{
  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "W54CX6vGVZ3j"
      },
      "source": [
        "# IRIS Dataset – Machine Learning Assignment\n",
        "\n",
        "This notebook completes the required IRIS dataset tasks:\n",
        "\n",
        "- Load the dataset using Pandas\n",
        "- Clean and explore the dataset\n",
        "- Display class counts and missing values\n",
        "- Plot histograms and scatterplots\n",
        "- Compute correlation matrix and heatmap\n",
        "- Encode the class variable\n",
        "- Split data into 70% training and 30% testing\n",
        "- Train Logistic Regression, KNN, and Decision Tree models\n",
        "- Compare model accuracies\n",
        "- Change hyperparameters and compare results\n",
        "\n",
        "> **Note:** The notebook first tries to load `Iris.csv` from Google Drive.  \n",
        "> If the file is not found, it automatically loads the standard Iris dataset from Scikit-learn."
      ],
      "id": "W54CX6vGVZ3j"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wzkuUpIyVZ3k"
      },
      "source": [
        "## 1. Upload the dataset\n",
        "\n",
        "Create a folder named:\n",
        "\n",
        "`IRIS_ML_Assignment`\n",
        "\n",
        "Upload the dataset file into that folder and name it:\n",
        "\n",
        "`Iris.csv`\n",
        "\n",
        "Expected path:\n",
        "\n",
        "`Iris.csv`"
      ],
      "id": "wzkuUpIyVZ3k"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xoGm_ngpVZ3k"
      },
      "source": [
        "## 2. Import the necessary modules"
      ],
      "id": "xoGm_ngpVZ3k"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "rEnCy-ThVZ3k"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "\n",
        "pd.set_option('display.max_columns', None)\n",
        "sns.set_context('notebook')"
      ],
      "id": "rEnCy-ThVZ3k"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0SJ2ZilrVZ3k"
      },
      "source": [
        "## 3. Load the dataset using Pandas"
      ],
      "id": "0SJ2ZilrVZ3k"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        },
        "id": "H0VxlV4pVZ3k",
        "outputId": "ae8ef3ad-15f9-41e3-a680-0effef59c39a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iris.csv was not found beside the notebook.\n",
            "The standard Iris dataset will be loaded from Scikit-learn instead.\n",
            "Dataset shape: (150, 6)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
              "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
              "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
              "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
              "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
              "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "      <th>3</th>\n",
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              "      <td>Iris-setosa</td>\n",
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              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43,\n        \"min\": 1,\n        \"max\": 150,\n        \"num_unique_values\": 150,\n        \"samples\": [\n          74,\n          19,\n          119\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8280661279778629,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.435866284936698,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7652982332594667,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7622376689603465,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Iris-setosa\",\n          \"Iris-versicolor\",\n          \"Iris-virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "# Path to the uploaded dataset\n",
        "file_path = 'Iris.csv'\n",
        "\n",
        "if os.path.exists(file_path):\n",
        "    df = pd.read_csv(file_path)\n",
        "    print('Dataset loaded successfully from current directory.')\n",
        "else:\n",
        "    print('Iris.csv was not found beside the notebook.')\n",
        "    print('The standard Iris dataset will be loaded from Scikit-learn instead.')\n",
        "\n",
        "    iris_data = load_iris(as_frame=True)\n",
        "    df = iris_data.frame.copy()\n",
        "\n",
        "    # Rename columns to commonly used Iris dataset names\n",
        "    df.columns = [\n",
        "        'SepalLengthCm',\n",
        "        'SepalWidthCm',\n",
        "        'PetalLengthCm',\n",
        "        'PetalWidthCm',\n",
        "        'target'\n",
        "    ]\n",
        "\n",
        "    # Convert numeric target to class names\n",
        "    class_map = {\n",
        "        0: 'Iris-setosa',\n",
        "        1: 'Iris-versicolor',\n",
        "        2: 'Iris-virginica'\n",
        "    }\n",
        "    df['Species'] = df['target'].map(class_map)\n",
        "    df.drop(columns=['target'], inplace=True)\n",
        "\n",
        "    # Add an ID column to match common Iris.csv versions\n",
        "    df.insert(0, 'Id', range(1, len(df) + 1))\n",
        "\n",
        "print('Dataset shape:', df.shape)\n",
        "df.head()"
      ],
      "id": "H0VxlV4pVZ3k"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "omV-3FoZVZ3l"
      },
      "source": [
        "## 4. Delete the ID column"
      ],
      "id": "omV-3FoZVZ3l"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GrvVhoxtVZ3l",
        "outputId": "e42ddf56-9aa6-4ce8-9aea-6c218ad56a24"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Deleted ID column(s): ['Id']\n",
            "Current columns:\n",
            "['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species']\n"
          ]
        }
      ],
      "source": [
        "# Delete the ID column if it exists\n",
        "id_columns = [col for col in df.columns if col.strip().lower() in ['id', 'index']]\n",
        "\n",
        "if id_columns:\n",
        "    df.drop(columns=id_columns, inplace=True)\n",
        "    print('Deleted ID column(s):', id_columns)\n",
        "else:\n",
        "    print('No ID column found.')\n",
        "\n",
        "print('Current columns:')\n",
        "print(df.columns.tolist())"
      ],
      "id": "GrvVhoxtVZ3l"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4OLysJEpVZ3l"
      },
      "source": [
        "## 5. Display the first 10 rows"
      ],
      "id": "4OLysJEpVZ3l"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
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          "height": 363
        },
        "id": "g4kx7HPkVZ3l",
        "outputId": "51836253-34eb-48e7-d8c4-b8a8a5ed2f35"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
              "0            5.1           3.5            1.4           0.2  Iris-setosa\n",
              "1            4.9           3.0            1.4           0.2  Iris-setosa\n",
              "2            4.7           3.2            1.3           0.2  Iris-setosa\n",
              "3            4.6           3.1            1.5           0.2  Iris-setosa\n",
              "4            5.0           3.6            1.4           0.2  Iris-setosa\n",
              "5            5.4           3.9            1.7           0.4  Iris-setosa\n",
              "6            4.6           3.4            1.4           0.3  Iris-setosa\n",
              "7            5.0           3.4            1.5           0.2  Iris-setosa\n",
              "8            4.4           2.9            1.4           0.2  Iris-setosa\n",
              "9            4.9           3.1            1.5           0.1  Iris-setosa"
            ],
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              "      <th></th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
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              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
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              "      <td>0.2</td>\n",
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              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
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              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
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              "      <td>Iris-setosa</td>\n",
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              "      <th>5</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.4</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "      <td>3.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "      <td>Iris-setosa</td>\n",
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              "      <th>9</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.1</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-727fb47c-826d-4602-8378-b7e9d876eed1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-727fb47c-826d-4602-8378-b7e9d876eed1 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-727fb47c-826d-4602-8378-b7e9d876eed1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8280661279778629,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.435866284936698,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7652982332594667,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7622376689603465,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Iris-setosa\",\n          \"Iris-versicolor\",\n          \"Iris-virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "df.head(10)"
      ],
      "id": "g4kx7HPkVZ3l"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4xUwIaM0VZ3l"
      },
      "source": [
        "## 6. Display a description of the dataset using Pandas"
      ],
      "id": "4xUwIaM0VZ3l"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "E2qzTfg2VZ3l",
        "outputId": "560c940e-5ade-42bd-ba68-36d6a89d6d8b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
              "count      150.000000    150.000000     150.000000    150.000000          150\n",
              "unique            NaN           NaN            NaN           NaN            3\n",
              "top               NaN           NaN            NaN           NaN  Iris-setosa\n",
              "freq              NaN           NaN            NaN           NaN           50\n",
              "mean         5.843333      3.057333       3.758000      1.199333          NaN\n",
              "std          0.828066      0.435866       1.765298      0.762238          NaN\n",
              "min          4.300000      2.000000       1.000000      0.100000          NaN\n",
              "25%          5.100000      2.800000       1.600000      0.300000          NaN\n",
              "50%          5.800000      3.000000       4.350000      1.300000          NaN\n",
              "75%          6.400000      3.300000       5.100000      1.800000          NaN\n",
              "max          7.900000      4.400000       6.900000      2.500000          NaN"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cdd9a429-3d37-41eb-8fc6-aac5fdb9860c\" 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>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.843333</td>\n",
              "      <td>3.057333</td>\n",
              "      <td>3.758000</td>\n",
              "      <td>1.199333</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.828066</td>\n",
              "      <td>0.435866</td>\n",
              "      <td>1.765298</td>\n",
              "      <td>0.762238</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.100000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.400000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cdd9a429-3d37-41eb-8fc6-aac5fdb9860c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cdd9a429-3d37-41eb-8fc6-aac5fdb9860c 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-cdd9a429-3d37-41eb-8fc6-aac5fdb9860c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.24711349471842,\n        \"min\": 0.8280661279778629,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.843333333333334,\n          5.8,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.08617800869866,\n        \"min\": 0.435866284936698,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.0573333333333337,\n          3.0,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.83521261418364,\n        \"min\": 1.0,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.7580000000000005,\n          4.35,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.636648242617504,\n        \"min\": 0.1,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.1993333333333336,\n          1.3,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          \"50\",\n          \"150\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df.describe(include='all')"
      ],
      "id": "E2qzTfg2VZ3l"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WTbhHsL9VZ3l"
      },
      "source": [
        "## 7. Display information about the attributes"
      ],
      "id": "WTbhHsL9VZ3l"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MnK3azkXVZ3l",
        "outputId": "4dc91e6c-7be5-4810-94e2-a7e21ff9bc94"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 5 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   SepalLengthCm  150 non-null    float64\n",
            " 1   SepalWidthCm   150 non-null    float64\n",
            " 2   PetalLengthCm  150 non-null    float64\n",
            " 3   PetalWidthCm   150 non-null    float64\n",
            " 4   Species        150 non-null    object \n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 6.0+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ],
      "id": "MnK3azkXVZ3l"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UHar4gxlVZ3l"
      },
      "source": [
        "## 8. Identify the class column"
      ],
      "id": "UHar4gxlVZ3l"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v6sd9DoHVZ3l",
        "outputId": "92936a09-c824-4340-f1ed-d1dc8e2a1e0f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class column: Species\n",
            "Unique classes: ['Iris-setosa' 'Iris-versicolor' 'Iris-virginica']\n"
          ]
        }
      ],
      "source": [
        "# Detect the class column automatically\n",
        "possible_class_columns = ['Species', 'species', 'Class', 'class', 'target', 'Target']\n",
        "\n",
        "class_column = None\n",
        "for col in possible_class_columns:\n",
        "    if col in df.columns:\n",
        "        class_column = col\n",
        "        break\n",
        "\n",
        "if class_column is None:\n",
        "    # Use the last non-numeric column as a fallback\n",
        "    non_numeric_columns = df.select_dtypes(exclude=np.number).columns.tolist()\n",
        "    if non_numeric_columns:\n",
        "        class_column = non_numeric_columns[-1]\n",
        "    else:\n",
        "        class_column = df.columns[-1]\n",
        "\n",
        "print('Class column:', class_column)\n",
        "print('Unique classes:', df[class_column].unique())"
      ],
      "id": "v6sd9DoHVZ3l"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EweTdyMKVZ3l"
      },
      "source": [
        "## 9. Display the number of samples in each class"
      ],
      "id": "EweTdyMKVZ3l"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sYQ_kL8qVZ3l",
        "outputId": "19801fdd-2246-454a-fec1-9cc04c20c9ee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Species\n",
            "Iris-setosa        50\n",
            "Iris-versicolor    50\n",
            "Iris-virginica     50\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "class_counts = df[class_column].value_counts()\n",
        "print(class_counts)"
      ],
      "id": "sYQ_kL8qVZ3l"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XoADAAzZVZ3l"
      },
      "source": [
        "## 10. Check for null values"
      ],
      "id": "XoADAAzZVZ3l"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yltIQLzZVZ3l",
        "outputId": "63e9cf34-b3a4-4c1c-9e57-aaecb62ee967"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Null values in each column:\n",
            "SepalLengthCm    0\n",
            "SepalWidthCm     0\n",
            "PetalLengthCm    0\n",
            "PetalWidthCm     0\n",
            "Species          0\n",
            "dtype: int64\n",
            "\n",
            "Total null values: 0\n"
          ]
        }
      ],
      "source": [
        "null_values = df.isnull().sum()\n",
        "print('Null values in each column:')\n",
        "print(null_values)\n",
        "\n",
        "print('\\nTotal null values:', df.isnull().sum().sum())"
      ],
      "id": "yltIQLzZVZ3l"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O9EjjqkbVZ3m"
      },
      "source": [
        "### Interpretation of null values\n",
        "\n",
        "If the total number of null values is **0**, the dataset is complete and no missing-value treatment is required."
      ],
      "id": "O9EjjqkbVZ3m"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4aA342XuVZ3m"
      },
      "source": [
        "## 11. Display a histogram for each numerical attribute"
      ],
      "id": "4aA342XuVZ3m"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 803
        },
        "id": "qjWDOIdmVZ3m",
        "outputId": "a0cd10ab-aa7b-46be-e325-e9cbc9cb8e38"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "numeric_columns = df.select_dtypes(include=np.number).columns.tolist()\n",
        "\n",
        "df[numeric_columns].hist(\n",
        "    bins=15,\n",
        "    figsize=(12, 8),\n",
        "    edgecolor='black'\n",
        ")\n",
        "\n",
        "plt.suptitle('Histograms of Iris Attributes', fontsize=16)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "id": "qjWDOIdmVZ3m"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Es00d4xJVZ3m"
      },
      "source": [
        "## 12. Prepare standardized class names and colors"
      ],
      "id": "Es00d4xJVZ3m"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1BxX82mrVZ3m",
        "outputId": "c27ed195-ac8e-4200-b39a-946e9f62b7c6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class_Name\n",
            "Setosa        50\n",
            "Versicolor    50\n",
            "Virginica     50\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Convert class names to lowercase text for reliable matching\n",
        "class_text = df[class_column].astype(str).str.lower()\n",
        "\n",
        "def normalize_class_name(value):\n",
        "    value = str(value).lower()\n",
        "    if 'virginica' in value:\n",
        "        return 'Virginica'\n",
        "    elif 'versicolor' in value or 'versicolour' in value:\n",
        "        return 'Versicolor'\n",
        "    elif 'setosa' in value:\n",
        "        return 'Setosa'\n",
        "    return str(value)\n",
        "\n",
        "df['Class_Name'] = df[class_column].apply(normalize_class_name)\n",
        "\n",
        "class_colors = {\n",
        "    'Virginica': 'red',\n",
        "    'Versicolor': 'orange',\n",
        "    'Setosa': 'blue'\n",
        "}\n",
        "\n",
        "print(df['Class_Name'].value_counts())"
      ],
      "id": "1BxX82mrVZ3m"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nm3FxrRJVZ3m"
      },
      "source": [
        "## 13. Display scatterplots for each class"
      ],
      "id": "Nm3FxrRJVZ3m"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z-KS_R4FVZ3m",
        "outputId": "fb4930c2-ea00-4533-e1dd-3589627f8a71"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Detected features:\n",
            "sepal_length: SepalLengthCm\n",
            "sepal_width: SepalWidthCm\n",
            "petal_length: PetalLengthCm\n",
            "petal_width: PetalWidthCm\n"
          ]
        }
      ],
      "source": [
        "# Detect the four feature columns\n",
        "feature_lookup = {}\n",
        "\n",
        "for col in numeric_columns:\n",
        "    name = col.lower().replace(' ', '').replace('_', '')\n",
        "\n",
        "    if 'sepallength' in name:\n",
        "        feature_lookup['sepal_length'] = col\n",
        "    elif 'sepalwidth' in name:\n",
        "        feature_lookup['sepal_width'] = col\n",
        "    elif 'petallength' in name:\n",
        "        feature_lookup['petal_length'] = col\n",
        "    elif 'petalwidth' in name:\n",
        "        feature_lookup['petal_width'] = col\n",
        "\n",
        "required_features = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']\n",
        "\n",
        "missing_features = [f for f in required_features if f not in feature_lookup]\n",
        "\n",
        "if missing_features:\n",
        "    raise ValueError(f'Could not detect these feature columns: {missing_features}')\n",
        "\n",
        "print('Detected features:')\n",
        "for key, value in feature_lookup.items():\n",
        "    print(f'{key}: {value}')"
      ],
      "id": "Z-KS_R4FVZ3m"
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9gAcvq_XVZ3m",
        "outputId": "1f9498dc-a0af-4473-8b44-78906e2310e8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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Xr4bdbm/Q3ykpKejatSueffbZBnmbc8UVV0Cv1yMnJ6dBPd3Vsf4PPbX6m4jajiPEROSxmTNnorq6GjfeeCPS0tJgs9mwa9cu1yjknXfeCUCMED711FN47LHHUFxcjBtuuAEdO3bEkSNH8M477+DXv/41Hn74YVe53bp1w/Lly1FcXIx+/fph06ZNyM/Px4svvuja5GHixInYunUrbrzxRlxzzTU4cuQINmzYgAsuuKDBDWSeysrKct3k1zjoHTFiBJ5++mlXvvNZsmQJPvjgA2RnZ+OBBx6Aw+FATk4OBgwYgL179zbIO3jwYHz88cdYvXo1unXrhl69euHSSy9t889xrosvvhh9+vTBwoULUVdX12C6BCDmYq9fvx433ngjevfujZ9++gl//vOfERMTgwkTJnj9+vPnz8e7776LiRMn4o477sDgwYNRVVWFffv24e2330ZxcbFr/eMLLrgAmzZtQr9+/dCpUydceOGFuPDCCzFq1Cg8++yzsNvt6N69O7Zv395ke2VPJSUloWfPnti9ezdSUlLQrVu3BudHjBiBLVu2QJKk8/4BZDab8fDDD+Ppp5/GxIkTMWHCBHz77bf497//7ZpLXS8zMxNarRbLly+H1WqFwWDA2LFj3a7dTUR+1k6rWxBREPv3v/+t3HXXXUpaWprSoUMHJSIiQunTp48yc+ZM5eTJk03yb9myRcnKylKio6OV6OhoJS0tTfntb3+rHDx40JVn9OjRyoABA5SvvvpKGT58uGI0GpXk5GRl3bp1DcqSZVlZtmyZkpycrBgMBuWiiy5Stm3bpvzqV79SkpOTG+RFK5ddUxRF+fDDDxUAik6nU6qqqhqcKysrUyRJUgAon3/+eZPnunud//znP8rgwYOViIgIJTU1VdmwYYNrKbVzFRYWKqNGjVIiIyMbLNNVn9disTTI39rlxeotXLhQAaD06dOnyblvvvlGufnmm5WkpCTFYDAoCQkJysSJE5WvvvrqvOXW99f5/PTTT8pjjz2m9OnTR4mIiFA6d+6sjBgxQlm5cmWDZfp27drlaq9z2/PYsWPKjTfeqMTGxiomk0mZOnWqcuLEiSZt7mm73HzzzQoAZcaMGU3OrV69WgGgpKenNznn7nWcTqeyZMkSpWvXrkpkZKQyZswYZf/+/UpycnKTJfX+/Oc/K6mpqa4l+OqXYEtOTlauueaaJq83evRoZfTo0a36mYio7SRF4Wx9Imp/Y8aMwenTp1u12QcREZEvcQ4xEREREYU1BsREREREFNYYEBMRERFRWOMcYiIiIiIKaxwhJiIiIqKwxoCYiIiIiMIaN+ZoRmxsLOrq6tC1a9f2rgoRERERufHjjz/CYDB4vQMkR4ibUVdX59pW1F9kWfbr65H62Kehif0aetinoYn9Gnoa96nD4fDJVvMcIW5G/chwUVGRX17PbrfDarXCZDK5tqil4MY+DU3s19DDPg1N7NfQ465PU1NTfVI2R4iJiIiIKKwxICYiIiKisMaAmIiIiIjCGgNiIiIiIgprDIiJiIiIKKxxlQkiIiIKaE6nE3a73aPn2O122O121NbWwul0qlQzUpNer4dWq/XLazEgJiIiooCkKAr+97//tWnTBUVRIMsyysvLIUmS7ytHfhEbG4suXbqo3ocMiImIiCgg1QfDCQkJiIqK8igokmUZTqcTWq0WGg1niAYbRVFQXV2NU6dOAYDqOwczICYiIqKA43Q6XcFwfHy8x89nQBz8IiMjAQCnTp1CQkKCqq/FK4SIiIgCTv2c4aioqHauCbWn+v73dA65pxgQExERUcDi/N/w5q/+Z0BMRERERGGNATERERGRSq699lr07du32fM5OTmQJAm33XYbLrzwQp+97uLFi9GhQwePn3fHHXf4tB7BIqAC4s2bN+P6669Hjx49EB0djczMTLz88stQFKXF56WkpECSpCb/amtr/VRzIiIioqZmzJiBQ4cO4csvv3R7fuPGjRg2bBgWL16MN954w2eve88992DHjh0eP+/xxx/3aT2CRUCtMrF69WqkpKRg1apVMJvN+Oijj3Dvvffi6NGjWLRoUYvPnTJlCubNm9cgzWAwqFldIiICAIcD2LIFKC0FkpKAyZMBXUB9vLQfWQb27gXKyoD4eCAjA+CKB2Hl+uuvR4cOHfDGG29g6NChDc4VFxdj9+7dWLt2LXr37n3esmpqalwrL5xPjx490KNHD4/r25p6hKKA+q3817/+hY0bN2L69OkYO3Ysnn76adx9991YvXo1ZFlu8bmJiYkYNmxYg3+ciE9EpLJ164CePYHbbgMWLBDHnj1FerjLzQUmTQJuugm45x5xnDRJpJP/yTKQnw988ok4nieu8JWoqChcf/31eOutt5rEMhs3boRWq8X06dObTFV49dVXIUkSdu/ejXHjxiE6Ohrz588HAHz33XcYNWoUjEYj+vbti9dffx033HADxowZ43p+4ykTn376KSRJwkcffYQZM2agY8eOSE5OxrPPPtugTu6mTBw/fhy33347EhMTERkZibS0NKxZs8Z1/rXXXkNWVhY6deqEuLg4jBkzBl988YXXbedPAfUnfOfOnZukXXTRRfjzn/+MqqoqdOzYsR1qRUREbq1bB8yfD9jtgMEgRoUdDsBiEekA8OCD7VvH9pKbC8ycCVRUAAkJgNEI1NYCBQUiPScHyM5u71qGj9xcYNUqoLAQqKsT12taGjBvnl/6YcaMGXj99dfx6aefYuzYsa70N954A+PGjWtxjd0ZM2bg17/+NRYsWICoqCjU1NRg/PjxiI2Nxd///ncAwJIlS1BRUdGq0d37778ft912G9555x384x//wKOPPoqMjAxcddVVbvOXlZVh+PDhAIClS5ciNTUVP/zwAw4fPuzKU1xcjNtvvx29e/eGzWbDxo0bMWrUKOzduxf9+vVrVRu1t4AKiN3Jy8tD9+7dzxsMv/766/jzn/8MvV6PUaNGYfny5Rg4cKCfaklEFGYcDmDpUhEMR0f/Mg0gIkIExlVVwLJlwP33h9/0CVkWwVdFBZCSAtR/WxkdDURFASUlwOrVwMiRnD7hD7m5wEMPtesfJ+PHj4fZbMbGjRtdAfH+/fuxf/9+PPLIIy0+9/7778ejjz7qerx+/XqcPHkSO3fuREpKCgBgyJAh6NOnT6sC4smTJ2Px4sUAgMsvvxzvvfce3n777WYD4tWrV+PUqVMoLCx0vd65QT0A/OEPf3D9X5ZljBs3Dl988QVeffVVLFu27Lx1CgQB/S6Vl5eHN998E6tWrWox33XXXYdLL70USUlJKCoqwtKlS5GVlYVvv/0WqampzT6vpXNHjx5Fz549VV8Iup7D4XD9o9DAPg1N7Nefbd0qgl6TSQTBjel0wNmzYm7xpEn+r58HfN6n+/YBRUVAjx7u26Z7d+DwYfG1PQdummW326EoCmRZPu+0yWbJMqTnnoPS0h8nq1ZBGT5c1T9ONBoNpkyZgjfffBM5OTmIiIjAG2+84ZpOIcuyawGB+p+1/nj11Vc3+Pm/+OILDBw4EElJSa70pKQkDBo0qMHzmivviiuuaFBeWloajh492uzzPvnkE1x22WUNXq+xAwcOYOHChdi9e7drq2UAOHjwYNv77mf1bVN/Paj1/huwAfGxY8cwffp0XHbZZZg1a1aLedeuXev6f3Z2NsaPH4+0tDSsXLkS69evb3MdZFmG1Wpt8/M94XA4UFVVBQDQhdtoSohin4Ym9uvPLBYgNVV89ewukJBl8dW0xQL46X20rXzepxYLkJgImM2AVtv0vNMp8gRB27Qnu93u2n7Z6XS2qQw5Px/KgQOiLwCg8apVnTsDBw7A+e23QGamdxU+j+nTp+NPf/oT3n//fVx77bV48803MXHiRERGRsLpdEJRFCiK4vpZ6wPTzp07N/j5f/zxxyZpAGA2m1FTU+NKrw9EGz+OiYlp8Fy9Xg+r1drgdc+tR1lZGS644IJm++Cnn37CVVddhc6dO+PZZ59FcnIyjEYj7rvvPtTW1ra57+o5nU7IsoyzZ89CkqQmv6uyLPtka+6AfDevqKjA1Vdfjfj4eGzZssXjH7Rr167IysrC119/3WK+oqKiZs/Vjx6bTCaPXrut6v/aMZlM4f0hG0LYp6GJ/fozs1mMgur17kdBbTYxncJsFqPIAcznfWo2AydPAjU1YhSysepqoLIyKNqmPdXW1qK8vBxarRZad39YtIJUUQHJZgMiI38ZHT5XZCRw+jS0FRXu/3jxoaysLKSkpOCtt95Cly5dcOTIETz33HOun61+ydhzHwMi8Dv35+/atSsKCgqatInFYkHHjh1d6fWxU+PHGo2mwXPdve65j+Pj4/Hjjz822wdffPEFjh07hnfffdc1Sg0AlZWVDV6/rbRaLTQaDTp06OD6/Tz3d9UXwTAQgAFxTU0NJk6cCKvVit27d/stIG2OXq/322vpdDrodDq/viapi30amtivEEurzZ4tRjnPnUMMiNHhqioxXzNIlmDzaZ9mZorR84ICIDm5YSCmKMDx4yJPZibnELfA6XRCkiRoNJo2BT2yLEPp1AmIiIBUWyuu08ZqawGDAZLZ7Je+uPnmm7FmzRpER0cjPj4eEyZMcP1s9QHwuYFr/fHcn/+SSy7B3/72N5SUlKBXr14AxE1tBQUFyMrK8ri8xvkaP77iiiuwcuVKHDt2DElJSU1+prq6OgCA0Wh0PWfXrl0oLi7GgAEDvA5YNRoNJEmCXq+HVqtV7f03oH4THQ4Hpk2bhgMHDuCDDz5A9+7d21TOiRMnkJeX12S9PyIi8hGdDli4UIwQV1WJEWFZFseqKpG+YEFQBMM+p9GI1QtMJjFHtapKTJOoqhKPTSZg7lwGw/6QkQGkp4s/3BpPl1AUkZ6eLvL5wYwZM1BdXY1XXnkFU6dObVNQd+edd6JLly6YOHEi3n77bbz99tuYOHEiunTp4rPR0nPNmTMHCQkJGDVqFF566SXs2LEDL730kutGv2HDhqFDhw747W9/i+3bt+OVV17BTTfd1OYYrr0E1G/jAw88gG3btmHhwoWorKzEnj17XP/q/wK5/PLL0adPH9dzNm7ciFtuuQWvv/66q5NGjRoFrVbbZKMOIiLyoQcfBFasEF/92+1iKoDdLkaGV6wI3yXXALFqQU4OMGiQmB5x7Jg4ZmZyyTV/0migzJkTMH+cXHjhhcjIyICiKJgxY0abyoiMjMT27dvRqVMn3HLLLXjkkUcwb9489O3bV5Vv1ePj47Fz505kZWXhkUcewYQJE7By5UrXph+JiYnYvHkzTp06heuvvx7PP/88XnjhhQaxWjCQlPPti+xHKSkpKCkpcXvuyJEjSElJwZgxY1BcXIzi4mIAwJ49e/DYY49h//79qKioQGxsLMaOHYsnnngC/fv3b3Nd6ucQtzTP2JfsdjusVitMJlN4fw0bQtinoYn96kaQ71Snap9yp7o2q62txZEjR9CrVy8YjUaPn19/Q55Wq4Vm585f1iG22cS89/R0EQyHwB8nZ86cQWpqKubMmXPenX2DzbnXgVarbfK76qt4LaDeseqD3JZ8+umnDR4PGzasTXt1ExGRj+h0wPTp7V2LwKTRqL56AbVCdrZY9zlE/jhZvnw5EhMTkZKSgh9//BErV66E0+nEXXfd1d5VC1oBFRATERERqSKE/jjRaDR46qmncPz4ceh0Olx66aX4v//7P/Ts2bO9qxa0GBATERERBZH58+djfv326OQTwfldARERERGRjzAgJiIiIqKwxoCYiIiIiMIaA2IiIiIiCmu8qY6I/IvrsrrHdmke24aIVMaAmIj8Jzf3l8Xx6+oAgwFISxPb3IbA4vhtxnZpHtuGiPyAf2ITkX/k5gIzZwL5+UBMDNCzpzgWFIj03Nz2rmH7YLs0j21DRH7CgJiI1CfLYpSvogJISQGiowGtVhyTkwGrFVi9WuQLJ2yX5rFtKERce+216Nu3b7Pnc3JyIEkSDh8+rFodFi9ejA4dOvi83FdffRWSJOH06dM+L9vfGBATkfr27hVfeSckAJLU8JwkAWYzcOCAyBdO2C7NY9tQiJgxYwYOHTqEL7/80u35jRs3YtiwYejdu7dqdbjnnnuwY8cO1coPBQyIiUh9ZWVi/qfR6P680QjYbCJfOGG7NI9tQyHi+uuvR4cOHfDGG280OVdcXIzdu3djxowZbSq7rq4Ociu+JenRoweGDh3aptfwB6fTCbvd3q51YEBMROqLjxc3Q9XWuj9fWwtERIh84YTt0jy2DfmaIgPl+cD/PhFHxT/TbaKionD99dfjrbfeahK8bty4EVqtFtOnT8exY8dw6623onPnzoiMjMSoUaPw9ddfN8ifkpKCBx98EM8++yySk5MRGRmJM2fO4NixY5g2bRoSExNhNBrRq1cvzJkzx/U8d1MmKioqMHPmTPTo0QMGgwG9evXCY4891iDPCy+8gP79+8NgMCAlJQVPPfXUeQPwM2fO4K677nL9HCNGjMBnn33WIM+YMWMwceJE/PWvf3WVX1BQ0Oo2VQNXmSAi9WVkiJUBCgqAqKiGX4ErCmCxAJmZIl84Ybs0j21DvnQqFyhcBVgLAbkO0BgAUxqQNg9IUH+1khkzZuD111/Hp59+irFjx7rS33jjDYwbNw56vR6XXHIJOnTogJycHJhMJuTk5GDs2LH44YcfkJCQ4HrOli1b0LdvX6xZswZarRbR0dGYNm0aTpw4gbVr1yIxMRGlpaX46quvmq1PXV0dxo4di+LiYixatAgDBw7E0aNHkZeX58qTk5ODWbNmYebMmZg4cSJ27dqFxYsXo6KiAitXrnRbrtPpxNVXX42ioiIsX74ciYmJWLt2LcaNG4ddu3Zh8ODBrrxfffUViouL8cQTTyAuLg49e/b0pom9xoCYiNSn0YhlsmbOBEpKxPxPo1GM8lksgMkEzJ0bfmvLsl2ax7YhXzmVC3zzEGCvAAwJgNYIOGuB8gLgq5nAkBzVg+Lx48fDbDZj48aNroB4//792L9/Px555BE8//zzqKiowBdffOEKfi+//HL069cPK1euxLPPPusqy26349///jeio6NdaV988QWefvppTJ8+3ZV2++23N1uf1157Dd9++y127dqF4cOHu9J/9atfARCB7RNPPIGbbroJa9eudf0MNpsNq1atwmOPPYZ4N9/OvPfee/jiiy/wwQcf4MorrwQAXHnllejTpw+WLVuGLVu2uPKeOXMGX375ZbsHwvX4TkJE/pGdDeTkAIMGAZWVwLFj4piZKdLDdU1Ztkvz2DbkLUWGdPA5EQxHpQC6aEDSimNUMmC3AoWrVZ8+odPpMHXqVGzZsgU2mw2AmC4RFRWFG2+8Edu3b8dll12GTp06weFwwOFwQKvVYvTo0U1uxhszZkyDYBgALr74YqxcuRJ/+tOfcOjQofPW55NPPkF6enqDYPhchYWFOH36NKZOndogffr06bDZbPjiiy/cPi83NxcxMTGuYBgA9Ho9Jk2a1GD0GQAyMjICJhgGOEJMRP6UnQ2MHMldxxpjuzSPbUPesO4FKg+IkWF3q5UYzID1AFCxF4jLVLUqM2bMwPr16/HBBx/guuuuw8aNG3HdddehQ4cOOH36NPbs2QO9Xt/keY1Xn0hMTGySZ9OmTVi4cCEWLlyIBx54AP3798eyZcswadIkt3UpKytDt27dmq1reXm529eqf3zmzJlmn3fu9I5zn9f4Oe5+jvbEgJiI/EujESN81BDbpXlsG2ojqe4MINvENAl3tEag7jRQp/5qJSNGjEBKSgo2btyIhIQEHDlyBGvWrAEAdOrUCVdddRWefPLJJs8zGAwNHkuNA3sAXbt2xcsvv4y//OUv+Prrr/HUU09h+vTpOHjwIFJTU5vkj4+Px94Wlizs1KkTAODUqVMN0k+ePNngvLvnNX5O/fMaP8fdz9Ge+Cc2ERERhSTF0AnQRIg5w+44a8V5g/qrlUiShJtvvhnvvvsu/vznPyM+Ph5XXXUVAOCKK67A999/j/T0dAwZMqTBv4EDB7b6NTQaDYYOHYqnnnoKDoej2ekTV1xxBQ4cOIDPP//c7fn+/fvDbDZj8+bNDdLfeustRERE4JJLLnH7vKysLFRWVmL79u2uNIfDgXfeeQdZWVmt/jnaA0eIiYiIKDSZMoCYdKCiANC6Wa2kziKmSsT6Z7WSGTNm4Omnn8Yrr7yC++67zzVFYu7cuXj99dcxevRoPPTQQ0hKSoLFYsHnn3+Obt26NVhCrTGr1Yorr7wSt912G/r37w+bzYacnBzExsbi4osvdvuc2267DevXr8c111yDRYsW4cILL8Tx48fx2Wef4cUXX4RWq8Xjjz+OWbNmISEhARMmTMCePXuwfPlyzJ492+0NdQBwzTXX4JJLLsGtt96KZ555BomJicjJycGPP/6IBQsWeN+AKmJATERERKFJ0kDpPwfSNw8B1SViznD9KhN1FkBvAtLmApJ/vjC/8MILkZGRgb179zbYjCM+Ph579uzB73//ezz66KMoKytDQkIChg0bhhtvvLHFMo1GIwYOHIicnByUlpYiMjISQ4YMwfbt29G5c2e3zzEYDPjkk0+wcOFCLFu2DGfOnEGPHj1w8803u/LMnDkTer0eq1evxvr169G1a1csXry4xcBWq9Xi/fffx8MPP4z58+ejqqoKF198MbZv395gybVAJCmKorR3JQJR/ZyboqIiv7ye3W6H1WqFyWRyO6megg/7NDSxX0MP+zQw1dbW4siRI+jVqxeMze1Y2AJZluF0OqHVaqE5vfOcdYhtYpqEKV0Ew35Yh5ja7tzrQKvVNvld9VW8xhFiIiIiCm0J2YB5pFhNoq5MzBmOzfDbyDAFPgbERMFIlrkMVXtwOICtW8XGEGYzMHkyoPPR26jafWqzAc89Bxw+DPTuDcyZI7Y+Dga83skXJI3qS6tR8GJATBRscnOBVauAwkKgrg4wGMQWt/PmcaMCNa1bByxdClRVAampQFERMHs2sHAh8OCD3pWtdp/Ong1s2CDKrrdoEXD//cDzz3tfvpp4vRORH/BPbKJgkpsrtrLNzwdiYoCePcWxoECk5+a2dw1D07p1wPz5YmRYrxdBmV4vHs+fL863ldp9Ons2sHatCCYlSYysSpJ4vHatOB+oeL0TkZ8wICYKFrIsRsoqKoCUFCA6GtBqxTE5GbBagdWrRT7yHYdDjAzb7aKtIyJEUBkRIR7b7cCyZSKfp9TuU5tNjAwriqizVtvwqCji/M9byQYUXu9E5EcMiImCxd694mvjhGa2IDWbgQMHRD7ynS1bxNxVg6HpvFWNRqSfPi3yeUrtPn3uuYYjw43rXj9S/NxzbStfTbzeiciPGBATBYuyMhG8NLf8kNEoRvrK1N+CNKyUloqR1OZuntPpxPnSUs/LVrtPDx8Wx+a2SK1Pr88XSHi9E5EfMSAmChbx8WI0sraZLUhra8XX+M3sIERtlJQkAsfmpkQ4HOJ8UpLnZavdp717i2Nzy83Xp9fnCyS83onIjxgQEwWLjAxxd73F0jTAURSRnp4u8pHvTJ4sgq66uqbzVWVZpHfuLPJ5Su0+nTNHBJWK4r7uiiLOt7AtbLvh9U5EfsSAmChYaDRiqSmTCSgpEct/OZ3iWFIi0ufO5fqsvqbTiaXV9HrR1jabCCZtNvFYrwcWLGjbesRq92lEhFhaTZJEnZ3OhkdJEucDcT1iXu8UYl5//XVccsklMJlMiImJQXp6Ou655x6cOnXKo3Ly8/OxePFiVFdXq1TT8MR3EqJgkp0N5OQAgwYBlZXAsWPimJkp0rkuqzoefBBYsULcyGW3i1Fhu13c8LVihXfrEKvdp88/D8ya1XCkuH5keNaswF6HmNc7hYhnn30Wt912G7Kzs7Fp0yZs2rQJd911F7766iucOHHCo7Ly8/OxZMkSBsQ+JilKc5PLwpuv9sZuLbvd3mR/bgpuqvYpd+5qHw4H7Fu2wGqxwGQ2Q8+d6vxD5bbh+29gqq2txZEjR9CrVy8Ym7u5sgWyLMPpdEKr1ULTzu+PPXr0wPjx4/Hyyy83OSfLskf1e/XVV3HnnXfCYrGgc+fOvqxmQDr3OtBqtU1+V30Vr/ETlCgYaTRilOzyy8WRwbB/6HTApEnATTeJo6+CYUD9Po2IAB59FHjxRXEMlmAY4PVOPiHLYo+XTz4RR38uYV1eXo6uXbu6Pdc4GH711VeRkZEBo9GI7t27Y+HChXA6na5zd955JwDAbDZDkiSkpKS4nrtv3z5ceeWViI6OhslkwpQpU1DaaAWcl19+GQMGDEBkZCTi4+ORlZWFL7/80nV+1apVGDp0KEwmExISEjBx4kT897//9UUzBDRu3UxEREQhrb13AB88eDA2bNiAXr16YeLEiejSpYvbfKtXr8YjjzyCOXPmYNWqVThw4IArIH7mmWdwzTXX4Pe//z2eeuopfPDBBzCZTDAYDACAo0ePYtSoUejduzf+/ve/o7a2FgsXLsTo0aOxd+9edOzYEZ999hnuvvtuPPzww5gwYQKqq6vxxRdfoKKiwlWHY8eO4cEHH0RycjIqKyuxYcMGjBgxAv/973/RqVMn9RurvSjkVq9evZRevXr57fVsNptisVgUm83mt9ckdbFPQxP7NfSwTwNTTU2N8v333ys1NTVter7T6VRsNpvy6adOZdAgRUlOVpShQxUlO1scU1IUZdAgRfnsM1/W2r19+/Ypffr0UQAoAJRevXops2bNUo4cOeLKU1lZqXTo0EF57LHHGjz3T3/6kxIZGamcPn1aURRFeeWVVxQAisViaZBvzpw5SnR0tFJWVuZKO3DggCJJkrJ27VpFURRlxYoVSqdOnVpdb4fDoVRXVysdOnRQXnjhBU9/bJ849zpw97vqq3iN3zsRERFRSJJl4LnnpHbfAfzCCy/Ed999h/feew8PPfQQTCYT1q5di4yMDOTn5wMAdu3ahbNnz2Lq1KlwOByuf1dccQVqamqwf//+Fl8jNzcXY8eObTCKm5aWhkGDBiEvLw8AcPHFF+PMmTO444478NFHH7m9MW/Pnj0YN24c4uPjodPpEBUVhbNnz4b8tAkGxERERBSS9u4VO3wHwg7gERERmDBhAp5//nl8++23+OCDD1BdXY0nnngCAHD69GkAImjV6/Wuf3379gUgpkS0pLy8HImJiU3SExMTcebMGQDA2LFj8be//Q3fffcdrrzySnTu3Bm3336763xpaSnGjx8Pp9OJF154ATt37sSXX36JhIQE1Da3SU6I4BxiIiIiCklnzkiw2VreAfz06fbZAfzKK6/EoEGDcODAAQBwjexu3boVPXv2bJK/V69eLZbXqVMnt2sanzx5Ev369XM9vvXWW3Hrrbfi9OnT+Oc//4k5c+ZAr9fjpZdewgcffICzZ89i69atiI2NBQA4HA5XwBzKGBATERFRSOrUSUFEhNjpOzq66Xl/7QB+8uTJJqO3NTU1OHr0KAYMGAAAGD58OKKionDs2DHceOONzZYV8fMKMY1HbLOysvDiiy+ivLwccXFxAICDBw9i7969uOuuu5qU07lzZ9x99914//33XUF5TU0NJElqsPzgW2+9BUdzW9eHEAbERORfwbyGsiwD+/aJbYPNZi4BRqEtmH9Xf5aRIXb4LigAoqIaTpuo3wE8M1P9HcAHDhyIa6+9FldeeSW6du2K48ePY926dTh9+jQeeughAEBsbCyeeOIJPPLIIzh27BjGjBkDrVaLoqIi/POf/8SWLVsQFRWF9PR0AMAf//hH3HDDDYiKisLAgQMxZ84cvPLKKxg/fjwWLlyI2tpa/P73v0dSUhLuuOMOAMCiRYtQVlaGMWPGICEhAfv27cMHH3yAuXPnAhBTKgDgzjvvxH333YfvvvsOq1atco0WhzSvb8sLUVxlgrzFPnXjs88U5frrFaV/f3GLd//+4rE/bvP21s91tw0cqFiuuEKxDRwYPHWnFvF31Y0A+F319SoTKSntt8rEH//4R+Wqq65SunfvrkRERCjdunVTrrrqKuX//u//muTduHGjMnToUCUyMlKJiYlRLrroIuXxxx9X7Ha7K8/ixYuVHj16KBqNRklOTnalFxQUKOPGjVOioqKUjh07KpMmTVKKi4td5//1r38pl19+uWI2mxWDwaD07t1bWbRoUYOyX3vtNSU1NVUxGo3KsGHDlC+++EJJTk5Wfvvb36rTOOfhr1UmuFNdM7hTHXmLfdpIbi4wcyZQUSHucDEaxfeVFgtgMgX2Vrzn1N3eowesSUkwlZZCf/x44Nedzou/q40EyO+qL3eq27lT41qH2GYT0yTS04G5c/mrG+j8tVMdp0wQkfpkWayKX7/2Uf33ltHR4nvMkhKx9tHIkYH3lWzjukdEiHWboqLEuk2BXHciTwXz72oLsrNFlYN8BgipiJcCEalv714xNBMIax95KpjrTuSpEL7euQM4tYSXAxGpr6xM7Jfa0tpHNlv7rH10PsFcdyJP8XqnMMWAmIjUFx8PGAxiHqI7/lr7qC2Cue5EnuL1TmGKATERqS8jA0hLEzflNL6Pt37to/R09dc+aotgrjuRp3i9U5hiQExE6tNogHnzxB3qJSVAVRXgdIpjSYlInzs3MCf1Na57dbWoe3V14NedyFMB+LvKxbDCm7/6n+/gROQf2dliuaZBg4DKSuDYMXHMzAz8Zcsa191iCZ66E3kqQH5X65fVqq6u9svrUWCq73+1l0TksmtE5D/BvPZRfd3z87lTHYW+APhd1Wq1iI2NxalTpwAAUVFRkBqvfNGCc9ch1vD3NOgoioLq6mqcOnUKsbGx0Gq1kGVZtddjQExE/lW/9lEw0miAgQMBq1V8dcwPWQplAfC72qVLFwBwBcWeUBQFsixDo9F4FEhTYImNjXVdB2piQExEREQBSZIkdO3aFQkJCbDb7R4912634+zZs+jQoQN3IAxSer0eWq3WL6/FgJiIiIgCmlar9Tgw0mq1qKurg9FoZEBM58Xv+4iIiIgorDEgJiIiIqKwxoCYiIiIiMIa5xATkX/JcnAuuwYANhuwZs0vdX/oIbGNrS8Ec7sEc92JiMCAmIj8KTcXWLUKKCwE6uoAg0FsEztvXuBvbjF7NrBhAyBJQJ8+wKFDwOOPA/ffDzz/vHdlB3O7BHPdiYh+xj/hicg/cnOBmTPFxhYxMUDPnuJYUCDSc3Pbu4bNmz0bWLtWBHyS9Mu/ujqRPnt228sO5nYJ5roTEZ2DATERqU+WxShiRQWQkgJERwNarTgmJ4uNLlavFvkCjc0mRoYVRUwD0GobHhVFnLfZPC87mNslmOtORNQIA2IiUt/eveIr9YQEMbJ6LkkS2yAfOCDyBZrnnvtlZLjxvFiN5peR4uee87zsYG6XYK47EVEjDIiJSH1lZSJoNBrdnzcaxQhrWZl/69Uahw+LY3Nbv9an1+fzRDC3SzDXnYioEQbERKS++Hhxs1VtrfvztbVitYb4eP/WqzV69xZHRXF/vj69Pp8ngrldgrnuRESNMCAmIvVlZIiVByyWpoGlooj09HSRL9DMmSMCP0VpOh9WlkW6wSDyeSqY2yWY605E1AgDYiJSn0YjluEymYCSEqCqCnA6xbGkRKTPnRuYa9dGRIil1SRJBMBOZ8OjJInzbVmPOJjbJZjrTkTUCN+piMg/srOBnBxg0CCgshI4dkwcMzNFeiCvWfv888CsWb+MFNf/MxhEujfrEAdzuwRz3YmIziEpSnMT48JbamoqAKCoqMgvr2e322G1WmEymaDX6/3ymqQu9mkzgnlXM5sN9jVrYC0rgyk+HnruVCcEc93B39VQxX4NPe761FfxGneqIyL/0mjECGIwiogQm3BYrWJKgC8/ZIO5XYK57kRE4JQJIiIiIgpzDIiJiIiIKKwxICYiIiKisMaAmIiIiIjCGgNiIiIiIgprARUQb968Gddffz169OiB6OhoZGZm4uWXX8b5VoZTFAXPPPMMkpKSEBkZieHDh2PPnj1+qjURERERBbOACohXr16NqKgorFq1Cv/6179w9dVX495778UTTzzR4vOWL1+ORYsWYc6cOdi2bRu6du2K8ePH+20NYaImZBnYtw/4+mtxbLzlbzhzOIBNm4AVK8TR4WjvGrVeMPerLAP5+cAnn4hjMNWd2gevGQojAbUxx+nTp9G5c+cGab/+9a+xadMmlJeXQ+Nmoffa2lokJibit7/9LZYtWwYAsNls6NevHyZMmID169e3qS7cmIPaLDcXWLUK9qIiWBMTYTp5EvrUVLHNbbjv3LVuHbB0qdjAQVHEtsfx8cDChcCDD7Z37VoWzP36c91RWAjU1Ykd9tLSgqPufsD3XzdC4Jphv4YeNTfmCKgR4sbBMABcdNFFqKysRFVVldvn7Nq1C5WVlZg2bZorLSIiApMmTcL777+vWl2J3MrNBWbOFKMpMTGA2SyOBQUiPTe3vWvYftatA+bPBywWsaFFVJQ4Wiwifd269q5h84K5XxvXvWfP4Kk7tQ9eMxSGAiogdicvLw/du3dHx44d3Z4vLCwEAKSlpTVIT09PR2lpKWpqalSvIxEA8XXiqlVARQWQkiICPq1WHJOTxe5mq1eH59eODocYGbbbgehoseObRiOO0dEifdmywJw+Ecz92rju0dGi7tHRgV93ah+8ZihMBfTWzXl5eXjzzTexatWqZvOUl5fDYDDAaDQ2SI+Li4OiKCgvL0dkZKTb59YPs7tz9OhR9OzZE3a7vW2V95DD4XD9oyC1bx9QVAT06AFERMCh18Oh08FR/1Vd9+7A4cNi1GXgwHatqt9t3QpUVYntjiMimp7X6YCzZ4EtW4BJk/xfv5YEc782qnsTgVx3P+L77zlC6Jphv4YeNfs0YAPiY8eOYfr06bjsssswa9asdqmDLMuwWq1+eS2Hw+GaFqLTBWy3UEssFiAxUXydrtXCodWiKi4OUBTonE7A6RR5LBYxyhJOLBYgNVXMQ3RzLwBkWcxTDMS2CeZ+bVT3JgK57n7E999zhNA1w34NPe76VJZlt/eYeSogr5CKigpcffXViI+Px5YtW1r8QePi4lBXV4fa2toGo8Tl5eWQJAlxcXHNPrelCdj1o8cmk6kNP4Hn6v/aMZlM/MUNVmYzcPIkUFMDREWJEURJgsligc5uB6qrgcpKkc9P11XAMJvFqJNe737UyWYT0yYCsW2CuV8b1b2JQK67H/H99xwhdM2wX0OPuz71RTAMBGBAXFNTg4kTJ8JqtWL37t3nDUjr5w4fPHgQgwYNcqUXFha61iX2hj/vTNXpdNDpdLwbNlhlZopR0IICMdcOgM7hgM5uh95mA44fF3kyM92PkoayyZOB2bPFqFJ0dMOfX5bFdIqEBJEv0D64grlfG9ddkn45pyiBXXc/4/vvz0LsmmG/hh61+jSgrmaHw4Fp06bhwIED+OCDD9C9e/fzPmfEiBGIiYnB5s2bXWl2ux1bt27FhAkT1KwuUUMajViSyGQCSkrESIrTKY4lJSJ97tyg+BDxOZ1OLK2m14vg12YTgbDNJh7r9cCCBYEXDAPB3a+N615VJepeVRX4daf2wWuGwlRAXdEPPPAAtm3bhoULF6KyshJ79uxx/aurqwMAXH755ejTp4/rOUajEY899hhWrlyJNWvW4P/+7/9w8803o6ysDA8//HB7/SgUrrKzgZwcYNAg8bWixSKOmZkiPUjW71TFgw+KzTjMZjE9orpaHBMSRHogr0MczP3auO7HjgVP3al98JqhMBRQG3OkpKSgpKTE7bkjR44gJSUFY8aMQXFxMYqLi13n6rduXr9+PSwWCzIzM/Hcc89h+PDhba4LN+Ygr8gy7Pn5sFosMJnN0AfJ14t+4XCI1SRKS4GkpMCcJtGcYO5XWQb27hWbosTHAxkZwVN3lfH9txlBfs2wX0OPmhtzBFRAHEgYEJO32Kehif0aetinoYn9GnrCZqc6IiIiIiJ/Y0BMRERERGGNATERERERhTUGxEREREQU1hgQExEREVFYC5K1joiCjCwD+/aJ9WrN5qDZ1QmA2CzjueeAw4eB3r2BOXPcb7fcVkG+lBMREYUeBsREvpabC6xaBRQVAYmJwMmTYivUefMCf0H72bOBDRuAnzfCAQAsWgTcfz/w/PPel1/fNoWF4jUMBiAtLTjahoiIQhaHZYh8KTcXmDkTyM8HYmLE6HBMDFBQINJzc9u7hs2bPRtYu1YEqpIkRm0lSTxeu1ac90bjtunZM3jahoiIQhoDYiJfkWUx+llRAaSkAFFRgFYrjsnJgNUKrF4t8gUam02MDCuKCIS12oZHRRHnbba2ld+4baKjRdnR0YHfNkREFPIYEBP5yt69YipAQoIYWT2XJInR4gMHRL5A89xzDUeGz3XuSPFzz7Wt/GBuGyIiCnkMiIl8paxMBI1Go/vzRqMYYS0r82+9WuPwYXFsHKzWq0+vz+epYG4bIiIKeQyIiXwlPl7cJFZb6/58ba1YrSE+3r/1ao3evcVRUdyfr0+vz+epYG4bIiIKeQyIiXwlI0OsmGCxNA0sFUWkp6eLfIFmzhwRsCpK03m8sizSDQaRry2CuW2IiCjkMSAm8hWNRiwfZjIBJSVAdTXgdIpjSYlInzs3MNfcjYgQS6tJkgiAnc6GR0kS59u6HnHjtqmqEmVXVQV+2xARUcjjpw+RL2VnAzk5wKBBQGWlGPmsrBQbc+TkBPZau88/D8ya1XCkuH5keNYs79chbtw2x44FT9sQEVFI48YcRL6WnQ2MHCnW2w22neqefx549ln1dqqrbxvuVEdERAGEATGRGjQaYOBAsb6uyRRcAV9EBPDoo+qVr9GIPxCIiIgCRBB9ShMRERER+R4DYiIiIiIKawyIiYiIiCisMSAmIiIiorDGgJiIiIiIwhoDYiIiIiIKa1x2jUgNsgzs2xd86xADou5qrhOsZvkOB7BlC1BaCiQlAZMnAzofvs3V1gILFogNRWJigGXLAKPRN2Wr3e5OB/DlFuBMKdApCRg6GdDyI4CICGBATOR7ubnAqlVAURGQmAicPAmkpoqtiwN9N7b6uhcWAnV1Ype6tDTf1V3N8tetA5YuFQGloojtpmfPBhYuBB580Pu6T50KbN0q1mnu0wc4dAhYswaYNAnYvNm7stVu94/XAd8sBYxlgEYBDknAZ7OBixcCV/igbYiIglyQDFkRBYncXGDmTLFLXUyMGB2OiQEKCkR6bm5717B5jeves6dv665m+evWAfPnixF5vR6IihJHi0Wkr1vnXd2nTgXefluM4p5LlkX61KltL1vtdv94HbBvPhBtAex6oCZKHKMsIv1jL9uGiCgEMCAm8hVZFqN8FRVASooIyrRacUxOFrvWrV7dNKgKBI3rHh0t6h4d7Zu6q1m+wyFGhu12UV5EhJhqEBEhHtvtYmqDw9G2utfWipHhepLU8AiI87W1npetdrs7HWJkOMIO1EQDSgQgacSxNhrQ24Fvlol8RERhjAExka/s3Su+8k5IaBgsAeKx2QwcOCDyBRq1665m+Vu2iGkSBkPTObcajUg/fVrka4tHH/0lIHVXd0Ccb8t212q3+5dbxDSJWgOavt1rgDoDYDwt8hERhTEGxES+UlYm5n82d5OV0QjYbCJfoFG77mqWX1oq5gw3d/OcTifOl5Z6XjYg5gr7Mt+51G73M6VizrDcTNsoOnH+TBvbhogoRDAgJvKV+HgxGtncV+e1teJr/Ph4/9arNdSuu5rlJyWJ0dTmpkQ4HOJ8UpLnZQPiBjpf5juX2u3eKQmQJUDTTNtIDnG+UxvbhogoRDAgJvKVjAyxMoDFIkYkz6UoIj09XeQLNGrXXc3yJ08WAWNdnfub3urqgM6dRb62WL78l6kY7uoOiPPLl3tettrtPnQyUBsPGOoANJ6HLIv02s4iHxFRGGNATOQrGo1YJstkAkpKgOpqwOkUx5ISkT53bmCuR9y47lVVou5VVb6pu5rl63RiaTW9XpRns4lA2GYTj/V6sXZwW9cjNhrF0mr16gPXcwPYSZPath6x2u2u1Yml1ex6wFgFSDYAsjgaq0T6xQu4HjERhb0A/GQmCmLZ2UBODjBokNi8wWIRx8xMkR7I6xA3rvuxY76tu5rlP/ggsGKFuAnNbhd/hNjt4ma1FSu8X4d482ZgyhT3N+1NmeLdOsRqt/sVDwIDVwDVZrGqhLFaHKsTRDrXISYigqQojb+nIwBITU0FABQVFfnl9ex2O6xWK0wmE/R6vV9ek1Qky7Dn58NqscBkNkPPner8U74fdqqzL1gAa2UlTDEx0HOnupDA99/QxH4NPe761FfxGt8NidSg0QADB4p1ZE2m4AmGAVHXzMzgLF+nA6ZPV6dsQAS/y5f/0q++/JBVu921OmCYim1DRBTEguhTmoiIiIjI9xgQExEREVFYY0BMRERERGGNATERERERhTUGxEREREQU1rjKBJEaZBnYt0+sQ2w2i9UDgmmlCXJPkYGKfUC5BVDMQOdMQGK/EhEFOwbERL6WmwusWgUUFQGJicDJk0BqqtiRLJA35qCWncoFClcBFUWAkghIJ4HYVCBtHpDAfiUiCmYc2iDypdxcYOZMID8fiIkRo8MxMUBBgUjPzW3vGlJbnMoFvpoJlOcD+hjAYBbH8gKRfor9SkQUzBgQE/mKLIuR4YoKICUFiIoCtFpxTE4WmzmsXi3yUfBQZDEybK8AolIAXRQgacUxKhmwW4HC1SIfEREFJQbERL6ydy9QWAgkJACS1PCcJInR4gMHRD4KHhV7AWshYGimXw1mwHpA5CMioqDEgJjIV8rKgLo6sb2vO0YjYLOJfBQ86soAuQ7QNtOvWiMg20Q+IiIKSgyIiXwlPh4wGIDaWvfna2uBiAiRj4KHIR7QGABnM/3qrAU0ESIfEREFJQbERL6SkQGkpYml1hSl4TlFEenp6SIfBY/YDMCUBtQ10691FsCULvIREVFQYkBM5CsajVhazWQCSkqA6mrA6RTHkhKRPncu1yMONpJGLK2mNwHVJYCjGlCc4lhdItLT5nI9YiKiIMZ3cCJfys4GcnKAQYOAykoxKlxZKTbmyMnhOsTBKiEbGJIDxA0C7JViVNheCcRlinSuQ0xEFNS4MQeRr2VnAyNHirWIuVNd6EjIBswjgdP5Yqe6OO5UR0QUKhgQE6lBowEGDhRrD5tMDIZDhaQBYgcC0s/9ymCYiCgk8N2ciIiIiMIaA2IiIiIiCmsMiImIiIgorDEgJiIiIqKwxoCYiIiIiMIaA2IiIiIiCmtcdo2IGlJkoGIvUFcGGOLFlsRcXkyQZWDfPq4v3Zja1wyvSSJSGQNiIvrFqVygcBVgLQTkOkBjAExpYuvicN+NLTcXWLUKKCoCEhOBkyeB1FSxXXc470Co9jXDa5KI/IB/YhORcCoX+GomUJ4P6GOAqJ7iWF4g0k/ltncN209uLjBzpth9MCZGjA7HxAAFBSI9N0zbRu1rhtckEfmJ1yPEVVVV2LJlC4qKilBeXg5FURqclyQJa9as8fZliEhNiixG4ewVQFQKIEkiXRcNaKOA6hKgcLXYujjcvqqWZTEyXFEBpKQAERGAVgtERQHJyUBJCbB6tdiuO5ymT6h9zfCaJCI/8iog/uSTTzB16lRUVFQ0m4cBMVEQqNgrvpI2JPwSeNSTJMBgBqwHRL64zHapYrvZuxcoLAQSmmkbsxk4cEDky8xslyq2C7WvGV6TRORHXv1Z/dvf/hbR0dH48MMPUVFRAVmWm/xzOp2+qisRqaWuTMzP1Brdn9caAdkm8oWbsjKgrg4wNtM2RiNgs4l84UTta4bXJBH5kVcBcWlpKR555BGMGzcOMTExvqoTEfmbIV7crOSsdX/eWQtoIkS+cBMfDxgMQG0zbVNbK6ZRxIdZ26h9zfCaJCI/8iogzsjIgNVq9VVdiKi9xGaIO/frLECj+wCgKCLdlC7yhZuMDCAtTSy15q5tLBYgPV3kCydqXzO8JonIj7wKiJcvX47169fjq6++8lV9iKg9SBqxjJXeJG5WclQBilMcq0tEetrc8Lx5SaMRS6uZTOIGuupqwOkUx5ISkT53bnjdUAeof83wmiQiP/LqprrRo0fj+eefx/Dhw5Geno6ePXtCq9U2yCNJEv75z396VUki8oOEbGBIzi9rvtadFl9Jx2WKwCOc13zNzgZycn5Zh9hiASorxU10c+eG7zrEal8zvCaJyE+8Coi3bNmCW2+9FU6nE8eOHcNPP/3UJI/U+O5gIgpcCdliGSvuCtZUdrZYWi0/nzvVnUvta4bXJBH5gVcB8e9+9zv0798fW7ZsQb9+/XxVJyJqT5KGy1g1R6MBBg4ErFYxVSLcg+F6al8zvCaJSGVevZufOHECv/nNbxgMExEREVHQ8iogHjp0KEpLS31VFyIiIiIiv/MqIM7JycGbb76Jt956y1f1ISIiIiLyK6/mEN9yyy1wOBy4+eabce+996JHjx5uV5koKCjwqpJERERERGrxKiDu1KkT4uPj0bdvX59U5tChQ1i5ciX27NmD/fv3Iy0tDfv37z/v81JSUlBSUtIkvaamBsbmtlslIiIiIoKXAfGnn37qo2oI3333Hd577z1ceumlkGUZsiy3+rlTpkzBvHnzGqQZDAaf1o/ccDqAL7cAZ0qBTknA0MmA1qvLKjQoMlCxDyi3AIoZ6JzJZaLqqXnNKLK6y3M5bUDhGqC8DIiLBy54CNBG+KZs2QEc3QJUlQLRSUDPyYDGh79LarcNEVEQC6jI5dprr8X1118PALjjjjs82gEvMTERw4YNU6tq5M7H64BvlgLGMkCjAIck4LPZwMULgSsebO/atZ9TuWIjgYoiQEkEpJNAbKrYdSvcNxJQ85qpb3drISDXARqD2PrXV+3+1Wzg0AZAlgBNH+DYIeC7x4E+9wNDnveu7IPrgO+WimAVCgAJMMwGBiwE+vvgd0nttiEiCnIeDw/8+OOPSEtLw+OPP95ivt///vdIT0/HqVOnWl8ZrukZPD5eB+ybD0RbALseqIkSxyiLSP94XXvXsH2cygW+mgmU5wP6GMBgFsfyApF+Kre9a9h+1LxmGrd7VE/ftvtXs4H/rhXBJKRf/sl1Iv2r2W0v++A64Nv5QK0F0OgBbZQ41lpE+kEvf5fUbhsiohDgcQS6Zs0anDlzBo8++miL+R599FGcOXMGOTk5ba6cJ15//XUYDAZ06NABEyZMwL59+/zyumHJ6RCjfBF2oCYaUCLEV69KBFAbDejtwDfLRL5woshiFM5eAUSlALooQNKKY1QyYLcChatFvnCj5jXTpN2jf273aN+0u9MmRoahANCIsqWfj9CI9EMbRD5PyQ4xMizbRX01P7eLJkI8lu3Ad8tEvrZQu22IiEKEx1Mm3nvvPdx8883o0KFDi/k6duyIGTNm4N1338WTTz7Z5gq2xnXXXYdLL70USUlJKCoqwtKlS5GVlYVvv/0WqampzT6vpXNHjx5Fz549Ybfb1ahyEw6Hw/Uv4H25FYisAupMgORm/qRNB0SeBT7fAgyd5P/6tZeKfWKaREQPABFwKHo4FB0cil4MKEZ0ByoOA6fzgdiB7VxZP1PzmmnU7lAanfe23QvXiGkSiAQkLRwwwoEIOGAU/ao4AVkBvl8DpM32rOyjW4G6KkDrpl0kiLnVdWeB4i1Azzb8LqndNiEiqN5/qdXYr6FHzT71OCA+fPgwZs+e3aq8AwYMwIsvvujpS3hs7dq1rv9nZ2dj/PjxSEtLw8qVK7F+/fo2lyvLMqxWqy+qeF4OhwNVVVUAAJ0uoKZ2N3XGAhhTAcnQzE05MhBRJ/L5qf0CQrnl5znDZkDWwqFoUSXHAVCgk5yA5AQUi8gnhVG7AOpeM43avQlv2728TMwZhgRIGjgQgSpNNwCADrafR1cVkc/Tup+xAJpUMafXXbtoZAA/t0tMW+quctuEiKB6/6VWY7+GHnd9KsuyT6bcenyFaLVa2Gyt+2rQbre3y7zgrl27IisrC19//XWL+YqKipo9Vz96bDKZfFq35tT/tWMymQL/F7eTGThSJOZ/Km5G+ySb+Kq3kxnwU/sFBMUsbqBTagBtlBgZhgSTxgKdZAcc1YBUCcSFWbsA6l4zjdq9CW/bPS5e3EAHyTVCDAAm+Qh0qBUjxFBEPk/L72QGfigCoBfTJBqTvfxdUrttQkRQvf9Sq7FfQ4+7PvVVnOnxFdK7d2/k5eXhN7/5zXnz7ty5E717925TxQKFXq/322vpdDrodDq/vmabXDoZyJstboaqjUbDqegyYKgCqhNEvnBagq1zplhNorwA0CUDEqCTHNBJduhhA2zHgbjM8FyCTc1rpkm7S7+cUxTv2/2Ch8RqEnItoGhEv8IGHWqhV6pF/TWGn5dg8/B3N2UykD9b3EAnRTesnyIDzirAmCDytWUJNrXbJoQEzfsveYT9GnrU6lOP3wFvvPFGbN68Gbt3724x3549e/DWW2/hxhtvbHPl2urEiRPIy8vD0KFD/f7aYUGrE8tk2fWAsUqM7kEWR2OVSL94QXgFw4AIKNLmAXoTUF0iRt8UpzhWl4j0tLnhGXioec00afeqn9u9yjftro0QS6tBEnVWnCJYVZziMSRxvi3rEWt0Ymk1jV7UV/55CoZsE481emDAgravR6x22xARhQiP3wXnzp2LHj16YPz48Vi+fDmOHz/e4Pzx48exfPlyjB8/Hj169MCcOXNaXXZ1dTXefvttvP322ygpKUFlZaXrscViAQBcfvnl6NOnj+s5GzduxC233ILXX38dO3bswEsvvYRRo0ZBq9U22aiDfOiKB4GBK4Bqs1ghwFgtjtUJIj1c1yFOyAaG5ABxgwB7JVBnEce4TJEezmu+qnnNNG736mO+bfchzwP9ZomRYCi//NMYRLo36xD3fxC4aAVgNIvpEc5qcTQmiHRv1yFWu22IiEKApChK4/uOz6uoqAiTJk3C3r17IUkSTCYTOnbsiJ9++glWqxWKomDgwIHYunWrR1MmiouL0atXL7fnduzYgTFjxmDMmDEoLi5GcXExADES/dhjj2H//v2oqKhAbGwsxo4diyeeeAL9+/f39EdzqZ9D3NI8Y1+y2+2wWq0wmUzB9dUOd6pzT5FhP50Pa7kFpjgz9PxK+hdBvlOd/fs1sJaXwRQXDz13qgsJQfv+Sy1iv4Yed33qq3itTQExADidTrz99tt49913UVhYiMrKSsTExCAtLQ3XXnstpkyZEtST2BkQk7fYp6GJ/Rp62Kehif0aetQMiNscsWq1WkyfPh3Tp0/3qgJERERERO2J35cRERERUVjzek7Dhx9+iJdeeglFRUUoLy9H4xkYkiTh8OHD3r4MEREREZEqvAqIV6xYgd/97ndITEzEJZdcgoEDw3frTyIiIiIKTl4FxGvWrMHYsWPx/vvvc8I6EREREQUlr+YQl5eXY8qUKQyGiYiIiChoeTVCfMkll+DgwYO+qgsRtZYsA3v3AmVlQHw8kJEB+Gg/d9XXq1WzfLXrLjuAo1uBMxagk7ntWyq7E8ztTkQU5Lx6J1+/fj2uvvpqDBkyBDNmzPBVnYioJbm5wKpVQGEhUFcHGAxAWhowbx6Q7eWuY6dygcJVgLUQkOvETmymNLH9ry92NFOzfLXrfnAd8N1SoK4K0KQCPxQB+bPF1sve7iYXzO1ORBQCPNqYIyMjo0namTNn8OOPP6JDhw7o0aMHtFptwxeQJBQUFHhfUz/jxhzkLVX6NDcXmDkTqKgAEhIAoxGorQUsFsBkAnJy2h4Un8oFvpoJ2CsAQwKgNQLOWrH9tN7k/Ta/apavdt0PrgO+nQ/Idti1Jlg1fWCSD0HvtAIavXdbLAdzu4cIvv+GJvZr6AmYjTk6deoESZIapMXHx6Nv375eVYKIWkGWxchwRQWQkgLU/y5GRwNRUUBJCbB6NTBypOfTJxRZjCDaK4Coc8rWRQPaKKC6BChcDZhHtu1rdjXLV7vuskOMDMt2UaYUIcrRRABSNOCoAr5bBvS93/PpE8Hc7kREIcSjd+9PP/1UpWoQ0Xnt3SumSSQk/BLY1JMkwGwGDhwQ+TIzPSu7Yq/4Ot3QTNkGM2A9IPLFeVi22uWrXfejW8S8W62hadAoaUR63WmRL9nDnTuDud2JiEKIV0MCr732GoqLi5s9X1JSgtdee82blyCiemVlYs6w0ej+vNEI2Gwin6fqysTcUm0zZWuNgGwT+dpCzfLVrntVKQAFkJoZP5B04nxVqedlB3O7ExGFEK8C4jvvvBO7du1q9vyePXtw5513evMSRFQvPl7cQFdb6/58bS0QESHyecoQL260cjZTtrNWTBEwtKFstctXu+7RSQAkQHG4P684xPnoJM/LDuZ2JyIKIV4FxOe7H6+qqgo6nY+WJCIKdxkZYjUJiwVo/LunKCI9PV3k81Rshlh1oK6ZsussgCld5GsLNctXu+49J4uA0Vkn5uQ2KF8W6YbOIl+g1V3t8omIQoTH0erevXuRn5/vepybmwuHo+nISUVFBTZs2IB+/fp5VUEi+plGI5ZWmzlT3EBnNjddZWLu3LatRyxpxBJcX80UN1oZzE1XI0ib2/Ybr9QsX+26a3RiabVv54sb6LQ6QCOLqQbOKrHKxIAFbVuPOJjbnYgohHi07BoALFmyBEuWLBFPlqQWR4ljY2Px2muvYeLEid7Vsh1w2TXylmp9eu46xDabmCaRni6CYZ+uQ2wTX6eb0kXQ5PP1cH1cvtp1/3kdYntdFayaVJjkIugNHUQw7NN1iIOs3UMA339DE/s19Ki57JrHAfGPP/6IEydOQFEUXHLJJXjiiSdw9dVXNyxUkhAdHY3evXsH7ZQJBsTkLVX7lDvV+b9sAJAdsBdvgfWMBaZOZui5U11I4PtvaGK/hp6AWYcYALp27YquXbsCAHbs2IH09HQkJCR4VQki8pBG4/nSaq0ladRdgkvN8tWuu0YH9JwExFjFFBVfBcNAcLc7EVGQ8+rdfPTo0b6qBxERERFRu/AoIB47dqzHLyBJEj755BOPn0dERERE5A8eBcSyLDfZuvno0aMoKiqCyWRyzeM4cuQIKioq0Lt3b/Ts2dN3tSUiIiIi8jGvtm7Oy8vDddddhz//+c/41a9+5bqBzuFw4JVXXsGjjz6KV1991Vd1JSIiIiLyOa/mED/88MO48847cffddzcsVKfDvffei8LCQsydOxeff/65V5UkIiIiIlKLV2vu7N271zVNwp1evXph37593rwEhTvZAZRsAr5fIY5yM9vnBhpFBir2AWe+FsfGO5wFMkUGyvOB/30ijr6uu9MGfLcc+PzX4ui0+a5sezWQNwP44FJxtFf7rmwguPuViIia5dUIcbdu3bBp0ybcd999TdYbdjgc2LRpE7p16+ZVBSmM/bwRAurKACgAJMAwW+wa5u1GCGqq3wShoghQEgHpJBCbKnYMC/RNEBps4FAHaAxi619f1f2r2cChDaLsevsWAX3uB4Y8713ZH40BLP/55fGZL4DSjYB5NDDuU+/KBoK7X4mIqEVejRA/8sgjyMvLw7Bhw/CXv/wFn376KT799FP8+c9/xqWXXopdu3Zh/vz5vqorhZOD68RWubUWsTWuNkocay0i/eC69q6he6dyxTa55fmAPkZslauPAcoLRPqp3PauYfMa1z2qp2/r/tVs4L9rfw6GJYi3H0k8/u9acb6tGgfD57L8R5z3RjD3KxERnZdXI8S//vWvodVqsXDhQvz61792rUChKArMZjM2bNiAe++91ycVpTAiO8TIsGwHdNG/7KYlRQCSDnBUAd8tA/re79uNEbylyGIE0V4BRKUAiABkrQjmdclAdQlQuBowjwy8HcIa171+NRldtKi/t3V32sTIMBQAmoZlKDIAWZy/6FlAG+FZ2fbq5oPhepb/iHz6KA8rjuDuVyIiahWv373vvvtuHDt2DLt27cIbb7yBN954A7t27cLx48cZDFPbHN0ipkloDU0DDEkj0utOi3yBpGKvmGpgSPgloKwnSWJU0XpA5As0ate98LlfRobd9Wn9SHHhc56X/fk9vs3XWDD3KxERtYpPhtd0Oh2GDRuGYcOG+aI4CndVpQAUMRrsjqQDYPs5XwCpKxNBndbo/rzWKAL5ujL/1qs11K772cM//0dqJoMEQDknX1vK9lG+xoK5X4mIqFU8Cog/++wzAMCoUaMaPD6f+vxErRKdBEACFIeYJtGY4hDno5P8XbOWGeLFTWjOWjHVoDFnLaCJEPkCjdp179D75/8ozWRQGuXzsOwzX3hQBw8Fc78SEVGreBQQjxkzBpIkoaamBhEREa7HzVEUBZIkwel0el1RCiM9J4vVJGotYjS48XxTZx1gTBD5AklshliRobxAzC89l6IAdRYgLlPkCzSN637u77Uv6p42R6wmIdeJPmwyh1gRQWfaHM/LvvQvYjWJ1uRri2DuVyIiahWPAuIdO3YAACIiIho8JvIpjU4srfbtfHEDndYgAmPFIYJhjR4YsCCwbqgDRJCXNk+sOlBdAkR0ByQn4KgGbMcBvQlImxuYN141rrvBLKYCOGtFwOdt3bURYmm1/64FIItAsn6aRP2Sen3u9/yGOkDcKGce3fKNdebRbbuhDgjufiUiolaRFEVp7jvMsFa/4UhRUZFfXs9ut8NqtcJkMkGv1/vlNQOe23WIO4tgOAjWIbZXFMGqJMIknYQ+trcImgJ9vdoG6xDbxFQAU7rv6u5uHWKNQZ11iOv5eB3ioOxXahHff0MT+zX0uOtTX8VrHgfEWVlZyM7OxsiRIzFy5EjExcV5VYFAxYA4QMgOsZpEVamYM9xzcuCNDLujyLCfzoe13AJTnBn6zpnBM4KoyGLFhLoyMS82NsO3dXfaxGoSZw+Leb1pc9o2MuyOvVqsJlFf9qV/afvIsDvB3K/ULL7/hib2a+hRMyD2OLIoLS3F8uXLIUkSJElCWloasrKyXP9SUlK8qhBRAxodkDy9vWvhOUkDxA4EJCtgMgVX0CRpxJxYtWgjgAGPqlO2PgrIekOdsoHg7lciImpWmwLiY8eOIS8vD3l5edi1axdeeuklvPjii5AkCd26dcPIkSNdAfKgQYNavPGOiIiIiKg9tem75x49euCmm27CTTfdBAA4e/Ysdu3ahZ07d2Lnzp147733sHnzZgBATEwMysvLfVdjIiIiIiIf8slkzA4dOmD8+PEYP348fvzxR+zYsQN//OMfsXv3blRWVvriJYiIiIiIVOF1QLx//37k5eW5RodLSkpgMBhw0UUXYd68eRg5cqQv6klEREREpAqPA+L//Oc/2LlzJ/Ly8rBnzx5UVFQgMTERI0aMwG9/+1uMGDECgwcPdq1VTEREREQUyDwOiC+77DLo9XpMnToVOTk5GD58uGvJCyIiIiKiYONxQDxw4EB899132LhxI/bt24cRI0YgKysLI0aMQK9evdSoIxE1pub6zGqvQ6x2+WpSZKBiH1BuARQzwHWIiYhCgsefoAUFBfjpp5+we/du17zhv//976iurkZCQgJGjBiBkSNHuqZOcDFsIh9zu4PfbLHdtbc7+DXYqa5O7CJnShNbF/tiNza1y1dTfd0rigAlEZBOArGpwVF3IiJqkU+2bnY6ncjPz8fOnTtdy6+dOHECBoMBQ4YMwWeffeaLuvoVd6ojb6nSpwfXAd/OB2Q7oDUAkg5QHICzDtDogYtWtD0oPpULfDUTsFcAhgRAawSctUCdBdCbgCE53gV+apevpnPqbo/oAauUBJNSCr3teODXnc6L77+hif0aetTcqc4n3/VptVoMHjwYs2bNwsKFC/HYY49h2LBhqK2txc6dO33xEkQkO8TIsGwHdNGAJkJ8Xa+JEI9lO/DdMpHPU4osRj/tFUBUiihP0opjVDJgtwKFq0W+tlC7fDU1qXvUz3WPCvy6ExFRq3g16bCurg6ff/65a9e6PXv2wGq1AgAMBgOys7ORlZXlk4oShb2jW8Q0Ca2h6bxVSSPS606LfJ5ud12xV0xjMCQAjXeWlCTAYAasB0S+tmzrrHb5ampc93O/Uwv0uhMRUat4HBD/85//dAXA3377Lex2OxRFQXx8vCsAzsrKwpAhQ/gVBZEvVZUCUMQ0CXckHQDbz/k8VFcm5vRqje7Pa40i2K4r87xsf5SvpmCuOxERtYrHAfGNN94IAOjVqxemT5/uCoDT09N9XjkiOkd0EgBJzBmW3KzzrTjE+egkz8s2xIsb3Jy1YhpDY85aMTXDEO952f4oX03BXHciImoVjwPiTZs2ISsrC127dlWjPkTUnJ6TxWoStRYxGnzutAlFFjfWGRNEPk/FZojVHsoLAG1Uw2kNiiJufIvLFPnaQu3y1dS47ucK9LoTEVGreHxT3dSpUxkME7UHjU4srabRA44qQLaJQFi2iccaPTBgQdvWI5Y0YvkwvQmoLhHlKU5xrC4R6Wlz277mrtrlq6lJ3at/rnt14NediIhaxaNPzieeeMLjF5AkCY8//rjHzyMiN+qXVHOtQ2wDIImR4QELvFuHOCFbLB9Wv05w3WkxFSAuUwR83i4rpnb5ajq37hVFgGIBpMrgqDsREZ2XR+sQazSej4BIkgSn0+nx89ob1yEmb6nap9yprn0oMuyn82Ett8AUZ4aeO9WFBL7/hib2a+hRcx1ijz5BZZnrbBIFBI3O86XVWkvSqLt8mNrlq0nSALEDAckKmEwMhomIQgTfzYmIiIgorDEgJiIiIqKw5vWkw7179yInJwfffPMNrFZrk2kVkiTh8OHD3r4MEREREZEqvBoh/vTTT3HJJZdg27Zt6NatG4qKipCamopu3bqhpKQEHTp0wKhRo3xVVyIiIiIin/MqIP7DH/6A1NRUHDx4EK+88goAYMGCBcjLy8OuXbtw7NgxTJs2zScVJSIiIiJSg1cB8TfffIO7774bMTEx0Gq1AOBaYu3SSy/FfffdxzWIKXApMlCeD/zvE3FUfLiKiuwAjm4Fit8UR9nhu7KJiIjIp7yaQ6zT6dCxY0cAQGxsLPR6PU6dOuU6n5qaiu+//967GhKp4VTuLxtEyHWAxiC2502b5/0mCwfX/bxxRhWgSQV+KALyZ4td5rzZOIOIiIhU4dUIcZ8+ffDDDz8AEDfPpaWl4Z133nGdf++999ClSxfvakjka6dyga9milFhfQwQ1VMcywtE+qnctpd9cB3w7Xyg1iK2UtYYxLHWItIPrvPZj0FERES+4VVAPGHCBGzcuBEOh/g6eO7cudi6dSv69u2Lvn374t1338V9993nk4oS+YQii5FhewUQlQLoogFJK45RyYDdChSubtv0CdkhRoZluyhPEyE2btBEiMeyHfhuGadPEBERBRivAuLHH38cBQUFrvnDv/rVr/Daa6/hwgsvxKBBg/Dyyy/j0Ucf9UlFiXyiYq+YJmFIACSp4TlJAgxmwHpA5PPU0S1iO2KtoekOZpJGpNedFvmIiIgoYHg1h1iv1yM+Pr5B2q233opbb73Vq0oRqaauTMwZ1hrdn9caRdBaV+Z52VWlABRAaubXStIBsP2cj4iIiAKFVyPEqampePfdd5s9v23bNqSmpnrzEkS+ZYgX83qdte7PO2vFFAdDvPvzLYlOAiABSjNTIhSHOB+d5HnZREREpBqvAuLi4mKcPXu22fNnz55FSUmJNy9B5FuxGWI1iToLoCgNzymKSDeli3ye6jlZBNLOuqZzkBVZpBs6i3xEREQUMLwKiAGxukRzvvzyS8TGxnr7EkS+I2nE0mp6E1BdAjiqAMUpjtUlIj1tbtM5wK2h0Yml1TR6UZ5sE4GwbBOPNXpgwAKRj4iIiAKGx5/Ma9aswZo1awCIYHj27NlYuHBhk3xWqxUVFRWYMWOG97Uk8qWEbGBIzi/rENedFtMk4jJFMOzNOsT16wzXr0OMOrG6hDFBBMNch5iIiCjgeBwQJyQkYMCAAQDElInu3buje/fuDfJIkoTo6GgMHjwYDzzwgG9qSuRLCdmAeaRYTaKuTEx1iM1o28hwY/0fBPreDxRvAc5YgE5mIGUyR4aJiIgClMef0DfffDNuvvlmAMBll12G3//+97j88st9XjEi1UkaMSqsBo0O6DkJiLECJhODYSIiogDm1af0jh07fFUPIiIiIqJ24fX3w5WVlXjmmWdw5ZVX4qKLLsIXX3wBADhz5gxWr16NQ4cOeV1JIiIiIiK1eDVCfOzYMYwePRpHjx5F3759UVhY6FqGrVOnTnjhhRdQUlLiugmPiIiIiCjQeBUQz58/Hz/99BPy8/ORkJCAhISEBudvuOEGbNu2zasKEhERERGpyaspE9u3b8esWbNwwQUXuF2PODU1FUePHvXmJYiIiIiIVOVVQFxTUwOz2dzs+Z9++smj8g4dOoT7778fmZmZ0Ol0uPDCC1v1PEVR8MwzzyApKQmRkZEYPnw49uzZ49FrExEREVF48iogvuCCC/DZZ581e/4f//gHLrroolaX99133+G9995Dnz59cMEFF7T6ecuXL8eiRYswZ84cbNu2DV27dsX48eNRVFTU6jLalSIDFfuAM1+LY+NtfwOZIgPl+cD/PhFHX9ddzfJlB1CyCfh+hTjKDt+VrTY120XtPiUiIgowXs0hnj17Nn71q18hIyMDU6dOBQDIsoxDhw5hyZIl2L17N7Zs2dLq8q699lpcf/31AIA77rgDX3311XmfU1tbi6effhrz5s3DnDlzAADZ2dno168fVq5cifXr17fhJ/OjU7lix7SKIkBJBKSTQGyq2F7Ymx3T/KG+7tZCQK4DNAbAlOa7uqtZ/sF1P+8mVwZAASABhtli6+VA301OzXZRu0+JiIgCkFcjxLfeeiueeOIJ/P73v0e/fv0AAFdddRX69++PN998E8uWLcMNN9zQ+spoPK/Orl27UFlZiWnTprnSIiIiMGnSJLz//vsel+dXp3KBr2aKUTh9DGAwi2N5gUg/ldveNWxe47pH9fRt3dUs/+A64Nv5QK0F0OgBbZQ41lpE+sF13tVdTWq2i9p9SkREFKDaNEJcW1uLf/7znzhy5AgSEhJw+PBhbN26FT/88ANkWUbv3r0xadIkpKam+rq+TRQWFgIA0tLSGqSnp6ejtLQUNTU1iIyMVL0eHlNkMRJnrwCiUgBEALJWBGe6ZKC6BChcLbYX9sV2wr7UuO71N1TqokX9va27muXLDjEyLNtFefXPlyIASQc4qoDvlomtlwNtdzk120XtPiUiIgpgHn/inzp1CiNGjMCRI0egKAokSUJUVBS2bt2K2bNnq1DFlpWXl8NgMMBoNDZIj4uLg6IoKC8vbzYgbilgP3r0KHr27Am73e7T+rpU7BPTJCJ6AIiAQ9HDoejgUPSABCCiO1BxGDidD8QOVKcObdWo7lAanfe27mqWf3QrUFcFaE0iCD6XBECrA+rOAsVbxNbLXnA4HK5/PqFmu6jdpyHE5/1K7Y59GprYr6FHzT71OCB+8sknUVxcjDlz5mDs2LE4dOgQnnzySdx///04fPiwzyvYnmRZhtVqVafwcsvPc4bNgKyFQ9GiSo4DoEAnOQHJCSgWkU9SqQ5t1ajuTXhbdzXLP2MBNKlibqy7kU6NDKBO5Ivxrt0dDgeqqqoAADqdD0ab1WwXtfs0hPi8X6ndsU9DE/s19LjrU1mW2zTltjGPr5Dt27fj9ttvx8qVK11piYmJmDFjBg4ePIj+/ft7XSlPxMXFoa6uDrW1tQ1GicvLyyFJEuLi4pp9bkurUNSPHptMJt9V9lyKWdxAp9QA2igxMgwJJo0FOskOOKoBqRKIMwNq1aGtGtW9CW/rrmb5nczAD0UA9IAmoul52SamU3Tyvt3r/4I1mUy+eTNWs13U7tMQ4vN+pXbHPg1N7NfQ465PfREMA20IiEtLS/Hoo482SMvKyoKiKDh58qTfA+L6ucMHDx7EoEGDXOmFhYWudYm9odfrvXp+szpnitUkygvEnGEJ0EkO6CQ79LABtuNAXKbIF2hzNpvU/ZxNWRTF+7qrWX7KZCB/triBTopu+HxFBpxVgDFB5PPBHGKdTgedTueb60jNdlG7T0OMT/uVAgL7NDSxX0OPWn3q8SdbXV1dk/m69Y/bY57OiBEjEBMTg82bN7vS7HY7tm7digkTJvi9Pq0macRSVnqTuGHJUQ0oTnGsLhHpaXMDM/hoUveqn+te5Zu6q1m+RieWVtPoRXmyTQTCsk081uiBAQsC74Y6QN12UbtPiYiIAlibPvWLi4vxzTffuB7Xz7P94YcfEBsb2yT/xRdf3Kpyq6urXUullZSUoLKyEm+//TYAYPTo0TCbzbj88stRUlKCQ4cOARDB+GOPPYbFixfDbDZj4MCBWL9+PcrKyvDwww+35cfzn4RsYEjOOesQW37+WjpTBB+BvO7ruXW3FgJ1p8UUBF/VXc3y69cZdq1DbAMgiZHhAQsCex1iNdtF7T4lIiIKUJKiKI3vJ2+RRqOBdO7XqT+rX3HCXZrT6WxV2cXFxejVq5fbczt27MCYMWMwZswYFBcXo7i4uMHrPPPMM1i/fj0sFgsyMzPx3HPPYfjw4a3/wRqpn0Psl93uFBn20/mwlltgijNDH0xfSysyULFXBJaGeCA2w7d1V7N82QEc3QJUlQLRSUBP30yTqGe322G1WmEymXz/dZ2a7aJ2nwY5VfuV2gX7NDSxX0OPuz71VbzmcUD817/+1eMX+dWvfuXxc9qbXwNi8Bc3FLFPQxP7NfSwT0MT+zX0qBkQezwcFozBLRERERFRc/g9KBERERGFNQbERERERBTWGBATERERUVhjQExEREREYY0BMRERERGFNQbERERERBTWGBATERERUVhjQExEREREYY0BMRERERGFNQbERERERBTWGBATERERUVhjQExEREREYY0BMRERERGFNQbERERERBTWGBATERERUVhjQExEREREYY0BMRERERGFNQbERERERBTWGBATERERUVhjQExEREREYY0BMRERERGFNQbERERERBTWGBATERERUVhjQExEREREYY0BMRERERGFNQbERERERBTWGBATERERUVhjQExEREREYY0BMRERERGFNQbERERERBTWGBATERERUVhjQExEREREYY0BMRERERGFNQbERERERBTWGBATERERUVhjQExEREREYY0BMRERERGFNQbERERERBTWGBATERERUVhjQExEREREYY0BMRERERGFNQbERERERBTWGBATERERUVhjQExEREREYY0BMRERERGFNQbERERERBTWGBATERERUVhjQExEREREYY0BMRERERGFNQbERERERBTWGBATERERUVhjQExEREREYY0BMRERERGFNQbERERERBTWGBATERERUVhjQExEREREYY0BMRERERGFNV17V4CIiKi9yDKwbx9gsQBmM5CZCWg4VEQUdhgQExFRWMrNBVatAoqKgMRE4ORJIDUVmDcPyM5u79oRkT/x72AiIgo7ubnAzJlAfj4QEyNGh2NigIICkZ6b2941JCJ/YkBMRERhRZbFyHBFBZCSAkRFAVqtOCYnA1YrsHq1yEdE4YEBMRERhZW9e4HCQiAhAZCkhuckSYwWHzgg8hFReGBATEREYaWsDKirA4xG9+eNRsBmE/mIKDwwICYiorASHw8YDEBtrfvztbVARITIR0ThgQExERGFlYwMIC1NLLWmKA3PKYpIT08X+YgoPDAgJiKisKLRiKXVTCagpASorgacTnEsKRHpc+dyPWKicMJfdyIiCjvZ2UBODjBoEFBZKUaFKyvFxhw5OVyHmCjccGMOIiIKS9nZwMiRYi1i7lRHFN4YEBMRUdjSaICBA8XawyYTg2GicMVffSIiIiIKawyIiYiIiCisMSAmIiIiorDGgJiIiIiIwlrABcSFhYUYN24coqOj0aVLFzzyyCOw2WznfV5KSgokSWryr7a5rYiIiIiIiBBgq0yUl5dj7Nix6Nu3L7Zu3Yrjx49j7ty5qK6uxrp16877/ClTpmDevHkN0gwGg1rVJSIiALIM7N0LlJWJ7Y4zMrhaAxEFl4AKiDds2IDKykq888476NSpEwDA4XDggQcewIIFC9CtW7cWn5+YmIhhw4b5o6pERAQgNxdYtQooLATq6gCDQWyLPG8eN7cgouARUH/D//vf/8YVV1zhCoYBYNq0aZBlGdu3b2/HmhERUWO5ucDMmWJji5gYoGdPcSwoEOm5ue1dQyKi1gmogLiwsBBpaWkN0mJjY9G1a1cUFhae9/mvv/46DAYDOnTogAkTJmDfvn1qVZWIKKzJshgZrqgAUlKA6GhAqxXH5GSx0cXq1SIfEVGgC6gpE+Xl5YiNjW2SHhcXhzNnzrT43Ouuuw6XXnopkpKSUFRUhKVLlyIrKwvffvstUlNT3T6nuXQAOHr0KHr27Am73e7Rz9BWDofD9Y9CA/s0NLFfhX37gKIioEcPICKi6fnu3YHDh8Xo8cCBfq+eR9inoYn9GnrU7NOACoi9sXbtWtf/s7OzMX78eKSlpWHlypVYv359m8qUZRlWq9VXVWyRw+FAVVUVAECnC5luCWvs09DEfhUsFiAxETCbxchwY06nyGOxiNHiQMY+DU3s19Djrk9lWYbGB3fxBtQVEhcX5zYALS8vbzCvuDW6du2KrKwsfP31183mKSoqavZc/eixyWTy6HXbqv6vHZPJxF/cEME+DU3sV8FsBk6eBGpqgKiopuerq4HKSpHPT2+jbcY+DU3s19Djrk99EQwDARYQp6WlNZkrbLVa8eOPPzaZW+wver3eb6+l0+mg0+n8+pqkLvZpaGK/ApmZQGqquIEuORmQpF/OKQpw/LjIk5kZHEuwsU9DE/s19KjVpwH1NnX11Vfj448/RkVFhStt8+bN0Gg0GD9+vEdlnThxAnl5eRg6dKiPa0lERBqNWFrNZAJKSoCqKjFNoqpKPDaZgLlzgyMYJiIKqLeq+++/Hx07dsQNN9yA7du345VXXsH8+fNx//33N1iD+PLLL0efPn1cjzdu3IhbbrkFr7/+Onbs2IGXXnoJo0aNglarbbJRBxER+UZ2NpCTAwwaJKZHHDsmjpmZIp3rEBNRsAioKRNxcXH45JNPMHPmTNxwww3o2LEj7rnnHixdurRBPqfT2eAOw169euHEiROYPXs2KioqEBsbi7Fjx+KJJ55Ar169/P1jEBGFjexsYORI7lRHRMEtoAJiAEhPT8fHH3/cYp5PP/20weNhw4Zhx44dKtaKiIiao9GIUWEiomDFv+GJiIiIKKwxICYiIiKisMaAmIiIiIjCGgNiIiIiIgprDIiJiIiIKKwxICYiIiKisBZwy64RERGdy+EAtmwBSkuBpCRg8mRAx08vyLK66z+rWT77NDSpfU2qiZcfEREFrHXrgKVLxQesogCSBMyeDSxcCDz4YHvXrv3k5gKrVgGFhUBdHWAwAGlpYjttX+wQqGb57NPQpPY1qbYgiduJiCjcrFsHzJ8PWCyAXg9ERYmjxSLS161r7xq2j9xcYOZMID8fiIkBevYUx4ICkZ6bG7jls09Dk9rXpD8wICYiooDjcIhRRLsdiI4GIiLEV68REeKx3Q4sWybyhRNZFqNwFRVASopoC61WHJOTAasVWL1a5Au08tmnoUnta9JfGBATEVHA2bJFfKVuMDSdg6jRiPTTp0W+cLJ3r/hKOiFBTDU4lyQBZjNw4IDIF2jls09Dk9rXpL8wICYiooBTWirmlzZ3o5VOJ86Xlvq3Xu2trEzMzzQa3Z83GgGbTeQLtPLZp6FJ7WvSXxgQExFRwElKEqNLzX197nCI80lJ/q1Xe4uPFyOptbXuz9fWiikI8fGBVz77NDSpfU36CwNiIiIKOJMniw/Qurqmcw9lWaR37izyhZOMDHHnvsUiRlPPpSgiPT1d5Au08tmnoUnta9JfGBATEVHA0enEMlx6PVBVJb5ylWVxrKoS6QsWhN/atRqNWMbKZAJKSkRbOJ3iWFIi0ufObfvar2qWzz4NTWpfk/4S4NUjIqJw9eCDwIoV4qYcux2orhbHhASRHq5r1mZnAzk5wKBBQGUlcOyYOGZminRv13xVs3z2aWhS+5r0B0lRGg9wEwCkpqYCAIqKivzyena7HVarFSaTCXq93i+vSepin4Ym9qv/qb2rWbD2KXeqa1mw9mswU/uadNenvorX+MUEEREFNJ0OmD69vWsReDQaMQIXjOWzT0OT2tekmjhlgoiIiIjCGgNiIiIiIgprDIiJiIiIKKwxICYiIiKisMaAmIiIiIjCGleZoICm9hIuRJ6QZWDfPrHzktks7qbm9ag+tZf/2rr1lz5VY/mvYOWPpdGIAgUvbQpYubnAqlVAYaHY0tNgENtDzpsXHIt8U2ipvx6LioDERODkSSA1ldej2tR8H1i3Dli6VOyolZoq+nb2bLGbWrhvEFHfNmVlYvtdSWLbUGjj2AYFpNxcYOZMID8fiIkBevYUx4ICkZ6b2941pHDS+Ho0m3k9+oOa7wPr1gHz54uRYb1eBNp6vXg8f744H64at01UFNuGQh8DYgo4sixGhCoqgJQUIDoa0GrFMTkZsFqB1atFPiK1Nb4eo6LE9RgVxetRTWq+DzgcYvTTbhflRUSIKRgREeKx3Q4sWybyhRu2DYUrBsQUcPbuFV+PJiSIr+nOJUlidO7AAZGPSG28HtuHmu2+ZYuYCmAwNJ2LrNGI9NOnRb5ww7ahcMWAmAJOWZmYK2g0uj9vNAI2m8hHpDZej+1DzXYvLRXzYpu7QUynE+dLSz0vO9ixbShcMSCmgBMfL0Yhamvdn6+tFV/fxcf7t14Unng9tg812z0pSYwyN/e1v8MhzicleV52sGPbULhiQEwBJyND3EVusYiRiHMpikhPTxf5iNTG67F9qNnukyeLQLqurukcZFkW6Z07i3zhhm1D4YoBMQUcjUYsqWQyASUlYkkkp1McS0pE+ty5XP+V/KPx9VhdLa7H6mpej2pS831ApxPLh+n1ojybTQR7Npt4rNcDCxaE55q7bBsKV3wLp4CUnQ3k5ACDBgGVlcCxY+KYmSnSue4r+VPj69Fi4fXoD2q+Dzz4ILBihbg5z24XI592u7iJb8WK8F5rt3HbVFezbSj0SYrS+MsoAoDU1FQAQFFRkV9ez263w2q1wmQyQa/X++U1g0Ew71THPg09sgzk59thsVhhNpuQmakPmusxmKm9U92WLb/06eTJeo5+/izYd6rje3DocdenvorXgujSpnCk0YjRIKJAoNEAAweKNXBNpuD54yzYqfk+oNMBkyb90qfBFPCpTacDpk9v71oQ+QffzomIiIgorDEgJiIiIqKwxoCYiIiIiMIaA2IiIiIiCmsMiImIiIgorDEgJiIiIqKwxgVmiChkqL1utc0GrFnzS/kPPQRERPiu7OeeAw4fBnr3BubM8V3ZgNhc4Z57fin/L38BoqJ8U7ba7X72LDB1KnDkCNCrF7B5M9Chg2/KVrNPAXXX8lV7neBgXgdeloF9+8QmOmazWLYvmOoerO0e1BRyq1evXkqvXr389no2m02xWCyKzWbz22uSutin/vXZZ4py/fWK0r+/oqSkiOP114t0X3joIUUxGBTFaLQpF15oUYxGm2IwiHRflQ388s9XZSuKoowe3bDs+n+jR3tfttrtPmiQ+7oPGuR92Wr2qaIoSk6OonTpoih6vaLodOLYpYtID+SyFUX9flVTfd0HDrQpV1xhUQYOtAVd3YOx3f3B3eeqr+I1/s1BREEvNxeYORPIzwdiYoCePcWxoECk5+Z6V/7s2cDatWJ7X0n65V9dnUifPdt3ZWs0visbAMaMAf7zH/fn/vMfcb6t1G73zExRljsFBd5t1qFmnwLAunXA/PlihFKvF6Pxer14PH++OB+IZQPq96uaGtfdbA7eugdTu4cCbt3cDG7dTN5in/qHLIudxvLzgZQUEdTUUxSgpEQETlu2tO1rR5tNfCjV1YnnR0XZ0bu3FYcPm1BdrYcsAwYDUFnp+Vftjcs+t36yDK/KBsQ0iejo8+erqvJ8+oTa7X72LNCx4/nz/fST59Mn1OxTQExl6NlTBKjR0U37taoKSEgQUx08neKgZtn1ZajZr2pqXPeICDsSE604edIEm00fVHUPpnb3JzW3bg7jZiWiULB3L1BYKIKAcz9EAPHYbAYOHBD52uK55xqO3p7r3NHc554LrLIBMWfYl/nOpXa7T53q23znUrvdt2wR8z8NBvflGwzA6dMiXyCVDajfr2pi3ckbDIiJKKiVlYngxWh0f95oFCOCZWVtK//wYXFs/CFVrz69Pl+glO3J89pSvtrtfuSIb/OdS+12Ly0Vo3rNjdDqdOJ8aWlglQ2o369qYt3JGwyIiSioxceLUbHaWvfna2vF197x8W0rv3dvcWxucll9en2+QCnbk+e1pXy1271XL9/mO5fa7Z6UJIJqh8P9eYdDnE9KCqyyAfX7VU2sO3mDATERBbWMDCAtTcypbBzgKIpIT08X+dpizhzxQaUoYp7fuWRZpBsMIl8glQ2IpdV8me9carf75s2+zXcutdt98mQRuNTVuS+/rg7o3FnkC6SyAfX7VU2sO3mDATERBTWNBpg3DzCZxI0nVVWA0ymOJSUife7ctt+IEhEB3H+/GHWTZVH2uUdJEufbcvOVmmUD4ka50aNbzjN6dNvWI1a73Tt0AAYNajnPoEFtW49Y7XbX6YCFC8XKD1VV4qtuWRbHqiqRvmBB2256U7NsQP1+VVPjuldXi7pXVwdf3YOp3UMFm5aIgl52NpCTIwKkykrg2DFxzMwU6dnZ3pX//PPArFm/jCrW/zMYRPrzz/uu7HNHKL0tGwA+/bT5oHj0aHG+rdRu9/z85oPiQYPE+bZSs08B4MEHgRUrxM1QdrsIyux2cdPUihXifCCWDajfr2pqXHeLJXjrHkztHgq47FozuOwaeYt96n/+2anOjrIyK+LjTXjoIT13qkMo7FSnTp8C3KmuvcgykJ9vh8VihdlsQmamPqjqHqztrjY1l11jQNwMBsTkLfZpaGK/hh72aWhiv4YerkNMRERERKQSBsREREREFNYYEBMRERFRWGNATERERERhjQExEREREYU1Hy7QQkQU2hwOYOtWsbap2ezbZa641FJoYr+GHvZpaGJATETUCuvWAUuXip2jUlOBoiJg9myxa5i3GyHk5gKrVgGFhWLrXYNBbOM6bx4X4w9m7NfQwz4NXfybhojoPNatA+bPFyPDer34ENTrxeP588X5tsrNBWbOFLuuxcQAPXuKY0GBSM/N9dmPQX7Efg097NPQxoCYiKgFDocYGbbbgehosXucRiOO0dEifdkykc9TsixGmyoqgJQUUZ5WK47JyYDVCqxeLfJR8GC/hh72aehjQExE1IItW8RcQYOh6TxBjUaknz4t8nlq717x1WtCAiBJDc9JkpinfOCAyEfBg/0aetinoY8BMRFRC0pLAUVp/uY5nU6cLy31vOyyMjEP0Wh0f95oBGw2kY+CB/s19LBPQx8DYiKiFiQliRGg5qZEOBzifFKS52XHx4sR5tpa9+dra8XUjPh4z8um9sN+DT3s09DHgJiIqAWTJ4sPubq6pvMDZVmkd+4s8nkqI0PcoW6xiFHmcymKSE9PF/koeLBfQw/7NPQxICYiaoFOJ5ZW0+vFkms2mwiEbTbxWK8HFixo23rEGo1YrslkAkpKRHlOpziWlIj0uXO5xmmwYb+GHvZp6GPXERGdx4MPAitWiBtn7HYxKmy3ixtsVqzwbh3i7GwgJwcYNAiorASOHRPHzEyRzrVNgxP7NfSwT0ObpCiNB/8JAFJTUwEARUVFfnk9u90Oq9UKk8kEvV7vl9ckdbFPQ4/DAWzZYofFYoXZbMLkyXruVBcC1PxdZb+2H7X6lX3aftz1qa/iNe5UR0TUSjodMGmSWHPUZPLdts2A+EDNzPRdeRQY2K+hh30amvg3DRERERGFNQbERERERBTWGBATERERUVhjQExEREREYY0BMRERERGFtYALiAsLCzFu3DhER0ejS5cueOSRR2Cz2c77PEVR8MwzzyApKQmRkZEYPnw49uzZ44caExEREVEwC6iAuLy8HGPHjoXNZsPWrVuxbNkyvPjii5g7d+55n7t8+XIsWrQIc+bMwbZt29C1a1eMHz/eb+sIExEREVFwCqh1iDds2IDKykq888476NSpEwDA4XDggQcewIIFC9CtWze3z6utrcXTTz+NefPmYc6cOQCA7Oxs9OvXDytXrsT69ev99jMQERERUXAJqBHif//737jiiitcwTAATJs2DbIsY/v27c0+b9euXaisrMS0adNcaREREZg0aRLef/99VetMRERERMEtoALiwsJCpKWlNUiLjY1F165dUVhY2OLzADR5bnp6OkpLS1FTU+P7yhIRERFRSAioKRPl5eWIjY1tkh4XF4czZ860+DyDwQCj0djkeYqioLy8HJGRkU2eV7//tTtHjx5Fz549YbfbW/8DeMHhcLj+UWhgn4Ym9mvoYZ+GJvZr6FGzTwMqIA40sizDarX65bUcDgeqqqoAADoduyUUsE9DE/s19LBPQxP7NfS461NZlqHReD/hIaCukLi4OLcBaHl5eYN5xe6eV1dXh9ra2gajxOXl5ZAkCXFxcW6f19IKFPWjxyaTqbXV90r9Xzsmk4m/uCGCfRqa2K+hh30amtivocddn/oiGAYCLCBOS0trMlfYarXixx9/bDI/uPHzAODgwYMYNGiQK72wsNC1LrGnfvzxRzgcDvTv39/j57aVr/7KocDBPg1N7NfQwz4NTezX0NO4T48ePeqTP3gC6iq5+uqr8fHHH6OiosKVtnnzZmg0GowfP77Z540YMQIxMTHYvHmzK81ut2Pr1q2YMGFCm+piMBj8+hfl0aNHcfz4cb+9HqmPfRqa2K+hh30amtivocddn+p0OhgMBq/LlhRFUbwuxUfKy8sxYMAA9OvXDwsWLMDx48cxd+5c3HLLLVi3bp0r3+WXX46SkhIcOnTIlfbMM89g8eLFWL58OQYOHIj169dj+/btyM/Pb/HmuUBRX0duJBI62Kehif0aetinoYn9GnrU7NOAmjIRFxeHTz75BDNnzsQNN9yAjh074p577sHSpUsb5HM6nU3uMHz00UehKApWrlwJi8WCzMxMfPjhh0ERDBMRERFR+wmoEeJwxr9kQw/7NDSxX0MP+zQ0sV9Dj5p9GlBziImIiIiI/I0BMRERERGFNQbERERERBTWGBATERERUVjjTXVEREREFNY4QkxEREREYY0BMRERERGFNQbERERERBTWGBATERERUVhjQExEREREYY0Bsco2b96M66+/Hj169EB0dDQyMzPx8ssv43yLeyiKgmeeeQZJSUmIjIzE8OHDsWfPHj/Vms6nrf2akpICSZKa/KutrfVTzak577//PkaPHg2z2QyDwYDU1FTMnTsXVqv1vM996aWX0K9fPxiNRgwaNAjbtm3zQ43pfNrap2PGjHH7e1pYWOinmlNrnT17Fj169IAkSfjqq69azMvP1eDhSb/66nNV502F6fxWr16NlJQUrFq1CmazGR999BHuvfdeHD16FIsWLWr2ecuXL8eiRYvwzDPPICMjA3/84x8xfvx45Ofnu/bypvbT1n4FgClTpmDevHkN0gwGg5rVpVY4c+YMLr30UsyaNQvx8fHYv38/Fi9ejP3792P79u3NPu/NN9/Evffei4ULF2Ls2LHYtGkTbrzxRuTm5mLYsGF+/Amosbb2KQCMHDkSK1eubJCWkpKiYm2pLZ588kk4HI5W5eXnavDwpF8BH32uKqQqi8XSJO3ee+9VYmJiFKfT6fY5NTU1SkxMjPLYY4+50urq6pTk5GTlN7/5jWp1pdZrS78qiqIkJycrv/3tb9WsGvnQiy++qABQjh8/3myefv36KTfffHODtOHDhytXX3212tWjNmhNn44ePVq55ppr/FgraosDBw4o0dHRyoYNGxQAypdfftlsXn6uBg9P+lVRfPe5yikTKuvcuXOTtIsuugiVlZWoqqpy+5xdu3ahsrIS06ZNc6VFRERg0qRJeP/991WrK7VeW/qVgk98fDwAwGazuT1fVFSE//73vw1+VwHgpptuwieffIK6ujrV60ieOV+fUvCYOXMm7r//fvTv3/+8efm5Gjw86VdfYkDcDvLy8tC9e3d07NjR7fn6eWppaWkN0tPT01FaWoqamhrV60ieO1+/1nv99ddhMBjQoUMHTJgwAfv27fNTDak1nE4namtr8c033+CJJ57Adddd1+xX5S39rtpsNhw5ckTt6lIreNKn9f7zn/8gOjoaRqMRo0ePxmeffeafylKrvP3229i3bx/+8Ic/tCo/P1eDg6f9Ws8Xn6sMiP0sLy8Pb775Jh5++OFm85SXl8NgMMBoNDZIj4uLg6IoKC8vV7ua5KHW9CsAXHfddVi3bh0+/vhj/PGPf8ShQ4eQlZWFoqIiP9WUzic5ORmRkZEYPHgwunbtijfeeKPZvPW/i7GxsQ3S4+LiAIg5rNT+POlTABg9ejTWrFmDDz74AH/9619RXV2NK664Art37/ZTjakl1dXVmDt3LpYtW4aYmJhWPYefq4GvLf0K+O5zlTfV+dGxY8cwffp0XHbZZZg1a1Z7V4d8xJN+Xbt2rev/2dnZGD9+PNLS0rBy5UqsX79e7apSK7z//vuoqqrCd999h6eeegrXXnstPvroI2i12vauGrWRp326ZMmSBo8nTpyIAQMG4Mknn+TX6wHgqaeeQmJiIu688872rgr5UFv71VefqwyI/aSiogJXX3014uPjsWXLFmg0zQ/Ox8XFoa6uDrW1tQ3+mi0vL4ckSa7RJ2p/nvSrO127dkVWVha+/vprlWpInsrIyAAADB8+HEOHDkVmZibeeecdTJkypUne+t9Fq9WKLl26uNLrR5s6derkhxrT+XjSp+5ER0fjmmuuwdtvv61mNakVSkpKsGrVKrzzzjuu5fPOnj3rOp49exYdOnRo8jx+rga2tvarO239XGVA7Ac1NTWYOHEirFYrdu/eDZPJ1GL++jlOBw8exKBBg1zphYWFrvUTqf152q8UfDIyMqDX63Ho0CG35+t/VwsLCxvcAFJYWIiIiAgu5RSAztenFNiOHDkCm82Ga665psm5yy67DJdeeqnbtYX5uRrY2tqvvsSAWGUOhwPTpk3DgQMHkJubi+7du5/3OSNGjEBMTAw2b97s+sW12+3YunUrJkyYoHaVqRXa0q/unDhxAnl5ebjtttt8XEPyhc8//xx2u73ZwDY1NRX9+vVzbdRSb9OmTbj88ssRERHhr6pSK52vT92pqqrCtm3bMHToUBVrRq2RmZmJHTt2NEjLz8/HnDlzsGHDhmb7iJ+rga2t/epOWz9XGRCr7IEHHsC2bduwatUqVFZWNvgL56KLLoLBYMDll1+OkpIS14iF0WjEY489hsWLF8NsNmPgwIFYv349ysrKznvTFvlHW/p148aN2LZtGyZMmIBu3bqhqKgITz/9NLRabZMFxcn/Jk2ahCFDhiAjIwORkZEoKCjAihUrkJGRgRtuuAEAcPfdd+Ovf/1rgwXjFy9ejFtuuQW9e/fGZZddhk2bNuHzzz/nqgQBoC19mpubixUrVuDGG29ESkoKTpw4gVWrVuF///sfNm/e3I4/DQHiBtYxY8a4PTd48GBcfPHFAMDP1SDT1n715ecqA2KV1e+G5K5jjhw5gpSUFDidziY7sjz66KNQFAUrV66ExWJBZmYmPvzwQ34FGyDa0q+9evXCiRMnMHv2bFRUVCA2NhZjx47FE088gV69evmt7uTeJZdcgk2bNuGZZ56BLMtISUnB/7d3/zFV1X0cwN+Hy68r4IUYVwILkd9qQfFrXogfLrPImBoZbtWgCVhbzjluhm4J6S1g0VZpQ5YpSTZKYaOFo+XA4RVRSQi1llNoTFqk3byEzsuP7/NHzz2Px4v8UHvoec77tTE5n+/3fM73HP/gs+8+99y8vDwUFhbKO72jo6MYHR1VnLdmzRpcu3YNpaWlKC0tRUREBOrr67F48eKZuA26yZ38n95///2w2WzYvHkzrly5Ag8PDxgMBlRWViIhIWGmboWmiX9X/z/9nX9XJSGEuNcLJiIiIiL6X8H3EBMRERGRqrEgJiIiIiJVY0FMRERERKrGgpiIiIiIVI0FMRERERGpGgtiIiIiIlI1FsREREREpGosiImI/uEkSUJxcfE9y9fS0gJJktDS0jLp3LS0tNt+g9R4cxctWnR3iyMimgEsiIlI9bq7u5GVlYWgoCC4u7sjMDAQS5cuxYcffjjTS3PwxRdfQJIk1NfXO4xFR0dDkiQ0Nzc7jD344IMwGAx3ff3+/n4UFxejs7PzrnNZrVaUlJQgOjoanp6e0Gq1WLRoETZt2oT+/v67zk9ENFUsiIlI1Y4dO4a4uDh0dXUhLy8PO3bswNq1a+Hk5IT3339/ppfnIDk5GQBw9OhRRdxqteLMmTNwdnaG2WxWjPX19aGvr08+NyUlBdevX0dKSsq0r9/f34+SkpK7LogvXryImJgYbNu2DQsWLEBZWRk++OADpKenY/fu3VPelSYiuhecZ3oBREQzyWQyQafT4eTJk/D29laMDQwMzMyiJhAQEIDg4GCHgritrQ1CCDz33HMOY/Zje0Hs5OQEd3f3/86CxzEyMoJVq1bh119/RUtLi7wuO5PJhLKyshlaHRGpEXeIiUjVLly4gIULFzoUwwCg1+sVxzU1NYiNjYVWq8V9992H7Oxs9PX1KebY+2g7OjpgMBig1WoRHByMyspKxTybzYY333wTsbGx0Ol08PDwwGOPPTZuu8OtkpOTcfr0aVy/fl2Omc1mLFy4EE899RSOHz+OsbExxZgkSUhKSgJw+x7iqqoqhISEQKvVIiEhAa2trYrxlpYWxMfHAwByc3MhSRIkScLevXsV886dO4f09HTMmjULgYGBKC8vV4wfPHgQXV1d2LJli0MxDACzZ8+GyWSSj+3P9Pvvv0dqaipmzZqF0NBQHDhwAABw5MgRJCYmQqvVIiIiAt9+++2kz5CI6GYsiIlI1YKCgtDR0YEzZ85MOM9kMuGll15CWFgY3nvvPWzYsAGHDx9GSkoK/vjjD8Vci8WCjIwMxMbGory8HHPnzsUrr7yCTz75RJ5jtVrx8ccfIy0tDWVlZSguLsZvv/2GZcuWTdqOkJycjOHhYbS3t8sxs9kMg8EAg8GAq1evKu7HbDYjMjISvr6+t825e/duFBQUwN/fH+Xl5UhKSkJmZqai4I+KisJbb70FAMjPz8e+ffuwb98+ReuFxWLBk08+iejoaFRUVCAyMhKbNm3CoUOH5DkNDQ0AgBdffHHC+7yZxWLB8uXLkZiYiPLycri5uSE7Oxu1tbXIzs5GRkYGSktLMTQ0hKysLAwODk45NxERBBGRin3zzTdCo9EIjUYjFi9eLF5//XXR1NQkbDabPKe3t1doNBphMpkU53Z3dwtnZ2dFPDU1VQAQFRUVcuzGjRsiJiZG6PV6Oe/IyIi4ceOGIp/FYhFz5swRL7/8siIOQGzdulU+Pnv2rAAgtm3bJoQQYnh4WHh4eIjq6mohhBBz5swRO3fuFEIIYbVahUajEXl5efL5zc3NAoBobm4WQghhs9mEXq8XMTExijVVVVUJACI1NVWOnTx5UgAQe/bscXiW9nv/9NNPFffu7+8vnn32WTn2yCOPCJ1O53D+7djz7t+/X479+OOPAoBwcnISx48fl+NNTU23XR8R0e1wh5iIVG3p0qVoa2tDZmYmurq6UF5ejmXLliEwMFDeyayrq8PY2BhWr16Ny5cvyz/+/v4ICwtzaHNwdnZGQUGBfOzq6oqCggIMDAygo6MDAKDRaODq6goAGBsbw++//46RkRHExcXhu+++m3DNUVFR8PX1lXuDu7q6MDQ0JL9FwmAwyB+sa2trw+jo6LitCXanTp3CwMAA1q1bJ68JAHJycqDT6ab0HO08PT3xwgsvKO49ISEBFy9elGNWqxVeXl7TzpudnS0fR0REwNvbG1FRUUhMTJTj9t9vvh4R0WRYEBOR6sXHx6Ourg4WiwUnTpxAUVERBgcHkZWVhXPnzuH8+fMQQiAsLAx+fn6Knx9++MHhw3cBAQHw8PBQxMLDwwEAvb29cqy6uhoPP/ww3N3d4evrCz8/P3z99de4evXqhOuVJAkGg0HuFTabzdDr9QgNDQWgLIjt/05UEP/8888AgLCwMEXcxcUF8+fPn3Att5o7dy4kSVLEfHx8YLFY5OPZs2dPu6VhvLw6nQ4PPPCAQwyA4npERJPhWyaIiP7N1dUV8fHxiI+PR3h4OHJzc/Hll19ibGwMkiTh0KFD0Gg0Dud5enpO+1o1NTXIycnBihUrYDQaodfrodFo8M477+DChQuTnp+cnIyvvvoK3d3dcv+wncFggNFoxKVLl3D06FEEBARMu7C9U+M9HwAQQsi/R0ZG4vTp0+jr63MoaKebdyrXIyKaDAtiIqJxxMXFAQB++eUXhISEQAiB4OBgead3Iv39/RgaGlLsEv/0008AgHnz5gEADhw4gPnz56Ourk6x87l169Ypre/m9xGbzWZs2LBBHouNjYWbmxtaWlrQ3t6OjIyMCXMFBQUBAM6fP48lS5bI8eHhYfT09CA6OlqO3bpLeyeeeeYZfP7556ipqUFRUdFd5yMiultsmSAiVWtubh53N7GxsRHAX72qq1atgkajQUlJicNcIQSuXLmiiI2MjGDXrl3ysc1mw65du+Dn54fY2FgA/9nZvDlfe3s72traprTuuLg4uLu747PPPsOlS5cUO8Rubm549NFHsXPnTgwNDU3YLmHP5efnh8rKSthsNjm+d+9ehzdo2Iv8W+PTkZWVhYceeggmk2nc+x0cHMSWLVvuOD8R0XRxh5iIVO21117DtWvXsHLlSkRGRsJms+HYsWOora3FvHnzkJubC29vb2zfvh1FRUXo7e3FihUr4OXlhZ6eHtTX1yM/Px+FhYVyzoCAAJSVlaG3txfh4eGora1FZ2cnqqqq4OLiAgBYvnw56urqsHLlSjz99NPo6elBZWUlFixYgD///HPSddvbO1pbW+Hm5iYX2nYGgwEVFRUAJu4fBv7qFd6+fTsKCgqwZMkSPP/88+jp6cGePXscWi1CQkLg7e2NyspKeHl5wcPDA4mJiQgODp7S87Zfr66uDo8//jhSUlKwevVqJCUlwcXFBWfPnsX+/fvh4+OjeBcxEdHfiTvERKRq7777LtLT09HY2IiNGzdi48aNOHHiBF599VW0t7fLX9jxxhtv4ODBg3ByckJJSQkKCwvR0NCAJ554ApmZmYqcPj4+aGxsxKlTp2A0GtHX14cdO3YgLy9PnpOTk4O3334bXV1dWL9+PZqamlBTUyO3akyFvdC1t0jczP4lHF5eXoqWh9vJz8/HRx99hP7+fhiNRrS2tqKhocGhx9fFxQXV1dXQaDRYt24d1qxZgyNHjkx5zXahoaHo7OzE5s2b0d3dDaPRiPXr1+Pw4cNYu3btHeUkIrpTkuAnD4iI7pm0tDRcvnx50i/6ICKifw7uEBMRERGRqrEgJiIiIiJVY0FMRERERKrGHmIiIiIiUjXuEBMRERGRqrEgJiIiIiJVY0FMRERERKrGgpiIiIiIVI0FMRERERGpGgtiIiIiIlI1FsREREREpGosiImIiIhI1VgQExEREZGq/QvWZYlARt2iCQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "scatter_pairs = [\n",
        "    ('sepal_length', 'sepal_width', 'Sepal Length vs Sepal Width'),\n",
        "    ('petal_length', 'petal_width', 'Petal Length vs Petal Width'),\n",
        "    ('sepal_length', 'petal_length', 'Sepal Length vs Petal Length'),\n",
        "    ('sepal_width', 'petal_width', 'Sepal Width vs Petal Width')\n",
        "]\n",
        "\n",
        "for x_key, y_key, title in scatter_pairs:\n",
        "    plt.figure(figsize=(8, 6))\n",
        "\n",
        "    for class_name in ['Virginica', 'Versicolor', 'Setosa']:\n",
        "        subset = df[df['Class_Name'] == class_name]\n",
        "\n",
        "        plt.scatter(\n",
        "            subset[feature_lookup[x_key]],\n",
        "            subset[feature_lookup[y_key]],\n",
        "            label=class_name,\n",
        "            color=class_colors[class_name],\n",
        "            alpha=0.75\n",
        "        )\n",
        "\n",
        "    plt.xlabel(feature_lookup[x_key])\n",
        "    plt.ylabel(feature_lookup[y_key])\n",
        "    plt.title(title)\n",
        "    plt.legend()\n",
        "    plt.grid(alpha=0.25)\n",
        "    plt.show()"
      ],
      "id": "9gAcvq_XVZ3m"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fS_y453HVZ3m"
      },
      "source": [
        "## 14. Interpretation of the scatterplots\n",
        "\n",
        "The scatterplots show that:\n",
        "\n",
        "- **Iris Setosa** is clearly separated from the other two classes, especially when petal length or petal width is used.\n",
        "- **Iris Versicolor** and **Iris Virginica** overlap in several plots.\n",
        "- Petal measurements provide stronger class separation than sepal measurements.\n",
        "- Sepal length versus sepal width shows more overlap, so these two features alone are less effective for classification.\n",
        "- Petal length versus petal width gives the clearest separation among the three classes."
      ],
      "id": "fS_y453HVZ3m"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rSfFSMq8VZ3m"
      },
      "source": [
        "## 15. Display the correlation matrix"
      ],
      "id": "rSfFSMq8VZ3m"
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "9UBs8E7XVZ3m",
        "outputId": "cab80d96-a731-4acd-e622-3d40ed23de5c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
              "SepalLengthCm       1.000000     -0.117570       0.871754      0.817941\n",
              "SepalWidthCm       -0.117570      1.000000      -0.428440     -0.366126\n",
              "PetalLengthCm       0.871754     -0.428440       1.000000      0.962865\n",
              "PetalWidthCm        0.817941     -0.366126       0.962865      1.000000"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
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              "      <th>SepalLengthCm</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.117570</td>\n",
              "      <td>0.871754</td>\n",
              "      <td>0.817941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <td>-0.117570</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.428440</td>\n",
              "      <td>-0.366126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <td>0.871754</td>\n",
              "      <td>-0.428440</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.962865</td>\n",
              "    </tr>\n",
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              "      <th>PetalWidthCm</th>\n",
              "      <td>0.817941</td>\n",
              "      <td>-0.366126</td>\n",
              "      <td>0.962865</td>\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ffbff973-826c-4eef-a02d-82eb3712caeb 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-ffbff973-826c-4eef-a02d-82eb3712caeb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_05668726-00d6-497a-87ff-4f84c18b844c\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('correlation_matrix')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_05668726-00d6-497a-87ff-4f84c18b844c button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('correlation_matrix');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "correlation_matrix",
              "summary": "{\n  \"name\": \"correlation_matrix\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5127858813273581,\n        \"min\": -0.11756978413300088,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -0.11756978413300088,\n          0.8179411262715758,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6657080809503223,\n        \"min\": -0.42844010433053864,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.0,\n          -0.3661259325364377,\n          -0.11756978413300088\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6887672414981271,\n        \"min\": -0.42844010433053864,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -0.42844010433053864,\n          0.962865431402796,\n          0.8717537758865838\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6512846518377995,\n        \"min\": -0.3661259325364377,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -0.3661259325364377,\n          1.0,\n          0.8179411262715758\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "correlation_matrix = df[numeric_columns].corr()\n",
        "correlation_matrix"
      ],
      "id": "9UBs8E7XVZ3m"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sAILeL88VZ3m"
      },
      "source": [
        "## 16. Display a heatmap for the correlation matrix"
      ],
      "id": "sAILeL88VZ3m"
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "id": "T0XDuPD-VZ3m",
        "outputId": "9b27260b-fbe8-40ed-dc9d-5346726632f7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(9, 7))\n",
        "sns.heatmap(\n",
        "    correlation_matrix,\n",
        "    annot=True,\n",
        "    fmt='.2f',\n",
        "    cmap='coolwarm',\n",
        "    square=True,\n",
        "    linewidths=0.5\n",
        ")\n",
        "\n",
        "plt.title('Correlation Matrix Heatmap')\n",
        "plt.show()"
      ],
      "id": "T0XDuPD-VZ3m"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NLpaORX8VZ3m"
      },
      "source": [
        "## 17. Interpretation of the correlation matrix\n",
        "\n",
        "- Correlation values close to **+1** indicate a strong positive relationship.\n",
        "- Values close to **-1** indicate a strong negative relationship.\n",
        "- Values close to **0** indicate a weak linear relationship.\n",
        "- Petal length and petal width usually have a very strong positive correlation.\n",
        "- Sepal length is also positively correlated with petal length and petal width.\n",
        "- Sepal width usually has weaker or negative correlations with some other attributes.\n",
        "- Strongly correlated petal features are useful for separating the Iris classes."
      ],
      "id": "NLpaORX8VZ3m"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pSazDD8IVZ3m"
      },
      "source": [
        "## 18. Encode the class variable using LabelEncoder"
      ],
      "id": "pSazDD8IVZ3m"
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "2-_TtSeZVZ3m",
        "outputId": "775780ff-3dd2-4830-b688-ae5a828a83d9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Class  Code\n",
              "0      Iris-setosa     0\n",
              "1  Iris-versicolor     1\n",
              "2   Iris-virginica     2"
            ],
            "text/html": [
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              "<style scoped>\n",
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              "      <td>Iris-versicolor</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Iris-virginica</td>\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('encoding_table')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
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              "      buttonEl.style.display =\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('encoding_table');\n",
              "      }\n",
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              "    </script>\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "encoding_table",
              "summary": "{\n  \"name\": \"encoding_table\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Class\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Iris-setosa\",\n          \"Iris-versicolor\",\n          \"Iris-virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "label_encoder = LabelEncoder()\n",
        "\n",
        "df['Class_Encoded'] = label_encoder.fit_transform(df[class_column])\n",
        "\n",
        "encoding_table = pd.DataFrame({\n",
        "    'Class': label_encoder.classes_,\n",
        "    'Code': label_encoder.transform(label_encoder.classes_)\n",
        "})\n",
        "\n",
        "encoding_table"
      ],
      "id": "2-_TtSeZVZ3m"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ViKL4GVCVZ3m"
      },
      "source": [
        "## 19. What is the code of the third class?"
      ],
      "id": "ViKL4GVCVZ3m"
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oKu3ICbHVZ3m",
        "outputId": "693fa8df-91b1-4a94-d1b2-ddb68eec47cb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Third class: Iris-virginica\n",
            "Code of the third class: 2\n"
          ]
        }
      ],
      "source": [
        "third_class = label_encoder.classes_[2]\n",
        "third_class_code = label_encoder.transform([third_class])[0]\n",
        "\n",
        "print('Third class:', third_class)\n",
        "print('Code of the third class:', third_class_code)"
      ],
      "id": "oKu3ICbHVZ3m"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tV1qnUsBVZ3m"
      },
      "source": [
        "### Answer\n",
        "\n",
        "With Scikit-learn `LabelEncoder`, classes are sorted alphabetically before encoding.  \n",
        "Therefore, the third class receives the code shown in the output above, which is normally **2**."
      ],
      "id": "tV1qnUsBVZ3m"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "enAZIMoJVZ3m"
      },
      "source": [
        "## 20. Split the dataset into 70% training and 30% testing"
      ],
      "id": "enAZIMoJVZ3m"
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eJ7tFSFIVZ3n",
        "outputId": "c6181e78-ee1d-4a1f-d60d-7438c7e64e7b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training samples: 105\n",
            "Testing samples: 45\n"
          ]
        }
      ],
      "source": [
        "X = df[numeric_columns]\n",
        "y = df['Class_Encoded']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X,\n",
        "    y,\n",
        "    test_size=0.30,\n",
        "    random_state=42,\n",
        "    stratify=y\n",
        ")\n",
        "\n",
        "print('Training samples:', X_train.shape[0])\n",
        "print('Testing samples:', X_test.shape[0])"
      ],
      "id": "eJ7tFSFIVZ3n"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c4lfP5U6VZ3n"
      },
      "source": [
        "## 21. Train a Logistic Regression model"
      ],
      "id": "c4lfP5U6VZ3n"
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QegyFu4UVZ3n",
        "outputId": "6828152e-f7b9-4d37-d995-4ce708d9dca7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression Accuracy: 0.9111111111111111\n",
            "\n",
            "Classification Report:\n",
            "                 precision    recall  f1-score   support\n",
            "\n",
            "    Iris-setosa       1.00      1.00      1.00        15\n",
            "Iris-versicolor       0.82      0.93      0.88        15\n",
            " Iris-virginica       0.92      0.80      0.86        15\n",
            "\n",
            "       accuracy                           0.91        45\n",
            "      macro avg       0.92      0.91      0.91        45\n",
            "   weighted avg       0.92      0.91      0.91        45\n",
            "\n"
          ]
        }
      ],
      "source": [
        "logistic_model = Pipeline([\n",
        "    ('scaler', StandardScaler()),\n",
        "    ('classifier', LogisticRegression(max_iter=1000, random_state=42))\n",
        "])\n",
        "\n",
        "logistic_model.fit(X_train, y_train)\n",
        "logistic_predictions = logistic_model.predict(X_test)\n",
        "logistic_accuracy = accuracy_score(y_test, logistic_predictions)\n",
        "\n",
        "print('Logistic Regression Accuracy:', logistic_accuracy)\n",
        "print('\\nClassification Report:')\n",
        "print(classification_report(y_test, logistic_predictions, target_names=label_encoder.classes_))"
      ],
      "id": "QegyFu4UVZ3n"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r6orDZ_ZVZ3n"
      },
      "source": [
        "## 22. Train a KNN model"
      ],
      "id": "r6orDZ_ZVZ3n"
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RrPamCGIVZ3n",
        "outputId": "c6374fef-86e4-498d-c942-66b2744793c0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KNN Accuracy: 0.9111111111111111\n",
            "\n",
            "Classification Report:\n",
            "                 precision    recall  f1-score   support\n",
            "\n",
            "    Iris-setosa       1.00      1.00      1.00        15\n",
            "Iris-versicolor       0.79      1.00      0.88        15\n",
            " Iris-virginica       1.00      0.73      0.85        15\n",
            "\n",
            "       accuracy                           0.91        45\n",
            "      macro avg       0.93      0.91      0.91        45\n",
            "   weighted avg       0.93      0.91      0.91        45\n",
            "\n"
          ]
        }
      ],
      "source": [
        "knn_model = Pipeline([\n",
        "    ('scaler', StandardScaler()),\n",
        "    ('classifier', KNeighborsClassifier(n_neighbors=5))\n",
        "])\n",
        "\n",
        "knn_model.fit(X_train, y_train)\n",
        "knn_predictions = knn_model.predict(X_test)\n",
        "knn_accuracy = accuracy_score(y_test, knn_predictions)\n",
        "\n",
        "print('KNN Accuracy:', knn_accuracy)\n",
        "print('\\nClassification Report:')\n",
        "print(classification_report(y_test, knn_predictions, target_names=label_encoder.classes_))"
      ],
      "id": "RrPamCGIVZ3n"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FyCxuQnSVZ3n"
      },
      "source": [
        "## 23. Train a Decision Tree model"
      ],
      "id": "FyCxuQnSVZ3n"
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SsriwZimVZ3n",
        "outputId": "ad5a91f2-ec2f-49ed-be54-d7a7816f9983"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree Accuracy: 0.9333333333333333\n",
            "\n",
            "Classification Report:\n",
            "                 precision    recall  f1-score   support\n",
            "\n",
            "    Iris-setosa       1.00      1.00      1.00        15\n",
            "Iris-versicolor       1.00      0.80      0.89        15\n",
            " Iris-virginica       0.83      1.00      0.91        15\n",
            "\n",
            "       accuracy                           0.93        45\n",
            "      macro avg       0.94      0.93      0.93        45\n",
            "   weighted avg       0.94      0.93      0.93        45\n",
            "\n"
          ]
        }
      ],
      "source": [
        "tree_model = DecisionTreeClassifier(\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "tree_model.fit(X_train, y_train)\n",
        "tree_predictions = tree_model.predict(X_test)\n",
        "tree_accuracy = accuracy_score(y_test, tree_predictions)\n",
        "\n",
        "print('Decision Tree Accuracy:', tree_accuracy)\n",
        "print('\\nClassification Report:')\n",
        "print(classification_report(y_test, tree_predictions, target_names=label_encoder.classes_))"
      ],
      "id": "SsriwZimVZ3n"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k87YPlR3VZ3n"
      },
      "source": [
        "## 24. Compare the accuracies of the three models"
      ],
      "id": "k87YPlR3VZ3n"
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "iBJP_TOgVZ3n",
        "outputId": "fbe9e810-24bc-4efe-c42a-09a56344b89b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Model  Accuracy\n",
              "2        Decision Tree  0.933333\n",
              "0  Logistic Regression  0.911111\n",
              "1                  KNN  0.911111"
            ],
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              "summary": "{\n  \"name\": \"baseline_results\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Decision Tree\",\n          \"Logistic Regression\",\n          \"KNN\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.012830005981991702,\n        \"min\": 0.9111111111111111,\n        \"max\": 0.9333333333333333,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.9111111111111111,\n          0.9333333333333333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "baseline_results = pd.DataFrame({\n",
        "    'Model': [\n",
        "        'Logistic Regression',\n",
        "        'KNN',\n",
        "        'Decision Tree'\n",
        "    ],\n",
        "    'Accuracy': [\n",
        "        logistic_accuracy,\n",
        "        knn_accuracy,\n",
        "        tree_accuracy\n",
        "    ]\n",
        "}).sort_values(by='Accuracy', ascending=False)\n",
        "\n",
        "baseline_results"
      ],
      "id": "iBJP_TOgVZ3n"
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "QdF8yWPvVZ3n",
        "outputId": "8103ffa0-c85a-4558-db71-6a2285fc4a3e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 5))\n",
        "sns.barplot(\n",
        "    data=baseline_results,\n",
        "    x='Model',\n",
        "    y='Accuracy'\n",
        ")\n",
        "\n",
        "plt.ylim(0, 1.05)\n",
        "plt.title('Baseline Model Accuracy Comparison')\n",
        "plt.xticks(rotation=15)\n",
        "plt.show()"
      ],
      "id": "QdF8yWPvVZ3n"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JqNLQGwnVZ3n"
      },
      "source": [
        "## 25. Change the hyperparameters of each model"
      ],
      "id": "JqNLQGwnVZ3n"
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "SMKpU_zXVZ3n",
        "outputId": "b2342fd2-493d-4c39-a57d-053c71302e9a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       Model  Accuracy\n",
              "1                  Tuned KNN  0.933333\n",
              "2        Tuned Decision Tree  0.888889\n",
              "0  Tuned Logistic Regression  0.866667"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b29af98c-798f-4074-b6a5-678cf44c62fb\" 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>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Tuned KNN</td>\n",
              "      <td>0.933333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Tuned Decision Tree</td>\n",
              "      <td>0.888889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Tuned Logistic Regression</td>\n",
              "      <td>0.866667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b29af98c-798f-4074-b6a5-678cf44c62fb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "\n",
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              "      buttonEl.style.display =\n",
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              "          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",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('tuned_results');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "tuned_results",
              "summary": "{\n  \"name\": \"tuned_results\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Tuned KNN\",\n          \"Tuned Decision Tree\",\n          \"Tuned Logistic Regression\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03394500514782104,\n        \"min\": 0.8666666666666667,\n        \"max\": 0.9333333333333333,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.9333333333333333,\n          0.8888888888888888,\n          0.8666666666666667\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "# Tuned Logistic Regression\n",
        "tuned_logistic = Pipeline([\n",
        "    ('scaler', StandardScaler()),\n",
        "    ('classifier', LogisticRegression(\n",
        "        C=10,\n",
        "        solver='liblinear',\n",
        "        max_iter=2000,\n",
        "        random_state=42\n",
        "    ))\n",
        "])\n",
        "\n",
        "# Tuned KNN\n",
        "tuned_knn = Pipeline([\n",
        "    ('scaler', StandardScaler()),\n",
        "    ('classifier', KNeighborsClassifier(\n",
        "        n_neighbors=7,\n",
        "        weights='distance',\n",
        "        metric='minkowski',\n",
        "        p=2\n",
        "    ))\n",
        "])\n",
        "\n",
        "# Tuned Decision Tree\n",
        "tuned_tree = DecisionTreeClassifier(\n",
        "    max_depth=3,\n",
        "    min_samples_split=4,\n",
        "    min_samples_leaf=2,\n",
        "    criterion='entropy',\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "tuned_models = {\n",
        "    'Tuned Logistic Regression': tuned_logistic,\n",
        "    'Tuned KNN': tuned_knn,\n",
        "    'Tuned Decision Tree': tuned_tree\n",
        "}\n",
        "\n",
        "tuned_accuracies = {}\n",
        "\n",
        "for model_name, model in tuned_models.items():\n",
        "    model.fit(X_train, y_train)\n",
        "    predictions = model.predict(X_test)\n",
        "    tuned_accuracies[model_name] = accuracy_score(y_test, predictions)\n",
        "\n",
        "tuned_results = pd.DataFrame({\n",
        "    'Model': list(tuned_accuracies.keys()),\n",
        "    'Accuracy': list(tuned_accuracies.values())\n",
        "}).sort_values(by='Accuracy', ascending=False)\n",
        "\n",
        "tuned_results"
      ],
      "id": "SMKpU_zXVZ3n"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BKm3IOG2VZ3n"
      },
      "source": [
        "## 26. Compare baseline and tuned accuracies"
      ],
      "id": "BKm3IOG2VZ3n"
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "b1jvqNVaVZ3n",
        "outputId": "2cd46362-8e63-4624-b727-2ca876c32b66"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Model  Baseline Accuracy  Tuned Accuracy  Accuracy Change\n",
              "0  Logistic Regression           0.911111        0.866667        -0.044444\n",
              "1                  KNN           0.911111        0.933333         0.022222\n",
              "2        Decision Tree           0.933333        0.888889        -0.044444"
            ],
            "text/html": [
              "\n",
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              "        vertical-align: middle;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Baseline Accuracy</th>\n",
              "      <th>Tuned Accuracy</th>\n",
              "      <th>Accuracy Change</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.911111</td>\n",
              "      <td>0.866667</td>\n",
              "      <td>-0.044444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>KNN</td>\n",
              "      <td>0.911111</td>\n",
              "      <td>0.933333</td>\n",
              "      <td>0.022222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Decision Tree</td>\n",
              "      <td>0.933333</td>\n",
              "      <td>0.888889</td>\n",
              "      <td>-0.044444</td>\n",
              "    </tr>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "        const element = document.querySelector('#df-02383793-09a2-4689-9100-0203bf98cf51');\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",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('comparison_results');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "comparison_results",
              "summary": "{\n  \"name\": \"comparison_results\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Logistic Regression\",\n          \"KNN\",\n          \"Decision Tree\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Baseline Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.012830005981991702,\n        \"min\": 0.9111111111111111,\n        \"max\": 0.9333333333333333,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.9333333333333333,\n          0.9111111111111111\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tuned Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03394500514782104,\n        \"min\": 0.8666666666666667,\n        \"max\": 0.9333333333333333,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.8666666666666667,\n          0.9333333333333333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Accuracy Change\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03849001794597508,\n        \"min\": -0.04444444444444451,\n        \"max\": 0.022222222222222254,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -0.0444444444444444,\n          0.022222222222222254\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "comparison_results = pd.DataFrame({\n",
        "    'Model': [\n",
        "        'Logistic Regression',\n",
        "        'KNN',\n",
        "        'Decision Tree'\n",
        "    ],\n",
        "    'Baseline Accuracy': [\n",
        "        logistic_accuracy,\n",
        "        knn_accuracy,\n",
        "        tree_accuracy\n",
        "    ],\n",
        "    'Tuned Accuracy': [\n",
        "        tuned_accuracies['Tuned Logistic Regression'],\n",
        "        tuned_accuracies['Tuned KNN'],\n",
        "        tuned_accuracies['Tuned Decision Tree']\n",
        "    ]\n",
        "})\n",
        "\n",
        "comparison_results['Accuracy Change'] = (\n",
        "    comparison_results['Tuned Accuracy']\n",
        "    - comparison_results['Baseline Accuracy']\n",
        ")\n",
        "\n",
        "comparison_results"
      ],
      "id": "b1jvqNVaVZ3n"
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "sV4XxdSWVZ3n",
        "outputId": "ed72f4c8-6fa1-48a5-8117-18416452ef88"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x500 with 1 Axes>"
            ],
            "image/png": 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fDh06ICMjA6tWrcLu3btl34YnJSXJvun97/Hnh6OjIzQ1NXHo0CG19kWLFuV7m+3bt0d2djYmT54sW6ZUKqVQEhAQAG1tbSxYsEDt2ObOnZvvfX/77bcYP348xo4dm2efatWqwcrKCkuWLFF77nbt2oW///4bzZs3BwDY2NjAx8cHq1atUrtkZu/evbh06ZLaNt/2mN9WztmE0NBQtGvXTu2nffv2aNCggdSncePGMDY2RlhYmOzGcjnPa9WqVeHk5IS5c+fKann1uXdxcUFcXBwePXoktZ09e1ZtdrE3yfkA/+p2T5w4gWPHjqn1a9u2LYQQmDhxomwb//1d79KlC5KSktC3b99cZ04jog+DZxSI6IPZtWsX4uLiALy8Tj8iIgJXrlzBiBEjUKpUKQAvB5DOmTMHQUFBCAkJQUJCAn788UeUL18e586dk7Y1adIkHDp0CM2bN4ejoyMSEhKwaNEilC1bVhrw26VLF2zYsAH9+vXDgQMHULduXWRnZyMuLg4bNmzAnj171G4AV1AKYr9Vq1ZF+fLlMXr0aGRkZMimVV21ahUWLVqENm3awMXFBc+ePcOyZctQqlQpNGvWLN+1m5iY4IsvvsCCBQugUCjg4uKC7du35+ua+hwNGjRA3759ERYWhjNnzqBx48bQ1tbGlStXEBkZiXnz5qFdu3bSPRjCwsLQokULNGvWDKdPn8auXbvyPBvwJpUrV85z3v0c2tramD59Onr06IEGDRqgU6dOePjwIebNm4dy5cphyJAhUt+wsDA0b94c9erVQ8+ePZGYmIgFCxbA09NTbfD52x7z21qzZg18fHzyvGytVatWGDhwIE6dOoWqVavihx9+QO/evVG9enWEhITAzMwMZ8+eRWpqKlatWgUNDQ0sXrwYLVu2hI+PD3r06AEbGxvExcXh4sWL2LNnDwCgZ8+emDNnDpo0aYJevXohISEBS5YsgaenJ54+ffpWtbdo0QJRUVFo06YNmjdvjhs3bmDJkiWoWLGi2nPWsGFDdOnSBfPnz8eVK1cQFBQElUqFw4cPo2HDhhgwYIDUt0qVKvDy8pImDqhatepbP5dE9B6KarolIiq5cpseVU9PT/j4+IjFixerTccohBDLly8Xrq6uQldXV7i7u4vw8HBpSssc+/btE61btxa2trZCR0dH2Nraik6dOol//vlHbVuZmZli+vTpwtPTU+jq6gozMzPh6+srJk6cKFJSUqR+bzs9qqenp+z4/jtV5Lvs93VGjx4tAIjy5cvLlp06dUp06tRJODg4CF1dXWFlZSVatGgh/vzzz7fado7/To8qhBCPHj0Sbdu2FQYGBsLMzEz07dtXXLhwIdfpUQ0NDWXb/O//qxxLly4Vvr6+Ql9fXxgbGwtvb28RGhoq7t+/L/XJzs4WEydOFDY2NkJfX1/4+fmJCxcuyP7/5AX/Pz3q6/x3etQc69evF1WqVBG6urrC3NxcdO7cWdy9e1e2/qZNm4SHh4fQ1dUVFStWFFFRUbn+DrztMb9petS//vpLABBjx47Ns8/NmzcFADFkyBCpLTo6WtSpU0fo6+uLUqVKiRo1aoi1a9eqrXfkyBERGBgojI2NhaGhoahUqZJYsGCBWp/Vq1cLZ2dnoaOjI3x8fMSePXvynB515syZstpUKpWYOnWqcHR0FLq6uqJKlSpi+/btuT5nSqVSzJw5U7i7uwsdHR1haWkpmjZtKv766y/ZdmfMmCEAiKlTp+b5vBBRwVIIkcuoJSIiIqJiZN68eRgyZAhu3rwJBweHoi6H6JPAoEBERETFmhAClStXhoWFRb7uSUFE+cMxCkRERFQsvXjxAtHR0Thw4ADOnz+PrVu3FnVJRJ8UnlEgIiKiYunmzZtwcnKCqakp+vfvjylTphR1SUSfFAYFIiIiIiKS4X0UiIiIiIhIhkGBiIiIiIhkOJg5D6ampsjIyICNjU1Rl0JEREREVGDi4+Ohq6v7xrvGMyjkISMjA0qlsqjLICIiIiIqUG/7GZdBIQ85ZxKuX79exJUQERERERUcZ2fnt+rHMQpERERERCTDoEBERERERDIMCkREREREJMOgQEREREREMhzMTERERCVOdnY2srKyiroMokKnra0NTU3NAtkWgwIRERGVGEIIPHjw4I3zwxOVZKamprC2toZCoXiv7TAoEBERUYmRExKsrKxgYGDw3h+UiD4mQgikpqYiISEBAN77xsEMCkRERFQiZGdnSyHBwsKiqMshKhL6+voAgISEBFhZWb3XZUgczExEREQlQs6YBAMDgyKuhKho5bwG3necDoMCERERlSi83Ig+dQX1GmBQICIiIiIimWIVFK5evYp+/frBx8cHWlpa8PLyeqv1hBCYNm0aHBwcoK+vj9q1a+P48eMfuFoiIiIiopKrWAWFixcvYseOHShfvjwqVqz41utNnz4d48ePx5AhQ7B9+3bY2NigcePGuH79+gesloiIiIje1sqVK6FQKHDz5s2iLoXeUrEKCi1btsSdO3ewceNGVK1a9a3WSU9PR1hYGIYNG4YhQ4bA398f69atg7m5OWbNmvWBKyYiIiIqnlq1agUDAwM8e/Yszz6dO3eGjo4Onjx5UoiV0ceiWAUFDY13L+fo0aN4+vQp2rdvL7Xp6Ojg888/x86dOwuyPCIiIqKPRufOnZGWlobNmzfnujw1NRVbt25FUFBQoUwn26VLF6SlpcHR0fGD74sKxkd/H4W4uDgAgLu7u1q7h4cHbt++jbS0NGk+2f9ydnbOc7t37tyBvb19wRVKREREVIhatWoFY2NjREREoGvXrrLlW7duxYsXL9C5c+d870OpVEKlUkFHR+eNfTU1Nd9rTn8qfMXqjEJ+JCUlQVdXF3p6emrtZmZmEEIgKSmpiCojIiIiKjr6+vr4/PPPsW/fPulOva+KiIiAsbExWrVqheTkZAwePBj29vbQ1dVF+fLlMX36dKhUKqn/zZs3oVAoMGvWLMydOxcuLi7Q1dXFpUuXAAALFiyAp6cnDAwMYGZmhmrVqiEiIkJaP68xCosWLYKnpyd0dXVha2uLb775BsnJyWp9/Pz84OXlhUuXLqFhw4YwMDCAnZ0dZsyYUXBPGMl89GcU3sfrBju/7mwDERER0cegc+fOWLVqFTZs2IABAwZI7YmJidizZw86deoEIQQaNGiAe/fuoW/fvnBwcMDRo0cxcuRIxMfHY+7cuWrbDA8PR3p6Or766ivo6urC3Nwcy5Ytw6BBg9CuXTv873//Q3p6Os6dO4cTJ04gJCQkz/omTJiAiRMnIiAgAF9//TUuX76MxYsX4+TJk/j999+hra0t9U1KSkJQUBA+//xztG/fHhs3bsTw4cPh7e2Npk2bFvhzRyUgKJiZmSEjIwPp6elqZxWSkpKgUChgZmZWhNURERERFZ1GjRrBxsYGERERakEhMjISWVlZ6Ny5M+bMmYNr167h9OnTcHV1BQD07dsXtra2mDlzJoYNG6Z2Ofbdu3dx9epVWFpaSm07duyAp6cnIiMj37q2R48eISwsDI0bN8auXbuksaru7u4YMGAAVq9ejR49ekj979+/j19++QVdunQBAPTq1QuOjo5Yvnw5g8IH8tFfepQzNuHy5ctq7XFxcdJ9FYiIiIg+RZqamujYsSOOHTumdslPREQEypQpA39/f0RGRuKzzz6DmZkZHj9+LP0EBAQgOzsbhw4dUttm27Zt1UICAJiamuLu3bs4efLkW9cWGxuLzMxMDB48WG1Cmz59+qBUqVLYsWOHWn8jIyN8+eWX0mMdHR3UqFGD0+F/QB99UKhTpw5KlSqllmCzsrIQFRWFZs2aFWFlREREREUvZ7ByzniBu3fv4vDhw+jYsSM0NTVx5coV7N69G5aWlmo/AQEBACAb3+Dk5CTbx/Dhw2FkZIQaNWrA1dUV33zzDX7//ffX1nXr1i0AgJubm1q7jo4OnJ2dpeU5ypYtC4VCodZmZmbG8agfULG69Cg1NVWa0vTWrVt4+vQpNm7cCABo0KABLC0t4e/vj1u3buHq1asAAD09PYwcORITJkyApaUlvL29sWjRIjx58gTffvttkR0LERERUXHg6+sLd3d3rF27FqNGjcLatWshhJAChEqlQmBgIEJDQ3Ndv0KFCmqPc7taw8PDA5cvX8b27duxe/dubNq0CYsWLcK4ceMwceLEAjmOvGZMEkIUyPZJrlgFhYSEBHzxxRdqbTmPDxw4AD8/P2RnZ0OpVKr1GT58OIQQmDVrFh49egQfHx/s2bOHA5KJiIiI8PKswtixY3Hu3DlERETA1dUV1atXBwC4uLjg+fPn0hmE/DI0NESHDh3QoUMHZGZm4vPPP8eUKVMwcuRI2eyUAKT7KVy+fFntM1tmZiZu3Ljx3vXQ+ytWlx6VK1cOQohcf/z8/AAABw8elE2rpVAoMHLkSNy5cwfp6ek4fvw4ateuXfgHQERERFQM5Zw9GDduHM6cOaN274T27dvj2LFj2LNnj2y95ORk2Re0ufnvnZ11dHRQsWJFCCGQlZWV6zoBAQHQ0dHB/Pnz1c4KLF++HCkpKWjevPlbHRt9OMXqjAIRERERFTwnJyfUqVMHW7duBQC1oPDdd98hOjoaLVq0QPfu3eHr64sXL17g/Pnz2LhxI27evInSpUu/dvuNGzeGtbU16tatizJlyuDvv//GwoUL0bx5cxgbG+e6jqWlJUaOHImJEyciKCgIrVq1wuXLl7Fo0SJUr15dbeAyFQ0GBSIiIqJPQOfOnXH06FHUqFED5cuXl9oNDAzw22+/YerUqYiMjMQvv/yCUqVKoUKFCpg4cSJMTEzeuO2+fftizZo1mDNnDp4/f46yZcti0KBBGDNmzGvXyxljunDhQgwZMgTm5ub46quvMHXqVLV7KFDRUAiOAMlVzrVynHKLiIjo45Ceno4bN27Ayckp12viiT4Vb3otvO3n3GI1RoGIiIiIiIoHBgUiIiIiIpJhUCAiIiIiIhkGBSIiIiIikmFQICIiIiIiGQYFIiIiIiKSYVAgIiIiIiIZBgUiIiIiIpJhUCAiIiIiIhkGBSIiIiIikmFQICIiIiIiGa2iLoCIiIjoQ/P97pci2/dfM7u+8zoTJkzAxIkTpce6urpwcnJCjx498O2330JDo/C/6z148CAaNmyIkydPolq1agAAhUKBmTNn4ttvvy3UWrKysmBjY4PExETcvHkTDg4Ohbr/TwWDAhEREVExpK+vj/379wMA0tLScODAAYwYMQIqlQojRowo4upeOnbsGBwdHQt9v3v27MGTJ08AAGvXrsXw4cMLvYZPAYMCEREVC7cneRd1CZ8ch3Hni7qEfMnr7IC1sQ6GBbggWy8JGlrahVxV3i7defzO6zxKSQUUCpSyKw8AKAWgY3lvHP3jL6xZtwGtOvcu4Cpfr6J96Vzba9WqVah15IiIiICpqSlcXFywZs2aYhUU0tLSoK+vX9RlFAiOUSAiIiL6SBgaGkGpzFJrmxM2CcGB9VHN3RENq3vj2wFf4dHDB2p9Tp08ga7tWqKmpzOqe5RDcGB9bIlcp9bnt30x6NiqCaq62qOejzsmjfoOqakvXluPQqHArFmzpMd+fn5o0aIFNm7cCDc3NxgZGaFRo0a4du2a2noZGRkYNWoUHB0doaurCw8PD0RERLzVc/DixQtER0ejXbt26N69O86fP4/z5+Wh99ixY2jcuDFKlSoFY2Nj1KxZE3v37lWrYcyYMXB2doauri7Kli2L7t27y47lVWfOnIFCocDBgwfVnoNp06Zh+PDhsLa2hpWVlbT/Vq1awdbWFoaGhvDx8cGvv/4qqzM5ORkDBw5E2bJlpUvMRo4cCQBYsGABDAwM8PTpU7V1/v77bygUCuzcufOtnrP84hkFIiIiomJKqVQCADLS03Hi6BHs3bUdfQb8T61P4pPH6DNgMKzKlEHikydYtWwxurVvjeh9v0NLSwvPnz1D/x4hqFqtJmYu+Ak6Orq4duUynj1NkbaxZ0c0vv2mD9q074RvhobiccJD/DDtezxNScasH5e9U81nzpzBzJkzMW3aNGRnZ2Po0KH48ssvcezYMalP+/btceTIEYwfPx4eHh7YuXMnvvzyS5iZmaFp06av3f6WLVvw4sULhISEwNPTE0OGDEFERATCwsKkPr///jsaNWqEWrVq4eeff4apqSn+/PNP3L59W+rTtm1b7N+/H6NGjUKtWrXw6NEjREVFvdOx5pg3bx5q1aqF5cuXS//Pbt26hbp166Jfv37Q09PD77//jl69ekGlUqFbt24AXoaVRo0a4ebNmxg/fjy8vb1x584dHDlyBADw5ZdfIjQ0FGvXrkXfvn2l/a1YsQJ2dnZo0qRJvup9WwwKRERERMVQWmoqKjvbqLU1bRmM3v3Vg8L3s+ZL/87OzoaPb3U0qlEJJ44eRt36DXHz+jU8e/oUg0eMQQX3igCAWvXqS+sIITBrygQEtQzGpBlzpfbSVmXwdbdO6DdoGCra13vrupOTk3H69GlYWloCAJ4/f44ePXrg7t27KFu2LA4cOIDo6Gjs2bMHjRs3BgAEBgYiPj4e48ePf2NQiIiIgJ2dHRo0aAANDQ34+/sjIiICU6dOhUKhAACEhoaifPny2L9/PzQ1NQFA2hcA7N27Fzt27EBERAQ6deoktb/673dhbm6OqKgoaf8A0LFjR+nfQgjUr18fd+/exU8//SQFhV9++QWnT5/G0aNHUbt2bal/znIzMzO0a9cOK1askIKCUqnEr7/+il69eknH9qHw0iMiIiKiYkhPTx/rt+3F+m178eum7Rg5YQqO/LYf44cPUet3+EAsOrdphpqezqjkZI1GNSoBAG5ef3m5j71jORgZG2PSqO+we9sWJD5RHzNx8/o13L97B0EtWkOpVEo/1WvVgYaGBi6cO/NOdfv4+EghAQAqVnwZTu7evQsAiImJgbm5ORo1aqS2v8DAQJw+fRrZ2dl5bvvx48eIiYlBhw4dpJmfQkJCcPv2belb+NTUVBw/fhzdunXL84P0vn37YGBgoPZh/n00bdpULSQAQFJSEgYNGgRHR0doa2tDW1sbS5cuxT///KNWh4eHh1pI+K8+ffrgjz/+wMWLFwEAO3fuREJCAnr27Fkgtb8OzygQERERFUMKDQW8KvtIj6tWrwlldjZmTh6Hbn2+hqubB86fPY0BvbqgYWAQen09CBalS0OhUKBT6yBkZmQAAExMTbFszUb8OGc6Rgz5BtlKJXxr1MKoSWGo4F4RSYkvZw8a1KdbrnU8iL/3TnWbmpqqPdbR0QEApKenA3j5YT8xMRHa2rkPOI+Pj0fZsmVzXbZhwwYolUo0b94cycnJAICGDRtCV1cXERER+Oyzz5CUlASVSgVbW9s8a3zy5AlsbGxkH+7zq0yZMrK27t274+jRoxg3bhw8PT1RqlQpLF68GOvXr1er43V1AkD9+vXh5uaG5cuXY86cOVixYgXq168PFxeXAqn9dRgUiIiIiD4SLuVdAQBXL8fB1c0D+3bvgJFxKcxZvFz6hv3+3Tuy9Sr5VMVPv6xHenoa/jh6BDO/n4BBvbth95GTMDE1AwCMnjwNlXx8ZetalbEu0GMwNzeHpaVlngNxcwYD5yZnwLO/v79sWWRkJObPnw9TU1NoaGjg/v37eW7HwsIC8fHxEELkGRb09PSQmZmp1paUlJRr3/9uIz09Hdu3b8ecOXMwcOBAqV2lUsnqOHfuXJ515ujduzdmzJiBoUOHYseOHVixYsUb1ykIvPSIiIiI6CNx5XIcAMDM3ALAyw+kWlpaah9Ut2/ZmOf6enr6qN8oEB27dMfdO7eQkZ4O5/KusLaxxd3bt+BV2Uf2Y2VdsEEhICAAjx49go6ODqpVqyb7yTkD8V+3bt3C0aNH0a9fPxw4cEDt54cffsCTJ0+we/duGBoaonbt2vjll1/yvIwpICAAqamp2LBhQ551li1bFpcvX4YQQmqLiYl5q2PMyMiASqVSO5Znz54hOjpaVsfff/+NEydOvHZ73bp1Q0pKCjp37gwDAwO0a9furep4XzyjQERERFQMCZXA2VN/AgCysjJx8fw5/LRgDlxc3eBb8+U17XU+a4Bfl/+EKeNGIKBJc5w5dRLboiLVtvPbvhhErY+Af5NmsLGzw+OEBKxZ+TOqVKsBXT09AEDo2EkIHdQPaampqN8oEPoGBoi/dwe/7duLwcPH5HkfhfwIDAxEy5YtERQUhNDQUFSqVAkvXrzAxYsXcfXqVfz888+5rpdzNuG7776Ds7Oz2rJ69eohLCwMERERaNmyJaZNm4ZGjRohICAA/fv3h5mZGU6dOoXSpUujZ8+eCAgIQLNmzdCzZ09cu3YNNWvWRGJiIjZu3ChdGtSuXTssX74cAwcORHBwMI4ePYqNG/MOYa8yMTFB9erVMW3aNFhaWkJLSwvTpk2DiYkJEhISpH5dunTBokWL0Lx5c4wfPx5eXl64d+8eDh06hKVLl0r9LC0t0bp1a0RGRqJv376Fdp8GBoUiUpS3kv9U/TWza1GXQB8RvkYL32bjoq6ASrJfBzUr6hLeWXp6GkKCX84ApKWlBWsbO7Ro0w79B38nXd9fv1Egho4ch4iVP2PLhnWoUq0GFoWvQbMG/94IzaGcMxQKBebNnIrEJ49hamqGOvX9MHj4GKlPkxatYVzKBEsX/oBtm18GDbuyDqjn1wgWpS1R0DZu3Ihp06Zh0aJFuHXrFkxMTODl5YUePXrkuU5ERATq1q0rCwnAy+cnJCQES5cuxfPnz1GvXj0cPHgQY8aMQffu3aGpqQlPT098//330jqbNm3CxIkT8dNPP2HChAkoU6aM2sxIQUFBmDFjBhYsWICVK1eiWbNmWLJkCQICAt7qGCMiItC3b19069YNFhYWGDRoEJ4/f6523wldXV3s27cPo0ePxtSpU5GYmIiyZcvmOvtSmzZtEBkZWSiDmHMoxKvnU0iS80t4/fr1D7J9fggpfAwK9C74Gi18m41nFnUJn5ySemdmK5uyxerOzCVBQZ5RoPzp2rUrTp8+nevN5f4rPT0dN27cgJOTE/T+/6zRq972cy7PKBARERERFVPnz5/HmTNnsG7dOixatKhQ982gQERERERUTLVs2RKPHj1Ct27dCvWyI4BBgYiIiIio2Lp582aR7ZvToxIRERERkQyDAhERERERyTAoEBERERGRDMcoEBEREdFrZdy/WNQlfHJ0bT2LugSeUSAiIiIiIjkGBSIiIiIikmFQICIiIipGPB0s3/izOXJtUZeJ6N37oGfnhZt37r1V//lLf4GenRf6Dhv7gSujgsIxCkRERFTiGYU3LLJ9P+9x4J36R2zZpfY4JLgpOnfvjebBbaU2e8dyBVFaoVq3eTsAYOuufZg/dSx0dXWKuCJ6EwYFIiIiomKkctVqsjYbu7K5tn8srly7iVPnLqHRZ7Ww//Bx7Nr3G4KbBRZ1WZK0tHTo6+sVdRnFDi89IiIiIvqIdG/fGv27h6i1/X3xPDwdLPHHsd+lNk8HSyxfvAA/zpmB+lUrom5lN4weNhCpqS/U1n0Qfx/D//c16lZ2Q1VXe3Rt1xIXz51V65OVlYVvx02DjWcdWLnXQt9hY/H8Repb17xuyw4oFAr8OGMCylhaYN3mHbI+GRmZGD99PtxrB6GUUxW4+Pqj9+DRan2O/3kGzTv1gaVbTZSuUAOfteiE2ENHAQC/Hf0DenZe+OvsBbV1vug5CIHtukuPJ8/+ERau1XHy9Hk0aNkZJs5VsWTVy0u5xkz9Ab7+bWDhWh3Ovo3Qpf93iH/4SFbrrtjf4Nf6S5i5VIN1xToIbNcdZy78jaysLDj6NMC4afNk63zZbxjqNe/41s9ZccCgQERERFRCRaxajls3r2PKnAX4+n/DsGNrFJbMmy0tT0lORpe2LRB38QJGTQrD3J9WQN/AAD07tcGTx/9+QB4bNhc//bIOQ/r1wJols5GdrcLYqXPfuo71m3eibk1fODmURduWTbBr3yGkPH2m1qdjn8GYv/QXdOvQBptXLcLUscOQmpomLT968hQaf9EDGZlZWDxzItYu+wEtGjfEnXvx7/y8ZGZloduAUHRq2wJbf12CgPp1AAAJj58gdGAfbP5lEWZNHIHbd+4jsG13KJVKad3IrbvwefcBsCptjlU/TsfKBdNQu1oV3I9/CG1tbXRpH4w1G6OhUqmkdRKTUrAt5gC6dfz8nWstSrz0iD4Ztyd5F3UJnxyHceeLugQiok+apVUZzJi/BADwmZ8/Ll04h5id2zB05DgAwK/Lf8KzpylYF70HFqUtAQC16tZHM79aCP9pEb4dPR7JyUn4adV6fPtNb4QO7AMACPSri4C23XHvwcM31vDnmfO4euMW/te3GwCgQ3BzLFoRgc0796L7/39wjj10FLv2HcKqH2egQ3Azad1X/z3q+zlwKeeAPRuWQ1NT82UdDerm63nJylJiYuggfNG6qVr70jnfS//Ozs5GTd/KcKnmjwO/n0Bgg7oQQmDE5FkIaFAHG5bPl/oG+deX/t2jU1vMXrQCew/+jiaNPgPwcnyGhkJD7Xg+BjyjQERERFRC1f6sgdpjF1c3PHzw7zfwRw8fRI3a9WBiagalUgmlUgkNTU1Ur1kHF86dBgBcibuEtPR0tG7qr7atNs0C3qqG9Zt3QltbC21bNAEA1PStDCfHslgX9e/lRwePnICBvj7a/+eDe47UtDT8ceocvvyitRQS3lfTgAaytj37D8OvVWdYudeCocPLkAAAV6/fAgD8c+0G7sU/RLcObfLcrouTA+rXro5V6zZLbb+s34I2zQNRytioQGovLDyjQERERFRClSplovZYW1sbmRkZ0uOkxCc4e+pPVHa2ka2bM7PSo4QEAIClhbnacqvSFm/cv0qlQmT0LtSvXR0aGgokpzwFALRs3AgLl6/G/QcJsLW2wpOkZFiXKQ2FQpHrdpKSn0KlUsGmjOUb9/k2DPT1YWRooNb255nzaNtjIFo2bohvB/SGpYU5FAoF6rcMQfr/P2dPkpIBADbWVq/dfs/O7dBnyGg8TkzC/fiHOHPhb0wf/12B1F6YGBSIiIiIPiI6urrIyspSa3uakpKvbZmYmqGenxMGDhuZy35eTl9qafXyQ/GjJ4mwsykjLU94/OSN2z9w5AQeJDzGg4THsK5YR7Y8cusu/K9vN1iYmeLBw8cQQuQaFkxNjKGhoZHrwOIcerq6AIDMTPXnJinlKf67ydzyyNZd+2BibIQ1P82GhsbLi25u3b2v1sfCzBQAEP8gIc86ACC4aQCGjpmKtZu248btO3AuZ4/6tau/dp3iiJceEREREX1ErG1sceP6VQghpLajh97tXg05aterj2tX/oGzqyu8Kvuo/VRwrwgAcHWvCH09PWzdtU9t3c07Y9+4/fVbdsDQQB871/2MPZEr1H4qVXSTZj9qWK8WUtPSsDF6d67bMTQwQE3fylizMRrZ2dm59skJMXFXr0ttjxOTcOb8pTc/EQDS0zOgra2lFlTWRW1X61PBxQl2NmXwy4Ytr92Wrq4OQtq2xIqIjVi/eSe6tg/O82xJccYzCkREREQfkcBmLbFp3RpMGTcC/o2b4fRfJxGzc1u+ttWtz9fYvmUTun/RGl/2/Ao2dnZIfPIE58+cgmUZa3Tr3Q+mpmbo06U9Zv34M/T1dFHFuyLWb9mJGzfvvHbb6ekZ2LprH4KbBaLRZ7Xk++7YBsPGTcM/V2/Av35tBDX6DH2HjcP1W3dQvUolJCWnYPOOGKxe8nKWpu9HDUFQ+55o2qE3+nbrCFOTUjhz4W9YmJuie8fPUdbWGjWqVMKUOYthYmwMLS1NzP5xBUyMjd/qufCvXxsLfv4VQ8ZMRasgf5z46ywiNqk/rwqFAtPGfouu34SiY5/B6NyuFXR1dHD8rzOoVtkLzQL9pL49O7fDgp9/haamJrq0D36rGoobnlEgIiIi+oh85uePYaPG48DePRjYpyuuXv4b46fOyte2TM3MEbFlF9w9vTAnbBL6fNkeMyaNxb07t1HJp6rU7/tRQ9CnS3vMWRyOzv2GAQAmjxr82m3nTIHauV2rXJd3aNMc2tpaWPv/ZxXWLZuL/r1C8PPqSLTu0g+hE2fA8JVxBHVrVEVMZDgUCgX6DBmDjn0GI3rXPjjY2Up9whdOg0s5B/QZMhojJs/CN707o2plz7d6LoL862PK6KHYtmc/2vUYiCMn/sTmVT/K+n3Ruik2rliA+/EJ6NL/O3T9JhRH/zgNOxtrtX4eFVzg6lwOAQ3qqF2y9TFRiFfPW5HE2dkZAHD9+vU39Mwf3+9++SDbpbxtNp5Z1CV8cj7m6VH5Gi18fI0Wvo/1NZrX69PaWAfDAlxgZVMWGlrahVxVyeai+eZpUEndtZu34VWvOSJ+moM2zd/9LtS6tm8XcHKTnp6OGzduwMnJCXp68jtOv+3nXF56RERERERUQJ4kJuOfazcwde4SOJS1RcsmDYu6pHzjpUdERERERAVkx96DaNSmK27evofwBWHQ0vp4v5f/eCsnIiIiIipmunYIRtcOwUVdRoHgGQUiIiIiIpJhUCAiIiIiIhkGBSIiIioRVABezuXICR3p01ZQk5oyKBAREVGJ8DRNCWW2CqqszKIuhahIpaamAgC0td9vmmAOZiYiIqISIV2pwpFrTxCoowVTc0BDWweAoqjLKhEyhKqoS/jkiPT0d19HCKSmpiIhIQGmpqbQ1NR8rxoYFIiIiKjE2HXpMQCgnosSWpoaUDAnFAiheFrUJXxytF7k/2O6qakprK2t39zxTTW89xaIiIiIigkBYOelx9j3TyJM9LV4jXUBWWC4oqhL+OTYfhOdr/W0tbXf+0xCDgYFIiIiKnEylCokPONYhYKihfiiLuGTo6enV9QlFL+gHRcXh8DAQBgaGsLa2hqhoaHIzHzzC/3Jkyfo168fHBwcYGhoCC8vLyxZsqQQKiYiIiIiKnmK1RmFpKQkNGrUCK6uroiKisK9e/cwdOhQpKamYuHCha9d94svvkBcXBymTp0KBwcH7Ny5E19//TU0NTXRp0+fQjoCIiIiIqKSoVgFhSVLluDp06fYvHkzzM3NAQBKpRL9+/fHqFGjYGtrm+t6Dx48wIEDBxAeHo7u3bsDABo1aoSTJ09i3bp1DApERERERO+oWF16tGvXLgQEBEghAQDat28PlUqFmJiYPNfLysoCAJiYmKi1m5iYFNgNJ4iIiIiIPiXF6oxCXFwcevbsqdZmamoKGxsbxMXF5bmevb09GjdujKlTp8LNzQ329vbYtWsXYmJisGbNmjzXc3Z2znPZnTt3YG9v/+4HQURERERUAhSroJCUlARTU1NZu5mZGRITE1+7blRUFDp06ABPT08AgKamJhYsWIC2bdt+iFKJiIiIiEq0YhUU8ksIgR49euDKlSuIiIiAjY0N9u7di8GDB8PMzAwdO3bMdb3r16/nuc3XnW0gIiIiIirpilVQMDMzQ0pKiqw9KSlJbdzCf+3YsQORkZE4d+4cvL29AQB+fn5ISEjAsGHD8gwKRERERESUu2I1mNnd3V02FiElJQXx8fFwd3fPc71Lly5BU1MTXl5eau1VqlTB/fv3kZqa+kHqJSIiIiIqqYpVUGjatCliY2ORnJwstUVGRkJDQwONGzfOcz1HR0dkZ2fj3Llzau1//fUXrKysYGBg8KFKJiIiIiIqkYpVUOjXrx+MjY0RHByMmJgYhIeH47vvvkO/fv3U7qHg7++P8uXLS4+bNWsGBwcHtGvXDqtXr8a+ffswfPhwrFy5EgMHDiyKQyEiIiIi+qgVuzEK+/btw8CBAxEcHAxjY2P07t0bU6ZMUeuXnZ0NpVIpPTY2Nsa+ffswevRoDB8+HMnJyXBycsKcOXMwYMCAwj4MIiIiIqKPXrEKCgDg4eGB2NjY1/Y5ePCgrK18+fJYv379B6qKiIiIiOjTUqwuPSIiIiIiouKBQYGIiIiIiGQYFIiIiIiISIZBgYiIiIiIZBgUiIiIiIhIhkGBiIiIiIhkGBSIiIiIiEiGQYGIiIiIiGQYFIiIiIiISIZBgYiIiIiIZBgUiIiIiIhIhkGBiIiIiIhkGBSIiIiIiEiGQYGIiIiIiGQYFIiIiIiISIZBgYiIiIiIZBgUiIiIiIhIhkGBiIiIiIhkGBSIiIiIiEiGQYGIiIiIiGQYFIiIiIiISIZBgYiIiIiIZBgUiIiIiIhIhkGBiIiIiIhkGBSIiIiIiEiGQYGIiIiIiGQYFIiIiIiISIZBgYiIiIiIZBgUiIiIiIhIhkGBiIiIiIhkGBSIiIiIiEiGQYGIiIiIiGQYFIiIiIiISIZBgYiIiIiIZBgUiIiIiIhIhkGBiIiIiIhkGBSIiIiIiEiGQYGIiIiIiGQYFIiIiIiISIZBgYiIiIiIZBgUiIiIiIhIhkGBiIiIiIhkGBSIiIiIiEiGQYGIiIiIiGQYFIiIiIiISIZBgYiIiIiIZBgUiIiIiIhIhkGBiIiIiIhkGBSIiIiIiEiGQYGIiIiIiGQYFIiIiIiISIZBgYiIiIiIZBgUiIiIiIhIhkGBiIiIiIhkGBSIiIiIiEiGQYGIiIiIiGQYFIiIiIiISIZBgYiIiIiIZPIVFE6cOFHQdRARERERUTGSr6BQu3ZtVKhQAZMnT8b169cLuiYiIiIiIipi+QoKq1evhqurKyZPngxXV1fUrVsXS5YsQWJi4nsXFBcXh8DAQBgaGsLa2hqhoaHIzMx8q3Xv3buHbt26wdLSEvr6+vDw8MCaNWveuyYiIiIiok9NvoJCSEgIduzYgfv372PevHkQQqB///6wtbVFcHAwNm7c+NYf7l+VlJSERo0aITMzE1FRUZg6dSqWLl2KoUOHvnHd+Ph41K5dG/fv38fSpUuxfft2fP3118jIyMjPIRIRERERfdK03mfl0qVLY8CAARgwYACuXbuGiIgIrFmzBh06dICJiQnatWuHrl27ol69em+1vSVLluDp06fYvHkzzM3NAQBKpRL9+/fHqFGjYGtrm+e6oaGhsLe3x+7du6GpqQkA8Pf3f5/DIyIiIiL6ZBXYrEf6+vowMDCAnp4ehBBQKBTYunUrGjRogOrVq+PSpUtv3MauXbsQEBAghQQAaN++PVQqFWJiYvJc7+nTp9iwYQP69+8vhQQiIiIiIsq/9woKz549Q3h4OAICAuDo6IhRo0ahXLly2LhxIx48eID79+9j/fr1SEhIQI8ePd64vbi4OLi7u6u1mZqawsbGBnFxcXmud+rUKWRmZkJbWxsNGjSAtrY2rK2tMXz4cGRlZb3PIRIRERERfZLydenR1q1bsWbNGmzfvh3p6emoXr065s6di44dO8LCwkKtb7t27ZCUlIRvvvnmjdtNSkqCqamprN3MzOy1A6UfPHgAAOjduzf69OmDCRMm4I8//sC4ceOgoaGBsLCwXNdzdnbOc5t37tyBvb39G2smIiIiIiqJ8hUU2rRpA3t7ewwZMgRdu3aFm5vba/tXrlwZnTt3zleBb0OlUgEAAgICMHv2bABAw4YN8ezZM8yaNQvjxo2Dvr7+B9s/EREREVFJk6+gsH//fvj5+b11/xo1aqBGjRpv7GdmZoaUlBRZe1JSktq4hdzWA4BGjRqptfv7+2PKlCm4evUqvL29Zeu97h4QrzvbQERERERU0uVrjMK7hIR34e7uLhuLkJKSgvj4eNnYhVdVrFjxtdtNT08vkPqIiIiIiD4V+QoKY8aMgY+PT57Lq1SpgokTJ77zdps2bYrY2FgkJydLbZGRkdDQ0EDjxo3zXM/R0RHe3t6IjY1Va9+7dy/09fXfGCSIiIiIiEhdvoLCxo0b0bRp0zyXN2vWDOvXr3/n7fbr1w/GxsYIDg5GTEwMwsPD8d1336Ffv35q91Dw9/dH+fLl1dadMmUKoqOjMXjwYOzduxdTp07FrFmzMHToUBgaGr5zLUREREREn7J8BYXbt2/DxcUlz+VOTk64devWO2/XzMwM+/btg5aWFoKDgzFixAj07t0bc+bMUeuXnZ0NpVKp1tayZUusXbsWsbGxaNGiBZYuXYqJEydi8uTJ71wHEREREdGnLl+DmY2MjF4bBG7cuAE9Pb18FeTh4SG7hOi/Dh48mGt7hw4d0KFDh3ztl4iIiIiI/pXvwcw//fQT7t27J1t2584dLF26FA0bNnzv4oiIiIiIqGjk64zC5MmTUaNGDXh6eqJXr17w9PQEAFy4cAErVqyAEIKX/BARERERfcTyFRTc3Nxw+PBhDBw4ED/88IPasvr162P+/Pnw8PAokAKJiIiIiKjw5SsoAEClSpXw22+/4fHjx9KNy5ydnVG6dOkCK46IiIiIiIpGvoNCjtKlSzMcEBERERGVMO8VFO7evYvTp08jJSUFKpVKtrxr167vs3kiIiIiIioi+QoK6enp6NatGzZt2gSVSgWFQgEhBABAoVBI/RgUiIiIiIg+TvmaHnXUqFGIiorClClTcPDgQQghsGrVKsTExKBp06aoXLkyzp49W9C1EhERERFRIclXUNi4cSN69OiB4cOHS1Oj2tnZISAgANu3b4epqSl+/PHHAi2UiIiIiIgKT76CQkJCAmrUqAEA0NfXBwC8ePFCWt62bVtERUUVQHlERERERFQU8hUUypQpgydPngAADAwMYGZmhsuXL0vLnz59ivT09IKpkIiIiIiICl2+BjPXrFkTR44cwfDhwwEALVu2xMyZM2FjYwOVSoUffvgBtWrVKtBCiYiIiIio8OTrjMKgQYPg7OyMjIwMAMDkyZNhamqKLl26oFu3bjAxMcH8+fMLtFAiIiIiIio8+TqjUK9ePdSrV096bG9vj7///hvnz5+HpqYm3N3doaX13vdyIyIiIiKiIvLOZxRSU1Px+eefY82aNeob0tBA5cqV4eXlxZBARERERPSRe+egYGBggNjYWKSmpn6IeoiIiIiIqBjI1xiFevXq4dixYwVdCxERERERFRP5CgoLFy7E4cOHMWbMGNy9e7egayIiIiIioiKWr6BQuXJl3L17F2FhYXB0dISuri5KlSql9mNiYlLQtRIRERERUSHJ16jjtm3bQqFQFHQtRERERERUTOQrKKxcubKAyyAiIiIiouIkX5ceERERERFRyZavMwq//PLLW/Xr2rVrfjZPRERERERFLF9BoXv37nkue3XsAoMCEREREdHHKV9B4caNG7K27Oxs3Lx5E4sWLcLt27exatWq9y6OiIiIiIiKRr6CgqOjY67tzs7OaNSoEZo3b46FCxfixx9/fK/iiIiIiIioaHyQwcwtWrTA+vXrP8SmiYiIiIioEHyQoHDt2jVkZGR8iE0TEREREVEhyNelR4cOHcq1PTk5GYcOHcL8+fMRHBz8PnUREREREVERyldQ8PPzy/XOzEIIaGpq4osvvsCCBQveuzgiIiIiIioa+QoKBw4ckLUpFAqYmZnB0dERpUqVeu/CiIiIiIio6OQrKDRo0KCg6yAiIiIiomIkX4OZb9y4gW3btuW5fNu2bbh582Z+ayIiIiIioiKWrzMK3377LZ4+fYqWLVvmuvzHH3+Eqakp1q1b917FERERERFR0cjXGYVjx44hMDAwz+X+/v44fPhwvosiIiIiIqKila+gkJSUBGNj4zyXGxkZ4cmTJ/kuioiIiIiIila+goKDgwN+//33PJcfPnwYZcuWzXdRRERERERUtPIVFDp16oS1a9di/vz5UKlUUnt2djbmzZuH9evXIyQkpMCKJCIiIiKiwpWvwcwjR47EkSNHMHjwYEyZMgVubm4AgMuXL+PRo0fw8/PD6NGjC7RQIiIiIiIqPPk6o6Crq4uYmBgsX74cNWrUwOPHj/H48WPUqFEDK1asQGxsLHR1dQu6ViIiIiIiKiT5OqMAABoaGujRowd69OhRkPUQEREREVExkK8zComJiTh37lyey8+fP4+kpKR8F0VEREREREUrX0FhyJAh+Oqrr/Jc3rdvX3z77bf5LoqIiIiIiIpWvoLC/v370apVqzyXt2zZErGxsfkuioiIiIiIila+gsKjR49QunTpPJdbWFggISEh30UREREREVHRyldQsLGxwenTp/Nc/tdff8HS0jLfRRERERERUdHKV1AIDg7G8uXLER0dLVu2detWhIeHo02bNu9dHBERERERFY18TY86YcIExMbGok2bNqhcuTK8vLwAABcuXMCZM2dQsWJFTJw4sUALJSIiIiKiwpOvMwomJiY4fvw4xowZg6ysLGzcuBEbN25EVlYWxo0bhz/++ANCiIKulYiIiIiICkm+ggIAGBoaYuLEiTh//jxSU1ORmpqKkydPwtPTEyEhIbCxsSnIOomIiIiIqBDl+87MOYQQ2LdvH9asWYPNmzfj2bNnKF26NEJCQgqiPiIiIiIiKgL5Dgp//fUX1qxZg3Xr1uHBgwdQKBTo2LEjBgwYgFq1akGhUBRknUREREREVIjeKShcv34da9aswZo1a3DlyhXY2dmhc+fOqFGjBjp06IC2bduidu3aH6pWIiIiIiIqJG8dFGrXro0//vgDpUuXRrt27fDzzz+jXr16AIBr1659sAKJiIiIiKjwvXVQOHHiBJycnDBnzhw0b94cWlrvPbyBiIiIiIiKqbee9WjhwoWwsbFBmzZtYG1tjb59++LAgQOcBpWIiIiIqAR666DQv39/HDlyBNeuXcPgwYNx+PBh+Pv7w87ODuPGjYNCoeAAZiIiIiKiEuKd76Pg5OSEMWPG4NKlSzh58iQ6duyIgwcPQgiB/v3746uvvsL27duRnp7+IeolIiIiIqJCkO8brgGAr68v5syZgzt37iAmJgZNmjTB+vXr0apVK5QuXbqgaiQiIiIiokL2XkFB2oiGBgICArBy5Uo8fPgQa9euhb+/f0FsmoiIiIiIikCBBIVX6enpoUOHDti6dWtBb5qIiIiIiApJgQeF9xUXF4fAwEAYGhrC2toaoaGhyMzMfKdtzJ07FwqFAi1atPhAVRIRERERlWzF6mYISUlJaNSoEVxdXREVFYV79+5h6NChSE1NxcKFC99qGw8ePMDEiRNhZWX1gaslIiIiIiq5ilVQWLJkCZ4+fYrNmzfD3NwcAKBUKtG/f3+MGjUKtra2b9xGaGgoWrVqhVu3bn3ocomIiIiISqxidenRrl27EBAQIIUEAGjfvj1UKhViYmLeuP6RI0ewZcsWTJs27UOWSURERERU4hWrMwpxcXHo2bOnWpupqSlsbGwQFxf32nWzs7MxYMAAjB49GjY2Nm+1P2dn5zyX3blzB/b29m+1HSIiIiKikqZYnVFISkqCqamprN3MzAyJiYmvXXfRokV48eIFhgwZ8oGqIyIiIiL6dBSrMwr5lZCQgHHjxuGXX36Bjo7OW693/fr1PJe97mwDEREREVFJV6yCgpmZGVJSUmTtSUlJauMW/mvcuHGoVKkSPvvsMyQnJwN4OQhaqVQiOTkZRkZG0NIqVodKRERERFSsFatPz+7u7rKxCCkpKYiPj4e7u3ue68XFxeHQoUMwMzOTLTMzM8OuXbsQFBRU4PUSEREREZVUxSooNG3aFFOnTkVycrI0ViEyMhIaGhpo3LhxnuvNnTtXOpOQY/DgwdDX10dYWBgqVar0AasmIiIiIip5ilVQ6NevHxYsWIDg4GCMGjUK9+7dw3fffYd+/fqp3UPB398ft27dwtWrVwEAPj4+sm2ZmprCyMgIfn5+hVQ9EREREVHJUaxmPTIzM8O+ffugpaWF4OBgjBgxAr1798acOXPU+mVnZ0OpVBZRlUREREREJV+xOqMAAB4eHoiNjX1tn4MHD75xO2/Th4iIiIiIcleszigQEREREVHxwKBAREREREQyDApERERERCTDoEBERERERDIMCkREREREJMOgQEREREREMgwKREREREQkw6BAREREREQyDApERERERCTDoEBERERERDIMCkREREREJMOgQEREREREMgwKREREREQkw6BAREREREQyDApERERERCTDoEBERERERDIMCkREREREJMOgQEREREREMgwKREREREQkw6BAREREREQyDApERERERCTDoEBERERERDIMCkREREREJMOgQEREREREMgwKREREREQkw6BAREREREQyDApERERERCTDoEBERERERDIMCkREREREJMOgQEREREREMgwKREREREQkw6BAREREREQyDApERERERCTDoEBERERERDIMCkREREREJMOgQEREREREMgwKREREREQkw6BAREREREQyDApERERERCTDoEBERERERDIMCkREREREJMOgQEREREREMgwKREREREQkw6BAREREREQyDApERERERCTDoEBERERERDIMCkREREREJMOgQEREREREMgwKREREREQkw6BAREREREQyDApERERERCTDoEBERERERDIMCkREREREJMOgQEREREREMgwKREREREQkw6BAREREREQyDApERERERCTDoEBERERERDIMCkREREREJMOgQEREREREMsUuKMTFxSEwMBCGhoawtrZGaGgoMjMzX7tOfHw8QkND4ePjA2NjY5QtWxYhISG4detWIVVNRERERFSyaBV1Aa9KSkpCo0aN4OrqiqioKNy7dw9Dhw5FamoqFi5cmOd6f/31F6KiotCzZ0/UqlULjx8/xuTJk1GjRg1cuHABlpaWhXgUREREREQfv2IVFJYsWYKnT59i8+bNMDc3BwAolUr0798fo0aNgq2tba7r1atXD3FxcdDS+vdw6tSpAwcHB/zyyy8YNmxYodRPRERERFRSFKtLj3bt2oWAgAApJABA+/btoVKpEBMTk+d6pqamaiEBAMqWLQtLS0vcv3//g9VLRERERFRSFaszCnFxcejZs6dam6mpKWxsbBAXF/dO2/rnn3+QkJAADw+PPPs4OzvnuezOnTuwt7d/p30SEREREZUUxeqMQlJSEkxNTWXtZmZmSExMfOvtCCEwaNAg2NraolOnTgVYIRERERHRp6FYnVEoKBMmTMC+ffuwe/duGBoa5tnv+vXreS573dkGIiIiIqKSrlgFBTMzM6SkpMjak5KS1MYtvM6yZcswadIkLF++HP7+/gVdIhERERHRJ6FYXXrk7u4uG4uQkpKC+Ph4uLu7v3H9zZs34+uvv8akSZNkYx2IiIiIiOjtFaug0LRpU8TGxiI5OVlqi4yMhIaGBho3bvzadQ8ePIhOnTqhT58+GDt27AeulIiIiIioZCtWQaFfv34wNjZGcHAwYmJiEB4eju+++w79+vVTu4eCv78/ypcvLz3++++/ERwcDFdXV3Tp0gXHjx+Xfq5du1YUh0JERERE9FErdmMU9u3bh4EDByI4OBjGxsbo3bs3pkyZotYvOzsbSqVSenzixAmkpKQgJSUFdevWVevbrVs3rFy5sjDKJyIiIiIqMYpVUAAADw8PxMbGvrbPwYMH1R53794d3bt3/3BFERERERF9YorVpUdERERERFQ8MCgQEREREZEMgwIREREREckwKBARERERkQyDAhERERERyTAoEBERERGRDIMCERERERHJMCgQEREREZEMgwIREREREckwKBARERERkQyDAhERERERyTAoEBERERGRDIMCERERERHJMCgQEREREZEMgwIREREREckwKBARERERkQyDAhERERERyTAoEBERERGRDIMCERERERHJMCgQEREREZEMgwIREREREckwKBARERERkQyDAhERERERyTAoEBERERGRDIMCERERERHJMCgQEREREZEMgwIREREREckwKBARERERkQyDAhERERERyTAoEBERERGRDIMCERERERHJMCgQEREREZEMgwIREREREckwKBARERERkQyDAhERERERyTAoEBERERGRDIMCERERERHJMCgQEREREZEMgwIREREREckwKBARERERkQyDAhERERERyTAoEBERERGRDIMCERERERHJMCgQEREREZEMgwIREREREckwKBARERERkQyDAhERERERyTAoEBERERGRDIMCERERERHJMCgQEREREZEMgwIREREREckwKBARERERkQyDAhERERERyTAoEBERERGRDIMCERERERHJMCgQEREREZEMgwIREREREckwKBARERERkQyDAhERERERyTAoEBERERGRTLELCnFxcQgMDIShoSGsra0RGhqKzMzMN64nhMC0adPg4OAAfX191K5dG8ePHy+EiomIiIiISp5iFRSSkpLQqFEjZGZmIioqClOnTsXSpUsxdOjQN647ffp0jB8/HkOGDMH27dthY2ODxo0b4/r164VQORERERFRyaJV1AW8asmSJXj69Ck2b94Mc3NzAIBSqUT//v0xatQo2Nra5rpeeno6wsLCMGzYMAwZMgQA8Nlnn6FChQqYNWsWFi1aVGjHQERERERUEhSrMwq7du1CQECAFBIAoH379lCpVIiJiclzvaNHj+Lp06do37691Kajo4PPP/8cO3fu/KA1ExERERGVRMUqKMTFxcHd3V2tzdTUFDY2NoiLi3vtegBk63p4eOD27dtIS0sr+GKJiIiIiEqwYnXpUVJSEkxNTWXtZmZmSExMfO16urq60NPTk60nhEBSUhL09fVl6zk7O+e5zRs3bkBLS+u1fd7HvcTnH2S7lLd6GslFXcInR2vlh3n9FAa+RgsfX6OF72N9jfL1Wfj4+ix8H/L1eefOHWhpvTkGFKugUJwoFIq3egLzy87c6INtm+Tu3LmDeAD29vZFXQp9JPgaLVx8jdK74OuzcPH1WfJoaWlBV1f3zf0KoZa3ZmZmhpSUFFl7UlKS2riF3NbLyMhAenq62lmFpKQkKBQKmJmZ5boeZ0T6dOScGeL/c6Liia9RouKLr89PV7Eao+Du7i4bi5CSkoL4+HjZ+IP/rgcAly9fVmuPi4uT7qtARERERERvr1gFhaZNmyI2NhbJyclSW2RkJDQ0NNC4ceM816tTpw5KlSqFyMhIqS0rKwtRUVFo1qzZhyyZiIiIiKhEKlZBoV+/fjA2NkZwcDBiYmIQHh6O7777Dv369VO7h4K/vz/Kly8vPdbT08PIkSMxa9YszJs3D/v370enTp3w5MkTfPvtt0VxKEREREREH7ViN0Zh3759GDhwIIKDg2FsbIzevXtjypQpav2ys7OhVCrV2oYPHw4hBGbNmoVHjx7Bx8cHe/bs+WCzFhERERERlWQKIYQo6iKIPjQOxCIq3vgaJSq++Pr8dBWrS4+IiIiIiKh44BkFIiIiIiKS4RkFIiIiIiKSYVAgIiIiIiIZBgUiIiIiIpJhUCAiIiIiIhkGBSIiIiIikmFQICKij45SqYRKpSrqMoiISjQGBaJPhBACSqUSnBGZPnZRUVGwsLDA1atXi7oUIqISjUGBqARTqVTSt64KhQJaWlpQKBTScoYGKs6uXbuGDRs2IDs7W63d2dkZz58/x+XLl4uoMiIqjoQQyM7OVntv4/vc+2FQICrBNDQ0oKHx8mV++/ZthIaGonHjxhgxYgQePnyoFhqIitqzZ8+kfz98+BCurq7o2LEjxo4di6dPn0rLXF1d4eLiggMHDshCBBF9uhQKBTQ1NaFQKHDnzh1cvnyZ73PviUGB6CPx4sULXLt2DQDUPhxlZ2erXav96rcnaWlpCAoKwpIlSzB16lScOnUKZcuWxcKFC9GtWzc8evSo8A6A6DXCw8NhZ2eHs2fPAgBKly6NgIAAAMCiRYuwaNEiqa+hoSHq16+PgwcPqoULIirZci6hzWt80osXLxAWFoayZcvCy8sLbdq0wcSJE/Hw4cNCrrTkYFAg+ghcv34dDRs2xMyZMwEAmpqa0jJNTU1oaGjg8ePH+Ouvv6R2IQT09fVx8uRJhIaGIiUlBcuXL8eKFSswd+5cnD59GlFRUVJfosLw999/Y9myZYiPjwcAZGVlAQAsLS1hYWGBQ4cOAXj5e12zZk3Y2Njg888/x+zZs6XffwAIDAzE33//jbt37xb+QRDRB5GSkvLa5TmX0OacKc+R8x62Zs0ahIeHo3///oiJiUHz5s2xYsUKjB49+oPVXOIJIir2srOzxdWrV4VSqZQt27Jli/D29hZ6enrC1tZWBAQEiEOHDknLhw4dKhQKhdi6davUdvfuXeHv7y8aNmwobZ/oQ1AqlSI8PFxMmDBBREVFCRcXF6FQKMTXX38tnj59KvW7fv26qF+/vmjVqpXUFhsbK/T09MSuXbvEoEGDhLa2tvjtt9+EEELcuHFD6OrqipUrVxb6MRFRwXjy5IlYvny5aNasmTA0NBRDhw4V6enpQgghVCqV1C/n33FxcWLs2LGiRYsW4ocffhDXrl2T+ty6dUt4enqKTp06iaysLKl9165dwsDAQJw9e7aQjqpk4RkFomImOzsbSqVSrU1DQwMuLi64cuWK9E0sAFy8eBGjR49GhQoVcOjQIXz//fdISEhAz5498fvvvwOAdPlGRkaGtJ6VlRVq166NY8eOSdsnKgg5ZwgyMzMBAH/88Qf69+8PDQ0N1KpVC9WqVQMAxMbGIjQ0VFrP3t4eXl5eOH/+vPT77+vrC21tbZw5cwZz5syBn58fevTogcOHD6NcuXLw9PTE/v37pX0SUfGmVCqxY8cOdOvWDe7u7ihdujTGjBmDMmXKYP78+Rg+fDh0dXUBQBpncPPmTSgUCuzfvx+tWrVCTEwMSpUqhXnz5iEwMBCXLl0C8PKyo0uXLmHSpEnQ0tJCZmYmtm3bhtWrVyMtLQ0HDhyQvbfSm/HTAVExo6mpCS0tLQDAkydPpGuwY2JiULt2bfz4449S340bN+LGjRtYuHAhqlWrhh49emDz5s1ITU3Fr7/+CgBo1KgRgJczyIj/Pz2rra2NqlWrIiMjAydOnADAy4/o/WRlZaF3795SMM35HZ4+fTpcXV0xbNgw2NjYwM3NDcbGxujRowfCw8OxYsUKZGVlQUtLC56ennj+/DmOHz8OADA1NUW1atVw4MABaGhoYNGiRbCzs8OgQYNw9uxZtGnTBocOHUJSUlKRHTcRvZ3Ro0dDR0cHXbt2xYsXL2BtbQ0HBwesWLECK1asQM+ePWFqaopTp07h4cOHqFmzJnx9fXHgwAG8ePECvXr1gpubG9atW4elS5fixIkTMDMzw7hx45Ceng6VSgVdXV2EhobC29sb+vr66NatG548eYK5c+eiTZs2apft0tthUCAqZOL/B2PlNltLdnY2du/ejY4dO6JcuXJo3bo1Nm7cCODlTC9VqlTBhQsXAACpqak4e/YsPDw8YG1tDYVCAZVKBWdnZ/j5+eHPP//EnTt3oKurCy8vLxw9ehRPnjyR9uXq6gpHR0dER0dLdRHll7a2NgwMDHDu3DkIIaChoYGoqChER0djwoQJMDAwAAB4enoiKysLderUwbBhwzB8+HCsWrUKAODt7Q0TExPExMRI223atClOnz6NuLg4lC9fHj/99BOUSiV69+6NSpUq4c6dO7h+/XqRHDMRvVnOe12HDh2wf/9+XLhwARs3bsSkSZPw4sUL3LhxA8DLLxumT5+OatWqYeTIkahZsyZ27tyJoKAgnDhxAvfu3cPChQtRrlw5GBoawsrKCkFBQdi9ezeOHz8OS0tLVK5cGQcPHkTHjh2xf/9+XLlyBbt27cKgQYPg4ODAGZDygUGBqJDlDMbS1NSEEEJt1pbt27ejX79+UKlUCA0NRd++fVGqVCkAgJOTE1xdXXHp0iUkJSXBwMAAmZmZMDY2xv379wH8+wfZ09MTT58+lT5ANWvWDKdOncK9e/ekfdna2sLNzQ3Lly8vrEOnEq5JkyZIS0uTzlKNGTMGrVu3RqtWraQ+7u7usLW1xc6dOzFlyhQ0b94co0ePxr59+1CtWjU4OztLA5qBl5fOPX78GP/88w8AwMPDA6tWrcLly5cxb948qFQqnDt3jndpJiqmcr7Fr1SpEvz8/GBjYwMAqFOnDgwNDXHu3DlkZWVBW1sbVapUgZWVFY4ePYoBAwagWrVqsLGxwdmzZ+Hs7IxLly4hMjISrVu3hqWlJWbOnImKFSsCACwsLFCnTh0YGBigd+/eaNCgAczNzZGeno6YmBh07NiRfyfygUGB6AP475Slr7px4wa+//571KpVC76+vpg7d660bNiwYahduzaWL1+O/v37o0uXLmjbtq20vFKlSkhNTcWRI0ekx3fv3sX58+fV9mFvb4/Hjx/D1NQUANCuXTvcv38fFy9elPqYmZmhW7du+N///geA4xTo/VWqVAlWVlbYtWsXNmzYIN27Q1NTU7o22N7eHtWqVcOWLVsAAD/88AO8vb3RtWtXPHz4ED4+Prh69ap0OZGHhwfKlCmDY8eOSeMeqlatilmzZuHBgwcAXo53eP78eeEfMBEBeHlzz//e6Ox1srOzoaWlBW9vb1y8eBG3b98G8PJmihYWFvD09ESFChWk91FHR0dcv34dzZs3x+DBg6Gjo4MZM2bgwoULOHbsGPz8/KCpqYnhw4fDzMwMDRo0QFhYGH766Sd89dVXGD16NPT19dXux0Jvh58MiApQzh/JnClL/+v27dvo3LkzoqKi4Ofnh379+sHExESaEk5PTw+ampq4du0a0tLSkJaWhocPH0p/LCtVqoRSpUrht99+AwC0bNkSQgisWLECwMvrwh8/foz169ejVKlSqFy5MgBIA0hf3ZZCoUCnTp0wcuTID/iM0KekTJkyqF69On7++WcsXLgQHTp0QO3ataFSqaQxC2ZmZvD19cWtW7cQHx8PMzMzhIeHQ09PDwMGDIBCoYCenp70O66np4f69evj2LFjalMnfvXVVxgxYgQMDQ3h6ekJbW3tIjlmok+VUqmU3vM0NDSkG529i0aNGuHWrVuIi4sDAFhbW8PHx0c6g5iz/Tp16qBs2bL4/PPPcffuXURGRqJHjx5wdXVFfHw89uzZg+fPn6NMmTKIjIxEq1atsHfvXkyZMgVJSUkYOnQo5s+fL315Ru+giGZbIvroZWdnq01XmjN924sXL0RkZKT47rvvxJYtW8SzZ8+kPn379hVOTk7izJkzQgihNoWbEEIsXrxY2NjYCCcnJ2FpaSn09fVFzZo1xZw5c4QQQjx8+FAEBQWJ+vXrCyFeTj25YMECoVAoRFBQkFi8eLHo1auXsLOzE9u3b5f6CCHUpqJ8VW5TrhLl1/fffy+0tbWFQqEQFSpUEHv37pV+z3N+13bu3ClMTEzEL7/8Iq23e/duUb16dWFlZSWcnZ3Ft99+Ky1btGiRUCgU4o8//ijcgyGit3Lu3DkxfPhw0a9fP7XpuV+d4vRVOe1nz54VpUuXFtOnTxdCvPwbsXDhQqGlpSUeP36sts7w4cOFtbW1WLt2rbh27Zq4f/++2L17t/jyyy/F0KFDxcOHD9X6JyUlFeARfroYFIjeU0ZGhkhNTRVCCLF//35RuXJlYWdnJ+rVqyfMzc1F+/btRVxcnBBCiK+//lp4e3uLkydPSus8ffpU7YPUtWvXxOLFi0VkZKTYvHmzaNeunVAoFOLixYtCCCGGDRsmypcvL27fvi3VsGrVKtGqVStRtmxZUbduXbF27Vq1EJLzRzmvP9pEBeXAgQPC1tZWfP3116J169ZCoVCIr776Sm2+87i4OOHr6yu++OILIcS/9/E4dOiQMDIyEgqFQri6ukr97927J2bMmCGSk5ML92CIPhE570evUqlUIisrK8/77Ozfv18MHTpUREZGisDAQFG3bl1RuXJlYWVlJRYvXiyEeLt79Li7u4suXbqI58+fCyGEOHjwoNDR0RGbNm0SQvz7BUN6eroYOHCgsLa2FlWqVBH29vbCyMhItGrVShw8eJD3A/pAGBSI8vC6D9eJiYkiLi5OhISECAMDA7Fp0yaRmpoqfH19RUBAgPjnn39EUlKSiIyMFFWrVhVt2rQRQgjxxx9/CEtLS1GuXDnh7u4uzM3NhZeXl5g0aZLIzMzMtY7Tp08LbW1tsXHjRiGEEEuXLhW6urpi+fLlQoh//xDn/JElKkoJCQnC3d1dDBkyRAghxMyZM4WhoaGwsrISS5YsEUK8DNdfffWVcHR0lK2/atUqUbNmTTFq1CiRkZFRmKUTfZKWLFkibG1txZ9//plnn+TkZJGQkCCE+PdM+ObNm4VCoRA+Pj5i2rRp4unTp+Lq1auiRYsWwsXF5Y37zQkAX375pfD19RXnzp0TQry8+WKVKlVE7969hRAv3+NefT++efOmWLx4sdi8ebPaGXv6MBgUiF6hUqly/Vbi1T9GQ4YMEXZ2duLrr78WHTt2FBs2bBD3798XMTExQl9fX/pjl2P+/PlCoVCIJ0+eCCFefjv6448/ipkzZ4o1a9aIPn36CIVCIZYtWyaEePmB/8KFC+L+/fsiLi5OdOvWTVSpUkWcP39eCCHExYsXxbRp06QzDP+lVCp5OREVGaVSKTp37iycnZ2ltlu3bong4GChUCiEv7+/iIuLE2FhYUKhUIgLFy4IIXi2i6iw5bzXXb16VZw+fVqoVCq112FqaqqYMWOGcHJyEhYWFqJmzZpi2rRp0vLMzEyhra0tPDw81La7du1aoampKY4fP662n//KeZ9atWqVKFu2rIiMjBRCCJGSkiK6du0q9PT0Cu5gKd8YFIhykZaWJg4cOCBOnz4tvv/+e7VvR6Kjo4VCoRAVK1YU//zzj9S+YsUKYWhoKLKyssTZs2fFyJEjReXKlYWOjo4wNTUVJ06cyHVfiYmJomrVqqJnz55CiJffwHp5eYkaNWoIIyMj4eXlJZ2CJfoYLFq0SOjp6Ym7d+8KIf4NAatXrxa2trbC3t5e/PTTT2Lt2rUiJSWlKEslKrFyPoi/+uFfqVTKxsbluHnzplr/8PBwYWNjI0aPHi2io6NFSEiIUCgUIiwsTBrzVrVqVVGrVi1x//59aTvnz58XDg4OYvjw4UII+Vi8HDn7uXXrljAwMBCDBw+Wlu3Zs0fMnDkzzzPtVHi0inowNVFhUqlUajOw/JcQAhMnTsSMGTNga2sLDw8PXL16FQ8ePMC9e/dgZ2eHGjVqwNDQEK6urnB1dZXWzczMhI6ODvT09KCtrY2KFSsiKCgIc+fOhZeXF0qXLi31vX37NvT19aFSqbBp0yY8evQI9evXBwC0bdsWWVlZMDExgZ+fnzRH9H/rFP9/Uyui4qZOnTrS7/agQYOkqRA7d+6MJk2aqL0WiKhgHTt2DH379sWoUaPQsWNHtZmIXr0zcWpqqnQjxMGDB+PXX3/F1atXYWZmhszMTIwaNQr+/v4YO3YsdHR00LJlSxgaGiI8PBxVqlRBkyZNEBgYiF9//RUPHz6U7o9ga2uLatWqYceOHZg2bVqeMyHltDs4OKBly5aoUqUKlEoltLS00LhxYzRu3PhDPUX0Dvgpg0qMFy9eYNeuXVi3bh1u3ryZax8NDQ0pJLx6l+Icx48fx9y5czF06FDs27cPo0aNQoUKFZCamorjx48DeDkFpJeXF1QqlTTXO/DvjaSaNm2Kp0+f4sSJE5g2bRr8/Pygq6uLkydPAgD279+P1q1bo2fPnvD19cXEiRPRuXNnfPnllwBe3lht5MiR6N+/vxQS/nsXZ4VCwZBAxVbOXcUNDQ0BQC2YMyQQfRji/6cSdXNzw8CBA9GoUSO15enp6YiKikLHjh1RpUoVDB06VLoJp7OzM4yMjKT3uZz7ltSvXx+6urrIysoCAHTp0gUKhQIHDhwAAAQHByM+Pl6azhR4OQVynTp1EBcXh/T0dLVwkpd169aha9eueX6JR0WHnzToo3br1i0MHjwYbm5uMDExQc+ePfHtt9+icePGWLVqFQCo3QDmzJkz6Nq1K8qUKYM6depg8ODBePjwobT8119/hZ2dHb766is4Ojqidu3aGD9+PJydnbF9+3apn7+/P86ePasWFKpUqYLAwEAcO3YM169fx7Nnz/D06VP88ccf+P7777FgwQJkZWWhRo0a6NChA7y8vDB//nxcu3YNYWFhsj+mr9685m3+0BIVFyYmJtiwYQN69epV1KUQlXjZ2dnIzs6WvqE3NzdHnz59YGVlJX3JpFQqERoaiv/973/Q0NBAp06d4OjoiMePHwN4eRNDQ0ND7Nu3DwCks+M573E5H+B9fHxQqlQp3L17FwBQq1Yt6Onp4dSpU1KYUCgU8PLyQnZ2tvS+yTsif7wY3eijJYRATEwM5s+fj6FDh+KLL76ApaUlrly5gt69e2PUqFFo1aoVzMzMpG//R4wYgbS0NMyYMQMPHz7E9OnTcfXqVUyfPh2enp54/vw5SpUqBTs7O6hUKmhoaMDNzQ1BQUHYsWMHsrOzoampicaNGyMsLAxXr16Fs7MzAKBUqVKYMGECzp49i7p166JWrVp48OABbty4AQ8PDwwfPhwKhQJGRkYYMWKE2rGoVCooFIo8TxETERHl5tX3ipz3qMWLF2Px4sWYP38+/Pz8sHPnTixfvhzz5s1D7969pUtlc3h4eMDR0RF//PEHgJdffOnp6SEuLk6tr7GxMdLT06GlpYXnz5/DyMgIdevWxfHjx5GQkAA7OzsAgIuLCzp16gR9fX0A4Bnwjxj/z9FHS6FQoGbNmjA0NETNmjVRs2ZNODs7o0mTJvj222+luzUCL/9IzZs3D8eOHcO0adPQsWNHhIaG4ujRo3j48CEiIiIAvLzsJ+eypZwP7Xp6eqhYsSJu3bqFK1euAHj57YupqSn+/PNPKJVKAC//QJuYmGD79u1Yt24dKlasiODgYOzfvx9HjhxBy5Yt1U6rZmdnS9+yaGhovPMdLYmIqOR79az4f9seP36Mn376CcHBwejcuTNiY2MBvBwncO/ePVy9ehXAyw/4WVlZcHR0REpKivTBPzMzEwBgYWGBihUr4vbt27hx4wb09PTQoEEDxMbG4uzZs9I+//rrL1y9ehXly5eHkZERAKBhw4Y4duwYHj16JNVXvnx5rFmzBs2bN/9AzwoVFgYF+qjZ29vDxcUFMTExAP69lt/HxweGhoa4c+cOACAlJQVnzpxBQEAAateuLV1zeerUKdy5c0cKCjVq1EBCQgKOHj0qfXDX1NSUTs8eO3YMAGBkZITKlStjy5YtSE5OlvoJIWBgYICAgADMmDEDY8aMQeXKlQHIT71qamryWxYiIpJRqVTS+1nOe9GrgUGhUODy5cto2rQp5syZAzMzMzg4OODvv/8G8PLyWENDQ5w/fx5CCDRs2BDly5dH165dERAQgHLlysHZ2RmjR49GXFwcAMDb2xvZ2dk4cuQIAEiXL7Vo0QIzZ87E9u3bERoaCh8fH/Ts2VN6Txs8eDDi4+Ph4+NTWE8PFSJeekTFUs4fyDddflOqVCnUr18fu3fvRnJyMkxNTQEAly9fRmZmJoKCggAAhoaGePz4MfT19fG///0Pe/bswZUrV2BmZgZ/f380bdoUWVlZaNiwITw9PREWFgZLS0u4u7vj77//lv5w7tmzBz169AAAdOrUCRcuXICenp5Uz6tnBYQQ0uVLHHxMREQAcO/ePVy5cgWOjo4oV65crmeTX32/OHfuHIyMjKTLXIGXZwK6d++O58+fY82aNahcuTKEENKZAiMjI7i7u+PChQu4ffs2HB0dcfDgQezduxfnz5+Hvb09bt26hUWLFuGff/7B1q1b4ePjA0tLSxw9ehRdunRBrVq18Ouvv2LatGlYvHgxHj9+jDp16mDChAmwtbWVajEwMJBmT6ISqAimZCUSQgjRu3dv0bRpU/Hw4cPX9rtx48Zr79C6adMmoaWlJW7cuCFu374tZs+eLSpUqCD69OmjdrfiDh06CIVCIapWrSq+//57cfjwYZGUlCQtz5nTedOmTcLR0VE4OTmJvn37irp164qvvvpKDB8+XNja2qr1JSIiep3U1FSxdu1a0aZNG1GmTBlRpkwZ4e3tLRwcHESDBg3E7t27ZevcunVLdOnSRRgaGgpra2vh6ekpFi9eLL0XnjlzRmhpaYno6GjZujn3LZg6dapwcnISe/fuFULIb3yWnZ0txowZI6ytrYUQQjx9+lS0aNEi1zum37p1672eA/p48StO+uCys7Px4MED6VrInP+2a9cOgwYNgoWFhVr/tLQ0LF++HDVr1kSZMmXQvXt3/P7773lu39vbG5aWlqhQoQKcnZ2xePFiZGRk4Oeff0aPHj2k06oNGzZEuXLl0K9fP4wePRr16tWDqakp7t27h2XLlmHDhg0AXk73Fh0djYYNG+LixYto0qQJZs6ciUePHqF06dJ48eKF9A3QqzMTERERvSohIQGdOnVCSEgITE1NMXnyZGzatAkzZ87EyJEjcf/+fTRt2lS6fBZ4ednR5MmT8ffff2PZsmXYunUrqlWrhnHjxmH58uUAgPPnz8PCwkJ6P311Cu2c96eAgAA8e/YMFy5cAPDyLMWzZ8/w6NEjJCYm4vjx49i2bRsCAwOhVCphbGyMgIAAfPnll3jx4oXacTg4OHzQ54mKsaJOKlTyHTt2TLRv3z7POxOnpaWpPV6yZIlwcnISvXv3Flu2bBHh4eHir7/+yvM28ElJSaJ58+aiYsWK4sKFC+LFixfi4cOH4ocffhAKhUIEBQUJIYRISEgQvXr1EqVLlxZhYWEiNjZWrFixQnzxxRfC399fxMbG5nkMR44cEaVKlRLDhw/Psw4iIqJXKZVKMXr0aGFmZiZ7rxPi5Tf17u7uolKlSuLSpUtCiJd3JTY3Nxfz5s1T6ztixAhRsWJFkZqaKo4dOyYsLS3F3Llzpf3kpnz58qJ3794iIyNDPHjwQAQFBYnWrVuLatWqCXNzc9GkSRNx48aNgj1oKlEYFOiDybk85+7du0KhUIi1a9cKIYR49uyZePDggRBCiMqVK4u+ffuK1NRUIYQQcXFxwsLCQvTq1Uukpqa+1SU+KpVKjBs3Ttja2opHjx6ptS9cuFAoFAoxZMgQIcTLP6YDBw4UtWrVEmXLlhWlS5cWnTt3FrGxsWq3mY+Pjxc///yzGDZsmAgODhbm5uaiadOm/INKRETvZN26dUJPT0/tEiCVSiVdRjR37lyhra0tZsyYIYQQYsGCBaJMmTLS+rdu3RJLliwR3t7eQlNTU+zdu1dkZGSIihUripYtW8reJ+Pj40VycrIQQojPP/9ceHt7SyFk2bJlom/fvmL69Oni3LlzH/zY6ePHS48oX+7cuYP09HS1NpVKJU0VCrw8/ZmdnQ07OzuYm5tj6tSpcHV1RalSpbB69WoAgJWVFc6cOYMHDx4AAH777TekpqaiV69e0NfXz3W2h/9SKBSoX78+4uPjpcuMctb55ptv8PXXX2Pu3LmYPHkyAGD+/PnYuHEjjh49ikePHmH16tXw9/dXm7rUxMQEQggcPXoURkZGWLp0KaKiolCuXDleakRERG/Nzc0N9vb2iI6OBvDvfXO0tbUBAM2aNYOtra10szMjIyM8efIEwcHBsLKyQrly5TBjxgzUqFED69evR9WqVaGjo4MuXbpg7969CAsLwz///IMnT57g4MGDGDVqlHSH5YYNG8LGxkaqpXfv3liyZAlCQ0Ph7e1dyM8EfYw46xG9s99++w0NGzbEyZMn4evrK7VraGhIMzVcv34dGRkZ8PDwQK9evZCYmIjs7Gz06dMHX3zxhTR7Q8eOHTFw4EDcvHkTTk5OMDY2hkKhkGYvyvGmewy4ubmhbNmyOHToEOrVqwfg33AxadIk1KxZE9WrV5emMM25KQzw8o6Vr9YOAPr6+ujevTt69+4t2xfvd0BERG/LwcEBPj4+UhDImc0v573E1dUVDg4OuH79Op4/fw4XFxeYmZnh/v37CAsLQ+3ateHk5CTdvCxnXMLgwYPx/PlzfP/99wgPD4empqY05qFcuXIAgG+++QYDBgwo5COmkoRnFOidVatWDVevXlULCQBw8uRJdOjQAdbW1qhduzZmz56NBw8eYNmyZfjxxx/x4sULBAYGonr16jA3NwcANG3aFGlpadKZABcXF6Snp+Pq1avvdMt3CwsLODo6Ytu2bdJZjZw/xhYWFujatSs8PDwAyD/oa2lp5Tp16atnGIiIiPLD3Nwcvr6+uHbtGh4/fqz2HpQzCNnW1hapqam4f/8+qlatCldXV5QpUwZffvklKlasCF1dXSQnJ2PevHkIDQ0F8PJmoBMnTsT58+fRr18/DBkyBOfPn8f69evh5uYGgF9s0ftjUKA8if+/D8B/2wwNDeHo6IjTp09Lf+Tu3LmD4cOH49GjR5g9ezZWr16NNm3aSDcV69q1K5RKJc6dOwfg5R8vlUoFGxsb2NnZ4cSJE3j+/Dl8fHzg7u6OpUuXIikpCcDL07TJyck4cuQIbt++nWut+vr6+PnnnxEdHZ3nB/x3CR5EREQFxdPTEzo6OrKbg+a8L+nq6kJXVxdlypSBoaEhxo8fj7i4ONSoUQPz5s1DWFgYOnXqhJUrV8La2lrarqamJlxcXDBs2DD07dsXjo6OhX9wVKLxK1PKk0KhUPs24vLly3Bzc4NSqcTChQsxdOhQ3LhxA46Ojvjrr79w8OBB3Lp1C/b29mrbyc7OhqGhIdzc3HDs2DEkJibC3NwcSqUSOjo6CAwMxLFjx3D37l24u7tjyJAhGDZsGD7//HMMHDgQlpaW2LZtG27duoXvv/8+z3pzvkHJC294RkRERcHV1RUuLi7Yvn07QkJCpHZtbW0kJSVh9+7d8PPzg4mJCQCgcePGiIiIwK+//oq1a9ciJSUF/v7+GDt2LOrUqVNUh0GfIAYFytPVq1fx22+/wdraGhcvXsSIESOgUqmgpaWFWrVqQVdXFxcvXoSjoyOUSiUMDAywfv16BAQEwNTUFA8fPkS5cuVQpkwZAC8vM4qKisK9e/dgbm4ufXBv06YNVq9ejRs3bsDd3R1dunSBqakpZs+ejaFDh+LRo0fw8PBA3759OZczERF9dOzs7FC1alXs3bsXwMszAdnZ2Th79ixGjRqFtLQ0DBkyRG2d6tWrw9fXFxkZGdL4BKLCxqDwCco55ZlzDX9uRo4cifnz56NChQqwsrJCfHw8AOD06dOoUqUKHB0dUbZsWezduxfNmjVDYGAgunbtiqVLlyIsLAxJSUkwNTVF1apVMWjQILRq1QotWrTA3LlzceXKFXh7e0uXCOXc7OWPP/5AUFAQdHV10a5dOwQGBuLatWuoUKECjIyMPvwTQ0RE9AEYGRnB19cX69evx4kTJ6ClpYUtW7bg4MGDEEJg+/btqFWrlmw9DQ0NhgQqUgwKJUx8fDxsbGygUqnyvNQmJyBkZ2fjn3/+gZubm1rf3377DT/99BMGDhyIkSNH4tmzZ1iyZAkuXLiALVu2oEqVKjAxMUG9evWk6UxNTEwwd+5cnD9/HhkZGShbtizOnDmDkSNHYu7cuWjVqhUaNWoECwsLzJkzBwqFAn/99ReqV6+O1q1bS2cosrKyoKOjA+DlFKVVq1YF8PI6TiHEa8MNERFRcVW+fHkoFArUrl0bGhoa8PDwQIcOHdCxY0eUL18eQggOPqZiRyE4KXyJoFQqMW7cOMyaNUuaOi0vv//+O8aOHYvjx4/D3NwcVapUwZAhQ9CoUSMAwMSJExEeHo5jx45J8y/fu3cPISEhSE5OxtmzZ5GdnY3Vq1ejb9++OHfuHCpUqJDrvpo3b47U1FRs2LABlpaWWLt2LX766SdcunQJenp6mDt3Lj7//POCfTKIiIiKmTt37mD37t2ws7ODv78/dHV1i7okojfiGYUSQktLC+XKlYOmpibOnDkDHx+fXM8q3Lt3D6NGjYJCoUB0dDRu3LiBhQsXomvXrli/fj3q1q0LDQ0NpKWlwcbGBkqlElpaWrC2tkaTJk0wZswYZGRkQFdXF1WrVoVCocCff/6JChUqIC0tDWfPnoWGhgZ0dXXx22+/4Z9//sGIESNgaWkJAOjUqRMCAgJgYWEhqy1nX0RERCWNvb09+vTpU9RlEL0TfiorQby9vWFhYYHo6Og8g8KePXtw+PBhnD17Fl5eXlAoFGjdujWqVq2KZcuWoW7durC0tMTjx4/x5MkTWFhYIDs7G5qamtLZhQMHDiAoKAi2trbw9vZGbGwsQkJCcOrUKcyePRuXL1/G3bt3YWRkhH79+qFt27ZqNeSEhuzsbAghpHDAkEBERERUfHC+yBKkXLly8PLywu7duwHIpwMVQuD48eNwdnaGt7c3FAoFsrOzYWVlhebNm+PPP//EnTt3ULVqVRgYGGDDhg0A1Mc0AJC2b2hoKA3Oev78OapXr44hQ4Zg8uTJOHPmDO7du4exY8fK7rKcQ1NTk+GAiIiIqJhiUChBypQpA19fX5w/fx6ZmZlqQSFnkFRGRgbMzc1x5coVAP/e7MXHxwfPnz/H5cuXUaNGDbRo0QKzZs1CbGwsMjMzcfr0aaxbtw4ODg7YuXMngJc3iAkJCcHo0aOhpaUFHR0dfPbZZ/j888/h5OQE4N+zBkRERET0cWFQKEE0NDRQqVIlKJVK/PbbbwD+DQI5/61SpQoePnyI8+fPq61bvnx5JCQkQE9PDwAwadIklCpVCm3btkWLFi0QEhKC8uXL46uvvsLz58+RlJQEhUKBzz77DKNGjZLWA16GkpxwoKmpyVkciIiIiD5CDAoljJubG+zt7bFt2zYA/waEHEFBQdDX10d4eDiAl3eFfPHiBTZt2gQtLS1UqVIFwMu7SMbGxmLcuHFwcHBAaGgolixZguvXr8PGxgapqanSNnOmLs3x3zs6ExEREdHHhxeIlzAODg7w8fFBbGwsgH/HKeSMM3B3d8fgwYPRv39/NGvWDB06dMA///yDrVu3Ys6cOTA0NATw8sO/hYUFhg4dKn3o3759O6KjoxESEgI7Oztpn3ndr4GIiIiIPl4MCiWMubk5fH19ER0djeTkZJiamkKpVCItLQ3x8fFQqVTo168fDA0NERERgeHDh8PMzAyhoaHo1KmTtB0NDQ08efIE69evx++//46rV6/i3LlzCAwMxNChQ4vwCImIiIioMDAolEBeXl4wNDTEmjVr4Ofnh927d+P3339HTEwMatSogRUrVqBLly5o3bo1dHR01MYXvMrMzAwODg44evQomjVrhsWLF0t3SiYiIiKiko13Zi6B/v77b3To0AEXLlyAtrY2dHR0ULduXbRo0QLt2rWDtbW1Wn+VSgWVSsWBx0REREQkYVAogZ49e4bly5cjMzMTQUFBqFSpUlGXREREREQfGQaFT4RSqYRCoYCGhgbPGhARERHRGzEolGDZ2dkMBkRERESULwwKREREREQkwwnwiYiIiIhIhkGBiIiIiIhkGBSIiIiIiEiGQYGIiIiIiGQYFIiIiIiISIZBgYiIiIiIZBgUiIioWFMoFJgwYcI7r3fz5k0oFAqsXLmywGsiIvoUMCgQEdFbWblyJRQKBRQKBY4cOSJbLoSAvb09FAoFWrRoUQQVEhFRQWJQICKid6Knp4eIiAhZ+2+//Ya7d+9CV1e3CKoiIqKCxqBARETvpFmzZoiMjIRSqVRrj4iIgK+vL6ytrYuoMiIiKkgMCkRE9E46deqEJ0+eYO/evVJbZmYmNm7ciJCQEFn/Fy9eYNiwYbC3t4euri7c3Nwwa9YsCCHU+mVkZGDIkCGwtLSEsbExWrVqhbt37+Zaw71799CzZ0+UKVMGurq68PT0xIoVKwr2QImIPnEMCkRE9E7KlSuH2rVrY+3atVLbrl27kJKSgo4dO6r1FUKgVatW+OGHHxAUFIQ5c+bAzc0N3333HYYOHarWt3fv3pg7dy4aN26MadOmQVtbG82bN5ft/+HDh6hVqxZiY2MxYMAAzJs3D+XLl0evXr0wd+7cD3LMRESfIgYFIiJ6ZyEhIdiyZQvS0tIAAGvWrEGDBg1ga2ur1i86Ohr79+/H5MmTsWzZMnzzzTeIjo5Gu3btMG/ePFy7dg0AcPbsWaxevRr9+/fHmjVr8M0332DTpk3w8vKS7Xv06NHIzs7G6dOnMXbsWPTr1w9bt25Fx44dMWHCBKkmIiJ6PwwKRET0ztq3b4+0tDRs374dz549w/bt23O97Gjnzp3Q1NTEoEGD1NqHDRsGIQR27dol9QMg6zd48GC1x0IIbNq0CS1btoQQAo8fP5Z+mjRpgpSUFJw6daoAj5T+r707BkktiuM4/tPeWCCEBEEUNEgJ1ha4JS4GDpKLETRJa+AYRDQVDkGIgoNOBQ5FBS5huIqDU0NBQW2RIErgdu1N+t71+BZ5vsd7fj/L4d77597zH3+ce+4FML6+/e0JAAD+PW63W8FgUBcXF2q327IsS9Fo1Kh7e3vT7OyspqambOeXlpZ617uj0+nU4uKirc7j8diO6/W6ms2mstmsstnswLl9fHwM3RcA4AeCAgBgKFtbW4rH43p/f1coFJLL5Rr5MzudjiRpe3tbOzs7A2t8Pt/I5wEA44CgAAAYSiQS0e7uriqVigqFwsCa+fl5lUolfX5+2lYVHh8fe9e7Y6fT0cvLi20V4enpyXa/7heRLMtSMBj83S0BAH7CHgUAwFAmJyeVyWR0eHiocDg8sGZjY0OWZSmVStnOn56eyuFwKBQKSVJvPDs7s9X1f8VoYmJCm5ubury81MPDg/G8er0+bDsAgD6sKAAAhvar13+6wuGw1tfXtb+/r9fXV62srOju7k43Nzfa29vr7UlYXV1VLBZTOp1Wq9WS3+/X/f29np+fjXseHx+rXC5rbW1N8Xhcy8vLajQaqtVqKpVKajQaI+kVAMYNQQEAMDJOp1O3t7c6ODhQoVBQPp/XwsKCksmkEomErTaXy8ntduv8/FzX19cKBAIqFouam5uz1c3MzKharero6EhXV1dKp9Oanp6W1+vVycnJn2wPAP5rjq/+X2MCAAAAGHvsUQAAAABgICgAAAAAMBAUAAAAABgICgAAAAAMBAUAAAAABoICAAAAAANBAQAAAICBoAAAAADAQFAAAAAAYCAoAAAAADAQFAAAAAAYCAoAAAAADN8ByvsJ7b492MQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "comparison_plot = comparison_results.melt(\n",
        "    id_vars='Model',\n",
        "    value_vars=['Baseline Accuracy', 'Tuned Accuracy'],\n",
        "    var_name='Version',\n",
        "    value_name='Accuracy'\n",
        ")\n",
        "\n",
        "plt.figure(figsize=(9, 5))\n",
        "sns.barplot(\n",
        "    data=comparison_plot,\n",
        "    x='Model',\n",
        "    y='Accuracy',\n",
        "    hue='Version'\n",
        ")\n",
        "\n",
        "plt.ylim(0, 1.05)\n",
        "plt.title('Baseline vs Tuned Model Accuracy')\n",
        "plt.xticks(rotation=15)\n",
        "plt.show()"
      ],
      "id": "sV4XxdSWVZ3n"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BXzymRSmVZ3n"
      },
      "source": [
        "## 27. Did the accuracies change?\n",
        "\n",
        "The table above shows whether changing the hyperparameters improved, reduced, or maintained the accuracy.\n",
        "\n",
        "Possible observations:\n",
        "\n",
        "- Logistic Regression may change slightly after changing `C` and the solver.\n",
        "- KNN may improve or decrease depending on `n_neighbors`, distance weighting, and feature scaling.\n",
        "- Decision Tree accuracy may change after limiting the tree depth and controlling minimum samples.\n",
        "- Hyperparameter tuning does not always guarantee higher accuracy, especially with a small dataset such as Iris.\n",
        "- A simpler model can sometimes perform as well as or better than a more complex model."
      ],
      "id": "BXzymRSmVZ3n"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZAIq1ubYVZ3n"
      },
      "source": [
        "## 28. Confusion matrices for the baseline models"
      ],
      "id": "ZAIq1ubYVZ3n"
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "qn7bfxEPVZ3n",
        "outputId": "e5a390eb-1ac1-483f-e150-b9f7016076e9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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IiBRhVwIRERGJ2JVAREREuoyJARERkSIyPdXdiuHu3bsYOHAgatasiVKlSqFq1aof3H/79u2QyWQf3a8g7EogIiJSRCJjDK5du4Zdu3bh888/R05ODnJychTum5aWhhEjRsDJyUmpc0njERMREZFCbdu2xcOHD7FlyxbUrl37g/vOmDEDbm5uaNWqlVLnYmJARESkiJ5MdbfihKFXuK/rmJgYzJkzBwsXLlT6XOxKICIiUkQiXQmFNWzYMHz99deoUaOG0nUwMSAiItIAT0/PD5bfu3evWPXv3LkTJ06cwO3bt4tVDxMDIiIiRUrIdQzS09MxfPhwTJ48Gfb29sWqi4kBERGRIirsSihui8CHzJ8/H3p6eujatStevHgBAMjMzEROTg5evHgBU1NTGBoaFqouJgZEREQl3M2bN3H37l04ODjkK7OxscGyZcswcODAQtUl2cQgJycHhw4dwu3bt5Geni5XJpPJMGLECC1FRkREOqOEdCWMHTsWYWFhcttmzpyJW7duYdWqVahQoUKh65JkYvD06VP4+/vj9u3bkMlkEAQBQG5CkIeJARERqZ1EZiW8efMGu3fvBgA8ePAAqamp2LJlCwDAz88PFStWRMWKFeWOWb16NR49egR/f/8inUuSicHIkSNhZ2eHhw8f4rPPPsPp06fh5OSEdevW4ffff8euXbu0HSIREZHGxMfH48svv5Tblnf/8OHDRf7y/xBJJgZHjx7FwoUL4eLiAgAQBAFubm4IDw+HIAj49ttvsWfPHi1HSUREnzyJdCV4eHiIreeFtXr1aqXOJY02kvekpKTAwcEBenp6sLS0RHx8vFjm6+uLY8eOaTE6IiLSGRJZREmTJBlp2bJlERcXBwCoUqUK1q5dK5ZFRUXB1tZWW6ERERF90iTZldCmTRvs378fXbp0wfjx4/HFF1/A0dERBgYGePr0KWbNmqXtEImISBdIpCtBkySZGMyYMUP8u3Xr1jhx4gS2bduG9PR0tGjRAq1bt9ZidEREpDNKUBeAqkgyMXhf3bp1UbduXW2HQURE9MmTZCr04MEDXLlyRbyfkZGB6dOno0ePHkqPsiQiIioymUx1txJCkolBv3795AYcjhkzBpMnT8bNmzfRv39/LF26VIvRERGRzuCsBGm4dOkSGjduDADIysrCmjVrMGvWLJw7dw4RERFYtmyZliMkIiL6NEkyMXj58iWsrKwAAKdPn0ZqaipCQ0MBAI0aNVLrClVEREQithhIg6urK06dOgUA2LZtGypXrixeBTE5ORmmpqbaDI+IiHSFDo4xkOSshD59+mD8+PHYvHkzLl68iHnz5ollp06dQqVKlbQYHRER0adLkonB2LFjUbp0aZw9exaDBg2SW0oyOTkZffv21V5wRESkO0pQF4CqyISirsrwiTCp9a22QyANSj67WNshEJGaGKvxJ65J+5Uqqytte3+V1aVOkmwxAHJXVNy9ezeOHTuGpKQk2NraonHjxmjdujVkJaivhoiIqCSRZGKQnJyMoKAgnD59GtbW1nBycsKzZ88wa9Ys1K9fH7t374a1tbW2wyQiok+dDnYlSPIRjx49GjExMdi3bx+SkpJw48YNJCUlYd++fYiJicHo0aO1HSIREekCHZyVIMnEYMeOHZg1axZatGght71FixaYMWMGoqOjtRQZERHRp02SXQmvX7+Gk5NTgWXOzs54/fq1hiMiIiJdpItj2iTZYlCrVi0sXrwY2dnZcttzcnKwaNEi1K5dW0uRERGRLpHJZCq7lRSSbDGYMWMGAgMDUb58eXzxxRdwcnJCfHw8tm/fjqdPn2L//v3aDpGIiOiTJMnEoEmTJjh+/DimT5+ODRs2IDk5Gba2tmjUqBHGjRvHFgMiItKMkvNDX2UkmRgAQJ06dbBt2zZth0FERDqsJHUBqIokxxg0a9YMN2/eLLDs9u3baNasmYYjIiIi0g2SbDE4cuQIUlNTCyxLTU3F0aNHNRxRyWBmYogRPQNQr6oH6lZ1h62VGfpNXIt1O0/L7bdycg981a5+vuNvxT5FzY7TNBUuqUlmZiaWLFqAXTujkZqaCq8K3vh26HD4Nmio7dBIDfh6q5cuthhIMjEAFL8YJ06cgKOjo4ajKRnsrM0xbkAQ/o1LwpXbj+FXr4LCfdMz3uKbKRvktqW+SlN3iKQBE8LH4uCBfej+1ddwc/PAjugofPtNf/zy2xrUrlNX2+GRivH1Vi8mBlo0Y8YMzJgxA0DuC9G0aVPo6cn3dGRkZCArKwuDBg3SRoiS9zQhFR4BP+BZ4kvUruyG4+u/V7hvVnYOInef1WB0pAlXLl/G3j27MHL09+jZqw8AoO0X7dHpi2DMnzsbv6+P1HKEpEp8vUkdJJMYNGjQAKNGjYIgCJgyZQq6du0KV1dXuX0MDQ1RqVIltG3bVktRSlvm2yw8S3xZ6P319GQwMzHCy9fpaoyKNOng/r3Q19dHpy9DxG1GRkbo0KkzFs6fi6dxcXB2cdFihKRKfL3Vjy0GWuTn5wc/Pz8AuS9Ev379ULp0aS1H9ekyNTZA/LHZMDMxQlLKa2zaex7jF2zH67RMbYdGxXDz5g24u3vA3NxcbnvVatXFcn5RfDr4emuA7uUF0kkM3jVp0iTx74cPH+Lhw4eoUaMGzMzMtBjVp+NpQirmrjmISzceQk9PDy0aVMLAkCaoXqEMAvstQHZ2jrZDJCU9f/4c9g4O+bbb2zv8tzxe0yGRGvH1JnWQZGIAACtXrsTkyZMRFxcHmUyGs2fPonbt2ujQoQP8/f0xbNgwbYdYYk1ctEPu/uZ953HnQTymDGmHjgG1sHnfeS1FRsWVkZEOQ0PDfNuNjIxyy9PZbfQp4eutfrrYlSDJ6xjMnz8fQ4YMwddff439+/dDEASxzN/fH5s3b/5oHZ6enh+8kbxF6w8jOzsHTT/31nYoVAxGRsbIzMzfHZSRkZFbbmys6ZBIjfh6qx/XSpCIRYsWYcKECRg/fny+hZS8vb1x69YtLUX26UrPeIvElNewtTTVdihUDA4ODoh/9izf9oSE5/8t51TfTwlfb1IHSSYGjx8/RoMGDQosMzAwwKtXrz5ax7179z5YblLrW6Vi+1SZmxrB3toMz5M//tySdHlXrIizZ07j1atXcgPSrlz+BwBQsWIlbYVGasDXW/1K0i99VZFkV4K7uzvOnDlTYNnp06dRoYLiC/fQhxkZloK5qVG+7T/0awU9PT0cOHFdC1GRqgQEtkJ2dja2bv5D3JaZmYnoqG2oVr0GR6h/Yvh6qx+7EiSiX79+iIiIgIODAzp27AgAePv2LXbt2oWff/4Z06dP13KE0jUwpAmsLEzg4mAFAGjjVw1lnKwBAMsi/w/WFqY4FTkWm/aew+37uU2QAb6V0LpxVew7fg07j1zRVuikAtWr10Bgy1ZYOH8ukhIT8ZmbO3ZGR+HJk8eImMrPzaeGrzepg0x4d2SfhAwdOhRLliyBTCZDTk6OeBXEQYMGYeHChcWu/1PtSri5azLcS9sVWOYdNBEpL9Mwd8yX8KnuARcHK+jr6SHm4XNE7jmH+b8fRFbWpzlVMfnsYm2HoDEZGRlYsmg+du3cidTUFHhV8MbgIcPQsFFjbYdGasDXGzBW409cu54bVVZX4pquKqtLnSSbGAC54wQOHjyIhIQE2Nraonnz5vDy8lJJ3Z9qYkAF06XEgEjXqDMxsA9T3WWlE1aHqqwudZJkV0IeT09P9O/fX9thEBER6QxJDj48f/48/vrrL/H+ixcv0K9fPzRq1AgRERHIyfk0m7uJiEhapDL48O7duxg4cCBq1qyJUqVKoWrVqnLlqampiIiIgI+PD6ytreHk5IS2bdviypWijxuTZGIwYsQIHDt2TLw/bNgwbNq0Cc7Ozpg9ezYHHxIRkUZIJTG4du0adu3ahfLly6Ny5cr5yv/991+sWLECgYGB2LRpE3755RekpKSgfv36uHHjRpHOJcnE4Pr16/Dx8QEApKWlYcuWLZg/fz62bNmCWbNmYe3atVqOkIiISHPatm2Lhw8fYsuWLahdu3a+8rJlyyImJgbTpk1DYGAg2rVrh927d8PY2BhLly4t0rkkOcbgzZs3MDXNvQLf8ePHkZGRgS+++AIAUL16dTx69Eib4RERka6QyOUH8mbmKVLQIoPm5uYoX748njx5UrRzFWlvDfH09MSePXsAAOvXr0edOnVga2sLAIiPj4elpaU2wyMiIh0hla4EZbx48QJXr15FpUpFuwKmJFsMRo4cib59++LXX39FUlKSXNfBkSNHUL16dS1GR0REVHQfW8DvY5fyL6rvv/8eMpkMAwcOLNJxkkwMevfujfLly4tLLTdt2lQss7Oz45LLRESkESXpUsbvWrVqFX755ResXr0arq6uRTpWkokBADRp0gRNmjTJtz0iIkLzwRARkU5SZWKg6hYBRfbs2YP+/ftjwoQJ6NmzZ5GPl+QYg3fl5OTA09MT165d03YoREREknbq1Cl07twZPXv2xJQpU5SqQ7ItBnkEQcD9+/eRkZGh7VCIiEjHlKSuhOvXr6NNmzZo1qwZli9frnQ9kk8MiIiItEYiecGbN2+we/duAMCDBw+QmpqKLVu2AAD8/PwgCAJatmwJExMTjBgxAufOnROPtbS0LPCiSIowMSAiIpK4+Ph4fPnll3Lb8u4fPnwYAMRr/DRv3lxuPz8/Pxw5cqTQ55J8YqCvr4/Y2FiULl1a26EQEZGOkUpXgoeHBz62GLKqFkuW/OBDAHB3d8fjx49x8OBBJCUlaTscIiLSESX5AkfKkmRiMGrUKAwfPly8HxUVBW9vbwQGBsLLywvnz5/XXnBERESfMEkmBlFRUahbt654Pzw8HEFBQbh8+TJ8fHwwfvx4LUZHRES6gi0GEhEXFwc3NzcAQExMDG7duoXx48ejatWqGDJkiNxoSyIiIrWRqfBWQkgyMbCyskJ8fDwA4MCBA7C1tUWdOnUAAEZGRkhLS9NmeERERJ8sSc5KaNKkCSZOnIhnz55h9uzZaN++vVh269YtsTWBiIhInUpSF4CqSLLFYN68eXB2dsbYsWPh5uaG6dOni2Vr165F48aNtRgdERHpCl0cYyDJFoMyZcrg0KFDBZbt27cPxsbGGo6IiIhIN0gyMfgQS0tLbYdAREQ6oiT90lcVySQG7dq1w5w5c+Dl5YV27dp9cF+ZTIbo6GgNRUZERLqKiYEWvXz5EtnZ2QCA1NRUnXwxiIiItE0yiUHeIhAAirTYAxERkdro4G9Uyc1KSE9PR40aNbB//35th0JERKRzJNNikMfY2BiPHz+Gnp7kchYiItIxutitLclv344dO2LTpk3aDoOIiHQcr2MgEQ0bNkR4eDiCg4MRFBQEJyenfE9qx44dtRQdERHRp0uSiUGvXr0A5C6mtHv37nzlMplMnMFARESkLiXoh77KSDIxiI2N1XYIREREJaoLQFUkmRi4u7trOwQiIiKdJJnEwMLCotCZmUwmQ0pKipojIiIiXaeDDQbSSQxGjRqlk002REQkXbr4vSSZxCAiIkLbIRAREek8ySQGREREUqODDQZMDIiIiBTR09O9zECSVz4kIiIi7WCLARERkQLsSiAiIiKRLs5KYFcCERERidhiQEREpIAONhgwMSAiIlKEXQlERESk09hiQEREpIAuthgwMSAiIlJAB/MCdiUQERHR/7DFgIiISAF2JRAREZFIB/MCdiUQERHR/7DFgIiISAF2JRAREZFIB/MCdiUQERFJ3d27dzFw4EDUrFkTpUqVQtWqVQvc79dff0WFChVgbGyMGjVq4M8//yzyuZgYEBERKSCTyVR2K45r165h165dKF++PCpXrlzgPpGRkejXrx9CQkKwZ88e+Pr6okOHDjh16lTRHrMgCEKxoi2hTGp9q+0QSIOSzy7WdghEpCbGauwU9/nxiMrqOhPur/SxOTk50NPL/S0fFhaGc+fO4erVq3L7eHt7o06dOtiwYYO4rUGDBrC2tsbu3bsLfS62GBAREUlcXlKgyL1793D79m106dJFbntoaCj++usvZGRkFP5cSkVIRESkA6TSlfAxN2/eBABUrFhRbnulSpWQmZmJ2NjYQtfFWQlEREQKqPL73NPT84Pl9+7dU7ru5ORkAIC1tbXcdhsbGwBAUlJSoevS2cSAfc66xaYex5ToEn6+iZSns4kBERHRx6iyC6A4LQIfk9cykJKSAmdnZ3F7XkuCra1toeviGAMiIiIFZDLV3dQpb2xB3liDPDdv3oShoeFHuzHexcSAiIiohPP09ESFChWwefNmue1//PEHmjdvDkNDw0LXxa4EIiIiBaSyVsKbN2/EaxE8ePAAqamp2LJlCwDAz88PDg4OiIiIQPfu3VGuXDk0bdoUf/zxB06fPo2jR48W6VxMDIiIiBSQSF6A+Ph4fPnll3Lb8u4fPnwY/v7+6Nq1K968eYOZM2di5syZ8Pb2RlRUFHx9fYt0LiYGREREEufh4YHCXKi4T58+6NOnT7HOxcSAiIhIAal0JWgSEwMiIiIFdDEx4KwEIiIiErHFgIiISAEdbDBgYkBERKQIuxKIiIhIp7HFgIiISAEdbDBQbYtBZmYmXr9+Xaw6srKycOHCBTx//lxFURERESlHJpOp7FZSKJUYREZGYsSIEXLbJk+eDHNzc1hbW6NDhw549eqVcgHp6aF+/fr4559/lDqeiIiIlKdUYjBnzhy5loETJ05g8uTJaNmyJUaMGIG9e/di+vTpygWkpwdPT09xqUgiIiJtKSmrK6qSUmMMYmJi0LNnT/H+hg0b4OzsjKioKJQqVQo5OTnYunUrZsyYoVRQ4eHhmDp1Kho2bIjSpUsrVQcREVFx6ZWkb3QVUSoxyMjIgLGxsXh///79aN26NUqVyq2ucuXKWLp0qdJBbd68Gc+fP4enpyeqV68OJycnuf4ZmUyG6OhopesnIiKiginVlVC2bFkcPHgQAHDu3DncvXsXrVq1EsufPXsGc3NzpYN69eoVKlasCF9fX5iZmeHVq1d4+fKleEtNTVW6biIiosJiV0IhDRgwAMOGDcP169fx6NEjuLq6Ijg4WCw/fvw4qlSponRQhw8fVvpYIiIiVSlJswlURanEYMiQITA2Nsbu3btRp04djBkzBiYmJgCApKQkPH36FAMHDlRpoERERKR+Sl/gqF+/fujXr1++7ba2tjh37lyxggKAixcv4scff8SxY8eQlJQEW1tbNG7cGD/88ANq1apV7PqJiIg+Rk/3GgxUd+VDQRBw+PBhZGRkoFGjRrCwsFC6rr///hstWrSAs7MzunbtCicnJzx79gxRUVFo0KABDhw4gEaNGqkqdCIiogLpYleCTBAEoagHjRs3DidOnBDHAgiCgMDAQBw6dAiCIMDNzQ1//fUXypUrp1RQDRs2hIWFBf78809xpgMAZGdno02bNnj16hWOHTumVN150rOKdTiVMDb1vtV2CKRByWcXazsE0iBjNV7cP2j5GZXVtXugj8rqUielZiVs3boVPj7/e4BbtmzBX3/9hWnTpuHPP/9EdnY2IiIilA7q4sWLGDp0qFxSAAD6+voYOnQoLly4oHTdREREhcVZCYX0+PFjlC9fXry/bds2VK5cGT/88AMA4JtvvsGyZcuUDsrMzAzx8fEFlj179gxmZmZK101ERFRYMpSgb3QVUarFoFSpUsjIyACQ243w119/yV3HwMnJCQkJCUoH1bZtW4wZM0a8VkKegwcP4ocffkC7du2UrpuIiIgUUyoxqFq1KtatW4fk5GSsWrUKiYmJaNOmjVj+4MED2NvbKx3UnDlz4OHhgZYtW8LGxgbe3t6wsbFBy5Yt4eHhgdmzZytdNxERUWHpyVR3KymU6kqYOHEi2rZtK375N2zYEE2bNhXLd+3ahXr16ikdlI2NDU6ePIk///wTx44dQ3JyMmxtbdGoUSO0adMGenoqXS2aiIioQLo4K0GpxKBFixa4cOECDhw4AGtra4SEhIhlycnJaNKkCb744otiBaanp4d27dqx24CIiEiDlJ7kUblyZVSuXDnfdhsbG8ybN6/I9SUlJRVpf1tb2yKfg4iIqCh0sMFAdRc4Ki57e/siNdlkZ2erMRoiIiIuu1wke/bswdy5c3HhwgWkpKSgoOskFeXL+7ffftPJvhwiIiIpUSox2Lp1K7p06YIqVaogNDQUy5YtQ7du3SAIAqKjo+Hl5YX27dsXqc6wsDBlQiEiIlIbXfy9qlRiMGPGDPj4+IgzBpYtW4bevXujWbNmuH//PurXr4+yZcsWO7jk5GScOXNGXETJx8cHNjY2xa6XiIioMHSxJVupeX/Xr19HaGgo9PX1xcsWv337FgDg4eGBQYMGYdasWUoHJQgCvv/+e5QuXRqtW7dG9+7d0bp1a5QuXRpjxoxRul4iIiL6MKVaDExNTWFoaAgAsLa2hpGREeLi4sRyJycnxMbGKh3Ujz/+iHnz5uH7779HSEiIuLriH3/8gZ9++gnW1tbi5ZeJiIjURQcbDJRLDLy9vXH9+nXxfs2aNbF27Vr06NEDWVlZ2LBhA9zc3JQO6j//+Q8mTJiAiRMnitucnJxQvXp1GBkZYeXKlUwMiIhI7XRxVoJSXQkdOnRAdHS0uF7CuHHjcOTIEVhbW8PBwQF///03xo4dq3RQcXFxaNCgQYFlvr6+cq0TREREpDpKtRiMHj0ao0ePFu8HBwfjyJEj2LZtG/T19dGmTRu5SyQXlYeHB3bt2oWAgIB8Zbt374aHh4fSdRMRERWW7rUXqPACR40bN0bjxo1VUteIESPwzTff4Pnz5+jcuTOcnJwQHx+PzZs3Y+PGjcVa0pmIiKiwdHFWgmSufPiuAQMGIDMzE1OnTsWGDRsgk8kgCAIcHBywYMEC9O/fX9shEhERfZIKlRiULVu2yFmTTCZDTEyMUkEBwJAhQzB48GDcvHlTXF3R29ubKysSEZHGlKTlklWlUImBn5+fVppT9PT0ClyoiYiISBPYlaDA6tWr1RyGvHHjxiEhIQErVqzIVzZgwAA4OTlhypQpGo2JiIhIW3bs2IHp06fj+vXrMDc3R+PGjTFz5kx4enqq/FySbJffuHEjGjVqVGBZ48aNsXHjRg1HREREukgmU91NWUeOHEGHDh1QuXJlREVFYf78+fjnn38QGBiItLQ01T3Y/yp0YhAXF4eKFStiwoQJH9xv/PjxqFSpEuLj45UO6smTJ/jss88KLHN1dcWjR4+UrpuIiKiwZDKZym7KioyMhLu7O3777TcEBAQgJCQEy5cvR0xMDM6dO6fCR5ur0InBggULkJSU9NG1CsaMGYOkpCQsWrRI6aAcHBxw9erVAsuuXr0KW1tbpesmIiIqSd6+fQsLCwu55MLKygpA7tpCqlboxGDXrl3o2rUrzM3NP7ifhYUFunXrhh07digdVPv27REREYEzZ87IbT979iymTJmCDh06KF03ERFRYenJVHdTVlhYGK5fv46lS5ciJSUF9+7dQ3h4OGrVqoWGDRuq7sH+V6GvYxATE4Phw4cXat8qVapg5cqVysaEadOm4fjx4/D19UWlSpVQunRpPHnyBDdu3EDNmjUxffp0pesmIiIqLFXOSvjYQMF79+4VuL1x48aIiopCt27dMHjwYAC5axTt3bsX+vr6KosvT6FbDPT19ZGZmVmofd++fVus6w1YWVnh1KlTWL58OapVqwYAqFatGlauXImTJ0+KTShERESfuhMnTuCrr75Cv379cOjQIWzevBk5OTlo06aNWgYfyoRCdlDUrFkTVapUwfr16z+6b48ePXD16lVcunSpuPGpTXqWtiMgTbKp9622QyANSj67WNshkAYZq/Eavr0jr6isrt9Cqyl1XN26deHu7o6tW7eK2x49egQ3NzcsX75c5VcDLvTP+g4dOmDz5s04efLkB/c7deoUNm3axHEARERU4unJZCq7Kev69euoWbOm3DZXV1fY29sX6wrDihQ6MRg5ciRcXV0RGBiIWbNm4fHjx3Lljx8/xqxZsxAYGAhXV1eMGDGiSIFYWlri/PnzAHIHMFpaWiq8sSuBiIh0hbu7Oy5cuCC37cGDB0hISFDLasOFboCxsLDAwYMH0bFjR/zwww8IDw+HlZUVLCws8PLlS6SkpEAQBFSrVg3btm2DpaVlkQIZNWoUXFxcxL918TKUREQkLVL4Kho4cCCGDx+OYcOGoW3btkhMTMS0adPg6OiILl26qPx8hR5jkCc7OxtbtmzBjh07cPPmTaSmpsLS0hIVK1ZE27Zt0blzZ5QqJclFG+Xo0hiDzMxMLFm0ALt2RiM1NRVeFbzx7dDh8G2g+mkuUvUpjjEwMzHEiJ4BqFfVA3WrusPWygz9Jq7Fup2n5fZbObkHvmpXP9/xt2KfombHaZoKV6N0aYwBP9/qHWPQf/M1ldW18ssqSh0nCAJWrFiBZcuWISYmBhYWFvD19cWPP/6IihUrqiy+PEV+OvX19RESEoKQkBCVB/Mh9+/fx927d1G7dm1e4KiIJoSPxcED+9D9q6/h5uaBHdFR+Pab/vjltzWoXaeutsMjJdlZm2PcgCD8G5eEK7cfw69eBYX7pme8xTdTNshtS32l+tHMpHn8fH/6ZDIZBg4ciIEDB2rkfJL8aT9q1ChkZ2dj/vz5AICoqCiEhobi7du3sLGxwf79+1GnTh3tBllCXLl8GXv37MLI0d+jZ68+AIC2X7RHpy+CMX/ubPy+PlLLEZKyniakwiPgBzxLfInald1wfP33CvfNys5B5O6zGoyONIGfb/WTQleCpklyEaWoqCjUrfu/TDc8PBxBQUG4fPkyfHx8MH78eC1GV7Ic3J97AYxOX/6vhcfIyAgdOnXGP5cu4mlcnBajo+LIfJuFZ4kvC72/np4MFmbGaoyINI2fb/WTwqwETZNkYhAXFwc3NzcAuVdcvHXrFsaPH4+qVatiyJAhalk04lN18+YNuLt75LuUddVq1cVy+vSZGhsg/thsxB+bjcdHZmHe2C4wMzHUdlhUTPx8kzpIsivByspKXJ3xwIEDsLW1FbsOjIyM1HKlp0/V8+fPYe/gkG+7vb3Df8uVXwWTSoanCamYu+YgLt14CD09PbRoUAkDQ5qgeoUyCOy3ANnZOdoOkZTEz7f6laAf+iojycSgSZMmmDhxIp49e4bZs2ejffv2YtmtW7fE1gT6uIyMdBga5v9laGRklFuenq7pkEjDJi6SX9Bs877zuPMgHlOGtEPHgFrYvO+8liKj4uLnW/10ceq8JLsS5s2bB2dnZ4wdOxZubm5yiyatXbsWjRs3/mgdnp6eH7zpCiMj4wLXuMjIyMgtN2afsy5atP4wsrNz0PRzb22HQsXAzzepQ6FaDKZMmVLkimUyGSZMmFDk4wRBgImJCXbv3g3jAt7U+/btK3A7FczBwQHxz57l256Q8Py/5Y6aDokkID3jLRJTXsPW0lTboVAx8POtfpL89axmhUoMIiIiilyxsonB27dv4ejoiOjoaLRp0yZfeWGvqKho+co8unKBI++KFXH2zGm8evVKboDSlcv/AAAqVqykrdBIi8xNjWBvbYbnya+0HQoVAz/f6seuBAVycnKKfMvOzlYqIENDQ7i6uip9PMkLCGyF7OxsbN38h7gtMzMT0VHbUK16DTj/9zLU9GkyMiwFc1OjfNt/6NcKenp6OHDiuhaiIlXh55vUQZKDDwcPHoy5c+ciMDCQ3QbFVL16DQS2bIWF8+ciKTERn7m5Y2d0FJ48eYyIqdM/XgFJ2sCQJrCyMIGLQ+7CYm38qqGMkzUAYFnk/8HawhSnIsdi095zuH0/t8k5wLcSWjeuin3Hr2HnEdUtKUuax8+3+unpXoNB0ddK0IQhQ4Zg69atyMrKgr+/P5ycnOSac2QyGRYsWFCsc+hKVwKQOxBpyaL52LVzJ1JTU+BVwRuDhwxDw0YfH8T5qfgU10oAgJu7JsO9tF2BZd5BE5HyMg1zx3wJn+oecHGwgr6eHmIePkfknnOY//tBZGV9mlMVdWmtBH6+1btWwsgdN1VW19x2ql/XQB2UTgwuX76MRYsW4cKFC0hJSUFOjvw/MDKZTOl1osuWLfvBcplM9tExBB+jS4kBfbqJARVMlxIDYmKgako9nUeOHEGrVq1gY2ODunXr4uLFi2jWrBnS09Nx8uRJVKlSpVhrGcTGxip9LBERkapw8GEhTZw4EZ6enrh16xZWrVoFIHc9g2PHjuHEiRN49OiRWtaIJiIi0iQ9mepuJYVSicGFCxfQp08fWFpaQl9fHwDEWQSff/45BgwYoNRUxXclJCRg7NixaN68OSpUqIBr13LXxF6wYAFOnTpVrLqJiIioYEolBqVKlYKFhQUAwNraGgYGBuLaBkDuVQevX1d+GtSFCxfg5eWFyMhIuLq6IiYmRryS1+PHjzFv3jyl6yYiIiosmUx1t5JCqcSgfPnyuHPnDoDc/peKFSsiKipKLN+1axecnZ2VDmrEiBHw9fXFnTt38Ouvv+Ld8ZGff/45WwyIiEgjuOxyIQUFBWHjxo3Iysod2j9y5Ehs27YNXl5e8PLywo4dOzBgwAClgzp79iyGDh0KAwODfAM/HBwc5FoniIiISHWUmpUwYcIEDBs2TBxf0LNnT+jr62Pr1q3Q19fHuHHjEBYWpnRQZmZmSE1NLbDs33//hZ1dwfO2iYiIVIlrJRSSgYFBvi/nHj16oEePHioJqmXLlpg2bRqaN28Oa2trALldFmlpaViwYAGCgoJUch4iIqIPKUE9ACojyWRo1qxZSE1NhZeXF7p06QKZTIbx48ejcuXKSExMxLRp07QdIhER0SdJqRaDZs2afXQfmUyGv/76S5nqUaZMGVy6dAnz5s3DgQMHUK5cOSQmJqJ79+4YOXIkbG1tlaqXiIioKErSoEFVUSoxyMnJyTcoMDs7Gw8ePMDDhw9Rvnx5lClTpliBWVtbY/LkyZg8eXKx6iEiIlKWDuYFyl8SWZE///wT/fv3x9y5c5WNCd27d0f37t0RGBiIUqUkuQAkERHRJ0nlYwyCg4PRo0cPDB8+XOk6bt26heDgYDg7O2PAgAE4evSo6gIkIiIqJF4SWUXKlSuHs2fPKn38uXPncOvWLQwdOhTHjh2Dv78/XF1dMWrUKJw/f16FkRIRESnGCxypQFZWFjZt2gR7e/ti1ePl5YWJEyfi2rVruHjxIr766itERUXBx8cH3t7eKoqWiIiI3qVUB37v3r0L3P7ixQucOnUKT58+LdYYg/fVqFEDbm5uKFu2LKZMmYK7d++qrG4iIiJFStAPfZVRKjE4dOhQvlkJMpkMNjY2aNSoEfr27YvAwMBiB/f69Wts374dGzduxMGDByGTydCyZUt07dq12HUTERF9TEkaG6AqSiUG9+/fV3EY8rZt24bIyEjs2rULGRkZaNq0KZYuXYqOHTuKV0IkIiIi1VNqjMHvv//+weTg/v37+P3335WNCZ07d8ajR48wc+ZMPH78GAcOHEDv3r2ZFBARkUbJVPhfSaFUi0GvXr2wdu1aeHh4FFh++vRp9OrVC19//bVSQcXGxsLd3V2pY4mIiFRFF7sSlGoxEAThg+WvX78u1oWJ3k0KcnJy4OnpiWvXrildHxERERVOob+9L1++jEuXLon3//77b2RlZeXb78WLF1i+fDkqVKigkgAFQcD9+/eRkZGhkvqIiIgKSxdbDAqdGERFRYnrFshkMqxYsQIrVqwocF9ra+tijTEgIiKSgvdn4OmCQicG/fv3R3BwMARBgI+PD6ZMmYLWrVvL7SOTyWBmZoZy5cpxjQMiIqISqNDf3i4uLnBxcQEAHD58GJUrV4aDg4PaAsujr6+P2NhYlC5dWu3nIiIiepcudiUoNfiwWrVqiIuLU1h+5coVJCcnKx3U+9zd3fH48WMcPHgQSUlJKquXiIjoQ2Qy1d1KCqUSgxEjRqB///4KywcMGIDRo0crHdSoUaPkVmeMioqCt7c3AgMD4eXlxYWUiIhIJ61Zswa1atWCsbEx7O3t0bp1a6Slpan0HEolBocOHUK7du0Ulrdt2xYHDx5UOqioqCjUrVtXvB8eHo6goCBcvnwZPj4+GD9+vNJ1ExERFZaUVlecPn06hgwZgpCQEOzbtw8rVqxA2bJlkZ2drYJH+j9KjRB8/vz5B1dPtLOzQ3x8vNJBxcXFwc3NDQAQExODW7duYd26dahatSqGDBmCnj17Kl03ERFRYUlljMGtW7cQERGBHTt2yA3879Spk8rPpVSLgYuLCy5evKiw/Pz588UamGhlZSUmFgcOHICtrS3q1KkDADAyMlJ5swkREZGUrVq1CmXLls03G1AdlEoM2rdvj19//RU7duzIVxYdHY1Vq1ahQ4cOSgfVpEkTTJw4EUuWLMGsWbPQvn17sezWrVtiawIREZE6SWXw4alTp1CtWjVMmzYNjo6OMDQ0RMOGDXH69GnVPNB3yISPXd+4ACkpKWjUqBGuX7+OGjVqoGrVqgCAq1ev4p9//kGlSpVw7NgxpRc9evz4Mb766iucPXsWtWvXxqZNm+Dk5AQA8PX1RfXq1RVeXKmw0vNftJE+YTb1vtV2CKRByWcXazsE0iBjNV42Z8nx+yqra85XzT5Yfu/ePYVlFStWxOPHj+Hi4oIff/wRpqam+PHHH3HlyhXcuXMHjo6OKotTqafTysoKp06dwk8//YRt27Zhy5YtAIBy5cphwoQJ+O6772BmZqZ0UGXKlMGhQ4cKLNu3bx+MjY2VrpuIiKikycnJwatXr7BlyxZUr14dAFC/fn14eHhg8eLFmDJlisrOpXSeZWZmhsmTJ4uXSX5fcnIybGxslA5MEUtLS5XXSUREVBBVXn/gQy0CH2NjYwM7OzsxKQAAW1tb1KpVS+WLDKq0ASYjIwM7duzA+vXrsXfvXqSnpxf62Hbt2mHOnDnw8vL64FRIIPfSy9HR0cUNl4iI6IOkMiuhSpUqiImJKbCsKN+1haHU4MN3CYKAgwcPolevXnByckJISAhOnjyJbt26Famely9finMxU1NT8fLlS4W31NTU4oZNRERUYgQHByMxMVFulePExERcuHBBnLWnKkoNPgRypySuX78ekZGRePr0KWQyGUJDQ/Htt9+ifv36kl+RioMPdQsHH+oWDj7ULeocfLjy1AOV1dW/vrvSx+bk5KB+/fpISkrC9OnTYWJighkzZuDOnTu4evUqnJ2dVRZnkVoM7t27h6lTp6JixYrw8fHBli1b0L17d/zxxx8QBAGdOnWCr69vsZKC9PR01KhRA/v371e6DiIiIlWQynRFPT097N69G76+vhgwYABCQ0NhaWmJo0ePqjQpAIowxsDX1xdnzpyBvb09OnfujP/85z9o1KgRACjs91CGsbExHj9+DD29YvdyEBERfTLs7e2xdu1atZ+n0InB6dOnUbZsWcydOxdt2rRBqVLqa7vp2LEjNm3ahICAALWdg4iI6GNUscZBSVPob/fFixdjw4YN6NChA2xtbdGpUyeEhobC399f5UE1bNgQ4eHhCA4ORlBQEJycnPJ1T3Ts2FHl5yUiInqXDuYFhU8MBg0ahEGDBiE2Nhbr16/Hhg0b8Msvv8DZ2RlNmzaFTCZT2YDDXr16AchdTGn37t35ymUymcpXkyIiIqJizEoA/jcz4Y8//kBcXBycnJzQtm1btGvXDgEBAUpfofDBg4+PAnV3V350J8BZCbqGsxJ0C2cl6BZ1zkpYffZfldUVVq9krPNTrMQgT05ODg4dOoR169YhKioKL1++hKmpKV69eqWKGNWCiYFuYWKgW5gY6BZ1JgZrzj1UWV09636msrrUSSVPp56eHgICAhAQEIDly5cjOjoaGzZsKFIdFhYWhe6KkMlkSElJUSZUIiIi+gCV51nGxsYICQlBSEhIkY4bNWqU5C+KREREukUXv5XU2ABTNBEREdoOgYiISI4uTlfkVYSIiIhIJJkWAyIiIqnRvfYCJgZEREQK6WBPArsSiIiI6H/YYkBERKSALs6WY2JARESkgC42q+viYyYiIiIF2GJARESkALsSiIiISKR7aQG7EoiIiOgdbDEgIiJSgF0JRJ+ofX9M0XYIpEHVw/dqOwTSoNs/tVJb3brYrK6Lj5mIiIgUYIsBERGRAuxKICIiIpHupQXsSiAiIqJ3sMWAiIhIAR3sSWBiQEREpIieDnYmsCuBiIiIRGwxICIiUoBdCURERCSSsSuBiIiIdBlbDIiIiBRgVwIRERGJOCuBiIiIdBpbDIiIiBRgVwIRERGJdDExYFcCERERidhiQEREpIAuXseAiQEREZECerqXF7ArgYiIiP6HiQEREZECMhX+pyqvXr2Cq6srZDIZzp07p7J687ArgYiISAEpzkqYOnUqsrKy1Fa/ZFsM1q5di0aNGsHR0RGWlpb5bkRERLrm5s2bWLJkCSZPnqy2c0gyMVi3bh369euHqlWrIiEhAV26dEGnTp1gaGgIR0dHjB49WtshEhGRDpBaV8KQIUMwcOBAeHt7q6S+gkgyMZgzZw4mTJiAJUuWAAAGDRqEVatWITY2Fg4ODjA3N9dyhEREpAv0ZKq7FdeWLVtw5coVTJw4sfiVfYAkxxjcuXMHDRs2hL6+PvT19ZGamgoAsLCwwJgxYzB8+HCMHDlSy1ESEREVnqen5wfL7927p7DszZs3GDlyJH788Ue1d6dLssXAysoKGRkZAIAyZcrg+vXrYll2djYSExO1FRoREekQqXQlTJs2DU5OTujVq5eKHplikmwxqFu3Li5fvoyWLVuiXbt2mDx5MnJycmBgYICZM2eifv362g6RiIh0gCpnJXyoReBDHjx4gDlz5iAqKgopKSkAcqcs5v3/1atXKu1il2Ri8MMPP+DBgwcAgClTpuDBgwcYPnw4cnJyUK9ePaxYsULLERIREWlGbGwsMjMz0aZNm3xlTZs2xeeff45Tp06p7HySTAzq168vtgpYW1sjOjoaGRkZyMjI4FRFIiLSGClcxqBmzZo4fPiw3LZLly5hxIgRWL58OerVq6fS80kyMSiIkZERjIyMtB0GERHpED0JXOHI2toa/v7+BZbVqVMHtWvXVun5JDn4sHfv3ggJCSmwLDQ0FP3799dwRERERLpBkonBgQMH0LFjxwLLOnXqhH379mk4IiIi0kUyFd5Uyd/fH4IgoG7duiquWaKJwfPnz+Hg4FBgmZ2dHZ49e6bhiIiIiHSDJBODMmXK4PTp0wWWnT59Gi4uLhqOiIiIdJJUmwzUSJKJQdeuXTF9+nRs2rRJbvvmzZvx448/olu3blqKjIiIdIlULnCkSZJMDCZOnAh/f3+EhobCwsICFSpUgIWFBUJDQ+Hn54dJkyZpO0QiIqJPkiSnKxoaGuLPP//EgQMHcOjQISQmJsLOzg4BAQFo3ry5tsMjIiIdIYHZihonycQgT4sWLdCiRQtth0FERDpKB/MC6SQGSUlJsLa2hp6eHpKSkj66v62trQaiIiIi0i2SSQwcHBxw8uRJ+Pj4wN7eHrKPtN9kZ2drKDIiItJZOthkIJnE4LfffkO5cuXEvz+WGBAREalbSZpNoCqSSQx69uwp/h0WFqa9QIiIiHSYZBIDIiIiqdHFxmtJXscgLS0N4eHhqFChAkxNTaGvr5/vRkREpG46eOFDabYYDB48GBs2bEDXrl1RuXJlGBoaajskIiIinSDJxGDnzp2YPXs2vv32W22HQkREuqwk/dRXEUkmBvr6+qhQoYK2wyAiIh2ni7MSJDnG4JtvvsHatWu1HQYREZHOkWSLgampKf7++280aNAAAQEBsLa2liuXyWQYMWKEdoIjIiKdoYuzEmSCIAjaDuJ9enofbsiQyWTFvvJhelaxDqcS5sy9j19mmz4dff9zRtshkAbd/qmV2ur+59+XKqurhpuFyupSJ0m2GOTk5Gg7BCIiIp0kycSAiIhIEnSwK0EyicGFCxdQqVIlmJiY4MKFCx/dv3bt2hqIioiIdJkuzkqQTGJQt25dnDp1Cj4+Pqhbt67CRZQEQVDJGAMiIiLKTzKJweHDh1G5cmXxbyIiIm3TxVkJkkkM/Pz8Cvybii8zMxNLFi3Arp3RSE1NhVcFb3w7dDh8GzTUdmikZrv+WI3t61agtJsnJi9Zr+1wqBhMDfXR168sqrtZofpnVrA2NcSYP64g6vxjuf2qf2aFDnXKoIabFbxdLGCgr4cK3+/VUtQlnw7mBdK8wBGp1oTwsVj3+2oEBbfF92PHQV9fH99+0x8Xzp/TdmikRkkJ8di9eQ2MjE20HQqpgI2ZIb5tUR7lHM1xM07xFDq/ig740scVAoCHSWmaC5A+GZJMDPT09ApcUVFfXx+lSpWCnZ0dmjdvjp07d2o7VMm7cvky9u7ZhaHDR2Lk6DHo3CUEv/y2Bi4upTF/7mxth0dqtOW3RfD0rgL38hW1HQqpQHxqOhpMOYSmM/4PP+26pXC/DSf/RZ2JB9Fp4UmcuJ2gwQg/UTq4vKIkE4Off/4Zrq6uKF++PEaMGIEZM2Zg+PDhKFeuHEqXLo3Bgwfj7du3aN++PSIjI7UdrqQd3L8X+vr66PRliLjNyMgIHTp1xj+XLuJpXJwWoyN1uX31Is4fP4yQfsO1HQqpyNtsAQmvMj+6X+KrTGRk8VowqiJT4X8lhWTGGLwrKSkJdevWxZYtW+RmJ8yePRudOnVCWloajh49itDQUMyaNQuhoaFajFbabt68AXd3D5ibm8ttr1qtulju7OKijdBITXKys7FxxVw0CmwLV4/y2g6HiEoYSbYY/Prrr+jXr1++KYsymQz9+/fHmjVrAADdunXDzZs3tRFiifH8+XPYOzjk225v7/Df8nhNh0Rq9n97o5D4/Cna9+iv7VCISjyZTHW3kkKSLQZv3rzBv//+W2DZgwcPkJ6eDgAwMzODoaGhJkMrcTIy0gt8joyMjHLL//tc0qfhVWoKotf/guCQXrCwstF2OEQlXgn6PlcZSSYG7dq1w9ixY2Fubo62bdvCwsICL1++RHR0NMaOHYv27dsDAK5cuYLy5QtuKvX09PzgOa7fvqfqsCXJyMgYmZn5+yUzMjJyy42NNR0SqdH2dStgZm6JZsFfajsUIiqhJJkYLF26FGFhYejRowdkMhkMDAzw9u1bCIKADh06YPHixQAANzc3zJgxQ8vRSpuDgwPinz3Ltz0h4fl/yx01HRKpybMnD3F0XzRC+g7Hi6T/jUZ/+zYT2dlZSHgWBxNTU5hZWGkxSqISRgebDCSZGFhaWmLbtm24ceMGzp49i7i4OLi4uKBu3bri1REBoGPHjgrruHfvwy0CurLssnfFijh75jRevXolNwDxyuV/AAAVK1bSVmikYi8Sn0PIyUHkyrmIXDk3X/kPfTuiebsuCO03QgvREZVMJWk2gapILjFIT0/H559/jp9//hmBgYGoVIlfXMURENgKa1b9hq2b/0DPXn0A5F4JMTpqG6pVr8EZCZ+Q0m6eGBQ+M9/27etWIj3tDUL7DYeDSxktREZEJYnkEgNjY2M8fvwYenqSnDBR4lSvXgOBLVth4fy5SEpMxGdu7tgZHYUnTx4jYup0bYdHKmRhZY1avvkvJ35wxx8AUGAZlSw9GrjBwrgUHC1zxwY1q+wAZ6vcgcRrT/yLV+lZKG1tjC9qlwYAVHXN7Tb6plnumKsnL9IRfeGJFiIvuUrSbAJVkVxiAOR2EWzatAkBAQHaDuWTMG3GT1iyaD7+3LkDqakp8KrgjYVLlqNO3XraDo2IiqB3k7Jwtf3fJa5bVnNGy2rOAIAdF+PwKj0LrramGNGqgtxxefdPxyQxMSgiHcwLIBMEQdB2EO9bs2YNwsPDUatWLQQFBcHJySnfNQ0+NL6gMHRljAHlOnMvSdshkAb1/c8ZbYdAGnT7p1bqq/vpG5XVVcHZVGV1qZMkWwx69eoFAIiLi8Pu3bvzlctkMmRnZ2s6LCIi0jU62GQgycQgNjZW2yEQERFJZlbC5s2bsW7dOpw/fx7Jycnw8vLC0KFD0atXr3wt6sUlycTA3d1d2yEQERFJxty5c+Hh4YE5c+bAwcEBBw4cQL9+/fDw4UNMmjRJpeeSTGKQlJQEa2tr6OnpISnp4/3Btra2GoiKiIh0mVRmJezcuRP29vbi/WbNmiExMRFz587FhAkTVDqTTzKJgYODA06ePAkfHx/Y29t/tGmEYwyIiEjdJJIXyCUFeWrVqoVffvkFr1+/hoWFhcrOJZnE4LfffkO5cuXEv1XdZ0JERPQpOXbsGMqUKaPSpACQUGLQs2dP8e+wsDDtBUJERJRHhb9RP7a438cu5f+uY8eOITIyEnPmzCluWPlI8vKC3bt3x+7du5GVxYsNEBGR9shU+J+qPHr0CCEhIWjatCmGDh2qsnrzSKbF4F23bt1CcHAwbG1t0alTJ3Tv3h1NmjTRdlhERERKK0qLgCIvXrxA69atYWdnh61bt6pl+QBJthicO3cOt27dwtChQ3Hs2DH4+/vD1dUVo0aNwvnz57UdHhER6QiZTHW34kpLS0NwcDBSUlKwZ88eWFmpZwl1SSYGAODl5YWJEyfi2rVruHjxIr766itERUXBx8cH3t7e2g6PiIh0gEyFt+LIyspCly5dcOPGDezduxdlyqhvpVRJdiW8r0aNGnBzc0PZsmUxZcoU3L17V9shERERacygQYPw559/Ys6cOUhNTcWpU6fEslq1asHIyEhl55J0YvD69Wts374dGzduxMGDByGTydCyZUt07dpV26EREZEukMjM+f379wMARo0ala8sNjYWHh4eKjuXJBODbdu2ITIyErt27UJGRgaaNm2KpUuXomPHjrC2ttZ2eEREpCOkslbC/fv3NXYuSSYGnTt3Rv369TFz5kx06dIFTk5O2g6JiIhIJ0gyMYiNjeVCSkREpHW6eBFeSc5KeDcpyMnJgaenJ65du6bFiIiISBdJZVaCJkkyMXiXIAi4f/8+MjIytB0KERHRJ0+SXQlERERSoItdCUwMiIiIFNK9zEDyiYG+vj5iY2NRunRpbYdCRET0yZP8GAMgdzDi48ePcfDgQSQlJWk7HCIi0hFSWitBUySZGIwaNQrDhw8X70dFRcHb2xuBgYHw8vLiQkpERKQRnJUgEVFRUahbt654Pzw8HEFBQbh8+TJ8fHwwfvx4LUZHRET06ZLkGIO4uDi4ubkBAGJiYnDr1i2sW7cOVatWxZAhQ9CzZ08tR0hERLqgJHUBqIokEwMrKyvEx8cDAA4cOABbW1vUqVMHAGBkZIS0tDRthkdERDpCKmslaJIkE4MmTZpg4sSJePbsGWbPno327duLZbdu3RJbE4iIiEi1JDnGYN68eXB2dsbYsWPh5uaG6dOni2Vr165F48aNtRgdERHpDB0cfSjJFoMyZcrg0KFDBZbt27cPxsbGGo6IiIh0UQn6PlcZSSYGH2JpaantEIiIiD5ZkkkM2rVrhzlz5sDLywvt2rX74L4ymQzR0dEaioyIiHQVZyVo0cuXL5GdnQ0ASE1NhUwXXw0iIpIUzkrQosOHD4t/HzlyRHuBEBER6TDJzUpIT09HjRo1sH//fm2HQkREuo6zErTP2NgYjx8/hp6e5HIWIiLSMSXo+1xlJPnt27FjR2zatEnbYRAREekcybUYAEDDhg0RHh6O4OBgBAUFwcnJKd9gxI4dO2opOiIi0hW6OA5ekolBr169AOQuprR79+585TKZTJzBQEREpC6clSARsbGx2g6BiIhIJ0kyMXB3d9d2CEREROxK0CYLC4tCX9RIJpMhJSVFzRERERHpHskkBqNGjeLVDomIiLRMMolBRESEtkMgIiKSo4u/VyWTGBAREUmNLs5KkOQFjoiIiEg72GJARESkALsSiIiISKSDeQG7EoiIiOh/2GJARESkiA42GTAxICIiUoCzEoiIiEinscWAiIhIAV2clcAWAyIiIgVkKrwVx82bN9GiRQuYmZnB2dkZ33//PTIzM4tZa8HYYkBERCRhycnJaNasGby8vLBt2zY8fvwYI0eOxJs3b7B48WKVn4+JARERkSIS6EpYvnw5UlNTERUVBVtbWwBAVlYWBg0ahPDwcJQuXVql52NXAhERkQIyFf6nrD179iAgIEBMCgCgS5cuyMnJwf79+1XxMOUwMSAiIpKwmzdvomLFinLbrK2t4eLigps3b6r8fOxKICIiUkCVsxI8PT0/WH7v3r0CtycnJ8Pa2jrfdhsbGyQlJakiNDk6mxgY6+Ajz3tTKnrzfcqaVLD9+E6fGF1+vW//1ErbIWicLr/e6qSL3xU6+JCJiIg0T9mkzcbGBikpKfm2Jycny407UBWOMSAiIpKwihUr5htLkJKSgri4uHxjD1SBiQEREZGEtW7dGgcPHsSLFy/EbZs3b4aenh4CAwNVfj4mBkRERBI2cOBAWFhYoH379ti/fz9WrVqF7777DgMHDlT5NQwAJgZERESSZmNjg7/++gulSpVC+/btMXbsWPTt2xdz585Vy/k4+JCIiEjiKlWqhIMHD2rkXGwxICIiIpFMEARB20EQERGRNLDFgIiIiERMDIiIiEjExICIiIhETAyIiIhIxMSAiIiIREwM1CQiIgLm5uYf3c/f3x/BwcEaiOjjjhw5gh9//FHbYUhCSXz9iqOwj7eoVq9eDZlMhoSEBJXXrW5Sew+o+rk8cuQIZDIZzp07p9U4SHp4gSMtW7p0KfT19bUdBoDcfyhmz56N8PBwbYdSYkjp9SuOvn37ok2bNtoOo0TS1HugTZs2OHnyJKytrVVSX+3atXHy5ElUqlRJq3GQ9DAx0JK0tDSYmJigcuXK2g6FlFBSXr+MjAwYGBhAT+/DjYOurq5wdXXVUFRFl52djZycHBgYGGg7FJGm3wMODg5wcHAoVEyFYWlpifr166slDirZ2JWgAffv34dMJsPq1avRr18/2NnZwcfHB0D+ZshHjx6hS5cucHJygrGxMcqWLYsRI0Z89By//fYbqlSpAhMTE9jZ2aFRo0Y4e/asWC4IAmbPno0KFSrAyMgInp6emDdvnlgeERGByZMn4/Xr15DJZJDJZPD39xfLjx49igYNGsDExAT29vbo3bs3kpKS5GKYOXMmypcvD2NjYzg4OCAgIACxsbFi+dixY1GtWjWYm5ujTJky6Nq1K+Li4or8fGqaOl+/vLq3bNmSr6xu3bro2rWrXN09evSAvb09TExM0KRJE5w/f17uGA8PD3z77bf46aef4O7uDhMTEyQlJX00roKazV+8eIEhQ4bA1dUVRkZGKFu2LH744Qe5fVasWAFvb28YGRnBw8MD06ZNQ05Ozgefz6SkJPTu3Vt8HA0aNMDRo0fl9sl7XtesWSPW/88//3ywXnWSwnvg/Sb8D8WUkpKCHj16wMLCAo6OjggPD8ecOXMgk8nEugvqSpDJZPjpp58QEREBJycn2Nvbo1evXnj9+rW4T0FdCRkZGRg/fjw8PT1hZGQEV1dXhIWFieUnT55Eu3btULp0aZiZmaFmzZpYu3ZtoZ570jy2GGjQDz/8gDZt2mDjxo0K//H8+uuv8eTJEyxcuBBOTk74999/P9oHePToUfTp0wejR49GUFAQ3rx5gzNnzsgt0Tls2DD85z//wbhx4/D555/jxIkTGDNmDExMTDBw4ED07dsXjx49woYNG3Do0CEAub8oAOD8+fNo0aIF/P39sXnzZjx79gxjx47FtWvXcOLECejr6+P333/HhAkTMGXKFPj6+iIlJQV///03UlNTxRji4+MRHh6O0qVL4/nz55gzZw78/Pxw/fp1lCol/beiOl4/Dw8P1K9fH5GRkejcubO4/c6dOzh//jwmTZoEAEhOTkajRo1gbm6ORYsWwcrKCosWLUKzZs1w584dODo6isdu3boVXl5eWLBgAfT19WFmZoYuXboUKa6MjAw0a9YM9+/fx6RJk1CtWjU8fPgQx44dE/dZtGgRhg4diiFDhiA4OBgnTpxAREQEXrx4gdmzZxdYb3Z2Nlq3bo179+5h1qxZcHJywsKFC9GiRQucOHECderUEfc9d+4c7t+/jylTpsDGxgafffaZwng1RZvvgaLE1KtXLxw6dEhMEH/55Zd8SaQiixcvRuPGjbFmzRrcvn0b3333HZycnDBz5kyFx3Tq1AmHDh1CeHg46tevj+fPn2Pbtm1i+YMHD9CwYUMMHDgQxsbGOH78OPr06YOcnBz07NmzUHGRBgmkFpMmTRLMzMwEQRCE2NhYAYDQqlWrfPv5+fkJbdq0Ee+bmZkJCxcuLNK5fv75Z8HW1lZh+d27dwWZTCasWLFCbvuYMWMEZ2dnITs7O1/M7+rQoYPg5uYmZGZmitv27dsnABB27NghCIIgDB48WKhdu3ahY87KyhIePXokABD27dtX6OM0RZOv34IFCwRjY2MhNTVV3DZ58mTBxsZGyMjIEARBECZOnChYWVkJz549E/dJT08X3NzchO+++07c5u7uLtjZ2QmvXr2SO8fH4nr/tV+5cqUAQDhx4kSB+2dlZQn29vZCaGio3PYffvhBMDQ0FBISEgRBEIRVq1YJAITnz58LgiAI0dHRAgBh79694jGZmZmCm5ub0LFjR3Gbn5+fYGBgIPz7778KY1Y3qb0H3n8uFcV07do1AYDw+++/i9uys7MFLy8v4d1/8g8fPiwAEM6ePStuAyD4+PjI1dezZ0+hXLly4v3349i/f78AQNiwYUOhHmtOTo7w9u1boX///oKvr2+hjiHNYleCBhVmcFft2rUxe/ZsLFu2DHfv3s1XnpWVJd6ys7PFY5KSkhAWFoYDBw7gzZs3csfkrcjVqVMnueMDAgLw9OlTPHz48IMx/f333/jiiy/k+ncDAwNhbW0t/oKsXbs2Ll68iJEjR+LYsWN4+/Ztvnr27NmDBg0awMrKCqVKlRL7tG/fvv3R50UK1PX6denSBZmZmdi+fbu4X2RkJDp16gRDQ0MAwP79+9G0aVPY2tqKx+vr68PPz0+uywjIbdo2MzMrUlzv++uvv1CpUiX4+voWWH7z5k0kJCTgyy+/lNseEhKCzMxMnDlzpsDj/v77b1haWqJly5biNgMDA3Ts2FGuNQIAqlevLolWgndp8z1Q2Jjy3g/t2rUTt+np6aFt27YfjR0AWrRoIXe/cuXKePTokcL9//rrL5iamiI0NFThPsnJyRg6dCjc3d1hYGAAAwMDrFy5ssR89nUNEwMNcnJy+ug+f/zxB5o3b45x48bBy8sLFStWFJvk7t+/L36oDAwMUK5cOQBAs2bNsHbtWly7dg0tW7aEvb09vv76a3EMQEJCAgRBgL29vdzxef8AfCwxSE5OLjB2Jycn8RxhYWGYN28e9u3bh8aNG8PBwQHDhg1DWloagNx/rPL6GNeuXYuTJ0/i1KlTAID09PTCPH1ap67Xz9nZGU2bNsXGjRsBAP/88w9u3LiBbt26ifUmJCRg+/btcscbGBhg7dq1+V6/guL8UFwFSUxMROnSpRWWJycnF3iuvPvvjz9597h3uz3ePe79YwrzfGuaNt8DhY0pLi4OBgYGsLKyktte0PNekPdnGxgaGiIjI0Ph/omJiXBxcZEbv/C+sLAwbNy4EaNHj8b+/ftx9uxZ9O7du8R89nWN9Dt2PyEf+uDkcXFxwW+//Yb//Oc/OH/+PKZNm4aQkBDcunULrq6ucr8OjYyMxL979OiBHj16ICEhAdHR0RgxYgQMDAzw66+/wtbWFjKZDMeOHSvw14e3t/cHY7K1tUV8fHy+7c+ePYOtrS2A3F8kw4YNw7Bhw/D48WNERkZi7NixsLe3x4QJExAVFQUrKyts2rRJHCH/4MGDjz4fUqLO169r16745ptvkJiYiMjISLi4uMDPz08st7W1RatWrTB16tR853y3HkVxfiguT0/PfPvb2dnh8uXLCh9n3uv+/vvi2bNncuUFHfex99KHHoe2afM9UNiYXFxc8PbtW6SkpMglBwU976pgZ2eHuLg4CIJQ4POTnp6OP//8E3PnzsWQIUPE7R8bpErawxYDidLT00O9evUwbdo0ZGVl4e7duzA0NETdunXFW7Vq1fIdZ29vjz59+qBFixa4ceMGAKB58+YAcjP7d4/Pu1lYWABQ/MugUaNG2L59O7KyssRtBw4cwIsXL9CoUaN8+5cpUwajRo1C9erVxRjS0tJgYGAg9w/H+vXri/EMSVtRX7+OHTuKI9MjIyMREhIiN8UwICAA169fR6VKlfK9fgW9D4oSV0ECAgJw48YNnD59usByb29vODg4YPPmzXLbN23aBENDQ3F0/PsaNWqE1NRU7N+/X9yWlZWFqKioAt9LJZmq3wOFVbduXQBAdHS0uC0nJwc7d+4s/oMqQEBAAN68eYNNmzYVWJ6RkYGcnBy5HyUvX77Ejh071BIPFR9bDCQkJSUFLVu2xFdffQVvb29kZmZi0aJFsLa2Ru3atRUeN2nSJCQmJsLf3x+Ojo64cuUK9u7di5EjRwIAKlSogMGDB+Orr77Cd999h88//xxv377F7du3cfjwYbFfs1KlSsjKysKCBQvQoEEDWFpawtvbG+PGjUODBg0QHByMIUOGiLMSfHx8EBQUBAAYMGAAbGxsUL9+fdjY2OD48eP4559/MGjQIAC5/Zbz58/HkCFD0KFDB5w8efKTm66k7OsHADY2NmjVqhWmTJmCJ0+e5GtCHjlyJNavXw8/Pz8MGzYMbm5ueP78OU6fPo3SpUt/cDqcMnF99dVXWLp0Kdq0aYNJkyahatWqePz4MY4ePYqVK1dCX18fEyZMwNChQ+Ho6IigoCCcOnUKs2bNwvDhw2FnZ1dgvW3atIGPjw969OiBmTNnwsnJCYsWLUJcXNwncWEtdb4HCqtKlSro0KEDhg4dijdv3sDd3R0rV65EWlqaWlphAgICEBQUhN69eyMmJgaff/45kpKSsGXLFvzxxx+wsrJCvXr1MHPmTDg4OKBUqVKYOXMmrKys1NaKQcWk7dGPn6qCRjRv3rw5337vjmhOT08X+vbtK3h7ewsmJiaCra2tEBgYKJw5c+aD59q5c6fQvHlzwcHBQTAyMhLKlSsnTJo0SXj79q24T05OjrBo0SKhatWqgqGhoWBrayv4+voKc+fOFfd5+/atMGjQIMHJyUmQyWSCn5+fWHbkyBHB19dXMDIyEmxtbYWwsDAhMTFRLF+9erXQsGFDwdbWVjA2NhYqV66cb2T2rFmzBFdXV8HU1FRo0aKFcPv2bQGA8PPPPxf+idUQTb5+eTZu3CgAkBsB/q64uDihT58+gouLi2BoaCi4uroKnTt3Fo4fPy7u4+7uLgwePFjuuMLEVdCMlKSkJOGbb74RnJ2dBUNDQ8HT01MYN26c3D7Lli0TvLy8BAMDA8HNzU2YOnWqOMtFEPKPYBcEQUhISBDCwsIEW1tbwcjISPD19RWOHDkiV+/7I/21QWrvAUWzEgqKKTk5WejevbtgZmYm2NnZCSNHjhTGjx8vWFtbi/sompXw/udx3rx5crMZCnpN09LShLFjxwpubm6CgYGB4OrqKvTu3Vssv3PnjtCsWTPB1NRU+Oyzz4Sff/5Z4Swo0j6ZIAiCVjISIiLSmCZNmkBfXx+HDx/WdigkcexKICL6xGzduhX//vsvqlWrhjdv3mDDhg34+++/ERUVpe3QqARgYkBE9IkxNzfH2rVrcefOHWRmZqJixYpYt24d2rdvr+3QqARgVwIRERGJOF2RiIiIREwMiIiISMTEgIiIiERMDIiIiEjExIBIAzw8PBAWFibeP3LkCGQyGY4cOaK1mN73foyqEBERIck1D4hIMSYG9MlbvXo1ZDKZeDM2NkaFChXw7bffiov+lBS7d+9GRESEtsNAeno65s2bh88//xxWVlZyzymX0iUq2XgdA9IZU6ZMQdmyZZGeno5jx45h2bJl2L17N65evQpTU1ONxtKkSROkpaUVuNrlh+zevRtLlizRanKQkJCAVq1a4fz58wgODka3bt1gbm6OW7duITIyEitXrkRmZqbW4iOi4mFiQDqjdevW4spzffv2hZ2dHebOnYvo6Gh07dq1wGNev34NMzMzlceip6cHY2NjlderCWFhYbh48SK2bNmCTp06yZVNnToV48aN01JkRKQK7EogndWsWTMAQGxsLIDcLzxzc3PExMQgKCgIFhYW6N69O4DcZWvnz5+PKlWqwNjYGE5OThgwYACSk5Pl6hQEAdOmTYOrqytMTU3RtGlTXLt2Ld+5FY0xOH36NIKCgmBjYwMzMzNUr14dCxYsEONbsmQJAMh1jeRRdYwFOX36NHbt2oU+ffrkSwoAwMjICLNnz/5gHatWrUKzZs3g6OgIIyMjVK5cGcuWLcu337lz59CyZUvY29vDxMQEZcuWRe/eveX2iYyMRJ06dWBhYQFLS0tUq1ZNfL6ISDlsMSCdFRMTAwBySwRnZWWhZcuWaNSoEWbPni12MQwYMACrV69Gr169MHToUMTGxmLx4sW4ePEijh8/DgMDAwDAxIkTMW3aNAQFBSEoKAgXLlxAYGBgoZrWDxw4gODgYLi4uGDYsGFwdnbGjRs38Oeff2LYsGEYMGAAnjx5ggMHDhS4ZLUmYtyxYweA3GWZlbVs2TJUqVIF7dq1Q6lSpbBz504MGjQIOTk5GDx4MAAgPj4egYGBcHBwwNixY2FtbY379+9j27Ztcs9X165d0bx5c8yaNQsAcOPGDRw/fhzDhg1TOj4inafNpR2JNCFvmdiDBw8Kz58/Fx4+fChERkYKdnZ2gomJifDo0SNBEAShZ8+eAgBh7Nixcsf//fffAgBh/fr1ctv37t0rtz0+Pl4wNDQU2rRpI+Tk5Ij7hYeHCwCEnj17itvylrw9fPiwIAiCkJWVJZQtW1Zwd3cXkpOT5c7zbl2DBw8WCvrYqiPGgnTo0EEAkC9GRSZNmpQv3jdv3uTbr2XLloKnp6d4PyoqKt+SwO8bNmyYYGlpKWRlZRUqFiIqHHYlkM4ICAiAg4MDPvvsM4SGhsLc3BxRUVEoU6aM3H7ffPON3P3NmzfDysoKLVq0QEJCgnirU6cOzM3NxWVsDx48iMzMTAwZMkSuiX/48OEfje3ixYuIjY3F8OHDYW1tLVdWmOl+mogRAFJTUwEAFhYWhdq/ICYmJuLfKSkpSEhIgJ+fH+7du4eUlBQAEJ+DP//8E2/fvi2wHmtra7x+/RoHDhxQOhYiyo9dCaQzlixZggoVKqBUqVJwcnKCt7c39PTkc+NSpUrB1dVVbtudO3eQkpICR0fHAuuNj48HADx48AAA4OXlJVfu4OAAGxubD8aW161RtWrVwj8gDccIAJaWlgCAly9f5ktgCuv48eOYNGkSTp48iTdv3siVpaSkwMrKCn5+fujUqRMmT56MefPmwd/fH+3bt0e3bt1gZGQEABg0aBA2bdqE1q1bo0yZMggMDESXLl3QqlUrpeIiolxMDEhn+Pj4iLMSFDEyMsqXLOTk5MDR0RHr168v8BgHBweVxagsTcVYsWJFAMCVK1fQuHHjIh8fExOD5s2bo2LFipg7dy4+++wzGBoaYvfu3Zg3bx5ycnIA5LaSbNmyBadOncLOnTuxb98+9O7dG3PmzMGpU6dgbm4OR0dHXLp0Cfv27cOePXuwZ88erFq1Cl9//TXWrFmjksdLpIuYGBB9RLly5XDw4EE0bNhQrhn8fe7u7gByf717enqK258/f55vZkBB5wCAq1evIiAgQOF+iroVNBEjALRt2xYzZszAunXrlEoMdu7ciYyMDOzYsQNubm7i9ryujvfVr18f9evXx/Tp07FhwwZ0794dkZGR6Nu3LwDA0NAQbdu2Rdu2bZGTk4NBgwZhxYoVmDBhAsqXL1/k+IiI0xWJPqpLly7Izs7G1KlT85VlZWXhxYsXAHLHMBgYGGDRokUQBEHcZ/78+R89R+3atVG2bFnMnz9frC/Pu3XlXVPh/X00ESMA+Pr6olWrVvjPf/6D7du35yvPzMzE6NGjFR6vr6+f7zGlpKRg1apVcvslJyfL7QMANWvWBABkZGQAABITE+XK9fT0UL16dbl9iKjo2GJA9BF+fn4YMGAAZsyYgUuXLiEwMBAGBga4c+cONm/ejAULFqBz585wcHDA6NGjMWPGDAQHByMoKAgXL17Enj17YG9v/8Fz6OnpYdmyZWjbti1q1qyJXr16wcXFBTdv3sS1a9ewb98+AECdOnUAAEOHDkXLli2hr6+P0NBQjcSY5/fff0dgYCA6duyItm3bonnz5jAzM8OdO3cQGRmJuLg4hdcyCAwMFH/lDxgwAK9evcIvv/wCR0dHxMXFifutWbMGS5cuRYcOHVCuXDm8fPkSv/zyCywtLREUFAQg9yJVSUlJaNasGVxdXfHgwQMsWrQINWvWRKVKlQr1WIioAFqdE0GkAXnTFT809U0QcqcrmpmZKSxfuXKlUKdOHcHExESwsLAQqlWrJnz//ffCkydPxH2ys7OFyZMnCy4uLoKJiYng7+8vXL16VXB3d//gdMU8x44dE1q0aCFYWFgIZmZmQvXq1YVFixaJ5VlZWcKQIUMEBwcHQSaT5ZsKqMoYP+TNmzfC7NmzhXr16gnm5uaCoaGh4OXlJQwZMkS4e/euuF9B0xV37NghVK9eXTA2NhY8PDyEWbNmCb/99psAQIiNjRUEQRAuXLggdO3aVXBzcxOMjIwER0dHITg4WDh37pxYz5YtW4TAwEDB0dFRMDQ0FNzc3IQBAwYIcXFxhXoMRFQwmSC8115HREREOotjDIiIiEjExICIiIhETAyIiIhIxMSAiIiIREwMiIiISMTEgIiIiERMDIiIiEjExICIiIhETAyIiIhIxMSAiIiIREwMiIiISMTEgIiIiERMDIiIiEj0/+MGkuCAAafaAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "models_and_predictions = {\n",
        "    'Logistic Regression': logistic_predictions,\n",
        "    'KNN': knn_predictions,\n",
        "    'Decision Tree': tree_predictions\n",
        "}\n",
        "\n",
        "for model_name, predictions in models_and_predictions.items():\n",
        "    matrix = confusion_matrix(y_test, predictions)\n",
        "\n",
        "    plt.figure(figsize=(6, 5))\n",
        "    sns.heatmap(\n",
        "        matrix,\n",
        "        annot=True,\n",
        "        fmt='d',\n",
        "        cmap='Blues',\n",
        "        xticklabels=label_encoder.classes_,\n",
        "        yticklabels=label_encoder.classes_\n",
        "    )\n",
        "\n",
        "    plt.title(f'Confusion Matrix - {model_name}')\n",
        "    plt.xlabel('Predicted Class')\n",
        "    plt.ylabel('Actual Class')\n",
        "    plt.show()"
      ],
      "id": "qn7bfxEPVZ3n"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3QDTQ3FeVZ3o"
      },
      "source": [
        "## 29. Final conclusion"
      ],
      "id": "3QDTQ3FeVZ3o"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "flx-59qJVZ3o"
      },
      "source": [
        "The Iris dataset contains three classes and four numerical features.  \n",
        "Exploratory analysis shows that petal length and petal width provide the clearest class separation.\n",
        "\n",
        "Three classification models were trained:\n",
        "\n",
        "1. Logistic Regression\n",
        "2. K-Nearest Neighbors\n",
        "3. Decision Tree\n",
        "\n",
        "The models were evaluated using a 70% training and 30% testing split.  \n",
        "Their baseline and tuned accuracies were compared. The best model is the one with the highest testing accuracy shown in the final comparison table.\n",
        "\n",
        "Because the Iris dataset is small and relatively clean, all three models can achieve high accuracy. However, their exact results may vary depending on the train-test split and selected hyperparameters."
      ],
      "id": "flx-59qJVZ3o"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mJMHC7kbVZ3o"
      },
      "source": [
        "## 30. Download the completed notebook\n",
        "\n",
        "After running all cells in Google Colab:\n",
        "\n",
        "1. Click **File**\n",
        "2. Select **Download**\n",
        "3. Choose **Download .ipynb**\n",
        "4. Upload the downloaded `.ipynb` file to your university platform"
      ],
      "id": "mJMHC7kbVZ3o"
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.x"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}