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      "display_name": "Python 3"
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      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_D43u6eNXuNP",
        "outputId": "8db801b6-18b2-41d5-e07d-72e7d43b9f53"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "# Walid Iris Project in machine Learning\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "metadata": {
        "id": "-5kzdh4YYhMd"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/Python/Iris.csv')"
      ],
      "metadata": {
        "id": "MrajO_T9YlP5"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "UvFW6VYcYtq-",
        "outputId": "622061e0-f5f6-47df-c942-803e5411294e"
      },
      "execution_count": 76,
      "outputs": [
        {
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            "text/plain": [
              "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
              "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
              "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
              "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
              "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
              "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
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              "      <td>Iris-setosa</td>\n",
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            }
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c939d141"
      },
      "source": [
        "df.drop('Id', axis=1, inplace=True)"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "fa43b2a5",
        "outputId": "679421d2-b98c-4aea-8ed1-1a5b575107d4"
      },
      "source": [
        "display(df.head())"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
              "0            5.1           3.5            1.4           0.2  Iris-setosa\n",
              "1            4.9           3.0            1.4           0.2  Iris-setosa\n",
              "2            4.7           3.2            1.3           0.2  Iris-setosa\n",
              "3            4.6           3.1            1.5           0.2  Iris-setosa\n",
              "4            5.0           3.6            1.4           0.2  Iris-setosa"
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              "summary": "{\n  \"name\": \"display(df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2073644135332772,\n        \"min\": 4.6,\n        \"max\": 5.1,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4.9,\n          5.0,\n          4.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2588435821108957,\n        \"min\": 3.0,\n        \"max\": 3.6,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3.0,\n          3.6,\n          3.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07071067811865474,\n        \"min\": 1.3,\n        \"max\": 1.5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.4,\n          1.3,\n          1.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.2,\n        \"max\": 0.2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Iris-setosa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "5402cfde",
        "outputId": "12a9a763-2fc9-4718-9182-6563c4950787"
      },
      "source": [
        "display(df.head(10))"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
              "0            5.1           3.5            1.4           0.2  Iris-setosa\n",
              "1            4.9           3.0            1.4           0.2  Iris-setosa\n",
              "2            4.7           3.2            1.3           0.2  Iris-setosa\n",
              "3            4.6           3.1            1.5           0.2  Iris-setosa\n",
              "4            5.0           3.6            1.4           0.2  Iris-setosa\n",
              "5            5.4           3.9            1.7           0.4  Iris-setosa\n",
              "6            4.6           3.4            1.4           0.3  Iris-setosa\n",
              "7            5.0           3.4            1.5           0.2  Iris-setosa\n",
              "8            4.4           2.9            1.4           0.2  Iris-setosa\n",
              "9            4.9           3.1            1.5           0.1  Iris-setosa"
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              "      <th></th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
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              "      <td>3.1</td>\n",
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              "      <td>0.1</td>\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(df\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.29135697844549546,\n        \"min\": 4.4,\n        \"max\": 5.4,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          5.1,\n          4.9,\n          5.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.30713731999438515,\n        \"min\": 2.9,\n        \"max\": 3.9,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.0,\n          3.9,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10801234497346433,\n        \"min\": 1.3,\n        \"max\": 1.7,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.3,\n          1.7,\n          1.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07888106377466154,\n        \"min\": 0.1,\n        \"max\": 0.4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.4,\n          0.1,\n          0.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Iris-setosa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {}
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    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "7f072a6d",
        "outputId": "e2c83c56-3dda-462c-a0f7-03acb98b4284"
      },
      "source": [
        "display(df.describe())"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
              "count     150.000000    150.000000     150.000000    150.000000\n",
              "mean        5.843333      3.054000       3.758667      1.198667\n",
              "std         0.828066      0.433594       1.764420      0.763161\n",
              "min         4.300000      2.000000       1.000000      0.100000\n",
              "25%         5.100000      2.800000       1.600000      0.300000\n",
              "50%         5.800000      3.000000       4.350000      1.300000\n",
              "75%         6.400000      3.300000       5.100000      1.800000\n",
              "max         7.900000      4.400000       6.900000      2.500000"
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
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              "      <th>PetalWidthCm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.843333</td>\n",
              "      <td>3.054000</td>\n",
              "      <td>3.758667</td>\n",
              "      <td>1.198667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.828066</td>\n",
              "      <td>0.433594</td>\n",
              "      <td>1.764420</td>\n",
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              "      <th>min</th>\n",
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              "      <td>0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.100000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>0.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.400000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>2.500000</td>\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7aa81661-3ae1-4bff-8a1c-705983a94203 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-7aa81661-3ae1-4bff-8a1c-705983a94203');\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.24711349471842,\n        \"min\": 0.8280661279778629,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.843333333333334,\n          5.8,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.08647211421483,\n        \"min\": 0.4335943113621737,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.0540000000000003,\n          3.0,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.835227940958106,\n        \"min\": 1.0,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.758666666666666,\n          4.35,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.63663424340991,\n        \"min\": 0.1,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.1986666666666668,\n          1.3,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f1ba720d",
        "outputId": "f80cc305-d629-4d33-a21f-7c02f6accb51"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 5 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   SepalLengthCm  150 non-null    float64\n",
            " 1   SepalWidthCm   150 non-null    float64\n",
            " 2   PetalLengthCm  150 non-null    float64\n",
            " 3   PetalWidthCm   150 non-null    float64\n",
            " 4   Species        150 non-null    object \n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 6.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Are there Null values? -- No Null Values --"
      ],
      "metadata": {
        "id": "DBjEVba9bUJ7"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 1000
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        "id": "86624858",
        "outputId": "882d3e47-6b32-43b0-8827-7aba5ad25a0a"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "for column in df.columns[:-1]:\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    sns.histplot(df[column], kde=True)\n",
        "    plt.title(f'Distribution of {column}')\n",
        "    plt.xlabel(column)\n",
        "    plt.ylabel('Frequency')\n",
        "    plt.grid(True, linestyle='--', alpha=0.7)\n",
        "    plt.show()"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "565a12b5",
        "outputId": "e1860e1d-81a5-46cb-ffa1-0bf40726d78d"
      },
      "source": [
        "species_colors = {'Iris-virginica': 'red', 'Iris-versicolor': 'orange', 'Iris-setosa': 'blue'}\n",
        "\n",
        "# Scatter plot 1: Sepal Length vs. Sepal Width\n",
        "plt.figure(figsize=(10, 7))\n",
        "sns.scatterplot(x='SepalLengthCm', y='SepalWidthCm', hue='Species', palette=species_colors, data=df, s=100)\n",
        "plt.title('Sepal Length vs. Sepal Width by Species')\n",
        "plt.xlabel('Sepal Length (cm)')\n",
        "plt.ylabel('Sepal Width (cm)')\n",
        "plt.grid(True, linestyle='--', alpha=0.7)\n",
        "plt.legend(title='Species')\n",
        "plt.show()"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "c5360a7b",
        "outputId": "351884ee-be74-416f-ae66-ed3a54ad4cc9"
      },
      "source": [
        "# Scatter plot 2: Petal Length vs. Petal Width\n",
        "plt.figure(figsize=(10, 7))\n",
        "sns.scatterplot(x='PetalLengthCm', y='PetalWidthCm', hue='Species', palette=species_colors, data=df, s=100)\n",
        "plt.title('Petal Length vs. Petal Width by Species')\n",
        "plt.xlabel('Petal Length (cm)')\n",
        "plt.ylabel('Petal Width (cm)')\n",
        "plt.grid(True, linestyle='--', alpha=0.7)\n",
        "plt.legend(title='Species')\n",
        "plt.show()"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "a8195c11",
        "outputId": "86cfc3d0-1f90-4e2b-ef86-12aa94a499db"
      },
      "source": [
        "# Scatter plot 3: Sepal Length vs. Petal Length\n",
        "plt.figure(figsize=(10, 7))\n",
        "sns.scatterplot(x='SepalLengthCm', y='PetalLengthCm', hue='Species', palette=species_colors, data=df, s=100)\n",
        "plt.title('Sepal Length vs. Petal Length by Species')\n",
        "plt.xlabel('Sepal Length (cm)')\n",
        "plt.ylabel('Petal Length (cm)')\n",
        "plt.grid(True, linestyle='--', alpha=0.7)\n",
        "plt.legend(title='Species')\n",
        "plt.show()"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "5e282138",
        "outputId": "bd9c942b-c51b-40a3-e0c9-ca9488dc8125"
      },
      "source": [
        "# Scatter plot 4: Sepal Width vs. Petal Width\n",
        "plt.figure(figsize=(10, 7))\n",
        "sns.scatterplot(x='SepalWidthCm', y='PetalWidthCm', hue='Species', palette=species_colors, data=df, s=100)\n",
        "plt.title('Sepal Width vs. Petal Width by Species')\n",
        "plt.xlabel('Sepal Width (cm)')\n",
        "plt.ylabel('Petal Width (cm)')\n",
        "plt.grid(True, linestyle='--', alpha=0.7)\n",
        "plt.legend(title='Species')\n",
        "plt.show()"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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sGHPmzJHESogqQmbzE0JUClarla5du9K1a1datGjB6NGjmTt3LhMmTEDTNEwmEz/88INzNrzCyjNl8pdffsmoUaMYOHAg48ePJy4uDovFwpQpU5xjczxVeMyM1WqlRo0aJCYmAtCxY0fn1NkFEyiUdiNttOLOG1DkF/+LXX755TRu3Jg5c+Zw++23880332Cz2VzGhJhMJr7++mvWrl3LN998w5IlSxgzZgxvvPEGa9euLfeU1j169HDO5ncxTdOIi4srdowOUCRxKM5LL73Es88+y5gxY5g8eTI1atTAbDbz6KOPlvtGt3v37rz77rusWrWK1atXOye3AJVM5ebmsn79elauXElQUFCxEy74Qknvv9FGjhzJ3Llznce+aNEiHnzwwSItPqUxmUy0aNGCFi1a0L9/f5o3b87MmTO55557PKqLpmm0a9eON998s9jHGzRo4NHrlff1w8LC+PXXX/n555/57rvvWLx4MbNnz6Z3794sXbq0wt4bIYRvSDIlhKh0kpKSANXNCaBp06bouk6TJk2cv/qW5ujRo2RlZbn8cr17925Aze4G8PXXX5OQkMD8+fNdfnEvbnFddxVeaygkJIQrrrjC+dpBQUF07dqVVatWkZqaSlxcXKnHEhsbS1hYGHv27Cny2K5du4ps82TdJU8NGTKEd955h4yMDGbPnk3jxo2LTQIuv/xyLr/8cl588UVmzZrFiBEj+O9//+vxTbA7mjZtyo8//ki3bt0ICwsrtWxJ5+brr7/m6quvLtLqkZaWVmISV5bCCfWaNWucLZOgWlMaNWrEqlWriqwxVpxGjRoBuBUDvnz/Afbu3Yuu6y77ufgzBXD99dcTGxvLzJkz+dvf/kZ2djZ33nlnufebkJBA9erVndeCAu58xps2bcrmzZvp06dPqeenadOmaJrGH3/8QceOHd2um7uvD2A2m+nTpw99+vThzTff5KWXXuKZZ57h559/5pprrnF7n0KIyke6+Qkh/Obnn38utmXk+++/By50ZbrllluwWCxMmjSpSHld1zlz5ozLNrvd7jKFeF5eHh999BGxsbHObmIFvwYXfr3ff/+dNWvWlPt4Cq81VNx6OVdeeSW//vora9eudbnJLo7FYqFv374sXLiQQ4cOObfv2LGDJUuWFCkfHh5eZFZDowwdOpTc3FxmzJjB4sWLGTJkiMvj586dK/K+FNyUFu7qt2/fvnK3+l1syJAhOBwOJk+eXOQxu93uci5KOjcWi6VIvefOncuff/5Z7nrVrVuXJk2asHz5cpKTk4uNgYULF7Jr164yWybj4+Pp2LEjM2bMcOl2uGzZMmcX2AIFSZmvYuDo0aMus0hmZGTw+eef07FjR+rUqePcHhQUxPDhw5kzZw7Tp0+nXbt2tG/fvszX//3338nKyiqyfd26dZw5c6ZIt0Z3PuNDhgzhzz//5N///neR17XZbM79DRw4ELPZzPPPP1+kRbK0llt3X//s2bNFHi/u8yGECEzSMiWE8JuHHnqI7OxsBg0aRGJiInl5eaxevdrZ+jF69GhA/QL8wgsv8NRTT3HgwAEGDhxIZGQkqampLFiwgHvvvZcnnnjC+bp169bllVde4cCBA7Ro0YLZs2eTkpLCxx9/THBwMAA33ngj8+fPZ9CgQfTv35/U1FQ+/PBDWrdu7TLZgqe6d+/uHHh+ccJ05ZVXMmXKFGe5skyaNInFixdz1VVX8eCDD2K323n33Xdp06YNW7ZscSnbpUsXfvzxR958803nDf3f/va3ch9HYZ07d6ZZs2Y888wz5ObmFplWesaMGXzwwQcMGjSIpk2bcv78ef79738TFRXFDTfc4CzXp08fwHXcWnn17NmT++67jylTppCSksJ1111HcHAwe/bsYe7cubzzzjvceuutgDo306ZN44UXXqBZs2bExcXRu3dvbrzxRp5//nlGjx7NlVdeydatW5k5cyYJCQle1a179+7OKb6Li4GC9ZbciYEpU6bQv39/unfvzpgxYzh79qwzBgrHaVhYGK1bt2b27Nm0aNGCGjVq0LZtW9q2bevVsRRo0aIFd999N+vXr6d27dp8+umnnDhxgs8++6xI2ZEjRzJ16lR+/vlnXnnlFbde/4svvmDmzJkMGjSILl26YLVa2bFjB59++imhoaFF1mRy5zN+5513MmfOHO6//35+/vlnunXrhsPhYOfOncyZM4clS5aQlJTkjO3Jkydz1VVXccsttxASEsL69eupW7eu8zN7MXdf//nnn+fXX3+lf//+NGrUiJMnT/LBBx9Qv379Cu3qK4TwEf9MIiiEELr+ww8/6GPGjNETExP1iIgI3Wq16s2aNdMfeugh/cSJE0XKz5s3T+/evbseHh6uh4eH64mJifrYsWP1Xbt2Ocv07NlTb9OmjZ6cnKxfccUVemhoqN6oUSP9vffec3ktTdP0l156SW/UqJEeEhKid+rUSf/222/1u+66y2XKaV13f2p0Xdf1JUuW6IAeFBSkZ2VluTx25swZ3WQy6YD++++/F3lucfv55Zdf9C5duuhWq1VPSEjQP/zwQ+fU4YXt3LlT79Gjhx4WFqYDzqmoC8pePC3zZ599pgN6amqqW8f1zDPP6IDerFmzIo9t3LhRHz58uN6wYUM9JCREj4uL02+88UY9OTnZpVyjRo2KnNvilFTn4nz88cd6ly5d9LCwMD0yMlJv166d/uSTT+pHjx51ljl+/Ljev39/PTIyUgec01vn5OTojz/+uB4fH6+HhYXp3bp109esWVNkCmx3p0YvUDBFeL169Yo8tnHjRh3QgSIxXtJ+5s2bp7dq1UoPCQnRW7durc+fP7/YOF29erUzVgrH0l133aWHh4cXqUtxcVScRo0a6f3799eXLFmit2/fXg8JCdETExP1uXPnlvicNm3a6GazWT9y5EiZr6/rur5lyxZ9/PjxeufOnfUaNWroQUFBenx8vH7bbbfpGzdudCnr7mdc19XU7K+88orepk0bPSQkRK9evbrepUsXfdKkSXp6erpL2U8//VTv1KmTs1zPnj31ZcuWuez34in23Xn95cuX6wMGDNDr1q2rW61WvW7duvrw4cP13bt3u3VuhBCVm0nXyxh9LIQQAaRXr16cPn3arYWAhRC+0alTJ2rUqMHy5csNf235jAshKhMZMyWEEEIIwyQnJ5OSksLIkSP9XRUhhPA5GTMlhBBCCK9t27aNDRs28MYbbxAfH19kbJ0QQlRF0jIlhBBCCK99/fXXjB49mvz8fL766itCQ0P9XSUhhPA5GTMlhBBCCCGEEOUgLVNCCCGEEEIIUQ6STAkhhBBCCCFEOVxyE1BomsbRo0eJjIzEZDL5uzpCCCGEEEIIP9F1nfPnz1O3bl3MZs/bmS65ZOro0aM0aNDA39UQQgghhBBCVBKHDx+mfv36Hj/vkkumIiMjAXXCoqKi/FwbsNvtbNq0iU6dOhEUdMm9HcIgEkfCKBJLwigSS8IoEkvCCCXFUUZGBg0aNHDmCJ665CKyoGtfVFRUpUmmwsPDiYqKkguEKDeJI2EUiSVhFIklYRSJJWGEsuKovMN/ZAIKPzObzcTGxparj6YQBSSOhFEkloRRJJaEUSSWhBF8FUeX3DpTGRkZREdHk56eXilapoQQQgghhBD+4W1uICm+n2maxr59+9A0zd9VEQFM4kgYRWJJGEViSRhFYkkYwVdxJB1P/UzTNE6dOkWjRo2k+VqUm8SRMIrEkjCKxJIwysWxpOs6drsdh8Ph76qJABEcHIyu6z65JkkyJYQQQgghAkJeXh7Hjh0jOzvb31URAcRkMhEfH++T15ZkSgghhBBCVHqappGamorFYqFu3bpYrdZyz8AmLh0FLVJHjx71yetLMuVnZrOZ+vXrSxcI4RWJI2EUiSVhFIklYZSCWLLb7WiaRoMGDahWrZq/qyUCSGxsLJmZmdSpU8fwa5IkU35WcIEQwhsSR8IoEkvCKBJLwigFsZSTk+P8fyE8YTKZMJlMPkmmJBr9zOFwsGPHDhlEKbwicSSMIrEkjCKxJIwisSSMoOs6+/btMzyOJJnyM13XSU9P5xJb7ksYTOJIGEViSRhFYkkYRWJJGOX8+fOGx5EkU0IIIYQQQlwCTCYTCxcu9Hc1qhRJpoQQQgghhPCxU6dO8cADD9CwYUNCQkKoU6cOffv2ZdWqVRVWh2PHjtGvX78K29+lQCag8DOz2UxCQoIMphRekTgSRpFYEkaRWBJGqSqxNHjwYPLy8pgxYwYJCQmcOHGC5cuXc+bMmQqrQ506dSpsX5VRgwYNZAKKqsZsNhMXFxfwFwjhXxJHwigSS8IoEkvCKFUhltLS0vjtt9945ZVXuPrqq2nUqBGXXXYZTz31FDfffDOguuBNmzaNfv36ERYWRkJCAl9//bXL6xw+fJghQ4YQExNDjRo1GDBgAAcOHHAp8+mnn9KmTRtCQkKIj49n3Lhxzscu7uZX1uutWLGCyy67jPDwcGJiYujWrRsHDx40/PxUBJPJRM2aNSWZqmocDgebN2+WGWqEVySOhFEklnwkKwvsdjh5Eo4ehdxcyMz0d618qkJjKScHHA44dUqd35wcyM72/X5FhagK16WIiAgiIiJYuHAhubm5JZZ79tlnGTx4MJs3b2bEiBEMGzaMHTt2AJCfn0/fvn2JjIzkt99+Y9WqVURERHD99deTl5cHwLRp0xg7diz33nsvW7duZdGiRTRr1qzYfZX1ena7nYEDB9KzZ0+2bNnCmjVruPfeewN2oWRd19m5c6fhcSTd/PxM13VsNpvMUCO8InEkjCKxZLDsbLDZYOpU+Pe/4dgxtT0sDIYNgyeegMaNoQouQFohsZSXpxLT996DDz+EQ4fUdqsVBg+GJ5+ExEQIDfVdHYTPVYXrUlBQENOnT+fvf/87H374IZ07d6Znz54MGzaM9u3bO8vddttt3HPPPQBMnjyZZcuW8e677/LBBx8we/ZsNE3jk08+cSY0n332GTExMaxYsYLrrruOF154gccff5xHHnnE+Zpdu3Yttk5lvV5SUhLp6enceOONNG3aFIBWrVr55PxUlJycHJnNTwghhAgI2dmweze0bAnPP38hkQKVYH32GbRrB198Ia0o5ZGXp5KnxER4+ukLiVTBY199BZ06qUT2r8VehfCnwYMHc/ToURYtWsT111/PihUr6Ny5M9OnT3eWueKKK1yec8UVVzhbpjZv3szevXuJjIx0tnTVqFGDnJwc9u3bx8mTJzl69Ch9+vRxqz5lvV6NGjUYNWoUffv25aabbuKdd97hWOHrmAD8nExNmTKFrl27EhkZSVxcHAMHDmTXrl2lPmf69OnOVYwL/kLlFychhBCVzfnz0KcPlDa4XNPggQfgxx/lht9TOTnQs6fq1leaf/4T5syR8ysqhdDQUK699lqeffZZVq9ezahRo5gwYYJbz83MzKRLly6kpKS4/O3evZvbb7+dsLAwj+pS1uuBaqlas2YNV155JbNnz6ZFixasXbvW4+OuyvyaTP3yyy+MHTuWtWvXsmzZMvLz87nuuuvIysoq9XlRUVEcO3bM+ReoA+EALBYLiYmJWCwWf1dFBDCJI2EUiSWDnD8PL78MZ8+WXVbX4V//gip2zn0aSwVdJ8tKpAo8+ywEyciGQFWVr0utW7d2ue+9OFFZu3ats2td586d2bNnD3FxcTRr1szlLzo6msjISBo3bszy5cvd2ndZr1egU6dOPPXUU6xevZq2bdsya9YsA4684plMJhISEgyPI78mU4sXL2bUqFG0adOGDh06MH36dA4dOsSGDRtKfZ7JZKJOnTrOv9q1a1dQjY1nMpmIiYkJ2MF8onKQOBJGkVgySEgIFOq6U6atWyElxVe18QufxpLVqsZIuevQIVi+XLUEioBTFa5LZ86coXfv3nz55Zds2bKF1NRU5s6dy6uvvsqAAQOc5ebOncunn37K7t27mTBhAuvWrXPOxjdixAhq1arFgAED+O2330hNTWXFihU8/PDDHDlyBICJEyfyxhtvMHXqVPbs2cPGjRt59913i61TWa+XmprKU089xZo1azh48CBLly5lz549AT1uKioqyvA4qlQ/06SnpwNQo0aNUstlZmbSqFEjNE2jc+fOvPTSS7Rp06bYsrm5uS6zpmRkZABgt9ux2+2AmnLTbDajaRpaoQttwXaHw+EyWK2k7RaLBZPJ5HzdwtuBIrOHWCwW7HY7GzdupGPHjs5yQUFB6LruUt5kMmGxWIrUsaTt/jym4rbLMfn2mHRdJyUlhfbt27v84hLIx1QV36dAOCZN09i8eXORWArkY/LL+7RrF0FpaehmM45CLSImXceSn49mNqNdvP3HH9Hat0cr5rxXimPCs/fJ4XCwZcsWOnTo4DIVsSHHdPw4lmPHMAF2q9W17vn5oOs4Lt6+fDlcdVWR7VUu9qrgMRXEUmJiIrquO/8K6l/chAL+2l6S8PBwLrvsMt566y327dtHfn4+DRo04J577uHpp592vtbEiRP573//y4MPPkh8fDyzZs2iVatW6LpOtWrV+OWXX/i///s/brnlFs6fP0+9evXo06cPkZGR6LrOyJEjsdlsvP322zzxxBPUqlWLwYMHF6mrruuEhYUV+3q9e/cmMjISm83Gzp07mTFjBmfOnCE+Pp4HH3yQe++9t8Rjr2zvR8H2gr/NmzfTuXNngoKCnDF2cWx6yqRXkqlRNE3j5ptvJi0tjZUrV5ZYbs2aNezZs4f27duTnp7O66+/zq+//sr27dupX79+kfITJ05k0qRJRbb/+OOPhIeHAxAbG0vTpk3Zt28fp06dcpapX78+9evXZ8eOHc5EDyAhIYG4uDg2b96MzWZzbk9MTCQmJob169e7XATat2+P1WolOTnZpQ5JSUnYbDZWrlzp/MXFYrHQtWtX0tLS2Llzp7NsWFgYHTp04OTJk+zfv9+5PTo6mlatWnHkyBHnrxL+Pqa8vDy2bNni3CbH5Ptjat68OXv27HF+8VWFY6qK71MgHFN8fDzHjh0jMjKS8+fPV4lj8sv7dP48Xa+7jrSEBHYOH37hmE6fpsNHH3GyY0f29+9/4Zj276dVixYcGTmSIydPVs5j8vB9Koihgpgy9JhsNhLvuYeY/ftZP368S4LU/qOPsGZkkDx+vOsxZWWR969/sWXPnnIfU0DEXhU8poLvtkaNGnHixAkaNGhASEgIZrOZatWqkZ+f7/LDucViISwsjLy8POeU4aCSzNDQUHJyclxuoK1WK1arFZvN5lLHkJAQgoODyc7OdvluDQ0NJSgoiKysLJcb+LCwMMxmc5HhKuHh4Wia5nJeTCYT4eHh2O12cv4azxcZGcl///tfhg4dWmWOCagU71NOTg6HDx/m5MmTXHHFFYSFhTljLysri2uuuYb09HSioqLwVKVJph544AF++OEHVq5cWWxSVJL8/HxatWrF8OHDmTx5cpHHi2uZatCgAWfOnHGeMH+3TCUnJ9O5c2dpmZJjKvcx6brOhg0b6NSpk7RMyTF53TK1cePGIrEUyMfkl/fp5EmC6tXzrGXq44/R7rwTrZg6VopjwvOWqU2bNtG5c2fjW6ays7HExmLKy3O/ZWrKFBg3DofZdYRDlYu9KnhMBbHUpk0bDh8+TJMmTZyTj/m7xcMbF7+G2Wxm/vz5DBo0qMocU2XZnpOTQ2pqKunp6SQlJbm0TGVkZFCzZs1yJ1OVopvfuHHj+Pbbb/n11189SqQAgoOD6dSpE3v37i328ZCQEEJCQopsDwoKIuiiwagFF4KLlTRQraTtF79uadsLZiS0WCwuj5tMpmLLl1RHT7f78phK2i7H5LtjKviSujiOSqt7SdsryzGVVkdPt8sxub+9rFgKxGMqUKHvU40a0KsXphUrCCr0i6uzjpqGufD2iAgYOhSzxVLsYOZKcUx/Kc/75El5t44pKAhuvBHmzy/2/AKu24ODYfRosFqLvfGpUrH3l6p6TIX/Cm8vjr+2e+Li1yj4/6p0TJVhe+HzWvBXEGMlxaC7/DoBha7rjBs3jgULFvDTTz/RpEkTj1/D4XCwdetW4uPjfVBD37NYLEXGJgjhKYkjYRSJJYNYLPDYY+6Xv/NOuOgX+kDn01gKC1ML8rpr8GA1KYgISJfKdUnXdQYOHOjvalRZJpOJli1bGh5Hfk2mxo4dy5dffsmsWbOIjIzk+PHjHD9+3KX/5ciRI3nqqaec///888+zdOlS9u/fz8aNG7njjjs4ePCgc7XoQGS9qCuCEOUhcSSMIrFkgOBguO46ePDBsst26QJvvKFap6oYn8WSyQQdOqgpz8uSmAgff6wSMBGw5LokjBAcHGz4a/o1mZo2bRrp6en06tWL+Ph459/s2bOdZQ4dOuQycPXcuXP8/e9/p1WrVtxwww1kZGSwevVqWrdu7Y9D8JrD4SA5OblIn2EhPCFxJIwisWSgkBB480149VWoVavo41Yr3HUX/Pab+u8qxuexFBoKzzyjpkgvrndKUBAMGQLr1qlEyoDuS8I/5LokjKDrOtu2bTM8jvw6ZsqdQW4rVqxw+f+33nqLt956y0c1EkIIIQwUEgIPPACPPgrz5sHatZCfD82awZgx6oZfWkzKLyQERo6Eu++G776DFSsgNxcaN1bbqlVTSZckUkIIH6kUE1AIIYQQVVZB973Bg6F/f9B11RL112xkwksFyeiNN8K116rzGxxcJVv7hBCVjyRTQgghREUIDlZ/wjcsFtUSJYQbbDYVMkFBYLer+V+kkViUR6VZZ6qiZGRkEB0dXe655I1WsA5EwToMQpSHxJEwisSSMIrEkjBKQSzl5+dz4MABl3WmPGWzQU4OvP++6nmblgYxMarheOxY1WAsSVXVU7DOVIMGDQgPD3e5JnmbG/h1Agqh5JWwRoYQnpA4EkaRWBJGkVgSRjEilnJzVRJVu7aaCDIlBQ4cUP8++6za/v77qpw/mEwmFi5c6J+dXyLy8/MNf01JpvzM4XCwZcsWmaFGeEXiSBhFYkkYRWJJGMWIWLLZ4N13Yfx4NQdMcfLz1ePvvafKG2nUqFFlriF17Ngx+vXrZ+yO3TRx4kQ6duzol31XFF3X2bVrl+HXJEmmhBBCCCFElZaTA08/7V7Zp55S5StKQatbnTp1CJHFpQOOJFNCCFGavDw4fx4yM9Wfpvm7RlVLfv6F83v+vBoFLoxjt1+I3fPn1f8LcYmx2VT3PXd7eOXnwwcfGN86VaBXr16MGzeORx99lFq1atG3b1/AtZtfXl4e48aNIz4+ntDQUBo1asSUKVNKfM2yyqelpXHPPfcQGxtLVFQUvXv3ZvPmzQBMnz6dSZMmsXnzZkwmEyaTienTpwNqvdcBAwYQERFBVFQUQ4YM4cSJE87X3bx5M1dffTWRkZFERUXRpUsXkpOTAThz5gzDhw+nXr16VKtWjXbt2vHVV18ZeSorBZnNrxKwWCz+roKoAiSODJadDWYzfPGFWr8mK0t1qB8zBnr0UDf9VfQXxAqJpexsNY3W7NmwYIG60a9ZU60ZdN116qZfpg4vP5tNnd+FC9U5Tk9Xo+yHD4ebb1Z3ixUwyl6uS8Io3sSSxaImm/DEvHnwxBPl3mWZZsyYwQMPPMCqVauKfXzq1KksWrSIOXPm0LBhQw4fPszhw4dLfL2yyt92222EhYXxww8/EB0dzUcffUSfPn3YvXs3Q4cOZdu2bSxevJgff/wRgOjoaDRNcyZSv/zyC3a7nbFjxzJ06FDnOrAjRoygU6dOTJs2DYvFQkpKCsF/zVqak5NDly5d+Oc//0lUVBTfffcdd955J02bNuWyyy4z6Ex6xifXJP0Sk56ergN6enq6v6sihKiscnJ0/e23dT0iQtfVqjWuf02a6Pr69bqene3vmgYmm03Xp0/X9erViz+/9evr+ooVcn7LKztb1+fN0/XY2OLPb1ycri9aJOdXBBybzab/8ccfus1m8+h5DoeuN25c/MehpL8mTdTzjHLXXXfpAwYM0HVd13v27Kl36tSpSBlAX7Bgga7ruv7QQw/pvXv31jVNc+v1Syv/22+/6VFRUXpOTo7L9qZNm+offfSRruu6PmHCBL1Dhw4ujy9dulS3WCz6oUOHnNu2b9+uA/q6det0Xdf1yMhIffr06W7VUdd1vX///vrjjz/udnmjlBY73uYG0s3Pz3RdJy0tDf3SmqFeGEziyEA5OfD66/Doo6prVHFSU+Gqq+CPP/w37ZOP+DyWbDaYPh1GjYJz54ovc+QIXHMNrF1bsQMXqgKbDb75Bm69FU6dKr7MyZMwYAAsWeK7fkzIdUkYx9tYsttVw6wnYmLc7xZYHl26dCn18VGjRpGSkkLLli15+OGHWbp0qfOx+++/n4iICOdfWeU3b95MZmYmNWvWdHleamoq+/btK7EOO3bsoEGDBjRo0MC5rXXr1sTExLBjxw4AHnvsMe655x6uueYaXn75ZZfXczgcTJ48mXbt2lGjRg0iIiJYsmQJhw4d8uxkGSgjI8Pwa5IkU37mcDjYuXOnzHYkvCJxZKBjx+Bf/yq7XE4ODBmi+o9UIT6PpawsGDeu7HJ2OwwbprqqCfdpmkpUy7pZ0HW4806fVkWuS8Io3saSw6HWkfLE4MG+HSIbHh5e6uOdO3cmNTWVyZMnY7PZGDJkCLfeeisAzz//PCkpKc6/sspnZmYSHx/v8pyUlBR27drF+PHjvTqOiRMnsn37dvr3789PP/1E69atWbBgAQCvvfYa77zzDv/85z/5+eefSUlJoW/fvn5bMkHXdfbv3y+z+QkhhM9kZalWKXft3w8rV8qkFO6y2WDqVPcnmTh5Ev73P5k0wV25ufDJJ+63NmVmwowZapIVIaqwsDC1IO9fQ3nKFBwMDz7o/8V7o6KiGDp0KP/+97+ZPXs28+bN4+zZs8TFxdGsWTPnX1nlO3fuzPHjxwkKCnJ5XrNmzahVqxYAVqu1SJLRqlWrImOv/vjjD9LS0mjdurVzW4sWLfjHP/7B0qVLueWWW/jss88AWLVqFQMGDOCOO+6gQ4cOJCQksHv3bl+eMr+QZEoIIQqEh8OsWZ495z//UZMpiLKFhsLMmZ4957PPqlxXSp/64gvPy8uPAeISEBoKL73kXtmXX/b//DdvvvkmX331FTt37mT37t3MnTuXOnXqEFNCf8XSyl9zzTVcccUVDBw4kKVLl3LgwAFWr17NM88845x5r3HjxqSmppKSksLp06fJzc3lmmuuoV27dowYMYKNGzeybt06Ro4cSc+ePUlKSsJmszFu3DhWrFjBwYMHWbVqFevXr6dVq1YANG/enGXLlrF69Wp27NjBfffd5zITYFUhyZSfmUwmwsLCMJlM/q6KCGASRwZxOCAtzbPnnDzpk6r4i09jyWQqeRxPSU6eVLMqirJZreU7v+7+XO8huS4JoxgRS2Fh8NBDqvNBSSEfHKweHzvW/61SkZGRvPrqqyQlJdG1a1cOHDjA999/j7mE62Fp5U0mE99//z09evRg9OjRtGjRgmHDhnHw4EFq164NwODBg7n++uu5+uqriY2N5auvvsJkMvG///2P6tWr06NHD6655hoSEhKYPXs2oGbGO3PmDCNHjqRFixYMGTKEfv36MWnSJAD+9a9/0blzZ/r27UuvXr2oU6dOmQsX+1poaKjh1ySTfomNDM3IyCA6Opr09HSioqL8XR0hRGUTEuJZt6dBg1RXqchI39WpKomPh+PH3S/fqxd8+61qNRSly8+HDh3gr4HhbunQAX7/vcpO8y+qlpycHFJTU2nSpAmh5Ww6stnUkNcPPlDTn6elqckmBg9WXftCQ/2fSAnjlRY73uYG8nOfn2maxsmTJ9Gkm4XwgsSRQWw2tcaRJ268sUrdiPo0lrKzPT+/N9xQ5Sb58Jm8PM/P7/XX+2yhZLkuCaMYGUthYVC9Ojz2GKxZA3v3qn8fe0xtl0Sq6tJ1nTNnzhh+TZJkys80TWP//v3yZSO8InFkkJAQePxx98tHRcGIEap7VRXh01iqVs2zVTBDQuC++/w/eCFQhIer+HW3C4vZrJYAqFbNJ9WR65Iwii9iKSxMXWLMZvWvJFGXhsOHD0syJYQQPmM2Q7du0Leve+Vfeslnv+pXWS1bwtCh7pV95hmfjeepsmJj4f773Ss7bpzni+8IIYRwIcmUEEIUFhwMCxeq7k8lMZnUdE/33OOzX/WrLKsVPv9cLSpbmv/7P/jnP+XnYk+FhsLbb8OYMaWXu+8+NdJeWv2EEMIrkkz5mclkIjo6WmY7El6RODJYaCgsWgS//AI333xhTFTNmvDww5Caqv6tQmOlClRILFmtagr6devUwscFCWlMDNx7L+zeDRMmVKnukxXKaoVp02DzZrUwb0SE2h4ZCXfdBVu3wjvv+LzVT65LwigSS8IokZGRMpuft2Q2PyGE2zRNrXEUFqb+22xWkyhIa5RxCs6nnF/fkPMrqhAjZvMTlyaZza8K0zSNI0eOyABd4RWJIx8xmy90MytY26OK34hWeCwVnM9L5PxWOD+eX7kuCaNILAkj6LrO8ePHZQKKqkYuEMIIEkfCKBJLwigSS8IoPokluw0ceaBr6l+7zbjXFpWWL5KpIENfTQghhBBCiMrKbgMtB3a/D4fnQV4aWGOgwWBoMRbMoRAkE98I90nLlBBCCCGEqPocubDnfZhfG7Y8C+dSIOuA+nfLs2r7nvdVOT8wmUwsXLjQL/sur8aNG/P2229X2terCNIy5Wdms5nY2FjMZslrRflJHAmjSCwJo0gsCaMYEkt2m0qUNo0vuYyW/9fjJmj+oKEtVKNGjSItLa3UZOnYsWNUr17dsH1WhPXr1xMeHu7varitRo0ahl+T5ArnZ2azmaZNm8qXjfCKxJEwisSSMIrEkjCKIbHkyIHNT7tXdvNTqitgBcnLywOgTp06hFSiJTcK6lWa2NhYqlWiiYNKq7PJZKJhw4aSTFU1mqaxb98+GaArvFKhcZSTA3a7WsNm6VJYvVpty872/b6FbzgcYLPBiRNoy5ax7/vv0Q4cUNPC5+f7br/Z2ZCXB2vXqljauFHFls2HA8FtNnVMO3bA4sXw88+QkQGZmb7bp79kZ6tjXbcOliyB9evV+a2gz6p8vwmjeB1LBa1SmpvXMy0fdn/gs0kpevXqxbhx43j00UepVasWffv2BVy7+eXl5TFu3Dji4+MJDQ2lUaNGTJkypdjX2717NyaTiZ07d7psf+utt2jatKnz/7dt20a/fv2IiIigdu3a3HnnnZw+fbrUeum6zsSJE2nYsCEhISHUrVuXhx9+2Pmci7vlpaWlcd9991G7dm1CQ0Np27Yt3377rfPxefPm0aZNG0JCQmjcuDFvvPFGqefq0KFDDBgwgIiICKKiohgyZAgnTpxwPj5x4kQ6duzIJ598UuaU+bquc+jQIZnNr6rRNI1Tp07Jl43wSoXEka6rG9/334cWLaBjR+jbF7p1g7g4GD8eTp1SZUTgyM5WN9m33QZ166LdeCOnVq9Ga9kSrr1WJTk5Bv9Cm58P587Bv/4FderAFVeoWOrSBZo0gTffVPs0Op7z8tRiwV26QOvW0K8f9O6t4veBB2DvXsjKMnaf/mC3q+TwhRegXj3429/g+uvhssugfn146SV1nA6HT6sh32/CKF7HksmiJpvwxOF5YPLdbfKMGTOwWq2sWrWKDz/8sMjjU6dOZdGiRcyZM4ddu3Yxc+ZMGjduXOxrtWjRgqSkJGbOnOmyfebMmdx+++2ASnJ69+5Np06dSE5OZvHixZw4cYIhQ4aUWq958+bx1ltv8dFHH7Fnzx4WLlxIu3btiq2Hpmn069ePVatW8eWXX/LHH3/w8ssvY7FYANiwYQNDhgxh2LBhbN26lYkTJ/Lss88yffr0El9vwIABnD17ll9++YVly5axf/9+hg4d6lJu7969zJs3j/nz55OSklLsaxU4e/aszOYnhPCT3Fy47jr47beij50/Dx98APPmwcqV0LAhWK0VX0fhmexsmDMH7r67+MTlt9/U33PPwVNPgRGLZNrtcOKESsIPHSr6+JEjKslasAB++QWM6ouflwd33QX//W/Rx3Jz4csv1T6//x6SkgJ3vSuHA9LS1Pndvbvo4ydOwIsvwvz5sGoVREdfWINKiKrKHKRm7fNEXhqYg31RGwCaN2/Oq6++WuLjhw4donnz5nTv3h2TyUSjRo1Kfb0RI0bw3nvvMXnyZEC1Vm3YsIEvv/wSgPfee49OnTrx0ksvOZ/z6aef0qBBA3bv3k2LFi2Krdd3331HnTp1uOaaawgODqZhw4Zcdtllxdbhxx9/ZN26dezYscP5egkJCc7H33zzTfr06cOzzz4LqCTwjz/+4LXXXmPUqFFFXm/58uVs3bqV1NRUGjRoAMDnn39OmzZtWL9+PV27dgVUK97nn39ObGxsqefIV+QKKoQom80Go0cXn0gVduIEXH21asUSlZvDAVu2wD33lN0C9PzzKtEwqoWqd+/iE6nCNmxQrWW5Bsyqdf68OobiEqnCsrLgxhtVt79Apeuqla+4RKqwHTtUy5y0GolLgWZX0597whrjfrfAcujSpUupj48aNYqUlBRatmzJww8/zNKlS52P3X///URERDj/AIYNG8aBAwdYu3YtoFqlOnfuTGJiIgCbN2/m559/dnlewWP79u0rsV633XYbNpuNhIQE/v73v7NgwQLsdnuxdU5JSaF+/frOROpiO3bsoFu3bi7bunXrxp49e3AU01K+Y8cOGjRo4EykAFq3bk1MTAw7duxwbmvUqJHfEimQZMrvzGYz9evXlwG6wis+j6P0dNWC4Y4jR2DmTOnuV9nl5qoWiou+wMwOB/V/+w3zxV9sU6bAX101ys3hgG+/hT173Cv/ww9w+LB3+wTVSvree+6VPX9edTM8f977/VY0TYM1a9TYM3f8/rtKWn3044d8vwmjeB1LukOtI+WJBoPVgr4+UtYMeJ07dyY1NZXJkydjs9kYMmQIt956KwDPP/88KSkpzj9Qk1f07t2bWbNmATBr1ixGjBjhfL3MzExuuukml+elpKSwZ88eevToUWK9GjRowK5du/jggw8ICwvjwQcfpEePHuQXM542LMw/63N5MptgnTp1ZAKKqka+bIQRfBpH2dkwdapnv2C/+y6YTMbXRRgnK0t1abuI2eGg/q+/Fk2mtm5VLVnesNvhnXc8e86bb3o3jik3V42TSk93/zmffgp+uinwSk4OvPWWZ8956y2fTUgh32/CKF7HUlAYNB/rfrc9czC0MHZq9PKIiopi6NCh/Pvf/2b27NnMmzePs2fPEhcXR7NmzZx/BUaMGMHs2bNZs2YN+/fvZ9iwYc7HOnfuzPbt22ncuLHLc5s1a1ZmMhIWFsZNN93E1KlTWbFiBWvWrGHr1q1FyrVv354jR46wu4SW8VatWrFq1SqXbatWraJFixbOcVUXlz98+DCHC/2o9scff5CWlkbr1q1LrXNxTCaTJFNVkcPhYMeOHcU2bwrhLp/GkcMBZQzoLGLrVgj2XV9zYYDt24tNkB3BwewYPhxHce/f+vXe7TMkRM0C6YnNm71LzHNy3G+pKXDmjPoLNOU9vz4a3yjfb8IohsSSJRQ6vFR2OYAOL4PZgDGiXnjzzTf56quv2LlzJ7t372bu3LnUqVOHmJiYEp9zyy23cP78eR544AGuvvpq6tat63xs7NixnD17luHDh7N+/Xr27dvHkiVLGD16dKnndfr06fznP/9h27Zt7N+/ny+//JKwsLBix3D17NmTHj16MHjwYJYtW0Zqaio//PADixcvBuDxxx9n+fLlTJ48md27dzNjxgzee+89nnjiiWL3fc0119CuXTtGjBjBxo0bWbduHSNHjqRnz54kJSW5eSYv0HWdffv2GX5NkmTKz3RdJz09HV3GmAgv+DSOTCbPuwFJPFd+JbQ06iYT6QkJ6MUlMEZ8AXkaG5rmfStneeIxUMcSVaLPqny/CaMYEktBYdDiIej0esktVOZg9XiLsX5vlYqMjOTVV18lKSmJrl27cuDAAb7//vtSW1UiIyO56aab2Lx5s0sXP4C6deuyatUqHA4H1113He3atePRRx8lJiam1NeMiYnh3//+N926daN9+/b8+OOPfPPNN9SsWbPY8vPmzaNr164MHz6c1q1b8+STTzqTl86dOzNnzhz++9//0rZtW5577jmef/75YiefANWS9L///Y/q1avTo0cPrrnmGhISEpg9e3YZZ69k58+fN/yaZNIvsatcRkYG0dHRpKenExUV5e/qYLfbSU5OJikpiaAgmVxRlI9P4ygrC55+WnX1c1fLlqrlw9sxNsJ3jhyBQoN6C9itVpLHjyfptdcIunjc2y+/QKG+9R7Lz4fu3dW6R+666y41HX95Z/XLyYFPPoGHHnL/OZGRcPp04M1IabPBoEFqTSl33XwzfPWVT2YvlO83YZSCWGrbti2HDx8ucz2h0l/Mphbk3f2Bmv48L01NNtFgsOraZw71eyIljJeTk8P+/fvJyMgock3yNjeQlikhROnCw+HRRz17zoMPqvExovKqVUvNvOiuhAS48krv9ztunGflH33Uu+nRQ0PVTJSejIG6805jZhGsaCEhUGgxTbc88ogxU94LESiCwsBaHRIfg+vWwM171b+Jj6ntkkgJD0ky5Wdms5mEhAQZoCu84vM4qltXTbfsjurVYcwYdWMnKq+gIPjnP4tsNtvtJHz3HeaLk+HHHvN+hsbgYBg6FOLj3Sv/t79Bmzbe7RNUV7bRo90ra7XC44+r1qlAYzarz2mhAemlSkxULY0+um7I95swik9iKSgMLCFqYV5LiCRRl4gGDRrIBBRVjdlsJi4uTr5shFd8HkdWq5oavazZcyIiYPFimXwiEAQFqRvpF15w2WzWNOJSUjAXHjM0ejT8/e/GdQVbtkwl3aVJSIBvvjHmRj8iQs0KWFZLXFCQWry3dm3v9+kvmgZLl5Z9DPHxqpwPyfebMIrEkjCCyWSiZs2akkxVNQ6Hg82bN8tsR8IrPo8jk0l1tVq3To09ubhPscUCAwZAcjK0by+tUoEiLEy1OM2ZAx06AGo2v8333adm82vaVI1X+vBD48YPWa2q5WTTJrUo78WJd3g43HuvmoEvJsa4cXchISrRf+654hONXr1gxQq44QbvuhX6W3Aw1KunZuC8446in8XQUDUOLSVFnQcfjmWS7zdhFIklYQRd19m5c6fhcSQjQv1M13VsNpvMdiS8UiFxZLGom8wpU+C119SN6Z9/qsSqb1/1639IiM+6DAkfCQuDm26CgQNh+3b05GRskZHod9wBl1+uuvYZPRFDSAg0bAjTp6uxSYsXw7lzUKcO9OunuuX5YEIErFZ44gl49ln48UfYv18df8+eqiur2Rx4k04Ux2pV5/Kjj+CDD9R6YmfOqHFyN9ygjtMX5/ci8v0mjCKxJIySk5NjeBxJMiWE8EzBr/YDBvi3HsI4BRMQdOwIbduqFsakJNVq4auWC5NJ3dBXqwbDh/tmH8UpGAt1/fUVt09/KUiYhg71bz2EEKIKk5+QhRBCCCGEEKIcJJnyM4vFQmJiIhZZj0d4QeJIGEViSRhFYkkYxSexZLOpbsyapv612Yx7bVEpmUwmEhISDL8mSTLlZyaTiZiYGEwmk7+rIgKYxJEwisSSMIrEkjCKobFks6kxmm+8oZZfaNpU/fvGG2q7JFVVWlRUlOHXJEmm/Mxut7N+/XrsssCp8ILEkTCKxJIwisSSMIphsZSbq2YorV1bTUSTkgIHDqh/n31WbX//fb8t2m0ymVi4cKFPXnvFihWYTCbS0tK8fi1P6zl9+nRiYmK83q+3dF1n69athl+TZAKKSkCm+hRGkDgSRpFYEkaRWBJG8TqWbDaVKI0fX3KZ/Hz1uMkEDz6oZvs0yKhRo0hLSys1CTl27BjVy1qDr5yuvPJKjh07RnR0tNev5Wk9hw4dyg033OD1fo3gi2uStEwJIURpsrKg4OKbnw85Of6tj69oGmRnq39B/TIrN8JCVD52+4Xr0MWfW1GynBx4+mn3yj71VIVe6/Py8gCoU6cOIT5ap9FqtVKnTp0Su7g5HA40N+PI03qGhYURFxfndvlAI8mUEEIUJzcXUlPhySchMVEthHrZZeqXzfPnq06/eodD3Zz98gsMGwatW6s1mAYPhm+/VY/99UUvhPCjvDz1eVy0SK0X1qABJCTAiBGwcqV6TJKq4hW0SuXnu1c+P1+t0eaj63yvXr0YN24cjz76KLVq1aJv376Aa/e5vLw8xo0bR3x8PKGhoTRq1IgpU6YU+3q7d+/GZDKxc+dOl+1vvfUWTZs2BYp28yvoerdo0SJat25NSEgIhw4d4tixY/Tv35+wsDCaNGnCrFmzaNy4MW+//bbzdQvX88CBA5hMJubPn8/VV19NtWrV6NChA2vWrHGWL66b3zfffEPXrl0JDQ2lVq1aDBo0yPnYF198QVJSEpGRkdSpU4fbb7+dkydPenqaK4wkU35msVho3769zHYkvCJxZLC8PNXFIyFBfaHu3QtHj6p+9U88AXFxsHBh4CdUdrsacN21K/TuDd98g2X/ftq/8w6WpUvVQr7NmsGhQ34bQyACl1yXDFTw405Cgvqh4+ef4cgROHhQXYt69lSLbKelVckWZa9jyWKBefM8e868eT5dhH7GjBlYrVZWrVrFhx9+WOTxqVOnsmjRIubMmcOuXbuYOXMmjRs3Lva1WrRoQVJSEjNnznTZPnPmTG6//fYS65Cdnc0rr7zCJ598wvbt24mLi2PkyJEcPXqUFStWMG/ePD7++GO3EplnnnmGJ554gpSUFFq0aMHw4cNLHJv03XffMWjQIG644QY2bdrE8uXLueyyy5yP5+fnM3nyZDZv3szChQs5cOAAo0aNKrMOZTGZTLRs2dLwa5KMmaoErFarv6sgqgCJI4Pk5cE998AXX5RcJidH/Ro8d676hdjAfvUVKicHunWD3bsvbNN1rBkZULBC/MGDcOWVsGmTap0TwgNyXTKArsOJE+pzePZsyeU2bIDu3dWi2wULNlchVquVfHdbli4WFKQSTU+kpUFwcPn254bmzZvz6quvlvj4oUOHaN68Od27d8dkMtGoUaNSX2/EiBG89957TJ48GVCtVRs2bODLL78s8Tn5+fl88MEHdOjQAYCdO3fy448/sn79epKSkgD45JNPaN68eZnH88QTT9C/f38AJk2aRJs2bdi7dy+JiYlFyr744osMGzaMSZMmObcV1AFgzJgxzv9OSEhg6tSpdO3alczMTCIiIsqsS2mCffCeSsuUnzkcDpKTk2WQrvCKxJGBtm4tPZEqoOvwwAPqSzoQZWfDSy+5JlKAw2olefx4HIVvgk+dUi1y2dkVXEkRyOS6ZBCbDf7xj9ITqQI7dsDrrwd+q/lFvI4lux08nU0uJsb9boHl0KVLl1IfHzVqFCkpKbRs2ZKHH36YpUuXOh+7//77iYiIcP4BDBs2jAMHDrB27VpAtUp17ty52GSmgNVqpX379s7/37VrF0FBQXTu3Nm5rVmzZm5NNlH4deLj4wFKbNFKSUmhT58+Jb7Whg0buOmmm2jYsCGRkZH07NkTUAmmN3RdZ9u2bYZfkySZEkKIAtnZ6kbEXadOwf/+p76oA01wMHzyifvl58+XsVNC+IPNpsZJueujj3zaohKQHA7VPdITgwf7dAxaeHh4qY937tyZ1NRUJk+ejM1mY8iQIdx6660APP/886SkpDj/QE0K0bt3b2bNmgXArFmzGDFiRKn7CAsLM2zNpcItPgWvWdKEFmGl9ObIysqib9++REVFMXPmTNavX8+CBQuACxN1VDaSTAkhRIFq1VRy5InZswPzV+DNm1Uy6K68PDUhhRCiYnn6g83Ro/DHH76rTyAKC4OxY91PMoODDZ8avTyioqIYOnQo//73v5k9ezbz5s3j7NmzxMXF0axZM+dfgREjRjB79mzWrFnD/v37GTZsmEf7a9myJXa7nU2bNjm37d27l3Pnzhl2TKBasZYvX17sYzt37uTMmTO8/PLLXHXVVSQmJlbqySdAkikhhLjA4fA8McrIUGuSBJryLNzoTjcjIYRxNA3OnPH8eenpxtcl0IWGqq7N7nj5ZVXej958802++uordu7cye7du5k7dy516tQpdfHbW265hfPnz/PAAw9w9dVXU7duXY/2mZiYyDXXXMO9997LunXr2LRpE/fee6+hLVgAEyZM4KuvvmLChAns2LGDrVu38sorrwDQsGFDrFYr7777Lvv372fRokXOcWCVlSRTfmaxWEhKSpLZjoRXJI4MYrFAGV0viqhe/cJkDYGkRo1iN1vy8kh67TUsxXWnqMLrhAjjyXXJAGYzxMZ6/rwSPt+BypBYCguDhx5SXblLaqEKDlaPjx3r91apyMhIXn31VZKSkujatSsHDhzg+++/x1zKDIORkZHcdNNNbN68ucwufiX5/PPPqV27Nj169GDQoEH8/e9/JzIyklADk8tevXoxd+5cFi1aRMeOHenduzfr1q0DIDY2lunTpzN37lxat27Nyy+/zOuedL8vhclkom3btoZfk0y6Hoh3AeWXkZFBdHQ06enpREVF+bs66LqOzWYzPOsXlxaJI4NkZ6tJJT7/3P3nFKz5Emg3jHY7NGqkugQVoptM2GrWJOzMGUyFvx5CQ+HkSYiMrOCKikAl1yWDpKVB7druj1ls1Aj27Qu8a1IpCmLJZDJx4MABmjRpUv6be5tNzWT6wQdq+vO0NDXZxODBqmtfaKjfE6nK5MiRIzRo0IAff/yx1EkjKrucnBxSU1OdrXuFr0ne5gbSMuVnDoeDLVu2yGxHwisSRwapVk3NWueu+Hjo1y8wb1ry8+Hee4tsdgQHs+W++3Bc/MvtkCGBeZzCb+S6ZBCrFW65xf3yDzzg01no/MHQWAoLUz0KHnsM1qxR6wiuWaP+v3r1Sz6R+umnn1i0aBGpqamsXr2aYcOG0bhxY3r06OHvqnlN13V27dols/kJIYRPtWihbkbKYjbDZ58F5kx+oG4YnnwSCk1nW6K6dVXXlyq4do0QlV61avD221CnTtllO3WCRx7x+3ifgBAWBiEh6loeEnLJJ1EF8vPzefrpp2nTpg2DBg0iNjaWFStW+GR9pqpCkikhhCgsJETduDz2mPqSLU50tOre16NHYN+0WK3w669w1VUll2nTBtatU8cshPCP6tXh99+hlDWDuPpq+OUX9bkWopz69u3Ltm3byM7O5sSJEyxYsKDMBYMvdZJMVQIyOFcYQeLIQFYrvPgiHDsGTz8NnTurm5gePeDTT+HECejdO/B/ybRY1Bio5cth0ya44w5o2xZLaCjcfDOsWAEpKWq8htygiXKQ65JBrFbVrXjrVvjpJxg4UP3Q0b49jBwJW7bAkiUQEVHyj0ABTmJJGMEXcSQTUAghRGmys9XNicWiuvRZLFU3scjKujDLVX6+Shar6I2ZEAFL09QkCsHBaiZRu93zWUgDVMEkAo0bNy514VchLmaz2UqcvEQmoAhwuq6TlpbGJZbTCoNJHPlQtWqqK19wsEouqmoiBRAejh4cTFp2Nnq1apJICa/IdclHzGaVPFmtqlvyJZBIFcRSUFAQANnZ2X6ukQg0eX/NhpmVlWX4NSnI0FcTHnM4HOzcuZOkpCTnRUIIT0kcCaNILAmjSCwJoxSOpZiYGE6ePAlAtWrVZNp9USZN0zh16hShoaHs2bPH8GuSXN2EEEIIIURAqPPXrIYFCZUQ7jCbzdSrV48TJ04Y/tqSTAkhhBBCiIBgMpmIj48nLi6O/Cq2npbwHavViqZpPnltSab8zGQyyerwwmsSR8IoEkvCKBJLwijFxZLFYpEZ/oRHdF33yTVJZvMTQoiy6Do4HHApjPuw29XxVuQCjZfS+dU0dbxyEyiEEJWCt7nBJfDNVblpmsbp06epVasWZpk5S5STxJEPOBwqsTh6VC3Qm5kJdevCrbeqG+Fq1fxdQ+NkZalZwVauRFu3jtNhYdRq3Bhzv36Qk6PWrjGaw6GmXz9zBubPh4wMiIuD225Ts5RVpfObm6tiZsMGtUiy3Q6tWkH//uocVKVjLUSuS8IoEkvCCL6KI0mm/EzTNPbv30+NGjXkAiHKTeLIYDYb7NgBjz0Gv/zi+tiDD8LQofDOO+omuCJbcHwhNxf++1+YMgX27UOzWtk/fjw1nngCc40a8NBD8MQTxk4Jn5MDBw7AI4/AsmWqpabA2LFwyy3w7rsQHR34U9Hn5KhkfOJEFVOF1aoF998Pzz4b+MdZDLkuCaNILAkj+CqOJCKFEKIwmw3WrIErryyaSIG6OZ4xAzp3hvPnVbetQJWTo27k77kH9u0r+vjx4/DMM3DHHfDXGh2G7HPbNkhKgqVLXRMpUC01s2dDx45w+rRqxQlUOTkq6R46tGgiBer4XngBbr7ZuPMrhBCiQkkyJYQQheXlqZvb3NzSy+3fD4MHq5v/QJSXB0uWwGuvlV127lyYOlV1dTTC9derroWlOXpUdYMLVJqmuvX93/+VXXbJEpgwAWQhUiGECDiSTPmZyWQiOjpaZjsSXpE4MkhODkybVvaNfoEVK+DQIZ9WyWd0vdhEyqTrRO/fj+niFqN334XQUO/2mZcHX36pxkm5IyVFJSSBOE9Sbi68/LL75T/+uMpNwCHXJWEUiSVhBF/FkczmJ4QQBTQNmjZV43ncNW6cumkOD/dZtXzi4EFo3Niz5yxfDr17l3+f+flw+eWwcaP7zxkxAj780DeTYPjSuXNqTJQn3UDnzFETcAghhKgw3uYG0jLlZ5qmceTIEZ8tJCYuDRJHBjp40LPy+/YFZsvJ3r3FbtYsFo706IFW3NTdxY378URwcPFjs0qzb19gTiN++LDn4+l27KhSY6fkuiSMIrEkjOCrOJJkys/kAiGMIHFkELMZPG3+t1gCM5kqIUHRLBaOXHVV8cmUEd3QPE2Mqtj5LfM5VWimMrkuCaNILAkjSDIlhBC+lpcHnTp59pyuXQNzrEv79p7X+4orvNtnTg506eLZc7p0CcxkqkkTz9eP6t49MGNJCCEuYZJMCSFEAZNJrX3krqAgtS5SWJjv6uQrISFq1kJ3demiFpr1htUK//iHZ8957LHAG48GKgG8/Xb3yyckQLduvquPEEIIn5Bkys/MZjOxsbGyCJ3wisSRQYKD1ZpATZq4V/6uuzxvfagswsLUGlMXLTps1jRiU1IwX9wN4tlnweHwbp9mM1x3HbRr5175W26BOnW826e/hIfD00+7Hx/PPhvYa2oVQ65LwigSS8IIvoojmc1PCCEKs9vVGkfdu6tJBErSrx8sWKBaeAJVVhb88INqQSlpvSyTCV5/HR54wJgWOIcDzp5V53f37pLLXXWVWn8pEFv9CthssHo13HST+u+S/OtfanFkb6eeF0II4TGZzS/AaZrGvn37ZFCl8IrEkYGCgiA+HrZsgaeegthY18fbt4f//AcWLgzsRApU60m/fmqq8ttvB6sVLSiIff37o1mtcOON8OuvcP/9xiU1FgtUr672+fzzULeu6+MtW8J778GPPwZ+chEWBldeCdu2wejRrufQZIJrr1XHWUUTKbkuCaNILAkj+CqOpGXKz+x2O8nJySQlJREkA49FOUkc+Uh2tuoGt2uX+u/YWKhfX42HsVr9XTtjZWWBw4F91y6Sz5whqXNngiIiVMLoq6nJbTZ1fvfsgfPnoUYNtfaVwxH4ierFsrNV3Ozdq1oBGzaEqCgVR1W065Jcl4RRJJaEEUqKI29zA4lIIYQoScF4l7Zt/VuPilAwyUOnTpCcrBIbX9+0FLTUXDyxRVW8WSqIpQ4d/FsPIYQQhqqaP4cJIYQQQgghhI9JMuVnZrOZ+vXryww1wisSR8IoEkvCKBJLwigSS8IIvoojGTMlhBBCCCGEuCTJbH4BzuFwsGPHDhzert8iLmkSR8IoEkvCKBJLwigSS8IIvoojSab8TNd10tPTucQaCIXBJI6EUSSWhFEkloRRJJaEEXwVR5JMCRHI7HbIy1P/OhygaWoKZmGM/HzIyVH/ZmWp85uZ6e9a+UZOjoqh3Fx1nJpW+kKzgazgM5KdrT4/+fklL1osRGVjs6lp9m029Xkt+NwKIfxCkikhApGmqZu/+fOhZ0+IjoYffoA6deAf/1Dr9uTk+LuWgavg/H77LVx/vVoLKCJCTR8+dqxa0LeqnN/cXJUgTpumFsytUQO+/x6aN4dXXoEzZ6rOsdpscPAg/POf6jMTHq7Ws7r8cvjvf1ViJd2IRGVls8GpU/DSS2ph8WrV1GLPrVrBRx+pH3zy8vxdSyEuOTIBhZ9pmsbp06epVauWzFIj3KNpkJEBffrAxo1qk9nM6fbtqbVlC2ZNA5NJ3Qg/9JD6shXuK2iRuf56WLmy5HL/938wcWJgLy6bm6sS7z594ORJoJhYCg+H//0PrrzywrpQgSgnB774Au6/X73HxWnVClasgJo1fbdQ8SVEvt8MZLPBr7/CoEEltxjXqQM//QQJCYF9XSqGxJIwQklxFNATUEyZMoWuXbsSGRlJXFwcAwcOZNeuXWU+b+7cuSQmJhIaGkq7du34/vvvK6C2vmE2m4mLi5OLg3CfwwG9ezsTKQCzphGXkqJufkF1AXnySZg1q+p21fIVTYN+/UpPpABefhneflv9Ghyozp6FXr2ciRQUE0tZWdC/P+zeXXISUtnl5MB338G995Z+DDt2QI8e8uu+QeT7zSAOB/zxB9x8c+nX8+PHVU+F9PSKq1sFkVgSRvBVHPk1Kn/55RfGjh3L2rVrWbZsGfn5+Vx33XVklXJzsnr1aoYPH87dd9/Npk2bGDhwIAMHDmTbtm0VWHPjOBwONm/eLDPUCPfk58PcubBpk8tmR3Awm++7D0dwsGv5//s/CAqqwAoGOIcDli2D335zr/zkyRCoX+5ZWTBpkurGV0ixsZSbq5LzQE0ygoLgiSfcK7trF3z6qYxBMYB8vxnEblfx687n79QpeOGFKjd2VmJJGMFXceTXu4DFixczatQo2rRpQ4cOHZg+fTqHDh1iw4YNJT7nnXfe4frrr2f8+PG0atWKyZMn07lzZ957770KrLlxdF3HZrPJDDXCPbquWkMu3mwyYatVC91kcn3g1CnVRctur5j6Bbq8vGLPb4mysuCzzwKz9c9shpkzi2wuMZaWLSuSeAUETYNffoEDB9x/zrvvSjc/A8j3m0GOH1fdT931+edVLn4lloQRfBVHleon6/S/mqZr1KhRYpk1a9bw2GOPuWzr27cvCxcuLLZ8bm4uuYV+YczIyADAbrdj/+sG02w2Yzab0TQNrVAXkILtDofD5cSXtN1isWAymZyvW3g7UCQTtlgs6LqOrusujwUFBRXZZjKZsFgsRepY0nZ/HlNx2+WYDDomux3z+vU4goNdbnYL/tthtbrWPT8ffvgBx7XXqrEvlfGYKtP7ZLVi+flnNIsFrdDNiFnTMNvtaEFBaIVaoswOB+Zly3AMG4ZeqCWnUh1TSe/Tpk2YMzOLHFPBcV/cymm22zEvXYpj5MjKe0zFxZ7Nhvn77zH/dUyFPzdmux2zphXZbtmzB1NGBvaL+s5XmmMiMD5PBWU0TXPZbyAfk1/epx9+wGIyga4Xvcbn5YHJ5Pp5tdkI2r4dvVOnyntMePY+Ffy3rusurxPIxwQBEHtV7JgKHr/43vviY/ZUpUmmNE3j0UcfpVu3brRt27bEcsePH6d27dou22rXrs3x48eLLT9lyhQmTZpUZPumTZsI/+vmMjY2lqZNm5KamsqpU6ecZerXr0/9+vXZvXu3M9EDSEhIIC4ujm3btmEr9It0YmIiMTExbNq0ySWI2rdvj9VqJTk52aUOSUlJ5OTkkJaWxsaNG51B1bVrV9LT09m5c6ezbFhYGB06dOD06dPs37/fuT06OppWrVpx9OhRjhw54tzuz2PKy8tjy5Ytzm1yTAYeU506xAHbxozBVquWc3vzr78GYPPYsWiFuvW1/+gjrHl5JG/d6tLdr1IdU2V6n6KiaJWfz9EePThy1VUXjiklhabffUdq376c6tjxwjH99hv1bTZ2Hz5MeqG6V6pjKul9ysigKRQ5pvjVqwHYO2gQ5xs1unBM331HXGZm5T6m4mLP4aB+dDT1gd233kp6QoLrMaWkFPk8JX71FTE5OWzat69yHhOB8XmKjIwE4NixYxw7dqxKHJNf3qfISNrXrIk1I4Pk8eNdj+m118iLimLLffddOKa8PLpmZ1fuY8Kz96lgjEtGRgZ79uypEscUELFXxY5J13XS0tLIyckhLCzMeUylDS9yR6WZze+BBx7ghx9+YOXKldSvX7/EclarlRkzZjB8+HDntg8++IBJkyZx4sSJIuWLa5lq0KABZ86ccc7Y4e+WqXPnzhEVFYXpr19GK3NW784xFbddjsmgY9J1zFFRanvhX9g1jYyGDQk/ehRTodex5OfDo4/imDTJZSa2SnVMle19ql0bLS3N/ZapO+7A8eab6IVaMSrdMRX3Pu3ahblt2yLHZNJ1zjdoQMSff6pupQXl7XbM8+fjuPHGyntMxcVeXh7m11/HPGGC+y1Tuo4pLQ37xa0AleWYCIzPE0BmZiaRkZHF1jEQj8kv79P8+ViGDXO/ZQpUy1TTppX3mPDsfdJ1naysLKKiotyqeyAcEwRA7FWxY9J1nYyMDKpXr47JZHLWMSMjg5o1a5Z7Nr9KkUyNGzeO//3vf/z66680adKk1LINGzbkscce49FHH3VumzBhAgsXLmTz5s1l7quyTY0uhEeys+HBB2HGDPefs3OnWj9IlC0zE5591rNxU7/9pqYNNwfYRBR2O7Rrp+LDHTExcOKEWnMr0Pz5JzRo4JIclmrAAPjqq8CeCl5UHTk5EBcH58+7V75dO9iwAS6ekEgIUayAnhpd13XGjRvHggUL+Omnn8pMpACuuOIKli9f7rJt2bJlXHHFFb6qpk/Z7XbWr1/vdX9NcYmoVg0u6uYBYLdaWT9+fJFf0rniCnDjcyX+EhEBjz3mfmKUmKgWfA20RArUzIUPP1xkc4mxNHp04E5kUrOmWjfMXU88IeuzGUC+3wyiaXDXXe6Xf/RR9384CBASS8IIvoojv94BjB07li+//JJZs2YRGRnJ8ePHOX78uEt/x5EjR/LUU085//+RRx5h8eLFvPHGG+zcuZOJEyeSnJzMuHHj/HEIhri4iVSIUjVtChMmFNl8cfcPatZUv64H4o2+P9WsCW++WXa5iAj4+ms1XX0gCgmBu+9Wa2pdpEgsdeoEL76okvlAZLXC9OlQt27ZZR9+GLp2VQtfC6/J95sBqlVTi7C3a1d22ZtvhjvuCMwW5DJILAkj+CKO/HqXNW3aNNLT0+nVqxfx8fHOv9mzZzvLHDp0yGXg6pVXXsmsWbP4+OOP6dChA19//TULFy4sddIKIaqU0FB46imYOlV1vSpO166QnAx16sg6U56qVg3uu0+tNRQbW3yZdu1g7Vpo3Diwu4JZrbBwIdx/f/E3X2YzDB6sFjAOCanw6hnGbFaflQ0boNDEIi4iI+Gll+C11wL7WEXVFBICq1fDwIHF/0AWEgJjx6p1CKtgIiVEZVYpxkxVpMo2Zsput5OcnExSUhJBctMrPJGdrRKlOXOw//gjyV27krRnD0H33gvNmqkvXImp8svMVDco8+fDd9+p8x0XB2PGQIcOqptcVekKlp2tWtg+/hj75s0kX3klSSdOEPTggxAVFdgJY2EOh+qqePgwfPCB+jckBHr1Ur/ma1rgtr5VQvL9ZjBdV4tJnzun4nf3brWeVJcucO+96r+raPxKLAkjlBRH3uYGkkz5WcECYmFhYc7Z/ITwSH4+us2GLTdXxVFEhL9rVLXk5alko+DzGR3t3/r4Uk4Oel6eiqXwcExV9MYMUO9pQXePkBD5Nd8H5PvNh2y2C2MYg4Orzg87JZBYEkYoKY68zQ0kva8ErPIlLrwRHAxBQVirVatyq95XClbrpXOjHRoKISGXRixV5USxEpHvNx+pKq3FHpBYEkbwRRzJyHQ/czgcJCcny8BK4RWJI2EUiSVhFIklYRSJJWEEX8WRJFNCCCGEEEIIUQ6STAkhhBBCCCFEOUgyJYQQQgghhBDlILP5+Zmu6zgcDiwWi8xQI8rHbke32VQcBQdjCg/3d418JyvrwiK5wcFQEcfqcKiZs3S9Sk89XECuScIoEkvCKBJLwgglxZHM5lcF5OXlEXYJzswjvJSdrRKKhQth8WLyzGbCYmLUgrMNGqgb/6qyHkdWlpoG+OOPYetWta1dO7W2SlCQb5Iqm02d3x9+gEWLLqwzdffd0KKFmio9ONj4/VYCck0SRpFYEkaRWBJG8EUcSTc/P3M4HGzZskVmqBGeyc2FL7+EunVhyBAcX37Jltq1cUydCs2bwzXXwPHjqlygy82FRx5RicyTT8IXX6i/J59U2x591PjjzMmB//0PGjaEm2+GTz6BWbPg7bdVEtetGxw4oMpVMXJNEkaRWBJGkVgSRvBVHEkyJUSgycmBt95SLVCnTxdfZuVK6NJFPV6wsGMgysuDfv3gP/9R/13c4598AjfcUPzj5WGzqWRt+HA4dqz4MsnJ0LUrHDp0oduhEEIIIS45kkwJEWgOHICnniq73MmTMGwYaJrPq+QTmZnw+uvw889ll/3pJ3jjDfUcb507B/ffX3a59HS45RbV3U8IIYQQlyRJpioBi8Xi7yqIQJGdDa+9VuxDluJaZlauVK0ngSg0FKZNc7/8hx+q53gjO1u1+rmbgG7fDhs2qMkpqhC5JgmjSCwJo0gsCSP4Io5kNj8hAonDAZGRqiuaux5/HJ5/PvBmofvtN+jRw7PnrFypxjN5Iy4OTp1yv/zo0TB1KkREeLdfIYQQQlQ4b3MDaZnyM13XSUtL4xLLaUV5ZWUVm0jpZjNpCQno5mI+0kePqiQs0Bw+XDHPKczh8CyRAnV+qxC5JgmjSCwJo0gsCSP4Ko4kmfIzh8PBzp07ZYYa4R6rtdjNjqAgdg4fjqO4qdBDQwNzXE95pi71drrT8kwnHxpapbr5yTVJGEViSRhFYkkYwVdxJMmUEIHEYlGz9Hniuuu8H0vkD1dd5VliExysnuONnBzo2dOz51xzTYlJrhBCCCGqNkmmhAg0jzziftlatWDw4MBcvDcsDAYMcL/8wIEQEuLdPq1WNcbMXdWqwZgx3u9XCCGEEAFJkik/M5lMhIWFYQrEblii4gUHw9Ch0L69y2aTrhN2+jSmi7ubTZ4cuOtMhYXBCy+4N3FGeLg6Vm+7+ZnNcO21cMUV7pV3Z4r6ACPXJGEUiSVhFIklYQRfxZHM5idEoHE4IC0Nrr4atm4tudwLL8ATTwR2q0lWlpp6/MYb4fz54stERcG330Lnziqp8pauq/Wqrr0Wfv+95HL/+Ae8/LJ08RNCCCECmMzmF+A0TePkyZNogbqwqqh4FgvExEByMnz+OXTujGY2c7JjR7TwcBg1Sq1/9PjjgZ1IgUqOunSB/fthwgSIj7/wWHw8TJyoHuvSxZhECtRkHeHhamr2OXNcW6msVhg+XJ37KVOqZCIl1yRhFIklYRSJJWEEX8VRAA6kqFo0TWP//v3UqFEDc3HTWgtRHItF/Q0bBsOGoeXmsj8lhRpdu2J2OAJvTanShIervyeegOeeU61VBduzs32zvpPZrP4GDVLjtjQNcnPVec3Pr1rn9yJyTRJGkVgSRpFYEkbwVRxJMiVEIAsOVv+aTGqSCYsl8FujSlKQNEVGFt3mK4Un7iiYEbHgnAshhBDikifpvRBCCCGEEEKUgyRTfmYymYiOjpYZaoRXJI6EUSSWhFEkloRRJJaEEXwVRzKbnxBCCCGEEOKSJLP5BThN0zhy5IjMUCO8InEkjCKxJIwisSSMIrEkjOCrOJJkys/kAiGMIHEkjCKxJIwisSSMIrEkjOCrOJLZ/IQIZNnZaq2jvXshIwNOnIBatdSsfkFV7OOdna0WLD50SP1/w4bqOH05TbnNpmbvO3BATckeG6vOr8nku1n98vLUwsFnz8Lx4xAWBs2aQU6Ob2cvzMxUx7Vvn4qljAw1M2RYmJom3hdyci6c38xMqFED6tRRx++rNbwcdsi3q0Wgj/4JIVZIaAb5eRDu49khhRBCVDnSMiVEIMrLg3Pn4IUXoG5d6NABfv0VEhLgmmvgm29UmarAZoNt2+Cee1Qi07at+qtVC/7+d7VAsc1m7D7z89XN9muvQaNG0Lw5dOwI9epBt24wd65vzm9ennrv+vRR72vnztCqldrvSy+pBCsnx9h9ZmXB7t3w0EMqWezaVcVSgwZw112wYYNKZI3kcKj3bOpUlSg2a6bOb8OGkJQEX3zhm/ObmwtLl0D//hAXBx07Qas2KoF79jk4eRJyDI4lIYQQVZpMQOFnmqaRmppKkyZNZCE64Z68PNU606MHHDsGgBYURGrfvjRZsgSz3a7K3XknfPKJ737hrwg2G8yfr27qHY7iywQFwYwZaoHdsDDv92m3qxa+q66C1NSSyw0YAHPmGHd+8/Lg7rvhyy9LLlO3Lvzyi0p0jFhPLCsLfvoJbrtNJRoUE0smk0p6xowxphXQ4YC0NBW/f/xRcrk+feDbby+s7+Wt3Fx44nF47/2Sy9SsCT8thxYtINSAWLrEyfebMIrEkjBCSXHkbW4gyZQQgSY7W93s/fln2WWfeQaeftq3XeF8xeGA339XN90lJVIFLBZYuRIuu8z7Lmn5+dCmDezZU3bZ+++HN9/0PonLzIQXX4SXXy67bP36sGuX9++ppqlWvaSksluBTKYLLWbeJjd2u3qfNm0qu+zQoTB9uvf7zMqEaR/C+PFll61VS53fGjW826cQQoiAILP5BThN09i3b58MqhTuyc1VrU0XJVJaUBD7+vdHu3ic1DvvqBvhQJSfDxMmlJ1IgSozYYL3XcPy81UXPncSKYD//MeYbndms2r9cceRI2q/3na9s9lg8uQi56zYWNJ1mDTJ+0RV01RLmDuJFKiWv1OnvNsngDUEXnnFvbKnT8O7UyE7y/v9XuLk+00YRWJJGMFXcSTJlJ9pmsapU6fkAiHcY7HAe+8V2ayZzZzq2BHt4pvdzEzVBS4Qx0+dPg3Ll7tfftkyOHPGu33qOrz9tvvl8/Ph/fe9G7Nls8Gnn3qWHL33nvfdC/PyYOHCIptLjKX169XYKm/3+eab7pfXdVU+y4vExp4P879W8eSujz5WCZjwiny/CaNILAkj+CqOJJkSIpDk5bnfalJg7VrneJiAsn69upl2l66ryRK8YbV6/hrr1rnXelaSvDzVndETu3er7nLe2LpVJYOeWL3au32GhkJysmfPSU72rnU1xwar13r2nGPH4NzZ8u9TCCHEJUOSKSECSXl+TfH0hrmyKE+9vT1WTfP8HBtxfsuTGHmbTJWn3ka0cHpa7/x877uq+uP8CiGEuCRIMuVnZrOZ+vXry+w0wj3VqkH16kU2mx0O6v/2G+biWkiaNg3MNaeaN6+Y51ysYUPPyickeHezb7Go1/BEjRreT0BRwj5LjaXERO/2abdDkyaePSchwbuWP6sVmnp4fsPCoEbN8u9TAPL9JowjsSSM4Ks4kqj0M7lACI/k5Khpwi9idjio/+uvRW+AzWZ44AFjpgyvaO3aeXbz3rq1+vNGbq5au8oTDz8M4eHl32dEBDz4oGcJ2ahR3k9AUa8eXH55kc0lxlK9etCrl3f71DS47z7PnjNunHfn1xoKY+72bIzZsGFqrJXwiny/CaNILAkjSDJVRTkcDnbs2IHDm19exaWjWjV4/HEIDnbZ7AgOZsfw4Tgu2s6NN0JMTMXVz0gOBzz2mPvl//GP8nWDLCwsDMaOdb/Vp3t3z1tailOjhlpI1h3BwepYIyK826euq1i6SImxNG6c9zMXWq3qx4BiWleL1batmkbd225+wcEqQXKH2Qzjn4BwL8+vkO83YRiJJWEEX8WRJFN+pus66enpXGLLfQlv1KwJX3zhMk21bjKRnpCAXvimMzFRlTNq0dOKFhKibrzvvLPssiNHqj8jFtANDVULBV+cTFysUSNVzmLxfp9Wq1qst2XL0stZLOo9dTcZKU1IiEq2H3rIZXOxsTRggEpsvU3gQCVG339fdmtp7drw3XeeTUJSkvBwmDYNOncuu27TPoBGjb3fp5DvN2EYiSVhBF/FkSRTQgSasDC4+WY1FXinTkUfDw1V3cDWr1ctLIG6zhSoJOOTT9QaQXXqFH08Ph5efRX+/W9jEilQ57dHD/jtt2K7wTlbOTZuVK1+RiRTFova7/r16r0rLgHu3BmWLIGbbvKu21thoaHw2mtqqvUGDYo+XqsWPPecWu/JqPMbGgodOqgZDIvrNmixwKBBai2qOnXKTmrdFRKiFna+//7iz1+bNrBwgUreA3GRayGEEH5h0i+xNN/bVY6NZrfbSU5OJikpiaBAnCRA+E/BbGw7dmD//nuS69YlKTeXoOHDVatVVbohtNnUTfXixSqJAZVcXH+9Og++GBNmt6tug6mpsGCBGqNUpw7cfrtKLHx1fjMzVWvMV1+pBXrDwuCGG9R4ME1TSYEv9hkaCj/+iD05meSGDUkKCyNo4EDVtS8y0vh9OhzqHB87phZKPn9eJW+3366SndBQ3/wQkJUJJjPM/i8cOKjO57XXQKfO4MiHkAAcX1hJyfebMIrEkjBCSXHkbW4gyZSfaZrG6dOnqVWrlgysFOWm5edz+uRJatWpg9mIlpLKrCCJNKrFwh0FN/7BwS7dK31K09SEGEFBFXqsWnY2p8+coVa9ehV3TfLH+dV1yMsBc8We30uJfL8Jo0gsCSOUFEeSTHmosiVTQgghhBBCCP/wNjeQ9N7PHA4HmzdvlhlqhFckjoRRJJaEUSSWhFEkloQRfBVHkkz5ma7r2Gw2maFGeEXiSBhFYkkYRWJJGEViSRjBV3EkyZQQQgghhBBClIMkU0IIIYQQQghRDpJM+ZnFYiExMRFLVZ+BTfhOdjaW3FwSExKw5OSoWdGEKA97FhbySGyRgEWzGbNgrrhkyfebMIrEkjCCr+JIJuv3M5PJRExMjL+rIQJRTg6kp8M772D66SdicnKgbl245x61qK/dXvzir0IUpuug58P5fbDzDUznUojRdYhqAS0fhhpdAROY5SZGeEa+34RRJJaEEXwVR9Iy5Wd2u53169djl9YE4Yn8fBg/XiVPU6Zg37SJ9dddh335chg8GBo0gJQUtditECXRNLBnwYr+8F1r2Pcf7Ge3sj67D/aD82HplbCkK+SeAkeev2srAox8vwmjSCwJI/gqjiSZqgRkqk/hkbw81fr03nvqZvgvDqv1Qpnjx+Hqq2H7drXwqxDF0fPgxx5w/EeXzQ4KxdK5FFhyGdjPV2zdRJUg32/CKBJLwgi+iCNJpoQIJLoO69bB55+XXTYnB+66C6SPuSiO3QY7Xodzm8oum30YNo1XrVhCCCGEcJJkSohAkpMDb7zhfvk//oANG2QiAVGUORj2fuR++YP/9V1dhBBCiAAlyZSfWSwW2rdvLzPUCPcEBcE33xTZbMnPp/1HH2HJzy/6nM8/h+zsCqicCCjp2yD7SJHNFvJpn/cRFi6KJYcN/vy2gionqgL5fhNGkVgSRvBVHEkyVQlYC491EaI0NhsU199X17FmZBTfAnX2rMvYKiEAyD1bwgM6Vj0DKCaWck74skaiCpLvN2EUiSVhBF/EkSRTfuZwOEhOTpaBlcI9oaFgMhXZ7LBaSR4/3nUSigKRkcU+R1zigiOL3ezASnLIeNdJKApYq/u4UqIqke83YRSJJWEEX8WRJFNCBBJNgz59PHvO0KFQrZpv6iMCV/WOEFLL/fLmYKh7o8+qI4QQQgQiSaaECCRWKzz2mPvlGzeGnj3BLB91cREtHxLudr98/YFgCfFZdYQQQohAJHdYQgQSsxmuuQb69XOv7IcfgixyKIoTVA3aPg0RCWWXtdaATq+DJcz39RJCCCECiEnXL605kzMyMoiOjiY9PZ2oqCh/Vwdd13E4HFgsFkwyrkW4KydHdd9btAhQUwU4rFYseXmYAMLCYNYs6NtX/bcQxdHy1UQUP/WB9O3AX7GEFQsFsVQPei9TSZe0TAkPyPebMIrEkjBCSXHkbW4gLVOVQF5enr+rIAJNaCh8/TVs3gx33gkNGpDXuDG0bw9vvgknTkgiJcpmDlbjpvqlQK8fIL4vhMaTF5oAtbpDt//CgFSIbCqJlCgX+X4TRpFYEkbwRRxJMuVnDoeDLVu2yAw1wnPBwSp5mjYNx/79bJkxA8e6dfDAA2oGP0mkhDvMFjAHQfx1cNUCHDcfYkvdz3D0WgINBquEyyxTEgvPyfebMIrEkjCCr+JIkikhAl14+IX/tlhUq5UQnjKZIahQAm62qiRLCCGEECWSZEoIIYQQQgghykGSqUrAYrH4uwqiCpA4EkaRWBJGkVgSRpFYEkbwRRzJbH5CCCGEEEKIS5LM5hfgdF0nLS2NSyynFQaTOBJGkVgSRpFYEkaRWBJG8FUcSTLlZw6Hg507d8oMNcIrEkfCKBJLwigSS8IoEkvCCL6KI5mqSQhR+Wl5gAkydsG5FLWtekeIagnoMnW3t+x20DRITYXkZAgJgaAg6NhRbbfK+fWKZgfdAbajcHqtWiw5qgXUSALdDhYfzcBpzwJMcPxHyD2t1hSrc416LKiab/YphBCXGEmmhBCVmyMHjvwPtk+BtM2uj1XvCK2fgvoDZFHZ8srJgeXLYdIkWL9eJU7jx8OIEdC0KTzxhPrvEDm/5WK3wZm1sGUCnPrN9bGIptDyUWh+r7E/CGh5kJ8Bm/4JB/8LjuwLjwWFQ6PboeMUCI5S64gJIYQoN+nm52cmk4mwsDBMJpO/qyICWJWNI0cObJsMq4YVTaRAtVKtGqrKOHIqvHoBz2aD99+HG29UiRRg0nXCTp/GpOuwYwfcfTc89hjk5vq5sgHIblPJzE/XFE2kADL3wYaHYM2ov1pfDaDZwXYCvu8I+z91TaRAtVbt+zf80Em1Vml2Y/ZbjCp7XRIVTmJJGMFXcSSz+QkhKictX7VIrbzNvfJXzYN6N8kv7e7SNFixAvr0ca/8xx/DnXfKotDu0nX1A8DiLqBrZZfvMAVaPuK6cHJ5aA74vo3qEluW6LbQb5MsziyEuKTJbH4BTtM0Tp48iaa58WUrRAmqbBxtn+Jh2UvqtyHv5OXBSy8V2ayZzZzs2BHNfNHXw2uvqXFUwj0OG2x/yb1ECmDX297/EKBrcOJH9xIpgPRtcGql+3X0UJW9LokKJ7EkjOCrOJJkys80TWP//v1ygRBeqZJxdH4fnNvofvmzyZB5wGfVqXLOnoWffiqyWQsKYn///mgXJ0579ji7Ago36HY4stD98jkn4OgP3u3TkQO7pnr2nF3v+KyLbJW8Lgm/kFgSRvBVHEkyJYSonNK3VcxzLlV79qiuaJ7YXMy4NVG8rIOqq6on0lI8f05hlhBI3+HZczJ2yGyYQgjhBUmmhBCVVHkGiMrgZLeVZwCuDP72QHnj18tz7PF7ZEK6xwohRPlJMuVnJpOJ6OhomaFGeKVKxlH1DhXznEtVYiJcPC4KNZtf9P79aja/i3XpUgEVqyLCG4PZw+nka3b1bjIIRy7EtPPsOTHtjJtJ8CJV8rok/EJiSRjBV3EkyZSfWSwWWrVqhcVi8XdVRACrknFUrSHUutz98rHdIKy+7+pT1URGwvXXF9lsyc+n1VdfYcm/qLtZ69ZqEV/hHpMZGt7qfvlq9aG2mzMrlsQSqtat8kTLf4DFNwv4VsnrkvALiSVhBF/FkSRTfqZpGkeOHJFBlcIrVTKOTGZo8y/3y7d5Rj1HuCckBP71ryKtU5rFwpEePdAu/rJ56ik1nbpwT1A1FZPuztCX+Lh346VAxX9sd4hxs4W2RhfVGuajX/ur5HVJ+IXEkjCCr+JI7jz8TC4QwghVMo7MQVCnD3R8peyyHV+F2lfLejmeMJtVS9OHH7rcTGsWC0euuso1mfrnP+G228AqExV4JLwxXDETTGXEZbN7ocWD3q8xBYAJei+FiITSi0U2h6sX48txhlXyuiT8QmJJGEGSKSHEpccSCi0fVjd9cT2KPh7XE65eCi0fUmWFZ8LC4I474Jdf4Npri7ZQ/O1vsGABTJqkWrKEZ4LCoN6N0Het+vfiltPqHeGKL6HLVONm1DNbwFpdLcab+Jj678JCakKr8XD9BgiOUeWFEEKUW7l+xs3NzeX333/n4MGDZGdnExsbS6dOnWjSpInR9RNCXOosoWosSVwvyDkO6dsBE0S3htA66ld/uSEsv7Aw6NYNFi2CM2dgyxbIz4dt26BRI9WCJYv1ll9QGFTvBN3mgP08nEtREz5ENIPIpmoiPYuXi/VezBys/tpPho5T4PTvkHcOrDWg1mWg2VU3RCGEEF7z6Bty1apVvPPOO3zzzTfk5+cTHR1NWFgYZ8+eJTc3l4SEBO69917uv/9+IiMjfVXnKsVsNhMbG4u5mFm1hHBXlY8jcxAQBOGN1J8wltkMoaFQrx7m+HhiU1MxN2lS7Gx/ohxMZpVUBYVB/HUVt9+ChCnuKtftFbSuVJW/LokKI7EkjOCrODLpunurNt58881s3LiR22+/nZtuuomkpCTCwi70796/fz+//fYbX331FZs3b+bzzz/n2muvNbSyRsjIyCA6Opr09HSioqL8XR0hhBBCCCGEn3ibG7idmvXv35/U1FReffVVrrrqKpdECiAhIYG77rqLxYsXs3z5cvn1wE2aprFv3z4ZVCm8InEkjCKxJIwisSSMIrEkjOCrOHI747nvvvsIDnavX3fr1q3p08fL9TIuEZqmcerUKblACK9IHAmjSCwJo0gsCaNILAkj+CqOvB5VnJmZWaRS0n1OCCGEEEIIUdWVqy9eamoq/fv3Jzw8nOjoaKpXr0716tWJiYmhevXqZb+AEEIIIYQQQgS4crVM3XHHHei6zqeffkrt2rUx+Wj19EuB2Wymfv36VXeMmcMB+XkQ+tcYO02DHBtUC/dvvaoKXQNHLmZzyIU4stsMWvxTFBuvWZkQHuHb/V78HtptYAkpuk6RkRy5gAmzOUjFEhrYs30/hXZ+JgRHlPz/vnDx+XTkqBnufHl+L0FV/vtNVBiJJWEEX8WR27P5FRYREcGGDRto2bKloZWpCDKbXwXRHKDpsHwZvPUOrFwJeXnQsCGMGQ0PPAghVkmqvKHZ4fRq2PEGnPgZtBwIqwtNRkKLh9QNqUWSqnLLz4eNG+CNN2HpUsjKgtq14fbh8Og/oHoMhBmcaNhtYM+E3VNh/+dgO6rew9pXQ6vHodaVf00Tb+Q+c9Q6XYfmwO734NxmlaRHtYRm90PCKECDIIM/q1oepG2HnW/C0e8g/7xaULbhELXYbGht438UsNvU52T3B7D/U8g+rJKo2KvUPmtfrRIqSaqEEOKS4W1uUK5k6uqrr+aZZ57hmmuu8XiH/lbZkimHw8Hu3btp0aIFFksVWXjU4YDM83BdX1i3rvgyoaHw1Uy47nqoJotHekTXwWGDX25USRTgIJjdwbfSIv9rLOSrhWz/9gk0GiIJVXnk5sJtt8E33xT/uNkMb7wO9z+gYtkIjhw4+F/4/e+g24svU7s39PzGuNYiuw1yTsDy3pCVqqpxcSyF1IReiyG6lXEJlZYHa8fAgZkll2n7HLR5WrUgGcFug2OLYdVw0HKLL1Pzb9B7KVgiZI0tA1TJ7zfhFxJLwgglxVGFTY1e2CeffMIrr7zCjBkz2LBhA1u2bHH5c9evv/7KTTfdRN26dTGZTCxcuLDU8itWrMBkMhX5O378eHkOo1LQdZ309HTKkdNWXg4H9O5TciIFkJMDtw6BNatUNyrhPt0OK/o5EykAHRPp5gR0TBfKrB0NR39QN5HCffn5cOutJSdSoLr//eMx+OJz1e3PW3YbHPlGvWclJVIAJ36CFf1By/d+nwCObFjW3ZlIQTGxlHtGJVvZf6oWZ6/3mQe/31N6IgWw7XnY+RbYswzYZy6cXgUrbys5kQI487s61tLeA+G2Kvn9JvxCYkkYwVdxVK5k6tSpU+zbt4/Ro0fTtWtXOnbsSKdOnZz/uisrK4sOHTrw/vvve7T/Xbt2cezYMedfXFycp4cgfCU3B76YARs3ll3W4YCHHoEgg7stVWWaA/78Dk7+6kZhHZIfBrN7SxoI1Pldsxq+/da98k/+E4Kt3u/XHAwbH3Wv7MkVcHSx94lN/nnY9gLY/iy7rP08pDxZeiLirvO7IfUL98pum6S6HHrLZIHkcaC7cc7OblCJnpbn/X6FEEJUeeW6ix0zZgydOnXiq6++8moCin79+tGvXz+PnxcXF0dMTEy59il8zGyBqe+5X37HDkhOhsv+Jt1q3KHbYddb7pe3/QnHl0J8X3VDKUqXkwtvenB+09Lgq1kwbDiElLM7muZQ3c9sR91/zs43If46wIv31BwC+6e7X/7Pb1UrkTddDPMzVd3d5ciBvR9D8wfKv19dhzPrIGOX+8/Z9Q40uaN8+xNCCHFJKVcydfDgQRYtWkSzZs2Mro9bOnbsSG5uLm3btmXixIl069atxLK5ubnk5l74NTUjIwMAu92O3a66cpjNZsxmM5qmuayZVbDd4XC4NAmWtN1isWAymZyvW3g7qL6aF283mUw0atQITdOczwsKCkLXdZfyJpMJi8VSpI4lbffbMaWnw9atOKyuv9YH5eWhm804CrVCmXQdS34+2vwFaG1aQ1h45TymErb75X3Sdcwnf8VB8IVuWIAJjQT7d2iYsXPh3FvIh0MLcNTo5jLepVIdU2V6n0JCsPzwA5rFglaoP7VZ0zDb7WhBQWiFkn6zw4F50Tc4BtyMXri8J8dkz8JyaIGqO66fGwt5gAkHrq2LQSdXoAOOQq/j8ft0JhlzfhoaQWgunRR0EuzfoWNyiSWzbsd89Dscje4s//tkCsVy9Ifij4k8dMw4Cn0tmdCxHP0OLeHvaJR9rMXGniMH86EFmKHI58aMHTNake2WtK2Y7JnYzZFlH1Mp2y/1z5Ou6yQkJAC47DeQj6kqvk+BcEyappGQkFDkdQL5mKDqvU+V/Zg0TaNRo0aYTCaXOl58zJ4qVzLVu3dvNm/eXOHJVHx8PB9++CFJSUnk5ubyySef0KtXL37//Xc6d+5c7HOmTJnCpEmTimzftGkT4eHq5jI2NpamTZuSmprKqVOnnGXq169P/fr12b17N+np6c7tCQkJxMXFsW3bNmy2C+NREhMTiYmJYdOmTS5B1L59e6xWK8nJyS51SEpKIi8vj4MHD3Lw4EFAvfFdu3YlPT2dnTt3OsuGhYXRoUMHTp8+zf79+53bo6OjadWqFUePHuXIkSPO7X47prrx5NWsyZb77nNus+Tl0fW110hv3Jidw4dfOKbTp+nw0UecDglh/5atYAmqnMf01/tUeDyg396nhnWIA7ZZx2Az1bpwTPlfEedIYX3IeJcb8vZ5H2G120jetFVNSlEZj6kyvU9RUbTKy+Nojx4cueqqC8eUkkLT774jtW9fTnXseOGYfvuN+pmZ7D54mPRCdffomHQ77fM0rFhJDhnveky5r5FnimKLtdDniTy65r5G+rkz7Nx7qOxjKul9CkmjKZAa1JdTlkLHZP+N+o5f2RE8nHRzwoVjsn9HXH6G9++TPZ88U83ij8ncmJ3Bha4R+mk62Ddy+mw6+w/+UfYxFRd7uoP6eZHUB3YH31r0mBwpxX6eYuw2Nm3fG3jXiMr0efrrmI4cOVLljqkqvk+BcExpaWlV7piq4vtU2Y8pOjra5Ziysrwbm1uu2fw+/vhjXnjhBcaMGUO7du0IDnb9hfHmm2/2vCImEwsWLGDgwIEePa9nz540bNiQL74ovg9+cS1TDRo04MyZM84ZO/yZ1TscDrZu3UqbNm2c895X5qy+zGPKzoLqNVxaoKCMlqlnn0X753gICaucx1SZfn1Bw/x1BA4Nl1/SwcR26yha5X2JmQuTE1jIh+ZjcbR/ESwXuklVqmOqbO9TzZpomZnut0wNHYrj/XfRIy7MAOTRMTmysWx5Gva8737LlFlHvy0bR6Grt8fvU/pWzEs6F2mZ0jHzh/UuWuV9gblQa5AZO+ZuX+GoP7j875OuY/m+JWQdcL9lqu41aFd8hWa50LLqUexpuZh3vIJ5+2T3W6ZwYLotDbvJdabGgLhGVKLPk6Zp7Nixg9atW7sMBwjkY6qK71MgHFNBLLVp08albCAfE1S996myH5OmaWzfvp127do578FB5QY1a9Ys92x+5WqZuv/++wF4/vnnizxmMpmKnFhfuuyyy1i5cmWJj4eEhBBSzFiGoKAggi664S94My5WEDDubr/4dcvanpubi9lsdnncZDIVW76kOnq63WfHZLVC794E/fhjkbImTSMor+igbvPoUZjDI4turyzHVMr2Cn+f7NlQfxCWQ3NcN2PFZqqFmXyCuOgcN7uboJDiLw6V4pjK2F6h71NWJgwbhvmjjzAXcx0z2+1FZ+0ZfReWqGg1XvAibh1TUBQkjIY97xd97wDQi25vMAyTnkdQMeOI3H4/qreD8MaYsw64HNOFWLK77jcoAurd5N37lJ8JjYbDHy8Ve6wmtKLbG9+JOSgMs6W8MRkETUfD9snqx4ViFNkefx2YgwgqZp9FjqmM7Zfy58lut2Oz2Tw+B5X5mMq7XY7Ju2MqiKWSygfiMRWoSu9Tgcp6THa73dnAUriOJR2bu8o14r8gUyzuryITKYCUlBTi4+MrdJ+iFMFWeOwf7pfv1Qvi6/qsOlVOUDVo9YT75WM6QHSbsssJJTwCHn/M/fKNG0Ov3sUmUh6p3h5i2rlfPvEJ79ea0nKg+YPul288wvspw4MjIPER9ydDCakFDW8Fi5czJobVhbie7pdPfAzMBszSKIQQosrz6/RpmZmZpKSkkJKSAkBqaiopKSkcOqTGATz11FOMHDnSWf7tt9/mf//7H3v37mXbtm08+uij/PTTT4wdO9Yf1RfFsVigzzVQaGxUiWrUgM8+hSCZZc4jMe2huRsxHxQO3Waq2cyE+xo0gGf/VXY5qxVmzYR8A6bQ1nW4cpZ7i+K2GAcxbb3fZ1AEtHwYal1ZdtnI5tDpVQgu2oJcrv12er3sciYLXPGFWiPKW6YguPwzsFYvu2zjEVD7ajDJ7KJCCCHKVq5vi4cffpipU6cW2f7ee+/x6KOPuv06ycnJdOrUybk21WOPPUanTp147rnnADh27JgzsQLIy8vj8ccfp127dvTs2ZPNmzfz448/0qdPn/IcRqVgsVhITEwssWkyIFmtMH06PDSu5DWkWrWCdb9DndoQJOsgecQSAl3ehDbPgFl1YbVgJzH/KywFY1wiEuC6NRDR1Ptf9S81oWHwzL/glZchLKz4MvXrw4qfoEMHCPOyhQjUexTZDK5brd674phDoM2/oPMbKgaMYAqGPj9Cg8Hw15ihIrEU1wOuW+uMNa8FVYPm90PXaRBUQnIWWht6fqv2HRzh/T7NFgiLh77rIKpVCWWCoeUjKumSVilDVMnvN+EXEkvCCL6Ko3JNQFGvXj0WLVpEly5dXLZv3LiRm2++2WVGjsomIyOD6Ojocg8yEx6wZYPNBu+/D7+thNxcaNgQHrgPLrscNDtYQ8t+HVE8uw30fNjzEZz4Ua3JExYPTe+DuKvUAqVG3XRfirKzQAf+8wl8/wNkZ0NcHIweBX2vh/xc53T+hnHkqhaZk7/Avn9D9lEICoM610Kz+1QLS1AJCZ5X+82BnJOw6204t1ktlBvVQrVcRTYH3eycbdMw+ZlgDoL9n8Of/1OLCIfUVC1D9QeqOhmRSBWm5QMmOPM77JkG2YdVkhh7lWrxCwoFiw/OrxBCiErL29ygXMlUaGgo27ZtKzI1+t69e2nbti05OTkeV6SiVLZkym63s2nTJjp16uT1ALhKKzsTHDqYTaBpalxKMQMKRTnZc7Dbc9m05Q86tW9NUEikdFEyUk425NtVzGoaVAtX3Vl9SdPAkQ0mk+oCaApSN/q+lp+B3aGxafMOOnVIJCjUjW5x3nLkgJanYlbXwBwGFh+3Vuu6WoDYeX4tvklSL3GXxPebqBASS8IIJcWRt7lBue64mjVrxuLFi4ts/+GHH5wL9An3VfSkHRWuWgRERqokKjJKEimjBYVCUDgOzaTG3EgiZazQaipuC+K3IrqZmM2qVSYo/K9/K6gFNzgKgiJw6KaSu+AZzRLq3C/BUb5PpEAlUS7nVxIpX6ny32+iwkgsCSP4Io7Kld4/9thjjBs3jlOnTtG7d28Ali9fzhtvvMHbb79tZP2EEEIIIYQQolIqVzI1ZswYcnNzefHFF5k8eTIAjRs3Ztq0aS6z7wkhhBBCCCFEVVWuMVOFnTp1irCwMCIiDB4o7COVbcyUruvYbDbCwsJcVogXwhMSR8IoEkvCKBJLwigSS8IIJcWRt7mB16P4YmNjvX2JS57VKtPwCu9JHAmjSCwJo0gsCaNILAkj+CKO3B6pfv3117N27doyy50/f55XXnmF999/36uKXSocDgfJyckysFJ4xS9xlJenpguvyNk7NQfYs9WfVoHH6sj5a58GLNDrLi1fzTjnyKm4hZc1O47cDJLXr8WRb8Biue5y5Krz66jo85v91xIDWsXt9xIi32/CKBJLwgi+iiO3W6Zuu+02Bg8eTHR0NDfddBNJSUnUrVuX0NBQzp07xx9//MHKlSv5/vvv6d+/P6+99pqhFRVCVAKappKoc+fUwswnTkB4OAwcCJ06qcd98euhPVtNX31oDpxLUduqd4SGQ0G3q4VgjVaw5tO5jXB4oZqqPLQ2JIwCawyYQoyfmVLX/kosMmH/Z2A7qma7q9sfal2hjtXig5n98jPVmmSHvobTmyAjEQ7shIQRar0yo9d7ApU4mUyQvkO9r/ZMsNaAhLvUeTYFq8V2jWbPBi0X9s+ArINqgd74ayCut1q3zRfnVwghRJXldjJ19913c8cddzB37lxmz57Nxx9/THp6OgAmk4nWrVvTt29f1q9fT6tWJawwL4QIXHl5cOYM3H03LF7s2lry0kvQpg1MnQpXXAFhBk41reXBthdhz/uQn+76WPLD0GIstHtO3RQbxW6D06tgwyOQ/ofrY1v+BfHXw9/+A9bqxi2MrOWrhWt/vwf+XKSSmAJ/vKIWz+30OsRfa+zCsloe7JoKu96E3DOAFULGQ/JrkPIPaHo3dHwVLAaf37TNsH6sSlYL2zoBaveGv32iFqE2KrnR7OCwwbr74PDXfy3g+5cdr0J4I2j/IjQcLAmVEEIIt3k0ZiokJIQ77riDO+64A4D09HRsNhs1a9YkOLgC1gYRQviH3Q4nT0JSkmqNKs727XDddTBnDvTrZ0xCpeXBymFwZEHxj+enwfYXIWMHXPmVMTf8Dhsc/R5WDXVNaAromnr8h05wfbJqRTF7OfxU01SiuDhJtZYU5/we+HWgSjIaDTdmbSRHLqy/H/ZPL/5xeybsegfSt0PP74w7v6dXw4obSu42eeInWNwF+v4O1Rp5v19dV/td8jcVK8XJOghr7oCcY9BinCRUQggh3OL1bH6BpjLO5udwOLBYLDJDjSg3n8dRfr5qcdqwoeyyISHw559Qs6Z3+3TkwN6PVeuQO7q8C83+7n1LUc4pWFjfvfFRNZLgutVg9vLHJEcO/HITHP+x7LImC9y0FyIae7nPXNW9bo3rchY64MCKhTxcIqndREh83Psuf/YsmF9HJWpliWwO/f/wPlm1Z8Pa0ep43dEvBap38G6fQr7fhGEkloQRSoojb3MDgzv8i/LIy6vAQdeiyvJpHG3f7l4iBZCbC++9Bzabd/s0BalWEXftnqoSDW/Ybep13J1o4myyGvPjrZwT7iVSoFrLdr6hkhJvmMyw8+3iHiDPFAVcdMOyZ5r3rTWOXNj7iXuJFKjWuFMrvZ8gwmGDw/PdL7/jNZWACa/J95swisSSMIIv4kiSKT9zOBxs2bJFZqgRXvFpHGVmgqezc/7nPxDq5Y33uU2Qud/98uf3QNoW7/YZFAb7PvXsOXvedz85KI49G3Z/4NlzUj/3ftxU1oGi45UAB8Fssd6Hg4ta23JOwPFl3u3TFAT7PvHsOXs+UJN/lJcjD/b9R03e4a5DX6tkU3hFvt+EUSSWhBF8FUfybSGEKNuhQ56VP3LE++m8PUmkvHlOYbqmZtDzRNYh1Teu3Pt0QHYJ46RKkp/hXYIBJY/NKs35fd7t02yBbA9jKesQ4EWLo5avEkePnpMLeefKv08hhBCXDEmmhBCl03XPpzsPCvJ+2vDydCkzezleymT2vKug2Yp32ZSpfDMRmrwcR1Sec2XEpAyeHqvZ+v/t3Xd8FGX+B/DPzG422YQUSkILLRCaGroKeDRRVNSz3OlxKoIn9oKcehZORO+n5yl6djk9xPMsp55dsSEgTSQQikgTkCIlCZJeNjvz/P54LgtLssnuzjPZ7Obzfr3yUibP7jzPzDez893nmecBYGGYnxbm8bX6HBwREbUIlu52PB4P9u3bhz179vj9UGgcDhvWUqEWx7Y4iosDTjsttNeceqp8dsqKtqeENtRK04F2p1jbp1ENtDs1tNekj7R24627gIzRob0m9UTrz4e1HhAwoXIgwJjyjFHW9umtlOtlhaLdcNR5fisUjnggY0xor0nsCsS1Dn+f5MPPN1KFsUQq2BFHYc3mt337dlx11VVYsWKF33YhBDRNa9ZjWpvbbH5EUaG4GMjIkGtNBePdd4HzzpM9VOEyKoGlv5HTkAej87nAyLesTRlueoF9HwDLfhNceT0euDgfiLN4LTGqgP9mAN7S4Mqf/CKQNdna2lrecrnm0k+vBVc+fSQwbqG12RKFCeQvBRaOCa68pgMX/Ay4O4S/T0Ce1/e7AFUHgys/4GGg73ROj05E1AJEZDa/KVOmQNd1fPzxx1izZg3Wrl2LtWvXIi8vD2vX1n2gmQITQqCoqAgtbIZ6Usz2OHK5gBtvDK5sTo71RAoAtHgg58Hgen10F3DSA9anRdedQOb5sucnGL1vBDQFw8GECfS/M7iyrXoCPa6wvkixMwk48T7AkehfFego0rMgjv140HR5Lqz0ENW+T/pIoN2I4Mr3mAy40qztE5DPTZ00K7iy7k5A7xuYSCnAzzdShbFEKtgVR2ElU+vWrcPcuXNx9tlnY+DAgRgwYIDfDwXPMAxs2bKlWffmUfNnexy53cBf/wr8/vcNl+vfH1i4UD6nYpWuA6n9gNPebvj5Hj0e+NV/gZS+imZg04HTFwEp/Rou1v0yYODDahbPdSYC/e4Esm9ouFxSD2D8YutD/GolZgJjPpaJ1f8YcGJL3CQYtWu6a07g1JflsEsVi/ZqDmDsAqDNkIbLdT4fOHmumqTG6QaypgInzGy4nLujPL5MpJTg5xupwlgiFeyKo7DuPPr374/CwkKlFSGiZs7lAl5+GfjkE+D00/1/17s38PTTci2q1q0BVWOSHW6gw5nAuZuBXtcBzmMWjHUmA9nXA+duAdqfriapAeSMc67WwNlrgCFPyYVjfTS5r9GfAKfOs947dCyHCxj8uBxK1+kc/8QwqTsw8G/AxI1AQgfri9jWciYCbU8Gzt0K9LkViEs7pj5uIOsq4JyNQJeLZVkVNE2exzOWAyf/o24vYPppMjn+1TuKj288cOK9wBkrgMwL/RNSd2fZszlxM5DUTe1+iYgopgX9zFRJSYnv/3NzczFz5kw89NBDOOmkkxAX5z/MpTk/i9Tcnpnyer3Izc3F0KFD4bQ6LIparCaNI8MAamqAqirgyBEgMRFo21Zuj7c4zK4h3nLZS1KVL/+d0B4QNX69KsoZ1fKmu7pQPsPlai17LfQ4db1DxzNNQFTLoWnVh+X+4tPlOkl29pjUlAEOF7xlB5C7cSeGDjsZTk0Aca0af224DI9MGj2/yPPrSpPDDjWHuoTxeELIZ9SEIc+rwwXEZ8hYsrp2F/nh5xupwlgiFQLFkdXcIOiITEtLg3bM0B0hBE4/7tvpaJiAornRNA1ut9vv2BKFqknjyOGQPwkJQFra0e12f8DVJk1JXY7ZaHMPQu0zWFYnQAiFrgNwyxt7v4ktbD6+/0uatKRMuFsVQXMkqOthDKR22GBChr37OZamHe3F9EsUeYOmGj/fSBXGEqlgVxwF3TO1ZMmSoN909OgQp/ltQs2tZ4qIiIiIiCKjyXqmjk2Q9uzZgy5dutTJ7IQQ2Lt3b8iVaMlM00RhYSHatWsH3eoip9RiMY5IFcYSqcJYIlUYS6SCXXEU1jv16NEDBQUFdbb/8ssv6NGjh+VKtSSmaWLnzp0wTTPSVaEoxjgiVRhLpApjiVRhLJEKdsVRWMlU7bNRxysrK0NCAqeUJSIiIiKi2BfSE7czZswAIB/g+vOf/4zExKNT5RqGgVWrVmHgwIFKK0hERERERNQchZRM5eXlAZA9Uxs3boTLdXQmLZfLhQEDBuD2229XW8MYp2kaUlNTOUONSqZXTiNteoCaEjkLnLMVIMyjs7PFiv9N3a15fkFqogOaUQnAKafQjrWY8lbIKcmrD8t/x7eT51jV+kf17rNS7rOm+OjU6NDlOkS6jVOjm1UABOA5Is9lXBpgVts7Dby3AtBd0KoLkJrkhCZqAK/Nx5diGj/fSBXGEqlgVxwFPZvfsaZOnYonn3wyKmfD42x+Mc70APs/A7b+HTi06Oj2NkOA3jcD3SbJtYqi/QFW0yvXIdrxIrDtGaB0u9yuOYHO5wH9bpdtjoXk0aiUCdTmR4Gd/wJqiuT2uDQg60qg3x1AfFu1azAZNQBMYOfL8vgWb5LbNR3oOAHocxuQMUr98TWqgMO5wJY5wM8fybWQALlwcPYNQK9rZR1U7teokl86bJ4D7Jwn114C5BcQ3S8H+t8BuDtyDSYiIopJVnODsJKpaNbckinTNLF//3506tSJM9RYZXqA5b8H9v43cJl2w4FxXwK6O3oTKtMLVBcAX40CSn+Um+DAfsdIdDKWQ8f/bsD7/RHI+b/oTqiMSuDQEmDphfKmvz4ONzDqPSB91NH1gyzt0wt4S4CFY4CijYHLZV0FnPy87KVSwfAA6+4Ctj4RuExyNjB+CeBqo+a8GlXAL2uBRWcB3lIA9cSSHgcMfw3ofK6a40stBj/fSBXGEqkQKI6abGr0iy66KOg3fffdd0OuSEtlmib27duHDh068AJhhVEJ5N7ccCIFAIUrgcXnyoQqvPlXIk8YwFdjfIkUIG+A9zl/hQ7Gt0eTqc1zgISOQPb10TlUS5iyx+2bC+QQt0CMSmDJr4GzVgOpJ8ieGys0AF+PbziRAmQvTkIGcOJM68PvasqA7c82nEgB8ngsHAucvd7a/mpVHgAWTQC8Zb5NdWLJrAFWTJJJXNtTAJ2L21Jw+PlGqjCWSAW74ijod0pNTfX9pKSkYOHChcjNzfX9fs2aNVi4cCFSU1OVVY4oaJ4iOSQrGPmLgcJv5bMp0cb0AD/9GyjdFlz57/8ih/5FI6MaWD+z4USqllkNbPizfI2lfXqAnz8EjuQFV37r32XSZ5UeB2x6OLiyJVtlDHgrre3TWwFsvN8vkQpIGMD6e44OOyQiIiIAIfRMvfzy0RvVP/3pT7jkkkvwwgsvwOGQD2EbhoEbbrihWQydoxampgzY+mRoN7VbHgdGDAb0aOux0WRbg1VTBOx+E+g+Sd6wRxNvKbD/k+DL//yxTAwsDUUToR1fowr4cS7Q6zogrlV4u/RWArvfkJNcBGvrU0CPyeHtr5YwgD1vBV8+/xug8megVZa1/RIREcWQsPq45s2bh9tvv92XSAGAw+HAjBkzMG/ePGWVawl0XUd6ejq7ra3QNKBgWWivKVwenUPfTG+9w890mEg31kFHPQll/qLAzxs1Z4UrQ0uQhSF7HK1wxAMFy0N7Tf5Saz02ZrXsLQ1F0QbrvURFG+qNiwZj6dhJXYgawc83UoWxRCrYFUdhvZvX68WWLVvqbN+yZQtXpw6Rruvo2bMnLxCW6HL4WyisDgeLFOGtd7MOL3p6P4GOen4frW0NJwE0LA59E0boSYpZbXEaei30+AXks0xWGPXvs8FY8lZY2ye1KPx8I1UYS6SCXXEU1rtNnToVf/jDH/D4449j2bJlWLZsGebMmYOrr74aU6dOVVrBWGeaJnbs2MEk1ArhBRK7hPaaxC6AGYXPfzgT5ZTVxzHhxA7nRJj1jdxN7AJoNq2JZKdWPcJ4jcUhaJpDTgMeisQugJVJUTUt9Ph1Jluf9CKp/n02GEvJvaztk1oUfr6RKowlUsGuOAormXrsscdw5513Ys6cORg1ahRGjRqFxx9/HHfccQceffRRpRWMdaZpoqCggBcIKxyJQM9pob2m51XBTWzQ3BjVQPfL6mw2oaPAMRBmfX/S0TqbX+vBQFL34Mu3ygJaD7S2z5pSoMeU0F6TfR3gsjDxTlyKfOYqFD0uB7zl4e8TAJK61Xu8AsZSfDrQ4Qxr+6QWhZ9vpApjiVSwK47CSqZ0Xcedd96Jn3/+GUVFRSgqKsLPP/+MO++80+85KqImoTvkTV5SkD0ZziSZfEVjguFMlAvyBjv9d/vTQ+9paS6EF+h9U/Dle98ccBhk0OKSgT63Bj9ZR+tBQFqOtX0CQGIm0H5scGU1Hej7R1lXS4RcfDhYva4NbzgiERFRDLM8aDAlJYUz+FHkmR5g9IfyW/6GaE7gtLctPuMSYe7OwJCnGy+X2AUY+Ub0To3uSAD63CwXi21M5/OA3jfI11gV1wo45WXIBacaEJ8uFwtWMTU6dHmuEjMbLzrkaTUJsu4Cuv8O6P77xstmjAZO/HN0fgFBRERko6CTqcGDB+PIkSMAgEGDBmHw4MEBfyh4uq4jMzOTD1Va5XTLYV4TvgPSR9ZfJvVE4PSF8sbQ6vMmkeR0y2GKI9+UQ7UA6DCQ6V0qF1nVdKDTROCsNUBcquy5i1a6C/jVf4G+M+p9VgzOZNlT96t3ZFkVnElAlwtlcp6cXX+Z9qfL4xufLmcAtMoRJ9ty1lp57urreUzqLs951lR1SY3uAk59GTjxPiAuTW46NpYcbjlMdOzn0Te1PkUcP99IFcYSqWBXHGlCBPfk9OzZs3HHHXcgMTER999/P7QGvtmfNWuWsgqqVlJSgtTUVBQXF7NHLRYZHgACKP8J2PUqUH1YDofq8hugzSA5tbildYiaEaMK0OKA/K+B/Qvkv92dgJ5Xy2d4tHggVj54vBWyN3HXv+WU3gCQNkA+OyQMe5Jjbzmg/2+q9J8/lHVIyJCJbEJ7ADrgUJTA1TK8gKgBPEeAnfOAip9lvHY8C2g/DjCr6k8qrfJWyB7MPf8BDq8GYAIp/YCsKfL30fzlAxERUQOs5gZBJ1OxorklU4ZhYNu2bejduzefN1PNWy6TJ80R/oKq0UCYMDzl2PbjLvTO7gWHK4aHYpneoxOH6PGA3gRDGE0DMMrljH26s+kSC2+FnP5c0+Ti0o6maKsXRk2ljKXeveGIUzBsklosfr6RKowlUiFQHFnNDUL6dO7WrRvGjRuHcePGYcyYMejSJcTpfKkOIQSKi4vRwnLaptFSvk3XdAiHG8WlFRCqhro1V7qzaRIov306AD0CX7xE4vkk3Xk0lqL1WTtqNvj5RqowlkgFu+IopDFAU6dOxa5du3DNNdege/fu6NWrF6ZNm4Y33ngDBw8eVFoxIiIiIiKi5iykrx7vv/9+AEB1dTWWL1+OJUuWYPHixXj11VdRU1OD3r17Y9y4cXj22WftqCsREREREVGzoeSZqSNHjmDOnDl4+umnUVZWBsMwVNTNFs3tmSnTNFFYWIh27dpxlhoKG+OIVGEskSqMJVKFsUQqBIqjJn1mqpbH48HKlSuxePFiLF68GKtWrULnzp3xm9/8BqNHjw7nLVssXdeRkZER6WpQlGMckSqMJVKFsUSqMJZIBbviKKT0/oEHHsC4cePQunVrXH/99Thw4ACuueYa/Pjjj9i+fTv++c9/YvLkycorGcsMw8D69eubdW8eNX9NHkeGAXhKgJpSwFMsp2VvCt4KoKZM/ngrmmafhke20/u//ZoqFultprwVMKqKsT4vF0Z1SaRrQ1GOn2+kCmOJVLArjkJ+Zqpr166YM2cOfvvb36Jt27ZKK9MSCSFQWVnJGWrIkiaLI2+FXOg1fxGw6xWg6pCcNbHjOUDWZEB47VkHyagCqgqArU8CxZvkttQTgL7Tgfh2gMOGKby9FXLx3F2vAvs/kVPtJ7QHsq4CMkbJ9a1ULNjbHBhVMine+iTEke9RWXE6hPc1oN/NgLuzXLC3voWEiRrAzzdShbFEKtgVRyElUwsWLMCiRYswf/583HrrrejduzfGjBmD0aNHY/To0UhPT1daOSJqRrzlQNEmYPnvgPJd/r/b9wGQdzuQ8yCQfZ26JMM0AbMSWP574OePABxzATzwGbDlcSDzfGDE64CeoG6RYqMa2PEisH6m7JE61k+vAUk9gNPeAtJOABxRvAi0EHLdrm+vkgv2ChOAC4jPAbY/B2z/O9DhDOC0t2WSrHN9FyIiomOFdOcxYcIE/PWvf8W3336LwsJCPPLII0hMTMTf/vY3ZGZm4oQTTsBNN91kV12JKFK8FbJHaOHouomUr0wpsHY6sHmOuiF4ohr4chTw84fwS6SOFpCJ3FejAOFRs0+jCtj8GLBmet1Eqlb5LuCrXwHFP8jEK1qJGmDRWcDuN/6XSNXj4JfAF6cCRmXT1o2IiCgKWJ7NzzAMfPfdd/jwww/x3HPPcTa/ENUuIJaamgpN0yJdHYpStseR6QU+6h04kTre+TuAVlnW9umtADY9BGz6v+DKnzgT6H8P4LTYU1S2C/gwyLq3ygLO3dr0CwmrYFQB258H1s7w2yygo1jvjlTzJ2g4JsHKvgEY9LeWsxg2WcbPN1KFsUQqBIojq7lByGNiTNPEd999h0ceeQRnn302WrdujdNOOw2vv/46LrzwQsybNy/kSrRkmqYhLS2NFweyxNY4Mg3g0KLgEykA2PKEnKzBCj1ODrUL1o8vytdY4S2XvVLBKtsJFCwL3KvTnGlOYNszdTfDRJq50z+RAuSzYxqH+VHw+PlGqjCWSAW74iikZKo2eTr11FPx9NNPo127dnjiiSewfft27N69G/Pnz8eVV16ptIKxzuv1YvXq1fB6vZGuCkUxW+PIWwbsmh/aa3a/AcRZnIiiYDlQlR98+apDQOFKa/t0JgE/vR7aa3b8s+lmFlSpZLNMBo/jhQur4++AF67jflEK7P+0iSpHsYCfb6QKY4lUsCuOQhqbkpaWhkcffRRjx45Fdna20oq0ZM15WCRFD9viSNPlTHqhqD4se2uszABXeTCM1xwIf3+AnKGvpii011SHkPA1Jw2cU+P4RKpWxT6bKkOxip9vpApjiVSwI45CSqbeeOMN5RUgomZOmKE/J+NIsD6Vdlxy07zmWJpDTv1uhjCZhSMJ9U+O0cyF8+xTXKr6ehAREUUxLhxCRA1zxAMdzw7tNR0nWB/6lvErQA9hinVHApB+mrV9eiuBjmeG9prO50bnelOtB4aWHGk60Oks26pDREQUjZhMRZjD4UBOTg4cDj7YTeGzNY4cCXJBXmcIvT59bgstEaqP5gS6/ib48l0vsT5BgiMe6PvH4MvHpQDdL5O9WdFGGECPyXU2O1CDHM9cOFDj/4tO5wDOyM+AStGDn2+kCmOJVLArjphMNQMuVxTeiFGzY2scmV5gwF+CK9vxLCB9hPUFXp2JwICHAFfrxsu6Wsv6OROt7VPTgfSRsmctGAMekklJNHImAifdByR0OO4XAi5RAr+hi84kYNCj0dkDRxHFzzdShbFEKtgRR0ymIswwDOTm5vLBSrLE9jiKawX0ukYmDw3pdDYw6j3rU5TXSmgPjF8CJGQ0UCZDlolvoEwo9Dhg1PsyKQxIAwb+Feh5tfUELpKcKcAZy4DELr5NBlzIjb/j6CQUcWnAuIVAUnfrz8FRi8LPN1KFsUQq2BVHQU9AUVJSEvSbNofFcIlIMUcC0Gc60O13ch2p3a/LWfscCbInp89t/+uRUpRIAbInJLk38OufgJ2vyHWRSjbL36X0B3rfBGRdCUAHHAq/bXIkAKM/lFOtb54DHPgcMKuB+LZyWF/fGTKJi/aeGocLSOwKnLddTme/9SmgaAugAWiVDfS+WiaMulMeEyIiIvITdDIVzCJXQghomsZvDohildMNtOohe6iGPnV0+nNvBaAnALoNPRe1CUvWVUdv7AE59FAY9iU0epyc0KLNMNnuY9sazb1Rx3P8L/ntdplMFE0NyM0FhtwH6CaTKCIiogYEnUwtWrTIznoQUTSpXZC3dthXUyQXx/c86U6EuLpD6DRdJlK1/w/EViJ1rNqkyvzfYoaa4t4+IiKiGKQJIaJwgZTwlZSUIDU1FcXFxc1iOKIQAoZhwOFwNNrzRxQI44hUYSyRKowlUoWxRCoEiiOruYGlr3UrKiqwZ88eeDz+C1zm5ORYedsWx+PxwO12R7oaFOUYR6QKY4lUYSyRKowlUsGOOArrAYeCggKce+65SE5OxgknnIBBgwb5/VDwDMPAhg0b+JwZWcI4IlUYS6QKY4lUYSyRCnbFUVjJ1PTp01FUVIRVq1bB7Xbjs88+wyuvvILs7Gx8+OGHSitIRERERETUHIU1zO/rr7/GBx98gKFDh0LXdXTr1g1nnHEGUlJS8PDDD2PixImq60lERERERNSshNUzVV5ejowMuUBm69atUVBQAAA46aSTsHbtWnW1ayEcDkekq0BWmV7AqAbK9wAHvwYOLQaqCgBvpZxS2y41ZYC3HMhfCoenACjeJOvirbBvnxTzeE0iVRhLpApjiVSwI47C6pnq06cPtm7diu7du2PAgAGYO3cuunfvjhdeeAEdO3ZUXceY5nQ6MWzYsEhXg6wwqoBDi4BNDwMFS49u13Sg0znAifcBqScenWJbBW+FXDB34/3A7jfhNCowDAD2AUjpKxez7TUN0Dm1NYWG1yRShbFEqjCWSAW74iisnqlbb70VBw4cAADMmjULCxYsQNeuXfHUU0/hoYceUlrBWCeEQFFREVrYDPWxw6iWSdTic/wTKUD2SP38MfDFcGDvO7KXSgVvJVCyBfg0B9g5DzAqIKCjSM+CgC5/l3sT8M1FgOFp/P2IjsFrEqnCWCJVGEukgl1xFFYydfnll2PKlCkAgCFDhmD37t1YvXo19u7di0svvVRl/WKeYRjYsmULZ6iJRoYH2Ptf4PsHGi4nDODbqXIInoohf8IDLDoTqCk6WhU4sSVuEoxjO5v3fwLk/VEOBSQKEq9JpApjiVRhLJEKdsVRWMnUAw88gIqKo89kJCYmYvDgwUhKSsIDDzRyY0kUKzQd2PR/wZUVBrDpL7InywpvObDteTnELxg7XgLAb/KIiIiI7BBWMjV79myUldX9truiogKzZ8+2XCmiqFC0Hij+IfjyP38MGOXW9ulIALY/H3x5o0omVKqGGBIRERGRT1jJlBACmqbV2b5+/Xq0adPGcqVaEk3T4Ha76z2e1MwVrgqtvDCA4i3W9mlUARV76mzWIOAWhdDq64U6kgeYfHaKgsNrEqnCWCJVGEukgl1xFNJsfq1bt4amadA0Db179/arjGEYKCsrw3XXXae0grHO4XBgwIABka4GhSWc4XMWn5kK8NCkAzUY4Jkb4DU2Ts1OMYfXJFKFsUSqMJZIBbviKKRk6u9//zuEELjqqqswe/ZspKam+n7ncrnQvXt3DB8+XHklY5lpmigsLES7du2g62F1FFKkpOWE+AINSM62tk9nIpCQAVTl+202oaPQkYN2xgboxydsKX0BPaxVEKgF4jWJVGEskSqMJVLBrjgK6Q7ryiuvBAD06NEDI0eOhNPJGzSrTNPEzp070aZNG14gok274UBSd6D8p+DKtx8HuFpb26e3Asi6GvjBfwkCE07sdE5EG+MH6DhmSJ/mBLKvA5xJ1vZLLQavSaQKY4lUYSyRCnbFUVjvNHr0aOzevRszZ87EpEmTkJ8vvyVfsGABNm3apKxyRM2a8AJ9/xh8+RPuBvQ4a/uMawX0vRVwJAZXvtslwZclIiIiopCElUwtWbIEJ510ElatWoV3333XN7Pf+vXrMWvWLKUVJGq2HAlAr2uArCmNlx3wEJA+EtAc1vfrbAWMehfQ4xsu1/Zk4OSX5NBAIiIiIlIurGTqrrvuwl/+8hd8+eWXcLlcvu3jxo3Dt99+q6xyLYGmaUhNTeUMNdHK4QKGzQWGPgu06ln3960HAb/6L9D3Npl8qeBMBNJPA85cAXScAECDBoFUc6eczS++LdDvDmD8EkB3Nfp2RMfiNYlUYSyRKowlUsGuONKECDA9WANatWqFjRs3okePHkhOTsb69euRlZWFn376CX379kVVVZXSSqpUUlKC1NRUFBcXIyUlJdLVoVhhVMnnkw6vBoo2ygkf2g6Tkz8IAA6Lw/vqYxqA8MgFfA9+JZ+nSuoqEyyjCohLVr9PIiIiohhiNTcIq2cqLS0NBw4cqLM9Ly8PnTt3DuctWyzTNLFv3z6YJqevjmqOBJlApQ8Hsq8Bel4FpJ0kn5GyI5ECAN0BONxAYibM7pOxL/F8mB3PkftkIkVh4jWJVGEskSqMJVLBrjgKK5n63e9+hz/96U84ePAgNE2DaZpYvnw5br/9dkyePFlpBWMdLxCkAuOIVGEskSqMJVKFsUQqNKtk6qGHHkLfvn3RpUsXlJWVoX///hg1ahRGjBiBmTNnKq0gERERERFRcxTWQlEulwsvvvgi7rvvPmzcuBFlZWUYNGgQsrMtLkhKREREREQUJUJKpkzTxKOPPooPP/wQHo8Hp59+OmbNmgW3221X/WKerutIT0/nInRkCeOIVGEskSqMJVKFsUQq2BVHIc3m9+CDD+L+++/H+PHj4Xa78fnnn2PSpEmYN2+e0krZibP5ERERERER0MSz+f3rX//Cc889h88//xzvv/8+PvroI7z22mt8IDAc3krArIFpmtjx43aYnvJI18g+3nJAGPL/jWrA9DTBPuXxBQCYXjltuN2MGrlfABCmbLdp2L9fyF7jHTt2NN3folF1zPE1mub4ClMeXyHkj7dSbiOlmjyWKGYxlkgVxhKpYFcchTTMb8+ePTjnnHN8/x4/fjw0TcP+/fuRmZmptGIxy1sJeEuBrU8Be9+FWVOOAv16dCsth97vZjmltSNGhk2aNUDxJmDzHKBguUyikroAPacB3X8vy6hayLaWtxIwq4BtzwK7/wN4fpHHtNNEoO8MuaCt8n1WyWnRf/4Q2P4cULod0HQgbQDQ51YgY9T/1poK6xHFoJimiYKCAnTr1s3eYRDeSkB4gR//Afz0b6AqH3AmAR3PBPr+EXB3AhzxavdpeuV/D34p/26KN8l/p54gj2+HMwAIeQ7IsiaLJYp5jCVShbFEKtgVRyHdfXi9XiQk+N+IxsXFoaamRlmFYppRDWx5HNg462hPDVxAfBmw+VFgy8PASbOAfneqvyFtakYl8M1FwIHP/LdX/gwUfgus/SMw+kOgzVDAqSh5NKqAnS8Da6cf7TUBgEoAJVuBLU8AfW8DBj4M6C41+/RWAhV7gUVnAuW7/X9XvlsmWK0HAWO/AJACOBTtNxKMSmDfe8CqP8hjfazS7TKB7fkHYNhz6o6v4QGq84Gvx8tzeKyKvTK+UvoC474C4jPsW9OLiIiIqB4hJVNCCEyZMgXx8Udv9KuqqnDdddchKSnJt+3dd99VV8NY4a0Etv4d2NDA1PHCADbcB0AH+s2I3h4q0wMsOhvIXxK4TE0R8PUZwBnLgNYD5EKzVngrgZ9eBXJvbKCQkMksBDDgYTUJq6cQ+HIkUF0YuMyRPOCr04AJudGbTHkrgf2fACsuh+xmC2DHP2Uie8qLahIqbynwxXCgYl/gMiVbZJmz1wGONtb3SURERBSkkPq4rrzySmRkZCA1NdX3c/nll6NTp05+24L1zTff4LzzzkOnTp2gaRref//9Rl+zePFiDB48GPHx8ejVqxfmz58fShMix6j8X6LkT4eBTO9S6Djm2ZqNs5rmGRQ7mDXAnncaTqR8ZauB765RtGMBrLktuKJbngAqD1jfpbdc9rA1lEjVKtkK/PAIUFNmfb/10HUdmZmZ9g1/0J3A6uvRYCJVa9e/gKJN1vfprQDW39twIlWrYq/8oiJa/26aEdtjiVoMxhKpwlgiFeyKo5B6pl5++WWlOy8vL8eAAQNw1VVX4aKLLmq0/K5duzBx4kRcd911eO2117Bw4UJcffXV6NixIyZMmKC0bkp5K4Ftz8hnTY6jw0Cm8Y3/RmEAW58G+v9J3RC4JiNkD1ywjuQBxZuB1jnh79KolsP7jBBupLfMAQY+AjgTre133/vBl9/xohzGaYPaC4QtTC+w973gksZaWx4DTn7R2vHVNOCn14Ivv+vfwODHw98fAbA5lqhFYSyRKowlUsGuOIroE9tnn302zj777KDLv/DCC+jRowfmzJkDAOjXrx+WLVuGJ554ImAyVV1djerqat+/S0pKAMjnv7xemdzoug5d12Gapt8MH7XbDcPAsTPIB9rucDigaZrvfX3boQF734MB13HbPTDgwta4S5Bd8x50yOd8nPBA7H0fRt8/Af97L03T4HA46tQx0Hbb2+RwAAAMw3+2OodRDhzOrdNWJzwQ0GEcE3IaBByogfnT6zATs3wTQ4TcJtMDfe+7MBAHAe3odnihw6yz3QEvtH3vwzvwCd/xbbBNAbY7f/4IwjT82uprE3SYx7e16hDMw2thth7ceJtCPE8AsH37dvTs2dPvG5eQ2+R0Qgjht13zlsGx563624QamHDAhONoHWFC3/cBzFNfhXnM8Q059g4thuYtg7fO300NAFH378lbChz6BkbGuMbbFC1/T6GcJ0VtEkLgxx9/rBNL0dymWDxP0dCm2pmzevXqBU075tocxW2KxfMUDW2qjaXs7Gy/stHcJiD2zlNzb5Npmti+fTv69OkDh8Phq+PxbQ5VVE1/tXLlSowfP95v24QJEzB9+vSAr3n44Ycxe/bsOtvz8vJ8z3mlp6ejZ8+e2LVrFwoKCnxlMjMzkZmZiW3btqG4uNi3PSsrCxkZGfj+++9RWVnp2963b1+kpaUhLy/PL4hyTjoRrpoK5Mbf4VeHodWPolJriz3O8SjRe0CDCQc8GFb9KIq9rbBl7XpfWbfbjQEDBqCwsBA7d+70bU9NTUW/fv2wf/9+7Nt3dDiU7W3KyYHL5UJubq5/m07oBI/WFhtc1/q2+dqkd8eWuElH2yQKMcAzF4Vlcdi5dj2gOcJrU6cMZNYUY1vcb1CsZx1tk/cTZBjr8L3rKlRq7Y62qeYNpNUUBt+moUPh8XiwYcOGo21yODAs+UjgNjlysNM50bc91dyJfjVvYP+hX7Bvx9H3V3WesrOzUVxcjHXr1vldeEJu07BhKC4uxpYtW462Kd6JATXFgdvkGIl9zl8dbZOxDj29n2DXrp0oKDjamxVy7KUUIw1AXvytfolTjmcuXKKk3r8nT/kRbDimrQHbFC1/T6GcJ0Vt6tixI4qLi7F9+3aUlpbGRJti8TxFQ5uSk5NRWlqK/fv348CBo0Oro7lNsXieoqFNtTfWtdemWGhTLJ6n5t4mIQSKiorQtWtXuN1uX5vKy60tTxTSor120jQN7733Hi644IKAZXr37o2pU6fi7rvv9m379NNPMXHiRFRUVMDtrjskrr6eqS5duuDw4cO+hbns75nyAl+MhFH0/XHbPfAiHrnxd2Jw9RNwQK6/5IQHIm0wjPHLfBMzNJes/tjtQH09U2XAf9vAgP+EEg32TPW/B2b/ewA9Prw2mZXQl10E4+CS4HumkrrAO/HH4NoU6NuXvW9ArJxSf5sC9eKcuQZm2tEhjarOkxACa9aswaBBg3z1DatNgXqmcq+Buee/wfdMxSXCvPiItdjL/xra4gnB90zBA4z9Ckb66MbbFC1/TxH45s80Taxdu7ZOLEVzm2LxPEVDmwzDQF5eHgYPHlxvL2c0tikWz1M0tKk2loYMGeLXyxnNbQJi7zw19zYZhoG1a9di6NChcDqdvjqWlJSgbdu2YS/aG1U9U+GIj4/3m32wltPphNPp3/zak3G8Y28ogtl+/PvC8ALdL4Fz3do6ZTUIX4+UE0cXs9W6/QZOHUCQdQx1u+U2BdquuYH0kXAWLKtTVoPp10ZfHbMmQ3cl1d0ebJvMeKDb7+A4+FW9dXSgnqn7u1ws61LPMz1BtzXz19D0ODjN6jpldZjQj2+ruzP0NjnQ61kPyep5qr1QOByOeusfdJsgL25+2/VkoNvvoe95u26bIJ/785tABQC6XAzdrIJez/ENOvbajwbiUuGsKa63fJ1YiksDMn4FZz3redVpU23dm/vfUwPb7WpTY7EUjW2qFUvnqVa0tCmU8tHSplg8T2wT2xTrbdI0zfdTuz1Q24IVVdOidOjQAYcOHfLbdujQIaSkpNTbK9VsOBKAXtehvqmidXiR5f0EOo75JkCPA7Kvj8LJJyCH6vWZHnz5tqcAST2s7VN3Ad0vA5zJQb5Akwv4WpkcAZAL83b9TfDle10rJ62wga7ryMrKqveiY/3NHUDnc4GEDsG/pu8frR9fYQA9JgdfPuvKeid5odDYGkvUojCWSBXGEqlgVxxFVVQOHz4cCxcu9Nv25ZdfYvjw4RGqUQh0FzB4Tt3NMJFhrIOOo92YGPSob8hb1NGdQOb5QKcgJhZxJgGnvCRnbbNKCODkucGVPXEmEN/W+j6dreTsce5OjZdNywH63w7E1e2BU0HXdWRkZNj3QWPWAKe+LBPIxvS+EUjuZX2fzkRgwF+AVj0bL9uqJ5DzgPUEjuyPJWoxGEukCmOJVLArjiIalWVlZVi3bh3WrVsHQE59vm7dOuzZswcAcPfdd2Py5KPfTF933XXYuXMn7rzzTmzZsgXPPfcc3nrrLdx2W5DrC0WS0w30nAYMfcY3ax0AGIjDete18hkjPR4Y+rTsxYrGXqlaehzwq/eAbpcGLuPuBIxfKm+6rS7YC8jj1eVCYPi/ZZIWqF45DwInzPQ7B5bEpQBnfguk9g9cJmMUcMZSAAraGYBhGFi/fn2dscvKON1y2N2oD2Sb66M5gH63ywRTxYLIAOBwA2euBNoMCVymzVBgwreAg4mUCrbHErUYjCVShbFEKtgVRxF9Zio3Nxdjx471/XvGjBkA5OLA8+fPx4EDB3yJFQD06NEDn3zyCW677TY8+eSTyMzMxEsvvdS815g6liMeyLpKDl368R/A3ncgqstRiV4Q/R4Gel8DaE51N6KR5IgHTn0FGPCwXNOpYLkc4pbUVQ5363yeHMalsq2OBKDLRTKp2vkysPsNoPowEJcMdD4fyL5RlnHUHW5paZ8J7YGz1wMF3wBbnwFKt8nEovUAoM9tQNqJAByAjd+oCSFQWVlZZ7p0pRxuoP3pwEX5cv2nXa8AVfkyee14FtDnFplo1TOcNWx6HOBqIxPWX3LlGma1CwKnnSCPb5shADQ5HJEsa5JYohaBsUSqMJZIBbviqNnM5tdUSkpKkJqaGvaMHcoYVQAEvIaO3DVrMHTIADjrmYQhJnjLZZKo6XK4mO6SwwHtZFQDMOV+hSGHAdrd22cagFH5v2RCAKZHJnJNwOv1Ijc31zdDje1Mj2yvXnt8TfuH2AkhF2bW/tfDJ2pkb5SKYaLk0+SxRDGLsUSqMJZIhUBxZDU3YERGSu0wM+GVSUa0PiMVjGOH3akY0hcMvx6vJtqn7gD0VgHqEGN01zGDhJvo+GracUM4FfaAEREREYWBT/JFmMPhQN++fQNO50gUDMYRqcJYIlUYS6QKY4lUsCuO2DMVYZqmIS0tLdLVoCjHOCJVGEukCmOJVGEskQp2xRF7piLM6/Vi9erVdVacJgoF44hUYSyRKowlUoWxRCrYFUdMppoBTvVJKjCOSBXGEqnCWCJVGEukgh1xxGSKiIiIiIgoDEymiIiIiIiIwsB1piKsdgExt9sNjevlUJgYR6QKY4lUYSyRKowlUiFQHFnNDdgz1Qy4XFwvh6xjHJEqjCVShbFEqjCWSAU74ojJVIQZhoHc3Fw+WEmWMI5IFcYSqcJYIlUYS6SCXXHEZIqIiIiIiCgMTKaIiIiIiIjCwGSKiIiIiIgoDJzNL8KEEDAMAw6HgzPUUNgYR6QKY4lUYSyRKowlUiFQHHE2vxjg8XgiXQWKAYwjUoWxRKowlkgVxhKpYEccMZmKMMMwsGHDBs5QQ5YwjkgVxhKpwlgiVRhLpIJdccRkioiIiIiIKAxMpoiIiIiIiMLAZKoZcDgcka4CxQDGEanCWCJVGEukCmOJVLAjjjibHxERERERtUiczS/KCSFQVFSEFpbTkmKMI1KFsUSqMJZIFcYSqWBXHDGZijDDMLBlyxbOUEOWMI5IFcYSqcJYIlUYS6SCXXHEZIqIiIiIiCgMTKaIiIiIiIjCwGQqwjRNg9vthqZpka4KRTHGEanCWCJVGEukCmOJVLArjjibHxERERERtUiczS/KmaaJ/Px8mKYZ6apQFGMckSqMJVKFsUSqMJZIBbviiMlUhJmmiZ07d/ICQZYwjkgVxhKpwlgiVRhLpIJdccRkioiIiIiIKAxMpoiIiIiIiMLAZCrCNE1DamoqZ6ghSxhHpApjiVRhLJEqjCVSwa444mx+RERERETUInE2vyhnmib27dvHhyrJEsYRqcJYIlUYS6QKY4lUsCuOmExFGC8QpALjiFRhLJEqjCVShbFEKjCZIiIiIiIiakaYTBEREREREYWByVSE6bqO9PR06DpPBYWPcUSqMJZIFcYSqcJYIhXsiiPO5kdERERERC0SZ/OLcqZpYseOHXyokixhHJEqjCVShbFEqjCWSAW74ojJVISZpomCggJeIMgSxhGpwlgiVRhLpApjiVSwK46YTBEREREREYWByRQREREREVEYmExFmK7ryMzM5Aw1ZAnjiFRhLJEqjCVShbFEKtgVR5zNj4iIiIiIWiTO5hflDMPA5s2bYRhGpKtCUYxxRKowlkgVxhKpwlgiFeyKIyZTESaEQHFxMVpYByEpxjgiVRhLpApjiVRhLJEKdsURkykiIiIiIqIwMJkiIiIiIiIKA5OpCNN1HVlZWZyhhixhHJEqjCVShbFEqjCWSAW74oiz+RERERERUYvE2fyinGEYWL9+PWeoIUsYR6QKY4lUYSyRKowlUsGuOGIyFWFCCFRWVnKGGrKEcUSqMJZIFcYSqcJYIhXsiiMmU0RERERERGFgMkVERERERBQGJlMR5nA40LdvXzgcjkhXhaIY44hUYSyRKowlUoWxRCrYFUdOpe9GIdM0DWlpaZGuBkU5xhGpwlgiVRhLpApjiVSwK47YMxVhXq8Xq1evhtfrjXRVKIoxjkgVxhKpwlgiVRhLpIJdccRkqhngVJ+kAuOIVGEskSqMJVKFsUQq2BFHTKaIiIiIiIjCwGSKiIiIiIgoDJpoYSuglZSUIDU1FcXFxUhJSYl0dXwLiLndbmiaFunqUJRiHJEqjCVShbFEqjCWSIVAcWQ1N2DPVDPgcrkiXQWKAYwjUoWxRKowlkgVxhKpYEccMZmKMMMwkJubywcryRLGEanCWCJVGEukCmOJVLArjphMERERERERhYHJFBERERERURiYTBEREREREYWBs/lFmBAChmHA4XBwhhoKG+OIVGEskSqMJVKFsUQqBIojzuYXAzweT6SrQDGAcUSqMJYoXKYJVFYCv/wCbNwI/PCDB1VVQEVFpGtG0Y7XJVLBjjhiMhVhhmFgw4YNnKGGLGEckSqMJQpXVRWwdClw8cVAejowbJiBt97agC5dDNxxB7BvnyxDFCpel0gFu+KIyRQRERFZUl0NPPQQMGYMsGCB7KGqVVoKPPcc0L8/sHYtEyoiii1MpoiIiChsVVXA668DDz7YcLnSUuCss+QQQCKiWMFkqhlwOByRrgLFAMYRqcJYolDExQEPPFD/7zwe/1gqLQX++lc+Q0Wh43WJVLAjjjibHxEREYVt4UJg/Pjgy6ekAPn5QHy8fXUiIgoWZ/OLckIIFBUVoYXltKQY44hUYSxRKDweOelEfXRdICurCLruH0slJcDevU1QOYoZvC6RCnbFEZOpCDMMA1u2bOEMNWQJ44hUYSxRKIQAvN76f+d0Gpg0aQuczrqxVFNjc8UopvC6RCrYFUdMpoiIiCgscXFA796hvcbhADp2tKc+RERNjckUERERhUXXgUsuAZKTg3/NuefyeSkiih1MpiJM0zS43W5omhbpqlAUYxyRKowlCpVhAFddVXe7EBoKC90Qwj+W7roLSEhoospRTOB1iVSwK444mx8RERFZUl0NnHFG4Mkoaj38MDB9OpMpImo+OJtflDNNE/n5+TCPXS6eKESMI1KFsUThcLmAL78EbrsNSE2V23TdxMCB+dB1E9nZwGuvMZGi8PC6RCrYFUdOpe9GITNNEzt37kSbNm2g68xtKTyMI1KFsUTh0DT5HNRf/iJ7n/77X2D7dhNZWTvRvXsbDB+uwzD4rBSFh9clUsGuOGIyRUREREokJsr/TpoEVFUB69YBw4YBTqf8ISKKNUzviYiISClNk9Omc74AIop1TKYiTNM0pKamcoYasoRxRKowlkgVxhKpwlgiFeyKI87mR0RERERELRJn84typmli3759nKGGLGEckSqMJVKFsUSqMJZIBbviiMlUhPECQSowjkgVxhKpwlgiVRhLpIJdccS5dYiIiJpAeTmg63JSBsMA3G75byIiil5MpoiIiGxUXQ3s2wfMmQPk5gJeL5CVBdx0E3DaaYAQcuY7IiKKPkymIkzXdaSnp3MROrKEcUSqMJbUEUKutTRpEvDBB/6/y8uTC9v27Qt8+inQqVPsLWjLWCJVGEukgl1xxNn8iIiIbODxAOPHA0uXNlwuPV0mVx07ctgfEVFTi4nZ/J599ll0794dCQkJOOWUU/Ddd98FLDt//nxomub3k5CQ0IS1Vcs0TezYsYMPVZIljCNShbGkhscDzJvXeCIFAAUFwI03yuGAsYSxRKowlkgFu+Io4snUf/7zH8yYMQOzZs3C2rVrMWDAAEyYMAH5+fkBX5OSkoIDBw74fnbv3t2ENVbLNE0UFBTwAkGWMI5IFcaSGroOPP108OU//hgoK7OvPpHAWCJVGEukgl1xFPFk6vHHH8e0adMwdepU9O/fHy+88AISExMxb968gK/RNA0dOnTw/bRv374Ja0xERNSwn38Gfvgh+PKGAbz5JsB7RSKi6BLRCSg8Hg/WrFmDu+++27dN13WMHz8eK1euDPi6srIydOvWDaZpYvDgwXjooYdwwgkn1Fu2uroa1ceMnSgpKQEAeL1eeL1e3z51XYdpmn7Zau12wzBw7KNlgbY7HA5omuZ732O3A4BhGHW2CyEghPD7ndPprLNN0zQ4HI46dQy0PZJtqm8722Rvm2r/P9i6R0ObYvE8RUObav//+H1Gc5sicZ4OHwYAJ3RdwOk8ul0IDTU1Dui6CafT9NteWOiAx+O/vTm1CQjtPNWWMU3Tb7/R3KZoiL1YbFPt/wsh/N4nmtsExN55au5tqv398ffex7c5VBFNpgoLC2EYRp2epfbt22PLli31vqZPnz6YN28ecnJyUFxcjMceewwjRozApk2bkJmZWaf8ww8/jNmzZ9fZnpeXh6SkJABAeno6evbsiV27dqGgoMBXJjMzE5mZmdi2bRuKi4t927OyspCRkYHvv/8elZWVvu19+/ZFWloa8vLy/IIoJycHLpcLubm5fnUYOnSoL9nLy8sDIE/8sGHDUFxc7HcM3G43BgwYgMLCQuzcudO3PTU1Ff369cP+/fuxb98+3/ZItsnj8WDDhg2+bWyT/W3q3bs3MjMzsX79er8LTzS3KRbPUzS0qVOnTsjMzMSOHTt8Xz5Fe5sicZ4qKx0AhqF792JMmnS0TYWFbsydOwA5OYWYOPFom3buTEVKSj/k5+/H/v3Ns02hnqeUlBRkZmbi4MGD2L9/f0y0KRpiLxbbpOs6MjMzUVpaim3btsVEm2LxPEVDm2rvuzVN87WpvLwcVkR0Nr/9+/ejc+fOWLFiBYYPH+7bfuedd2LJkiVYtWpVo+9RU1ODfv36YdKkSXjwwQfr/L6+nqkuXbrg8OHDvhk7mNWzTWwT28Q2sU0q22QYQN++TuzZE1zPFKDhxx8d6Ny5+bYJiL3zxDaxTWwT21RSUoK2bduGPZtfRHum2rVrB4fDgUOHDvltP3ToEDp06BDUe8TFxWHQoEH48ccf6/19fHw84utZvMPpdMLp9G9+7ck4Xu1BD3b78e/b0HbDMLB9+3b07t3b7/00Tau3fKA6hrrdzjYF2s422dcmwzCwbdu2OnHUUN0DbW8ubWqojqFuZ5uC324YBjZv3ozevXvX+/7R2KZaTXmevF7ghhuAO+/U4PHULW+aOjyeo3UcP15Okd6c21Qr2PPUWCxFY5vC3c42WWtTuLHUnNtUK5bOU63m2qbj75VqtwdqW7AiOgGFy+XCkCFDsHDhQt820zSxcOFCv56qhhiGgY0bN6Jjx452VdNWQggUFxf7ZdJEoWIckSqMJTUSEoCbbgJOPLHxsklJwFNPAS6X/fVqSowlUoWxRCrYFUcRn81vxowZePHFF/HKK69g8+bNuP7661FeXo6pU6cCACZPnuw3QcUDDzyAL774Ajt37sTatWtx+eWXY/fu3bj66qsj1QQiIqI6XC7gm2+AYcMCl2nXDli8GOjRA6jnC1giImrmIjrMDwAuvfRSFBQU4L777sPBgwcxcOBAfPbZZ75JKfbs2ePXxXfkyBFMmzYNBw8eROvWrTFkyBCsWLEC/fv3j1QTiIiI6nA4gNRUYMUKYPly4PHHgdxcoKYGyMoCrrsOmDRJlq1nNDoREUWBiE5AEQklJSVITU0N+yEz1UzTRGFhIdq1a1fvuFCiYDCOSBXGkj1ME6iqAhIT5b89HkDTgLi4yNbLTowlUoWxRCoEiiOruQGTKSIiIiIiapGs5gZM7yPMMAysX7++ztSSRKFgHJEqjCVShbFEqjCWSAW74ojJVIQJIVBZWckZasgSxhGpwlgiVRhLpApjiVSwK46YTBEREREREYWByRQREREREVEYmExFmMPhQN++fQOu2kwUDMYRqcJYIlUYS6QKY4lUsCuOIr7OVEunaRrS0tIiXQ2KcowjUoWxFDvKyuT0619+CRw+DLRtC5x5ppymvVUr+/cf67FUXi7XElu4EMjPB1q3Bk4/XW6rnQJftepq+f7r1wM//CD/f9gwoFs3ueiz04a7OsOQ+y0pkQtMV1TI/Y0eLaf7ZyxRsGpqACGAnTuBNWvktejEE4GTTpL/73LZu3+74og9UxHm9XqxevVqeL3eSFeFohjjiFRhLEW/qiqgoAC49VYgIwO48ELg6qvlfzMygBkzZHJVXW1vPWI1lrxemVjccYc8nueeC1x11dHje/31wIED6o+vxwO8+qq8+Rw6FJg8GbjsMqB3b5nYfPUVUFmpdp/l5cCmTcAllwCdO8tFpv/wB2D8eKBLF+Cxx2S9TFPtfo8Xq7HUklRVAZ99BowcCfTrB1x+uYzhwYOBgQOBN96I3msSk6lmgFN9kgqMI1KFsRS9PB7ZSzJoEDBvnuxFOFZ5OfDii/L3hw/L8naKtVgyDKCoSN4APv88UFrq//uqKuBf/5I3h/v2qTu+Hg9w443AtGnA1q11f//tt8DZZ8tzqyqhKi8HVqwATjkF+OSTugnTwYPA7NnAGWfIHge7xVostSRVVcCTTwLnnw/k5tb9/aZNwJQp8gsKuxMqO+KIyRQREVGMcDjkUL6ff2643N69wIQJcmgYBc80gXPOAXbsaLhcfr4c8qfi+FZVySTppZcaLzt9OrB2rZqeovJy2dtWVdVwuW++AW64QZYnOp5hAMuWAXfd1XjZp59umh4q1XgZJSIiigGGAXzxRf09F/X5/nt5I8wv/IO3fj2wenVwZXfvBj74QA4LtCIuTg6nC4YQwEMPWb8ZLSsDnngi+ATp3/9umt4pij5eL/B//xd8+UcekV8KRRMmUxHmcDiQk5PDGWrIEsYRqcJYil5VVcAzz4T2mqefVv+cTa1Yi6XycuDvfw/tNU89ZT1ZXbYM+Omn4Mt/9plMhqxwu4F//jP48h4P8I9/1B1WqkqsxVJLUlAgJy4J1pYtQF6ePXWxK46YTDUDLrunL6EWgXFEqjCWolNCArB5c2iv2bwZiI+3pz5AbMWSwyFv9EKxZYu14ytE6DeWptn4MMTGlJXJm+BQbN5sb+9ULMVSSxJsT/mxNmxQX49adsQRk6kIMwwDubm5fLCSLGEckSqMpeimafaWD0WsxZIQkTm+4byH1f1GYp8NibVYakmaUyzZFUdMpoiIiGJAVZVcryUUJ50UfQ97R4pphn58Tzyx8QkcGqJpwMknh/YahwPo1Sv8fQJy7ahOnUJ7TU6O/esEUfTp1y/05GjwYHvqYhcmU0RERDEgIQG45ZbQXnPrrfYtMBtrkpKA224L7TXTp8sJJKwYNgzIzg6+/HnnWT+nlZVyGvZgJSTItczcbmv7pdjTurWcPj9YOTnACSfYVx87MJkiIiKKAQ4HMGoUMGBAcOWHDJFrCHF69OD17SuPcTB69wbOOsv6zGSGAdx9d3BlHQ7g3ntlcmNFUhJw881Aampw5a+6ytr+KHa5XMDMmcH3Tt1zjxxSG000IaKtytaUlJQgNTUVxcXFSElJiXR1IISAYRhwOBzQ7BxwTDGNcUSqMJaiW00N8MsvwIgRwM6dgctlZ8sFWdPSAKfTnrrEYiyZplyo97TT5NTygXTpIo9vhw5qjm91tbzJfPzxwGV0Xc7Ad+mlanqIKiqAdevkemQNzQ549tnA++/bO8QvFmOpJamsBObPlwtPN5R1zJwpf+yaFCdQHFnNDfh9VDPgsXsJemoRGEekCmMpesXFyQRp7Vpgxgw5xOZYbdsCt98OrFkDpKTYl0jVirVY0nX5PNGqVTK5ycjw/31qquzRWbcOaN9e3fGNj5frR739thz2dyxNkwnPsmXqEilADhUcOFDOJnjZZXWTpV695FpUdidStWItlloStxuYMgVYtEguZn18Pjx8uFyT7d577Z1dFLAnjtgzFWFerxe5ubkYOnQonHZ/qlHMYhyRKoyl2FFaKm9MVqwAjhwB2rSRNy0ej0wI7BbrsVRRIZOI774DCgtlInXqqXJYnl3PodXUyG/2d+8Gtm2TyVpOjkyaXS77hmyWlcl2rVol292lixxO6vE0zTN3sR5LLYVhyBg+fBjYuFH29PbtC2Rmyti1+9QGiiOruQEjkoiIKAYlJ8v/jhnjv50zrqlRm0SMGNF0+6ydzCI7O7RJKayqTb7PPNN/O/MaCoXDIX86d5Y/sYLD/IiIiIiIiMLAZKoZcFid6ocIjCNSh7FEqjCWSBXGEqlgRxzxmSkiIiIiImqROJtflBNCoKioCC0spyXFGEekCmOJVGEskSqMJVLBrjhiMhVhhmFgy5YtMAwj0lWhKMY4IlUYS6QKY4lUYSyRCnbFEZMpikkVFQ3/m4j8eb1ymuNahuH/b4pO5eUN/9uufR57r1JdLadDJjUqK/2Pr8fDv1WiSGIyRTGlqkquv/GnP8mV5+Pi5Noq110HbNjADxyi49XUyJ+33wZGjwbatQM+/RQYORKYN0/euFVXR7qWFKrqamDrVmD6dHlO4+KA9HS5kO/27fac0/JyubbV888DJ5wg1z5asAC48ELgk09kws6kKnwVFfLnpZfkYroJCXJ69nHjgHfflceWn3FETY/JVIRpmga32w3t+OWgKWTV1XKF+O7dgWeeAQ4dkh/eR44Ar74qFxi86qrY/LBhHFE4PB5g/36gTx/g978Hvv0WqKjQkJ/vxoYNGq6/Xi7O+f338osKig41NcBtt8nFMF96SS6Q6fXKhWX/8Q+gd2/gzjvVXgvLy+XiwJ06AXfcIRO56moNhw65sXixhgsvBAYPBoqKmFCFo7xcfiGYmQnccov8m6yull92LF8OTJoE9O8vP/di8csPfsaRCnbFEWfzo5hQWQn8/e/APfc0XnbyZODFF7lwJdEvvwA5OcDPPzdcLjkZWLMG6NlTrlJPzVdVFXDXXcCTTzZe9o47gAcekD0cVng8QF4eMGpU4wla374ylmoXvKXGGQawbRswbFjjwzS7dgXWrwfS0pqkakQxgbP5RTnTNJGfnw/TNCNdlajm9QKzZwdX9l//AnbutLc+TY1xRKGqqAAefLBuIqXrJgYOzIeuH42l0lJ54x2L33jHmsOHgaeeCq7snDlASYma/U6fXjeRqi+WtmyRIwea4tmtWOHxAH/8Y3DHbM8e4OGHgbIy++vVlPgZRyrYFUdMpiLMNE3s3LmTFwgLqqqAuXNDu9F77LHY+jBnHFGonE5g/vz6tpuYOHEnnE7/WPr449i7QYs15eXA448DwY43MU3Zg2V1gp4dO+QQ0eMFiqXnnwfi463tsyU5cgT4/PPgy8+bF3vHl59xpIJdccRkiqKeacobvVAsWAAkJdlTH6JosG6dfH4lWIYh/26o+XK5Qj9Hn3wCOBzh79PjAd57L7TX/PQTsHdv+PtsaT76SH7OBauwUD5fRURNg8kURT1dD72Xid+wU0sXzt9AKMkXNb24uPCuhU5n+PusqQkvlngNDo5hyGG2oQrnNUQUHiZTEaZpGlJTUzlDjQWGIaf+DUW7dsEPhYkGjCMKVaC/GSE07NyZCiHqxlKnTjZXiizxeEK/FqanW5tdLz4eyMio/3cNxVLbtuHvsyVxOID27UN/XaBzEq34GUcq2BVHTKYizOFwoF+/fnBYGWfRwrlcwBVXhPaa3/8+tqZ6ZhxRqPr3l8sIHK+mxoE33uiHmhr/WGrVCpg4sWnqRuExTeCyy0J7zeWXA1buK5xOuc/6Lj2BYmnYsNCTvpbsoosAtzv48tnZcvr7WMLPOFLBrjhiMhVhpmli3759fKjSgrg44De/kYvzBsPhkOt0hPLh1NwxjihUXi9w4411tzscJkaN2geHwz+WLrsstOc2qOklJADTpgU/1XliIjB1qvXJCpKSgPPOq7s9UCzdcktsjQywmxByHalg3Xhj7K2nyM84UsGuOGIyFWG8QKhhGMBrrwX3IPUTT8hv2WMJ44hClZAA3HwzMGKE/3aHw8SvfuV/A5ydDfztb5y0JRo4nXJh3sZompz1TcVol/h4OUNfhw7+2+uLpXPPBS65JPZmm7NTq1ZyGvusrMbLjhoFXH997K3jxc84UoHJFFED3G5g9Gg5M1WgseLJyfImY9q02OqVIgqXywV89RVw8cWBb6pHjZLTXsfazVmscruB3/4WeOMNoHXr+su0aQO89RZw/vlqroUOB5CaCqxeDQwaVH8ZXZe9YP/9LxdMD4fbDaxaBYwcWf/vNU0mqZ9/bm1CESIKHf/kKGa43fLG7+efgQ8/BF59VS5gmZwMXHihfDZAiOCHwBDFOk2Tfzf//jdQUCB7bTdtkpMDXHWVHI7Vs6e8OdP51VvUSEiQidJFF8mk6T//AYqLgbQ04He/k8OiDUPtl0put/wia9UqOe3+c8/J6c/btQP+9Cc59CwlhYlUuOLjZdL69dfA1q3A008D27fLbQMHykWT27Xj5xtRJGhCtKyRyyUlJUhNTUVxcTFSUlIiXR2Ypoldu3ahR48e0Hm3oozXKyeY0HX5nIfLFdsf4owjUqGiAvB6Tfz88y5kZvZAcjJjKdrV1MgFzWuvhfHx8jlTu5WUAICJfftkLKWkMJZUKimR57T2Di45ObL1sRs/40iFQHFkNTdgMkVERERERC2S1dyA6X2EmaaJHTt28KFKsoRxRKowlkgVxhKpwlgiFeyKIyZTEWaaJgoKCniBIEsYR6QKY4lUYSyRKowlUsGuOGIyRUREREREFAYmU0RERERERGHg1OgRpus6MjMzOTsNWcI4ij01NYDHI6c+jo9Xs7hqMPusqNDRtm0mvF69ydarqaiQU3XHxTXd1M5VVfL4appcQyuYBb+tMk05s54QcnbRpjq+5eVyhtO4uKZbL6y6GqislLEkRNNcl0xTxlLt8W2qhYErK+XxdTia7vh6vUfjNz4+tpcu8HgAj0dHRkZsf8YJIf9uTFNeG2J5BuJIseteibP5ERE1E7U322VlwMsvA/v3y+Ri4kRg+HB5A2VHslFaKt/3nXeANWvkh3q/fsBll8kkp1Ur9fusqpI3n5s2yf2WlcnFZCdPBjp0kL+zYwrv8nJ5czZ/PrBnj7xhGTcOGD9e1ikpSf0+KytlWz75BFi+XJ7H7GzgiivkTbAdN+C1S0Ps2AG8+SZQVCTXmbrsMqBbN/vW3Csrk+/9yivAzp3ypnDkSBnDVVX2xFJ5uUwovvwSWLxYnt/u3YEpU2Qc2bHPmhr5t7F/v1yn7Zdf5PTkv/0t0LevPAZ2JHMVFfK/r70GbN4s23fKKcCvfy3rFCuLaxuGbM/hw/JvtaBA/m1edBGQk3M0YY4F1dXyPK5eDXz0kTzHHTrI+E1Njf1kuTmwnBuIFqa4uFgAEMXFxZGuihBCCK/XK3744Qfh9XojXRWKYoyj6OfxCHH4sBAXXiiEwyGEvF04+pOdLcQHHwhRUaF2v9XVQvzf/wnRtq3cT1ycV0ya9IOIi/OKVq2EuPVWWUalsjIhli8XYvDguu0EhBg3TogdO4QoL1e3z6oqIUpKhJg0SYi4uLr77NZNiH/9S4jKSnX7rN3vU08J0aFD3X263UJcc43cp2Go22dZmRDr1glx6qn1H98RI4TYuFGWU6WmRsbmVVcJkZBQN5Y6dRLimWfUH9/KSiH++U8hunSp206XS4jLLxeitFT+falSXi7Etm1CjB5d//EdOlSIVavUHl/TlLF0001CJCXV3Wd6uhCPPKL+bzUSqqqE2LdPiAkThNC0urF00klCLFqk/loYCRUVQnz+uRD9+tU9p7ouxMSJQhw8GBvntTkIdK9kNTdgMhVhNTU1YuXKlaKmpibSVaEoxjiKboYhREGBvKGv7+as9kfT5I2jqpuIqiohpkw5/ga0Rtx770rhctX4to0fr+7DvKxMiC++kDe6DbW1dWshtm5Vc/NtGDKRqu+G5fifv/1N7fGdMaPxfZ5yiroko6xMiG+/FSIxseF9JiUJsWaNuhv+igqZRDQWS3/6k7q2VlQI8Ze/NH58TzxRJlSq9rlpkxCpqQ3vMz5e3vCrOr7V1UKMGdN4W6+9VsZdtPJ4hNi9WyaHDcWSw2HPl0tNqaJCiDfflElTQ+e0Uych9u+XX1iQNYHulazmBuw4JCKKMI8HmDQJ2L274XJCANdcAxw6ZH2fVVXAW2/JITSN+eor4OGH5XBAFS66SLa5IUeOAOeeq+a5ospK4Oqr5bCoxtx5J7B1q/V9GgawZAnw+OONl121Su63vNz6fuPigPPOOzocLJDycjn0TsVQtNJS4LbbgNzcxss+8sjRoY5WmCawcSMwc2bjZb//Hrj+enXH99xzgeLihstVVwMXXKBmeFZFBTB7thzC2Ji5c4GPP7Z+fCPp/PPlsL6GGAZw6aXyOhatSkrkUN/GZunevx+4+OLGy1HkMJkiIoqwQ4dkwhIMwwDmzLF+Y+hwAH//e/Dln38ecLut7bOiAnjxRflcTTC2bwe++Ua22YrKSuDdd4Mv/9hjwdcxkJoa4G9/C778/PnWJ8GoqgL+85/Gb0RrHTwIvPeevPG3wuEAXn01+PJz5jSeTDemslKep2C99ZY8J1YYhnwua9eu4MoXF8vzWllpbb9xcTJJCtZjj0Xvjff69fInGFVVwHPPWT++kVBRATz5ZPAxuXIl8OOP9taJwsdkKsJ0XUdWVlZMz1BD9mMcRa+KCnlDEIp//ct6YvPTT8DatXW3e706PvkkC16vfywdOiRvJK2Ijwf++c/QXvPCC433sjSkokLuM5Rv6t95x3qPWGkp8PXXoZV/5x3Z+xguIWSyGop//MNasmoYwBtv1H+OAsXS55+r6VF4//3gy3o8clIXK4ljRUVoSQ0AvPSS9VhasEBOxBCsb7+ViXK0KSsDnn22/t8FiqV//rPpZm1UKTERmDcvtNc8+6ya3tWWzK57Jd55RZiu68jIyOBNMFnCOIpehtH48L7jlZRYSzCAwPs0TR3r1mXANOvG0o4d1vbpcMgZ9EKxZ4+1HpuamtCPb3W1nJ3Niv37Q0+Mduyw1nsSHx/68d2929qsaFVVcta++gSKJdMEDhwIf5+A7H0L9Vjt3m2tRywuLrz4tTIrpWmG1yOxd2/4+4wUTQt8fAPF0p490TnTXU1N6MO19+yx9mUL2XevFIUhGFsMw8D69ethWB3HQi0a4yh6aVp4N7NWv+0O9G1uXJyBa69dj7i4urGkYirtUNvqclkfshTO8bU6LXs4+3S5rCWOptn0x1fXA++zoViyOq11OOfH5bK2Xlu4x9cKXQ+v5yUae2samu48UCzZsXxCU3A4Qo9Fl4vJlFV23SsxmYowIQQqKysh+BdCFjCOopfLBYweHdprTjzR+vM1AwbUf8OlaQLt2lVC0+rG0qhR1vZZUSHXywrFqada22diYuj17tpVrnllRVYWEOpyJaefbu28VlaGfnyHD7c29M3tBsaOrf93gWKpTRu51pUV7dsDnTqF9ppRo6wNjzWM8OLX6tCsQMc3ELdbrhMXbRwOuSZZfQLF0vDh0TkJhdcr1wcLxciRsbO2VqTYda/EZIqIKIJcLrmQanJy8K+59Vbr31DqOvCb3wRffuRI6zfA8fFy1rdg6bosb2XR1bg4OTtYhw7Bv+a666w/1G4YwJVXBl++f39g8GBr+0xOBmbMCO01M2aEFnv1GTFCLkIcrKlTrU96UVUFTJsWfPnMTOCss6wlq8nJMh5D6VGYMcP68429egHDhgVfftIkaz1wkeJ2AzfeGFpv04wZ0dk7pevyOh6shAQZ79HY49gSMJkiIoow05RTYwejZ085na7VbyhbtQLuu0/23DRG14EHH7S2P0DeyP7qV/LmOxiTJwNpadb3W1UF/PnPwZXt1Ene0FlJ4AB5XO++O/j6/+UvamZg69cPOPvs4Mqef76MJ6uqq4EHHgiubJs2MtatJnCtWgHTp8seqmDMmmU9gQOAjAyZrARj1CjZ+2D18QxNA/7v/4JLkFq1krEezN91c5SQIL/MCMbAgdYT5EhxOuV05/37B1f+lluis50thrXlr6JPc1u01zRNceTIEWGaZqSrQlGMcRT9qquFuOGGhhdv7NFDiL171S3eWFYmxMKFcgHX2n3ouimyso4IXTcFIITTKcQrrwhRXq5mn4YhRHGxEEOGNNzW889Xt1CwEHIh03vvbXifHTvKhYJVLtq7erUQaWkNL8T81FNqFx8tLxfiV79quK1jx6rdZ0WFEI8+KtsTKJbatBFi7Vq1CwVv2iRE+/YNt3X2bHULBQshz+s55zS8z5NPlgsFG4aafVZUCDFvnlysNtA+k5OFWLZMbVsjobpaiEsv9W/b8bF04olCHD6s7vhGgtcrxKFDQvTp03AsXXml2mthSxboXslqbsBkioiomaiqksnNOecIoetHP0y7dxfib3+TN6GqEqlaZWVC7NsnxK23+t/0u91CXHWVEJs3q7v5reX1yra+8IK8KTr2xuG004R4+217bh4qK+XN5oUX+t+Udu4sb7iLitQmGELIdh46JMRddwnRrt3RfcbHC3HZZUKsW6d+n4Yhj9/8+UIMHux/fIcNE+Jf/7Ln+JaXC5GbK8TvfieEy3V0nxkZQtxzjxAFBeqS8loVFUL88osQs2bJZLh2n06nEBdfLMTKleqPrxDy+L3xhhDDh/sf35wcIf7xD/l7r1ftPisqhPjhB3lznZBwdJ9t2ggxY4YQBw5EfyJVq7paiI8+kkn/sce3Tx8hnnlGtlP18Y0Er1ee1yeeEKJXL/8vWc44Q4gFC5hINQWruYEmRMt6Yr2kpASpqakoLi5GSqhPB9vA6/UiLy8PgwYNgtPq9FzUYjGOYodpyuFINTVybZmEBCA9XT6wrGI2vUBKS+V4/AMHvPjppzwMHToIQjgtD3drSGWlfN7hl1/kGjNpaUBSkhwWZddzEKYph/2Zppxe2+WSQ8WqqqwP7WtIRYXcV36+PLfp6XK7ncOxqqrk0KCiInl+U1Lkj2naG0u1Ey4cOuTFvn15OOWUQfB4nJaH9jWkrEy26dAhOf15Robc7nbbN3W2xyNvfcvK5AK9rVoBrVvL5+XsPL4VFbJN+fny/GZkyJiK1qF9gRiGvO5VVgKHD3uxf38eTj55EABnzD07VF0tz2VhoWxvmzbyeuxyRefU781VoHslq7kB77qaAU5nTSowjmKDrssbQLfbfzY4u3Pk2hvdzp2BAwcMxMfbv8/aB/MzMo7e/NpN14/edB6bPNmZSAFH9xnqDHRW1N7Qp6cfTd6aQlKS/G/XrkB+vgGHw/ozUo2pPX+dO9u7n2PVPrcYHw+0bXt0u90TItTGUteuTbfPSHA45E98vDy/BQUylmLx+8La5DCUiXIoPHbcKzHfJSIiIiIiCgOTKSIiIiIiojDwmakIE/9bQMztdkOLxoUhqFlgHJEqjCVShbFEqjCWSIVAcWQ1N2DPVDPg4pLWpADjiFRhLJEqjCVShbFEKtgRR0ymIswwDOTm5nLyALKEcUSqMJZIFcYSqcJYIhXsiqMYnBOFiIhCdezU0rVTs9s9XXjt1N1lZXI67dRUOSuZwxGbs5MRUfNXUSGvP4cPA5omZ2r0eGJv6nlShz1TREQtWEWFTKAeeADo2BHIygK+/BLIzATuuAPYsUOWUck05c3Jm28Cp54q11Tp0kVOBT9xIvDZZ/L3RERNpbIS2LMHuOsuuZRAx45yqvKMDGDmTGD/flmG6HhMpoiIWqiKCmDtWiA7G3j0UblgZK2yMuCFF4B+/YCPPlKXUJmmvCEZPRqYOlXu/1hffw2cfz5w2WVMqIioaVRWAgsXAr17A08/LRdhrnXkCPDEE/I6uXw5Eyqqi7P5RZgQAoZhwOFwcIYaChvjiMKxYwcwcKBMnI4ScLkMeDwOADKWHA5g8WLglFOsD7/zeoHTTwe++abxstdcA/z970cX96XowusSqWJnLJkmsH697CVv7AuchARgzRr5JRNDOvoEiiPO5hcDPPz6lRRgHFEoysrk0D7/REreIKSkePxuFAwDuPdeedNhhWkCK1YEl0gBwEsvASUl1vZJkcXrEqliVyxVV8vrWzBvX1UF3Hcfe6eimR1xxGQqwgzDwIYNGzhDDVnCOKJQCQG89Vbd7XFxBq69dgPi4vxj6ZtvgH37rO2zqkoOlwmWaQJPPgmUllrbL0UGr0ukip2xVFQkn9MM1gcfyGsZRR+74ojJFBFRC7RuXeg3BIsWWdtnYiKwbFlor1m2jMNpiMg+y5fLL5eC5fUC331nX30o+jCZIiJqgaqrQ3+NiqEtoY6wqK4GdH5SEZFNwull4jA/OhY/opoBh8MR6SpQDGAcUSi6dg38Ozn5RF1ZWdb2aRhyCvRQdOkiX0fRidclUsWuWOrRo2leQ82DHXHE2fyIiFqgmhrg5JPlcL9gpKfLdVacFpZ6Ly+X063ffnvwr/n8czn7H+/JicgOXq/8omjv3uDK9+4NbNpk7VpIzQtn84tyQggUFRWhheW0pBjjiEJlGMCtt9bdrusCWVlF0HX/WLrmGusPXSclyfdJSgqufFYWMG4cE6loxesSqWJnLHm9wI03Bl/+llvYWx6t7IojJlMRZhgGtmzZwtmOyBLGEYUqIQH4/e/lz7GcTgOTJm2B03k0lkaPltMBt2plfb+6LmcRbOxb3ZQUuVgwZ9aOXrwukSp2xlJCAnDbbcBZZzVe9oILgGnTgPh45dWgJmBXHDGZIiJqoVwu4OWXZaKUllb39243cP31cqidqiEtSUkyOfvqK+CEE+ovc9ppwOrVQLducgZAIiI7uVxyyvPp0+vvOU9OBv70J+A//5FliY7FEZ9ERC2YywXccYdctPKNN4C8PKB7d+Dxx4EpU+SUwaq/hU1KAoYNk89rrV0LvPOOXEuqbVtg8mSZRAH89peImo7LBfzf/wEPPQS8+iqwcaNclmHgQNmDbxhMpKh+TKYiTNM0uN1uaFxIhSxgHJEVtcP3Jk0Cfv1rDdu3u3HCCZqtvUK1733yyUC/fnKBXodDzVBCah54XSJVmiqWaq9LU6ceHWIcH8/JJmKFXXHE2fyIiIiIiKhF4mx+Uc40TeTn58M0zUhXhaIY44hUYSyRKowlUoWxRCrYFUdMpiLMNE3s3LmTFwiyhHFEqjCWSBXGEqnCWCIV7IojJlNERERERERhYDJFREREREQUBiZTEaZpGlJTUznbEVnCOCJVGEukCmOJVGEskQp2xRFn8yMiIiIiohaJs/lFOdM0sW/fPj5USZYwjkgVxhKpwlgiVRhLpIJdccRkKsJ4gSAVGEekCmOJVGEskSqMJVIhppOpZ599Ft27d0dCQgJOOeUUfPfddw2Wf/vtt9G3b18kJCTgpJNOwqefftpENSUiIiIiIpIinkz95z//wYwZMzBr1iysXbsWAwYMwIQJE5Cfn19v+RUrVmDSpEn4wx/+gLy8PFxwwQW44IIL8P333zdxzYmIiIiIqCWLeDL1+OOPY9q0aZg6dSr69++PF154AYmJiZg3b1695Z988kmcddZZuOOOO9CvXz88+OCDGDx4MJ555pkmrrkauq4jPT0duh7xU0FRjHFEqjCWSBXGEqnCWCIV7Iojp9J3C5HH48GaNWtw9913+7bpuo7x48dj5cqV9b5m5cqVmDFjht+2CRMm4P3336+3fHV1Naqrq33/LikpAQB4vV54vV7fPnVdh2mafuMoa7cbhoFjJz0MtN3hcEDTNN/7HrsdAAzDqLNd0zR069bNb99OpxNCCL/ymqbB4XDUqWOg7ZFsU33b2Sb729SzZ094vV6/94n2NsXieYqGNvXs2ROGYfjVM9rbFIvnKRra1LNnT5imWW8sRWubYvE8RUObevbsCSGE3/tEe5ti8Tw19zZ169YNmqb51fH4NocqoslUYWEhDMNA+/bt/ba3b98eW7Zsqfc1Bw8erLf8wYMH6y3/8MMPY/bs2XW25+XlISkpCQCQnp6Onj17YteuXSgoKPCVyczMRGZmJrZt24bi4mLf9qysLGRkZOD7779HZWWlb3vfvn2RlpaGvLw8vyDKycmBy+VCbm6uXx2GDh2KqqoqfPfdd0hMTAQgT/ywYcNQXFzsdwzcbjcGDBiAwsJC7Ny507c9NTUV/fr1w/79+7Fv3z7f9ki2yePxYMOGDb5tbJP9berduzeOHDmCw4cP+114orlNsXieoqFNnTp1Qk1NDaqrq31fPkV7m2LxPEVDm1JSUhAfH4+4uDjs378/JtoUi+cpGtqk6zratm2L1q1bY9u2bTHRplg8T9HQpoqKCpx88slISEjwtam8vBxWRHSdqf3796Nz585YsWIFhg8f7tt+5513YsmSJVi1alWd17hcLrzyyiuYNGmSb9tzzz2H2bNn49ChQ3XK19cz1aVLFxw+fNg3l3wks3qv14vc3FwMHjzYV665Z/WNtam+7WyTvW0SQmDNmjUYNGiQr77R3qZYPE/R0CbTNLF27do6sRTNbYrF8xQNbTIMA3l5eRg8eDB0/eiwmmhuUyyep2hoU20sDRkyxG/B1WhuExB756m5t8kwDKxduxZDhw6F0+n01bGkpARt27YNe52piPZMtWvXDg6Ho04SdOjQIXTo0KHe13To0CGk8vHx8YiPj6+z3el0wun0b37tyTjesTcUwWw//n0b2q5pmi+Yjv29pmn1lg9Ux1C329mmQNvZJvvaVHuhOD6OGqp7oO3NpU0N1THU7WxT8Nsbi6VobFOtWDpPtaKlTaGUj5Y2xeJ5YpvYplhvU+1997F1DNS2YEU0mXK5XBgyZAgWLlyICy64AID8VnThwoW46aab6n3N8OHDsXDhQkyfPt237csvv/Tr2WpIbcZ67PCVSPJ6vSgvL0dJSYnlk0ktF+OIVGEskSqMJVKFsUQqBIqj2pwg7MF6IsLefPNNER8fL+bPny9++OEHcc0114i0tDRx8OBBIYQQV1xxhbjrrrt85ZcvXy6cTqd47LHHxObNm8WsWbNEXFyc2LhxY1D727t3rwDAH/7whz/84Q9/+MMf/vCHPwKA2Lt3b1i5TMTT+0svvRQFBQW47777cPDgQQwcOBCfffaZb5KJPXv2+HXxjRgxAq+//jpmzpyJe+65B9nZ2Xj//fdx4oknBrW/Tp06Ye/evUhOTvYbdxsptc9w7d27N6xxmkQA44jUYSyRKowlUoWxRCoEiiMhBEpLS9GpU6ew3jeiE1CQPLGpqalhP/RGBDCOSB3GEqnCWCJVGEukgl1xxNXPiIiIiIiIwsBkioiIiIiIKAxMpiIsPj4es2bNqnf6dqJgMY5IFcYSqcJYIlUYS6SCXXHEZ6aIiIiIiIjCwJ4pIiIiIiKiMDCZIiIiIiIiCgOTKSIiIiIiojAwmSIiIiIiIgoDkykbPfzwwxg2bBiSk5ORkZGBCy64AFu3bm30dW+//Tb69u2LhIQEnHTSSfj000+boLbUXIUTR/Pnz4emaX4/CQkJTVRjaq6ef/555OTkICUlBSkpKRg+fDgWLFjQ4Gt4PaL6hBpLvCZRMP76179C0zRMnz69wXK8LlFjgoklVdclJlM2WrJkCW688UZ8++23+PLLL1FTU4MzzzwT5eXlAV+zYsUKTJo0CX/4wx+Ql5eHCy64ABdccAG+//77Jqw5NSfhxBEApKSk4MCBA76f3bt3N1GNqbnKzMzEX//6V6xZswa5ubkYN24cfv3rX2PTpk31luf1iAIJNZYAXpOoYatXr8bcuXORk5PTYDlel6gxwcYSoOi6JKjJ5OfnCwBiyZIlActccsklYuLEiX7bTjnlFHHttdfaXT2KEsHE0csvvyxSU1ObrlIUtVq3bi1eeumlen/H6xGFoqFY4jWJGlJaWiqys7PFl19+KUaPHi1uvfXWgGV5XaKGhBJLqq5L7JlqQsXFxQCANm3aBCyzcuVKjB8/3m/bhAkTsHLlSlvrRtEjmDgCgLKyMnTr1g1dunRp9BtjankMw8Cbb76J8vJyDB8+vN4yvB5RMIKJJYDXJArsxhtvxMSJE+tcb+rD6xI1JJRYAtRcl5whv4LCYpompk+fjpEjR+LEE08MWO7gwYNo376937b27dvj4MGDdleRokCwcdSnTx/MmzcPOTk5KC4uxmOPPYYRI0Zg06ZNyMzMbMIaU3OzceNGDB8+HFVVVWjVqhXee+899O/fv96yvB5RQ0KJJV6TKJA333wTa9euxerVq4Mqz+sSBRJqLKm6LjGZaiI33ngjvv/+eyxbtizSVaEoFmwcDR8+3O8b4hEjRqBfv36YO3cuHnzwQburSc1Ynz59sG7dOhQXF+Odd97BlVdeiSVLlgS8CSYKJJRY4jWJ6rN3717ceuut+PLLLzkhCVkSTiypui4xmWoCN910Ez7++GN88803jWa6HTp0wKFDh/y2HTp0CB06dLCzihQFQomj48XFxWHQoEH48ccfbaodRQuXy4VevXoBAIYMGYLVq1fjySefxNy5c+uU5fWIGhJKLB2P1yQCgDVr1iA/Px+DBw/2bTMMA9988w2eeeYZVFdXw+Fw+L2G1yWqTzixdLxwr0t8ZspGQgjcdNNNeO+99/D111+jR48ejb5m+PDhWLhwod+2L7/8ssFx6BTbwomj4xmGgY0bN6Jjx4421JCimWmaqK6urvd3vB5RKBqKpePxmkQAcPrpp2Pjxo1Yt26d72fo0KG47LLLsG7dunpvfnldovqEE0vHC/e6xJ4pG9144414/fXX8cEHHyA5Odk3njc1NRVutxsAMHnyZHTu3BkPP/wwAODWW2/F6NGjMWfOHEycOBFvvvkmcnNz8Y9//CNi7aDICieOHnjgAZx66qno1asXioqK8Oijj2L37t24+uqrI9YOiry7774bZ599Nrp27YrS0lK8/vrrWLx4MT7//HMAvB5R8EKNJV6TqD7Jycl1nv9NSkpC27Ztfdt5XaJghBNLqq5LTKZs9PzzzwMAxowZ47f95ZdfxpQpUwAAe/bsga4f7SAcMWIEXn/9dcycORP33HMPsrOz8f777zc42QDFtnDi6MiRI5g2bRoOHjyI1q1bY8iQIVixYgWfi2nh8vPzMXnyZBw4cACpqanIycnB559/jjPOOAMAr0cUvFBjidckChevS6SKXdclTQghVFeWiIiIiIgo1vGZKSIiIiIiojAwmSIiIiIiIgoDkykiIiIiIqIwMJkiIiIiIiIKA5MpIiIiIiKiMDCZIiIiIiIiCgOTKSIiIiIiojAwmSIiIiIiIgoDkykiImq2NE3D+++/r/Q9Fy9eDE3TUFRUFLDM/PnzkZaW1uh7hVu/rVu3okOHDigtLQ35tcH64YcfkJmZifLyctv2QUTU0jGZIiIiPwUFBbj++uvRtWtXxMfHo0OHDpgwYQKWL18e6ar5eeGFF5CcnAyv1+vbVlZWhri4OIwZM8avbG0CtWPHDowYMQIHDhxAampq0Pu6//77MXDgQEU1B+6++27cfPPNSE5OVvaex+vfvz9OPfVUPP7447btg4iopWMyRUREfi6++GLk5eXhlVdewbZt2/Dhhx9izJgxOHz4cKSr5mfs2LEoKytDbm6ub9vSpUvRoUMHrFq1ClVVVb7tixYtQteuXdGzZ0+4XC506NABmqZFotrYs2cPPv74Y0yZMsX2fU2dOhXPP/+8X8JJRETqMJkiIiKfoqIiLF26FI888gjGjh2Lbt264eSTT8bdd9+N888/36/c1VdfjfT0dKSkpGDcuHFYv3697/e1PTlz585Fly5dkJiYiEsuuQTFxcW+MqtXr8YZZ5yBdu3aITU1FaNHj8batWuDrmufPn3QsWNHLF682Ldt8eLF+PWvf40ePXrg22+/9ds+duxY3/8fP8xv/vz56Nq1KxITE3HhhRf6JY7z58/H7NmzsX79emiaBk3TMH/+fN/vCwsLceGFFyIxMRHZ2dn48MMPG6z3W2+9hQEDBqBz585+25cvX44xY8YgMTERrVu3xoQJE3DkyBEAwJgxY3DzzTdj+vTpaN26Ndq3b48XX3wR5eXlmDp1KpKTk9GrVy8sWLDA7z3POOMM/PLLL1iyZElQx5SIiELDZIqIiHxatWqFVq1a4f3330d1dXXAcr/97W+Rn5+PBQsWYM2aNRg8eDBOP/10/PLLL74yP/74I9566y189NFH+Oyzz5CXl4cbbrjB9/vS0lJceeWVWLZsGb799ltkZ2fjnHPOCek5orFjx2LRokW+fy9atAhjxozB6NGjfdsrKyuxatUqXzJ1vFWrVuEPf/gDbrrpJqxbtw5jx47FX/7yF9/vL730Uvzxj3/ECSecgAMHDuDAgQO49NJLfb+fPXs2LrnkEmzYsAHnnHMOLrvsMr/jcLylS5di6NChftvWrVuH008/Hf3798fKlSuxbNkynHfeeTAMw1fmlVdeQbt27fDdd9/h5ptvxvXXX4/f/va3GDFiBNauXYszzzwTV1xxBSoqKnyvcblcGDhwIJYuXRrkESUiopAIIiKiY7zzzjuidevWIiEhQYwYMULcfffdYv369b7fL126VKSkpIiqqiq/1/Xs2VPMnTtXCCHErFmzhMPhEPv27fP9fsGCBULXdXHgwIF692sYhkhOThYfffSRbxsA8d577wWs64svviiSkpJETU2NKCkpEU6nU+Tn54vXX39djBo1SgghxMKFCwUAsXv3biGEEIsWLRIAxJEjR4QQQkyaNEmcc845fu976aWXitTUVN+/Z82aJQYMGFBn/wDEzJkzff8uKysTAMSCBQsC1nnAgAHigQce8Ns2adIkMXLkyICvGT16tDjttNN8//Z6vSIpKUlcccUVvm0HDhwQAMTKlSv9XnvhhReKKVOmBHxvIiIKH3umiIjIz8UXX4z9+/fjww8/xFlnnYXFixdj8ODBvqFt69evR1lZGdq2bevryWrVqhV27dqFHTt2+N6na9eufkPZhg8fDtM0sXXrVgDAoUOHMG3aNGRnZyM1NRUpKSkoKyvDnj17gq7rmDFjUF5ejtWrV2Pp0qXo3bs30tPTMXr0aN9zU4sXL0ZWVha6du1a73ts3rwZp5xyit+24cOHB12HnJwc3/8nJSUhJSUF+fn5ActXVlYiISHBb1ttz1Sw+3E4HGjbti1OOukk37b27dsDQJ19u91uv94qIiJSxxnpChARUfOTkJCAM844A2eccQb+/Oc/4+qrr8asWbMwZcoUlJWV1XlWqVYw04nXuvLKK3H48GE8+eST6NatG+Lj4zF8+HB4PJ6g36NXr17IzMzEokWLcOTIEYwePRoA0KlTJ3Tp0gUrVqzAokWLMG7cuKDfM1RxcXF+/9Y0DaZpBizfrl0737NQtdxud1j7OXZb7YQax+/7l19+QeoK7scAAAN7SURBVM+ePRt9fyIiCh17poiIqFH9+/f3rVc0ePBgHDx4EE6nE7169fL7adeune81e/bswf79+33//vbbb6HrOvr06QNATrhwyy234JxzzsEJJ5yA+Ph4FBYWhly3sWPHYvHixVi8eLHflOijRo3CggUL8N133wV8XgoA+vXrh1WrVvltO3byCkA+e3Ts80tWDBo0CD/88IPftpycHCxcuFDJ+x/v+++/x6BBg2x5byKilo7JFBER+Rw+fBjjxo3Dv//9b2zYsAG7du3C22+/jb/97W/49a9/DQAYP348hg8fjgsuuABffPEFfvrpJ6xYsQL33nuv3zTlCQkJuPLKK7F+/XosXboUt9xyCy655BJ06NABAJCdnY1XX30VmzdvxqpVq3DZZZcF1UNzvLFjx2LZsmVYt26dr2cKAEaPHo25c+fC4/E0mEzdcsst+Oyzz/DYY49h+/bteOaZZ/DZZ5/5lenevTt27dqFdevWobCwsMHJORozYcIErFy50i85u/vuu7F69WrccMMN2LBhA7Zs2YLnn38+rOTyWD/99BN+/vlnjB8/3tL7EBFR/ZhMERGRT6tWrXDKKafgiSeewKhRo3DiiSfiz3/+M6ZNm4ZnnnkGgBxO9umnn2LUqFGYOnUqevfujd/97nfYvXu377kdQA7Bu+iii3DOOefgzDPPRE5ODp577jnf7//5z3/iyJEjGDx4MK644grccsstyMjICLnOY8eORWVlJXr16uW3/9GjR6O0tNQ3hXogp556Kl588UU8+eSTGDBgAL744gvMnDnTr8zFF1+Ms846C2PHjkV6ejreeOONkOtZ6+yzz4bT6cRXX33l29a7d2988cUXWL9+PU4++WQMHz4cH3zwAZxOa6Px33jjDZx55pno1q2bpfchIqL6aUIIEelKEBFRbLn//vvx/vvvY926dZGuSrP07LPP4sMPP8Tnn39u2z48Hg+ys7Px+uuvY+TIkbbth4ioJeMEFERERE3s2muvRVFREUpLS5GcnGzLPvbs2YN77rmHiRQRkY2YTBERETUxp9OJe++919Z91E4KQkRE9uEwPyIiIiIiojBwAgoiIiIiIqIwMJkiIiIiIiIKA5MpIiIiIiKiMDCZIiIiIiIiCgOTKSIiIiIiojAwmSIiIiIiIgoDkykiIiIiIqIwMJkiIiIiIiIKw/8DBGDiiSaQewUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Interpretation of scatterplots ? -- the petal measurements (length and width) appear to be the most effective features for distinguishing between the three Iris species"
      ],
      "metadata": {
        "id": "pt56KWY3cG_N"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "9f9a27de",
        "outputId": "ad92334e-818a-424d-9d03-9c0c5fbd9d10"
      },
      "source": [
        "# Walid - Correlation\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(df.iloc[:,:-1].corr(), annot=True, cmap='coolwarm', fmt='.2f')\n",
        "plt.title('Correlation Matrix of Iris Attributes')\n",
        "plt.show()"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a0fb90dd"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n"
      ],
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "23f8fdd9"
      },
      "source": [
        "df['Species'] = le.fit_transform(df['Species'])\n"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "6e537057",
        "outputId": "2ef81ad7-c329-45cc-8fe8-d83ff7aebc28"
      },
      "source": [
        "display(df.head())"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Species\n",
              "0            5.1           3.5            1.4           0.2        0\n",
              "1            4.9           3.0            1.4           0.2        0\n",
              "2            4.7           3.2            1.3           0.2        0\n",
              "3            4.6           3.1            1.5           0.2        0\n",
              "4            5.0           3.6            1.4           0.2        0"
            ],
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              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
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              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
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              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2c4b4c60-0680-4cb4-b628-c2372938964b 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-2c4b4c60-0680-4cb4-b628-c2372938964b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2073644135332772,\n        \"min\": 4.6,\n        \"max\": 5.1,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4.9,\n          5.0,\n          4.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2588435821108957,\n        \"min\": 3.0,\n        \"max\": 3.6,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3.0,\n          3.6,\n          3.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07071067811865474,\n        \"min\": 1.3,\n        \"max\": 1.5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.4,\n          1.3,\n          1.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.2,\n        \"max\": 0.2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "76cfc84e",
        "outputId": "b011f6cc-df82-472a-9619-06fd9d2c8809"
      },
      "source": [
        "third_class_name = le.inverse_transform([2])\n",
        "print(f\"The original name of the third class (encoded as 2) is: {third_class_name[0]}\")"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The original name of the third class (encoded as 2) is: Iris-virginica\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The original name of the third class is: **Iris-virginica**\n",
        "\n"
      ],
      "metadata": {
        "id": "PJ1fZEjIdL2N"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "018ccbd2",
        "outputId": "3b116831-2c65-4c39-8c95-93f38d4dbe5d"
      },
      "source": [
        "\n",
        "#### Model Training: Logistic Regression\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "\n",
        "# Initialize the Logistic Regression model\n",
        "model = LogisticRegression(max_iter=200) # Increased max_iter for convergence\n",
        "\n",
        "# Train the model\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "print(\"Logistic Regression model trained successfully.\")"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression model trained successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "57178240",
        "outputId": "3e357f69-60aa-4362-edff-641f32744ba6"
      },
      "source": [
        "# Model Evaluation -- Make predictions on the test set\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Evaluate the model's accuracy\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f\"Accuracy: {accuracy:.4f}\")\n",
        "\n",
        "# Display classification report\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "# Display confusion matrix\n",
        "print(\"\\nConfusion Matrix:\")\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "display(pd.DataFrame(cm, index=['Actual 0', 'Actual 1', 'Actual 2'], columns=['Predicted 0', 'Predicted 1', 'Predicted 2']))"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 1.0000\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        19\n",
            "           1       1.00      1.00      1.00        13\n",
            "           2       1.00      1.00      1.00        13\n",
            "\n",
            "    accuracy                           1.00        45\n",
            "   macro avg       1.00      1.00      1.00        45\n",
            "weighted avg       1.00      1.00      1.00        45\n",
            "\n",
            "\n",
            "Confusion Matrix:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          Predicted 0  Predicted 1  Predicted 2\n",
              "Actual 0           19            0            0\n",
              "Actual 1            0           13            0\n",
              "Actual 2            0            0           13"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-76fb7e72-a3c4-47e6-bb62-2f45c9269ef0\" 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>Predicted 0</th>\n",
              "      <th>Predicted 1</th>\n",
              "      <th>Predicted 2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Actual 0</th>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actual 1</th>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actual 2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</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-76fb7e72-a3c4-47e6-bb62-2f45c9269ef0')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\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-76fb7e72-a3c4-47e6-bb62-2f45c9269ef0 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-76fb7e72-a3c4-47e6-bb62-2f45c9269ef0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(pd\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Predicted 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 0,\n        \"max\": 19,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted 1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 0,\n        \"max\": 13,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          13,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted 2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 0,\n        \"max\": 13,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          13,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "34d74745",
        "outputId": "cd3ebd26-670e-4c2d-eb9c-381a2dfda4a0"
      },
      "source": [
        "#K-Nearest Neighbors (KNN)\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "# Initialize the KNN model\n",
        "# A common practice is to start with k=5, but this can be tuned.\n",
        "knn_model = KNeighborsClassifier(n_neighbors=5)\n",
        "\n",
        "# Train the KNN model\n",
        "knn_model.fit(X_train, y_train)\n",
        "\n",
        "print(\"K-Nearest Neighbors model trained successfully.\")"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "K-Nearest Neighbors model trained successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "7435ecc0",
        "outputId": "20087ff3-31e0-4139-f850-0cd4b6b8bdfb"
      },
      "source": [
        "### KNN Model Evaluation\n",
        "# Make predictions on the test set using the KNN model\n",
        "y_pred_knn = knn_model.predict(X_test)\n",
        "\n",
        "# Evaluate the KNN model's accuracy\n",
        "accuracy_knn = accuracy_score(y_test, y_pred_knn)\n",
        "print(f\"KNN Accuracy: {accuracy_knn:.4f}\")\n",
        "\n",
        "# Display classification report for KNN\n",
        "print(\"\\nKNN Classification Report:\")\n",
        "print(classification_report(y_test, y_pred_knn))\n",
        "\n",
        "# Display confusion matrix for KNN\n",
        "print(\"\\nKNN Confusion Matrix:\")\n",
        "cm_knn = confusion_matrix(y_test, y_pred_knn)\n",
        "display(pd.DataFrame(cm_knn, index=['Actual 0', 'Actual 1', 'Actual 2'], columns=['Predicted 0', 'Predicted 1', 'Predicted 2']))"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KNN Accuracy: 1.0000\n",
            "\n",
            "KNN Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        19\n",
            "           1       1.00      1.00      1.00        13\n",
            "           2       1.00      1.00      1.00        13\n",
            "\n",
            "    accuracy                           1.00        45\n",
            "   macro avg       1.00      1.00      1.00        45\n",
            "weighted avg       1.00      1.00      1.00        45\n",
            "\n",
            "\n",
            "KNN Confusion Matrix:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          Predicted 0  Predicted 1  Predicted 2\n",
              "Actual 0           19            0            0\n",
              "Actual 1            0           13            0\n",
              "Actual 2            0            0           13"
            ],
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              "\n",
              "  <div id=\"df-9c1d6d39-feb8-46f7-ac9b-ead6e59aafe9\" 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",
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              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9c1d6d39-feb8-46f7-ac9b-ead6e59aafe9')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "    </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",
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              "      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",
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              "    }\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-9c1d6d39-feb8-46f7-ac9b-ead6e59aafe9 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-9c1d6d39-feb8-46f7-ac9b-ead6e59aafe9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(pd\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Predicted 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 0,\n        \"max\": 19,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted 1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 0,\n        \"max\": 13,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          13,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted 2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 0,\n        \"max\": 13,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          13,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "69e7b562",
        "outputId": "f2eba0c5-1b07-4036-f1c4-e9b7666cc2d8"
      },
      "source": [
        " #Decision Tree Classifier\n",
        " from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "# Initialize the Decision Tree Classifier model\n",
        "decision_tree_model = DecisionTreeClassifier(random_state=42)\n",
        "\n",
        "# Train the Decision Tree model\n",
        "decision_tree_model.fit(X_train, y_train)\n",
        "\n",
        "print(\"Decision Tree Classifier model trained successfully.\")"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree Classifier model trained successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "c696a827",
        "outputId": "0fa0c7e9-6eb0-4f3c-d674-ea848ffbfc9f"
      },
      "source": [
        "# Decision Tree Model Evaluation\n",
        "# Make predictions on the test set using the Decision Tree model\n",
        "y_pred_dt = decision_tree_model.predict(X_test)\n",
        "\n",
        "# Evaluate the Decision Tree model's accuracy\n",
        "accuracy_dt = accuracy_score(y_test, y_pred_dt)\n",
        "print(f\"Decision Tree Accuracy: {accuracy_dt:.4f}\")\n",
        "\n",
        "# Display classification report for Decision Tree\n",
        "print(\"\\nDecision Tree Classification Report:\")\n",
        "print(classification_report(y_test, y_pred_dt))\n",
        "\n",
        "# Display confusion matrix for Decision Tree\n",
        "print(\"\\nDecision Tree Confusion Matrix:\")\n",
        "cm_dt = confusion_matrix(y_test, y_pred_dt)\n",
        "display(pd.DataFrame(cm_dt, index=['Actual 0', 'Actual 1', 'Actual 2'], columns=['Predicted 0', 'Predicted 1', 'Predicted 2']))"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree Accuracy: 1.0000\n",
            "\n",
            "Decision Tree Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        19\n",
            "           1       1.00      1.00      1.00        13\n",
            "           2       1.00      1.00      1.00        13\n",
            "\n",
            "    accuracy                           1.00        45\n",
            "   macro avg       1.00      1.00      1.00        45\n",
            "weighted avg       1.00      1.00      1.00        45\n",
            "\n",
            "\n",
            "Decision Tree Confusion Matrix:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          Predicted 0  Predicted 1  Predicted 2\n",
              "Actual 0           19            0            0\n",
              "Actual 1            0           13            0\n",
              "Actual 2            0            0           13"
            ],
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>Predicted 0</th>\n",
              "      <th>Predicted 1</th>\n",
              "      <th>Predicted 2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Actual 0</th>\n",
              "      <td>19</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actual 1</th>\n",
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              "      <td>13</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>Actual 2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-39d07687-7cb0-470d-b575-46eed8cee04f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
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              "      background-color: #E2EBFA;\n",
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              "\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
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              "        document.querySelector('#df-39d07687-7cb0-470d-b575-46eed8cee04f 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-39d07687-7cb0-470d-b575-46eed8cee04f');\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",
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              "\n",
              "\n",
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            }
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "0db25e07",
        "outputId": "fff5d747-2181-4b4a-a7b9-f4338089ffbb"
      },
      "source": [
        "# Model Accuracy Comparison\n",
        "model_accuracies = {\n",
        "    'Logistic Regression': accuracy,\n",
        "    'K-Nearest Neighbors': accuracy_knn,\n",
        "    'Decision Tree': accuracy_dt\n",
        "}\n",
        "\n",
        "accuracy_df = pd.DataFrame(model_accuracies.items(), columns=['Model', 'Accuracy'])\n",
        "accuracy_df.set_index('Model', inplace=True)\n",
        "\n",
        "display(accuracy_df)\n"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                     Accuracy\n",
              "Model                        \n",
              "Logistic Regression       1.0\n",
              "K-Nearest Neighbors       1.0\n",
              "Decision Tree             1.0"
            ],
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              "        vertical-align: middle;\n",
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              "    }\n",
              "\n",
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              "        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>Accuracy</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Logistic Regression</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>K-Nearest Neighbors</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Decision Tree</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    }\n",
              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-0fb39b9d-3d04-4a3e-96a1-e9d963a4303f 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-0fb39b9d-3d04-4a3e-96a1-e9d963a4303f');\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",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_f73b0bd5-2516-4772-80d1-355435cd6496\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        cursor: pointer;\n",
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              "        background-color: #E2EBFA;\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
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              "    </button>\n",
              "    <script>\n",
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              "        document.querySelector('#id_f73b0bd5-2516-4772-80d1-355435cd6496 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('accuracy_df');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "accuracy_df",
              "summary": "{\n  \"name\": \"accuracy_df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Logistic Regression\",\n          \"K-Nearest Neighbors\",\n          \"Decision Tree\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "35f371f2",
        "outputId": "275aeebd-4b60-46b8-9ccc-337060856292"
      },
      "source": [
        "# Hyperparameter Tuning for Logistic Regression\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# Define the parameter grid for Logistic Regression\n",
        "# C is the inverse of regularization strength; smaller values specify stronger regularization.\n",
        "# solver specifies the algorithm to use in the optimization problem.\n",
        "param_grid_lr = {\n",
        "    'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
        "    'solver': ['liblinear', 'lbfgs', 'saga']\n",
        "}\n",
        "\n",
        "# Initialize GridSearchCV\n",
        "# cv=5 for 5-fold cross-validation\n",
        "# scoring='accuracy' to optimize for accuracy\n",
        "grid_search_lr = GridSearchCV(LogisticRegression(max_iter=200), param_grid_lr, cv=5, scoring='accuracy', verbose=1)\n",
        "\n",
        "# Fit GridSearchCV to the training data\n",
        "grid_search_lr.fit(X_train, y_train)\n",
        "\n",
        "print(\"GridSearchCV for Logistic Regression completed.\")\n",
        "print(f\"Best parameters for Logistic Regression: {grid_search_lr.best_params_}\")\n",
        "print(f\"Best cross-validation accuracy for Logistic Regression: {grid_search_lr.best_score_:.4f}\")"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 18 candidates, totalling 90 fits\n",
            "GridSearchCV for Logistic Regression completed.\n",
            "Best parameters for Logistic Regression: {'C': 1, 'solver': 'saga'}\n",
            "Best cross-validation accuracy for Logistic Regression: 0.9714\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "e3af4809",
        "outputId": "c7c874fd-4671-4a22-a632-31d6b36af717"
      },
      "source": [
        "## Evaluate Tuned Logistic Regression Model\n",
        "# Get the best Logistic Regression model from GridSearchCV\n",
        "best_lr_model = grid_search_lr.best_estimator_\n",
        "\n",
        "# Make predictions on the test set with the best model\n",
        "y_pred_lr_tuned = best_lr_model.predict(X_test)\n",
        "\n",
        "# Evaluate the tuned Logistic Regression model's accuracy\n",
        "accuracy_lr_tuned = accuracy_score(y_test, y_pred_lr_tuned)\n",
        "print(f\"Tuned Logistic Regression Accuracy on test set: {accuracy_lr_tuned:.4f}\")\n",
        "\n",
        "# Display classification report for tuned Logistic Regression\n",
        "print(\"\\nTuned Logistic Regression Classification Report:\")\n",
        "print(classification_report(y_test, y_pred_lr_tuned))\n",
        "\n",
        "# Display confusion matrix for tuned Logistic Regression\n",
        "print(\"\\nTuned Logistic Regression Confusion Matrix:\")\n",
        "cm_lr_tuned = confusion_matrix(y_test, y_pred_lr_tuned)\n",
        "display(pd.DataFrame(cm_lr_tuned, index=['Actual 0', 'Actual 1', 'Actual 2'], columns=['Predicted 0', 'Predicted 1', 'Predicted 2']))"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tuned Logistic Regression Accuracy on test set: 1.0000\n",
            "\n",
            "Tuned Logistic Regression Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        19\n",
            "           1       1.00      1.00      1.00        13\n",
            "           2       1.00      1.00      1.00        13\n",
            "\n",
            "    accuracy                           1.00        45\n",
            "   macro avg       1.00      1.00      1.00        45\n",
            "weighted avg       1.00      1.00      1.00        45\n",
            "\n",
            "\n",
            "Tuned Logistic Regression Confusion Matrix:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          Predicted 0  Predicted 1  Predicted 2\n",
              "Actual 0           19            0            0\n",
              "Actual 1            0           13            0\n",
              "Actual 2            0            0           13"
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-59adea4e-3541-4eb0-a4e4-38f68c6f7576 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-59adea4e-3541-4eb0-a4e4-38f68c6f7576');\n",
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              "                                                    [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",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(pd\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Predicted 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 0,\n        \"max\": 19,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted 1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 0,\n        \"max\": 13,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          13,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted 2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 0,\n        \"max\": 13,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          13,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4d154039",
        "outputId": "7a325257-f6eb-4c3d-879c-f5e4d2b00621"
      },
      "source": [
        "### Hyperparameter Tuning for K-Nearest Neighbors\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "# Define the parameter grid for KNN\n",
        "# n_neighbors: number of neighbors to consider\n",
        "# weights: weighting function used in prediction\n",
        "# metric: distance metric to use\n",
        "param_grid_knn = {\n",
        "    'n_neighbors': list(range(1, 21)), # Test k from 1 to 20\n",
        "    'weights': ['uniform', 'distance'],\n",
        "    'metric': ['euclidean', 'manhattan']\n",
        "}\n",
        "\n",
        "# Initialize GridSearchCV for KNN\n",
        "grid_search_knn = GridSearchCV(KNeighborsClassifier(), param_grid_knn, cv=5, scoring='accuracy', verbose=1)\n",
        "\n",
        "# Fit GridSearchCV to the training data\n",
        "grid_search_knn.fit(X_train, y_train)\n",
        "\n",
        "print(\"GridSearchCV for K-Nearest Neighbors completed.\")\n",
        "print(f\"Best parameters for KNN: {grid_search_knn.best_params_}\")\n",
        "print(f\"Best cross-validation accuracy for KNN: {grid_search_knn.best_score_:.4f}\")"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 80 candidates, totalling 400 fits\n",
            "GridSearchCV for K-Nearest Neighbors completed.\n",
            "Best parameters for KNN: {'metric': 'euclidean', 'n_neighbors': 18, 'weights': 'distance'}\n",
            "Best cross-validation accuracy for KNN: 0.9619\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "dfbc59fc",
        "outputId": "7daa817a-7acf-4def-9890-6fd79fa1e496"
      },
      "source": [
        "### Evaluate Tuned K-Nearest Neighbors Model\n",
        "# Get the best KNN model from GridSearchCV\n",
        "best_knn_model = grid_search_knn.best_estimator_\n",
        "\n",
        "# Make predictions on the test set with the best model\n",
        "y_pred_knn_tuned = best_knn_model.predict(X_test)\n",
        "\n",
        "# Evaluate the tuned KNN model's accuracy\n",
        "accuracy_knn_tuned = accuracy_score(y_test, y_pred_knn_tuned)\n",
        "print(f\"Tuned K-Nearest Neighbors Accuracy on test set: {accuracy_knn_tuned:.4f}\")\n",
        "\n",
        "# Display classification report for tuned KNN\n",
        "print(\"\\nTuned K-Nearest Neighbors Classification Report:\")\n",
        "print(classification_report(y_test, y_pred_knn_tuned))\n",
        "\n",
        "# Display confusion matrix for tuned KNN\n",
        "print(\"\\nTuned K-Nearest Neighbors Confusion Matrix:\")\n",
        "cm_knn_tuned = confusion_matrix(y_test, y_pred_knn_tuned)\n",
        "display(pd.DataFrame(cm_knn_tuned, index=['Actual 0', 'Actual 1', 'Actual 2'], columns=['Predicted 0', 'Predicted 1', 'Predicted 2']))"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tuned K-Nearest Neighbors Accuracy on test set: 1.0000\n",
            "\n",
            "Tuned K-Nearest Neighbors Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        19\n",
            "           1       1.00      1.00      1.00        13\n",
            "           2       1.00      1.00      1.00        13\n",
            "\n",
            "    accuracy                           1.00        45\n",
            "   macro avg       1.00      1.00      1.00        45\n",
            "weighted avg       1.00      1.00      1.00        45\n",
            "\n",
            "\n",
            "Tuned K-Nearest Neighbors Confusion Matrix:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          Predicted 0  Predicted 1  Predicted 2\n",
              "Actual 0           19            0            0\n",
              "Actual 1            0           13            0\n",
              "Actual 2            0            0           13"
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
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              "      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-b952ca28-be35-4bf8-8b90-b124fe114d76 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-b952ca28-be35-4bf8-8b90-b124fe114d76');\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",
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              "\n",
              "\n",
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              "  </div>\n"
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              "summary": "{\n  \"name\": \"display(pd\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Predicted 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 0,\n        \"max\": 19,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted 1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 0,\n        \"max\": 13,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          13,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted 2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 0,\n        \"max\": 13,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          13,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "136390bc",
        "outputId": "89d2d821-fa71-4dff-e729-b570ab8adc80"
      },
      "source": [
        "### Hyperparameter Tuning for Decision Tree Classifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# Define the parameter grid for Decision Tree Classifier\n",
        "# criterion: The function to measure the quality of a split\n",
        "# max_depth: The maximum depth of the tree\n",
        "# min_samples_split: The minimum number of samples required to split an internal node\n",
        "# min_samples_leaf: The minimum number of samples required to be at a leaf node\n",
        "param_grid_dt = {\n",
        "    'criterion': ['gini', 'entropy'],\n",
        "    'max_depth': [None, 5, 10, 15, 20],\n",
        "    'min_samples_split': [2, 5, 10],\n",
        "    'min_samples_leaf': [1, 2, 4]\n",
        "}\n",
        "\n",
        "# Initialize GridSearchCV for Decision Tree Classifier\n",
        "grid_search_dt = GridSearchCV(DecisionTreeClassifier(random_state=42), param_grid_dt, cv=5, scoring='accuracy', verbose=1)\n",
        "\n",
        "# Fit GridSearchCV to the training data\n",
        "grid_search_dt.fit(X_train, y_train)\n",
        "\n",
        "print(\"GridSearchCV for Decision Tree Classifier completed.\")\n",
        "print(f\"Best parameters for Decision Tree: {grid_search_dt.best_params_}\")\n",
        "print(f\"Best cross-validation accuracy for Decision Tree: {grid_search_dt.best_score_:.4f}\")"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 90 candidates, totalling 450 fits\n",
            "GridSearchCV for Decision Tree Classifier completed.\n",
            "Best parameters for Decision Tree: {'criterion': 'gini', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 10}\n",
            "Best cross-validation accuracy for Decision Tree: 0.9429\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "42b23cfc",
        "outputId": "3ac5e194-1b5f-4175-a355-a0ddac8edc38"
      },
      "source": [
        "# Evaluate Tuned Decision Tree Classifier Model\n",
        "# Get the best Decision Tree model from GridSearchCV\n",
        "best_dt_model = grid_search_dt.best_estimator_\n",
        "\n",
        "# Make predictions on the test set with the best model\n",
        "y_pred_dt_tuned = best_dt_model.predict(X_test)\n",
        "\n",
        "# Evaluate the tuned Decision Tree model's accuracy\n",
        "accuracy_dt_tuned = accuracy_score(y_test, y_pred_dt_tuned)\n",
        "print(f\"Tuned Decision Tree Classifier Accuracy on test set: {accuracy_dt_tuned:.4f}\")\n",
        "\n",
        "# Display classification report for tuned Decision Tree\n",
        "print(\"\\nTuned Decision Tree Classifier Classification Report:\")\n",
        "print(classification_report(y_test, y_pred_dt_tuned))\n",
        "\n",
        "# Display confusion matrix for tuned Decision Tree\n",
        "print(\"\\nTuned Decision Tree Classifier Confusion Matrix:\")\n",
        "cm_dt_tuned = confusion_matrix(y_test, y_pred_dt_tuned)\n",
        "display(pd.DataFrame(cm_dt_tuned, index=['Actual 0', 'Actual 1', 'Actual 2'], columns=['Predicted 0', 'Predicted 1', 'Predicted 2']))"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tuned Decision Tree Classifier Accuracy on test set: 1.0000\n",
            "\n",
            "Tuned Decision Tree Classifier Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        19\n",
            "           1       1.00      1.00      1.00        13\n",
            "           2       1.00      1.00      1.00        13\n",
            "\n",
            "    accuracy                           1.00        45\n",
            "   macro avg       1.00      1.00      1.00        45\n",
            "weighted avg       1.00      1.00      1.00        45\n",
            "\n",
            "\n",
            "Tuned Decision Tree Classifier Confusion Matrix:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          Predicted 0  Predicted 1  Predicted 2\n",
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    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "25941e4e",
        "outputId": "d9ae1fe9-8e23-493f-f0b3-4f21958a2661"
      },
      "source": [
        "# Hyperparameter Tuning for Decision Tree Classifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# Define the parameter grid for Decision Tree Classifier\n",
        "# criterion: The function to measure the quality of a split\n",
        "# max_depth: The maximum depth of the tree\n",
        "# min_samples_split: The minimum number of samples required to split an internal node\n",
        "# min_samples_leaf: The minimum number of samples required to be at a leaf node\n",
        "param_grid_dt = {\n",
        "    'criterion': ['gini', 'entropy'],\n",
        "    'max_depth': [None, 5, 10, 15, 20],\n",
        "    'min_samples_split': [2, 5, 10],\n",
        "    'min_samples_leaf': [1, 2, 4]\n",
        "}\n",
        "\n",
        "# Initialize GridSearchCV for Decision Tree Classifier\n",
        "grid_search_dt = GridSearchCV(DecisionTreeClassifier(random_state=42), param_grid_dt, cv=5, scoring='accuracy', verbose=1)\n",
        "\n",
        "# Fit GridSearchCV to the training data\n",
        "grid_search_dt.fit(X_train, y_train)\n",
        "\n",
        "print(\"GridSearchCV for Decision Tree Classifier completed.\")\n",
        "print(f\"Best parameters for Decision Tree: {grid_search_dt.best_params_}\")\n",
        "print(f\"Best cross-validation accuracy for Decision Tree: {grid_search_dt.best_score_:.4f}\")"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 90 candidates, totalling 450 fits\n",
            "GridSearchCV for Decision Tree Classifier completed.\n",
            "Best parameters for Decision Tree: {'criterion': 'gini', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 10}\n",
            "Best cross-validation accuracy for Decision Tree: 0.9429\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "c2bd1411",
        "outputId": "6a4df7ff-50d0-44dc-ad8d-b20ee81a4880"
      },
      "source": [
        "### Evaluate Tuned Decision Tree Classifier Model:\n",
        "# Get the best Decision Tree model from GridSearchCV\n",
        "best_dt_model = grid_search_dt.best_estimator_\n",
        "\n",
        "# Make predictions on the test set with the best model\n",
        "y_pred_dt_tuned = best_dt_model.predict(X_test)\n",
        "\n",
        "# Evaluate the tuned Decision Tree model's accuracy\n",
        "accuracy_dt_tuned = accuracy_score(y_test, y_pred_dt_tuned)\n",
        "print(f\"Tuned Decision Tree Classifier Accuracy on test set: {accuracy_dt_tuned:.4f}\")\n",
        "\n",
        "# Display classification report for tuned Decision Tree\n",
        "print(\"\\nTuned Decision Tree Classifier Classification Report:\")\n",
        "print(classification_report(y_test, y_pred_dt_tuned))\n",
        "\n",
        "# Display confusion matrix for tuned Decision Tree\n",
        "print(\"\\nTuned Decision Tree Classifier Confusion Matrix:\")\n",
        "cm_dt_tuned = confusion_matrix(y_test, y_pred_dt_tuned)\n",
        "display(pd.DataFrame(cm_dt_tuned, index=['Actual 0', 'Actual 1', 'Actual 2'], columns=['Predicted 0', 'Predicted 1', 'Predicted 2']))"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tuned Decision Tree Classifier Accuracy on test set: 1.0000\n",
            "\n",
            "Tuned Decision Tree Classifier Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        19\n",
            "           1       1.00      1.00      1.00        13\n",
            "           2       1.00      1.00      1.00        13\n",
            "\n",
            "    accuracy                           1.00        45\n",
            "   macro avg       1.00      1.00      1.00        45\n",
            "weighted avg       1.00      1.00      1.00        45\n",
            "\n",
            "\n",
            "Tuned Decision Tree Classifier Confusion Matrix:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          Predicted 0  Predicted 1  Predicted 2\n",
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              "                                                    [key], {});\n",
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              "          + ' to learn more about interactive tables.';\n",
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    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "34b34414",
        "outputId": "205c81a9-39da-42e0-9328-43691624431a"
      },
      "source": [
        "# Hyperparameter Tuning for K-Nearest\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "# Define the parameter grid for KNN\n",
        "# n_neighbors: number of neighbors to consider\n",
        "# weights: weighting function used in prediction\n",
        "# metric: distance metric to use\n",
        "param_grid_knn = {\n",
        "    'n_neighbors': list(range(1, 21)), # Test k from 1 to 20\n",
        "    'weights': ['uniform', 'distance'],\n",
        "    'metric': ['euclidean', 'manhattan']\n",
        "}\n",
        "\n",
        "# Initialize GridSearchCV for KNN\n",
        "grid_search_knn = GridSearchCV(KNeighborsClassifier(), param_grid_knn, cv=5, scoring='accuracy', verbose=1)\n",
        "\n",
        "# Fit GridSearchCV to the training data\n",
        "grid_search_knn.fit(X_train, y_train)\n",
        "\n",
        "print(\"GridSearchCV for K-Nearest Neighbors completed.\")\n",
        "print(f\"Best parameters for KNN: {grid_search_knn.best_params_}\")\n",
        "print(f\"Best cross-validation accuracy for KNN: {grid_search_knn.best_score_:.4f}\")"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 80 candidates, totalling 400 fits\n",
            "GridSearchCV for K-Nearest Neighbors completed.\n",
            "Best parameters for KNN: {'metric': 'euclidean', 'n_neighbors': 18, 'weights': 'distance'}\n",
            "Best cross-validation accuracy for KNN: 0.9619\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "e5e8ff9b",
        "outputId": "e6315d28-4051-497a-9754-9dc4fe2a9a1f"
      },
      "source": [
        "### Evaluate Tuned K-Nearest Neighbors Model\n",
        "# Get the best KNN model from GridSearchCV\n",
        "best_knn_model = grid_search_knn.best_estimator_\n",
        "\n",
        "# Make predictions on the test set with the best model\n",
        "y_pred_knn_tuned = best_knn_model.predict(X_test)\n",
        "\n",
        "# Evaluate the tuned KNN model's accuracy\n",
        "accuracy_knn_tuned = accuracy_score(y_test, y_pred_knn_tuned)\n",
        "print(f\"Tuned K-Nearest Neighbors Accuracy on test set: {accuracy_knn_tuned:.4f}\")\n",
        "\n",
        "# Display classification report for tuned KNN\n",
        "print(\"\\nTuned K-Nearest Neighbors Classification Report:\")\n",
        "print(classification_report(y_test, y_pred_knn_tuned))\n",
        "\n",
        "# Display confusion matrix for tuned KNN\n",
        "print(\"\\nTuned K-Nearest Neighbors Confusion Matrix:\")\n",
        "cm_knn_tuned = confusion_matrix(y_test, y_pred_knn_tuned)\n",
        "display(pd.DataFrame(cm_knn_tuned, index=['Actual 0', 'Actual 1', 'Actual 2'], columns=['Predicted 0', 'Predicted 1', 'Predicted 2']))"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tuned K-Nearest Neighbors Accuracy on test set: 1.0000\n",
            "\n",
            "Tuned K-Nearest Neighbors Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        19\n",
            "           1       1.00      1.00      1.00        13\n",
            "           2       1.00      1.00      1.00        13\n",
            "\n",
            "    accuracy                           1.00        45\n",
            "   macro avg       1.00      1.00      1.00        45\n",
            "weighted avg       1.00      1.00      1.00        45\n",
            "\n",
            "\n",
            "Tuned K-Nearest Neighbors Confusion Matrix:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          Predicted 0  Predicted 1  Predicted 2\n",
              "Actual 0           19            0            0\n",
              "Actual 1            0           13            0\n",
              "Actual 2            0            0           13"
            ],
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
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      },
      "execution_count": 108,
      "outputs": []
    }
  ]
}