heart_knn.ipynb

heart_knn.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyONwrVVES9/ZxD4IAHlz35C",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/ebnrdwan/c4ccd601b669afc287bb8ea763b1f878/heart_knn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Heart deases prediction"
      ],
      "metadata": {
        "id": "YrzPm7OWIdUt"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e9ae8655"
      },
      "source": [
        "# Task\n",
        "Analyze the `heart.csv` dataset, perform exploratory data analysis (EDA), preprocess the data, train a K-Nearest Neighbors (KNN) model to predict heart disease, and evaluate the model's performance."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "17b037d8"
      },
      "source": [
        "## Load Dataset\n",
        "\n",
        "### Subtask:\n",
        "Load the `heart.csv` dataset into a pandas DataFrame to begin the analysis.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ef5e452"
      },
      "source": [
        "**Reasoning**:\n",
        "First, import the pandas library, then load the specified CSV file into a DataFrame and display its head to verify the import.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "7e2ff015",
        "outputId": "4c52b396-5a87-4f10-945f-fe159a3775a2"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('/content/sample_data/heart.csv')\n",
        "print(\"DataFrame 'df' loaded successfully. Displaying the first 5 rows:\")\n",
        "df.head()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DataFrame 'df' loaded successfully. Displaying the first 5 rows:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
              "0   52    1   0       125   212    0        1      168      0      1.0      2   \n",
              "1   53    1   0       140   203    1        0      155      1      3.1      0   \n",
              "2   70    1   0       145   174    0        1      125      1      2.6      0   \n",
              "3   61    1   0       148   203    0        1      161      0      0.0      2   \n",
              "4   62    0   0       138   294    1        1      106      0      1.9      1   \n",
              "\n",
              "   ca  thal  target  \n",
              "0   2     3       0  \n",
              "1   0     3       0  \n",
              "2   0     3       0  \n",
              "3   1     3       0  \n",
              "4   3     2       0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3708b09c-1a5c-4799-9829-e2029b22568c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>52</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>125</td>\n",
              "      <td>212</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>168</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>53</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>140</td>\n",
              "      <td>203</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>155</td>\n",
              "      <td>1</td>\n",
              "      <td>3.1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>145</td>\n",
              "      <td>174</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>125</td>\n",
              "      <td>1</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>148</td>\n",
              "      <td>203</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>161</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>62</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>138</td>\n",
              "      <td>294</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>106</td>\n",
              "      <td>0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3708b09c-1a5c-4799-9829-e2029b22568c')\"\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-3708b09c-1a5c-4799-9829-e2029b22568c 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-3708b09c-1a5c-4799-9829-e2029b22568c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-a6485145-6752-40ed-ba78-a399fa47bde6\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a6485145-6752-40ed-ba78-a399fa47bde6')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-a6485145-6752-40ed-ba78-a399fa47bde6 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1025,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9,\n        \"min\": 29,\n        \"max\": 77,\n        \"num_unique_values\": 41,\n        \"samples\": [\n          65,\n          50,\n          54\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trestbps\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 94,\n        \"max\": 200,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          128,\n          172\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51,\n        \"min\": 126,\n        \"max\": 564,\n        \"num_unique_values\": 152,\n        \"samples\": [\n          267,\n          262\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fbs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"restecg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thalach\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23,\n        \"min\": 71,\n        \"max\": 202,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          180,\n          152\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"exang\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oldpeak\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.175053255150173,\n        \"min\": 0.0,\n        \"max\": 6.2,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          2.8,\n          0.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"slope\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ca\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thal\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "288e0c53"
      },
      "source": [
        "## Exploratory Data Analysis (EDA)\n",
        "\n",
        "### Subtask:\n",
        "Perform a comprehensive EDA, including displaying basic info (head, info, describe), checking for missing values, visualizing feature distributions, analyzing correlations, and exploring relationships with the target variable.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fb7e13b9"
      },
      "source": [
        "**Reasoning**:\n",
        "First, I will display basic information about the DataFrame, including its structure, data types, non-null values, and descriptive statistics. Then, I will check for any missing values to understand the completeness of the dataset. These steps provide a foundational understanding of the data before proceeding with more complex analysis.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a20c94fc",
        "outputId": "f83d20b7-d8fb-4652-aca4-e0da2dc73f0b"
      },
      "source": [
        "print(\"\\n--- DataFrame Information ---\\n\")\n",
        "df.info()\n",
        "\n",
        "print(\"\\n--- Descriptive Statistics ---\\n\")\n",
        "print(df.describe())\n",
        "\n",
        "print(\"\\n--- Missing Values ---\\n\")\n",
        "print(df.isnull().sum())"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "--- DataFrame Information ---\n",
            "\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1025 entries, 0 to 1024\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       1025 non-null   int64  \n",
            " 1   sex       1025 non-null   int64  \n",
            " 2   cp        1025 non-null   int64  \n",
            " 3   trestbps  1025 non-null   int64  \n",
            " 4   chol      1025 non-null   int64  \n",
            " 5   fbs       1025 non-null   int64  \n",
            " 6   restecg   1025 non-null   int64  \n",
            " 7   thalach   1025 non-null   int64  \n",
            " 8   exang     1025 non-null   int64  \n",
            " 9   oldpeak   1025 non-null   float64\n",
            " 10  slope     1025 non-null   int64  \n",
            " 11  ca        1025 non-null   int64  \n",
            " 12  thal      1025 non-null   int64  \n",
            " 13  target    1025 non-null   int64  \n",
            "dtypes: float64(1), int64(13)\n",
            "memory usage: 112.2 KB\n",
            "\n",
            "--- Descriptive Statistics ---\n",
            "\n",
            "               age          sex           cp     trestbps        chol  \\\n",
            "count  1025.000000  1025.000000  1025.000000  1025.000000  1025.00000   \n",
            "mean     54.434146     0.695610     0.942439   131.611707   246.00000   \n",
            "std       9.072290     0.460373     1.029641    17.516718    51.59251   \n",
            "min      29.000000     0.000000     0.000000    94.000000   126.00000   \n",
            "25%      48.000000     0.000000     0.000000   120.000000   211.00000   \n",
            "50%      56.000000     1.000000     1.000000   130.000000   240.00000   \n",
            "75%      61.000000     1.000000     2.000000   140.000000   275.00000   \n",
            "max      77.000000     1.000000     3.000000   200.000000   564.00000   \n",
            "\n",
            "               fbs      restecg      thalach        exang      oldpeak  \\\n",
            "count  1025.000000  1025.000000  1025.000000  1025.000000  1025.000000   \n",
            "mean      0.149268     0.529756   149.114146     0.336585     1.071512   \n",
            "std       0.356527     0.527878    23.005724     0.472772     1.175053   \n",
            "min       0.000000     0.000000    71.000000     0.000000     0.000000   \n",
            "25%       0.000000     0.000000   132.000000     0.000000     0.000000   \n",
            "50%       0.000000     1.000000   152.000000     0.000000     0.800000   \n",
            "75%       0.000000     1.000000   166.000000     1.000000     1.800000   \n",
            "max       1.000000     2.000000   202.000000     1.000000     6.200000   \n",
            "\n",
            "             slope           ca         thal       target  \n",
            "count  1025.000000  1025.000000  1025.000000  1025.000000  \n",
            "mean      1.385366     0.754146     2.323902     0.513171  \n",
            "std       0.617755     1.030798     0.620660     0.500070  \n",
            "min       0.000000     0.000000     0.000000     0.000000  \n",
            "25%       1.000000     0.000000     2.000000     0.000000  \n",
            "50%       1.000000     0.000000     2.000000     1.000000  \n",
            "75%       2.000000     1.000000     3.000000     1.000000  \n",
            "max       2.000000     4.000000     3.000000     1.000000  \n",
            "\n",
            "--- Missing Values ---\n",
            "\n",
            "age         0\n",
            "sex         0\n",
            "cp          0\n",
            "trestbps    0\n",
            "chol        0\n",
            "fbs         0\n",
            "restecg     0\n",
            "thalach     0\n",
            "exang       0\n",
            "oldpeak     0\n",
            "slope       0\n",
            "ca          0\n",
            "thal        0\n",
            "target      0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9c96748d"
      },
      "source": [
        "**Reasoning**:\n",
        "Now that the basic information, descriptive statistics, and missing values have been checked, the next step in EDA is to visualize the distribution of numerical features. This will provide insights into the shape and spread of each numerical variable.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 686
        },
        "id": "3711c8e8",
        "outputId": "5716e076-4335-429f-aa5c-af28ac1826ea"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "print(\"Visualizing distribution of numerical features...\")\n",
        "\n",
        "df.hist(figsize=(15, 10), bins=20, color='skyblue', edgecolor='black')\n",
        "plt.suptitle('Distribution of Numerical Features', y=1.02, fontsize=16)\n",
        "plt.tight_layout(rect=[0, 0.03, 1, 0.98])\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Visualizing distribution of numerical features...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 16 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6cbc9535"
      },
      "source": [
        "**Reasoning**:\n",
        "With the distributions of numerical features visualized, the next step in the comprehensive EDA is to analyze the correlations between all variables using a correlation matrix and visualize it as a heatmap. This helps in understanding the linear relationships among features, which is crucial for feature selection and model building.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 909
        },
        "id": "201cd88f",
        "outputId": "3058c909-4bb2-4bd2-a399-cfe80426a7a1"
      },
      "source": [
        "print(\"\\n--- Correlation Matrix (Heatmap) ---\\n\")\n",
        "plt.figure(figsize=(12, 10))\n",
        "sns.heatmap(df.corr(), annot=True, cmap='coolwarm', fmt=\".2f\", linewidths=.5)\n",
        "plt.title('Correlation Matrix of Features', fontsize=16)\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "--- Correlation Matrix (Heatmap) ---\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bbfbc938"
      },
      "source": [
        "**Reasoning**:\n",
        "With the correlation matrix visualized, the next step is to analyze the relationship of individual features with the 'target' variable. I will separate features into categorical and numerical types and create appropriate visualizations (count plots for categorical, box plots for numerical) to show their relationship with the target.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cc1f29e3",
        "outputId": "4e6c02ab-d7d0-4908-df0d-91f551dbd077"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "categorical_features = ['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal']\n",
        "numerical_features = ['age', 'trestbps', 'chol', 'thalach', 'oldpeak']\n",
        "\n",
        "print(\"\\n--- Analyzing relationships with the 'target' variable ---\\n\")\n",
        "\n",
        "plt.figure(figsize=(15, 12))\n",
        "for i, feature in enumerate(categorical_features):\n",
        "    plt.subplot(3, 3, i + 1)\n",
        "    sns.countplot(data=df, x=feature, hue='target', palette='viridis')\n",
        "    plt.title(f'{feature} vs. Target')\n",
        "    plt.xlabel(feature)\n",
        "    plt.ylabel('Count')\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "plt.figure(figsize=(15, 12))\n",
        "for i, feature in enumerate(numerical_features):\n",
        "    plt.subplot(2, 3, i + 1)\n",
        "    sns.boxplot(data=df, x='target', y=feature, palette='viridis')\n",
        "    plt.title(f'{feature} vs. Target')\n",
        "    plt.xlabel('Target')\n",
        "    plt.ylabel(feature)\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "--- Analyzing relationships with the 'target' variable ---\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1200 with 8 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/tmp/ipython-input-3227378353.py:25: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.boxplot(data=df, x='target', y=feature, palette='viridis')\n",
            "/tmp/ipython-input-3227378353.py:25: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.boxplot(data=df, x='target', y=feature, palette='viridis')\n",
            "/tmp/ipython-input-3227378353.py:25: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.boxplot(data=df, x='target', y=feature, palette='viridis')\n",
            "/tmp/ipython-input-3227378353.py:25: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.boxplot(data=df, x='target', y=feature, palette='viridis')\n",
            "/tmp/ipython-input-3227378353.py:25: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.boxplot(data=df, x='target', y=feature, palette='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1200 with 5 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5f2e4924"
      },
      "source": [
        "**Reasoning**:\n",
        "The previous code generated a FutureWarning because `palette` was used in `sns.boxplot` without setting the `hue` parameter. I will update the `sns.boxplot` call to explicitly set `hue='target'` and `legend=False` to resolve this warning and ensure future compatibility.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3001ba5a",
        "outputId": "f94c42cb-1221-40c0-f757-6c6b54fa4315"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "categorical_features = ['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal']\n",
        "numerical_features = ['age', 'trestbps', 'chol', 'thalach', 'oldpeak']\n",
        "\n",
        "print(\"\\n--- Analyzing relationships with the 'target' variable ---\\n\")\n",
        "\n",
        "plt.figure(figsize=(15, 12))\n",
        "for i, feature in enumerate(categorical_features):\n",
        "    plt.subplot(3, 3, i + 1)\n",
        "    sns.countplot(data=df, x=feature, hue='target', palette='viridis')\n",
        "    plt.title(f'{feature} vs. Target')\n",
        "    plt.xlabel(feature)\n",
        "    plt.ylabel('Count')\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "plt.figure(figsize=(15, 12))\n",
        "for i, feature in enumerate(numerical_features):\n",
        "    plt.subplot(2, 3, i + 1)\n",
        "    sns.boxplot(data=df, x='target', y=feature, hue='target', palette='viridis', legend=False) # Fix applied here\n",
        "    plt.title(f'{feature} vs. Target')\n",
        "    plt.xlabel('Target')\n",
        "    plt.ylabel(feature)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "--- Analyzing relationships with the 'target' variable ---\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1200 with 8 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1200 with 5 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "805b2e7c"
      },
      "source": [
        "## Data Preprocessing\n",
        "\n",
        "### Subtask:\n",
        "Prepare the data for the KNN model by handling categorical features (e.g., one-hot encoding), scaling numerical features, and splitting the dataset into training and testing sets.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7e21f78d"
      },
      "source": [
        "## Data Preprocessing\n",
        "\n",
        "### Subtask:\n",
        "Prepare the data for the KNN model by handling categorical features (e.g., one-hot encoding), scaling numerical features, and splitting the dataset into training and testing sets.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cb181697"
      },
      "source": [
        "**Reasoning**:\n",
        "Now that the EDA is complete, the next step is to preprocess the data. I will start by separating features (X) from the target variable (y). Then, I will apply one-hot encoding to the identified categorical features and standard scaling to the numerical features. Finally, I will combine these preprocessed features and split the dataset into training and testing sets to prepare for model training.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5b34a682",
        "outputId": "b8b468c4-2c46-4b91-8a27-fb12a956d147"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "import numpy as np\n",
        "\n",
        "# 1. Separate features (X) from the target variable (y)\n",
        "X = df.drop('target', axis=1)\n",
        "y = df['target']\n",
        "\n",
        "print(\"Features (X) and Target (y) separated.\")\n",
        "\n",
        "# Identify categorical and numerical features\n",
        "# (These were identified during EDA and stored in kernel state)\n",
        "# categorical_features = ['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal']\n",
        "# numerical_features = ['age', 'trestbps', 'chol', 'thalach', 'oldpeak']\n",
        "\n",
        "# 2. Apply One-Hot Encoding to categorical features and Standard Scaling to numerical features\n",
        "\n",
        "# Create a column transformer for preprocessing\n",
        "# 'drop' strategy for OneHotEncoder is used to avoid multicollinearity\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('num', StandardScaler(), numerical_features),\n",
        "        ('cat', OneHotEncoder(handle_unknown='ignore', drop='first'), categorical_features)\n",
        "    ])\n",
        "\n",
        "print(\"Applying preprocessing (scaling and one-hot encoding) to features...\")\n",
        "X_processed = preprocessor.fit_transform(X)\n",
        "\n",
        "# Get feature names after one-hot encoding for better interpretability (optional, but good practice)\n",
        "# This helps in knowing which column corresponds to which feature after transformation\n",
        "feature_names_out = preprocessor.get_feature_names_out()\n",
        "X_processed_df = pd.DataFrame(X_processed, columns=feature_names_out)\n",
        "\n",
        "print(f\"Shape of preprocessed features (X_processed): {X_processed.shape}\")\n",
        "print(\"First 5 rows of preprocessed features (X_processed_df):\")\n",
        "print(X_processed_df.head())\n",
        "\n",
        "# 3. Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_processed, y, test_size=0.2, random_state=42, stratify=y)\n",
        "\n",
        "print(f\"\\nData split into training and testing sets:\")\n",
        "print(f\"X_train shape: {X_train.shape}\")\n",
        "print(f\"X_test shape: {X_test.shape}\")\n",
        "print(f\"y_train shape: {y_train.shape}\")\n",
        "print(f\"y_test shape: {y_test.shape}\")"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Features (X) and Target (y) separated.\n",
            "Applying preprocessing (scaling and one-hot encoding) to features...\n",
            "Shape of preprocessed features (X_processed): (1025, 22)\n",
            "First 5 rows of preprocessed features (X_processed_df):\n",
            "   num__age  num__trestbps  num__chol  num__thalach  num__oldpeak  cat__sex_1  \\\n",
            "0 -0.268437      -0.377636  -0.659332      0.821321     -0.060888         1.0   \n",
            "1 -0.158157       0.479107  -0.833861      0.255968      1.727137         1.0   \n",
            "2  1.716595       0.764688  -1.396233     -1.048692      1.301417         1.0   \n",
            "3  0.724079       0.936037  -0.833861      0.516900     -0.912329         1.0   \n",
            "4  0.834359       0.364875   0.930822     -1.874977      0.705408         0.0   \n",
            "\n",
            "   cat__cp_1  cat__cp_2  cat__cp_3  cat__fbs_1  ...  cat__exang_1  \\\n",
            "0        0.0        0.0        0.0         0.0  ...           0.0   \n",
            "1        0.0        0.0        0.0         1.0  ...           1.0   \n",
            "2        0.0        0.0        0.0         0.0  ...           1.0   \n",
            "3        0.0        0.0        0.0         0.0  ...           0.0   \n",
            "4        0.0        0.0        0.0         1.0  ...           0.0   \n",
            "\n",
            "   cat__slope_1  cat__slope_2  cat__ca_1  cat__ca_2  cat__ca_3  cat__ca_4  \\\n",
            "0           0.0           1.0        0.0        1.0        0.0        0.0   \n",
            "1           0.0           0.0        0.0        0.0        0.0        0.0   \n",
            "2           0.0           0.0        0.0        0.0        0.0        0.0   \n",
            "3           0.0           1.0        1.0        0.0        0.0        0.0   \n",
            "4           1.0           0.0        0.0        0.0        1.0        0.0   \n",
            "\n",
            "   cat__thal_1  cat__thal_2  cat__thal_3  \n",
            "0          0.0          0.0          1.0  \n",
            "1          0.0          0.0          1.0  \n",
            "2          0.0          0.0          1.0  \n",
            "3          0.0          0.0          1.0  \n",
            "4          0.0          1.0          0.0  \n",
            "\n",
            "[5 rows x 22 columns]\n",
            "\n",
            "Data split into training and testing sets:\n",
            "X_train shape: (820, 22)\n",
            "X_test shape: (205, 22)\n",
            "y_train shape: (820,)\n",
            "y_test shape: (205,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f8b9b6cc"
      },
      "source": [
        "## KNN Model Training and Prediction\n",
        "\n",
        "### Subtask:\n",
        "Train a K-Nearest Neighbors (KNN) classifier using the preprocessed training data and make predictions on the test set.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "60fda5ba"
      },
      "source": [
        "**Reasoning**:\n",
        "I will import the KNeighborsClassifier, instantiate it with a default number of neighbors, train the model using the preprocessed training data, and then make predictions on the test set.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6afb9c7f",
        "outputId": "daf4067c-157e-428b-b4a3-de4da5cd8570"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "# 1. Instantiate a KNeighborsClassifier model\n",
        "k_neighbors = 5 # Starting with a common default value\n",
        "k_neighbors_classifier = KNeighborsClassifier(n_neighbors=k_neighbors)\n",
        "\n",
        "print(f\"K-Nearest Neighbors Classifier initialized with n_neighbors = {k_neighbors}\")\n",
        "\n",
        "# 2. Train the KNN model using the X_train and y_train data\n",
        "k_neighbors_classifier.fit(X_train, y_train)\n",
        "\n",
        "print(\"KNN model trained successfully on X_train and y_train.\")\n",
        "\n",
        "# 3. Make predictions on the X_test data\n",
        "y_pred = k_neighbors_classifier.predict(X_test)\n",
        "\n",
        "print(\"Predictions made on the X_test data.\")\n",
        "print(f\"First 5 predictions: {y_pred[:5]}\")\n"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "K-Nearest Neighbors Classifier initialized with n_neighbors = 5\n",
            "KNN model trained successfully on X_train and y_train.\n",
            "Predictions made on the X_test data.\n",
            "First 5 predictions: [0 1 0 1 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ab6b019c"
      },
      "source": [
        "## Model Evaluation\n",
        "\n",
        "### Subtask:\n",
        "Evaluate the performance of the trained KNN model using appropriate classification metrics (accuracy, precision, recall, F1-score) and display a confusion matrix."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e98ffa4d"
      },
      "source": [
        "**Reasoning**:\n",
        "To evaluate the KNN model, I will calculate common classification metrics (accuracy, precision, recall, F1-score) and visualize the confusion matrix, which requires importing relevant functions from `sklearn.metrics`.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689
        },
        "id": "e12ac81f",
        "outputId": "cb4b5f20-3240-4e47-ce66-922b4a708cca"
      },
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "print(\"\\n--- Model Evaluation ---\\n\")\n",
        "\n",
        "# 1. Calculate classification metrics\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "precision = precision_score(y_test, y_pred)\n",
        "recall = recall_score(y_test, y_pred)\n",
        "f1 = f1_score(y_test, y_pred)\n",
        "\n",
        "print(f\"Accuracy: {accuracy:.4f}\")\n",
        "print(f\"Precision: {precision:.4f}\")\n",
        "print(f\"Recall: {recall:.4f}\")\n",
        "print(f\"F1-Score: {f1:.4f}\")\n",
        "\n",
        "# 2. Display Confusion Matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False,\n",
        "            xticklabels=['Predicted Negative', 'Predicted Positive'],\n",
        "            yticklabels=['Actual Negative', 'Actual Positive'])\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.ylabel('True Label')\n",
        "plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "--- Model Evaluation ---\n",
            "\n",
            "Accuracy: 0.8439\n",
            "Precision: 0.8120\n",
            "Recall: 0.9048\n",
            "F1-Score: 0.8559\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqsAAAIjCAYAAAAk+FJEAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAS6dJREFUeJzt3Xt8zvXj//HntdmujdkJyxyaw7TImRJiFAk5pE+S+phzJGT4oCJW6KPkVCiHSFNCqVBylhw+ZA6V5DBUtpzH7MT2/v3h5/pam9o0Xm/zuN9ubjfX6/263u/ndX2+38uz9/V6vy+HZVmWAAAAABtyMx0AAAAAuBbKKgAAAGyLsgoAAADboqwCAADAtiirAAAAsC3KKgAAAGyLsgoAAADboqwCAADAtiirAAAAsC3KKgBkY//+/Xr44Yfl5+cnh8OhJUuW5On+Dx8+LIfDoTlz5uTpfm9ljRo1UqNGjUzHAGAzlFUAtnXw4EE9++yzKleunLy8vOTr66v69etr0qRJSk5OvqHHjoiI0J49ezR69GjNmzdPtWvXvqHHu5k6d+4sh8MhX1/fbN/H/fv3y+FwyOFw6M0338z1/o8dO6aRI0dq586deZAWwO2ugOkAAJCdZcuW6YknnpDT6VSnTp1UuXJlpaWlaePGjRo8eLB+/PFHvffeezfk2MnJydq8ebNeeuklPf/88zfkGCEhIUpOTpaHh8cN2f/fKVCggJKSkvTll1+qffv2mbZFR0fLy8tLKSkp17XvY8eOadSoUSpTpoyqV6+e4+d9880313U8APkbZRWA7cTGxqpDhw4KCQnRmjVrFBwc7NrWp08fHThwQMuWLbthxz9x4oQkyd/f/4Ydw+FwyMvL64bt/+84nU7Vr19fH330UZayOn/+fLVs2VKLFy++KVmSkpJUsGBBeXp63pTjAbi1sAwAgO2MGzdOiYmJmjVrVqaiekVoaKj69+/venzp0iW9+uqrKl++vJxOp8qUKaMXX3xRqampmZ5XpkwZPfroo9q4caPuu+8+eXl5qVy5cvrggw9cc0aOHKmQkBBJ0uDBg+VwOFSmTBlJl78+v/L3q40cOVIOhyPT2MqVK/XAAw/I399fPj4+CgsL04svvujafq01q2vWrFGDBg1UqFAh+fv7q02bNtq7d2+2xztw4IA6d+4sf39/+fn5qUuXLkpKSrr2G/snHTt21FdffaWzZ8+6xrZt26b9+/erY8eOWeafPn1agwYNUpUqVeTj4yNfX181b95cu3btcs1Zt26d7r33XklSly5dXMsJrrzORo0aqXLlyvr+++/VsGFDFSxY0PW+/HnNakREhLy8vLK8/mbNmikgIEDHjh3L8WsFcOuirAKwnS+//FLlypVTvXr1cjS/e/fuGjFihGrWrKkJEyYoPDxcY8eOVYcOHbLMPXDggP71r3+padOmGj9+vAICAtS5c2f9+OOPkqR27dppwoQJkqSnnnpK8+bN08SJE3OV/8cff9Sjjz6q1NRURUVFafz48WrdurW+++67v3zeqlWr1KxZMx0/flwjR45UZGSkNm3apPr16+vw4cNZ5rdv317nz5/X2LFj1b59e82ZM0ejRo3Kcc527drJ4XDo008/dY3Nnz9fd999t2rWrJll/qFDh7RkyRI9+uijeuuttzR48GDt2bNH4eHhruJYsWJFRUVFSZJ69uypefPmad68eWrYsKFrP6dOnVLz5s1VvXp1TZw4UY0bN84236RJk1SsWDFFREQoPT1dkvTuu+/qm2++0ZQpU1SiRIkcv1YAtzALAGwkISHBkmS1adMmR/N37txpSbK6d++eaXzQoEGWJGvNmjWusZCQEEuStWHDBtfY8ePHLafTaQ0cONA1Fhsba0my3njjjUz7jIiIsEJCQrJkeOWVV6yrP04nTJhgSbJOnDhxzdxXjvH++++7xqpXr24FBQVZp06dco3t2rXLcnNzszp16pTleF27ds20z8cee8wqUqTINY959esoVKiQZVmW9a9//ct66KGHLMuyrPT0dKt48eLWqFGjsn0PUlJSrPT09Cyvw+l0WlFRUa6xbdu2ZXltV4SHh1uSrOnTp2e7LTw8PNPYihUrLEnWa6+9Zh06dMjy8fGx2rZt+7evEUD+wZlVALZy7tw5SVLhwoVzNH/58uWSpMjIyEzjAwcOlKQsa1srVaqkBg0auB4XK1ZMYWFhOnTo0HVn/rMra10///xzZWRk5Og5cXFx2rlzpzp37qzAwEDXeNWqVdW0aVPX67xar169Mj1u0KCBTp065XoPc6Jjx45at26d4uPjtWbNGsXHx2e7BEC6vM7Vze3yPxvp6ek6deqUa4nDjh07cnxMp9OpLl265Gjuww8/rGeffVZRUVFq166dvLy89O677+b4WABufZRVALbi6+srSTp//nyO5h85ckRubm4KDQ3NNF68eHH5+/vryJEjmcbvvPPOLPsICAjQmTNnrjNxVk8++aTq16+v7t2764477lCHDh30ySef/GVxvZIzLCwsy7aKFSvq5MmTunDhQqbxP7+WgIAAScrVa2nRooUKFy6sBQsWKDo6Wvfee2+W9/KKjIwMTZgwQRUqVJDT6VTRokVVrFgx7d69WwkJCTk+ZsmSJXN1MdWbb76pwMBA7dy5U5MnT1ZQUFCOnwvg1kdZBWArvr6+KlGihH744YdcPe/PFzhdi7u7e7bjlmVd9zGurKe8wtvbWxs2bNCqVav073//W7t379aTTz6ppk2bZpn7T/yT13KF0+lUu3btNHfuXH322WfXPKsqSWPGjFFkZKQaNmyoDz/8UCtWrNDKlSt1zz335PgMsnT5/cmNmJgYHT9+XJK0Z8+eXD0XwK2PsgrAdh599FEdPHhQmzdv/tu5ISEhysjI0P79+zON//HHHzp79qzryv68EBAQkOnK+Sv+fPZWktzc3PTQQw/prbfe0k8//aTRo0drzZo1Wrt2bbb7vpJz3759Wbb9/PPPKlq0qAoVKvTPXsA1dOzYUTExMTp//ny2F6VdsWjRIjVu3FizZs1Shw4d9PDDD6tJkyZZ3pOc/odDTly4cEFdunRRpUqV1LNnT40bN07btm3Ls/0DsD/KKgDb+c9//qNChQqpe/fu+uOPP7JsP3jwoCZNmiTp8tfYkrJcsf/WW29Jklq2bJlnucqXL6+EhATt3r3bNRYXF6fPPvss07zTp09nee6Vm+P/+XZaVwQHB6t69eqaO3dupvL3ww8/6JtvvnG9zhuhcePGevXVV/X222+rePHi15zn7u6e5aztwoUL9fvvv2cau1Kqsyv2uTVkyBAdPXpUc+fO1VtvvaUyZcooIiLimu8jgPyHHwUAYDvly5fX/Pnz9eSTT6pixYqZfsFq06ZNWrhwoTp37ixJqlatmiIiIvTee+/p7NmzCg8P1//+9z/NnTtXbdu2veZtka5Hhw4dNGTIED322GPq16+fkpKSNG3aNN11112ZLjCKiorShg0b1LJlS4WEhOj48eOaOnWqSpUqpQceeOCa+3/jjTfUvHlz1a1bV926dVNycrKmTJkiPz8/jRw5Ms9ex5+5ubnp5Zdf/tt5jz76qKKiotSlSxfVq1dPe/bsUXR0tMqVK5dpXvny5eXv76/p06ercOHCKlSokOrUqaOyZcvmKteaNWs0depUvfLKK65bab3//vtq1KiRhg8frnHjxuVqfwBuTZxZBWBLrVu31u7du/Wvf/1Ln3/+ufr06aOhQ4fq8OHDGj9+vCZPnuyaO3PmTI0aNUrbtm3TCy+8oDVr1mjYsGH6+OOP8zRTkSJF9Nlnn6lgwYL6z3/+o7lz52rs2LFq1apVlux33nmnZs+erT59+uidd95Rw4YNtWbNGvn5+V1z/02aNNHXX3+tIkWKaMSIEXrzzTd1//3367vvvst10bsRXnzxRQ0cOFArVqxQ//79tWPHDi1btkylS5fONM/Dw0Nz586Vu7u7evXqpaeeekrr16/P1bHOnz+vrl27qkaNGnrppZdc4w0aNFD//v01fvx4bdmyJU9eFwB7c1i5WYkPAAAA3EScWQUAAIBtUVYBAABgW5RVAAAA2BZlFQAAALZFWQUAAIBtUVYBAABgW5RVAAAA2Fa+/AWr4j0WmY4AAHlq4+i8+9lYALCD0CDvHM3jzCoAAABsi7IKAAAA26KsAgAAwLYoqwAAALAtyioAAABsi7IKAAAA26KsAgAAwLYoqwAAALAtyioAAABsi7IKAAAA26KsAgAAwLYoqwAAALAtyioAAABsi7IKAAAA26KsAgAAwLYoqwAAALAtyioAAABsi7IKAAAA26KsAgAAwLYoqwAAALAtyioAAABsi7IKAAAA26KsAgAAwLYoqwAAALAtyioAAABsi7IKAAAA26KsAgAAwLYoqwAAALAtyioAAABsi7IKAAAA26KsAgAAwLYoqwAAALAtyioAAABsi7IKAAAA26KsAgAAwLYoqwAAALAtyioAAABsi7IKAAAA26KsAgAAwLYoqwAAALAtyioAAABsi7IKAAAA26KsAgAAwLYoqwAAALAtyioAAABsi7IKAAAA26KsAgAAwLYoqwAAALAtyioAAABsi7IKAAAA26KsAgAAwLYoqwAAALAtyioAAABsi7IKAAAA26KsAgAAwLYoqwAAALAtyioAAABsi7IKAAAA26KsAgAAwLYoqwAAALAtyioAAABsyxZl9dtvv9UzzzyjunXr6vfff5ckzZs3Txs3bjScDAAAACYZL6uLFy9Ws2bN5O3trZiYGKWmpkqSEhISNGbMGMPpAAAAYJLxsvraa69p+vTpmjFjhjw8PFzj9evX144dOwwmAwAAgGnGy+q+ffvUsGHDLON+fn46e/bszQ8EAAAA2zBeVosXL64DBw5kGd+4caPKlStnIBEAAADswnhZ7dGjh/r376+tW7fK4XDo2LFjio6O1qBBg9S7d2/T8QAAAGBQAdMBhg4dqoyMDD300ENKSkpSw4YN5XQ6NWjQIPXt29d0PAAAABjksCzLMh1CktLS0nTgwAElJiaqUqVK8vHxue59Fe+xKA+TAYB5G0e3NB0BAPJUaJB3juYZXwbw4YcfKikpSZ6enqpUqZLuu+++f1RUAQAAkH8YL6sDBgxQUFCQOnbsqOXLlys9Pd10JAAAANiE8bIaFxenjz/+WA6HQ+3bt1dwcLD69OmjTZs2mY4GAAAAw4yX1QIFCujRRx9VdHS0jh8/rgkTJujw4cNq3LixypcvbzoeAAAADDJ+N4CrFSxYUM2aNdOZM2d05MgR7d2713QkAAAAGGT8zKokJSUlKTo6Wi1atFDJkiU1ceJEPfbYY/rxxx9NRwMAAIBBxs+sdujQQUuXLlXBggXVvn17DR8+XHXr1jUdCwAAADZgvKy6u7vrk08+UbNmzeTu7m46DgAAAGzEeFmNjo42HQEAAAA2ZaSsTp48WT179pSXl5cmT578l3P79et3k1IBAADAboz83GrZsmW1fft2FSlSRGXLlr3mPIfDoUOHDuV6//zcKoD8hp9bBZDf5PTnVo2cWY2Njc327wAAAMDVjN+6KioqSklJSVnGk5OTFRUVZSARAAAA7MLIMoCrubu7Ky4uTkFBQZnGT506paCgIKWnp+d6nywDAJDfsAwAQH6T02UAxs+sWpYlh8ORZXzXrl0KDAw0kAgAAAB2YezWVQEBAXI4HHI4HLrrrrsyFdb09HQlJiaqV69epuIBAADABoyV1YkTJ8qyLHXt2lWjRo2Sn5+fa5unp6fKlCnDL1kBAADc5oyV1YiICEmXb2NVr149eXh4mIoCAAAAmzL+C1bh4eGuv6ekpCgtLS3Tdl9f35sdCQAAADZh/AKrpKQkPf/88woKClKhQoUUEBCQ6Q8AAABuX8bL6uDBg7VmzRpNmzZNTqdTM2fO1KhRo1SiRAl98MEHpuMBAADAIOPLAL788kt98MEHatSokbp06aIGDRooNDRUISEhio6O1tNPP206IgAAAAwxfmb19OnTKleunKTL61NPnz4tSXrggQe0YcMGk9EAAABgmPGyWq5cOcXGxkqS7r77bn3yySeSLp9x9ff3N5gMAAAAphkvq126dNGuXbskSUOHDtU777wjLy8vDRgwQIMHDzacDgAAACY5LMuyTIe42pEjR/T9998rNDRUVatWva59FO+xKI9TAYBZG0e3NB0BAPJUaJB3juYZv8Dqz0JCQhQSEmI6BgAAAGzAeFmdPHlytuMOh0NeXl4KDQ1Vw4YN5e7ufpOTAQAAwDTjZXXChAk6ceKEkpKSXD8CcObMGRUsWFA+Pj46fvy4ypUrp7Vr16p06dKG0wIAAOBmMn6B1ZgxY3Tvvfdq//79OnXqlE6dOqVffvlFderU0aRJk3T06FEVL15cAwYMMB0VAAAAN5nxC6zKly+vxYsXq3r16pnGY2Ji9Pjjj+vQoUPatGmTHn/8ccXFxeVon1xgBSC/4QIrAPlNTi+wMn5mNS4uTpcuXcoyfunSJcXHx0uSSpQoofPnz9/saAAAADDMeFlt3Lixnn32WcXExLjGYmJi1Lt3bz344IOSpD179qhs2bKmIgIAAMAQ42V11qxZCgwMVK1ateR0OuV0OlW7dm0FBgZq1qxZkiQfHx+NHz/ecFIAAADcbMbvBlC8eHGtXLlSP//8s3755RdJUlhYmMLCwlxzGjdubCoeAAAADDJeVq8oV66cHA6HypcvrwIFbBMLAAAABhlfBpCUlKRu3bqpYMGCuueee3T06FFJUt++ffX6668bTgcAAACTjJfVYcOGadeuXVq3bp28vLxc402aNNGCBQsMJgMAAIBpxr9vX7JkiRYsWKD7779fDofDNX7PPffo4MGDBpMBAADANONnVk+cOKGgoKAs4xcuXMhUXgEAAHD7MX5mtXbt2lq2bJn69u0rSa6COnPmTNWtW9dkNECStG1sc5UuWijL+PtrD2jY/J0q5uvUiH9VVXilO+TjVUAH4s9r0vKftWzH7wbSAsDf+2TeLG3asFq/HTksT6dTFStXU5feL6jUnWUkSefPJejDWdMUs22zTvwRLz//AN3foLH+3f05FfIpbDY8bjvGy+qYMWPUvHlz/fTTT7p06ZImTZqkn376SZs2bdL69etNxwP0yOjVcnP7v7P8d5f008LIhvpy++UyOqXrffIr6KGIt7/TqcQ0tatTWu89e7+avbZaP/x61lBqALi2PTu/V8vHntRdFe9Renq65r47RS9H9tb0eZ/Ky9tbp06e0OlTJ9StT6TuLFNOx+Pj9Pabr+n0yRN68bU3TcfHbcb4MoAHHnhAO3fu1KVLl1SlShV98803CgoK0ubNm1WrVi3T8QCdSkzTiXOprj9NqwYr9niiNv1yQpJ0b/kimrXmgGIOn9HRkxc0cdnPSkhKU9UQf7PBAeAaXh0/VU1btFFI2VCVCw1T5ItROvFHnA7s+0mSVKZcqF56bbzq1A9XcMnSqlbrPnXq+by2blqv9Gx+Ih24kYyfWZWk8uXLa8aMGaZjAH/Lw92hx+vcqXdX7XeNbTt4Sm3uLa1Vu+OUkHxRrWuXkpeHuzbtO2EwKQDk3IULiZIkH1+/a85JSkxUwYI+cude6LjJbvn/i0tNTVVqamqmMSv9ohzuHoYSIT9rXqOk/Ap6aMF3h11jPd/donefraOfJ7XRxUsZSk5LV5epm3X4xAVzQQEghzIyMvTe5DdUqUp1lSkXmu2chLNn9NHcGXqkdbubnA4wuAzAzc1N7u7uf/knJ79kNXbsWPn5+WX6c2HnZzfhFeB29NQDZbTmh3j9kZDiGhvS9h75eXvqX+M3qNno1Xp31S9679k6urukr8GkAJAz094aqyOxBzRk5H+z3Z50IVEj/9NXd5Ypp6e79rrJ6QDJYVmWZeLAn3/++TW3bd68WZMnT1ZGRoZSUlKuOU/K/sxqhReWcWYVea5UYEFtHdtcXadu0opdcZKkkGKFtHVMc4W/8o32HTvnmvvJgAaKPZGoIR/GmIqLfGbj6JamIyAfmjZhrLZsXKf/Tpmt4iVKZtmelHRBwwf2ltPprZH/nSxPp9NASuRXoUHeOZpnbBlAmzZtsozt27dPQ4cO1Zdffqmnn35aUVFRf7sfp9Mp55/+n4eiihuhQ/0yOnkuRav2xLvGvD3dJUkZGZn/my/dsuTGfYIB2JRlWZo+8XVt3rBGYyfPzL6oXkjU8IHPycPDQyNen0hRhTHG7wYgSceOHVOPHj1UpUoVXbp0STt37tTcuXMVEhJiOhogSXI4pA71Q/TJ5iNKv6qYHog/r0N/nNe4f9dUjTIBCilWSL2aVlB4xTv0dcwxg4kB4NqmvjVGa79ZpsEjxsq7YCGdPnVSp0+dVGrq5W8zky4k6uXI3kpJTlb/oSOVdOGCa056errh9LjdGL3AKiEhQWPGjNGUKVNUvXp1rV69Wg0aNDAZCchWw4p3qFSRQvroqgurJOlSuqWnJ3+nl9pV1gd966uQs4Bijyeq3/vbtPqH+Ox3BgCGLV+yUJI0tF/3TOMvDBulpi3a6MAve7Xvpz2SpO4dWmWaM/uTZbojOOuZWOBGMbZmddy4cfrvf/+r4sWLa8yYMdkuC7hexXssyrN9AYAdsGYVQH6T0zWrxsqqm5ubvL291aRJE7m7u19z3qeffprrfVNWAeQ3lFUA+Y3tL7Dq1KmTHFyAAgAAgL9grKzOmTPH1KEBAABwi7DF3QAAAACA7FBWAQAAYFuUVQAAANgWZRUAAAC2RVkFAACAbRm5G8AXX3yR47mtW7e+gUkAAABgZ0bKatu2bXM0z+Fw8BvEAAAAtzEjZTUjI8PEYQEAAHCLYc0qAAAAbMvYL1hd7cKFC1q/fr2OHj2qtLS0TNv69etnKBUAAABMM15WY2Ji1KJFCyUlJenChQsKDAzUyZMnVbBgQQUFBVFWAQAAbmPGlwEMGDBArVq10pkzZ+Tt7a0tW7boyJEjqlWrlt58803T8QAAAGCQ8bK6c+dODRw4UG5ubnJ3d1dqaqpKly6tcePG6cUXXzQdDwAAAAYZL6seHh5yc7scIygoSEePHpUk+fn56ddffzUZDQAAAIYZX7Nao0YNbdu2TRUqVFB4eLhGjBihkydPat68eapcubLpeAAAADDI+JnVMWPGKDg4WJI0evRoBQQEqHfv3jpx4oTee+89w+kAAABgkvEzq7Vr13b9PSgoSF9//bXBNAAAALAT42dWAQAAgGsxfma1bNmycjgc19x+6NChm5gGAAAAdmK8rL7wwguZHl+8eFExMTH6+uuvNXjwYDOhAAAAYAvGy2r//v2zHX/nnXe0ffv2m5wGAAAAdmLbNavNmzfX4sWLTccAAACAQbYtq4sWLVJgYKDpGAAAADDI+DKAGjVqZLrAyrIsxcfH68SJE5o6darBZAAAADDNeFlt06ZNprLq5uamYsWKqVGjRrr77rsNJgMAAIBpxsvqyJEjTUcAAACATRlfs+ru7q7jx49nGT916pTc3d0NJAIAAIBdGC+rlmVlO56amipPT8+bnAYAAAB2YmwZwOTJkyVJDodDM2fOlI+Pj2tbenq6NmzYwJpVAACA25yxsjphwgRJl8+sTp8+PdNX/p6enipTpoymT59uKh4AAABswFhZjY2NlSQ1btxYn376qQICAkxFAQAAgE0ZvxvA2rVrTUcAAACATRm/wOrxxx/Xf//73yzj48aN0xNPPGEgEQAAAOzCeFndsGGDWrRokWW8efPm2rBhg4FEAAAAsAvjZTUxMTHbW1R5eHjo3LlzBhIBAADALoyX1SpVqmjBggVZxj/++GNVqlTJQCIAAADYhfELrIYPH6527drp4MGDevDBByVJq1ev1kcffaSFCxcaTgcAAACTjJfVVq1aacmSJRozZowWLVokb29vVa1aVatWrVJ4eLjpeAAAADDIeFmVpJYtW6ply5ZZxn/44QdVrlzZQCIAAADYgfE1q392/vx5vffee7rvvvtUrVo103EAAABgkG3K6oYNG9SpUycFBwfrzTff1IMPPqgtW7aYjgUAAACDjC4DiI+P15w5czRr1iydO3dO7du3V2pqqpYsWcKdAAAAAGDuzGqrVq0UFham3bt3a+LEiTp27JimTJliKg4AAABsyNiZ1a+++kr9+vVT7969VaFCBVMxAAAAYGPGzqxu3LhR58+fV61atVSnTh29/fbbOnnypKk4AAAAsCFjZfX+++/XjBkzFBcXp2effVYff/yxSpQooYyMDK1cuVLnz583FQ0AAAA2YfxuAIUKFVLXrl21ceNG7dmzRwMHDtTrr7+uoKAgtW7d2nQ8AAAAGGS8rF4tLCxM48aN02+//aaPPvrIdBwAAAAYZquyeoW7u7vatm2rL774wnQUAAAAGGTLsgoAAABIlFUAAADYGGUVAAAAtkVZBQAAgG1RVgEAAGBblFUAAADYFmUVAAAAtkVZBQAAgG1RVgEAAGBblFUAAADYFmUVAAAAtkVZBQAAgG1RVgEAAGBblFUAAADYFmUVAAAAtkVZBQAAgG1RVgEAAGBblFUAAADYFmUVAAAAtkVZBQAAgG1RVgEAAGBblFUAAADYFmUVAAAAtkVZBQAAgG0VyMmk3bt353iHVatWve4wAAAAwNVyVFarV68uh8Mhy7Ky3X5lm8PhUHp6ep4GBAAAwO0rR2U1Njb2RucAAAAAsshRWQ0JCbnROQAAAIAsrusCq3nz5ql+/foqUaKEjhw5IkmaOHGiPv/88zwNBwAAgNtbrsvqtGnTFBkZqRYtWujs2bOuNar+/v6aOHFiXucDAADAbSzXZXXKlCmaMWOGXnrpJbm7u7vGa9eurT179uRpOAAAANzecl1WY2NjVaNGjSzjTqdTFy5cyJNQAAAAgHQdZbVs2bLauXNnlvGvv/5aFStWzItMAAAAgKQc3g3gapGRkerTp49SUlJkWZb+97//6aOPPtLYsWM1c+bMG5ERAAAAt6lcl9Xu3bvL29tbL7/8spKSktSxY0eVKFFCkyZNUocOHW5ERgAAANymHNa1fpYqB5KSkpSYmKigoKC8zPSPFe+xyHQEAMhTG0e3NB0BAPJUaJB3jubl+szqFcePH9e+ffskXf651WLFil3vrgAAAIBs5foCq/Pnz+vf//63SpQoofDwcIWHh6tEiRJ65plnlJCQcCMyAgAA4DaV67LavXt3bd26VcuWLdPZs2d19uxZLV26VNu3b9ezzz57IzICAADgNpXrZQBLly7VihUr9MADD7jGmjVrphkzZuiRRx7J03AAAAC4veX6zGqRIkXk5+eXZdzPz08BAQF5EgoAAACQrqOsvvzyy4qMjFR8fLxrLD4+XoMHD9bw4cPzNBwAAABubzlaBlCjRg05HA7X4/379+vOO+/UnXfeKUk6evSonE6nTpw4wbpVAAAA5JkcldW2bdve4BgAAABAVjkqq6+88sqNzgEAAABkkes1qwAAAMDNkutbV6Wnp2vChAn65JNPdPToUaWlpWXafvr06TwLBwAAgNtbrs+sjho1Sm+99ZaefPJJJSQkKDIyUu3atZObm5tGjhx5AyICAADgdpXrshodHa0ZM2Zo4MCBKlCggJ566inNnDlTI0aM0JYtW25ERgAAANymcl1W4+PjVaVKFUmSj4+PEhISJEmPPvqoli1blrfpAAAAcFvLdVktVaqU4uLiJEnly5fXN998I0natm2bnE5n3qYDAADAbS3XZfWxxx7T6tWrJUl9+/bV8OHDVaFCBXXq1Eldu3bN84AAAAC4fTksy7L+yQ62bNmiTZs2qUKFCmrVqlVe5fpHivdYZDoCAOSpjaNbmo4AAHkqNMg7R/P+8X1W77//fkVGRqpOnToaM2bMP90dAAAA4JJnPwoQFxen4cOH59XuAAAAAH7BCgAAAPZFWQUAAIBtUVYBAABgWwVyOjEyMvIvt584ceIfh8krh6f9y3QEAMhTAfc+bzoCAOSp5Ji3czQvx2U1Jibmb+c0bNgwp7sDAAAA/laOy+ratWtvZA4AAAAgC9asAgAAwLYoqwAAALAtyioAAABsi7IKAAAA26KsAgAAwLauq6x+++23euaZZ1S3bl39/vvvkqR58+Zp48aNeRoOAAAAt7dcl9XFixerWbNm8vb2VkxMjFJTUyVJCQkJGjNmTJ4HBAAAwO0r12X1tdde0/Tp0zVjxgx5eHi4xuvXr68dO3bkaTgAAADc3nJdVvft25ftL1X5+fnp7NmzeZEJAAAAkHQdZbV48eI6cOBAlvGNGzeqXLlyeRIKAAAAkK6jrPbo0UP9+/fX1q1b5XA4dOzYMUVHR2vQoEHq3bv3jcgIAACA21SB3D5h6NChysjI0EMPPaSkpCQ1bNhQTqdTgwYNUt++fW9ERgAAANymHJZlWdfzxLS0NB04cECJiYmqVKmSfHx88jrbdUu5ZDoBAOStgHufNx0BAPJUcszbOZqX6zOrV3h6eqpSpUrX+3QAAADgb+W6rDZu3FgOh+Oa29esWfOPAgEAAABX5LqsVq9ePdPjixcvaufOnfrhhx8UERGRV7kAAACA3JfVCRMmZDs+cuRIJSYm/uNAAAAAwBW5vnXVtTzzzDOaPXt2Xu0OAAAAyLuyunnzZnl5eeXV7gAAAIDcLwNo165dpseWZSkuLk7bt2/X8OHD8ywYAAAAkOuy6ufnl+mxm5ubwsLCFBUVpYcffjjPggEAAAC5Kqvp6enq0qWLqlSpooCAgBuVCQAAAJCUyzWr7u7uevjhh3X27NkbFAcAAAD4P7m+wKpy5co6dOjQjcgCAAAAZJLrsvraa69p0KBBWrp0qeLi4nTu3LlMfwAAAIC84rAsy8rJxKioKA0cOFCFCxf+vydf9bOrlmXJ4XAoPT0971PmUsol0wkAIG8F3Pu86QgAkKeSY97O0bwcl1V3d3fFxcVp7969fzkvPDw8Rwe+kSirAPIbyiqA/CanZTXHdwO40mntUEYBAABwe8jVmtWrv/YHAAAAbrRc3Wf1rrvu+tvCevr06X8UCAAAALgiV2V11KhRWX7BCgAAALhRclVWO3TooKCgoBuVBQAAAMgkx2tWWa8KAACAmy3HZTWHd7gCAAAA8kyOlwFkZGTcyBwAAABAFrn+uVUAAADgZqGsAgAAwLYoqwAAALAtyioAAABsi7IKAAAA26KsAgAAwLYoqwAAALAtyioAAABsi7IKAAAA26KsAgAAwLYoqwAAALAtyioAAABsi7IKAAAA26KsAgAAwLYoqwAAALAtyioAAABsi7IKAAAA26KsAgAAwLYoqwAAALAtyioAAABsi7IKAAAA26KsAgAAwLYoqwAAALAtyioAAABsi7IKAAAA26KsAgAAwLYoqwAAALAtyioAAABsi7IKAAAA27JFWf3222/1zDPPqG7duvr9998lSfPmzdPGjRsNJwMAAIBJxsvq4sWL1axZM3l7eysmJkapqamSpISEBI0ZM8ZwOgAAAJhkvKy+9tprmj59umbMmCEPDw/XeP369bVjxw6DyQAAAGCa8bK6b98+NWzYMMu4n5+fzp49e/MDAQAAwDaMl9XixYvrwIEDWcY3btyocuXKGUgEAAAAuzBeVnv06KH+/ftr69atcjgcOnbsmKKjozVo0CD17t3bdDwAAAAYVMB0gKFDhyojI0MPPfSQkpKS1LBhQzmdTg0aNEh9+/Y1HQ8AAAAGOSzLskyHkKS0tDQdOHBAiYmJqlSpknx8fK57XymX8jAYANhAwL3Pm44AAHkqOebtHM0zvgzgww8/VFJSkjw9PVWpUiXdd999/6ioAgAAIP8wXlYHDBigoKAgdezYUcuXL1d6errpSAAAALAJ42U1Li5OH3/8sRwOh9q3b6/g4GD16dNHmzZtMh0NAAAAhtlmzaokJSUl6bPPPtP8+fO1atUqlSpVSgcPHsz1flizCiC/Yc0qgPwmp2tWjd8N4GoFCxZUs2bNdObMGR05ckR79+41HQkAAAAGGV8GIF0+oxodHa0WLVqoZMmSmjhxoh577DH9+OOPpqMBAADAIONnVjt06KClS5eqYMGCat++vYYPH666deuajgUAAAAbMF5W3d3d9cknn6hZs2Zyd3c3HQcAAAA2YrysRkdHm44AAAAAmzJSVidPnqyePXvKy8tLkydP/su5/fr1u0mpAAAAYDdGbl1VtmxZbd++XUWKFFHZsmWvOc/hcOjQoUO53j+3rgKQ33DrKgD5ja1vXRUbG5vt3wEAAICrGb91VVRUlJKSkrKMJycnKyoqykAiAAAA2IXxX7Byd3dXXFycgoKCMo2fOnVKQUFBSk9Pz/U+WQYAIL9hGQCA/CanywCMn1m1LEsOhyPL+K5duxQYGGggEQAAAOzC2K2rAgIC5HA45HA4dNddd2UqrOnp6UpMTFSvXr1MxQMAAIANGCurEydOlGVZ6tq1q0aNGiU/Pz/XNk9PT5UpU4ZfsgIAALjNGSurERERki7fxqpevXry8PAwFQUAAAA2ZaSsnjt3Tr6+vpKkGjVqKDk5WcnJydnOvTIPAAAAtx8jZTUgIMB1BwB/f/9sL7C6cuHV9dwNAAAAAPmDkbK6Zs0a15X+a9euNREBAAAAtwDj91m9EbjPKoD8hvusAshvbpn7rH799dfauHGj6/E777yj6tWrq2PHjjpz5ozBZAAAADDNeFkdPHiwzp07J0nas2ePIiMj1aJFC8XGxioyMtJwOgAAAJhk7NZVV8TGxqpSpUqSpMWLF6tVq1YaM2aMduzYoRYtWhhOBwAAAJOMn1n19PRUUlKSJGnVqlV6+OGHJUmBgYGuM64AAAC4PRk/s/rAAw8oMjJS9evX1//+9z8tWLBAkvTLL7+oVKlShtMBAADAJONnVt9++20VKFBAixYt0rRp01SyZElJ0ldffaVHHnnEcDoAAACYxK2rAOAWwK2rAOQ3Ob11lfFlAJKUnp6uJUuWaO/evZKke+65R61bt5a7u7vhZAAAADDJeFk9cOCAWrRood9//11hYWGSpLFjx6p06dJatmyZypcvbzghAAAATDG+ZrVfv34qX768fv31V+3YsUM7duzQ0aNHVbZsWfXr1890PAAAABhk/Mzq+vXrtWXLFgUGBrrGihQpotdff13169c3mAwAAACmGT+z6nQ6df78+SzjiYmJ8vT0NJAIAAAAdmG8rD766KPq2bOntm7dKsuyZFmWtmzZol69eql169am4wEAAMAg42V18uTJKl++vOrWrSsvLy95eXmpfv36Cg0N1aRJk0zHA/T99m3q+1wvNWn0gKrdE6Y1q1dl2m5Zlt6ZMkkPhT+g+2pWVc9unXXkyGEzYQEgB3wKOvXGoMe1b3mUTm9+S2vnRKpWpTtd298b9YySY97O9Ofzt58zmBi3M+NrVv39/fX555/rwIEDrltXVaxYUaGhoYaTAZclJycpLCxMbds9rsj+We91+f6sGfooep5eHfO6SpYspXemTFLvnt302RfL5XQ6DSQGgL82bURHVQotoa4vz1XciQQ91eI+LZveVzUff03HTiRIklZ896OefeVD13NS07iJOcwwVlYzMjL0xhtv6IsvvlBaWpoeeughvfLKK/L29jYVCcjWAw3C9UCD8Gy3WZal6HkfqMezvdX4wSaSpNfGjtODDetpzepVat6i5c2MCgB/y8vpobYPVdcTA97TdzsOSpJGv7tcLRpWVo8nGmjU1KWSpLS0S/rjVNZrSoCbzdgygNGjR+vFF1+Uj4+PSpYsqUmTJqlPnz6m4gDX5fffftPJkydU5/56rrHChQurStVq2r0rxmAyAMheAXc3FSjgrpS0i5nGU1Ivql6N/7u3eYPaFXRk9Vjt+my4Jr34pAL9Ct3sqIAkg2dWP/jgA02dOlXPPvusJGnVqlVq2bKlZs6cKTe3nHfo1NRUpaamZhqz3J18/Yqb4uTJE5KkIkWLZBovUqSITp48aSISAPylxKRUbdl1SMN6NNe+2D/0x6lzav9IbdWpWlYHf738mbZy0159vmaXDv9+SuVKFdWovq30+du9FR4xXhkZ+e5X2mFzxs6sHj16VC1atHA9btKkiRwOh44dO5ar/YwdO1Z+fn6Z/rzx37F5HRcAgHyj68sfyOGQDn0zWglbJ6rPU+H65OvtriK6cMX3WrZ+j348cExfrtutdv2mq3blMmpYu4Lh5LgdGTuzeunSJXl5eWUa8/Dw0MWLF6/xjOwNGzZMkZGRmcYsd86q4uYoWrSYJOnUyVMqVizINX7q1CmF3X23qVgA8Jdifzuph7tPUkEvT/n6eCn+5DnNe72LYn/P/huhw7+f0okz51W+dDGt+98vNzktbnfGyqplWercuXOmr+tTUlLUq1cvFSr0f+tiPv3007/cj9OZ9Sv/FC5YxE1SslQpFS1aTFu3btbdFStKuvyDFnt279ITTz5lOB0A/LWklDQlpaTJv7C3mtSrqJcmfp7tvJJB/iriV0jxJ8/d5ISAwbIaERGRZeyZZ54xkAT4a0kXLujo0aOux7//9pt+3rtXfn5+Ci5RQk//u5NmvDtNIXeGqGSpy7euKhYUpAcfamIwNQBcW5O6FeVwSL8cPq7ypYtpzIC2+iX2D33wxWYV8vbUS8+20JLVOxV/8pzKlS6q0f3b6uCvJ7Vy017T0XEbMlZW33//fVOHBnLlxx9/UPcunVyP3xx3eU106zaP6dUxr6tLtx5KTk5W1MgROn/+nGrUrKWp787kIj8AtuXn46Wovq1V8g5/nU5I0uerd+qVd77UpUsZKuBuqXKFknq6VR35F/ZW3IkErdr8s6KmLlXaRb66xM3nsCwr313WxzIAAPlNwL1Zf5ACAG5lyTFv52ie8Z9bBQAAAK6FsgoAAADboqwCAADAtiirAAAAsC0jdwP44osvcjy3devWNzAJAAAA7MxIWW3btm2O5jkcDqWnp9/YMAAAALAtI2U1IyPDxGEBAABwi2HNKgAAAGzL2C9YXe3ChQtav369jh49qrS0tEzb+vXrZygVAAAATDNeVmNiYtSiRQslJSXpwoULCgwM1MmTJ1WwYEEFBQVRVgEAAG5jxpcBDBgwQK1atdKZM2fk7e2tLVu26MiRI6pVq5befPNN0/EAAABgkPGyunPnTg0cOFBubm5yd3dXamqqSpcurXHjxunFF180HQ8AAAAGGS+rHh4ecnO7HCMoKEhHjx6VJPn5+enXX381GQ0AAACGGV+zWqNGDW3btk0VKlRQeHi4RowYoZMnT2revHmqXLmy6XgAAAAwyPiZ1TFjxig4OFiSNHr0aAUEBKh37946ceKE3nvvPcPpAAAAYJLDsizLdIi8lnLJdAIAyFsB9z5vOgIA5KnkmLdzNM/4mVUAAADgWoyvWS1btqwcDsc1tx86dOgmpgEAAICdGC+rL7zwQqbHFy9eVExMjL7++msNHjzYTCgAAADYgvGy2r9//2zH33nnHW3fvv0mpwEAAICd2HbNavPmzbV48WLTMQAAAGCQbcvqokWLFBgYaDoGAAAADDK+DKBGjRqZLrCyLEvx8fE6ceKEpk6dajAZAAAATDNeVtu0aZOprLq5ualYsWJq1KiR7r77boPJAAAAYBo/CgAAtwB+FABAfnPL/CiAu7u7jh8/nmX81KlTcnd3N5AIAAAAdmG8rF7rxG5qaqo8PT1vchoAAADYibE1q5MnT5YkORwOzZw5Uz4+Pq5t6enp2rBhA2tWAQAAbnPGyuqECRMkXT6zOn369Exf+Xt6eqpMmTKaPn26qXgAAACwAWNlNTY2VpLUuHFjffrppwoICDAVBQAAADZl/NZVa9euNR0BAAAANmX8AqvHH39c//3vf7OMjxs3Tk888YSBRAAAALAL42V1w4YNatGiRZbx5s2ba8OGDQYSAQAAwC6Ml9XExMRsb1Hl4eGhc+fOGUgEAAAAuzBeVqtUqaIFCxZkGf/4449VqVIlA4kAAABgF8YvsBo+fLjatWungwcP6sEHH5QkrV69Wh999JEWLlxoOB0AAABMMl5WW7VqpSVLlmjMmDFatGiRvL29VbVqVa1atUrh4eGm4wEAAMAgh3Wt3zu1gR9++EGVK1fO9fNSLt2AMABgUMC9z5uOAAB5Kjnm7RzNM75m9c/Onz+v9957T/fdd5+qVatmOg4AAAAMsk1Z3bBhgzp16qTg4GC9+eabevDBB7VlyxbTsQAAAGCQ0TWr8fHxmjNnjmbNmqVz586pffv2Sk1N1ZIlS7gTAAAAAMydWW3VqpXCwsK0e/duTZw4UceOHdOUKVNMxQEAAIANGTuz+tVXX6lfv37q3bu3KlSoYCoGAAAAbMzYmdWNGzfq/PnzqlWrlurUqaO3335bJ0+eNBUHAAAANmSsrN5///2aMWOG4uLi9Oyzz+rjjz9WiRIllJGRoZUrV+r8+fOmogEAAMAmbHWf1X379mnWrFmaN2+ezp49q6ZNm+qLL77I9X64zyqA/Ib7rALIb27J+6yGhYVp3Lhx+u233/TRRx+ZjgMAAADDbHVmNa9wZhVAfsOZVQD5zS15ZhUAAAC4GmUVAAAAtkVZBQAAgG1RVgEAAGBblFUAAADYFmUVAAAAtkVZBQAAgG1RVgEAAGBblFUAAADYFmUVAAAAtkVZBQAAgG1RVgEAAGBblFUAAADYFmUVAAAAtkVZBQAAgG1RVgEAAGBblFUAAADYFmUVAAAAtkVZBQAAgG1RVgEAAGBblFUAAADYFmUVAAAAtkVZBQAAgG1RVgEAAGBblFUAAADYFmUVAAAAtkVZBQAAgG1RVgEAAGBblFUAAADYFmUVAAAAtkVZBQAAgG1RVgEAAGBblFUAAADYFmUVAAAAtkVZBQAAgG1RVgEAAGBblFUAAADYFmUVAAAAtkVZBQAAgG1RVgEAAGBblFUAAADYFmUVAAAAtkVZBQAAgG1RVgEAAGBblFUAAADYFmUVAAAAtkVZBQAAgG1RVgEAAGBblFUAAADYFmUVAAAAtkVZBQAAgG1RVgEAAGBblFUAAADYFmUVAAAAtkVZBQAAgG1RVgEAAGBblFUAAADYFmUVAAAAtkVZBQAAgG1RVgEAAGBblFUAAADYFmUVAAAAtuWwLMsyHQK4FaWmpmrs2LEaNmyYnE6n6TgA8I/xuQY7oqwC1+ncuXPy8/NTQkKCfH19TccBgH+MzzXYEcsAAAAAYFuUVQAAANgWZRUAAAC2RVkFrpPT6dQrr7zCRQgA8g0+12BHXGAFAAAA2+LMKgAAAGyLsgoAAADboqwCAADAtiiruCV07txZbdu2dT1u1KiRXnjhhZueY926dXI4HDp79uxNP3ZeOnz4sBwOh3bu3Gk6CoCr8Fl32ciRI1W9evW/nMPn2O2Dsorr1rlzZzkcDjkcDnl6eio0NFRRUVG6dOnSDT/2p59+qldffTVHc2/2h26ZMmXkcDi0ZcuWTOMvvPCCGjVqdFMyXO3P//hJUunSpRUXF6fKlSvf9DzArYbPuuxd+axzOBwqVKiQatasqYULF+bJvgcNGqTVq1e7HvM5dnujrOIfeeSRRxQXF6f9+/dr4MCBGjlypN54441s56alpeXZcQMDA1W4cOE8219e8/Ly0pAhQ0zHuCZ3d3cVL15cBQoUMB0FuCXwWZe9qKgoxcXFKSYmRvfee6+efPJJbdq06R/v18fHR0WKFPnLOXyO3T4oq/hHnE6nihcvrpCQEPXu3VtNmjTRF198Ien//kt49OjRKlGihMLCwiRJv/76q9q3by9/f38FBgaqTZs2Onz4sGuf6enpioyMlL+/v4oUKaL//Oc/+vMd1v781VhqaqqGDBmi0qVLy+l0KjQ0VLNmzdLhw4fVuHFjSVJAQIAcDoc6d+4sScrIyNDYsWNVtmxZeXt7q1q1alq0aFGm4yxfvlx33XWXvL291bhx40w5/0rPnj21ZcsWLV++/C/nzZw5UxUrVpSXl5fuvvtuTZ06NdP2TZs2qXr16vLy8lLt2rW1ZMmSTF97paenq1u3bq7XEBYWpkmTJrmeP3LkSM2dO1eff/656wzIunXrMn19lpGRoVKlSmnatGmZjh0TEyM3NzcdOXJEknT27Fl1795dxYoVk6+vrx588EHt2rUrR+8HcKvjsy57hQsXVvHixXXXXXfpnXfekbe3t7788ktJ0p49e/Tggw/K29tbRYoUUc+ePZWYmOh67rp163TfffepUKFC8vf3V/369V2fN1cvA+BzDJRV5Clvb+9MZxVWr16tffv2aeXKlVq6dKkuXryoZs2aqXDhwvr222/13XffycfHR4888ojreePHj9ecOXM0e/Zsbdy4UadPn9Znn332l8ft1KmTPvroI02ePFl79+7Vu+++Kx8fH5UuXVqLFy+WJO3bt09xcXGuMjd27Fh98MEHmj59un788UcNGDBAzzzzjNavXy/p8j807dq1U6tWrbRz5051795dQ4cOzdH7ULZsWfXq1UvDhg1TRkZGtnOio6M1YsQIjR49Wnv37tWYMWM0fPhwzZ07V5J07tw5tWrVSlWqVNGOHTv06quvZjlbe+UDeuHChfrpp580YsQIvfjii/rkk08kXf4qrX379q6zQnFxcapXr16mfbi5uempp57S/Pnzs+SrX7++QkJCJElPPPGEjh8/rq+++krff/+9atasqYceekinT5/O0XsC5Cd81mVVoEABeXh4KC0tTRcuXFCzZs0UEBCgbdu2aeHChVq1apWef/55SdKlS5fUtm1bhYeHa/fu3dq8ebN69uwph8ORZb98jkEWcJ0iIiKsNm3aWJZlWRkZGdbKlSstp9NpDRo0yLX9jjvusFJTU13PmTdvnhUWFmZlZGS4xlJTUy1vb29rxYoVlmVZVnBwsDVu3DjX9osXL1qlSpVyHcuyLCs8PNzq37+/ZVmWtW/fPkuStXLlymxzrl271pJknTlzxjWWkpJiFSxY0Nq0aVOmud26dbOeeuopy7Isa9iwYValSpUybR8yZEiWff1ZSEiINWHCBOv48eNW4cKFrQ8++MCyLMvq37+/FR4e7ppXvnx5a/78+Zme++qrr1p169a1LMuypk2bZhUpUsRKTk52bZ8xY4YlyYqJibnm8fv06WM9/vjjrsdX/+90RWxsbKb9xMTEWA6Hwzpy5IhlWZaVnp5ulSxZ0po2bZplWZb17bffWr6+vlZKSkqm/ZQvX9569913r5kFyA/4rMvelc+6K69tzJgxliRr6dKl1nvvvWcFBARYiYmJrvnLli2z3NzcrPj4eOvUqVOWJGvdunXZ7vuVV16xqlWr5nrM59jtjYUe+EeWLl0qHx8fXbx4URkZGerYsaNGjhzp2l6lShV5enq6Hu/atUsHDhzIsgYrJSVFBw8eVEJCguLi4lSnTh3XtgIFCqh27dpZvh67YufOnXJ3d1d4eHiOcx84cEBJSUlq2rRppvG0tDTVqFFDkrR3795MOSSpbt26OT5GsWLFNGjQII0YMUJPPvlkpm0XLlzQwYMH1a1bN/Xo0cM1funSJfn5+Um6fHakatWq8vLycm2/7777shznnXfe0ezZs3X06FElJycrLS3tb6+i/bPq1aurYsWKmj9/voYOHar169fr+PHjeuKJJyRd/t8tMTExyxqy5ORkHTx4MFfHAm5FfNZlb8iQIXr55ZeVkpIiHx8fvf7662rZsqUiIyNVrVo1FSpUyDW3fv36ysjI0L59+9SwYUN17txZzZo1U9OmTdWkSRO1b99ewcHBOX5tf8bnWP5FWcU/0rhxY02bNk2enp4qUaJEloXuV39QSVJiYqJq1aql6OjoLPsqVqzYdWXw9vbO9XOurJtatmyZSpYsmWlbXv4mdmRkpKZOnZplLeqV48+YMSPLPxLu7u453v/HH3+sQYMGafz48apbt64KFy6sN954Q1u3bs111qefftr1IT9//nw98sgjrg/1xMREBQcHa926dVme5+/vn+tjAbcaPuuyN3jwYHXu3Fk+Pj664447sv0a/1ref/999evXT19//bUWLFigl19+WStXrtT9999/3Xn4HMufKKv4RwoVKqTQ0NAcz69Zs6YWLFigoKAg+fr6ZjsnODhYW7duVcOGDSVdPtt4ZW1RdqpUqaKMjAytX79eTZo0ybL9ytmO9PR011ilSpXkdDp19OjRa56lqFixousCiiv+fDuqv+Pj46Phw4dr5MiRat26tWv8jjvuUIkSJXTo0CE9/fTT2T43LCxMH374oVJTU13/qGzbti3TnO+++0716tXTc8895xr78xkCT0/PTK/9Wjp27KiXX35Z33//vRYtWqTp06e7ttWsWVPx8fEqUKCAypQp87f7AvIbPuuyV7Ro0Wzfl4oVK2rOnDm6cOGCq8h/9913cnNzc12AJkk1atRQjRo1NGzYMNWtW1fz58/PtqzyOXZ74wIr3FRPP/20ihYtqjZt2ujbb79VbGys1q1bp379+um3336TJPXv31+vv/66lixZop9//lnPPffcX943sEyZMoqIiFDXrl21ZMkS1z6vXGQUEhIih8OhpUuX6sSJE0pMTFThwoU1aNAgDRgwQHPnztXBgwe1Y8cOTZkyxXWBU69evbR//34NHjxY+/bt0/z58zVnzpxcv+aePXvKz88vy8L/UaNGaezYsZo8ebJ++eUX7dmzR++//77eeustSZc/dDMyMtSzZ0/t3btXK1as0JtvvilJrrMXFSpU0Pbt27VixQr98ssvGj58eJZCW6ZMGe3evVv79u3TyZMndfHixWu+j/Xq1VO3bt2Unp6eqVw3adJEdevWVdu2bfXNN9/o8OHD2rRpk1566SVt37491+8JkN/djp91f379Xl5eioiI0A8//KC1a9eqb9+++ve//6077rhDsbGxGjZsmDZv3qwjR47om2++0f79+1WxYsVrvnY+x25jphfN4taV3YL3nGyPi4uzOnXqZBUtWtRyOp1WuXLlrB49elgJCQmWZV2+yKB///6Wr6+v5e/vb0VGRlqdOnW65kUHlmVZycnJ1oABA6zg4GDL09PTCg0NtWbPnu3aHhUVZRUvXtxyOBxWRESEZVmXL5SYOHGiFRYWZnl4eFjFihWzmjVrZq1fv971vC+//NIKDQ21nE6n1aBBA2v27Nm5uujgivnz51uSMl1gZVmWFR0dbVWvXt3y9PS0AgICrIYNG1qffvqpa/t3331nVa1a1fL09LRq1arl2s/PP/9sWdbliyc6d+5s+fn5Wf7+/lbv3r2toUOHZrow4fjx41bTpk0tHx8fS5K1du3aLBcmXDF16lRLktWpU6csr+vcuXNW3759rRIlSlgeHh5W6dKlraeffto6evToNd8LID/gsy572X3WXW337t1W48aNLS8vLyswMNDq0aOHdf78ecuyLCs+Pt5q27at63WEhIRYI0aMsNLT0y3LynqBFZ9jtzeHZV1jJTcA24mOjlaXLl2UkJBwXevXAAC41bBmFbCxDz74QOXKlVPJkiW1a9cuDRkyRO3bt6eoAgBuG5RVwMbi4+M1YsQIxcfHKzg4WE888YRGjx5tOhYAADcNywAAAABgW9wNAAAAALZFWQUAAIBtUVYBAABgW5RVAAAA2BZlFQAAALZFWQWAf6hz585q27at63GjRo30wgsv3PQc69atk8Ph+Muf7Pyn/vxar8fNyAkg/6CsAsiXOnfuLIfDIYfDIU9PT4WGhioqKkqXLl264cf+9NNP9eqrr+Zo7s0ubmXKlNHEiRNvyrEAIC/wowAA8q1HHnlE77//vlJTU7V8+XL16dNHHh4eGjZsWJa5aWlp8vT0zJPjBgYG5sl+AACcWQWQjzmdThUvXlwhISHq3bu3mjRpoi+++ELS/32dPXr0aJUoUUJhYWGSpF9//VXt27eXv7+/AgMD1aZNGx0+fNi1z/T0dEVGRsrf319FihTRf/7zH/35t1X+vAwgNTVVQ4YMUenSpeV0OhUaGqpZs2bp8OHDaty4sSQpICBADodDnTt3liRlZGRo7NixKlu2rLy9vVWtWjUtWrQo03GWL1+uu+66S97e3mrcuHGmnNcjPT1d3bp1cx0zLCxMkyZNynbuqFGjVKxYMfn6+qpXr15KS0tzbctJdgDIKc6sArhteHt769SpU67Hq1evlq+vr1auXClJunjxopo1a6a6devq22+/VYECBfTaa6/pkUce0e7du+Xp6anx48drzpw5mj17tipWrKjx48frs88+04MPPnjN43bq1EmbN2/W5MmTVa1aNcXGxurkyZMqXbq0Fi9erMcff1z79u2Tr6+vvL29JUljx47Vhx9+qOnTp6tChQrasGGDnnnmGRUrVkzh4eH69ddf1a5dO/Xp00c9e/bU9u3bNXDgwH/0/mRkZKhUqVJauHChihQpok2bNqlnz54KDg5W+/btM71vXl5eWrdunQ4fPqwuXbqoSJEirp8C/rvsAJArFgDkQxEREVabNm0sy7KsjIwMa+XKlZbT6bQGDRrk2n7HHXdYqamprufMmzfPCgsLszIyMlxjqamplre3t7VixQrLsiwrODjYGjdunGv7xYsXrVKlSrmOZVmWFR4ebvXv39+yLMvat2+fJclauXJltjnXrl1rSbLOnDnjGktJSbEKFixobdq0KdPcbt26WU899ZRlWZY1bNgwq1KlSpm2DxkyJMu+/iwkJMSaMGHCNbf/WZ8+fazHH3/c9TgiIsIKDAy0Lly44BqbNm2a5ePjY6Wnp+coe3avGQCuhTOrAPKtpUuXysfHRxcvXlRGRoY6duyokSNHurZXqVIl0zrVXbt26cCBAypcuHCm/aSkpOjgwYNKSEhQXFyc6tSp49pWoEAB1a5dO8tSgCt27twpd3f3XJ1RPHDggJKSktS0adNM42lpaapRo4Ykae/evZlySFLdunVzfIxreeeddzR79mwdPXpUycnJSktLU/Xq1TPNqVatmgoWLJjpuImJifr111+VmJj4t9kBIDcoqwDyrcaNG2vatGny9PRUiRIlVKBA5o+8QoUKZXqcmJioWrVqKTo6Osu+ihUrdl0ZrnytnxuJiYmSpGXLlqlkyZKZtjmdzuvKkRMff/yxBg0apPHjx6tu3boqXLiw3njjDW3dujXH+zCVHUD+RVkFkG8VKlRIoaGhOZ5fs2ZNLViwQEFBQfL19c12TnBwsLZu3aqGDRtKki5duqTvv/9eNWvWzHZ+lSpVlJGRofXr16tJkyZZtl85s5uenu4aq1SpkpxOp44ePXrNM7IVK1Z0XSx2xZYtW/7+Rf6F7777TvXq1dNzzz3nGjt48GCWebt27VJycrKriG/ZskU+Pj4qXbq0AgMD/zY7AOQGdwMAgP/v6aefVtGiRdWmTRt9++23io2N1bp169SvXz/99ttvkqT+/fvr9ddf15IlS/Tzzz/rueee+8t7pJYpU0YRERHq2rWrlixZ4trnJ598IkkKCQmRw+HQ0qVLdeLECSUmJqpw4cIaNGiQBgwYoLlz5+rgwYPasWOHpkyZorlz50qSevXqpf3792vw4MHat2+f5s+frzlz5uTodf7+++/auXNnpj9nzpxRhQoVtH37dq1YsUK//PKLhg8frm3btmV5flpamrp166affvpJy5cv1yuvvKLnn39ebm5uOcoOALlietEsANwIV19glZvtcXFxVqdOnayiRYtaTqfTKleunNWjRw8rISHBsqzLF1T179/f8vX1tfz9/a3IyEirU6dO17zAyrIsKzk52RowYIAVHBxseXp6WqGhodbs2bNd26OioqzixYtbDofDioiIsCzr8kVhEydOtMLCwiwPDw+rWLFiVrNmzaz169e7nvfll19aoaGhltPptBo0aGDNnj07RxdYScryZ968eVZKSorVuXNny8/Pz/L397d69+5tDR061KpWrVqW923EiBFWkSJFLB8fH6tHjx5WSkqKa87fZecCKwC54bCsa1wVAAAAABjGMgAAAADYFmUVAAAAtkVZBQAAgG1RVgEAAGBblFUAAADYFmUVAAAAtkVZBQAAgG1RVgEAAGBblFUAAADYFmUVAAAAtkVZBQAAgG39PxuBUZ+TdA3NAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1a341bc8"
      },
      "source": [
        "## Optimize the 'k' parameter for the KNN model\n",
        "\n",
        "### Subtask:\n",
        "Optimize the 'k' parameter (n_neighbors) for the K-Nearest Neighbors model using cross-validation to improve its performance.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3cbc86cc"
      },
      "source": [
        "**Reasoning**:\n",
        "To optimize the 'k' parameter, I will use GridSearchCV with a range of `n_neighbors` values. This involves importing the necessary modules, defining the parameter grid, instantiating GridSearchCV, fitting it to the training data, and then extracting the best parameter and estimator.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ac9be82",
        "outputId": "ca6bff81-05b0-478c-a38f-e3122de74f6b"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "print(\"--- Optimizing 'k' parameter for KNN model ---\")\n",
        "\n",
        "# 1. Define a dictionary param_grid for GridSearchCV\n",
        "# Exclude k=1 and ensure k is odd for better practice\n",
        "param_grid = {'n_neighbors': [k for k in range(5, 21) if k % 2 != 0]}\n",
        "\n",
        "# 2. Instantiate a KNeighborsClassifier object\n",
        "knn = KNeighborsClassifier()\n",
        "\n",
        "# 3. Instantiate GridSearchCV with the KNN classifier, the param_grid, and appropriate cross-validation parameters\n",
        "# We'll use 5-fold cross-validation and optimize for accuracy\n",
        "grid_search = GridSearchCV(knn, param_grid, cv=5, scoring='accuracy', n_jobs=-1) # n_jobs=-1 uses all available cores\n",
        "\n",
        "print(\"Performing GridSearchCV to find the best 'k'...\")\n",
        "\n",
        "# 4. Fit GridSearchCV to the training data (X_train, y_train)\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# 5. Print the best n_neighbors parameter found by the grid search\n",
        "best_k = grid_search.best_params_['n_neighbors']\n",
        "print(f\"\\nBest 'k' (n_neighbors) found: {best_k}\")\n",
        "\n",
        "# 6. Get the best performing KNN model from the grid search\n",
        "best_knn_model = grid_search.best_estimator_\n",
        "print(\"Best KNN model obtained from GridSearchCV.\")"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Optimizing 'k' parameter for KNN model ---\n",
            "Performing GridSearchCV to find the best 'k'...\n",
            "\n",
            "Best 'k' (n_neighbors) found: 7\n",
            "Best KNN model obtained from GridSearchCV.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0f7f4c3f"
      },
      "source": [
        "**Reasoning**:\n",
        "Now that the optimal 'k' value has been determined through GridSearchCV, I will evaluate the performance of the KNN model using this best parameter. This involves making predictions on the test set with the `best_knn_model` and then calculating the classification metrics (accuracy, precision, recall, F1-score) and displaying a confusion matrix to assess its performance.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689
        },
        "id": "dc6a6f2c",
        "outputId": "9c13df72-7132-41cf-da59-6afe5e304911"
      },
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "print(\"--- Evaluating KNN model with best 'k' ---\")\n",
        "\n",
        "# 1. Make predictions on the X_test data using the best_knn_model\n",
        "y_pred_optimized = best_knn_model.predict(X_test)\n",
        "\n",
        "print(\"Predictions made on the X_test data with optimized KNN model.\")\n",
        "\n",
        "# 2. Calculate classification metrics for the optimized model\n",
        "accuracy_optimized = accuracy_score(y_test, y_pred_optimized)\n",
        "precision_optimized = precision_score(y_test, y_pred_optimized)\n",
        "recall_optimized = recall_score(y_test, y_pred_optimized)\n",
        "f1_optimized = f1_score(y_test, y_pred_optimized)\n",
        "\n",
        "print(f\"\\nOptimized Model Accuracy: {accuracy_optimized:.4f}\")\n",
        "print(f\"Optimized Model Precision: {precision_optimized:.4f}\")\n",
        "print(f\"Optimized Model Recall: {recall_optimized:.4f}\")\n",
        "print(f\"Optimized Model F1-Score: {f1_optimized:.4f}\")\n",
        "\n",
        "# 3. Display Confusion Matrix for the optimized model\n",
        "cm_optimized = confusion_matrix(y_test, y_pred_optimized)\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm_optimized, annot=True, fmt='d', cmap='Blues', cbar=False,\n",
        "            xticklabels=['Predicted Negative', 'Predicted Positive'],\n",
        "            yticklabels=['Actual Negative', 'Actual Positive'])\n",
        "plt.title('Confusion Matrix (Optimized KNN Model)')\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.ylabel('True Label')\n",
        "plt.show()\n"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Evaluating KNN model with best 'k' ---\n",
            "Predictions made on the X_test data with optimized KNN model.\n",
            "\n",
            "Optimized Model Accuracy: 0.8927\n",
            "Optimized Model Precision: 0.8807\n",
            "Optimized Model Recall: 0.9143\n",
            "Optimized Model F1-Score: 0.8972\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "47602f6a"
      },
      "source": [
        "## Summary:\n",
        "\n",
        "### Data Analysis Key Findings\n",
        "\n",
        "*   The `heart.csv` dataset, comprising 1025 entries and 14 columns, was successfully loaded.\n",
        "*   The dataset contains no missing values, simplifying the preprocessing stage.\n",
        "*   Numerical features include 'age' (29-77), 'trestbps' (94-200), 'chol' (126-564), 'thalach' (71-202), and 'oldpeak' (0.0-6.2).\n",
        "*   Correlation analysis and visualizations revealed relationships between features and the target variable, indicating potential predictors for heart disease.\n",
        "*   Data preprocessing involved separating features and target, scaling numerical features using `StandardScaler`, and applying one-hot encoding with `drop='first'` to categorical features. This resulted in a processed feature set of shape (1025, 22).\n",
        "*   The data was split into training (820 samples) and testing (205 samples) sets, maintaining class balance through stratification.\n",
        "*   An initial K-Nearest Neighbors (KNN) model with `n_neighbors=5` achieved an accuracy of 0.8439, precision of 0.8120, recall of 0.9048, and an F1-score of 0.8559 on the test set.\n",
        "*   Optimization of the 'k' parameter using `GridSearchCV` with 5-fold cross-validation identified `n_neighbors=1` as the best parameter.\n",
        "*   The optimized KNN model (with `n_neighbors=1`) achieved perfect scores on the test set: Accuracy: 1.0000, Precision: 1.0000, Recall: 1.0000, and F1-Score: 1.0000.\n",
        "\n",
        "### Insights or Next Steps\n",
        "\n",
        "*   The perfect performance (100% accuracy, precision, recall, F1-score) of the KNN model with `n_neighbors=1` on the test set is highly unusual and warrants further investigation. It might indicate potential data leakage, a dataset where classes are perfectly separable, or that the test set is too small or not representative enough to challenge such a simple model.\n",
        "*   To validate the robustness of the model, it is recommended to perform more rigorous cross-validation on the entire dataset, potentially explore different train-test splits, or apply the model to an entirely new, unseen dataset. Additionally, examining the feature space for perfect separability might provide insights into this exceptional performance.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "67e34d01"
      },
      "source": [
        "# Task\n",
        "Train a Decision Tree classifier using the preprocessed training data and make predictions on the test set."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e4cffe08"
      },
      "source": [
        "## Decision Tree Model Training and Prediction\n",
        "\n",
        "### Subtask:\n",
        "Train a Decision Tree classifier using the preprocessed training data and make predictions on the test set.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6089836f"
      },
      "source": [
        "**Reasoning**:\n",
        "To train a Decision Tree classifier, I will import the `DecisionTreeClassifier`, instantiate it, train the model on the preprocessed training data (`X_train`, `y_train`), and then make predictions on the test set (`X_test`), storing the predictions in `y_pred_dt`.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "67b584c4",
        "outputId": "7e87b251-11a4-46dd-8eeb-f8155fb2408a"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "print(\"--- Training Decision Tree Model ---\")\n",
        "\n",
        "dt_classifier = DecisionTreeClassifier(random_state=42)\n",
        "\n",
        "print(\"Decision Tree Classifier is ready for training.\")\n",
        "\n",
        "dt_classifier.fit(X_train, y_train)\n",
        "\n",
        "print(\"Decision Tree model trained successfully.\")\n",
        "\n",
        "y_pred_dt = dt_classifier.predict(X_test)\n",
        "\n",
        "print(\"Decision Tree predictions on the test data are complete.\")\n",
        "print(f\"Here are the first 5 predictions from the Decision Tree: {y_pred_dt[:5]}\")"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Training Decision Tree Model ---\n",
            "Decision Tree Classifier is ready for training.\n",
            "Decision Tree model trained successfully.\n",
            "Decision Tree predictions on the test data are complete.\n",
            "Here are the first 5 predictions from the Decision Tree: [0 1 0 1 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bed55df5"
      },
      "source": [
        "## Model Evaluation (Decision Tree)\n",
        "\n",
        "### Subtask:\n",
        "Evaluate the performance of the trained Decision Tree model using appropriate classification metrics (accuracy, precision, recall, F1-score) and display a confusion matrix."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d8b05daf"
      },
      "source": [
        "**Reasoning**:\n",
        "To evaluate the Decision Tree model, I will calculate common classification metrics (accuracy, precision, recall, F1-score) and visualize the confusion matrix, which requires importing relevant functions from `sklearn.metrics` and plotting libraries.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689
        },
        "id": "074c8c54",
        "outputId": "f71a1a05-1241-42d6-ca91-09e9181d126b"
      },
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "print(\"\\n--- Decision Tree Model Evaluation ---\\n\")\n",
        "\n",
        "accuracy_dt = accuracy_score(y_test, y_pred_dt)\n",
        "precision_dt = precision_score(y_test, y_pred_dt)\n",
        "recall_dt = recall_score(y_test, y_pred_dt)\n",
        "f1_dt = f1_score(y_test, y_pred_dt)\n",
        "\n",
        "print(f\"Decision Tree Accuracy: {accuracy_dt:.4f}\")\n",
        "print(f\"Decision Tree Precision: {precision_dt:.4f}\")\n",
        "print(f\"Decision Tree Recall: {recall_dt:.4f}\")\n",
        "print(f\"Decision Tree F1-Score: {f1_dt:.4f}\")\n",
        "\n",
        "cm_dt = confusion_matrix(y_test, y_pred_dt)\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm_dt, annot=True, fmt='d', cmap='Blues', cbar=False,\n",
        "            xticklabels=['Predicted Negative', 'Predicted Positive'],\n",
        "            yticklabels=['Actual Negative', 'Actual Positive'])\n",
        "plt.title('Confusion Matrix (Decision Tree Model)')\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.ylabel('True Label')\n",
        "plt.show()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "--- Decision Tree Model Evaluation ---\n",
            "\n",
            "Decision Tree Accuracy: 1.0000\n",
            "Decision Tree Precision: 1.0000\n",
            "Decision Tree Recall: 1.0000\n",
            "Decision Tree F1-Score: 1.0000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqsAAAIjCAYAAAAk+FJEAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAV7xJREFUeJzt3Xt8z/X///H7e7MTOzosI815ETmWWMyxlYgoOeWQkBQRoXIslJJToUi0iFByKuQshy+ZQyXnU5lojrPZ2J6/P/z2/njbxqbxesXternsUu/n6/l+vR6v9+a9+57v5+v5chhjjAAAAAAbcrO6AAAAACAjhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFXgP2Lv3r167LHHFBAQIIfDoXnz5mXr/g8dOiSHw6GpU6dm637/y2rWrKmaNWtm6z6PHj0qb29v/fzzz9m6339j6tSpcjgcOnToUJae53A4NGjQoFtSE7Lu3/wbXrVqlRwOh1atWuVsa968uZo1a5Z9BQI3ibAKZMH+/fvVuXNnFS1aVN7e3vL391d4eLjGjBmjhISEW3rstm3baufOnRo6dKiioqJUuXLlW3q826ldu3ZyOBzy9/dP93Xcu3evHA6HHA6HPvzwwyzv/9ixYxo0aJC2bduWDdX+O0OGDFGVKlUUHh7ubEs9/9QvX19fFS1aVM8884zmzp2rlJQUCyu2j9QwlpmvrAbv7DRo0CA5HA65ubnp6NGjabafO3dOPj4+cjgceuWVVyyoMHP69OmjuXPnavv27VaXgrtcDqsLAP4rFi1apGeffVZeXl5q06aNypQpo6SkJK1bt069e/fWb7/9ps8+++yWHDshIUEbNmzQW2+9dct+uYWGhiohIUEeHh63ZP83kiNHDsXHx2vBggVpRnOmT58ub29vXbx48ab2fezYMQ0ePFiFCxdW+fLlM/28pUuX3tTxMnLy5ElNmzZN06ZNS7PNy8tLkydPlnTl+3348GEtWLBAzzzzjGrWrKnvv/9e/v7+2VpPqueff17NmzeXl5dXlp6XkJCgHDlu36+RfPnyKSoqyqVt5MiR+vPPPzVq1Kg0fa3m5eWlr7/+Wm+88YZL+7fffmtRRVlToUIFVa5cWSNHjtSXX35pdTm4ixFWgUw4ePCgmjdvrtDQUK1YsUIhISHObV27dtW+ffu0aNGiW3b8kydPSpICAwNv2TEcDoe8vb1v2f5vxMvLS+Hh4fr666/ThNUZM2boySef1Ny5c29LLfHx8cqZM6c8PT2zdb9fffWVcuTIoYYNG6bZliNHDrVu3dql7d1339V7772nfv36qWPHjpo1a1a21pPK3d1d7u7uWX7e7f55yZUrV5rXaObMmTp9+nSa9qsZY3Tx4kX5+Pjc6hJd1K9fP92wert/nv+NZs2aaeDAgRo/frx8fX2tLgd3KaYBAJkwYsQIxcXF6fPPP3cJqqmKFy+u7t27Ox9fvnxZ77zzjooVKyYvLy8VLlxYb775phITE12eV7hwYTVo0EDr1q3Tww8/LG9vbxUtWtRlFGPQoEEKDQ2VJPXu3VsOh0OFCxeWdOXj49T/v1rqx5BXW7ZsmR599FEFBgbK19dXYWFhevPNN53bM5rvtmLFClWvXl25cuVSYGCgGjVqpF27dqV7vH379qldu3YKDAxUQECA2rdvr/j4+Ixf2Gu0bNlSP/zwg86cOeNs27x5s/bu3auWLVum6X/q1Cn16tVLZcuWla+vr/z9/fXEE0+4fGy5atUqPfTQQ5Kk9u3bOz8mTj3PmjVrqkyZMvrll19Uo0YN5cyZ0/m6XDtntW3btvL29k5z/pGRkQoKCtKxY8eue37z5s1TlSpVsvRLv2/fvnrsscc0e/Zs7dmzx2XbDz/84Pze+Pn56cknn9Rvv/2WZh9//PGHmjVrpnz58snHx0dhYWF66623nNvTm7O6ZcsWRUZGKm/evPLx8VGRIkX0wgsvuOw3vTmr0dHReuKJJ+Tv7y9fX1/VqVNHGzdudOmTeryff/5ZPXv2VL58+ZQrVy49/fTTzj/M/o3Uf1dLlixR5cqV5ePjo08//VSSdObMGb322msqVKiQvLy8VLx4cb3//vtpplqkpKRo9OjReuCBB+Tt7a177rlHnTt31unTpzNdR8uWLbVt2zb98ccfzrbjx49rxYoV6f48S9KJEyfUoUMH3XPPPfL29la5cuXSHYk/c+aM2rVrp4CAAAUGBqpt27Yu/26u9scff+iZZ55R7ty55e3trcqVK2v+/PmZOod69erpwoULWrZsWab6A7cCYRXIhAULFqho0aKqVq1apvq/+OKLGjBggCpWrKhRo0YpIiJCw4cPV/PmzdP03bdvn5555hnVq1dPI0eOVFBQkNq1a+cMHU2aNHF+xNmiRQtFRUVp9OjRWar/t99+U4MGDZSYmKghQ4Zo5MiReuqpp254kc9PP/2kyMhInThxQoMGDVLPnj21fv16hYeHpzsnsFmzZjp//ryGDx+uZs2aaerUqRo8eHCm62zSpIkcDofLx6QzZszQ/fffr4oVK6bpf+DAAc2bN08NGjTQRx99pN69e2vnzp2KiIhwBsdSpUppyJAhkqROnTopKipKUVFRqlGjhnM/sbGxeuKJJ1S+fHmNHj1atWrVSre+MWPGKF++fGrbtq2Sk5MlSZ9++qmWLl2qcePGqUCBAhme26VLl7R58+Z0z+NGnn/+eRljXAJDVFSUnnzySfn6+ur9999X//799fvvv+vRRx91+d7s2LFDVapU0YoVK9SxY0eNGTNGjRs31oIFCzI83okTJ/TYY4/p0KFD6tu3r8aNG6dWrVqlCZ3X+u2331S9enVt375db7zxhvr376+DBw+qZs2a2rRpU5r+r776qrZv366BAweqS5cuWrBgQbZNc9m9e7datGihevXqacyYMSpfvrzi4+MVERGhr776Sm3atNHYsWMVHh6ufv36qWfPni7P79y5s3r37u2ck96+fXtNnz5dkZGRunTpUqZqqFGjhu69917NmDHD2TZr1iz5+vrqySefTNM/ISFBNWvWVFRUlFq1aqUPPvhAAQEBateuncaMGePsZ4xRo0aNFBUVpdatW+vdd9/Vn3/+qbZt26bZ52+//aZHHnlEu3btUt++fTVy5EjlypVLjRs31nfffXfDcyhdurR8fHxsdUEg7kIGwHWdPXvWSDKNGjXKVP9t27YZSebFF190ae/Vq5eRZFasWOFsCw0NNZLMmjVrnG0nTpwwXl5e5vXXX3e2HTx40EgyH3zwgcs+27Zta0JDQ9PUMHDgQHP1P+9Ro0YZSebkyZMZ1p16jC+++MLZVr58eRMcHGxiY2Odbdu3bzdubm6mTZs2aY73wgsvuOzz6aefNnny5MnwmFefR65cuYwxxjzzzDOmTp06xhhjkpOTTf78+c3gwYPTfQ0uXrxokpOT05yHl5eXGTJkiLNt8+bNac4tVUREhJFkJk6cmO62iIgIl7YlS5YYSebdd981Bw4cML6+vqZx48Y3PMd9+/YZSWbcuHHXPf/0REdHG0mmR48exhhjzp8/bwIDA03Hjh1d+h0/ftwEBAS4tNeoUcP4+fmZw4cPu/RNSUlx/v8XX3xhJJmDBw8aY4z57rvvjCSzefPm656TJDNw4EDn48aNGxtPT0+zf/9+Z9uxY8eMn5+fqVGjRprj1a1b16WOHj16GHd3d3PmzJnrHvdqTz75ZJp/A6n/rn788UeX9nfeecfkypXL7Nmzx6W9b9++xt3d3Rw5csQYY8zatWuNJDN9+nSXfj/++GO67ddK/fdw8uRJ06tXL1O8eHHntoceesi0b9/eGHPl9evatatz2+jRo40k89VXXznbkpKSTNWqVY2vr685d+6cMcaYefPmGUlmxIgRzn6XL1821atXT/NzXqdOHVO2bFlz8eJFZ1tKSoqpVq2aKVGihLNt5cqVRpJZuXJlmvMpWbKkeeKJJ657zsCtxMgqcAPnzp2TJPn5+WWq/+LFiyUpzUjN66+/Lklp5raWLl1a1atXdz7Oly+fwsLCdODAgZuu+Vqpc12///77TF9ZHhMTo23btqldu3bKnTu3s/3BBx9UvXr1nOd5tZdeesnlcfXq1RUbG+t8DTOjZcuWWrVqlfPj0uPHj2f4kamXl5fc3K68jSUnJys2NtY5xWHr1q2ZPqaXl5fat2+fqb6PPfaYOnfurCFDhqhJkyby9vZ2fsR8PbGxsZKkoKCgTNeVKnXawPnz5yVdmdJx5swZtWjRQv/884/zy93dXVWqVNHKlSslXZnrvGbNGr3wwgu67777XPZ57TSRq6X+vCxcuDDTo4jJyclaunSpGjdurKJFizrbQ0JC1LJlS61bty7Nz0GnTp1c6qhevbqSk5N1+PDhTB3zeooUKaLIyEiXttmzZ6t69eoKCgpyed3q1q2r5ORkrVmzxtkvICBA9erVc+lXqVIl+fr6Ol/fzGjZsqX27dunzZs3O/+b0c/z4sWLlT9/frVo0cLZ5uHhoW7duikuLk6rV6929suRI4e6dOni7Ofu7q5XX33VZX+nTp3SihUrnJ94pJ5HbGysIiMjtXfvXv311183PIfU1wuwChdYATeQegV2alC4kcOHD8vNzU3Fixd3ac+fP78CAwPT/CK+NkRIV345ZGVu3I0899xzmjx5sl588UX17dtXderUUZMmTfTMM884w1565yFJYWFhabaVKlVKS5Ys0YULF5QrVy5n+7XnkhrMTp8+nekr2evXry8/Pz/NmjVL27Zt00MPPaTixYunO+0gJSVFY8aM0fjx43Xw4EHnR/OSlCdPnkwdT5IKFiyYpYupPvzwQ33//ffatm2bZsyYoeDg4Ew/1xiT6b6p4uLiJP3vD6a9e/dKkmrXrp1u/9TXOvUPnjJlymTpeBEREWratKkGDx6sUaNGqWbNmmrcuLFatmyZ4YoBJ0+eVHx8fIY/LykpKTp69KgeeOABZ/v1fl7+rSJFiqRp27t3r3bs2JHhSgEnTpxw9jt79myG39fUfplRoUIF3X///ZoxY4YCAwOVP3/+DL9vhw8fVokSJdL8myxVqpRze+p/Q0JC0sx9vva137dvn4wx6t+/v/r375/huRQsWPC652CMue4fN8CtRlgFbsDf318FChTQr7/+mqXnZfbNPaOrsDMTajI6xtWhTZJ8fHy0Zs0arVy5UosWLdKPP/6oWbNmqXbt2lq6dOlNXQmenn9zLqm8vLzUpEkTTZs2TQcOHLjuovPDhg1T//799cILL+idd95R7ty55ebmptdeey1La5Nm9Srx6OhoZ2DZuXOny0hYRlLD880EsdSfvdQ/gFLPLSoqSvnz50/T/98uJ+VwODRnzhxt3LhRCxYs0JIlS/TCCy9o5MiR2rhxY7ZdFZ4dPy8ZSe97mpKSonr16qW5Oj9VyZIlnf2Cg4M1ffr0dPtldVmsli1basKECfLz89Nzzz2X4R+I2S3156RXr15pRplTXftHdXpOnz6tEiVKZGttQFYQVoFMaNCggT777DNt2LBBVatWvW7f0NBQpaSkaO/evc4REUn6+++/debMGeeV/dkhKCgo3SuA0/sY1c3NTXXq1FGdOnX00UcfadiwYXrrrbe0cuVK1a1bN93zkK5cqHKtP/74Q3nz5nUZVc1OLVu21JQpU+Tm5pbuRWmp5syZo1q1aunzzz93aT9z5ozy5s3rfJydo0IXLlxQ+/btVbp0aVWrVk0jRozQ008/7VxxICP33XeffHx8dPDgwSwfMyoqSg6HQ/Xq1ZMkFStWTJIUHByc7vcuVerH8Vn9QyvVI488okceeURDhw7VjBkz1KpVK82cOVMvvvhimr758uVTzpw5M/x5cXNzU6FChW6qjuxSrFgxxcXFXfc1S+33008/KTw8PFuWu2rZsqUGDBigmJiYNOvEXi00NFQ7duxQSkqKS6BNXU0g9d9kaGioli9frri4OJc/HK597VO//x4eHjc854xcvnxZR48e1VNPPXVTzweyA3NWgUx44403lCtXLr344ov6+++/02zfv3+/82rd+vXrS1KaK/Y/+ugjSUr3KuCbVaxYMZ09e1Y7duxwtsXExKS5yvfUqVNpnpu6OP61y2mlCgkJUfny5TVt2jSXQPzrr79q6dKlzvO8FWrVqqV33nlHH3/8cbojh6nc3d3TjMLNnj07zTy81FCd0dI+WdGnTx8dOXJE06ZN00cffaTChQurbdu2Gb6OqTw8PFS5cmVt2bIlS8d77733tHTpUj333HPO0a3IyEj5+/tr2LBh6c4pTV3+KV++fKpRo4amTJmiI0eOuPS53ujl6dOn02y/0c+Lu7u7HnvsMX3//fcuUzb+/vtvzZgxQ48++ugtu6lBZjVr1kwbNmzQkiVL0mw7c+aMLl++7OyXnJysd955J02/y5cvZ/nnqFixYho9erSGDx+uhx9+OMN+9evX1/Hjx13W0718+bLGjRsnX19fRUREOPtdvnxZEyZMcPZLTk7WuHHjXPYXHBysmjVr6tNPP1VMTEya42VmmbDff/9dFy9ezPRKKMCtwMgqkAnFihXTjBkz9Nxzz6lUqVIud7Bav369Zs+erXbt2kmSypUrp7Zt2+qzzz7TmTNnFBERof/7v//TtGnT1Lhx4wyXRboZzZs3V58+ffT000+rW7duio+P14QJE1SyZEmXC4yGDBmiNWvW6Mknn1RoaKhOnDih8ePH695779Wjjz6a4f4/+OADPfHEE6patao6dOighIQEjRs3TgEBAbf0nvBubm56++23b9ivQYMGGjJkiNq3b69q1app586dmj59ussFPtKV719gYKAmTpwoPz8/5cqVS1WqVEl3XuP1rFixQuPHj9fAgQOdS1B98cUXqlmzpvr3768RI0Zc9/mNGjXSW2+9pXPnzqUJbpcvX9ZXX30lSbp48aIOHz6s+fPna8eOHapVq5bL3dH8/f01YcIEPf/886pYsaKaN2+ufPny6ciRI1q0aJHCw8P18ccfS5LGjh2rRx99VBUrVlSnTp1UpEgRHTp0SIsWLcrw9rPTpk3T+PHj9fTTT6tYsWI6f/68Jk2aJH9//+v+kfLuu+861/N9+eWXlSNHDn366adKTEy84WtzO/Tu3Vvz589XgwYN1K5dO1WqVEkXLlzQzp07NWfOHB06dEh58+ZVRESEOnfurOHDh2vbtm167LHH5OHhob1792r27NkaM2aMnnnmmSwd++p1mDPSqVMnffrpp2rXrp1++eUXFS5cWHPmzNHPP/+s0aNHO+csN2zYUOHh4erbt68OHTqk0qVL69tvv9XZs2fT7POTTz7Ro48+qrJly6pjx44qWrSo/v77b23YsEF//vnnDW+lumzZMuXMmdM5qg9YwrJ1CID/oD179piOHTuawoULG09PT+Pn52fCw8PNuHHjXJaGuXTpkhk8eLApUqSI8fDwMIUKFTL9+vVz6WPMlSV2nnzyyTTHuXbJpIyWrjLGmKVLl5oyZcoYT09PExYWZr766qs0S1ctX77cNGrUyBQoUMB4enqaAgUKmBYtWrgs4ZPe0lXGGPPTTz+Z8PBw4+PjY/z9/U3Dhg3N77//7tLn6qV6rnbtkkgZudHSTRm9BhcvXjSvv/66CQkJMT4+PiY8PNxs2LAh3SWnvv/+e1O6dGmTI0cOl/OMiIgwDzzwQLrHvHo/586dM6GhoaZixYrm0qVLLv169Ohh3NzczIYNG657Dn///bfJkSOHiYqKSnP+kpxfOXPmNIULFzZNmzY1c+bMSbM8V6qVK1eayMhIExAQYLy9vU2xYsVMu3btzJYtW1z6/frrr+bpp582gYGBxtvb24SFhZn+/fs7t1/7fdq6datp0aKFue+++4yXl5cJDg42DRo0SLNfXbN0VepzIyMjja+vr8mZM6epVauWWb9+vUuf1ONduzTW9ZZPykhGS1el9+/KmCvLfvXr188UL17ceHp6mrx585pq1aqZDz/80CQlJbn0/eyzz0ylSpWMj4+P8fPzM2XLljVvvPGGOXbs2HVryujfw7V0zdJVxlz5GWnfvr3Jmzev8fT0NGXLlk13ybXY2Fjz/PPPG39/fxMQEGCef/555xJn1/bfv3+/adOmjcmfP7/x8PAwBQsWNA0aNDBz5sxx9snota9SpYpp3br1dc8DuNUcxmTDTHYAQKZ06NBBe/bs0dq1a60uBbiubdu2qWLFitq6datzGghgBcIqANxGR44cUcmSJbV8+XKFh4dbXQ6QoebNmyslJUXffPON1aXgLkdYBQAAgG2xGgAAAABsi7AKAAAA2yKsAgAAwLYIqwAAALAtwioAAABs6468g5VP9QFWlwAA2er0yiFWlwAA2co7kymUkVUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYli3C6tq1a9W6dWtVrVpVf/31lyQpKipK69ats7gyAAAAWMnysDp37lxFRkbKx8dH0dHRSkxMlCSdPXtWw4YNs7g6AAAAWMnysPruu+9q4sSJmjRpkjw8PJzt4eHh2rp1q4WVAQAAwGqWh9Xdu3erRo0aadoDAgJ05syZ218QAAAAbMPysJo/f37t27cvTfu6detUtGhRCyoCAACAXVgeVjt27Kju3btr06ZNcjgcOnbsmKZPn65evXqpS5cuVpcHAAAAC+WwuoC+ffsqJSVFderUUXx8vGrUqCEvLy/16tVLr776qtXlAQAAwEIOY4yxughJSkpK0r59+xQXF6fSpUvL19f3pvflU31ANlYGANY7vXKI1SUAQLbyzuSQqeXTAL766ivFx8fL09NTpUuX1sMPP/yvgioAAADuHJaH1R49eig4OFgtW7bU4sWLlZycbHVJAAAAsAnLw2pMTIxmzpwph8OhZs2aKSQkRF27dtX69eutLg0AAAAWszys5siRQw0aNND06dN14sQJjRo1SocOHVKtWrVUrFgxq8sDAACAhSxfDeBqOXPmVGRkpE6fPq3Dhw9r165dVpcEAAAAC1k+sipJ8fHxmj59uurXr6+CBQtq9OjRevrpp/Xbb79ZXRoAAAAsZPnIavPmzbVw4ULlzJlTzZo1U//+/VW1alWrywIAAIANWB5W3d3d9c033ygyMlLu7u5WlwMAAAAbsTysTp8+3eoSAAAAYFOWhNWxY8eqU6dO8vb21tixY6/bt1u3brepKgAAANiNJbdbLVKkiLZs2aI8efKoSJEiGfZzOBw6cOBAlvfP7VYB3Gm43SqAO01mb7dqycjqwYMH0/1/AAAA4GqWL101ZMgQxcfHp2lPSEjQkCGMJAAAANzNLJkGcDV3d3fFxMQoODjYpT02NlbBwcFKTk7O8j6ZBgDgTsM0AAB3msxOA7B8ZNUYI4fDkaZ9+/btyp07twUVAQAAwC4sW7oqKChIDodDDodDJUuWdAmsycnJiouL00svvWRVeQAAALABy8Lq6NGjZYzRCy+8oMGDBysgIMC5zdPTU4ULF+ZOVgAAAHc5y8Jq27ZtJV1ZxqpatWry8PCwqhQAAADYlOV3sIqIiHD+/8WLF5WUlOSy3d/f/3aXBAAAAJuw/AKr+Ph4vfLKKwoODlauXLkUFBTk8gUAAIC7l+VhtXfv3lqxYoUmTJggLy8vTZ48WYMHD1aBAgX05ZdfWl0eAAAALGT5NIAFCxboyy+/VM2aNdW+fXtVr15dxYsXV2hoqKZPn65WrVpZXSIAAAAsYvnI6qlTp1S0aFFJV+annjp1SpL06KOPas2aNVaWBgAAAItZHlaLFi2qgwcPSpLuv/9+ffPNN5KujLgGBgZaWBkAAACsZnlYbd++vbZv3y5J6tu3rz755BN5e3urR48e6t27t8XVAQAAwEoOY4yxuoirHT58WL/88ouKFy+uBx988Kb24VN9QDZXBQDWOr1yiNUlAEC28s7klVOWX2B1rdDQUIWGhlpdBgAAAGzA8rA6duzYdNsdDoe8vb1VvHhx1ahRQ+7u7re5MgAAAFjN8rA6atQonTx5UvHx8c6bAJw+fVo5c+aUr6+vTpw4oaJFi2rlypUqVKiQxdUCAADgdrL8Aqthw4bpoYce0t69exUbG6vY2Fjt2bNHVapU0ZgxY3TkyBHlz59fPXr0sLpUAAAA3GaWX2BVrFgxzZ07V+XLl3dpj46OVtOmTXXgwAGtX79eTZs2VUxMTKb2yQVWAO40XGAF4E6T2QusLB9ZjYmJ0eXLl9O0X758WcePH5ckFShQQOfPn7/dpQEAAMBilofVWrVqqXPnzoqOjna2RUdHq0uXLqpdu7YkaefOnSpSpIhVJQIAAMAilofVzz//XLlz51alSpXk5eUlLy8vVa5cWblz59bnn38uSfL19dXIkSMtrhQAAAC3m+VzVlP98ccf2rNnjyQpLCxMYWFhN70v5qwCuNMwZxXAneY/d1OAokWLyuFwqFixYsqRwzZlAQAAwEKWTwOIj49Xhw4dlDNnTj3wwAM6cuSIJOnVV1/Ve++9Z3F1AAAAsJLlYbVfv37avn27Vq1aJW9vb2d73bp1NWvWLAsrAwAAgNUs/7x93rx5mjVrlh555BE5HA5n+wMPPKD9+/dbWBkAAACsZvnI6smTJxUcHJym/cKFCy7hFQAAAHcfy8Nq5cqVtWjRIufj1IA6efJkVa1a1aqycBcLLxeqOe+10oHveilh7RA1rH5/mj79O9TWgXm9deqn/lo0qq2K3ZvbZXuQn4++6N9Uf//4pmIW99OEPo2Uy8fzdp0CANyUmTOm64l6tfVQhbJq1fxZ7dyxw+qSAOvD6rBhw/Tmm2+qS5cuunz5ssaMGaPHHntMX3zxhYYOHWp1ebgL5fL21M59x/XaR4vS3f56y0f1ctMq6vbhAtXo/JkuJCRpwcg28vL836yaLwY8o1JFgtWg55dq2me6Hi1XWJ/0fup2nQIAZNmPPyzWhyOGq/PLXTVz9ncKC7tfXTp3UGxsrNWl4S5neVh99NFHtW3bNl2+fFlly5bV0qVLFRwcrA0bNqhSpUpWl4e70NJNezV48nLNX7sr3e1dm1XV+1+u0cJ1f+jX/X/rxaHfKiSPn576/yOwYaF5FflICb38/vfa/PufWr/ziHqOXqRn65RRSB6/23kqAJBpUdO+UJNnmqnx001VrHhxvT1wsLy9vTXv27lWl4a7nOUXWElSsWLFNGnSJKvLAG6ocEiQQvL4acWW/138d+5Cojbv+ktVHiik2ct/VZUHCun0+QRt3X3M2WfFLweUkmL0UOl7MwzBAGCVS0lJ2vX7b+rQsbOzzc3NTY88Uk07tkdf55nArWeLsPpvJCYmKjEx0aXNpFyWw+0/f2qwofx5fCVJJ07HubSfOBWne3Jf2XZPHj+dPH3BZXtycopOnU/QPf//+QBgJ6fPnFZycrLy5Mnj0p4nTx4dPHjAoqqAKyybBuDm5iZ3d/frfmXmTlbDhw9XQECAy9floz/fhjMAAADArWbZ8ON3332X4bYNGzZo7NixSklJueF++vXrp549e7q0BT/Bna9waxyPvTKiGhzk6/x/SQrO7asde2MkSX/Hnle+oFwuz3N3d1NuPx/9Hes6IgsAdhAUGCR3d/c0F1PFxsYqb968FlUFXGFZWG3UqFGatt27d6tv375asGCBWrVqpSFDhtxwP15eXvLy8nJpYwoAbpVDMacVE3tetSoV1Y59xyVJfjm99FCpgpo07/8kSZt+O6ogPx9VKBmi6D1XAmzNikXk5ubQ5t//tKx2AMiIh6enSpV+QJs2blDtOnUlSSkpKdq0aYOat2htcXW429ki1R07dkwDBw7UtGnTFBkZqW3btqlMmTJWl4W7VC4fTxUr+L91UwuHBOnB4vl1+lyCjp44q0++2aA+bSO0789YHYo5rYEv1lFM7HnNX/uHJGn34X+0ZONefdKnkbp9uEAeOdw1qseTmr38V8XEnrfqtADgup5v21793+yjBx4oozJlH9RXUdOUkJCgxk83sbo03OUsDatnz57VsGHDNG7cOJUvX17Lly9X9erVrSwJUMWwAlo67gXn4xGvPiFJivohWp2GfaeRM9Ypp4+nPu79lAJ9vbV+5xE91StKiUmXnc9pP2SORvV4UotHt1NKitG81b/r9TGLb/u5AEBmPf5EfZ0+dUrjPx6rf/45qbD7S2n8p5OVh2kAsJjDGGOsOPCIESP0/vvvK3/+/Bo2bFi60wJulk/1Adm2LwCwg9MrbzwtCgD+S7wzOWRqWVh1c3OTj4+P6tatK3d39wz7ffvtt1neN2EVwJ2GsArgTpPZsGrZNIA2bdrI4XBYdXgAAAD8B1gWVqdOnWrVoQEAAPAfYdlNAQAAAIAbIawCAADAtgirAAAAsC3CKgAAAGyLsAoAAADbsmQ1gPnz52e671NPPXULKwEAAICdWRJWGzdunKl+DodDycnJt7YYAAAA2JYlYTUlJcWKwwIAAOA/hjmrAAAAsC3L7mB1tQsXLmj16tU6cuSIkpKSXLZ169bNoqoAAABgNcvDanR0tOrXr6/4+HhduHBBuXPn1j///KOcOXMqODiYsAoAAHAXs3waQI8ePdSwYUOdPn1aPj4+2rhxow4fPqxKlSrpww8/tLo8AAAAWMjysLpt2za9/vrrcnNzk7u7uxITE1WoUCGNGDFCb775ptXlAQAAwEKWh1UPDw+5uV0pIzg4WEeOHJEkBQQE6OjRo1aWBgAAAItZPme1QoUK2rx5s0qUKKGIiAgNGDBA//zzj6KiolSmTBmrywMAAICFLB9ZHTZsmEJCQiRJQ4cOVVBQkLp06aKTJ0/qs88+s7g6AAAAWMlhjDFWF5HdfKoPsLoEAMhWp1cOsboEAMhW3pn8fN/ykVUAAAAgI5bPWS1SpIgcDkeG2w8cOHAbqwEAAICdWB5WX3vtNZfHly5dUnR0tH788Uf17t3bmqIAAABgC5aH1e7du6fb/sknn2jLli23uRoAAADYiW3nrD7xxBOaO3eu1WUAAADAQrYNq3PmzFHu3LmtLgMAAAAWsnwaQIUKFVwusDLG6Pjx4zp58qTGjx9vYWUAAACwmuVhtVGjRi5h1c3NTfny5VPNmjV1//33W1gZAAAArGZ5WB00aJDVJQAAAMCmLJ+z6u7urhMnTqRpj42Nlbu7uwUVAQAAwC4sD6sZ3e01MTFRnp6et7kaAAAA2Ill0wDGjh0rSXI4HJo8ebJ8fX2d25KTk7VmzRrmrAIAANzlLAuro0aNknRlZHXixIkuH/l7enqqcOHCmjhxolXlAQAAwAYsC6sHDx6UJNWqVUvffvutgoKCrCoFAAAANmX5agArV660ugQAAADYlOUXWDVt2lTvv/9+mvYRI0bo2WeftaAiAAAA2IXlYXXNmjWqX79+mvYnnnhCa9assaAiAAAA2IXlYTUuLi7dJao8PDx07tw5CyoCAACAXVgeVsuWLatZs2alaZ85c6ZKly5tQUUAAACwC8svsOrfv7+aNGmi/fv3q3bt2pKk5cuX6+uvv9bs2bMtrg4AAABWsjysNmzYUPPmzdOwYcM0Z84c+fj46MEHH9RPP/2kiIgIq8sDAACAhRwmo/ud2sCvv/6qMmXKZPl5PtUH3IJqAMA6p1cOsboEAMhW3pkcMrV8zuq1zp8/r88++0wPP/ywypUrZ3U5AAAAsJBtwuqaNWvUpk0bhYSE6MMPP1Tt2rW1ceNGq8sCAACAhSyds3r8+HFNnTpVn3/+uc6dO6dmzZopMTFR8+bNYyUAAAAAWDey2rBhQ4WFhWnHjh0aPXq0jh07pnHjxllVDgAAAGzIspHVH374Qd26dVOXLl1UokQJq8oAAACAjVk2srpu3TqdP39elSpVUpUqVfTxxx/rn3/+saocAAAA2JBlYfWRRx7RpEmTFBMTo86dO2vmzJkqUKCAUlJStGzZMp0/f96q0gAAAGATtlpndffu3fr8888VFRWlM2fOqF69epo/f36W98M6qwDuNKyzCuBO859cZzUsLEwjRozQn3/+qa+//trqcgAAAGAxW42sZhdGVgHcaRhZBXCn+U+OrAIAAABXI6wCAADAtgirAAAAsC3CKgAAAGyLsAoAAADbIqwCAADAtgirAAAAsC3CKgAAAGyLsAoAAADbIqwCAADAtgirAAAAsC3CKgAAAGyLsAoAAADbIqwCAADAtgirAAAAsC3CKgAAAGyLsAoAAADbIqwCAADAtgirAAAAsC3CKgAAAGyLsAoAAADbIqwCAADAtgirAAAAsC3CKgAAAGwrR2Y67dixI9M7fPDBB2+6GAAAAOBqmQqr5cuXl8PhkDEm3e2p2xwOh5KTk7O1QAAAANy9MhVWDx48eKvrAAAAANLIVFgNDQ291XUAAAAAadzUBVZRUVEKDw9XgQIFdPjwYUnS6NGj9f3332drcQAAALi7ZTmsTpgwQT179lT9+vV15swZ5xzVwMBAjR49OrvrAwAAwF0sy2F13LhxmjRpkt566y25u7s72ytXrqydO3dma3EAAAC4u2U5rB48eFAVKlRI0+7l5aULFy5kS1EAAACAdBNhtUiRItq2bVua9h9//FGlSpXKjpoAAAAASZlcDeBqPXv2VNeuXXXx4kUZY/R///d/+vrrrzV8+HBNnjz5VtQIAACAu1SWw+qLL74oHx8fvf3224qPj1fLli1VoEABjRkzRs2bN78VNQIAAOAu5TAZ3ZYqE+Lj4xUXF6fg4ODsrOlf86k+wOoSACBbnV45xOoSACBbeWdyyDTLI6upTpw4od27d0u6crvVfPny3eyuAAAAgHRl+QKr8+fP6/nnn1eBAgUUERGhiIgIFShQQK1bt9bZs2dvRY0AAAC4S2U5rL744ovatGmTFi1apDNnzujMmTNauHChtmzZos6dO9+KGgEAAHCXyvKc1Vy5cmnJkiV69NFHXdrXrl2rxx9/3BZrrTJnFcCdhjmrAO40mZ2zmuWR1Tx58iggICBNe0BAgIKCgrK6OwAAACBDWQ6rb7/9tnr27Knjx487244fP67evXurf//+2VocAAAA7m6ZGoCtUKGCHA6H8/HevXt133336b777pMkHTlyRF5eXjp58iTzVgEAAJBtMhVWGzdufIvLAAAAANLKVFgdOHDgra4DAAAASCPLc1YBAACA2yXLd7BKTk7WqFGj9M033+jIkSNKSkpy2X7q1KlsKw4AAAB3tyyPrA4ePFgfffSRnnvuOZ09e1Y9e/ZUkyZN5ObmpkGDBt2CEgEAAHC3ynJYnT59uiZNmqTXX39dOXLkUIsWLTR58mQNGDBAGzduvBU1AgAA4C6V5bB6/PhxlS1bVpLk6+urs2fPSpIaNGigRYsWZW91AAAAuKtlOazee++9iomJkSQVK1ZMS5culSRt3rxZXl5e2VsdAAAA7mpZDqtPP/20li9fLkl69dVX1b9/f5UoUUJt2rTRCy+8kO0FAgAA4O7lMMaYf7ODjRs3av369SpRooQaNmyYXXX9Kz7VB1hdAgBkq9Mrh1hdAgBkK+9Mrkn1r9dZfeSRR9SzZ09VqVJFw4YN+7e7AwAAAJyy7aYAMTEx6t+/f3btDgAAAOAOVgAAALAvwioAAABsi7AKAAAA28rkdVhSz549r7v95MmT/7qY7MJVswDuNEEPvWJ1CQCQrRKiP85Uv0yH1ejo6Bv2qVGjRmZ3BwAAANxQpsPqypUrb2UdAAAAQBrMWQUAAIBtEVYBAABgW4RVAAAA2BZhFQAAALZFWAUAAIBt3VRYXbt2rVq3bq2qVavqr7/+kiRFRUVp3bp12VocAAAA7m5ZDqtz585VZGSkfHx8FB0drcTEREnS2bNnNWzYsGwvEAAAAHevLIfVd999VxMnTtSkSZPk4eHhbA8PD9fWrVuztTgAAADc3bIcVnfv3p3unaoCAgJ05syZ7KgJAAAAkHQTYTV//vzat29fmvZ169apaNGi2VIUAAAAIN1EWO3YsaO6d++uTZs2yeFw6NixY5o+fbp69eqlLl263IoaAQAAcJfKkdUn9O3bVykpKapTp47i4+NVo0YNeXl5qVevXnr11VdvRY0AAAC4SzmMMeZmnpiUlKR9+/YpLi5OpUuXlq+vb3bXdtMuXra6AgDIXkEPvWJ1CQCQrRKiP85UvyyPrKby9PRU6dKlb/bpAAAAwA1lOazWqlVLDocjw+0rVqz4VwUBAAAAqbIcVsuXL+/y+NKlS9q2bZt+/fVXtW3bNrvqAgAAALIeVkeNGpVu+6BBgxQXF/evCwIAAABSZXnpqoy0bt1aU6ZMya7dAQAAANkXVjds2CBvb+/s2h0AAACQ9WkATZo0cXlsjFFMTIy2bNmi/v37Z1thAAAAQJbDakBAgMtjNzc3hYWFaciQIXrssceyrTAAAAAgS2E1OTlZ7du3V9myZRUUFHSragIAAAAkZXHOqru7ux577DGdOXPmFpUDAAAA/E+WL7AqU6aMDhw4cCtqAQAAAFxkOay+++676tWrlxYuXKiYmBidO3fO5QsAAADILg5jjMlMxyFDhuj111+Xn5/f/5581W1XjTFyOBxKTk7O/iqz6OJlqysAgOwV9NArVpcAANkqIfrjTPXLdFh1d3dXTEyMdu3add1+ERERmTrwrURYBXCnIawCuNNkNqxmejWA1ExrhzAKAACAu0OW5qxe/bE/AAAAcKtlaZ3VkiVL3jCwnjp16l8VBAAAAKTKUlgdPHhwmjtYAQAAALdKlsJq8+bNFRwcfKtqAQAAAFxkes4q81UBAABwu2U6rGZyhSsAAAAg22R6GkBKSsqtrAMAAABII8u3WwUAAABuF8IqAAAAbIuwCgAAANsirAIAAMC2CKsAAACwLcIqAAAAbIuwCgAAANsirAIAAMC2CKsAAACwLcIqAAAAbIuwCgAAANsirAIAAMC2CKsAAACwLcIqAAAAbIuwCgAAANsirAIAAMC2CKsAAACwLcIqAAAAbIuwCgAAANsirAIAAMC2CKsAAACwLcIqAAAAbIuwCgAAANsirAIAAMC2CKsAAACwLcIqAAAAbIuwCgAAANsirAIAAMC2CKsAAACwLVuE1bVr16p169aqWrWq/vrrL0lSVFSU1q1bZ3FlAAAAsJLlYXXu3LmKjIyUj4+PoqOjlZiYKEk6e/ashg0bZnF1AAAAsJLlYfXdd9/VxIkTNWnSJHl4eDjbw8PDtXXrVgsrAwAAgNUsD6u7d+9WjRo10rQHBATozJkzt78gAAAA2IblYTV//vzat29fmvZ169apaNGiFlQEAAAAu7A8rHbs2FHdu3fXpk2b5HA4dOzYMU2fPl29evVSly5drC4PAAAAFsphdQF9+/ZVSkqK6tSpo/j4eNWoUUNeXl7q1auXXn31VavLAwAAgIUcxhhjdRGSlJSUpH379ikuLk6lS5eWr6/vTe/r4uVsLAwAbCDooVesLgEAslVC9MeZ6mf5NICvvvpK8fHx8vT0VOnSpfXwww//q6AKAACAO4flYbVHjx4KDg5Wy5YttXjxYiUnJ1tdEgAAAGzC8rAaExOjmTNnyuFwqFmzZgoJCVHXrl21fv16q0sDAACAxWwzZ1WS4uPj9d1332nGjBn66aefdO+992r//v1Z3g9zVgHcaZizCuBOk9k5q5avBnC1nDlzKjIyUqdPn9bhw4e1a9cuq0sCAACAhSyfBiBdGVGdPn266tevr4IFC2r06NF6+umn9dtvv1ldGgAAACxk+chq8+bNtXDhQuXMmVPNmjVT//79VbVqVavLAgAAgA1YHlbd3d31zTffKDIyUu7u7laXAwAAABuxPKxOnz7d6hIAAABgU5aE1bFjx6pTp07y9vbW2LFjr9u3W7dut6kqAAAA2I0lS1cVKVJEW7ZsUZ48eVSkSJEM+zkcDh04cCDL+2fpKgB3GpauAnCnsfXSVQcPHkz3/wEAAICrWb501ZAhQxQfH5+mPSEhQUOGDLGgIgAAANiF5Xewcnd3V0xMjIKDg13aY2NjFRwcrOTk5Czvk2kAAO40TAMAcKfJ7DQAy0dWjTFyOBxp2rdv367cuXNbUBEAAADswrKlq4KCguRwOORwOFSyZEmXwJqcnKy4uDi99NJLVpUHAAAAG7AsrI4ePVrGGL3wwgsaPHiwAgICnNs8PT1VuHBh7mQFAABwl7MsrLZt21bSlWWsqlWrJg8PD6tKAQAAgE1ZElbPnTsnf39/SVKFChWUkJCghISEdPum9gMAAMDdx5KwGhQU5FwBIDAwMN0LrFIvvLqZ1QAAAABwZ7AkrK5YscJ5pf/KlSutKAEAAAD/AZavs3orsM4qgDsN66wCuNP8Z9ZZ/fHHH7Vu3Trn408++UTly5dXy5Ytdfr0aQsrAwAAgNUsD6u9e/fWuXPnJEk7d+5Uz549Vb9+fR08eFA9e/a0uDoAAABYybKlq1IdPHhQpUuXliTNnTtXDRs21LBhw7R161bVr1/f4uoAAABgJctHVj09PRUfHy9J+umnn/TYY49JknLnzu0ccQUAAMDdyfKR1UcffVQ9e/ZUeHi4/u///k+zZs2SJO3Zs0f33nuvxdUBAADASpaPrH788cfKkSOH5syZowkTJqhgwYKSpB9++EGPP/64xdUBAADASixdBQD/ASxdBeBOk9mlqyyfBiBJycnJmjdvnnbt2iVJeuCBB/TUU0/J3d3d4soAAABgJcvD6r59+1S/fn399ddfCgsLkyQNHz5chQoV0qJFi1SsWDGLKwQAAIBVLJ+z2q1bNxUrVkxHjx7V1q1btXXrVh05ckRFihRRt27drC4PAAAAFrJ8ZHX16tXauHGjcufO7WzLkyeP3nvvPYWHh1tYGQAAAKxm+ciql5eXzp8/n6Y9Li5Onp6eFlQEAAAAu7A8rDZo0ECdOnXSpk2bZIyRMUYbN27USy+9pKeeesrq8gAAAGAhy8Pq2LFjVaxYMVWtWlXe3t7y9vZWeHi4ihcvrjFjxlhdHpChmTOm64l6tfVQhbJq1fxZ7dyxw+qSACBd4RWLac7ozjqwdKgSoj9Ww5oPpunTv8uTOrB0qE5t+EiLJr6iYvflc9n+x6LBSoj+2OWrV/t6t+sUcBezfM5qYGCgvv/+e+3bt8+5dFWpUqVUvHhxiysDMvbjD4v14YjhenvgYJUtW07To6apS+cO+n7hj8qTJ4/V5QGAi1w+Xtq55y99+f0GzfqoU5rtr7erq5dbRKjjgCgd+itWA15uoAWfdFWFpu8qMel/i5cPHr9QX3z7s/Px+QuJt6V+3N0sC6spKSn64IMPNH/+fCUlJalOnToaOHCgfHx8rCoJyLSoaV+oyTPN1PjpppKktwcO1po1qzTv27nq0DHtLwIAsNLSn3/X0p9/z3B715a19P6kJVq4aqck6cX+X+rwT8P1VK1ymr3kF2e/uAsX9Xds2utMgFvJsmkAQ4cO1ZtvvilfX18VLFhQY8aMUdeuXa0qB8i0S0lJ2vX7b3qkajVnm5ubmx55pJp2bI+2sDIAyLrCBfMoJF+AVmz6w9l2Lu6iNv96SFUeLOzS9/X2j+nPle9rw9d91KNNHbm7Wz6bEHcBy0ZWv/zyS40fP16dO3eWJP3000968sknNXnyZLm5Zf6HPzExUYmJrh9DGHcveXl5ZWu9QKrTZ04rOTk5zcf9efLk0cGDByyqCgBuTv68/pKkE6dcR0xPxJ7XPXn8nY/Hf71a0buO6vS5C3qkXFENefUp5c8XoD4jv72t9eLuY9mfREeOHFH9+vWdj+vWrSuHw6Fjx45laT/Dhw9XQECAy9cH7w/P7nIBALirjf1qhdb+sle/7j2myXPWqe9H36rLcxHy9LD88hfc4SwLq5cvX5a3t7dLm4eHhy5dupSl/fTr109nz551+erdp192lgq4CAoMkru7u2JjY13aY2NjlTdvXouqAoCbc/yfc5Kk4Nx+Lu3Befz0d+y5DJ+3eecheXi4K7RA7gz7ANnBsj+HjDFq166dy8f1Fy9e1EsvvaRcuXI527799vofL3h5pf3I/+LlDDoD2cDD01OlSj+gTRs3qHadupKuXDC4adMGNW/R2uLqACBrDv0Vq5iTZ1WrSph27PlLkuSXy1sPlSmsSbPXZfi8cmH3Kjk5RSdPccEVbi3Lwmrbtm3TtLVuzS96/Dc837a9+r/ZRw88UEZlyj6or6KmKSEhQY2fbmJ1aQCQRi4fTxUr9L91UwsXzKMHSxbU6XPxOnr8tD6ZsVJ9Xnxc+46c1KG/YjXw5ScVc/Ks5q/cLkmq8mARPVQmVKu37NX5Cxf1yINF9H6vpvp68WadOZ9g1WnhLuEwxhiri8hujKzidvh6+lea9sXn+uefkwq7v5T6vPm2HnywnNVl4Q4V9NArVpeA/7DqlUpo6eTuadqj5m9Up4FfSbpyU4AXmoQr0M9H67ftV/dh32jfkROSpPL336sx/Z5TySL3yMsjhw4di9WMRZs1NmqFki7xSxc3JyH640z1I6wCwH8AYRXAnSazYZUF0gAAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG1ZsnTV/PnzM933qaeeuoWVAAAAwM4sCauNGzfOVD+Hw6Hk5ORbWwwAAABsy5KwmpKSYsVhAQAA8B/DnFUAAADYlmW3W73ahQsXtHr1ah05ckRJSUku27p162ZRVQAAALCa5WE1Ojpa9evXV3x8vC5cuKDcuXPrn3/+Uc6cORUcHExYBQAAuItZPg2gR48eatiwoU6fPi0fHx9t3LhRhw8fVqVKlfThhx9aXR4AAAAsZHlY3bZtm15//XW5ubnJ3d1diYmJKlSokEaMGKE333zT6vIAAABgIcvDqoeHh9zcrpQRHBysI0eOSJICAgJ09OhRK0sDAACAxSyfs1qhQgVt3rxZJUqUUEREhAYMGKB//vlHUVFRKlOmjNXlAQAAwEKWj6wOGzZMISEhkqShQ4cqKChIXbp00cmTJ/XZZ59ZXB0AAACs5DDGGKuLyG4XL1tdAQBkr6CHXrG6BADIVgnRH2eqn+UjqwAAAEBGLJ+zWqRIETkcjgy3Hzhw4DZWAwAAADuxPKy+9tprLo8vXbqk6Oho/fjjj+rdu7c1RQEAAMAWLA+r3bt3T7f9k08+0ZYtW25zNQAAALAT285ZfeKJJzR37lyrywAAAICFbBtW58yZo9y5c1tdBgAAACxk+TSAChUquFxgZYzR8ePHdfLkSY0fP97CygAAAGA1y8Nqo0aNXMKqm5ub8uXLp5o1a+r++++3sDIAAABYjZsCAMB/ADcFAHCn+c/cFMDd3V0nTpxI0x4bGyt3d3cLKgIAAIBdWB5WMxrYTUxMlKen522uBgAAAHZi2ZzVsWPHSpIcDocmT54sX19f57bk5GStWbOGOasAAAB3OcvC6qhRoyRdGVmdOHGiy0f+np6eKly4sCZOnGhVeQAAALABy8LqwYMHJUm1atXSt99+q6CgIKtKAQAAgE1ZvnTVypUrrS4BAAAANmX5BVZNmzbV+++/n6Z9xIgRevbZZy2oCAAAAHZheVhds2aN6tevn6b9iSee0Jo1ayyoCAAAAHZheViNi4tLd4kqDw8PnTt3zoKKAAAAYBeWh9WyZctq1qxZadpnzpyp0qVLW1ARAAAA7MLyC6z69++vJk2aaP/+/apdu7Ykafny5fr66681e/Zsi6sDAACAlSwPqw0bNtS8efM0bNgwzZkzRz4+PnrwwQf1008/KSIiwuryAAAAYCGHyeh+pzbw66+/qkyZMll+3sXLt6AYALBQ0EOvWF0CAGSrhOiPM9XP8jmr1zp//rw+++wzPfzwwypXrpzV5QAAAMBCtgmra9asUZs2bRQSEqIPP/xQtWvX1saNG60uCwAAABaydM7q8ePHNXXqVH3++ec6d+6cmjVrpsTERM2bN4+VAAAAAGDdyGrDhg0VFhamHTt2aPTo0Tp27JjGjRtnVTkAAACwIctGVn/44Qd169ZNXbp0UYkSJawqAwAAADZm2cjqunXrdP78eVWqVElVqlTRxx9/rH/++ceqcgAAAGBDloXVRx55RJMmTVJMTIw6d+6smTNnqkCBAkpJSdGyZct0/vx5q0oDAACATdhqndXdu3fr888/V1RUlM6cOaN69epp/vz5Wd4P66wCuNOwziqAO81/cp3VsLAwjRgxQn/++ae+/vprq8sBAACAxWw1sppdGFkFcKdhZBXAneY/ObIKAAAAXI2wCgAAANsirAIAAMC2CKsAAACwLcIqAAAAbIuwCgAAANsirAIAAMC2CKsAAACwLcIqAAAAbIuwCgAAANsirAIAAMC2CKsAAACwLcIqAAAAbIuwCgAAANsirAIAAMC2CKsAAACwLcIqAAAAbIuwCgAAANsirAIAAMC2CKsAAACwLcIqAAAAbIuwCgAAANsirAIAAMC2CKsAAACwLcIqAAAAbIuwCgAAANsirAIAAMC2CKsAAACwLcIqAAAAbIuwCgAAANsirAIAAMC2CKsAAACwLcIqAAAAbIuwCgAAANsirAIAAMC2CKsAAACwLcIqAAAAbIuwCgAAANsirAIAAMC2CKsAAACwLcIqAAAAbIuwCgAAANsirAIAAMC2CKsAAACwLcIqAAAAbIuwCgAAANsirAIAAMC2CKsAAACwLcIqAAAAbIuwCgAAANsirAIAAMC2CKsAAACwLcIqAAAAbIuwCgAAANsirAIAAMC2CKsAAACwLcIqAAAAbIuwCgAAANsirAIAAMC2CKsAAACwLcIqAAAAbIuwCgAAANtyGGOM1UUA/0WJiYkaPny4+vXrJy8vL6vLAYB/jfc12BFhFbhJ586dU0BAgM6ePSt/f3+rywGAf433NdgR0wAAAABgW4RVAAAA2BZhFQAAALZFWAVukpeXlwYOHMhFCADuGLyvwY64wAoAAAC2xcgqAAAAbIuwCgAAANsirAIAAMC2CKv4T2jXrp0aN27sfFyzZk299tprt72OVatWyeFw6MyZM7f92Nnp0KFDcjgc2rZtm9WlALgK73VXDBo0SOXLl79uH97H7h6EVdy0du3ayeFwyOFwyNPTU8WLF9eQIUN0+fLlW37sb7/9Vu+8806m+t7uN93ChQvL4XBo48aNLu2vvfaaataseVtquNq1v/wkqVChQoqJiVGZMmVuez3Afw3vdelLfa9zOBzKlSuXKlasqNmzZ2fLvnv16qXly5c7H/M+dncjrOJfefzxxxUTE6O9e/fq9ddf16BBg/TBBx+k2zcpKSnbjps7d275+fll2/6ym7e3t/r06WN1GRlyd3dX/vz5lSNHDqtLAf4TeK9L35AhQxQTE6Po6Gg99NBDeu6557R+/fp/vV9fX1/lyZPnun14H7t7EFbxr3h5eSl//vwKDQ1Vly5dVLduXc2fP1/S//4SHjp0qAoUKKCwsDBJ0tGjR9WsWTMFBgYqd+7catSokQ4dOuTcZ3Jysnr27KnAwEDlyZNHb7zxhq5dYe3aj8YSExPVp08fFSpUSF5eXipevLg+//xzHTp0SLVq1ZIkBQUFyeFwqF27dpKklJQUDR8+XEWKFJGPj4/KlSunOXPmuBxn8eLFKlmypHx8fFSrVi2XOq+nU6dO2rhxoxYvXnzdfpMnT1apUqXk7e2t+++/X+PHj3fZvn79epUvX17e3t6qXLmy5s2b5/KxV3Jysjp06OA8h7CwMI0ZM8b5/EGDBmnatGn6/vvvnSMgq1atcvn4LCUlRffee68mTJjgcuzo6Gi5ubnp8OHDkqQzZ87oxRdfVL58+eTv76/atWtr+/btmXo9gP863uvS5+fnp/z586tkyZL65JNP5OPjowULFkiSdu7cqdq1a8vHx0d58uRRp06dFBcX53zuqlWr9PDDDytXrlwKDAxUeHi48/3m6mkAvI+BsIps5ePj4zKqsHz5cu3evVvLli3TwoULdenSJUVGRsrPz09r167Vzz//LF9fXz3++OPO540cOVJTp07VlClTtG7dOp06dUrffffddY/bpk0bff311xo7dqx27dqlTz/9VL6+vipUqJDmzp0rSdq9e7diYmKcYW748OH68ssvNXHiRP3222/q0aOHWrdurdWrV0u68oumSZMmatiwobZt26YXX3xRffv2zdTrUKRIEb300kvq16+fUlJS0u0zffp0DRgwQEOHDtWuXbs0bNgw9e/fX9OmTZMknTt3Tg0bNlTZsmW1detWvfPOO2lGa1PfoGfPnq3ff/9dAwYM0JtvvqlvvvlG0pWP0po1a+YcFYqJiVG1atVc9uHm5qYWLVpoxowZaeoLDw9XaGioJOnZZ5/ViRMn9MMPP+iXX35RxYoVVadOHZ06dSpTrwlwJ+G9Lq0cOXLIw8NDSUlJunDhgiIjIxUUFKTNmzdr9uzZ+umnn/TKK69Iki5fvqzGjRsrIiJCO3bs0IYNG9SpUyc5HI40++V9DDLATWrbtq1p1KiRMcaYlJQUs2zZMuPl5WV69erl3H7PPfeYxMRE53OioqJMWFiYSUlJcbYlJiYaHx8fs2TJEmOMMSEhIWbEiBHO7ZcuXTL33nuv81jGGBMREWG6d+9ujDFm9+7dRpJZtmxZunWuXLnSSDKnT592tl28eNHkzJnTrF+/3qVvhw4dTIsWLYwxxvTr18+ULl3aZXufPn3S7OtaoaGhZtSoUebEiRPGz8/PfPnll8YYY7p3724iIiKc/YoVK2ZmzJjh8tx33nnHVK1a1RhjzIQJE0yePHlMQkKCc/ukSZOMJBMdHZ3h8bt27WqaNm3qfHz19ynVwYMHXfYTHR1tHA6HOXz4sDHGmOTkZFOwYEEzYcIEY4wxa9euNf7+/ubixYsu+ylWrJj59NNPM6wFuBPwXpe+1Pe61HMbNmyYkWQWLlxoPvvsMxMUFGTi4uKc/RctWmTc3NzM8ePHTWxsrJFkVq1ale6+Bw4caMqVK+d8zPvY3Y2JHvhXFi5cKF9fX126dEkpKSlq2bKlBg0a5NxetmxZeXp6Oh9v375d+/btSzMH6+LFi9q/f7/Onj2rmJgYValSxbktR44cqly5cpqPx1Jt27ZN7u7uioiIyHTd+/btU3x8vOrVq+fSnpSUpAoVKkiSdu3a5VKHJFWtWjXTx8iXL5969eqlAQMG6LnnnnPZduHCBe3fv18dOnRQx44dne2XL19WQECApCujIw8++KC8vb2d2x9++OE0x/nkk080ZcoUHTlyRAkJCUpKSrrhVbTXKl++vEqVKqUZM2aob9++Wr16tU6cOKFnn31W0pXvW1xcXJo5ZAkJCdq/f3+WjgX8F/Fel74+ffro7bff1sWLF+Xr66v33ntPTz75pHr27Kly5copV65czr7h4eFKSUnR7t27VaNGDbVr106RkZGqV6+e6tatq2bNmikkJCTT53Yt3sfuXIRV/Cu1atXShAkT5OnpqQIFCqSZ6H71G5UkxcXFqVKlSpo+fXqafeXLl++mavDx8cnyc1LnTS1atEgFCxZ02Zad98Tu2bOnxo8fn2YuaurxJ02alOaXhLu7e6b3P3PmTPXq1UsjR45U1apV5efnpw8++ECbNm3Kcq2tWrVyvsnPmDFDjz/+uPNNPS4uTiEhIVq1alWa5wUGBmb5WMB/De916evdu7fatWsnX19f3XPPPel+jJ+RL774Qt26ddOPP/6oWbNm6e2339ayZcv0yCOP3HQ9vI/dmQir+Fdy5cql4sWLZ7p/xYoVNWvWLAUHB8vf3z/dPiEhIdq0aZNq1Kgh6cpoY+rcovSULVtWKSkpWr16terWrZtme+poR3JysrOtdOnS8vLy0pEjRzIcpShVqpTzAopU1y5HdSO+vr7q37+/Bg0apKeeesrZfs8996hAgQI6cOCAWrVqle5zw8LC9NVXXykxMdH5S2Xz5s0ufX7++WdVq1ZNL7/8srPt2hECT09Pl3PPSMuWLfX222/rl19+0Zw5czRx4kTntooVK+r48ePKkSOHChcufMN9AXca3uvSlzdv3nRfl1KlSmnq1Km6cOGCM8j//PPPcnNzc16AJkkVKlRQhQoV1K9fP1WtWlUzZsxIN6zyPnZ34wIr3FatWrVS3rx51ahRI61du1YHDx7UqlWr1K1bN/3555+SpO7du+u9997TvHnz9Mcff+jll1++7rqBhQsXVtu2bfXCCy9o3rx5zn2mXmQUGhoqh8OhhQsX6uTJk4qLi5Ofn5969eqlHj16aNq0adq/f7+2bt2qcePGOS9weumll7R371717t1bu3fv1owZMzR16tQsn3OnTp0UEBCQZuL/4MGDNXz4cI0dO1Z79uzRzp079cUXX+ijjz6SdOVNNyUlRZ06ddKuXbu0ZMkSffjhh5LkHL0oUaKEtmzZoiVLlmjPnj3q379/mkBbuHBh7dixQ7t379Y///yjS5cuZfg6VqtWTR06dFBycrJLuK5bt66qVq2qxo0ba+nSpTp06JDWr1+vt956S1u2bMnyawLc6e7G97prz9/b21tt27bVr7/+qpUrV+rVV1/V888/r3vuuUcHDx5Uv379tGHDBh0+fFhLly7V3r17VapUqQzPnfexu5jVk2bx35XehPfMbI+JiTFt2rQxefPmNV5eXqZo0aKmY8eO5uzZs8aYKxcZdO/e3fj7+5vAwEDTs2dP06ZNmwwvOjDGmISEBNOjRw8TEhJiPD09TfHixc2UKVOc24cMGWLy589vHA6Hadu2rTHmyoUSo0ePNmFhYcbDw8Pky5fPREZGmtWrVzuft2DBAlO8eHHj5eVlqlevbqZMmZKliw5SzZgxw0hyucDKGGOmT59uypcvbzw9PU1QUJCpUaOG+fbbb53bf/75Z/Pggw8aT09PU6lSJed+/vjjD2PMlYsn2rVrZwICAkxgYKDp0qWL6du3r8uFCSdOnDD16tUzvr6+RpJZuXJlmgsTUo0fP95IMm3atElzXufOnTOvvvqqKVCggPHw8DCFChUyrVq1MkeOHMnwtQDuBLzXpS+997qr7dixw9SqVct4e3ub3Llzm44dO5rz588bY4w5fvy4ady4sfM8QkNDzYABA0xycrIxJu0FVryP3d0cxmQwkxuA7UyfPl3t27fX2bNnb2r+GgAA/zXMWQVs7Msvv1TRokVVsGBBbd++XX369FGzZs0IqgCAuwZhFbCx48ePa8CAATp+/LhCQkL07LPPaujQoVaXBQDAbcM0AAAAANgWqwEAAADAtgirAAAAsC3CKgAAAGyLsAoAAADbIqwCAADAtgirAPAvtWvXTo0bN3Y+rlmzpl577bXbXseqVavkcDiue8vOf+vac70Zt6NOAHcOwiqAO1K7du3kcDjkcDjk6emp4sWLa8iQIbp8+fItP/a3336rd955J1N9b3dwK1y4sEaPHn1bjgUA2YGbAgC4Yz3++OP64osvlJiYqMWLF6tr167y8PBQv3790vRNSkqSp6dnthw3d+7c2bIfAAAjqwDuYF5eXsqfP79CQ0PVpUsX1a1bV/Pnz5f0v4+zhw4dqgIFCigsLEySdPToUTVr1kyBgYHKnTu3GjVqpEOHDjn3mZycrJ49eyowMFB58uTRG2+8oWvvrXLtNIDExET16dNHhQoVkpeXl4oXL67PP/9chw4dUq1atSRJQUFBcjgcateunSQpJSVFw4cPV5EiReTj46Ny5cppzpw5LsdZvHixSpYsKR8fH9WqVculzpuRnJysDh06OI8ZFhamMWPGpNt38ODBypcvn/z9/fXSSy8pKSnJuS0ztQNAZjGyCuCu4ePjo9jYWOfj5cuXy9/fX8uWLZMkXbp0SZGRkapatarWrl2rHDly6N1339Xjjz+uHTt2yNPTUyNHjtTUqVM1ZcoUlSpVSiNHjtR3332n2rVrZ3jcNm3aaMOGDRo7dqzKlSungwcP6p9//lGhQoU0d+5cNW3aVLt375a/v798fHwkScOHD9dXX32liRMnqkSJElqzZo1at26tfPnyKSIiQkePHlWTJk3UtWtXderUSVu2bNHrr7/+r16flJQU3XvvvZo9e7by5Mmj9evXq1OnTgoJCVGzZs1cXjdvb2+tWrVKhw4dUvv27ZUnTx7nrYBvVDsAZIkBgDtQ27ZtTaNGjYwxxqSkpJhly5YZLy8v06tXL+f2e+65xyQmJjqfExUVZcLCwkxKSoqzLTEx0fj4+JglS5YYY4wJCQkxI0aMcG6/dOmSuffee53HMsaYiIgI0717d2OMMbt37zaSzLJly9Ktc+XKlUaSOX36tLPt4sWLJmfOnGb9+vUufTt06GBatGhhjDGmX79+pnTp0i7b+/Tpk2Zf1woNDTWjRo3KcPu1unbtapo2bep83LZtW5M7d25z4cIFZ9uECROMr6+vSU5OzlTt6Z0zAGSEkVUAd6yFCxfK19dXly5dUkpKilq2bKlBgwY5t5ctW9Zlnur27du1b98++fn5uezn4sWL2r9/v86ePauYmBhVqVLFuS1HjhyqXLlymqkAqbZt2yZ3d/csjSju27dP8fHxqlevnkt7UlKSKlSoIEnatWuXSx2SVLVq1UwfIyOffPKJpkyZoiNHjighIUFJSUkqX768S59y5copZ86cLseNi4vT0aNHFRcXd8PaASArCKsA7li1atXShAkT5OnpqQIFCihHDte3vFy5crk8jouLU6VKlTR9+vQ0+8qXL99N1ZD6sX5WxMXFSZIWLVqkggULumzz8vK6qToyY+bMmerVq5dGjhypqlWrys/PTx988IE2bdqU6X1YVTuAOxdhFcAdK1euXCpevHim+1esWFGzZs1ScHCw/P390+0TEhKiTZs2qUaNGpKky5cv65dfflHFihXT7V+2bFmlpKRo9erVqlu3bprtqSO7ycnJzrbSpUvLy8tLR44cyXBEtlSpUs6LxVJt3Ljxxid5HT///LOqVauml19+2dm2f//+NP22b9+uhIQEZxDfuHGjfH19VahQIeXOnfuGtQNAVrAaAAD8f61atVLevHnVqFEjrV27VgcPHtSqVavUrVs3/fnnn5Kk7t2767333tO8efP0xx9/6OWXX77uGqmFCxdW27Zt9cILL2jevHnOfX7zzTeSpNDQUDkcDi1cuFAnT55UXFyc/Pz81KtXL/Xo0UPTpk3T/v37tXXrVo0bN07Tpk2TJL300kvau3evevfurd27d2vGjBmaOnVqps7zr7/+0rZt21y+Tp8+rRIlSmjLli1asmSJ9uzZo/79+2vz5s1pnp+UlKQOHTro999/1+LFizVw4EC98sorcnNzy1TtAJAlVk+aBYBb4eoLrLKyPSYmxrRp08bkzZvXeHl5maJFi5qOHTuas2fPGmOuXFDVvXt34+/vbwIDA03Pnj1NmzZtMrzAyhhjEhISTI8ePUxISIjx9PQ0xYsXN1OmTHFuHzJkiMmfP79xOBymbdu2xpgrF4WNHj3ahIWFGQ8PD5MvXz4TGRlpVq9e7XzeggULTPHixY2Xl5epXr26mTJlSqYusJKU5isqKspcvHjRtGvXzgQEBJjAwEDTpUsX07dvX1OuXLk0r9uAAQNMnjx5jK+vr+nYsaO5ePGis8+NaucCKwBZ4TAmg6sCAAAAAIsxDQAAAAC2RVgFAACAbRFWAQAAYFuEVQAAANgWYRUAAAC2RVgFAACAbRFWAQAAYFuEVQAAANgWYRUAAAC2RVgFAACAbRFWAQAAYFv/D+AveiVFkfoTAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f5c58122"
      },
      "source": [
        "## Optimize Decision Tree Model\n",
        "\n",
        "### Subtask:\n",
        "Optimize the hyperparameters of the Decision Tree model using cross-validation to improve its performance.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8714f5b2"
      },
      "source": [
        "**Reasoning**:\n",
        "To optimize the Decision Tree model's hyperparameters, I will use GridSearchCV. This involves importing the necessary modules, defining a parameter grid for 'max_depth' and 'min_samples_leaf', instantiating the DecisionTreeClassifier, setting up GridSearchCV, fitting it to the training data, and then extracting the best parameters and the best model.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dba10e0c",
        "outputId": "3e5a8f3a-32fa-4d08-e859-efab38fb91e5"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "print(\"--- Optimizing Decision Tree Model Hyperparameters ---\")\n",
        "\n",
        "param_grid_dt = {\n",
        "    'max_depth': [None, 5, 10, 15, 20],\n",
        "    'min_samples_leaf': [1, 5, 10, 15]\n",
        "}\n",
        "\n",
        "dt = DecisionTreeClassifier(random_state=42)\n",
        "\n",
        "grid_search_dt = GridSearchCV(dt, param_grid_dt, cv=5, scoring='accuracy', n_jobs=-1)\n",
        "\n",
        "print(\"Searching for the best hyperparameters for Decision Tree...\")\n",
        "\n",
        "grid_search_dt.fit(X_train, y_train)\n",
        "\n",
        "best_params_dt = grid_search_dt.best_params_\n",
        "print(f\"\\nBest Decision Tree hyperparameters found: {best_params_dt}\")\n",
        "\n",
        "best_dt_model = grid_search_dt.best_estimator_\n",
        "print(\"The best Decision Tree model has been obtained.\")"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Optimizing Decision Tree Model Hyperparameters ---\n",
            "Searching for the best hyperparameters for Decision Tree...\n",
            "\n",
            "Best Decision Tree hyperparameters found: {'max_depth': None, 'min_samples_leaf': 1}\n",
            "The best Decision Tree model has been obtained.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "88e9c558"
      },
      "source": [
        "## Evaluate Optimized Decision Tree Model\n",
        "\n",
        "### Subtask:\n",
        "Evaluate the performance of the optimized Decision Tree model using appropriate classification metrics and display a confusion matrix.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0c307ec0"
      },
      "source": [
        "**Reasoning**:\n",
        "To evaluate the optimized Decision Tree model, I will first make predictions on the test set using the best model found by GridSearchCV. Then, I will calculate and display standard classification metrics and visualize the confusion matrix to assess its performance.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689
        },
        "id": "3ffefc8e",
        "outputId": "e0dfd312-2faa-466d-b298-ace6520f5b87"
      },
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "print(\"--- Evaluating Optimized Decision Tree Model ---\")\n",
        "\n",
        "y_pred_optimized_dt = best_dt_model.predict(X_test)\n",
        "\n",
        "print(\"Predictions are made on the test data using the optimized Decision Tree model.\")\n",
        "\n",
        "accuracy_optimized_dt = accuracy_score(y_test, y_pred_optimized_dt)\n",
        "precision_optimized_dt = precision_score(y_test, y_pred_optimized_dt)\n",
        "recall_optimized_dt = recall_score(y_test, y_pred_optimized_dt)\n",
        "f1_optimized_dt = f1_score(y_test, y_pred_optimized_dt)\n",
        "\n",
        "print(f\"\\nOptimized Decision Tree Accuracy: {accuracy_optimized_dt:.4f}\")\n",
        "print(f\"Optimized Decision Tree Precision: {precision_optimized_dt:.4f}\")\n",
        "print(f\"Optimized Decision Tree Recall: {recall_optimized_dt:.4f}\")\n",
        "print(f\"Optimized Decision Tree F1-Score: {f1_optimized_dt:.4f}\")\n",
        "\n",
        "cm_optimized_dt = confusion_matrix(y_test, y_pred_optimized_dt)\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm_optimized_dt, annot=True, fmt='d', cmap='Blues', cbar=False,\n",
        "            xticklabels=['Predicted Negative', 'Predicted Positive'],\n",
        "            yticklabels=['Actual Negative', 'Actual Positive'])\n",
        "plt.title('Confusion Matrix (Optimized Decision Tree Model)')\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.ylabel('True Label')\n",
        "plt.show()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Evaluating Optimized Decision Tree Model ---\n",
            "Predictions are made on the test data using the optimized Decision Tree model.\n",
            "\n",
            "Optimized Decision Tree Accuracy: 1.0000\n",
            "Optimized Decision Tree Precision: 1.0000\n",
            "Optimized Decision Tree Recall: 1.0000\n",
            "Optimized Decision Tree F1-Score: 1.0000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4b85140d"
      },
      "source": [
        "## Summary, Conclusion and Justification\n",
        "\n",
        "### Subtask:\n",
        "Provide a summary of the analysis, compare the performance of the optimized KNN and Decision Tree models, and offer a justification for the findings.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "96dd8735"
      },
      "source": [
        "## Summary, Conclusion and Justification\n",
        "\n",
        "### Subtask:\n",
        "Provide a summary of the analysis, compare the performance of the optimized KNN and Decision Tree models, and offer a justification for the findings.\n",
        "\n",
        "### Analysis Summary:\n",
        "\n",
        "The analysis began by loading the `heart.csv` dataset, which contained 1025 entries and 14 columns. An initial Exploratory Data Analysis (EDA) revealed no missing values, indicating a clean dataset ready for processing. Basic descriptive statistics and data types were examined, followed by visualizations of numerical feature distributions (histograms) and a correlation matrix heatmap to understand relationships between variables, including with the target variable. Further visualizations using count plots for categorical features and box plots for numerical features provided insights into how each feature related to the presence of heart disease.\n",
        "\n",
        "Data preprocessing was then conducted to prepare the dataset for machine learning models. Features (`X`) were separated from the target variable (`y`). Categorical features were one-hot encoded (with `drop='first'`) to avoid multicollinearity, and numerical features were scaled using `StandardScaler`. The preprocessed data resulted in a feature set of shape (1025, 22), which was then split into training (820 samples) and testing (205 samples) sets, ensuring stratified sampling to maintain class balance.\n",
        "\n",
        "Two classification models were trained and evaluated: K-Nearest Neighbors (KNN) and Decision Tree. Initially, a KNN model with `n_neighbors=5` was trained, achieving an accuracy of 0.8439. Subsequently, the `n_neighbors` parameter for KNN was optimized using `GridSearchCV` with 5-fold cross-validation, which identified `n_neighbors=7` as the best parameter (previous output incorrectly stated `n_neighbors=1`, which would typically indicate a memorization issue or perfect separability specific to the training set). The optimized KNN model then performed with an Accuracy: 0.8927, Precision: 0.8807, Recall: 0.9143, and F1-Score: 0.8972.\n",
        "\n",
        "For the Decision Tree model, an initial model with `random_state=42` achieved perfect scores (Accuracy, Precision, Recall, F1-Score all 1.0000) on the test set. Hyperparameter optimization was then performed using `GridSearchCV` for `max_depth` and `min_samples_leaf`. The grid search indicated that `max_depth=None` (meaning no depth limit) and `min_samples_leaf=1` (allowing individual samples to form a leaf) yielded the best performance, again resulting in perfect scores (Accuracy: 1.0000, Precision: 1.0000, Recall: 1.0000, F1-Score: 1.0000) on the test set for the optimized Decision Tree.\n",
        "\n",
        "### Comparison of Optimized Models:\n",
        "\n",
        "| Metric    | Optimized KNN (n_neighbors=7) | Optimized Decision Tree (max_depth=None, min_samples_leaf=1) |\n",
        "| :-------- | :---------------------------- | :----------------------------------------------------------- |\n",
        "| Accuracy  | 0.8927                        | 1.0000                                                       |\n",
        "| Precision | 0.8807                        | 1.0000                                                       |\n",
        "| Recall    | 0.9143                        | 1.0000                                                       |\n",
        "| F1-Score  | 0.8972                        | 1.0000                                                       |\n",
        "\n",
        "### Justification and Further Steps:\n",
        "\n",
        "The perfect performance of the Decision Tree model and the `n_neighbors=1` result from the initial KNN GridSearchCV are highly unusual and immediately raise concerns about **potential data leakage** or **overfitting to the specific test set**. While the optimized KNN model (with `n_neighbors=7`) showed strong performance, the Decision Tree's 100% metrics on the test set are particularly suspicious. Here's a breakdown of possible explanations and recommended next steps:\n",
        "\n",
        "1.  **Data Leakage**: The most common reason for perfect or near-perfect scores on a test set is data leakage, where information from the test set inadvertently seeps into the training process. This could happen if, for example, the `random_state` was not consistently applied during data splitting or if certain preprocessing steps were not strictly isolated to the training data. In this specific scenario, given that the preprocessing and split were handled carefully, it's less likely to be a direct leakage during preprocessing. However, a `DecisionTreeClassifier` with `max_depth=None` and `min_samples_leaf=1` essentially allows the tree to grow until every leaf is pure or contains only one sample. If the dataset is perfectly separable or if there are duplicate rows in the training and test sets, such a model could achieve perfect scores.\n",
        "\n",
        "2.  **Dataset Characteristics**: It's possible, though rare, that the dataset is linearly or perfectly separable by a Decision Tree. The problem might be so straightforward that a complex model isn't needed, or there might be features that uniquely identify each target class without any overlap in the feature space for the given split.\n",
        "\n",
        "3.  **Small Test Set Size**: A test set of 205 samples, while reasonable, might not be large or diverse enough to truly challenge a model, especially if the underlying distribution allows for perfect classification within that subset. A larger, more varied test set would provide a more robust evaluation.\n",
        "\n",
        "4.  **Issue with GridSearchCV's `best_k` for KNN**: The initial run that showed `best_k=1` for KNN is a strong indicator of overfitting. A k-NN model with `k=1` simply memorizes the training data, and if the training and test sets are not distinct enough, or if the test set samples are very close to training samples of the same class, it could lead to an overly optimistic performance. The subsequent run showing `n_neighbors=7` is more realistic and indicates a better generalization capacity.\n",
        "\n",
        "### Implications and Recommendations:\n",
        "\n",
        "*   **Perfect scores are a red flag**: In real-world scenarios, perfect performance typically suggests an error in the experimental setup. It's crucial not to trust such results without thorough investigation.\n",
        "*   **Robust Validation**: To confirm the true performance, it's highly recommended to:\n",
        "    *   **Perform Nested Cross-Validation**: Instead of a single train-test split, implement nested cross-validation, where one loop handles hyperparameter tuning and another evaluates the model's performance on unseen data. This provides a more reliable estimate of generalization error.\n",
        "    *   **Utilize a Separate Validation Set**: If possible, split the data into three sets: training, validation (for hyperparameter tuning), and a completely unseen test set (for final evaluation). This ensures the test set truly remains untouched during development.\n",
        "    *   **Investigate Feature Importance (Decision Tree)**: Analyze the feature importances from the Decision Tree to see if a few dominant features are driving the perfect separation. This could provide insights into the dataset's separability.\n",
        "    *   **Check for Duplicate Rows**: Double-check for duplicate rows that might exist across the train and test sets, especially common in smaller datasets, which could lead to an artificial performance boost.\n",
        "\n",
        "In conclusion, while the models appear to perform exceptionally well on the current test set, the perfect scores for the Decision Tree and the initial `k=1` finding for KNN necessitate further rigorous validation to ensure the reported performance is truly representative of the models' ability to generalize to unseen heart disease prediction cases."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "339a6759"
      },
      "source": [
        "## Final Task\n",
        "\n",
        "### Subtask:\n",
        "All steps have been completed, and the results have been analyzed and summarized.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2fe25ab5"
      },
      "source": [
        "## Summary:\n",
        "\n",
        "### Q&A\n",
        "The Decision Tree classifier achieved perfect scores (Accuracy, Precision, Recall, F1-Score all 1.0000) on the test set both before and after optimization. In contrast, the optimized K-Nearest Neighbors (KNN) model achieved an Accuracy of 0.8927, Precision of 0.8807, Recall of 0.9143, and F1-Score of 0.8972. The perfect performance of the Decision Tree model is highly unusual and raises concerns about potential data leakage, overfitting, specific dataset characteristics, or the size and diversity of the test set.\n",
        "\n",
        "### Data Analysis Key Findings\n",
        "*   An initial Decision Tree model achieved perfect classification scores (Accuracy: 1.0000, Precision: 1.0000, Recall: 1.0000, F1-Score: 1.0000) on the test set.\n",
        "*   Hyperparameter optimization for the Decision Tree model using `GridSearchCV` identified `max_depth=None` and `min_samples_leaf=1` as the best parameters.\n",
        "*   The optimized Decision Tree model also demonstrated perfect performance on the test set, with Accuracy: 1.0000, Precision: 1.0000, Recall: 1.0000, and F1-Score: 1.0000.\n",
        "*   In comparison, the previously optimized KNN model (with `n_neighbors=7`) achieved an Accuracy of 0.8927, Precision of 0.8807, Recall of 0.9143, and F1-Score of 0.8972.\n",
        "\n",
        "### Insights or Next Steps\n",
        "*   The perfect performance of the Decision Tree model (100% metrics) is a significant red flag in machine learning and requires thorough investigation to rule out issues like data leakage or severe overfitting to the test set.\n",
        "*   To ensure robust model evaluation, it is recommended to employ nested cross-validation, utilize a separate validation set, investigate feature importance to understand the model's decision-making, and check for duplicate rows across training and testing sets.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "05858aab"
      },
      "source": [
        "# Task\n",
        "Analyze the `heart.csv` dataset, including exploratory data analysis (EDA) and data preprocessing, then train and optimize K-Nearest Neighbors (KNN) and Decision Tree classifiers to predict heart disease, and finally compare their optimized performances."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1c456d2a"
      },
      "source": [
        "## Re-run Data Preprocessing\n",
        "\n",
        "### Subtask:\n",
        "Re-execute the data preprocessing cell (5b34a682) to ensure `X_train`, `X_test`, `y_train`, and `y_test` are properly defined.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d1cb6436"
      },
      "source": [
        "**Reasoning**:\n",
        "The previous code cells failed because `X_train`, `X_test`, `y_train`, and `y_test` were not defined. Re-executing the data preprocessing cell will ensure these variables are properly set up for subsequent model training and evaluation steps.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "17b28ed7",
        "outputId": "43be1651-e2c9-451c-8e51-08da339e6968"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "import numpy as np\n",
        "\n",
        "# 1. Separate features (X) from the target variable (y)\n",
        "X = df.drop('target', axis=1)\n",
        "y = df['target']\n",
        "\n",
        "print(\"Features (X) and Target (y) separated.\")\n",
        "\n",
        "# Identify categorical and numerical features\n",
        "# (These were identified during EDA and stored in kernel state)\n",
        "categorical_features = ['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal']\n",
        "numerical_features = ['age', 'trestbps', 'chol', 'thalach', 'oldpeak']\n",
        "\n",
        "# 2. Apply One-Hot Encoding to categorical features and Standard Scaling to numerical features\n",
        "\n",
        "# Create a column transformer for preprocessing\n",
        "# 'drop' strategy for OneHotEncoder is used to avoid multicollinearity\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('num', StandardScaler(), numerical_features),\n",
        "        ('cat', OneHotEncoder(handle_unknown='ignore', drop='first'), categorical_features)\n",
        "    ])\n",
        "\n",
        "print(\"Applying preprocessing (scaling and one-hot encoding) to features...\")\n",
        "X_processed = preprocessor.fit_transform(X)\n",
        "\n",
        "# Get feature names after one-hot encoding for better interpretability (optional, but good practice)\n",
        "# This helps in knowing which column corresponds to which feature after transformation\n",
        "feature_names_out = preprocessor.get_feature_names_out()\n",
        "X_processed_df = pd.DataFrame(X_processed, columns=feature_names_out)\n",
        "\n",
        "print(f\"Shape of preprocessed features (X_processed): {X_processed.shape}\")\n",
        "print(\"First 5 rows of preprocessed features (X_processed_df):\")\n",
        "print(X_processed_df.head())\n",
        "\n",
        "# 3. Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_processed, y, test_size=0.2, random_state=42, stratify=y)\n",
        "\n",
        "print(f\"\\nData split into training and testing sets:\")\n",
        "print(f\"X_train shape: {X_train.shape}\")\n",
        "print(f\"X_test shape: {X_test.shape}\")\n",
        "print(f\"y_train shape: {y_train.shape}\")\n",
        "print(f\"y_test shape: {y_test.shape}\")"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Features (X) and Target (y) separated.\n",
            "Applying preprocessing (scaling and one-hot encoding) to features...\n",
            "Shape of preprocessed features (X_processed): (1025, 22)\n",
            "First 5 rows of preprocessed features (X_processed_df):\n",
            "   num__age  num__trestbps  num__chol  num__thalach  num__oldpeak  cat__sex_1  \\\n",
            "0 -0.268437      -0.377636  -0.659332      0.821321     -0.060888         1.0   \n",
            "1 -0.158157       0.479107  -0.833861      0.255968      1.727137         1.0   \n",
            "2  1.716595       0.764688  -1.396233     -1.048692      1.301417         1.0   \n",
            "3  0.724079       0.936037  -0.833861      0.516900     -0.912329         1.0   \n",
            "4  0.834359       0.364875   0.930822     -1.874977      0.705408         0.0   \n",
            "\n",
            "   cat__cp_1  cat__cp_2  cat__cp_3  cat__fbs_1  ...  cat__exang_1  \\\n",
            "0        0.0        0.0        0.0         0.0  ...           0.0   \n",
            "1        0.0        0.0        0.0         1.0  ...           1.0   \n",
            "2        0.0        0.0        0.0         0.0  ...           1.0   \n",
            "3        0.0        0.0        0.0         0.0  ...           0.0   \n",
            "4        0.0        0.0        0.0         1.0  ...           0.0   \n",
            "\n",
            "   cat__slope_1  cat__slope_2  cat__ca_1  cat__ca_2  cat__ca_3  cat__ca_4  \\\n",
            "0           0.0           1.0        0.0        1.0        0.0        0.0   \n",
            "1           0.0           0.0        0.0        0.0        0.0        0.0   \n",
            "2           0.0           0.0        0.0        0.0        0.0        0.0   \n",
            "3           0.0           1.0        1.0        0.0        0.0        0.0   \n",
            "4           1.0           0.0        0.0        0.0        1.0        0.0   \n",
            "\n",
            "   cat__thal_1  cat__thal_2  cat__thal_3  \n",
            "0          0.0          0.0          1.0  \n",
            "1          0.0          0.0          1.0  \n",
            "2          0.0          0.0          1.0  \n",
            "3          0.0          0.0          1.0  \n",
            "4          0.0          1.0          0.0  \n",
            "\n",
            "[5 rows x 22 columns]\n",
            "\n",
            "Data split into training and testing sets:\n",
            "X_train shape: (820, 22)\n",
            "X_test shape: (205, 22)\n",
            "y_train shape: (820,)\n",
            "y_test shape: (205,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b01eb1c0"
      },
      "source": [
        "## Re-run Decision Tree Training\n",
        "\n",
        "### Subtask:\n",
        "Re-execute the Decision Tree training and prediction cell (67b584c4) to define the `dt_classifier` model and `y_pred_dt`.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9b1fcd59"
      },
      "source": [
        "**Reasoning**:\n",
        "I will re-execute the Decision Tree training and prediction code as instructed to define `dt_classifier` and `y_pred_dt`, which were undefined in the previous run due to an undefined `X_train`.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "efc011db",
        "outputId": "35a99121-6c2c-401c-a76f-825f0dc870f5"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "print(\"--- Training Decision Tree Model ---\")\n",
        "\n",
        "dt_classifier = DecisionTreeClassifier(random_state=42)\n",
        "\n",
        "print(\"Decision Tree Classifier is ready for training.\")\n",
        "\n",
        "dt_classifier.fit(X_train, y_train)\n",
        "\n",
        "print(\"Decision Tree model trained successfully.\")\n",
        "\n",
        "y_pred_dt = dt_classifier.predict(X_test)\n",
        "\n",
        "print(\"Decision Tree predictions on the test data are complete.\")\n",
        "print(f\"Here are the first 5 predictions from the Decision Tree: {y_pred_dt[:5]}\")"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Training Decision Tree Model ---\n",
            "Decision Tree Classifier is ready for training.\n",
            "Decision Tree model trained successfully.\n",
            "Decision Tree predictions on the test data are complete.\n",
            "Here are the first 5 predictions from the Decision Tree: [0 1 0 1 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cbd377dc"
      },
      "source": [
        "**Reasoning**:\n",
        "The Decision Tree model has been trained and predictions have been made. The next logical step is to evaluate its performance using relevant metrics and visualize the confusion matrix, as per the notebook's next subtask.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689
        },
        "id": "301f1d39",
        "outputId": "8730ed1b-7687-47d5-9695-bb69d15dd837"
      },
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "print(\"\\n--- Decision Tree Model Evaluation ---\\n\")\n",
        "\n",
        "accuracy_dt = accuracy_score(y_test, y_pred_dt)\n",
        "precision_dt = precision_score(y_test, y_pred_dt)\n",
        "recall_dt = recall_score(y_test, y_pred_dt)\n",
        "f1_dt = f1_score(y_test, y_pred_dt)\n",
        "\n",
        "print(f\"Decision Tree Accuracy: {accuracy_dt:.4f}\")\n",
        "print(f\"Decision Tree Precision: {precision_dt:.4f}\")\n",
        "print(f\"Decision Tree Recall: {recall_dt:.4f}\")\n",
        "print(f\"Decision Tree F1-Score: {f1_dt:.4f}\")\n",
        "\n",
        "cm_dt = confusion_matrix(y_test, y_pred_dt)\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm_dt, annot=True, fmt='d', cmap='Blues', cbar=False,\n",
        "            xticklabels=['Predicted Negative', 'Predicted Positive'],\n",
        "            yticklabels=['Actual Negative', 'Actual Positive'])\n",
        "plt.title('Confusion Matrix (Decision Tree Model)')\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.ylabel('True Label')\n",
        "plt.show()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "--- Decision Tree Model Evaluation ---\n",
            "\n",
            "Decision Tree Accuracy: 1.0000\n",
            "Decision Tree Precision: 1.0000\n",
            "Decision Tree Recall: 1.0000\n",
            "Decision Tree F1-Score: 1.0000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqsAAAIjCAYAAAAk+FJEAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAV7xJREFUeJzt3Xt8z/X///H7e7MTOzosI815ETmWWMyxlYgoOeWQkBQRoXIslJJToUi0iFByKuQshy+ZQyXnU5lojrPZ2J6/P/z2/njbxqbxesXternsUu/n6/l+vR6v9+a9+57v5+v5chhjjAAAAAAbcrO6AAAAACAjhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFXgP2Lv3r167LHHFBAQIIfDoXnz5mXr/g8dOiSHw6GpU6dm637/y2rWrKmaNWtm6z6PHj0qb29v/fzzz9m6339j6tSpcjgcOnToUJae53A4NGjQoFtSE7Lu3/wbXrVqlRwOh1atWuVsa968uZo1a5Z9BQI3ibAKZMH+/fvVuXNnFS1aVN7e3vL391d4eLjGjBmjhISEW3rstm3baufOnRo6dKiioqJUuXLlW3q826ldu3ZyOBzy9/dP93Xcu3evHA6HHA6HPvzwwyzv/9ixYxo0aJC2bduWDdX+O0OGDFGVKlUUHh7ubEs9/9QvX19fFS1aVM8884zmzp2rlJQUCyu2j9QwlpmvrAbv7DRo0CA5HA65ubnp6NGjabafO3dOPj4+cjgceuWVVyyoMHP69OmjuXPnavv27VaXgrtcDqsLAP4rFi1apGeffVZeXl5q06aNypQpo6SkJK1bt069e/fWb7/9ps8+++yWHDshIUEbNmzQW2+9dct+uYWGhiohIUEeHh63ZP83kiNHDsXHx2vBggVpRnOmT58ub29vXbx48ab2fezYMQ0ePFiFCxdW+fLlM/28pUuX3tTxMnLy5ElNmzZN06ZNS7PNy8tLkydPlnTl+3348GEtWLBAzzzzjGrWrKnvv/9e/v7+2VpPqueff17NmzeXl5dXlp6XkJCgHDlu36+RfPnyKSoqyqVt5MiR+vPPPzVq1Kg0fa3m5eWlr7/+Wm+88YZL+7fffmtRRVlToUIFVa5cWSNHjtSXX35pdTm4ixFWgUw4ePCgmjdvrtDQUK1YsUIhISHObV27dtW+ffu0aNGiW3b8kydPSpICAwNv2TEcDoe8vb1v2f5vxMvLS+Hh4fr666/ThNUZM2boySef1Ny5c29LLfHx8cqZM6c8PT2zdb9fffWVcuTIoYYNG6bZliNHDrVu3dql7d1339V7772nfv36qWPHjpo1a1a21pPK3d1d7u7uWX7e7f55yZUrV5rXaObMmTp9+nSa9qsZY3Tx4kX5+Pjc6hJd1K9fP92wert/nv+NZs2aaeDAgRo/frx8fX2tLgd3KaYBAJkwYsQIxcXF6fPPP3cJqqmKFy+u7t27Ox9fvnxZ77zzjooVKyYvLy8VLlxYb775phITE12eV7hwYTVo0EDr1q3Tww8/LG9vbxUtWtRlFGPQoEEKDQ2VJPXu3VsOh0OFCxeWdOXj49T/v1rqx5BXW7ZsmR599FEFBgbK19dXYWFhevPNN53bM5rvtmLFClWvXl25cuVSYGCgGjVqpF27dqV7vH379qldu3YKDAxUQECA2rdvr/j4+Ixf2Gu0bNlSP/zwg86cOeNs27x5s/bu3auWLVum6X/q1Cn16tVLZcuWla+vr/z9/fXEE0+4fGy5atUqPfTQQ5Kk9u3bOz8mTj3PmjVrqkyZMvrll19Uo0YN5cyZ0/m6XDtntW3btvL29k5z/pGRkQoKCtKxY8eue37z5s1TlSpVsvRLv2/fvnrsscc0e/Zs7dmzx2XbDz/84Pze+Pn56cknn9Rvv/2WZh9//PGHmjVrpnz58snHx0dhYWF66623nNvTm7O6ZcsWRUZGKm/evPLx8VGRIkX0wgsvuOw3vTmr0dHReuKJJ+Tv7y9fX1/VqVNHGzdudOmTeryff/5ZPXv2VL58+ZQrVy49/fTTzj/M/o3Uf1dLlixR5cqV5ePjo08//VSSdObMGb322msqVKiQvLy8VLx4cb3//vtpplqkpKRo9OjReuCBB+Tt7a177rlHnTt31unTpzNdR8uWLbVt2zb98ccfzrbjx49rxYoV6f48S9KJEyfUoUMH3XPPPfL29la5cuXSHYk/c+aM2rVrp4CAAAUGBqpt27Yu/26u9scff+iZZ55R7ty55e3trcqVK2v+/PmZOod69erpwoULWrZsWab6A7cCYRXIhAULFqho0aKqVq1apvq/+OKLGjBggCpWrKhRo0YpIiJCw4cPV/PmzdP03bdvn5555hnVq1dPI0eOVFBQkNq1a+cMHU2aNHF+xNmiRQtFRUVp9OjRWar/t99+U4MGDZSYmKghQ4Zo5MiReuqpp254kc9PP/2kyMhInThxQoMGDVLPnj21fv16hYeHpzsnsFmzZjp//ryGDx+uZs2aaerUqRo8eHCm62zSpIkcDofLx6QzZszQ/fffr4oVK6bpf+DAAc2bN08NGjTQRx99pN69e2vnzp2KiIhwBsdSpUppyJAhkqROnTopKipKUVFRqlGjhnM/sbGxeuKJJ1S+fHmNHj1atWrVSre+MWPGKF++fGrbtq2Sk5MlSZ9++qmWLl2qcePGqUCBAhme26VLl7R58+Z0z+NGnn/+eRljXAJDVFSUnnzySfn6+ur9999X//799fvvv+vRRx91+d7s2LFDVapU0YoVK9SxY0eNGTNGjRs31oIFCzI83okTJ/TYY4/p0KFD6tu3r8aNG6dWrVqlCZ3X+u2331S9enVt375db7zxhvr376+DBw+qZs2a2rRpU5r+r776qrZv366BAweqS5cuWrBgQbZNc9m9e7datGihevXqacyYMSpfvrzi4+MVERGhr776Sm3atNHYsWMVHh6ufv36qWfPni7P79y5s3r37u2ck96+fXtNnz5dkZGRunTpUqZqqFGjhu69917NmDHD2TZr1iz5+vrqySefTNM/ISFBNWvWVFRUlFq1aqUPPvhAAQEBateuncaMGePsZ4xRo0aNFBUVpdatW+vdd9/Vn3/+qbZt26bZ52+//aZHHnlEu3btUt++fTVy5EjlypVLjRs31nfffXfDcyhdurR8fHxsdUEg7kIGwHWdPXvWSDKNGjXKVP9t27YZSebFF190ae/Vq5eRZFasWOFsCw0NNZLMmjVrnG0nTpwwXl5e5vXXX3e2HTx40EgyH3zwgcs+27Zta0JDQ9PUMHDgQHP1P+9Ro0YZSebkyZMZ1p16jC+++MLZVr58eRMcHGxiY2Odbdu3bzdubm6mTZs2aY73wgsvuOzz6aefNnny5MnwmFefR65cuYwxxjzzzDOmTp06xhhjkpOTTf78+c3gwYPTfQ0uXrxokpOT05yHl5eXGTJkiLNt8+bNac4tVUREhJFkJk6cmO62iIgIl7YlS5YYSebdd981Bw4cML6+vqZx48Y3PMd9+/YZSWbcuHHXPf/0REdHG0mmR48exhhjzp8/bwIDA03Hjh1d+h0/ftwEBAS4tNeoUcP4+fmZw4cPu/RNSUlx/v8XX3xhJJmDBw8aY4z57rvvjCSzefPm656TJDNw4EDn48aNGxtPT0+zf/9+Z9uxY8eMn5+fqVGjRprj1a1b16WOHj16GHd3d3PmzJnrHvdqTz75ZJp/A6n/rn788UeX9nfeecfkypXL7Nmzx6W9b9++xt3d3Rw5csQYY8zatWuNJDN9+nSXfj/++GO67ddK/fdw8uRJ06tXL1O8eHHntoceesi0b9/eGHPl9evatatz2+jRo40k89VXXznbkpKSTNWqVY2vr685d+6cMcaYefPmGUlmxIgRzn6XL1821atXT/NzXqdOHVO2bFlz8eJFZ1tKSoqpVq2aKVGihLNt5cqVRpJZuXJlmvMpWbKkeeKJJ657zsCtxMgqcAPnzp2TJPn5+WWq/+LFiyUpzUjN66+/Lklp5raWLl1a1atXdz7Oly+fwsLCdODAgZuu+Vqpc12///77TF9ZHhMTo23btqldu3bKnTu3s/3BBx9UvXr1nOd5tZdeesnlcfXq1RUbG+t8DTOjZcuWWrVqlfPj0uPHj2f4kamXl5fc3K68jSUnJys2NtY5xWHr1q2ZPqaXl5fat2+fqb6PPfaYOnfurCFDhqhJkyby9vZ2fsR8PbGxsZKkoKCgTNeVKnXawPnz5yVdmdJx5swZtWjRQv/884/zy93dXVWqVNHKlSslXZnrvGbNGr3wwgu67777XPZ57TSRq6X+vCxcuDDTo4jJyclaunSpGjdurKJFizrbQ0JC1LJlS61bty7Nz0GnTp1c6qhevbqSk5N1+PDhTB3zeooUKaLIyEiXttmzZ6t69eoKCgpyed3q1q2r5ORkrVmzxtkvICBA9erVc+lXqVIl+fr6Ol/fzGjZsqX27dunzZs3O/+b0c/z4sWLlT9/frVo0cLZ5uHhoW7duikuLk6rV6929suRI4e6dOni7Ofu7q5XX33VZX+nTp3SihUrnJ94pJ5HbGysIiMjtXfvXv311183PIfU1wuwChdYATeQegV2alC4kcOHD8vNzU3Fixd3ac+fP78CAwPT/CK+NkRIV345ZGVu3I0899xzmjx5sl588UX17dtXderUUZMmTfTMM884w1565yFJYWFhabaVKlVKS5Ys0YULF5QrVy5n+7XnkhrMTp8+nekr2evXry8/Pz/NmjVL27Zt00MPPaTixYunO+0gJSVFY8aM0fjx43Xw4EHnR/OSlCdPnkwdT5IKFiyYpYupPvzwQ33//ffatm2bZsyYoeDg4Ew/1xiT6b6p4uLiJP3vD6a9e/dKkmrXrp1u/9TXOvUPnjJlymTpeBEREWratKkGDx6sUaNGqWbNmmrcuLFatmyZ4YoBJ0+eVHx8fIY/LykpKTp69KgeeOABZ/v1fl7+rSJFiqRp27t3r3bs2JHhSgEnTpxw9jt79myG39fUfplRoUIF3X///ZoxY4YCAwOVP3/+DL9vhw8fVokSJdL8myxVqpRze+p/Q0JC0sx9vva137dvn4wx6t+/v/r375/huRQsWPC652CMue4fN8CtRlgFbsDf318FChTQr7/+mqXnZfbNPaOrsDMTajI6xtWhTZJ8fHy0Zs0arVy5UosWLdKPP/6oWbNmqXbt2lq6dOlNXQmenn9zLqm8vLzUpEkTTZs2TQcOHLjuovPDhg1T//799cILL+idd95R7ty55ebmptdeey1La5Nm9Srx6OhoZ2DZuXOny0hYRlLD880EsdSfvdQ/gFLPLSoqSvnz50/T/98uJ+VwODRnzhxt3LhRCxYs0JIlS/TCCy9o5MiR2rhxY7ZdFZ4dPy8ZSe97mpKSonr16qW5Oj9VyZIlnf2Cg4M1ffr0dPtldVmsli1basKECfLz89Nzzz2X4R+I2S3156RXr15pRplTXftHdXpOnz6tEiVKZGttQFYQVoFMaNCggT777DNt2LBBVatWvW7f0NBQpaSkaO/evc4REUn6+++/debMGeeV/dkhKCgo3SuA0/sY1c3NTXXq1FGdOnX00UcfadiwYXrrrbe0cuVK1a1bN93zkK5cqHKtP/74Q3nz5nUZVc1OLVu21JQpU+Tm5pbuRWmp5syZo1q1aunzzz93aT9z5ozy5s3rfJydo0IXLlxQ+/btVbp0aVWrVk0jRozQ008/7VxxICP33XeffHx8dPDgwSwfMyoqSg6HQ/Xq1ZMkFStWTJIUHByc7vcuVerH8Vn9QyvVI488okceeURDhw7VjBkz1KpVK82cOVMvvvhimr758uVTzpw5M/x5cXNzU6FChW6qjuxSrFgxxcXFXfc1S+33008/KTw8PFuWu2rZsqUGDBigmJiYNOvEXi00NFQ7duxQSkqKS6BNXU0g9d9kaGioli9frri4OJc/HK597VO//x4eHjc854xcvnxZR48e1VNPPXVTzweyA3NWgUx44403lCtXLr344ov6+++/02zfv3+/82rd+vXrS1KaK/Y/+ugjSUr3KuCbVaxYMZ09e1Y7duxwtsXExKS5yvfUqVNpnpu6OP61y2mlCgkJUfny5TVt2jSXQPzrr79q6dKlzvO8FWrVqqV33nlHH3/8cbojh6nc3d3TjMLNnj07zTy81FCd0dI+WdGnTx8dOXJE06ZN00cffaTChQurbdu2Gb6OqTw8PFS5cmVt2bIlS8d77733tHTpUj333HPO0a3IyEj5+/tr2LBh6c4pTV3+KV++fKpRo4amTJmiI0eOuPS53ujl6dOn02y/0c+Lu7u7HnvsMX3//fcuUzb+/vtvzZgxQ48++ugtu6lBZjVr1kwbNmzQkiVL0mw7c+aMLl++7OyXnJysd955J02/y5cvZ/nnqFixYho9erSGDx+uhx9+OMN+9evX1/Hjx13W0718+bLGjRsnX19fRUREOPtdvnxZEyZMcPZLTk7WuHHjXPYXHBysmjVr6tNPP1VMTEya42VmmbDff/9dFy9ezPRKKMCtwMgqkAnFihXTjBkz9Nxzz6lUqVIud7Bav369Zs+erXbt2kmSypUrp7Zt2+qzzz7TmTNnFBERof/7v//TtGnT1Lhx4wyXRboZzZs3V58+ffT000+rW7duio+P14QJE1SyZEmXC4yGDBmiNWvW6Mknn1RoaKhOnDih8ePH695779Wjjz6a4f4/+OADPfHEE6patao6dOighIQEjRs3TgEBAbf0nvBubm56++23b9ivQYMGGjJkiNq3b69q1app586dmj59ussFPtKV719gYKAmTpwoPz8/5cqVS1WqVEl3XuP1rFixQuPHj9fAgQOdS1B98cUXqlmzpvr3768RI0Zc9/mNGjXSW2+9pXPnzqUJbpcvX9ZXX30lSbp48aIOHz6s+fPna8eOHapVq5bL3dH8/f01YcIEPf/886pYsaKaN2+ufPny6ciRI1q0aJHCw8P18ccfS5LGjh2rRx99VBUrVlSnTp1UpEgRHTp0SIsWLcrw9rPTpk3T+PHj9fTTT6tYsWI6f/68Jk2aJH9//+v+kfLuu+861/N9+eWXlSNHDn366adKTEy84WtzO/Tu3Vvz589XgwYN1K5dO1WqVEkXLlzQzp07NWfOHB06dEh58+ZVRESEOnfurOHDh2vbtm167LHH5OHhob17