HeartDisease.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/abdulrahman-nuzha/HeartDisease/blob/main/HeartDisease.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Libraries"
      ],
      "metadata": {
        "id": "Epoh6qHtvjEn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.dummy import DummyClassifier\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "hKFuvRKVvlJm"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Dataset"
      ],
      "metadata": {
        "id": "dptxg74Ivgjn"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "z86tkE9fcR54"
      },
      "outputs": [],
      "source": [
        "! pip install -q kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "AE5NndJEcRzN"
      },
      "outputs": [],
      "source": [
        "! mkdir ~/.kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "KUAgjpSMcaur"
      },
      "outputs": [],
      "source": [
        "! cp kaggle.json ~/.kaggle/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Z5veVHSBcaET"
      },
      "outputs": [],
      "source": [
        "! chmod 600 ~/.kaggle/kaggle.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s8BB6ybNck1O",
        "outputId": "2ce7c0f9-5c3a-4fd2-8502-c7be954dd5ef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading personal-key-indicators-of-heart-disease.zip to /content\n",
            " 96% 3.00M/3.13M [00:00<00:00, 5.78MB/s]\n",
            "100% 3.13M/3.13M [00:00<00:00, 5.09MB/s]\n"
          ]
        }
      ],
      "source": [
        "! kaggle datasets download -d kamilpytlak/personal-key-indicators-of-heart-disease"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tbYimF-UbnOO",
        "outputId": "bf4bea8a-5135-46ce-eed6-c6c6483a028c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  personal-key-indicators-of-heart-disease.zip\n",
            "  inflating: personal-key-indicators-of-heart-disease/heart_2020_cleaned.csv  \n"
          ]
        }
      ],
      "source": [
        "! unzip personal-key-indicators-of-heart-disease.zip -d personal-key-indicators-of-heart-disease"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "8BbI0_uaacqN"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('/content/personal-key-indicators-of-heart-disease/heart_2020_cleaned.csv')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CteChep7XU-9",
        "outputId": "aa589323-c3cf-434c-f8ac-206d51710dde"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 319795 entries, 0 to 319794\n",
            "Data columns (total 18 columns):\n",
            " #   Column            Non-Null Count   Dtype  \n",
            "---  ------            --------------   -----  \n",
            " 0   HeartDisease      319795 non-null  object \n",
            " 1   BMI               319795 non-null  float64\n",
            " 2   Smoking           319795 non-null  object \n",
            " 3   AlcoholDrinking   319795 non-null  object \n",
            " 4   Stroke            319795 non-null  object \n",
            " 5   PhysicalHealth    319795 non-null  float64\n",
            " 6   MentalHealth      319795 non-null  float64\n",
            " 7   DiffWalking       319795 non-null  object \n",
            " 8   Sex               319795 non-null  object \n",
            " 9   AgeCategory       319795 non-null  object \n",
            " 10  Race              319795 non-null  object \n",
            " 11  Diabetic          319795 non-null  object \n",
            " 12  PhysicalActivity  319795 non-null  object \n",
            " 13  GenHealth         319795 non-null  object \n",
            " 14  SleepTime         319795 non-null  float64\n",
            " 15  Asthma            319795 non-null  object \n",
            " 16  KidneyDisease     319795 non-null  object \n",
            " 17  SkinCancer        319795 non-null  object \n",
            "dtypes: float64(4), object(14)\n",
            "memory usage: 43.9+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum().sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B2CVmc3SXU4s",
        "outputId": "d9b33d86-8322-4f64-99cf-b04f2e7e8be8"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "HeartDisease        0\n",
              "BMI                 0\n",
              "KidneyDisease       0\n",
              "Asthma              0\n",
              "SleepTime           0\n",
              "GenHealth           0\n",
              "PhysicalActivity    0\n",
              "Diabetic            0\n",
              "Race                0\n",
              "AgeCategory         0\n",
              "Sex                 0\n",
              "DiffWalking         0\n",
              "MentalHealth        0\n",
              "PhysicalHealth      0\n",
              "Stroke              0\n",
              "AlcoholDrinking     0\n",
              "Smoking             0\n",
              "SkinCancer          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "KersmrXtacqP"
      },
      "outputs": [],
      "source": [
        "x = df.loc[: , df.columns != 'HeartDisease']\n",
        "y = df['HeartDisease']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "MqA03fk2acqQ"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train , x_test ,y_train , y_test = train_test_split(x,y,test_size=0.2)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Preprocessing X"
      ],
      "metadata": {
        "id": "Jd7tcLqYwddQ"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HVixhbEndI1-"
      },
      "source": [
        "##Label Encoder"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train['PhysicalActivity'].unique().size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Isi9sJJikB6L",
        "outputId": "db644d60-1db0-438d-c25c-2376076a3617"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "l = LabelEncoder()\n",
        "\n",
        "# Fit and transform on x_train\n",
        "tmp = x_train['PhysicalActivity'].values.ravel()\n",
        "x_train['PhysicalActivity'] = l.fit_transform(tmp)\n",
        "\n",
        "# Transform on x_test using the parameters from x_train\n",
        "tmp = x_test['PhysicalActivity'].values.ravel()\n",
        "x_test['PhysicalActivity'] = l.transform(tmp)"
      ],
      "metadata": {
        "id": "HP64wHUV0DLc"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train['Asthma'].unique().size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I8f6oXRokG9c",
        "outputId": "7c90b752-d867-47c2-b268-f6b6fe251d0b"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "FbVoi-tUacqa"
      },
      "outputs": [],
      "source": [
        "l = LabelEncoder()\n",
        "\n",
        "# Fit and transform on x_train\n",
        "tmp = x_train['Asthma'].values.ravel()\n",
        "x_train['Asthma'] = l.fit_transform(tmp)\n",
        "\n",
        "# Transform on x_test using the parameters from x_train\n",
        "tmp = x_test['Asthma'].values.ravel()\n",
        "x_test['Asthma'] = l.transform(tmp)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train['KidneyDisease'].unique().size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2EBYUffikLO-",
        "outputId": "71a92e32-ec04-4273-893c-1bb7b7629e3b"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "mhdrH46Wacqc"
      },
      "outputs": [],
      "source": [
        "l = LabelEncoder()\n",
        "\n",
        "# Fit and transform on x_train\n",
        "tmp = x_train['KidneyDisease'].values.ravel()\n",
        "x_train['KidneyDisease'] = l.fit_transform(tmp)\n",
        "\n",
        "# Transform on x_test using the parameters from x_train\n",
        "tmp = x_test['KidneyDisease'].values.ravel()\n",
        "x_test['KidneyDisease'] = l.transform(tmp)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train['SkinCancer'].unique().size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8tyW4QmfkPW4",
        "outputId": "77033e08-6f46-4642-924a-61c7303df035"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "VWY5W9Vracqe"
      },
      "outputs": [],
      "source": [
        "l = LabelEncoder()\n",
        "\n",
        "# Fit and transform on x_train\n",
        "tmp = x_train['SkinCancer'].values.ravel()\n",
        "x_train['SkinCancer'] = l.fit_transform(tmp)\n",
        "\n",
        "# Transform on x_test using the parameters from x_train\n",
        "tmp = x_test['SkinCancer'].values.ravel()\n",
        "x_test['SkinCancer'] = l.transform(tmp)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x['Smoking'].unique().size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u1BuoPZikSP-",
        "outputId": "d5d61724-2015-4ddb-cca3-2a076577ca04"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "czs7Hn_vacqg"
      },
      "outputs": [],
      "source": [
        "l = LabelEncoder()\n",
        "\n",
        "# Fit and transform on x_train\n",
        "tmp = x_train['Smoking'].values.ravel()\n",
        "x_train['Smoking'] = l.fit_transform(tmp)\n",
        "\n",
        "# Transform on x_test using the parameters from x_train\n",
        "tmp = x_test['Smoking'].values.ravel()\n",
        "x_test['Smoking'] = l.transform(tmp)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train['AlcoholDrinking'].unique().size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6TA5bQLGkUtO",
        "outputId": "aab8216a-cf99-4892-aeec-b0211560fe8b"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "4wrVMfUnacqh"
      },
      "outputs": [],
      "source": [
        "l = LabelEncoder()\n",
        "\n",
        "# Fit and transform on x_train\n",
        "tmp = x_train['AlcoholDrinking'].values.ravel()\n",
        "x_train['AlcoholDrinking'] = l.fit_transform(tmp)\n",
        "\n",
        "# Transform on x_test using the parameters from x_train\n",
        "tmp = x_test['AlcoholDrinking'].values.ravel()\n",
        "x_test['AlcoholDrinking'] = l.transform(tmp)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train['DiffWalking'].unique().size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mlAlQxF7kXm-",
        "outputId": "da26ca26-4977-4755-d13c-3579c7e600b5"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "gsQXQe85acqi"
      },
      "outputs": [],
      "source": [
        "l = LabelEncoder()\n",
        "\n",
        "# Fit and transform on x_train\n",
        "tmp = x_train['DiffWalking'].values.ravel()\n",
        "x_train['DiffWalking'] = l.fit_transform(tmp)\n",
        "\n",
        "# Transform on x_test using the parameters from x_train\n",
        "tmp = x_test['DiffWalking'].values.ravel()\n",
        "x_test['DiffWalking'] = l.transform(tmp)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train['Sex'].unique().size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P-VOmteRkaT3",
        "outputId": "1990acfd-8dee-4ba1-f583-e9629bda974a"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "VIzbyb_0acqk"
      },
      "outputs": [],
      "source": [
        "l = LabelEncoder()\n",
        "\n",
        "# Fit and transform on x_train\n",
        "tmp = x_train['Sex'].values.ravel()\n",
        "x_train['Sex'] = l.fit_transform(tmp)\n",
        "\n",
        "# Transform on x_test using the parameters from x_train\n",
        "tmp = x_test['Sex'].values.ravel()\n",
        "x_test['Sex'] = l.transform(tmp)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train['Stroke'].unique().size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TJZCZC_bkiCs",
        "outputId": "2d297955-c3d7-4152-f5d6-becb060a5f79"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "f7CKNGyBacqm"
      },
      "outputs": [],
      "source": [
        "l = LabelEncoder()\n",
        "\n",
        "# Fit and transform on x_train\n",
        "tmp = x_train['Stroke'].values.ravel()\n",
        "x_train['Stroke'] = l.fit_transform(tmp)\n",
        "\n",
        "# Transform on x_test using the parameters from x_train\n",
        "tmp = x_test['Stroke'].values.ravel()\n",
        "x_test['Stroke'] = l.transform(tmp)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "Td3BbNyD6rhx",
        "outputId": "aec27443-a997-4400-e530-e06c1fb46e11"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          BMI  Smoking  AlcoholDrinking  Stroke  PhysicalHealth  MentalHealth  \\\n",
              "50555   23.75        1                0       0             0.0          30.0   \n",
              "281263  22.24        0                0       0             4.0           0.0   \n",
              "246896  26.63        0                0       0             1.0           0.0   \n",
              "46463   27.32        0                0       0            30.0           5.0   \n",
              "31755   31.93        0                0       0            30.0           5.0   \n",
              "...       ...      ...              ...     ...             ...           ...   \n",
              "12059   31.52        0                0       0             0.0           1.0   \n",
              "94660   18.31        0                0       0             0.0          30.0   \n",
              "187762  36.58        1                1       0             0.0          23.0   \n",
              "263886  44.01        0                0       0             0.0           5.0   \n",
              "262444  20.81        0                0       0             5.0           0.0   \n",
              "\n",
              "        DiffWalking  Sex AgeCategory      Race                 Diabetic  \\\n",
              "50555             1    1       60-64     Black                       No   \n",
              "281263            0    0       55-59     White                       No   \n",
              "246896            0    1       55-59     White                       No   \n",
              "46463             0    1       55-59     Black  No, borderline diabetes   \n",
              "31755             1    0       40-44     White                       No   \n",
              "...             ...  ...         ...       ...                      ...   \n",
              "12059             0    1       60-64     White  No, borderline diabetes   \n",
              "94660             0    1       18-24     White                       No   \n",
              "187762            0    0       50-54     White   Yes (during pregnancy)   \n",
              "263886            0    0       35-39  Hispanic                       No   \n",
              "262444            0    1       30-34     Black                       No   \n",
              "\n",
              "        PhysicalActivity  GenHealth  SleepTime  Asthma  KidneyDisease  \\\n",
              "50555                  0       Fair        8.0       0              1   \n",
              "281263                 1  Excellent        8.0       0              0   \n",
              "246896                 1  Very good        7.0       0              0   \n",
              "46463                  1       Fair        9.0       0              0   \n",
              "31755                  0       Poor        6.0       0              0   \n",
              "...                  ...        ...        ...     ...            ...   \n",
              "12059                  1  Very good        7.0       0              0   \n",
              "94660                  1  Excellent        6.0       0              0   \n",
              "187762                 1       Good        6.0       0              0   \n",
              "263886                 0       Fair        8.0       0              0   \n",
              "262444                 1  Excellent        8.0       0              0   \n",
              "\n",
              "        SkinCancer  \n",
              "50555            0  \n",
              "281263           0  \n",
              "246896           1  \n",
              "46463            0  \n",
              "31755            0  \n",
              "...            ...  \n",
              "12059            0  \n",
              "94660            0  \n",
              "187762           0  \n",
              "263886           0  \n",
              "262444           0  \n",
              "\n",
              "[255836 rows x 17 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-db3580e9-32cb-463b-8382-4ee23f774a82\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BMI</th>\n",
              "      <th>Smoking</th>\n",
              "      <th>AlcoholDrinking</th>\n",
              "      <th>Stroke</th>\n",
              "      <th>PhysicalHealth</th>\n",
              "      <th>MentalHealth</th>\n",
              "      <th>DiffWalking</th>\n",
              "      <th>Sex</th>\n",
              "      <th>AgeCategory</th>\n",
              "      <th>Race</th>\n",
              "      <th>Diabetic</th>\n",
              "      <th>PhysicalActivity</th>\n",
              "      <th>GenHealth</th>\n",
              "      <th>SleepTime</th>\n",
              "      <th>Asthma</th>\n",
              "      <th>KidneyDisease</th>\n",
              "      <th>SkinCancer</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>50555</th>\n",
              "      <td>23.75</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>60-64</td>\n",
              "      <td>Black</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Fair</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>281263</th>\n",
              "      <td>22.24</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>55-59</td>\n",
              "      <td>White</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>Excellent</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246896</th>\n",
              "      <td>26.63</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>55-59</td>\n",
              "      <td>White</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>Very good</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46463</th>\n",
              "      <td>27.32</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>55-59</td>\n",
              "      <td>Black</td>\n",
              "      <td>No, borderline diabetes</td>\n",
              "      <td>1</td>\n",
              "      <td>Fair</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31755</th>\n",
              "      <td>31.93</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>40-44</td>\n",
              "      <td>White</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Poor</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12059</th>\n",
              "      <td>31.52</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>60-64</td>\n",
              "      <td>White</td>\n",
              "      <td>No, borderline diabetes</td>\n",
              "      <td>1</td>\n",
              "      <td>Very good</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94660</th>\n",
              "      <td>18.31</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>18-24</td>\n",
              "      <td>White</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>Excellent</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>187762</th>\n",
              "      <td>36.58</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50-54</td>\n",
              "      <td>White</td>\n",
              "      <td>Yes (during pregnancy)</td>\n",
              "      <td>1</td>\n",
              "      <td>Good</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>263886</th>\n",
              "      <td>44.01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35-39</td>\n",
              "      <td>Hispanic</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Fair</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>262444</th>\n",
              "      <td>20.81</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>30-34</td>\n",
              "      <td>Black</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>Excellent</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>255836 rows × 17 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-db3580e9-32cb-463b-8382-4ee23f774a82')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-db3580e9-32cb-463b-8382-4ee23f774a82 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-db3580e9-32cb-463b-8382-4ee23f774a82');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TjT4xWVlkeQ_"
      },
      "source": [
        "## OneHot Encoder"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train['Race'].unique().size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T032XuDnkkkA",
        "outputId": "ee75984e-16f1-42a8-fa02-72ec5bf40c84"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.unique(x_train['Race'].values))\n",
        "print(x_train['Race'].unique().size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aVncMdAWhgmQ",
        "outputId": "ea7b2b21-b599-4bc0-97b7-e8588b0ac0bc"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['American Indian/Alaskan Native' 'Asian' 'Black' 'Hispanic' 'Other'\n",
            " 'White']\n",
            "6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "JeuZE3ackdJ5"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "OHE = OneHotEncoder(sparse_output=False,handle_unknown = 'ignore')\n",
        "OHE.fit(x_train[['Race']])\n",
        "\n",
        "x_train['Race'] = OHE.transform(x_train[['Race']])\n",
        "x_test['Race'] = OHE.transform(x_test[['Race']])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.unique(x_train['AgeCategory'].values))\n",
        "print(x_train['AgeCategory'].unique().size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dvEVWrmDk5-P",
        "outputId": "b77478df-8526-41b4-bf03-e8acf7345dcd"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['18-24' '25-29' '30-34' '35-39' '40-44' '45-49' '50-54' '55-59' '60-64'\n",
            " '65-69' '70-74' '75-79' '80 or older']\n",
            "13\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "q1wECeTOacqo"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "OHE = OneHotEncoder(sparse_output=False,handle_unknown = 'ignore')\n",
        "OHE.fit(x_train[['AgeCategory']])\n",
        "\n",
        "x_train['AgeCategory'] = OHE.transform(x_train[['AgeCategory']])\n",
        "x_test['AgeCategory'] = OHE.transform(x_test[['AgeCategory']])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.unique(x_train['Diabetic'].values))\n",
        "print(x_train['Diabetic'].unique().size)"
      ],
      "metadata": {
        "id": "4cGJEXHolQd-",
        "outputId": "0d7af572-e48b-4be6-deda-0dd8a8d8d6d2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['No' 'No, borderline diabetes' 'Yes' 'Yes (during pregnancy)']\n",
            "4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "1AKzviNzacqp"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "OHE = OneHotEncoder(sparse_output=False,handle_unknown = 'ignore')\n",
        "OHE.fit(x_train[['Diabetic']])\n",
        "\n",
        "x_train['Diabetic'] = OHE.transform(x_train[['Diabetic']])\n",
        "x_test['Diabetic'] = OHE.transform(x_test[['Diabetic']])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.unique(x_train['GenHealth'].values))\n",
        "print(x_train['GenHealth'].unique().size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gtHh8Z3koFk0",
        "outputId": "f68309b6-e743-4dcf-9213-b48301143248"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Excellent' 'Fair' 'Good' 'Poor' 'Very good']\n",
            "5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "OFlNOlfyacqq"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "OHE = OneHotEncoder(sparse_output=False,handle_unknown = 'ignore')\n",
        "OHE.fit(x_train[['GenHealth']])\n",
        "\n",
        "x_train['GenHealth'] = OHE.transform(x_train[['GenHealth']])\n",
        "x_test['GenHealth'] = OHE.transform(x_test[['GenHealth']])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tm2xgslTmTpk"
      },
      "source": [
        "## Normalization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "CdlZ3rV1mWBc"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "x_train['BMI'] = scaler.fit_transform(x_train[['BMI']])\n",
        "x_test['BMI'] = scaler.transform(x_test[['BMI']])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "RHUfSvJwmnlK"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "x_train['MentalHealth'] = scaler.fit_transform(x_train[['MentalHealth']])\n",
        "x_test['MentalHealth'] = scaler.transform(x_test[['MentalHealth']])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "5OxKESpxofV2",
        "outputId": "755adac0-b836-41ba-c7cd-fc4c206c0b07"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        BMI  Smoking  AlcoholDrinking  Stroke  PhysicalHealth  MentalHealth  \\\n",
              "50555   1.0        1                0       0             0.0      1.000000   \n",
              "281263  0.0        0                0       0             4.0      0.000000   \n",
              "246896  0.0        0                0       0             1.0      0.000000   \n",
              "46463   1.0        0                0       0            30.0      0.166667   \n",
              "31755   0.0        0                0       0            30.0      0.166667   \n",
              "...     ...      ...              ...     ...             ...           ...   \n",
              "12059   0.0        0                0       0             0.0      0.033333   \n",
              "94660   0.0        0                0       0             0.0      1.000000   \n",
              "187762  0.0        1                1       0             0.0      0.766667   \n",
              "263886  1.0        0                0       0             0.0      0.166667   \n",
              "262444  0.0        0                0       0             5.0      0.000000   \n",
              "\n",
              "        DiffWalking  Sex  AgeCategory  Race  Diabetic  PhysicalActivity  \\\n",
              "50555             1    1          0.0   0.0       1.0                 0   \n",
              "281263            0    0          0.0   0.0       1.0                 1   \n",
              "246896            0    1          0.0   0.0       1.0                 1   \n",
              "46463             0    1          0.0   0.0       0.0                 1   \n",
              "31755             1    0          0.0   0.0       1.0                 0   \n",
              "...             ...  ...          ...   ...       ...               ...   \n",
              "12059             0    1          0.0   0.0       0.0                 1   \n",
              "94660             0    1          1.0   0.0       1.0                 1   \n",
              "187762            0    0          0.0   0.0       0.0                 1   \n",
              "263886            0    0          0.0   0.0       1.0                 0   \n",
              "262444            0    1          0.0   0.0       1.0                 1   \n",
              "\n",
              "        GenHealth  SleepTime  Asthma  KidneyDisease  SkinCancer  \n",
              "50555         0.0        8.0       0              1           0  \n",
              "281263        1.0        8.0       0              0           0  \n",
              "246896        0.0        7.0       0              0           1  \n",
              "46463         0.0        9.0       0              0           0  \n",
              "31755         0.0        6.0       0              0           0  \n",
              "...           ...        ...     ...            ...         ...  \n",
              "12059         0.0        7.0       0              0           0  \n",
              "94660         1.0        6.0       0              0           0  \n",
              "187762        0.0        6.0       0              0           0  \n",
              "263886        0.0        8.0       0              0           0  \n",
              "262444        1.0        8.0       0              0           0  \n",
              "\n",
              "[255836 rows x 17 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6f1bd371-e215-4089-b52c-78218784af32\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BMI</th>\n",
              "      <th>Smoking</th>\n",
              "      <th>AlcoholDrinking</th>\n",
              "      <th>Stroke</th>\n",
              "      <th>PhysicalHealth</th>\n",
              "      <th>MentalHealth</th>\n",
              "      <th>DiffWalking</th>\n",
              "      <th>Sex</th>\n",
              "      <th>AgeCategory</th>\n",
              "      <th>Race</th>\n",
              "      <th>Diabetic</th>\n",
              "      <th>PhysicalActivity</th>\n",
              "      <th>GenHealth</th>\n",
              "      <th>SleepTime</th>\n",
              "      <th>Asthma</th>\n",
              "      <th>KidneyDisease</th>\n",
              "      <th>SkinCancer</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>50555</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>281263</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246896</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46463</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31755</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12059</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94660</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>187762</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.766667</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>263886</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>262444</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>255836 rows × 17 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6f1bd371-e215-4089-b52c-78218784af32')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6f1bd371-e215-4089-b52c-78218784af32 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-6f1bd371-e215-4089-b52c-78218784af32');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tHFaGOi6dUZe"
      },
      "source": [
        "#Preprocessing Y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "nrCvTkaNacqs"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "l = LabelEncoder()\n",
        "\n",
        "# Fit and transform on y_train\n",
        "tmp = y_train.ravel()\n",
        "y_train = l.fit_transform(tmp)\n",
        "\n",
        "# Transform on y_test using the parameters from y_train\n",
        "tmp = y_test.ravel()\n",
        "y_test = l.transform(tmp)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IRbOu9fSq76G",
        "outputId": "aafeb3c8-5c02-495c-a5ef-bb77fcd75248"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0          No\n",
              "1          No\n",
              "2          No\n",
              "3          No\n",
              "4          No\n",
              "         ... \n",
              "319790    Yes\n",
              "319791     No\n",
              "319792     No\n",
              "319793     No\n",
              "319794     No\n",
              "Name: HeartDisease, Length: 319795, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wF_2oz_cqt-n",
        "outputId": "0567ed31-6cf2-475b-c0de-ccfb1050087e"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SPwnfrV-VNQx"
      },
      "source": [
        "# Models"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BCV7IK31eAiU",
        "outputId": "d01488a8-9368-49ea-8b3a-4a753e63e871"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(255836,)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KT3PBNLGeC2-",
        "outputId": "fa864af5-823f-4da5-e848-27c6aaa6f942"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(255836, 17)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##SVM"
      ],
      "metadata": {
        "id": "mxFC9uqT4_CY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#It takes a long time"
      ],
      "metadata": {
        "id": "djFCq-8f66Jc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # Define the parameter grid to search over\n",
        "# param_grid = {\n",
        "#     'C': [0.1, 1, 10],\n",
        "#     'kernel': ['linear', 'sigmoid']\n",
        "# }\n",
        "\n",
        "# # Perform grid search with 5-fold cross-validation\n",
        "# grid_search = GridSearchCV(svc, param_grid, cv=5)\n",
        "# grid_search.fit(x_train, y_train)"
      ],
      "metadata": {
        "id": "T12k7uMFeMqg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the best hyperparameters and corresponding score\n",
        "# print(\"Best hyperparameters: \", grid_search.best_params_)\n",
        "# print(\"Best score: \", grid_search.best_score_)"
      ],
      "metadata": {
        "id": "Y_dBlkHathRz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "DgjBh_bzacqs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "f3dfed9f-09b3-4302-cf8f-d0f614490364"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1, kernel='sigmoid')"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=1, kernel=&#x27;sigmoid&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=1, kernel=&#x27;sigmoid&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "svc = SVC(C = 1,kernel = 'sigmoid')\n",
        "svc.fit(x_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "wgU9ynlfRL_b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa4d7360-4877-484e-c428-6e6a4a6ed004"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " TEST :  0.8482152629027971\n",
            " TRAIN :  0.8470739067214934\n",
            "Support vector machine classifier (linear kernel, C=1)\n",
            " [[53725  4862]\n",
            " [ 4846   526]]\n",
            "SVM \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.92      0.92     58587\n",
            "           1       0.10      0.10      0.10      5372\n",
            "\n",
            "    accuracy                           0.85     63959\n",
            "   macro avg       0.51      0.51      0.51     63959\n",
            "weighted avg       0.85      0.85      0.85     63959\n",
            "\n"
          ]
        }
      ],
      "source": [
        "svc_train = svc.score(x_train,y_train)\n",
        "svc_test = svc.score(x_test,y_test)\n",
        "\n",
        "print(' TEST : ',svc_test)\n",
        "print(' TRAIN : ',svc_train)\n",
        "\n",
        "svm_predicted = svc.predict(x_test)\n",
        "confusion = confusion_matrix(y_test, svm_predicted)\n",
        "\n",
        "print('Support vector machine classifier (linear kernel, C=1)\\n', confusion)\n",
        "svm_classification_report = classification_report(y_test, svm_predicted, target_names = ['0', '1'])\n",
        "print('SVM \\n', svm_classification_report)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Logistic Regression"
      ],
      "metadata": {
        "id": "oygCJje7R7Yh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "c_values = [0.01, 0.1, 1, 10, 100]\n",
        "\n",
        "k = 5\n",
        "\n",
        "# Initialize an array to store the cross-validation scores for each value of C\n",
        "scores = np.zeros(len(c_values))\n",
        "\n",
        "# Split the data into K folds\n",
        "kf = KFold(n_splits=k, shuffle=True)\n",
        "\n",
        "# Perform cross-validation for each value of C\n",
        "for i, c in enumerate(c_values):\n",
        "    # Initialize an array to store the validation scores for each fold\n",
        "    fold_scores = np.zeros(k)\n",
        "\n",
        "    # Train and evaluate the model for each fold\n",
        "    for j, (train_idx, val_idx) in enumerate(kf.split(x_train)):\n",
        "        # Split the data into training and validation sets\n",
        "        x_train_k, y_train_k = x_train.iloc[train_idx], y_train[train_idx]\n",
        "        x_val_k, y_val_k = x_train.iloc[val_idx], y_train[val_idx]\n",
        "\n",
        "        # Train a logistic regression model with the current value of C\n",
        "        model = LogisticRegression(C=c,max_iter = 100000)\n",
        "        model.fit(x_train_k, y_train_k)\n",
        "\n",
        "        # Evaluate the model on the validation set\n",
        "        y_pred = model.predict(x_val_k)\n",
        "        fold_scores[j] = accuracy_score(y_val_k, y_pred)\n",
        "\n",
        "    # Calculate the average validation score across all folds\n",
        "    scores[i] = np.mean(fold_scores)\n",
        "\n",
        "# Print the scoress array\n",
        "for i,value in enumerate(scores):\n",
        "  print(\"Accuracy :\",value,\",C value :\",c_values[i])\n",
        "\n",
        "# Choose the value of C with the highest cross-validation score\n",
        "best_c = c_values[np.argmax(scores)]\n",
        "\n",
        "# Train the final model on the entire training set using the best value of C\n",
        "LR = LogisticRegression(C=best_c,max_iter = 100000)\n",
        "LR.fit(x_train, y_train)"
      ],
      "metadata": {
        "id": "4mLPgcZET1ny",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "outputId": "d7d9cc28-b8d4-438b-b2b2-a642d774163b"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy : 0.9146132683278594 ,C value : 0.01\n",
            "Accuracy : 0.9147813419712001 ,C value : 0.1\n",
            "Accuracy : 0.914793073473753 ,C value : 1\n",
            "Accuracy : 0.9148634272728673 ,C value : 10\n",
            "Accuracy : 0.914796980715259 ,C value : 100\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=10, max_iter=100000)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=10, max_iter=100000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=10, max_iter=100000)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "9mWDc1on9z7D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7473e32d-e264-46cf-c49f-54dddcf45c61"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " TEST :  0.9166653637486515\n",
            " TRAIN :  0.9148829719038759\n",
            "Logistic regression classifier\n",
            " [[58205   382]\n",
            " [ 4948   424]]\n",
            "Log\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.99      0.96     58587\n",
            "           1       0.53      0.08      0.14      5372\n",
            "\n",
            "    accuracy                           0.92     63959\n",
            "   macro avg       0.72      0.54      0.55     63959\n",
            "weighted avg       0.89      0.92      0.89     63959\n",
            "\n"
          ]
        }
      ],
      "source": [
        "LR_train = LR.score(x_train,y_train)\n",
        "LR_test = LR.score(x_test,y_test)\n",
        "\n",
        "print(' TEST : ',LR_test)\n",
        "print(' TRAIN : ',LR_train)\n",
        "\n",
        "lr_predicted = LR.predict(x_test)\n",
        "confusion = confusion_matrix(y_test, lr_predicted)\n",
        "lr_classification_report = classification_report(y_test, lr_predicted, target_names = ['0', '1'])\n",
        "\n",
        "print('Logistic regression classifier\\n', confusion)\n",
        "print('Log\\n', lr_classification_report)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "QNqV9ru5V00X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "outputId": "188e7f1e-7e5d-46d1-be83-b406f07916f6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAo0AAAI4CAYAAAAPqtOLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABiP0lEQVR4nO3deVxU1f/H8feAssjqCqK4kgu5pZWR5VIkmvXN7ZuaFZpalmZqbi3uFX01970sUbNy6aullkuamkqlmOaeW2EpaiogKovM/f3hl/k5gg7YRXB4PXvcR8y9Z849Zxzgw+csYzEMwxAAAABwEy753QAAAAAUfASNAAAAcIigEQAAAA4RNAIAAMAhgkYAAAA4RNAIAAAAhwgaAQAA4BBBIwAAABwiaAQAAIBDBI1wOocOHVLz5s3l5+cni8WiZcuWmVr/77//LovFoujoaFPrvZM1bdpUTZs2Na2+ESNGqFKlSqbVZ7YRI0bIYrGYVl+XLl0KdH/vRGa/JwEQNCKPHDlyRC+99JKqVKkiDw8P+fr6qlGjRpo0aZIuX76cp/eOjIzU7t279e6772r+/Pm699578/R+t1OXLl1ksVjk6+ub7et46NAhWSwWWSwWffDBB7mu/8SJExoxYoR27txpQmvzXmYAfyt9vd1u12ub+R7JPNzd3VWtWjUNGzZMKSkpeXpvAM6tSH43AM5n5cqV+ve//y13d3c9//zzqlWrltLS0rR582YNHDhQe/fu1Ycffpgn9758+bJiYmL01ltvqXfv3nlyj4oVK+ry5csqWrRontTvSJEiRXTp0iUtX75cTz/9tN21BQsWyMPD45aDgxMnTmjkyJGqVKmS6tWrl+PnrVmz5pbud6d6++23NWTIkFw952av7UcffSSr1Wpa+9zd3TV79mxJUmJior766iuNHj1aR44c0YIFC0y7T0FW2N6TwO1A0AhTHTt2TB07dlTFihW1fv16lS1b1natV69eOnz4sFauXJln9z9z5owkyd/fP8/uYbFY5OHhkWf1O+Lu7q5GjRrp888/zxI0fvbZZ2rVqpW+/PLL29KWS5cuqVixYnJzc7st9ysoihQpoiJFzPvxafYfIEWKFNGzzz5re/zKK6/owQcf1Oeff67x48crICDA1PvdzJUrV2S1Wm/7e6SwvSeB24HhaZhqzJgxSk5O1scff2wXMGYKCQnRa6+9Znt85coVjR49WlWrVpW7u7sqVaqkN998U6mpqXbPq1Spkp544glt3rxZ999/vzw8PFSlShXNmzfPVmbEiBGqWLGiJGngwIGyWCy2eWI3mjOW3dy0tWvX6qGHHpK/v7+8vb1VvXp1vfnmm7brN5rTuH79ej388MPy8vKSv7+/nnrqKe3fvz/b+x0+fFhdunSRv7+//Pz81LVrV126dOnGL+x1nnnmGX377bdKSEiwndu2bZsOHTqkZ555Jkv5c+fOacCAAapdu7a8vb3l6+urli1bateuXbYyGzZs0H333SdJ6tq1q214M7OfTZs2Va1atRQbG6vGjRurWLFittfl+vljkZGR8vDwyNL/iIgIFS9eXCdOnMhxX81w+vRpdevWTQEBAfLw8FDdunU1d+7cLOXOnj2r5557Tr6+vvL391dkZKR27dqV5d87t+8bR69tdu9Pq9WqSZMmqXbt2vLw8FDp0qXVokULbd++Pdf9t1gseuihh2QYho4ePWp37dtvv7W9b318fNSqVSvt3bs3Sx2LFy9WaGioPDw8VKtWLS1dujRLu6+dLjBx4kTb9/W+ffskSQcOHFD79u1VokQJeXh46N5779XXX39td5/09HSNHDlSd911lzw8PFSyZEk99NBDWrt2ra1MfHy8unbtqvLly8vd3V1ly5bVU089pd9//91WJrs5jTl5H1zbhw8//NDWh/vuu0/btm3LzcsOOB0yjTDV8uXLVaVKFT344IM5Kt+9e3fNnTtX7du31+uvv66ffvpJUVFR2r9/v5YuXWpX9vDhw2rfvr26deumyMhIffLJJ+rSpYsaNGigu+++W23btpW/v7/69eunTp066fHHH5e3t3eu2r9371498cQTqlOnjkaNGiV3d3cdPnxYW7ZsuenzvvvuO7Vs2VJVqlTRiBEjdPnyZU2ZMkWNGjXSjh07sgQETz/9tCpXrqyoqCjt2LFDs2fPVpkyZfSf//wnR+1s27atevbsqf/+97964YUXJF3NMtaoUUP169fPUv7o0aNatmyZ/v3vf6ty5co6deqUZs2apSZNmmjfvn0KCgpSzZo1NWrUKA0bNkwvvviiHn74YUmy+7c8e/asWrZsqY4dO+rZZ5+9YcZq0qRJWr9+vSIjIxUTEyNXV1fNmjVLa9as0fz58xUUFJSjfprh8uXLatq0qQ4fPqzevXurcuXKWrx4sbp06aKEhATbHzFWq1VPPvmkfv75Z7388suqUaOGvvrqK0VGRjq8h6P3TU5e2+t169ZN0dHRatmypbp3764rV67ohx9+0I8//nhL83QzA6rixYvbzs2fP1+RkZGKiIjQf/7zH126dEkzZszQQw89pF9++cX2vl25cqU6dOig2rVrKyoqSufPn1e3bt1Urly5bO81Z84cpaSk6MUXX5S7u7tKlCihvXv3qlGjRipXrpyGDBkiLy8vLVq0SK1bt9aXX36pNm3aSLoakEdFRal79+66//77lZSUpO3bt2vHjh167LHHJEnt2rXT3r179eqrr6pSpUo6ffq01q5dq7i4uBsuKMrp+yDTZ599pgsXLuill16SxWLRmDFj1LZtWx09ejTfpqYA+c4ATJKYmGhIMp566qkcld+5c6chyejevbvd+QEDBhiSjPXr19vOVaxY0ZBkbNq0yXbu9OnThru7u/H666/bzh07dsyQZIwdO9auzsjISKNixYpZ2jB8+HDj2m+DCRMmGJKMM2fO3LDdmfeYM2eO7Vy9evWMMmXKGGfPnrWd27Vrl+Hi4mI8//zzWe73wgsv2NXZpk0bo2TJkje857X98PLyMgzDMNq3b288+uijhmEYRkZGhhEYGGiMHDky29cgJSXFyMjIyNIPd3d3Y9SoUbZz27Zty9K3TE2aNDEkGTNnzsz2WpMmTezOrV692pBkvPPOO8bRo0cNb29vo3Xr1g77aBhXX6fs/r2ud6N/72tNnDjRkGR8+umntnNpaWlGWFiY4e3tbSQlJRmGYRhffvmlIcmYOHGirVxGRobxyCOPZHlNbuV9c7PX9vr35/r16w1JRp8+fbKUtVqtN7xHZl1eXl7GmTNnjDNnzhiHDx82PvjgA8NisRi1atWyPf/ChQuGv7+/0aNHD7vnx8fHG35+fnbna9eubZQvX964cOGC7dyGDRsMSXbtzvz38PX1NU6fPm1X76OPPmrUrl3bSElJsevLgw8+aNx11122c3Xr1jVatWp1w/6dP3/e4b+5YWR9T+b0fZDZh5IlSxrnzp2zlf3qq68MScby5ctvel/AmTE8DdMkJSVJknx8fHJU/ptvvpEk9e/f3+7866+/LklZ5j6GhobaMjSSVLp0aVWvXj3LcNs/kTkX8quvvsrxwoSTJ09q586d6tKli0qUKGE7X6dOHT322GO2fl6rZ8+edo8ffvhhnT171vYa5sQzzzyjDRs2KD4+XuvXr1d8fHy2Q9PS1XmQLi5Xv90zMjJ09uxZ2xDqjh07cnxPd3d3de3aNUdlmzdvrpdeekmjRo1S27Zt5eHhoVmzZuX4Xmb55ptvFBgYqE6dOtnOFS1aVH369FFycrI2btwoSVq1apWKFi2qHj162Mq5uLioV69eDu9xK++bm/nyyy9lsVg0fPjwLNdystXPxYsXVbp0aZUuXVohISEaMGCAGjVqpK+++sr2/LVr1yohIUGdOnXS33//bTtcXV3VsGFDff/995KuLuDZvXu3nn/+ebvMfZMmTVS7du1s79+uXTuVLl3a9vjcuXNav369nn76aV24cMF2r7NnzyoiIkKHDh3SX3/9Jenqa7l3714dOnQo27o9PT3l5uamDRs26Pz58w5fi0w5fR9k6tChg11WNvNnj5k/b4A7DUEjTOPr6ytJunDhQo7K//HHH3JxcVFISIjd+cDAQPn7++uPP/6wO1+hQoUsdRQvXjxXvzgc6dChgxo1aqTu3bsrICBAHTt21KJFi24aCGS2s3r16lmu1axZU3///bcuXrxod/76vmT+cspNXx5//HH5+Pho4cKFWrBgge67774sr2Umq9WqCRMm6K677pK7u7tKlSql0qVL69dff1ViYmKO71muXLlcLTD44IMPVKJECe3cuVOTJ09WmTJlcvxcs/zxxx+66667bEFzppo1a9quZ/6/bNmyKlasmF25G72m17qV983NHDlyREFBQXZ/hOSGh4eH1q5dq7Vr12rOnDmqWbOmTp8+LU9PT1uZzKDskUcesQWYmceaNWt0+vRpSf//+mT3OtzotalcubLd48OHD8swDA0dOjTLvTID48z7jRo1SgkJCapWrZpq166tgQMH6tdff7XV5e7urv/85z/69ttvFRAQoMaNG2vMmDGKj4+/6WuS0/dBJjO+RwFnw5xGmMbX11dBQUHas2dPrp6X002SXV1dsz1vGMYt3yMjI8PusaenpzZt2qTvv/9eK1eu1KpVq7Rw4UI98sgjWrNmzQ3bkFv/pC+Z3N3d1bZtW82dO1dHjx7ViBEjblj2vffe09ChQ/XCCy9o9OjRKlGihFxcXNS3b99cBTbXBh058csvv9iCgd27d9tleZzJ7Xrf5JSrq6vCw8NtjyMiIlSjRg299NJLtoUnmf/u8+fPV2BgYJY6/snq8OvfJ5n3GjBggCIiIrJ9TmYA2rhxYx05ckRfffWV1qxZo9mzZ2vChAmaOXOmunfvLknq27evnnzySS1btkyrV6/W0KFDFRUVpfXr1+uee+655XZfy4zvUcDZkGmEqZ544gkdOXJEMTExDstWrFhRVqs1yzDUqVOnlJCQYFsJbYbixYvbrTTOdH12Qbo6JPnoo49q/Pjx2rdvn959912tX7/eNlx3vcx2Hjx4MMu1AwcOqFSpUvLy8vpnHbiBZ555Rr/88osuXLigjh073rDckiVL1KxZM3388cfq2LGjmjdvrvDw8CyviZmfcnLx4kV17dpVoaGhevHFFzVmzJh8WX1asWJFHTp0KEtwfODAAdv1zP+fPHkyyyr2w4cP5+g+jt43uXltq1atqhMnTujcuXM5fs7NlC1bVv369dPy5cv1448/2u4hSWXKlFF4eHiWI3Plcebrk93rkNPXpkqVKpKuDgdnd6/w8HC7aS0lSpRQ165d9fnnn+v48eOqU6dOlj+Kqlatqtdff11r1qzRnj17lJaWpnHjxt2wDTl9HwC4MYJGmGrQoEHy8vJS9+7dderUqSzXjxw5okmTJkm6OrwqSRMnTrQrM378eElSq1atTGtX1apVlZiYaDfMdfLkySwrtLP7JZ25EfP12wBlKlu2rOrVq6e5c+faBWF79uzRmjVrbP3MC82aNdPo0aM1derUbLNFmVxdXbNkSBYvXmybR5YpM7jNLsDOrcGDBysuLk5z587V+PHjValSJUVGRt7wdcwrjz/+uOLj47Vw4ULbuStXrmjKlCny9vZWkyZNJF3NxqWnp+ujjz6ylbNarZo2bZrDe+TkfZOb17Zdu3YyDEMjR47Mcu1WM12vvvqqihUrpvfff1/S1f76+vrqvffeU3p6epbymXueBgUFqVatWpo3b56Sk5Nt1zdu3Kjdu3fn6N5lypRR06ZNNWvWLJ08efKG95KurtC/lre3t0JCQmyv46VLl7JsXl+1alX5+Pjc9L2V0/cBgBtjeBqmqlq1qj777DN16NBBNWvWtPtEmK1bt9q2uJCkunXrKjIyUh9++KESEhLUpEkT/fzzz5o7d65at26tZs2amdaujh07avDgwWrTpo369Olj21qkWrVqdgtBRo0apU2bNqlVq1aqWLGiTp8+renTp6t8+fJ66KGHblj/2LFj1bJlS4WFhalbt262LXf8/PxuOmz8T7m4uOjtt992WO6JJ57QqFGj1LVrVz344IPavXu3FixYYMsAZapatar8/f01c+ZM+fj4yMvLSw0bNswyR82R9evXa/r06Ro+fLhtC6A5c+aoadOmGjp0qMaMGZOr+hxZt25dtp+C07p1a7344ouaNWuWunTpotjYWFWqVElLlizRli1bNHHiRFuGq3Xr1rr//vv1+uuv6/Dhw6pRo4a+/vprW0B4s0xhTt43uXltmzVrpueee06TJ0/WoUOH1KJFC1mtVv3www9q1qzZLX3aUcmSJdW1a1dNnz5d+/fvV82aNTVjxgw999xzql+/vjp27KjSpUsrLi5OK1euVKNGjTR16lRJV6c3PPXUU2rUqJG6du2q8+fPa+rUqapVq5ZdIHkz06ZN00MPPaTatWurR48eqlKlik6dOqWYmBj9+eeftj1DQ0ND1bRpUzVo0EAlSpTQ9u3btWTJEluff/vtNz366KN6+umnFRoaqiJFimjp0qU6derUTbPtOX0fALiJfFy5DSf222+/GT169DAqVapkuLm5GT4+PkajRo2MKVOm2G25kZ6ebowcOdKoXLmyUbRoUSM4ONh444037MoYxtUtd7LbhuP6bTVutgXLmjVrjFq1ahlubm5G9erVjU8//TTL1inr1q0znnrqKSMoKMhwc3MzgoKCjE6dOhm//fZblntcv3XKd999ZzRq1Mjw9PQ0fH19jSeffNLYt2+fXZnM+12/NcucOXMMScaxY8du+Joahv2WOzdyoy13Xn/9daNs2bKGp6en0ahRIyMmJibbrXK++uorIzQ01ChSpIhdP5s0aWLcfffd2d7z2nqSkpKMihUrGvXr1zfS09PtyvXr189wcXExYmJibtqH3G65c6Nj/vz5hmEYxqlTp4yuXbsapUqVMtzc3IzatWtnu/XNmTNnjGeeecbw8fEx/Pz8jC5duhhbtmwxJBlffPGFXfty+74xjBu/ttltCXXlyhVj7NixRo0aNQw3NzejdOnSRsuWLY3Y2NibviY3e48cOXLEcHV1NSIjI23nvv/+eyMiIsLw8/MzPDw8jKpVqxpdunQxtm/fbvfcL774wqhRo4bh7u5u1KpVy/j666+Ndu3aGTVq1LCVcbQF0pEjR4znn3/eCAwMNIoWLWqUK1fOeOKJJ4wlS5bYyrzzzjvG/fffb/j7+xuenp5GjRo1jHfffddIS0szDMMw/v77b6NXr15GjRo1DC8vL8PPz89o2LChsWjRIrt7Zffezsn74GZ9kGQMHz48274BhYHFMJjVC6BgGTFihKKjo+0+4SO/LFu2TG3atNHmzZvVqFGj/G5OgVKvXj2VLl3a7tNaADgv5jQCwP9cvnzZ7nFGRoamTJkiX1/fbD9pp7BIT0/XlStX7M5t2LBBu3btyvJRfQCcF3MaAeB/Xn31VV2+fFlhYWFKTU3Vf//7X23dulXvvfderrcbciZ//fWXwsPD9eyzzyooKEgHDhzQzJkzFRgYmGWjegDOi6ARAP7nkUce0bhx47RixQqlpKQoJCREU6ZMuaWFJ86kePHiatCggWbPnq0zZ87Iy8tLrVq10vvvv6+SJUvmd/MA3CbMaQQAAIBDzGkEAACAQwSNAAAAcIigEQAAAA4RNAIAAMAhgkYAAAA4RNAIAAAAhwgaAQAA4BBBIwAAABwiaAQAAIBDBI0AAABwiKARAAAADhE0AgAAwCGCRgAAADhE0AgAAACHCBoBAADgEEEjAAAAHCJoBAAAgEMEjQAAAHCIoBEAAAAOETQCAADAIYJGAAAAOETQCAAAAIcIGgEAAOBQkfxuwJ3AarXqxIkT8vHxkcViye/mAABQoBmGoQsXLigoKEguLrc/P5WSkqK0tLQ8qdvNzU0eHh55UndBR9CYAydOnFBwcHB+NwMAgDvK8ePHVb58+dt6z5SUFHn6lJSuXMqT+gMDA3Xs2LFCGTgSNOaAj4+PJMktNFIWV7d8bg2Aa8Vt+CC/mwDgOheSkhRSOdj2+/N2SktLk65ckntopGT27+yMNMXvm6u0tDSCRmQvc0ja4upG0AgUML6+vvndBAA3kK9Tuop4mP4727AU7qUgBI0AAMD5WCSZHbQW8mUNhTtkBgAAQI6QaQQAAM7H4nL1MLvOQqxw9x4AAAA5QqYRAAA4H4slD+Y0Fu5JjWQaAQAA4BCZRgAA4HyY02i6wt17AAAA5AiZRgAA4HyY02g6gkYAAOCE8mB4upAP0Bbu3gMAACBHyDQCAADnw/C06cg0AgAAwCEyjQAAwPmw5Y7pCnfvAQAAkCNkGgEAgPNhTqPpyDQCAADAITKNAADA+TCn0XQEjQAAwPkwPG26wh0yAwAAIEfINAIAAOfD8LTpCnfvAQAAkCNkGgEAgPOxWPIg08icRgAAAOCmyDQCAADn42K5ephdZyFGphEAAAAOkWkEAADOh9XTpiNoBAAAzofNvU1XuENmAAAA5AiZRgAA4HwYnjZd4e49AAAAcoRMIwAAcD7MaTQdmUYAAAA4RKYRAAA4H+Y0mq5w9x4AAAA5QqYRAAA4H+Y0mo6gEQAAOB+Gp01XuHsPAACAHCHTCAAAnA/D06Yj0wgAAACHyDQCAAAnlAdzGgt5rq1w9x4AAAA5QqYRAAA4H+Y0mo5MIwAAABwi0wgAAJyPxZIH+zQW7kwjQSMAAHA+bO5tusLdewAAAOQImUYAAOB8WAhjOjKNAAAAcIhMIwAAcD7MaTRd4e49AAAAcoRMIwAAcD7MaTQdmUYAAAA4RKYRAAA4H+Y0mo6gEQAAOB+Gp01XuENmAAAA5AiZRgAA4HQsFossZBpNRaYRAAAADhE0AgAAp5OZaTT7yI0RI0ZkeX6NGjVs11NSUtSrVy+VLFlS3t7eateunU6dOmVXR1xcnFq1aqVixYqpTJkyGjhwoK5cuWJXZsOGDapfv77c3d0VEhKi6OjoLG2ZNm2aKlWqJA8PDzVs2FA///xzrvoiETQCAADkmbvvvlsnT560HZs3b7Zd69evn5YvX67Fixdr48aNOnHihNq2bWu7npGRoVatWiktLU1bt27V3LlzFR0drWHDhtnKHDt2TK1atVKzZs20c+dO9e3bV927d9fq1attZRYuXKj+/ftr+PDh2rFjh+rWrauIiAidPn06V30haAQAAM7HkkdHLhUpUkSBgYG2o1SpUpKkxMREffzxxxo/frweeeQRNWjQQHPmzNHWrVv1448/SpLWrFmjffv26dNPP1W9evXUsmVLjR49WtOmTVNaWpokaebMmapcubLGjRunmjVrqnfv3mrfvr0mTJhga8P48ePVo0cPde3aVaGhoZo5c6aKFSumTz75JFd9IWgEAADIhaSkJLsjNTX1hmUPHTqkoKAgValSRZ07d1ZcXJwkKTY2Vunp6QoPD7eVrVGjhipUqKCYmBhJUkxMjGrXrq2AgABbmYiICCUlJWnv3r22MtfWkVkms460tDTFxsbalXFxcVF4eLitTE4RNAIAAKeTl3Mag4OD5efnZzuioqKybUPDhg0VHR2tVatWacaMGTp27JgefvhhXbhwQfHx8XJzc5O/v7/dcwICAhQfHy9Jio+PtwsYM69nXrtZmaSkJF2+fFl///23MjIysi2TWUdOseUOAABwOnm55c7x48fl6+trO+3u7p5t8ZYtW9q+rlOnjho2bKiKFStq0aJF8vT0NLdttwGZRgAAgFzw9fW1O24UNF7P399f1apV0+HDhxUYGKi0tDQlJCTYlTl16pQCAwMlSYGBgVlWU2c+dlTG19dXnp6eKlWqlFxdXbMtk1lHThE0AgAAp1MQtty5XnJyso4cOaKyZcuqQYMGKlq0qNatW2e7fvDgQcXFxSksLEySFBYWpt27d9utcl67dq18fX0VGhpqK3NtHZllMutwc3NTgwYN7MpYrVatW7fOVianCBoBAADywIABA7Rx40b9/vvv2rp1q9q0aSNXV1d16tRJfn5+6tatm/r376/vv/9esbGx6tq1q8LCwvTAAw9Ikpo3b67Q0FA999xz2rVrl1avXq23335bvXr1smU3e/bsqaNHj2rQoEE6cOCApk+frkWLFqlfv362dvTv318fffSR5s6dq/379+vll1/WxYsX1bVr11z1hzmNAADA6RSEjxH8888/1alTJ509e1alS5fWQw89pB9//FGlS5eWJE2YMEEuLi5q166dUlNTFRERoenTp9ue7+rqqhUrVujll19WWFiYvLy8FBkZqVGjRtnKVK5cWStXrlS/fv00adIklS9fXrNnz1ZERIStTIcOHXTmzBkNGzZM8fHxqlevnlatWpVlcYzD7huGYeTqGYVQUlKS/Pz85F67hyyubvndHADXOL9tan43AcB1kpKSFFDST4mJiXYLRm7Xvf38/OTTbpYsRc1dbGKkX9aFL1/Kl34VBGQaAQCA87nFzbgd1lmIMacRAAAADpFpBAAATqcgzGl0NmQaAQAA4BCZRgAA4HQsFuVBptHc6u40BI0AAMDpWJQHw9OFPGpkeBoAAAAOkWkEAABOh4Uw5iPTCAAAAIfINAIAAOfD5t6mI9MIAAAAh8g0AgAA55MHcxoN5jQCAAAAN0emEQAAOJ28WD1t/r6PdxaCRgAA4HQIGs3H8DQAAAAcItMIAACcD1vumI5MIwAAABwi0wgAAJwOcxrNR6YRAAAADpFpBAAATodMo/nINAIAAMAhMo0AAMDpkGk0H0EjAABwOgSN5mN4GgAAAA6RaQQAAM6Hzb1NR6YRAAAADpFpBAAAToc5jeYj0wgAAACHyDQCAACnQ6bRfGQaAQAA4BCZRgAA4HTINJqPoBEAADgfttwxHcPTAAAAcIhMIwAAcDoMT5uPTCMAAAAcItMIAACcDplG85FpBAAAgEN3ZKYxOjpaffv2VUJCQn43BbfB4B6Pa8iLj9ud++33eDX89zuSpDIlfTSqTxs1bVhD3sXcdfiP0xr3yWot/36nJCm4bAkN7NZCje+tpjIlfRX/d6IWfbtN4z5ZrfQrGbY67w4J0thBT+ue0Io6m5CsDxdu1OT539mud3qioaYPf86uHSmp6Sr7UL886jlw5/t4yQ/65MsfdPzkOUlSjSqBGtitpR5rdLck6dTfSRo2eak2/HRAyZdSFVKxjF5/IUL/euQeSVLcibMa+/Eqbdr+m06fTVJgKT893fI+vf5ChNyK3pG/wnCbWJQHmcZCvnw6X7/junTporlz52Y5f+jQIYWEhORDi1BQ7T9yQq17TbE9vnLFavt6xojn5efjqWf6z9LZxGS1j7hXc6JeULPnx2j3b3+qWqUAubi4qF/UFzr65xmFVg3SxDc7qZinu4ZNWipJ8vHy0JdTe2vjzwfU//0vFFq1nKYM66zE5Muau3SL7V5JyZd1X/tRtseGcRs6D9zBgsr4a3jvp1Q1uLQMw9DnK39S5wEfauOnQ1Szalm9PGKeEi9c1mfjX1JJP28tWb1dXd/4RN/PG6Q61YP12++nZLVaNeGNjqpSvrT2HTmhvu99rkuXUzW6b9v87h5QqOT7n2ktWrTQnDlz7M6VLl06n1qDgupKhlWnz17I9tr9dapowPtfaMe+PyRJ4z5ZrVc6PaJ6NYO1+7c/tS5mv9bF7LeV/+OvswqpUEYvtH/YFjT+u8W9civiqt6jFij9SoYOHI1X7erl9MozzeyCRsMwbtgOAFm1bFzb7vHQV/6lT77crO17jqlm1bL6+dej+mBIRzW4u5IkaUC3Fpr++Xrt3H9cdaoHK/zBUIU/GGp7fqXypXQ47rQ+WfIDQSNuijmN5sv3OY3u7u4KDAy0OyZNmqTatWvLy8tLwcHBeuWVV5ScnHzDOnbt2qVmzZrJx8dHvr6+atCggbZv3267vnnzZj388MPy9PRUcHCw+vTpo4sXL96O7sEkVYJLa9837+qXZSP04ehIlQ8obrv2869H1eaxBvL3LSaLxaK2jzWQu3sRbY49dMP6fL09dT7xku3xfbUra+svh+2Gq9fF7Fe1SoHy8/G0nfPydNevX4/SnhWjteCDF1WjSqDJPQWcV0aGVV+u2a5Ll9N0X+3Kkq7+0bd0bazOJ16U1Xr1emrqFT3U4K4b1pOUfFnF/YrdrmbjTmXJo6MQy/egMTsuLi6aPHmy9u7dq7lz52r9+vUaNGjQDct37txZ5cuX17Zt2xQbG6shQ4aoaNGikqQjR46oRYsWateunX799VctXLhQmzdvVu/evW9YX2pqqpKSkuwO5J/Yvb+r18hP9e8+0/T6+wtVMaikvvmon7yLuUuSur7xiYoUcdWxdWN0autETXizo54b+JGO/fl3tvVVLl9KL3Zoouilm23nypT01Zlz9hnEzMcBJX0lSYf/OK3eoxfomQGz9OKwuXJxsWj1x68rqIx/HvQacB57D/+l8o37K6BRX/WPWqj5Y3uoRpWykqQ5US/oypUMVQkfrIAH+6rfe19o/tgeqhKc/YjT0eNn9OHCjerS5qHb2QUAKgDD0ytWrJC3t7ftccuWLbV48WLb40qVKumdd95Rz549NX369GzriIuL08CBA1WjRg1J0l13/f9fqFFRUercubP69u1ruzZ58mQ1adJEM2bMkIeHR5b6oqKiNHLkSDO6BxN8t3Wf7eu9h09o+57ftXv5KLUOr69Pv47RWz2fkJ+Pp556ZbLOJVzU403qaE7UC3q8x0TtO3LCrq6ypf20ZHIvLfvuF81btjVX7di2+5i27T5me/zzrqP6afFQdWnbSO/NXPnPOgk4sbsqBmjTgjeUlHxZX637Ra+MmK8Vs15TjSpl9e7MFUq8cFnLpr2qEv5e+mbjr+r6xif65qO+ujuknF09J04nqH2faWodfo8i2zTKp97gTsHwtPnyPWhs1qyZZsyYYXvs5eWl7777TlFRUTpw4ICSkpJ05coVpaSk6NKlSypWLOuQRP/+/dW9e3fNnz9f4eHh+ve//62qVatKujp0/euvv2rBggW28oZhyGq16tixY6pZs2aW+t544w3179/f9jgpKUnBwcFmdhv/QFLyZR2OO60qwaVVqdzVrGFYh3d04Gi8JGnPob8Udk9Vdf93Y/V//wvb8wJL+enrGa/p51+Pqu97n9vVefpskkqX8LE7l/n41NnsM81XMqz69eBxVSnPHFzgZtyKFrFlDuvVrKBf9sVp5hcb9Nrz4fpo0SZt/eIt1ax6NfNYu1p5xfxyRLMXb9KENzrZ6jh5JkH/enmS7q9TRRPf7JTtfQDkrXwfnvby8lJISIjtSE1N1RNPPKE6deroyy+/VGxsrKZNmyZJSktLy7aOESNGaO/evWrVqpXWr1+v0NBQLV16dYFDcnKyXnrpJe3cudN27Nq1S4cOHbIFltdzd3eXr6+v3YGCw8vTTZXLlVL834kq5uEmSbJa7ZcxZ2QYsrj8/1+EZUv7afnM17TrQJx6jfpUxnXLnrftPqYH7wlREdf//5Zo1rCGfvs9XokXLmfbDhcXi0JDghT/N9MXgNywGobS0q7oUsrVn+kuLvbZG1dXi4xrvqdPnE7Qkz0nqW6NCpo27Fm5uOT7ry7cATIzjWYfhVm+ZxqvFxsbK6vVqnHjxtl+MCxatMjh86pVq6Zq1aqpX79+6tSpk+bMmaM2bdqofv362rdvH1v43MFGvdZGq37YreMnz6lsaT8NebGVMqxWfbk6VokXLulI3GlNeKOThk5aqnOJF9WqaR01a1hdHfvNlPT/AePx+HMaOmmpShX//+kQmSuhl6zarkE9HteUoZ01ad5a1awapJc6NtVbE/5rKzuwewtt3/27jv55Rn7enurzXLiCA0to/le5G+YGCpORU79S+IN3KziwuC5cStGSVdu1OfaQvpzyiqpVClSV4NLqF/W5Rr/WRiX8vLRyw6/6/qeD+mJCT0n/HzAGB5bQ6Nfa6O/z/78oMqAUf9ADt1OBCxpDQkKUnp6uKVOm6Mknn9SWLVs0c+bMG5a/fPmyBg4cqPbt26ty5cr6888/tW3bNrVr106SNHjwYD3wwAPq3bu3unfvLi8vL+3bt09r167V1KlTb1e38A+UK+Ov2e90VQm/Yvr7fLJ+2nVUj3Udp7MJV395PN13hob3fkqfj39JXsXcdez4Gb0yYr7W/m8uZNOGNVS1QhlVrVBG+755167u4vddXRCVdDFF7XpP1dhBT+v7eYN1NiFZY2d/a7fdjr9PMU166xmVKemjhAuXtWt/nCK6jdfBY/G36ZUA7jx/n0/WyyPm6dTfSfL19tDdIeX05ZRX1Kzh1alBiya+rJFTv1Kn/rN08VKqKgeX1vQRz6n5/zb/3vDTAR09fkZHj5/R3a3etqv7/DZ+huPGLJarh9l1FmYW4/pxutuoS5cuSkhI0LJly+zOT5gwQWPHjlVCQoIaN26szp076/nnn9f58+fl7+9v94kwaWlpioyM1JYtW3Tq1CmVKlVKbdu21dixY22LXLZt26a33npLMTExMgxDVatWVYcOHfTmm2/mqJ1JSUny8/OTe+0esri6mf0yAPgHCByAgicpKUkBJf2UmJh426d4Zf7Ortx7iVzczd2ayZp6Scemts+XfhUE+Ro03ikIGoGCi6ARKHgKQtBY5dUlcnH3MrVua+pFHZ1SeIPGAjc8DQAA8I/lwfA0m3sDAAAADpBpBAAATofNvc1HphEAAAAOkWkEAABOhy13zEemEQAAAA6RaQQAAE7HxcWS5SMq/ynD5PruNGQaAQAA4BCZRgAA4HSY02g+gkYAAOB02HLHfAxPAwAAwCEyjQAAwOkwPG0+Mo0AAABwiEwjAABwOsxpNB+ZRgAAADhEphEAADgdMo3mI9MIAAAAh8g0AgAAp8PqafMRNAIAAKdjUR4MT6twR40MTwMAANwG77//viwWi/r27Ws7l5KSol69eqlkyZLy9vZWu3btdOrUKbvnxcXFqVWrVipWrJjKlCmjgQMH6sqVK3ZlNmzYoPr168vd3V0hISGKjo7Ocv9p06apUqVK8vDwUMOGDfXzzz/nqv0EjQAAwOlkDk+bfdyqbdu2adasWapTp47d+X79+mn58uVavHixNm7cqBMnTqht27a26xkZGWrVqpXS0tK0detWzZ07V9HR0Ro2bJitzLFjx9SqVSs1a9ZMO3fuVN++fdW9e3etXr3aVmbhwoXq37+/hg8frh07dqhu3bqKiIjQ6dOnc9wHgkYAAIA8lJycrM6dO+ujjz5S8eLFbecTExP18ccfa/z48XrkkUfUoEEDzZkzR1u3btWPP/4oSVqzZo327dunTz/9VPXq1VPLli01evRoTZs2TWlpaZKkmTNnqnLlyho3bpxq1qyp3r17q3379powYYLtXuPHj1ePHj3UtWtXhYaGaubMmSpWrJg++eSTHPeDoBEAADidzC13zD4kKSkpye5ITU29aVt69eqlVq1aKTw83O58bGys0tPT7c7XqFFDFSpUUExMjCQpJiZGtWvXVkBAgK1MRESEkpKStHfvXluZ6+uOiIiw1ZGWlqbY2Fi7Mi4uLgoPD7eVyQmCRgAAgFwIDg6Wn5+f7YiKirph2S+++EI7duzItkx8fLzc3Nzk7+9vdz4gIEDx8fG2MtcGjJnXM6/drExSUpIuX76sv//+WxkZGdmWyawjJ1g9DQAAnE5ebrlz/Phx+fr62s67u7tnW/748eN67bXXtHbtWnl4eJjbmHxAphEAACAXfH197Y4bBY2xsbE6ffq06tevryJFiqhIkSLauHGjJk+erCJFiiggIEBpaWlKSEiwe96pU6cUGBgoSQoMDMyymjrzsaMyvr6+8vT0VKlSpeTq6pptmcw6coKgEQAAOJ28nNOYU48++qh2796tnTt32o57771XnTt3tn1dtGhRrVu3zvacgwcPKi4uTmFhYZKksLAw7d69226V89q1a+Xr66vQ0FBbmWvryCyTWYebm5saNGhgV8ZqtWrdunW2MjnB8DQAAEAe8PHxUa1atezOeXl5qWTJkrbz3bp1U//+/VWiRAn5+vrq1VdfVVhYmB544AFJUvPmzRUaGqrnnntOY8aMUXx8vN5++2316tXLluHs2bOnpk6dqkGDBumFF17Q+vXrtWjRIq1cudJ23/79+ysyMlL33nuv7r//fk2cOFEXL15U165dc9wfgkYAAOB07pSPEZwwYYJcXFzUrl07paamKiIiQtOnT7ddd3V11YoVK/Tyyy8rLCxMXl5eioyM1KhRo2xlKleurJUrV6pfv36aNGmSypcvr9mzZysiIsJWpkOHDjpz5oyGDRum+Ph41atXT6tWrcqyOOZmLIZhGOZ023klJSXJz89P7rV7yOLqlt/NAXCN89um5ncTAFwnKSlJASX9lJiYaLdg5Hbd28/PTw2GrZSrh5epdWekXFTsqFb50q+CgDmNAAAAcIjhaQAA4HzyYHhaeTA8fSch0wgAAACHyDQCAACncytb5OSkzsKMTCMAAAAcItMIAACczp2y5c6dhEwjAAAAHCLTCAAAnA5zGs1H0AgAAJwOw9PmY3gaAAAADpFpBAAATofhafORaQQAAIBDZBoBAIDTIdNoPjKNAAAAcIhMIwAAcDqsnjYfmUYAAAA4RKYRAAA4HeY0mo+gEQAAOB2Gp83H8DQAAAAcItMIAACcDsPT5iPTCAAAAIfINAIAAKdjUR7MaTS3ujsOmUYAAAA4RKYRAAA4HReLRS4mpxrNru9OQ6YRAAAADpFpBAAATod9Gs1H0AgAAJwOW+6Yj+FpAAAAOESmEQAAOB0Xy9XD7DoLMzKNAAAAcIhMIwAAcD6WPJiDSKYRAAAAuDkyjQAAwOmw5Y75yDQCAADAITKNAADA6Vj+95/ZdRZmBI0AAMDpsOWO+RieBgAAgENkGgEAgNPhYwTNR6YRAAAADpFpBAAAToctd8xHphEAAAAOkWkEAABOx8VikYvJqUGz67vTkGkEAACAQ2QaAQCA02FOo/kIGgEAgNNhyx3zMTwNAAAAh8g0AgAAp8PwtPlyFDR+/fXXOa7wX//61y03BgAAAAVTjoLG1q1b56gyi8WijIyMf9IeAACAf4wtd8yXo6DRarXmdTsAAABQgP2jhTApKSlmtQMAAMA0ljw6CrNcB40ZGRkaPXq0ypUrJ29vbx09elSSNHToUH388cemNxAAAAD5L9dB47vvvqvo6GiNGTNGbm5utvO1atXS7NmzTW0cAADArcjcp9HsozDLddA4b948ffjhh+rcubNcXV1t5+vWrasDBw6Y2jgAAIBb4WLJm6Mwy3XQ+NdffykkJCTLeavVqvT0dFMaBQAAgIIl10FjaGiofvjhhyznlyxZonvuuceURgEAAPwTDE+bL9efCDNs2DBFRkbqr7/+ktVq1X//+18dPHhQ8+bN04oVK/KijQAAAMhnuc40PvXUU1q+fLm+++47eXl5adiwYdq/f7+WL1+uxx57LC/aCAAAkGuZHyVo1lHY3dJnTz/88MNau3at2W0BAABAAXVLQaMkbd++Xfv375d0dZ5jgwYNTGsUAADAP5EXcxCZ05hLf/75pzp16qQtW7bI399fkpSQkKAHH3xQX3zxhcqXL292GwEAAJDPcj2nsXv37kpPT9f+/ft17tw5nTt3Tvv375fValX37t3zoo0AAAC5wj6N5st1pnHjxo3aunWrqlevbjtXvXp1TZkyRQ8//LCpjQMAALgVDE+bL9eZxuDg4Gw38c7IyFBQUJApjQIAAEDBkuugcezYsXr11Ve1fft227nt27frtdde0wcffGBq4wAAAG6FJY+OwixHw9PFixe3S8levHhRDRs2VJEiV59+5coVFSlSRC+88IJat26dJw0FAABA/slR0Dhx4sQ8bgYAAIB5XCwWuZg8B9Hs+u40OQoaIyMj87odAAAAKMBueXNvSUpJSVFaWprdOV9f33/UIAAAgH8qLz76r5AnGnO/EObixYvq3bu3ypQpIy8vLxUvXtzuAAAAgDRjxgzVqVNHvr6+8vX1VVhYmL799lvb9ZSUFPXq1UslS5aUt7e32rVrp1OnTtnVERcXp1atWqlYsWIqU6aMBg4cqCtXrtiV2bBhg+rXry93d3eFhIQoOjo6S1umTZumSpUqycPDQw0bNtTPP/+c6/7kOmgcNGiQ1q9frxkzZsjd3V2zZ8/WyJEjFRQUpHnz5uW6AQAAAGbL3KfR7CM3ypcvr/fff1+xsbHavn27HnnkET311FPau3evJKlfv35avny5Fi9erI0bN+rEiRNq27at7fkZGRlq1aqV0tLStHXrVs2dO1fR0dEaNmyYrcyxY8fUqlUrNWvWTDt37lTfvn3VvXt3rV692lZm4cKF6t+/v4YPH64dO3aobt26ioiI0OnTp3P3mhqGYeTmCRUqVNC8efPUtGlT+fr6aseOHQoJCdH8+fP1+eef65tvvslVA+4ESUlJ8vPzk3vtHrK4uuV3cwBc4/y2qfndBADXSUpKUkBJPyUmJt72aWuZv7Mjo3+UWzFvU+tOu5SsuV0e+Ef9KlGihMaOHav27durdOnS+uyzz9S+fXtJ0oEDB1SzZk3FxMTogQce0LfffqsnnnhCJ06cUEBAgCRp5syZGjx4sM6cOSM3NzcNHjxYK1eu1J49e2z36NixoxISErRq1SpJUsOGDXXfffdp6tSrPy+tVquCg4P16quvasiQITlue64zjefOnVOVKlUkXZ2/eO7cOUnSQw89pE2bNuW2OgAAANNlzmk0+5CuBqbXHqmpqQ7bk5GRoS+++EIXL15UWFiYYmNjlZ6ervDwcFuZGjVqqEKFCoqJiZEkxcTEqHbt2raAUZIiIiKUlJRky1bGxMTY1ZFZJrOOtLQ0xcbG2pVxcXFReHi4rUxO5TporFKlio4dO2br3KJFiyRJy5cvl7+/f26rAwAAMF3mljtmH9LVT8fz8/OzHVFRUTdsx+7du+Xt7S13d3f17NlTS5cuVWhoqOLj4+Xm5pYldgoICFB8fLwkKT4+3i5gzLyeee1mZZKSknT58mX9/fffysjIyLZMZh05levV0127dtWuXbvUpEkTDRkyRE8++aSmTp2q9PR0jR8/PrfVAQAA3FGOHz9uNzzt7u5+w7LVq1fXzp07lZiYqCVLligyMlIbN268Hc00Xa6Dxn79+tm+Dg8P14EDBxQbG6uQkBDVqVPH1MYBAADcirzccidzNXROuLm5KSQkRJLUoEEDbdu2TZMmTVKHDh2UlpamhIQEu2zjqVOnFBgYKEkKDAzMsso5c3X1tWWuX3F96tQp+fr6ytPTU66urnJ1dc22TGYdOZXr4enrVaxYUW3btiVgBAAAcMBqtSo1NVUNGjRQ0aJFtW7dOtu1gwcPKi4uTmFhYZKksLAw7d69226V89q1a+Xr66vQ0FBbmWvryCyTWYebm5saNGhgV8ZqtWrdunW2MjmVo0zj5MmTc1xhnz59ctUAAAAAs93KFjk5qTM33njjDbVs2VIVKlTQhQsX9Nlnn2nDhg1avXq1/Pz81K1bN/Xv318lSpSQr6+vXn31VYWFhemBBx6QJDVv3lyhoaF67rnnNGbMGMXHx+vtt99Wr169bEPiPXv21NSpUzVo0CC98MILWr9+vRYtWqSVK1fa2tG/f39FRkbq3nvv1f3336+JEyfq4sWL6tq1a676k6OgccKECTmqzGKxOHXQuHvle/LhE28AAEAOnD59Ws8//7xOnjwpPz8/1alTR6tXr9Zjjz0m6Wp85eLionbt2ik1NVURERGaPn267fmurq5asWKFXn75ZYWFhcnLy0uRkZEaNWqUrUzlypW1cuVK9evXT5MmTVL58uU1e/ZsRURE2Mp06NBBZ86c0bBhwxQfH6969epp1apVWRbHOJLrfRoLo8w9n36LO0PQCBQw/l7snQoUNAVhn8YXP/05T/Zp/PDZ+/OlXwXBP57TCAAAAOeX69XTAAAABV1BmNPobAgaAQCA07FYJJc82nKnsGJ4GgAAAA6RaQQAAE7HJQ8yjWbXd6e5pUzjDz/8oGeffVZhYWH666+/JEnz58/X5s2bTW0cAAAACoZcB41ffvmlIiIi5OnpqV9++UWpqamSpMTERL333numNxAAACC3MhfCmH0UZrkOGt955x3NnDlTH330kYoWLWo736hRI+3YscPUxgEAAKBgyPWcxoMHD6px48ZZzvv5+SkhIcGMNgEAAPwjzGk0X64zjYGBgTp8+HCW85s3b1aVKlVMaRQAAAAKllwHjT169NBrr72mn376SRaLRSdOnNCCBQs0YMAAvfzyy3nRRgAAgFyxWPLmKMxyPTw9ZMgQWa1WPfroo7p06ZIaN24sd3d3DRgwQK+++mpetBEAACBXXCwWuZgc5Zld350m10GjxWLRW2+9pYEDB+rw4cNKTk5WaGiovL3N/VBwAAAAFBy3vLm3m5ubQkNDzWwLAACAKVxk/sfeFfaP0ct10NisWbOb7lO0fv36f9QgAAAAFDy5Dhrr1atn9zg9PV07d+7Unj17FBkZaVa7AAAAblleLFwp5FMacx80TpgwIdvzI0aMUHJy8j9uEAAAAAoe04bnn332WX3yySdmVQcAAHDLXGSxraA27VDhTjWaFjTGxMTIw8PDrOoAAABQgOR6eLpt27Z2jw3D0MmTJ7V9+3YNHTrUtIYBAADcKuY0mi/XQaOfn5/dYxcXF1WvXl2jRo1S8+bNTWsYAADAreKzp82Xq6AxIyNDXbt2Ve3atVW8ePG8ahMAAAAKmFzNaXR1dVXz5s2VkJCQR80BAAD45ywWmb4QprAPT+d6IUytWrV09OjRvGgLAAAACqhcB43vvPOOBgwYoBUrVujkyZNKSkqyOwAAAPJb5kIYs4/CLMdzGkeNGqXXX39djz/+uCTpX//6l93HCRqGIYvFooyMDPNbCQAAgHyV46Bx5MiR6tmzp77//vu8bA8AAMA/xupp8+U4aDQMQ5LUpEmTPGsMAAAACqZcbbljKeyD+QAA4I5g+d9/ZtdZmOUqaKxWrZrDwPHcuXP/qEEAAAD/FMPT5stV0Dhy5MgsnwgDAAAA55eroLFjx44qU6ZMXrUFAADAFGQazZfjfRqZzwgAAFB45Xr1NAAAQEFnsVhMT3gV9gRajoNGq9Wal+0AAABAAZarOY0AAAB3AuY0mi/Xnz0NAACAwodMIwAAcDoWy9XD7DoLM4JGAADgdFwsFrmYHOWZXd+dhuFpAAAAOESmEQAAOB0WwpiPTCMAAAAcItMIAACcTx4shBGZRgAAAODmyDQCAACn4yKLXExODZpd352GTCMAAAAcItMIAACcDpt7m4+gEQAAOB223DEfw9MAAABwiEwjAABwOnyMoPnINAIAAMAhMo0AAMDpsBDGfGQaAQAA4BCZRgAA4HRclAdzGtncGwAAALg5Mo0AAMDpMKfRfASNAADA6bjI/OHUwj48W9j7DwAAgBwg0wgAAJyOxWKRxeTxZLPru9OQaQQAAIBDZBoBAIDTsfzvMLvOwoxMIwAAABwi0wgAAJyOiyUPNvdmTiMAAABwc2QaAQCAUyrceUHzETQCAACnwyfCmI/haQAAADhEphEAADgdNvc2H5lGAAAAOETQCAAAnI5LHh25ERUVpfvuu08+Pj4qU6aMWrdurYMHD9qVSUlJUa9evVSyZEl5e3urXbt2OnXqlF2ZuLg4tWrVSsWKFVOZMmU0cOBAXblyxa7Mhg0bVL9+fbm7uyskJETR0dFZ2jNt2jRVqlRJHh4eatiwoX7++edc9YegEQAAIA9s3LhRvXr10o8//qi1a9cqPT1dzZs318WLF21l+vXrp+XLl2vx4sXauHGjTpw4obZt29quZ2RkqFWrVkpLS9PWrVs1d+5cRUdHa9iwYbYyx44dU6tWrdSsWTPt3LlTffv2Vffu3bV69WpbmYULF6p///4aPny4duzYobp16yoiIkKnT5/OcX8shmEY//A1cXpJSUny8/PTb3Fn5OPrm9/NAXANfy+3/G4CgOskJSUpoKSfEhMT5Xubf29m/s6e88MBFfP2MbXuS8kX1PXhGrfcrzNnzqhMmTLauHGjGjdurMTERJUuXVqfffaZ2rdvL0k6cOCAatasqZiYGD3wwAP69ttv9cQTT+jEiRMKCAiQJM2cOVODBw/WmTNn5ObmpsGDB2vlypXas2eP7V4dO3ZUQkKCVq1aJUlq2LCh7rvvPk2dOlWSZLVaFRwcrFdffVVDhgzJUfvJNAIAAORCUlKS3ZGampqj5yUmJkqSSpQoIUmKjY1Venq6wsPDbWVq1KihChUqKCYmRpIUExOj2rVr2wJGSYqIiFBSUpL27t1rK3NtHZllMutIS0tTbGysXRkXFxeFh4fbyuQEQSMAAHA6ljw6JCk4OFh+fn62IyoqymF7rFar+vbtq0aNGqlWrVqSpPj4eLm5ucnf39+ubEBAgOLj421lrg0YM69nXrtZmaSkJF2+fFl///23MjIysi2TWUdOsOUOAABALhw/ftxueNrd3d3hc3r16qU9e/Zo8+bNedm0PEXQCAAAnE5e7tPo6+ubqzmNvXv31ooVK7Rp0yaVL1/edj4wMFBpaWlKSEiwyzaeOnVKgYGBtjLXr3LOXF19bZnrV1yfOnVKvr6+8vT0lKurq1xdXbMtk1lHTjA8DQAAnE5B2HLHMAz17t1bS5cu1fr161W5cmW76w0aNFDRokW1bt0627mDBw8qLi5OYWFhkqSwsDDt3r3bbpXz2rVr5evrq9DQUFuZa+vILJNZh5ubmxo0aGBXxmq1at26dbYyOUGmEQAAIA/06tVLn332mb766iv5+PjY5g/6+fnJ09NTfn5+6tatm/r3768SJUrI19dXr776qsLCwvTAAw9Ikpo3b67Q0FA999xzGjNmjOLj4/X222+rV69etmHxnj17aurUqRo0aJBeeOEFrV+/XosWLdLKlSttbenfv78iIyN177336v7779fEiRN18eJFde3aNcf9IWgEAABOpyB8jOCMGTMkSU2bNrU7P2fOHHXp0kWSNGHCBLm4uKhdu3ZKTU1VRESEpk+fbivr6uqqFStW6OWXX1ZYWJi8vLwUGRmpUaNG2cpUrlxZK1euVL9+/TRp0iSVL19es2fPVkREhK1Mhw4ddObMGQ0bNkzx8fGqV6+eVq1alWVxzE37zz6NjrFPI1BwsU8jUPAUhH0aP93yW57s0/hso2r50q+CgEwjAABwOtdukWNmnYUZC2EAAADgEJlGAADgdCyWq4fZdRZmZBoBAADgEJlGAADgdFxkkYvJsxDNru9OQ9AIAACcDsPT5mN4GgAAAA6RaQQAAE7H8r//zK6zMCPTCAAAAIfINAIAAKfDnEbzkWkEAACAQ2QaAQCA07HkwZY7zGkEAAAAHCDTCAAAnA5zGs1H0AgAAJwOQaP5GJ4GAACAQ2QaAQCA02Fzb/ORaQQAAIBDZBoBAIDTcbFcPcyuszAj0wgAAACHyDQCAACnw5xG85FpBAAAgENkGgEAgNNhn0bzETQCAACnY5H5w8mFPGZkeBoAAACOkWkEAABOhy13zEemEQAAAA6RaQQAAE6HLXfMR6YRAAAADhE04o43Y8E6VW7aX6OmLLWd++Ovv/XS25+owVNDVfvxN9RrxFydOXch2+enpl3R490+UOWm/bXv0F921zb+fEBtXp6oWi3fUIOnhurlYXP058lzedofwFlNiF6j4vf11hvjlkiSzide1KCxi3Rfu1Eq+1A/1XpiqAZ/sFiJyZezff65hGTd3eptFb+vtxIvXLqdTccdKHPLHbOPwqxABY0Wi+Wmx4gRI/K7iShgdh2I02fLY1SjalnbuUuXU/X8wFmyWCxaMOFlLZ76qtLTM9T9zdmyWq1Z6nh/1nIFlPLNcv74ybN68a1P9GD9u7Ry9uuaO/YlnUu8qJ7D5uRpnwBntGPvH4peukV331XOdu7kmUTFn0nUqNfaaOsXb2r68Ge1Lmaf+oxekG0dr77zmUJDgm5XkwFcp0AFjSdPnrQdEydOlK+vr925AQMG2MoahqErV67kY2uR3y5eSlXfdxYoasDT8vMuZju/fc/v+jP+nMYO6aQaVYJUo0qQPnijk3Yf/FNbdxy2q2PDT/v1w7aDevPlf2Wpf/fBP2W1WvV6t5aqWK6UalUrrx4dmmnf4RNKv5KR5/0DnEXypVS9OCxak97sJH8fT9v50JAgzRvTQy0b11bl8qXV+L7qevvlJ7Xqhz26ct332MdLflDihUt69dlHb3fzcYey5NFRmBWooDEwMNB2+Pn5yWKx2B4fOHBAPj4++vbbb9WgQQO5u7tr8+bN6tKli1q3bm1XT9++fdW0aVPbY6vVqqioKFWuXFmenp6qW7eulixZcns7B9MNm/SlHnmgph66t5rd+bT0K7LIIrei/7/Oy92tqFwsFm3ffdR27sy5C3pj7CKNf7OzPN3dstRfu3p5ubhYtPjbn5WRYVVS8mUtXbNdjRrcpaJFXPOuY4CTGThmoZo3qqWmDWs4LJuUnCIfLw8VueZ77MDRkxo7+1vNGPm8XAr7nifIMRdZ5GIx+SjkYeMdt3p6yJAh+uCDD1SlShUVL148R8+JiorSp59+qpkzZ+quu+7Spk2b9Oyzz6p06dJq0qRJlvKpqalKTU21PU5KSjKt/TDH8nW/aO9vf+qrmf2yXLsntKKKebrpP7OWa2CPVjIMQ//5cKUyrFadPnf139IwDA18/3M9868HVadGcLbzFIPLltTcsS+p98h5emvcEmVYrap/dyXNeb9HnvcPcBZfrtmuXQeOa/3cQQ7Lnk1I1tiPv1Vkmwdt51LT0tX97WiN7NNawYEl9Mdff+dlcwHcxB0XNI4aNUqPPfZYjsunpqbqvffe03fffaewsDBJUpUqVbR582bNmjUr26AxKipKI0eONK3NMNeJ0+c1cupSzf+gp9zdi2a5XtLfW1NHRGrohCWK/u9muVgsevLRe1SrWnm5WK4m16P/+4MuXkrVK51vPNR15myS3vhgsdpF3Kd/PXqPki+lasInq/TK8GjNH9dTlsI+Ixpw4M/483pj3Jf679Te8sjme/VaScmX1aHvDFWvXFZDXmxlOz9q2teqVilAHR6/P6+bCyeTF8PJhf2n/h0XNN577725Kn/48GFdunQpS6CZlpame+65J9vnvPHGG+rfv7/tcVJSkoKDg3PfWOSJPQf/1NnzyXqyx3jbuQyrVT//elTzlm7RwbVj1Pi+6tr42Vs6l5CsIq6u8vXx1H1thuuJR0pIkmJ2HNaOfb+r+mP22Y9/vTRBTz1WX+PeeEbzlm2Rr5eH3uj5pO36hLc668GnR2nnvj90z92Vbkt/gTvVrgNxOnPugpo+9x/buYwMq7b+ckQfLd6kU1smytXVRRcupqh9n+nyLuahT8f2sJv+sWnbb9p35IRKPdBH0tVRAkmq+tgQvd41Qm+81EoAbo87Lmj08vKye+zi4mL7IZIpPT3d9nVycrIkaeXKlSpXrpxdOXd392zv4e7ufsNryH8PNrhLqz4ZaHdu0H++UJUKZdSz0yNydf3/qbol/L0lSVt3HNLZhGSFP1hLkjS8Txu93q2lrdyps0mKHDhLU4Y/p3o1K0qSLqemyXLd/KnMuq3XvecAZNX4vura8vmbdud6j/pUd1UK0GvPPyZXVxclJV9W+z7T5Fa0iD4b/1KWjOS8Md11OeX/f6b/su8P9R69QN982FeVy5e+Lf3AHYpUo+nuuKDxeqVLl9aePXvszu3cuVNFi179wRMaGip3d3fFxcVlOxSNO493MQ9Vr1LW7pynh5uK+xaznV/87c8KqVBGJfy9tWPv7xo1dZle+HdjVa1QRpJULsB+PqyX59U/EioGlVLZMv6SpEceCNUnizdp8tzVevLR+rp4KVVjP1qpcgHF7bYNAZA9Hy+PLFvkFPN0Uwk/L4WGBCkp+bLavTpNl1LSNGtUpC4kp+hCcookqVRxb7m6umQJDM8lXk0EVK8cKD+fYgJw+9zxQeMjjzyisWPHat68eQoLC9Onn36qPXv22IaefXx8NGDAAPXr109Wq1UPPfSQEhMTtWXLFvn6+ioyMjKfe4C8cDTutMZ8uFKJFy6pXGAJ9Xo2XN3+nbs/Gh6sf5cmvf2sZn2xXrM+/16eHm665+6KmjvmRXlks9oaQO78evC4tu/5XZJUv439PPJdX41UhaCS+dAqOAs+RtB8FuP6sd0CIjo6Wn379lVCQoIkacOGDWrWrJnOnz8vf39/u7LDhw/XrFmzlJKSohdeeEHp6enavXu3NmzYIOnqHJjJkydrxowZOnr0qPz9/VW/fn29+eabaty4scO2JCUlyc/PT7/FnZGPb9ZNoAHkH38vAnigoElKSlJAST8lJibK9zb/3sz8nb3ulzh5+Zh774sXkvToPRXypV8FQYENGgsSgkag4CJoBAqeAhE07oyTt8lBY/KFJD1ar/AGjXf88DQAAMD1WAdjvgL1iTAAAAAomMg0AgAA50Oq0XRkGgEAAOAQmUYAAOB02HLHfGQaAQAA4BCZRgAA4HQslquH2XUWZmQaAQAA4BCZRgAA4HRYPG0+gkYAAOB8iBpNx/A0AAAAHCLTCAAAnA5b7piPTCMAAAAcItMIAACcDlvumI9MIwAAABwi0wgAAJwOi6fNR6YRAAAADpFpBAAAzodUo+kIGgEAgNNhyx3zMTwNAAAAh8g0AgAAp8OWO+Yj0wgAAACHyDQCAACnwzoY85FpBAAAgENkGgEAgPMh1Wg6Mo0AAABwiEwjAABwOuzTaD4yjQAAAHCIoBEAADidzH0azT5ya9OmTXryyScVFBQki8WiZcuW2V03DEPDhg1T2bJl5enpqfDwcB06dMiuzLlz59S5c2f5+vrK399f3bp1U3Jysl2ZX3/9VQ8//LA8PDwUHBysMWPGZGnL4sWLVaNGDXl4eKh27dr65ptvctUXgkYAAOB0LHl05NbFixdVt25dTZs2LdvrY8aM0eTJkzVz5kz99NNP8vLyUkREhFJSUmxlOnfurL1792rt2rVasWKFNm3apBdffNF2PSkpSc2bN1fFihUVGxursWPHasSIEfrwww9tZbZu3apOnTqpW7du+uWXX9S6dWu1bt1ae/bsyXFfLIZhGLfwGhQqSUlJ8vPz029xZ+Tj65vfzQFwDX8vt/xuAoDrJCUlKaCknxITE+V7m39vZv7O/vnACXn7mHvv5AtJur9G0C33y2KxaOnSpWrdurWkq1nGoKAgvf766xowYIAkKTExUQEBAYqOjlbHjh21f/9+hYaGatu2bbr33nslSatWrdLjjz+uP//8U0FBQZoxY4beeustxcfHy83t6s/EIUOGaNmyZTpw4IAkqUOHDrp48aJWrFhha88DDzygevXqaebMmTlqP5lGAADgfPIw1ZiUlGR3pKam3lITjx07pvj4eIWHh9vO+fn5qWHDhoqJiZEkxcTEyN/f3xYwSlJ4eLhcXFz0008/2co0btzYFjBKUkREhA4ePKjz58/bylx7n8wymffJCYJGAACAXAgODpafn5/tiIqKuqV64uPjJUkBAQF25wMCAmzX4uPjVaZMGbvrRYoUUYkSJezKZFfHtfe4UZnM6znBljsAAMDp5OWWO8ePH7cbnnZ3dzf1PgUVmUYAAIBc8PX1tTtuNWgMDAyUJJ06dcru/KlTp2zXAgMDdfr0abvrV65c0blz5+zKZFfHtfe4UZnM6zlB0AgAAJxOQdly52YqV66swMBArVu3znYuKSlJP/30k8LCwiRJYWFhSkhIUGxsrK3M+vXrZbVa1bBhQ1uZTZs2KT093VZm7dq1ql69uooXL24rc+19Mstk3icnCBoBAADySHJysnbu3KmdO3dKurr4ZefOnYqLi5PFYlHfvn31zjvv6Ouvv9bu3bv1/PPPKygoyLbCumbNmmrRooV69Oihn3/+WVu2bFHv3r3VsWNHBQUFSZKeeeYZubm5qVu3btq7d68WLlyoSZMmqX///rZ2vPbaa1q1apXGjRunAwcOaMSIEdq+fbt69+6d474wpxEAADidW91X0VGdubV9+3Y1a9bM9jgzkIuMjFR0dLQGDRqkixcv6sUXX1RCQoIeeughrVq1Sh4eHrbnLFiwQL1799ajjz4qFxcXtWvXTpMnT7Zd9/Pz05o1a9SrVy81aNBApUqV0rBhw+z2cnzwwQf12Wef6e2339abb76pu+66S8uWLVOtWrVy3n/2aXSMfRqBgot9GoGCpyDs0xh76GSe7NPY4K6y+dKvgoDhaQAAADjE8DQAAHA6ebnlTmFFphEAAAAOkWkEAADOJw+2yCnkiUYyjQAAAHCMTCMAAHA6BWXLHWdCphEAAAAOkWkEAADOh1Sj6QgaAQCA02HLHfMxPA0AAACHyDQCAACnY8mDLXdM38LnDkOmEQAAAA6RaQQAAE6HdTDmI9MIAAAAh8g0AgAA50Oq0XRkGgEAAOAQmUYAAOB02KfRfASNAADA6ViUB1vumFvdHYfhaQAAADhEphEAADgd1sGYj0wjAAAAHCLTCAAAnA4fI2g+Mo0AAABwiEwjAABwQsxqNBuZRgAAADhEphEAADgd5jSaj6ARAAA4HQanzcfwNAAAABwi0wgAAJwOw9PmI9MIAAAAh8g0AgAAp2P5339m11mYkWkEAACAQ2QaAQCA82H5tOnINAIAAMAhMo0AAMDpkGg0H0EjAABwOmy5Yz6GpwEAAOAQmUYAAOB02HLHfGQaAQAA4BCZRgAA4HxYCWM6Mo0AAABwiEwjAABwOiQazUemEQAAAA6RaQQAAE6HfRrNR9AIAACckPlb7hT2AWqGpwEAAOAQmUYAAOB0GJ42H5lGAAAAOETQCAAAAIcIGgEAAOAQcxoBAIDTYU6j+cg0AgAAwCEyjQAAwOlY8mCfRvP3fbyzEDQCAACnw/C0+RieBgAAgENkGgEAgNOxyPwP/SvkiUYyjQAAAHCMTCMAAHA+pBpNR6YRAAAADpFpBAAAToctd8xHphEAAAAOkWkEAABOh30azUemEQAAAA6RaQQAAE6HxdPmI2gEAADOh6jRdAxPAwAAwCEyjQAAwOmw5Y75yDQCAADAITKNAADA6bDljvkIGnPAMAxJUvKFC/ncEgDXc8lwy+8mALjOhaQkSf//+zM/JP2vDQW9zjsJQWMOXPhfsFj/7ir53BIAAO4cFy5ckJ+f3229p5ubmwIDA3VX5eA8qT8wMFBuboXzj1WLkZ9/BtwhrFarTpw4IR8fH1kKe27aCSQlJSk4OFjHjx+Xr69vfjcHwP/wvek8DMPQhQsXFBQUJBeX2798IiUlRWlpaXlSt5ubmzw8PPKk7oKOTGMOuLi4qHz58vndDJjM19eXX0xAAcT3pnO43RnGa3l4eBTawC4vsXoaAAAADhE0AgAAwCGCRhQ67u7uGj58uNzd3fO7KQCuwfcmULCxEAYAAAAOkWkEAACAQwSNAAAAcIigEQAAAA4RNKJQiY6Olr+/f343AwCAOw5BI+5IXbp0kcViyXIcPnw4v5sGFHrZfW9ee4wYMSK/mwjgFvCJMLhjtWjRQnPmzLE7V7p06XxqDYBMJ0+etH29cOFCDRs2TAcPHrSd8/b2tn1tGIYyMjJUpAi/joCCjkwj7lju7u4KDAy0OyZNmqTatWvLy8tLwcHBeuWVV5ScnHzDOnbt2qVmzZrJx8dHvr6+atCggbZv3267vnnzZj388MPy9PRUcHCw+vTpo4sXL96O7gF3rGu/J/38/GSxWGyPDxw4IB8fH3377bdq0KCB3N3dtXnzZnXp0kWtW7e2q6dv375q2rSp7bHValVUVJQqV64sT09P1a1bV0uWLLm9nQMKMYJGOBUXFxdNnjxZe/fu1dy5c7V+/XoNGjTohuU7d+6s8uXLa9u2bYqNjdWQIUNUtGhRSdKRI0fUokULtWvXTr/++qsWLlyozZs3q3fv3rerO4DTGjJkiN5//33t379fderUydFzoqKiNG/ePM2cOVN79+5Vv3799Oyzz2rjxo153FoAEsPTuIOtWLHCbpirZcuWWrx4se1xpUqV9M4776hnz56aPn16tnXExcVp4MCBqlGjhiTprrvusl2LiopS586d1bdvX9u1yZMnq0mTJpoxY4Y8PDzyoFdA4TBq1Cg99thjOS6fmpqq9957T999953CwsIkSVWqVNHmzZs1a9YsNWnSJK+aCuB/CBpxx2rWrJlmzJhhe+zl5aXvvvtOUVFROnDggJKSknTlyhWlpKTo0qVLKlasWJY6+vfvr+7du2v+/PkKDw/Xv//9b1WtWlXS1aHrX3/9VQsWLLCVNwxDVqtVx44dU82aNfO+k4CTuvfee3NV/vDhw7p06VKWQDMtLU333HOPmU0DcAMEjbhjeXl5KSQkxPb4999/1xNPPKGXX35Z7777rkqUKKHNmzerW7duSktLyzZoHDFihJ555hmtXLlS3377rYYPH64vvvhCbdq0UXJysl566SX16dMny/MqVKiQp30DnJ2Xl5fdYxcXF13/qbbp6em2rzPnJq9cuVLlypWzK8dnVQO3B0EjnEZsbKysVqvGjRsnF5er03UXLVrk8HnVqlVTtWrV1K9fP3Xq1Elz5sxRmzZtVL9+fe3bt88uMAWQN0qXLq09e/bYndu5c6dtjnFoaKjc3d0VFxfHUDSQT1gIA6cREhKi9PR0TZkyRUePHtX8+fM1c+bMG5a/fPmyevfurQ0bNuiPP/7Qli1btG3bNtuw8+DBg7V161b17t1bO3fu1KFDh/TVV1+xEAbIA4888oi2b9+uefPm6dChQxo+fLhdEOnj46MBAwaoX79+mjt3ro4cOaIdO3ZoypQpmjt3bj62HCg8CBrhNOrWravx48frP//5j2rVqqUFCxYoKirqhuVdXV119uxZPf/886pWrZqefvpptWzZUiNHjpQk1alTRxs3btRvv/2mhx9+WPfcc4+GDRumoKCg29UloNCIiIjQ0KFDNWjQIN133326cOGCnn/+ebsyo0eP1tChQxUVFaWaNWuqRYsWWrlypSpXrpxPrQYKF4tx/SQSAAAA4DpkGgEAAOAQQSMAAAAcImgEAACAQwSNAAAAcIigEQAAAA4RNAIAAMAhgkYAAAA4RNAIIE916dJFrVu3tj1u2rSp+vbte9vbsWHDBlksFiUkJNywjMVi0bJly3Jc54gRI1SvXr1/1K7ff/9dFotFO3fu/Ef1AEBeI2gECqEuXbrIYrHIYrHIzc1NISEhGjVqlK5cuZLn9/7vf/+r0aNH56hsTgI9AMDtUSS/GwAgf7Ro0UJz5sxRamqqvvnmG/Xq1UtFixbVG2+8kaVsWlqa3NzcTLlviRIlTKkHAHB7kWkECil3d3cFBgaqYsWKevnllxUeHq6vv/5a0v8PKb/77rsKCgpS9erVJUnHjx/X008/LX9/f5UoUUJPPfWUfv/9d1udGRkZ6t+/v/z9/VWyZEkNGjRI139S6fXD06mpqRo8eLCCg4Pl7u6ukJAQffzxx/r999/VrFkzSVLx4sVlsVjUpUsXSZLValVUVJQqV64sT09P1a1bV0uWLLG7zzfffKNq1arJ09NTzZo1s2tnTg0ePFjVqlVTsWLFVKVKFQ0dOlTp6elZys2aNUvBwcEqVqyYnn76aSUmJtpdnz17tmrWrCkPDw/VqFFD06dPz3VbACC/ETQCkCR5enoqLS3N9njdunU6ePCg1q5dqxUrVig9PV0RERHy8fHRDz/8oC1btsjb21stWrSwPW/cuHGKjo7WJ598os2bN+vcuXNaunTpTe/7/PPP6/PPP9fkyZO1f/9+zZo1S97e3goODtaXX34pSTp48KBOnjypSZMmSZKioqI0b948zZw5U3v37lW/fv307LPPauPGjZKuBrdt27bVk08+qZ07d6p79+4aMmRIrl8THx8fRUdHa9++fZo0aZI++ugjTZgwwa7M4cOHtWjRIi1fvlyrVq3SL7/8oldeecV2fcGCBRo2bJjeffdd7d+/X++9956GDh2quXPn5ro9AJCvDACFTmRkpPHUU08ZhmEYVqvVWLt2reHu7m4MGDDAdj0gIMBITU21PWf+/PlG9erVDavVajuXmppqeHp6GqtXrzYMwzDKli1rjBkzxnY9PT3dKF++vO1ehmEYTZo0MV577TXDMAzj4MGDhiRj7dq12bbz+++/NyQZ58+ft51LSUkxihUrZmzdutWubLdu3YxOnToZhmEYb7zxhhEaGmp3ffDgwVnqup4kY+nSpTe8PnbsWKNBgwa2x8OHDzdcXV2NP//803bu22+/NVxcXIyTJ08ahmEYVatWNT777DO7ekaPHm2EhYUZhmEYx44dMyQZv/zyyw3vCwAFAXMagUJqxYoV8vb2Vnp6uqxWq5555hmNGDHCdr127dp28xh37dqlw4cPy8fHx66elJQUHTlyRImJiTp58qQaNmxou1akSBHde++9WYaoM+3cuVOurq5q0qRJjtt9+PBhXbp0SY899pjd+bS0NN1zzz2SpP3799u1Q5LCwsJyfI9MCxcu1OTJk3XkyBElJyfrypUr8vX1tStToUIFlStXzu4+VqtVBw8elI+Pj44cOaJu3bqpR48etjJXrlyRn59frtsDAPmJoBEopJo1a6YZM2bIzc1NQUFBKlLE/seBl5eX3ePk5GQ1aNBACxYsyFJX6dKlb6kNnp6euX5OcnKyJGnlypV2wZp0dZ6mWWJiYtS5c2eNHDlSERER8vPz0xdffKFx48bluq0fffRRliDW1dXVtLYCwO1A0AgUUl5eXgoJCclx+fr162vhwoUqU6ZMlmxbprJly+qnn35S48aNJV3NqMXGxqp+/frZlq9du7asVqs2btyo8PDwLNczM50ZGRm2c6GhoXJ3d1dcXNwNM5Q1a9a0LerJ9OOPPzru5DW2bt2qihUr6q233rKd++OPP7KUi4uL04kTJxQUFGS7j4uLi6pXr66AgAAFBQXp6NGj6ty5c67uDwAFDQthAORI586dVapUKT311FP64YcfdOzYMW3YsEF9+vTRn3/+KUl67bXX9P7772vZsmU6cOCAXnnllZvusVipUiVFRkbqhRde0LJly2x1Llq0SJJUsWJFWSwWrVixQmfOnFFycrJ8fHw0YMAA9evXT3PnztWRI0e0Y8cOTZkyxba4pGfPnjp06JAGDhyogwcP6rPPPlN0dHSu+nvXXXcpLi5OX3zxhY4cOaLJkydnu6jHw8NDkZGR2rVrl3744Qf16dNHTz/9tAIDAyVJI0eOVFRUlCZPnqzffvtNu3fv1pw5czR+/PhctQcA8htBI4AcKVasmDZt2qQKFSqobdu2qlmzprp166aUlBRb5vH111/Xc889p8jISIWFhcnHx0dt2rS5ab0zZsxQ+/bt9corr6hGjRrq0aOHLl68KEkqV66cRo4cqSFDhiggIEC9e/eWJI0ePVpDhw5VVFSUatasqRYtWmjlypWqXLmypKvzDL/88kstW7ZMdevW1cyZM/Xee+/lqr//+te/1K9fP/Xu3Vv16tXT1q1bNXTo0CzlQkJC1LZtWz3++ONq3ry56tSpY7elTvfu3TV79mzNmTNHtWvXVpMmTRQdHW1rKwDcKSzGjWaoAwAAAP9DphEAAAAOETQCAADAIYJGAAAAOETQCAAAAIcIGgEAAOAQQSMAAAAcImgEAACAQwSNAAAAcIigEQAAAA4RNAIAAMAhgkYAAAA4RNAIAAAAh/4PQmvzssSiFdkAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = confusion, display_labels = [False, True])\n",
        "\n",
        "# Plot the confusion matrix heatmap with a title\n",
        "fig, ax = plt.subplots(figsize=(8, 6))\n",
        "cm_display.plot(cmap=plt.cm.Blues,ax=ax)\n",
        "ax.set_title('Confusion Matrix | Logistic Regression\\n')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Gaussian"
      ],
      "metadata": {
        "id": "GtmN06M_5tU5"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "UJqM_FKljQKS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "a162e728-e612-4204-a8af-ee5925df9fa8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "gnb = GaussianNB()\n",
        "gnb.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "kHWMjhH77c4z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0564f345-6cab-491e-b481-7c2f44099365"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " TEST :  0.8086117669131787\n",
            " TRAIN :  0.8056723838709173\n",
            "GaussianNBn classifier\n",
            " [[48670  9917]\n",
            " [ 2324  3048]]\n",
            "GaussianNB\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.83      0.89     58587\n",
            "           1       0.24      0.57      0.33      5372\n",
            "\n",
            "    accuracy                           0.81     63959\n",
            "   macro avg       0.59      0.70      0.61     63959\n",
            "weighted avg       0.89      0.81      0.84     63959\n",
            "\n"
          ]
        }
      ],
      "source": [
        "gnb_train = gnb.score(x_train,y_train)\n",
        "gnb_test = gnb.score(x_test,y_test)\n",
        "\n",
        "print(' TEST : ',gnb_test)\n",
        "print(' TRAIN : ',gnb_train)\n",
        "\n",
        "gnb_predicted = gnb.predict(x_test)\n",
        "confusion = confusion_matrix(y_test, gnb_predicted)\n",
        "gnb_classification_report = classification_report(y_test, gnb_predicted, target_names = ['0', '1'])\n",
        "\n",
        "print('GaussianNBn classifier\\n', confusion)\n",
        "print('GaussianNB\\n', gnb_classification_report)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = confusion, display_labels = [False, True])\n",
        "\n",
        "# Plot the confusion matrix heatmap with a title\n",
        "fig, ax = plt.subplots(figsize=(8, 6))\n",
        "cm_display.plot(cmap=plt.cm.Blues,ax=ax)\n",
        "ax.set_title('Confusion Matrix | GaussianNBn\\n')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "MPlyx9iyuNB_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "outputId": "c96e7a12-a3b5-4995-b88f-3993f6d2a0cb"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##KNeighborsClassifier"
      ],
      "metadata": {
        "id": "7U54NhnV5zLg"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "PYxia-J-pDnl"
      },
      "outputs": [],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# Define a range of values for k to try\n",
        "k_range = range(10, 15)\n",
        "\n",
        "# Define a list to store the cross-validation scores\n",
        "cv_scores = []\n",
        "\n",
        "# Loop over the range of k values and compute the scores\n",
        "for k in k_range:\n",
        "    knn = KNeighborsClassifier(n_neighbors=k)\n",
        "    scores = cross_val_score(knn, x_train, y_train, cv=10, scoring='accuracy')\n",
        "    cv_scores.append(scores.mean())\n",
        "\n",
        "# Find the k value with the highest score\n",
        "best_k = k_range[np.argmax(cv_scores)]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the KNN model using the best value of k\n",
        "knn = KNeighborsClassifier(n_neighbors=best_k)\n",
        "knn.fit(x_train, y_train)"
      ],
      "metadata": {
        "id": "0Z633X8phQ3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "af7a5123-ca0e-4cdd-a75f-a07cbc216d14"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=14)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=14)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=14)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "GyaljnO77ZEl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "30f4995e-f048-4b88-cb08-9be17645e17b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " TEST :  0.9161025031660908\n",
            " TRAIN :  0.915985240544724\n",
            "KNeighborsClassifier\n",
            " [[58446   141]\n",
            " [ 5225   147]]\n",
            "KNeighbors\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      1.00      0.96     58587\n",
            "           1       0.51      0.03      0.05      5372\n",
            "\n",
            "    accuracy                           0.92     63959\n",
            "   macro avg       0.71      0.51      0.50     63959\n",
            "weighted avg       0.88      0.92      0.88     63959\n",
            "\n"
          ]
        }
      ],
      "source": [
        "knn_train = knn.score(x_train,y_train)\n",
        "knn_test = knn.score(x_test,y_test)\n",
        "print(' TEST : ',knn_test)\n",
        "print(' TRAIN : ',knn_train)\n",
        "\n",
        "knn_predicted = knn.predict(x_test)\n",
        "confusion = confusion_matrix(y_test, knn_predicted)\n",
        "knn_classification_report = classification_report(y_test, knn_predicted, target_names = ['0', '1'])\n",
        "\n",
        "print('KNeighborsClassifier\\n', confusion)\n",
        "print('KNeighbors\\n',knn_classification_report)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = confusion, display_labels = [False, True])\n",
        "\n",
        "# Plot the confusion matrix heatmap with a title\n",
        "fig, ax = plt.subplots(figsize=(8, 6))\n",
        "cm_display.plot(cmap=plt.cm.Blues,ax=ax)\n",
        "ax.set_title('Confusion Matrix | KNeighbors\\n')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "6aYVVVDrvhGL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "outputId": "13a5f6de-e075-4131-feb8-9dab4503ed45"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##DecisionTreeClassifier"
      ],
      "metadata": {
        "id": "ynsU7HX8581w"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "zvIgq9NZQ9Jt"
      },
      "outputs": [],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "dt = DecisionTreeClassifier(max_depth=25).fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt_train = dt.score(x_train,y_train)\n",
        "dt_test = dt.score(x_test,y_test)\n",
        "\n",
        "print(' TEST : ',dt_train)\n",
        "print(' TRAIN : ',dt_test)\n",
        "\n",
        "tree_predicted = dt.predict(x_test)\n",
        "confusion = confusion_matrix(y_test, tree_predicted)\n",
        "dt_classification_report = classification_report(y_test, tree_predicted, target_names=['Not 1', '1'],zero_division='warn')\n",
        "print('Decision tree classifier (max_depth = 25)\\n', confusion)\n",
        "print('Decision tree\\n',dt_classification_report)"
      ],
      "metadata": {
        "id": "jNiInCM8ycMy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d45ae5d2-577b-437d-a9ae-8a8ff47152bc"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " TEST :  0.9465008833784143\n",
            " TRAIN :  0.898466204912522\n",
            "Decision tree classifier (max_depth = 25)\n",
            " [[56707  1880]\n",
            " [ 4614   758]]\n",
            "Decision tree\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       Not 1       0.92      0.97      0.95     58587\n",
            "           1       0.29      0.14      0.19      5372\n",
            "\n",
            "    accuracy                           0.90     63959\n",
            "   macro avg       0.61      0.55      0.57     63959\n",
            "weighted avg       0.87      0.90      0.88     63959\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = confusion, display_labels = [False, True])\n",
        "\n",
        "# Plot the confusion matrix heatmap with a title\n",
        "fig, ax = plt.subplots(figsize=(8, 6))\n",
        "cm_display.plot(cmap=plt.cm.Blues,ax=ax)\n",
        "ax.set_title('Confusion Matrix | Decision tree \\n')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "qzekqdIKyeGS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "outputId": "2603b132-ddc0-4cd5-b03d-337f28aa9e06"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Comparisons"
      ],
      "metadata": {
        "id": "l8XG_DesOchF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tabulate import tabulate\n",
        "headers = [\"Classifier\", \"Results\"]\n",
        "table = [\n",
        "    [\"Support Vector Machine\", svm_classification_report],\n",
        "    [\"Logistic regression\",lr_classification_report],\n",
        "    [\"Decision tree\",dt_classification_report],\n",
        "    [\"KNeighbors\",knn_classification_report],\n",
        "    [\"GaussianNB\",gnb_classification_report],\n",
        "]\n",
        "print(tabulate(table, headers=headers,stralign='center', tablefmt='grid', numalign='center', colalign=('center', 'center')))"
      ],
      "metadata": {
        "id": "BbhbtIlR9uhL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d77c2ca9-2c1e-498f-8c1a-3e79be32de88"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+------------------------+-------------------------------------------------------+\n",
            "|       Classifier       |                        Results                        |\n",
            "+========================+=======================================================+\n",
            "| Support Vector Machine |        precision    recall  f1-score   support        |\n",
            "|                        |                                                       |\n",
            "|                        |            0       0.92      0.92      0.92     58587 |\n",
            "|                        |            1       0.10      0.10      0.10      5372 |\n",
            "|                        |                                                       |\n",
            "|                        |     accuracy                           0.85     63959 |\n",
            "|                        |    macro avg       0.51      0.51      0.51     63959 |\n",
            "|                        | weighted avg       0.85      0.85      0.85     63959 |\n",
            "+------------------------+-------------------------------------------------------+\n",
            "|  Logistic regression   |        precision    recall  f1-score   support        |\n",
            "|                        |                                                       |\n",
            "|                        |            0       0.92      0.99      0.96     58587 |\n",
            "|                        |            1       0.53      0.08      0.14      5372 |\n",
            "|                        |                                                       |\n",
            "|                        |     accuracy                           0.92     63959 |\n",
            "|                        |    macro avg       0.72      0.54      0.55     63959 |\n",
            "|                        | weighted avg       0.89      0.92      0.89     63959 |\n",
            "+------------------------+-------------------------------------------------------+\n",
            "|     Decision tree      |        precision    recall  f1-score   support        |\n",
            "|                        |                                                       |\n",
            "|                        |        Not 1       0.92      0.97      0.95     58587 |\n",
            "|                        |            1       0.29      0.14      0.19      5372 |\n",
            "|                        |                                                       |\n",
            "|                        |     accuracy                           0.90     63959 |\n",
            "|                        |    macro avg       0.61      0.55      0.57     63959 |\n",
            "|                        | weighted avg       0.87      0.90      0.88     63959 |\n",
            "+------------------------+-------------------------------------------------------+\n",
            "|       KNeighbors       |        precision    recall  f1-score   support        |\n",
            "|                        |                                                       |\n",
            "|                        |            0       0.92      1.00      0.96     58587 |\n",
            "|                        |            1       0.51      0.03      0.05      5372 |\n",
            "|                        |                                                       |\n",
            "|                        |     accuracy                           0.92     63959 |\n",
            "|                        |    macro avg       0.71      0.51      0.50     63959 |\n",
            "|                        | weighted avg       0.88      0.92      0.88     63959 |\n",
            "+------------------------+-------------------------------------------------------+\n",
            "|       GaussianNB       |        precision    recall  f1-score   support        |\n",
            "|                        |                                                       |\n",
            "|                        |            0       0.95      0.83      0.89     58587 |\n",
            "|                        |            1       0.24      0.57      0.33      5372 |\n",
            "|                        |                                                       |\n",
            "|                        |     accuracy                           0.81     63959 |\n",
            "|                        |    macro avg       0.59      0.70      0.61     63959 |\n",
            "|                        | weighted avg       0.89      0.81      0.84     63959 |\n",
            "+------------------------+-------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tabulate import tabulate\n",
        "headers = [\"Classifier\", \"Train\", \"Test\"]\n",
        "table = [\n",
        "    [\"Support Vector Machine\", svc_train,svc_test],\n",
        "    [\"Logistic regression\",LR_train,LR_test],\n",
        "    [\"Decision tree\",dt_train,dt_test],\n",
        "    [\"KNeighbors\",knn_train,knn_test],\n",
        "    [\"GaussianNB\",gnb_train,gnb_test],\n",
        "]\n",
        "print(tabulate(table, headers=headers,stralign='center', tablefmt='grid', numalign='center', colalign=('center', 'center')))"
      ],
      "metadata": {
        "id": "wmExx9IOKWaj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24e19ba7-225e-48b9-8f73-1b388d6dabe1"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+------------------------+----------+----------+\n",
            "|       Classifier       |  Train   |   Test   |\n",
            "+========================+==========+==========+\n",
            "| Support Vector Machine | 0.847074 | 0.848215 |\n",
            "+------------------------+----------+----------+\n",
            "|  Logistic regression   | 0.914883 | 0.916665 |\n",
            "+------------------------+----------+----------+\n",
            "|     Decision tree      | 0.946501 | 0.898466 |\n",
            "+------------------------+----------+----------+\n",
            "|       KNeighbors       | 0.915985 | 0.916103 |\n",
            "+------------------------+----------+----------+\n",
            "|       GaussianNB       | 0.805672 | 0.808612 |\n",
            "+------------------------+----------+----------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6LTnj9XWbyft"
      },
      "source": [
        "# Plots"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def prc(classifier):\n",
        "  from sklearn.metrics import precision_recall_curve\n",
        "\n",
        "  classifier_probs = classifier.predict_proba(x_test)\n",
        "  classifier_probs = classifier_probs[:, 1]\n",
        "\n",
        "  precision, recall, thresholds = precision_recall_curve(y_test, classifier_probs)\n",
        "  closest_zero = np.argmin(np.abs(thresholds))\n",
        "  closest_zero_p = precision[closest_zero]\n",
        "  closest_zero_r = recall[closest_zero]\n",
        "\n",
        "  plt.figure()\n",
        "  plt.xlim([0.0, 1.01])\n",
        "  plt.ylim([0.0, 1.01])\n",
        "  plt.plot(recall, precision, label='Precision-Recall Curve')\n",
        "  plt.plot(closest_zero_r, closest_zero_p, 'o', markersize = 12, fillstyle = 'none', c='r', mew=3)\n",
        "  plt.xlabel('Recall', fontsize=16)\n",
        "  plt.ylabel('Precision', fontsize=16)\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "gBzUe7wPMxhN"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install scikit-plot"
      ],
      "metadata": {
        "id": "q1PyFkUySjze",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "224aef00-ca4b-4756-cd0c-71fd2b871c59"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting scikit-plot\n",
            "  Downloading scikit_plot-0.3.7-py3-none-any.whl (33 kB)\n",
            "Requirement already satisfied: matplotlib>=1.4.0 in /usr/local/lib/python3.10/dist-packages (from scikit-plot) (3.7.1)\n",
            "Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.10/dist-packages (from scikit-plot) (1.2.2)\n",
            "Requirement already satisfied: scipy>=0.9 in /usr/local/lib/python3.10/dist-packages (from scikit-plot) (1.10.1)\n",
            "Requirement already satisfied: joblib>=0.10 in /usr/local/lib/python3.10/dist-packages (from scikit-plot) (1.2.0)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=1.4.0->scikit-plot) (1.1.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=1.4.0->scikit-plot) (0.11.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=1.4.0->scikit-plot) (4.40.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=1.4.0->scikit-plot) (1.4.4)\n",
            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=1.4.0->scikit-plot) (1.22.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=1.4.0->scikit-plot) (23.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=1.4.0->scikit-plot) (8.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=1.4.0->scikit-plot) (3.1.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=1.4.0->scikit-plot) (2.8.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.18->scikit-plot) (3.1.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=1.4.0->scikit-plot) (1.16.0)\n",
            "Installing collected packages: scikit-plot\n",
            "Successfully installed scikit-plot-0.3.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "import scikitplot as skplt\n",
        "\n",
        "def roc(classifier):\n",
        "  y_pred_proba = classifier.predict_proba(x_test)\n",
        "  skplt.metrics.plot_precision_recall(y_test, y_pred_proba)\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "VWf8lEZUR_Jk"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prc(LR)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "4UJB4aDJOvpw",
        "outputId": "79504deb-ed36-4f0b-afc8-1faee66ff5f2"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prc(gnb)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "D7YnOIbpQ_s8",
        "outputId": "3ba8bb65-5ccc-45f1-a84f-70fd346c8f9c"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prc(knn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "Te53lpPuRNFx",
        "outputId": "75bb3257-6aa8-4da6-ff6a-b1ec1414ebf5"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkMAAAG4CAYAAABGhOPcAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABGTElEQVR4nO3deXxU1f3/8fdkmwSyQsgCJGFfFNkhQhBE+UrrilSl4k8Qty8u/bbSqmBRtFpwr1axVFzbquCCO+ISoQhGURIUgYDIkrBkIysJ2Wbu74/AwEwCJMyQmcx9PR+PeWjunDv3E64478c5555jMQzDEAAAgEkFeLsAAAAAbyIMAQAAUyMMAQAAUyMMAQAAUyMMAQAAUyMMAQAAUyMMAQAAUyMMAQAAUyMMAQAAUwvydgG+wm63a9++fYqIiJDFYvF2OQAAoBkMw1BFRYU6d+6sgIBT7OMxfNB///tf4+KLLzYSExMNSca777570nNWrlxpDBkyxAgJCTF69uxpvPzyyy26Zm5uriGJFy9evHjx4tUGX7m5uacWOgzD8MmeocrKSg0aNEjXX3+9Jk+efNL2O3fu1EUXXaSZM2fqtddeU3p6um688UYlJiZq4sSJzbpmRESEJCk3N1eRkZFu1Q8AAFpHeXm5kpKSHN/jp8Inw9Cvf/1r/frXv252+0WLFql79+564oknJEn9+/fXmjVr9Le//a3ZYejI0FhdgJUwBABAG+POFBe/mECdkZGhCRMmOB2bOHGiMjIyjntOTU2NysvLnV6SlJlTclprBQAAvsUvwlBeXp7i4+OdjsXHx6u8vFyHDh1q8pwFCxYoKirK8UpKSmqNUgEAgI/xizB0KubMmaOysjLHKzc3t+ENw7t1AQCA1uWTc4ZaKiEhQfn5+U7H8vPzFRkZqbCwsCbPsVqtslqtjY6ThQAAMBe/6BkaNWqU0tPTnY59/vnnGjVqVIs/yyANAQBgKj4Zhg4ePKgNGzZow4YNkhoend+wYYNycnIkNQxxTZs2zdF+5syZ2rFjh+666y5lZ2frueee05tvvqk77rijxdc26BsCAMBUfDIMff/99xoyZIiGDBkiSZo1a5aGDBmi++67T5K0f/9+RzCSpO7du+vjjz/W559/rkGDBumJJ57QCy+80OzH6o9FzxAAAOZiMQy+/qWGRZuioqK0ZE22pqT19XY5AACgGY58f5eVlZ3yOoE+2TPkTSRDAADMhTDkgo4yAADMhTAEAABMjTDkgo4hAADMhTDkgkfrAQAwF8KQC3qGAAAwF8IQAAAwNcKQC3qGAAAwF8KQC7IQAADmQhhywTpDAACYC2HIBVEIAABzIQy5Ig0BAGAqhCEAAGBqhCEXLLoIAIC5EIZcMH8aAABzIQy5IAsBAGAuhCEXhCEAAMyFMOSCOUMAAJgLYcgVWQgAAFMhDAEAAFMjDLmgYwgAAHMhDLni2XoAAEyFMOSCKAQAgLkQhlzQMQQAgLkQhlwYpCEAAEyFMAQAAEyNMOSCfiEAAMyFMOSCUTIAAMyFMOSCLAQAgLkQhlwwgRoAAHMhDAEAAFMjDAEAAFMjDLlglAwAAHMhDLkwmEINAICpEIZc0DMEAIC5EIZckIUAADAXwpALeoYAADAXwpAL5gwBAGAuhCEAAGBqhCEXDJMBAGAuhCEAAGBqhCEX9AwBAGAuhCEXhCEAAMyFMAQAAEyNMAQAAEyNMOSCYTIAAMyFMOSCRRcBADAXwpALeoYAADAXwpALshAAAOZCGHJF1xAAAKZCGHJBFAIAwFwIQwAAwNQIQy4YJQMAwFwIQy54tB4AAHMhDLmgZwgAAHMhDLkgCwEAYC6EIReEIQAAzIUwBAAATI0w5IIJ1AAAmAthyBVZCAAAUyEMuSALAQBgLoQhFwbP1gMAYCo+G4YWLlyobt26KTQ0VKmpqVq3bt0J2z/11FPq27evwsLClJSUpDvuuEPV1dUtvi5ZCAAAc/HJMLR06VLNmjVL8+bNU2ZmpgYNGqSJEyeqoKCgyfavv/66Zs+erXnz5mnLli168cUXtXTpUt1zzz0tvjZZCAAAc/HJMPTkk0/qpptu0owZM3TGGWdo0aJFateunV566aUm23/99ddKS0vT1KlT1a1bN11wwQW6+uqrT9qbBAAA4HNhqLa2VuvXr9eECRMcxwICAjRhwgRlZGQ0ec7o0aO1fv16R/jZsWOHli9frgsvvPC416mpqVF5ebnTS2KYDAAAswnydgGuioqKZLPZFB8f73Q8Pj5e2dnZTZ4zdepUFRUVacyYMTIMQ/X19Zo5c+YJh8kWLFigBx54oNFx1hkCAMBcfK5n6FSsWrVK8+fP13PPPafMzEwtW7ZMH3/8sR588MHjnjNnzhyVlZU5Xrm5uQ1vkIUAADAVn+sZio2NVWBgoPLz852O5+fnKyEhoclz7r33Xl177bW68cYbJUlnnXWWKisrdfPNN+vPf/6zAgIaZz6r1Sqr1er5XwAAALQpPtczFBISomHDhik9Pd1xzG63Kz09XaNGjWrynKqqqkaBJzAwUFLL1w1inSEAAMzF53qGJGnWrFmaPn26hg8frpEjR+qpp55SZWWlZsyYIUmaNm2aunTpogULFkiSLrnkEj355JMaMmSIUlNTtX37dt1777265JJLHKEIAACgKT4ZhqZMmaLCwkLdd999ysvL0+DBg7VixQrHpOqcnBynnqC5c+fKYrFo7ty52rt3rzp16qRLLrlEf/3rX1t8bfqFAAAwF4vBuJAkqby8XFFRUbrztQw9OvVsb5cDAACa4cj3d1lZmSIjI0/pM3xuzpC38Wg9AADmQhhyQT8ZAADmQhhyQRYCAMBcCEMAAMDUCEMuGCYDAMBcCEONkIYAADATwpALeoYAADAXwpALwhAAAOZCGHLBOkMAAJgLYcgFPUMAAJgLYQgAAJgaYcgFHUMAAJgLYcgFw2QAAJgLYcgFE6gBADAXwpArshAAAKZCGHJBFgIAwFwIQwAAwNQIQy4MZlADAGAqhCEXRCEAAMyFMOSKNAQAgKkQhlyQhQAAMBfCkAvmDAEAYC6EIRdEIQAAzIUwBAAATI0w5IquIQAATIUw5IK9yQAAMBfCkAuiEAAA5kIYcsHDZAAAmAthyAXDZAAAmAthCAAAmBphyAXDZAAAmAthyAVZCAAAcyEMuaBnCAAAcyEMNUIaAgDATAhDLugZAgDAXAhDAADA1AhDLugYAgDAXAhDLgzGyQAAMBXCkAtD0p6SKj2yIlv55dXeLgcAAJxmhCEXhiG9+vUu/WPVL3pjXY63ywEAAKcZYciFIelAZa0kqfxQvXeLAQAApx1hyIVhSBXVDSGout7m5WoAAMDpRhhqwsEjYaiWMAQAgL8jDDWhoqZOEj1DAACYAWHIlXFMz1Cd3cvFAACA0y3I2wX4GkOGKqobeoSq6+gZAgDA39Ez5MJpAjVhCAAAv0cYclFTb1etrWF4jGEyAAD8H2HIxcHDk6cleoYAADADwpCLI0NkEmEIAAAzIAy5OFhzTBiqZ5gMAAB/RxhyUVlztDeIniEAAPwfYegEqutsMgzD22UAAIDTiDB0AnZDqrMRhgAA8GeEoZM4xFAZAAB+jTB0EjWEIQAA/Bph6CRYeBEAAP/mkb3Jvv32W33xxRfau3evqqurm2xjsVj04osveuJyrYqd6wEA8G9uhaHa2lpdffXVeu+99yTphE9etdkwxDAZAAB+za0w9OCDD+rdd99V+/btde2116p///6KjIz0VG0+gWEyAAD8m1th6I033lC7du307bff6owzzvBUTT6Fp8kAAPBvbk2g3rNnj9LS0vw2CEkMkwEA4O/cCkMxMTHq0KGDp2pxsnDhQnXr1k2hoaFKTU3VunXrTti+tLRUt912mxITE2W1WtWnTx8tX778lK/fPiRQEmEIAAB/51YYmjBhgr799luPb1mxdOlSzZo1S/PmzVNmZqYGDRqkiRMnqqCgoMn2tbW1+p//+R/t2rVLb7/9trZu3arFixerS5cup1xDpwirJKmGOUMAAPg1t8LQgw8+qOLiYt1///0eKqfBk08+qZtuukkzZszQGWecoUWLFqldu3Z66aWXmmz/0ksvqbi4WO+9957S0tLUrVs3jRs3ToMGDTrlGo6EIR6tBwDAv7k1gXr16tWaMWOGHnroIa1YsUIXXXSRkpOTFRDQdMaaNm3aST+ztrZW69ev15w5cxzHAgICNGHCBGVkZDR5zgcffKBRo0bptttu0/vvv69OnTpp6tSpuvvuuxUYGNjkOTU1NaqpqXH8XF5e7vS+IwwxTAYAgF9zKwxdd911slgsMgxD3333nb7//vsTtm9OGCoqKpLNZlN8fLzT8fj4eGVnZzd5zo4dO/Tll1/qmmuu0fLly7V9+3bdeuutqqur07x585o8Z8GCBXrggQeafC8owKKosBBJ0qFahskAAPBnboWhadOmyWKxeKqWU2a32xUXF6fnn39egYGBGjZsmPbu3avHHnvsuGFozpw5mjVrluPn8vJyJSUlSZLCQ4MUGtzQu8UwGQAA/s2tMPTKK694qIyjYmNjFRgYqPz8fKfj+fn5SkhIaPKcxMREBQcHOw2J9e/fX3l5eaqtrVVISEijc6xWq6xWa5OfFxEapNBgniYDAMAMfG6j1pCQEA0bNkzp6emOY3a7Xenp6Ro1alST56SlpWn79u2y248OaW3btk2JiYlNBqGTCbcGK8wRhhgmAwDAn3k0DBmGoaKiIhUVFTkFk5aaNWuWFi9erFdffVVbtmzRLbfcosrKSs2YMUNSw/DcsROsb7nlFhUXF+v3v/+9tm3bpo8//ljz58/XbbfddkrXjzhmmKyGniEAAPyaR3atT09P12OPPaavvvrKsWt9aGioxo4dqz/96U86//zzW/R5U6ZMUWFhoe677z7l5eVp8ODBWrFihWNSdU5OjtMTa0lJSfr00091xx13aODAgerSpYt+//vf6+677z6l3yfCeswwGXOGAADwaxbDzRUT//KXv+iBBx447sKLFotFDzzwgObOnevOZU678vJyRUVFKekPb2pyai+N7hmru975UeP7dtLLM0Z6uzwAANCEI9/fZWVlp7xZvFvDZF988YXuv/9+BQcH6/bbb1dWVpbKy8tVXl6uDRs26He/+51CQkI0b948ffnll+5cqlWFhwbJeuRpMuYMAQDg19wKQ3//+99lsVj0/vvv6+9//7sGDRqk8PBwhYeHa+DAgXr66af1/vvvS5KefvppjxTcGiJCgxkmAwDAJNwKQ99++61Gjx6tiRMnHrfNBRdcoNGjRx939WhfFG4N4mkyAABMwq0wVFpaqpSUlJO2S0lJUVlZmTuXalWRx6wzxNNkAAD4N7fCUGxs7HG3yDhWdna2YmNj3blUq3JagZowBACAX3MrDKWlpSkrK0uvv/76cdu89tpryszM1JgxY9y5VKuKsB6dM3SIMAQAgF9za52hO++8U8uWLdO0adP03nvvafr06erevbukhs1TX3nlFb333nsKDAzUn/70J48U3BrCQ4MUGsScIQAAzMCtMDRixAj94x//0G233aa3335b77zzjtP7hmEoKChICxcu1IgRI9wqtDVFuGzUahiGT2xICwAAPM/t7ThuuukmZWZm6vrrr1ePHj0cG6D26NFDN9xwgzIzM3XTTTd5otZWE2ENVmhIQ8+QYUi1NnqHAADwVx7ZjmPAgAF64YUXPPFRPiHimGEyqWGozHrMzwAAwH/43K71viA8NEjBgRYFHB4Z44kyAAD8F2HIhTU4QMGBAbJYLEdXoSYMAQDgt1o0THb99dfLYrFo/vz5io+P1/XXX9/scy0Wi1588cUWF9jawkOO/pGEBgeqqtbGE2UAAPixFoWhV155RRaLRXfffbfi4+P1yiuvNPvcthKGzup6dMfbMHqGAADwey0KQy+//LIkKTEx0elnf/L0b4c6/t3KKtQAAPi9FoWh6dOnn/BnfxAYcHQ9IcfCi/UMkwEA4K+YQH0CRxZePFRLzxAAAP7KI+sMNWXnzp368ccflZKSosGDB5+uy5xWjp3r6wlDAAD4K7d6hj744ANNnjxZ69atczr+2GOPqU+fPpo8ebKGDRvWoqfOfAmP1gMA4P/cCkP/+te/tGLFCvXv399xLDs7W7Nnz5ZhGBo0aJDatWunV199VR9++KHbxba2o0+TMWcIAAB/5VYYysrK0qBBgxQREeE49tprr0mSnnvuOWVmZuq7775TYGCgnn/+efcq9QKeJgMAwP+5FYaKiorUpUsXp2OrVq1SWFiYrrvuOklSv379NGbMGG3atMmdS3lFKD1DAAD4PbfCUHV1tQIDj25garPZlJmZqdTUVIWEhDiOd+7cWXl5ee5cyiuOPFp/iJ4hAAD8llthKC4uTj///LPj52+++UaHDh1SWlqaU7tDhw6pffv27lzKK0IZJgMAwO+5FYZGjx6tH374QUuWLFFZWZnmz58vi8WiCRMmOLXbsmWLOnfu7Fah3tCcR+vrbXYZhtFaJQEAAA9zKwzdfffdCgoK0jXXXKMOHTrok08+0dChQzV27FhHm9zcXGVnZ2vEiBFuF9vaTvY02RvrcjTwgc/00MdbWrMsAADgQW6FoaFDh2r58uUaN26c+vfvr+uuu04fffSRU5s333xTUVFROv/8890q1BuON0xmtxta8MkWzVm2UVW1Nr3y9S7tLT3kjRIBAICb3F6B+vzzzz9h0PnjH/+oP/7xj+5exiusTSy6WF1n06w3N2j5xoYJ4bHhVhUdrNG/MnZpzq/7N/k5AADAd7E32QkcmTN05GmyooM1+u3z32j5xjwFB1r05FWD9PDksyRJb3ybo8qaeq/VCgAATg1h6ARCg44Mk9m1vaBClz+3VhtySxUVFqx/35CqyUO76rx+cUrp2E7l1fValrnHyxUDAICWatEw2XnnnSeLxaJXX31VXbt21Xnnndfscy0Wi9LT01tcoDeFhTT0DO0+UKnJz32t8up6JXdop5dnjFDPTuGSpIAAi2aM7qb7P9ysl9fu0jWpKQoIsHizbAAA0AItCkOrVq2SxWJRVVWV4+fmsljaXkA4MkxWUlUnSRqWEqPnrx2mjuFWp3ZXDk/SE59v046iSv13W6HG94tr9VoBAMCpaVEYWrlypSQpOTnZ6Wd/1T7k6B/PxQMT9fiVgxwByamdNUi/HZGkxV/t1EtrdxKGAABoQywGKwZKksrLyxUVFaWysjJFRkZKaniEft4Hm5TcoZ1uGNP9hMNfucVVGvfYStkN6dM/jFXfhIjjtgUAAJ7R1Pd3SzGB+gQCAix6cNIA3TS2x0nnASV1aKeJZyZIkl5eu7M1ygMAAB7gVhgqKSnR6tWrtXfv3uO22bt3r1avXq3S0lJ3LtUmXD+muyRpWdZeHThY4+VqAABAc7gVhp5++mmNHz9e+/fvP26b/fv3a/z48Vq4cKE7l2oThqfEaGDXKNXW2/XGuhxvlwMAAJrBrTC0fPly9ejRQ8OHDz9um+HDh6t79+6NtunwRxaLRdenNfQO/Stjt2rrm97TDAAA+A63wtCuXbvUt2/fk7br16+fdu40xzyaC89KVFyEVQUVNfp44z5vlwMAAE7CrTB0ZAb3yURGRppizpAkhQQFaNqoFEnSi2t2iof1AADwbW6FoU6dOik7O/uk7bZu3aoOHTq4c6k2ZWpqiqxBAfppb7m+313i7XIAAMAJuBWGzj77bG3YsEGrV68+bpuvvvpKWVlZOvvss925VJvSoX2IJg/tIkl6aY05hgcBAGir3ApDt9xyiwzD0BVXXKH333+/0fvvv/++rrjiClksFs2cOdOdS7U5Mw5PpP50U55yi6u8XA0AADget8LQeeedp9tvv11FRUWaPHmy4uPjNXbsWI0dO1YJCQmaPHmyCgsLNXPmTF1wwQWeqrlN6BMfoXN6x8puSK9+vcvb5QAAgONwewXqv//973r66afVsWNHFRYWas2aNVqzZo0KCgrUsWNH/e1vfzPFGkNNOfKY/dLvcnWwpt7L1QAAgKZ4bG8ym82m9evXa/fu3ZIaNnMdPny4AgMbb2zqizyxt4kru93QhL/9VzsKK3X/JWfousPhCAAAeIYnvr/ZqPWw0xGGJOnf3+zWve/9pJSO7bTyj+eedI8zAADQfD63Uev27duVkZGhbdu2efJj27TfDO2iyNAg7T5QpS+zC7xdDgAAcOF2GLLZbHrooYeUkJCgvn37asyYMXr44Ycd77/22msaPXq0Nm3a5O6l2qR2IUG6OjVZUsMijAAAwLe4FYZsNpsuvvhizZs3TyUlJerfv3+jFZfT0tL0zTffaNmyZW4V2pZNH9VNgQEWZew4oM37yr1dDgAAOIZbYWjRokX69NNPNX78eO3cuVM//fRTozbdunVTz5499dlnn7lzqTatc3SYfj0gQZL05Ofb2KIDAAAf4lYYevXVV9WhQwe99dZb6ty583Hb9e/fXzk5Oe5cqs27/bxeCg606Ist+Xp7/R5vlwMAAA5zKwxlZ2dr5MiRiomJOWG7qKgoFRSYe/Jwv4RI3fE/fSRJD3y4mVWpAQDwEW7PGbJarSdtt3///ma183f/O7anhqfE6GBNvf745g+y2RkuAwDA29wKQykpKfrxxx9P2Kaurk4//fSTevfu7c6l/EJggEVPXjVY7UMCtW5XsRZ/tcPbJQEAYHpuhaFf/epX2rVrl55//vnjtnnmmWdUWFioiy66yJ1L+Y3kju0075IzJUlPfLaVp8sAAPAyt8LQnXfeqaioKN166636wx/+oK+//lqSVFlZqczMTM2ePVuzZ89WbGysbr/9do8U7A+uHN5VE/rHq85maNabG1RdZ/N2SQAAmJbb23GsXr1akydPVnFxsSwW560mDMNQdHS0PvjgA40ZM8atQk+307Udx/EUHazRxL+t1oHKWt08tofuubD/ab8mAAD+xie24xg7dqw2bdqku+66S2eeeabCwsJktVrVq1cv/d///Z82btzo80HIG2LDrXr4NwMlSYu/2qFvdhzwckUAAJiTWz1DOTk5slgsSkpK8mRNXtHaPUNHzH7nRy35LlddosP0yR/OUWRocKtdGwCAts7rPUPdunXTb3/7W3c+wvTmXnyGkjqEaW/pIT3wwWZvlwMAgOm4FYYiIyPVvXt3T9ViSuHWIP3tqsEKsEjvZO7Rip/2e7skAABMxa0wdMYZZyg3N9dTtZjW8G4dNHNcT0nSnGUbVVBR7eWKAAAwD7fC0E033aS1a9fqu+++81Q9ThYuXKhu3bopNDRUqampWrduXbPOW7JkiSwWiyZNmnRa6jod/jChj85IjFRJVZ3ufvtHNnMFAKCVuBWGZsyYoVtvvVUXXHCB5s+fr61bt6qmpsYjhS1dulSzZs3SvHnzlJmZqUGDBmnixIkn3eNs165d+tOf/qRzzjnHI3W0lpCgAD3128EKCQrQyq2Fen2duTe2BQCgtbj1NFlgYGDzL2SxqL6+vtntU1NTNWLECD377LOSJLvdrqSkJP3ud7/T7NmzmzzHZrNp7Nixuv766/XVV1+ptLRU7733XpNta2pqnIJbeXm5kpKSWv1pMlcvfLVDD328RWHBgfrk9+eoW2x7r9UCAICv8/rTZIZhNPtlt9ub/bm1tbVav369JkyYcLTQgABNmDBBGRkZxz3vL3/5i+Li4nTDDTec9BoLFixQVFSU4+UrywNcn9Zdo3p01KE6m+54c4Pqbc3/cwMAAC3nVhiy2+0tejVXUVGRbDab4uPjnY7Hx8crLy+vyXPWrFmjF198UYsXL27WNebMmaOysjLHy1cmggcEWPT4VYMUERqkrJxS/WPVL94uCQAAvxZ0Kidt375dy5Yt065du2S1WjVkyBBdeeWVCgsL83R9zVJRUaFrr71WixcvVmxsbLPOsVqtslqtp7myU9MlOkx/uexM3bH0Bz2d/rPO7Runs7pGebssAAD8UovD0FNPPaW77rpLNpvz5qJz587V8uXLNWDAALeLio2NVWBgoPLz852O5+fnKyEhoVH7X375Rbt27dIll1ziOHakJyooKEhbt25Vz5493a6rNU0a3EVfbC7Qxxv36w9Ls/Tx/52j0ODmz9ECAADN06JhsjVr1uiPf/yj6uvr1a5dOw0ZMkQ9e/aUxWLRnj179Jvf/KZFw2HHExISomHDhik9Pd1xzG63Kz09XaNGjWrUvl+/ftq4caM2bNjgeF166aUaP368NmzY4DPzgVrCYrHooUkDFBdh1S+FlXr4k2xvlwQAgF9qURh69tlnZRiGpk+frry8PH3//ffatm2bMjMz1bNnT23fvl0rVqzwSGGzZs3S4sWL9eqrr2rLli265ZZbVFlZqRkzZkiSpk2bpjlz5kiSQkNDNWDAAKdXdHS0IiIiNGDAAIWEhHikptYW0z5Ej17RsJnrK1/v0lc/F3q5IgAA/E+LwlBGRoa6du2qf/7zn2rf/ugj3wMHDtTTTz8twzD0zTffeKSwKVOm6PHHH9d9992nwYMHa8OGDVqxYoVjUnVOTo727/f/rSvO7Runa89OkSTd+daPKquq83JFAAD4lxatMxQaGqoLL7xQy5Yta/ReeXm5oqOjdfPNN2vRokUeLbI1eGvX+uaoqq3XxX9fox1Flbp0UGf9/eoh3i4JAACf0OrrDNXW1io6OrrJ944UUFtbe0qF4PjahQTpySmDFRhg0Qc/7NMHP+zzdkkAAPgNt9YZQusZnBSt28f3kiTNfXej8srYzBUAAE9o8aP127dv17/+9a9Ten/atGktvRyOcft5vbRqa4F+2FOmO9/+Qa/OGKmAAIu3ywIAoE1r0ZyhgIAAWSyn9uXb0r3JWpsvzxk61i+FB3XR379SdZ1d919yhq5L6+7tkgAA8BpPfH+3qGcoOTn5lMMQPKNnp3D9+cL+uvf9TVrwSbbG9I5Vr7gIb5cFAECb1aIwtGvXrtNUBlri/52dos+3FGj1tkLdsfQHLbt1tIIDmf4FAMCp4Bu0DbJYLHrsioGKCgvWxr1leib9Z2+XBABAm0UYaqPiI0M1//KzJEnPrtyuzJwSL1cEAEDbRBhqwy4amKjLh3SR3ZD+99/r9dBHm7Vya4Gqan13ojoAAL6mRU+T+bO28jSZq7JDdbr02TXafaDKcSw40KIhyTE6p1es0nrHamCXKAUxpwgA4Ic88f1NGDqsrYYhSTpYU69VWwu05uciffVzkfaWHnJ6P8IapLN7dtSYXrEa0ztWPWLb81QgAMAvEIY8qC2HoWMZhqGc4iqt2V6ktduLtHb7AZUdct7cNTEqVGm9YjWmV6xG9+qouIhQL1ULAIB7CEMe5C9hyJXNbmjTvjJHOPpuV4lq6+1ObfrGR2hM74ZwNLJ7B7W3tnhhcgAAvIIw5EH+GoZcVdfZ9N2uYkc42rSvXMf+FxAUYNHQ5JiGnqPeHTWoazTzjQAAPosw5EFmCUOuiitrlfHLAa3ZXqivfi7SnhLn+Ubh1iCd3aOjxvTqqDG9Y9WzUzjzjQAAPoMw5EFmDUOucg4cM9/olyKVVjnPN0qIDHX0GqX1jFVcJPONAADeQxjyIMJQYza7oc37yh3haN2u4kbzjfrEhyutV6zO6R2rkd07Kpz5RgCAVkQY8iDC0MlV19m0fneJvvq5IRz9tK+s0XyjIcnRjifVBiVFs2caAOC0Igx5EGGo5Uoqa5Wx44Cj5+jYhR+lI/ONOmhsn04a3zdOSR3aealSAIC/Igx5EGHIfbmH1zdas71IX28vUonLfKOendprfN84je8Xp+HdYmQNCvRSpQAAf0EY8iDCkGfZ7YY27y/XVz8XadXWAn2/u0Q2+9H/1NqHBCqtV6zO7Runc/t2UufoMC9WCwBoqwhDHkQYOr3KDtVp7fYircwu0KpthSqsqHF6v19ChM7tG6fxfTtpaEoMc40AAM1CGPIgwlDrOdJrtDK7QCu3FmhDbqmO6TRSRGiQxvbupHP7dtK4vp3YLgQAcFyEIQ8iDHlPSWWtVv9cqFVbC/XfbYUqrqx1en9Al0iN7xunc/vGaXBStAIDWPQRANCAMORBhCHfYLMb+nFPqVZuLdSqrQX6cU+Z0/sx7YIdT6eN7dNJHdqHeKlSAIAvIAx5EGHINxVW1Oi/2xqC0epthSqvrne8Z7FIg5OiG55Q6xunMztHKoBeIwAwFcKQBxGGfF+9za6s3NLDc40KtWV/udP7seFWjevTSeP7ddI5vTspKizYS5UCAFoLYciDCENtT15ZtVZtbZiEvXb7AR2sOdprFBhg0bDkGJ3br2FIrV9CBBvMAoAfIgx5EGGobautt+v73cVatbVQK7ML9HPBQaf3EyJDNb5fJ43rE6cxvWPZQw0A/ARhyIMIQ/4lt7hKq7YValV2gdb+UqTquqMbzAYHWjSiW4fDq2F3Us9O4fQaAUAbRRjyIMKQ/6qus+nbncVatbVAq7YWamdRpdP7XWPCHMFoVI9YhYWwTQgAtBWEIQ8iDJnHzqLKw3ONCvXNjgOqrT/aaxQSFKBRPTpqfN9OOrdvnLrFtvdipQCAkyEMeRBhyJyqauuV8csBrdxaoJXZhdpbesjp/e6x7XVu34ZJ2CO7d1BoML1GAOBLCEMeRBiCYRj6pfCgVmYXauXWAq3bWaz6Y/YJsQYFaGhyjFJ7dNDI7h00NDmGcAQAXkYY8iDCEFxVVNdp7fYDjsf388udN5cNDrRoUNdopfbooNTuHTUsJUbteUoNAFoVYciDCEM4kSO9Rt/uLNa3O4r17c4DjcJRYIBFA7pEKbV7B6V276Dh3Tqw8CMAnGaEIQ8iDKElDMNQTnHV4WDUEI72lDjPN7JYpP4JkYd7jjpoZPeO7KUGAB5GGPIgwhDctbf0kNbtPKBvdxRr3c5i7XB5hF+SeseFH55z1FFnd++guMhQL1QKAP6DMORBhCF4WkF5tdbtOjqsti3/YKM23WPba2S3Do5J2V1j2nmhUgBouwhDHkQYwulWXFmrdTsbeo2+3XlAm/eXy/VvX5foMMewWmr3jkrp2I7VsQHgBAhDHkQYQmsrO1Sn9buLHfOONu4tk83u/NcxLsKq1B4dNbJ7B53dvYN6xbF1CAAcizDkQYQheFtlTb0yc0occ4425Jaq1mZ3atOhfYjTsFr/hEgFBBCOAJgXYciDCEPwNdV1NmXllDqG1TJzSpw2nJWkyNAgjXCEo44a0DlSQYEBXqoYAFofYciDCEPwdbX1dm3cW6pvDvccfb+rWJW1Nqc27UMCNaxbB8daR2d1jZI1iFWyAfgvwpAHEYbQ1tTb7Nq8v9zxtNq6ncUqr653anNkC5GR3Rt6j9hCBIC/IQx5EGEIbZ3dbig7r6JhraPDT60dqKx1anPsFiIjD28hEs4WIgDaMMKQBxGG4G+ObCFyZFjtuFuIdI5Uao+ObCECoE0iDHkQYQj+7tgtRL45PKx2vC1ERnbvoLN7dNCIbh3UMdzqpYoB4OQIQx5EGIIZHbuFyLc7i7WTLUQAtDGEIQ8iDAENW4h8e8wq2U1tIdI1JkxDk2M0JDlaQ5JjdEZipEKCeJwfgHcQhjyIMAQ0duBgjb7bVeJ4Wq2pLURCggJ0VpcoDUlqCEdDU6KVGBXmnYIBmA5hyIMIQ8DJlVfX6cfcMmXllCgzp0RZuaUqrapr1C4hMlRDkqMdPUgDukTxSD+A04Iw5EGEIaDlDMPQrgNVytxdoqzcEmXllCo7r6LRHmtBARad0Tny6PBaUoySOoSxzxoAtxGGPIgwBHhGVW29ftxTpqyc0sM9SKUqOljTqF1seIgGJ8U4epAGdo1Se9Y8AtBChCEPIgwBp4dhGNpTckhZuaWHe5BKtXlfmepszv/rCbBIfRMinYbXesS2p/cIwAkRhjyIMAS0nuo6mzbtK1dWTomjB2lfWXWjdlFhwY5htSHJ0RqcHK3IUBaFBHAUYciDCEOAd+WVVTeEo8M9SBv3lqmm3u7UxmKRenUKP6b3KEa94sIVGEDvEWBWhCEPIgwBvqW23q7svHLH0FpWTqlyiqsatQu3BmlQUpRjaG1wUow6tA/xQsUAvIEw5EGEIcD3FR2scQyrZeWU6oc9paqqtTVq1z22/eF1jxrWPuqXEKGgQBaGBPwRYciDCENA21Nvs2tb/kFl5ZYoc3epsnJLtKOw8ZYiYcGBOqtrlNPk7LgIthUB/AFhyIMIQ4B/KK2q1YbcUmUe7kHakFuqiur6Ru26RIdpaEqMowfpzM5RbCsCtEGEIQ8iDAH+yW439EvhwYbhtcM9SNsKKprcVmRA50gNST669lFiVCiP9gM+zu/D0MKFC/XYY48pLy9PgwYN0jPPPKORI0c22Xbx4sX617/+pZ9++kmSNGzYMM2fP/+47V0RhgDzqKiuO7wwZImjB6mkiW1F4iOtGpLUsN/akOQYncW2IoDP8eswtHTpUk2bNk2LFi1SamqqnnrqKb311lvaunWr4uLiGrW/5pprlJaWptGjRys0NFSPPPKI3n33XW3atEldunQ56fUIQ4B5GYah3QeqGvZbO9yDtGX/8bcVObIp7ZDkaCV3aEfvEeBFfh2GUlNTNWLECD377LOSJLvdrqSkJP3ud7/T7NmzT3q+zWZTTEyMnn32WU2bNu2k7QlDAI5VVVuvjXvKDj/W39CDVFjReFuRju1DHE+tDUmO1qCu0WwrArQiT3x/++Tf2NraWq1fv15z5sxxHAsICNCECROUkZHRrM+oqqpSXV2dOnTo0OT7NTU1qqk5+j+28vJy94oG4FfahQQptUdHpfboKKmh92hv6SFl5ZQ6epA27SvTgcpafbGlQF9sKZDUsK1In/iIYyZnx6hHbHsFsDAk4LN8MgwVFRXJZrMpPj7e6Xh8fLyys7Ob9Rl33323OnfurAkTJjT5/oIFC/TAAw+4XSsAc7BYLOoa005dY9rpkkGdJblsK5JbqqzdDduKZOdVKDuvQq9/myOpYVuRwcesezQ4KVpRYWwrAvgKnwxD7nr44Ye1ZMkSrVq1SqGhTa8lMmfOHM2aNcvxc3l5uZKSklqrRAB+IDQ4UMNSYjQsJcZxLK+sWhtySxw9SD/uKVPZoTr9d1uh/rut0NGuV1y4hiRFN/QgJUerd1wE24oAXuKTYSg2NlaBgYHKz893Op6fn6+EhIQTnvv444/r4Ycf1hdffKGBAwcet53VapXVavVIvQBwREJUqH4VlahfDUiUJNXZ7MreX3F4aK2hB2n3gSptLzio7QUH9db6PZKObivieHotKUYxbCsCtAqfDEMhISEaNmyY0tPTNWnSJEkNE6jT09N1++23H/e8Rx99VH/961/16aefavjw4a1ULQAcX3BggM7qGqWzukZp+uhukhq2Fdlw+Km1rJxS/ZBbqoM19Vq7/YDWbj/gOLdHp/YamhzT8Eqh9wg4XXz2abKlS5dq+vTp+uc//6mRI0fqqaee0ptvvqns7GzFx8dr2rRp6tKlixYsWCBJeuSRR3Tffffp9ddfV1pamuNzwsPDFR4eftLr8TQZAG+x2Q1ty2/oPcrc3fD02o6ixtuKhFuDNPjw0NrQ5Ibeo6h2zD2Cufnt02SSNGXKFBUWFuq+++5TXl6eBg8erBUrVjgmVefk5Cgg4OjS+f/4xz9UW1urK664wulz5s2bp/vvv781SweAFgkMsKh/YqT6J0bqmtQUSVJJZa2ycku0fndDQPphT0Pv0ZrtRVqzvchxbq+4cA09vGL20JQY9eoUzpNrQAv5bM9Qa6NnCIAvq7fZtTW/omHF7N0lyswp0a4DVY3aRYQGaUhyjCMgDU6OVmQovUfwX3696GJrIwwBaGuKDtY4nlrL3N3w5NqhOptTG4tF6h0XfszcI9Y9gn8hDHkQYQhAW1dvsys7r8IRjjJzSpVT3Lj3KCos2LEZ7dDkGA1KilIEvUdoowhDHkQYAuCPCitqGsJRTomyDs89qqm3O7WxWKS+h1fNHnp4iK17bHv2XEObQBjyIMIQADOos9m1ZX+5o+do/e4S7S091KhdTLtgp7lHg5LYcw2+iTDkQYQhAGZVUF59uPeotGHu0d4y1br0HgVYpH4JkRqacnR4LaVjO3qP4HWEIQ8iDAFAg9p6uzY7eo8a5h/tK6tu1K5j+5CGuUeHh9cGdo1SuxB6j9C6CEMeRBgCgOPLK6s+ZmJ2iX7aW65am3PvUcN6SRFHn1xLjlFShzB6j3BaEYY8iDAEAM1XU2/Tpn3H9h6VKq+8ce9RbLi1Yd7RMb1HocGBXqgY/oow5EGEIQBwz77SQ45glJlTok37ylRnc/6KCQqw6IzOkY41j4YmR6tLNL1HOHWEIQ8iDAGAZ1XX2fTT3jKngFRQUdOoXVyE1bEZ7dDkGA3oQu8Rmo8w5EGEIQA4vQzD0N7SQ46n1rJySrRpX7nq7c5fQ8GBFp3ZOcopIHWODvNS1fB1hCEPIgwBQOurrrPpxz1lTqtmFx1s3HuUEBnqCEZDkmM0oEukrEH0HoEw5FGEIQDwPsMwtKfkkCMcrc8p0Zb9FbK59B6FBAZoQJdj5x7FKCEq1EtVw5sIQx5EGAIA31RVW39M71GpsnJKdKCytlG7zlGhGpISo2GHA9IZiZEKCQrwQsVoTYQhDyIMAUDbYBiGcoqrHOFo/e4SZeeVy6XzSNagAJ3VJcrx1NrQ5BjFRdJ75G8IQx5EGAKAtquypl4/7ClV1uHJ2Zk5JSqpqmvUrmtMmGMz2qEpMeqfGKngQHqP2jLCkAcRhgDAfxiGoV0HqrT+mC1FtuVXNOo9Cg0O0MAu0RrQJUq948PVOy5cveLCFd0uxDuFo8UIQx5EGAIA/3awpl4/5B7tOcrMKVXZoca9R5LUKcLqCEYN/4xQ7/hwdWwfwgKRPoYw5EGEIQAwF7vd0I6iSmXllGhrXoV+Ljio7QUHtbf00HHPiWkXrN5xEep1TC9S77gIxUdaCUleQhjyIMIQAEBqmH/0S+FB/Zx/8HBAaghKOcVVOt43ZoQ1yBGQjg1LnaPCFBBASDqdCEMeRBgCAJxIdZ1NvxQ29B5tLzgSliq060BVo3WQjmgXEqienQ73IsU3BKXeceFK6tBOgYQkjyAMeRBhCABwKmrr7dp1oNIRjn4uOKjt+Qe1o+hgo41qjwgJCnCEJMdwW3y4Ujq25+m2FvLE93eQh2sCAMBUQoIC1Cc+Qn3iIyQlOo7X2+zaXVx1TE/S0XlJNfV2bdlfri37y50+KyjAou6x7dU7/vCk7cMhqXtse7YfOY3oGTqMniEAQGuw2Q3tLTnk6EX6Ob9hXtL2goOqrLU1eU6ARUrp2N7xdFvvw0NuPTuFKyzE3CGJYTIPIgwBALzJMAztL6s+HJAawtGRfy+vrm/yHIulYSHJI3OReh4z7BYRGtzKv4F3EIY8iDAEAPBFhmGosKLmaEgqPNKbdLDJPdqOSIwKdTz636YXlCwrk/79b+mdd6Q9e6SDB6XwcKlrV+k3v1H5pEmKSkoiDHkCYQgA0NYcOFjj6EFq+GeFfs4/qIKKmuOeExtuPWaozYcXlMzNlf76V+k//5EqK4/brLxdO0VVVRGGPIEwBADwF2WH6g5P3K44Zr2k5i0o2dNlXpJXFpRcu1a69FKpuPikTcslRUmEIU8gDAEA/N3Bmnr9cmQuUkGFth8OSrklPrSg5Nq10oQJUnV1s5oThjyIMAQAMKtjF5Q8dr2k3SdYUDIsOPDo3m2eWlAyN1caPLhxj1BgoHTZZdLll0vx8VJ+vrRsmfT++yq32wlDnkIYAgDAWU29TbuKqhxzkbYXNn9BSccyAC1ZUPKWW6RFi5yPTZwoPf+8lJzcuH1Ojsqvv15R6emEIU8gDAEA0Dx1NrtyiqscayQdWS/pl8KGBSWb0tSCkr3iGhaUDA0OlMrLpc6dnSdLT5woffSRFHT8NaLLi4sV1bEjK1ADAIDWExzY0PvTs1O4pATHcZvd0J6SqmPWSDoalqpqbYfnKh2UlOc458iCkjf9sFxTjwlCRmCgLIsXnzAISTr5+81AGAIAAB4RGGBRSsf2SunYXuf3j3cct9sN7S+vPrqY5DHzkiqq67WzqFLdV69w+qwVPVM1/42f1TsuT73ijg67nY4FJQlDAADgtAoIsKhLdJi6RIfp3L5xjuPHLih55mvO+7R92meUcosPKbf4kL7MLnB6b86v++l/x/X0WH2EIQAA4BUWi0VxkaGKiwyV6p0XivzLTeP12wFnN6yRdMzK2wUVNeoSE+bROghDAADA+8LDnX6MLC/R2T066uweHZ2Ol1XVKTjIs+sbneQZNwAAgFbQtavzz+++22SzqHbBahfi2b4cwhAAAPC+3/zG+ef3329YhLEVEIYAAID3TZsmtW9/9GebTbrpJqm+/sTnnez9ZiAMAQAA74uMlK691vnYp59KF18s5eQ0fU5OjnTllW5fmhWoD2MFagAAvCw3VxoyRDpwwPl4YGDDLvaue5N98AF7k3kSYQgAAB/ghV3rGSYDAAC+Iy1N+uILqWPHk7f1EMIQAADwLWlpUlaWNHOm86Tqppzs/WZgmOwwhskAAPBB5eXSv/8tvf22tHevVFEhRURIXbpIV1yh8ssuU1RSEnOGPIEwBABA2+OJ72+GyQAAgKkRhgAAgKkRhgAAgKkRhgAAgKkRhgAAgKkRhgAAgKkRhgAAgKkRhgAAgKkRhgAAgKkRhgAAgKkRhgAAgKkRhgAAgKkRhgAAgKkRhgAAgKkRhgAAgKn5dBhauHChunXrptDQUKWmpmrdunUnbP/WW2+pX79+Cg0N1VlnnaXly5e3UqUAAKCt8tkwtHTpUs2aNUvz5s1TZmamBg0apIkTJ6qgoKDJ9l9//bWuvvpq3XDDDcrKytKkSZM0adIk/fTTT61cOQAAaEsshmEY3i6iKampqRoxYoSeffZZSZLdbldSUpJ+97vfafbs2Y3aT5kyRZWVlfroo48cx84++2wNHjxYixYtOun1ysvLFRUVpbKyMkVGRnruFwEAAKeNJ76/gzxck0fU1tZq/fr1mjNnjuNYQECAJkyYoIyMjCbPycjI0KxZs5yOTZw4Ue+9916T7WtqalRTU+P4uaysTFLDHyoAAGgbjnxvu9O345NhqKioSDabTfHx8U7H4+PjlZ2d3eQ5eXl5TbbPy8trsv2CBQv0wAMPNDqelJR0ilUDAABvqaioUFRU1Cmd65NhqDXMmTPHqSeptLRUKSkpysnJOeU/TLivvLxcSUlJys3NZbjSy7gXvoH74Bu4D76hqftgGIYqKirUuXPnU/5cnwxDsbGxCgwMVH5+vtPx/Px8JSQkNHlOQkJCi9pbrVZZrdZGx6OiovgP3QdERkZyH3wE98I3cB98A/fBN7jeB3c7MXzyabKQkBANGzZM6enpjmN2u13p6ekaNWpUk+eMGjXKqb0kff7558dtDwAAIPloz5AkzZo1S9OnT9fw4cM1cuRIPfXUU6qsrNSMGTMkSdOmTVOXLl20YMECSdLvf/97jRs3Tk888YQuuugiLVmyRN9//72ef/55b/4aAADAx/lsGJoyZYoKCwt13333KS8vT4MHD9aKFSsck6RzcnIUEHC0Y2v06NF6/fXXNXfuXN1zzz3q3bu33nvvPQ0YMKBZ17NarZo3b16TQ2doPdwH38G98A3cB9/AffANp+s++Ow6QwAAAK3BJ+cMAQAAtBbCEAAAMDXCEAAAMDXCEAAAMDVThaGFCxeqW7duCg0NVWpqqtatW3fC9m+99Zb69eun0NBQnXXWWVq+fHkrVerfWnIfFi9erHPOOUcxMTGKiYnRhAkTTnrf0Hwt/TtxxJIlS2SxWDRp0qTTW6BJtPQ+lJaW6rbbblNiYqKsVqv69OnD/588oKX34amnnlLfvn0VFhampKQk3XHHHaqurm6lav3T6tWrdckll6hz586yWCzH3V/0WKtWrdLQoUNltVrVq1cvvfLKKy2/sGESS5YsMUJCQoyXXnrJ2LRpk3HTTTcZ0dHRRn5+fpPt165dawQGBhqPPvqosXnzZmPu3LlGcHCwsXHjxlau3L+09D5MnTrVWLhwoZGVlWVs2bLFuO6664yoqChjz549rVy5/2npvThi586dRpcuXYxzzjnHuOyyy1qnWD/W0vtQU1NjDB8+3LjwwguNNWvWGDt37jRWrVplbNiwoZUr9y8tvQ+vvfaaYbVajddee83YuXOn8emnnxqJiYnGHXfc0cqV+5fly5cbf/7zn41ly5YZkox33333hO137NhhtGvXzpg1a5axefNm45lnnjECAwONFStWtOi6pglDI0eONG677TbHzzabzejcubOxYMGCJttfddVVxkUXXeR0LDU11fjf//3f01qnv2vpfXBVX19vREREGK+++urpKtE0TuVe1NfXG6NHjzZeeOEFY/r06YQhD2jpffjHP/5h9OjRw6itrW2tEk2hpffhtttuM8477zynY7NmzTLS0tJOa51m0pwwdNdddxlnnnmm07EpU6YYEydObNG1TDFMVltbq/Xr12vChAmOYwEBAZowYYIyMjKaPCcjI8OpvSRNnDjxuO1xcqdyH1xVVVWprq5OHTp0OF1lmsKp3ou//OUviouL0w033NAaZfq9U7kPH3zwgUaNGqXbbrtN8fHxGjBggObPny+bzdZaZfudU7kPo0eP1vr16x1DaTt27NDy5ct14YUXtkrNaOCp72qfXYHak4qKimSz2RyrVx8RHx+v7OzsJs/Jy8trsn1eXt5pq9Pfncp9cHX33Xerc+fOjf7jR8ucyr1Ys2aNXnzxRW3YsKEVKjSHU7kPO3bs0JdffqlrrrlGy5cv1/bt23Xrrbeqrq5O8+bNa42y/c6p3IepU6eqqKhIY8aMkWEYqq+v18yZM3XPPfe0Rsk47Hjf1eXl5Tp06JDCwsKa9Tmm6BmCf3j44Ye1ZMkSvfvuuwoNDfV2OaZSUVGha6+9VosXL1ZsbKy3yzE1u92uuLg4Pf/88xo2bJimTJmiP//5z1q0aJG3SzOVVatWaf78+XruueeUmZmpZcuW6eOPP9aDDz7o7dJwCkzRMxQbG6vAwEDl5+c7Hc/Pz1dCQkKT5yQkJLSoPU7uVO7DEY8//rgefvhhffHFFxo4cODpLNMUWnovfvnlF+3atUuXXHKJ45jdbpckBQUFaevWrerZs+fpLdoPncrficTERAUHByswMNBxrH///srLy1Ntba1CQkJOa83+6FTuw7333qtrr71WN954oyTprLPOUmVlpW6++Wb9+c9/dto7E6fP8b6rIyMjm90rJJmkZygkJETDhg1Tenq645jdbld6erpGjRrV5DmjRo1yai9Jn3/++XHb4+RO5T5I0qOPPqoHH3xQK1as0PDhw1ujVL/X0nvRr18/bdy4URs2bHC8Lr30Uo0fP14bNmxQUlJSa5bvN07l70RaWpq2b9/uCKOStG3bNiUmJhKETtGp3IeqqqpGgedIQDXY8rPVeOy7umVzu9uuJUuWGFar1XjllVeMzZs3GzfffLMRHR1t5OXlGYZhGNdee60xe/ZsR/u1a9caQUFBxuOPP25s2bLFmDdvHo/We0BL78PDDz9shISEGG+//baxf/9+x6uiosJbv4LfaOm9cMXTZJ7R0vuQk5NjREREGLfffruxdetW46OPPjLi4uKMhx56yFu/gl9o6X2YN2+eERERYbzxxhvGjh07jM8++8zo2bOncdVVV3nrV/ALFRUVRlZWlpGVlWVIMp588kkjKyvL2L17t2EYhjF79mzj2muvdbQ/8mj9nXfeaWzZssVYuHAhj9afzDPPPGMkJycbISEhxsiRI41vvvnG8d64ceOM6dOnO7V/8803jT59+hghISHGmWeeaXz88cetXLF/asl9SElJMSQ1es2bN6/1C/dDLf07cSzCkOe09D58/fXXRmpqqmG1Wo0ePXoYf/3rX436+vpWrtr/tOQ+1NXVGffff7/Rs2dPIzQ01EhKSjJuvfVWo6SkpPUL9yMrV65s8v/5R/7sp0+fbowbN67ROYMHDzZCQkKMHj16GC+//HKLr2sxDPrzAACAeZlizhAAAMDxEIYAAICpEYYAAICpEYYAAICpEYYAAICpEYYAAICpEYYAAICpEYYAAICpEYYAtJpu3brJYrE4vaxWq7p27arLLrtMH330kbdLbLEjv4erc889VxaLRatWrWr9ogC0iCl2rQfgW9LS0tSrVy9JUllZmbKysvTBBx/ogw8+0B133KEnn3zSyxUCMBPCEIBWd+ONN+q6665z/FxfX6877rhDzz77rP72t7/p6quv1ogRI7xXIABTYZgMgNcFBQXpscceU2RkpCTpww8/9HJFAMyEMATAJ4SGhqp3796SpPz8/Ebvp6ena/LkyUpMTFRISIji4uJ0+eWXKyMj47ifWVVVpaeeekpjxoxRTEyMrFarUlJSdMkll+j11193art792498sgjOu+885ScnCyr1aro6GiNGTNG//znP2W32z37CwPwGQyTAfAZ5eXlkqT4+Hin43/605/0xBNPKCAgQMOHD9c555yjnJwcvf/++/rwww+1ePFizZgxw+mc3Nxc/epXv9LmzZvVrl07paWlqWPHjtq7d6+++uorbdy4UVOnTnW0//e//617771X3bt3V58+fZSWlqb9+/crIyNDa9eu1Weffaa33367ycnSANo2whAAn7Blyxbt2LFDknTppZc6ji9evFhPPPGEevXqpXfeeUcDBw50vLd69WpdfPHFmjlzpsaMGePoWbLb7Zo8ebI2b96sCy64QP/5z3/UqVMnx3nV1dX68ssvna4/ceJETZo0SQMGDHA6vm/fPl144YVatmyZ3n77bV155ZUe/90BeBfDZAC8qqysTJ999pkmT54sm82muXPnavjw4ZIaQs39998vSVqyZIlTEJKksWPH6t5771Vtba3++c9/Oo5/+OGH+v7775WYmKh33nnHKQhJDUNyF154odOxESNGNApCktS5c2c9+uijkqS33nrL7d8XgO+hZwhAq5sxY0ajYa3AwED95z//0TXXXOM4lpWVpX379qlnz54aNmxYk5917rnnSpK+/vprx7EVK1ZIkqZOnarw8PBm11VTU6PPPvtM3333nQoKClRTUyPDMFRRUSFJ2rp1a7M/C0DbQRgC0OqOXWeosLBQX331lSoqKnTLLbeod+/eGjlypCQ5hs1++eWXk87VKSwsdPz77t27JUn9+vVrdk3ffPONpkyZopycnOO2OTKnCYB/IQwBaHWu6wyVlZXp8ssv18qVK3XVVVc5Jj0feYIrISFBEydOPOFnxsbGnnI9VVVVmjRpkvLz8zVjxgzdcsst6tWrlyIjIxUYGKht27apb9++MgzjlK8BwHcRhgB4XVRUlJYuXap+/fpp9+7devLJJzV37lwlJSVJkjp27KhXXnml2Z+XnJwsScrOzm5W+9WrVys/P19Dhw7VSy+91Oj9n3/+udnXBtD2MIEagE/o1KmT5s6dK0l6/PHHVVpaqhEjRig2NlabN2/Wpk2bmv1Zv/rVryRJb7zxhiorK0/avri4WNLREOXqP//5T7OvDaDtIQwB8Bm33nqrkpOTVVZWpieeeELBwcGaN2+eDMPQ5ZdfrjVr1jQ6x2az6csvv9Q333zjOHbppZdqyJAh2rdvn6688kodOHDA6Zzq6mp98sknjp/79+8vqWFhx82bNzu1ff7557V06VJP/poAfAxhCIDPsFqtjkfpn376aRUXF+v222/XnXfeqZ9//lnnnHOOBgwYoEmTJunqq6/W+PHjFRsbq/PPP18bNmxwfE5AQIDeffdd9e3bV5988omSk5M1ceJETZ06VePGjVNCQoJuueUWR/shQ4bosssuU0VFhYYMGaKJEyfq6quvVv/+/TVz5kzdc889rfwnAaA1EYYA+JRp06bpjDPOUEVFhR577DFJ0qOPPqq1a9fqmmuu0cGDB7VixQp9/PHH2rdvn84991y98MILmjJlitPnpKSk6Pvvv9cjjzyiM888UxkZGVq2bJl2796tcePG6ZFHHnFq/9Zbb+mxxx5T3759tWbNGn322WdKTk7Wp59+qhtvvLHVfn8Arc9i8HgEAAAwMXqGAACAqRGGAACAqRGGAACAqRGGAACAqRGGAACAqRGGAACAqRGGAACAqRGGAACAqRGGAACAqRGGAACAqRGGAACAqRGGAACAqf1/w3hv5C52L2AAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prc(dt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "iyKlEOSWRc4P",
        "outputId": "f1c3254a-c4c0-4406-af55-b750b6bec6be"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "roc(LR)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "4WEU0lEqUjZU",
        "outputId": "338b5421-4613-4102-f814-544aaae013af"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "roc(gnb)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "z2Qx8rWSUqNM",
        "outputId": "5300043d-9cd4-4a15-88f5-8816e971dfcc"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "roc(knn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "5L2rD20cUseC",
        "outputId": "79a61890-cdcc-43dd-ea96-7d91b2aaf3b9"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "roc(dt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "jELFTeFoUvV1",
        "outputId": "1938b350-171d-41fb-9aaa-41075bccc163"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "id": "1xBSPbmTVbUf"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import roc_curve, auc\n",
        "def logRege_roc_curve(classifier):\n",
        "\n",
        "  cls = classifier\n",
        "  y_score_cls = cls.fit(x_train, y_train).predict_proba(x_test)[:, 1]\n",
        "  fpr_cls, tpr_cls, _ = roc_curve(y_test, y_score_cls)\n",
        "  roc_auc_cls = auc(fpr_cls, tpr_cls)\n",
        "\n",
        "  plt.figure()\n",
        "  plt.xlim([-0.01, 1.00])\n",
        "  plt.ylim([-0.01, 1.01])\n",
        "  plt.plot(fpr_cls, tpr_cls, lw=3, label='LogRegr ROC curve (area = {:0.2f})'.format(roc_auc_cls))\n",
        "  plt.xlabel('False Positive Rate', fontsize=16)\n",
        "  plt.ylabel('True Positive Rate', fontsize=16)\n",
        "  plt.title('ROC curve', fontsize=16)\n",
        "  plt.legend(loc='lower right', fontsize=13)\n",
        "  plt.plot([0, 1], [0, 1], color='navy', lw=3, linestyle='--')\n",
        "  # plt.axes().set_aspect('equal')\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "logRege_roc_curve(LR)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "r5pO4GOwVSB0",
        "outputId": "bcb23501-2de6-4b0f-b2ea-25450f5208ba"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "logRege_roc_curve(gnb)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "ecDMN0mVVnLD",
        "outputId": "f995c13e-107d-4893-ba74-208ea536dc7c"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "logRege_roc_curve(knn)"
      ],
      "metadata": {
        "id": "zufALH6SWFRB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "outputId": "13c77f0d-f268-45a5-8602-e64dae291dd5"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkgAAAHPCAYAAACoQyVSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACc50lEQVR4nOzdd1jTVxcH8G8IhL03iIIDARcqgrhwYN0bxQ3O6uuo2tZVZ2vV1mq1dVdxD9wTJ4pbURQXqCiIirJkb0ju+wclNSRAiIEEOJ/n4XnKvb9fckIoOd51OIwxBkIIIYQQIqSi6AAIIYQQQpQNJUiEEEIIIcVQgkQIIYQQUgwlSIQQQgghxVCCRAghhBBSDCVIhBBCCCHFUIJECCGEEFIMJUiEEEIIIcVQgkQIIYQQUgwlSIQQ2NragsPhiHypq6ujVq1a6NevH86cOSPV44SFhWHatGlo1KgR9PX1oampCVtbWwwfPhznzp2T6jEYYzhy5AiGDRsGOzs7aGtrQ0NDAzY2Nujduze2bt2K9PT0r3m5hBBSJg6VGiGE2NraIjo6Gm3btkX9+vUBAKmpqXj06BGio6MBADNnzsSaNWsk3s8Yw8KFC7Fy5Urw+XxYWVmhVatWUFdXR3h4OJ4+fQoA6NmzJw4cOAA9PT2JjxMZGQkvLy88evQIAODo6IiGDRtCXV0dMTExCA4ORl5eHkxMTPDgwQPUqVNH3j8KQggpxAghNV6dOnUYALZjxw6R9vz8fDZ16lQGgAFgwcHBEu+fMWMGA8A0NDSYn58fEwgEIv137txh9erVYwCYm5sby83NFXuM6OhoZmZmxgAwd3d39vjxY7Fr0tLS2G+//cb09PTYo0ePZH69hBBSFppiI4SUSFVVFatWrRKO+Jw+fVrsmkuXLmHt2rUAgIMHD2LMmDHgcDgi17Ru3RpXr16FoaEh7t27h19++UXscUaOHIn4+Hi4urriypUraNq0qdg1urq6mD17NkJCQmBubi6HV0gIIZJRgkQIKZWGhgYaNGgAAIiLixPrX758OQCgT58+6NevX4mPY2Njg4ULFwIA/vrrL5F1RNeuXcONGzcAAJs3b4aGhkapMdWvXx+Wlpbleh0hISHw8fGBnZ0dNDQ0YGRkhGbNmuHHH38UTiMCwM6dO8HhcODr6yvxcd6+fQsOhwNbW9sS2/l8PtasWYPmzZtDR0cHHA4HKSkp0NTUBJfLRUxMTIlxenl5gcPhYN26dWJ9R44cQffu3WFqagoejwdra2uMHDkSYWFh5fpZEELKRgkSIaRMaWlpACA2apOcnIzr168DAEaPHl3m44waNUr4eEFBQcL2kydPAgCaNGmC5s2byyNkEatWrYKrqyt2794NHo+Hfv36oV27dsjPz8cff/yBq1evyu25GGMYOHAg5s2bB2NjY/Tt2xdNmzaFgYEBBgwYAIFAgD179ki89/Pnzzh9+jR4PB5GjhwpbC8oKIC3tzcGDx6MoKAg2Nvbo3///jA1NcW+ffvg4uKC8+fPy+01EEIAVUUHQAhRbuHh4YiMjAQA9O3bV6Tv0aNHEAgEAABXV9cyH8vExAR2dnaIiorCgwcP0KdPHwDAgwcPAACtWrWSZ+gAgFOnTmH27NnQ0NDArl27MGTIEJH+sLAwsSnBr/Hu3TsIBAI8ffoU9vb2In1jx47FgQMHsGvXLsydO1fs3n379iEvLw+DBg2CsbGxsH3x4sU4dOgQ3NzccODAAdjZ2Qn7jhw5gqFDh2L48OGIjIyEgYGB3F4LITUZjSARQiRKTU3FxYsXMXDgQPD5fCxYsAAuLi4i1yQkJAj/W9o1QUXXfXlv0X+bmZl9bdhiFi9eDAD49ddfxZIjAHBycoKjo6Ncn3P58uViyREAdOnSBXXq1MGLFy9w584dsf4dO3YAAMaMGSNsS0pKwp9//gkNDQ0cPXpUJDkCCqfkvv32WyQnJ2Pv3r1yfR2E1GSUIBFChIoWWHM4HBgYGKBbt26IiIjA3r17JS6slgWrxJNFYmNjERoaChUVFYwbN67SnnfQoEES2zkcDnx8fAAUrnX6UmhoKEJDQ2FpaYnu3bsL269evYrs7Gy0bdsW1tbWEh+3Y8eOAIDbt29/ffCEEACUIBFCvtC2bVv4+PjAx8cHPXv2hK6uLvh8PiZPnozg4GCx601MTIT/LWkBtyTx8fEAAFNTU2Fb0X8X9cnLu3fvAACWlpbQ19eX62OXxMzMDFpaWiX2FyWh/v7+yM7OFrYXjR6NHj0aXC5X2F40vRkYGCh2mGfRV9HI2JejcoSQr0NrkAghQuPHjxfZvZWamooBAwbg6tWrGDJkCMLCwkQ+/Js3bw4OhwPGGO7du4fatWuX+vgJCQmIiooCALRs2VLY3rJlS9y4cQP379+X7wuqAEVrrkqiqalZar+trS06deqEK1eu4Pjx4xg+fDjy8/Oxf/9+AKLTa18+X/369dG2bdtSH9vBwaGs8AkhUqIEiRBSIn19ffj7+8PBwQHR0dFYs2YNFixYIOw3MjJC+/btcf36dezevRuDBw8u9fGKdm/p6uoKp4UAoF+/fli7di2ePn2KR48eyW0nW1HC9unTJ6Smpko1isTj8QCgxHImXx4JIKsxY8bgypUr2LFjB4YPH47Tp08jMTERbdq0QcOGDUWutbGxAQA0bNhQbFqOEFJxaIqNEFIqU1NTYVL0xx9/ICUlRaR//vz5AIAzZ84It+tL8v79eyxbtgwAMHXqVJFyIx07dhSOjkyePBm5ubmlxvTmzRt8+vSpzNgtLCzQrFkzCAQC+Pn5lXk9AOE6nxcvXkjsP3v2rFSPU5pBgwZBX18fV65cwfv37yUuzi7SpUsX8Hg8BAUFyX0KkhBSCgWf5E0IUQIllRopkpOTw2rXrs0AsAULFoj1T5s2jQFgmpqabMeOHWKlRu7evSssNeLi4sJycnLEHiMqKoqZmJgwAKxNmzbsyZMnYtdkZGSw1atXM319falLjRw7dkwY25EjR8T6nz9/zsLCwoTfZ2VlMT09PQaA7d69W+TaQ4cOMTU1NQaA1alTRyx+Se0l+fbbbxkANmXKFKaqqsq0tLRYWlqaxGu///57BoC5urpK/Lnk5OSwkydPsvDwcKmemxBSNkqQCCFlJkiMMebn58cAMF1dXfb582eRPoFAwObMmcNUVFQYAGZtbc369+/PvL29WdOmTYW13Lp168ZSUlJKfI6IiAiR652cnNjAgQPZ0KFDWfv27Zm6ujoDwMzNzVl0dLTUr+/XX39lHA6HAWAODg7M29ub9e3blzk5OUl83X/++acwBnd3d+bl5cUaNWrEOBwOW7hwoVwSpLt37wqfAwAbPXp0idfm5+ez4cOHMwBMRUWFNW/enA0aNIh5e3uztm3bMm1tbQaAnTt3TuqfCSGkdJQgEUKkSpAKCgqECcXcuXMlXvPs2TM2ZcoU5uDgwHR0dJi6ujqzsbFh3t7e7MyZM1LFwufzmb+/PxsyZAirU6cO09TUZOrq6qxWrVqsd+/e7J9//mGZmZnlfo137txhw4YNY9bW1kxNTY0ZGRmxZs2asdmzZ0tMtnbt2sVatGjBNDQ0mJ6eHuvcuTO7dOlSiYlQeRMkxhhr1KiRMEG6evVqmdcHBASwgQMHCl+DgYEBc3R0ZEOHDmX79++X6edCCJGMw1glHkpCCCGEEFIF0CJtQgghhJBiKEEihBBCCCmGEiRCCCGEkGIoQSKEEEIIKYYSJEIIIYSQYihBIoQQQggpRilrsV2/fh2rVq1CSEgIPn36hOPHj6N///6l3hMUFIRZs2bh+fPnsLGxwYIFC0SKbpZFIBDg48eP0NXVBYfD+boXQAghhJBKwRhDeno6rKysoKIiv3EfpUyQMjMz0axZM4wdOxYDBw4s8/qoqCj06tULkyZNwr59+xAYGIjx48fD0tIS3bp1k+o5P378KCwKSQghhJCq5f3796hVq5bcHk/pD4rkcDhljiDNmTMHZ8+exbNnz4RtQ4cORUpKCs6fPy/V86SmpsLAwADv378XKaJJCCGEEOXEGMODiBh4tmqElJQU6Ovry+2xlXIEqbzu3LkDT09PkbZu3bphxowZUj9G0bSanp4eJUiEEEKIksot4OPOm8+48PQTdv11HzlafACQ+/KYapEgxcbGwtzcXKTN3NwcaWlpyM7Ohqamptg9ubm5yM3NFX6flpZW4XESQgghpPySMvNw9UU8LofH4fqrBKQmZCHhZCTyPmUCankV8pzVIkGSxYoVK7B06VJFh0EIIYQQCaISM3EpLBaXw+LxIDoJgn8XBGW+TMbnc2/BcgtHjpAvqJDnrxYJkoWFBeLi4kTa4uLioKenJ3H0CADmzZuHWbNmCb9PS0ujRdqEEEKIgvAFDI/eJeNSeBwuh8XhTUKmSD8rECDpyntkPEqolHiqRYLk7u6OgIAAkbZLly7B3d29xHvU1dWhrq5e0aERQgghpASZuQW4EZGIy+FxuPIiHkmZkqfL8j/nIOHUG+THZ4v11aqtjw/v5B+bUiZIGRkZeP36tfD7qKgohIaGwsjICLVr18a8efMQExOD3bt3AwAmTZqE9evXY/bs2Rg7diyuXLmCQ4cO4ezZs4p6CYQQQgiRIC4tB4Hh8bgUFotbbz4jr6D0KbKMp4lIuvQOTMJU2vDhTfDbb+1hYzNP7nEqZYL04MEDdOrUSfh90VSYj48Pdu7ciU+fPuHdu//SRTs7O5w9exYzZ87EunXrUKtWLWzbtk3qM5AIIYQQUjEYY3gRm47LYXG4HB6Hxx9SpbpPkMdH1tUP+BwqPqWmqamK9et7YswYZ6Snp8s7ZABV4BykypKWlgZ9fX2kpqbSNn9CCCHkK+QVCBAclYTL4XG4FBaHmBTxqbGSOFjowpHHw/HV9xAdmSLW37ixGfz9veDkZAqg4j6/lXIEiRBCCCFVS2p2PoJexuNyeDyCXsYjPadAqvtUVThwq2sET0dzeDqaQ4sBdeqsRWZmvti1Eye2wNq13aGpqSbv8MXjqvBnIIQQQki19D4pC5f+nToLjkpCgUC6SSldDVV0amgGTydzeNibQr9YwrNgQQfMmxco/F5PTx3//NMHQ4Y0kmv8paEEiRBCCCFSEQgYnsSkCtcTvYiVfv2PtYEmujqZo6uTOVrZGoGnWnJh2dmz2+Lq1be4ePENWrWywsGDXqhb11AeL0FqlCARQgghpEQ5+Xzcel24Ff9yeDwS0nPLvulfzWrpo6uTOTydzNHQXFfqciAqKhzs3t0fGzfex08/dQCPx5U1fJnRIu1/0SJtQgghpFBiRi6uhMfjUngcbkQkIEfK06p5qipoV98Eno7m6OJoBnM9jRKvjYvLwPXr0Rg8+OumzWiRNiGEEEIqBGMMbxIycCmssN7Zw3fJkHb4xFibh84OheuJ2jcwgRav7NQiMDASI0ceR0JCJmrV0oO7u/JVsqAEiRBCCKmBCvgCPIhOFq4nevs5S+p765lqw9PJHN84mcPZxhBcFemmzgoKBFiyJAjLl98QJmBDhx5FaOi3MDSUXBpMUShBIoQQQmqIjNwCXH+VgEthhaU9UrPFt9JLosIBXGyN0PXfqbO6pjrlfu7371MxbNhR3Lr1XqT93btUzJlzGVu39in3Y1YkSpAIIYSQauxjSjYCw+NwKTwed998Rh5fuvVE2jwuPBqawtPRHJ0amsFQmydzDKdOvYSv7wkkJ+eI9fXs2QC//tpZ5seuKJQgEUIIIdUIYwzPP6YJzyd6/jFN6nst9DTg6WQGT0dzuNczhrrq1+0ey80twOzZl/DXX8FifaqqKli5sgtmznSHipRTdJWJEiRCCCGkisst4ONuZJJwPdGnVPGRmpI0stKDp2Ph+USNrPSk3opfloiIzxg69CgePvwk1mdnZ4CDB73g6motl+eqCJQgEUIIIVVQcmYerr4s3HV27WUCMvP4Ut2nxuXAvZ4JujqaobOjOawN5L84ev/+p/j22zPIyMgT6xs82An//NMH+volHwGgDChBIoQQQqqIqMRMXA6Lw6XwODx4mwQpK3tAX1OtcCu+ozk62JtAV6NiapllZuZh+vRz8PMLFevT0FDFunXdMWFCC7mNUlUkSpAIIYQQJcUXMIS+TxaeT/Q6PkPqe+sYa6GrY+Ep1i51DKHKLbm0hzwwxtCt216xXWoA4OhoAn9/LzRpYl6hMcgTJUiEEEKIEsnKK8CNiERc/ncr/udM8WkqSTgcoLmNAbo6WaCrkxnqmepU6kgNh8PBrFnuYgnS2LHO+OuvHtD+il1wikAJEiGEEKJg8Wk5CHwRj8thcbj5OhG5BdJtxddQU0H7Bqbo6miOTg5mMNVVr+BISzdwoCP+9z8XbNz4ADo6PGzZ0hvDhzdRaEyyogSJEEIIqWSMMbyMS/93PVE8Hr9PkfpeEx11dP13K37b+ibQUKv8Qq6lWb26GzIy8rFgQXs0aGCs6HBkRgkSIYQQUgny+QLcj0rCxX+34n9Izpb63obmusLziZrVMlDouUECAcPFi2/QvXt9if0aGqrYtat/5QZVAShBIoQQQipIanY+rr1KwOWwOFx9GY/0nAKp7uOqcOBmZwRPR3N4OpqjtrFWBUcqnYSETPj6nkRAQAT27h2AESOaKjqkCkMJEiGEECJH75OycDm8cJToXmQSCqTci6+rrgqPhqbo6mSOjvZm0NeqmK34srp27S2GDz+Gjx/TAQCTJp2Fq6t1lZ5GKw0lSIQQQshXEAgYnsak4nJ4HC6FxeFFbLrU91obaKKrU+EokaudEXiqFbsVXxZ8vgDLll3Hzz9fh+CLZC8jIw/Dhx/DvXvjlbJUyNeiBIkQQggpp5x8Pm6/ScSlsHgEhschPj1X6nub1dIvnDpzMoeDha5SH5r48WM6Row4hqCgt2J95ubaWL68c7VMjgBKkAghhBCpJGbk4sq/W/FvRCQiO1+60h48VRW0rWcMTydzdHEwh4WSl9gocu5cBEaPPoHExCyxvq5d62L37gGwsNBRQGSVgxIkQgghRALGGN4kZBauJwqLQ8i7ZDApS3sYafOEpT3aNzCBtnrV+bjNy+Nj/vxArF59R6yPy+Xgl186Yc6cdtV25KhI1XnHCCGEkApWwBcgJDr530XW8YhKzJT63rqm2ujqZI6ujuZoXtsQ3CqYQERGJmPYsKMIDo4R67Ox0cOBA4PQtm1tBURW+ShBIoQQUqNl5BbgxqsEXAqLw5WX8UjJypfqPhUO4FLHCJ5OZujiaI56plV7uunw4ecYP/400tLE11P17++A7dv7wshIUwGRKQYlSIQQQmqcT6nZuBxeuJ7ozpvPyONLV9pDi8eFh70pPP8t7WFUxeqLleSXX65h0aIgsXYej4vVq7/BlCmtlHoxeUWgBIkQQki1xxhD2Kc0XPr3FOtnMWlS32uupw5PR3N0dTJH67rGSlfaQx569myAX365jvz8/xLFBg2M4O/vhebNLRUYmeJQgkQIIaRayi3g415kknCR9cfUHKnvdbLUg+e/64kaW+tV+9GTli2tsGpVV8yYcQEAMGpUU2zY0BO6Ci5+q0iUIBFCCKk2UrLycPVlPC6HxePaqwRk5EpX2kONy0Hrusbo6mSOLo7msDaoOWttikyf7oZ792LQrVs9+Pg4KzochaMEiRBCSJUW/TkTl8IKT7F+EJ0MvpSlPfQ0VAu34juZo4O9KfQ0lKu0R0UIC0tAw4bG4HLFT+zmcDjYv3+QAqJSTpQgEUIIqVL4AobQ9ynCqbOI+Ayp761tpCUs7eFiawg1CYlCdcQYw99/B+PHHy9h/vx2WLy4o6JDUnqUIBFCCFF62Xl83IhIwOXwOFx5EY/EjDyp7uNwAGcbA+Ei6wZmOtV+PVFxSUnZGDv2JE6efAkA+Pnn6/DwsEXHjraKDUzJUYJECCFEKcWn5+BKeDwuhxeW9sgtkG4rvoaaCtrVN0VXJzN0cjCDmW7VKO1REW7efIfhw4/i/fv/du0JBAzDhx/FkyeTYWKipcDolBslSIQQQpQCYwyv4jJwObxwPVHo+xSp7zXRUYenY2Fpj7b1TaDJq35b8cuDzxdg5cqbWLw4CHy++JqskSObQl+/5u5QkwYlSIQQQhQmny/A/agkXAovPJ/ofVK21Pfam+vA09Ecnk7mcK5lUO1rg0krNjYDI0ceQ2BglFifqakWdu8egO7d6ysgsqqFEiRCCCGVKi0nH9deFq4nuvoiHmk50m3F56pw4GprBE8nc3g6mqGOsXYFR1r1XLz4BqNGHUd8vHgNuU6dbLF370BYWekqILKqhxIkQgghFe59UhYC/y0AezfyMwqk3Iqvo64Kj4am+MbJHB3tzaCvVf234ssiP5+PRYuuYuXKW2J9KiocLFnigfnz20vc3k8kowSJEEKI3AkEDM8+puJyWBwuhsXhRWy61PdaG2gWridyMoebnTF4qvShXpro6BQMG3YUd+58EOuzttbFgQOD0L59HQVEVrVRgkQIIUQucvL5uPPmMy6FxyEwPA5xEqrCl6RpLf3C9USO5nC01K1xW/Flde3aW/Tv74+UFPEyKr1722Pnzn4wNqadarKgBIkQQojMPmfk4urLBFwKi8WNiERk5fGluo/HVUGb+sbCpMhCv+Zuxf8a9esbQbXYCJuamgp+/70rvvvOjRLNr0AJEiGEkHJ5k5CBy2GFu85CopMh5XIiGGqpobODObo6maF9A1Noq9NH0NeyttbDrl390avXfgBAvXqGOHjQCy4uVgqOrOqj305CCCGl4gsYQqKThaU9IhPFd0iVpK6JdmFpDydztKhtCC5txZe7nj0b4Pvv3RETk44tW3pDT4/ON5IHSpAIIYSIycwtwI2IBFwKi8eVF3FIzsqX6j4VDtCyjqHwfKJ6pjoVHGnNkJGRB8YYdHUlJz+//eYJFRUOTanJESVIhBBCAACxqTmFo0Thcbj9+jPy+NKV9tDicdGhgSk8nczRqaEpjHVoBEOeHj+Ohbf3EbRoYYl9+wZKTIJo+778UYJECCE1FGMMYZ/ScDmssN7Z05hUqe8101WHp5M5ujqaw72eMTTUanZpj4rAGMOmTQ8wa9YF5Oby8fLlZ3h61sXYsc0VHVqNQAkSIYTUIHkFAtyL+vzvIut4xKRIX9rD0VIPXf89n6ixlT6V9qhAKSk5GD/+FI4eDRdpnzo1AK1b14KTk6mCIqs5KEEihJBqLiUrD0EvE3ApPA7XXiYgI1e60h6qKhy0rmuMrk7m6OJohlqGdJ5OZbh79wOGDj2C6GjxET0nJ1Ooq9NoXWWgBIkQQqqh6M+ZuPTvVvz7b5PBl3Ivvp6GKjo5mMHT0RweDU2hp0GlPSqLQMCwevVtzJ9/BQUF4uu/Zsxww8qVnlCn4xEqBf2UCSGkGhAIGEI/pAjPJ3oVlyH1vTZGmujqaAFPJzO0sjWCGi34rXTx8Znw8TmB8+dfi/UZGWli585+6NOnoQIiq7koQSKEkCoqO4+Pm68TcTksDoEv4pGYIX1pD2cbA3R1MkdXJ3M0MNOh7eEKdOVKFEaOPIZPn8ST2nbtamP//oGwsdFXQGQ1GyVIhBBShSSk5+LKizhcCovHzdcJyMmXbiu+uqoK2jcwgaejOTo7msFMl0p7KFpBgQA//3wNy5ZdBys2A8rhAD/91B6LF3cUKyVCKgclSIQQosQYY4iIzxCuJwp9nyL2YVoSEx0eujgUHtjYrr4JNHm0uFdZxMdnwsvrEG7ceCfWZ2Ghg337BqJzZzsFREaKUIJECCFKpoAvwP23ycJDG6M/Z0l9bwMzHXg6FRaAdbYxoNIeSkpbWw2JieLva7du9bB79wCYmWkrICryJUqQCCFECaTn5OPaqwRcDovD1ZcJSM2WrrQHV4WDVrb/lvZwNIetCX2wVgXa2jwcOjQYrVr9g5ycAqiqquDXXzvjhx/a0PlSSoISJEIIUZAPyVkIDC88xfpu5Gfk86WbO9NRV4WHvSm6OpmjY0NTGGjxKjhSUhEaNzbDunXdsXz5DRw86IXWrWspOiTyBQ5j0s5mV29paWnQ19dHamoq9PT0FB0OIaQaYozhWUwaLoXH4XJYHMI+pUl9r5W+hnDqzK2uEdRVaT1RVZGdnQ9NTcnnSTHGkJmZDx0dSnJlVVGf3zSCRAghFSgnn487kYWlPQLD4xGbliP1vU2s9QunzpzM4GSpR1vxq5isrHx89905PHuWgOvXfaEmoV4dh8Oh5EhJUYJECCFylpSZhysv4nE5LA7XIxKQlceX6j4eVwXu9Yz/HSkyg6W+ZgVHSirK8+fx8PY+gufPEwAACxdexcqVngqOipQHJUiEECIHkQkZhbvOwuLxIDoJUlb2gIGWGjo7mKGrozna25tCh8pIVGmMMWzb9hDffXce2dn/1bz77bdb6NjRFt2711dgdKQ86P9EQgiRAV/A8PBdMi6HxeFSeBwiEzKlvtfWWOvfU6wt0KK2AVSptEe1kJaWi4kTT8Pf/7lYn7a2GtLSpD/pnCgeJUiEECKlzNwC3IhIxKWwOFx9GY+kzDyp7uNwgJa1DYWLrOuZatN6omrmwYOP8PY+gsjIZLG+Zs3M4e/vhYYNTRQQGZGV0iZIGzZswKpVqxAbG4tmzZrh77//hqura4nXr127Fps2bcK7d+9gYmICLy8vrFixAhoadJw+IUR2cWk5/06dxeHWm8/Ik1BlXRJNNS462BeW9ujkYAYTHfUKjpQoAmMMa9fexZw5l5EvoezL1KmtsGrVN9DQUNqPW1ICpXzH/P39MWvWLGzevBlubm5Yu3YtunXrhpcvX8LMzEzs+v3792Pu3Lnw8/NDmzZt8OrVK/j6+oLD4WDNmjUKeAWEkKqKMYbwT+nCU6yffEiV+l4zXXV0cTRHVycztKlnAg0Ju5ZI9ZGYmIUxY07izJlXYn0GBhrw8+uLAQMcFRAZkQelPAfJzc0NrVq1wvr16wEAAoEANjY2mDZtGubOnSt2/dSpUxEeHo7AwEBh2/fff4979+7h5s2bUj0nnYNESM2VVyBAcFQSLofH4VJYHGJSsqW+18FCF13/nTprYq1PpyDXEDduRGPYsKOIiUkX63N3r4UDBwahTh2Dyg+sBqox5yDl5eUhJCQE8+bNE7apqKjA09MTd+7ckXhPmzZtsHfvXgQHB8PV1RWRkZEICAjAqFGjSnye3Nxc5Ob+t2AuLU36A9sIIVVfalY+gl7F41JYHK69TEB6bkHZNwFQVeHAra4Rujqao4ujOWyMtCo4UqJM+HwBli+/gSVLrkEgYavi3Llt8fPPnSSeeUSqFqVLkBITE8Hn82Fubi7Sbm5ujhcvXki8Z/jw4UhMTES7du3AGENBQQEmTZqE+fPnl/g8K1aswNKlS+UaOyFEub37nCWcOguOSkKBlHvxdTVU0amhGTydzOFhbwr9Ek5FJtUfh8PBrVvvxZIjU1Mt7NkzAN260Tb+6kLpEiRZBAUFYfny5di4cSPc3Nzw+vVrfPfdd/jll1+wcOFCiffMmzcPs2bNEn6flpYGGxubygqZEFIJBAKGxx9ShOcTvYwTnw4pSS1DzcKt+I7maGVnBDXaik8AqKhwsHv3ADRrthmxsRkAgC5d7LBnzwBYWuoqODoiT0qXIJmYmIDL5SIuLk6kPS4uDhYWFhLvWbhwIUaNGoXx48cDAJo0aYLMzExMnDgRP/30E1RUxP+wqaurQ12ddpUQUt3k5PNxMyLx35GieCRmSH/2TDMbA3R1NENXJwvYm+vQVnwikZmZNvbuHYAePfZh8WIPzJ3bDlxKoKsdpUuQeDweWrZsicDAQPTv3x9A4SLtwMBATJ06VeI9WVlZYkkQl1s4/6uEa9AJIXKWkJ6Lqy/icSk8DjciEpAjYbu1JOqqKmhX3wSeTubo4mAGMz06FoT8RyBgJS6679KlLt68mQ4bG/1KjopUFqVLkABg1qxZ8PHxgYuLC1xdXbF27VpkZmZizJgxAIDRo0fD2toaK1asAAD06dMHa9asQfPmzYVTbAsXLkSfPn2EiRIhpPpgjOF1fAYu/Xs+0aP3KZD230LG2jx0cTSDp6M52jUwgRZPKf8MEgU7ejQMv/xyHVev+sDQUHJNPEqOqjel/Mvg7e2NhIQELFq0CLGxsXB2dsb58+eFC7ffvXsnMmK0YMECcDgcLFiwADExMTA1NUWfPn3w66+/KuolEEIqyPVXCVgeEI4XsdKvJ6pvpgPPf88ncrYxBJe24pMS5OQUYNasC9i06QEAYNy4Uzh6dAhNt9ZASnkOkiLQOUiEKLf3SVn45UwYLobFlXmtCgdoZWuErk6FW/HtTLQrIUJS1b14kQhv7yN48kT0d2z9+h6YMqXkSg5EsWrMOUiEEPKlnHw+NgW9weZrb5BbSpkPbR4XHg1NC0t7NDSDoTavEqMkVd2uXaH43/8CkJWVL9YXEvJJARERRaMEiRCilBhjuPA8Dr+cCSvxZGtLfQ14OprD08kcresaQV2V1hyS8snIyMP//ncWe/Y8EevT1FTF+vU9MWaMc+UHRhSOEiRCiNJ5HZ+Bpaef40ZEosR+Y20eZndviMEtbai0B5FZaGgsvL2P4NWrz2J9jRubwd/fC05OpgqIjCgDSpAIIUojPScff195Db+bURJPueaqcDCqdR3M7GpPp1kTmTHGsHHjfXz//UXk5vLF+idObIG1a7tDk37HarSvTpBSU1Nx//59JCQkoE6dOmjTpo084iKE1CCMMRx/FIMV514gIV3ywY5udkZY2q8RHCxoEwWRXXJyNsaNO4Xjx8VLV+npqeOff/pgyJBGCoiMKBuZE6T09HTMnDkTe/bsQUFBYZFHHx8fYYK0bds2LFq0CMePH4ebm5t8oiWEVDvPYlKx+NRzhEQnS+y30NPAT70c0bupJW21Jl/lzp33GDr0KN69SxXra9XKCgcPeqFuXUMFREaUkUxno2dnZ6Njx47w8/ODoaEhevToIXZide/evREXF4cTJ07II05CSDWTnJmHn44/RZ/1NyUmRzyuCv7XsR4Cv/dAn2ZWlByRr3bgwDOJydH337vj5s2xlBwRETKNIK1ZswaPHj3CsGHDsHXrVmhra4uV+rCwsICjoyOuXr0ql0AJIdUDX8BwIPgd/rj4EikStlQDQGcHMyzs7UTnFxG5+v33rrhx4x1CQ2MBAMbGmti1qz969bJXcGREGcmUIPn7+8PCwgLbt2+HhkbJtYvs7e0RHBwsc3CEkOrlwdskLD71HM8/pknsr2OshUW9ndDF0bySIyM1gYaGKvz9vdCixRa4uFhh376BsLamNW1EMpkSpDdv3qBr166lJkcAoKWlhcREydt0CSE1R3xaDlace4Hjj2Ik9muqcTG1c32Ma2cHDTU6y4hUHHt7Y9y8ORZNmpiBy5VplQmpIWRKkLhcLvLzJQ+Nf+nDhw/Q1qYhckJqqrwCAXbejsK6yxHIzBPfTg0AvZpa4qeejrAykFwQlJDyePcuFd9+ewZ//dUdDRoYS7zG2dmikqMiVZFMCVK9evXw+PFjFBQUQFVV8kNkZGTgyZMncHJy+qoACSFV0/VXCVhy+jkiEzIl9tub62BJ30ZoU8+kkiMj1dXJky8wZsxJJCfnYOjQo7h9eyzU1em4PyIbmcYX+/bti0+fPmHZsmUlXrNs2TKkpqZiwIABMgdHCKl63idl4ds9DzDaL1hicqSroYpFvZ1wdnp7So6IXOTmFmD69HPo398fyck5AICHDz9h9uxLCo6MVGUcVnx/vhRSUlLQtGlTxMTEoE+fPhgyZAhGjhyJHj16YNy4cTh06BAOHz4MW1tbPHnypEpMs1VUNWBCaoqcfD42X3uDTUElF5Ud4lILs7s7wERHvZKjI9VVRMRneHsfwaNHsWJ9dnYGCA2dBD09+n2rzirq81umBAkAwsLC0LdvX0RGRoqdT8IYQ506dXD27NkqM8VGCRIhsikqKrvsbBg+JEsuKtuslj6W9G2E5rXpnBkiP/v3P8W3355BRkaeWN/gwU74558+0NcvfTMRqfoq6vNb5slZJycnPHv2DDt37kRAQAAiIyMhEAhgY2ODHj16YOLEidDS0pJboIQQ5VNWUVkjbR7mUFFZImeZmXmYPv0c/PxCxfo0NFSxbl13TJjQgg4XJV9F5hGk6oZGkAiRXllFZVU4wGh3W8z0tIe+FhX8JPLz9GkcvL2PIDxcPCl3cDDBoUNeaNKEztGqSZRqBGn37t2oX79+mYVp7969i1evXmH06NEyBUcIUS6MMZwIjcHygJKLyrraGWFp30ZwtKR/aBD5YYxh69YQzJhxATk5BWL9Y8Y44++/e0Bbm6eA6Eh1JNMIkoqKCnx9feHn51fqdRMmTICfnx/4fMnnnygTGkEipHTPYlKx5NRzPCilqOz8Xo7oQ0VliZylpuZgwoTTOHw4TKxPR4eHzZt7YcSIpgqIjCgDpRpBkhbN3hFS9SVn5uGPiy9xIPgdJMymgcdVwfj2dpjSqT606cwZUgEOHw6TmBw1b24Bf3+vEg+EJORrVOhfs/j4eFqoTUgVJU1R2U4NTbGoTyMqKksq1LhxzXHmzCucPPlS2DZ9uit+/70rHQRJKozUv1nXr18X+T42NlasrUhBQQGeP3+OixcvokmTJl8XISGk0lFRWaJMOBwO/Pz6wdl5MzIy8rBjRz/06+eg6LBINSf1GiQVFRXhugLGmFRrDBhj2LZtG8aOHft1UVYCWoNESGFR2ZXnXuBYCUVlNdRUMLVTfYxvX5eKypJKFxLyEaam2qhdW1/RoRAlovA1SB06dBAmRdeuXYOZmRkcHCRn8DweD7Vq1cKgQYPQs2dP+URKCKkwRUVl/wp8jYxc8R1CABWVJRWLzxdg2bLr8PCwRceOthKvadnSqnKDIjWa1AlSUFCQ8L9VVFTQo0ePMnexEUKU342IBCw59RxvqKgsUZCYmDSMGHEM165Fw9IyBI8fT4KpKa1rI4ol0+q2q1evwsLCQt6xEEIq0fukLCw7G4YLz+Mk9uuqq2JmV3uMcq8DNa5Mda0JKdO5cxEYPfoEEhOzAACfPmXAx+cEzpwZTqevE4WSKUHy8PCQdxyEkEoiTVHZwS0Li8qa6lKRT1Ix8vL4mD8/EKtX3xHru3jxDe7fj4GbWy0FREZIIbnsj0xNTUVaWlqJ5x7Vrl1bHk9DCPkK0hSVbVpLH0upqCypYJGRyRg27CiCg8U3A9SurY8DBwZRckQUTuYEKTk5GYsWLcLhw4eRkJBQ4nUcDgcFBZIXfRJCKoc0RWVnd2uIIS5UVJZUrMOHn2P8+NNISxMvVdO/vwO2b+8LIyPaCEAUT6YEKTU1Fa1bt8br16/B5XKhqamJrKwsWFpaIjY2VngMAI0cEaJYGbkF+CswgorKEoXLzs7HzJkXsGVLiFgfj8fF6tXfYMqUVlSmhigNmVZerlq1ChERERg9ejRSU1Ph5eUFDoeDmJgYpKenY9OmTTAwMICHhweioqLkHTMhpAyMMRx/9AGd/wjC1uuREpMjVzsjnJ3eHkv6NqLkiFSo8PAEuLltk5gcNWhghLt3x2HqVFdKjohSkWkE6dSpUzAxMcGmTZugoaEh8kutpaWFb7/9Fs2aNUO7du3Qpk0bTJw4UW4BE0JKR0VlibJgjGHHjlBMm3YOWRLK1Ywa1RQbNvSELm0GIEpIpgQpMjIS7du3h4aGBgAI/8jy+XxwuYWn67Zu3Rru7u7Yvn07JUiEVILkzDysvvQS++9JLiqrxuVgfPu6mEpFZUklefw4DuPGnRJr19JSw8aNPeHj41z5QREiJZkPNzE0/G+XS1FB2uRk0X+x1q5dGy9evJD1KQghUuALGPbejUan1UHYe1dyctSxoSkuzvTAnO4OlByRSuPsbIEffnAXaWva1BwhIRMpOSJKT6a/lFZWVoiJ+W97ZtFi7CdPnqBz587C9sjISKiq0h9jQipKSHQSFp0suahsbaOiorJmNJ1GFOLXX7vg+vV3CA6OweTJLli9+htoatKaN6L8ZMpemjRpglu3bgm/b9++PRhjWLx4MVq1agVdXV3s3bsX9+7dQ9u2beUWLCGkEBWVJVUFj8fFwYOD8PDhJwwa5KTocAiRmkwJUvfu3XH8+HFcvXoVnTp1gru7O9q2bYtbt27ByMgIenp6SElJAYfDwezZs+UdMyE1llRFZZtYYn4vR1hTUVlSSW7efIcPH9IwdGhjif12doaws6PDR0nVIlOCNGzYMDg5OcHW1lbYduzYMYwbNw7nzp1DcnIyDA0NsWjRIvTp00desRJSo0lVVLZPI7SpT0VlSeXg8wVYufImFi8OAo/HRZMmZmjUyEzRYREiFxxWUn0QGWVlZSE1NRXm5uZQUak6BS7T0tKgr6+P1NRU6OnpKTocQoTeJ2Xh17PhOP88VmK/rroqZnS1x2gqKksq0adP6Rg16jgCA/87665RI1MEB0+AFp2rRSpRRX1+y30FtZaWlnBXGwC8fPkSDRs2lPfTEFLtUVFZoqwuXHiN0aNPID5edDTz+fMEbNp0H99/30ZBkREiPxW2xez169dYsmQJDh06hLy8vIp6GkKqHcYYLobF4ZczpReVXdK3EVpQUVlSifLz+Vi48Cp+++2WWJ+KCgdLlnhgxozWCoiMEPkrd4L09u1bxMXFwdzcXGQN0pf9S5cuxb59+1BQUEBbiwkpByoqS5RVdHQKhg07ijt3Poj1WVvrYv/+QejQoY4CIiOkYkidIN27dw/jx49HWFiYsK1p06bYvn07WrRogYKCAixYsADr1q1DXl4eGGPo0KEDVq5cWSGBE1KdUFFZosyOHQvHuHGnkJKSI9bXu7c9duzoBxMTLQl3ElJ1SbVIOzo6Gk2aNEFmZiaKX25sbIzHjx9j2LBhuHnzJhhjcHZ2xvLly9G9e/cKC1zeaJE2UQTGGE6ExmBFwAvEp+dKvMbVzghL+zaCoyX9XpLKlZNTgB9+uIgNG+6L9ampqeC33zwxY0ZrmikgCqXQRdpr1qxBRkYGGjRogCVLlqBp06ZIS0vDmTNnsHr1anTs2BGvX7+Grq4u/vzzT4wdO1ZuARJSXT3/mIrFJ0suKmuup475PR3Rt5kVfQCRSvfyZSK8vY/g8eM4sb66dQ3h7+8FFxcrBURGSOWQKkEKDAyEtrY2rly5Amtra2G7u7s7DA0NMXv2bHA4HJw/fx7u7u6lPBIhJCUrD39cpKKyRHllZuahXbsdSEzMEuvz9m6ELVt6Q19fQwGREVJ5pDo0JTo6Gq1btxZJjooMHToUAODm5kbJESGlKCoq2/GP0ovKXpjRgYrKEoXS1ubh1187i7Rpaqrin3/64MCBQZQckRpBqr/AmZmZqFWrlsS+ovZ69erJLypCqhkqKkuqmgkTWiAwMAqHDj2Hk5MpDh3yolOySY0i9T9Ry/qjraZGO2sIKY6KypKqisPhYOvW3qhXzxALFnSg07FJjUNj+IRUgHy+ADtvvcW6wAgqKkuUVkpKDgICIjB8eBOJ/fr6Gli+vEslR0WIcpBqm7+KiorMw/4cDgcFBZI/IJQJbfMn8lJWUdkGZjpY2peKyhLFunfvA4YOPYq3b1Nw/vwIdOtWX9EhESIThddik3NNW0KqHSoqS6oCgYBh9erbmD//Cgr+rfE3atRxPH48CZaWugqOjhDlIVWCdPXq1YqOg5Aqi4rKkqoiPj4TPj4ncP78a5H2hIQsTJ9+HocPD1ZQZIQoH6kSJA8Pj4qOg5Aqh4rKkqrkypUojBx5DJ8+ZYj1tW9fG3/+2U0BURGivGiRNiEyeJOQgSWnqKgsUX4FBQL8/PM1LFt2HcVXSnA4wIIFHbBokQdUVWnal5AvUYJESDlk5Bbg78AI+N2KQj5fclHZUa3rYFbXhlRUlijchw9pGD78KG7ceCfWZ2Ghg337BqJzZzsFREaI8qMEiRApMMZwMvQjlgeEl1xU1tYIS/o2gpMV7YIkinfmzCv4+p7A58/i07/dutXD7t0DYGamrYDICKkaKEEipAzPP6ZiyannuP+WisoS5ZebW4C5cy9j7dp7Yn2qqipYvrwzvv++DU39ElIGSpAIKQEVlSVV0eDBh3H69CuxdltbAxw4MAitW0suG0UIEUV/1Qkphi9gOHj/Hf648BLJWfkSr+nY0BSLejuhrqlOJUdHSOm++84NZ868ElmQPWiQI7Zt6wsDAyoyS4i0KEEi5Ash0UlYfOo5nsVQUVlSNXXpUhc//dQey5bdgLo6F3/+2Q2TJrnQ7ysh5aS0+zo3bNgAW1tbaGhowM3NDcHBwaVen5KSgilTpsDS0hLq6uqwt7dHQEBAJUVLqrr49BzMOhSKQZvuSEyONNRU8H1Xe1yc2QGeTub0YUOU2uLFHTFmjDPu3RuPyZNb0e8rITL46hGk1NRU3L9/HwkJCahTpw7atGnz1UH5+/tj1qxZ2Lx5M9zc3LB27Vp069YNL1++hJmZmdj1eXl56Nq1K8zMzHDkyBFYW1sjOjoaBgYGXx0Lqd6kKSrbs4kFfurlREVlidJgjOHixTf45pt6EpMfVVUV+Pn1U0BkhFQfMo8gpaenY/z48TAzM0O3bt0wcuRIbNu2Tdi/bds2WFlZ4d498Z0UZVmzZg0mTJiAMWPGwMnJCZs3b4aWlhb8/PwkXu/n54ekpCScOHECbdu2ha2tLTw8PNCsWTNZXx6pAW5EJKD72uv4NSBcYnLUwEwH+8a7YeOIlpQcEaWRlpaLYcOOonv3fdi48b6iwyGk2pIpQcrOzkbHjh3h5+cHQ0ND9OjRQ6yYbe/evREXF4cTJ06U67Hz8vIQEhICT0/P/4JUUYGnpyfu3Lkj8Z5Tp07B3d0dU6ZMgbm5ORo3bozly5eDz+eX+7WR6u9DchYm7QnBqO3BeJOQKdavq66Khb2dEPBde7Stb6KACAmR7MGDj2jefAv8/Z8DAGbNuojQUMnFkQkhX0emKbY1a9bg0aNHGDZsGLZu3QptbW2oqIjmWhYWFnB0dCx3odvExETw+XyYm5uLtJubm+PFixcS74mMjMSVK1cwYsQIBAQE4PXr1/jf//6H/Px8LF68WOI9ubm5yM3978C/tDTJi3JJ9ZGTz8eWa5HYGPS6xKKyXi1rYQ4VlSVKhjGGtWvvYs6cy8jP/+93Ny+PD2/vI3j6dDJ4PK4CIySk+pEpQfL394eFhQW2b98ODY2St43a29uXubhaHgQCAczMzLB161ZwuVy0bNkSMTExWLVqVYkJ0ooVK7B06dIKj40oHmMMl8Li8HMpRWWbWOtjaT8qKkuUT2JiFsaMOYkzZ8TPNjIw0MDKlV0oOSKkAsiUIL158wZdu3YtNTkCAC0tLSQmSi7mWRITExNwuVzExcWJtMfFxcHCwkLiPZaWllBTUwOX+98fCUdHR8TGxiIvLw88Hk/snnnz5mHWrFnC79PS0mBjY1OuWInyi0zIwJLTYbj+KkFiv6GWGmZ3d8AQFxtw6WRhomSuX4/G8OFHEROTLtbn7l4LBw4MQp06BpUfGCE1gExrkLhcLvLzJR+g96UPHz5AW7t8tX54PB5atmyJwMBAYZtAIEBgYCDc3d0l3tO2bVu8fv0aAsF/Q8+vXr2CpaWlxOQIANTV1aGnpyfyRaqXkOgk9P77psTkSIUD+LjXQdAPnTDMtTYlR0Sp8PkC/PzzNXTqtEticjR3bltcu+ZLyREhFUimEaR69erh8ePHKCgogKqq5IfIyMjAkydP4OTkVO7HnzVrFnx8fODi4gJXV1esXbsWmZmZGDNmDABg9OjRsLa2xooVKwAAkydPxvr16/Hdd99h2rRpiIiIwPLlyzF9+nRZXh6pBqI/Z2LC7hBk5Ykv1KeiskSZffyYjhEjjiEo6K1Yn5mZNvbsGYBvvqlX+YERUsPIlCD17dsXy5Ytw7Jly7BkyRKJ1yxbtgypqakYMGBAuR/f29sbCQkJWLRoEWJjY+Hs7Izz588LF26/e/dOZFG4jY0NLly4gJkzZ6Jp06awtrbGd999hzlz5sjy8kgVl5qVjzE77yMpM0+knYrKEmV37lwERo8+gcTELLG+Ll3ssHfvQFhYUHkbQioDhxXfny+FlJQUNG3aFDExMejTpw+GDBmCkSNHokePHhg3bhwOHTqEw4cPw9bWFk+ePCn3NJsipKWlQV9fH6mpqTTdVoXlFQgw2u8e7kYmibR72Jti44gWVFSWKK3164Mxbdo5sXYVFQ5+/rkj5s5tBy5XaYsfEKIwFfX5LVOCBABhYWHo27cvIiMjxf41zhhDnTp1cPbsWZmm2BSBEqSqjzGGHw4/wdGHH0TaHSx0cWRyG+hQckSU2KtXn9GixRZkZv63vrNWLT0cODAI7drVVmBkhCi3ivr8lvkTw8nJCc+ePcPOnTsREBCAyMhICAQC2NjYoEePHpg4cSK0tLTkFighZVl/5bVYcmSmq44dY1pRckSUnr29MTZt6oXRo08AAPr2bQg/v74wNqa/o4QogswjSNUNjSBVbSdDY/DdwVCRNk01Lg5Pckdja33FBEWIDL799jScnEwxfbobrZUjRApKNYL0+fNnGBsbyy0IQr7G/bdJ+PHwE5E2Dgf4e1hzSo6I0omI+IzatfWhXsKo5pYtfSo5IkKIJDKt+LO2tsaQIUNw7tw5kbOHCKlsbxMzMXH3A+TxRX8PF/ZygqeTeQl3EaIYu3aFwtl5C2bPvqToUAghZZApQWKM4ciRI+jduzdsbGwwf/58vHolfgw+IRUpJSsPY3feR3KW6KGlPu51MKatrWKCIkSC9PRcjB59HL6+J5GVlY+//grGyZOSa0sSQpSDTAnSp0+fsG7dOjRr1gyfPn3Cb7/9BkdHR7Rr1w5+fn7IyMiQd5yEiMgt4GPinhBEJmaKtHd2MMPC3k60doMojdDQWLRsuRV79ohOA48ZcxLv36cqKCpCSFlkSpCMjIwwbdo0PHz4EKGhoZg2bRqMjY1x+/ZtTJgwARYWFvD19cW1a9fkHS8hYIxh3tGnCI4SPevI0VIPfw1rDlU6K4YoAcYY1q8PhpvbNkREJIn1DxnSCCYmtEONEGUlt11sBQUFOHXqFHbu3Inz58+joKAAHA4HdnZ2eP36tTyeokLRLraqY93lCPx5WXRK11xPHSemtIWlvqaCoiLkP8nJ2Rg37hSOHxefRtPTU8e2bX0weHAjBURGSPVTUZ/fcvuntqqqKgYOHIhTp07hw4cPmDZtGhhjiIqKktdTEIITj2LEkiMtHhfbfVpRckSUwu3b7+HsvEVictSqlRUePfqWkiNCqgC5np6Xm5uLY8eOYefOnbhy5Yo8H5oQBEclYfYR0XUcKrSdnygJgYDh999vYcGCK+DzxQfmf/jBHb/+2gU8HlcB0RFCyksuCdK9e/ewY8cOHDp0CKmpqWCMwcDAAEOHDsXYsWPl8RSkhotKzMTEPeLb+Rf1dkIXR9rOTxQrLi4Do0Ydx6VLkWJ9JiZa2LWrP3r2bKCAyAghspI5Qfr06RP27NmDnTt34uXLl2CMgcPhoHPnzhg7diwGDhwIdXV1ecZKaqjkzMLt/CnFtvP7trGFb1s7BUVFSKGQkI/o1Ws/4uIyxfo6drTF3r0DYG1N6xoJqWpkSpB69uyJS5cuQSAQgDEGW1tb+Pr6wtfXF7VrU1FFIj+5BXx8uycEUcW283s6Fm7nJ0TR7OwMxabNVFQ4WLSoAxYs6AAu7aokpEqSKUE6f/48NDU1MXDgQIwdOxadOnWSd1yEgDGGuUefIvit6BbpRlZ6WDe0ObgqdNYRUTwjI00cODAIHh47weczWFnpYt++gejY0VbRoRFCvoJMCdLmzZsxdOhQ2g5PKtS6wAgcfxQj0mahp4HtPq2gXUIdK0IUoW3b2vj55064des9du7sB1NTbUWHRAj5SnI7B6mqo3OQlMvxRx8w0/+xSJs2j4vDk9rAyYreH1L5cnMLkJWVD0NDycdJCASFf0pVaGSTkEql9OcgESIv9yI/S9zOv354C0qOiEJERHyGu/t2jBhxTJgIFaeiwqHkiJBqRKp5irFjx4LD4WD58uUwNzcv19Z9DoeD7du3yxwgqVkiEzLw7d4Q5Bc7R2ZJ30bo5GCmoKhITbZv3xNMmnQWGRl5AIA1a+7ghx/aKDgqQkhFk2qKTUVFBRwOB+Hh4bC3t4eKivQDTxwOB3w+/6uCrAw0xaZ4SZl5GLjxFt5+zhJpH9vWDov60I41UrkyM/Mwbdo57NgRKtKuqqqCmzfHwM2tlmICI4SIqKjPb6lGkHbs2AEAsLS0FPmeEHnJyedj4u4HYsmRp6M5furlqKCoSE319GkcvL2PIDw8UayvQQMjaGvzFBAVIaQy0SLtf9EIkuIwxjDDPxQnQz+KtDe21oP/RHfasUYqDWMMW7eGYMaMC8jJKRDrHzvWGX/91YMSJEKUiEJHkAipSH9ejhBLjiz1aTs/qVypqTmYMOE0Dh8OE+vT0eFhy5beGD68iQIiI4Qogky72LhcLsaNG1fmdRMmTICqKn3AkZIdDfmAvwIjRNq0eVz4+baCuZ6GgqIiNU1wcAyaN98iMTlq3twCDx9OpOSIkBpGpgSJMQZpZ+ZoBo+U5M6bz5h7TMJ2/hEt4GhJ05yk4gkEDKtX30bbtn6IikoR658+3RV37oxDgwbGlR8cIUShKnR4JysrC2pqahX5FKSKepOQgUkStvMv7dcYnRrSdn5S8VJTczB8+DEEBESI9RkaamDHjn7o189BAZERQpRBhSVIKSkpuHnzpnDnGyFFPmfkYsyO+0jNzhdpH9fODqNa11FQVKSm0dRUQ2Jillh727Y22L9/EGrX1ldAVIQQZSF1glS3bl2R748cOYKgoCCJ1xYUFCA2NhZ8Ph/ffvvtVwVIqpecfD4m7gnBuyTRD6auTuaY35O285PKw+NxcfDgIDRvvgWpqbngcIB589ph6dJOUFWlIgOE1HRSJ0hv374V/jeHw0FGRgYyMjJKvJ7H46F///5Yvnz5VwVIqg+BgOHHI08QEp0s0t7EWh/rhjqDS2UaSCWzszPEtm19MXVqAPbuHQhPz7pl30QIqRGkTpCioqIAFC66rlu3Lry8vLBq1SqJ1/J4PJiamtIONiLiz8uvcPqx6HZ+K30NbPdxgRaPfldIxcnOzoempuT1kF5eTujWrR50ddUrOSpCiDKT+lOpTp3/1ob4+Pigffv2Im2ElObwg/f4+8prkTYddVX4jWkFM9rOTypIXh4f8+cH4sKFN7h3bzy0tCQnSZQcEUKKk+mf7VRqhJTH7TeJmH/8qUgbV4WDDSNawMGCtvOTihEZmYyhQ4/g/v3CUcsZM85j69Y+Co6KEFJV0EpEUqFex2dg0h7x7fw/92sED3tTBUVFqrtDh56jefMtwuQIAP755yH8/Z8pMCpCSFUi1QjS2LFjweFwsHz5cpibm2Ps2LFSPwGHw8H27dtlDpBUXZ8zcjFmZzDSitW0mtDeDiPcaHqWyF92dv6/I0UPxfp4PC7S0/MUEBUhpCqSqlitiooKOBwOwsPDYW9vDxUV6QeeOBwO+Hz+VwVZGahYrXzl5PMx/J+7ePguRaS9WyNzbBrREiq0Y43IWVhYAry9j+DZs3ixPnt7Y/j7e8HZ2UIBkRFCKpJCi9UWrTkqOvSR1iCR0ggEDD8cfiyWHDWrpY+13s0pOSJyxRjDjh2hmDo1ANnZBWL9o0Y1xcaNvaCjw1NAdISQqkqqEaSagEaQ5GfVhRfYcPWNSJu1gSaOT2kDM13asUbkJy0tF5Mnn8X+/U/F+rS01LBxY0/4+DhXfmCEkEqj0BEkQqR16MF7seRIV10Vfr6tKDkichUS8hFDhx7F69dJYn1Nm5rD398LDg4mCoiMEFIdyH0XW1RUFE6ePInQ0FB5PzRRcrdfJ2L+Mcnb+Rta6CooKlLdMMawbt1duLtvl5gcTZ7sgrt3x1FyRAj5KjIlSKdOncLAgQMRHBws0r5q1SrY29tj4MCBaNmyZbl2u5Gq7XV8Or7dG4ICgeiM7S/9GqMDbecncnbnzgfk5wtE2vT11XHkyGBs3NirxFOzCSFEWjIlSLt378b58+fh6PhfcdEXL15g7ty5YIyhWbNm0NLSwq5du3D69Gm5BUuUU2JGLnx33Ed6se3833aoi+FutRUUFamuOBwOtm7tg7p1DYVtbm7WCA2dhEGDnBQYGSGkOpEpQXr06BGaNWsGXd3/pk327dsHANi4cSMePnyI+/fvg8vlYuvWrfKJlCilnHw+Jux+gA/J2SLtPRpbYE53BwVFRao7PT11+Pt7QV2di9mz2+DGjTGwtTVQdFiEkGpEpkXaiYmJaN68uUhbUFAQNDU14evrCwBwcHBAu3bt8Pz5868OkigngYDh+0OP8aj4dn4bA6wZ4kzb+clXEwhYib9HLi5WiIiYBhsb/UqOihBSE8g0gpSTkwMulyv8ns/n4+HDh3BzcwOP999ZI1ZWVoiNjf36KIlSWnXxJc4+/STSZm2giX9Gt4Qmj1vCXYRI58KF12jadBM+fkwv8RpKjgghFUWmBMnMzAwRERHC7+/evYvs7Gy0bdtW5Lrs7Gxoa2t/XYREKfnff4dNQeLb+XeMoe385Ovk5/Mxd+5ldO++D8+fJ2DkyGPg8wVl30gIIXIkU4LUpk0bPH78GAcPHkRqaiqWL18ODocDT09PkevCw8NhZWUll0CJ8rgZkYifjosW/VRV4WDTyJawN6ft/ER20dEp8PDYid9+uyVsu3r1LZYvv6HAqAghNZFMCdKcOXOgqqqKESNGwMjICOfOnUOLFi3QoUMH4TXv37/Hixcv0KpVK7kFSxQvIi4dk/eJb+df1r8x2jWgc2eI7I4fD4ez8xbcufNBrC8k5BMEAjr0nxBSeWRKkFq0aIGAgAB4eHjA0dERvr6+OHPmjMg1hw4dgr6+Prp06SKXQIniJaTnYsxO8e38kzzqYagrbecnssnJKcDUqQEYOPAQUlJyRPrU1FSwZs03OH7cmxb9E0IqFdVi+xfVYitddh4fQ/+5i8fvU0TaezaxwPphLejDi8jk1avP8PY+gtBQ8c0cdesawt/fCy4uNE1PCCkZ1WIjCiMQMMw6FCqWHDnTdn7yFfbseYzJk88iMzNfrM/buxG2bOkNfX1a8E8IUYyvTpDu3r2Lq1evIiYmBgBgbW2NTp06oXXr1l8dHFEOv114gXPPRP+FX8tQE/+MdoGGGm3nJ+WTkZGHqVMDsGvXY7E+TU1V/PVXD4wb1xwcDiXehBDFkTlBevfuHUaMGIHbt28DKCwgCUD4R61t27bYu3cvatemtSlV2YHgd9hyLVKkTVdDFTt8W8FUV11BUZGq6vHjWHh7H8HLl5/F+pycTHHokBcaNTJTQGSEECJKpgQpJSUFnTp1QlRUFDQ0NNCtWzfUq1cPABAZGYnz58/j5s2b6NKlCx48eAB9fTrMrSq6EZGABSfEt/NvHtkSDWg7P5HBsWPhEpOj8eObY926HtDSoiKzhBDlIFOCtHr1akRFRaFnz57YunWr2FlHsbGxmDBhAgICArB69Wr8/PPPcgmWVJ6Xsen4396H4BfbWv3rgMZoW5+28xPZLFzogatX3+LGjXcAAF1dHrZu7YOhQxsrODJCCBEl0y62xo0bIyEhAVFRUdDS0pJ4TVZWFuzs7GBqaopnz55JvEaZ0C62/8Sn52DAhtuISREtQPu/jvUwmwrQkq/04UManJ03w9bWAAcPeqF+fSNFh0QIqcKUahdbVFQUevXqVWJyBABaWlrw8PDA2bNnZQ6OVL7sPD4m7Hoglhz1amKJH75pqKCoSHVSq5YerlzxQcOGxlBXp420hBDlJNNBkVwuF/n54ltziysoKICKikxPQRRAIGCY6R+Kxx9SRdqb1zbA6iHNaDs/kUp8fCb69z+IR48+lXhN06bmlBwRQpSaTNlLgwYNEBQUhJSUlBKvSUpKwtWrV2Fvby9rbKSS/Xb+Bc4/F93Ob2NE2/mJ9K5ciUKzZptx8uRLeHsfQXp6rqJDIoQQmciUIA0ePBipqano1asXnj9/Ltb/9OlT9O7dG2lpafD29v7qIEnF23cvGluuS97Ob6JD2/lJ6QoKBFi48Ao8PXcjNjYDABARkYT//S8AdFg/IaQqkmmRdnZ2Ntq0aYPHjx9DRUUFzZs3h52dHYDCbf6hoaEQCARwdnbG7du3oaGh/Kfh1uRF2tdeJWDszvsiO9ZUVTjYPdYVbWjHGinDhw9pGD78qHBn2pcsLXXw8OG3sLDQUUBkhJCaoKI+v2WuxZacnIzJkyfjyJEjEAgEIn0qKioYPHgwNmzYACOjqrFDpaYmSC9j0zFo021k5IoWoP3dqymGuNgoKCpSVZw+/RK+vieRlJQt1te9e33s2tUfZmbaCoiMEFJTKNUuNgAwNDTEwYMH8f79e1y/fl2k1EiHDh1gY0MfrsouO4+P8bvviyVHUzrVo+SIlCo3twBz517G2rX3xPpUVVWwYkUXzJrlTgv7CSFVVrkTpLi4OERHR0NdXR0ODg6wsbHBiBEjKiI2bNiwAatWrUJsbCyaNWuGv//+G66urmXed/DgQQwbNgz9+vXDiRMnKiS26mDbjUi8L/Yv/95NLfF9V9rOT0r2+nUShg49gpAQ8V1qhWcbDYKbWy0FREYIIfIj9SLtV69eoWPHjrCysoK7uztatGgBIyMjzJo1S6ot/+Xl7++PWbNmYfHixXj48CGaNWuGbt26IT4+vtT73r59ix9++AHt27eXe0zVSUJ6LjZfeyPS5mxjgD8G03Z+UrIDB56iRYstEpMjLy8nPHr0LSVHhJBqQaoEKSEhAR4eHrhx4wYYY8Kv7OxsrFu3DuPGjZN7YGvWrMGECRMwZswYODk5YfPmzdDS0oKfn1+J9/D5fIwYMQJLly5F3bp15R5TdbIu8BUy8/jC7zkcYMXAJrSdn0iUlZWP8eNPYfjwY0hPzxPpU1fnYtOmXjh0yAsGBsq/IYMQQqQhVYK0Zs0axMXFwdraGjt27MCTJ09w8+ZNfP/99+Byudi3bx/CwsLkFlReXh5CQkLg6en5X6AqKvD09MSdO3dKvO/nn3+GmZmZVAlbbm4u0tLSRL5qitfxGTgQ/F6kbVCLWnC0rDmL00n5nD//Gtu3PxJrd3AwQXDwBEya5AIOh0YeCSHVh1RrkM6dOwcNDQ1cuXIF9evXF7a3adMGBgYGWLhwIc6fPw8nJye5BJWYmAg+nw9zc3ORdnNzc7x48ULiPTdv3sT27dsRGhoq1XOsWLECS5cu/dpQq6Tfzr8Q2dKvoaaC77+hAz1JyQYOdISPTzPs2vVY2Obr64z163tAW5unwMgIIaRiSDWCFBkZidatW4skR0VGjx4NoLA+m6Kkp6dj1KhR+Oeff2BiIt25PfPmzUNqaqrw6/3792XfVA3ci/yMS2FxIm3j2tnBUl9TQRGRqmL9+p5o2NAYOjo87NkzADt29KPkiBBSbUk1gpSRkYE6depI7Cvazp+VlSW3oExMTMDlchEXJ/pBHhcXBwsLC7Hr37x5g7dv36JPnz7CtqKzmVRVVfHy5UvUq1dP5B51dXWoq9esE6IFAoblAeEibcbaPEzyqFfCHYT8R0eHh6NHh0BNjQt7e2NFh0MIIRVK6l1sZa0vkGc5AR6Ph5YtWyIwMFDYJhAIEBgYCHd3d7HrHRwc8PTpU4SGhgq/+vbti06dOiE0NJTOZPrXmaefxArRfufZALoaagqKiCgTxhj+/PMOTp6UPI0NAI0amVFyRAipEZS2nPasWbPg4+MDFxcXuLq6Yu3atcjMzMSYMWMAFE7tWVtbY8WKFdDQ0EDjxo1F7jcwMAAAsfaaKreAj9/Pi37w1TXRxjDX2gqKiCiTxMQs+PqewNmzETA01EBoqCVq19ZXdFiEEKIwUo8g7dq1C1wuV+IXh8MpsV9VVbYczNvbG3/88QcWLVoEZ2dnhIaG4vz588KF2+/evcOnT+JnsRDJ9tyJxodk0UMhZ3d3gBpXpnrFpBq5fj0azs6bcfZsBAAgOTkHw4YdRX4+v4w7CSGk+pKqFpuKiuwfohwOB3y+8v+hrc612FKz8tFh1VWkZv93oKdLHUMcnuROW7NrMD5fgF9/vYGlS69BIBD/M3D+/Ah06ya+MYMQQpSJQmuxXb16VW5PSCrf+qsRIskRAPzUy5GSoxrs48d0jBx5DFevvhXrMzPTxp49A/DNN7R4nxBSc0mVIHl4eFR0HKSCvE/Kwq7b0SJtvZpaonltQwVFRBTt/PnXGD36OBISxHeedulih717B8LCQkcBkRFCiPKgBSjV3KoLL5HHFwi/V+NyMKebgwIjIoqSn8/H7NmX0KPHPrHkiMvl4NdfO+PChZGUHBFCCJR4Fxv5ek8+pODU448ibaNa26K2sZaCIiKKEhWVjGHDjuLevRixPhsbPezfPwjt2tGORkIIKUIJUjXFGMOvZ0UPhdTVUMW0zrTotqY5ejQM48adQmpqrlhf374NsWNHPxgZ0UnqhBDyJUqQqqnA8Hjci0oSaZvaqT4MqTREjRIZmQxv7yPg80V3qfF4XKxa1RXTprnSYn1CCJGA1iBVQwV8AVacEx09sjbQhE8bW8UERBSmbl1D/PxzJ5G2+vWNcOfOOEyf7kbJESGElIASpGrI/8F7vEnIFGn7sVtDaKhxFRQRUaS5c9vB07MuAGD48CZ4+HAiWrSwVHBUhBCi3GiKrZrJyC3An5ciRNoaW+uhbzMrBUVEFE1FhYM9ewbg4sU3GDWqKY0aEUKIFGgEqZrZej0SiRmii3Hn93SEigp9KFZnoaGx2LHjUYn9FhY6GD26GSVHhBAipa8eQUpNTcX9+/eRkJCAOnXqoE2bNvKIi8ggLi0H/1yPFGnr7GCGNvVMFBQRqWiMMWzYcB/ff38RAgGDo6MpWreupeiwCCGkypN5BCk9PR3jx4+HmZkZunXrhpEjR2Lbtm3C/m3btsHKygr37t2TS6CkbH9eeoXsLwqMqnCAeT3oUMjqKjk5G4MGHcK0aeeQl8dHQYEAw4YdRUpKjqJDI4SQKk+mBCk7OxsdO3aEn58fDA0N0aNHDxSvedu7d2/ExcXhxIkT8oiTlOFVXDoOPXgv0ubdygYNzHUVFBGpSLdvv4ez8xYcP/5CpP3t2xSsWnVLQVERQkj1IVOCtGbNGjx69AjDhg3DmzdvcObMGbFrLCws4OjoSIVuK8mKgHB8WZBdi8fFTE97xQVEKoRAwLBy5U106LAD796livX/8IM7Fi/uWPmBEUJINSPTGiR/f39YWFhg+/bt0NDQKPE6e3t7BAcHyxwckc6t14m4+jJBpG1C+7ow0yv5vSFVT1xcBkaNOo5LlyLF+kxMtLBrV3/07NlAAZERQkj1I1OC9ObNG3Tt2rXU5AgAtLS0kJiYKFNgRDoCAcPyANFDIU111TGxQ10FRUQqwuXLkRg58hji4jLF+jp2tMXevQNgba2ngMgIIaR6kmmKjcvlIj8/v8zrPnz4AG1tbVmegkjpRGgMnn9ME2mb6WkPbXU64qo6KCgQ4KefAvHNN3vEkiMVFQ6WLPHA5cujKDkihBA5k+lTtF69enj8+DEKCgqgqir5ITIyMvDkyRM4OTl9VYCkZDn5fPxx4aVIW30zHQxxoW3e1cG7d6kYPvwobt16L9ZnZaWL/fsHwsPDtvIDI4SQGkCmEaS+ffvi06dPWLZsWYnXLFu2DKmpqRgwYIDMwZHS7bj1Fh9TRbd0z+vhAFUunf9Z1RUUCNCp0y6JyVHPng0QGvotJUeEEFKBZPoknTlzJqytrfHLL7+gf//+2L9/PwAgLi4Ox44dw9ChQ7Fq1SrY2tpi0qRJcg2YFErKzMPGq69F2lrXNUJnBzMFRUTkSVVVBb/95inW9scfXXH69DCYmtLUNSGEVCQOK36AkZTCwsLQt29fREZGipUvYIyhTp06OHv2bJWZYktLS4O+vj5SU1Ohp6f86zmWnHqOnbffirSdntoOTWrpKyYgUiEmTz6DzZtDYGdngIMHveDqaq3okAghRKlU1Oe3zCt5nZyc8OzZM+zcuRMBAQGIjIyEQCCAjY0NevTogYkTJ0JLS0tugZL/vE3MxN670SJt/ZytKDmqhtas6QY9PXXMn98e+vp0bAMhhFQWmUeQqpuqNIL0v30hCHgaK/yex1VB4PcesDGihLSqyczMw9Gj4Rg9upmiQyGEkCpJ6UaQiGKERCeLJEcA4NvWlpKjKujp0zh4ex9BeHgieDwuhg5trOiQCCGE/Iu2O1UhjIkfCqmvqYYpHesrKCIiC8YYtmx5AFfXbQgPLzxIdeLE03j9OknBkRFCCCki0whS3brSn9LM4XDw5s0bWZ6GFHPheSxCopNF2qZ3aQB9LTUFRUTKKzU1BxMmnMbhw2Ei7enpeZg48TSuXPFRUGSEEEK+JFOC9Pbt2zKv4XA4YIyJ7XAjssnnC/DbedFDIWsbaWFU6zoKioiUV3BwDIYOPYKoqBSxvhYtLLFlS+/KD4oQQohEMiVIUVFREtsFAgGio6Nx5swZ/P3335g3bx7GjBnzVQGSQvvvvUNUomipidndG4KnSrOkyk4gYPjzzzuYOzcQBQUCsf7p013x++9doU7lYQghRGnI9Be5Tp2SRy3s7OzQsWNHuLm5YdiwYfDw8Cj1elK2tJx8rAuMEGlztjFAryaWCoqISCshIRO+vicREBAh1mdoqIGdO/ujb9+GCoiMEEJIaSps+GHw4MFwdHTEihUrKuopaozNQW+QlJkn0vZTL0eavlRyQUFv4ey8RWJy1LatDR4/nkTJESGEKKkKnZ9xdHTE/fv3K/Ipqr2PKdnYflN0SvMbJ3O0sjVSUESkLHy+AEuWBKFLl934+DFdpI/DAX76qT2CgnxhY0MHexJCiLKq0EUPMTExyMvLK/tCUqLVF18h94t1K1wVDub0cFBgRKQs48efxs6doWLt5uba2Lt3IDw9pd8FSgghRDEqbARp7969uHPnTpWpxaaMwj6m4dijDyJtw11ro56pjoIiItKYMqUV1NRE/9f65pt6ePx4EiVHhBBSRcg0gjR27NgS+9LT0/HixQuEhYWBw+Hgu+++kzm4mqzoUMgvC8HoqKviO88GiguKSMXFxQq//eaJWbMugsvl4NdfO+PHH9tCRYXWjBFCSFUhU4K0c+fOMq/R09PD0qVLMXLkSFmeosa79ioBN18nirRN8qgLEx11BUVEymPGjNZ49eozRo9uBnd3G0WHQwghpJxkSpB27NhRYh+Px4O1tTVcXV2hoUHVx2XBFzCsCHgh0mahp4Fx7Wh6RplcvhyJTp1sweWKz1RzOBxs2kQHPxJCSFUlU4Lk40PlECrS0Ycf8DJOdPfTrG/socnjKigi8qXs7HzMmHEeW7c+xNKlHbFokYeiQyKEECJnMi3SHjt2LGbPni3vWAj+LWR6TbR2nYOFLga1qKWgiMiXwsIS4Oq6DVu3PgQALF16DdeuvVVsUIQQQuROpgRp7969JZYbIV8nOCoJbxJES4rM6eEALi3wVSjGGPz8HsHFZSuePYsXtgsEDMOHH0N6eq4CoyOEECJvMk2xWVhY0CnOFeRA8DuR7+uaaKOjvamCoiEAkJaWi8mTz2L//qdifdraalixogt0dWnxPCGEVCcyJUhdu3bF+fPnkZ+fDzU1NXnHVGMlZ+Yh4FmsSNsw19qUjCpQSMhHDB16FK9fJ4n1NW1qjkOHvNCwoYkCIiOEEFKRZJpiW7JkCXJzczFhwgSkp6eXfQORytGHH5D3xanZPK4KBrWktUeKwBjDunV34e6+XWJy9L//ueDevfGUHBFCSDUl8zb/7t27Y/fu3Th79iw8PT1ha2sLTU1NsWs5HA4WLlz41YFWd4wxsem17o0tYKTNU1BENdfnz1kYO/YUTp16Kdanr6+O7dv7YtAgOiGeEEKqMw5jX57VLFnnzp3RvXt34c41FRUVcDgclHZrUT+HwwGfz5dfxBUkLS0N+vr6SE1NhZ6eXqU/f3BUEoZsuSPSdmBCa7jXM670WGqymzffYdiwo/jwIU2sz83NGgcPesHW1qDyAyOEECJRRX1+SzWCFBQUBFtbW+H3ixYtonUxcrb/XrTI93VNtNG6rpGCoqmZ9u9/itGjj4PPF0/8Z89ug2XLOkNNjc6iIoSQmkCmKbYlS5bIOYyajRZnK4cOHerAwEADnz9nC9tMTbWwe/cAdO9eX4GREUIIqWwyLdIm8nXsUQwtzlYCtWrpYefO/sLvO3WyRWjoJEqOCCGkBpJpBInID2NMbHqNFmcrTu/e9vjhB3fo6alj/vz2EuusEUIIqf4oQVKw+2+TxU7OHuZaW0HR1Azv3qXC0FCjxMMdV636ppIjIoQQomyk/ufxrl27wOVyy/2lqko5WGkknZxNi7MrzvHj4WjWbDMmTz5b6i5MQgghNZvUCRJjTOYvIllyZh7OPv0k0kaLsytGTk4Bpk4NwMCBh5CSkoN9+55i167Hig6LEEKIkpJ6eKd79+6YM2dORcZS49Di7Mrx6tVneHsfQWio6E7BKVMC4OZmDUdHqnVHCCFElNQJkoWFBTw8PCoylhpF0snZ3Whxttzt2fMYkyefRWZmvlhf374NYW1d+YeCEkIIUX60QEhB7r9Nxuv4DJG24bQ4W24yMvIwdWqAxGk0TU1V/PVXD4wb15ymMwkhhEhECZKCFB89sqPF2XLz+HEsvL2P4OXLz2J9jRqZwt/fC40amSkgMkIIIVUFHfKiAClZkhZn29BoxldijGHjxvtwc9smMTmaMKEFgoMnUHJECCGkTDSCpABHH0pYnN2CFmd/jZSUHIwffwpHj4aL9enq8vDPP33g7d1YAZERQgipiqRKkAQCQdkXEamUtDjbWEfyoYWkbC9eJKJ7972Ijk4V63NxscLBg4NQrx5NXxJCCJEeTbFVsgfR4ouzh7naKCia6sHGRg+ammpi7TNntsatW2MpOSKEEFJulCBVsv33xBdnu9c1VlA01YO2Ng/+/l5QV+cCAIyNNXH69DCsWdMNPB5XwdERQgipiihBqkS0OLviNG1qjrVru6NDhzoIDZ2E3r3tFR0SIYSQKkypE6QNGzbA1tYWGhoacHNzQ3BwcInX/vPPP2jfvj0MDQ1haGgIT0/PUq9XhGPFFmercTm0OLscCgoESEzMKrH/229b4sqV0ahViw5/JIQQ8nWUNkHy9/fHrFmzsHjxYjx8+BDNmjVDt27dEB8fL/H6oKAgDBs2DFevXsWdO3dgY2ODb775BjExMZUcuWSMMewvvji7ES3OltaHD2no3HkX+vY9gPx8vsRrOBwOuFyl/ZUmhBBShXCYklaTdXNzQ6tWrbB+/XoAhTvpbGxsMG3aNMydO7fM+/l8PgwNDbF+/XqMHj26zOvT0tKgr6+P1NRU6OnJfwTi/tskDN58R6Rt/wQ3tKlnIvfnqm5On34JX9+TSErKBgDMndsWK1Z4KjgqQgghyqCiPr+V8p/beXl5CAkJgafnfx+CKioq8PT0xJ07d0q58z9ZWVnIz8+HkZFy7GA6UGxxtq2xFi3OLkNubgFmzjyPvn0PCpMjAFi58hYuXnyjwMgIIYRUd0p5UGRiYiL4fD7Mzc1F2s3NzfHixQupHmPOnDmwsrISSbK+lJubi9zcXOH3aWlpsgdchpSsPJwRW5xdmxZnl+L16yQMHXoEISGfxPpsbQ1gYKChgKgIIYTUFEo5gvS1Vq5ciYMHD+L48ePQ0JD8QbpixQro6+sLv2xsKu4sIkmLs71a0uLskhw48BQtWmyRmBx5eTnh0aNv4epqrYDICCGE1BRKmSCZmJiAy+UiLi5OpD0uLg4WFhal3vvHH39g5cqVuHjxIpo2bVridfPmzUNqaqrw6/3793KJvTiJJ2fT4myJsrLyMX78KQwffgzp6XkiferqXGza1AuHDnnR6BEhhJAKp5QJEo/HQ8uWLREYGChsEwgECAwMhLu7e4n3/f777/jll19w/vx5uLi4lPoc6urq0NPTE/mqCA+ikxFR7OTs4a61K+S5qrJnz+LRqtU/2L79kVhfw4bGuHdvPCZNcqFpSUIIIZVCKdcgAcCsWbPg4+MDFxcXuLq6Yu3atcjMzMSYMWMAAKNHj4a1tTVWrFgBAPjtt9+waNEi7N+/H7a2toiNjQUA6OjoQEdHR2GvQ+Li7Hq0OLsIYwzbtj3E9OnnkZNTINbv6+uM9et7QFubp4DoCCGE1FRKmyB5e3sjISEBixYtQmxsLJydnXH+/Hnhwu13795BReW/AbBNmzYhLy8PXl5eIo+zePFiLFmypDJDF6LF2aXLysrH2LEn4e//XKxPW1sNmzf3xsiRJU+TEkIIIRVFaRMkAJg6dSqmTp0qsS8oKEjk+7dv31Z8QOUk8eRsWpwtpKGhKrJ9v4izswX8/b1gb08jbYQQQhRDKdcgVQclLc42ocXZQioqHOzZMwDm5trCtqlTW+HOnXGUHBFCCFEoSpAqSAgtzpaKubkO9u4dCCMjTRw7NgR//90TGhpKPbBJCCGkBqBPogpy6IHosQG2xlpoXYNPzs7OzoempprEPk/Punj79jvo6tLoGiGEEOVAI0gVQCBguPJCtKjuUNfaUFGpeYuz+XwBfvnlGpo02YTkZPH1RkUoOSKEEKJMKEGqAE9iUpGYIXrQYY/GpR9wWR19/JiOrl33YNGiILx5k4wJE05DSWsjE0IIISIoQaoAV8JFTwCva6qNOsbaJVxdPZ0//xrOzptx9epbYdvRo+HYvPmB4oIihBBCpEQJUgW48lJ0eq1zQzMFRVL58vP5mD37Enr02IeEhCyRPi6Xg8zMfAVFRgghhEiPFmnLWXxaDp7FpIm0dXasGQlSVFQyhg07inv3YsT6bGz0cODAILRtSzv5CCGEKD9KkOTsarHRI111VbSyNVJQNJXnyJEwjB9/CqmpuWJ9/fo1hJ9fPxgZaSogMkIIIaT8KEGSs+K719rbm0CNW31nMrOz8zFr1gVs3hwi1sfjcfHHH10xdaorlVchhBBSpVCCJEe5BXzcjEgUaetUjdcfvXiRCG/vI3jyJE6sr359I/j7e6FFC0sFREYIIYR8HUqQ5Cg4KgmZeXyRto7VNEHatSsU//tfALKyxBddjxjRBJs29aKzjQghhFRZlCDJUfHptWa19GFaTZOEe/dixJIjLS01rF/fA76+zjSlRgghpEqrvotjFOBqsQSps4O5giKpeGvWdEPTpv+9viZNzPDgwQSMGdOckiNCCCFVHiVIchKZkIG3n0XP/ensUD2n1wBAQ0MV/v5e0NZWw6RJLXHv3ng4OpoqOixCCCFELmiKTU6KT6+Z6qqjkZWegqKRH4GAlVhDzsHBBOHhU2Bjo1/JURFCCCEVi0aQ5KT4+UedGppW+eK0t2+/R9Omm/D6dVKJ11ByRAghpDqiBEkO0nPycS9SNImoytNrAgHDihU30KHDDjx/ngBv7yPIzS1QdFiEEEJIpaEESQ5uRiSiQPBflXo1LgftGlTN9ThxcRno3n0v5s+/Aj6/8DU9fPgJc+deVnBkhBBCSOWhNUhyUHz9kZudMXTUq96P9tKlNxg16jji4jLF+kJD45CXxwePx1VAZIQQQkjlohGkryQQMFx9mSDS1qmKTa8VFAgwf34gunXbK5YcqahwsHixBy5fHkXJESGEkBqj6g1zKJlnH1ORmCFaoLUqrT969y4Vw4Ydxe3b78X6rKx0sW/fQHTsaFv5gRFCCCEKRAnSVyo+vWZnog07E20FRVM+J068wNixJ5GcnCPW17NnA+zc2Q+mplXjtRBCCCHyRAnSVyqeIFWF4rS5uQX48cdL+PvvYLE+VVUVrFzZBTNnulf5YwoIIYQQWVGC9BXi03Pw5EOqSJuyT6+9evUZQ4cewaNHsWJ9dnYGOHjQC66u1gqIjBBCCFEelCB9haBii7O1eVy42hkpKBrpnD//WmJyNHiwE/75pw/09TUUEBUhhBCiXGgX21coXpy2fQNT8FSV+0c6bZor+vSxF36voaGKLVt6w9/fi5IjQggh5F80giSjvAIBbkQkirQp+/QaAHA4HOzY0Q/Nmm2Gnp46/P290KSJuaLDqnR8Ph/5+fmKDoMQQkgJ1NTUwOUq7ngZSpBk9OBtEjKKld/o6FA1Ts82NtbCxYujUKeOPrS1eYoOp1IxxhAbG4vU1FQwxsq+gRBCiEJwOBzo6+vDwsICHE7lbxqiBElGgcWm15pY68NMVzmmqFJScjB58llMmtQSHh62Eq9xcqoayZy8paamIiUlBaamptDW1lbI/3SEEEJKxxhDZmYmEhISoKmpCQMDg0qPgRIkGRVff6Qsp2cHB8fA2/sI3r5NwfXr0Xj8eBJMTLQUHZZSYIwhPj4eenp6MDExUXQ4hBBCSqGpqYnc3FzEx8dDX1+/0v9Bq9wripVUVGImIhNFS3Ioev2RQMDwxx+30batH96+TQEAfPyYDl/fEzSV9C8+nw8+nw89PT1Fh0IIIUQKenp6wr/dlY1GkGQQGB4n8r2JjjqaWusrKBogISETPj4ncO7ca7G+27ff482bZNSvr9zHD1SGgoLCNWOqqvRrTwghVUHR3+uCgoJK/9tNnxQyuBQmmiB1cTBT2KnTQUFvMWLEMXz8mC7W17atDQ4cGAQbG8Ulb8qI1h0RQkjVoMi/1zTFVk4pWXl4EJ0s0ubpVPnb5Pl8ARYvvorOnXeJJUccDvDTT+0RFORLyREhhBAiAxpBKqeglwngC/5b06OuqoJ29St3wW9MTBqGDz+G69ejxfrMzbWxd+9AeHrWrdSYCCGEkOqERpDK6XKx9Udt65tAk1d5B1mdPfsKzZptlpgcffNNPTx+PImSI0JItTVnzhzY2dkhLy9P0aEQGQwYMACdOnVSdBhSoQSpHPIKBLj2SrT+mqdj5Uyv5eXx8cMPF9G79wF8/pwt0sflcrByZRecOzcC5uY6lRIPUW5BQUHgcDj4448/FPr8X37p6OigRYsW+PPPP4UL5pVVx44dRWJXU1ODlZUVvL298ezZsxLvS0tLwy+//IIWLVpAV1cXWlpacHJywo8//oi4uLgS7ysoKICfnx+6du0KU1NT8Hg8GBsbo1OnTvj777+RlZVVES+zyomKisK6deuwaNEi8Hg165Db8vj48SNGjx4NU1NTaGpqwsXFBYcPH5b6fltbW7H/f7/8mjBhgvDamJgYrFixAh4eHrC0tIS2tjYaNWqEH3/8EZ8/fxZ77CVLluDatWs4deqUXF5rRaIptnK4/zYJ6Tmif9i7OFbO9v7bt99j9eo7Yu21a+vj4MFBcHe3qZQ4CCmPYcOGoWfPnsITzHfv3o1Zs2YhPDwcW7duVXR4pVJXV8e2bdsAANnZ2QgJCcGOHTsQEBCABw8eoGHDhiLXv3r1Ct26dUN0dDQGDhyIcePGQU1NDXfv3sW6deuwY8cOnD59Gu7u7iL3JSQkoG/fvrh79y7c3NwwY8YMWFpaIiUlBdevX8fMmTNx48YNHDp0qNJeu7JauXIl9PT0MHLkSEWHorSSkpLQrl07xMfHY9asWahVqxb279+PIUOGwM/PD2PGjCnzMdauXYuMjAyx9g0bNuDu3bvo06ePsO306dNYsmQJevXqhR9//BG6uroIDg7G2rVrcfDgQdy/fx8WFhbC65s1a4aOHTvil19+Qd++feXzoisKI4wxxlJTUxkAlpqaWuI1S049Y3XmnBF+9fn7RiVGyNisWecZsET4NWDAQZaUlFWpMVRl2dnZLCwsjGVnZys6lAp39epVBoCtWrVKqZ4/IyOD1apVi3E4HBYfH6+Q2BhjLC0trdR+Dw8Ppq2tLda+bt06BoBNnTpVpD0zM5PZ29szNTU1dubMGbH77t+/z/T19ZmpqSmLjY0VtgsEAtahQwcGgP31118SY3n16hX79ddfpXlZlaKsn11FSU1NZdra2mz69Olyf+ysrCyWn58v98dVhB9//JEBYKdOnRK2FRQUsFatWjEjIyOWnp4u0+NmZWUxfX19ZmlpKfKzevbsGfv06ZPY9f/88w8DwL7//nuxPj8/PwaAhYSElPm80vzdlubzWxY0xSYlxpjY+qMuDpW7e23FCk+4uFiBx+Ni/foeOHp0CAwNNSs1hupGIGD4nJGr1F8CQcUe9Hn9+nV07doV+vr60NTURIsWLbB9+3aJ1x49ehTNmjWDhoYGateujaVLl+Ly5cvgcDjYuXNnmc+lra2N1q1bgzGGN2/eiPR9+vQJkydPRu3atcHj8WBlZYWJEyciPj5e7HGePHmCb775Btra2jA2NoaPjw8SExPB4XDg6+srvO7t27fgcDhYsmQJ/P390bJlS2hqamLatGnl+hkV6dKlCwAgIiJCpH379u149eoVZsyYgV69eond5+LiguXLlyMhIQGrVq0Stp85cwbXr1+Ht7d3iTE1aNAA8+fPlyq+q1evolevXjA2NoaGhgbq1q2LcePGITGxsLB20dSnpPfK19dXbEt1x44dYWtri8jISHh5ecHIyAh6enoIDw8Hh8PBrFmzJMYxbNgw8Hg8JCT8tyShPO+vJAEBAcjMzETPnj3F+oKDg+Hr6wt7e3toaWlBV1cXbdu2xfHjx0t8nQkJCRg7dizMzc2hra2NDx8+ACgsRzRnzhzUr18f6urqMDU1xbBhwxAZGSnyOOnp6ViwYAHc3NxgYmICdXV11K9fH3PnzlXolOj+/ftRr149kVEeLpeLadOmISkpCQEBATI97pEjR5CamgofHx+R84gaNWokMkJUxNvbGwAkTkn36NEDAJR+VJSm2KQUEZ+B90mia388nSr39Gwejwt/fy+kpeXC2Vn8F5KUX3JWHlouu6zoMEoVssATxjrqFfLYp0+fxoABA2BhYYHvv/8eurq6OHjwIMaPH4/IyEj8+uuvwmv9/f0xbNgw1KtXD4sXL4aqqip27dqF06dPl+s5ixIjI6P/Di999+4d3N3dkZeXh3HjxqFevXp4/fo1Nm3ahKtXr+LBgwfQ1y88siIiIgLt27eHQCDA9OnTYW1tjYCAAHTv3r3E5zxx4gT++usvTJ48GZMmTZL5NHVJsQOFHx4AMHHixBLv9fX1xYwZM3D06FHh2jBp7pPWli1bMHnyZFhbW2Py5MmoU6cO3r17h9OnT+PDhw8yl9fJyMiAh4cH2rZti19//RXx8fFwdHREq1atsH//fqxatUqk4npaWhpOnjyJHj16wNS0sOZjed7fkly7dg0A0KpVK7G+48eP48WLFxgyZAjq1KmDz58/Y9euXRg4cCD27duH4cOHi93TtWtXWFhYYOHChcjMzISOjg5SU1PRpk0bvHv3DmPHjkWjRo3w6dMnbNy4EW5ubnjw4AHq1KkDoHDtzbZt2zBo0CAMHz4cqqqquHbtGn7//Xc8evQIFy5cKPNnm5+fj9TU1DKvK1LWe/jp0yfExMRgxIgRYn2tW7cGANy/fx9DhgyR+jmLbN++HRwOB+PGjZPq+qKE09xcfCDBwsICtra2CAoKKncclYkSJCkVHz2y0teAk6V8S1YwxuDn9wg8HhejRjWTeE3duoZyfU5Sc/H5fEydOhU6OjoIDg6GlZUVAGDKlCno1KkTVq5cCV9fXzRo0AAFBQWYNWsWTE1NERwcDEPDwt/DyZMno2nTpiU+R1ZWFhITE4VrkDZv3oxHjx7B1dUV9vb2wuumTZuG/Px8PHr0CLVq1RK2Dx48GK1bt8aff/6JJUuWAAB++uknpKWl4ebNm2jbti0AYOrUqfD29kZISIjEOJ4/f44nT57A0dGxXD+jopGXojVIM2bMAACxNTDPnj2Drq4u6tevX+JjaWlpwcHBAU+fPkVGRgZ0dHSE/7p2dnYuV1zFffjwAdOnT4eDgwNu374tUtjzl19+gUAgkPmxP3/+jJ9++gnLli0Taffx8cHUqVNx4cIFkVGdw4cPIzs7Gz4+PsK28ry/JQkLC4OhoaFYcgoACxYswIoVK0Tapk+fjubNm2PZsmUSE6TGjRtj7969Im3fffcdIiMjcffuXTRr9t/fYF9fXzRp0gSLFy8Wjr7VrVsX79+/h5qamvC6KVOmYOHChVi2bBmCg4Ph6upa6mu6detWuXZ0sTLKRn38+BEAYG1tLdZX1BYTEyP18xV5/fo1rl+/Dg8Pj1J/x7+0ePFiABD5PfhSvXr1cO/evXLHUploik1Kl4ufnu1oLtcTPtPScjFixDGMH38akyadRXh4Qtk3EfIVQkJChP9SLkqOAIDH42H27NkQCAQ4efKk8NqPHz/C19dXmBwBgI6ODiZNmlTicyxevBimpqYwMzND06ZNsXHjRgwcOFD4uEDhlMaZM2fQt29faGhoIDExUfhla2uL+vXr4+LFiwAKk7qAgAC4uroKk6Mi33//fYlx9OrVq9zJUWZmJkxNTWFqaoratWtjwIAByMvLw65du8SmedLS0socAQEgHLkqGjVIS0sTaZfV4cOHkZeXh8WLF0useq6i8nV/6n/44QextqJptN27d4u07969G0ZGRujduzeA8r2/pUlISJCYHAGFU7dFsrKy8PnzZ2RlZaFz584IDw8X/pxLe02MMezbtw8dOnSAtbW1SJxFU8Nfxsnj8YTJUUFBAZKTk5GYmAhPT08AkOrDv1mzZrh06ZLUX2UpmtpTVxcfcdbQ0BC5pjy2b98OxpjUo0erV6/G4cOHMXHiRHTu3FniNcbGxsjIyEB2drbEfmVAI0hSSMzIxaP3KSJt8ty9FhLyEd7eR/DmTeEJ3VlZ+Rgy5AiCg8dDU1OtjLsJkU1UVBSAwjUExRW1Fa27KLq2+M6tktqKTJw4EYMHD0Z+fj6ePn2K3377DR8+fBD+sQaAly9fQiAQYPv27SWufapbt/Bsr4SEBGRmZpY7ji9Hq6SloaEhnD5MSkrC7t27cenSJYmjMXp6ehI/hIsruqYomSpKjNLT00USz/IqWhPVvHlzmR+jJKamphKTrqIk6OTJk0hLS4Oenh7evn2LGzduYPLkycJt+OV5f0vD4XBKHEGJj4/HggULcPLkSYlrmlJSUsSS0OK/EwkJCfj8+TMuXrwonBosrniiuXHjRmzevBnPnz8X+71IThatuCCJoaGhMKGSBy0tLQBAbm6uWF9OTo7INdLi8/nYtWsXDAwM4OXlVeb127Ztw48//ohevXph/fr1JV5X9F4qc+knSpCkcOVFPL78/1Kbx4V7PeOvflzGGP766x5+/PES8vNF/+d69iweJ0++xNChjb/6eUjJDLV4CFkgvz9QFcFQq+qe99KgQQPhB0CPHj3Qrl07tGvXDpMmTcLBgwcB/PeHcuTIkSUOx2tqft1mhPJ+KACFC1u//PDy8vJC7969MXHiRLRo0UJkarFx48a4fv06Xr9+XeIURFZWFl68eAFbW1vo6OgI73v48CEePXpU4r+05am0D6OSzqYq7Wc3evRoHDt2DIcOHcL48eOxZ88eMMZE3kd5vb+mpqZ4/PixWDtjDN988w3Cw8Px3XffwcXFBfr6+uByudixYwf2798vMakt/rqK4vT09MScOXPKjGfNmjX4/vvv8c0332D69OmwsrICj8dDTEwMfH19pZrWzMvLQ1JSUpnXFZG0GPpLRSPBkqbRitokTb+VJiAgAJ8+fcKUKVNE/mEjiZ+fHyZOnIhvvvkGR48eFZl+LC4pKQk6OjplPqYiUYIkhcBi64/aNzCFuurXnZ79+XMWxo49hVOnXor16eurY/v2vhg0yOmrnoOUTUWFU2ELoJVd0b/anz9/LtYXFhYmco2trS2AwtGA4iS1laRNmzYYNWoUdu/ejenTp6NNmzaoX78+OBwO8vLyyvzXtKmpKbS1tb86DlmoqKhg3bp1cHJywg8//CAy3TJw4EBcv34d27Ztw8qVKyXev3v3buTn52PgwIHCtkGDBmH37t3Ytm3bVyVIRaMhoaGhpY6WFU1RSfpQLr5LSxo9e/aEiYkJdu/eLUyQHBwcRNbelOf9LU3jxo1x7do1JCYmiixWfvLkCR4/foxFixZh6dKlIvcUnWMljaKRsrS0NKni3LNnD2xtbXHu3DmRkaXz589L/Zy3b9+W6xokS0tLWFtb4+7du2J9RW0uLi5SPx/w389w/PjxpV7n5+eH8ePHw9PTEydOnJA4zfel169fo3Fj5R4AoDVIZcgrEOBGRKJI29cWp7158x2cnbdITI7c3KwRGjqJkiNS4Vq0aIHatWtjx44diI2NFbbn5+dj1apV4HA46NevH4DCP6qWlpbYuXOnyNRBRkYGNm/eXK7nXbhwIbhcLhYtWgSgcC1Cz549cezYMYl/2Bljwu3iXC4XPXr0QHBwMG7duiVy3erVq8sVhywaNGiA4cOH49KlS7h586awffz48ahfvz7WrFkj8QPy4cOHmDdvHkxNTfHjjz8K2/v06YMOHTrgwIED2Lhxo8TnfP36tdgC5OK8vLzA4/GwdOlSiVN9RR+sdnZ2UFVVxeXLojs3b9++LfFnXxY1NTUMHz4cN2/exP79+xERESE2SlSe97c0HTt2BACxxyjaQVc8eXj27JnEbf4lUVFRwYgRIxAcHCzcXVjcl9N3XC5XbNqvoKCgxARZEnmvQQIK14a9efNGZHcpn8/H33//DQMDA5H1c0Wjmp8+fZL4WLGxsQgICECLFi1K3Uiwc+dOTJgwAZ07d8bJkyfLHBWKjY1FdHQ0PDw8pHpNikIjSGV4EZuGrDy+SFunhpLnp8vC5wuwcuVNLF4cBD5f/F8Cs2e3wbJlnaGmVnm13Uj1FhgYKFx78CUTExNMmjQJ69evx4ABA9CqVStMnDgRurq68Pf3x927dzF//nw0aNAAAKCqqoo//vgDI0aMgKurK8aNGwdVVVXs3LkTxsbGiIqKknotQf369TF06FDs27cPN27cQPv27bFp0ya0a9cOHTp0wOjRo9G8eXMIBAJERkbi5MmTGD16tHCX07Jly3DhwgV0794dU6dORa1atXD27Fnhh2xFr2mYP38+9u7di8WLFyMwMBBA4SLhU6dOoXv37ujVqxcGDRqEjh07QlVVFcHBwdizZw90dHRw4sQJkWkSDoeDI0eOoE+fPpgyZQr27NmDvn37wsLCAikpKbh58yZOnTqFQYMGlRpTrVq1sHbtWkyZMgVNmjTB6NGjUadOHcTExODkyZPw8/ODs7MzdHR04Ovri23btmHYsGHo2LEjIiIisGPHDjRt2lTiFFZZfHx8hEcoqKioSDzlujzvb0m6d+8OXV1dBAQECBeAA4CjoyMaNWqE33//HVlZWWjYsCFevXqFLVu2oEmTJiXubJTk119/xa1btzBkyBAMGTIErVu3Bo/HQ3R0NAICAtCyZUvhLjYvLy/MmzcPPXr0wMCBA5GWlob9+/eXOq1UnLzXIAHA3LlzcfjwYQwfPhyzZs2CtbU1Dhw4gPv372Pbtm3Q1dUVXhscHIxOnTrBx8dH4tlYu3btQkFBQamjR6dOncK4ceOgp6cHb29vHD16VKRfR0cH/fv3F2krOotp8ODBsr/QyiDXYyersJJO4txz563I6dmdVl2V6fE/fUpnXbrsEjkJu+jL1PR3du5chBxeBSlNTTxJu6Svhg0bCq8NCgpinp6eTFdXl6mrqzNnZ2e2bds2iY976NAh1qRJE8bj8ZiNjQ1bsmQJO3bsGAPA/P39xZ6/pJO8w8LCmIqKCuvYsaOwLSEhgf3www+sQYMGTF1dnenr67PGjRuz6dOns+fPn4vc/+jRI9alSxemqanJDA0N2ahRo1hkZCQDwCZPniy8LioqigFgixcvLtfPr6STtIsMHTqUAWBBQUEi7SkpKWzp0qWsWbNmTFtbm2loaLCGDRuy77//XuJpw0Xy8vLYtm3bWJcuXZixsTFTVVVlRkZGrFOnTmzDhg0sK0u6E/MvXLjAPD09mZ6eHlNXV2d2dnZs/PjxLDExUXhNeno6GzduHDMyMmKampqsXbt27NatW8zHx4cV/0jw8PBgderUKfN5GzduzAAwT0/PEq8pz/tbksmTJzMjIyOWm5sr0v727Vvm5eXFTExMmKamJmvVqhU7duwYW7x4MQPAoqKihNdKep1fyszMZD///DNr3Lgx09DQYDo6OszBwYGNHz+e3b17V3hdQUEBW758OatXrx7j8Xisdu3a7Mcff2RhYWEy/c7J04cPH9jIkSOZsbExU1dXZ82bN2cHDx4Uu67o/1MfHx+Jj2Nvb880NTVZSkpKic9V9DMu6UvS70/Hjh2Zi4uLVK9FkSdpcxgrY1Kzhijappuamiqy22Hu0Sc4eP+98Pt+zlZYN7R8O0UuXnyDUaOOIz4+U6yvUydb7N07EFZWuhLuJPKUk5ODqKgo2NnZKfXCwKpm9erV+OGHH3Dnzh3hYXSKEBISAhcXF6xYsQJz585VWByk4rx9+xYODg5Yv359mWtiiHIKDQ1FixYtcOLECalqsUnzd7ukz++vRWuQyvDkg+gpp02syz7r5EsJCZkYMMBfLDlSUeHg55874tKlUZQckSohLy8PfL7odHNGRgY2bNgAY2NjtGjRotJiKX52CmMMv//+O4DCE5JJ9WRra4sZM2Zg2bJlyMvLU3Q4RAZLliyBh4eH8heqBa1BKlVOPh+v4tJF2sqbIJmaamP16m8wefJZYZu1tS4OHBiE9u3ryCVOQipDZGQkevTogaFDh8LOzg6fPn3Crl27EBUVhU2bNgnPvakMzs7O6Ny5M5o0aYLMzEycPn0aN27cgLe3N1q2bFlpcZDKt3LlynIthCbK5cSJE4oOQWqUIJUi/FMaCr4oFMrhAI3KmSABwLfftkRgYBSOHAlD79722LmzH4yNy38uCyGKZGpqitatW2Pfvn2Ij4+HqqoqmjRpgpUrV8pU2+lr9OvXD6dPn8aePXtQUFAAOzs7/PLLL1KdX0MIIdKgBKkUz2JEp9fqmepAR738PzIOh4N//umDrl3rYsKEFkp9cighJTE2NsaBAwcUHQYA4PfffxdOqRFCSEWgNUilKL7+qGkpo0cvXyZi48b7JfYbGGhg4sSWlBwRQgghVQCNIJXi7WfRhdVOVpJXx+/e/Rj/+99ZZGbmo359I3zzTb3KCI8QQgghFYRGkErxMUX0gL1ahqLrhjIy8uDjcwI+PieQmZkPABg16jhiYzMqLUZSfnSyBSGEVA2K/HtNCVIJ+AKGuDTRBMnK4L8zGB4/joWLy1bs3i168mx8fCaWLAmqjBBJOamqFg6YllSUkxBCiHIp+ntd9Pe7MlGCVILEjFyRHWwAYKmvCcYYNm68Dze3bXj58rPYfRMmtMCaNd0qK0xSDlwuF1wuV2KtKkIIIconLS1N+Le7stEapBJ8TBE9iE6NywE3XwAvr8M4dixc7HpdXR7++acPvL2VuzpxTcbhcGBmZoZPnz5BXV0d2tratGieEEKUEGMMmZmZSEtLg6WlpUL+VlOCVILi64+0UvLQosUWREenil3r4mKFgwcHoV49o8oKj8hIX18f2dnZSExMlKqCOCGEEMXgcDgwMDCAvn75zx+UB6VOkDZs2IBVq1YhNjYWzZo1w99//w1XV9cSrz98+DAWLlyIt2/fokGDBvjtt9/Qs2dPmZ776b9nIDHGkBYci3c3PoLxxReLzZzZGitXeoLHq/zhP1J+HA4HlpaWMDMzQ35+vqLDIYQQUgI1NTWFTK0VUdoEyd/fH7NmzcLmzZvh5uaGtWvXolu3bnj58iXMzMzErr99+zaGDRuGFStWoHfv3ti/fz/69++Phw8fonHj8k97Pf+YCn5mPhLPRiEnSnzNirGxJnbu7I/eve1len1EsRQ1p00IIaRq4DAl3fPs5uaGVq1aYf369QAAgUAAGxsbTJs2TWKlbm9vb2RmZuLMmTPCttatW8PZ2RmbN28u8/mKVwPu/dcNXFp+F3lxWWLXduhQB/v2DUStWvKrGkwIIYSQ8iv++S0vSrmLLS8vDyEhIfD09BS2qaiowNPTE3fu3JF4z507d0SuB4Bu3bqVeH1ubi7S0tJEvr6UkVsAg461RNo4HGDRog4IDBxNyREhhBBSjSllgpSYmAg+nw9zc3ORdnNzc8TGxkq8JzY2tlzXr1ixAvr6+sIvGxsbkf6E9Fxo2upBz90SAGBipoXAwNFYurQTVFWV8sdGCCGEEDmpsZ/08+bNQ2pqqvDr/fv3wr6M3AJk5vEBAAbtrKDnZoHz13zQqZOdosIlhBBCSCVSykXaJiYm4HK5iIuLE2mPi4uDhYWFxHssLCzKdb26ujrU1dUl9umoq+Llsu5ISM9FXFoO4kblonF9YxleCSGEEEKqIqVMkHg8Hlq2bInAwED0798fQOEi7cDAQEydOlXiPe7u7ggMDMSMGTOEbZcuXYK7u7tUz1m0Vv3LtUh6XEDPUBUNDFWRm5WJXNleDiGEEEIqSNHnttz3nDEldfDgQaaurs527tzJwsLC2MSJE5mBgQGLjY1ljDE2atQoNnfuXOH1t27dYqqqquyPP/5g4eHhbPHixUxNTY09ffpUqud7//49A0Bf9EVf9EVf9EVfVfDrzZs3cs1DlHIECSjctp+QkIBFixYhNjYWzs7OOH/+vHAh9rt376Ci8t8SqjZt2mD//v1YsGAB5s+fjwYNGuDEiRNSn4FkZWWF9+/fQ1dXV3ikeVpaGmxsbPD+/Xu5bh0k5UPvg/Kg90I50PugHOh9UA6pqamoXbs2jIzkW81Cac9BUgYVdbYCKR96H5QHvRfKgd4H5UDvg3KoUecgEUIIIYQoEiVIhBBCCCHFUIJUCnV1dSxevLjE4wBI5aD3QXnQe6Ec6H1QDvQ+KIeKeh9oDRIhhBBCSDE0gkQIIYQQUgwlSIQQQgghxVCCRAghhBBSDCVIhBBCCCHF1PgEacOGDbC1tYWGhgbc3NwQHBxc6vWHDx+Gg4MDNDQ00KRJEwQEBFRSpNVbed6Hf/75B+3bt4ehoSEMDQ3h6elZ5vtGpFfe/yeKHDx4EBwOR1g/kXyd8r4PKSkpmDJlCiwtLaGurg57e3v6+yQH5X0f1q5di4YNG0JTUxM2NjaYOXMmcnJyKina6un69evo06cPrKyswOFwcOLEiTLvCQoKQosWLaCuro769etj586d5X9iuRYuqWIOHjzIeDwe8/PzY8+fP2cTJkxgBgYGLC4uTuL1t27dYlwul/3+++8sLCyMLViwoFz13ohk5X0fhg8fzjZs2MAePXrEwsPDma+vL9PX12cfPnyo5Mirn/K+F0WioqKYtbU1a9++PevXr1/lBFuNlfd9yM3NZS4uLqxnz57s5s2bLCoqigUFBbHQ0NBKjrx6Ke/7sG/fPqaurs727dvHoqKi2IULF5ilpSWbOXNmJUdevQQEBLCffvqJHTt2jAFgx48fL/X6yMhIpqWlxWbNmsXCwsLY33//zbhcLjt//ny5nrdGJ0iurq5sypQpwu/5fD6zsrJiK1askHj9kCFDWK9evUTa3Nzc2LfffluhcVZ35X0fiisoKGC6urps165dFRVijSHLe1FQUMDatGnDtm3bxnx8fChBkoPyvg+bNm1idevWZXl5eZUVYo1Q3vdhypQprHPnziJts2bNYm3btq3QOGsSaRKk2bNns0aNGom0eXt7s27dupXruWrsFFteXh5CQkLg6ekpbFNRUYGnpyfu3Lkj8Z47d+6IXA8A3bp1K/F6UjZZ3ofisrKykJ+fL/dChTWNrO/Fzz//DDMzM4wbN64ywqz2ZHkfTp06BXd3d0yZMgXm5uZo3Lgxli9fDj6fX1lhVzuyvA9t2rRBSEiIcBouMjISAQEB6NmzZ6XETArJ67NaVZ5BVSWJiYng8/kwNzcXaTc3N8eLFy8k3hMbGyvx+tjY2AqLs7qT5X0obs6cObCyshL7H4KUjyzvxc2bN7F9+3aEhoZWQoQ1gyzvQ2RkJK5cuYIRI0YgICAAr1+/xv/+9z/k5+dj8eLFlRF2tSPL+zB8+HAkJiaiXbt2YIyh4P/t3XtQVGUfB/DvunDYBcwLF7kooICiqImKhtgLRk7kAI6CIChsgJoxkqNOxigGZiohoY5i3hJ0Gu/BDELgBcEMxgYnSBOkjEAr78olVOTye//wZV/2grIIu4W/z8z+89zO8zzn4Pl59jzPNjdj8eLFWLVqlTa6zP6no3t1XV0dHj9+DKlU2ql2XtknSKx3SEhIwOHDh5GRkQGJRKLr7rxS6uvrERoaij179sDU1FTX3Xmltba2wtzcHLt378aECRMQFBSE1atXY+fOnbru2iuloKAAGzZswI4dO/Djjz8iPT0d2dnZWLduna67xrrglX2CZGpqCrFYjNu3byuk3759GxYWFmrrWFhYaFSevVhXzkObpKQkJCQk4MyZMxg7dmxPdvOVoOm5+O2331BVVQVfX195WmtrKwBAT08PFRUVsLe379lO90Jd+ZuwtLSEvr4+xGKxPG3kyJG4desWnj59CkEQerTPvVFXzsOaNWsQGhqKBQsWAADGjBmDhoYGLFq0CKtXr0afPvxMQhs6ule/9tprnX56BLzCT5AEQcCECROQl5cnT2ttbUVeXh7c3NzU1nFzc1MoDwCnT5/usDx7sa6cBwBITEzEunXrkJubi4kTJ2qjq72epufCyckJly9fRmlpqfzj5+eHadOmobS0FEOGDNFm93uNrvxNuLu749q1a/IAFQB++eUXWFpacnDURV05D48ePVIJgtqCVuKfPdWabrtXa/b+eO9y+PBhMjAwoLS0NCorK6NFixZR//796datW0REFBoaSjExMfLyhYWFpKenR0lJSVReXk5xcXG8zL8baHoeEhISSBAEOn78ON28eVP+qa+v19UQeg1Nz4UyXsXWPTQ9D9evX6e+ffvSkiVLqKKigrKyssjc3Jw+++wzXQ2hV9D0PMTFxVHfvn3p0KFDVFlZSadOnSJ7e3sKDAzU1RB6hfr6eiopKaGSkhICQMnJyVRSUkLV1dVERBQTE0OhoaHy8m3L/D/66CMqLy+nlJQUXubfFdu2bSMbGxsSBIEmTZpEFy5ckOd5eHiQTCZTKH/06FEaPnw4CYJAzs7OlJ2dreUe906anAdbW1sCoPKJi4vTfsd7IU3/JtrjAKn7aHoeioqKaPLkyWRgYEDDhg2j9evXU3Nzs5Z73ftoch6ampooPj6e7O3tSSKR0JAhQygqKooePnyo/Y73Ivn5+Wr/zW+be5lMRh4eHip1xo0bR4Ig0LBhwyg1NVXj44qI+LkfY4wxxlh7r+w7SIwxxhhjHeEAiTHGGGNMCQdIjDHGGGNKOEBijDHGGFPCARJjjDHGmBIOkBhjjDHGlHCAxBhjjDGmhAMkxnqAnZ0dRCLRcz9btmzpcvvx8fEQiUSIj4/vtj53F3VjNzAwgI2NDYKCgnD+/Hmd9KutL9quqw0dzfngwYMxc+ZMZGVl6bqLjP3rvLI/VsuYNri7u8PBwUFt3qhRo7TcG+1qP/aamhpcvHgRR48exbFjx5CUlITly5fruIfPeHp64ty5c8jPz4enp6euu/NS2s95bW0tSkpKkJmZiczMTCxbtgzJycndcpy0tDSEh4dDJpMhLS2tW9pk7J+GAyTGetCCBQvw3nvv6bobOqE89idPnuD999/HgQMHsHLlSvj4+GD48OFa6095eblO6mqT8pw3Nzdj2bJl2L59OzZv3ozg4GC4urrqroOM/YvwV2yMMa2QSCRISUmBkZERWlpakJ6ertXjOzk5wcnJSet1dUlPTw+bNm3Ca6+9BgA4ceKEjnvE2L8HB0iM6Vh6ejoWLFiA0aNHY8CAAZBIJBg6dCgiIiJQUVGhcXvHjh3D22+/DRMTE+jr68PExASjRo3CwoULcenSJbV1jh8/Dm9vb5iZmUEQBFhbW2P+/PkoKyt72eEpMDY2xogRIwAAVVVVCnknT56Ej48PzM3NIQgCrKysEBQUhIsXL6ptq7a2FrGxsRgzZgyMjIxgYGAAKysruLu745NPPkFTU5NCeeX3iAoKCiASiXDu3DkAwLRp0xTe4Wn/1ZFy3ZqaGkilUojFYvz5558djjcgIAAikQhbt25VydPWnEskEjg6OgIAbt++rZJ/5swZREdHY9y4cTA1NZW/uxQUFITi4mKV8nZ2dggPDwcA7N+/X2HO1H1Fqa1xMtbtXvZXdhljqmxtbQlAp35BWiwWk6GhIU2cOJFmz55Nfn5+NGzYMAJARkZGVFhYqFInLi6OAFBcXJxC+tq1awkA6enp0X/+8x8KDg6mGTNm0OjRo0kkEtHmzZsVyjc1NVFgYCABIAMDA5oyZQrNmTOHXn/9dQJAUqmUcnJyunXsDg4OBIA+/PBDeVpsbCwBIJFIRO7u7hQcHEzjxo0jACQWi+mrr75SaKOhoYFGjx5NAMjMzIx8fX1p7ty55OnpSRYWFgRA5RfU8b9fAG9TXl5OMpmMBg0aRADonXfeIZlMJv+cP3++w7pERMHBwQSANm7cqHac9+7dI0EQSBAEunfvnjxdF3Pu6OhIAGjNmjUqefb29iQIArm4uJCfnx/Nnj2bRo0aJb+Ojh8/rlB+xYoV5O7uTgDI3t5eYc7az0VPjJMxbeIAibEeoEmAdPjwYfr7778V0lpbWyklJYUAkLOzM7W2tirkqwuQnjx5QlKplIyNjenq1asqx6mqqqLy8nKFtFWrVhEAmjx5MlVWVirkHTt2jMRiMQ0YMEAl2Hie5439p59+oj59+hAA2rdvHxER5eTkEACSSCR06tQphfJ79+4lAKSvr08///yzPH3//v0EgN599116+vSpQp2WlhYqKCigxsZGhXR1QQ4RkYeHBwGg/Pz8Dsekru7p06cJADk5Oamts3XrVgJA/v7+CunanvOysjISi8UEgIqLi1XyMzIy6MGDB2rT9fT0yMTEhB49eqSQl5qaSgBIJpN12KeeGCdj2sQBEmM9oO2G1dHHw8OjU+24ubkRALpy5YpCuroA6c6dOwSAxo4d26m279+/T1KplCQSCf3xxx9qy0RFRREA2rZtW6faJFJ/s66pqaHs7Gyyt7cnAGRlZSUPCr28vAgALV++XG17Pj4+BIAWLlwoT0tMTCQAlJyc3Ol+dXeA1NraKh9rUVGRSp22J2BZWVnyNG3P+cmTJ8nJyYkAUGxsbKfba9P2lCw7O1sh/UUBUk+NkzFt4lVsjPWgjpb5K7/we+3aNeTm5uLatWuor69HS0sLgP+/M1JRUfHCbQHMzMxgZ2eHS5cuYcWKFYiMjHxunfz8fDx+/BheXl6wtrZWW8bT0xM7duxAUVERlixZ8tzjKwsPD5e/q9Kevb09vvnmGxgZGaG5uRmFhYUA0OFqv8jISGRlZSE/P1+e1rYSKzExESYmJvDx8cHAgQM16t/LEolEkMlk+PTTT5GWlgY3Nzd5XmlpKUpLS2FpaQlvb295ui7mXCwW4+uvv8a8efM6rPfXX38hOzsbV69eRW1tLZqbmwEAV65cAfDs+psxY0an+9HT42RMGzhAYqwHvWiZf0tLC5YsWYJdu3aBiDosV1dX16njHThwAAEBAUhOTkZycjIGDhyIyZMnY/r06QgNDYWpqam8bGVlJQAgLy/vhZsg3r17t1PHb699cCgIAszNzfHGG2/A29sbenrP/um5f/8+njx5AgAYOnSo2nbs7e0BQOFlaE9PT3z88cfYtGkTZDIZRCIRHB0d4e7ujpkzZ8LX1xd9+vT8GpTw8HCsW7cOR44cwZYtWyCVSgEAqampAICwsDCIxWJ5eW3O+d27d3H+/HnU19fjgw8+gKOjIyZNmqRSZ+3atVi/fr3KS+3tdfb6a9PT42RMGzhAYkyHtm7dip07d8LCwgLJycmYMmUKBg0aBIlEAgAICQnBoUOHnhs8tffmm2+iqqoK2dnZOHfuHIqKinDy5Enk5OQgLi4OGRkZ8PLyAgC0trYCABwcHODu7v7cdruyxL2n94BKSEjA4sWLceLECXz//fcoLCxEamoqUlNT4erqivz8fBgZGfXY8YFnK7qmTZuGs2fPIiMjAyEhIWhqasLBgwcBQOVpjrbnvLa2FrNmzUJ+fj4CAwNRVlYGQ0NDeX56ejri4+NhbGyM7du346233oKVlRWkUilEIhFWrVqFjRs3dvr6a9PT42RMGzhAYkyHjh49CgDYtWsX/Pz8VPJ//fVXjduUSqUICAhAQEAAgGf/Q4+NjcXu3bsRERGB6upqAMCQIUMAACNGjNDZbsgmJiYwMDBAY2MjKisrMXbsWJUybU8j1H1VY2dnh+joaERHRwMAiouLMX/+fBQXFyMxMRFr167t2QHgWRB09uxZpKamIiQkBCdOnMC9e/cwZcoU+ZYGbbQ95/369cORI0fg5OSE6upqJCcnIzY2Vp7fdv2tX78eixYtUqnflesP+GdcW4y9LN4HiTEdevDgAQDA1tZWJe/KlSsoLS196WOYmZkhMTERAHD9+nU8fPgQAODl5QVBEFBQUIA7d+689HG6Qk9PD1OnTgWADm+k+/btA/Bsn6IXcXV1RVRUFAB0eu4EQQAA+Xs3mvL390e/fv1w9uxZ3LhxQ/71mrr3r3Qx52ZmZvKgKCkpCTU1NfK8511/d+7cwenTp9W2+aI5+ydcW4y9LA6QGNOhkSNHAgBSUlLkX0sAwM2bNxEWFqbRTbu6uhp79+5V+75I2w7KAwYMkO+qPGjQIERHR6OhoQG+vr64fPmySr3GxkZkZmbi6tWrGo1LEytWrAAAfPnll8jLy1PIS0tLQ2ZmJvT19bF06VJ5ekZGBr777juFOQOApqYm5ObmAlB/01dn8ODBAP7/QrKmpFIp5s6di9bWVnz++efIzc2FoaEhgoKCVMrqas6joqJgY2OD2tpafPHFF/L0tutv9+7dePr0qTy9trYWMpkMtbW1attrm7OONnv8p1xbjL0UHa+iY6xX6uw+SBcuXCBBEAgAOTg4UGBgIHl7e5NUKiVnZ2eaNWuW2nbULfMvKSmR7xnk6upKgYGBFBgYSC4uLvJNGPfu3avQTlNTE4WEhBAA6tOnD7m4uJC/vz8FBQWRu7s7GRkZEQCNNvTTZA+oNu03ipw6dSqFhITQ+PHjO9wocunSpQSATE1Nafr06TRv3jzy8/Mjc3NzAkDW1tZ048YNhTroYJl/VlYWASBBEMjHx4ciIiIoMjJSYYPOjuq2uXDhgsI2DmFhYR2W1dWc79u3jwBQ37596f79+0REVFlZSf3795fPmb+/P/n5+VG/fv3I0tKSIiIi1G5I2tjYSFZWVgSAXFxcKCwsjCIjIykxMbFHx8mYNnGAxFgP0CRIuHTpEvn5+ZGlpSVJJBJydHSklStXUl1dHclksk4HSHV1dbRlyxaaNWsWOTo6krGxMRkZGdHw4cMpLCyMLl682GEfvv32W5o9ezZZW1uTvr4+9e/fn0aOHElz586lgwcPUkNDQ4+Mvb2cnByaMWMGmZiYkJ6eHllYWNCcOXPohx9+UClbUlJCMTExNHXqVLK2tiZBEMjMzIwmTJhAGzZsUNi5us3zgpw9e/bQ+PHjydDQUF6uff9fFCARETk7O8vLPW9PpTbanvPm5mb5DtkxMTHy9N9//53mzZtHNjY2ZGBgQLa2trR48WK6detWhzu2ExFdvnyZ/Pz8yMzMTL75p7r9vbpznIxpk4hIw+UJjDHGGGO9HL+DxBhjjDGmhAMkxhhjjDElHCAxxhhjjCnhAIkxxhhjTAkHSIwxxhhjSjhAYowxxhhTwgESY4wxxpgSDpAYY4wxxpRwgMQYY4wxpoQDJMYYY4wxJRwgMcYYY4wp4QCJMcYYY0wJB0iMMcYYY0r+CzYbwQ7k6CqyAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "logRege_roc_curve(dt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "-Z_i7kbfVn9z",
        "outputId": "e82266a1-97ae-4a67-d239-93c4c0c61153"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "Epoh6qHtvjEn",
        "Jd7tcLqYwddQ",
        "TjT4xWVlkeQ_",
        "Tm2xgslTmTpk",
        "tHFaGOi6dUZe",
        "oygCJje7R7Yh",
        "GtmN06M_5tU5",
        "7U54NhnV5zLg",
        "ynsU7HX8581w",
        "l8XG_DesOchF"
      ],
      "toc_visible": true,
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "vscode": {
      "interpreter": {
        "hash": "a90aeebcf29d64a654773811cc170cb25061cb2498f10ac689db374c7bf325de"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}