{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 362,
      "metadata": {
        "id": "Vvcj31qpfCwY"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import pickle "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 363,
      "metadata": {
        "id": "frLJTPPefM7G"
      },
      "outputs": [],
      "source": [
        "# Load the dataset\n",
        "data = pd.read_csv('indeed.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 364,
      "metadata": {},
      "outputs": [],
      "source": [
        "# from sklearn.preprocessing import MinMaxScaler\n",
        "# import pandas as pd\n",
        "\n",
        "# # Create a MinMaxScaler object\n",
        "# scaler = MinMaxScaler()\n",
        "\n",
        "# # Apply the scaler to the 'New Salary' column\n",
        "# data['New Salary'] = scaler.fit_transform(data['New Salary'].values.reshape(-1, 1))\n",
        "\n",
        "# # Print the normalized data\n",
        "# print(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 365,
      "metadata": {
        "id": "-9bDf-NOfOLo"
      },
      "outputs": [],
      "source": [
        "# seperate features and label\n",
        "X = data[[\"New Salary\",\"Experience\"]]\n",
        "y = data[\"Job_Pos\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 366,
      "metadata": {
        "id": "83HqtwnBfP0F"
      },
      "outputs": [],
      "source": [
        "X_train, X_test,y_train,y_test = train_test_split(x,y, test_size=0.3, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 367,
      "metadata": {},
      "outputs": [],
      "source": [
        "# for bernoulli\n",
        "a_train = X_train\n",
        "a_test = X_test\n",
        "b_train = y_train.astype(int)\n",
        "b_test = y_test.astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 368,
      "metadata": {},
      "outputs": [],
      "source": [
        "class_prob = [0.25,0.25,0.25,0.25]"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "lnd1249Ig9ov"
      },
      "source": [
        "# **MultinomialNB**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 369,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-39 {color: black;background-color: white;}#sk-container-id-39 pre{padding: 0;}#sk-container-id-39 div.sk-toggleable {background-color: white;}#sk-container-id-39 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-39 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-39 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-39 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-39 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-39 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-39 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-39 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-39 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-39 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-39 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-39 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-39 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-39 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-39 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-39 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-39 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-39 div.sk-item {position: relative;z-index: 1;}#sk-container-id-39 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-39 div.sk-item::before, #sk-container-id-39 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-39 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-39 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-39 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-39 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-39 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-39 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-39 div.sk-label-container {text-align: center;}#sk-container-id-39 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-39 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-39\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB(alpha=10.0, class_prior=[0.25, 0.25, 0.25, 0.25], fit_prior=False)</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-39\" type=\"checkbox\" checked><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB(alpha=10.0, class_prior=[0.25, 0.25, 0.25, 0.25], fit_prior=False)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "MultinomialNB(alpha=10.0, class_prior=[0.25, 0.25, 0.25, 0.25], fit_prior=False)"
            ]
          },
          "execution_count": 369,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "\n",
        "# Define the hyperparameters to tune and their possible values\n",
        "param_grid = {'alpha': [0.1, 1.0, 10.0],\n",
        "              'fit_prior': [True, False]}\n",
        "\n",
        "# Perform hyperparameter tuning using GridSearchCV\n",
        "M_clf = MultinomialNB()\n",
        "grid_search = GridSearchCV(M_clf, param_grid, cv=5)\n",
        "grid_search.fit(X_train, y_train)\n",
        "best_params = grid_search.best_params_\n",
        "\n",
        "# Train the MNB classifier on the training data using the best hyperparameters\n",
        "M_clf = MultinomialNB(alpha=best_params['alpha'], fit_prior=best_params['fit_prior'], class_prior=class_prob)\n",
        "M_clf.fit(X_train, y_train)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 370,
      "metadata": {
        "id": "bLbwDzxtf3NP"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.38372093023255816\n"
          ]
        }
      ],
      "source": [
        "M_accuracy = M_clf.score(X_test, y_test)\n",
        "print(f'Accuracy: {M_accuracy}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 371,
      "metadata": {
        "id": "BJHM6YE3n8vH"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.51      0.83      0.63        23\n",
            "           2       0.50      0.05      0.09        21\n",
            "           3       0.31      0.65      0.42        20\n",
            "           4       0.00      0.00      0.00        22\n",
            "\n",
            "    accuracy                           0.38        86\n",
            "   macro avg       0.33      0.38      0.28        86\n",
            "weighted avg       0.33      0.38      0.29        86\n",
            "\n"
          ]
        }
      ],
      "source": [
        "y_test_pred = M_clf.predict(X_test)\n",
        "pred_prob = M_clf.predict_proba(X_test)\n",
        "report = classification_report(y_test, y_test_pred)\n",
        "print(report)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 372,
      "metadata": {
        "id": "A9DHvgxXoIxy"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[19  0  4  0]\n",
            " [ 8  1  7  5]\n",
            " [ 7  0 13  0]\n",
            " [ 3  1 18  0]]\n"
          ]
        }
      ],
      "source": [
        "M_cm = confusion_matrix(y_test, y_test_pred)\n",
        "print(M_cm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 373,
      "metadata": {
        "id": "YROyQwkcPf8B"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 900x900 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(9,9))\n",
        "sns.heatmap(M_cm, annot=True, fmt=\".3f\", linewidths=.5, square=True, cmap='Blues_r');\n",
        "plt.ylabel('Actual label');\n",
        "plt.xlabel('Predicted label');\n",
        "all_sample_title= 'Accuracy score: {0}'.format(M_accuracy)\n",
        "plt.title(all_sample_title, size=15);"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "zFL9KiWLhu4g"
      },
      "source": [
        "# **GaussianNB**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 374,
      "metadata": {
        "id": "XOaUTNwPhMdE"
      },
      "outputs": [],
      "source": [
        "from sklearn.naive_bayes import GaussianNB"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 375,
      "metadata": {
        "id": "0WrPIlH_hf4I"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-40 {color: black;background-color: white;}#sk-container-id-40 pre{padding: 0;}#sk-container-id-40 div.sk-toggleable {background-color: white;}#sk-container-id-40 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-40 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-40 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-40 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-40 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-40 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-40 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-40 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-40 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-40 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-40 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-40 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-40 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-40 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-40 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-40 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-40 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-40 div.sk-item {position: relative;z-index: 1;}#sk-container-id-40 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-40 div.sk-item::before, #sk-container-id-40 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-40 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-40 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-40 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-40 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-40 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-40 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-40 div.sk-label-container {text-align: center;}#sk-container-id-40 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-40 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-40\" 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-40\" type=\"checkbox\" checked><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "GaussianNB()"
            ]
          },
          "execution_count": 375,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "G_clf = GaussianNB()\n",
        "G_clf.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 376,
      "metadata": {
        "id": "G_QAOnVlhh0P"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.7790697674418605\n"
          ]
        }
      ],
      "source": [
        "G_accuracy = G_clf.score(X_test, y_test)\n",
        "print(f'Accuracy: {G_accuracy}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 377,
      "metadata": {
        "id": "umT3Db2DmKQl"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       1.00      0.91      0.95        23\n",
            "           2       0.75      0.71      0.73        21\n",
            "           3       0.56      0.75      0.64        20\n",
            "           4       0.89      0.73      0.80        22\n",
            "\n",
            "    accuracy                           0.78        86\n",
            "   macro avg       0.80      0.78      0.78        86\n",
            "weighted avg       0.81      0.78      0.79        86\n",
            "\n"
          ]
        }
      ],
      "source": [
        "y_test_pred = G_clf.predict(X_test)\n",
        "pred_prob = G_clf.predict_proba(X_test)\n",
        "report = classification_report(y_test, y_test_pred)\n",
        "print(report)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 378,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1 4 3 3 2 4 4 3 1 4 1 3 3 3 1 3 4 2 3 1 2 1 1 4 1 3 2 3 4 2 4 2 2 1 2 3 2\n",
            " 2 3 3 1 1 1 3 2 4 4 4 2 2 4 3 3 1 3 4 4 4 1 2 3 2 3 1 4 2 1 1 2 1 1 2 4 1\n",
            " 3 3 3 2 3 3 1 2 3 3 3 4]\n"
          ]
        }
      ],
      "source": [
        "print(y_test_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 379,
      "metadata": {
        "id": "j4PpcnEOljgd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[21  2  0  0]\n",
            " [ 0 15  6  0]\n",
            " [ 0  3 15  2]\n",
            " [ 0  0  6 16]]\n"
          ]
        }
      ],
      "source": [
        "G_cm = confusion_matrix(y_test, y_test_pred)\n",
        "print(G_cm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 380,
      "metadata": {
        "id": "Tx15t50_Syyq"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 900x900 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(9,9))\n",
        "sns.heatmap(G_cm, annot=True, fmt=\".3f\", linewidths=.5, square=True, cmap='Blues_r');\n",
        "plt.ylabel('Actual label');\n",
        "plt.xlabel('Predicted label');\n",
        "all_sample_title= 'Accuracy score: {0}'.format(G_accuracy)\n",
        "plt.title(all_sample_title, size=15);"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "cbkRybvginwO"
      },
      "source": [
        "# **BernoulliNB**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 381,
      "metadata": {
        "id": "68HQyE7fis45"
      },
      "outputs": [],
      "source": [
        "from sklearn.naive_bayes import BernoulliNB"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 382,
      "metadata": {
        "id": "GwM1i8J6h4x-"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-41 {color: black;background-color: white;}#sk-container-id-41 pre{padding: 0;}#sk-container-id-41 div.sk-toggleable {background-color: white;}#sk-container-id-41 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-41 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-41 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-41 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-41 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-41 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-41 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-41 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-41 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-41 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-41 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-41 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-41 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-41 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-41 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-41 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-41 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-41 div.sk-item {position: relative;z-index: 1;}#sk-container-id-41 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-41 div.sk-item::before, #sk-container-id-41 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-41 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-41 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-41 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-41 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-41 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-41 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-41 div.sk-label-container {text-align: center;}#sk-container-id-41 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-41 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-41\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BernoulliNB(alpha=0.1, binarize=0.5)</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-41\" type=\"checkbox\" checked><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BernoulliNB</label><div class=\"sk-toggleable__content\"><pre>BernoulliNB(alpha=0.1, binarize=0.5)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "BernoulliNB(alpha=0.1, binarize=0.5)"
            ]
          },
          "execution_count": 382,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "B_clf = BernoulliNB(alpha=0.1, fit_prior=True, binarize=0.5)\n",
        "B_clf.fit(a_train, b_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 383,
      "metadata": {
        "id": "9fiIE0jMi1c3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.23255813953488372\n"
          ]
        }
      ],
      "source": [
        "B_accuracy = B_clf.score(X_test, y_test)\n",
        "print(f'Accuracy: {B_accuracy}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 384,
      "metadata": {
        "id": "eetJ31CSkc4n"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.00      0.00      0.00        23\n",
            "           2       0.00      0.00      0.00        21\n",
            "           3       0.23      1.00      0.38        20\n",
            "           4       0.00      0.00      0.00        22\n",
            "\n",
            "    accuracy                           0.23        86\n",
            "   macro avg       0.06      0.25      0.09        86\n",
            "weighted avg       0.05      0.23      0.09        86\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Users\\Ayooo\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "c:\\Users\\Ayooo\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "c:\\Users\\Ayooo\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ],
      "source": [
        "y_test_pred = B_clf.predict(X_test)\n",
        "pred_prob = B_clf.predict_proba(X_test)\n",
        "report = classification_report(y_test, y_test_pred)\n",
        "print(report)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 385,
      "metadata": {
        "id": "5LSGMWcfnQwR"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[ 0  0 23  0]\n",
            " [ 0  0 21  0]\n",
            " [ 0  0 20  0]\n",
            " [ 0  0 22  0]]\n"
          ]
        }
      ],
      "source": [
        "B_cm = confusion_matrix(y_test, y_test_pred)\n",
        "print(B_cm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 386,
      "metadata": {
        "id": "z9WTZjl6S8sA"
      },
      "outputs": [
        {
          "data": {
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A+fnnn536JiQkOAWZdrvdVKxY0Ugyr732mlMt4hdffGG8vLxMjhw5zN9//+1oT+/aJiYmOs45duxYp+Bs3759pmjRosbb29v88ccfTsfdSOCcK1cul1+Vv/TSS0aSmThxouVzpmX//v2OXwpdBalPPfWU6dKli2nVqpXJnz+/o9QprV+erqd79+5GknnooYdS7Uu5Rmlt7dq1SzMATAmc09oqVqxo9u7dm+Y43n//fePl5WV8fHxMo0aNTMeOHU21atWMzWYzrVq1Svc9ZTRw/r//+z/TpUsX07FjR1OjRg0jyQQFBZnFixc79du7d6+RZGw2m7l48WKq8/zvf/9zvL9r77HrXYeQkBDz7bffuhzf2LFjTZcuXcwjjzxiypcvbySZfPnyXfd+6NevX6pSBFeBc2Jionn00UcdteUtW7Y07dq1M0WKFDH+/v7m1VdfTTNAf/XVVx1lHM2aNTPt27c3ZcuWNb6+vqZr165pXqMUN3u/ppQvXT2/wIqPP/7YSDK5c+dO9cshPJu7g2UCZ3islGD0888/d7RFRUUZX19fkzVrVqeMiDHGPP/880aSee6559I9919//WW8vb2Nn59fmhN4rnWzgfPvv/+e7mtc64033kiVXc/ouA8fPmy8vLxMuXLlUu2LiIgwksymTZssjads2bJGUqrrnpYWLVoYSWb06NHp9l22bJmRi0k6bdu2NZLMiBEjHG3pXduvvvrKEcil5csvvzSSTJ8+fZza33//fVO6dOkM1Ur6+voaSWbfvn1p7k/5OY4cOdLyOa+VkJBg6tWrZySZjh07uuzr7e3tFJA1bNgw1bcBrnz33XfGZrMZX19fs2XLllT7P/roI/POO++YHTt2mNjYWHPs2DHzySefOIKeNm3apDpm06ZN5tVXXzVr16410dHRJjo62ixdutTUrl3bka283n01f/58x7cEKVt4eLh5//33nX7JSktGA+eUX0pTtuDgYPO///0vVT+73W7Cw8ONJDN16tRU+1u2bOk4x+rVq532Pfnkk2bx4sXmr7/+MrGxsWbz5s2OCXFZsmQx69evv+74UuqtU7bChQubFStWpNl348aNxtvbO9Uvga4CZ2OSg+fXXnstVWBfrVq1VL9AXG3ChAnGx8fH6ZgSJUqkSmJc62bu17///tt4e3sbb29vc/z4cUvHGGPM0aNHHd9wpfXzg2dzd7BM4AyPdOTIEWOz2UzOnDlTZQNat25tJJkZM2Y4tZcsWdJIMps3b073/J988kmqbLYrNxM4h4eHuzx3YmKiWbJkiRkyZIh55plnTJcuXUyXLl0cpQrvvvvuDY/bGGMeeOABIzlPYtq2bZuRZGrUqGH5PI8//rgjC7l+/frrfsWekJBg/P39jSRz5syZdM87bNgwI8m88sorae5PCYKbNWvmaEvv2qb8EnW9f7RPnz5tJJnatWunO7703I7AOeX9FCtWzFG2lJ6///7bfP7556ZEiRImR44cLoOeFLt27XJk0CdMmJChMf79998mJCTESErzW5S0JCYmmnvvvddIMqNGjXLaZ7fbzYsvvuj4ZXjv3r0mNjbWrFu3znHMCy+84PL8N1qqceHCBbNhwwbToUMHI8n06NEjVZ+UFShy5Mhh/u///s9ER0ebQ4cOmT59+hhJjiDS6ipAKd8ONW3aNN2+Z8+eNStXrjRNmjRJ9UulMcnXtWrVqiYkJCTVaiCuAuczZ86YBg0amGzZspn33nvP/Pnnn+bMmTNm4cKFpmDBgsbLy8vMnz/f6ZjLly+bDh06GG9vbzN48GBz6NAhExMTY5YtW2YqVKjg+NYnPTdyv7777rtGkmnevHm6fVPExsY6vk1I65c8eL4T5694zOaJCJzvUqNGjTKSTPfu3VPt+/zzz42kVPWNWbNmNZLSXFbqWqNHjzaSTL9+/SyN52YC54iIiOue99ixY44a2ettV6+gkNFxG/PvyiRdu3Z1tPXu3dtIMtOnT7d8nqNHjzqNNVeuXOahhx4yM2bMcFoq68SJE45snRXPPvuskWQmTZqU5v7NmzcbyXllivSubUrGO72tRIkSlt//9dzqUo0RI0YYKXnJsesF564cPnzYBAQEmLx585rY2Njr9vvzzz8dQVVG7q+rvfLKKy7/nqTl22+/TfPvc8rfubR+SYyJiTH58uUzNpvNbN++/brnzoxVNVq1amWk1CVKdrvd8ffo6s1ms5kRI0aY3LlzG0lmz549ll7n3Llzxtvb22TJkiXVSibXc+XKFVO9enVjs9mcMtXvvPOOkZTmHAlXgXOXLl2MlHZp1++//25sNpvJnz+/0zdDKaU7V88JSXHkyBGTPXt2ky1btlQB/PVYvV+NMY5lE9PLaqe4cuWK47OhXr16LktI4LncHSx7euDMqhp3qY8++khS8oMT6tWr57SNHTtWkrRy5UodOXLEncN0cPV0uKxZs153X/fu3bV161a1a9dO69at07lz55SUlCRjjKZPny5JMjf5DKAHHnhABQsW1GeffaaYmBhdvnxZH3/8sXLkyKFOnTpZPk/BggW1YcMGLVmyRL1791bBggX1zTffqEePHqpUqdItW33B1YNDrndtU34ezZs3V5cuXa67tW7d+qbHV6hQIUm67pPYUtoLFy6c4XNPmzZNb775poKCgrR48eI0HziSnsKFC+vee+/ViRMntG7dujT7nDlzRk2bNtWRI0fUrVs3vfPOOxl+HUmOh00cP378po9J+QxIa7WSgIAANW/eXMYYrVq16obGalXKaj7XrpBhs9k0ceJEbd68WUOHDlWPHj30xhtvaOPGjXrllVd05swZ+fv7q1ixYpZeJygoSGFhYbpy5Yrlv0u+vr7q2LGjjDH65ptvHO3ffPONbDab5syZo4YNGzptJ06ckCS1b99eDRs21OLFiyUlP0Rk3rx5ktK+5jVq1FDRokX1119/OT1B0NXPqVChQoqIiNDFixedVilyxcr9KiWvbLJ582blyJFDbdq0Sfe8drtdXbp00Q8//KAqVarom2++kb+/v6UxAXcSD3wkC261jRs3ateuXZKSn/y0f//+NPsZY/TJJ59o4MCBkpIDu3379unAgQOqUqWKy9coWLCgJOnAgQOWxpSyNNL1HkN87NgxS+e5WlxcnH766SflyZNHCxYskLe3t9P+tB5vm9FxS5K3t7d69OihwYMH65NPPlFgYKDOnj2r7t27KyAgIENj9vHxUdOmTdW0aVNJ0pEjR/TUU09p2bJlGjNmjMaOHavQ0FD5+/vrzJkzOnfunHLmzOnynPny5XOcKy0py7ilPDHNipQnonXv3l3t2rWzfNyNqFy5srZu3apNmzY5PR0tRcpT7ipVqpSh886fP189e/ZUtmzZ9N1336V7T7sSGhoqSTp9+nSqfbGxsWrRooV27typtm3basaMGZafcnits2fPSpKyZ89+08ek/MJxvWXMUtpTjr9VXF07KflJhdf+bFauXKmkpCTVrVtXPj7W/hmz2+2KiYmRlLHrd73xGWO0cuXK6x63du1aSVLXrl0lSadOndKVK1ckZeya34qfU3rXXPo3YG/btq2yZcuW7jl79+6tefPmqVSpUlqyZEm6n0vwXDeXSvrvI+N8F0pZu/mVV16RSS7XSbUtX77cqa8kNWnSRJL0wQcfpPsaDRs2lLe3t5YsWWIp6A0PD5ck7d27N9W+vXv36ujRo+me41rnz5+X3W5XeHh4qqA5ISFBX3311U2PO0X37t3l4+OjGTNmONZQTnnc8s0oXLiwXn/9dUnJj9GVkgP1hg0bSrL2s7j33nslSZ9//rmSkpJS7U/5Gaf0s+L++++XpDSvYWZr2bKlpOR1s6+1efNmHTx4UBUqVFCRIkUsn/P777/Xk08+KR8fH3311VeqW7fuDY8vKSnJkZW9dh3f+Ph4tW7dWuvXr1ezZs00b968VPeiVcYYx/WuVq2a5eP+97//pXlM3rx5JUkbNmxI87iU9oxc1xuxYsUKSamvnSvvv/++JKV6HLgrixcvVlxcnIoXL67AwMCbGt/y5cuv+9mZ8s3HsWPHZIxxBM7BwcGOBEFa1zwmJkZ79uyR5PztiaufU1JSkjZv3izJ+s/J1f2awhijTz/9VJK1tZvffPNNTZkyRYUKFdJPP/2U6jHywH/KbS0MgdslJiaaPHnyGElm48aN1+2XlJTkmMWfshTTnj17HMvRXTuBxW63mx9//NFpomG3bt2MJNOoUaNUSx+dPHnSaVLPhQsXTLZs2YyPj4/T0k+nT592TFTSdWqcr7fWbEJCggkKCjI+Pj5m1apVTtegb9++jnNeWy+akXFfLWV1CkmmUqVKafZxZdy4cWnOXE+ZwPXMM8842pYvX25sNpsJDAxMtUaqq+XoBg4c6LRSwpdffulyOTpX17ZcuXJGkhk+fHiqCaZ2u92sWrXK6bobc2OralzvkduxsbEuH7nduHFjU7p06VRPvlu1apXx9/c3Pj4+5quvvrI0hnnz5plt27alao+OjjZPPfWUkZKXfbv62iYmJpqHH37YSDL33nuvpbW8T506ZSZNmpRqHsGFCxcctep58+ZNda7x48ebo0ePOrXZ7XYzbdo04+PjY2w2W6ol1caNG+eYfHftNUp5qmBAQIDLyZJWapx37txpFixYkKqu2G63m3nz5hl/f/9UNcTGJP9dO3LkiFNbQkKCGTx4sOPv57XmzZuX5qoZy5cvN/ny5Ut1DxmTfD/88MMPqSbjXrlyxUycONF4eXkZf3//VNf3elzVOKfUc1epUsXp79ulS5fMY489ZqTkpeKuljIZskCBAk713FevzlG4cGGnR4LfyP16tRUrVhhJJn/+/NedpJwi5T7KmzfvdZc9xJ3l+LkrHrN5IgLnu8z3339vJJlSpUql2zdljdKrJ6XMmzfPscpBuXLlzKOPPmpatGiR5gNQYmJiHCtXZM+e3bRo0cJ07NjR1K5d22TJkiXVZJeUfxCzZs1qmjVrZpo3b25y5cpl7rnnHkeAlJHA2RhjRo4caSQZb29vxwNQUtZM7dmzZ5qBc0bHneLHH390BM7Xm4jnSlBQkPHy8jJVq1Y1HTp0MO3btzelSpVyTAS89h+lt99+29hsNiMlr97RqVMnc//991/3ASgpKzKULVvWdOrUydStW9fon9UJFixY4NTfyrXdu3evI6ANCwszTZo0MZ07dzZNmzZ1PPjg2klQN7KOszHJD8RIWUkkIiLCdOjQwbFc2SOPPJJmAJASwPzyyy9O7Tlz5jSSTNGiRR0rrFy7XbuiTMqkrmLFipnWrVubTp06mfr165scOXI4AoydO3c6HTNhwgTH/fDwww9f97WuntSVct1z5MhhGjVqZDp37mzuv/9+x88uZ86cqX4ZSXmv3t7epmbNmqZDhw6mVatWjp+Nl5eXef/991Mdc+nSJccSfF5eXqZu3bqmffv2jl+IvL29zdy5c1MdN2PGDBMREWEiIiJMtWrVjJS8zFtKW0REhNMv5SlrTAcFBZnGjRubzp07mwceeMAUKVLE8dppTZb75ZdfjM1mM9WqVTPt2rUzbdu2dfzMq1atmmZAn/JzKlWqlHn44YdNhw4dTJUqVRw/h0cffTRVIJgySTI0NNQ0a9bMcQ+nvFbWrFlT/f1wxVXgvH//fkfiIiAgwDRt2tS0bt3aEdQHBwenWpc6KirKlC5d2kjJj2hv1KiRadeunSlWrJiRktd2Xrp0aZrXISP369V69OhhJJlXX33V5XvdvHmz4zOoTp06173Hf/31V8vXD+7n7mCZwBkepVOnTpZn5f/++++OoOjqbMbWrVvN448/bvLnz298fX1NWFiYqVu3rnn33Xed+hljTHx8vHnvvfdMrVq1TI4cOYy/v78pXry46datW6qMt91uN2+//bYpUaKE8fX1NQUKFDAvv/yyiYuLc7mqRnpPN5szZ46pWrWqyZYtmwkJCTGtW7c2W7dudbmSR0bGneLSpUvG19fX+Pv7p/kwlvTMnTvXdO7c2ZQuXdoEBASYgIAAU65cOdOvXz/z559/pnnMypUrzcMPP2zCwsKMr6+vCQ8PN/fdd5+ZOXNmqr5HjhwxPXr0MAULFjS+vr4mNDTUtGnTJlW20Rjr1/bcuXNmxIgRplq1aiZHjhwma9aspkiRIqZZs2Zm8uTJqWb632jgbIwx27dvN+3atTMhISEma9aspnz58mbcuHHXzYhdL3BOCaJcbdeO79dffzUvvPCCqVy5sgkNDXU8ca927dpm5MiRaa6R7OpBJldvV9/TMTEx5vXXXzcNGjQw+fPnN35+fiZbtmymfPny5uWXX77ufTBx4kTz4IMPmqJFi5rs2bObLFmymMKFC5vHH3/c5brF8fHx5t133zW1atUyAQEBxsfHx4SHh5tHHnnkukveWXlfV1/zU6dOmeHDh5vGjRubAgUKGD8/P+Pv729Klixpnnrqqev+fTp69Kjp2rWrKVmypMmWLZvJkSOHqV69unn33XevuyrG999/bx577DFTpkwZkzNnTuPj42PCwsJMixYtnNarv9rBgwfNwIEDTd26dU14eLjx9fU12bNnN+XLlze9e/fO8Eor6a3jfOLECdO3b19TpkwZkzVrVuPn52dKlChhevbsed1jYmJizJAhQ0ylSpVM9uzZja+vrylUqJDp0qVLmgHwjdyvKS5fvpzuSjYpXD145+otrW+E4LncHSx7euBsM+YmlxQAIEmaN2+eOnfurC5dumj27NnuHg4AABl2/PwVdw/BITwoi7uHkAqTA4FMkJCQoDFjxkiSevbs6ebRAACAW4Hl6ICbsGjRIi1cuFDr16/Xjh071KZNG9WsWdPdwwIAALcAgTNwEzZt2qRZs2YpV65c6ty5s2OZLAAA7kQU8LpGjTMAAAAkSX+f85wa53w5Pa/GmYwzAAAAJIknB6aDyYEAAACABQTOAAAAgAWUagAAAEASkwPT858NnP2r9nL3EHCHuLR5EvcLLLm0eZIuJ7p7FLhTZPXh3yJYc2nzJHcPARZRqgEAAABY8J/NOAMAACCjqNVwhYwzAAAAYAGBMwAAAGABpRoAAACQxKoa6SHjDAAAAFhAxhkAAACSmBqYHjLOAAAAgAUEzgAAAIAFlGoAAABAEpMD00PGGQAAALCAwBkAAACwgFINAAAASJIM62q4RMYZAAAAsICMMwAAAJKRcHaJjDMAAABgAYEzAAAAYAGBMwAAACQlV2p4ypYRkZGRqlmzpgICAhQWFqY2bdpoz549Tn0uX76snj17KiQkRDly5FC7du108uTJDL0OgTMAAADuaCtWrFDPnj21du1a/fTTT0pISFDTpk0VFxfn6NO3b1998803+vzzz7VixQr9/fffatu2bYZeh8mBAAAAuKMtXrzY6c+zZ89WWFiYNm7cqPr16+v8+fP68MMP9emnn6px48aSpFmzZqls2bJau3atateubel1CJwBAAAgybMeuR0fH6/4+HinNj8/P/n5+aV77Pnz5yVJwcHBkqSNGzcqISFBTZo0cfQpU6aMChUqpDVr1lgOnCnVAAAAgMeJjIxUUFCQ0xYZGZnucXa7XS+99JLq1q2rChUqSJJOnDihLFmyKGfOnE598+TJoxMnTlgeExlnAAAAeJwBAwaoX79+Tm1Wss09e/bU9u3btWrVqkwfE4EzAAAAJHnWI7f9/LJaCpSv1qtXL3377bdauXKlChQo4GjPmzevrly5onPnzjllnU+ePKm8efNaPj+lGgAAALijGWPUq1cvffXVV1q2bJmKFi3qtL969ery9fXV0qVLHW179uzR0aNHVadOHcuvQ8YZAAAAyTwn4ZwhPXv21Keffqqvv/5aAQEBjrrloKAg+fv7KygoSE8//bT69eun4OBgBQYGqnfv3qpTp47liYESgTMAAADucFOnTpUkNWzY0Kl91qxZ6tq1qyRp/Pjx8vLyUrt27RQfH69mzZppypQpGXodAmcAAADc0YyFdfSyZs2qyZMna/LkyTf8OgTOAAAAkHTHVmrcNkwOBAAAACwgcAYAAAAsoFQDAAAAkjzrkdueiIwzAAAAYAEZZwAAAEjyrCcHeiIyzgAAAIAFBM4AAACABZRqAAAAIBmVGi6RcQYAAAAsIHAGAAAALKBUAwAAAJKo1EgPGWcAAADAAjLOAAAAkMSTA9NDxhkAAACwgMAZAAAAsIBSDQAAAEjikdvpIeMMAAAAWEDgDAAAAFhAqQYAAACSUanhEhlnAAAAwAICZwAAAMACSjUAAAAgiUqN9JBxBgAAACwg4wwAAABJPHI7PWScAQAAAAsInAEAAAALKNUAAACAJB65nR4yzgAAAIAFBM4AAACABZRqAAAAIBmVGi6RcQYAAAAsIOMMAAAASSSc00PGGQAAALCAwPkO8WyH+tr93TCdXTteK+e+ohrlC7vs37ZJVW358k2dXTtev382UM3qlUvVZ9DzLXXwx5E6s2acvpvWS8UL5Xbanyswm2aN7KKTv76t4yvHauqQzsrunyVT3xduDe4XXOvDGdPVuUM71alZVQ3vraOXer+gw4cOOvUZPnSwWjZvolrVKqlhvdp6sdfzOnTwgMvzGmM0+f33dF+DeqpVrZKeebqrjhw57NTn/LlzGvDay7qnVjXVq11DQwYN1MW4OKc+e/fsVtcnOqtm1Ypqel8DzfpwRqa8b2QuPltwtyNwvgM80rSaxrz8sEZO/0F1Oo/Rtr1/adGUnsqdK0ea/WtXLqo5kV01Z+Ea1e40Wt8s36rPxj2jcsXDHX1e7tpEL3RqoD6j5qv+k+8o7tIVfTO5p/yy/Fu9M2tUF5UtHq4Hn5+kdn2mqV61Epo8qPMtf7+4OdwvSMuG39erY6fH9NG8zzR9xiwlJibquR5P6+LFi44+5cqV1/ARkfrqm+819YMPZYzRcz2eVlJS0nXPO+vDGZr3yUd6c8hQfTzvM/n7++v5Z55WfHy8o8+A11/Rgf37NW3mLE2cPE2bNmzQ8KGDHftjY2P1XI+nFZ4vn+Z9/qX6vvyapk2ZpC8+W3BrLgZuCJ8tdwdjPGfzRATOd4A+jzfWrC9X66NFa7X74An1Hjlfly5fUZc2ddLs37NTQ/24epfGz12qPYdOaviU77Rl1zE992iDf/t0bqQxM5bo2+V/aPu+v9V90FyF5w5Sq0aVJUmli+ZRs7rl9cLwT/X79iNaveWg+o35XO2bVVN47qDb8r5xY7hfkJapH3yo1g+3VYkSJVW6TBkNHzlax4//rV07dzj6PNKho6rXqKn8+QuobLny6tXnJZ04cVx///VXmuc0xuiTj+aqx7PPq1HjJipVuoxGRI7V6VOntGzpz5KkgwcO6LdVv2rI8BGqVKmyqlWvof4D39TiH77TqVMnJUnff7tICQkJGv7WKJUoUVItHmipTo89oY/mzrr1FwaW8dkCEDh7PF8fb1UtW1DL1u1xtBljtGzdHtWqVDTNYyIqFdUv63Y7tf20ZpciKhWRJBXJH6Lw3EFadlWfmNjL+n37YUefiEpFdTbmojbtPOros2zdHtntRjUruP5qDu7D/QKrYi9ckCQFBqUdfFy8eFFff/Wl8hcooLx586bZ568//1RU1GlF1L7H0RYQEKCKlSpr29bNkqStWzcrIDBQ5StUdPSJqHOPvLy89Me2bf/02aLqNWrIN8u/X7/fU7eeDh86pJjz52/ujSJT8NkCJHPrqhpRUVH6v//7P61Zs0YnTpyQJOXNm1f33HOPunbtqty5c6dzhv++0Fw55OPjrVNnLji1n4qOUekiedI8Jk9oYBr9LyhPSKAkKW9o8n9d9ckTEqjT1+xPSrLrTMxF5fnneHge7hdYYbfbNXbMKFWpWk0lS5Zy2rdg3ica/+47unTpoooULarpM2Y5BbRXi4o6LUkKCQ1xag8JCVFUVJQkKToqSsHBwU77fXx8FBgUpOh/jo+KilL+/AWuOUeoY9/1gnvcPny23D145LZrbss4//777ypVqpQmTpyooKAg1a9fX/Xr11dQUJAmTpyoMmXKaMOGDemeJz4+XjExMU7b1bV1AABno0YM04F9+zT2nfGp9j3wYCst+N9X+r85H6tw4SJ69eWX+EwFgH+4LePcu3dvtW/fXtOmTZPNZnPaZ4zRc889p969e2vNmjUuzxMZGalhw4Y5tQ0ZMiTTx+suUWdjlZiYpLDgAKf2sJBAnYiOSfOYk1ExafQP0Ml/+p+ISv5vWHCA4/+n9Nm258/kc0THKPc15/D29lJwYDadjEr7deF+3C9Iz6gRw7VyxXL935yPlSeNEoyAgAAFBASocOEiqlSpsurdU0vLfv5JLVo+mKpvaGjyt4LRUdHKnTvM0R4dHa3SZcpIkkJCQ3XmzBmn4xITExVz/rxC/jk+NDRUZ6KjnPpE//Pn0NDQm3i3yCx8tgDJ3JZx3rp1q/r27ZsqaJYkm82mvn37asuWLemeZ8CAATp//rzTNmDAgFswYvdISEzS5l3H1CiitKPNZrOpUa1SWr/tUJrHrNt2SA1rlXZqu692Ga3bdliSdPivaB0/fd7pnAHZs6pmhSKOPuu2HVKuwGyqWrago0/DmqXk5WXT79uPZNK7Q2bjfsH1GGM0asRwLVv6k2b83xwVKFAw/WOSD9SVK1fS3J+/QAGFhubWunX/JjhiY2P1x7atqlS5qiSpcuWquhATo507tjv6rF+3Vna7XRUrVfqnTxVt3LBBCQkJjj5r16xWkaJFKdPwEHy23EWMB20eyG2Bc968ebV+/frr7l+/fr3y5Em7bupqfn5+CgwMdNr8/Pwyc6huN/HjZer28D167KEIlS6aRxMHdlQ2fz/N/XqtJGnmW09oeO9Wjv6T5y1X03vK6cUnGqtUkTx649kHVK1cIU2bv+LfPp/+ote7N1fLBhVVvkQ+ffjWEzp++rwW/bJVkrTn0Ekt+W2HJg/qrBrlC6tO5WIa37+DPl+yScdPM1nHk3G/IC2j3hqm779dpNFj31X2bNkVdfq0ok6f1uXLlyVJfx47pg9nTNfOHdt1/O+/tWXzJr3St4/8/LKqXv1/V0Fo/WBzLf35J0nJgdNjTzypGdOnavmypdq3d4/eHPCacoeFqfF9TSRJxYoXV91692rYkEH6Y9s2bd60UZEj31LzFi0VFpb8Gd+i5UPy9fXV0MFvaP/+fVr8w/f65OO5euLJbrf5KsEVPlsAN5ZqvPLKK3rmmWe0ceNG3XfffY4g+eTJk1q6dKlmzJihd955x13D8yhf/LhJoblyaPDzLZUnJEDb9vyl1j0nOyZUFMwbLLv931/N1m49pK4DZ2tIzwc1rNdD2n/0tDr0+0A7Dxx39Hl39s/K5u+nSW92Us4Af63eckCtek5R/JVER59uA+dofP8O+n56b9ntRguXbtHLYz+/fW8cN4T7BWn5bME8SdLTXZ9wah8+IlKtH26rLH5ZtGnjBn380RzFnI9RSGiIqlevobmfzFNIyL+T/w4fOuRYkUOSuj3dQ5cuXdLwoYN14UKMqlarrinTZzolMCLHvKPIkW/pmae7yMvLS/fd31T9B7zp2B8QEKBpMz7UqBHD1al9W+XMlUvPPveCHunQ8VZdDtwAPlvuDh6a6PUYNmPct8T0ggULNH78eG3cuNGxwL63t7eqV6+ufv36qUOHDjd8bv+qvTJrmPiPu7R5EvcLLLm0eZIuJ6bfD5CkrD78WwRrLm2e5O4hOGw5eiH9TrdJlUIB6Xe6zdy6HF3Hjh3VsWNHJSQkOJYuCg0Nla+vrzuHBQAAAKTi1sA5ha+vr8LDw9PvCAAAgFvGUx917Sl4ciAAAABgAYEzAAAAYIFHlGoAAADA/XjktmtknAEAAAALyDgDAAAgGQlnl8g4AwAAABYQOAMAAAAWUKoBAAAASVRqpIeMMwAAAGABgTMAAABgAaUaAAAAkMQjt9NDxhkAAACwgMAZAAAAsIBSDQAAAEjikdvpIeMMAAAAWEDGGQAAAMlIOLtExhkAAACwgMAZAAAAsIBSDQAAAEiiUiM9ZJwBAAAACwicAQAAAAso1QAAAIAkHrmdHjLOAAAAgAVknAEAACCJJwemh4wzAAAAYAGBMwAAAGABpRoAAABIRqWGS2ScAQAAAAsInAEAAAALKNUAAACAJCo10kPGGQAAALCAjDMAAAAkSXYeHegSGWcAAADAAgJnAAAAwAJKNQAAACCJyYHpIeMMAAAAWEDgDAAAAFhAqQYAAAAkSSyq4RoZZwAAAMACAmcAAADAAko1AAAAIEkyrKvhEhlnAAAAwAIyzgAAAJAk2Uk4u0TGGQAAALCAwBkAAACwgFINAAAASGJyYHrIOAMAAAAWEDgDAAAAFlCqAQAAAEk8cjs9ZJwBAAAAC8g4AwAAQBKTA9NDxhkAAACwgMAZAAAAsIBSDQAAAEjikdvpIeMMAAAAWEDgDAAAAFhAqQYAAAAksapGesg4AwAAABYQOAMAAAAWUKoBAAAASTxyOz1knAEAAAALyDgDAABAEhnn9JBxBgAAACywGcPvFgAAAJAW7zjt7iE4NC+f291DSOU/W6rhX7WXu4eAO8SlzZO4X2DJpc2TdPz8FXcPA3eI8KAsfLbAkkubJ7l7CA521nF2iVINAAAAwAICZwAAAMCC/2ypBgAAADKGmW+ukXEGAAAALCDjDAAAAEmSYXKgS2ScAQAAAAsInAEAAAALKNUAAACAJCYHpoeMMwAAAGABgTMAAABgAaUaAAAAkMQjt9NDxhkAAACwgIwzAAAAJDE5MD1knAEAAAALCJwBAAAACyjVAAAAgCQxNTAdZJwBAAAACwicAQAAAAso1QAAAIAkybCshktknAEAAAALCJwBAAAACyjVAAAAgCTJ7u4BeDgyzgAAAIAFZJwBAAAgicmB6SHjDAAAAFhA4AwAAABYQKkGAAAAJPHI7fSQcQYAAAAsIHAGAADAHW3lypV66KGHlC9fPtlsNi1cuNBpf9euXWWz2Zy25s2bZ/h1KNUAAACApDt3VY24uDhVrlxZTz31lNq2bZtmn+bNm2vWrFmOP/v5+WX4dQicAQAAcEdr0aKFWrRo4bKPn5+f8ubNe1OvQ+AMAAAASZ715MD4+HjFx8c7tfn5+d1QpliSli9frrCwMOXKlUuNGzfWiBEjFBISkqFzUOMMAAAAjxMZGamgoCCnLTIy8obO1bx5c82dO1dLly7VmDFjtGLFCrVo0UJJSUkZOg8ZZwAAAHicAQMGqF+/fk5tN5ptfvTRRx3/v2LFiqpUqZKKFy+u5cuX67777rN8HgJnAAAASPKsyYE3U5aRnmLFiik0NFT79+/PUOBMqQYAAADuKn/++aeio6MVHh6eoePIOAMAAOCOFhsbq/379zv+fOjQIW3ZskXBwcEKDg7WsGHD1K5dO+XNm1cHDhzQa6+9phIlSqhZs2YZeh0CZwAAAEiSPKhSI0M2bNigRo0aOf6cUhvdpUsXTZ06Vdu2bdOcOXN07tw55cuXT02bNtVbb72V4VIQAmcAAADc0Ro2bOiyPnvJkiWZ8jrUOAMAAAAWkHEGAACAJOkOrdS4bcg4AwAAABaQcQYAAIAkyX6nzg68Tcg4AwAAABYQOAMAAAAWUKoBAAAASUwOTA8ZZwAAAMACAmcAAADAAko1AAAAIEkun74HMs4AAACAJWScAQAAIEmyu3sAHo6MMwAAAGABgTMAAABgAaUaAAAAkCQxN9A1Ms4AAACABQTOAAAAgAWUagAAAECSZKdWwyUyzgAAAIAFZJwBAAAgicmB6SHjDAAAAFhA4AwAAABYQKkGAAAAJDE5MD1knO8Qz3aor93fDdPZteO1cu4rqlG+sMv+bZtU1ZYv39TZteP1+2cD1axeuVR9Bj3fUgd/HKkza8bpu2m9VLxQbqf9uQKzadbILjr569s6vnKspg7prOz+WTL1feHW4H7BtT6ZPVPPdnlULRpGqE2zBnrjlT46euSQU59vvvpcLz7XTQ80qq2GtSrqwoUYS+f+6vN56ti6me6vV13Pd+usXTv+cNofHx+vCWNHqFWTemreoJYGv95XZ6KjnPqcPHFc/fu+oGb31lSbZg00deK7SkxMvLk3jUzHZwvudgTOd4BHmlbTmJcf1sjpP6hO5zHatvcvLZrSU7lz5Uizf+3KRTUnsqvmLFyj2p1G65vlW/XZuGdUrni4o8/LXZvohU4N1GfUfNV/8h3FXbqibyb3lF+Wf7+EmDWqi8oWD9eDz09Suz7TVK9aCU0e1PmWv1/cHO4XpGXLpg1q0/5RTfnwE73z/gdKSkrUq72f1aVLFx19Ll++rFp16uqxrt0tn3fZT4s1ZcLb6tr9Oc2Y+5mKlyylV/s8q7Nnoh19Jo8fq9W/rtDQyHf13rRZijp9SoNf7+vYn5SUpP59X1BCQoImffiRBgwZocXffq1ZH0zOnDePTMFnC0DgfEfo83hjzfpytT5atFa7D55Q75HzdenyFXVpUyfN/j07NdSPq3dp/Nyl2nPopIZP+U5bdh3Tc482+LdP50YaM2OJvl3+h7bv+1vdB81VeO4gtWpUWZJUumgeNatbXi8M/1S/bz+i1VsOqt+Yz9W+WTWF5w66Le8bN4b7BWl5e+I0tXiwjYoWL6ESpUqr/+AROnniuPbu2uno077TE3qsS3eVq1DZ8nk//3SuWrZppxYPPawixYqrX//ByprVX99/85UkKTb2gr5f9KVeeOlVVasZodJly+v1wW9p+7Yt2vHHVknShnWrdeTQQb0xLFIlS5VRxD336qlne2nh5/OVkJCQuRcCN4zPlruD3XjO5okInD2cr4+3qpYtqGXr9jjajDFatm6PalUqmuYxEZWK6pd1u53aflqzSxGVikiSiuQPUXjuIC27qk9M7GX9vv2wo09EpaI6G3NRm3YedfRZtm6P7HajmhVcfzUH9+F+gVWxsbGSpICgGw8+EhIStGf3TlWvWdvR5uXlpeo1a2vnP0Hx3l07lZiYqOq1/u1TuEgx5ckb7uiz44+tKlq8pIJDQh19atW+R3FxsTp8cP8Njw+Zh88WIJlHB87Hjh3TU0895bJPfHy8YmJinLb4+PjbNMJbLzRXDvn4eOvUmQtO7aeiY5Q3JDDNY/KEBqbR/4Ly/NM/b2jyf131yRMSqNPX7E9KsutMzEXlCU37deF+3C+wwm63a9K4MapQuaqKFS95w+c5f+6s7ElJCg4OcWrPFRyiM9HJpRpnoqPk6+urgIDANPpEOfqkOkdIiGMf3I/PFiCZRwfOZ86c0Zw5c1z2iYyMVFBQkNMWGRl5m0YIAHeeCWNH6tDB/Ro8Yqy7hwLAwxjjOZsncutydIsWLXK5/+DBg+meY8CAAerXr59Tm5+fn8Z8/fJNjc1TRJ2NVWJiksKCA5zaw0ICdSI67RnvJ6Ni0ugfoJP/9D8RlfzfsOAAx/9P6bNtz5/J54iOUe5rzuHt7aXgwGw6GWVtpj1uP+4XpGfC2yO1ZtUKTZw+W2F58t7UuYJy5pKXt7fOXDURUJLOnolW8D8Z4+CQUCUkJOjChRinrHNyn1BHn107tjuf45+M9dXlG3AfPluAZG7NOLdp00YPP/yw2rRpk+Z2bUCcFj8/PwUGBjptfn5+t2H0t0dCYpI27zqmRhGlHW02m02NapXS+m2H0jxm3bZDalirtFPbfbXLaN22w5Kkw39F6/jp807nDMieVTUrFHH0WbftkHIFZlPVsgUdfRrWLCUvL5t+334kk94dMhv3C67HGKMJb4/UquXLNH7KhwrPX+Cmz+nr66vSZcpp0+/rHG12u10bN6xVuYrJk7tKlS0nHx8fpz5HjxzSyRPHHX3KV6ysQwf2Oa3EsWH9GmXPnkOFixa/6XHi5vHZcvewy3jM5oncGjiHh4fryy+/lN1uT3PbtGmTO4fnMSZ+vEzdHr5Hjz0UodJF82jiwI7K5u+nuV+vlSTNfOsJDe/dytF/8rzlanpPOb34RGOVKpJHbzz7gKqVK6Rp81f82+fTX/R69+Zq2aCiypfIpw/fekLHT5/Xol+SJ+vsOXRSS37bocmDOqtG+cKqU7mYxvfvoM+XbNLx0+dv7wVAhnC/IC0Txo7UTz98pzffGi3/bNkVHRWl6KgoxV++7OgTHRWlfXt3669jyROxDu3fp317dyvm/L8/w34vdNeXn33q+HP7zk/q26//p8Xffq0jhw5q/Ji3dPnSJbV4sI0kKUeOAD3Qqq2mTHhbmzes155dOzRm+CCVr1hZ5f8JnGtE3KPCRYtp1JCB2r93j9av+U0fTpukNu0fVZYsrNfrKfhsAdxcqlG9enVt3LhRrVu3TnO/zWaT8dQil9voix83KTRXDg1+vqXyhARo256/1LrnZMeEioJ5g2W/at2WtVsPqevA2RrS80EN6/WQ9h89rQ79PtDOA8cdfd6d/bOy+ftp0pudlDPAX6u3HFCrnlMUf+XfBw50GzhH4/t30PfTe8tuN1q4dIteHvv57XvjuCHcL0jL1/9bIEl66TnnCdevD37LEeQu+vIzzZk51bGvz7NdU/X5669jOn/unKNP4/ub69zZM5r1wWSdiY5SiVJlNPa9aU4lFj37viYvL5sG9++rhCsJqln7Hr302puO/d7e3oocN1njx7ylnk8/rqz+/mrWspW6PdMzE68AbhafLYBkM26MTH/99VfFxcWpefPmae6Pi4vThg0b1KBBgzT3u+JftdfNDg93iUubJ3G/wJJLmyfp+Pkr7h4G7hDhQVn4bIEllzZPcvcQHCauSrv0xh361Et7qUN3cmvG+d5773W5P3v27DcUNAMAAACZzaOXowMAAAA8hVszzgAAAPAcnvqoa09BxhkAAACwgIwzAAAAJEl2VjNziYwzAAAAYAGBMwAAAGABpRoAAACQJFGp4RoZZwAAAMACAmcAAADAAko1AAAAIIl1nNNDxhkAAACwgMAZAAAAsIBSDQAAAEiSDMtquETGGQAAALCAjDMAAAAkMTkwPWScAQAAAAsInAEAAAALKNUAAACAJEo10kPGGQAAALCAwBkAAACwgFINAAAASJKMqNVwhYwzAAAAYAEZZwAAAEhicmB6yDgDAAAAFljKOE+cONHyCfv06XPDgwEAAAA8laXAefz48ZZOZrPZCJwBAADuUIZSDZcsBc6HDh261eMAAAAAPNoN1zhfuXJFe/bsUWJiYmaOBwAAAPBIGQ6cL168qKefflrZsmVT+fLldfToUUlS7969NXr06EwfIAAAAG4PuzEes3miDAfOAwYM0NatW7V8+XJlzZrV0d6kSRMtWLAgUwcHAAAAeIoMr+O8cOFCLViwQLVr15bNZnO0ly9fXgcOHMjUwQEAAACeIsOB8+nTpxUWFpaqPS4uzimQBgAAwJ2FB6C4luFSjRo1aui7775z/DklWJ45c6bq1KmTeSMDAAAAPEiGM86jRo1SixYttHPnTiUmJuq9997Tzp07tXr1aq1YseJWjBEAAAC3gYfOyfMYGc4416tXT1u2bFFiYqIqVqyoH3/8UWFhYVqzZo2qV69+K8YIAAAAuF2GM86SVLx4cc2YMSOzxwIAAAB4rBsKnJOSkvTVV19p165dkqRy5cqpdevW8vG5odMBAADAA3jq+smeIsOR7o4dO9SqVSudOHFCpUuXliSNGTNGuXPn1jfffKMKFSpk+iABAAAAd8twjXP37t1Vvnx5/fnnn9q0aZM2bdqkY8eOqVKlSnrmmWduxRgBAAAAt8twxnnLli3asGGDcuXK5WjLlSuXRo4cqZo1a2bq4AAAAHD7UKnhWoYzzqVKldLJkydTtZ86dUolSpTIlEEBAAAAnsZSxjkmJsbx/yMjI9WnTx8NHTpUtWvXliStXbtWw4cP15gxY27NKAEAAHDL2d09AA9nKXDOmTOn0+O0jTHq0KGDo838k9d/6KGHlJSUdAuGCQAAALiXpcD5l19+udXjAAAAADyapcC5QYMGt3ocAAAAcDPWcXbthp9YcvHiRR09elRXrlxxaq9UqdJNDwoAAADwNBkOnE+fPq1u3brphx9+SHM/Nc4AAAD4L8rwcnQvvfSSzp07p3Xr1snf31+LFy/WnDlzVLJkSS1atOhWjBEAAAC3gTGes3miDGecly1bpq+//lo1atSQl5eXChcurPvvv1+BgYGKjIxUy5Ytb8U4AQAAALfKcMY5Li5OYWFhkpKfGHj69GlJUsWKFbVp06bMHR0AAABuG7vxnM0TZThwLl26tPbs2SNJqly5sqZPn66//vpL06ZNU3h4eKYPEAAAAPAEGS7VePHFF3X8+HFJ0pAhQ9S8eXN98sknypIli2bPnp3Z4wMAAAA8QoYD58cff9zx/6tXr64jR45o9+7dKlSokEJDQzN1cAAAALh9jKfOyvMQN7yOc4ps2bKpWrVqmTEWAAAAwGNZCpz79etn+YTjxo274cEAAAAAnspS4Lx582ZLJ7PZbDc1GAAAALiPp65m4SksBc6//PLLrR4HAAAA4NEyvBwdAAAAcDe66cmBAAAA+G+gVMM1Ms4AAACABWScAQAAIIl1nNNDxhkAAACwwFLGedGiRZZP2KpVqxseDAAAAOCpbMZCTt7Ly1pi2mazKSkp6aYHBQAAgNuv51e73D0Eh8kPl3X3EFKxlHG22+23ehyZzr9qL3cPAXeIS5sncb/AkkubJ+ng6cvuHgbuEMVyZ+WzBZZc2jzJ3UOARdQ4AwAAABbc0KoacXFxWrFihY4ePaorV6447evTp0+mDAwAAAC3F6tquJbhwHnz5s164IEHdPHiRcXFxSk4OFhRUVHKli2bwsLCCJwBAADwn5ThUo2+ffvqoYce0tmzZ+Xv76+1a9fqyJEjql69ut55551bMUYAAADcBsZ4zuaJMhw4b9myRS+//LK8vLzk7e2t+Ph4FSxYUGPHjtXAgQNvxRgBAAAAt8tw4Ozr6+tYni4sLExHjx6VJAUFBenYsWOZOzoAAADAQ2S4xrlq1ar6/fffVbJkSTVo0ECDBw9WVFSUPvroI1WoUOFWjBEAAAC3gd1TayQ8RIYzzqNGjVJ4eLgkaeTIkcqVK5eef/55nT59Wh988EGmDxAAAADwBBnOONeoUcPx/8PCwrR48eJMHRAAAADgiW5oHWcAAAD891Cp4VqGA+eiRYvKZrNdd//BgwdvakAAAACAJ8pw4PzSSy85/TkhIUGbN2/W4sWL9eqrr2bWuAAAAACPkuHA+cUXX0yzffLkydqwYcNNDwgAAADuwSO3XcvwqhrX06JFC/3vf//LrNMBAAAAHiXTJgd+8cUXCg4OzqzTAQAA4DYj4ezaDT0A5erJgcYYnThxQqdPn9aUKVMydXAAAACAp8hw4Ny6dWunwNnLy0u5c+dWw4YNVaZMmUwdHAAAAOApMhw4Dx069BYMAwAAAO7GI7ddy/DkQG9vb506dSpVe3R0tLy9vTNlUAAAAICnyXDgfL1lSuLj45UlS5abHhAAAADgiSyXakycOFGSZLPZNHPmTOXIkcOxLykpSStXrqTGGQAA4A5GoYZrlgPn8ePHS0rOOE+bNs2pLCNLliwqUqSIpk2blvkjBAAAADyA5cD50KFDkqRGjRrpyy+/VK5cuW7ZoAAAAHD78eRA1zK8qsYvv/xyK8YBAAAAeLQMTw5s166dxowZk6p97Nixat++faYMCgAAAPA0GQ6cV65cqQceeCBVe4sWLbRy5cpMGRQAAABuP7vxnM0TZThwjo2NTXPZOV9fX8XExGTKoAAAAABPk+HAuWLFilqwYEGq9vnz56tcuXKZMigAAADA02R4cuCgQYPUtm1bHThwQI0bN5YkLV26VPPmzdPnn3+e6QMEAADA7cGqGq5lOHB+6KGHtHDhQo0aNUpffPGF/P39ValSJf38889q0KDBrRgjAAAA4HYZDpwlqWXLlmrZsmWq9u3bt6tChQo3PSgAAADcfiScXctwjfO1Lly4oA8++EC1atVS5cqVM2NMAAAAgMe54cB55cqVevLJJxUeHq533nlHjRs31tq1azNzbAAAAIDHyFCpxokTJzR79mx9+OGHiomJUYcOHRQfH6+FCxeyogYAAMAdjsmBrlnOOD/00EMqXbq0tm3bpgkTJujvv//W+++/fyvHBgAAAHgMyxnnH374QX369NHzzz+vkiVL3soxAQAAAB7HcsZ51apVunDhgqpXr66IiAhNmjRJUVFRt3JsAAAAuI3c/Zjt/8wjt2vXrq0ZM2bo+PHjevbZZzV//nzly5dPdrtdP/30ky5cuHArxwkAAAC4VYZX1ciePbueeuoprVq1Sn/88YdefvlljR49WmFhYWrVqtWtGCMAAADgdje1jnPp0qU1duxY/fnnn5o3b15mjQkAAABuYIzxmC0jVq5cqYceekj58uWTzWbTwoULU72vwYMHKzw8XP7+/mrSpIn27duX4etz0w9AkSRvb2+1adNGixYtyozTAQAAAJbFxcWpcuXKmjx5cpr7x44dq4kTJ2ratGlat26dsmfPrmbNmuny5csZep0beuQ2AAAA/ns8dE5eulq0aKEWLVqkuc8YowkTJujNN99U69atJUlz585Vnjx5tHDhQj366KOWXydTMs4AAABAZoqPj1dMTIzTFh8fn+HzHDp0SCdOnFCTJk0cbUFBQYqIiNCaNWsydC4CZwAAAHicyMhIBQUFOW2RkZEZPs+JEyckSXny5HFqz5Mnj2OfVZRqAAAAQJJk96BHbg8YMED9+vVzavPz83PTaJIROAMAAMDj+Pn5ZUqgnDdvXknSyZMnFR4e7mg/efKkqlSpkqFzUaoBAACA/6yiRYsqb968Wrp0qaMtJiZG69atU506dTJ0LjLOAAAAkCR5UKVGhsTGxmr//v2OPx86dEhbtmxRcHCwChUqpJdeekkjRoxQyZIlVbRoUQ0aNEj58uVTmzZtMvQ6BM4AAAC4o23YsEGNGjVy/DmlNrpLly6aPXu2XnvtNcXFxemZZ57RuXPnVK9ePS1evFhZs2bN0OsQOAMAAECSMvzEPk/RsGFDl2O32WwaPny4hg8fflOvQ40zAAAAYAGBMwAAAGABpRoAAACQdOdODrxdyDgDAAAAFhA4AwAAABZQqgEAAABJnvXIbU9ExhkAAACwgMAZAAAAsIBSDQAAAEhiVY30kHEGAAAALCDjDAAAAEl37iO3bxcyzgAAAIAFZJzvEM92qK++Xe5TnpBA/bH3L/Ub87k27Dhy3f5tm1TV4BdaqnC+EO0/elpvTlyoJat2OvUZ9HxLdXv4HuUM8NearQfVZ9QCHTh62rE/V2A2jXu9vR6oX0F2Y7Rw6Ra9MvYLxV26csveJzIH9wuuteCjD/XbiqX688ghZfHzU7mKVfTU8y+pQKEijj5X4uM1Y9K7WrF0sRISrqh6rXvU8+U3lCs45LrnNcboow+naPE3XyruwgWVq1hFvV55Q/kLFnb0uRBzXlPGj9a631bIy8tLdRvcp+defF3+2bI5+hzav1eTx43S3t07FJQzl1q166T2j3W7JdcCN47PFtztyDjfAR5pWk1jXn5YI6f/oDqdx2jb3r+0aEpP5c6VI83+tSsX1ZzIrpqzcI1qdxqtb5Zv1WfjnlG54uGOPi93baIXOjVQn1HzVf/JdxR36Yq+mdxTfln+/V1q1qguKls8XA8+P0nt+kxTvWolNHlQ51v+fnFzuF+Qlj82b9BDbTtq/PSPNGr8dCUmJuqNvs/p8qWLjj7T339b635boYFvva2x7/+foqNOa8Qb/Vye9/NPZmnRF/PU+5U3NeGDj5XV319v9nteV+LjHX3GDhugo4cOaNT4aRo6ZqK2b92kiWOHO/bHxcXqjX7PKSxvuN6fOU9Pv9BXn/zfNH3/9ReZfyFww/hsuTvYjedsnojA+Q7Q5/HGmvXlan20aK12Hzyh3iPn69LlK+rSpk6a/Xt2aqgfV+/S+LlLtefQSQ2f8p227Dqm5x5t8G+fzo00ZsYSfbv8D23f97e6D5qr8NxBatWosiSpdNE8ala3vF4Y/ql+335Eq7ccVL8xn6t9s2oKzx10W943bgz3C9IyYtxU3f9AaxUuVkLFSpZWv4HDderkce3bs0uSFBd7QT9++5V69H5FVapHqGSZcuo3cLh2/rFFu7ZvS/Ocxhgt/PwTPfpkD9W5t5GKliilV94coejo01r96zJJ0tHDB7Vh3W96sf8QlSlfSRUqV9PzL/XXiqWLFR11SpL0y4/fKyEhQX0HDFfhYiXUsEkLtXqkk75a8NHtuTiwhM8WgMDZ4/n6eKtq2YJatm6Po80Yo2Xr9qhWpaJpHhNRqah+Wbfbqe2nNbsUUamIJKlI/hCF5w7Ssqv6xMRe1u/bDzv6RFQqqrMxF7Vp51FHn2Xr9shuN6pZobDgmbhfYNXFuFhJUkBgoCRp356dSkxMVNUaEY4+BQsXVViecO3esTXNc5z4+y+djY5S1Zr/HpM9R4BKl6uo3f8E27u2b1WOHAEqVaa8o0/VGhGyeXlp944/JEm7t29VxSrV5evr6+hTPeIe/Xn0sC7ExGTSO8bN4LMFSEbg7OFCc+WQj4+3Tp254NR+KjpGeUMC0zwmT2hgGv0vKM8//fOGJv/XVZ88IYE6fc3+pCS7zsRcVJ7QtF8X7sf9AivsdrumTxyrchWrqEixkpKks9HR8vH1VY4A559XzuBgnYmOSvM8Z88kt+fK5VwDnStXiGPf2TPRCsoV7LTf28dHAQGBOnsmWpJ05kyUcl7TJ+c/50w5D9yLz5a7h/Gg/3kitwfOly5d0qpVq7Rz585U+y5fvqy5c+e6PD4+Pl4xMTFOW/xVtXUAAGeTx43S4YMH1H/YWHcPBQDuKG4NnPfu3auyZcuqfv36qlixoho0aKDjx4879p8/f17durmeVR0ZGamgoCCnLTIy8lYP/baJOhurxMQkhQUHOLWHhQTqRHTaX2GejIpJo3+ATv7T/0RU8n9d9TkZHaPc1+z39vZScGA2nYziq1NPxf2C9EwZN0rrV6/UmIkzlDssj6M9V0iIEhMSFHvB+ed17swZBYeEpnmuXMHJ7WfPRju1nz0b7diXKzhE58+ecdqflJioCxdiHKt1BAeH6tw1fc79c86U88C9+Gy5exjjOZsncmvg/Prrr6tChQo6deqU9uzZo4CAANWtW1dHjx5N/+B/DBgwQOfPn3faBgwYcAtHfXslJCZp865jahRR2tFms9nUqFYprd92KM1j1m07pIa1Sju13Ve7jNZtOyxJOvxXtI6fPu90zoDsWVWzQhFHn3XbDilXYDZVLVvQ0adhzVLy8rLp9+3XX3oI7sX9gusxxmjKuFFavXKZRr83Q3nzFXDaX7J0Ofn4+GjLxvWOtj+PHtapk8dVpnzlNM+ZN19+5QoJ1ZYN6xxtcXGx2rPzD5WpUEmSVLZCZcXGXtC+3f9+q7hl03oZu11lyleUJJWpUFl/bNmoxMQER5/Nv69VgUJFHDXYcC8+W4Bkbg2cV69ercjISIWGhqpEiRL65ptv1KxZM9177706ePCgpXP4+fkpMDDQafPz87vFI7+9Jn68TN0evkePPRSh0kXzaOLAjsrm76e5X6+VJM186wkN793K0X/yvOVqek85vfhEY5UqkkdvPPuAqpUrpGnzV/zb59Nf9Hr35mrZoKLKl8inD996QsdPn9eiX5InAe05dFJLftuhyYM6q0b5wqpTuZjG9++gz5ds0vHT52/vBUCGcL8gLZPfHaVlP36v14aMln+27DoTHaUz0VGKj78sKXlSX9MHH9aM99/R1k3rtW/3To0bNVhlK1RW2X+CYEnq0bm1fluxVFJy4NSm/WOaP2eG1q5arkMH9undEW8qJCS37rm3sSSpUJFiqhFRV++NHaY9O//Qjm2bNXVcpBrc11whoWGSpEb3t5Cvr68mRA7VkYP7tWLpYi38/BM93PGJ23yV4AqfLYCbH4By6dIl+fj8OwSbzaapU6eqV69eatCggT799FM3js5zfPHjJoXmyqHBz7dUnpAAbdvzl1r3nOyYUFEwb7DsVy14uHbrIXUdOFtDej6oYb0e0v6jp9Wh3wfaeeDfMph3Z/+sbP5+mvRmJ+UM8NfqLQfUqucUxV9JdPTpNnCOxvfvoO+n95bdnrzo/MtjP799bxw3hPsFaflu4WeSpNd7P+3U3m/gcN3/QGtJ0rO9X5WXzUsj3njZ6QEoV/vz6GHHihyS1P6xbrp8+ZImjh2u2NgLKl+xqt56d4qyXJXAeG1IpKaMi9SAF5+R7Z8HoDz/Un/H/uw5AjRy3DRNHjdKvbt3UmBQTnXu+qweaP1Ipl8H3Dg+W+4OPHLbNZtx4xWqVauWevfurSeeSJ1V6NWrlz755BPFxMQoKSkpw+f2r9orM4aIu8ClzZO4X2DJpc2TdPD0ZXcPA3eIYrmz8tkCSy5tnuTuITi0mLou/U63yQ/PR6Tf6TZza6nGww8/rHnz5qW5b9KkSerUqRO/+QAAAMAjuDVwHjBggL7//vvr7p8yZYrsdvttHBEAAMDdy92P2eaR2wAAAMB/AIEzAAAAYIFbV9UAAACA52BumWtknAEAAAALyDgDAABAkuc+6tpTkHEGAAAALCBwBgAAACygVAMAAACSJDu1Gi6RcQYAAAAsIHAGAAAALKBUAwAAAJJYVSM9ZJwBAAAAC8g4AwAAQBJPDkwPGWcAAADAAgJnAAAAwAJKNQAAACCJyYHpIeMMAAAAWEDgDAAAAFhAqQYAAAAksapGesg4AwAAABaQcQYAAIAkJgemh4wzAAAAYAGBMwAAAGABpRoAAACQxOTA9JBxBgAAACwgcAYAAAAsoFQDAAAAkijVSA8ZZwAAAMACAmcAAADAAko1AAAAIIkHoKSHjDMAAABgARlnAAAASGJyYHrIOAMAAAAWEDgDAAAAFlCqAQAAAElMDkwPGWcAAADAAgJnAAAAwAJKNQAAACCJVTXSQ8YZAAAAsICMMwAAACQxOTA9ZJwBAAAACwicAQAAAAso1QAAAIAkJgemh4wzAAAAYAGBMwAAAGABpRoAAACQxKoa6SHjDAAAAFhA4AwAAABYQKkGAAAAJLGqRnrIOAMAAAAWkHEGAACAJCYHpoeMMwAAAGABgTMAAABgAaUaAAAAkMTkwPSQcQYAAAAsIHAGAAAALKBUAwAAAJJYVSM9ZJwBAAAAC8g4AwAAQBKTA9NDxhkAAACwgMAZAAAAsIBSDQAAAEhicmB6yDgDAAAAFtgMVeAAAACQVHnIUncPwWHrsPvcPYRU/rOlGv5Ve7l7CLhDXNo8ifsFllzaPElnLya5exi4Q+TK5s1nCyy5tHmSu4fgQD7VNUo1AAAAAAv+sxlnAAAAZAwZZ9fIOAMAAAAWEDgDAAAAFlCqAQAAAEms45weMs4AAACABQTOAAAAgAWUagAAAEASq2qkh4wzAAAAYAGBMwAAAGABpRoAAACQxKoa6SHjDAAAAFhAxhkAAACSJLudlLMrZJwBAAAACwicAQAAAAso1QAAAIAkJgemh4wzAAAAYAGBMwAAAGABpRoAAACQxCO300PGGQAAALCAjDMAAAAkMTkwPWScAQAAAAsInAEAAAALKNUAAACAJCYHpoeMMwAAAGABgTMAAABgAaUaAAAAkMSqGukh4wwAAABYQOAMAAAAWECpBgAAACSxqkZ6yDgDAAAAFpBxBgAAgCQyzukh4wwAAABYQOAMAAAAWECpBgAAAJJRqeESGWcAAADAAgJnAAAAwAJKNQAAACCJVTXSQ8YZAAAAsICMMwAAACSRcU4PGWcAAADAAgJnAAAAwAJKNQAAACCJUo30kHEGAAAALCBwBgAAACygVAMAAACSKNVIDxlnAAAAwAIyzgAAAEhGwtklMs4AAACABQTOAAAAgAUEzgAAAJCUPDnQU7aMGDp0qGw2m9NWpkyZTL8+1DgDAADgjle+fHn9/PPPjj/7+GR+mEvgDAAAgDuej4+P8ubNe2tf45aeHQAAAHcMT1rHOT4+XvHx8U5tfn5+8vPzS7P/vn37lC9fPmXNmlV16tRRZGSkChUqlKljosYZAAAAHicyMlJBQUFOW2RkZJp9IyIiNHv2bC1evFhTp07VoUOHdO+99+rChQuZOiYyzgAAAPA4AwYMUL9+/ZzarpdtbtGiheP/V6pUSRERESpcuLA+++wzPf3005k2JgJnAAAASPKsUg1XZRnpyZkzp0qVKqX9+/dn6pgo1QAAAMB/SmxsrA4cOKDw8PBMPS+BMwAAAJIZD9oy4JVXXtGKFSt0+PBhrV69Wg8//LC8vb3VqVOnG7kK10WpBgAAAO5of/75pzp16qTo6Gjlzp1b9erV09q1a5U7d+5MfR0CZwAAANzR5s+ff1teh8AZAAAAkjxrcqAnosb5DvFsh/ra/d0wnV07XivnvqIa5Qu77N+2SVVt+fJNnV07Xr9/NlDN6pVL1WfQ8y118MeROrNmnL6b1kvFCzl/nZErMJtmjeyik7++reMrx2rqkM7K7p8lU98Xbg3uF1xrzocfqNtjHdS4bg21aFxPr/XtpSOHDzn2nz9/Tu+MHqEObR5Qg9pV1bpFY707ZqRi01kD1RijD6a8r5b311eD2lXV69mndPTIYac+58+f0+CBr6pxvZpqcm+ERg59Uxcvxjn12bd3j5596nHVj6iiVs0b66PZH2bae0fm4bMFdzsC5zvAI02raczLD2vk9B9Up/MYbdv7lxZN6ancuXKk2b925aKaE9lVcxauUe1Oo/XN8q36bNwzKlf835mlL3dtohc6NVCfUfNV/8l3FHfpir6Z3FN+Wf79EmLWqC4qWzxcDz4/Se36TFO9aiU0eVDnW/5+cXO4X5CWzZs2qF3HTpo5d54mTp2pxMREvfh8d126dFGSFHX6tKJOn1bvvq/qk8+/1qBho7R29SqNHDbI5Xk/mv2hPpv3sV4fOEQz586Xv7+/Xur5jNPTvoYMfE2HDuzXxKkz9c7EKdq8aYNGvzXUsT8uNlYvvtBdecPzafann6v3S69o5vTJWvi/z27JtcCN4bMFIHC+I/R5vLFmfblaHy1aq90HT6j3yPm6dPmKurSpk2b/np0a6sfVuzR+7lLtOXRSw6d8py27jum5Rxv826dzI42ZsUTfLv9D2/f9re6D5io8d5BaNaosSSpdNI+a1S2vF4Z/qt+3H9HqLQfVb8znat+smsJzB92W940bw/2CtEyY/IEebPWwihUvqZKly2jQsFE6ceK4du/cKUkqXqKkRr/7nu5t0EgFChZSjVq19VyvF7Vq5S9KTExM85zGGC34dK669XhW9Rvdp5KlSmvIW6MVdfqUVv6yVJJ06OABrV29SgMHv6UKFSurStXqevn1N/TTku91+tQpSdLi779VYkKC3hw6QsWKl9T9zR9Qh0cf17yP59yeiwNL+Gy5OxhjPGbzRATOHs7Xx1tVyxbUsnV7HG3GGC1bt0e1KhVN85iISkX1y7rdTm0/rdmliEpFJElF8ocoPHeQll3VJyb2sn7fftjRJ6JSUZ2NuahNO486+ixbt0d2u1HNCq6/moP7cL/AqtjY5BKMwKDrBx+xF2KVPXsO+fikPR3m77/+VHRUlGpG/Bs45QgIUPkKlfTHti2SpO3btiggIFBly1dw9KkZUUdeXl7asX2bo0+VajXk6/vv1++176mrI4cPKSbm/A2/R2QePluAZG6fHLhr1y6tXbtWderUUZkyZbR792699957io+P1+OPP67GjRu7PD4+Pt7pK0Hp+o9jvBOF5sohHx9vnTrjXGd4KjpGpYvkSfOYPKGBafS/oDwhgZKkvKHJ/3XVJ09IoE5fsz8pya4zMReV55/j4Xm4X2CF3W7XhHdGq1KVaipeomSafc6dPatZM6aqdbv21z1PdFSUJCk4ONSpPTgkRNHRyfuio6OUKzjYab+Pj48CA4Mcx0dHRylf/vzO5wgOcbxGYCCZRXfjs+Xu4amZXk/h1ozz4sWLVaVKFb3yyiuqWrWqFi9erPr162v//v06cuSImjZtqmXLlrk8R2RkpIKCgpy2yMjI2/QOAODO83bkWzqwf59GjH4nzf1xsbHq1+c5FSlWXD2e7XmbRwcAnsutgfPw4cP16quvKjo6WrNmzVLnzp3Vo0cP/fTTT1q6dKleffVVjR492uU5BgwYoPPnzzttAwYMuE3v4NaLOhurxMQkhQUHOLWHhQTqRHRMmsecjIpJo3+ATv7T/0RU8n9d9TkZHaPc1+z39vZScGA2nYxK+3XhftwvSM87o0fot19XaMqM2QrLkzfV/ri4OL3U8xlly5ZdY8a9Lx9f3+ueKyQ0OdN85kyUU/uZ6GiFhCTvCwkJ1dkzZ5z2JyYmKibmvOP4kJBQnYmOdj7HmWin14B78dkCJHNr4Lxjxw517dpVktShQwdduHBBjzzyiGP/Y489pm3btrk8h5+fnwIDA522/1KpRkJikjbvOqZGEaUdbTabTY1qldL6bYfSPGbdtkNqWKu0U9t9tcto3bbDkqTDf0Xr+OnzTucMyJ5VNSsUcfRZt+2QcgVmU9WyBR19GtYsJS8vm37ffiST3h0yG/cLrscYo3dGj9CKZT9r0vT/U778BVL1iYuN1YvPd5ePr6/emTA53c/SfPkLKCQ0VL+vW+t0jh3bt6lipSqSpAqVqujChRjt3rnD0Wfj7+tkt9tVvkIlR58tmzYoMSHB0Wf92tUqXKQoZRoegs+Wu4e7JwQyOTAdNptNkuTl5aWsWbMq6KqJKgEBATp/nokhEz9epm4P36PHHopQ6aJ5NHFgR2Xz99Pcr5P/sZr51hMa3ruVo//kecvV9J5yevGJxipVJI/eePYBVStXSNPmr/i3z6e/6PXuzdWyQUWVL5FPH771hI6fPq9Fv2yVJO05dFJLftuhyYM6q0b5wqpTuZjG9++gz5ds0vHT/Ew8GfcL0vJ25Fta/N03GjbqbWXPnl3RUacVHXValy9flpQc8PZ5obsuXb6kN4a8pbi4WEefpKQkx3k6PtxSy5f9LCn587tj5yc1e+Z0rVy+TPv37dWwQf0VmjtM9RvdJ0kqWqy4at9TT6PeGqwd27dp65ZNemf0CN3f7AHlDguTJDVr0VI+vr4aOWyQDh7Yp5+W/KAFn36sTo93uc1XCa7w2QK4eXJgkSJFtG/fPhUvXlyStGbNGhUqVMix/+jRowoPD7/e4XeNL37cpNBcOTT4+ZbKExKgbXv+Uuuekx0TKgrmDZbd/u9vZmu3HlLXgbM1pOeDGtbrIe0/elod+n2gnQeOO/q8O/tnZfP306Q3OylngL9WbzmgVj2nKP7Kv8tOdRs4R+P7d9D303vLbjdauHSLXh77+e1747gh3C9Iy5efJz+O9oUezsHom8NG6sFWD2v37p3a8UfyN3yPtGrufOx3PylfvuTJe0cOH3KsyCFJT3R9WpcvXdLoEUMUe+GCKlWppgmTP3DKVg8bNVbvjh6p3s8+JZuXlxrdd7/6vTbQsT9HQIDemzJT74x+S107t1dQzlx66pnn1aZdh8y9CLgpfLYAks24MRc+bdo0FSxYUC1btkxz/8CBA3Xq1CnNnDkzw+f2r9rrZoeHu8SlzZO4X2DJpc2TdPZiUvodAUm5snnz2QJLLm2e5O4hOIQ95TkPHjr1f573y7NbM87PPfecy/2jRo26TSMBAAAAXHN7jTMAAABwJ3D7A1AAAADgGTx1NQtPQcYZAAAAsICMMwAAACSRcU4PGWcAAADAAgJnAAAAwAJKNQAAACCJUo30kHEGAAAALCBwBgAAACygVAMAAADJqNRwiYwzAAAAYAEZZwAAAEhicmB6yDgDAAAAFhA4AwAAABZQqgEAAABJlGqkh4wzAAAAYAGBMwAAAGABpRoAAACQRKlGesg4AwAAABYQOAMAAAAWUKoBAAAASZRqpIeMMwAAAGABGWcAAAAkI+HsEhlnAAAAwAICZwAAAMACSjUAAAAgicmB6SHjDAAAAFhA4AwAAABYQKkGAAAAJFGqkR4yzgAAAIAFZJwBAAAgiYxzesg4AwAAABYQOAMAAAAWUKoBAAAASZRqpIeMMwAAAGABgTMAAABgAaUaAAAASEalhktknAEAAAALyDgDAABAEpMD00PGGQAAALCAwBkAAACwgFINAAAASKJUIz1knAEAAAALCJwBAAAACyjVAAAAQDJKNVwi4wwAAABYQOAMAAAAWECpBgAAAJIZu7tH4NHIOAMAAAAWkHEGAABAMiYHukTGGQAAALCAwBkAAACwgFINAAAAJGNyoEtknAEAAAALCJwBAAAACyjVAAAAQDJW1XCJjDMAAABgARlnAAAAJGNyoEtknAEAAAALCJwBAAAACyjVAAAAQDJKNVwi4wwAAABYQOAMAAAAWGAzhgX77gbx8fGKjIzUgAED5Ofn5+7hwMNxv8Aq7hVkBPeL5/N/4D13D8Hh0vcvunsIqRA43yViYmIUFBSk8+fPKzAw0N3DgYfjfoFV3CvICO4Xz0fg7BqlGgAAAIAFrKoBAACAZKyq4RIZZwAAAMACMs53CT8/Pw0ZMoTJGLCE+wVWca8gI7hf7gBMfXOJyYEAAACQJPk3H+fuIThcWtzP3UNIhVINAAAAwAJKNQAAAJCMyYEukXEGAAAALCBwBgAAACwgcL5LTJ48WUWKFFHWrFkVERGh9evXu3tI8EArV67UQw89pHz58slms2nhwoXuHhI8VGRkpGrWrKmAgACFhYWpTZs22rNnj7uHBQ80depUVapUSYGBgQoMDFSdOnX0ww8/uHtYuB5jPGfzQATOd4EFCxaoX79+GjJkiDZt2qTKlSurWbNmOnXqlLuHBg8TFxenypUra/Lkye4eCjzcihUr1LNnT61du1Y//fSTEhIS1LRpU8XFxbl7aPAwBQoU0OjRo7Vx40Zt2LBBjRs3VuvWrbVjxw53Dw3IMJajuwtERESoZs2amjRpkiTJbrerYMGC6t27t/r37+/m0cFT2Ww2ffXVV2rTpo27h4I7wOnTpxUWFqYVK1aofv367h4OPFxwcLDefvttPf300+4eCq7hf/8Ydw/B4dJPr7t7CKmQcf6Pu3LlijZu3KgmTZo42ry8vNSkSROtWbPGjSMD8F9y/vx5SckBEXA9SUlJmj9/vuLi4lSnTh13DwfIMJaj+4+LiopSUlKS8uTJ49SeJ08e7d69202jAvBfYrfb9dJLL6lu3bqqUKGCu4cDD/THH3+oTp06unz5snLkyKGvvvpK5cqVc/ewgAwjcAYA3JSePXtq+/btWrVqlbuHAg9VunRpbdmyRefPn9cXX3yhLl26aMWKFQTPnogKXpcInP/jQkND5e3trZMnTzq1nzx5Unnz5nXTqAD8V/Tq1UvffvutVq5cqQIFCrh7OPBQWbJkUYkSJSRJ1atX1++//6733ntP06dPd/PIgIyhxvk/LkuWLKpevbqWLl3qaLPb7Vq6dCn1ZQBumDFGvXr10ldffaVly5apaNGi7h4S7iB2u13x8fHuHgaQYWSc7wL9+vVTly5dVKNGDdWqVUsTJkxQXFycunXr5u6hwcPExsZq//79jj8fOnRIW7ZsUXBwsAoVKuTGkcHT9OzZU59++qm+/vprBQQE6MSJE5KkoKAg+fv7u3l08CQDBgxQixYtVKhQIV24cEGffvqpli9friVLlrh7aEgLj9x2icD5LtCxY0edPn1agwcP1okTJ1SlShUtXrw41YRBYMOGDWrUqJHjz/369ZMkdenSRbNnz3bTqOCJpk6dKklq2LChU/usWbPUtWvX2z8geKxTp07pySef1PHjxxUUFKRKlSppyZIluv/++909NCDDWMcZAAAAkiT/xiPdPQSHS8vecPcQUqHGGQAAALCAwBkAAACwgBpnAAAAJGNyoEtknAEAAAALCJwBAAAACyjVAAAAQDI7i625QsYZAAAAsIDAGQAAALCAwBnAf0rXrl3Vpk0bx58bNmyol1566baPY/ny5bLZbDp37tx1+9hsNi1cuNDyOYcOHaoqVarc1LgOHz4sm82mLVu23NR5APxHGbvnbB6IwBnALde1a1fZbDbZbDZlyZJFJUqU0PDhw5WYmHjLX/vLL7/UW2+9ZamvlWAXAHD3YnIggNuiefPmmjVrluLj4/X999+rZ8+e8vX11YABA1L1vXLlirJkyZIprxscHJwp5wGAu4KHZno9BRlnALeFn5+f8ubNq8KFC+v5559XkyZNtGjRIkn/lleMHDlS+fLlU+nSpSVJx44dU4cOHZQzZ04FBwerdevWOnz4sOOcSUlJ6tevn3LmzKmQkBC99tprMsZ5Rvi1pRrx8fF6/fXXVbBgQfn5+alEiRL68MMPdfjwYTVq1EiSlCtXLtlsNnXt2lWSZLfbFRkZqaJFi8rf31+VK1fWF1984fQ633//vUqVKiV/f381atTIaZxWvf766ypVqpSyZcumYsWKadCgQUpISEjVb/r06SpYsKCyZcumDh066Pz58077Z86cqbJlyypr1qwqU6aMpkyZkuGxAABSI3AG4Bb+/v66cuWK489Lly7Vnj179NNPP+nbb79VQkKCmjVrpoCAAP3666/67bfflCNHDjVv3txx3LvvvqvZs2fr//7v/7Rq1SqdOXNGX331lcvXffLJJzVv3jxNnDhRu3bt0vTp05UjRw4VLFhQ//vf/yRJe/bs0fHjx/Xee+9JkiIjIzV37lxNmzZNO3bsUN++ffX4449rxYoVkpID/LZt2+qhhx7Sli1b1L17d/Xv3z/D1yQgIECzZ8/Wzp079d5772nGjBkaP368U5/9+/frs88+0zfffKPFixdr8+bNeuGFFxz7P/nkEw0ePFgjR47Url27NGrUKA0aNEhz5szJ8HgAAM4o1QBwWxljtHTpUi1ZskS9e/d2tGfPnl0zZ850lGh8/PHHstvtmjlzpmw2myRp1qxZypkzp5YvX66mTZtqwoQJGjBggNq2bStJmjZtmpYsWXLd1967d68+++wz/fTTT2rSpIkkqVixYo79KWUdYWFhypkzp6TkDPWoUaP0888/q06dOo5jVq1apenTp6tBgwaaOnWqihcvrnfffVeSVLp0af3xxx8aM2ZMhq7Nm2++6fj/RYoU0SuvvKL58+frtddec7RfvnxZc+fOVf78+SVJ77//vlq2bKl3331XefPm1ZAhQ/Tuu+86rknRokW1c+dOTZ8+XV26dMnQeADchQzrOLtC4Azgtvj222+VI0cOJSQkyG63q3Pnzho6dKhjf8WKFZ3qmrdu3ar9+/crICDA6TyXL1/WgQMHdP78eR0/flwRERGOfT4+PqpRo0aqco0UW7Zskbe3txo0aGB53Pv379fFixd1//33O7VfuXJFVatWlSTt2rXLaRySHEF2RixYsEATJ07UgQMHFBsbq8TERAUGBjr1KVSokCNoTnkdu92uPXv2KCAgQAcOHNDTTz+tHj16OPokJiYqKCgow+MBADgjcAZwWzRq1EhTp05VlixZlC9fPvn4OH/8ZM+e3enPsbGxql69uj755JNU58qdO/cNjcHf3z/Dx8TGxkqSvvvuO6eAVUqu284sa9as0WOPPaZhw4apWbNmCgoK0vz58x1Z7IyMdcaMGakCeW9v70wbKwDcrQicAdwW2bNnV4kSJSz3r1atmhYsWKCwsLBUWdcU4eHhWrdunerXry8pObO6ceNGVatWLc3+FStWlN1u14oVKxylGldLyXgnJSU52sqVKyc/Pz8dPXr0upnqsmXLOiY6pli7dm36b/Iqq1evVuHChfXGG2842o4cOZKq39GjR/X3338rX758jtfx8vJS6dKllSdPHuXLl08HDx7UY489lqHXBwBJrKqRDiYHAvBIjz32mEJDQ9W6dWv9+uuvOnTokJYvX64+ffrozz//lCS9+OKLGj16tBYuXKjdu3frhRdecLkGc5EiRdSlSxc99dRTWrhwoeOcn332mSSpcOHCstls+vbbb3X69GnFxsYqICBAr7zyivr27as5c+bowIED2rRpk95//33HhLvnnntO+/bt06uvvqo9e/bo008/1ezZszP0fkuWLKmjR49q/vz5OnDggCZOnJjmRMesWbOqS5cu2rp1q3799Vf16dNHHTp0UN68eSVJw4YNU2RkpCZOnKi9e/fqjz/+0KxZszRu3LgMjQcAkBqBMwCPlC1bNq1cuVKFChVS27ZtVbZsWT399NO6fPmyIwP98ssv64knnlCXLl1Up04dBQQE6OGHH3Z53qlTp+qRRx7RCy+8oDJlyqhHjx6Ki4uTJOXPn1/Dhg1T//79lSdPHvXq1UuS9NZbb2nQoEGKjIxU2bJl1bx5c3333XcqWrSopOS64//9739auHChKleurGnTpmnUqFEZer+tWrVS37591atXL1WpUkWrV6/WoEGDUvUrUaKE2rZtqwceeEBNmzZVpUqVnJab6969u2bOnKlZs2apYsWKatCggWbPnu0YKwC4ZIznbB7IZq43iwYAAAB3Ff97Brp7CA6XVmcsAXE7kHEGAAAALGByIAAAAJIxOdAlMs4AAACABQTOAAAAgAWUagAAACAZa0a4RMYZAAAAsIDAGQAAALCAUg0AAAAkY1UNl8g4AwAAABaQcQYAAEAyJge6RMYZAAAAsIDAGQAAALCAUg0AAAAkY3KgS2ScAQAAAAsInAEAAAALKNUAAABAMlbVcImMMwAAAGABGWcAAAAkY3KgS2ScAQAAAAsInAEAAAALKNUAAABAMiYHukTGGQAAAHe8yZMnq0iRIsqaNasiIiK0fv36TH8NAmcAAADc0RYsWKB+/fppyJAh2rRpkypXrqxmzZrp1KlTmfo6NmPIyQMAAEDyr9rL3UNwuLR5kuW+ERERqlmzpiZNSj7GbrerYMGC6t27t/r3759pYyLjDAAAgDvWlStXtHHjRjVp0sTR5uXlpSZNmmjNmjWZ+lpMDgQAAIDHiY+PV3x8vFObn5+f/Pz8nNqioqKUlJSkPHnyOLXnyZNHu3fvztQxETgDAABAUsbKI261oUOHatiwYU5tQ4YM0dChQ90zIBE4AwAAwAMNGDBA/fr1c2q7NtssSaGhofL29tbJkyed2k+ePKm8efNm6piocQYAAIDH8fPzU2BgoNOWVuCcJUsWVa9eXUuXLnW02e12LV26VHXq1MnUMZFxBgAAwB2tX79+6tKli2rUqKFatWppwoQJiouLU7du3TL1dQicAQAAcEfr2LGjTp8+rcGDB+vEiROqUqWKFi9enGrC4M1iHWcAAADAAmqcAQAAAAsInAEAAAALCJwBAAAACwicAQAAAAsInAEAAAALCJwBAAAACwicAQAAAAsInAEAAAALCJwBAAAACwicAQAAAAsInAEAAAALCJwBAAAAC/4fnePBS29MHxwAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 900x900 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(9,9))\n",
        "sns.heatmap(B_cm, annot=True, fmt=\".3f\", linewidths=.5, square=True, cmap='Blues_r');\n",
        "plt.ylabel('Actual label');\n",
        "plt.xlabel('Predicted label');\n",
        "all_sample_title= 'Accuracy score: {0}'.format(B_accuracy)\n",
        "plt.title(all_sample_title, size=15);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 387,
      "metadata": {},
      "outputs": [],
      "source": [
        "import pickle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 388,
      "metadata": {},
      "outputs": [],
      "source": [
        "with open('nbB_model.pkl', 'wb') as f:\n",
        "    pickle.dump(B_clf, f)\n",
        "nbB_model_dump = pickle.load(open('nbB_model.pkl', 'rb'))\n",
        "\n",
        "with open('nbG_model.pkl', 'wb') as g:\n",
        "    pickle.dump(G_clf, g)\n",
        "nbG_model_dump = pickle.load(open('nbG_model.pkl', 'rb'))\n",
        "\n",
        "with open('nbM_model.pkl', 'wb') as h:\n",
        "    pickle.dump(M_clf, h)\n",
        "nbM_model_dump = pickle.load(open('nbM_model.pkl', 'rb'))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 392,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[3]\n",
            "[1]\n",
            "[3]\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Users\\Ayooo\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but GaussianNB was fitted with feature names\n",
            "  warnings.warn(\n",
            "c:\\Users\\Ayooo\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but MultinomialNB was fitted with feature names\n",
            "  warnings.warn(\n",
            "c:\\Users\\Ayooo\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but BernoulliNB was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "salary = 120000\n",
        "exp = 4\n",
        "sample = np.array([salary,exp])\n",
        "print(G_clf.predict(sample.reshape(1, -1)))\n",
        "print(M_clf.predict(sample.reshape(1, -1)))\n",
        "print(B_clf.predict(sample.reshape(1, -1)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 393,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Gaussian:  0.7790697674418605\n",
            "Multinomial:  0.38372093023255816\n",
            "Bernoulli: 0.23255813953488372\n"
          ]
        }
      ],
      "source": [
        "print(\"Gaussian: \", G_accuracy) \n",
        "print(\"Multinomial: \", M_accuracy)\n",
        "print(\"Bernoulli:\", B_accuracy)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
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      "codemirror_mode": {
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      },
      "file_extension": ".py",
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