4sxF5bNsUQPy5Q9t5L9TadfmBXHG7n3eTbv85y+b3HgeCwpQCnx4Hzfypgi4C5gttyxKbH4c2PrrXMCbVmENsuNLM7K/j7f38KESR9iPFkpmVppssVAfo7RA+hXrffK+/oMFj/rPEyAdyCmZNq2hHHuavRrKCagftVpxtbY98ULJEo17NurCN/ZZWjYBNrYG3P7P6EZ9RVub9UNiBKl3pIjhCdiblD3Ytr+13DGiTYBz+fAZSXWP9breRjz630fWNqP7e0E6SbMquZFCmgjO6h/sPf7c+Ce3LFMwPM2iUtLSfXOBkxNmzCrYRYLOeZgqWwhw5jDzKrjONpSY58B/C53jz0wbdFEp7zM1TuZLyZZf3fzxXxH2rKgDcc+UkqNmO8L62uYn/Nkyf4dfN6NJ+Dx40U/yA7HTKdHYQFPj8PcGI/045mJ8JvAXmXO66QNGWO3EMY03okHp0zOqdv6Lt9mLGDejrjPNWZhcAbGWB2J+ZQvjWlnny37fe6JBQtK+BKmgV0Acx19zenWl8DcNa5pKQupnkzDsHXouazdXscS2Nq0fXLegt7GUrIWdTH6tR6m8d0CT+Lg9T8DrFxGHf6/6fQrN8cmxQIov1jjfX4TU7DNU+O6ulIW+/8sDXNmobG2P+fzcJeN5NxUsFiG0kV8PL/ArDeyWIvHACf7/8kxPvE/wB8L1LUqbXTjKCxxR7M+anss/fL7bonF8+vv79RpmFXQYt7vr7G16hESt56CdVZOv3LtWRDLYPkGZrF1DSZcmyJ3Xj0Wni3Bg/l4vwPM1ei6Smlv1Q2IEqVowcxe3wUW9e2RmGDlDTxrle/fgZJ8rbFgeB/Qlsp1I69zveScPbBgY6cmRKhUVwIsaODh5GIX+LGZsHTofyiL+GGMyxuYOfUf8PhBZYxpjbryPvyvkpjzYlqyOXPX3AOclmwvjGk/Nq2z3oMwU+4nMI3gXL4/9eMfiLkTPFD2OGABIh/BNH+T5Y7tgAl9HqTc4KNDMdPTPXL7f+bzbFRy3k5lz+lcndkH0k9oE/CMqnFeUWHW8ZiQ9FWMifqF929aP/YuZhb8rLeh5V2DukLBtN5P+Bz/DDMt3wITCrxCLn5Gq5WeTsOw9elpp1WrY5YrT/q+D2n7WOlVq+111tll6FdS9w/uIZhrxe2Ym3ZZFrSV0i/MDelUzI2jYe9zbp7shVlt3Ip9+GXZItfzMTiXnICnQc92fcxy+kMsIOtI7KN7Mz8+AFNQZHOgsxmipvD7PY3xc19QQja5Osa7J9KvQdgH/aOYgGWoP+9HaLMsXdfb9VAZddPF6BdtPNggYB5/vlmygxVLrKdL8mDYt90lJN97rVAqb0CUKEWKL3yn4qmRMUn3p1jWgjsxs80Va1xXlIEcQxtjuokTnV18e1hyXp9a/0seg6l94f0vLsVPjg2nhmlrnfVsgFknbYQF03sAk6hnQc0aolH3RfV9THs1dY3jk2EuS7dj5qJ9csfqkrT7gvIhprlZw/v6Jm1xBAZg5sAPYIxXYT/+Gm0QLKDbX72eqXPH98A0KGXWOSWmNdotaUPmDnMdFhCzf+6aZgl47vaxrisYY417CubX/iCmEeqDmXf/x+fdJMk8WoUkcF+j3udGj2NXK8AK2Mf/msAg3zcY08K2fPB370+PpGGYKftNmJXKOMyNYDVMC34DcFwDxrpL0a+kjn5OWx7EBFyF3c+qol+017ZnrhNZ0OjlG/0+Y8KkrI83+r3/Rpt71LrYB+g1lKj4yLUhFUBMjgmV/oWlZn4Gs1gqy0VoZuzD90tgqWbN2aT+Hkm//F79MAHTPV7/opiHwB3JOdMl72TRb4suSb9y9e1JEnaizntURsPy4zWh8fM2XQrM28wxLvyMqm5AlChFC8YsTo+ZDr6KS7wxYcQ4XxBLlbhiDGvGqP4Q6MsJ1cEUDGpbR3uGY+a6X5CT+GftKnj/jTHf4h2SfUs5QX6NNgFP2ZZJczmzsLpvT4aZyB6I+bQPwLKMPIRp8ErJjIC5nj2Fm81jjNvHtA/oNhDYHjM9L9WPP31mPqc2xTS9d5NLOVqEqejoGmcg7qPN5Dmr40Lg+pL6N9HtTer/KRY0sZRAmBhTPitmOt832b8vxmAdS5JqtZ62d4WS6/M0GAM5RVXtSZ9pbl8/TFN3G7kUtq1aeioNI2HAsVgsi+SOP0SJwd9z+7sU/fLz+2JuQ8cVGeuuRL8w9/OjgLEdHC/9fcas/V4jcWnz+ZW55Gcf/htgqYubRquxj/xTfG6PowYvVudzngcLwv0OZjUytMzn+CPt6JH0y+/1g7WVv2fnYnGUrsMyRG3b0TPr7DPO7W84/aqzveO9v61AwzDrsWG5fYLFxho0Edd32axYHba56gZEiTKx5cdeZiwN+f20+bWv40R/TL0EfwLEd1dMO/NlbrEZhgXUK0UjSe4DKL+dOzYcC145jgLZCGrcdyHMj/7LGovZkpjW5BVca1byM18E89WeH/uwPwuL4fBvLAPFWn7ecpRoUYEFffwA0zyvg2nNsswQg4Hd84tCrYWvrOfvC9FmWJydO7M5PjHzYgL3ThfYGUm0a5gF3JOYuXlmfjwAY6BPL7mPo8gFwJzQWJQ13s40PIEJZ58np13EsnK8j2Wfm6pIXVUW2mvZj3Aa+Tr2wbVls+qutZ071g9jtu6gfQrblhbw9GQaRm79xFyS5vBn/FyRfrYa/apxXdGP30rpl69Jf8D4jdtpn7VGynqfa9CQVTHr7LmTfb0xS6G/Aqv92DwsWH9NGkabIGAgFm/o+HqfcW5uLwhM4vN3LozXehwX8CTnDainroloS4+lXx08/81oyxD12wL37RL0K6lvomhYrt1FlcYNp2FOF8ZhFl5Dk/0HUSOcRXcplTcgSpSJKTmCsr0vMOeTaEaw2DP/wYQcU2JClrHJ8SJZsZbH0ozP5NuzYhqUlzE3pQGYduWPmLahjMWtHfGfyGumwwKSlam96IeZWb/kTMWQ3PEsKOo1ZfU3t/9+TIL/BWa1sa7vf5lcsOiO7vEj9Ur+vz/fu7HUtZ+TxArAGLebgeVKGNsfXRwZ34Ln7yQ+7SW0YQymCf0SEx5lAfp2xBiMtzAm/imMeS4liB3mKz8n5l+93o/ds55nm45h7n0ajTENuzst+QQLlJjPsnI4JgTpki5NnRyDo/09Wtfp2e0YczNzg+qrh36t6M+klBS2FYxxj6JhnX0vMKXIw5iLQynCu6BfzaFfte6B8QbnYMGSR9Y4Xtr7DMzhv3P5e7NlbkwGY25Re9Vbx4Se2cTQsA7GqEjA6GMxwdg6mCujYMKWVzD+czKfhxcA+5TZ39z+bkm/OnpmE/FcpvVnUgafXwn9yvrUSRpWJPh6ZTQMC7D+byxuUZZJeSjm/pUPCt3yvJ5qCHeitFjBTPf+iUlyj8Y+Tk7yY5Nh5qNfYO5Zz5OY+RWo8wRMsvwmZhY60vfP7Yve350wPYmZAZcdrHCsE6dOSa4xjUdZgZT7YZZRz2AuaXkBz3xF+psj+ktgcTgyc9ws29lyufMewP2SS6q3V/LsBmGMxThgTHLOIEyAdxsFmPWsX50919u4SoljvSNm7r0ZZmJ9CcZEjPHji2MajvN8kW2E6fPv/L2ZbALnpMzfnsBBBepbEQtMuVWybx+nGScDs3Yw9i276AMzYIzjar69ltOsLE5Yw4QoBehXr1Ya855Gw+p5NpiL1igKfOwH/Wo+/cqN+ZTADLnjV2O80SoTuEeRNWtTn6+TYpYx92BCj2WScyb1Zz+6rGeb3HuiaVh+nOsdd9ri3KwHTJ47tjAm4HkfU7i9VnRO9zT6Vc+zqXV+Z8e9q9Evv1+3pGG0d/M6y9+XHWlz3cy/q9n4tpTbfc2+V92AKF2/5IhRYWFJgXasjAXuW9a3R2JB83ZKzhmMaQe3rJcQ5ojYTzAT1KWx1IenA98A2/jxqfycrXxBLENDlda/GLaIL9nJ5zRe0LtOtmEzTHh2aDLe/TGrqScxAc/gGtfVHRzS/4/BtBOvYa5gd5OYG2ML++yYb3chs/5cGw7AtCM3AOv4vsmwII3P+HM/BGNmXqDEwH2+2BzbmXEqqc+rY37zuyb7BmJMxPPUyEpV7zOu1f5kDEf6s1yi1v1z82MnLPXoZhNZ5/kk6VexOFGvY4KNnXPnZszFWMbPANIyQoYOxmF2jImcDrPcSU3cB2CZZ+Ysqa566Vfv5P/wMtrSxPHtsTSsE/Sr7EyCQb/an9sQ+pVr/68wocoXmOXEAcmxazDL6ULp3WvNFcz9+wcXeCyRxl+xD+GTsayS9/hzLyXNefK/MzQsva5TPFju2kX8Oa/k20Mxd8ZtaEs5Pok/j0Np43ULz216GP3ye0wUDcuPVcE6m0q/arWdbk7DaBMKLYEpFT7H4lb9jPHdGo/EYh01xL2x2aXyBkTp2oX2AoOfYaZ0lcSfALYF7vX/G5CYavpCN6LGNUVcsXpjgpuj0+NOeL6lg3gVRYhv7j4HYIzLGRNxbkp8d8YCr3Uohf+Re40F3sZ85W/AzHHX9mMDMEbqMcwSoDRCiPnnf4Qxcr2wwNQ/pFv0faO93gcplm0kfc5HYhkZzsXi2YyjjYmcHNMwPIRpik6jRO0JZhF1BXB3J5/x7ExEILgJ3Gsm7+c44Jgaxx8DLi3x2aZtXxVPW5vsexy48Ueu2wWLtbDBRNY5G2bW3je3f3fMCu92xvfx3tvpyu4l9LkjE/eGCopq1Ytlo3gE0wZ/QnuGcn4s48yqJbejEvpVZelpNCzo1w/7uh39qtGeI31ubwVsjaW1f4n2ru+X+zNZvKQ6p8V5DEwA/S/cFQf78D8LEzY9iQmXyraabgoNy73Lg7HYNi9g7kfLYK5QL2EuOq9Q48O/jD7Tw+iX36PpNIwm068abe8xNAyzfPsOE4Ie73PoS+x7dkgyB67ChJnTlDnuVZXKGxCl65bcS30iFtxsBwpahRRoz6aYtmg7f/l3SY6tjqWrGy+qep11HY5Zp7yCmYBOmo6Lj8dXwI4N7O/5Tvz/RM4NagLPaWdMqr5hnXXuhgl2lvTt7ZJFaGPf19+J8+8oN2XuZXg2LmB97CN0J98e6L8z+jwoJSaHLz4H02adNAltQamzVJS9sEwnqWVBGUxFplWYG9OEdmhOnnvGe2KL//QF618Wc3F8gCSVpx870ed9GW6NKRO3udf3kc+1TAu5JhNIce7z+lNgo4msM5svGRO4A7BvcnwPTFN1Fh7LITn2w/wqqc8LY8zUvDQ4y0mu3j2BPZPts31e/zrZNwRjdu4qu01UQL+qLD2NhgX96r70q0Z7psOEKKOTfcN93r2Y9Qtzu/tV0bnl99oHE2YcillI98fcv07DFYw+r/thgp5sPpbpctdwGpa79hyMr5oVcyF5yu91FmZ5MCdm1bFDPf35kXb0KPqVjj0V0DCaRL+y8Uv+9xgahlmZPUzC8/j+39Em4JnM9w0mJ/Bq5VJ5A6J0zZIjNHv7QvOTZF/vCS12BevuSOO9AmZSNw7YP9k/CPtAuYg6teI54rcPpkU4DbNc+c6JQL/kHMG0DA+WPd65/cd7/dtMxHPKpOr1CnaGep+yOBzrYrGGDsJ8gcfRZsHTN1kYyzCL7e+EfmtMUJe6jfTGzHE3zl1TVFu0gffpH8DCyf7BmCbpe3JmoxN6VgX6PgDTgl6AMcd5k/T8M/4E2LykulfwOXMVZvbd19+nx4GLypzXPpcfwLJ/HIotum9gWsnRWOyq8VLHOv35YmLnNcYY3UKbVmY4Zu79NO2DMu4LPEsN5qLe+VWjz5mJ+7ve93tpgnAcs757x+fxjFm7gGt9/pyLZeJ4gCQ2Wb3vckfvBE2kX1WXnkrDgn51L/qVn2O+PSmmad8rt39KzGpmTI17FHGH74d98H+CuQX9HYs/sp/3eYladRSZ11XQsNy1s2C0eIRvT4O5C61Ce97zMRqgVKSH0i+/XyU0jAbTrxpt7zE0LJnTT+PxgWif0e9uzPVwd1rQSvhH+151A6J0rYJrDZJtcYL3/+zdd5gsVbWw8bc5xEOSHCSriCAZJBwURTCBKIIgIIogwYAYQREUDEgQveaAXvWimFARETAgBoKiiBivCcPnNeccT39/rFX0njo9hzPdNd1T0+/vefqZ6eru2pV61e61d+06L59vRWR+bwQuayLA1covA9FxRGKhbH1+Sp4MXp4nhgPyS3rHtb/DBH6idf0i4EHFtPOJsX2OZcnuhY2MWF/8vyVx6/ENi2mvJ3oJTXt7c2Kcod+zjFn1pcxnW6J16J4Z+KqR+6uT8GKKyzcGWX/6XzayYq7nJykGes3XNiCu7z5lpmXVyigD+0o537fkvj2wXDbiBHtmru8SdwEZch8/HTiDopcZ0dX9r8D25XZlBD9+icHtfk9cB301kdD8MlmhHPQYry37IqJSunsxbXOiR8stRJf6xUQSubzFbSe/60cuY5kLcr99nkj2VndG2CGff4GplyQ9nWgdvZTaIKENbNenE3doqFokLyJ+JDyoyXL6lPtUIjm9c7ldiv9Py+39AWJcraG6uDOH4teoHpMWw4xfkxe/cv57EYmdtYjLYt7MkjdTeHuuf1O9eKv9tm1u21Py2PkKkWD4A/CpQffrMhzfI49hxNgr7yKS7gtYMmm1CrA+can8LTTYs7Tc7szT+NVnH8+ZGMYsxa8+yz6pMexK4KbyOM+/r8v9/R2KKzPmy2PsC+Bj7jyIga7eWJ5Y8qRyFdFt/5QM/B8hum++j8gC39EltsFleSnw2wwEPwVuoPcj5FQiuVTdOvByhrzWOgPY/hncfkveFrB4/Xyie+zjKVpRqs8OsZ5l8H0JcHMG+quBNxWvvToD0RIVh1ymP/Z7bRmXYbk+y3IkUZmrAvP9iWvIT2CI7rBMPcFuQyQLF+bzA4gK1I30bnu6UR5/Nw66b3M+D6PXCvVm4Mb8/555UvkzvevKq5P6qsQA0k12896baDH5DXGntf8hKjjL5/a9hKICVHyuqlQMuo+XeowSd8H4CZHQux+9LtdNXNZwJFFxfUc+X6n2+prEj4hX5HapuoUvWJZlr68jUbl4Wm7fS+hdCrV9bu965eJM4gdKUz9OOkTF9f30xg04kGgJfWI+X4Xsttzkg2j5eyvwgny+TcaHW4gfRA8ut23xuSZ6KY0lfo36wQTHMIxf1NePeRa/qmUhBiH9O3lHKuDRRP3oLHp3m1klj+vzGir3KOJy+PXz+SFEfWzTPPZeRFwqvxh4QlPrWvw/8hhGXBr7X0TcuK6Yvlzuh2rsmy/QwN1YmeD4lfMceQy7sxjALMavnM+8j2FF2WsTA65vkM/3yO36ztr7LyR6xbXq5g3LvD3GvQA+5taj+IIcB+yQ/+9IZFa/TbSc7JbTTyAuM1giEA5QbnnCWYW4tnrHDO6Lsuwym70BMaDZesUyN3EN7lnkIGdkYqN47dx87cBZ2O5nEK3tD8h1ejtxV649i/e8Kst/QO2zzwUOGrDcJxEJvcsyGFfJnGOz/D2IyscVwBuLzw17nfW5RNLuh8QPz+rSkYNzO3yZGEDwhjz2hq3QvIu4fv/jOf/ti9fukSegP9C7O0XfWyQOUO7h9Ho/vYIcsI7ocn0s0brxbeDduY0/CaxbLgPRU+5vzKC1iKV0K17KZ/YhEhDvJFrNBr1Ep172JTnfW+rv6bOdXwd8bdhy83t0MfHD4OLiuL43cYnh55naMjnUJYZ91mMBkUx5EL3ETlWxXZ7obn/osuyXAZblLURF8dj8/nyUSJZ/FriVBuJ1nzLHEr/G+WACYhjGL5iA+DXdZ/OY+wbZsk3UC/+V+/nDxKC6Xx/k2OpXPjGux7XEbdWPIy5Vel5u11WJusheebw1fTvokcSwfscy0VvoRRTjzNSOs02IXuuNjHOT85j38Ss/N/IYVv8u9dvnfT7TSPyapvx5HcOKzx6c++97RL3njJx+LL3fkOflvv4bfS4Fmy+PsS+Aj7nxqH0p705kVm+l10XxLhR3ySJ+oFyZAXuoHyi1srci7lB1Jdllkjjp707cEvFL9M+qD3s71/KyhZcSl0+czJJdkE8e9CQzTbkdIsv8MXoDFj84A/Hx+by8DeUzmiqf3iDZzyNajm4nKmzL5374IFGR+zZRgatO7kNdikUM3PZT4kfv44nK3C+Be+fruxJdZM8ifgAPXKFhaqvcrUx/d4J7EC0KvwEe0tD2XUBUGBcTFZo/U1xXXrzv8UQrwr/yvefWXt+eGVzKU9vWxxMVmg8QAzKufiefvR8x0N4V1JKby1j2LsX/p+e+XpGoFP8fcR39wj7LWfXK2yP306ZDbPdXEuNAvJPoyv9LojKxZr5+b6J78O0Ud30Y5Ljusx7VQJ8LiMrFV4kfKmUlZiPi+/6kJo6zPsuzPdGr8if53d45px9AtPyu02BZY4tfo34wYTEM49dExK8+5S+i1+q9BZEUfiG9hrV9ibHE3kH0dGnszkXFMrw4t+MHiSTLW4C9+ryvkUF1GVEMqx0zGwF3K56vmd+jv5ODGPdbR5rpsTPv41e1rRhxDGOM8SvnMZExLI/lvxGXed2XXmP8ImJMpd2JTgPXEr8vd2jiGJurj7EvgI/xP+jfYnMg0RXzi8B2xfQ1gSOIS7O+xhA/+PuU+bI84VTBYNvitU5+Ob9BtDQ00l1xuu1AZHf/RZ8ET74+zKVJ9UsiViXGDNqBuG1f2cq/ItGatW9T5efn70ckbaqBCQ8mLnM7rnjPRkS36GNoqMWIyKA/kaldMjcjukBP2edL22YD7M+FxA/eK4gk4cnUTrRE5eJq4ONDruObmXq9/i0UFQZ63a3rx8GORC+qTxIDVXb6fTdnsBzVoLqvz/8XU5zYl/K5/Yk7pm08w/K2yDIuyscfqthBnFwvznjybHq3t623ML2SaNVbe8B1fhhRObpPMe3M3Advo9c9eOec3uRdsU4nLhGt1vnuwHeJitLC/J6vR8TVG4YtexmWbZ1yOYkfL+9j+GT82OPXOB/zPYYZvyYnfvUpvxpb7z307pJ0OtFD525L+VzjsYxIRp9HtPwvBm5ucN4jj2FMTXK8iKg//4poOHsyUbdejWh0+wt5Ce8sbNdjmcfxK+cz9hjGiONXfnYLJjCG5bpdCjwvn69P9BJ7bb/jmFn4/TjXHmNfAB9jPgCmBt+TmXqry4cQPwhuBu6V07YkfsC8jyFbbGplH0aM2n4U0f30W1lu+QOlQ2RhL20iICzDMr2MaEV5Ng2Nj8HUwapPJrpirkEktD5IVGSeVLxnK+LH4BENr+ehwJeK//9IrzKzOvDQegAcdpsTg7d9N08+zy63N3FN/VVEcm/7Ycrpsx9PZOoA2e8gBlE7maJ1JE8Qw3aH3YDo/VS1ci4gWkxen+v9tH7LWEzbibiuf6hBd4mWxx/Ru4Ry9yx/2lt91j4/4+OdqDAdSHRl/yM5KF+xLVYmWmBvBp5VLyO/32dRVAoGWIbHERWjdct1IQYE/CsxYOVdap9ponJxITEQ4dHkj6DcHg8jWiK/RfTiuZ4GurjfybKUPyIWEj/YPk78eBkqGc8ciV/DrMOQZc7rGGb8mqz4Vf8OAfsRjWfvIep5lxNJ6h8Dr2+izBke96sQY/98h7iMo4k7c441hhE9SX5NjINyf+IH721EL6hViaRC1etg6IGEa2XP6/iV8xh7DGMM8ataHyYshlXrR1xydQhxud1PgDcXrx8J7DfsMd2mx9gXwMcYd/7UHwHn5xfiHIoBpogfJx8nLtPaNqdtUJwQmvhh9BhikOSnVMsFbEf8IPoCtQRP8f8oEjyvJ7olN9EzaccM8IcQPwZ/RW/QukcSXQovL7bBmsSlHdc1ta7FfjuKuPPCIUQrVVmZeVCu9xZNHV/5fPk86d2U+3bl2jJtmiedjzS4Ly/I4/pFwF2L6W/PZTiVqPB8Gvhov2NgiLKPAzYvnlfdg0+pvW/XcnsRCYAZtdr12dZHF8fSEUxtiVwT2LqpbVwr9yG5jv8ELiymV0mFlYlWtR+zjBWdGZZ/IHG9dVWpqo6tjfM4+Dlwdr9tNkSZhxAtdLsV01al12K2DlGROo2oZDQ2bsIyLNsu+R24Y4yKQctlbsSvPTKG7MIIbl86yTHM+DUZ8SvndY/i//OJBM9mxOUklxE9tf8DHD4b2/3OjgsihlbbYpgf/mOLYTm/tTJ2PKn22ouIpMtD8vnmNDAEQJ/v1cTEr5zHSGJYn+08lviV85+IGFbsqw7R4+2DRFL09ly/coDltxIDYs9qb+m59Bj7AvgY/wN4JkveOrdMcDyQSAT8kKmtHk0E3zWAn2UwuqD22rbEgG43Uoz3M6JtUq7/ErdEHHCeC4ms+N+JuzFsmdMXEHfXOTW3wzVEj6nPEK1JjbfyE5dcVd2dy+u7VyZab4YaS6lc1ly31fL/FYjWwa8TPRhWKbc30Z2yqbsWHU90M96l2Iflcr05T0I/ILqqrjhkedWP9uXyuP4t0RW1GjtqZWIMlH8TvcE2JFpG31nM44m5TwYa6K1Yz1OIStrhRNfcMnn3GKLn3dBjr/TbV8RJ/BDi9qav6vP6ChlzZqPXyhrA94lrqjcqpt+LOPkf29TxVcz7aeStNomk9POIVsl/Av+9tGNlth9ExWej4rgY5nLSscYvoifl7TnPHxM/OhfN4rabqBhm/Lpj2qTFr8OIXjHnF9PeCzzBl0ywAAAgAElEQVQ//38C0btkMfCaptd5JvuogRgy0hhW31fED/tvkIMmM/XW4J8DPthnHoMm4ycqfpXzZkwxjBHHr37HWE6btzGs2Mb18VBPyf32WYpL34gxVL9H8dt1Eh5jXwAfYz4Aorvce+iNJn8PItN8fU6vboN5CDEw2GwEg82JysXX6wE1A8JvKbrYDTD/aSsHSztx1l9r4oRXnDj+Qw7eV3v9vkQX0ldnsGpksMIs9xVE9nqbnHZQbttLiVar6vKNrxblzijBA+xee35GzvPzwGOq7UhULm4jTugr18tqaFu/iqyMFutTr2w9kOid1uRdKPYojutvEr3PysrFM/IY+CbFuFXFZ+41YLknAZ/O/7fNcv8FnFa8ZxWiy/LbZ7pv+5RXfq92JloE70Fv0LzHEpXoVxTvey2wf/G8yYRltQ/vRXR7/2Qe9/vlMXjHeDMNl/tQYqDCjxCVmnfkvjgo9/PAXZyn2dbLHMOWNp8Bl2Nc8esUohFgUT4/l2gNbbyrNRMewzB+TVr82pb4oXc7MRbYg4kxYN5A77KOuxC3Qm/iEvyZ1MHqPSJaWQcDDij+/zTwqeJ5lTh6NfDeBtZvouNXzmPkMYwRx6/6tmSCYhgxLtYNRJ3r3GL6i/N7/RZi7KR3EDe02KmpdWzLY+wL4GP8D6Lb6S3ED/xrgU8Qt8P7NnBNn/fPRoJnM2KQtc9TdKPM17YYtMxa8HsS0QL1XuCFd/K58iT3aIpuy4OWn89Xy8B3dgahY+5smw67vYnusL8iWqG+lfu46jK5X57gfpAno/cyeCtVVWk6OJ+fQdyN63wiUbiYXmvgcsStEm8lrk0eqsVmmuW5mqJ7cXFSWYn+d95o4hLDvYkeG/fN55vn9+iOykVO3zFPUHdUaOrHygzL7RCtZLfQq5Cfnfv7bcTlLAcSLZIDJ+/q2zL/f1mu4+3Edc9X0EsgHklULq4lTsbfoeGu5rXXqtbHLYmu9N/JY/uzxXHddMv3OkQl6n3E9eab5PRNie7vQ41fwBhjWH1bMYb4lfN4J3Bm/v8oojW06ua+Mg1dosWExzCMXxMVv2rbYX2i1+4niDrhd/vFmJmuPxNeB8vv0B23OCd6sfwGeFe5PfP4GqpnFBMev3IeI49hjDh+1T/LBMUwYgyuP+QxfSmRoLy6eP2knH4tMaj0QI0NbX+MfQF8jHBnT/OFAvYkuoT+mrjlZZX1Pjq/IAPdkm+A5dssA9RN1BI8+fowJ9jqtt9n5jr+FrhymveWQfMk4jrs/Qcos6zUbEmc1FcvTibnE5WLchDrFwF7NrQ9O3kSfSe9ZM6BRHfJLxb7efXc9uvSO/kOcsvLtYjWp79nOacBDyhef1Kub/UjbTmi18M7hty3S5xoct4vIk62u5fzJ350fwq43ywcw1sTlYhTi2lV5WKJxOWgx/U067wBcenkK4tpzyVOsP/O8i+nwcv8iG7sP6dXkXoV0aOibKlcRIz58spBywZeAxy6tPWvb0/iR/+GuU+qCsesjXNTlLEgv1NXEhWaprq4jzSGMeb4VcxzJeKccACwVx5fJ+VrKwBPBQ5qqKyJjmEYvyYyftXixROIu+oszkcjveOYkDpYv2Mj1/eTZKKfSFD/kuitfhUx9MA3h92/THj8ynnPegybZn1HHr9yPvM+htXiwYOBs/L/hcQNYf6X4g5r9LnV+6Q9xr4APka0o6ee5I4gssrPBB5YTN+0fD/Rje7tI17OzYhrkr9PcSvDIee5iMge75XPH0ltEOGc3mHJSsUfgEcNUGY5n5cQ2fTfZaB/HdF6tBJxPejiDMifzXVvKuhvS9zp4mMUgyMTP5I+kifAfq0nM70U647tRnTffl2ezP4PuH/tvSfna8+vlzXIeteO67sSg6etlc/vRlzK8TGi6+9C4iR/BZHMbOw22LXpZxMV1/JuAY0f19OUfTTRCrd3MW154N65bYYee6XY5wuA9wPPzGkHEXdoODGf39GjorafZtryezeiJfmbwMOW5Tjt91pT36s7WdZViMHKr2PqXbGGvRxqpDGMuRG/yvPR64jeh38Dji6mr038SHhOA8fzRMWw6Y5JjF8TGb+Y+uN7DeJH4yeaKJcJrIMBBxfH605EAqc61pYnEhwXET+4z2GIy7+YwPhVL7s2/WzGEMMYUfwq9vm8j2HFNtuDGDrijUwdH2wlegmeq2Zjv7bxMfYF8DHiHd67Q8A1+UX9CVOvx1yDqd0Hh7p17oDLuBWRZR76Uqx8fhDwlfy/ujtU1fK7GnBIn3lUlYpDB1mGYj7PJVpo9s8g9D6iS+6e+fryxPXtnyFaT5r6MXhelvtjYuDA3Wqv70+0IvyQIbstUtxOkWi9WJVehem4+vrktl0MHDtkueU8n0+0jHyLSEpW23dbolL5baJn2i00fCtqigpEPl+H6GlwDnHyrU5OQx3Xfco9kxiQ8MHFtK1z/aoxtPq1IjbVk2QBUVFelMdTvUfF8fndW774zKCXUewG/A9xAj9wWeY3yphVHgt5fL+E4SrrcyKGMb749XzgErLXADGewHXEwIh3yWNvA+I8ddOw3ykmOIZh/DJ+3fnyD3wpVj6fqDpYruNiovfmEUTS/xjiVtDTXl426HdrkuNXzmPkMWzc8aua/yTEMKKn21+JxNkvgJtrr69EJIx/AVzW1PZt82PsC+BjhDs7kjbloJQbEF3afwW8OKftSdwG+wMM8eMkP1cG/tUGnMcwg9g9leil8nCip8rjyuCX77k/8N8UAzkTg+j9jgFai4p5VLfR/ATZ0kzcYvzP5C0WgRWLE9zqTaxzfn4v4tbMBwBPIVqjbgd2qb3vIOI2lcO0/j6IHNCMGIDx87leaxMtYf+id/132UL0yGHXs5jXS/IYPpwYmO/TREKr6qa6MXF7yCcTXZCbHLjvCKKb83nAvsW+v4i49K0qqz6IZBOVi6OJ1sivEJWp6tK7J+fx29gd5pi+hezDxAn3j8Dji+nrE5d0njJkueUxsxtxieGdVi5qn3sYeVvyQdeZGcQvpla0V5hpubV5jSWGMd74dUF+nx9J3jqX6En6SOIOJH8kxon4Uj4f6kcCExzDMH7N2/hVX29mFsPuuO34dNtuGeczEXWw+jYiBrX9VR5br8h134kYSPcaYNWlHZczLHti41fOYywxjBHGr6UdK8zTGEYvIbcK0TvpGKJn2GFEj7RP1t6/EhFn7tbkdm/rY+wL4GOEOztOmF9m6gl/LSIDfQtxy9yViAGMh+o+WCvjFKL15E6/dMOc7GplPjVPLvciuhT+imipKK/DXYUY8O3SYn23Jn44PKaB7b1qbtcticTan+gNAroScByRcS+D4LB3AHla7s9yhP5FeQL4NsXt7mufG/SH0UVED68biFaZbYrX7kJcp9u3cjHM8VV8/kG5v6ru3gfmfr+NuA3k3tOUO+j61uezAnFrzQ/l/n0jkVxbSAwi99xhj6P6sV2bvj4xKOJ1RJfjjxGXBd1CDKbYxI+w8nu1Y36fqq6+98iY8p3imF83v1c3zkL59+FOKhe1/6sWyhndMpsxxK8+5Y41hjGe+PVIoqK6Y7U98ni6T7G+Tybi3KNp4EcCExTD+szD+DUP41efcmcSw4YZ5HXOxK9if44shgGb0EvcPJroDfSE3PZ/IpIAPyASA031apiY+DXNfGY9hjHG+FUvn8mKYffP4/qDZI83oqfdg4gEz7VNbN/5+Bj7AvgYwU7unTSPILq1b1t7fRExuvze/T43ZNkXEF1iHwtsvLT51wLCsdzJ3RSWUubuwOlMHSRvP2K8hkuIDPAhxAB3d4xcn+9bnWJE/RmUuUTwJyoPX89yfkdeB5uvbZ7Tj2xwP1eZ+sUUg7oV+/hy4lK8PRo+vq7LMt9EracCUbl4dW77I5osN+e/K3BB/v+QPNaeRFQQf0h0625k0L7aCW6F2vG6QZZ/A3FrzY8TAyV+jux90FC5BxM/bE8Atqy971CiYvPP3B9vbnhbn09Ulv5EtEAekNMfTvQU+xFRybiJIbtd19a5Hif2ZJrKBUtWKn7HcJcljTx+5edHGsOYA/Er53s02doK3JNIVN9O/Gj5zDSfaaLyOu9jGMaviYtfOR/rYCOIYcRdiX5A3I55UyJx9UYymUX0WLk8j+03Nnxsz/v4Vd/HjCiGMUfiV5YxUTEM2CfX96/A1sX0KsHzQ+BLTW/n+fAY+wL4mIWdOn2WeY/8MpxHcZInBt39Ks3/6D8B+CnZCpvTViwDbbWstYBwcgavhw9Q5k707vJwYu21BxAZ9R8TGe3ytt/DtPzW78iwOnlpBtEd8efAJ/L5CsS4Rh8luq42OkgicaK9jDih1pN4ewPXA+9uqKwViW7Prya6VX+J6J67dm3frklU6Pr+OGvguF6PaOG/Enhp9d7cxj8fttw++/hJxHX77yFH7a8ty75ET6nFRMWiqdtdVif2LxJJvO+VJ7zifYuI698bu90lcT33/+a6PZHo6n4d8JB8/S5EZf6ZxA/0gXtU1Lb1cUSF9dVM7XJcVS6+RQ7wV/tcE+PNjDx+5edHGsOYW/HrMOLH2EeJiurbiF4H+xHdzh/ccHkTEcMwfk1c/Mr5WAcbYQwjBvL9CDFo7z5E7LqVHByeGNT3wKbKZELiV599PJIYxhjjV5/yJy6G5Xd179zunyy/N0SC5yCiwXrGyeD5/hj7AvhoeIdODQbHAWcR3QOr3jsnECft1xMV6Z2JroRfmC5wD7Es5wHvzP/vDpxIZNS/DDxvmmU+iejSuUwBod8yEy1UfwPeSl5HXaz/GsSt+RoduT7n85IMND8gbgF5z5z+DOKa4M8S11t/lrhOt8kB5cptuAPRYvEzate5AtsPs5+X9lng5bled1QucvqmRCWkqUvu7pHruLCYtglRYXxcPl+TuE53P4b4cdJnOc7P7XoevevML57mvQ+gd4Id9nKdU4luqNXt659EVFx+B9y7Oo7r6zrosV1f3lyXC4vni4hK7KeBR0wzj2GvaT+fqBi+AXgXcVnhS4vX9yQGwPwdxV3fiAr1r5c1hiyl/FmPX9MdG4whhjHG+FVbjhOJO8gcCWyU07Ygb6nbwPwnNoZh/Kqez/v4lfOyDtad/RjG1J5HWwP/lfv1HKIny4eAFWfj2K69Nq/jV8535DGMEcevfsvLPI9hRSxYJ4+dO8alynX9CdEbq57gWTjo+s3nx9gXwEeDO3Nq8L2QuCPATUTLzdfJAb6AxxPZ7D8SJ/rPMOQdAvoFtTzB3Ux0C/5CBvqLgHOJk++WtWU+kQEzvfnZ+9FrqTiOOJmfzVIGNW3qpEMkyn5EdDV+ZW7399OrXOxGZL9fTnTrHGqw6mVYnh2JVpSf0uduWIPs59q+Op7ogvpacnDCnH4+0Xp0IVHJuZbi9oSDHl/F5y8gWsX+QfRCurDY51fkcX4S0aJxx/XGw5ab8ziauOtDdYJ/FPAX4iT//uJ9K9c+N+wJdgPiDg/VoJDV2AFn5Xr+mt71yE3/0H4acfL+MP0v9buCaFEZenyE2rxPIFrGds/nRxE/Fn4FvLp4377AC4r9vDnwd2bY/bweBxhx/Co+P5YYxhyIX7VtWW2DBcS4cFcQ56nGbp3LhMUwjF/zNn7lZ62DjTGG9dn+hxI/iL+X37GBenLW5jmx8SvnMfIYxhjjV85z3scweomdA/N7+xWil9L9a+v6E6LhuvHtPN8eY18AH7OwU6N73qVEL42ViQz7FzMobpDv2YDIvm9XBOYmWn+fBeyf/29GdNu7PgPUdjn9YcR1sesVnzshTxYzvjsCcZ3nT3L99izW54lE5eKFDHnXmj5l1rPqRwKnF8+PIRJoHwB2mGYes3EiKFvgdszAvxjYosEyLiBuOfgeotvt4jz5VAH63AzOPyEqlCsOUVZZmXkcUXl7OFGJvIBogbwkX9+TaCH7Wq530wnLU8kxCHIZfke0UBxLg9dY91teotVmq9ynPwSektNPptcFfui7BNS291lEAvi9RIvcb6lV+okuszcCr2mw3A7R4nlOPn9EbutnEy11f6NoPSo+V1XWNxpiOUYev4p1HlkMqx9jzJH4VZv/QuJuf9cQvXaa7Ok472MYxq+Ji1/5eetg3TlTB9ucGOvyg02WxQTEr/q2zOezHsP6LSsjil99tvfExLDcn38irjRZRCRGf0kk9Kp4sneW/YEmtvV8fox9AXw0vEPjTiLfJ7Kb6xbTtyESPN8G1u/zuSay6msT3QT/Ajwgp63C1FtMrkz0KLmiOBGtS7SoHLKM5SwxKjtxbeatRJfcvZhaufgH0VrV1HXOZfnHZxB8F/C02vseS7Q2v5+8TWL98yM4HnYjWlaaWvf7EV1i96nWhbjV6R+ANxTv24mpXWKHHT/hIbmdn1FMW5WokH4NOD6nLcjjqcm7va1TTSNO8OvlsXZ6Tt+a6CG1GDh/yPUsyz0aOLb2+slEZW5hPj+EGFjvzGG3ca2cXYiTeHUr092Ja9y/UP+eMvylfhsW/z8w/65EtCpvQtyF4tk5fVeigrOY4o5w1bE45DqPJH7Vl5URxzDaFb9OIlr+G2tlZwJiGMaviYtfOQ/rYFPfN/YYVlueJuL3vI9f+bmRxzDmSPzKeU9MDCOSoJ8Dnp7PNyF6DX2b6AV0DL06wB5kTykfS9mm414AH0PuwKnBaAHRSvBVImM/pTskcfeRzxMZ2LWaLLuYtg3wP8Q12/sW09ckbgn50Vy+KqNfnQDuMkD51YB51XqukPOuKhfV9FMZcmDIabb3y4gT6ueBP2cw2r72/qNyeZbIcjewLDNanyZOPkRX4+8UJ7dOMf3P5T4vj8th1pHoZVa1jlxUe32FPKbeuyzH5wD7+FnAWyha/ojWqe+Rd0sgxjK4hLhtY1OV1wuJFqJnMXXwy+fn93cD4uR7OVOvw25iHx9IdL39fnkSzfV+L9HN/5FNbG/ggbn/dia60v+DqRWNh+b3pxp7ZbdchkcNu637LS8jjF/5uZHGMFoSv/q91sSxnfOZ1zEM49dExK/plhnrYI3HsH7LPZN1aWK9i3nN6/jVZx+PPIYxxviV85mYGJbzvBuRIFsd2IhI6lycr11BJO6OG/R4msTH2BfAR0M7ErbJv6sQt+v7P/LuADm9OgHcOwNlk2MXrF177Z5EK8rv6bUurANcTAywd0cr7KAnSKKycD29cYSqSsSKGYi+SHTtW1B+dibl9Sm/HMhra2JQ6t3y+WHAp4jBqeuDGD+owSC4oDyBLC3Y1ZZ31YbK35vIpN+3Nv3uGYAPaqCMfhWp7Yguml8hWinKY+EFROvcKk2sYzHf84muz0cBW9WO718TXZ+3Bq4mTvLVMTbsd+uELHeJu9cRrXHXE111v060qFTfp6bGLtiL6OL9d+Dw2mt7EJd8/pBmbs+8P1FR+Q4xRtjW5TYkruf+KfBUYhDOjxItZUNta8YQv+r7iBHHMNodv1Zrovyc10TEMIxf8zZ+5Wetg40ghrFkA+qyxq9yWzU26CsTEr9yviOPYYw5fuW8JiKG1ZZji/x7ETG+UDUQ+2uJBO7PgTWbPL7m82PsC+BjwB039YSzD5FRPyifr0RcI/kD4JriffXrV2f8pQQeW3t+JJFlvWdt+jZE9+Df0Bv8bLWGgu+KRNfA32RAryoXVZB9RG6PH5Aj2fdb/yHW+XCiZ9QXKS5xIyoX1xIjum/bZz7DVpqfQlw//mHg+Xfy3vLE+ziixWHlGZQ13S0v1yEqTx+guHMN0Q33G8DBDR7Xa5XrQpxY/5zH1R5ERWsNYuyAdw1Tbp/lOIA4ee7T57XVgOcSXVN/QHSTndIKOmCZ1Xq+lRywrphWbpdtiRPtKcUxP3TFtTZ9F6ICcTtLdgG+L9E9e6jWwOL/1xDjMlxHrUJFtOK8kWjF+jFTx16Z8bbu810eefzKz48shvVZ53kfv+7k2J73MQzj17yMX/m5sccwJqQOVjtujV/zuA7GGOLXnezneRnDim26BZGYK4cPWYG4ZPQVxbRXET2y1mny+Jrvj7EvgI8BdtrUQHMycBq92/I9MqdXCZ7bgY82VO7jiO6vy1XLQK+15AYy41t7f9WVc9di+kxbu/erTlj5RT8j/9+RyOZ+hKkDAx5MdKt8xzDBbynrfDjwCSJ7X1/nw4iKxZfJLqMNbfvziOz5BcDpuU3/iz7dQGtB+0TgX8CBAx5fx2eZ7yC6Z65CXMd9PXELxhOAg3Kdbxl2exflPj+Pq0/l/lw3p+9CDLr2m9wHH8r9s9Igx9ZSyj8BuA1YY7rjNk9Oi4rjoqkuuVcBb+0zfRX6tNQMus1rx8kxRIXl+GLa7rnfv02fLsCDll07vlYlrm1/bB5Tl5NjRRTv2TD3+0H0WpMGGYdkLPErPzOWGDbNOs/r+NXnGJu4GIbxC+ZZ/MrPWQfrjj6GYfyamDoYI4pffY6ViYhh+dlDge8Sv1mvAZ5QvPYmoofYk4mrTH4L3L2JY2qSHmNfAB9D7Lzokvgz4tbmpxEtCnfc7YBI8BwM/JXadbIDlrdB8cVeVEw/KIP858nLw3L6fsDbgGcMGgiIgdM+SlRcLiO6KZbX3u5EVC6uJroybpLb4ZnFe4YJvmsV67xPMf2hxC37bmHJFrNjiApQI9eHEuMofQfYM58fmPv5P8R1xn27CRODkf6ewe/gcyHR9fUSoivud3N/rk6cUP87j60vZYBu5G42wJOIbrHPJq7T/yZx14DqTm87EBWL7xMj7Fcn9oHvCFE/Vogu598gu4ES1513iArmkcDO/T43w7Kma7F5c67berXpGxEDQ96/ieOqmO+5xEn2K8RJ9critd2JLrjfBI5soKzy+DwDeB3Z3ZpoqbuJqFzsW7zvkbV5DPpjcOTxK+czthjGBMevnMdExTCMX/M2fuVnrYP1po8khmH8mpd1sOmODUYcv3LekxTDtsnt+1QiyXMZ0fOqGrB5ITGmzzeJu4Dt1PT2noTH2BfAx4A7Du6aB//RxbR1ie5zf6fXyrIK0YVvmC/jecX/nTypLAbOLKYfTHQVvZU4wW9G3P6x7F43aOViZ2LwtMXAKcX06oSydQaLXxFddb9MA7fdrAXBvbP8s4ppDydaS75ArfWo3zwGXQbiRPusfH5gngROzsBYtSCtUPtcVbE4dMBy9ye6YJZ3mHgK8FmiC2fVHXUDovLXyJ1d8vnpteP6FUQLzgvoVS52JCo1H8hjbdCuuNOd4HfPbXt6bfpCopXy6Q0eW/chrmG/Wz5fM4/nzxLX0a+T2/mqnNbIeFn5XV6DqIjvQNxp5UH5Hfpk8f7diEHt3j1MubVlOJ9ITJ8IbFY77m7IdT2J6KL742G+R4w5fuVnRx7DmOD4VRxL8zqGTbd/MH7Nm/iV87UONoYYhvGrej6v6mCMMX6V5TNBMSznvR3wQqYOQr0Z8bv1i0y9E9tGNDjW3qQ9xr4APgbccXE7wL9QDJ6WgWJT4m4FfyC7gtLLhA/SOrddlnNjMW0TIov/B7J7bk5/CJHxXUx0I/wqAw42Vgu+9yJaJT5NXBP6iOK1qqViNaL16mAa6DZYW5YDiNap5/dZ54OJysWN9LnGu6HyVydGk1+PqDSdltO3JlpXFgPnFO9/InFd9DAVi0OBH1HcKSCnP4+4DnrDPp8ZZKT+slvqwUTX0LcDD6m97yKiVeMseqP270RUtD5JDsY2RNmPAZ5DtNJV4wecBvyb6JK9HzG21cdyOZo6ti4gTuS/Jbo/Pzan7wL8L3Hy/SHRMlde69zEXSg2ISpoH6z2J3EN/f1zmcoB2e81aJl9lmE/4P8xtRW23Bf7EnHkq0Rr9DDXd48lfvXZ1mOLYUxg/Mr5zOsYhvFr3sev/Jx1sBHFsH7bCONX9dq8q4Mx4vhV/ywTEsPys2sQybE/Ah+svVYleG4CXtDEOk76Y+wL4GMZdtLUL135/1VEl7b6nRLeQ2TZ/0Gt6+IAZa8A3I9otfliMX2jPMn8hWJwOeIEvz9RyRg4qVTM7+nALvn/7hlwPsfUysUC8nrfctoQZZbB96VEt9hNiex9v3U+KAP/xQ3u87VzW65ZTNuNqLBVLQybE9ek7lls61WJQdj6Xp+7DMdX1aJwKHHHtWrk/Cq4r5Lr/5iGj+uXE5WEHxGVpfdTG0CNOBH/nLglYrW+uxInwU2GKPtCoivs/+b2vZKsUBHXff+SaJH8KlGJHLjrc63c+xHdrBcR4wS8JZ+X1x+fQLQcHk6zP/jPJSotXyZO8lsWry3IZfshcNt0340hyj46vy8r0WttnDLIZx7/GxXH46AtzmONX/n5kcYwJix+lcdPuf7M4xiG8Wsi4ld13GIdbNZjWK3MtYmkztLi12YsGb8WEncpM37NrOyRxDDmSPzK+UxEDKtt892IROD3qV2uSHy/30UMiL72IGX5KLbnuBfAx53soKknnOUoTqBEF82bgZeRt7rOwP8h4nrkq4nkz8oM1vJcXku8X55oPl5M25jeifZ508xjmBP8yhn0ymC7KNfvOnpjC10FPG0Wtv2GRNfFA4pp6xbrfEZtuZrKqj+PaJ34Ru6/B+T0rYlWjLOJbqRXE61pVVCuTnhN3BVrAVGZ/ATF7S2JAey+QW3AtSHXd0eim+19iOvrzyVOeBfUgzxxne6Ukyy1SuUMy96OqKzulMfbUcQtPT8DbFqs8/bE7TcbGbiPqLi9nqnd6rchbvv4PeDE6fbLgOWVceQoogJ3HNFS9v+IE+6q5fuJ1tIPDfMdnmZZnkBcr79xPi+vpT8QuMeyHKPLUM5Y41d+fmwxjAmIX0s7PpiAGIbxa97Gr/p+wjpYNa3xGMbUH6BnMDV+7ZfTq/h1DnEJjfGrpXUwRhy/6vuZCYhhxfeiuqV59f3YLWPH1dTu7Eb0ZFqiR5qPAfbxuBfAx1J2ztRg8DTi2tZPA88tpp9DZGBvI67D/RJwS752McWt0GdYdnmyey7wbiKjvhi4vvy7CNcAACAASURBVHitqlz8AXhpU+ubz1fMdTunNn1vYsCtn+RJ7ns0cH13rYwjcl1vp7jdZL62brHO5y1tHQYot2qlOoy4nvwGoovvhhl4TyV6ZH2XIW//WNvHTwX+h+j2vE9O2zFPOjcRmf6DiIEVv9TUyYYYGO86ohK1QjH9JVnOBeTtOGufW9BvPWZY9lFEK8EHmZo0PYToPvoZimuRG9zHW+a8f0dtoHOigvGa/K6d2uQxXazbqcAJ1bYjWmP/H9EiNqVy0W97z6Cs6Sqti4BvEbFr42L6ykR8O62B9Rx5/Oq3zowphjEB8avPfp6oGIbxC+Zp/OpzbFsHm/rarMQwjF/zvg7GGONXsW6TEsMOzP37MaJRZ+2cvmcec9dQDC3io8HjbNwLMJcfgwaspsslrjf9v/z7NHqDuFW9dR6aAekjRDa6uiXhJUSCZ4Uhgu9pxAn0gAxATyZO6F8o3rMRken/ZBPbjDiRrpX/vxx4S/5fnni2Bx5NjOZftR400l0y57Ul0UXwP1XwqZ3M1i3XuaH13ooYVKxq6X4YMSjfybX3bUFkv5tqhT2T6PJ6GdHt9fNkt+Is67NEZeabRMWiqTsydIjK1PeJu1EsqL3+4lyWN5PZ/wb3b1X296Yp+xDiGuxvUrtrwiBllX/z/wOIE/kPgQfV3n9P4J1E5Xmo44qplYN1idbOxcDZtfftRgyadzUNDGJXK/e+RKvz/sW084lK+xuBB+b2+BjRWtjk93jk8SvnOdYYxgTFr5zHRMUwjF9n1943L+NXlmUdbAQxDOPXvKyDMcb4lfObyBhGJHD+keV8KI+jq4B1i9c/QSQuHzJseT5q23/cCzAXH8B2xf8jTfDUAyhx8vwesEc+fwDwL6J76LuAuxTvrU42q+YX6jfAvYZYlpWJ3kIvKaatADyYaN34VDF93X5BdIAyT87l/gkxdtD/ESe7Hand7rL2uUa6S9amb0Rc+/trYPv6e4lR9Yde5/z8isTI/D8lBh57OPAnsmJBXMv9ZGrXNU+37DNZ3wzui/L/PfPE9nWKAQGJ62HvWqzv0HdkKI6nZ+VJ5i0U17jn668G3trA9h2k7KOIbrpNdcVdjamtMvsSJ/JPUZx087XNiu9zExWMav9uR1SWPt/nWNo1Y8srhyyrrERVlbfv5nf5rcVrZxKV1sVE6/DHaKjSmvMYefzKz480hk0XA5in8avf55jnMWzAco1fg5U1J+JXzsc62CzFsNp8jF/zsA7GHIlfOZ95G8OKY7K8e96p9G5v3sl9eWOWUSV49iE6JSzRO8vHkMfbuBdgrj2I7phfAY4ppo0kwUN0yXxyVWYGvmOBp+a0qiXh8URvnX8TPXg2KeaxOfAm4GsMOZhyzu864MO1aZ0MuIuBb9Vfm+H8672UNiIyyMcRt8z7RJbzFWJE+y8SJ72HNbTNy+B/aJ5oTqOXTFuXyDb/kkz61YPesMcH0V3xmRnYb6DX3fik4j07Eq06+wxZVv0WkPfO9du2mL4b0evrq8BhS5vHgOXukOXukM+XJ269eTNRyaknOIeqsA5TdvG5obrEZhmfIU6gl9MbJHHf3P6fAh7YxLbuM49d8ztUtUhuR1TcP0bt+maiW3JTP0zOIO4msjdReT4rl+PdxXvuQrQAb8wQldalLMOsxq9+n2GEMYwJi1991nnex7Bhyi0+Z/yaebljj185P+tgsxjDMH7daRyhhXUw5kj8yvnMyxhWvH9hMW1r4PpcvxOL6csTCZ6bcpuvn9NnNEaVj2Xc9+NegLn2yCD+UYrb4uX0WU/wACdVBzo5gBqwPtE9dX3i+tfn5PQtidv0LaZ267gMnnedYdl91w94BnFt8UNr058MvA94x6CBqBZ8ly8DRDH97kRl4jBgDyKxdf6yBp8ZLMsFRKvNu4js8reAZ+RrmxDZ5Z8BOzVc7iOI7rg75fMP5T4tW+oW5jF5VYMnmwuJitovgL8Bh9de35W4HeYvgPsNWVbZivAyogvwT4lE5X/lyWUBMa7AF4DXsWQLThPXtA9UdgPb+iW5j6tK65eJE98B+foBwBVEQna3JsvO+a9PVCKeV0yrKhfXABv0+cywPwbvlsfrw/L5w4tt/nuiZXKJQRgHPb6nOz6YxfjVZ53HFsOYsPiV8533MayJchvYzsavWY5fSzs+sA4GDcaw2jobv+Z5HYwxx68sY97GMOIOdj+idxnhGvRuMf/h2jItR4yl9S2iR+Jygx5XPu5kv4x7AebSo/oy5ZfiA8Q1mUcWr8/KQZhB/lnF85OBi8iuazlth/xC7JzP70p0l9yT3vXOTWTVtyBabqqBr+5JZFo/QrSqdDIYX87UW1EOk1V/RgaBLwFvIDK/1ejtWxEDn+073f5qYPsfQVzruns+fzxxrejhxXs2JE48Vza434/KdX9OMW1VoqXux3lcvDiff41ed8mBR67P/7ejdwvIRxMVxJ9R3No037cXkelvajs/k+hevW/O+wjgr8RAkQuInmrPI7roPqeJMsddNtGT7lssedvHq4gTYjWuwcNzfw97Df103dvPJiqJqxXTts1luIUhbz1Z+z7fP/+emLFiUR7PVRf3VxIV6Ctp5trukcevPuWOLYYxAfErPzexMWyM5Rq/Zjl+9Sl/C6yDzXoMw/g17+tgjDh+Le34YJ7GMCJevZdIVD48p62W63sb8ApgxeL9C4g4tkWTx5eP2n4Z9wLMpUf15ciA8IL80n2FolskDSd4iCznh4mWiqqV4iLimsyzi+Bzr/wynkl0sau6Eg7VFbgWEM7M9f1Wll+daO9NDFr3v0Q29uvEHRKGSirlZ88lsvjPIq4jX0yMnL9esW43AsfP4n5/PnBp/v9ooktuFQRXo9cVeN3pAvcAZa5OXP+6GHhDfZ8QibtriBajl9PQgIXE4IevorirRu7ftxKj9T9ims8N8uO3rMwsl/v13Np79iQGTDw9n68IHDNIeXOh7Pp3gajE/YZsEaI32PkKwA+oDao36LbuM48tgDWK5wuJFquzKFpLiJ6Klw96XBOxsowh5+T3eWN6yfJzidbYagD45wLvp9aqM2D5Y41fxfqNLYYxQfEr5zHvY9hcKDefG79mMX5V+7f43zrYCGIYxq/yPfOmDlb/HjCm+JXz2YLJiWFbEWMm/Ylegmd1otfUF6kleHzM/mPsCzDXHkRG8Y9EZvOtRDb9JuCo4j1NDbBVfbnXBf4b+Bx5jSKR2Pkq8CJ6g089lRhn59vEYFxD3YaxtiwvJbLKhxLdQT+bQbHq0rcRMfjVGcAJ9E52w7QW7UhUYqrrUBcR3VOPr33my8CrG9rm/QZ1exWRtd+bqYPodYDHZiBcuLR5DLgsmxLXeH+fXuWlPCGuUNtew/YuWJsY1+k/wLtq79s+g/MPgSOa3M70rq39BnnrSeLktmL+fy7RIlfvBtxEV/ORlV0rd71qPrlNX1m8tgJRifkMRffvBrf3o4gYdhnR7byKExcB1053HM/0uCZaZcr12p2oMOxTzpNoHbomn69MVGSOH7TcaZZlJPGrz7YeaQzrt62Yx/Grz/ae9zFsjpRr/Opt61mPXzkf62CzFMOmKdP4NY/qYIwxfvUpf17GsD7Lu3zxf5Xg+TNLJnhuJO64ZoJnRI+xL8BYVz6uWd67eL4h0SJyWjFtB2JE9S/QZ2CzIcsvb+u4iGgl+CbwuJx2Dr0ET9WD5+5Elr/qZdREj537EANg7ZvPH050w72Z6DbZ9zZ1AwTfeiZ/EXBbPn8kU0/qawCH5P8PGnQ9l7LO2xAn94W5HIvz8ejiPasS18m+qsF9/hDgMeTJO5fhNqJb5hbLsu0GXN/HErdD3AV4DVG5OLD2/nsTrSsfanA7P4eozGxOtM79GLhPuU5ES8anG9q+Yym7T7nvoNfqehrRGvvs2vF/M8U12A2V/Zjcx08mKhJ/Jy4xPQm4B3Ebzsc1UOZZuU5VHDqciJM3kHfwK147hLgDxPX5ma8N+31mDPGrPG6KfTiyGMaExa8+6zzvY9gcKtf4NYvxq896WwebpRhWK9P4NQ/rYH3KHFn86lP+vI5h+Z0pL5csf8duBVxMJKWrMY1Wze1wLX3GFvIxO4+xL8DYVjy6nH4IuFsxbU3ies+T8nkVfLYnBuS6GXjCLCzLRURip2qp+QmZVSUSO7cRPXnWr32uiRbvc4gR6p+Xzw8Afp6BaR0iufQL4OAhy9mPXtLqjcSgXjsSyaznU3TDzffsRWTW71VMa6qb+/nErQF/Ddyawf9ZxDXex2SA2pHolnsrzV2+8TKiK/DNROvY+4lrbjfN7fxFGroOtba+5+U+fCKRSd+c6JX2Z+Cg2ue2auK4KrbzL4mT3RbAzkQLwifonXhXIypv72yizHGXXZR7BLBVTtuE6Jb6PWLchJfmd/2ObvUN7udfA08g7zhBtOS8lOiCfGu+/gFglWH2c67Pl/L/s4kKwzeJHwf3r713NeLHysW5LAP3mumzHCOJXznvscUwJix+TXNsT0wMmwPlGr967521+JXzsQ42SzGsVqbxa57XwRhx/JpmP8/bGEYMun4p8Zv06GJ6meDZhhiD5zP0em0tJHtT+RjNY+wLMNaV711/eR96A1B9FXhZ/r88vQTP+4gT7gdodiT3Y4gWml2JlpJ1s4xbyURSfnF/DjyxgfLKDPNhxLhCO9G79OtDwMuL9f8wkWy6bogyVycGp/5cBtc/ED2i7pLl/a0qs9ovxOj1Hxwm+E2zzocT16MemP+/kMis/w9wai7Lz3P7X0uvO+Ww1x8/h6hYVK0WJxCtVFfTq2DcSrRsbNTg8XU6UanYtVqXnL41cV35H6m1HtW32YDlPoBIlN63Nv3huW//SrSWfY2pgxU2cYnhWMruV24RPzYixhK4LpfhYppNciyxn5l6wl2RGM/hw0QFepcBy6nWZ19i/IevEvFrLeChuT0vJytvS5lPEz0ORxK/cl5ji2FMcPya7tjO6fMyhs2lcjF+TTefgX8UYh1s5DEM41c5fV7WwfqVyYji13T7mXkYw4irRy4nkjfHFNPLdT00t8XdBinDRwPH47gXYNwPIqP4cWIMm3sS10oupshK5vveSIysv3HD5Z+dZS+oBaJPEeP9PD6nHd9UEMr5PQB4Pb1BnDtEK9F3gWOLbfMB4vaXw1Yc185AtBg4o5h+ENFi8jniBPxUYuDAoe9O0GcZ9sugXnbPXJnenRkOI1oY9iUqW0Nd+laUsTHwNuAx+fxRGYxfQPQI+xjRO2wLooLT1Mlmpdx/T8/nmwMHE4Nxn0+0Il2U+2Svho/rxxPX8ld3/Cgrd1sRAzeeRXRVbWywwnGWXS93umOXqS09TZTbbz8flMfVK4D9i/euTrRYXkJUNoapTH08j52rimlHEndcuZTitqJNfYeL+Y00fuX8xhrDmLD4tZRje17HsLlS7nTHLcavpo4v62Dd2Y9hGL8mog5WL3O645aG49dS9vO8jWHAlkQvrHqCp4oX2xNJp7s3eUz7WPbH8ky4brf7106ncwoxgPLriS6yzwYu6XQ6uxPZ/s2Ik86O3W73p02U2+l0Ot34FvyNOLkt7Ha7f+p0Ost3u92fdTqds4gTwNmdTucP3W73rfm5Bd1u9z9Dlr0hMfDVesRgZuSy/KbT6dwGnN/pdNYisq8rEV3/up1OZ7lut7t4wGIXEwPX/QJ4YKfT+Wm32317t9u9stPpLCauhX4GMebRj4hrzP+d2+PfQ6wucMc6X0ys84XV9G63+/dOp3NZln9Qt9u9jBiArfrccg2U/1uiteDTnU5nV+LuCy/sdruv7nQ6vwJeRyQPj+l2u4/Lcofez0TCcBvgL51O5zDgcURl8R/EwIzrEgMZ/oao3A2tOK5XIe8IkC91i3XaA/hKt9v9WPG5BcNu53GVPV25nU6nk2V3yFu9drvdG/O91eeGPraZfj//nehav06n07kB+HvGmFuIysY/By2w0+msTRxHLwYO73Q67+52u0d2u9135/o+Azi10+m8Idd50LjRr+xxxC8YYwyb0PgFExTD5lq5xq/ZiV9ZvnWwNIIYZvwK87IONgfiF0xYDOt2uz/I382vAZ7Y6XRW6Ha7/93tdv+VbzmG+G37m6bK1Ax1h8wOzZcHMdDVNcT1oIuI7oM3Ea0XNwI7z1K52xEDXZ1Tm74/0X3v+cxOq9EORAvRzeW6EQO6XUr0JrqMhrr1F/PfkF7G99jaa+vVnjeSVa+t8/eI60V3qb32WuCTs3h8VdvxdCJpVw149uTc3o10f+5T7v5EV+OfE4nLfXL6OcCHZ2t7Eye6fwEvrk1fSHQLf84sbuuxlL2UclcZwTpPt5/PBq6svfdc4o57qw9Z5nL5ODHnd2nx2pHED4QXztL6jiV+5bzGEsMmMX5lGRMVw+Zgucav2Vln62BTX5u1GGb8mjJ9XtbBxhm/7mQ/z+cYtmV+d75EJEmPzO/xL4jOELO2vX3cyb4Z9wLMpQdxDew1xHW+2+S0lYA1ZrncY4F/Et339srl+Gg+ry7VauzHSVHuDsQgXG8Ddqi9tlZRdtMn+KpL37XEtc8LiIrGecV7Grnd/DTrfBvwzqpyQXST/Czwtlncx9W2fAuRLFyL6LF1BcXI+bNRwSBayjYtyyCuNb94ttY3yzkxj+vXEq1y+xHdSG9r+piaK2WPeZ2n289vKqZtBryHAa/3nqbc1fJ7/G2K27sSA4M2HreK+Y8lfuU8xxLDJjF+5XwnKoZNWrl3so/nZfzKMqyDdWc/hhm/JqMONs71vZP9PJ9j2F2JcbO+CnyZGI9nu9ne1j6W/qgCnlKn07kH0U1yfeL6yetHVO6hRObzP0T32V8Ce3a73X8V3Q5no9ydiVH7vwK8stvtfq32+qyU3el0tiS6x25HXHf6F2DX7hDdFGdQ9k5Ea8169O6csCVxzfM/Z3l770Fc2/5dYAWi2+Yu3ea6hy6t7NWJXmlPIe9e0I1u17O1jxcQ3cpfQazrL4jLHA/O47qprs9zpuxxrnOxDH33c/H6qt1u9y8Nl7kacVeMZwI/6na7Dy1em839PJb4lfMeSwyb1PiV5U9EDJu0cmvLMDHxK+dvHWxEMcz4Nb/rYHMhfuVyTFQMyzKWI3pJ/bvb7f5jNsvSnTO500en09kGeAnwzG63++MRlrsxMfjbqsD13W73P01d73wn5e5EXAv9E+BZ3W739tksryh3I2Jk+Q2Ad3QbvL57Gcq+N9Fq81PiNoxvzOkrjuDH2S7AI4k7VrxqVOudlchzgC5wWJ7sRlHu+sRgjv8Cbu92u90R7uexlD3mde67n4H/zNaP/ix3NWKAyD2IAekbHadiKeWOJX5l2WOJYZMYv7LsiYphk1Zulj1R8SvLtg42ohhm/Jr/dbBxrm+WP1ExbDYblDQYkzvTGEUleRmWYSRZ5izrPsCTgONHWampLcPI1jfL2wl4E/ANojvyd0ZVdm05RnnSuRvwg263u3iU5daWYdiBbVtX9qjLHdd+7nQ6qxCDBjYxgPFMyh17/MrlGGXMnrj4leVNbAyblHInLX5l2WOPYZNYBzN+zf+yx1TmxMUwzR0md3SHKvs6SQEhM+xvIgYee0G32/3WmBdpJCZpH0+yMf4YHHlLjvFrcuIXGMMmwSTFr7LcSTq2JzWGTdI+nmSTFsM0Nyw37gXQ3JGVis4knXC63e6txHWx6wG/H/PijMwk7eNJNq79PI5KhfFrcuIXGMMmwSTFr6pcY9hkmKR9PMkmLYZpbrDnjgR0Op2Vu93u38e9HJI0U8YvSW1mDJOkZoy1506n01mt0+mc0+l0rup0Or/qdDrdTqdz9jiXSZPJSoWktjJ+SWozY5gkNWPcl2WtC7wA2AH48piXRZIkSZIkqXWWH3P5PwPu2u12f9rpdDYB/t+Yl0eSJEmSJKlVxprc6Xa7/wB+Os5lkCRJkiRJarNxX5YlSZIkSZKkIYz7sqyhHPBSRn6rr5c/Nv4++52TUe44y3adJ6Ns13n+lzvOsl3nySh70sodZ9mu82SUPWnljrNs13kyyp4L67zj5qMvO3XGVvKEseeOJEmSJElSi5nckSRJkiRJajGTO5IkSZIkSS1mckeSJEmSJKnFTO5IkiRJkiS12NjvltXpdJ4K3AVYIyfdr9PpnJn/X9Ltdn80niWTJEmSJEma+8ae3AGeDZQ3ZntAPgCuB0zuSJIkSZIkTWPsyZ1ut7vFuJdBkiRJkiSprRxzR5IkSZIkqcVM7kiSJEmSJLWYyR1JkiRJkqQWM7kjSZIkSZLUYiZ3JEmSJEmSWszkjiRJkiRJUouZ3JEkSZIkSWoxkzuSJEmSJEktZnJHkiRJkiSpxUzuSJIkSZIktZjJHUmSJEmSpBYzuSNJkiRJktRiJnckSZIkSZJazOSOJEmSJElSi5nckSRJkiRJajGTO5IkSZIkSS1mckeSJEmSJKnFTO5IkiRJkiS1mMkdSZIkSZKkFjO5I0mSJEmS1GImdyRJkiRJklrM5I4kSZIkSVKLmdyRJEmSJElqMZM7kiRJkiRJLWZyR5IkSZIkqcVM7kiSJEmSJLWYyR1JkiRJkqQWM7kjSZIkSZLUYiZ3JEmSJEmSWszkjiRJkiRJUouZ3JEkSZIkSWoxkzuSJEmSJEktZnJHkiRJkiSpxUzuSJIkSZIktZjJHUmSJEmSpBYzuSNJkiRJktRiJnckSZIkSZJazOSOJEmSJElSi5nckSRJkiRJajGTO5IkSZIkSS1mckeSJEmSJKnFTO5IkiRJkiS1mMkdSZIkSZKkFjO5I0mSJEmS1GImdyRJkiRJklrM5I4kSZIkSVKLmdyRJEmSJElqMZM7kiRJkiRJLWZyR5IkSZIkqcVM7kiSJEmSJLWYyR1JkiRJkqQWM7kjSZIkSZLUYiZ3JEmSJEmSWszkjiRJkiRJUouZ3JEkSZIkSWoxkzuSJEmSJEktZnJHkiRJkiSpxUzuSJIkSZIktZjJHUmSJEmSpBYzuSNJkiRJktRiJnckSZIkSZJazOSOJEmSJElSi5nckSRJkiRJajGTO5IkSZIkSS1mckeSJEmSJKnFTO5IkiRJkiS1mMkdSZIkSZKkFjO5I0mSJEmS1GImdyRJkiRJklrM5I4kSZIkSVKLmdyRJEmSJElqMZM7kiRJkiRJLWZyR5IkSZIkqcVM7kiSJEmSJLWYyR1JkiRJkqQWM7kjSZIkSZLUYiZ3JEmSJEmSWszkjiRJkiRJUouZ3JEkSZIkSWoxkzuSJEmSJEktZnJHkiRJkiSpxUzuSJIkSZIktZjJHUmSJEmSpBYzuSNJkiRJktRiJnckSZIkSZJazOSOJEmSJElSi5nckSRJkiRJajGTO5IkSZIkSS1mckeSJEmSJKnFTO5IkiRJkiS1mMkdSZIkSZKkFjO5I0mSJEmS1GImdyRJkiRJklrM5I4kSZIkSVKLmdyRJEmSJElqMZM7kiRJkiRJLWZyR5IkSZIkqcVM7kiSJEmSJLWYyR1JkiRJkqQWM7kjSZIkSZLUYiZ3JEmSJEmSWszkjiRJkiRJUouZ3JEkSZIkSWoxkzuSJEmSJEktZnJHkiRJkiSpxUzuSJIkSZIktZjJHUmSJEmSpBYzuSNJkiRJktRiJnckSZIkSZJazOSOJEmSJElSi5nckSRJkiRJajGTO5IkSZIkSS1mckeSJEmSJKnFTO5IkiRJkiS1mMkdSZIkSZKkFjO5I0mSJEmS1GImdyRJkiRJklrM5I4kSZIkSVKLmdyRJEmSJElqMZM7kiRJkiRJLWZyR5IkSZIkqcVM7kiSJEmSJLWYyR1JkiRJkqQWM7kjSZIkSZLUYiZ3JEmSJEmSWszkjiRJkiRJUouZ3JEkSZIkSWoxkzuSJEmSJEktZnJHkiRJkiSpxUzuSJIkSZIktZjJHUmSJEmSpBYzuSNJkiRJktRiJnckSZIkSZJazOSOJEmSJElSi5nckSRJkiRJajGTO5IkSZIkSS1mckeSJEmSJKnFTO5IkiRJkiS1mMkdSZIkSZKkFjO5I0mSJEmS1GImdyRJkiRJklrM5I4kSZIkSVKLmdyRJEmSJElqMZM7kiRJkiRJLWZyR5IkSZIkqcVM7kiSJEmSJLWYyR1JkiRJkqQWM7kjSZIkSZLUYiZ3JEmSJEmSWszkjiRJkiRJUouZ3JEkSZIkSWoxkzuSJEmSJEktZnJHkiRJkiSpxUzuSJIkSZIktZjJHUmSJEmSpBYzuSNJkiRJktRiJnckSZIkSZJazOSOJEmSJElSi5nckSRJkiRJajGTO5IkSZIkSS1mckeSJEmSJKnFTO5IkiRJkiS1mMkdSZIkSZKkFjO5I0mSJEmS1GImdyRJkiRJklrM5I4kSZIkSVKLmdyRJEmSJElqMZM7kiRJkiRJLWZyR5IkSZIkqcVM7kiSJEmSJLWYyR1JkiRJkqQWM7kjSZIkSZLUYiZ3JEmSJEmSWszkjiRJkiRJUouZ3JEkSZIkSWoxkzuSJEmSJEktZnJHkiRJkiSpxUzuSJIkSZIktZjJHUmSJEmSpBYzuSNJkiRJktRiJnckSZIkSZJazOSOJEmSJElSi5nckSRJkiRJajGTO5IkSZIkSS1mckeSJEmSJKnFTO5IkiRJkiS1mMkdSZIkSZKkFjO5I0mSJEmS1GImdyRJkiRJklrM5I4kSZIkSVKLmdyRJEmSJElqMZM7kiRJkiRJLWZyR5IkSZIkqcVM7kiSJEmSJLWYyR1JkiRJkqQWM7kjSZIkSZLUYiZ3JEmSJEmSWszkjiRJkiRJUouZ3JEkSZIkSWoxkzuSJEmSJEktZnJHkiRJkiSpxUzuSJIkSZIktZjJHUmSJEmSpBYzuSNJkiRJktRiJnckSZIkSZJazOSOJEmSJElSi5nckSRJkiRJajGTO5IkSZIkSS1mckeSJEmSJKnFTO5IkiRJkiS1mMkdSZIkSZKkFjO5I0mSJEmS1GImdyRJkiRJLVJW/gAAD+pJREFUklrM5I4kSZIkSVKLmdyRJEmSJElqMZM7kiRJkiRJLWZyR5IkSZIkqcVM7kiSJEmSJLWYyR1JkiRJkqQWM7kjSZIkSZLUYiZ3JEmSJEmSWszkjiRJkiRJUouZ3JEkSZIkSWoxkzuSJEmSJEktZnJHkiRJkiSpxUzuSJIkSZIktZjJHUmSJEmSpBYzuSNJkiRJktRiJnckSZIkSZJazOSOJEmSJElSi5nckSRJkiRJajGTO5IkSZIkSS1mckeSJEmSJKnFTO5IkiRJkiS1mMkdSZIkSZKkFjO5I0mSJEmS1GImdyRJkiRJklrM5I4kSZIkSVKLmdyRJEmSJElqMZM7kiRJkiRJLWZyR5IkSZIkqcVM7kiSJEmSJLWYyR1JkiRJkqQWM7kjSZIkSZLUYiZ3JEmSJEmSWszkjiRJkiRJUouZ3JEkSZIkSWoxkzuSJEmSJEktZnJHkiRJkiSpxUzuSJIkSZIktZjJHUmSJEmSpBYzuSNJkiRJktRiJnckSZIkSZJazOSOJEmSJElSi5nckSRJkiRJajGTO5IkSZIkSS1mckeSJEmSJKnFTO5IkiRJkiS1mMkdSZIkSZKkFjO5I0mSJEmS1GImdyRJkiRJklrM5I4kSZIkSVKLmdyRJEmSJElqMZM7kiRJkiRJLWZyR5IkSZIkqcVM7kiSJEmSJLWYyR1JkiRJkqQWM7kjSZIkSZLUYiZ3JEmSJEmSWszkjiRJkiRJUouZ3JEkSZIkSWoxkzuSJEmSJEktZnJHkiRJkiSpxUzuSJIkSZIktZjJHUmSJEmSpBYzuSNJkiRJktRiJnckSZIkSZJazOSOJEmSJElSi5nckSRJkiT9//buJ9TSuo7j+OdbWRT9dShIxgIHk5ZlSAWBRC6TitlItamIEjIoslq0CUrEUBAqImjTHyuIwqKCFlEwikROi6CEcECDajIqJ6rpj78W5yiXy9zu3BkXfrivF1we7u98zznPs31zfs8DFBN3AAAAAIqJOwAAAADFxB0AAACAYuIOAAAAQDFxBwAAAKCYuAMAAABQTNwBAAAAKCbuAAAAABQTdwAAAACKiTsAAAAAxcQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBM3AEAAAAoJu4AAAAAFBN3AAAAAIqJOwAAAADFxB0AAACAYuIOAAAAQDFxBwAAAKCYuAMAAABQTNwBAAAAKCbuAAAAABQTdwAAAACKiTsAAAAAxcQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBM3AEAAAAoJu4AAAAAFBN3AAAAAIqJOwAAAADFxB0AAACAYuIOAAAAQDFxBwAAAKCYuAMAAABQTNwBAAAAKCbuAAAAABQTdwAAAACKiTsAAAAAxcQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBM3AEAAAAoJu4AAAAAFBN3AAAAAIqJOwAAAADFxB0AAACAYuIOAAAAQDFxBwAAAKCYuAMAAABQTNwBAAAAKCbuAAAAABQTdwAAAACKiTsAAAAAxcQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBM3AEAAAAoJu4AAAAAFBN3AAAAAIqJOwAAAADFxB0AAACAYuIOAAAAQDFxBwAAAKCYuAMAAABQTNwBAAAAKCbuAAAAABQTdwAAAACKiTsAAAAAxcQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBM3AEAAAAoJu4AAAAAFBN3AAAAAIqJOwAAAADFxB0AAACAYuIOAAAAQDFxBwAAAKCYuAMAAABQTNwBAAAAKCbuAAAAABQTdwAAAACKiTsAAAAAxcQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBM3AEAAAAoJu4AAAAAFBN3AAAAAIqJOwAAAADFxB0AAACAYuIOAAAAQDFxBwAAAKCYuAMAAABQTNwBAAAAKCbuAAAAABQTdwAAAACKiTsAAAAAxcQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBM3AEAAAAoJu4AAAAAFBN3AAAAAIqJOwAAAADFxB0AAACAYuIOAAAAQDFxBwAAAKCYuAMAAABQTNwBAAAAKCbuAAAAABQTdwAAAACKiTsAAAAAxcQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBM3AEAAAAoJu4AAAAAFBN3AAAAAIqJOwAAAADFxB0AAACAYuIOAAAAQDFxBwAAAKCYuAMAAABQTNwBAAAAKCbuAAAAABQTdwAAAACKiTsAAAAAxcQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBM3AEAAAAoJu4AAAAAFBN3AAAAAIqJOwAAAADFxB0AAACAYuIOAAAAQDFxBwAAAKCYuAMAAABQTNwBAAAAKCbuAAAAABQTdwAAAACKiTsAAAAAxcQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBM3AEAAAAoJu4AAAAAFBN3AAAAAIqJOwAAAADFxB0AAACAYuIOAAAAQDFxBwAAAKCYuAMAAABQTNwBAAAAKCbuAAAAABQTdwAAAACKiTsAAAAAxcQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBM3AEAAAAoJu4AAAAAFBN3AAAAAIqJOwAAAADFxB0AAACAYuIOAAAAQDFxBwAAAKCYuAMAAABQTNwBAAAAKCbuAAAAABQTdwAAAACKiTsAAAAAxcQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBM3AEAAAAoJu4AAAAAFBN3AAAAAIqJOwAAAADFxB0AAACAYuIOAAAAQDFxBwAAAKCYuAMAAABQTNwBAAAAKCbuAAAAABQTdwAAAACKiTsAAAAAxcQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBM3AEAAAAoJu4AAAAAFBN3AAAAAIqJOwAAAADFxB0AAACAYuIOAAAAQDFxBwAAAKCYuAMAAABQTNwBAAAAKCbuAAAAABQTdwAAAACKiTsAAAAAxcQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBM3AEAAAAoJu4AAAAAFBN3AAAAAIqJOwAAAADFxB0AAACAYuIOAAAAQDFxBwAAAKCYuAMAAABQTNwBAAAAKCbuAAAAABQTdwAAAACKiTsAAAAAxcQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi4g4AAABAMXEHAAAAoJi4AwAAAFBs37gzM9fOzNrj7x3nmL9+Zn4yM4/OzJmZuX+PuffNzC9n5uzM/H5mvjAzlz5ZFwYAAABwGDzjALOfT3LPrrUTO/+ZmZuT3JrkO0k+luSxJFcledmuuU8n+XiS7yX5bJKXJ/lAktfMzOvXWmcPcF4AAAAAh9ZB4s49a62v7PXizFyd5JYkN6+1bvs/cy9N8pEk31prHd+xfiLJ3Unek03wAQAAAGAfB7rnzsw8d2Yu2ePlDyU5neT27ezz9ph7bTZR6a6di2ut7yb5W5IbDnJOAAAAAIfZQeLO55KcSXJ2Zu6bmet2vX5dkp8lef/MnE7y6Mw8MjOfnJmd3/Os7fHv5/iOfyR51a55AAAAAPZwPhHl30m+nc0vc65P8uEklyX54cy8OUlm5oVJXpzkddncc+f2JMeT/CjJJ5Ls3Kb1wPb4hp1fMjNXbT/jOUledGGXAwAAAHC4zFrr4G+aOZLkV0nOrLWOzczRJA9vX377WutrO2a/n+RNSS5baz2yXTuR5NXZBKMfZHND5TuTvDLJJUkuX2v99oKvCgAAAOCQuKDtT2utPyX5UpIrZuZYNtupkuQ/Sb6xa/yr2QSba3asHU9yXzZbvU4l+XGSX2Tz9Kxks/0LAAAAgH0c5GlZuz20PR7JJtD8M8lf1lr/3TX3h+3xia1Wa63fJbl2Zq5IcjTJqbXWwzNzb5LTa62/XsR5AQAAABwaFxN3jm2Pf1xrPTYzJ5NcMzPPXGv9a8fc0cfndn/AWuvBJA8mycxcmuTqJN+8iHMCAAAAOFT23ZY1My85x9rlSd6d5NdrrVPb5buSPH27/vjc05K8K5ttVvfu81W3bt9/x3mdOQAAAADn9cudr8/M2SQnstlidSzJe5M8O8mNO+a+mE3YuXNmXpHkN0nems1TsT641nriPjozc0eS5ye5f7v0tiRvTPLRtdbPL+qKAAAAAA6RfZ+WNTM3JbkhyZVJXpDkz0l+muRTa62Tu2aPJLklyVu2sw8kuW2t9eVdc+/M5klZVyZZSU4m+cxa6+4n4ZoAAAAADo0LehQ6AAAAAE8NF/QodAAAAACeGsQdAAAAgGLiDgAAAEAxcQcAAACgmLgDAAAAUEzcAQAAACgm7gAAAAAUE3cAAAAAiok7AAAAAMXEHQAAAIBi/wP1DUNpYrjluQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1296x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Printing the matrix of each column to see where are the missing values\n",
        "msno.matrix(df, sparkline=False, figsize=(18,12), fontsize=14, color=(0.27, 0.52, 1.0));\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "shxF13hohAEf"
      },
      "outputs": [],
      "source": [
        "# Dropping 'Unnamed: 32' column\n",
        "df.drop(\"Unnamed: 32\", axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "Q33nnnZOhBAo"
      },
      "outputs": [],
      "source": [
        "# Dropping id column\n",
        "df.drop('id',axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6MbFzQ9rGlh_"
      },
      "source": [
        "## **2. Observations**\n",
        "In this section we make some observations on our dataset, also with the help of some graphs."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ypww1MIwqhjV"
      },
      "source": [
        "We print the descriptive statistics table of the dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "vZudrUm1hBuD",
        "outputId": "07418e40-b206-45ca-85c5-daa93b87e6cd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-bf52740b-a8f1-4652-9984-f4091aab922c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>14.127292</td>\n",
              "      <td>19.289649</td>\n",
              "      <td>91.969033</td>\n",
              "      <td>654.889104</td>\n",
              "      <td>0.096360</td>\n",
              "      <td>0.104341</td>\n",
              "      <td>0.088799</td>\n",
              "      <td>0.048919</td>\n",
              "      <td>0.181162</td>\n",
              "      <td>0.062798</td>\n",
              "      <td>0.405172</td>\n",
              "      <td>1.216853</td>\n",
              "      <td>2.866059</td>\n",
              "      <td>40.337079</td>\n",
              "      <td>0.007041</td>\n",
              "      <td>0.025478</td>\n",
              "      <td>0.031894</td>\n",
              "      <td>0.011796</td>\n",
              "      <td>0.020542</td>\n",
              "      <td>0.003795</td>\n",
              "      <td>16.269190</td>\n",
              "      <td>25.677223</td>\n",
              "      <td>107.261213</td>\n",
              "      <td>880.583128</td>\n",
              "      <td>0.132369</td>\n",
              "      <td>0.254265</td>\n",
              "      <td>0.272188</td>\n",
              "      <td>0.114606</td>\n",
              "      <td>0.290076</td>\n",
              "      <td>0.083946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.524049</td>\n",
              "      <td>4.301036</td>\n",
              "      <td>24.298981</td>\n",
              "      <td>351.914129</td>\n",
              "      <td>0.014064</td>\n",
              "      <td>0.052813</td>\n",
              "      <td>0.079720</td>\n",
              "      <td>0.038803</td>\n",
              "      <td>0.027414</td>\n",
              "      <td>0.007060</td>\n",
              "      <td>0.277313</td>\n",
              "      <td>0.551648</td>\n",
              "      <td>2.021855</td>\n",
              "      <td>45.491006</td>\n",
              "      <td>0.003003</td>\n",
              "      <td>0.017908</td>\n",
              "      <td>0.030186</td>\n",
              "      <td>0.006170</td>\n",
              "      <td>0.008266</td>\n",
              "      <td>0.002646</td>\n",
              "      <td>4.833242</td>\n",
              "      <td>6.146258</td>\n",
              "      <td>33.602542</td>\n",
              "      <td>569.356993</td>\n",
              "      <td>0.022832</td>\n",
              "      <td>0.157336</td>\n",
              "      <td>0.208624</td>\n",
              "      <td>0.065732</td>\n",
              "      <td>0.061867</td>\n",
              "      <td>0.018061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6.981000</td>\n",
              "      <td>9.710000</td>\n",
              "      <td>43.790000</td>\n",
              "      <td>143.500000</td>\n",
              "      <td>0.052630</td>\n",
              "      <td>0.019380</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.106000</td>\n",
              "      <td>0.049960</td>\n",
              "      <td>0.111500</td>\n",
              "      <td>0.360200</td>\n",
              "      <td>0.757000</td>\n",
              "      <td>6.802000</td>\n",
              "      <td>0.001713</td>\n",
              "      <td>0.002252</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.007882</td>\n",
              "      <td>0.000895</td>\n",
              "      <td>7.930000</td>\n",
              "      <td>12.020000</td>\n",
              "      <td>50.410000</td>\n",
              "      <td>185.200000</td>\n",
              "      <td>0.071170</td>\n",
              "      <td>0.027290</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.156500</td>\n",
              "      <td>0.055040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>11.700000</td>\n",
              "      <td>16.170000</td>\n",
              "      <td>75.170000</td>\n",
              "      <td>420.300000</td>\n",
              "      <td>0.086370</td>\n",
              "      <td>0.064920</td>\n",
              "      <td>0.029560</td>\n",
              "      <td>0.020310</td>\n",
              "      <td>0.161900</td>\n",
              "      <td>0.057700</td>\n",
              "      <td>0.232400</td>\n",
              "      <td>0.833900</td>\n",
              "      <td>1.606000</td>\n",
              "      <td>17.850000</td>\n",
              "      <td>0.005169</td>\n",
              "      <td>0.013080</td>\n",
              "      <td>0.015090</td>\n",
              "      <td>0.007638</td>\n",
              "      <td>0.015160</td>\n",
              "      <td>0.002248</td>\n",
              "      <td>13.010000</td>\n",
              "      <td>21.080000</td>\n",
              "      <td>84.110000</td>\n",
              "      <td>515.300000</td>\n",
              "      <td>0.116600</td>\n",
              "      <td>0.147200</td>\n",
              "      <td>0.114500</td>\n",
              "      <td>0.064930</td>\n",
              "      <td>0.250400</td>\n",
              "      <td>0.071460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>13.370000</td>\n",
              "      <td>18.840000</td>\n",
              "      <td>86.240000</td>\n",
              "      <td>551.100000</td>\n",
              "      <td>0.095870</td>\n",
              "      <td>0.092630</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>0.033500</td>\n",
              "      <td>0.179200</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>0.324200</td>\n",
              "      <td>1.108000</td>\n",
              "      <td>2.287000</td>\n",
              "      <td>24.530000</td>\n",
              "      <td>0.006380</td>\n",
              "      <td>0.020450</td>\n",
              "      <td>0.025890</td>\n",
              "      <td>0.010930</td>\n",
              "      <td>0.018730</td>\n",
              "      <td>0.003187</td>\n",
              "      <td>14.970000</td>\n",
              "      <td>25.410000</td>\n",
              "      <td>97.660000</td>\n",
              "      <td>686.500000</td>\n",
              "      <td>0.131300</td>\n",
              "      <td>0.211900</td>\n",
              "      <td>0.226700</td>\n",
              "      <td>0.099930</td>\n",
              "      <td>0.282200</td>\n",
              "      <td>0.080040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>15.780000</td>\n",
              "      <td>21.800000</td>\n",
              "      <td>104.100000</td>\n",
              "      <td>782.700000</td>\n",
              "      <td>0.105300</td>\n",
              "      <td>0.130400</td>\n",
              "      <td>0.130700</td>\n",
              "      <td>0.074000</td>\n",
              "      <td>0.195700</td>\n",
              "      <td>0.066120</td>\n",
              "      <td>0.478900</td>\n",
              "      <td>1.474000</td>\n",
              "      <td>3.357000</td>\n",
              "      <td>45.190000</td>\n",
              "      <td>0.008146</td>\n",
              "      <td>0.032450</td>\n",
              "      <td>0.042050</td>\n",
              "      <td>0.014710</td>\n",
              "      <td>0.023480</td>\n",
              "      <td>0.004558</td>\n",
              "      <td>18.790000</td>\n",
              "      <td>29.720000</td>\n",
              "      <td>125.400000</td>\n",
              "      <td>1084.000000</td>\n",
              "      <td>0.146000</td>\n",
              "      <td>0.339100</td>\n",
              "      <td>0.382900</td>\n",
              "      <td>0.161400</td>\n",
              "      <td>0.317900</td>\n",
              "      <td>0.092080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>28.110000</td>\n",
              "      <td>39.280000</td>\n",
              "      <td>188.500000</td>\n",
              "      <td>2501.000000</td>\n",
              "      <td>0.163400</td>\n",
              "      <td>0.345400</td>\n",
              "      <td>0.426800</td>\n",
              "      <td>0.201200</td>\n",
              "      <td>0.304000</td>\n",
              "      <td>0.097440</td>\n",
              "      <td>2.873000</td>\n",
              "      <td>4.885000</td>\n",
              "      <td>21.980000</td>\n",
              "      <td>542.200000</td>\n",
              "      <td>0.031130</td>\n",
              "      <td>0.135400</td>\n",
              "      <td>0.396000</td>\n",
              "      <td>0.052790</td>\n",
              "      <td>0.078950</td>\n",
              "      <td>0.029840</td>\n",
              "      <td>36.040000</td>\n",
              "      <td>49.540000</td>\n",
              "      <td>251.200000</td>\n",
              "      <td>4254.000000</td>\n",
              "      <td>0.222600</td>\n",
              "      <td>1.058000</td>\n",
              "      <td>1.252000</td>\n",
              "      <td>0.291000</td>\n",
              "      <td>0.663800</td>\n",
              "      <td>0.207500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bf52740b-a8f1-4652-9984-f4091aab922c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bf52740b-a8f1-4652-9984-f4091aab922c 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-bf52740b-a8f1-4652-9984-f4091aab922c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       radius_mean  texture_mean  ...  symmetry_worst  fractal_dimension_worst\n",
              "count   569.000000    569.000000  ...      569.000000               569.000000\n",
              "mean     14.127292     19.289649  ...        0.290076                 0.083946\n",
              "std       3.524049      4.301036  ...        0.061867                 0.018061\n",
              "min       6.981000      9.710000  ...        0.156500                 0.055040\n",
              "25%      11.700000     16.170000  ...        0.250400                 0.071460\n",
              "50%      13.370000     18.840000  ...        0.282200                 0.080040\n",
              "75%      15.780000     21.800000  ...        0.317900                 0.092080\n",
              "max      28.110000     39.280000  ...        0.663800                 0.207500\n",
              "\n",
              "[8 rows x 30 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# descriptive statistics of data\n",
        "df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EcfPt1fHhEEU"
      },
      "source": [
        "**1.3 Data Visualizations**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "iLV6bEDjhUue",
        "outputId": "cca37a86-af68-4c3b-defb-e7602a68a6c1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fae033c8950>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize = (8,7))\n",
        "sns.countplot(x=\"diagnosis\", data=df, palette='viridis')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4sjl4jSsHAEd"
      },
      "source": [
        "**Observation:**\n",
        "* In our dataset we can see that we have more benign than malignant tumors"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "dPz-519j-a6C",
        "outputId": "5e1dd068-8347-44d7-a43c-ebaa8c090036"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-2.8.3.min.js\"></script>                <div id=\"ed21d253-513a-4f47-a0ae-984bf658cf33\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"ed21d253-513a-4f47-a0ae-984bf658cf33\")) {                    Plotly.newPlot(                        \"ed21d253-513a-4f47-a0ae-984bf658cf33\",                        [{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"hovertemplate\":\"diagnosis=M<br>radius_mean=%{x}<br>count=%{y}<extra></extra>\",\"legendgroup\":\"M\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"x\"}},\"name\":\"M\",\"offsetgroup\":\"M\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[17.99,20.57,19.69,11.42,20.29,12.45,18.25,13.71,13.0,12.46,16.02,15.78,19.17,15.85,13.73,14.54,14.68,16.13,19.81,15.34,21.16,16.65,17.14,14.58,18.61,15.3,17.57,18.63,11.84,17.02,19.27,16.13,16.74,14.25,14.99,13.48,13.44,10.95,19.07,13.28,13.17,18.65,13.17,18.22,15.1,19.21,14.71,14.25,12.68,14.78,18.94,17.2,13.8,16.07,18.05,20.18,25.22,19.1,18.46,14.48,19.02,15.37,15.06,20.26,14.42,13.61,13.11,22.27,14.87,15.78,17.95,18.66,24.25,13.61,19.0,19.79,15.46,16.16,18.45,12.77,14.95,16.11,11.8,17.68,19.19,19.59,23.27,16.78,17.47,13.43,15.46,16.46,27.22,21.09,15.7,15.28,18.31,14.22,12.34,14.86,13.77,18.08,19.18,14.45,17.54,23.29,13.81,15.12,17.01,20.58,28.11,17.42,14.19,13.86,19.8,19.53,15.75,12.83,17.05,20.51,23.21,20.48,17.46,19.4,20.94,19.73,17.3,19.45,13.96,19.55,15.32,15.66,15.53,20.31,17.35,17.29,15.61,17.19,20.73,21.75,17.93,18.81,19.16,19.4,16.24,11.76,19.53,20.09,18.22,20.16,20.34,16.27,16.26,16.03,17.06,18.77,23.51,19.68,15.75,25.73,15.08,20.44,20.2,21.71,22.01,16.35,21.37,20.64,11.08,14.6,19.55,15.49,21.61,17.91,17.99,15.13,15.5,14.9,20.18,18.82,13.98,17.27,18.03,17.75,21.1,19.59,17.08,27.42,17.6,16.25,19.44,16.69,18.01,18.49,20.59,13.82,23.09,15.46,13.4,15.05,18.31,19.89,24.63,20.47,20.55,14.27,15.22,20.92,21.56,20.13,16.6,20.6],\"xaxis\":\"x\",\"yaxis\":\"y\",\"type\":\"histogram\"},{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"hovertemplate\":\"diagnosis=B<br>radius_mean=%{x}<br>count=%{y}<extra></extra>\",\"legendgroup\":\"B\",\"marker\":{\"color\":\"#EF553B\",\"pattern\":{\"shape\":\"x\"}},\"name\":\"B\",\"offsetgroup\":\"B\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[13.54,13.08,9.504,13.03,8.196,12.05,13.49,11.76,13.64,11.94,11.52,13.05,8.618,10.17,8.598,9.173,9.465,11.31,9.029,12.78,8.888,12.31,13.53,12.86,11.45,13.34,12.0,12.36,14.64,14.62,13.27,13.45,12.18,9.787,11.6,6.981,12.18,9.876,10.49,11.64,12.36,11.34,9.777,12.63,14.26,10.51,8.726,11.93,8.95,11.41,14.5,13.37,13.85,15.1,12.19,15.71,11.71,11.43,11.28,9.738,11.43,12.9,10.75,11.9,14.95,14.44,13.74,13.0,8.219,9.731,11.15,13.15,12.25,16.84,12.06,10.9,11.75,12.34,14.97,10.8,14.97,12.32,11.08,10.66,8.671,9.904,13.01,12.81,11.41,10.08,11.71,11.81,12.3,12.77,9.72,12.91,12.23,12.47,9.876,13.11,15.27,11.84,11.89,10.2,13.65,13.56,10.18,13.27,14.34,10.44,15.0,12.62,11.32,11.22,9.567,14.03,14.22,13.64,12.42,11.3,13.75,10.48,13.2,12.89,10.65,11.52,11.5,10.6,13.59,12.87,10.71,14.29,11.29,9.742,11.89,11.33,13.59,13.85,11.74,12.89,12.58,11.94,12.89,11.26,11.37,14.41,14.96,12.95,11.85,12.72,13.77,10.91,14.26,10.51,12.46,10.49,11.46,11.6,13.2,9.0,13.5,13.05,11.7,14.61,12.76,11.54,8.597,12.49,12.18,9.042,12.43,10.25,12.86,12.2,12.67,14.11,12.03,12.98,11.22,11.25,12.3,12.99,10.05,14.42,9.606,11.06,11.71,10.26,12.06,14.76,11.47,11.95,11.66,11.14,12.56,13.05,13.87,8.878,9.436,12.54,13.3,12.76,16.5,13.4,12.21,15.19,13.69,16.17,10.57,13.46,13.66,11.27,11.04,12.05,12.39,13.28,12.21,13.88,11.27,10.26,8.734,12.1,14.06,13.51,12.8,11.06,11.8,11.93,12.96,12.94,12.34,10.94,16.14,12.85,12.27,11.36,11.04,9.397,14.99,11.89,9.405,12.7,11.16,11.57,14.69,11.61,13.66,9.742,10.03,10.48,10.8,11.13,12.72,12.4,14.86,12.87,14.04,13.85,14.02,10.97,13.78,10.57,11.99,14.8,14.53,11.87,12.0,14.53,12.62,13.38,11.63,13.21,13.0,9.755,14.4,11.6,13.17,13.24,13.14,9.668,11.62,9.667,12.04,14.92,12.27,10.88,12.83,14.2,13.9,11.49,12.16,13.9,13.47,13.7,15.73,12.45,14.64,11.68,12.25,17.85,12.46,13.16,14.87,12.65,12.47,15.04,12.54,9.268,9.676,12.22,11.06,16.3,11.74,14.81,14.58,11.34,12.88,12.75,9.295,11.26,13.71,9.847,8.571,13.46,12.34,13.94,12.07,11.75,11.67,13.68,10.96,11.69,7.729,7.691,11.54,14.47,14.74,13.21,13.87,13.62,10.32,10.26,9.683,10.82,10.86,11.13,12.77,9.333,12.88,10.29,10.16,9.423,14.59,11.51,14.05,11.2,7.76],\"xaxis\":\"x\",\"yaxis\":\"y\",\"type\":\"histogram\"}],                        {\"template\":{\"data\":{\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"rgb(17,17,17)\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"bar\":[{\"error_x\":{\"color\":\"#f2f5fa\"},\"error_y\":{\"color\":\"#f2f5fa\"},\"marker\":{\"line\":{\"color\":\"rgb(17,17,17)\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#A2B1C6\",\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"minorgridcolor\":\"#506784\",\"startlinecolor\":\"#A2B1C6\"},\"baxis\":{\"endlinecolor\":\"#A2B1C6\",\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"minorgridcolor\":\"#506784\",\"startlinecolor\":\"#A2B1C6\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"line\":{\"color\":\"#283442\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatter\":[{\"marker\":{\"line\":{\"color\":\"#283442\"}},\"type\":\"scatter\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#506784\"},\"line\":{\"color\":\"rgb(17,17,17)\"}},\"header\":{\"fill\":{\"color\":\"#2a3f5f\"},\"line\":{\"color\":\"rgb(17,17,17)\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#f2f5fa\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#f2f5fa\"},\"geo\":{\"bgcolor\":\"rgb(17,17,17)\",\"lakecolor\":\"rgb(17,17,17)\",\"landcolor\":\"rgb(17,17,17)\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"#506784\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"dark\"},\"paper_bgcolor\":\"rgb(17,17,17)\",\"plot_bgcolor\":\"rgb(17,17,17)\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"},\"bgcolor\":\"rgb(17,17,17)\",\"radialaxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"rgb(17,17,17)\",\"gridcolor\":\"#506784\",\"gridwidth\":2,\"linecolor\":\"#506784\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#C8D4E3\"},\"yaxis\":{\"backgroundcolor\":\"rgb(17,17,17)\",\"gridcolor\":\"#506784\",\"gridwidth\":2,\"linecolor\":\"#506784\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#C8D4E3\"},\"zaxis\":{\"backgroundcolor\":\"rgb(17,17,17)\",\"gridcolor\":\"#506784\",\"gridwidth\":2,\"linecolor\":\"#506784\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#C8D4E3\"}},\"shapedefaults\":{\"line\":{\"color\":\"#f2f5fa\"}},\"sliderdefaults\":{\"bgcolor\":\"#C8D4E3\",\"bordercolor\":\"rgb(17,17,17)\",\"borderwidth\":1,\"tickwidth\":0},\"ternary\":{\"aaxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"},\"bgcolor\":\"rgb(17,17,17)\",\"caxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"updatemenudefaults\":{\"bgcolor\":\"#506784\",\"borderwidth\":0},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#283442\",\"linecolor\":\"#506784\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#283442\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#283442\",\"linecolor\":\"#506784\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#283442\",\"zerolinewidth\":2}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"radius_mean\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"count\"}},\"legend\":{\"title\":{\"text\":\"diagnosis\"},\"tracegroupgap\":0},\"title\":{\"text\":\"<b>diagnosis vs radius mean</b>\"},\"barmode\":\"relative\",\"bargap\":0.2},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('ed21d253-513a-4f47-a0ae-984bf658cf33');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-2.8.3.min.js\"></script>                <div id=\"a8d900c5-7b98-4587-95f2-c736418dee15\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"a8d900c5-7b98-4587-95f2-c736418dee15\")) {                    Plotly.newPlot(                        \"a8d900c5-7b98-4587-95f2-c736418dee15\",                        [{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"hovertemplate\":\"diagnosis=M<br>texture_mean=%{x}<br>count=%{y}<extra></extra>\",\"legendgroup\":\"M\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"x\"}},\"name\":\"M\",\"offsetgroup\":\"M\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[10.38,17.77,21.25,20.38,14.34,15.7,19.98,20.83,21.82,24.04,23.24,17.89,24.8,23.95,22.61,27.54,20.13,20.68,22.15,14.26,23.04,21.38,16.4,21.53,20.25,25.27,15.05,25.11,18.7,23.98,26.47,17.88,21.59,21.72,25.2,20.82,21.58,21.35,24.81,20.28,21.81,17.6,18.66,18.7,22.02,18.57,21.59,22.15,23.84,23.94,21.31,24.52,15.79,19.65,16.15,23.97,24.91,26.29,18.52,21.46,24.59,22.76,19.83,23.03,19.77,24.98,15.56,19.67,16.67,22.91,20.01,17.12,20.2,24.69,18.91,25.12,19.48,21.54,21.91,22.47,17.57,18.05,16.58,20.74,15.94,18.15,22.04,18.8,24.68,19.63,11.89,20.11,21.87,26.57,20.31,22.41,18.58,23.12,26.86,23.21,22.29,21.84,22.49,20.22,19.32,26.67,23.75,16.68,20.26,22.14,18.47,25.56,23.81,16.93,21.56,32.47,20.25,22.33,19.08,27.81,26.97,21.46,39.28,23.5,23.56,19.82,17.08,19.33,17.05,28.77,17.27,23.2,33.56,27.06,23.06,22.13,19.38,22.07,31.12,20.99,24.48,19.98,26.6,18.18,18.77,18.14,18.9,23.86,18.87,19.66,21.51,20.71,21.88,15.51,21.0,21.43,24.27,21.68,19.22,17.46,25.74,21.78,26.83,17.25,21.9,23.29,15.1,17.35,18.83,23.29,23.21,19.97,22.28,21.02,20.66,29.81,21.08,22.53,19.54,21.97,19.62,25.42,16.85,28.03,20.52,25.0,27.15,26.27,23.33,19.51,18.82,20.2,20.56,17.52,21.24,24.49,19.83,23.95,20.52,19.07,20.58,20.26,21.6,20.67,20.86,22.55,30.62,25.09,22.39,28.25,28.08,29.33],\"xaxis\":\"x\",\"yaxis\":\"y\",\"type\":\"histogram\"},{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"hovertemplate\":\"diagnosis=B<br>texture_mean=%{x}<br>count=%{y}<extra></extra>\",\"legendgroup\":\"B\",\"marker\":{\"color\":\"#EF553B\",\"pattern\":{\"shape\":\"x\"}},\"name\":\"B\",\"offsetgroup\":\"B\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[14.36,15.71,12.44,18.42,16.84,14.63,22.3,21.6,16.34,18.24,18.75,19.31,11.79,14.88,20.98,13.86,21.01,19.04,17.33,16.49,14.64,16.52,10.94,18.0,20.97,15.86,15.65,21.8,15.24,24.02,14.76,18.3,17.84,19.94,12.84,13.43,20.52,19.4,19.29,18.33,18.54,21.26,16.99,20.76,19.65,20.19,15.83,21.53,15.76,10.82,10.89,16.39,17.21,16.39,13.29,13.93,16.67,15.39,13.39,11.97,17.31,15.92,14.97,14.65,18.77,15.18,17.91,20.78,20.7,15.34,13.08,15.34,17.94,19.46,12.74,12.96,20.18,22.22,19.76,9.71,16.95,12.39,14.71,15.15,14.45,18.06,22.22,13.06,14.92,15.11,17.19,17.39,15.9,21.41,18.22,16.33,19.56,18.6,17.27,22.54,12.91,18.94,18.35,17.48,13.16,13.9,17.53,17.02,13.47,15.46,15.51,23.97,27.08,33.81,15.91,21.25,27.85,15.6,15.04,18.19,23.77,19.86,17.43,14.11,25.22,14.93,18.45,18.95,21.84,16.21,20.39,16.82,13.04,15.67,17.36,14.16,17.84,15.18,14.02,15.7,18.4,20.76,13.12,19.96,18.89,19.73,19.1,16.02,17.46,13.78,13.27,12.35,18.17,23.09,19.89,18.61,18.16,24.49,15.82,14.4,12.71,13.84,19.11,15.69,13.37,10.72,18.6,16.85,14.08,18.9,17.0,16.18,13.32,15.21,17.3,12.88,17.93,19.35,19.86,14.78,19.02,14.23,17.53,16.54,16.84,14.96,15.45,14.71,18.9,14.74,16.03,14.96,17.07,14.07,19.07,18.59,16.21,15.49,18.32,18.07,21.57,18.84,18.29,16.95,18.02,13.21,16.07,16.07,20.22,28.21,15.15,12.96,14.93,22.72,17.48,13.72,14.09,16.16,15.5,12.22,16.84,17.72,17.18,18.89,17.46,14.83,17.26,10.91,18.29,16.17,14.95,18.59,14.86,21.37,17.92,17.57,16.83,21.68,22.11,21.17,21.7,12.17,21.41,19.04,13.98,16.02,19.13,19.12,21.28,14.98,21.98,16.62,17.67,17.68,16.94,19.54,15.98,19.6,15.66,17.2,15.79,18.32,24.89,17.66,19.34,21.54,28.23,13.98,17.15,30.72,29.29,25.25,25.13,28.2,26.99,18.36,18.22,20.13,20.74,18.1,18.18,18.49,28.14,14.93,29.97,15.62,15.73,20.53,16.62,14.59,18.03,19.24,14.06,17.64,11.28,16.41,16.85,16.17,22.44,13.23,12.83,20.54,20.21,18.17,17.31,16.74,16.32,12.87,13.14,20.04,17.12,15.7,14.69,14.7,13.66,18.61,18.22,16.7,13.9,19.83,18.68,15.68,13.1,18.75,12.27,13.17,13.44,17.56,20.02,16.33,17.62,24.44,25.49,25.44,14.44,24.99,25.42,28.06,20.7,23.23,16.35,16.58,19.34,24.21,21.48,22.44,29.43,21.94,28.92,27.61,19.59,27.88,22.68,23.93,27.15,29.37,24.54],\"xaxis\":\"x\",\"yaxis\":\"y\",\"type\":\"histogram\"}],                        {\"template\":{\"data\":{\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"rgb(17,17,17)\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"bar\":[{\"error_x\":{\"color\":\"#f2f5fa\"},\"error_y\":{\"color\":\"#f2f5fa\"},\"marker\":{\"line\":{\"color\":\"rgb(17,17,17)\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#A2B1C6\",\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"minorgridcolor\":\"#506784\",\"startlinecolor\":\"#A2B1C6\"},\"baxis\":{\"endlinecolor\":\"#A2B1C6\",\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"minorgridcolor\":\"#506784\",\"startlinecolor\":\"#A2B1C6\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"line\":{\"color\":\"#283442\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatter\":[{\"marker\":{\"line\":{\"color\":\"#283442\"}},\"type\":\"scatter\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#506784\"},\"line\":{\"color\":\"rgb(17,17,17)\"}},\"header\":{\"fill\":{\"color\":\"#2a3f5f\"},\"line\":{\"color\":\"rgb(17,17,17)\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#f2f5fa\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#f2f5fa\"},\"geo\":{\"bgcolor\":\"rgb(17,17,17)\",\"lakecolor\":\"rgb(17,17,17)\",\"landcolor\":\"rgb(17,17,17)\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"#506784\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"dark\"},\"paper_bgcolor\":\"rgb(17,17,17)\",\"plot_bgcolor\":\"rgb(17,17,17)\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"},\"bgcolor\":\"rgb(17,17,17)\",\"radialaxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"rgb(17,17,17)\",\"gridcolor\":\"#506784\",\"gridwidth\":2,\"linecolor\":\"#506784\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#C8D4E3\"},\"yaxis\":{\"backgroundcolor\":\"rgb(17,17,17)\",\"gridcolor\":\"#506784\",\"gridwidth\":2,\"linecolor\":\"#506784\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#C8D4E3\"},\"zaxis\":{\"backgroundcolor\":\"rgb(17,17,17)\",\"gridcolor\":\"#506784\",\"gridwidth\":2,\"linecolor\":\"#506784\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#C8D4E3\"}},\"shapedefaults\":{\"line\":{\"color\":\"#f2f5fa\"}},\"sliderdefaults\":{\"bgcolor\":\"#C8D4E3\",\"bordercolor\":\"rgb(17,17,17)\",\"borderwidth\":1,\"tickwidth\":0},\"ternary\":{\"aaxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"},\"bgcolor\":\"rgb(17,17,17)\",\"caxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"updatemenudefaults\":{\"bgcolor\":\"#506784\",\"borderwidth\":0},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#283442\",\"linecolor\":\"#506784\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#283442\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#283442\",\"linecolor\":\"#506784\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#283442\",\"zerolinewidth\":2}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"texture_mean\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"count\"}},\"legend\":{\"title\":{\"text\":\"diagnosis\"},\"tracegroupgap\":0},\"title\":{\"text\":\"<b>diagnosis vs texture mean</b>\"},\"barmode\":\"relative\",\"bargap\":0.2},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('a8d900c5-7b98-4587-95f2-c736418dee15');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig = px.histogram(data_frame = df,\n",
        "             x = \"radius_mean\",\n",
        "             color=\"diagnosis\", title=\"<b>diagnosis vs radius mean</b>\",\n",
        "             pattern_shape_sequence=['x'],\n",
        "             template='plotly_dark')\n",
        "\n",
        "fig1 = px.histogram(data_frame = df,\n",
        "             x = \"texture_mean\",\n",
        "             color=\"diagnosis\", title=\"<b>diagnosis vs texture mean</b>\",\n",
        "             pattern_shape_sequence=['x'],\n",
        "             template='plotly_dark')\n",
        "\n",
        "fig.update_layout(bargap=0.2)\n",
        "fig1.update_layout(bargap = 0.2)\n",
        "\n",
        "fig.show()\n",
        "fig1.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BoOaEHh_-4pw"
      },
      "source": [
        "**Observations:**\n",
        "* All cancer tumors of type B have radius mean less than 18 and most of them are between 12-12.99 of radius mean\n",
        "* All cancer tumors of type M have radius mean greater than 10 and most of them are between 19-19.99 of radius mean\n",
        "* Up to 19.99 texture mean value cancer tumors of type B are more\n",
        "* From 19.99 texture mean value cancer tumors of type M are more"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Iok6Bijj_qXS",
        "outputId": "d9903158-9f99-45a4-8e4e-5109d55ac8cf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-2.8.3.min.js\"></script>                <div id=\"2c0c8163-cc75-45c7-b1f7-ccece1ee808f\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"2c0c8163-cc75-45c7-b1f7-ccece1ee808f\")) {                    Plotly.newPlot(                        \"2c0c8163-cc75-45c7-b1f7-ccece1ee808f\",                        [{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"hovertemplate\":\"diagnosis=M<br>perimeter_mean=%{x}<br>count=%{y}<extra></extra>\",\"legendgroup\":\"M\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"x\"}},\"name\":\"M\",\"offsetgroup\":\"M\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[122.8,132.9,130.0,77.58,135.1,82.57,119.6,90.2,87.5,83.97,102.7,103.6,132.4,103.7,93.6,96.73,94.74,108.1,130.0,102.5,137.2,110.0,116.0,97.41,122.1,102.4,115.0,124.8,77.93,112.8,127.9,107.0,110.1,93.63,95.54,88.4,86.18,71.9,128.3,87.32,85.42,123.7,85.98,120.3,97.26,125.5,95.55,96.42,82.69,97.4,123.6,114.2,90.43,104.1,120.2,143.7,171.5,129.1,121.1,94.25,122.0,100.2,100.3,132.4,94.48,88.05,87.21,152.8,98.64,105.7,114.2,121.4,166.2,87.76,123.4,130.4,101.7,106.2,120.2,81.72,96.85,105.1,78.99,117.4,126.3,130.7,152.1,109.3,116.1,85.84,102.5,109.3,182.1,142.7,101.2,98.92,118.6,94.37,81.15,100.4,90.63,117.4,127.5,94.49,115.1,158.9,91.56,98.78,109.7,134.7,188.5,114.5,92.87,90.96,129.7,128.0,102.6,85.26,113.4,134.4,153.5,132.5,113.4,129.1,138.9,130.7,113.0,126.5,91.43,133.6,103.2,110.2,103.7,132.9,111.0,114.4,100.0,111.6,135.7,147.3,115.2,120.9,126.2,127.2,108.8,75.0,129.5,134.7,118.7,131.1,135.9,106.9,107.5,105.8,111.8,122.9,155.1,129.9,107.1,174.2,98.0,133.8,133.7,140.9,147.2,109.0,141.3,134.8,73.3,93.97,128.9,102.4,144.4,124.4,117.8,96.71,102.9,102.1,133.8,123.7,91.12,112.4,117.5,117.3,138.1,127.7,111.2,186.9,119.0,109.8,128.1,107.1,118.4,121.3,137.8,92.33,152.1,103.8,88.64,97.26,120.8,130.5,165.5,134.7,137.8,93.77,103.4,143.0,142.0,131.2,108.3,140.1],\"xaxis\":\"x\",\"yaxis\":\"y\",\"type\":\"histogram\"},{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"hovertemplate\":\"diagnosis=B<br>perimeter_mean=%{x}<br>count=%{y}<extra></extra>\",\"legendgroup\":\"B\",\"marker\":{\"color\":\"#EF553B\",\"pattern\":{\"shape\":\"x\"}},\"name\":\"B\",\"offsetgroup\":\"B\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[87.46,85.63,60.34,82.61,51.71,78.04,86.91,74.72,87.21,75.71,73.34,82.61,54.34,64.55,54.66,59.2,60.11,71.8,58.79,81.37,58.79,79.19,87.91,83.19,73.81,86.49,76.95,79.78,95.77,94.57,84.74,86.6,77.79,62.11,74.34,43.79,77.22,63.95,67.41,75.17,79.01,72.48,62.5,82.15,97.83,68.64,55.84,76.53,58.74,73.34,94.28,86.1,88.44,99.58,79.08,102.0,74.72,73.06,73.0,61.24,73.66,83.74,68.26,78.11,97.84,93.97,88.12,83.51,53.27,63.78,70.87,85.31,78.27,108.4,76.84,68.69,76.1,79.85,95.5,68.77,96.22,78.85,70.21,67.49,54.42,64.6,82.01,81.29,73.53,63.76,74.68,75.27,78.83,82.02,60.73,82.53,78.54,81.09,62.92,87.02,98.17,75.51,77.32,65.05,87.88,88.59,65.12,84.55,92.51,66.62,97.45,81.35,71.76,70.79,60.21,89.79,92.55,87.38,78.61,73.93,88.54,66.72,84.13,84.95,68.01,73.87,73.28,69.28,87.16,82.38,69.5,90.3,72.23,61.5,76.2,71.79,86.24,88.99,74.24,84.08,79.83,77.87,81.89,73.72,72.17,96.03,97.03,83.14,75.54,81.78,88.06,69.14,91.22,66.85,80.43,66.86,73.59,74.23,84.07,56.36,85.69,82.71,74.33,92.68,82.29,73.73,54.09,79.19,77.25,60.07,78.6,66.52,82.82,78.01,81.25,90.03,76.09,84.52,71.94,71.38,77.88,84.08,64.41,94.15,61.64,71.49,75.03,66.2,76.66,94.87,73.02,77.23,73.7,71.24,81.92,85.09,88.52,56.74,59.82,79.42,85.24,81.87,106.6,85.48,78.31,97.65,87.84,106.3,70.15,85.89,88.27,73.16,70.67,78.75,80.64,85.79,78.78,88.37,73.38,65.75,55.27,78.07,89.75,88.1,83.05,70.31,75.26,76.14,84.18,83.18,78.29,70.39,104.3,82.63,78.41,72.49,70.92,59.75,97.53,76.39,59.6,80.88,70.95,74.2,98.22,75.46,89.46,61.93,63.19,67.49,68.79,70.47,80.98,81.47,94.89,82.67,89.78,88.68,89.59,71.73,88.37,66.82,77.61,95.88,94.25,76.83,76.77,93.86,80.62,86.34,74.87,84.1,82.61,61.68,92.25,73.88,84.28,86.87,85.98,61.06,76.38,61.49,76.85,96.45,77.42,70.41,82.89,92.41,88.97,73.99,78.29,88.73,87.32,87.76,102.8,82.85,94.21,75.49,78.18,114.6,78.83,84.06,96.12,82.69,80.45,98.73,81.25,61.49,64.12,79.47,71.25,104.7,76.31,94.66,94.29,72.76,84.45,82.51,59.96,71.3,88.73,63.0,54.53,87.44,78.94,90.31,77.83,75.89,75.21,87.76,70.79,76.37,47.98,48.34,74.65,95.81,94.7,84.88,89.77,87.19,65.31,65.85,61.05,68.89,68.51,71.49,81.35,59.01,82.5,65.67,64.73,59.26,96.39,74.52,91.38,70.67,47.92],\"xaxis\":\"x\",\"yaxis\":\"y\",\"type\":\"histogram\"}],                        {\"template\":{\"data\":{\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"rgb(17,17,17)\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"bar\":[{\"error_x\":{\"color\":\"#f2f5fa\"},\"error_y\":{\"color\":\"#f2f5fa\"},\"marker\":{\"line\":{\"color\":\"rgb(17,17,17)\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#A2B1C6\",\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"minorgridcolor\":\"#506784\",\"startlinecolor\":\"#A2B1C6\"},\"baxis\":{\"endlinecolor\":\"#A2B1C6\",\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"minorgridcolor\":\"#506784\",\"startlinecolor\":\"#A2B1C6\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"line\":{\"color\":\"#283442\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatter\":[{\"marker\":{\"line\":{\"color\":\"#283442\"}},\"type\":\"scatter\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#506784\"},\"line\":{\"color\":\"rgb(17,17,17)\"}},\"header\":{\"fill\":{\"color\":\"#2a3f5f\"},\"line\":{\"color\":\"rgb(17,17,17)\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#f2f5fa\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#f2f5fa\"},\"geo\":{\"bgcolor\":\"rgb(17,17,17)\",\"lakecolor\":\"rgb(17,17,17)\",\"landcolor\":\"rgb(17,17,17)\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"#506784\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"dark\"},\"paper_bgcolor\":\"rgb(17,17,17)\",\"plot_bgcolor\":\"rgb(17,17,17)\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"},\"bgcolor\":\"rgb(17,17,17)\",\"radialaxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"rgb(17,17,17)\",\"gridcolor\":\"#506784\",\"gridwidth\":2,\"linecolor\":\"#506784\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#C8D4E3\"},\"yaxis\":{\"backgroundcolor\":\"rgb(17,17,17)\",\"gridcolor\":\"#506784\",\"gridwidth\":2,\"linecolor\":\"#506784\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#C8D4E3\"},\"zaxis\":{\"backgroundcolor\":\"rgb(17,17,17)\",\"gridcolor\":\"#506784\",\"gridwidth\":2,\"linecolor\":\"#506784\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#C8D4E3\"}},\"shapedefaults\":{\"line\":{\"color\":\"#f2f5fa\"}},\"sliderdefaults\":{\"bgcolor\":\"#C8D4E3\",\"bordercolor\":\"rgb(17,17,17)\",\"borderwidth\":1,\"tickwidth\":0},\"ternary\":{\"aaxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"},\"bgcolor\":\"rgb(17,17,17)\",\"caxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"updatemenudefaults\":{\"bgcolor\":\"#506784\",\"borderwidth\":0},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#283442\",\"linecolor\":\"#506784\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#283442\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#283442\",\"linecolor\":\"#506784\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#283442\",\"zerolinewidth\":2}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"perimeter_mean\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"count\"}},\"legend\":{\"title\":{\"text\":\"diagnosis\"},\"tracegroupgap\":0},\"title\":{\"text\":\"<b>diagnosis vs perimeter mean</b>\"},\"barmode\":\"relative\",\"bargap\":0.2},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('2c0c8163-cc75-45c7-b1f7-ccece1ee808f');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-2.8.3.min.js\"></script>                <div id=\"9d08f407-54e0-43ee-9262-434b837012fa\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"9d08f407-54e0-43ee-9262-434b837012fa\")) {                    Plotly.newPlot(                        \"9d08f407-54e0-43ee-9262-434b837012fa\",                        [{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"hovertemplate\":\"diagnosis=M<br>area_mean=%{x}<br>count=%{y}<extra></extra>\",\"legendgroup\":\"M\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"x\"}},\"name\":\"M\",\"offsetgroup\":\"M\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[1001.0,1326.0,1203.0,386.1,1297.0,477.1,1040.0,577.9,519.8,475.9,797.8,781.0,1123.0,782.7,578.3,658.8,684.5,798.8,1260.0,704.4,1404.0,904.6,912.7,644.8,1094.0,732.4,955.1,1088.0,440.6,899.3,1162.0,807.2,869.5,633.0,698.8,559.2,563.0,371.1,1104.0,545.2,531.5,1076.0,534.6,1033.0,712.8,1152.0,656.9,645.7,499.0,668.3,1130.0,929.4,584.1,817.7,1006.0,1245.0,1878.0,1132.0,1075.0,648.2,1076.0,728.2,705.6,1264.0,642.5,582.7,530.2,1509.0,682.5,782.6,982.0,1077.0,1761.0,572.6,1138.0,1192.0,748.9,809.8,1075.0,506.3,678.1,813.0,432.0,963.7,1157.0,1214.0,1686.0,886.3,984.6,565.4,736.9,832.9,2250.0,1311.0,766.6,710.6,1041.0,609.9,477.4,671.4,588.9,1024.0,1148.0,642.7,951.6,1685.0,597.8,716.6,904.3,1290.0,2499.0,948.0,610.7,578.9,1230.0,1223.0,761.3,503.2,895.0,1319.0,1670.0,1306.0,920.6,1155.0,1364.0,1206.0,928.2,1169.0,602.4,1207.0,713.3,773.5,744.9,1288.0,933.1,947.8,758.6,928.3,1419.0,1491.0,998.9,1102.0,1138.0,1145.0,805.1,431.1,1217.0,1247.0,1027.0,1274.0,1264.0,813.7,826.8,793.2,918.6,1092.0,1747.0,1194.0,758.6,2010.0,716.6,1293.0,1234.0,1546.0,1482.0,840.4,1386.0,1335.0,361.6,664.7,1174.0,744.7,1407.0,994.0,991.7,719.5,803.1,685.0,1250.0,1110.0,599.5,928.8,990.0,981.6,1384.0,1191.0,930.9,2501.0,980.5,815.8,1167.0,857.6,1007.0,1068.0,1320.0,595.9,1682.0,731.3,556.7,701.9,1052.0,1214.0,1841.0,1299.0,1308.0,629.8,716.9,1347.0,1479.0,1261.0,858.1,1265.0],\"xaxis\":\"x\",\"yaxis\":\"y\",\"type\":\"histogram\"},{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"hovertemplate\":\"diagnosis=B<br>area_mean=%{x}<br>count=%{y}<extra></extra>\",\"legendgroup\":\"B\",\"marker\":{\"color\":\"#EF553B\",\"pattern\":{\"shape\":\"x\"}},\"name\":\"B\",\"offsetgroup\":\"B\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[566.3,520.0,273.9,523.8,201.9,449.3,561.0,427.9,571.8,437.6,409.0,527.2,224.5,311.9,221.8,260.9,269.4,394.1,250.5,502.5,244.0,470.9,559.2,506.3,401.5,520.0,443.3,466.1,651.9,662.7,551.7,555.1,451.1,294.5,412.6,143.5,458.7,298.3,336.1,412.5,466.7,396.5,290.2,480.4,629.9,334.2,230.9,438.6,245.2,403.3,640.7,553.5,588.7,674.5,455.8,761.7,423.6,399.8,384.8,288.5,398.0,512.2,355.3,432.8,689.5,640.1,585.0,519.4,203.9,300.2,381.9,538.9,460.3,880.2,448.6,366.8,419.8,464.5,690.2,357.6,685.9,464.1,372.7,349.6,227.2,302.4,526.4,508.8,402.0,317.5,420.3,428.9,463.7,507.4,288.1,516.4,461.0,481.9,295.4,529.4,725.5,428.0,432.2,321.2,568.9,561.3,313.1,546.4,641.2,329.6,684.5,496.4,395.7,386.8,279.6,603.4,623.9,575.3,476.5,389.4,590.0,337.7,541.6,512.2,347.0,406.3,407.4,346.4,561.0,512.2,344.9,632.6,388.0,289.9,435.6,396.6,572.3,587.4,427.3,516.6,489.0,441.0,515.9,394.1,396.0,651.0,687.3,513.7,432.7,492.1,582.7,363.7,633.1,334.2,471.3,334.3,403.1,417.2,537.3,246.3,566.2,530.6,418.7,664.9,504.1,409.1,221.2,481.6,461.4,244.5,477.3,324.2,504.8,457.9,489.9,616.5,446.0,514.0,387.3,390.0,464.4,514.3,310.8,641.2,280.5,373.9,420.3,321.6,445.3,668.7,402.7,426.7,421.0,384.6,485.8,512.0,593.7,241.0,278.6,491.9,546.1,496.6,838.1,552.4,458.4,711.8,579.1,788.5,338.3,562.1,580.6,386.3,372.7,447.8,462.9,541.8,462.0,596.6,392.0,321.6,234.3,446.2,609.1,558.1,508.3,378.2,431.9,442.7,525.2,507.6,469.1,370.0,800.0,514.5,466.1,399.8,373.2,268.8,693.7,433.8,271.2,495.0,380.3,409.7,656.1,408.2,575.3,289.7,307.3,333.6,359.9,381.1,501.3,467.8,673.7,509.2,611.2,592.6,606.5,371.5,585.9,340.9,441.3,674.8,659.7,432.0,442.5,644.2,492.9,557.2,415.1,537.9,520.2,290.9,646.1,412.7,537.3,542.9,536.9,286.3,408.8,289.1,449.9,686.9,465.4,358.9,506.9,618.4,599.4,404.9,455.3,602.9,546.3,571.1,747.2,476.7,666.0,420.5,466.5,992.1,477.3,538.7,680.9,485.6,480.1,689.4,476.3,248.7,272.5,453.1,366.5,819.8,426.0,680.7,658.8,391.2,493.1,493.8,257.8,388.1,571.0,293.2,221.3,551.1,468.5,594.2,445.2,422.9,416.2,575.5,365.6,406.4,178.8,170.4,402.9,656.4,668.6,538.4,584.8,573.2,324.9,320.8,285.7,361.6,360.5,378.4,507.9,264.0,514.3,321.4,311.7,271.3,657.1,403.5,600.4,386.0,181.0],\"xaxis\":\"x\",\"yaxis\":\"y\",\"type\":\"histogram\"}],                        {\"template\":{\"data\":{\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"rgb(17,17,17)\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"bar\":[{\"error_x\":{\"color\":\"#f2f5fa\"},\"error_y\":{\"color\":\"#f2f5fa\"},\"marker\":{\"line\":{\"color\":\"rgb(17,17,17)\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#A2B1C6\",\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"minorgridcolor\":\"#506784\",\"startlinecolor\":\"#A2B1C6\"},\"baxis\":{\"endlinecolor\":\"#A2B1C6\",\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"minorgridcolor\":\"#506784\",\"startlinecolor\":\"#A2B1C6\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"line\":{\"color\":\"#283442\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatter\":[{\"marker\":{\"line\":{\"color\":\"#283442\"}},\"type\":\"scatter\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#506784\"},\"line\":{\"color\":\"rgb(17,17,17)\"}},\"header\":{\"fill\":{\"color\":\"#2a3f5f\"},\"line\":{\"color\":\"rgb(17,17,17)\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#f2f5fa\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#f2f5fa\"},\"geo\":{\"bgcolor\":\"rgb(17,17,17)\",\"lakecolor\":\"rgb(17,17,17)\",\"landcolor\":\"rgb(17,17,17)\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"#506784\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"dark\"},\"paper_bgcolor\":\"rgb(17,17,17)\",\"plot_bgcolor\":\"rgb(17,17,17)\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"},\"bgcolor\":\"rgb(17,17,17)\",\"radialaxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"rgb(17,17,17)\",\"gridcolor\":\"#506784\",\"gridwidth\":2,\"linecolor\":\"#506784\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#C8D4E3\"},\"yaxis\":{\"backgroundcolor\":\"rgb(17,17,17)\",\"gridcolor\":\"#506784\",\"gridwidth\":2,\"linecolor\":\"#506784\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#C8D4E3\"},\"zaxis\":{\"backgroundcolor\":\"rgb(17,17,17)\",\"gridcolor\":\"#506784\",\"gridwidth\":2,\"linecolor\":\"#506784\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#C8D4E3\"}},\"shapedefaults\":{\"line\":{\"color\":\"#f2f5fa\"}},\"sliderdefaults\":{\"bgcolor\":\"#C8D4E3\",\"bordercolor\":\"rgb(17,17,17)\",\"borderwidth\":1,\"tickwidth\":0},\"ternary\":{\"aaxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"},\"bgcolor\":\"rgb(17,17,17)\",\"caxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"updatemenudefaults\":{\"bgcolor\":\"#506784\",\"borderwidth\":0},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#283442\",\"linecolor\":\"#506784\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#283442\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#283442\",\"linecolor\":\"#506784\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#283442\",\"zerolinewidth\":2}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"area_mean\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"count\"}},\"legend\":{\"title\":{\"text\":\"diagnosis\"},\"tracegroupgap\":0},\"title\":{\"text\":\"<b>diagnosis vs area mean</b>\"},\"barmode\":\"relative\",\"bargap\":0.2},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('9d08f407-54e0-43ee-9262-434b837012fa');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig = px.histogram(data_frame = df,\n",
        "             x = \"perimeter_mean\",\n",
        "             color=\"diagnosis\", title=\"<b>diagnosis vs perimeter mean</b>\",\n",
        "             pattern_shape_sequence=['x'],\n",
        "             template='plotly_dark')\n",
        "\n",
        "fig1 = px.histogram(data_frame = df,\n",
        "             x = \"area_mean\",\n",
        "             color=\"diagnosis\", title=\"<b>diagnosis vs area mean</b>\",\n",
        "             pattern_shape_sequence=['x'],\n",
        "             template='plotly_dark')\n",
        "\n",
        "fig.update_layout(bargap=0.2)\n",
        "fig1.update_layout(bargap = 0.2)\n",
        "\n",
        "fig.show()\n",
        "fig1.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lb9Q_8b0_ukW"
      },
      "source": [
        "**Observations:**\n",
        "* All cancer tumors of type B have perimeter mean less than 110-115 and most of them are between 75-80 of perimeter mean.\n",
        "* All cancer tumors of type M have radius greater than 70 and most of them are between 130-135 of perimeter mean\n",
        "* Up to 700 area mean value cancer tumors of type B are more\n",
        "* From 700 texture mean value cancer tumors of type M are more"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "FVIUB67yAL3j",
        "outputId": "3fdd5bee-52ad-4696-9315-62dc85ec9db6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-2.8.3.min.js\"></script>                <div id=\"c9035213-79eb-4639-a085-e5ee3c95c045\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"c9035213-79eb-4639-a085-e5ee3c95c045\")) {                    Plotly.newPlot(                        \"c9035213-79eb-4639-a085-e5ee3c95c045\",                        [{\"marker\":{\"color\":\"cornflowerblue\",\"opacity\":0.5,\"size\":6},\"mode\":\"markers\",\"name\":\"M\",\"showlegend\":false,\"text\":\"M\",\"x\":[122.8,132.9,130.0,77.58,135.1,82.57,119.6,90.2,87.5,83.97,102.7,103.6,132.4,103.7,93.6,96.73,94.74,108.1,130.0,102.5,137.2,110.0,116.0,97.41,122.1,102.4,115.0,124.8,77.93,112.8,127.9,107.0,110.1,93.63,95.54,88.4,86.18,71.9,128.3,87.32,85.42,123.7,85.98,120.3,97.26,125.5,95.55,96.42,82.69,97.4,123.6,114.2,90.43,104.1,120.2,143.7,171.5,129.1,121.1,94.25,122.0,100.2,100.3,132.4,94.48,88.05,87.21,152.8,98.64,105.7,114.2,121.4,166.2,87.76,123.4,130.4,101.7,106.2,120.2,81.72,96.85,105.1,78.99,117.4,126.3,130.7,152.1,109.3,116.1,85.84,102.5,109.3,182.1,142.7,101.2,98.92,118.6,94.37,81.15,100.4,90.63,117.4,127.5,94.49,115.1,158.9,91.56,98.78,109.7,134.7,188.5,114.5,92.87,90.96,129.7,128.0,102.6,85.26,113.4,134.4,153.5,132.5,113.4,129.1,138.9,130.7,113.0,126.5,91.43,133.6,103.2,110.2,103.7,132.9,111.0,114.4,100.0,111.6,135.7,147.3,115.2,120.9,126.2,127.2,108.8,75.0,129.5,134.7,118.7,131.1,135.9,106.9,107.5,105.8,111.8,122.9,155.1,129.9,107.1,174.2,98.0,133.8,133.7,140.9,147.2,109.0,141.3,134.8,73.3,93.97,128.9,102.4,144.4,124.4,117.8,96.71,102.9,102.1,133.8,123.7,91.12,112.4,117.5,117.3,138.1,127.7,111.2,186.9,119.0,109.8,128.1,107.1,118.4,121.3,137.8,92.33,152.1,103.8,88.64,97.26,120.8,130.5,165.5,134.7,137.8,93.77,103.4,143.0,142.0,131.2,108.3,140.1],\"y\":[1001.0,1326.0,1203.0,386.1,1297.0,477.1,1040.0,577.9,519.8,475.9,797.8,781.0,1123.0,782.7,578.3,658.8,684.5,798.8,1260.0,704.4,1404.0,904.6,912.7,644.8,1094.0,732.4,955.1,1088.0,440.6,899.3,1162.0,807.2,869.5,633.0,698.8,559.2,563.0,371.1,1104.0,545.2,531.5,1076.0,534.6,1033.0,712.8,1152.0,656.9,645.7,499.0,668.3,1130.0,929.4,584.1,817.7,1006.0,1245.0,1878.0,1132.0,1075.0,648.2,1076.0,728.2,705.6,1264.0,642.5,582.7,530.2,1509.0,682.5,782.6,982.0,1077.0,1761.0,572.6,1138.0,1192.0,748.9,809.8,1075.0,506.3,678.1,813.0,432.0,963.7,1157.0,1214.0,1686.0,886.3,984.6,565.4,736.9,832.9,2250.0,1311.0,766.6,710.6,1041.0,609.9,477.4,671.4,588.9,1024.0,1148.0,642.7,951.6,1685.0,597.8,716.6,904.3,1290.0,2499.0,948.0,610.7,578.9,1230.0,1223.0,761.3,503.2,895.0,1319.0,1670.0,1306.0,920.6,1155.0,1364.0,1206.0,928.2,1169.0,602.4,1207.0,713.3,773.5,744.9,1288.0,933.1,947.8,758.6,928.3,1419.0,1491.0,998.9,1102.0,1138.0,1145.0,805.1,431.1,1217.0,1247.0,1027.0,1274.0,1264.0,813.7,826.8,793.2,918.6,1092.0,1747.0,1194.0,758.6,2010.0,716.6,1293.0,1234.0,1546.0,1482.0,840.4,1386.0,1335.0,361.6,664.7,1174.0,744.7,1407.0,994.0,991.7,719.5,803.1,685.0,1250.0,1110.0,599.5,928.8,990.0,981.6,1384.0,1191.0,930.9,2501.0,980.5,815.8,1167.0,857.6,1007.0,1068.0,1320.0,595.9,1682.0,731.3,556.7,701.9,1052.0,1214.0,1841.0,1299.0,1308.0,629.8,716.9,1347.0,1479.0,1261.0,858.1,1265.0],\"type\":\"scatter\",\"xaxis\":\"x\",\"yaxis\":\"y\"},{\"marker\":{\"color\":\"darkorange\",\"opacity\":0.5,\"size\":6},\"mode\":\"markers\",\"name\":\"B\",\"showlegend\":false,\"text\":\"B\",\"x\":[87.46,85.63,60.34,82.61,51.71,78.04,86.91,74.72,87.21,75.71,73.34,82.61,54.34,64.55,54.66,59.2,60.11,71.8,58.79,81.37,58.79,79.19,87.91,83.19,73.81,86.49,76.95,79.78,95.77,94.57,84.74,86.6,77.79,62.11,74.34,43.79,77.22,63.95,67.41,75.17,79.01,72.48,62.5,82.15,97.83,68.64,55.84,76.53,58.74,73.34,94.28,86.1,88.44,99.58,79.08,102.0,74.72,73.06,73.0,61.24,73.66,83.74,68.26,78.11,97.84,93.97,88.12,83.51,53.27,63.78,70.87,85.31,78.27,108.4,76.84,68.69,76.1,79.85,95.5,68.77,96.22,78.85,70.21,67.49,54.42,64.6,82.01,81.29,73.53,63.76,74.68,75.27,78.83,82.02,60.73,82.53,78.54,81.09,62.92,87.02,98.17,75.51,77.32,65.05,87.88,88.59,65.12,84.55,92.51,66.62,97.45,81.35,71.76,70.79,60.21,89.79,92.55,87.38,78.61,73.93,88.54,66.72,84.13,84.95,68.01,73.87,73.28,69.28,87.16,82.38,69.5,90.3,72.23,61.5,76.2,71.79,86.24,88.99,74.24,84.08,79.83,77.87,81.89,73.72,72.17,96.03,97.03,83.14,75.54,81.78,88.06,69.14,91.22,66.85,80.43,66.86,73.59,74.23,84.07,56.36,85.69,82.71,74.33,92.68,82.29,73.73,54.09,79.19,77.25,60.07,78.6,66.52,82.82,78.01,81.25,90.03,76.09,84.52,71.94,71.38,77.88,84.08,64.41,94.15,61.64,71.49,75.03,66.2,76.66,94.87,73.02,77.23,73.7,71.24,81.92,85.09,88.52,56.74,59.82,79.42,85.24,81.87,106.6,85.48,78.31,97.65,87.84,106.3,70.15,85.89,88.27,73.16,70.67,78.75,80.64,85.79,78.78,88.37,73.38,65.75,55.27,78.07,89.75,88.1,83.05,70.31,75.26,76.14,84.18,83.18,78.29,70.39,104.3,82.63,78.41,72.49,70.92,59.75,97.53,76.39,59.6,80.88,70.95,74.2,98.22,75.46,89.46,61.93,63.19,67.49,68.79,70.47,80.98,81.47,94.89,82.67,89.78,88.68,89.59,71.73,88.37,66.82,77.61,95.88,94.25,76.83,76.77,93.86,80.62,86.34,74.87,84.1,82.61,61.68,92.25,73.88,84.28,86.87,85.98,61.06,76.38,61.49,76.85,96.45,77.42,70.41,82.89,92.41,88.97,73.99,78.29,88.73,87.32,87.76,102.8,82.85,94.21,75.49,78.18,114.6,78.83,84.06,96.12,82.69,80.45,98.73,81.25,61.49,64.12,79.47,71.25,104.7,76.31,94.66,94.29,72.76,84.45,82.51,59.96,71.3,88.73,63.0,54.53,87.44,78.94,90.31,77.83,75.89,75.21,87.76,70.79,76.37,47.98,48.34,74.65,95.81,94.7,84.88,89.77,87.19,65.31,65.85,61.05,68.89,68.51,71.49,81.35,59.01,82.5,65.67,64.73,59.26,96.39,74.52,91.38,70.67,47.92],\"y\":[566.3,520.0,273.9,523.8,201.9,449.3,561.0,427.9,571.8,437.6,409.0,527.2,224.5,311.9,221.8,260.9,269.4,394.1,250.5,502.5,244.0,470.9,559.2,506.3,401.5,520.0,443.3,466.1,651.9,662.7,551.7,555.1,451.1,294.5,412.6,143.5,458.7,298.3,336.1,412.5,466.7,396.5,290.2,480.4,629.9,334.2,230.9,438.6,245.2,403.3,640.7,553.5,588.7,674.5,455.8,761.7,423.6,399.8,384.8,288.5,398.0,512.2,355.3,432.8,689.5,640.1,585.0,519.4,203.9,300.2,381.9,538.9,460.3,880.2,448.6,366.8,419.8,464.5,690.2,357.6,685.9,464.1,372.7,349.6,227.2,302.4,526.4,508.8,402.0,317.5,420.3,428.9,463.7,507.4,288.1,516.4,461.0,481.9,295.4,529.4,725.5,428.0,432.2,321.2,568.9,561.3,313.1,546.4,641.2,329.6,684.5,496.4,395.7,386.8,279.6,603.4,623.9,575.3,476.5,389.4,590.0,337.7,541.6,512.2,347.0,406.3,407.4,346.4,561.0,512.2,344.9,632.6,388.0,289.9,435.6,396.6,572.3,587.4,427.3,516.6,489.0,441.0,515.9,394.1,396.0,651.0,687.3,513.7,432.7,492.1,582.7,363.7,633.1,334.2,471.3,334.3,403.1,417.2,537.3,246.3,566.2,530.6,418.7,664.9,504.1,409.1,221.2,481.6,461.4,244.5,477.3,324.2,504.8,457.9,489.9,616.5,446.0,514.0,387.3,390.0,464.4,514.3,310.8,641.2,280.5,373.9,420.3,321.6,445.3,668.7,402.7,426.7,421.0,384.6,485.8,512.0,593.7,241.0,278.6,491.9,546.1,496.6,838.1,552.4,458.4,711.8,579.1,788.5,338.3,562.1,580.6,386.3,372.7,447.8,462.9,541.8,462.0,596.6,392.0,321.6,234.3,446.2,609.1,558.1,508.3,378.2,431.9,442.7,525.2,507.6,469.1,370.0,800.0,514.5,466.1,399.8,373.2,268.8,693.7,433.8,271.2,495.0,380.3,409.7,656.1,408.2,575.3,289.7,307.3,333.6,359.9,381.1,501.3,467.8,673.7,509.2,611.2,592.6,606.5,371.5,585.9,340.9,441.3,674.8,659.7,432.0,442.5,644.2,492.9,557.2,415.1,537.9,520.2,290.9,646.1,412.7,537.3,542.9,536.9,286.3,408.8,289.1,449.9,686.9,465.4,358.9,506.9,618.4,599.4,404.9,455.3,602.9,546.3,571.1,747.2,476.7,666.0,420.5,466.5,992.1,477.3,538.7,680.9,485.6,480.1,689.4,476.3,248.7,272.5,453.1,366.5,819.8,426.0,680.7,658.8,391.2,493.1,493.8,257.8,388.1,571.0,293.2,221.3,551.1,468.5,594.2,445.2,422.9,416.2,575.5,365.6,406.4,178.8,170.4,402.9,656.4,668.6,538.4,584.8,573.2,324.9,320.8,285.7,361.6,360.5,378.4,507.9,264.0,514.3,321.4,311.7,271.3,657.1,403.5,600.4,386.0,181.0],\"type\":\"scatter\",\"xaxis\":\"x\",\"yaxis\":\"y\"}],                        {\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"perimeter_mean\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"area_mean\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('c9035213-79eb-4639-a085-e5ee3c95c045');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "tpc = pd.DataFrame(df.groupby(['diagnosis']).mean())\n",
        "tpc['tpc'] = tpc.index\n",
        "tpc = tpc.reset_index()\n",
        "fig = make_subplots(rows=1,\n",
        "                    cols=1)\n",
        "\n",
        "fig.add_trace((go.Scatter(x=df[df['diagnosis']=='M']['perimeter_mean'],\n",
        "                          showlegend=False,\n",
        "                          text='M',\n",
        "                   y = df[df['diagnosis']=='M']['area_mean'],\n",
        "                          name='M',\n",
        "                          mode='markers',\n",
        "                  marker = dict(color= 'cornflowerblue',\n",
        "                                size=6,\n",
        "                                opacity = 0.5))),\n",
        "              row=1,col=1)\n",
        "fig.add_trace((go.Scatter(x=df[df['diagnosis']=='B']['perimeter_mean'],\n",
        "                          showlegend=False,\n",
        "                          text='B',\n",
        "                   y = df[df['diagnosis']=='B']['area_mean'],\n",
        "                          name='B',\n",
        "                          mode='markers',\n",
        "                  marker = dict(color= 'darkorange',\n",
        "                                size=6,\n",
        "                                opacity = 0.5))),\n",
        "              row=1,col=1)\n",
        "\n",
        "fig.update_xaxes(title_text=\"perimeter_mean\", row=1, col=1)\n",
        "fig.update_yaxes(title_text=\"area_mean\", row=1, col=1)\n",
        "\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LcH_fMypIcJo"
      },
      "source": [
        "**Observation:**\n",
        "* The majority of tumors of type M have more perimeter and area mean than type B tumors"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bKvSGTJ6hovK"
      },
      "source": [
        "## **3. Data Preprocessing**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4xlfd9lYhv3u"
      },
      "source": [
        "**3.1 Data Preprocessing**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xruPh_2JhsJf",
        "outputId": "1c520747-c121-43d3-dfc4-7fade84e7ac9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "B    357\n",
              "M    212\n",
              "Name: diagnosis, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# counts of unique rows in the 'diagnosis' column\n",
        "df['diagnosis'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "yP3CS0v4jfbo",
        "outputId": "18cc2d4c-d77a-4ece-f4f4-fdedd0c1d869"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-e13fc5ba-862b-4f5e-ac26-9bf81be8b52a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>M</td>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>1.0950</td>\n",
              "      <td>0.9053</td>\n",
              "      <td>8.589</td>\n",
              "      <td>153.40</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>0.04904</td>\n",
              "      <td>0.05373</td>\n",
              "      <td>0.01587</td>\n",
              "      <td>0.03003</td>\n",
              "      <td>0.006193</td>\n",
              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>M</td>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.005225</td>\n",
              "      <td>0.01308</td>\n",
              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
              "      <td>0.01389</td>\n",
              "      <td>0.003532</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M</td>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>0.7456</td>\n",
              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.04006</td>\n",
              "      <td>0.03832</td>\n",
              "      <td>0.02058</td>\n",
              "      <td>0.02250</td>\n",
              "      <td>0.004571</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>M</td>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>0.4956</td>\n",
              "      <td>1.1560</td>\n",
              "      <td>3.445</td>\n",
              "      <td>27.23</td>\n",
              "      <td>0.009110</td>\n",
              "      <td>0.07458</td>\n",
              "      <td>0.05661</td>\n",
              "      <td>0.01867</td>\n",
              "      <td>0.05963</td>\n",
              "      <td>0.009208</td>\n",
              "      <td>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>M</td>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.1980</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>0.7572</td>\n",
              "      <td>0.7813</td>\n",
              "      <td>5.438</td>\n",
              "      <td>94.44</td>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.02461</td>\n",
              "      <td>0.05688</td>\n",
              "      <td>0.01885</td>\n",
              "      <td>0.01756</td>\n",
              "      <td>0.005115</td>\n",
              "      <td>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e13fc5ba-862b-4f5e-ac26-9bf81be8b52a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e13fc5ba-862b-4f5e-ac26-9bf81be8b52a 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-e13fc5ba-862b-4f5e-ac26-9bf81be8b52a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  diagnosis  radius_mean  ...  symmetry_worst  fractal_dimension_worst\n",
              "0         M        17.99  ...          0.4601                  0.11890\n",
              "1         M        20.57  ...          0.2750                  0.08902\n",
              "2         M        19.69  ...          0.3613                  0.08758\n",
              "3         M        11.42  ...          0.6638                  0.17300\n",
              "4         M        20.29  ...          0.2364                  0.07678\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3SHwQWe-FYP7"
      },
      "source": [
        "**3.2 Label Encoder**\n",
        "\n",
        "We convert the Categorical Columns into Numerical by mapping each category to an integer value using LabelEncoder()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "Uennr5PdhtgU"
      },
      "outputs": [],
      "source": [
        "# Trasforming categorical values to numerical values\n",
        "le = LabelEncoder()   # we use LabelEncoder()\n",
        "\n",
        "# We apply the transformation to the data of the column diagnosis\n",
        "df[\"diagnosis\"] = le.fit_transform(df[\"diagnosis\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "o_fVhPW1jeZL",
        "outputId": "00d6b6cf-196e-4113-e609-7d921863ee8e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-993d15c2-3aa9-4017-ba23-8e910a33ca7a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>1.0950</td>\n",
              "      <td>0.9053</td>\n",
              "      <td>8.589</td>\n",
              "      <td>153.40</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>0.04904</td>\n",
              "      <td>0.05373</td>\n",
              "      <td>0.01587</td>\n",
              "      <td>0.03003</td>\n",
              "      <td>0.006193</td>\n",
              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.005225</td>\n",
              "      <td>0.01308</td>\n",
              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
              "      <td>0.01389</td>\n",
              "      <td>0.003532</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>0.7456</td>\n",
              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.04006</td>\n",
              "      <td>0.03832</td>\n",
              "      <td>0.02058</td>\n",
              "      <td>0.02250</td>\n",
              "      <td>0.004571</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>0.4956</td>\n",
              "      <td>1.1560</td>\n",
              "      <td>3.445</td>\n",
              "      <td>27.23</td>\n",
              "      <td>0.009110</td>\n",
              "      <td>0.07458</td>\n",
              "      <td>0.05661</td>\n",
              "      <td>0.01867</td>\n",
              "      <td>0.05963</td>\n",
              "      <td>0.009208</td>\n",
              "      <td>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.1980</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>0.7572</td>\n",
              "      <td>0.7813</td>\n",
              "      <td>5.438</td>\n",
              "      <td>94.44</td>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.02461</td>\n",
              "      <td>0.05688</td>\n",
              "      <td>0.01885</td>\n",
              "      <td>0.01756</td>\n",
              "      <td>0.005115</td>\n",
              "      <td>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-993d15c2-3aa9-4017-ba23-8e910a33ca7a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-993d15c2-3aa9-4017-ba23-8e910a33ca7a 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-993d15c2-3aa9-4017-ba23-8e910a33ca7a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   diagnosis  radius_mean  ...  symmetry_worst  fractal_dimension_worst\n",
              "0          1        17.99  ...          0.4601                  0.11890\n",
              "1          1        20.57  ...          0.2750                  0.08902\n",
              "2          1        19.69  ...          0.3613                  0.08758\n",
              "3          1        11.42  ...          0.6638                  0.17300\n",
              "4          1        20.29  ...          0.2364                  0.07678\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VtQs96_SpSp5"
      },
      "source": [
        "**3.3 Train Test Split**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UzyTboc2h3eP",
        "outputId": "7f12b19e-a8e2-4085-8dbd-3bda477f69c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number transations x_train df (398, 30)\n",
            "Number transations x_test df (171, 30)\n",
            "Number transations y_train df (398,)\n",
            "Number transations y_test df (171,)\n"
          ]
        }
      ],
      "source": [
        "# we split x and y into random train and test subsets (20% of the data of the dataset is used in the test split)\n",
        "rs = 12\n",
        "X_train, X_test, y_train, y_test = train_test_split(df.drop('diagnosis', axis=1), df['diagnosis'], test_size=0.3, random_state=rs)\n",
        "\n",
        "print(\"Number transations x_train df\", X_train.shape)\n",
        "print(\"Number transations x_test df\", X_test.shape)\n",
        "print(\"Number transations y_train df\", y_train.shape)\n",
        "print(\"Number transations y_test df\", y_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "imYgpY8jGOx0"
      },
      "source": [
        "**3.4 SMOTE**\n",
        "\n",
        "We have an imbalanced data. Standard ML methods, such as Decision Tree, tend to deviate from the majority class and tend to ignore the minority class. They tend to only predict the majority class, hence have a significant misclassification of the minority class as compared to the majority class. So we use a technique called as SMOTE (Synthetic Minority Oversampling Technique)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PaWjGZDvGcZw",
        "outputId": "fbe675a8-7e30-48cd-f392-2a3045059b9f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Before OverSampling, counts of label 1: 148\n",
            "Before OverSampling, counts of label 0: 250\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"Before OverSampling, counts of label 1: {}\".format(sum(y_train==1)))\n",
        "print(\"Before OverSampling, counts of label 0: {}\\n\".format(sum(y_train==0)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V8enLci6GSEV",
        "outputId": "0028fd27-ccaf-4bef-b1ac-597ede76e5ee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "After OverSampling, the shape of train_x: (500, 30)\n",
            "After OverSampling, the shape of train_y: (500,)\n",
            "After OverSampling, counts of label 1: 250\n",
            "After OverSampling, counts of label 0: 250\n"
          ]
        }
      ],
      "source": [
        "# Dataset scaling\n",
        "scaler = MinMaxScaler()\n",
        "scaler.fit(X_train)\n",
        "X_train = scaler.transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "sm = SMOTE(random_state=rs)\n",
        "X_train_res, y_train_res = sm.fit_resample(X_train, y_train)\n",
        "\n",
        "print(\"After OverSampling, the shape of train_x: {}\".format(X_train_res.shape))\n",
        "print(\"After OverSampling, the shape of train_y: {}\".format(y_train_res.shape))\n",
        "\n",
        "print(\"After OverSampling, counts of label 1: {}\".format(sum(y_train_res == 1)))\n",
        "print(\"After OverSampling, counts of label 0: {}\".format(sum(y_train_res == 0)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZjjnF8VEHAgn"
      },
      "source": [
        "## **4. Prediction without tuning of model parameters**\n",
        "In this section we train our models on our dataset without tuning of model parameters. Finally we compare the models."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LhdsaJ5rh-fD"
      },
      "source": [
        "### **Decision Tree Classifier**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "AEW1Na4diF52"
      },
      "outputs": [],
      "source": [
        "dtc = DecisionTreeClassifier(random_state=rs)\n",
        "dtc.fit(X_train_res, y_train_res)\n",
        "predictions1 = dtc.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "id": "MGhdjMOwiGqT",
        "outputId": "8d7395fd-0f74-48f8-fdc2-973df09cb39c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgUAAAFDCAYAAACqdX3hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAfmElEQVR4nO3deZgdVZ3/8fc3C6SzEwgYEEjYiQESRURZDLKICy4RBJUlgjDoIIwz6IgyGBwQRX+/EWSQTUyQNSIIIvsSQQRZJJCwI0lAhATIHjoh6Zz5o04n1Te9Jum+6c779Tz15NxTp6pO3Zt776dO1a2OlBKSJEndqt0BSZK0bjAUSJIkwFAgSZIyQ4EkSQIMBZIkKTMUSJIkwFAgdQkR8bWIeDIiFkREytPoDthuKk1D23t7XV1EjC49n9Or3R+tfwwFUhtERLeI+GxETIyI6RFRGxHzI+LZiLgyIg6JiOjgPo0BLgV2Bfp25LbXRRExriKsLImIzRpp968V7dYo2ETEyLztcRExdg12QaqaHtXugNRZ5C+WicC+FbN6ATvl6SvARsDcDuza50vlm4H/AZYBUzpg2/uUyq93wPZWxwbAvwA/rK/Iwe2ktbydkcAPcvlPwPjVWMcTrHxOF6+FPkltYiiQWiEiegN3ALvlquUUH/q3APOALYFPAGOq0L0tSuXfp5QmddSGU0p/7qhtraETI+KclNLS/PggihC3ToiInkCklOYBneU5VRfk6QOpdU5hZSAA+EpK6biU0o0ppXtTShNSSkcAI4B36htFYWxE3BcRsyNiaUS8ERE3RcT+lRupGMoeHhFnR8SMPAT+XEQcWWo7NiISsF9pFZfXL5/blIfSx1dsa1Jp3thS/RYRcXFEvJy3WxsRr0bEXRFxZjP9HVoxb7eIuKLU//kR8UhEnBoRG1a0HV9az7iI+ExEPJy3/WbuT5/mX6JGzc//DgG+UKo/uWL+KiLiS/l1eiki5ubX7u2I+FNEHFs+TZSf71+XFv9o+bnJbYZWPF9D8n7PApYAwxu7piAiekbEX0v1Z5S2+76IWFw6TTJqNZ4jaaWUkpOTUwsT8ByQ8nRPK5fpDtxYWq6x6XsVy5TnvdDEMh/Obcc2t+7cZlypbnzFtiaV5o3NdT2Bl5pZ7+Jm+ju0VH8E8G4z63kM6FdqP74078Umlrmolc97eZ8fBv6ay3/O87ejGOlJwM+b2YdrW3jtft7E89DUazG0hdd3JDC69Hh6af3bUASYlJ/XUfm1erzU/pRqv0+cOv/kSIHUgnyEumOp6s5WLvqvwOdyeSlwBvBJ4LJSm7MjYo8mlt8C+Hfgs8DUUn39Ue6tFOefJ5fm/SjXlc/1t8VuwLa5/BTF9QoHAscA51EEhmZFxHuAX1F8aQHcBhwCfIPiVAvAB4AfN7GK7YBrgE8DvyzVHxcRq3Mh5QX5373ykfQ3gQAW0fDovtLNwInAZyhGY/YHjgPeyvNPyvsKxfP9o9Kyk1n5OjT1WmxF8X/i48AJpfWuIqX0MvD1/LAncAVwFvD+XHdLSum8ZvZFahWvKZBaNrDi8dutXG5sqXxRSum/c/m2iBgJ7J4fHwM80sjy41JK/wMQETUUR64AOwCklGYBsyJiXmmZF9Oanecvr+tNiqPZF1NxLv6KVq7ji0Dv0jrGpJQWQ/HrDVZ+SR8ZESenlOoqln+a4vRMiojbKJ6f3hSfV8No+wWUE4GfAZsC36O4ngDgNzTc30p3AN+mCHfb5D6Uf1nSHfgg8IeU0p8jYrvSvHmteB2+nVL6RbmiYh0NpJSuioj6gDYiTwD/BL7awrakVnGkQGpZ5S8JNm7lcuUL2Sq/IP7cRLuye0rlchAZ1Mrtr46XgLtzeX+KL+jaiHg+n//esxXrKO/PY/WBICvvd39g80aWvzelVIzJp7QcmFOa1+Z9TyktAS7JDw/N24WV4WQVOYQ9CPwnsAvQh4aBoN5Gbe1Pye9WY5mTgL9X1B2VUmpylEFqC0OB1IKU0iLg+VLVAR206dml8rJSuS33QUilcuXI4OBVGhdfxodQDJvfSLHfyylGJ44B7o+I3SuXW8tmVzxe3X0vu6hiPfemlJ5upv3nWXnKaBHFKZv9KE4FlEcq1uQzdHV+wrkpq75uXlyotcZQILXO+FL5gIg4rLFGEbF9RGyQHz5XmrVXRdPy4+doP+Wj7PfWFyJiexpeJ1FfHymlxSmli1NKY1JKO1EcJZ+fm/SkONpuTnl/PhARvUqPy/s9nw66t0FK6TXghlLVL5pqm21VKt+eUvpFKn7q+RSl57HC8lK5xc/W+tGQ1oqIHsDVrBzpqA85P4qI9ze+lNQ2XlMgtc55FFfU1/8s8ZqIOIjiPgXzKS4KPBg4DNiM4grx8aw8ijsx//TscYqj0A+W1j2hHfv9Qqm8b0T8f+AViiPf7o203ywiHqQY2p5C8aXdm5XXP0Bxs6bmTATOycttClwfERdRfJmeXWp3ZUppWSPLt5cfAs9SfJn+oYW2L5fK+0fEURTXH5xK06cMyqd4do3iTpOzgLkppalNLNMWZwEfyuXbKK4xmUBxc6ZrI+L9KaWFa2E7Wp9V++cPTk6dZQLeQ3GnumZ/fgYMzO1b85PE71dso6mfx40u1U+vWGZSad7YinndKb4IK7c7lyIcVP4k8T0t9HcpsEcr+tuanyT2L7UfX5o3rmIfppfmjW7F6zSu1P7hFtoObWwfKALN3xvp9+sVz+fY0ro2ojjVULnM3Y1tq4n+NPo6U1zfUZfr3wLek+snltpPqPZ7xKnzT54+kFoppfQGxXnlzwPXU3ypLgYWUpx7v5ri54Pzcvs6ijscHksRJuZSHKXOovi52wEppbNpR7kPnwVup7ip0gLgJmBPGh4N15sL/BfFzy5fAWpzn/9JMfy+T0qpsV9KVG73WmAP4ErgVYowsZBipOQ7wN4ppSZvHFRtKaV3gI9RhLrZFK/pzcDewMwmlplD8Xo/RnEzorUiIjah+KVE/ef1ifn/IhS3b34tl4+OiC+vre1q/RQptem0liRJ6qIcKZAkSYChQJIkZYYCSZIEGAokSVJmKJAkSYA3L4KGt4GVJKmra/J24Y4USJIkwFAgSZIyTx+U1Iw6qdpdkLq02ica/2vFizvyLyBI66Ferfy2d6RAkiQBhgJJkpQZCiRJEmAokCRJmaFAkiQBhgJJkpQZCiRJEmAokCRJmaFAkiQBhgJJkpQZCiRJEmAokCRJmaFAkiQBhgJJkpQZCiRJEmAokCRJmaFAkiQBhgJJkpQZCiRJEmAokCRJmaFAkiQBhgJJkpQZCiRJEmAokCRJmaFAkiQBhgJJkpQZCiRJEmAokCRJmaFAkiQBhgJJkpQZCiRJEmAokCRJmaFAkiQBhgJJkpQZCiRJEmAokCRJmaFAkiQBhgJJkpQZCiRJEmAokCRJmaFAkiQBhgJJkpQZCiRJEmAokCRJmaFAkiQBhgJJkpQZCiRJEmAokCRJmaFAkiQBhgJJkpQZCiRJEmAokCRJmaFAkiQBhgJJkpQZCiRJEmAokCRJmaFAkiQB0KPaHZDqbbPlJnzr6AP40K7DGL7tEB584u98/PjzVmn37WMP4oTD9mHjgX14/JlX+I+f/JanXnhtxfwxB4ziS5/eg/fvvCX9+9bwwvSZnPebe5h4++MduTtSl3LTjTdwxumnrVL//TPG8cXDv1SFHqk9GAq0zhi+7RAO3vt9PDJlGj17dG+0zanHHsRpxx/M937+e56fPpOTj/wYf7zom+x+2NnMfHsBACcf9TGmv/Y23/nZ73hr7iIO3vt9TDjnq2w8sC+/vPZPHblLUpdz6eUT6NWr14rHW7x3yyr2Rmtbu4aCiKgDpgAB1AEnpZT+sprr+iFwf0rp7rXYRa1D/vinqdwyaQoAV//0ODYe2LfB/A036MGpYw/kp7++k4uuux+Avz45jeduPZMTD/8oZ154CwBfOOUi3p67aMVyf3r0BYYMHsDJR+5nKJDW0IgRu9C7T59qd0PtpL2vKahNKY1MKe0GnAacs7orSimdYSDo2lJKzc7fc7dtGNCvht/d+cSKuncWv8utf5rKQXsNX1FXDgT1nnzuVYYMHrD2OitJXVBHXmjYH5hT/yAivh0Rj0bEUxFxZq4bGhHPRsSlEfF0RNwZETV53viIODSXPxkRz0XE4xFxfkTckuvHRcTlETEpIl6OiJM7cP/UznYcuhnLltXx0iuzGtQ/P+0Ndhy2WbPLfmjXYbw448327J60XvjUJw7k/bsO5zOf+ji/nXhttbujtay9rymoiYjJQC9gCPAxgIg4CNge2IPi1MLNEbEv8Equ/1JK6fiImAh8AbiyfoUR0Qu4GNg3pTQtIq6p2OZOwH5AP+D5iPhlSmlpe+6kOsbA/r1ZWLuE5csbjijMWfAOfWo2pGeP7ixdVrfKcqP32IFD9tuVfxl3VUd1VepyNhk8mH/95imM2GVXli+v4/Zbb+WsM3/A4trFHHXM2Gp3T2tJe4eC2pTSSICI+DBwRUSMAA7KU/04cF+KMPAKMC2lNDnXPw4MrVjnTsDLKaVp+fE1wAml+X9MKS0BlkTELGAz4B9rda/UaWw1ZBDjfzSWWyZN4co//LXa3ZE6rb323oe99t5nxeO99/koS95dwqUX/5KvHHU03br5C/euoMNexZTSQ8AmwGCK0YFz8vUGI1NK26WUfpWbLiktVkfbg0uLy0fECRHxWEQ8dskll7Rx9aqWufPfoW/NhnTrFg3qN+rXm0W1S1YZJdiof29uuuAbvPr6bMZ+f3wH9lRaPxx40MeZN28u/3zttZYbq1PosJ8kRsROQHfgbeAO4L8j4qqU0sKI2AJo7RD/88A2ETE0pTQdOLytfUkpXQLUp4Hmr27TOuP56TPp0aM72245mBdnrLyuYIdhm/H8tJkN2tb06skN55/IBj27M+bki6hd7BkkaW0Lor6gLqK9RwpqImJyvq7gOuCYlFJdSulO4GrgoYiYAlxPcQ1Ai1JKtcA3gNsj4nFgATCvfbqvdcnDT77MvAW1jDlw1Iq6ml49+eS+u3Dng8+sqOvevRtXnXsc2241mM+cdCFvzllYje5KXd5dd93BRhttxOabb1HtrmgtadeRgpRS43egKeadB6x6uzoYUWrzs1J5bKnNfSmlnSIigP8FHsttxlVsYwTqNGp69eTgvd8HwOabDqRfn158/oCRANz+56epXbyUn42/i9O+djBz59fy/PQ3OPnIj9EtosH9B8477XA+sc8I/uPc37LxgD5svMvK31RPfu4fvLt0WcfumNQF/Psp32TELruww447Ule3nDtuv5U7bruV//ze6V5P0IV01jsaHh8RxwAbUFyseHGV+6O1YPBG/bj6p19rUFf/eMdPnsErr8/mZ5ffSbcIvn3sgQwa0Ie/PfMKn/76BcyavWDFMgd8eCcA/t93DltlG/XrkdQ2Q4cN4/c3/o6Zb7xBSolttt2Os875CYd85nPV7prWomjphjHrgRVPQM2ok6rZD6nLq33igkbrFzt4I7WrXg2HAJq8CsQxH0mSBBgKJElSZiiQJEmAoUCSJGWGAkmSBBgKJElSZiiQJEmAoUCSJGWGAkmSBBgKJElSZiiQJEmAoUCSJGWGAkmSBBgKJElSZiiQJEmAoUCSJGWGAkmSBBgKJElSZiiQJEmAoUCSJGWGAkmSBBgKJElSZiiQJEmAoUCSJGWGAkmSBBgKJElSZiiQJEmAoUCSJGWGAkmSBBgKJElSZiiQJEmAoUCSJGWGAkmSBBgKJElSZiiQJEmAoUCSJGWGAkmSBBgKJElSZiiQJEmAoUCSJGWGAkmSBBgKJElSZiiQJEmAoUCSJGWGAkmSBBgKJElSZiiQJEmAoUCSJGWGAkmSBBgKJElSZiiQJEmAoUCSJGWGAkmSBBgKJElSZiiQJEmAoUCSJGWGAkmSBBgKJElSZiiQJEkAREqp2n2otvX+CZAkrVeiqRmOFEiSJMBQIEmSMkOBJEkCDAWSJCkzFEiSJAB6VLsD65LZi+qq3QWpSxvUp3uj9d+44ZkO7om0frlwzPBWtXOkQJIkAYYCSZKUGQokSRJgKJAkSZmhQJIkAYYCSZKUGQokSRJgKJAkSZmhQJIkAYYCSZKUGQokSRJgKJAkSZmhQJIkAYYCSZKUGQokSRJgKJAkSZmhQJIkAYYCSZKUGQokSRJgKJAkSZmhQJIkAYYCSZKUGQokSRJgKJAkSZmhQJIkAYYCSZKUGQokSRJgKJAkSZmhQJIkAYYCSZKUGQokSRJgKJAkSZmhQJIkAYYCSZKUGQokSRJgKJAkSZmhQJIkAYYCSZKUGQokSRJgKJAkSZmhQJIkAYYCSZKUGQokSRJgKJAkSZmhQJIkAYYCSZKUGQokSRJgKJAkSZmhQJIkAYYCSZKUGQokSRJgKJAkSZmhQJIkAYYCSZKUGQokSRJgKJAkSZmhQJIkAdCj2h2QmvPqKzO46orLmfrUk0x7+SV2G/UBLrx0QoM2KSUmXH4JN15/HXPnzmH48BF86zvfY4cdd65Sr6XObc+tBnD07lusUn/NE6/zwLQ5q9R/YZfN2H/7jbn7hbe5YerMjuii2omhQOu0aS+/xEMPPsD7dtmVZcuWNdrmil9fyq8vu4iTTjmVrYcN49orJ3Dy14/jqok3sfEmgzu4x1LX8fP7p/NuXVrx+K133l2lzXv6bcBHhg6kdmldR3ZN7aTF0wcRkSLiytLjHhHxZkTc0sJyo+vbRMRnIuK7a97d1omIkRHxyY7antrP3vvux0233cuPzv0522y73SrzlyxZwm/GX8bRXz2ew474Cnt86COcfe7/EATXX3d1FXosdR0z5tQyvTQtXLLqF/8XdxvCfX+fzTvvGgq6gtZcU7AIGBERNfnxgcBrbdlISunmlNKP29q5NTASMBR0Ad26Nf9fdMqTT7Bo4UL2P/DgFXU1Nb3Za9/RPPTgA+3dPWm9Nmrzfryn3wbc+fxb1e6K1pLWXmh4K/CpXP4ScE39jIjYIyIeiognIuIvEbFj5cIRMTYiLsjlbSPi4YiYEhFnRcTCXD86IiZFxPUR8VxEXBURkeedERGPRsTUiLikVD8pIn4SEY9ExAsRsU9EbAD8EDg8IiZHxOGr++Ro3Tdj+jS6d+/Olltt3aB+6LBtmDF9WpV6JXUNZ358e37xuZ35wYHbsvewgQ3m9ewWjNl1M34/dVaDUwzq3FobCq4FjoiIXsCuwF9L854D9kkpjQLOAH7UwrrOA85LKe0C/KNi3ijg34DhwDbAXrn+gpTSB1NKI4Aa4NOlZXqklPbIy/0gpfRu7sd1KaWRKaXrWrmP6oQWzJ9HTU1vunfv3qC+f/8BLF5cy9Klq54DldS8eYuXcfPTs5jw2Gv88qFXmTa7li+P2pyPbTdoRZuP77gJ8xcv45FX51Wxp1rbWnWhYUrpqYgYSjFKcGvF7AHAhIjYHkhAzxZW92Hgc7l8NfCz0rxHUkr/AIiIycBQ4M/AfhHxHaA3MAh4GvhDXuaG/O/jub0kaQ08O2sRz85atOLxMzMX0rN7cPCOm3DfS7MZ1LsnB2y/MT9/YEYVe6n20Jb7FNxM8QV+TUX9fwP35aP4Q4Bea9CfJaVyHdAjj05cCByaRxcurdjGknL71mwkIk6IiMci4rFLLrlkDbqrauvXfwC1te9QV9fwIqf58+fRq1cNPXtuUKWeSV3L316bT98NezCod08+N2JTnp65kJkLl1DTsxs1PbsREfToHtT09PY3nVlbfpJ4OTA3pTQlIkaX6gew8sLDsa1Yz8PAF4DrgCNa0b4+ALwVEX2BQ4HrW1hmAdCvqZkppUuA+jTgybBObOuhw6irq+Mfr77C1kOHraifMX1ag8eS1lDpk3LTvhuy5cBejNqif4Mmo7cdxOhtB/G9215gbm3jPyHWuq3VoSAP65/fyKxzKU4fnA78sRWr+jfgyoj4PnA70OwJqZTS3Ii4FJgKvAE82opt3Ad8N5+COMfrCrquXXYbRZ++fbn37jv46tdOBGBxbS0P3j+Jz445rMq9k7qOUVv0Z8GSZcx+ZylX/e2fbNij4YjAsXu8l5feWsT9L89p9KeL6hxaDAUppb6N1E0CJuXyQ8AOpdmnN9JmPDA+z38N2DOllCLiCGDHyvb58Uml8un1663ox+hS+S3yNQUppdnAB1vaN637FtfW8pcH7wfgzVkzWbRoIffefQcAH9lrX3rV1HDU2K/x68suol+//mw9dBjXXjWB5Wk5hx3xlWp2Xeq0jv/Qe5k+p5bX5i2hW8AH3tuf3bccwMTJr5OAV+YuXmWZZXXLmfPOMl58652O77DWmmrc0fADwAX5Z4VzgWOr0Ad1EnPmzOb73/lWg7r6xzfcchdDarbg6K8eT1q+nCt+fSnz5s1l553fx3kXXsagjTepRpelTm/mwnf5yNYD2aimJwS8MX8J4x99zV8arAcipfX+lPqKJ2D2Ioe8pPY0qE/3Ruu/ccMzHdwTaf1y4Zjh5YfRVDsvE5UkSYChQJIkZYYCSZIEGAokSVJmKJAkSYChQJIkZYYCSZIEGAokSVJmKJAkSYChQJIkZYYCSZIEGAokSVJmKJAkSYChQJIkZYYCSZIEGAokSVJmKJAkSYChQJIkZYYCSZIEGAokSVJmKJAkSYChQJIkZYYCSZIEGAokSVJmKJAkSYChQJIkZYYCSZIEGAokSVJmKJAkSYChQJIkZYYCSZIEGAokSVJmKJAkSYChQJIkZYYCSZIEGAokSVJmKJAkSYChQJIkZYYCSZIEGAokSVJmKJAkSYChQJIkZYYCSZIEGAokSVJmKJAkSYChQJIkZYYCSZIEGAokSVJmKJAkSYChQJIkZYYCSZIEGAokSVJmKJAkSYChQJIkZYYCSZIEGAokSVJmKJAkSYChQJIkZYYCSZIEQKSUqt2HalvvnwBJ0nolmprhSIEkSQIMBZIkKetR7Q6sA5ocRtG6LSJOSCldUu1+SOsj339dkyMF6sxOqHYHpPWY778uyFAgSZIAQ4EkScoMBerMPJ8pVY/vvy7I+xRIkiTAkQJJkpQZCtSoiKiLiMkRMTUifhsRvddgXeMj4tBcviwihjfTdnREfGQ1tjE9IjZpon5K3pcpEfHZtq67tK4TI+Lo1V1eqtfF3l8PVNRNjoipLaxvaH2biNg9Is5va59WV972lztqe52NoUBNqU0pjUwpjQDeBU4sz4yI1brHRUrpaymlZ5ppMhpo84dWC/ZLKY0EDgVW+8MnpXRRSumKtdctrce60vurX0RsCRARO7d14ZTSYymlk9dyn5ozFDAUNMFQoNZ4ANguH2U8EBE3A89ERPeI+GlEPBoRT0XEvwBE4YKIeD4i7gY2rV9RREyKiN1z+eCI+FtEPBkR90TEUIoPx2/lo419ImJwRPwub+PRiNgrL7txRNwZEU9HxGW07iZU/YE5pb4cGRGP5G1dHBHdc/3CiDg79+vhiNgs14+LiFNz+YN5nyfn56D+qGdsRNwQEbdHxIsRce6aPfVaD3T299dE4PBc/hJwTak/Q/M+/S1PqwSSvN+35PLgiLirfrsRMSMiNsnreTYiLs3z7oyImrzM8bnvT+Z96Z3rx0fE+RHxl4h4OfJoCvBjYJ/8HHyrbS/VeiCl5OS0ygQszP/2AG4Cvk5xlLEIGJbnnQCcnssbAo8Bw4AxwF1Ad2BzYC5waG43CdgdGAy8WlrXoPzvOODUUj+uBvbO5a2AZ3P5fOCMXP4UxR+22qSR/ZgOTAGmAu8An871OwN/AHrmxxcCR+dyAg7J5XNL+7iib3l9H87lHwNTc3ks8DIwAOgFzAC2rPbr6bRuTV3s/bUj8Jf8+AlgeOn90BvolcvbA4/l8tBSm9HALbl8AXBaLh9cv93cfhkwMs+bCByZyxuX+nMW8M1cHg/8luLgdzjwUuX2nFadvM2xmlITEZNz+QHgVxTDjo+klKbl+oOAXUsJfADFG39f4JqUUh3wz4i4t5H17wncX7+ulNLsJvpxADA8YsWBSv+I6Ju3MSYv+8eImNPE8lCcPngrIrYF7omIScD+wAeAR/O6a4BZuf27wC25/DhwYHllETEQ6JdSeihXXQ18utTknpTSvNz2GWBrig9oqV5Xen+9DcyJiCOAZynCd72ewAURMRKoA3ZoZj0AewOfz9u9vWK701JK9c/Z4xRBAWBERJwFDAT6AneUlvl9Smk5xcjLZi1sW/i3D9S02lSch18hf3AsKldRpPI7Ktp9ci32oxuwZ0ppcSN9aZOU0t8jYibFUUMAE1JKpzXSdGnKhxQUH2RtfZ8sKZVXZ3l1fV3t/XUd8L8UI2Vl3wJmArvlbS1m9VW+r2pyeTzwuZTSkxExlmIkoLFl/Ds3reA1BVoTdwBfj4ieABGxQ0T0Ae4HDs/nRIcA+zWy7MPAvhExLC87KNcvAPqV2t0JfLP+QT7iIG/jy7nuE8BGLXU2IjalGH6dAdwDHJrriIhBEbF1a3Y6pTQXWBARH8pVR7RmOamNOtP760aKU213VNQPAF7PR+tHUZzyaM6DwBfzdg9qxXah2J/X8/P0lVa0r3wOVGIo0Jq4DHgG+Fu+0O5iiqPiG4EX87wrgIcqF0wpvUlxzvSGiHiS4kgDivP8n6+/EAo4Gdg9X2j1DCuv0j6T4kPvaYphzlea6ed9eaj2PuC7KaWZqbhC+3Tgzoh4iuIc7ZA27PtxwKV5vX2AeW1YVmqNzvL+IqW0IKX0k5TSuxWzLgSOyX3YiYYjIY05Ezgo7+9hwBsUX+LN+S/grxSB4rkW2gI8BdTlCxO90LCCdzSUVkNE9E0pLczl7wJDUkqnVLlbUqcWERsCdSmlZRHxYeCXladZ1L481ymtnk9FxGkU76EZrHouVVLbbQVMjIhuFBf8Hl/l/qx3HCmQJEmA1xRIkqTMUCBJkgBDgSRJygwFkiQJMBRIkqTMUCBJkgD4P0sKlF7i1lquAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.91      0.95      0.93       107\n",
            "           1       0.92      0.84      0.88        64\n",
            "\n",
            "    accuracy                           0.91       171\n",
            "   macro avg       0.91      0.90      0.90       171\n",
            "weighted avg       0.91      0.91      0.91       171\n",
            "\n"
          ]
        }
      ],
      "source": [
        "cm = confusion_matrix(y_test, predictions1)\n",
        "\n",
        "# We visualize the confusion matrix\n",
        "plt.figure(figsize = (8, 5))\n",
        "sns.heatmap(cm, cmap=\"Blues\", annot=True, fmt=\"d\", linewidths=5, cbar=False, annot_kws={\"fontsize\":15}, yticklabels=[\"Benign\", \"Malignant\"], xticklabels=[\"Predicted Benign\", \"Predicted Malignant\"])\n",
        "plt.yticks(rotation=0)\n",
        "plt.title(\"Confusion Matrix\", y=1.1, weight=\"bold\", fontsize=18)\n",
        "plt.show()\n",
        "\n",
        "print(\"\\n\")\n",
        "print(classification_report(y_test, predictions1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dUv_eo4riIDb",
        "outputId": "035d6d55-4929-498b-d7e6-c3c6d3cc5855"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of Decision Tree Model is:  0.9122807017543859\n"
          ]
        }
      ],
      "source": [
        "print(\"Accuracy of Decision Tree Model is: \", accuracy_score(y_test, predictions1))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sGayVMoliJD9"
      },
      "source": [
        "### **Random Forest Classifier**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "WRtGaYgBiL0o"
      },
      "outputs": [],
      "source": [
        "rfc = RandomForestClassifier(random_state=rs)\n",
        "rfc.fit(X_train_res, y_train_res)\n",
        "predictions2 = rfc.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "id": "8EwjI91oiNId",
        "outputId": "d529cc1b-406d-40fc-ef7d-f077baf3c06f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.95      0.94       107\n",
            "           1       0.92      0.86      0.89        64\n",
            "\n",
            "    accuracy                           0.92       171\n",
            "   macro avg       0.92      0.91      0.91       171\n",
            "weighted avg       0.92      0.92      0.92       171\n",
            "\n"
          ]
        }
      ],
      "source": [
        "cm = confusion_matrix(y_test, predictions2)\n",
        "\n",
        "# We visualize the confusion matrix\n",
        "plt.figure(figsize = (8, 5))\n",
        "sns.heatmap(cm, cmap=\"Blues\", annot=True, fmt=\"d\", linewidths=5, cbar=False, annot_kws={\"fontsize\":15}, yticklabels=[\"Benign\", \"Malignant\"], xticklabels=[\"Predicted Benign\", \"Predicted Malignant\"])\n",
        "plt.yticks(rotation=0)\n",
        "plt.title(\"Confusion Matrix\", y=1.1, weight=\"bold\", fontsize=18)\n",
        "plt.show()\n",
        "\n",
        "print(\"\\n\")\n",
        "print(classification_report(y_test, predictions2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IRJWMGSHiOGB",
        "outputId": "a604fbfe-3163-4f8f-b399-cb713e09946c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of Random Forests Model is:  0.9181286549707602\n"
          ]
        }
      ],
      "source": [
        "print(\"Accuracy of Random Forests Model is: \", accuracy_score(y_test, predictions2))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dHsVrIruhw3O"
      },
      "source": [
        "**2.2 Splitting the data into train and test**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LkiUm2nppklT"
      },
      "source": [
        "## **5. Prediction with tuning of model parameters**\n",
        "In this section we find the best parameters for each model and use them to test all models. In this case to evaluate and estimate the skill of machine learning models we use metrics such as: Accuracy Score, ROC AUC Score, Precision, Recall and F1 Score. Finally, thanks to these metrics, we are able to compare the models at the end of testing."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I7JL_a-iJhoo"
      },
      "source": [
        "We use as tuned paramiters of classifier models:\n",
        "\n",
        "*   **Decision Tree**\n",
        "    \n",
        "    *Parameters*:\n",
        "    \n",
        "    * **`max_depth`**:\n",
        "    \n",
        "     The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.\n",
        "    \n",
        "    * **`min_samples_leaf`**:\n",
        "    \n",
        "     The minimum number of samples required to be at a leaf node.\n",
        "    \n",
        "    * **`max_leaf_nodes`**:\n",
        "\n",
        "     Grow a tree with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes.\n",
        "\n",
        "---\n",
        "\n",
        "*   **Random Forest**\n",
        "    \n",
        "    *Parameters*:\n",
        "    \n",
        "    * **`max_depth`**:\n",
        "    \n",
        "     The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.\n",
        "    \n",
        "    * **`max_features`**:\n",
        "    \n",
        "     The number of features to consider when looking for the best split. If \"auto\", then `max_features=sqrt(n_features)`.\n",
        "    \n",
        "    * **`min_samples_leaf`**:\n",
        "    \n",
        "     The minimum number of samples required to be at a leaf node.\n",
        "    \n",
        "    * **`min_samples_split`**:\n",
        "    \n",
        "     The minimum number of samples required to split an internal node."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "7WbX00D_wRps"
      },
      "outputs": [],
      "source": [
        "DT_grid_models = dict(max_depth=list(range(1, 11)), random_state=[rs])\n",
        "RF_grid_models = dict(max_depth=list(range(1, 21)), max_features=list(range(1, 11)), n_estimators=[100], random_state=[rs])\n",
        "\n",
        "DT = DecisionTreeClassifier()\n",
        "RF = RandomForestClassifier()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v6ZoDB8mwlNk",
        "outputId": "35342678-5252-45e1-be67-a80d47c9bf8c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " DecisionTreeClassifier():\n",
            " Best Parameters: {'max_depth': 5, 'random_state': 12}\n",
            "\n",
            "\u001b[1m \u001b[96m ---------------------------------------------------------------------------------------------------- \n",
            " \u001b[0m\n",
            " RandomForestClassifier():\n",
            "Best Parameters: {'max_depth': 4, 'max_features': 2, 'n_estimators': 100, 'random_state': 12}\n",
            "\n",
            "\u001b[1m \u001b[96m ---------------------------------------------------------------------------------------------------- \n",
            " \u001b[0m\n"
          ]
        }
      ],
      "source": [
        "DTC_grid = GridSearchCV(estimator=DT, param_grid=DT_grid_models, scoring=\"accuracy\", cv=10)\n",
        "RFC_grid = GridSearchCV(estimator=RF, param_grid=RF_grid_models, scoring=\"accuracy\", cv=10)\n",
        "\n",
        "DT_best = DTC_grid.fit(X_train_res, y_train_res)\n",
        "best_param = DT_best.best_params_\n",
        "print(\" {}:\\n Best Parameters: {}\\n\".format(DT, best_param))\n",
        "print(\"\\033[1m\", \"\\033[96m\", \"-\"*100, \"\\n\", \"\\033[0m\")\n",
        "\n",
        "RF_best = RFC_grid.fit(X_train_res, y_train_res)\n",
        "best_param = RF_best.best_params_\n",
        "print(\" {}:\\nBest Parameters: {}\\n\".format(RF, best_param))\n",
        "print(\"\\033[1m\", \"\\033[96m\", \"-\"*100, \"\\n\", \"\\033[0m\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9jnQj93tqMIW",
        "outputId": "00f94676-c081-43ea-e051-3df4838e5b9b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m \u001b[96m #####  DecisionTreeClassifier algorithm  #####\n",
            " \u001b[0m\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.94      0.93       107\n",
            "           1       0.90      0.86      0.88        64\n",
            "\n",
            "    accuracy                           0.91       171\n",
            "   macro avg       0.91      0.90      0.91       171\n",
            "weighted avg       0.91      0.91      0.91       171\n",
            "\n",
            "ROC AUC score:  0.9\n",
            "Accuracy Score: 0.912\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfgAAAIlCAYAAAA5YOoTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeViU5f4G8HtmYFhmQAQB2RQGTc09NUU096XcFa20jmXZ8jMr29zKNUvbO5btWaZtpJZ6yiw71hHcUktzl0VAkH0GmAHmZeb9/YFMTiKOyMw7y/25Li9npgfm+xJy8zzvs8hEURRBREREbkUudQFERETU9BjwREREbogBT0RE5IYY8ERERG6IAU9EROSGGPBERERuiAFPRETkhrykLoDIE23atAnz58+/7HUfHx+Eh4ejR48euO+++9C2bdt6P76wsBAbNmzA//73P2RlZaGyshIBAQFo164dhg0bhkmTJsHX17fejzWbzfjll1+wdetWHD16FEVFRfDy8kJ4eDg6duyI2267DYMGDYJMJrPpWvLz8/HFF18gNTUVmZmZMBgMaN68OaKiojBo0CCMHTsWERERtn9xiKhJyLjRDZHjXSngL+Xv748vvvgC7du3t3r9559/xjPPPAO9Xn/Fj42NjcWaNWsQHx9v9XpRUREef/xxHDhwoMH3PnDgAAIDA69yFcA333yD5cuXo6qq6opt/vWvf2HhwoVX/VxE1LTYgydyAhs2bEBNTQ3++usvvPbaazCZTDAYDNiwYQOWL19uaXf48GE8/vjjEAQBANCuXTvMmDED4eHhOHnyJN59911otVpkZmZi5syZ2Lx5M5o1awYAqKysxH333YeTJ08CAORyOSZMmIBBgwZBrVbjwoUL+O233/DTTz/ZVPOWLVusgjs2NhZ333034uPjUV1djWPHjuG7775rqi/RZcxmM4xG4xVHKog8HQOeyAn07NkTANCnTx8cOHAAu3btAgDk5uZatVu1apUl3CMjI/H5559DrVYDABISEtCzZ09MnjwZoiji/Pnz+Oijj/DEE08AANatW2cJdwB45ZVXMGrUKKvPP2HCBGRkZFw1NCsqKrBixQrL806dOmHdunVQqVSW1wYOHIiHHnoImZmZltcGDx6M8+fPW+rp3bs3ACAnJwdDhgyxtDt16pTlcbt27SyPv/vuO3zzzTf48ccfUVRUhDlz5uDVV18FAAQEBCAlJQU+Pj6W9m+//Tb+/e9/AwBGjx5taWsymZCcnIwtW7bgzJkzqKysRGhoKBITE/Hggw8iJiamwesncgWcZEfkxFq2bGl5nJubi8OHD1ueT58+3RLudTp37owBAwZYnv/www+Wx5s3b7Y87tOnz2XhXicuLg5KpbLBun799VdotVrL8/nz51uFex2FQnHZbYLr8dhjj+Gzzz5DQUEBzGYzNBoNbrrpJgBAeXm55RejOtu2bbM8TkpKAgBUVVXh3nvvxeLFi3Hw4EGUlZVBEATk5uYiOTkZEydOxJEjR5qsZiKpsAdP5AR+//13mEwmHDt2DLt37wYAeHt7484777S0ubRXC9T2muvTuXNnS9BlZWXBYDAAADIyMixtEhMTr6vev/76y/LYz8/PErL2lpWVhRkzZiAhIQGlpaWIiYlBUlISDh06BADYunUrRowYAQA4duwY0tPTAQDR0dHo06cPAGD16tXYt2+f5fVZs2YhPDwcO3bswJdffomysjI88cQT2L59O7y8+COSXBe/e4mcwLRp06yed+rUCfPnz7cK8fLycqs2wcHB9X6ukJAQq+f//DgAaN68eWNLvexzBgYGQi53zGDg9OnTMXfuXKvXYmJisGLFCuj1evz6668oKytDYGAgtm7damkzadIkyGQyiKKITZs2WV6/++670apVKwDAmDFjsHPnThQWFiI7Oxt79uxB//79HXJdRPbAgCdyQmlpacjPz7d6LSAgwOp5SUkJNBrNZR9bXFzc4McBQGlp6XXVd+nnLC8vh9lsdkjIDx8+/LLX/P39cdtttyE5ORlGoxE//vgjJk2ahO+//x5A7WTCiRMnAqj9mpWUlFg+9sUXX7zie505c4YBTy6N9+CJnMCpU6ewZ88ejB8/HkDtjPe5c+fi7NmzljaXTjYDrIfJr/R6TEwM/P394e/vj7i4OMvre/bsua56Lx1ZMBgMVnMDbGUymSyPLw3dhoSGhtb7+qRJkyyPt2zZgv3791t+QerXr5/VXAZb1d3aIHJVDHgiJxEcHIzly5cjOjoaACAIAl555RXLf4+MjES3bt0sz9etW3dZCB07dsxqotltt91meVzXiwWA1NRUqwl4l8rMzITRaGyw1gEDBlgN869cubLeQDSZTEhLS7M8r1uyBwAXLlywPP7vf//b4PvVudLmO927d7dM5vv999/x4YcfWv7b5MmTLY+Dg4Ot6v7oo49w6tSpy/4cPnwYjzzyiE01ETkrxZIlS5ZIXQSRpzlx4gR27txpeT579mwAtbPO/f398csvvwCoDdshQ4ZYeq4ajQZbtmyB2WxGeXk5fv31V/j7+0Or1eLnn3/GokWLUFlZCaD2F4JVq1ZZlrx17NgRu3btQlFREQDgp59+Ql5eHsxmMwoKCnDw4EF8+OGHWLZsGaZPn2613OyflEolwsLCLGvm8/PzsWPHDshkMuj1eqSnp2P79u1YtGgRysvLccsttwAA9u3bhzNnzgCoHWnw8vLC999/j48//hiX7rlV9/UAgLfeesvyePr06VfcgKe6uhopKSkQRRFZWVkAagN92bJlUCgUAGp/QSgpKbGMOKSmpkKhUMBgMODUqVPYvXs31q1bh0WLFuHBBx+84vUTuQLuZEckgX/uZHfpDHlBEDB8+HDLGvihQ4fi7bfftvz3H3/8EfPmzWtwCLl169ZYs2YN2rRpY/V6YWEh5syZI9lOdgcPHsTUqVMva3PDDTfg9OnTludXWge/c+dOywjHPxUXF2PAgAGWfQIAYMaMGZdNyquqqsLMmTOxf//+Bq/tn6sWiFwNJ9kRORlvb2/cf//9WLZsGYDaUDt58qRly9oRI0agW7du+Pzzz/G///0P586dQ1VVFQICAnDDDTdg2LBhSEpKgp+f32WfOzQ0FOvWrcMvv/yCLVu24OjRoyguLoZCobDai76+iXn1SUpKQv/+/fH5559b9qKvrKxEUFAQoqOjMXDgQIwbN87SvkePHli1ahXeffdd5OTkIDQ0FOPHj8fo0aOtbic0RkhICAYNGoQdO3ZY1fdPvr6++OSTT7Bx40Zs3boVp0+fRkVFBYKCghAeHo5evXpZbbpD5KrYgyciInJDnGRHRETkhhjwREREbogBT0RE5IYY8ERERG6IAU9EROSGGPBERERuiAFPRETkhhjwREREbogBT0RE5IYY8ERERG6IAU9EROSGGPBERERuiAFPRETkhhjwREREbogBT0RE5IYY8ERERG6IAU9EROSGGPBERERuiAFPRETkhhjwREREbogBT0RE5IYY8ERERG6IAU9EROSGGPBERERuiAFPRETkhhjwREREbogBT0RE5IYY8ERERG6IAU9EROSGGPBERERuiAFPRETkhhjwREREbogBT0RE5IYY8ERERG6IAU9EROSGGPBERERuiAFPRETkhhjwREREbogBT0RE5IYY8ERERG6IAU9EROSGGPBERERuiAFPRETkhhjwREREbogBT0RE5IYY8ERERG6IAU9EROSGvKQuoKlUVVXhr7/+QmhoKBQKhdTlEBER2ZXJZEJhYSE6deoEX1/fy/672wT8X3/9hWnTpkldBhERkUNt2LABPXv2vOx1twn40NBQALUX2rJlS4mrISIisq8LFy5g2rRplvz7J7cJ+Lph+ZYtWyI6OlriaoiIiBzjSrelOcmOiIjIDTHgiYiI3BADnoiIyA0x4ImIiNwQA56IiMgNMeCJiIjcEAOeiIjIDTHgiYiI3BADnoiIyA0x4ImIiNwQA56IiMgNMeCJiIjcEAOeiIjIDTkk4FetWoXBgwejXbt2OH36dL1tTCYTli5diqFDh2LYsGFITk52RGlERERuySEBP2TIEGzYsAFRUVFXbLN161ZkZWVhx44d+Oqrr7B69Wrk5OQ4ojwiIiK345CA79mzJyIiIhps8/3332Py5MmQy+UIDg7G0KFDsX37dkeUR0RE5Ha8pC6gTl5eHiIjIy3PIyIicOHCBQkrIiIiuj41BgP0mZnQp6XhzPEstGkXhajx4x3y3k4T8ERERK7IbDRCf+4c9BkZqEhLs/xdkZGBqtxcAMCJgPZ4u80s3PfHUSzxtICPiIhAbm4uunTpAuDyHj0REZFURJMJhpycy0M8PR2G7GzAbLa09W7eHGqNBi369oU6Ph5qjQans4KhOVaNWS8+7bCanSbgR44cieTkZAwfPhxarRY///wzNmzYIHVZRETkIURRRFV+PvQXe991f1ekpcFw7hzMRqOlrUKlglqjQfNu3RA9cSLUGg1UGg3UcXFQBgdb2umraqDy9cLTAGZV1cDf13Gx65B3ev7557Fjxw4UFRXh3nvvRVBQEP7zn/9g5syZePTRR9G5c2eMGzcOf/75J4YPHw4AmDVrFmJiYhxRHhEReRBjSQkq0tOteuL6jAxUpKfDZDBY2sl9fKCKjYW6TRu0HD787xDXaOATFgaZTNbg+/wnJRdz1xxB8oq+6BAb6NBwBwCZKIqiQ9/RTnJycjBkyBDs3LkT0dHRUpdDREQSEioqoE9PR0V6+mV/C1qtpZ1MoYB/q1ZQxcVZhtPrQtwvMhIyhaJR7//tb+cx6+WD6HZDEL5YloBAlXdTXZrF1XLPaYboiYiIroWpshL6zEyr4fS6EK8uKLBq6xcVBZVGg6ixY6GOj7cEun+rVpB7N234Jv+SjcdeP4SbO4Rg/ZLeUPs3fbjbggFPREROyywIMGRnXz6cnpaGytxc4JJBaJ/QUKg0GoQPGWLphas1Gvi3bg0vf3+H1LvrUAEefe0QEru0wKeLekPl4GH5SzHgiYhIUqLZjMrc3HqH0w1ZWRBraixtvZs1gyouDiF9+lgNp6vi4uAdGCjhVdRK6ByCp6a2x/9NagM/n8YN7zcVBjwREdmdKIqoLiy0BLdVmGdmwlxVZWmr8PODSqNBsxtvROSYMVBfHE5XxcVBGRJy1cltUvjml2wM7hmO4EAlnpzaTupyADDgiYioCRm12r/D+5LZ6fr0dNRUVFjayby9a2eoazQIGzjQ6r64b8uWThniV/L2xjNY/vFxzJ7cFgvvuVHqciwY8EREdE1q9HpLcP9zWN1YUvJ3Q7kc/tHRUMfHI7hnz9p74vHxUGk08IuKgtzL9SPo9S9PYdVnJzH+lig8c1d7qcux4vpfXSIianKm6moYzp2r97541T/OCfGNiIA6Lg4Ro0ZBHRcHVXw81HFx8G/dGgofH4muwL5EUcRL60/i9S9PI2lwNN58/CYoFM416sCAJyLyUHXbr9ZtuXppkBtycqy2X1UGB0MdH4/QW26xGk5XxcbCS6WS8CqkUW6oweZfz2Pq8FZ4+ZFuThfuAAOeiMitiWYzqi5cuLwnnpEBfWYmREGwtPVSq6HSaND8ppsQk5T09wx1jQbKoCAJr8J5iKIIsxkIVHlj26v9ERyghFzufOEOMOCJiFyeKIowFhfXO5yuz8iAqbLS0lbu6wtVbCwCbrgBLUeMsKwVV8XHw6dFC5ea3OZoZrOIBe8eQUVlDf495ya0aObctx8Y8ERELkIoK6s3xCvS01FTVmZpJ/Pygn/r1lDHxSG0f/+/h9Pj4mq3X5XLJbwK12Q2i3j6rT+x4cdzmDWpDVzh9yAGPBGRE7Fsv/qPE830aWmoLir6u6FMBr+oKKg1GstpZnXD6f4xMU2+/aonM5lEPP7mYSTvzMacO27AM3e1d4mRDgY8EZGDmY1G6LOy6h1Orzx/3qqtT1gY1BoNwv9xmpkqNhYKX1+JrsCzPP3WH0jemY25d7fHnDucYxMbWzDgiYjsQDSZUHn+vOU8casZ6tnZEE0mS1vvoCCoNRqEJCRY9cRVGg281WoJr4IAYOLAaLSNCcDDE9tIXco1YcATETWSKIqoLiiwOgClbjhdf+4czNXVlrYKf3+oNRo069IFUePH1/bEL64XVwYHS3gVVJ9qwYTfDhdi2M0t0a9rKPp1DZW6pGvGgCciugpjScnfPfFL/05Ph0mvt7STK5VQxcbWnmg2dKjVMjPf8HCXuG9LQGW1CTNW7MeuQwX47Z3BaBsTIHVJjcKAJyICIFRUQP+Pmel168WF0lJLO5lCAf+YGKg0GoT07m11LKlfVBRkCmlPEKPro6+qwT3L9mH3kSK8Oruby4Y7wIAnIg9iqqqCPjPzsnPFKzIyUJ2fb9XWNzISao0GUWPGWPXEVa1aQa5USnQFZE8VBgF3Ld2H/ceL8eac7pgypJXUJV0XBjwRuRVzTQ0M2dmW5WWXBnnl+fOAKFra+rRoUTucPmiQdYjHxsLL31/CqyAp/LQ/HweOl2DNUz0wfkC01OVcNwY8Ebkc0WxGVV6e9VD6xUA3nDsHsabG0tYrMLB2hvrNN1uFuFqjgXdgoIRXQc5CFEXIZDJMGBiNLm2DEB/lHisXGPBE5JREUUR1UVG954pXZGTAXFVlaavw84MqLg6B7dsjctQoy85tao0GypAQTm6jKyopM+L+Fw7g2Xs64Kb2wW4T7gADnogkJuh0l+3aVhfoNeXllnYyb2+oWreGWqP5+0Sziz1x35Ytuf0qXbNCbTWmLExF+vkKaCuEq3+Ai2HAE5Hd1ej1tZPb6maoX3Jf3FhS8ndDmcwyQz24Z0/rGerR0ZB78UcWNY38kiokLUhBTkEl1i/pg/7dXG+d+9XwXwsRNQmz0Qj9uXP1HoZSlZdn1da3ZUuo4uIQcdttVgehqFq35varZHeFpVWYMHc3LpRUYcPSPujbuYXUJdkFA56IbCaaTDDk5FhNaqsLcUN2NmA2W9oqg4Oh0mgQ2q8fVBd3bKsLci+VSsKrIE8XFKBEjw7BuHtka9x8Y4jU5dgNA56IrIiiiKoLFy4fTk9Ph+HcOZiNRktbhUoFdXw8mnfvXnuiWd198bg4KJs3l/AqiC53Lk8Pf18FQpv7YvUTN0ldjt0x4Ik8kCiKMJaUXL5r28UTzUwGg6Wt3McHqthYBLRti5bDh1t64er4ePiEhnKGOrmEtPMVSJqfgthIFTa9mOgR37cMeCI3JpSXX3Y/vO6xoNNZ2skUCvi3bg11XBxaJCZCHRdnGVb3i4riDHVyaaeyyjB5QSpMZhEvPNTFI8IdYMATuTxTZSX0mZn1hnh1YeHfDWUy+EVGQh0fj6jx46164v4xMZB7e0t3EUR2cjxDh8kLU6GQy7BpZSLatfKczY0Y8EQuwCwIMGRl1Rvilbm51tuvhoZCHR+P8KFDrXZtU8XGQuHnJ+FVEDmWKIqY/84RKL3k+ObFRLfaxMYWDHgiJyGazag8f77eZWaGrCyIJpOlrXezZrWnmSUkWA2nqzQaeAe47ulXRE1JJpPh/Xm9UFVtQusIz1u5wYAnciBRFFFdWFg7M/0fPXF9ZibM1dWWtgo/P6jj49GsUydEjRtntV5cGRzsMfcRia7V/uPF+GJHFl6e3RXhwZ67rwIDnsgOjKWlf2+9esn+6RVpaTDp9ZZ2cqWydnKbRoPwwYOtDkPxbdmSIU50jVKPFuGuJXvRMtgXpeUCQoN8pC5JMgx4okaq0evrHU7XZ2RYb78ql8M/JgZqjQbBvXpZtl5VaTTwj46GTKGQ7iKI3Mhvhwswffl+RIf54ZsXEj063AEGPFGDTNXVtXuo/+NccX1GBqouXLBq6xsZCXVcHCJHj75s+1W5UinRFRB5hl9+z8e9z++HJkqNr1f09fhwBxjwRDDX1KAyJ+eyE830aWkwnD9vvf1qSEjtaWYDBlj1xFWxsdx+lUhCan8vdG/XHB8vvBnBgfyFGmDAk4cQzWZU5eX9fRTpJcPp+nPnIAp/HxXpFRAAtUaD5j17ImbKFKsTzbybNZPwKojonzJyKxAXqcbNN4Zg80rP2KHOVgx4chuiKMJYVGR1nnhdmOszM2GqrLS0lfv6Qh0Xh4B27RBx661WIa5s0YI/JIhcwOZdOXjk1UNY80wPjOsfxX+3/8CAJ5cj6HSW3velQV6RkYGasjJLO5mXl2WGeugtt1iFuG9EBLdfJXJhX+/MwuNvHEbvjiEY2jNc6nKcEgOenFKNwVA7ue1icF8a5Mbi4r8bymTwj46GSqNBzKRJVsvM/GNiIPfitziRu/n8x3N4cvUf6N81FJ88dzP8ffnvvD78qpBkzEYj9FlZ9R6GUpWba9XWJzwcao0GESNHWoW4qnVrKHw9dyMLIk9zKqsMT67+A4N6hOHjhTfDV8llplfCgCe7Ek0mGHJy6l1mps/Kspqh7t28OdQaDVr07Qt13darF5eaeas9aw9pIqpfu1aB+OTZmzGwRxh8vBnuDWHA03UTRRFV+flWy8vqhtUN587BbDRa2ipUKqg1GgR17YqoCRP+Pgzl4varRET1eXfzWXRr2xx9OoVgRJ8IqctxCQx4spmxpMRq7/RL/zYZDJZ2ch8fqGJjoW7TBi2HD7c60cwnLIwzXYnomrz2xSm8tP4k7hrRGn06hUhdjstgwJMVoaLi8q1XL/4taLWWdjKFAv6tWkEVF4eQhATLzm3quDj4RUVx+1Uium6iKOKl9Sfx+penMXlIDFbN6ip1SS6FAe+BTJWV0GdmWu/adjHEqwsKrNr6RUVBpdEgauxYy9ar6vj42hnq3H6ViOxEFEUsX3scazaexbQRrfHyI10hl3P071ow4N2UWRBgyM6+7EjSirQ0VObmAqJoaevTogVU8fEIHzLEaq24f+vW8PL3l/AqiMhTmc1AXlEl7hkVixce6sJwbwQGvAsTzWZU5ubWO5xuyMqCWFNjaesVGAi1RoOQPn2sDkJRazTwDgyU8CqIiP5mNovQVggIDlRi9ZM3QSGXcd5OIzHgnZwoiqguKrKcK24V5pmZMFdVWdoq/Pyg0mjQ7MYbETlmDNSXBLkyJIT/SIjIqZlMIp5+6w/sO1aM7W8MQIC/t9QluTQGvJMwarX1bviiT09HTUWFpZ3M27t2hrpGg7CBA63ui/u2bMkQJyKXVGMy4/E3DuObX3LwxJ3toPZjPF0vfgUlcHbNGpxYtervF0TRaq045HL4R0dDHR+P4J49/15mFh8Pv6gobr9KRG5FqDHjkVcP4bvfzmPu3e0x5452UpfkFpgUEij5/Xd4BwQg5o47LK/5BAdDdXGZmX/r1lD4+EhYIRGR46xcdwLf/XYez824EbMmtZW6HLfBgJeAoNVC3aYNOj77rNSlEBFJ7qGJbXBDqwDcPrSV1KW4FZ6XKQGjTgfvZs2kLoOISDKV1Sa8/uUpGAUzQoN8GO52wICXgKDVMuCJyGPpq2pw99K9eGn9Sez9q0jqctwWh+glYNRq4R0UJHUZREQOV2EQcNeSfdh/ohirn7gJt3QPk7okt8WAdzCzIMCk10PJHjwReZgyvYCpi/bg8Gkt1jzdE+NviZK6JLfGgHcwQacDAA7RE5HHyc43ID1Xj/fn9cSoxEipy3F7DHgHswR88+YSV0JE5BiV1Sb4+SjQUdMM+z8aCjV3qHMITrJzMOPFgOcQPRF5gsLSKtw651e8s+ksADDcHYg9eAerO1Odk+yIyN1dKK7E5AWpyCmsROd4dmocjQHvYJaAZw+eiNzY+cJKJC1IQUFpFb5YloA+nUKkLsnjMOAdzMgePBG5ucpqEybNT0GxrhpfLe+Lnh2CpS7JIzHgHUzgPXgicnN+Pgo8OqUtOsQGovsNnFAsFQa8gwlaLRR+fpArlVKXQkTUpM7mlONCcRX6dQ3F1OGtpS7H4zHgHUzQ6bhEjojczqmsMiTNT4WfjwK73xsCpTcXaUmN/wcczKjTcXieiNzK8QwdJs5LgVwOrF/Sh+HuJPh/wcF40AwRuZMjZ7WYND8FPt5ybF7ZDze0CpC6JLqIAe9gAg+aISI38vXObKj8vLB5VT9ootRSl0OX4D14BzPqdGjGHjwRuTizWYRcLsPS+zvhsSltEdrcV+qS6B/Yg3cwQauFkj14InJhKUeKMGT2LuQWVUKhkDHcnZTDevAZGRmYN28etFotgoKCsGrVKsTGxlq1KS4uxvz585GXl4eamhr07t0bzz77LLy83GOgwSwIMBkMvAdPRC7r18MFuGf5fsSE+8NLLpO6HGqAw3rwixcvxtSpU/Hjjz9i6tSpWLRo0WVt3n33XcTHx2Pr1q3YsmULjh07hh07djiqRLvjPvRE5Mp+PpCPfy3dh7hIFTatTERYMHvuzswhAV9cXIzjx49j9OjRAIDRo0fj+PHjKCkpsWonk8mg1+thNpthNBohCALCw8MdUaJDWE6SY8ATkYv53x+FuPf5fWjXKgDfvJCIFs18pC6JrsIhAZ+Xl4fw8HAoFAoAgEKhQFhYGPLy8qza/d///R8yMjLQr18/y58ePXo4okSHsJwFzyF6InIxneKbYfLgGCS/kIjgQO7E6QqcapLd9u3b0a5dO+zevRu//fYbfv/9d2zfvl3qspqMUFoKgEP0ROQ6fjtcgCqjCc0DlHjtse5opuZ57q7CIQEfERGB/Px8mEwmAIDJZEJBQQEiIiKs2q1fvx5jx46FXC5HQEAABg8ejH379jmiRIcwsgdPRC7kq5+zcPtze/DmV6elLoUawSEBHxISgg4dOmDbtm0AgG3btqFDhw4IDrY+QjA6Ohq//fYbAMBoNGLPnj1o27atI0p0iLpJdtyqloic3frtmXj8jcO4pVsoZk92n5/DnsRhQ/RLlizB+vXrMWLECKxfvx5Lly4FAMycORNHjx4FACxYsAAHDx7EmDFjMH78eMTGxmLKlCmOKtHuLPfgOURPRE7s423peGr1nxjcIxyfLuoNf1/3WKrsaRz2fy0+Ph7JycmXvf7BBx9YHrdq1Qpr1651VEkOJ+h0UKhUkHvzHhYROaeSMiNe2XAKI/u0xHvzesLHWyF1SdRI/LXMgYzcxY6InFxwoBJbX+mPVuH+8PZyqrRkOLoAACAASURBVHnYdI0Y8A4k6HScYEdETkcURbz2xWmIEPHU1PaI56ExboG/njkQj4olImcjiiJWrjuBlzecRHa+AaIoSl0SNREGvAMZeVQsETkRURSx9KNjePPrM7hrRGu8/lh3yGTcX95dcIjegQSdjkvkiMhpLP7gL7z/XTruHR2HFQ92hpyHx7gVBrwDCezBE5ET6RTfDA9NiMfi+zqy5+6GGPAOYjYaYaqsZMATkaRMJhHHM3XoHB+EKUNaSV0O2RHvwTuI5SQ5DtETkURqTGY8+tohjHrif8jM00tdDtkZA95BeNAMEUlJqDHj4ZcOYuOuHDw1rR1iI1RSl0R2xiF6B+FRsUQklWrBhIdW/Y4f9lzA4vs64uGJbaQuiRyAAe8gxosHzbAHT0SO9uVPWfhhzwU8/2Bn3D9WI3U55CAMeAcReA+eiCRy98hYtIkOQGKXFlKXQg7Ee/AOIrAHT0QOpK+swayXD+LcBT3kchnD3QMx4B2E9+CJyFHKDQKmLt6Dzb/l4GiaTupySCIconcQo04HL7Uaci9+yYnIfnQVteH+x2kt3n2mJ0YnRkpdEkmEaeMgQmkpe+9EZFel5Ubc8dweHM/Q4cMFvXBrQoTUJZGEGPAOIuh0vP9ORHalkMvgp1Tg44U3Y9jNLaUuhyTGgHcQIw+aISI7KdRWQ+WrQKDKG5tXJXJfeQLASXYOw4NmiMgeLhRXYuLc3Zj1ykEAYLiTBQPeQThET0RN7XxhJSbMS0FucSUeHM/d6cgah+gdhGfBE1FTOndBj6QFqdBVGPH1833Ro32w1CWRk2HAO4CpqopHxRJRkxFFEQ+s/B3legFfr+iLbm2bS10SOSEGvANwkxsiakoymQxvPN4dJpOITvH8uUL14z14B+BBM0TUFE6eK8ObX52GKIroEBvIcKcGMeAdgAfNENH1Opauw8R5Kfh4WzqKy4xSl0MugAHvADxohoiuxx9nSjFpfgr8fBT4dlU/tGjmI3VJ5AIY8A5guQfPgCeia3TwZAkmL0hFgMobm1f1Q1ykWuqSyEUw4B3AyCF6Imqk84WVCA/2xber+qFVuL/U5ZAL4Sx6B7AM0TPgichG2nIjggKUGNs/CiP7REDpzf4YXRt+xziAoNXCKyAAMoVC6lKIyAXsOlSAXjN+wq+HCwCA4U6Nwu8aBzBqtey9E5FNftp/Af9aug+twv3RUcOfG9R4DHgHEHQ6KDnBjoiu4vvUXMxYsR8d4gLxzYuJnC1P14UB7wA8aIaIruavNB1mvvg7urQJQvKKvmgeoJS6JHJxnGTnAIJWC3XbtlKXQUROrKMmEEtndsIdQ2Og9veWuhxyA+zBO4CRQ/REdAUb/5uNtPMVkMlkuH+shuFOTYYB7wACJ9kRUT3W/ZCJWa8cwptfnZa6FHJDHKK3M1NlJczV1bwHT0RWPtySjmffO4qhvcLx0iNdpS6H3BAD3s6MPCqWiP5hzcazWPbxMdya0BLvze3Fde5kFwx4O6vbxY7b1BIRAAg1Zvy4Lw9j+0fi7ad6wNuL4U72wYC3M8tBM82bS1wJEUlJFEUYa8zw8Vbg86UJ8FHK4aVguJP98LvLzgQO0RN5PFEU8cKnJ3D7wj2orDZB5efFcCe743eYnRnrhug5yY7II4miiCUfHcPq5DO4oXUAfHi/nRyEQ/R2xpPkiDyX2Szi2feO4uNtGbh/rAbLH+gEmUwmdVnkIRjwdmYJ+MBAiSshIkdb+dkJfLwtAw9NiMfi+zoy3MmhGPB2ZtTp4BUYyKNiiTzQlCExUPt5Yfbktgx3cjjeDLIzQafjEjkiD1JjMiP5l2yIoog20QF4dMoNDHeSBHvwdiZotVwiR+QhhBozHn7pILal5CIixBf9uoZKXRJ5MAa8nQk6HSfYEXmAasGEB1f+ju17L2Dp/Z0Y7iQ5DtHbmVGr5RA9kZurMpow4/kD2L73Al54uDMenBAvdUlE7MHbm6DV8qAZIjf3x2kt/vdHIV6Z3RV3jYyVuhwiAAx4uxJFkUP0RG5MFEXIZDL06RSCPR8ORVSon9QlEVlwiN6OTJWVMBuN7METuaFyg4CJ81Ow5X/nAYDhTk6HAW9HdfvQc5taIveiqxBw+7N7cOB4idSlEF0Rh+jtiNvUErmfkjIj7ng2FSfOleHD+b0wMiFC6pKI6sWAtyPLSXLswRO5BX1lDZIWpCAtpwJrn+2Nob3CpS6J6IoY8HZkrBuiZw+eyC34+yow/OaWSLgvBAO6h0ldDlGDGPB2JJSWAmAPnsjV5RVVoswgoF2rQMz7VwepyyGyCQPejixD9OzBE7msnAIDkhakQi4Dfnt3MLwUnJtMroEBb0dGrRaQyXhULJGLOpenx6QFKSjTC/hyWQLDnVwKA96O6ja5kcn5Q4HI1aSfr0DSghRUVpvwzQuJ6NKGt9rItTDg7Yi72BG5rlc/P4VqwYyNLybixjj+OybXY3PAp6Sk4D//+Q9KSkrw7rvv4ujRo6ioqEBCQoI963NpglbLgCdyUS/N7or84ipootRSl0LUKDaNHX/22WdYsmQJYmNjceDAAQCAr68v3nzzTbsW5+qMWi13sSNyIUfTtLhryV6UGwSofL0Y7uTSbAr4Tz/9FGvXrsUDDzwA+cX7yRqNBhkZGXYtztVxiJ7IdRw+XYqk+ak4kVmG0nJB6nKIrptNAa/X6xERUbsdo0wmAwDU1NTA29vbfpW5AR4VS+QaDhwvwZSFqWim9sa3L/VDq3B/qUsium42BXyvXr3w/vvvW722bt069O7d2y5FuQNRFGFkD57I6e07Vozbn0tFaJAPvn2pH2LCGO7kHmyaZPfss8/ioYceQnJyMvR6PUaMGAGVSoX33nvP3vW5LJPBAFEQeA+eyMm1DPZFrw7B+PcTNyE82FfqcoiajE0BHxYWho0bN+Lo0aM4f/48IiIi0KVLF8v9eLocD5ohcm4nMsvQrlUAWkeo8NXzfaUuh6jJ2ZTQDz/8MGQyGbp06YJbb70V3bp1g1wuxyOPPGLv+lyWwINmiJzWjn0XMOKxX/HO5rNSl0JkNzYF/L59++p9ff/+/U1ajDsx8qAZIqf0n5RczFixHzdqAjFteGupyyGymwaH6OvWuQuCcNma9+zsbERGRtr8RhkZGZg3bx60Wi2CgoKwatUqxMbGXtbu+++/xzvvvANRFCGTybB27Vq0aNHC5vdxFjxohsj5fPvbecx6+SC63xCEz5clIFDFlUDkvhoM+AsXLgConRFe97hOREQEZs+ebfMbLV68GFOnTsW4cePw3XffYdGiRVi3bp1Vm6NHj+Ktt97Cp59+itDQUJSXl0OpVNr8Hs7EqNUCYMATOYvC0irMeeMwenUIxvolvaH2Z7iTe2sw4F988UUAQPfu3TFlypRGv0lxcTGOHz+OtWvXAgBGjx6N5cuXo6SkBMHBwZZ2n3zyCWbMmIHQ0FAAQEBAQKPfU2qWe/AcoidyCqHNffHFsgR0btMMKl8ew0Huz6bv8rpwr6ioQOnFe8t1YmJirvrxeXl5CA8Ph0KhAAAoFAqEhYUhLy/PKuDT0tIQHR2NadOmwWAwYNiwYZYJfq5G0OkAuRxeLvxLCpE7+PT7DPj7emHy4Bj06RQidTlEDmNTwKelpeHJJ5/EyZMnIZPJLPfHAeDEiRNNVozJZMKpU6ewdu1aGI1G3H///YiMjMT48eOb7D0cpe6gGR4VSySdD7ek4dn3/sKtCS2RNCjaJTsLRI1lU/osWbIEvXv3xv79+6FWq3HgwAHcfvvtWLlypU1vEhERgfz8fJhMJgC1QV5QUGDZ/rZOZGQkRo4cCaVSCbVajSFDhuDIkSPXeEnOQdDpuESOSEJvbzyDZ9/7C6P6RuC9ub0Y7uRxbAr4kydP4qmnnkJgYCBEUURAQACeeeYZm0+TCwkJQYcOHbBt2zYAwLZt29ChQwer4Xmg9t787t27IYoiBEHA3r170b59+2u8JOdgLC3lBDsiibzx5Sks//g4xt0ShXfn9oTSmyNp5Hls+q738fFBTU0NAKB58+bIzc2F2WyG9uJMcVssWbIE69evx4gRI7B+/XosXboUADBz5kwcPXoUADBq1CiEhITgtttuw/jx49GmTRskJSVd6zU5BUGn4xp4IokYa0QkDY7G20/dBG8vhjt5Jpvuwffo0QM//PADJk6ciBEjRmDmzJlQKpXo06ePzW8UHx+P5OTky17/4IMPLI/lcjnmz5+P+fPn2/x5nZVRp4NfdLTUZRB5DFEUkVdchcgWfnh6WjuIIiCXc1iePJdNAX/pUPwTTzyBNm3awGAwYMKECXYrzNUJWi2XyBE5iCiKWPzBX0j+JRs/rx6EqFA/8JY7ebprHruSy+UYP348kpKSsGnTJnvU5PJEUeQQPZGDmM0i5r9zBO9/l46kwTGIbMET4YgAGwJ+z549+Pjjj/Hzzz8DAGpqarBu3ToMGTIEX375pd0LdEUmgwFiTQ0n2RHZmdks4um3/sQn/8nErEltsGxmJ86WJ7qowSH6999/H++88w7atGmDs2fP4s4778T+/fuhVCqxfPlyDBw40EFluhbh4uRDDtET2ddHW9Ox4cdzmHPHDXjmrvYMd6JLNBjwX331FT777DN06tQJf/zxB+68807MnTsX99xzj4PKc03ch57IMe6+NRbBgUpMGnT1HTWJPE2DQ/SlpaXo1KkTAKBbt25QKpWYPn26QwpzZXU9eN6DJ2p6RsGMFz49Dm25Eb5KBcOd6Aqueg9eFEWYzWaYTCb4+PgAAMxms+UPXc7Io2KJ7KJaMOH+Fw7g31+fwS8HC6Quh8ipNThEbzAYcOONN1qei6JoeV63H31T7kXvLngPnqjpVVabMGPFfvz3YAFefLgLJg7kPhNEDWkw4Hfu3OmoOtxK3VGxHKInahr6qhrcs2wfdh8pwquPdsO0Ea2lLonI6TUY8FFRUY6qw60IOh1kCgW81GqpSyFyC2UVArILKvHmnO6YMqSV1OUQuQSbdrKja1N30AyX7BBdH31lDXyVCkS08MOuNYPgq1RIXRKRy+ApDHYg6HScYEd0nbTlRkxakIL579YeGc1wJ7o2DHg7YMATXZ+SMiMmL0zF8fQyDO0ZLnU5RC7pmgI+Ly8Pf/zxh71qcRtGrZYT7IgaqVBbjUnzU3AmuxyfPHczhvduKXVJRC7JpoDPzc3FHXfcgVtvvRX33nsvAGD79u1YuHChXYtzVYJOxyVyRI1gNou4a8leZObp8dniPhjM3jtRo9kU8IsWLcLAgQNx6NAheHnVzstLTExEamqqXYtzVYJWyyF6okaQy2VYML0DPl/WB/27hUpdDpFLs2kW/dGjR/H+++9DLpdbZoYHBASgvLzcrsW5Ih4VS3TtsgsMOHC8BBMHRmNA9zCpyyFyCzYFfEhICM6dO4e4uDjLa2fPnkVERITdCnNVNRUVEE0mKNmDJ7JJZp4ek+anwFBVg8E9whAUoJS6JCK3YNMQ/YwZM/DQQw9h48aNqKmpwbZt2zBnzhzMnDnT3vW5HB40Q2S7sznlmDB3NwxVNfh6RV+GO1ETsqkHn5SUhKCgIHz11VeIiIjAt99+i8ceewxDhw61d30uR+BBM0Q2OZVVhskLUmEyi9i0sh86xAZKXRKRW7Ep4E0mE4YOHcpAtwHPgieyzf/+KAQAbFqZiHatGO5ETc2mgE9MTMTIkSMxZswY9OjRw941ubS6HryyeXOJKyFyTkKNGd5ectw/Nh6TBsWgOYfliezCpnvwH3/8Mfz9/fHkk09i8ODBePXVV3Hq1Cl71+aSOERPdGWHTpWi7wM78eeZ2pEuhjuR/dgU8DfeeCOeeeYZ7Nq1CytXroROp8P06dMxZswYe9fncjjJjqh+B46XYMrCVMhlQHAzBjuRvV3zXvQajQbx8fGIjIzE+fPn7VGTSzNqtbVHxapUUpdC5DRSjxbh9udSEdbcB5tX9UNMmL/UJRG5PZvuwZeVleHHH3/Etm3b8OeffyIxMRH3338/hgwZYu/6XE7dLnY8Kpao1pGzWkxbvBfRYX745oVEhAf7Sl0SkUewKeD79++P7t27Y/To0Vi9ejUCAznj9Uq4ix2RtfatA/GvW2PxyOS2CA3ykbocIo9hU8D/9NNPCAvj9pG2MPKgGSIAwK+HC9BR0wwtmvlg6cxOUpdD5HGuGPAHDhxAr169AABpaWlIS0urt11CQoJ9KnNRglbLJXLk8bal5OKhVb8jaXAM3ni8u9TlEHmkKwb80qVLsW3bNgC44rGwMpkMO3futE9lLkrQ6aC6ZM9+Ik+zeVcOHnn1EG5q1xzLH2DPnUgqVwz4unAHgF9++cUhxbgDQafjGnjyWF/9nIU5bx7GzTeGYMOSPlD52XQXkIjswKZlcg8//HC9rz/yyCNNWoyrE81mGLVa3oMnj1QtmLA6+QwSO7fAhqUMdyKp2fQvcN++ffW+vn///iYtxtXVVFQAZjN78ORxRFGEj7cCG19MRKDKG34+CqlLIvJ4DQb8m2++CQAQBMHyuE52djYiIyPtV5kLEnjQDHmg9zan4chZLf79xE1c407kRBoM+AsXLgCo/e287nGdiIgIzJ49236VuSAjD5ohD7M6+QxWfHIcoxIjYBZFKMANnoicRYMB/+KLLwIAunfvjilTpjikIFfGg2bIk7z2xSm8tP4kJgyIwuonb4KX4pp3viYiO7piwOfk5CA6OhpA7Vr37OzsetvFxMTYpzIXxINmyFO8/mVtuE8eEoM3HusOhYI9dyJnc8WAHzNmDA4fPgwAGDZsGGQyGURRtGojk8lw4sQJ+1boQowXA17JHjy5ud4dQzBjdByef7Az5HKGO5EzumLA14U7AJw8edIhxbg69uDJnYmiiL3HipHQqQX6dq79Q0TOq1E3zbKzs5GTk9PUtbg8QaeDzMsLCn8ehUnuxWwWMXfNEUyYm4IDx0ukLoeIbGBTwD/xxBM4dOgQAGDjxo0YNWoURo8ejeTkZLsW52rqdrHjUbHkTkwmEU/++w+s+z4TjyS1Qc8OXCVC5ApsCvg9e/agU6faPaU/+eQTrF27FsnJyfjggw/sWpyrMfKgGXIzNSYzHnvjEL74KQtP3NkOC++5kb/AErkIm3ayEwQBSqUS+fn50Gq16NGjBwCgqKjIrsW5Gu5DT+7m18OF+OaXHMy9uz3m3NFO6nKI6BrYFPAdOnTAe++9h/Pnz2PgwIEAgPz8fKjVanvW5nIErRbKkBCpyyBqMkN6huOH129B9xs4MkXkamwaol+xYgVOnz6N6upqPPbYYwBqZ9mPGTPGrsW5Gh40Q+6gWjDh4Zd+x+8naifTMdyJXJNNPfhWrVrh1VdftXpt5MiRGDlypF2KclUcoidXV1ltwozn9+O/hwrQr2soenYIlrokImokm89z3LhxI7777jvk5+cjPDwc48aNw6RJk+xZm0sRzebagGcPnlyUvqoG05ftQ8qRIrz2WDdMHd5a6pKI6DrYFPDvvPMOvv32W8yYMQORkZHIzc3Fhx9+iIKCgiueFe9pasrLAVFkD55ckr6yBtMW78X+E8VY/cRNSBrMLaiJXJ1NAZ+cnIzPPvsMUVFRltf69euHu+66iwF/kWWbWvbgyQX5KOWIaOGLNU/3xPhboq7+AUTk9GwK+MrKSgQHW9+LCwoKQlVVlV2KckWWk+QY8ORCtOVGGAUzwoJ98c4zPaUuh4iakE2z6Pv374+nnnoK6enpqKqqQlpaGubNm4d+/frZuz6XwaNiydUU66qRtCAVUxfvhckkXv0DiMil2BTwixYtgkqlwtixY9G9e3eMHz8efn5+eO655+xdn8swlpYC4BA9uYbC0ipMmp+CsznlWHjPjTzulcgNXXWIvry8HFlZWVi0aBFWrlyJ0tJSNG/eHHJ5o86pcVvswZOruFBcickLUpFTWIn1S/qgX9dQqUsiIjtoMKV37dqF/v37Y9KkSRgwYAD279+PkJAQhns9LEfFMuDJyc1bcwS5xZX4YlkCw53IjTWY1G+++SaeeuopHD58GI8++ijeeOMNR9Xlcow6HWTe3jwqlpzeS7O6InlFIvp04rbKRO6swYDPzs7GXXfdBT8/P0ybNg3nzp1zVF0uR9DpoAwK4klb5JQy8/SY/84RCDW1M+ZvasftZ4ncXYMBbzabLY+9vLxgMpnsXpCrErRaLpEjp3Q2pxzj5+7Gd7+dR06BQepyiMhBGpxkV1VVhWnTplme6/V6q+cAsGHDBvtU5mK4Dz05o5PnyjB5QSpEABtfTERcJE+AJPIUDQb8ihUrrJ4nJSXZtRhXZtRq4RvKCUvkPI6l6zB5YSq8vWRIXpGIG1oFSF0SETlQgwE/YcIER9Xh8gStFgFt20pdBpFFldGEkGZKfPpcb2ii2HMn8jQ2nyZHDeMQPTmLgpIqhAX7okf7YOx6ezA3sSHyUFzQ3gREsxlCWRl3sSPJ7TtWjL4P7MTnP9aueGG4E3kuBnwTEMrKao+KZcCThFKOFOHORXvQMsQXg3qESV0OEUmMQ/RNgNvUktR+PVyAe5bvR0y4P75Z0Rdhwb5Sl0REErOpB280GvH6669jyJAh6NGjBwBg9+7dWL9+vV2LcxWWbWrZgycJ5BZVYvqyfYiLVGHTykSGOxEBsDHgX3jhBZw+fRqvvPKKZae2tm3b4osvvrBrca7CeDHglezBkwQiW/jhjce745sXEtGimY/U5RCRk7BpiP7nn3/Gjh074O/vbzloJjw8HPn5+XYtzlXwoBmSwtbduQgOVCKxSwuMHxAtdTlE5GRs6sF7e3tftk1tSUkJgjgkDeCSe/D8epCDbNqVgwdXHcBbyWcgiqLU5RCRE7Ip4EeOHIm5c+ciOzsbAFBQUIBly5Zh1KhRdi3OVRgvBjyXyZEjfPVzFma9chB9OrbAhwt68YAjIqqXTQE/Z84cREdHY+zYsSgrK8OIESMQFhaGWbNm2bs+lyBotZD7+EDh5yd1KeTm1m/PxONvHEb/rqFYv6Q3VH5cCENE9bMp4JVKJRYsWIDDhw8jNTUVhw4dwoIFC6BUKm1+o4yMDNx+++0YMWIEbr/9dmRmZl6xbXp6Orp27YpVq1bZ/PmlxF3syBFEUcTeY8UY1CMM6xb3hr8vw52IrsymnxB1Q/N19Hq95XFMTIxNb7R48WJMnToV48aNw3fffYdFixZh3bp1l7UzmUxYvHgxhg4datPndQbG0lIGPNmVvqoGKl8vvPF4d5jMIny8FVKXREROzqaAHzZsGGQymdVknrr7fidOnLjqxxcXF+P48eNYu3YtAGD06NFYvnw5SkpKEBwcbNX2/fffx8CBA2EwGGAwuMbZ1YJOx/vvZDervz6Nz3/KwpaX+yM0yAdezHYisoFNAX/y5Emr54WFhXjrrbfQs2dPm94kLy8P4eHhUChqfzIpFAqEhYUhLy/PKuBPnjyJ3bt3Y926dVizZo2t1yA5QaeDb3i41GWQmxFFEa99cRovbziJCQOi0DzAW+qSiMiFNGov+tDQUCxcuBCvvfZakxUiCAKee+45LF261PKLgKsQtFoukaMmJYoiVq47gZc3nMSUITF468ke8FLw6Agisl2jZ+mkp6ejsrLSprYRERHIz8+HyWSCQqGAyWRCQUEBIiIiLG0KCwuRlZWFBx54AABQVlYGURRRUVGB5cuXN7ZMhzByiJ6a2Idb0vHm12dw14jWeOmRrpDLuRSOiK6NTQE/depUq7W2lZWVOHv2rM3L5EJCQtChQwds27YN48aNw7Zt29ChQwer4fnIyEjs27fP8nz16tUwGAyYO3eurdciCdFkQk1ZGSfZUZOaODAaVdUmzEpqy3AnokaxKeAnT55s9dzPzw/t27dHbGyszW+0ZMkSzJs3D2vWrEFgYKBlCdzMmTPx6KOPonPnzrZX7USEsjIA3MWOrp/ZLGLdD5mYOrw1Qpr5YPaUG6QuiYhc2FUD3mQyYe/evVi+fPk1rXv/p/j4eCQnJ1/2+gcffFBv+9mzZzf6vRzJyH3oqQmYTCKeXP0HvvwpC2o/LyQNtm35KRHRlVw14BUKBVJSUrgd5hXUHTTDe/DUWDUmMx577TA27srBU9PaYdIgHhxDRNfPpmm506dPx+rVqyEIgr3rcTmWg2bYg6dGEGrMePilg9i4KwcLpnfAU1Pb85dpImoSDfbgt23bhtGjR2P9+vUoKirC2rVrERwcbPUDaNeuXfau0anxqFi6Hln5Bvx2uBBL7u+Ihya0kbocInIjDQb8okWLMHr0aLz88suOqsflWE6Sa95c4krIldSYzPBSyBEfpUbKB0PQopmP1CURkZtpMODrtqa9+eabHVKMK+IQPV0rQ1UNZqzYj76dW+DRKTcw3InILhoMeLPZjL1791rtQf9PCQkJTV6UKxG0Wsh9faHw9ZW6FHIB+qoa/GvpPqQeLcK4W6KkLoeI3FiDAW80GrFw4cIrBrxMJsPOnTvtUpirMGq17L2TTcoNAu5ashcHTpTgrSdvwqRBXApHRPbTYMD7+fl5fIBfjaDTQcmAp6swmURMXbQXh0+X4t1nemJsf/beici+Gr0XPdXiQTNkC4VChjuHt8L/TWqDWxMirv4BRETXyaZJdnRlgk4Hv8hIqcsgJ1Wkq8bprHL07dwCU4e3lrocIvIgDW50c/jwYUfV4bJ4D56upKCkCpPmp+C+FftRbuAmUUTkWByiv06CTscherpMXlElJi9MxfnCSny2uDcC/L2lLomIPAwD/jqYa2pQU17OHjxZySkwIGlBKgq1VfhyeQJ6dwyRuiQi8kAM+Otg2eSGPXi6xGc/ZKKkrBpfP98XPdoHS10OEXkoBvx1qAt4LpMjoHZSqkwmw9y7O+COYa0QF6mWuiQi8mA2nSZH9eNBM1TnTHY5xj69GzkFBsjlMoY7EUmOPfjrIPCgGQJwIrMMkxemQgagorJG6nKIiACwB39djDxoUbI/uAAAIABJREFUxuP9labDpPkp8FLIsHlVItq3DpS6JCIiAAz462IZouckO490PEOHpAUp8PNRYPPKRLSJDpC6JCIiCwb8dTDyHrxHiwr1R2KXFti0MpH33InI6fAe/HUQtFoo/Pyg8OF53p7kyFkt2sYEoJnaGx8tvFnqcoiI6sUe/HUQdDr23j3M7j8LMe6Z3Vj60V9Sl0JE1CAG/HVgwHuWXYcKcNeSvYgJ98cTd7STuhwiogYx4K+DoNVyiZyH+Gn/Bfxr6T7ER6mxaWUiwoJ9pS6JiKhBDPjrYGQP3iNUVpvw9Ft/on1sAL55MREtmnHOBRE5P06yuw6CVgvvjh2lLoPszM9HgS+XJyAixA/N1DwVjohcA3vw18Go1XINvBvb+N9svLzhJACgfetAhjsRuRQGfCOZBQEmvZ4HzbipL346h0dePYQ9R4thFMxSl0NEdM0Y8I0kcJtat7Xuh0zMeeMP3NItFOuX9IbSm/9MiMj18B58I/EsePf00dZ0LHz3KIb2CseHC3rBV6mQuiQiokZhwDdS3UEzSga8WwlSe2NUYgTeebone+5E5NIY8I3Eg2bcS0ZuBeIi1Zg0KAYTB0ZDJpNJXRIR0XVhF6WReA/ePYiiiJc3nMSAh/+LI2drf2ljuBORO2APvpGMpaUA2IN3ZaIo4oVPT2B18hncMawVOsbxlzUich8M+Eaq68FzmZxrEkURSz48hve+TcO/bovFyoe7QC5nz52I3AcDvpHqjoqVK5VSl0KNsC0lD+99m4b7x2qw/IFOHJYnIrfDgG8kQaeDNw+acVmjEyPw0YJeuK1vBMOdiNwSJ9k1klGn4/C8izGZRCz96Bgy8/SQyWQYlRjJcCcit8WAbyRBq+UMehdSYzJj9muH8M6ms/hp/wWpyyEisjsGfCMJOh1n0LsIocaMh1YdxKZdOVh4z42YOS5e6pKIiOyOAd9IRvbgXUK1YMLMFw9gW0oult7fCbMnt5W6JCIih+Aku0YStFpuU+sCampEFOuMePHhLrh3dJzU5RAROQwDvhHMggCTwcAevBMzVNVABKDy88LmVYnwUnCwiog8CwO+EbgPvXPTV9bg7qX74KWQ4avnExjuROSR+JOvEXiSnPMqNwi4c9Ee7DtWjDuGteIyOCLyWOzBNwIPmnFOuoracD9yVot35/bEmH6RUpdERCQZBnwjCDxoxinNeuUgjqZp8eH8XhiZECF1OUREkmLAN4KRPXin9NyMG3FvQRyG9AyXuhQiIsnxHnwj1E2y41a10isoqcI7m85CFEW0axXIcCciuog9+Eaw3IPnEL2k8ooqkbQgBXnFVbg1IQKxESqpSyIichoM+EYQdDooVCrIvb2lLsVj5RQYkLQgFUXaany5PIHhTkT0Dwz4RjByFztJncvTY9KCFJTpBXz9fAJuah8sdUlERE6HAd8Igk7HCXYSOnmuDFVGM5JXJKJrW/6iRURUHwZ8I/CoWGlUVpvg56PAiD4R6Nc1FCo/fvsSEV0JZ9E3glGr5QQ7BzuRWYa+M3/Gjn21Z7kz3ImIGsaAbwRBp+MSOQc6mqbFpPkpEAFoojiZjojIFgz4RuAQveMcPl2K/2/v3uOiqvP/gb/mwkVEFAgQRPOSEWoaiuIFNhMTL4OQgpiXUjY1L+uubW6iSajkbXVLXVofWWq/zFRSMUcWzVveAZNCIy0JryAgOJhcnGHmfP9wmV/E7aAwZxhez8eDx6MZzjBvDsaLz+d8zucdFnUGLWwU2LtyEJ7xbCV1SURETQIDvp4MWi30paWwcnSUuhSLdyO3BOMWnUFreyvsXeWPTh72UpdERNRk8EJmPRk7yXEE3+jau7bA3PCuGPNSe7RzaSF1OURETQoDvp7YC77xnfohH25OtujavhX+Mu5ZqcshImqSOEVfT8aA5wi+URz7Lg+TYs5h8ccXpS6FiKhJY8DXk5Yj+EZzKPkOXl+ajGc8WyHu7T5Sl0NE1KQx4OtJx2vwjeLA6WxEvp8C704OiF8+EM6tbaQuiYioSWPA1xOvwTc8QRCw9UAWXujaBvHvD4RjK2upSyIiavK4yK6ejK1iOYJvEHq9AIVChq2L/SAIAuzt2KGPiKghcARfT9qiIijt7SFX8m+jJ7X90HWMjTqN4tJytGyhZLgTETUgBnw9cRe7hrH1QBbeWvc9WtgoIJfLpC6HiMjicBhaTzo2mnlim/ZlYvHHl/ByPzdsiuoLW2uF1CUREVkcBnw9adlo5ol8lpiFxR9fwqiB7vjPP3xhbcVJJCKixsDfrvXEEfyTCejlgkhVJ2x8h+FORNSYTPYbNisrCxEREQgKCkJERASuXbtW5Zi4uDiMGjUKwcHBGDNmDE6ePGmq8kTTFRUx4OtJEAR8k3IHgiCgczt7LJ/ZE1ZKhjsRUWMy2W/Z9957DxMmTMDBgwcxYcIEREdHVzmmZ8+e+Oqrr7B//34sX74c8+bNQ1lZmalKFIW94OtHEATEbsnA5CXJ+PpUttTlEBE1GyYJ+IKCAmRkZEClUgEAVCoVMjIyUFhYWOm4gIAAtGjxqGuYl5cXBEGA5n8by5gDfVnZo1axHMGLIggCojddQtzuq3h9ZEcED/KQuiQiombDJAGfk5MDNzc3KBSPVksrFAq4uroiJyenxtckJCSgQ4cOaNu2rSlKFIWb3IhnMAhY8FE6Nu37FdNCOmPlrJ68HY6IyITMchV9SkoK1q1bh82bN0tdSiVsNCNeRtZ9bD90HbPGPoPFU7tBJmO4ExGZkkkC3t3dHbm5udDr9VAoFNDr9cjLy4O7u3uVY9PS0jB//nx89NFH6Ny5synKE42NZsTr0aU1Dm8YjGfbt2K4ExFJwCRT9M7OzvD29oZarQYAqNVqeHt7w8nJqdJx6enpmDdvHtavX4/u3buborR6YS/42pXrDZiz5jskfHsLAODVwYHhTkQkEZOtoo+JicG2bdsQFBSEbdu2YcmSJQCAadOm4eLFiwCAJUuWoKysDNHR0QgJCUFISAiuXLliqhLrZLwG7+gocSXmR6szYMaq8/jq2C3cvlsqdTlERM2eya7Bd+nSBfHx8VWe37Rpk/G/d+/ebapyHouWU/TVeqjTY9ry8ziUcgdLp/XA9NAuUpdERNTsmeUiO3PFKfqqdOUGTFmWgmPf5WHFzJ6YquokdUlERAQGfL3oNBooW7WCTMHmKBWUChl6dmkN1SAPTAx6WupyiIjofxjw9aBlq1ijByU65BSUoWv7Voh6vZvU5RAR0R9wQ/B60BUVwZr3wON+sQ7jF59F2MLTKC4rl7ocIiKqBkfw9aArKmr2I3jNb1qMjz6LS5lF2PiOL1ra8p8QEZE54gi+Hpp7q9iCoocIX3QGGb/ex+ZF/aDi3vJERGaLw6960DbzKfq126/g5xu/Yevifhji6yZ1OUREVAsGfD009yn6d6d2Q9hLnuj9nFPdBxMRkaQ4RS+SvrQUhrKyZjdFn323FDNXn8f9Yh3sbJUMdyKiJoIBL5K2GbaKvZlXglfeOYVvUnKRlV0sdTlERFQPnKIXqWIXu+ayTe21nGKMjTqNByXl2PX+QPTq2rxmLoiImjoGvEjGRjPNYIo+8/YDhEWdRplWj/jlA9HzGcv/nomILA2n6EVqTgGvVMjg6mSLPSv9Ge5ERE0UR/AiaSum6C044G/mlaDdUy3wdNuWSPrgT+zlTkTUhHEEL5LOwhfZpV/VYNjc41i97TIAMNyJiJo4BrxIunv3AABWDg4SV9LwLly5h/CFZ2Bnq8T4lztIXQ4RETUABrxI2qIiKB0cLK5VbGpGIcYtOoM2rayQsNofHd1bSl0SERE1AAa8SLqiIou7Ra64tByvL0uGm5MN9q7yR3tXO6lLIiKiBsJFdiLpNBpYOTpKXUaDatlCiY8X+KJr+1Zwc7KVuhwiImpADHiRLGkf+qPnc5GveYiIoR3g38tF6nKIiKgRcIpeJK1GYxFT9IeS72DKshRsVmehXG+QuhwiImokDHiRLKEXvPp0NiLfT0H3zg7YuWwAlAr++ImILBWn6EUQBKHJT9HvPX4Lc9ZeQG8vR2xf2h+t7KykLomIiBoRh3Ai6EtLYdBqm/QI/tqdYvTr7oQdywYw3ImImgGO4EWo2MWuKV6Dv/ebFo6trDFvvBdmj+0Kayv+TUdE1Bzwt70IFa1im9oIfos6CwOnHcbVW78BAMOdiKgZ4W98EZpiJ7mPEzIR9Z909PV2Rns3bmBDRNTccIpeBG0Tm6LfEP8L3t+agVGD3PGf+b4cuRMRNUMMeBGMjWaawAg+4cRtvL81A6F/aod/v92bt8IRETVTDHgRmlKr2BED2mLJGz3wxujOUCjY8pWIqLni8E4ErUYDyGRm2ypWEAR8nJCJwvta2FgpMOOVLgx3IqJmjiN4EXRFRbBycIBMbn5/DwmCgOhNl7Bp368o1wuYNfYZqUsiIiIzwIAXQVdUZJbX3w0GAQv+k47/l3gN00M6Y+aYLlKXREREZoIBL4JOozG76+96vYC3N3yPL7+5gTlhz2DRlG6QyTgtT0REjzDgRdAWFcHazEbw9x5ocebiXbz1qhfmT/RiuBMRUSUMeBF0Gg1auLtLXQYAQFdugFwmw1OtbfDN+sFwaMl95YmIqCrzWzVmhsylVaxWZ8CMlecxb10aBEFguBMRUY0Y8HUQBAFaM2gVW6bV48/LU5B4Ngc9n2nNKXkiIqoVp+jroC8pgaDTSbpNbelDPabGJuP4hXysntMLr43oKFktRETUNDDg62Dcxc7RUbIaZqw8j2/T8vGvv76ACcOelqwOIiJqOhjwdTCHXvDTQjpjdIAHwoa0l6wGIiJqWhjwddBK1Av+frEOJ77Ph2qQBwJecDHpexMRUdPHgK+DriLgTTiC1/ymRcTis/gp6z58PnFEO5cWJntvIiKyDAz4OmhNHPAFRQ8R8e5Z/HzjN3yyqC/DnYiIHgsDvg7Ga/AmmKLPv1eG8EVncC2nGJ9F++GlPq6N/p5ERGSZGPB10BUVAXI5lK1aNfp7fZOai+t3SrAtpj/8e/G6OxERPT4GfB0qGs00ZqtYg0GAXC7DhGFP40UfV07LExHRE+NOdnXQFRU16i1yN3JLMOyv3+LClXsAwHAnIqIGwRF8HbSN2Cr2Wk4xxkadxoOScnDnWSIiakgM+Do0VqOZq7d+Q9jCM9DqDPhqxUA830X6ZjZERGQ5OEVfh8ZoNHP9TjFeeec0yvUCdq8YxHAnIqIGxxF8HXQaTYPfIufxVAsM798W00K64NkOjb86n4iImh8GfC0EQYCuqKjBpugvZRbBzdkWLm1s8M+/vNAgX5OIiKg6nKKvhb6kBEJ5eYNM0V+4XIgxUafw1rq0BqiMiIiodgz4WlTsQ/+kU/TJPxZg3Ltn4eRggxUzezZEaURERLViwNeiIfahP51+F69Gn0VbZ1vsXTkInq52DVUeERFRjXgNvha6J2wVazAIiPnkEjxd7fDV+wPh6mTbkOURERHViAFfC+3/Gs087gheLpfh8/f8oFTK8VRrm4YsjYiIqFacoq+F8Rp8PQM+6WwO5qz9DuV6A9o6t2C4ExGRyTHga1HRKtbK0VH0a74+eRtvrEjFr7eLUfpQ31ilERER1YoBXwtdURFkCgWU9vaijt9z/BbeXH0evb0csTN2AFrZWTVyhURERNVjwNeiotGMTEQnmPijNzF7zXfo3/0pfLmU4U5ERNLiIrta6OrRSa6Dmx2G92+LuLf7wM6Wp5WIiKTFJKqFTkSjmR9/LUL3zq3h190Zft2dTVQZERFR7ThFXwttHa1iN+69isC/HMc3KXdMWBUREVHdGPC10BUV1XiL3PpdPyPmkx8R7O+Bwb1dTVwZERFR7ThFXwtdNSN4QRCw9ssrWPPFFYwZ7In1b/lAqeDfSUREZF4Y8DWoqVXshSv3sOaLK4gY2h7/musDhaLuFfZERESmxoCvQXlxMQS9vsoUfZ/nnLAzdgACerlALme4ExGReWLA10B37x6AR41mBEHAsi0ZCPJrC7/uznjRh9fciahpMhgMuHv3LjQaDfR67rbZVNja2sLT0xNWVuL3WDFZwGdlZWHBggXQaDRo06YNVq1ahY4dO1Y6Rq/XIzY2FidPnoRMJsP06dMRHh5uqhIrqdimVuHggH/8+wd8nnQdNlZy3gpHRE3arVu3IJPJ0LFjR1hZWYnayIukJQgCCgoKcOvWLXTq1En060y2Ouy9997DhAkTcPDgQUyYMAHR0dFVjtm/fz9u3LiBQ4cOYefOndiwYQNu3bplqhIr0Wo0MECGZaeU+DzpOuaO64p/THpOklqIiBpKcXEx2rVrB2tra4Z7EyGTyeDs7IyysrJ6vc4kAV9QUICMjAyoVCoAgEqlQkZGBgoLCysdl5iYiPDwcMjlcjg5OWHo0KFISkoyRYlVlN7TYEvHqdh38SHenuiFqNe8+T8DEVkEuZx3/jQ1j5M/Jvkp5+TkwM3NDQqFAgCgUCjg6uqKnJycKsd5eHgYH7u7u+POHWk2kZHJ5TDIFZg/tgPenvAcw52IiJoU/hlXg3ZBw7D986n4e6SP1KUQEVm8oqIi9OzZE7GxsZWe37BhA1atWlXpuW3btmHBggXGx1lZWZg9ezYCAwMxZswYjB8/HocPH26w2uLi4jB06FAMHToUcXFxNR63e/duBAcHY9SoUXjzzTeh0WiMn/v+++8xevRoBAUFITIyEgUFBcbP/f3vf4e/vz+8vLxQXFzcYHWbJODd3d2Rm5trXLGp1+uRl5cHd3f3KsdlZ2cbH+fk5KBt27amKLEKuVIJhy6dJXlvIqLmRq1Wo1evXjhw4AC0Wq3o1+Xl5WHSpEl4+eWXceTIEezZswcbNmzAgwcPGqSu1NRUJCUlQa1WQ61WIykpCampqVWOy8zMxIcffojPPvsMBw4cQM+ePfGvf/0LwKM7F+bPn4/o6GgcPHgQvr6+WLNmjfG1YWFh2LdvX4PU+3smCXhnZ2d4e3tDrVYDePSD9Pb2hpOTU6Xjhg8fjvj4eBgMBhQWFuLw4cMICgoyRYlERCSh3bt3Y9asWfDy8sKRI0dEv+6LL76An58fQkNDjc+5uLhUevwkEhMTERoaCltbW9ja2iI0NBSJiYlVjvv5558r5dqLL76I/fv3AwAuXboEGxsb+Pr6AgDGjx9faX3ZgAED4Ozc8Hdomew2uZiYGCxYsAAfffQRHBwcjFMu06ZNw9y5c/H8888jJCQEP/zwA4YNGwYAmD17Ntq3b2+qEomImp0bu3bhxpdfNsrX7vDqq+gwblydx12+fBkajQb9+/dHfn4+du/ejREjRoh6j4yMDAwaNEjUsffv38fkyZOr/dwzzzyDtWvXVnk+JycH/fr1Mz52d3evdgT/3HPP4eLFi7h58yY8PT2hVqtRUlICjUZTZX2Zk5MTDAaD8bbxxmKygO/SpQvi4+OrPL9p0ybjfysUCixZssRUJRERkRn46quvEBISAplMhmHDhiE2Nha5ublwc3OrcYHz4yx8dnBwaJSpcADo1KkT3n33XcybNw8ymQyBgYEAAKVSuv3kuJMdEVEz1mHcOFGj7Mai1WqhVqthbW1tDF+dToc9e/Zg5syZcHR0xO3btyu95t69e8ap8G7duuHixYui3utxRvDVrQ374/qxCqNGjcKoUaMAAOnp6di+fTvs7e2rfI3CwkLI5fJGHb0DXEVPREQSOnLkCDp16oQTJ07g6NGjOHr0KDZv3oy9e/cCAPr374+TJ08ab5nWaDRITEyEv78/AGDChAk4e/as8Xo38GjvlYSEhCrvVTGCr+6junAHHq0NS0hIQFlZGcrKypCQkFDj5YP8/HwAwMOHD7F+/XpERkYCAHr06IGysjKcP38eALBjxw4MHz78cU5XvXAET0REkqm4tez3fHx8YDAYkJKSgn79+mHhwoWYNWsW9Ho9BEHApEmTMGDAAACAm5sbPv/8c6xZswYffvgh7OzsYGdnh2nTpjVIfX5+fhg2bJhxZB4aGmq8Jn/kyBEcPXoU77//PgAgKioK2dnZ0Ol0GDlyJF577TUAjzYWWr16Nd577z08fPgQ7dq1wz//+U/je8yZMwfp6ekAHv1B8eyzz+LTTz994tplgiAIT/xVzMCtW7cQGBiII0eOwNPTU+pyiIjM0k8//QRvb2+py6DH8MefXV25xyl6IiIiC8SAJyIiskAMeCIiIgvEgCciamYMBoPUJVA9Pc5yOQY8EVEz0rJlS9y+fRtarfaxQoNMTxAEFBQUwNbWtl6v421yRETNiKenJ+7evYvr16+jvLxc6nJIJFtb23rfIcaAJyJqRuRyOVxdXeHq6ip1KdTIOEVPRERkgRjwREREFshipuj1ej0AGPcrJiIismQVeVeRf39kMQFfscn/xIkTJa6EiIjIdPLz8/H0009Xed5i9qIvKyvDpUuX4OLiAoVCIXU5REREjUqv1yM/Px89evSo9hY6iwl4IiIi+v+4yI6IiMgCMeCJiIgsEAOeiIjIAjHgiYiILBADnoiIyAIx4ImIiCwQA56IiMgCNfuAz8rKQkREBIKCghAREYFr165VOUav12PJkiUYOnQoXn75ZcTHx5u+UDMn5jzGxcVh1KhRCA4OxpgxY3Dy5EnTF2rGxJzDCr/++it69eqFVatWma7AJkDsOUxMTERwcDBUKhWCg4Nx9+5d0xZqxsScw4KCAkyfPh3BwcEYMWIEYmJi2Hr2d1atWoUhQ4bAy8sLP//8c7XHmCRXhGZu8uTJQkJCgiAIgpCQkCBMnjy5yjF79+4VIiMjBb1eLxQUFAgBAQHCzZs3TV2qWRNzHk+cOCGUlJQIgiAIP/30k9CnTx+htLTUpHWaMzHnUBAEoby8XJg0aZLw1ltvCStXrjRliWZPzDlMT08XRowYIeTl5QmCIAj3798XysrKTFqnORNzDmNjY43/9rRarRAWFiYcOHDApHWas9TUVCE7O1t46aWXhCtXrlR7jClypVmP4AsKCpCRkQGVSgUAUKlUyMjIQGFhYaXjEhMTER4eDrlcDicnJwwdOhRJSUlSlGyWxJ7HgIAAtGjRAgDg5eUFQRCg0WhMXq85EnsOAeDjjz/G4MGD0bFjRxNXad7EnsOtW7ciMjISLi4uAIBWrVrBxsbG5PWaI7HnUCaTobi4GAaDAVqtFjqdDm5ublKUbJZ8fX3h7u5e6zGmyJVmHfA5OTlwc3Mz7l2vUCjg6uqKnJycKsd5eHgYH7u7u7Nr3e+IPY+/l5CQgA4dOqBt27amKtOsiT2Hly9fxqlTpzBlyhQJqjRvYs9hZmYmbt68iYkTJ+KVV17BRx99BIE7dgMQfw5nzZqFrKws+Pv7Gz/69OkjRclNlilypVkHPEkjJSUF69atw9q1a6UupUnR6XRYvHgxlixZwoZKT0Cv1+PKlSvYsmULPv/8c5w4cQL79u2TuqwmJSkpCV5eXjh16hROnDiB8+fPc1bTDDXrgHd3d0dubq6xl65er0deXl6VqRV3d3dkZ2cbH+fk5HDk+TtizyMApKWlYf78+YiLi0Pnzp1NXarZEnMO8/PzcePGDUyfPh1DhgzBZ599hl27dmHx4sVSlW1WxP479PDwwPDhw2FtbQ17e3sEBgYiPT1dipLNjthzuG3bNowePRpyuRytWrXCkCFDkJycLEXJTZYpcqVZB7yzszO8vb2hVqsBAGq1Gt7e3nBycqp03PDhwxEfHw+DwYDCwkIcPnwYQUFBUpRslsSex/T0dMybNw/r169H9+7dpSjVbIk5hx4eHkhOTsbRo0dx9OhRvP766xg3bhyWLVsmVdlmRey/Q5VKhVOnTkEQBOh0Opw7dw7PPfecFCWbHbHn0NPTEydOnAAAaLVanD17Fl27djV5vU2ZSXKlQZfsNUFXr14VwsLChGHDhglhYWFCZmamIAiC8MYbbwjp6emCIDxatRwdHS0EBgYKgYGBwo4dO6Qs2SyJOY9jxowR/Pz8hNGjRxs/Ll++LGXZZkXMOfy99evXcxX9H4g5h3q9Xli+fLkwfPhwYeTIkcLy5csFvV4vZdlmRcw5vH79ujBlyhRBpVIJI0aMEGJiYgSdTidl2WZl2bJlQkBAgODt7S0MHDhQGDlypCAIps8V9oMnIiKyQM16ip6IiMhSMeCJiIgsEAOeiIjIAjHgiYiILBADnoiIyAIx4InM2OTJk82+e+HXX3+NyMjIGj9//vx57htBJAEGPJGJDBkyBD179oSPj4/xIzc31+R1TJ48Gc8//zx8fHzg5+eHOXPmIC8v77G/3ujRo7F582bjYy8vL1y/ft342NfXFwcPHnyimquzYcMGdO/eHT4+PvD19cX48eORlpYm+vV/rJPI0jDgiUxo48aNSEtLM35I1YErOjoaaWlpOHjwIO7fv48VK1ZIUseTGjFiBNLS0nDu3Dn4+fnhr3/9q9QlEZkNBjyRhIqKijBjxgz0798fffv2xYwZM2rsKHX9+nVMmjQJffr0gZ+fH/72t78ZP5eZmYmpU6eiX79+CAoKQmJioqj3b9OmDYKCgvDLL78AAC5cuICxY8eiT58+GDt2LC5cuGA8ds+ePQgMDISPjw+GDBmCr7/+2vj8q6++CgCYOHEiACAkJAQ+Pj5ITExEcnIy/vSnPwF41Op27ty5lWqIjY1FbGwsAOC3337DwoUL4e/vj4CAAHzwwQfGfdFro1QqERwcjNzcXGNr0/T0dERERMDX1xf+/v5YunQptFptjXUCwLFjxxASEmKcEbh8+bKo80hkjhjwRBIyGAwYM2YMjh07hmPHjsHGxgZLly6t9th169Zh0KBBSE1NxYkTJzBp0iQAQElJCSIjI6FSqXDmzBl88MEHWLJkCa5evVrn+xcWFuLgwYPw9vaGRqPBjBkzMHnyZCQnJ2Pq1KmYMWMG7t27h5KSEsTGxmLTpk1IS0vDjh074O3tXeXrffFQGHz/AAAFOUlEQVTFFwCAffv2IS0tDSNHjqz0+VGjRuHbb7/FgwcPADxqZpKUlGTsP75gwQIolUocOnQICQkJOH36tKg1CFqtFgkJCWjTpg0cHBwAAHK5HFFRUTh37hx27NiBs2fPYvv27TXWmZGRgYULF2Lp0qVITk5GREQEZs2aZfyjgKipYcATmdDs2bPh6+sLX19fzJo1C46OjggKCkKLFi1gb2+PmTNnIjU1tdrXKpVKZGdnIy8vDzY2NvD19QUAHD9+HO3atcPYsWOhVCrRrVs3BAUF1dq+MzY2Fr6+vggJCYGLiwuioqJw/PhxPP300wgNDYVSqYRKpULnzp1x7NgxAI8C85dffkFZWRlcXV0fq7lIu3bt0K1bNxw+fBgAcO7cOdja2uKFF17A3bt38e2332LhwoWws7ODs7MzpkyZggMHDtT49ZKSkuDr64tevXohPj4e69evh1KpBAD06NEDL7zwApRKJTw9PREREVHjuQWAnTt3IiIiAr169YJCocArr7wCKysrfP/99/X+PonMgVLqAoiak7i4OAwcOND4uLS0FCtWrMDJkydRVFQEACguLoZer6/S833+/PlYt24dwsLC0Lp1a0ydOhVhYWG4ffs20tPTjYEPPBoZjx49usY63n33XYSHh1d6Li8vDx4eHpWe8/DwQG5uLuzs7PDBBx9g8+bNWLRoEXr37o133nkHXbp0qfc5UKlUUKvVCA0NhVqtNo7es7OzUV5eDn9/f+OxBoOh2rbDFYYPH441a9agsLAQc+fOxY8//gg/Pz8AQFZWFlauXIlLly6htLQUer2+1i6G2dnZSEhIwLZt24zP6XS6J1qASCQlBjyRhDZv3oysrCzs2rULLi4u+OmnnxAaGorqekC5uLgYr1WfP38eU6dORd++feHu7o6+fftiy5YtT1SLq6trpf7UwKMe1QEBAQCAgIAABAQEoKysDB9++CEWL15snPKujxEjRmDVqlW4c+cOvvnmG+zcuRMA0LZtW1hbW+PcuXPGUbhYTk5OWLp0KcaOHQuVSgVXV1fExMSgW7duWLt2Lezt7bF169ZaV/O7u7vjzTffxMyZM+v9PRGZI07RE0mouLgYNjY2cHBwgEajwb///e8aj/3vf/9rXIDXunVryGQyyOVyDB48GNeuXUNCQgJ0Oh10Oh3S09ORmZlZr1pefPFFXLt2Dfv370d5eTkSExNx9epVDB48GHfv3sXhw4dRUlICa2tr2NnZQS6v/tfHU089hZs3b9b4Pk5OTujXrx+ioqLg6elpnAVwdXXFoEGDsHLlSjx48AAGgwE3btxASkqKqPo7d+6MgIAAfPLJJwAenduWLVuiZcuWyMzMxJdffllrneHh4dixYwd++OEHCIKAkpISHD9+3LhegKipYcATSej111/Hw4cP0b9/f0RERBhHy9W5ePEiwsPD4ePjg5kzZ2LRokVo37497O3t8emnnyIxMREBAQHw9/fHmjVr6r04zNHRERs3bsSWLVvg5+eHTz75BBs3boSTkxMMBgO2bt2KgIAA9OvXD6mpqYiJian268yZMwcLFiyAr69vjav5KxYEVkzPV1i9ejV0Oh1GjhyJvn37Yu7cucjPzxf9Pfz5z3/Grl27UFBQgHfeeQdqtRq9e/fG4sWLqyz4+2Odzz//PJYtW4alS5eib9++GDZsGPbs2SP6vYnMDfvBExERWSCO4ImIiCwQA56IiMgCMeCJiIgsEAOeiIjIAjHgiYiILBADnoiIyAIx4ImIiCwQA56IiMgCMeCJiIgs0P8BdpozTqb3nsUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m \u001b[96m \n",
            " ---------------------------------------------------------------------------------------------------- \n",
            " \u001b[0m\n",
            "\u001b[1m \u001b[96m #####  RandomForestClassifier algorithm  #####\n",
            " \u001b[0m\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.94      0.94       107\n",
            "           1       0.90      0.88      0.89        64\n",
            "\n",
            "    accuracy                           0.92       171\n",
            "   macro avg       0.91      0.91      0.91       171\n",
            "weighted avg       0.92      0.92      0.92       171\n",
            "\n",
            "ROC AUC score:  0.91\n",
            "Accuracy Score: 0.918\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m \u001b[96m \n",
            " ---------------------------------------------------------------------------------------------------- \n",
            " \u001b[0m\n"
          ]
        }
      ],
      "source": [
        "results = pd.DataFrame(columns=[\"DT\",\"RF\"], index=range(3))\n",
        "\n",
        "# Some parameters previously found as best parameters have not been set because they are already present by default\n",
        "classifier = [DecisionTreeClassifier(max_depth=DT_best.best_params_[\"max_depth\"],random_state=DT_best.best_params_[\"random_state\"]),RandomForestClassifier(max_depth=RF_best.best_params_[\"max_depth\"], max_features=RF_best.best_params_[\"max_features\"], n_estimators=RF_best.best_params_[\"n_estimators\"], random_state=12)]\n",
        "names = [\"DecisionTreeClassifier\", \"RandomForestClassifier\"]\n",
        "k = 0\n",
        "\n",
        "for i in classifier:\n",
        "    print(\"\\033[1m\", \"\\033[96m\", \"##### \", names[k], \"algorithm  #####\\n\", \"\\033[0m\")\n",
        "    i.fit(X_train_res, y_train_res)   # fit testing data\n",
        "    y_pred = i.predict(X_test)   # use predict(...) function that works on top of the testing model and makes use of the learned label\n",
        "    y_prob = i.predict_proba(X_test)[:,1]   # predict probabilities, using predict_proba(...) function\n",
        "\n",
        "    # Metrics\n",
        "    results.iloc[0, k] = round(precision_score(y_test, y_pred), 2)   # compute the precision\n",
        "    results.iloc[1, k] = round(roc_auc_score(y_test, y_pred), 2)   # compute the Area Under the Receiver Operating Characteristic Curve (ROC AUC)\n",
        "    results.iloc[2, k] = round(accuracy_score(y_test, y_pred), 3)   # compute the accuracy classification score\n",
        "    cm = confusion_matrix(y_test, y_pred)   # compute the confusion matrix\n",
        "    \n",
        "    print(classification_report(y_test, y_pred))   # build a text report showing the main classification metrics, using classification_report(...) function\n",
        "    print(\"ROC AUC score: \", results.iloc[1, k])\n",
        "    print(\"Accuracy Score: {}\\n\".format(results.iloc[2, k]))\n",
        "\n",
        "    # We visualize the confusion matrix\n",
        "    plt.figure(figsize = (8, 5))\n",
        "    sns.heatmap(cm, cmap=\"Blues\", annot=True, fmt=\"d\", linewidths=5, cbar=False, annot_kws={\"fontsize\":15}, yticklabels=[\"Benign\", \"Malignant\"], xticklabels=[\"Predicted Benign\", \"Predicted Malignant\"])\n",
        "    plt.yticks(rotation=0)\n",
        "    plt.title(\"Confusion Matrix\", y=1.1, weight=\"bold\", fontsize=18)\n",
        "    plt.show()\n",
        "\n",
        "    # We visualize the ROC curve\n",
        "    print()\n",
        "    false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_prob)\n",
        "    roc_auc = auc(false_positive_rate, true_positive_rate)\n",
        "\n",
        "    sns.set_theme(style=\"white\")\n",
        "    plt.figure(figsize=(8, 8))\n",
        "    plt.plot(false_positive_rate, true_positive_rate, color =\"#b01717\", label=\"AUC = %0.3f\" % roc_auc)\n",
        "    plt.legend(loc=\"lower right\")\n",
        "    plt.plot([0, 1], [0, 1], linestyle=\"--\", color=\"#174ab0\")\n",
        "    plt.axis(\"tight\")\n",
        "    plt.ylabel(\"True Positive Rate\")\n",
        "    plt.xlabel(\"False Positive Rate\")\n",
        "    plt.title(\"ROC Curve\", y=1.1, weight=\"bold\", fontsize=18)\n",
        "    plt.show()\n",
        "    k += 1\n",
        "\n",
        "    print(\"\\033[1m\", \"\\033[96m\", \"\\n\", \"-\"*100, \"\\n\", \"\\033[0m\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "50MkcNyZpmNz"
      },
      "source": [
        "## **6. Conclusion**\n",
        "In this section we conclude by viewing the metrics of the models obtained previously in a heatmap, allowing us to compare them with each other."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "vI-ILX_EA_We",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 686
        },
        "outputId": "055561d7-b975-4c98-ed97-dfcee8b0219c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DT 0.8985105140186915\n",
            "DT TUNING 0.9016501168224299\n",
            "RF 0.9063230140186916\n",
            "RF TUNING 0.9094626168224299\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fadfaf90d50>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABIUAAAJBCAYAAADY5QU1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeZxcdZ3/+/c5p+rUXr2kOztZCCTkGoyC+yhiRDZxcMBtEOYhjDAPYeQ6Mo+fgo4KjjP6u+N1BHcGGRAeLlxzB2VEGX6Mo4yacRqvJqLwE0GMJJ3upKu7a68659w/aumqrqU7Saeru+r1fExMqur06W+H1CR557MYnud5AgAAAAAAQE8xO30AAAAAAAAALD5CIQAAAAAAgB5EKAQAAAAAANCDCIUAAAAAAAB6EKEQAAAAAABAD/J1+gCSlM1mtW/fPg0PD8uyrE4fBwAAAAAAYNlzHEdjY2PasWOHgsFgw+tLIhTat2+f3v72t3f6GAAAAAAAAF3n3nvv1Yte9KKG55dEKDQ8PCypdMjVq1d3+DTHb9++fdqxY0enjwEsebxXgPnhvQLMD+8VYG68T4D56Zb3ysGDB/X2t7+9mrvMtiRCoUrL2OrVq7V+/foOn+b4jY6OdsXXAZxovFeA+eG9AswP7xVgbrxPgPnptvdKq1E9DJoGAAAAAADoQYRCAAAAAAAAPWhJtI+1UygUtH//fmWz2U4fZd58Pp9+9atfdfoYJ0QwGNT69evl9/s7fRQAAAAAAHAclnwotH//fsViMW3atEmGYXT6OPOSSqUUiUQ6fYwF53meDh8+rP3792vz5s2dPg4AAAAAADgOS759LJvNasWKFcsmEOpmhmFoxYoVy6pqCwAAAAAANLfkQyFJBEJLCP8tAAAAAADoDssiFAIAAAAAAMDCWvIzhZaaXbt2ybZt2batTCajU045RVdffbWefvpp3X333ZKk5557TqFQSAMDA5KkW265RTt37uzksQEAAAAAAOoQCh2DW2+9VVu3bpUkPfTQQ7rmmmt0xx136P7775ck/fVf/7Ve8IIX6PLLL+/kMQEAAAAAAFqifew4nXvuuXrb296mO+64o9NHAQAAAAAAmLdlVSn0yH8/q3/7r2dPyL1f95IN2vWiDcf0sTt37tQjjzyywCcCAAAAAAA4cagUWgCe53X6CAAAAAAAAEdlWVUK7XrRsVfznEh79+7Vqaee2uljAAAAAAAAzBuVQsfp4Ycf1le/+lVdddVVnT4KAAAAAADAvC2rSqGl4vrrr6+upN+yZYu+9KUvsXIeAAAAAAAsK4RCR2k+A6VvvvlmRSKRRTgNAAAAAADAsaF9DAAAAAAAoAcRCgEAAAAAAPSgOUOhT3ziE9q1a5e2bdumJ598suk1juPo5ptv1jnnnKPXve51uu+++xb8oAAAAAAAAFg4c4ZCr33ta3Xvvfdq3bp1La/59re/rWeffVYPPfSQvv71r+u2227T/v37F/SgAAAAAAAAWDhzDpp+0YteNOdNvvOd7+jNb36zTNPU4OCgzjnnHH33u9/VO9/5zgU5JACg0cHvPaSxHzza6WNgEeSmp7V39/2dPgaw5PFeAebG+wTLmufJK/2g9H3tY0/VVzx5Kv9fzWuzr6197Ml1HTlFR47jyHUdTW9apzPPPHPRvrROWZDtYwcOHNDatWurj9esWaODBw8e9X327dun0dHRuud8Pp9SqdRxn3GxLcczz1c+n9fIyEinj4Euwa+lY5f71wflHRyVsXpVp4+CRTA9Pd3pIwDLAu8VYG68TzptJsCYiS3Kz9de5XlNrpj1v17tx7T4sVcbf7T4X6/Nx1cDmOM/S+XzHN1ZZj9aHE84ea3/6U9lmMt7FPPY2Fjb15fUSvodO3Zo/fr1dc/96le/Wnbr3VOp1LI789GwbVs7d+7s9DHQBUZGRnoifT9R9u6+X4rFdPrHbun0UXCC8V4B5of3CjC3432feJ4n13PL3zw5nlP9seu5cl235vXSN6fm+upzDdd5cmffq8V1DZ+zybeZj/OanKPmNddpuI/TcC53XteVzjXrTG79NYsXbhhzPG7NNMzyN0OWYck0jJrnTJmmWXeNaZhNr7PM+mvMZtcYjfeq3l+GDKcoFQvy8gUVczk5+ZycXE5ePicV8jIKeZnFvCwnL5+bl+0V5PNcmZJMz5MhyfRKc3MMeXI8S0XXr4Lhl2sG5VkByReSZYdlBcOyg1EFIjGFo30ayDp60YtfvID/TTpjrtE+CxIKrVmzRs8995ye//znS2qsHOomu3btkm3bsm1bmUxGp5xyiq6++mqdccYZ+uY3v6m7775brutqdHRUwWBQAwMDkqRbbrmlGqQ88cQT+h//439IkiYnJ5VMJqszm97ylrfoyJEjSqfTet/73lf9vPfcc4/27dunj3/849qzZ4/+7M/+TDfccIOuueYaSdKePXv0iU98Qrt375Ykbdu2TY899pgikYiuuOIK/eY3v9HDDz9cDauuuOIKXXXVVXrNa14jSXr00Uf1uc99TqOjo+rr65NlWfrTP/1TXXLJJYvwswoAAAAsjtmhxtyBwdzXOU0Cg+bXzONzNglZmoUUDQFEy/Ck/lzJVFJfG3uw/b1aBDmu63akYuNoGU3CjPrwoRxAmPMLKWzLJ9NnyzRMGQ3Xzedz1IYns69pFp40+2bImnWvlp+z8rWbrb+u0s/PrM9Zvn6heZ4nL5eWk5mWk0mqmJpUKpFQZmpS2elJFVKTctLT8nJJKZeSr5iW30nL9vIt7+l4hlJeQGkvoJwRVMGKyvGH5QUiMoJR+UJx+aNxBWN9Cvf1KTowoPjAgOLxqGy/Na9z90pHw4KEQueff77uu+8+nXvuuUokEnr44Yd17733LsStl6Rbb71VW7dulSQ99NBDuuaaa3THHXfo0ksv1aWXXqpUKqWPfvSj2rFjhy6//PKGj9+2bZvuv7/Ux7t79259//vf16233lp9/bbbbpvzDMPDw7rrrrv0tre9TfF4fM7rQ6GQ7rzzTv3lX/5lw2uPPvqobrrpJt166616wQteIKmUJlbOCAAAgKXD87y6YGN2sNDqL/PzqbZoXb0xKzBw575X4+duHlIca1VGs6+reXhSf02lJWcpM1r+pb91VYbZJFiofKzf9FXDB2VdDUQH24cUrSo35hEsWA3X1oQUba5pF1LMVblS+zUYhiHDmH9VDI6OW8jJzSTlZKblZqblpKeVTU4qMzmpXHJSheSUnMy0vGxKRj4pq5iW38nIbBEm2pKKrq2cF1Das5U1gipYw3L9Ebl2RGYoIl8oLjsaVyDWp3C8T7HBQcX64loTCyoS9PHf+zjNGQr97d/+rR566CGNj4/ryiuvVH9/v/71X/9VV199ta6//nqdfvrpuvjii/Xzn/9c5557riTpuuuu00knnbTgh53+xfc1/fNHFvy+khTbuUux55991B937rnn6he/+IXuuOOOumDnRFu5cqVe+MIX6vbbb9cNN9ww5/XXXHONbr31Vl122WUaHByse+2zn/2srr322mogJEnr16/Xddddt+DnBgAAmB1qtA0pGsKFxpCics3T6f0yDwRaVmW0rt5Y+NYRr0V40qoqw51nq4vjOcsj1JDR0GIy11/m2wUQPtMne96BQZOqjLrAZH7VFs2qMlpXb8zxdc2zwsMyrBMeatBmCUnyXGcm3Mkm5aRLIU8xNa3M9KRy05MqpCoBT1JGIS1fISXLK7a8Z9GzlHYD1QqetBdR0Tckxx+R7KiMUES+UEx2rE/BWJ8i8X5F+/sVj4e0JmIrHrHl982vigcLZ85Q6IMf/KA++MEPNjx/++23V39sWZZuvvnmhT3ZMrJz50498siJCavaede73qU3vOENuuKKK+a8dtWqVbr44ov1hS98QTfddFPda48//rg+9KEPnahjAgDQldoFBq3+Mj8TarQJKeZRlVH6ftbnnGfryOz5Fi1DimYVF/OpNpnnPJET5rmFuY0hoyakaPeX/jZ/ya9t3TAt+VtUZbSr8JgrfGisyGisImlduTF3hcexzhPhX+6BxeF5nrx8phTupKfrQh4nM618ckq55JQKqSm5lYAnn5bPybS8p+MZSnvlcMe1yyHPkPLmSaWAJxCRGYrKF47LjpRatCL9fYrFY4pHba2J2OqLBBSmimdZWFKDpucSe/7Zx1TNc6It9L/WtHrjzH5+aGhIb3nLW/S5z31OF1xwwZz3veaaa/T6179eV155Zdvrrr/+ej3zzDM6fPiw/vM//3P+BwcALCtNqyjmGRg0hhTzmJXhtggpmoYPC9c6UheeuK1DiqOdJ7IctAspjqV1xOfzta3KaPY5jrW9pOFsZpu2mfK3//3kk9p+2vb5z91oE/bwFxkAi80rFmbassrf3Eyy+riYKoU8hXQl4EnJLKRktPk9Ke36y1U7AaXcgFJeXClvWFkF5Poj8gIRmcGY/JGY7GifQrE+ReIxxWNBxSO21kYDikdsxcK2/L7lvYULzS2rUGip2rt3r0499dQFu9/AwID+8Ic/1D03MTHR0PYlSe985zt1wQUX6HnPe9687nv55Zc3tLlt375de/fu1fbt2yWVZialUimdccYZx/FVAPV++Mx/ae/or+ueGz88rj17ftmhE5UM/uwZDfyy/UT+pSo0OqnMqj79cM/dnT4KjpOr9q0jicmEHpj6wZwhRbuqjGbzRJbDsNB2VRnH0jri81nNKySOMjCo+xzzqN6wDKsaarS7V9NgZJ5bXpoFPr0m8/spbR06udPHANDjPNeRm03JqQl03PKQ5erj9LQK6elyBU9SyiVlOK0HKxc8S6lysJP27PKPY9WKnqIvLAUisoIx+SJxBaJxheJxxaJhxSN2tYInHgmoL2orFKCKByWEQsfp4Ycf1le/+lXdcccdC3bPl73sZfr85z+vgwcPavXq1UokEvrOd77TtMUrFovpyiuv1Oc//3n19/fPee93vOMdOu+882RZM72a1157rf7mb/5Gp512WnWDXCbTupwQOBb/76++q0OpccUDsepz+XxeBw8d7uCppHN+9jvZR3KaGAx09BzHIjXg1zMb/PrNoSc6fRQcJ0NqCCBqA4OCV5TjOrJMSz6fv/q80SqkmB0YzHdQZ5NQYf4hRatApBKwtFhX2ywYqfm5AACgU0qtWdlqxU5tFc/sx056WsV06Xkvn5bR4h9eXBnKeAGlXFtJt1zB4w0o7a4ut2nZyigkIxiRGYrJF44pGO1TJBZRvFy101fTohWP2IpFbPksfs/EsSEUOgbXX399dSX9li1b9KUvfam6bn4hbNmyRTfddJOuvfZaOU5pmODll1+ul7/85U2vv/zyy3X33fOrFAiHw/qLv/gLffSjH60+d9ZZZ+mWW27R3/3d32lsbExDQ0OybVu33HLLgnw9QMUZa07Xe//o6urjpTDocO9PPiQNSa/5GL/esXQthfcKAADLmecU5KSTLUOdhpCnXNUj12l5z7xspRVQ0rE17dpKuyGlvP6aVq1S6OP5SzN4/NG4gpFYTbhT+n5NzY/jEap4sLgIhY7SfAdKf/zjH5/XdZdccokuueSShucvvPBCXXjhhU0/5qUvfal2795dfRwIBPQf//Efddc88cRM5cBXvvKVutcuv/xyXX755XXPvfrVr9arX/3qeZ0ZAAAAADrB81y52dRMO1Z6Wk62HOqUN2g52WR1XXrlsZfPtrynI0tZI6iMAkq6AU0V/Uo6w0p766otW5WAJ2+GZIWi8ofjisZC1SCnEuqsLVfvxKN2dRYPVTxYygiFAAAAAACLyvM8eYVstUKnfqhyTdVOurRNa6aiJyW1aM3yZChvBksBjxdQ0rU1VezXZHG4uiq9tFEroJRnK+0FZASjCoXDikeDdeFOX9TW2vIMntrgJ2hbVPGgqxAKAQAAAACOmecUyu1WrVqykk2HLcsptrynY9rKmyFlygHPtBvRVGFAiYJPSceuVu7UVvE4VlCxSrhTCXSipR+vjdgzbVvl12JhvyyqeNDjCIUAAAAAAOXWrPQ85u7Uf+/lWy+p8QxLRV9YOTOkrBFU2gso6UQ16WzURN6nyby/ukGrdruWa1iKhuxq1U5tFc/quuqd8katiK0AVTzAUSMUAgAAAIAu4nmevGK+Ya6Om55W8KkndPjIL6qPq5U72VKljzy3+T1lyPWHVLRCylshZRRU2hvWtLFOU5ZfE3m/DmctpcpVPJWAJy+fJEO235oJd+Izoc4pNaFObfATDduyTAIe4EQjFAJwXP4wdVAf/+HnlC/m2143mZvW+r41i3Qq6eD3HtLYDx6d87rU008rsnnzIpwIAADg6HlOUW421TBjp/axk6mZu1PesOU5hab3C0lK+AJy/WEVrLDyZlAZY0Bp32pNh2xNFv2ayPt0OGtpIlep4rGV8Wx5KrVaGYYUC9eHOPGIrS2zHvfVDF0O2vzVE1iKeGcCOC5/mDqo0eSYXrLuBYoFom2vfdXGFy/SqaSxHzw6r8Ansnmzhs965SKdCgAA9CrP8+Tm0g1zdZo9nhm+nJSXS7e+qWnJsyNy7YgKVkh5M6ZMYEgpf0DTjq3JQiXg8WksY1Zn8Tiy6m4TsK1qpU68vxTkbIjYOj0SqGvRqoQ9VPEA3YNQ6Cjt2rVLtm3Ltm0VCgVdddVVevOb36w9e/bommuu0aZNm+S6rkzT1I033qiXvexlnT4ysCje9LwLtWngpE4fo05k82ad/rFbOn0MAADQZdxCruXw5HZzeFq1ZkmSEYjIC0SqFTy5YJ8ygaBSXkDTjl+TBb+O5EoVPGNpU0dyPuXkl1QfzpiGFKsEPJGA4oO21kQD2lYesHz40HPauWNbdWV6PEIVD9DLePcfg1tvvVVbt27Vk08+qUsuuURnnXWWJGnLli3avXu3UqmUIpFIh08JAAAAoB3PdebYkDW7oqd0ndembd7wl9acy47I8YdVCK5SLrxRGQWVcgOaKvqrFTzjWVOHUpbG0pLjNd+CFbStma1ZfbZWrLW1ORKozueJz6rmiYb8MttU8YyMJHTGaSuP++cOQHcgFDoOW7duVTwe1+joaKePAgAAAPQsz/Pk5TOl0KZmxk5pyHKrip5pue1aswxTVjg2E/AEB1WIrFPODFXn7EwVyy1aOZ/GM6YOZUwdmXCVyztNb2kaKq1Cr4Q4/bbW1czdqR24XFmnHvBbTe8FAAthWYVC//H0T/TvT//ohNz7NZtfoVdvPrpWr5GREQ0MDOi0007Tz372Mz311FO6+OKL5bqugsGg7rvvvhNyVgAAAKBbucW83JphypWgp/q4PEi5bthyJim5zYMYSTIDYZmhmKxQTApEpOiwilZIOSOkjAJKOgFN1bRojWdMjaekqSMFTafz8rzm9w0FrJlKnb6Atq+dacmK11TzVIYvR4Ltq3gAYLEtq1Boqbj++uvleZ6effZZffrTn5Zt25JoHwMAAAAqPNep2ZrVas5OpXpn5rFXyLW8p+GzZYaiskIxmaGY/CvWy/GH5ZYDnrQXUNK1NV20lSj4SgFP2tRkpqjJI3lNpfLKF1pU8ZiG4hFT8YhPfZGANq6xq3N3+loMXLap4gGwzC2rUOjVm1921NU8J0JlptCDDz6oG2+8UWeccUanjwScEOlCRuOpI22vGU+3fx0AACxvpdasbMsWrNnDlCvfu9mUpBYlNoZZDneiMkMx+eIrZK/cKM+OKG+FlTWCpRYt19ZU0a9Ewa/DWZ8SGVeTybymDuc1lcopmSk0qeJxJGUUCviqlToDsaA2ro7PWpdeX80TpooHQA9aVqHQUnPBBRfowQcf1Be/+EWdc845nT4O5uHg9x7S2A8e7fQxloV0Pq3fTx2Q06YUu+JSSYcf+6ymffZRfY7c9LT27r7/GE/Y3nzW0QMA0Gu8YmHOdeiNm7SSkltseU8jEJYVLIU7Vjgq38AqWaGYjEBEBSusjBEstWgVbU05fk3k/ZrIGppKFTWZypUCnmSuVMVTrN3OlS9/kyzTmAlzogFtWhtvCHX6yjN4Ktf5fVTxAMBcCIWO0w033KBLLrlEO3fu7PRRMA9jP3iUsGAeJjIJHZwek9/yaXV8WIba/6uZZVqyLf8inW5+Ips3a/isV3b6GAAAnBCe58rNpORmyyFOw3DlZo+T8grZlvc0LH812DGDMflXrJMVilWHLTu+cgWPgko6pQqeiZxPUxlHk+VQZ2q8VMEzmcwrmSmU75wtf5sRDvqqLVkr+oLavDZeN2S5WtFTbteKBH0yDKp4AGChEQodpUceeaTu8caNGzUyMiJJuuiiizpxJBylyObNOv1jt3T6GEtS0XX0zz/7hh76zQ/0gtWv0P/58j9XxA6fsM83MjKi088884TdHwCApc7zPHmFbGMLVnrWIOXy45kKnjlas4KR6twdX2xQ5sqN1VYtKxSVAlFlzaDSblBJ19ZkwafJrDSZKmgqVRvw5EvVPKm8CsWMpEzDp/NZhmpXo29e21cNdfpqtmjVDmD2+5qvXwcALC5CIQCSpKnstD75o9v1q7H/rT8+7XW67PQ3yjT5AxsAAPPlObWtWbNbsFoPW5bTpjXLDlbDHSsUk69vuPx4ZtiyGYyq4Asp7QU1XfRrqmBpKl0szd6pCXgq1TyTqbxSmcMtP2ck6FO8HOoM9Yd08rq+aotWqXonUDd8OUwVDwAsW4RCAPTMxH79X49+XonctN790iv1qk0v6fSRAADoGM9z5WbTc8zZadyg5eUbq2iqLF9dmOMfXDsr3Jn53rOjyngBTTm2prNOOdyZmbszNTZTvTOZzGoqNaWi4zb9tKUqnpnNWVsG+qstWtVwp2ajVixsU8UDAD2EUAjocT/5/WP67J67FLHDumXXDdoyuLHTRwIAYEGUWrNyjXN2Gtahzwp9sinJax6ySIbMULk1KxiVFemXPXxSqZKnOmy5HPIEYzJCUeWMkKbzhqbSlXBnpoJn8lBti9a0ppLjSmVbVw5FQv5qqLNyIKxT1vfXtW5Vhy6XA59QgCoeAEBrhEJAj3I9V9/Y94B2P/6gtq44WX/9R9eoP9TX6WMBANCU5xRLwU1lrk56poqnEvpEDvxezz1+f2n4crp0necUWt7T8Afr5uz44ptmWrLKlTtWKCYzXHrdDMbk+IJKZmsGK5cDnslyBU+peienqdQRTaUOaCqVV9FpPvvHZ5nqi860Ya0aCFcHK9dt1CqHQLGILZ9FFQ8AYOEQCqGjFntF/EJuHvvtkd9p36EnF+RenfDLQ0/qZwf26TWbX6F3nvk2+ZfY9jAAQHfyPE9uLl03PHn2MOWZx8nqdi0vl259U9MqBTiGT7KH5etfrcCaaHUOTyX0mf1Ylk+pbLG6LWsqNbM5a+rQTIvWVPJgtZon3aaKJxryV1uxVg2GdepJ/TNbtGo3apW/UcUDAOg0QiF01GKviF+oNeX/9psf6suPfU1Oy9Lypc9n+nTlC9+i8089mz+QAgCOiVvItWjFavc42aY1SzKD0ZkqnUhc9tC6uqHKs4csW6GYDDsowzC057/+W4Pbnlet4KnO3RmtVPOkNZ1K1FTz5OW4zat4/D6z1KZVDnJWrQi33KjVFwkoFvbLoooHALDMEAqh45bTiviiU9SdP/uG/u2pH+oFq/8PXfuSP1PQF+j0sY6JZVpUBwEAJEme67Qcntx8Dk/psVfMt7yn4Q/MzNkJx2Sv3Fj3uGnIE4zIMK3SmTxPqUyhHODUzOAZrbRoTWoqNTbzfDKvTK4o6Q9NzxML+6tzd1avCGvrhoH6jVrVmTyl74O2xT+aAAC6HqHQUdq1a5ds25Zt2yoUCrrqqqv05je/WZK0Z88eXXPNNdqwYUN1lfeNN96ol73sZdWP/+Y3v6m7775bknTgwAEFg0ENDAxIkm655Rb9wz/8g6666iq95jWvqX7M9ddfr7PPPluXXHKJbrvtNn3mM5/RN77xDe3cuVOSdNtttymdTut973uf9uzZo0984hPavXu3JGnbtm161atepX/6p3+q3m/btm167LHHFIlE5Hme7rnnHn39619XsVhUMBjUihUrdN111+mMM844gT+Ty0/9yvZzddnpF7OyHQCwpHieJy+XlpNNyk3Pbx26m5mWO2drVrRmJfpK2au3yApHq8OWKyFP7WPTZ9fdplB0NJXKK5GsadE6WKnmGddU8rmZ9q1UXtNtqnhsn6l4tDx3J2xrzYqo+qK2pifHtX3r5ppqnlLYQxUPAADNEQodg1tvvVVbt27Vk08+qUsuuURnnXWWVq1aJUnasmWLvvKVrygSiTT92EsvvVSXXnqpJOn973+/duzYocsvv/yoPv+6dev0yU9+shouzeW3v/2tfvrTn+rFL35xw2v/+I//qJ/+9Ke64447ql/DY489pt/+9reEQjWemfi9/uejX9AkK9sBAIvELeZngp3KnJ10uXqnZpBy5fVKZY9cp+U9zUC4bq6Of3BNk5as6Mxw5WBURiDcUDHjup5S2UJdG9bUwcqPpzWV+l39a6mcMrnm5zIMKRqa2Zq1djiq0zbNrt6pH7ocDDT/I+zIyIjOPHPTMf+cAwDQa5ZVKHToke9r9H89ckLuveq1u7Ry19lH9TFbt25VPB7X6OhoNVBZDOeee65+9KMf6Yc//KFe9apXzXn9u9/9bn3yk5/U1772tbrnU6mUvvzlL+v++++vO/8ZZ5xBIFTjR8+O6HP/dZeidoSV7QCAo+a5jtxsqnlLVnq6VNVTeb4mBPIKuZb3NHx23Vwde+ikWXN2ok3Dnkpr1mz5QqmKZ7wc4kwezGsqldRU8kjNuvSaderpvNwWVTwB26qbu7NuOFo3e2f2wOVo2JZl0qYFAEAnLKtQaKkZGRnRwMCATjvttOpzTz31lN72trfJNE3Ztq377rtvwT+vYRh673vfq0996lN65SvnHpp87rnn6t5779XDDz+sc845p+6sgUBAJ5988oKfsRuUVrZ/W7sf/662rThZN7CyHQB6mud58vLZWS1Yrebu1LRmZVOtb2qY5dCmFOL4+oZkr97cdFNWbchj+lvPs3NdT8lMQROVFq1EXlOphKZSo3XVO5PVkCenbL51FU8sbFc3aq0bjmr7pvpQp7oyvRz8BA2aJBsAACAASURBVG3+eAkAwHKxrH7XXrnr7KOu5jkRrr/+enmep2effVaf/vSnZdszPfNztY8dq9ll22effba++MUv6sEHH5zXx7/3ve/V3//932vXrl0tr5mamtIVV1yhfD6vLVu26DOf+cxxnXk5Sxcyuu0nd2rkub3atfkV+nNWtgNAV/GKhTnn7DSGPEnJbb2O3AiEZdVU5fgGVrVdh26GYjIDIRlG+1k3uYIzs0krkddU8lBdqDPTolVq05pO5dWiiEfBchVPZaPW+pXRmuqdxoHLkZCfKh4AALrYsgqFlorKTKEHH3xQN954o8444wwNDQ0tyL0HBweVSCTqnpuYmNDg4GDDtTfccIM+8IEP6Pzzz5/zvq94xSu0YsUKfetb36o+t2XLFuVyOT3zzDPatGmT4vG47r//fv37v/+7vvzlLx//F7ME/df+/0/f+83357zu4PSYDmcSuuqMt+q8U17N9hEAWKJKrVnpmbk65farSpDTvKInKa+QbXlPw/KXgptwKbjxr1hXGqIcLgU6ZjDa5HFUhjX3H6tc19N0uhzgTGQ1lZqs2a5V06ZVU82Ta1HFYxpSrCbEOWlVVPHIipkBy7OreaK2Av7m7WMAAKA3EQodhwsuuEAPPvigvvjFL+oDH/jAgtzzj/7oj/Qv//IvuvDCCxUIBPTrX/9aTz31lJ7//Oc3XPuiF71ImzZt0re//W2dd955c977hhtu0Hve857q40gkoiuvvFIf/OAH9clPfrI6VyiTySzI17IU/fj3I/r12FM6eY65QKuiw3rXS/5MO1ZtW6STAUBv8zxPXqHcmpVOysmWq3PS03XDlGc/djMpSS3KYgxTZjBSrc7xxQZlrtxYX7VTHqZs1oQ8hs+e9z8GZPNFJVJ5TU0k60Kd+uqdmW1byXTrKp5QwFIsElBfuUpnw+p4k3Xp9kwVT9AvkyoeAABwHAiFjtMNN9ygSy65RFdfffWC3O9Nb3qTnnvuOb3pTW+SaZoKBoP61Kc+VV1bP9tf/dVf6U/+5E/mde/TTz9dz3ve87R///66j7/rrrt01VVXyXEcDQwMKB6P67rrrluQr2cpGgoP6qOv/etOHwMAupbnFGYNU64JeRqGK888ltOmNcsO1a9F7185a4jy7KHKMZnB8JytWbWcShXPkWRdqDNTvZOfFfzklS+0qOIxDcXDM3N2NqyKK36y3TBwuRLwxCJU8QAAgMVHKHSUHnmkfvvZxo0bNTIyIklauXKldu/erVSqzUDJGh//+McbnjNNU+95z3vqKnpqvfvd7657fNppp+lXv/pV9fFLX/pS7d69u/r4iSeeqLv+1ltvrXtsGIbe8Y536B3veMe8zgwA6B2e58rNpuaYs9M4h8fLt27NkuWrm6vjH1wra12Tdeh1c3iiMo5yrpvnecrlHU2lsnUhTqVqZ3Y1z2Qyr2QmL69lFY+vWqkzEAtqY00VT2XocqVFqy9iK0wVDwAAWAYIhQAA6HKl1qxck01Z9XN2GkKfbEry3BZ3NWSGZlqzrEi/7OGTZip2gtFyO1Z92GP4g8c0p81xPU1X2rBq27KStYFPrm6jVr7Y/OymadStTN+4Jj5rXfpMy1Zf1FYsbMumigcAAHQhQiEAAJYRzylWA5zKjJ25qnjczLQ8p9DynoY/OFOVE47JFx+aCXsqwU6wPH+nujUrLMM8tqDE8zxl886sap36ap7ZG7WSmULLKp5w0FcNdQbjQW1aE6+p3qmv5olHA4oEfSwQAAAA0DIJhTzP4w9vS4TX6k/kAICj4nmu3FymFOZUhidX5uzMehwbH9WzP/4nOdmkvFy69U1NXzm0KVXl+AdWy1rbfu6OFYrK8B1da9ZsjuNqqrJRa/YWrdqAp6aap9CiiscyjXKlTinE2bw23tiiFZmZzROP2PL7qOIBAAA4Fks+FLIsS4VCQbZtd/ookFQoFOTzze+Xjeu2ajmoUQ6Z5nXtAiDUAnAiuIVcm7k7zR4n5WaS7VuzgpFqeOPZEQVXr5s1Z2f23J2YDPvYWrNqeZ6nTK5YtzlrMjnHRq1M6yqkSNBXCniitlb0B3Xyur6aWTx29bVK4BOmigcAAGDRLPlQqL+/X6Ojo1q3bp1Mc/4bRLDwXNfV6Oio+vr65rz24btv0+Ef/uec1w1PFDU24NNH71u8bWfrYqsX7XMBWF4812k5PLndsGWvmG95T8MfqKvQsVdubBnqVB8HI3WtWSMjI9p25pnH9DUVHbc8iyfffOByk8qeVlU8PsuoW49+8rr+uhateHTWRq2wLb+P37sBAACWqiUfCg0NDWn//v0NW7SWsnw+37WVTZFIRENDQ3Ne5/33LzU8UZTWr5rjhlJ05xa9ZcdpC3TCuW1dsXnRPheAzvA8T14uLadmgHIlxKmdszN72LLbtjXLaliJbq/eIitcswK9XNlT+9j0LdzvB7VVPJPJVhu1an6cyivVroon5K+GOsP9YZ2yvr8a6sRrNmlVBi6HAlTxAAAAdJMlHwqZpqkNGzZ0+hhHZWRkRDt37uz0MTruyGBAb/rHz3b6GACWObeYl1s7TDmbLD9utjVrJvRp3ZqlUmtWsBLmxOVfsa7mcXlrVuVxuNKaFVrwQKTouE3m7tTM4imHPQfHJlV44HuaSuVVdFpV8Zg1W7NsnTLQX1e9U7dRK2IrFrHls6jiAQAA6GVLPhQCAHQHz3XkZlMz4U26vD0rM10f8lRDn9J1bVuzfHZdC5Y9vGFWS1a0yXDl6DFvzWr79Xme0tlifSvWrGqe2hatqWROqWyx5f2ilSqeaED9EUsb1q2sW5Net1ErQhUPAAAAjh6hEADgqHieJy+faazQqQ15MslZj6flZlOtb2qYdaGNr29I9urNTebu1Ic8pj9wwr7OQtGttmG13ahV07pVdJoPs/f7zGobVjxia9VguO1GrVi4vopnZGREZ575whP2tQIAAKA3EQoBQA/zioWGuTrNH9cOX05KbusKFyMQllWz8tw3sKrtOnQzFJMZCMkwTlwrk+d5SmWL9YOVk7m6Fq3JZL5uIHO6TRVPLOyvVu2sGgxr64aBxo1aNQOXg7ZFFQ8AAACWHEIhAOgCpdas9ExVTt1Q5cZ16NWtWYVsy3salr8U3ITLrVlD60tzdsI1AU/D44gM68T/1lIoOk3WpdesSU+VAp7aah7HbV7FY/vMuq1Zq1dEagYsz9qoFQkoFvbLYhYPAAAAugChEI7aMxO/15HMZNtr8k7rbTcAWvM8T14hW56zk5x3FU+pNat56FFtzQqWwh1fbFD2qo3Vx7XDlGtDnhPZmlXLdT2ls4VSxU5yJtRp1aI1mcwrk2texWMYUjQ0U7GzZiii0zYN1m/Uqqnm6YvYClDFAwAAgB5FKISjkivmdeO/fVxOm60+knRpPiW/5V+kUwFLk+cU5KSTcrPT9SFP3XDlWY+zSclp05plh+pasHz9K5vM3ZnVmhUMn9DWrNnyhSZVPHUDlhvn87itqnj81szWrLCttUPRmtk7gWo1T2U2TzRsyzIJeAAAAID5IBTCUXFcR47n6sJTX6NXbnxJy+uOjHxOfpNfXugOnufKzabmmLPTWMHj5Vu3Zsny1c3Z8Q+ulbWufkNW4xyeqIxFDltd11MqW2jSptV64HIm5zS9l2FIsfBMiLNuOKrtm2rWpEfthqHLQZv/PwIAAACcKPxpG8dkKLJCp6zY1PL1vf7g4h0GmKdSa1auSahTG+Y0adPKpqSW1XGGzFBkZqhydEDm8EnVMKdarVMOeSrXGf5AR1qW8gWnGupMpmZWo1cDnpoWrelUXlPp1lU8AduambsTCWjdymhdqDN74DJVPAAAAMDSQigEYFnynOJRbMuaGbbstZl3ZdjBmTk74Zh8fcOz5uw02ZoVjCxqa1Yt1/WUzBRazt1pFvxk882reExDitUEPOtXRmvWpdfM46m0ckWo4gEAAACWO/5ED6CjSq1Z6dJcnfT0TKjT9HFta1am9U1NXym0CcdkBaPyD6yWtfbU6uOZ4cpRWcGZkMfwdXYOVq7gzAQ8s0Odujat0vfTqbxaFPEoaFvVrVl9EVsnrYzWtWjNHrgcDfllUsUDAAAA9BRCIQALxq22ZtVU7JSHJ9c9zpSGL/dNTejp7+Xat2YFIzOtV5G47KF1s+bsxBpCHsMOdnyblON6SqbzdRU8k7MHLNfM5plM5ZVrU8UTjwQUK4c4J62K1c3dqQY8NdU8Ab+1yF8xAAAAgOWGUAhAA891Wg5Pbv04Ka+Yb3lPwx+om7Pji29UMrxKqzac3DrkCYZlmEsj3Mjmiw1bs2YPXK6t5kmmW1fxhAJWNcjpjwW1YXW8bmV6deByudInEqSKBwAAAMDCIxTCUdvxm4yiP/kX7Q3/R8trUk8/rcjmzYt4KjTjeZ68XFpOTZBTCXtq5+zMhDulx24u3fqmplW3+tzXv0qBNae02JYVqw5ZNn12w632j4xo8MwzT+DPQHOVKp7JugHL5YAnWRPupPPV8CdfaFHFYxrVQKcvEtDG1fHq3J2+miHLlYAnHrFlU8UDAAAAYAkgFMJR2/ZMVtZUTjploOU1kc2bNXzWKxfxVN2v1Jo117asxoCndWuWyq1ZlTk7cflXrCuFOcFyqBOOzbweLoU9hh3qeGtWLc/zlMs71VCn0qLVbGV65bVkJi+vZRWPr1qpMxALlkKemlCnb1Y1T5gqHgAAAADLFKEQjomzdkinf+yWTh9jWaq2ZlXm7FSGKWeTM/N2moQ8bVuzfHZdhY49vKFuQ1b1+9qQJxRdMq1ZtRzH1XS6MDN3p2Zz1mR5Ds/s1/LF5sGXVVvFEw1o09p4Q6gzu4rH71t6PycAAAAAcCIQCgHHyPM8eflM4zDldDnMyZa/rw19MtNys6nWNzXMurYrX9+w7NWbG9ag17ZvmaGoTH9g8b7wo+B5nrJ5p2FleqVi57e/m9CDP99TV9mTzBRaVvGEg75qS9aKvqA2r42XAp7Z4U55+HIk6FtSVU0AAAAAsJQQCgGSvGKhoe2q+ePa75OSW2x5TzMQrgt4/INrSgFOddhyrOZx6TojEF7SIYbjuKU5O8mawcrVocu1G7Vm1qkXWlTx+CxDQdvQUL+leMTW5rV9DS1aMwFP6bHfZy7yVwwAAAAA3YtQCF3Fcx252fQcAc9MqFNtzSrkWt6z1JoVrVbn2EPrm1TrzBquHIzIsJb228vzPGVyxRZzd1ps1MoUWt4vEvQpXg51hvpDOnldX7l6pxzqVAOfQHkWj0+PPfaYzuzAoGkAAAAAAKEQlqhSa1Z2jqqdxiqeUmtWi96jamtWVGYwJl9sUPaqjTOhTs0w5drQZ6m2Zs1WdFxN127RqhmsPDOTp/75otO6iqdSqdMXtbVlfX+1RSsebdyoFQvbVPEAAAAAwDJDKIQTznMKctLleTvZabnpmlCnOly5cdhyu9Ysww7NhDfhqHz9K2sex2QGow2PzWBYhrE8gotKFU+lamey2pZVu1Gr5rVUXql2VTwhfzXUWTkQ1inr+6stWbXVPJWQJxRgFg8AAAAAdDtCIVT99x9+rnt//i9y1XqFueu6eqWk4uSYfv/5d7e9n+eU5vR4+WzriyxfXfuVf3CtrHWxulk8jW1a0SXfmjVboehqOp2vH7hcu1ErNeu1tlU85kyIEwlo5UC4Oli59vlKNU8sYstnLY8wDAAAAACweJbX36xxQv16/Ck9lxzVy09qPePFy2UULT6hiGnLXr257f0My1czVDnaOHcnFJPhDyy7ihTP85TOFmfWoifrN2rVr0svvZbKtq56iob85TAnoFWDYZ16Un9d1U7tunSqeAAAAAAAC4VQCHX8pk/vefmft3w9/dTP9MvCv8tetUqr/uS9i3iyE6dQdGfaspItNmrNquZx3OZzi/w+c2ZzVtTWqsFw40at6EzAEw/bsqjiAQAAAAB0AKEQjkphYlRSaSPXUuR5nlLZ4kxr1qwWrUpFz3RN8JNuU8UTC/urc3dWrwhr64aB+o1akfpqnqBtUcUDAAAAAFgWCIVwVIqJUckwFm2mT6HoNKxJb2jTqhm4PN2misf2mTVr0W2tXhFpsi59JuyJhf1U8QAAAAAAuhahEI5KYeKgDJ9fOoZqGNf1lM4Wqpu0WrVo1c7nyeSaV/EYhhQNzVTsrB2O6rRNs6t36ocuBwP8cgcAAAAAoIK/JeOoFBOj1daxfMFpCHUmW8zmmUrmNZXOy21RxROwreqcnb5IQGuHotXZO32RxoHL0bAty6RNCwAAAACAY0UotIRlCllN51OL9vlS+Yw8T3r86cPVSp2pmk1ak9NZvW3sOSUytsZ/e1g3vf+BpvcxDCkWtqsbtdYNR7V9U32oU12ZXg5+gja/FAEAAAAAWEz8TXwJe893PqKJ7OSC3GvHbzLa9ky27TX9kv7YM/Tz/+dv6p6Pmob6LENbTE/TbkF2xtPA0LCuuGB7w8BlqngAAAAAAFgeCIWWsMnctM5Ye7petv6Fx30va8/XZEzl5a0bbnnNkamscjlp09q4LMuUzzRkWabMcsDj5tLKH5RiG9bq1PPP0+pzth73uQAAAAAAQGcQCi1xm/rX6ezNLz/u++wNPSid0qfTP3ZLy2ve95kfyjAMveW6VzZ9Pbnvhzp0/z9q/V/cIHto/XGfCQAAAAAAdA77tlE1nshouD/U8vVCYlSS5OtrXW0EAAAAAACWB0IhSJIc19PhyayG2oVCEwdlRQdl+gOLeDIAAAAAAHAiEApBkpSYzspxPQ0PtA6FiolR+QdWLeKpAAAAAADAiUIoBEml1jFJc1QKjcpHKAQAAAAAQFdg0HQH/O5fH9Cz/+vf5Mlre92fTB/W0H9+T3ujI8f9OVNPP63I5s0tXx8rh0KtZgq5xbyc6SPy9xMKAQAAAADQDQiFOuDph78n9/cHNDYw90+/31yY/0SRzZs1fFbzrWLS3JVCxcQhSZ58A6sX5DwAAAAAAKCzCIU6wPM8jQ34dPanbpNt2S2vMwxD/cH4opxpLJFRwLYUDfmbvl4sbx6jUggAAAAAgO5AKNRBA8E+2b7WodBiGk9kNNQXkmEYTV8vTJTX0RMKAQAAAADQFRg0DUmlUKjVPCFJKiRGZfiDsiJ9i3gqAAAAAABwohAKQVK5UqhNKFScGJV/YGXLSiIAAAAAALC8EApBhaKrielc+3X0iVFaxwAAAAAA6CKEQtCRqaw8TxoeaB4KeZ6nYmKUIdMAAAAAAHQRBk0vsO/c+X+ruOcXGr2z9U9taHRS6rcW8VTtzbWO3kkl5BVyrKMHAAAAAKCLEAotsNSP/1srjuQ1PRxpeU1hKKzcjg3yW83Xvy+2sXIo1GrQNOvoAQAAAADoPoRCJ0B2uE8Xf/6OTh9j3sYm0pJaVwoVJg5KknwDhEIAAAAAAHQLZgpB44mMIiG/QoHmGWFx4pAkQ/6+lYt7MAAAAAAAcMLMq1Lo6aef1vvf/34lEgn19/frE5/4hDZt2lR3zeHDh3XjjTfqwIEDKhaLeulLX6oPfvCD8vkoRlrqxhPZlq1jUmnzmBVfIcO3NNrdAAAAAADA8ZtXpdCHP/xhXXbZZfre976nyy67TB/60IcarvnCF76gLVu26Nvf/ra+9a1v6Ze//KUeeuihBT8wFt54ItN2HT2bxwAAAAAA6D5zhkKHDx/W448/rosuukiSdNFFF+nxxx/XkSNH6q4zDEOpVEqu6yqfz6tQKGjVKoKE5WBsjlCoMHFQPkIhAAAAAAC6ypy9XQcOHNCqVatkWaUV6pZlaeXKlTpw4IAGBwer11177bV697vfrVe+8pXKZDJ6+9vfrjPPPPOoDrNv3z6Njo4e5ZewtHieJ0kaGRnp8EnmJ190NZ3OK5860vzMTkEDyQmNZRz9fpl8TVhelst7Beg03ivA/PBeAebG+wSYn254r4yNjbV9fcEG/nz3u9/Vtm3bdNdddymVSunqq6/Wd7/7XZ1//vnzvseOHTu0fv36hTpSR/zWMCTpqAOxTvnDWFLSc9r5vFN05pknNbyeH/u99kva+LwXKvq85fE1YfkYGRlZNu8VoJN4rwDzw3sFmBvvE2B+uuW9sn///ravz9k+tmbNGo2OjspxHEmS4zg6dOiQ1qxZU3fdPffcoz/+4z+WaZqKxWLatWuX9uzZcxxHx2IYn8hIUstB09V19LSPAQAAAADQVeYMhVasWKHt27frgQcekCQ98MAD2r59e13rmCStX79eP/jBDyRJ+XxeP/7xj3XqqaeegCNjIY0lSqFQq5lCxUSpnY9B0wAAAAAAdJd5bR/7yEc+onvuuUfnnXee7rnnHt18882SpKuvvlp79+6VJN10000aGRnRG97wBr3xjW/Upk2b9Ja3vOXEnRwLYiYUCjZ9vZAYlWGHZIbji3ksAAAAAABwgs1rptCWLVt03333NTx/++23V3+8YcMG3XnnnQt3MiyK8URG/dGA/D6r6evFidI6eqM8KwkAAAAAAHSHeVUKoXuNJzItq4SkUqWQb4DWMQAAAAAAug2hUI8bS2RazhPyPFfFxCH5CYUAAAAAAOg6hEI9brxNKOQkE/KKeYZMAwAAAADQhQiFelgqU1AmV2QdPQAAAAAAPYhQqIeNlzePDfeHm75eXUc/sHrRzgQAAAAAABYHoVAPm1lH36pSaFQyTPn6hhbzWAAAAAAAYBEQCvWw8TlCoWJiVL74ChmWfzGPBQAAAAAAFgGhUA8bT2RkGtJgPND09cLEKPOEAAAAAADoUoRCPWwskdFgPCjLav7LoJgYZZ4QAAAAAABdilCoh7VbR+/ms3JSCSqFAAAAAADoUoRCPWysTSg0s3mMUAgAAAAAgG5EKNSjPM/T4TahUGGiHApRKQQAAAAAQFciFOpRU6m88kVXw61CoXKlkI9KIQAAAAAAuhKhUI8am2sd/cRBmYGwzGB0MY8FAAAAAAAWCaFQjxovh0LDA60rhXz9q2QYxmIeCwAAAAAALBJCoR41PlelEOvoAQAAAADoaoRCPWo8kZHPMtUXCTS85nmuColDzBMCAAAAAKCLEQr1qLGJjIb6gzLNxvYwZ/qI5BTZPAYAAAAAQBcjFOpRY/NYR0+lEAAAAAAA3YtQqEeNT7YOhYrldfRUCgEAAAAA0L0IhXqQ43o6PJnVcMtKoYOSYcoXH1rkkwEAAAAAgMVCKNSDEtNZua7Xun0sMSpf35AMy7fIJwMAAAAAAIuFUKgHjc21jn6CdfQAAAAAAHQ7QqEeNF4OhVq2jyVG5WOeEAAAAAAAXY1QqAe1C4XcXFpueooh0wAAAAAAdDlCoR40lsgoaFuKhPwNrxUShyRJPtrHAAAAAADoaoRCPWg8UVpHbxhGw2vFCdbRAwAAAADQCwiFetDYRKbN5rGDkiTfAKEQAAAAAADdjFCoB40nMi2HTBcnRmWGorKCkUU+FQAAAAAAWEyEQj2mUHSVSObaVAqN0joGAAAAAEAPIBTqMYcnM/I8tQ6FJg6yjh4AAAAAgB5AKNRjKuvom4VCnuuoODkmP/OEAAAAAADoeoRCPaYSCjWbKVScPiy5jnz9rKMHAAAAAKDbEQr1mLF2oVBlHT2VQgAAAAAAdD1CoR4znsgoGvIrGPA1vFYoh0LMFAIAAAAAoPsRCvWY8US25ZDpYmJUMi354isW+VQAAAAAAGCxEQr1mLFEuu06el/fsAzTWuRTAQAAAACAxUYo1GPGE5mm84QkqThxkHlCAAAAAAD0CEKhHpLNFzWdLrSvFGKeEAAAAAAAPYFQqIdU1tE3C4WcbEpuJin/AOvoAQAAAADoBYRCPWR8PuvoqRQCAAAAAKAnEAr1kHaVQoUE6+gBAAAAAOglhEI9ZCyRlSQN9QcbXiuWQyEGTQMAAAAA0BsIhXrIeCKj/lhAfl/jyvnCxKjMcFxmINyBkwEAAAAAgMVGKNRDxhOZlpvHiomDzBMCAAAAAKCHEAr1kLFEpumQaalUKeTrX7nIJwIAAAAAAJ1CKNQjPM/TeCLdtFLIcx0VJ8dYRw8AAAAAQA8hFOoRqWxRmZyjob4m6+gnxyTPZfMYAAAAAAA9hFCoR1TW0TdrHyuweQwAAAAAgJ5DKNQjKqFQs/ax4kQlFKJ9DAAAAACAXkEo1CPG2oRChcSoZPlkRQcW+1gAAAAAAKBDCIV6xHgiI9M0NBgPNLxWnBiVv2+lDNPqwMkAAAAAAEAnEAr1iPFERoPxoCyr8T95ITEqH/OEAAAAAADoKYRCPWI8kWk6ZFqSiolR+dk8BgAAAABATyEU6hFjiUzTeUJOZlpuNkWlEAAAAAAAPYZQqAd4nqfxFqFQdfMYlUIAAAAAAPQUQqEeMJnMq1B0NdQfbHitkGAdPQAAAAAAvYhQqAeMl9fRN5spVCyHQr7+lYt6JgAAAAAA0FmEQj1grBwKNWsfK0yMyor0ybSbD6EGAAAAAADdiVCoB4y3C4USo/L10zoGAAAAAECvIRTqAeOJjPw+U32RQMNrxYlR+dk8BgAAAABAzyEU6gHjiYyG+kIyTaPuec8pqDg1zjwhAAAAAAB6EKFQDxhrtY5+clzyXNbRAwAAAADQgwiFesD4ZKbtOnof6+gBAAAAAOg5hEJdznE9HZ7MNq8UmjgoSVQKAQAAAADQgwiFutzEVFau62m4xeYxw/LLig104GQAAAAAAKCTCIW6XNt19BOj8g2skmHwywAAAAAAgF5DGtDlxtqEQsXEIVrHAAAAAADoUYRCXa5SKTS7fczzPBUmDspHKAQAAAAAQE8iFOpy44mMQgFLkZC/7nk3My0vn5F/gFAIAAAAAIBeRCjU5cYSGQ31h2QYRt3zhYnyOnoqhQAAAAAA6EmEQl1uPJHRUF+zeULldfRUCgEAAAAA0JMIhbrceLlSaDYqhQAAAAAA6G2EQl2sUHQ0MZ1rGDItScXEqKzogEx/oAMnAwAAAAAAnUYo1MUOT2YlNV9H2ejM1gAAIABJREFUX0iMUiUEAAAAAEAPIxTqYmPldfSt2seYJwQAAAAAQO8iFOpi4y1CIa9YkDN1WP7+1Z04FgAAAAAAWAIIhbpYJRSaPVOoMDkmyZNvYGUHTgUAAAAAAJYCQqEuNpbIKBryKxjw1T1fnCivo6dSCAAAAACAnkUo1MXGExkNDzQfMi2xjh4AAAAAgF5GKNTFxhOZpkOmi4lRGT5bVrS/A6cCAAAAAABLAaFQF2sVChUmDso3sEqGYXTgVAAAAAAAYCkgFOpS2XxR0+lCw5BpqVQp5Kd1DAAAAACAnkYo1KVarqP3PBUmDsk3wJBpAAAAAAB6GaFQlxqbaB4KuekpeYUslUIAAAAAAPQ4QqEuVakUmt0+VqiuoycUAgAAAACglxEKdalKKLSiL1j3fHUd/QChEAAAAAAAvYxQqEuNJTLqjwXk91l1zxcnyqFQ/8pOHAsAAAAAACwRhEJdajyRabp5rJA4KCs2KNNnd+BUAAAAAABgqSAU6lLjk5mGIdNSqVKIeUIAAAAAAGBeodDTTz+tt771rTrvvPP01re+Vc8880zT677zne/oDW94gy666CK94Q1v0Pj4+EKeFfPkeV6bSqFR1tEDAAAAAAD55nPRhz/8YV122WW6+OKLdf/99+tDH/qQ7r777rpr9u7dq8985jO66667NDw8rOnpadk2LUqdkMoWlck5jevoi3k500eoFAIAAAAAAHNXCh0+fFiPP/64LrroIknSRRddpMcff1xHjhypu+6f//mfddVVV2l4eFiSFIvFFAgETsCRMZfK5rHZoVAxcUgSm8cAAAAAAMA8KoUOHDigVatWybJKW6wsy9LKlSt14MABDQ4OVq976qmntH79er397W9XOp3W6173Or3rXe+SYRjzPsy+ffs0Ojp6DF/G0uF5niRpZGSkY2d48g+lUOjwwd9pZORg9Xnfod8oJumpgxNycp07H1Crk+8VYDnhvQLMD+8VYG68T4D56Yb3ytjYWNvX59U+Nh+O4+iJJ57QnXfeqXw+r3e+851au3at3vjGN877Hjt27ND69esX6kgd8dtyCHbmmWd27AyHck9LOqxXvuyFddVCkz8d1WFJp7/8bFmRvo6dD6gYGRnp6HsFWC54rwDzw3sFmBvvE2B+uuW9sn///ravz9k+tmbNGo2OjspxHEml8OfQoUNas2ZN3XVr167V+eefL9u2FY1G9drXvla/+MUvjuPoOFZjiYxM09BAPFj3fGHioAx/UGY43qGTAQAAAACApWLOUGjFihXavn27HnjgAUnSAw88oO3bt9e1jkmlWUOPPvqoPM9ToVDQT37yE5122mkn5tRoazyR0WA8KMusb90rJkblH1h5VC19AAAAAACgO81rJf1HPvIR3XPPPTrvvPN0zz336Oabb5YkXX311dq7d68k6fWvf71WrFihCy+8UG984xt1yimn6E1vetOJOzlaGk9kW6+j72cdPQAAAAAAmOdMoS1btui+++5reP7222+v/tg0Td1444268cYb///27i5G7ru+4/1nHtbe9eOuvWtnQ6AoUU/wIYVzFI56AxKCUFPVadRWYMlUKkIKF61aKRcIpIo4LlWrVOoFpKAKJPrkVqJuJWjcHMhBXKSpWjiN0CGpFXoEyUlT7PWMvX8/ztqzM3MuvOvYXieeOLv7n9l9vSQEHo/DVzJ/JX7z+/++yzcdt6VZtPLzbx+/7rNer5f52Zlsuvt/K2kqAAAAYJD0dVKI4dHr9dI801qyjr5zvkhv/nLq49bRAwAAAKLQmnPm/OW057tLotB8MZMkGZnw+hgAAAAgCq05zaKVJEuiULs4kSROCgEAAABJRKE1p7EQhW68aLo9O5OkkpHtUyVMBQAAAAwaUWiNaRQXkyw9KTRfzKS2bWcq9ZEyxgIAAAAGjCi0xjSLuYzUq9m+ZcN1n7dnZzIy4dUxAAAA4ApRaI1pFq1Mbh9LpVK57vP52RMZcZ8QAAAAsEAUWmOaxdJ19N32pXQuFC6ZBgAAAK4ShdaYRtHK1IR19AAAAMAbE4XWkE63l9Nn55auo5+9EoWcFAIAAAAWiUJryOzZuXS7vZtuHkviomkAAADgKlFoDWkWrSTJ1E1OClU2jKU6trWMsQAAAIABJAqtIY2FKHSzk0IjE3cs2UgGAAAArF+i0BrSmL15FGrPnkh9fFcZIwEAAAADShRaQ5pnWhnbWMvm0frVz3q9buaLk+4TAgAAAK4jCq0hzaKVyfGx614T65ybTa/Tzsi4dfQAAADAa0ShNaRRtDK5/YZXx4oTSZK6k0IAAADANUShNaRZtDI1sem6z+ZnF9bRj4tCAAAAwGtEoTWiPd9Jce7S0kumi5mkUk19+2RJkwEAAACDSBRaI06dmUuSTI2PXvf5fHEy9W2TqdRGyhgLAAAAGFCi0BrRKN5gHb37hAAAAIAbiEJrRPN1otB8MeM+IQAAAGAJUWiNuFkU6l5upXPhTEacFAIAAABuIAqtEY3ZVrZuGsnohvrVz9oLm8fqTgoBAAAANxCF1ohG0brpq2OJdfQAAADAUqLQGtG8SRRqL0Sh+sQdZYwEAAAADDBRaI1oFq1M3XhSaHYm1dHNqY1tKWkqAAAAYFCJQmvA3KX5nG+1b7KOfsZ9QgAAAMBNiUJrQGNh89iSk0LW0QMAAACvQxRaA262jr7X7aR95mTq1tEDAAAANyEKrQE3i0Kdc6eTzryTQgAAAMBNiUJrQLNopVJJdm5/LQq9tnlMFAIAAACWEoXWgEbRyviWjRmpv/bb2Z69EoVGrKMHAAAAbkIUWgMaRWvJ5rH5YiapVFPfNlnSVAAAAMAgE4XWgOZNolB79kTq26dSqdZKmgoAAAAYZKLQkOv1emkWrZusoz+ZEfcJAQAAAK9DFBpyF1rtzF3uZGrihpNCxUzq4+4TAgAAAG5OFBpyjZuso+9eupjuxbNOCgEAAACvSxQacs2bRKHFzWP1cVEIAAAAuDlRaMgtRqFr7xSaLxbX0YtCAAAAwM2JQkOuUbRSq1YyvnX06mft2RNJkhEnhQAAAIDXIQoNuUbRyo7to6lVK1c/axczqY5tSXV0c4mTAQAAAINMFBpyzaKVye03rqOfcUoIAAAAeEOi0JBrFq3r7hNKrlw0XZ+wjh4AAAB4faLQEOt2e2kWc9dtHut1O5k/03BSCAAAAHhDotAQO3PhUuY73UxNXLN57OyppNuxjh4AAAB4Q6LQEFtcRz9pHT0AAADwJolCQ+xmUWhxHX1dFAIAAADegCg0xBoLUWjqxpNC1XrqW3eWNRYAAAAwBEShIdYs5rKhXs22zRuuftaencnI+FQq1VqJkwEAAACDThQaYs2ilZ3jY6lUKlc/my9mXDINAAAA3JIoNMQasxeve3UsWTwpJAoBAAAAb0wUGmLNonXdJdOd1vl05867ZBoAAAC4JVFoSHU63Zw+O3fDOvqTSZKR8TvKGgsAAAAYEqLQkDp99lK6ves3j7UL6+gBAACA/ohCQ6q5sI7+upNCszNJ4k4hAAAA4JZEoSG1GIWuPyk0k+qmbaluHHu9XwYAAACQRBQaWo2bnhQ64ZQQAAAA0BdRaEg1z7QytrGezWMjVz9rFzPuEwIAAAD6IgoNqRvX0fc685k/03RSCAAAAOiLKDSkGrMXr7tPaP5sM+l1MzJhHT0AAABwa6LQkGoWc9edFGovbB6rOykEAAAA9EEUGkLt+U6K85euv2S6WFhH704hAAAAoA+i0BBqFnNJlq6jT62e2tYdZY0FAAAADBFRaAg1F9bRTy1ZR78rlYrfUgAAAODWFIQh1FiIQpMT198p5D4hAAAAoF+i0BBaPCm0c/tokqTX66VdzFhHDwAAAPRNFBpCzaKVrZs2ZHRDPUnSnTuf3qWLqVtHDwAAAPRJFBpCjaJ1/SXTC+vonRQCAAAA+iUKDaFm0bKOHgAAAHhLRKEh1ChamRwfvfrjxZNCLpoGAAAA+iUKDZnWpflcaLWvPyk0eyK1zeOpbhh9g18JAAAA8BpRaMgsbh677k6hwjp6AAAA4M0RhYZMYzEKTWy6+tl8MeM+IQAAAOBNEYWGzOJJocXXx3qddubPnnJSCAAAAHhTRKEh0yxaqVSSnduv3B80f6aR9LpOCgEAAABviig0ZJpFKxNbN6Zeu/JbZ/MYAAAAcDtEoSFzZR39NZvHiitRaGTijrJGAgAAAIaQKDRkmjdEofbsTCr1DaltGS9xKgAAAGDYiEJDpNfrLTkpdGUd/a5UKn4rAQAAgP4pCUPkfKudS5c7mbr29bHZmYy4TwgAAAB4k0ShIbJkHX2vl3ZxInWbxwAAAIA3SRQaIo2FKLR4Uqh78Wx6l+ecFAIAAADeNFFoiNx4UqhdWEcPAAAA3B5RaIg0i1Zq1UrGt44msY4eAAAAuH2i0BBpFK3s3D6aWrWS5Mo6+iSpj+8qcywAAABgCIlCQ6R54zr62ZnUtkykOrKxxKkAAACAYSQKDZEbo9B8MePVMQAAAOC2iEJDotvtpVnMXd08liTt2RMumQYAAABuS19R6KWXXsr+/fuzd+/e7N+/Py+//PLrfvenP/1p3vve9+bxxx9frhlJcub8pcx3uldPCnXnL6dz7rR19AAAAMBt6SsKHTx4MAcOHMh3vvOdHDhwII8++uhNv9fpdHLw4ME88MADyzokVy6ZTl5bRz9/ppGkl/qES6YBAACAN++WUejUqVM5duxY9u3blyTZt29fjh07ltOnTy/57le/+tV88IMfzDvf+c5lH3S9ay5EocXXx+ZnTySxjh4AAAC4PbeMQsePH8/u3btTq9WSJLVaLbt27crx48ev+96LL76YZ599Np/85CdXZND1rnnDSaHX1tF7fQwAAAB48+rL8Rdpt9v5/Oc/nz/+4z++Go9uxwsvvJCZmZnlGKk0vV4vSfLcc88t61/3hR8XqdeS//fF51OpVDL24+ezsTaS/+fFnySVyrL+d8FqWu5nBdYqzwr0x7MCt+Y5gf6shWel0Wi84c/fMgpNT09nZmYmnU4ntVotnU4nJ0+ezPT09HX/Ja+88ko+/elPJ0nOnj2bXq+X8+fP5wtf+ELfw95333256667+v7+IPrpQqC5//77l/Wv+93//L+za6KX973vfUmSEz/9v9LeMZ37F34Mw+i5555b9mcF1iLPCvTHswK35jmB/qyVZ+XVV199w5+/ZRTauXNn9uzZk6NHj+ahhx7K0aNHs2fPnuzYsePqd+688858//vfv/rjJ554IhcvXsxnP/vZtzA612oWrauvjiVXXh+zeQwAAAC4XX1tH3vsscdy+PDh7N27N4cPH86hQ4eSJA8//HCef/75FR2QK66NQr1eL/PFTOoTohAAAABwe/q6U+iee+7JkSNHlnz+ta997abf/93f/d23NhXX6XS6OX127urmsc6FM+m1LzkpBAAAANy2vk4KUa5TZ+fS7b22eWy+WFxHLwoBAAAAt0cUGgLW0QMAAADLTRQaAotRaOrqSaGZJJXUx3eVOBUAAAAwzEShIXA1Ck0snBQqZlLbuiPV+oYyxwIAAACGmCg0BBpFK5tG69k0OpIkmZ+dcZ8QAAAA8JaIQkPg2nX0yZU7hdwnBAAAALwVotAQuDYKdduX0jl/OiMTd5Q8FQAAADDMRKEh0Czmrrlk+mSSZMRJIQAAAOAtEIUGXHu+k+L8pdfW0RcL6+jdKQQAAAC8BaLQgGsWc0mSye3XrqN3UggAAAB4a0ShAdcoLibJ1dfH2rMnUtkwmuqmbWWOBQAAAAw5UWjANYtWkmRyYuGk0OxMRsZ3p1KplDkWAAAAMOREoQHXWIxC19wpZB09AAAA8FaJQgOuWcxl2+YN2ThSS6/Xy3xx0jp6AAAA4C0ThQZcs2hdPSXUOT+b3vxlJ4UAAACAt0wUGnDNonX1kumrm8esowcAAADeIlFowDWuOSnUnr0ShZwUAgAAAN4qUWiAtS7N50KrfU0UOpGkkpHxqXIHAwAAAIaeKDTAmjdsHpsvZlLftjOV2kiZYwEAAABrgCg0wBqzV6LQ1LXr6N0nBAAAACwDUWiANW48KTQ7k5Fx6+gBAACAt04UGmDNopVKJdm5fTTdy3PpXCicFAIAAACWhSg0wJpFKxNbR1OvVTNfnEySjNg8BgAAACwDUWiANYvWdfcJJUl9wutjAAAAwFsnCg2wRtG6YR29k0IAAADA8hCFBlSv10vzTOu6dfSVjZtSHdtS8mQAAADAWiAKDajzrXYuXe5cc1JoJiPju1OpVEqeDAAAAFgLRKEB1VxYRz91zUmhEZvHAAAAgGUiCg2oxkIUmhwfTa/XzXxxMnX3CQEAAADLRBQaUI3ZxSg0ls652fQ6bZdMAwAAAMtGFBpQzaKVeq2Sia2jaRdXNo9ZRw8AAAAsF1FoQDWLVnZsH0u1Wsn87EySuFMIAAAAWDai0IBqFK2rl0y3Z2eSSjX1bZMlTwUAAACsFaLQgGoWrUxuf23zWH3bZCq1eslTAQAAAGuFKDSAut1eTp1pZXJ8NEnSnj3h1TEAAABgWYlCA+jM+UuZ7/Ree32smLGOHgAAAFhWotAAahSvraPvXmqle/Gsk0IAAADAshKFBlDzmijULq5sHrOOHgAAAFhOotAAuvak0NV19F4fAwAAAJaRKDSAmkUrG+rVbNu84bWTQqIQAAAAsIxEoQHUKFqZmhhLpVLJfDGT6ujm1Ma2lD0WAAAAsIaIQgOoWbQyubh5bPZE6uPuEwIAAACWlyg0gK6NQvPFTEYmdpU8EQAAALDWiEIDptPpZvbsXCbHx9LrdtIuGu4TAgAAAJadKDRgTp2dS7eXTI2PpXPudNKdz4h19AAAAMAyE4UGTPOadfTt2RNJrKMHAAAAlp8oNGCui0KL6+gnRCEAAABgeYlCA2YxCk2Nj2V+diap1lLfNlnyVAAAAMBaIwoNmMZsK5tG69k0OpJ2MZP69qlUqrWyxwIAAADWGFFowDSKVqYW19HPzrhPCAAAAFgRotCAaZ5pZXIhCrWLGevoAQAAgBUhCg2YZnElCnXnLqTbOpcRl0wDAAAAK0AUGiCX252cOX85UzaPAQAAACtMFBogzTNL19G7UwgAAABYCaLQAFlcRz+5uI4+ohAAAACwMkShAbIYhabGx9KenUl1bGuqo5tLngoAAABYi0ShAdJYiEI7x8cyX1hHDwAAAKwcUWiANGZb2bZ5QzaO1K6so3fJNAAAALBCRKEB0ixamZoYS6/byfyZhpNCAAAAwIoRhQZIs2hlcvtY5s82k27HSSEAAABgxYhCA6RZtDJl8xgAAACwCkShAXFxrp0Lc/OZHB9Lu1iIQhN3lDwVAAAAsFaJQgNicR395PhY2rMnkmo9ta07Sp4KAAAAWKtEoQHRLOaSXIlCV9bRT6VSrZU8FQAAALBWiUIDorFwUmhqfCzt2ZOpu08IAAAAWEGi0IBoFq1UKsmO7aOZL064TwgAAABYUaLQgGgWrUxsHU3l8sV05y44KQQAAACsKFFoQDSKi1fW0RfW0QMAAAArTxQaEM2ilcmJhc1jSeoTohAAAACwckShAdDr9dIo5pwUAgAAAFaNKDQAzl1s53K7k8nxsbRnZ1LdtC3VjWNljwUAAACsYaLQAGgurKOfXDgpZPMYAAAAsNJEoQGwGIWmFk4KeXUMAAAAWGmi0ABoLEShnVtHMn+2aR09AAAAsOJEoQHQLFqp1yrZ0j2X9LoZsXkMAAAAWGGi0ABoFq3s2D6Wzpkrm8esowcAAABWmig0ABpF68o6+tnFdfQumgYAAABWlig0ABajULuYSaU2ktrWibJHAgAAANY4Uahk3W4vp8+0rq6jr4/vSqXitwUAAABYWepDyYrzlzLf6WVyYR29zWMAAADAahCFStZcWEc/tX007WLG5jEAAABgVYhCJWssRKHJsW56ly46KQQAAACsClGoZIsnhbbnTJJkZMLmMQAAAGDliUIlaxatbBipZUPrVJJkxEkhAAAAYBWIQiW7so5+NPNnTiZJ6uO7Sp4IAAAAWA9EoZI1i4V19LMnUts8nuqG0bJHAgAAANYBUahkjdlWpsY3pV3MpG7zGAAAALBKRKESzXe6mT03t3BSaMZ9QgAAAMCqEYVKdPrMXHq9ZHLbSObPnrKOHgAAAFg1olCJGgvr6HePXEzSs44eAAAAWDX1fr700ksv5XOf+1yKosj4+Hgef/zxvPOd77zuO1/+8pfz1FNPpVqtZmRkJI888kg+8IEPrMTMa0ZzIQqNV86mm2TEnUIAAADAKukrCh08eDAHDhzIQw89lG9961t59NFH89d//dfXfec973lPPvWpT2VsbCwvvvhifvM3fzPPPvtsRkdt03o9i1Foy3yRs4nXxwAAAIBVc8vXx06dOpVjx45l3759SZJ9+/bl2LFjOX369HXf+8AHPpCxsbEkyb333pter5eiKFZg5LWjWbSyebSeyvlGKvUNqW2ZKHskAAAAYJ24ZRQ6fvx4du/enVqtliSp1WrZtWtXjh8//rq/5pvf/Gbe8Y535I473JHzRhpFK5PjY2nPzqQ+viuVSqXskQAAAIB1oq/Xx96MH/zgB/niF7+Yr3/962/6177wwguZmZlZ7pFWVa/XS5I899xzt/zuKz87lc2jtZw9/nK6o9v6+jWw1vjfPfTHswL98azArXlOoD9r4VlpNBpv+PO3jELT09OZmZlJp9NJrVZLp9PJyZMnMz09veS7P/zhD/OZz3wmX/nKV3L33Xe/6WHvu+++3HXXXW/61w2Sny6c9rn//vtv+d0L3/o/857/ZVdG/r+z2fqu/yPv6uPXwFry3HPP9fWswHrnWYH+eFbg1jwn0J+18qy8+uqrb/jzt3x9bOfOndmzZ0+OHj2aJDl69Gj27NmTHTt2XPe9H/3oR3nkkUfypS99Ke9+97vfwsjrw6V2J2cvXM701m56l+dsHgMAAABW1S2jUJI89thjOXz4cPbu3ZvDhw/n0KFDSZKHH344zz//fJLk0KFDmZuby6OPPpqHHnooDz30UH784x+v3ORD7tTC5rE7Ri4ksXkMAAAAWF193Sl0zz335MiRI0s+/9rXvnb1P//jP/7j8k21DjQWotBE5VySZEQUAgAAAFZRXyeFWH7NhSi0Zb5IktTHd5U5DgAAALDOLPv2MfqzGIU2zJ1Ob8uOVEc2ljwRAAAAsJ44KVSSRtHK9i0b0j170iXTAAAAwKoThUrSLFqZHB9Le/ZE6qIQAAAAsMpEoZI0i1Z2bRtJ59xpl0wDAAAAq04UKkmzaOXtmy4lsY4eAAAAWH2iUAkuzrVzYW4+d2y8kCTuFAIAAABWnShUgsbC5rEdlXNJkvr4HWWOAwAAAKxDolAJFtfRb+0UqYxsTG3z9pInAgAAANabetkDrEeLUWjjpdlUx3enUqmUPBEAAACw3jgpVIJG0Uq1klTPN9wnBAAAAJRCFCpBs2hlYtvGzBcz1tEDAAAApRCFStAsWnn7tl5685etowcAAABKIQqVoFm08o7Nc0msowcAAADKIQqtsl6vl0Yxl+kNF5Mk9Qnr6AEAAIDVJwqtsrMXLudyu5Od1XNJKqlvnyp7JAAAAGAdEoVW2eI6+m3dM6lt3ZFqfUPJEwEAAADrkSi0yhaj0Oil0xnx6hgAAABQElFolS1GoerFps1jAAAAQGlEoVXWKFoZq3XSu1DYPAYAAACURhRaZc1iLndvbydJRpwUAgAAAEoiCq2y5plW3rF5LklSd1IIAAAAKIkotMoaRSt3bryYxEkhAAAAoDyi0Crqdns5faaVndXzqWwYTXXTtrJHAgAAANYpUWgVFecvZb7Ty/bemYyM35FKpVL2SAAAAMA6JQqtosbsldfGRi/Puk8IAAAAKJUotIqaxVwq6aV+8ZT7hAAAAIBSiUKrqFG0sq3SSjrtjDgpBAAAAJRIFFpFzaKVOzZeSJLUnRQCAAAASiQKraJm0crPbb6UJE4KAQAAAKUShVZRs2jlztGLSaWa+vapsscBAAAA1jFRaBU1ilamaudT37YzldpI2eMAAAAA65gotErmO93MnpvL9t5Z9wkBAAAApROFVsnpM3Pp9ZKx9qx19AAAAEDpRKFV0iha2ZB26pfPpT5xR9njAAAAAOucKLRKGkUrO2vnk9g8BgAAAJRPFFolzaKVndVzSeJOIQAAAKB0otAqaRat3LmxlSTuFAIAAABKJwqtkmbRyttGL6a6cVOqY1vKHgcAAABY50ShVdIoWpmqn099fHcqlUrZ4wAAAADrnCi0SppFK9tz1iXTAAAAwEAQhVbBpXYn5y7MZVO7sI4eAAAAGAii0Co4VbSyvdpKtddxyTQAAAAwEEShVdCwjh4AAAAYMKLQKmjMtjK5EIXcKQQAAAAMAlFoFTTPtLKzdj6pVFPfNln2OAAAAACi0GpoFq1Mb7iY+vbJVGr1sscBAAAAEIVWQ6NoZdfIeZdMAwAAAANDFFoFzaKV8Zy1jh4AAAAYGKLQKjhXnMlot+WkEAAAADAwRKEVdnGunbF2kSSp2zwGAAAADAhRaIU1imvW0Y97fQwAAAAYDKLQCmsWC+vo46QQAAAAMDhEoRXWmF04KbRxc2qjm8seBwAAACCJKLTiFk8KbXBKCAAAABggotAKaxSt7Kqfz4h19AAAAMAAEYVW2KnZCxmvnM+Ik0IAAADAABGFVtiloplauqmPi0IAAADA4BCFVlCv10vlfCNJvD4GAAAADBRRaAWdvXA523M2SZwUAgAAAAaKKLSCmsWVdfS9SjX1bTvLHgcAAADgKlFoBTWLVnZWzyVbdqZSrZU9DgAAAMBVotAKahatTNasowcAAAAGjyi0ghpFKzur5zO2c7rsUQAAAACuIwqtoOL0bDZXL2VkwiXTAAAAwGARhVZQe3YmiXX0AAAAwOARhVbSuUYS6+gBAACAwSMKrZC6tkSEAAAK3ElEQVROt5cNc6eTJCPju0qeBgAAAOB6otAKKc7NZUflbOZHNqU6urnscQAAAACuIwqtkMV19L0tU2WPAgAAALCEKLRCmsVcdlbPZcR9QgAAAMAAEoVWSGP2fCaqF7Jp6s6yRwEAAABYQhRaIecax1Or9LJparrsUQAAAACWEIVWSPv0TJJkZOKOkicBAAAAWEoUWiG9c40kcacQAAAAMJBEoRWyodVMJ7XUtu4oexQAAACAJUShldBLNs+fyeWNE6lUa2VPAwAAALCEKLQCur1kZ/Vculsmyx4FAAAA4KZEoRXQ6/Wys3ou9fFdZY8CAAAAcFP1sgdYi7q9XjZV26lO3ln2KAAAAAA35aTQSuh2kyTb77ir5EEAAAAAbk4UWgm9K1Fo09R0yYMAAAAA3JwotAIqC1FoZGJ3yZMAAAAA3JwotAIq6aVV3ZTqhrGyRwEAAAC4KVFoBVR63VzaMFH2GAAAAACvSxRaAdX00tk8WfYYAAAAAK9LFFp2vVQr3dS2u08IAAAAGFyi0DLr9a78+9ikzWMAAADA4BKFVsjW3W8rewQAAACA1yUKLbsrR4V2vv0dJc8BAAAA8Pr6ikIvvfRS9u/fn71792b//v15+eWXl3yn0+nk0KFDeeCBB/KRj3wkR44cWe5Zh0YvlYyO7yx7DAAAAIDX1VcUOnjwYA4cOJDvfOc7OXDgQB599NEl33nyySfzyiuv5Omnn843vvGNPPHEE3n11VeXfeCB17sShSoVh7AAAACAwXXLcnHq1KkcO3Ys+/btS5Ls27cvx44dy+nTp6/73lNPPZWPfexjqVar2bFjRx544IF8+9vfXpmpB1zPW3kAAADAgLtlvTh+/Hh2796dWq2WJKnVatm1a1eOHz++5Ht33nnn1R9PT0/nxIkTyzzu4KsmiVNCAAAAwICrlz3AtV544YXMzMyUPcZbMva//u/ZPLYlzz33XNmjwFDwrEB/PCvQH88K3JrnBPqzFp6VRqPxhj9/yyg0PT2dmZmZdDqd1Gq1dDqdnDx5MtPT00u+97Of/Szvec97kiw9OdSP++67L3fdddeb+jUD5/7789xzz+X+++8vexIYeJ4V6I9nBfrjWYFb85xAf9bKs3Kru55v+Z7Tzp07s2fPnhw9ejRJcvTo0ezZsyc7duy47nsf/ehHc+TIkXS73Zw+fTrf/e53s3fv3rcwOgAAAAArpa/Lbx577LEcPnw4e/fuzeHDh3Po0KEkycMPP5znn38+SfLQQw/lrrvuyi/90i/l4x//eH7nd34nb3/721ducgAAAABuW193Ct1zzz05cuTIks+/9rWvXf3PtVrtaiwCAAAAYLBZkwUAAACwDolCAAAAAOuQKAQAAACwDolCAAAAAOuQKAQAAACwDolCAAAAAOuQKAQAAACwDolCAAAAAOuQKAQAAACwDolCAAAAAOuQKAQAAACwDolCAAAAAOuQKAQAAACwDolCAAAAAOuQKAQAAACwDolCAAAAAOuQKAQAAACwDolCAAAAAOtQvewBkqTT6SRJTpw4UfIky6PRaOTVV18tewwYeJ4V6I9nBfrjWYFb85xAf9bKs7LYWRa7y40GIgo1Go0kySc+8YmSJwEAAABYWxqNRn7u535uyeeVXq/XK2Ge68zNzeWFF17I1NRUarVa2eMAAAAADL1Op5NGo5H77rsvo6OjS35+IKIQAAAAAKvLRdMAAAAA65AoBAAAALAOiUIAAAAA65AoBAAAALAOiUIAAAAA65AoBAAAALAOiUIAAAAA65AodJteeuml7N+/P3v37s3+/fvz8ssvL/lOp9PJoUOH8sADD+QjH/lIjhw5svqDQsn6eVa+/OUv51d+5Vfy4IMP5td//dfzL//yL6s/KJSsn2dl0U9/+tO8973vzeOPP756A8KA6PdZeeqpp/Lggw9m3759efDBB9NsNld3UChZP8/KqVOn8ulPfzoPPvhgfvmXfzmPPfZY5ufnV39YKMnjjz+eD33oQ7n33nvzX//1Xzf9zlr/c70odJsOHjyYAwcO5Dvf+U4OHDiQRx99dMl3nnzyybzyyit5+umn841vfCNPPPFEXn311RKmhfL086y85z3vyT/8wz/kySefzB/90R/lkUceydzcXAnTQnn6eVaSK/9gcvDgwTzwwAOrPCEMhn6eleeffz5/9md/lq9//es5evRo/u7v/i5bt24tYVooTz/Pyp//+Z/nnnvuyZNPPpl/+qd/yn/+53/m6aefLmFaKMeHP/zh/O3f/m3e9ra3ve531vqf60Wh23Dq1KkcO3Ys+/btS5Ls27cvx44dy+nTp6/73lNPPZWPfexjqVar2bFjRx544IF8+9vfLmNkKEW/z8oHPvCBjI2NJUnuvffe9Hq9FEWx6vNCWfp9VpLkq1/9aj74wQ/mne985ypPCeXr91n5y7/8y3zqU5/K1NRUkmTr1q3ZuHHjqs8LZen3WalUKrlw4UK63W4uX76cdrud3bt3lzEylOJ973tfpqen3/A7a/3P9aLQbTh+/Hh2796dWq2WJKnVatm1a1eOHz++5Ht33nnn1R9PT0/nxIkTqzorlKnfZ+Va3/zmN/OOd7wjd9xxx2qNCaXr91l58cUX8+yzz+aTn/xkCVNC+fp9Vn7yk5/kv//7v/OJT3wiv/Zrv5avfOUr6fV6ZYwMpej3Wfnt3/7tvPTSS3n/+99/9V/3339/GSPDwFrrf64XhYCB8YMf/CBf/OIX86d/+qdljwIDp91u5/Of/3wOHTp09R/ygZvrdDr58Y9/nL/4i7/I3/zN3+SZZ57Jt771rbLHgoHz7W9/O/fee2+effbZPPPMM/mP//iPNXUCArg1Ueg2TE9PZ2ZmJp1OJ8mVf/A4efLkkmNn09PT+dnPfnb1x8ePH3f6gXWl32clSX74wx/mM5/5TL785S/n7rvvXu1RoVT9PCuNRiOvvPJKPv3pT+dDH/pQ/uqv/ip///d/n89//vNljQ2rrt+/r9x555356Ec/mg0bNmTLli358Ic/nB/96EdljAyl6PdZOXz4cH71V3811Wo1W7duzYc+9KF8//vfL2NkGFhr/c/1otBt2LlzZ/bs2ZOjR48mSY4ePZo9e/Zkx44d133vox/9aI4cOZJut5vTp0/nu9/9bvbu3VvGyFCKfp+VH/3oR3nkkUfypS99Ke9+97vLGBVK1c+zcuedd+b73/9+vve97+V73/tefuu3fisf//jH84UvfKGssWHV9fv3lX379uXZZ59Nr9dLu93Ov//7v+dd73pXGSNDKfp9Vu66664888wzSZLLly/n3/7t3/LzP//zqz4vDLK1/uf6Ss8L1rflJz/5ST73uc/l7Nmz2bZtWx5//PHcfffdefjhh/N7v/d7+YVf+IV0Op38wR/8Qf71X/81SfLwww9n//79JU8Oq6ufZ+U3fuM38j//8z/XXWz4J3/yJ7n33ntLnBxWVz/PyrWeeOKJXLx4MZ/97GdLmhjK0c+z0u128/jjj+eZZ55JtVrN+9///nz2s59Nter/D2X96OdZeeWVV3Lw4ME0m810Op384i/+Yn7/938/9Xq97PFhVfzhH/5hnn766TSbzUxMTGR8fDz//M//vK7+XC8KAQAAAKxD/u8SAAAAgHVIFAIAAABYh0QhAAAAgHVIFAIAAABYh0QhAAAAgHVIFAIAAABYh0QhAAAAgHVIFAIAAABYh/5/NurzZInkKRAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(20,10))\n",
        "plt.grid()\n",
        "auc_plot(dtc, X_test, y_test, label=\"DT\")\n",
        "auc_plot(classifier[0], X_test, y_test, label=\"DT TUNING\")\n",
        "auc_plot(rfc, X_test, y_test, label=\"RF\")\n",
        "auc_plot(classifier[1], X_test, y_test, label=\"RF TUNING\")\n",
        "plt.legend(loc=\"bottom right\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "pOZoQSFppqUB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "outputId": "73d407ab-2140-404f-e489-12bd0b4f2351"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 7))\n",
        "sns.heatmap(results[results.columns.to_list()].astype(float), cmap=\"Blues\", annot=True, linewidths=1, cbar=False, annot_kws={\"fontsize\":12}, yticklabels = [\"Precision\", \"ROC AUC\", \"Accuracy\"])\n",
        "sns.set(font_scale=1.5)\n",
        "plt.yticks(rotation=0)\n",
        "plt.title(\"Final Heatmap Result for Conclusions\", y=1.1, weight=\"bold\", fontsize=18)\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "6MbFzQ9rGlh_"
      ],
      "name": "Breast Cancer.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}