Model-B.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"k_B33jTlLHjf"},"source":["#Hello!\n","In this notebook, we will build our second ML/DL model using the all data to obtain a specific idea about the user."]},{"cell_type":"markdown","metadata":{"id":"uv795gSfLTfH"},"source":["##Import Data set from Google Drive"]},{"cell_type":"code","execution_count":28,"metadata":{"executionInfo":{"elapsed":290,"status":"ok","timestamp":1670143774685,"user":{"displayName":"Hamza ROUAISSI","userId":"09320853917146431941"},"user_tz":-60},"id":"gcceTKanLy2M"},"outputs":[],"source":["#Import library\n","import pandas as pd"]},{"cell_type":"code","execution_count":29,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1985,"status":"ok","timestamp":1670143776932,"user":{"displayName":"Hamza ROUAISSI","userId":"09320853917146431941"},"user_tz":-60},"id":"zvDQOo9BLy4I","outputId":"8b18570e-a53b-45b7-c456-eabea7b7a4d9"},"outputs":[{"name":"stdout","output_type":"stream","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}],"source":["#drive mount\n","from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":30,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":16,"status":"ok","timestamp":1670143776933,"user":{"displayName":"Hamza ROUAISSI","userId":"09320853917146431941"},"user_tz":-60},"id":"JNGm2URlLy6V","outputId":"717e0a5e-9036-4c52-badd-2e7a42ff0716"},"outputs":[{"name":"stdout","output_type":"stream","text":[" data_for_model1.csv\n"," data_for_model2.csv\n","'Data_preparation&cleaning&FeatureSelection.ipynb'\n"," Final_data.csv\n"," Mental-Health-Questionnaire-2.0.csv\n"," Mental-Health-Survey-Form-responses.csv\n"," Model-A.ipynb\n"," Model-B.ipynb\n"]}],"source":["#verify the path\n","folder_path = \"/content/drive/MyDrive/My_work/TuniHack_work/data\"\n","!ls \"/content/drive/MyDrive/My_work/TuniHack_work/data\""]},{"cell_type":"code","execution_count":31,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":490},"executionInfo":{"elapsed":579,"status":"ok","timestamp":1670143777504,"user":{"displayName":"Hamza ROUAISSI","userId":"09320853917146431941"},"user_tz":-60},"id":"DNwcz1E5Ly9r","outputId":"8d213a22-f3b7-4034-ecca-e1fb40051912"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-139512c3-dba2-4dad-911e-57e1bfd814ea\">\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>Unnamed: 0</th>\n","      <th>Gender</th>\n","      <th>Are you above 30 years of age?</th>\n","      <th>Employment Status</th>\n","      <th>City</th>\n","      <th>How are you feeling today?</th>\n","      <th>Have you been eating and sleeping well?</th>\n","      <th>Is your sadness momentarily or has it been constant for a long time? (last month)</th>\n","      <th>At what time of the day are you extremely low?</th>\n","      <th>How frequently have you had little pleasure or interest in the activities you usually enjoy?</th>\n","      <th>...</th>\n","      <th>conc_Yes</th>\n","      <th>conc_maybe</th>\n","      <th>conc_no</th>\n","      <th>conc_yes</th>\n","      <th>fbad_Maybe</th>\n","      <th>fbad_No</th>\n","      <th>fbad_Yes</th>\n","      <th>fbad_maybe</th>\n","      <th>fbad_no</th>\n","      <th>fbad_yes</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      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  </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 80 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-139512c3-dba2-4dad-911e-57e1bfd814ea')\"\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","  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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","  "],"text/plain":["   Unnamed: 0  Gender  Are you above 30 years of age?  Employment Status  \\\n","0           0       0                               1                  0   \n","1           1       1                               0                  0   \n","2           2       1                               0                  0   \n","3           3       1                               0                  0   \n","4           4       0                               0                  0   \n","\n","   City  How are you feeling today?  Have you been eating and sleeping well?  \\\n","0     3                           0                                        0   \n","1     3                           0                                        1   \n","2     3                           0                                        1   \n","3     3                           0                                        0   \n","4     3                           2                                        1   \n","\n","   Is your sadness momentarily or has it been constant for a long time? (last month)  \\\n","0                                                  0                                   \n","1                                                  0                                   \n","2                                                  2                                   \n","3                                                  0                                   \n","4                                                  0                                   \n","\n","   At what time of the day are you extremely low?  \\\n","0                                               1   \n","1                                               2   \n","2                                               1   \n","3                                               1   \n","4                                               1   \n","\n","   How frequently have you had little pleasure or interest in the activities you usually enjoy?  \\\n","0                                                  2                                              \n","1                                                  3                                              \n","2                                                  1                                              \n","3                                                  1                                              \n","4                                                  2                                              \n","\n","   ...  conc_Yes  conc_maybe  conc_no  conc_yes  fbad_Maybe  fbad_No  \\\n","0  ...         0           0        0         1           0        0   \n","1  ...         0           0        1         0           0        0   \n","2  ...         0           0        0         1           0        0   \n","3  ...         0           0        1         0           0        0   \n","4  ...         0           0        1         0           0        0   \n","\n","   fbad_Yes  fbad_maybe  fbad_no  fbad_yes  \n","0         0           0        0         1  \n","1         0           0        1         0  \n","2         0           0        1         0  \n","3         0           1        0         0  \n","4         0           0        1         0  \n","\n","[5 rows x 80 columns]"]},"execution_count":31,"metadata":{},"output_type":"execute_result"}],"source":["#assign data \n","data_path = folder_path + \"/data_for_model2.csv\"\n","#read data\n","data = pd.read_csv(data_path)\n","data.head()"]},{"cell_type":"markdown","metadata":{"id":"reBDxGuhLaaX"},"source":["##Splitting/Scaling"]},{"cell_type":"code","execution_count":32,"metadata":{"executionInfo":{"elapsed":35,"status":"ok","timestamp":1670143777507,"user":{"displayName":"Hamza ROUAISSI","userId":"09320853917146431941"},"user_tz":-60},"id":"xLufwVzUMDIv"},"outputs":[],"source":["#importing libraries\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from sklearn.preprocessing import StandardScaler\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import confusion_matrix\n","import itertools\n","import tensorflow as tf"]},{"cell_type":"code","execution_count":33,"metadata":{"executionInfo":{"elapsed":35,"status":"ok","timestamp":1670143777508,"user":{"displayName":"Hamza ROUAISSI","userId":"09320853917146431941"},"user_tz":-60},"id":"4FRs5Ew0MDLA"},"outputs":[],"source":["y=data['Prediction_status'].copy()\n","X=data.drop('Prediction_status',axis=1).copy()"]},{"cell_type":"code","execution_count":34,"metadata":{"executionInfo":{"elapsed":35,"status":"ok","timestamp":1670143777509,"user":{"displayName":"Hamza ROUAISSI","userId":"09320853917146431941"},"user_tz":-60},"id":"f9T_OpwnMDOa"},"outputs":[],"source":["scaler = StandardScaler()\n","X = scaler.fit_transform(X)"]},{"cell_type":"code","execution_count":35,"metadata":{"executionInfo":{"elapsed":34,"status":"ok","timestamp":1670143777509,"user":{"displayName":"Hamza ROUAISSI","userId":"09320853917146431941"},"user_tz":-60},"id":"SVMlecLaMG7T"},"outputs":[],"source":["X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, random_state=100)"]},{"cell_type":"code","execution_count":35,"metadata":{"executionInfo":{"elapsed":34,"status":"ok","timestamp":1670143777510,"user":{"displayName":"Hamza ROUAISSI","userId":"09320853917146431941"},"user_tz":-60},"id":"hMiUaEcbMG-v"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"r8p1WnWkLaco"},"source":["##Training using ML algorithms"]},{"cell_type":"code","execution_count":36,"metadata":{"executionInfo":{"elapsed":34,"status":"ok","timestamp":1670143777511,"user":{"displayName":"Hamza ROUAISSI","userId":"09320853917146431941"},"user_tz":-60},"id":"5eJIzPFKMJza"},"outputs":[],"source":["models = {}\n","\n","# Logistic Regression\n","from sklearn.linear_model import LogisticRegression\n","models['Logistic Regression'] = LogisticRegression()\n","\n","# Support Vector Machines\n","from sklearn.svm import LinearSVC\n","models['Support Vector Machines'] = LinearSVC()\n","\n","# Decision Trees\n","from sklearn.tree import DecisionTreeClassifier\n","models['Decision Trees'] = DecisionTreeClassifier()\n","\n","# Random Forest\n","from sklearn.ensemble import RandomForestClassifier\n","models['Random Forest'] = RandomForestClassifier()\n","\n","# Naive Bayes\n","from sklearn.naive_bayes import GaussianNB\n","models['Naive Bayes'] = GaussianNB()\n","\n","# K-Nearest Neighbors\n","from sklearn.neighbors import KNeighborsClassifier\n","models['K-Nearest Neighbor'] = KNeighborsClassifier()"]},{"cell_type":"code","execution_count":37,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":34,"status":"ok","timestamp":1670143777511,"user":{"displayName":"Hamza ROUAISSI","userId":"09320853917146431941"},"user_tz":-60},"id":"WfPmH1HwMJ17","outputId":"ebd6a257-aa37-4eda-8c3e-ae2e1b612725"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n","  warnings.warn(\n"]}],"source":["from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n","\n","\n","accuracy, precision, recall, f1_scores = {}, {}, {}, {}\n","\n","for key in models.keys():\n","    \n","    # Fit the classifier\n","    models[key].fit(X_train, y_train)\n","    \n","    # Make predictions\n","    predictions = models[key].predict(X_test)\n","    \n","    # Calculate metrics\n","    accuracy[key] = accuracy_score(predictions, y_test)\n","    precision[key] = precision_score(predictions, y_test)\n","    recall[key] = recall_score(predictions, y_test)\n","    f1_scores[key] = f1_score(predictions, y_test)"]},{"cell_type":"code","execution_count":38,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":237},"executionInfo":{"elapsed":30,"status":"ok","timestamp":1670143777512,"user":{"displayName":"Hamza ROUAISSI","userId":"09320853917146431941"},"user_tz":-60},"id":"0TwjoG0nMJ5R","outputId":"0293d2d2-5255-4b68-8605-c947cfc16121"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-119ecbfe-436a-41bb-9fba-3baa8be56160\">\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>Accuracy</th>\n","      <th>Precision</th>\n","      <th>Recall</th>\n","      <th>F1-score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Logistic Regression</th>\n","      <td>0.809524</td>\n","      <td>0.882353</td>\n","      <td>0.714286</td>\n","      <td>0.789474</td>\n","    </tr>\n","    <tr>\n","      <th>Support Vector Machines</th>\n","      <td>0.761905</td>\n","      <td>0.823529</td>\n","      <td>0.666667</td>\n","      <td>0.736842</td>\n","    </tr>\n","    <tr>\n","      <th>Decision Trees</th>\n","      <td>0.666667</td>\n","      <td>0.647059</td>\n","      <td>0.578947</td>\n","      <td>0.611111</td>\n","    </tr>\n","    <tr>\n","      <th>Random Forest</th>\n","      <td>0.880952</td>\n","      <td>0.764706</td>\n","      <td>0.928571</td>\n","      <td>0.838710</td>\n","    </tr>\n","    <tr>\n","      <th>Naive Bayes</th>\n","      <td>0.571429</td>\n","      <td>0.058824</td>\n","      <td>0.333333</td>\n","      <td>0.100000</td>\n","    </tr>\n","    <tr>\n","      <th>K-Nearest Neighbor</th>\n","      <td>0.761905</td>\n","      <td>0.529412</td>\n","      <td>0.818182</td>\n","      <td>0.642857</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-119ecbfe-436a-41bb-9fba-3baa8be56160')\"\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 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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","  "],"text/plain":["                         Accuracy  Precision    Recall  F1-score\n","Logistic Regression      0.809524   0.882353  0.714286  0.789474\n","Support Vector Machines  0.761905   0.823529  0.666667  0.736842\n","Decision Trees           0.666667   0.647059  0.578947  0.611111\n","Random Forest            0.880952   0.764706  0.928571  0.838710\n","Naive Bayes              0.571429   0.058824  0.333333  0.100000\n","K-Nearest Neighbor       0.761905   0.529412  0.818182  0.642857"]},"execution_count":38,"metadata":{},"output_type":"execute_result"}],"source":["import pandas as pd\n","\n","df_model = pd.DataFrame(index=models.keys(), columns=['Accuracy', 'Precision', 'Recall', 'F1-score'])\n","df_model['Accuracy'] = accuracy.values()\n","df_model['Precision'] = precision.values()\n","df_model['Recall'] = recall.values()\n","df_model['F1-score'] = f1_scores.values()\n","df_model"]},{"cell_type":"markdown","metadata":{"id":"SljG264gLagN"},"source":["##Training using Dl algorithms"]},{"cell_type":"code","execution_count":39,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":27,"status":"ok","timestamp":1670143777513,"user":{"displayName":"Hamza ROUAISSI","userId":"09320853917146431941"},"user_tz":-60},"id":"rAA9SydPMmQ8","outputId":"61738ada-116e-4a19-9894-1d419126027e"},"outputs":[{"data":{"text/plain":["(209, 79)"]},"execution_count":39,"metadata":{},"output_type":"execute_result"}],"source":["X.shape"]},{"cell_type":"code","execution_count":40,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":21,"status":"ok","timestamp":1670143777513,"user":{"displayName":"Hamza ROUAISSI","userId":"09320853917146431941"},"user_tz":-60},"id":"FaR_jy29MmTF","outputId":"cf1cbe7f-e624-4e0f-f39b-c868e16581eb"},"outputs":[{"name":"stdout","output_type":"stream","text":["Class Distribution (Positive to Negative): 52.7% / 47.3%\n"]}],"source":["print(\"Class Distribution (Positive to Negative): {:.1f}% / {:.1f}%\".format(y_train.mean() * 100, (1 - y_train.mean()) * 100))"]},{"cell_type":"code","execution_count":41,"metadata":{"executionInfo":{"elapsed":4086,"status":"ok","timestamp":1670143781988,"user":{"displayName":"Hamza ROUAISSI","userId":"09320853917146431941"},"user_tz":-60},"id":"9WFZUsezMmVX"},"outputs":[],"source":["inputs = tf.keras.Input(shape=(X.shape[1],))\n","x = tf.keras.layers.Dense(1024, activation='relu')(inputs)\n","x = tf.keras.layers.Dense(1024, activation='relu')(x)\n","outputs = tf.keras.layers.Dense(1, activation='sigmoid')(x)\n","\n","model = tf.keras.Model(inputs, outputs)\n","\n","\n","model.compile(\n","    optimizer='adam',\n","    loss='binary_crossentropy',\n","    metrics=[\n","        'accuracy',\n","        tf.keras.metrics.AUC(name='auc')\n","    ]\n",")\n","\n","\n","batch_size = 64\n","epochs = 50\n","\n","history = model.fit(\n","    X_train,\n","    y_train,\n","    validation_split=0.2,\n","    batch_size=batch_size,\n","    verbose=0,\n","    epochs=epochs,\n","    callbacks=[\n","        tf.keras.callbacks.ReduceLROnPlateau()\n","    ]\n",")"]},{"cell_type":"code","execution_count":42,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":404},"executionInfo":{"elapsed":15,"status":"ok","timestamp":1670143781989,"user":{"displayName":"Hamza ROUAISSI","userId":"09320853917146431941"},"user_tz":-60},"id":"hrCYeA3oMmYN","outputId":"89a87c48-bd19-4ec6-823a-3e2c95ebbba4"},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAtAAAAGDCAYAAAACpSdYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXhU1f3H8fc3CRAChMWwCRFQdsSwBMSFRXFfQKUotO4tLq1abNWibdVa7eqvVdtq61KtrQW3CqgIighYkbK5QVhEQdmEsE2AJGSSOb8/7iQGTGCSzJ3JMJ/X8+Qhc+fOvd8JPPqZk+85x5xziIiIiIhIZFLiXYCIiIiISCJRgBYRERERqQEFaBERERGRGlCAFhERERGpAQVoEREREZEaUIAWEREREakBBWgREYkbM7vLzJ6Mdx0iIjWhAC0iSc3M5prZLjNrFO9a/GJmLczsMTP7yswKzewTM7smRvf+q5ntDX+VmFmw0uM3nHO/cs59Lxa1iIhEiwK0iCQtM+sMDAUcMCrG906L0X0aArOBTsBJQHPgduA3ZvYjH+53wPtyzt3gnGvqnGsK/Ap4vvyxc+7caN9fRCQWFKBFJJldCSwEngGuqvyEmWWb2X/MLN/MdpjZnys9N8HMVprZHjPLM7MB4ePOzLpWOu8ZM7s//P0IM9toZj8xs6+Ap82spZm9Fr7HrvD3HSu9vpWZPW1mm8PPTw0fX25mF1Y6r4GZbTez/lW8xyuAY4Cxzrl1zrmgc24mcAtwn5llhmt66aD3/7CZPRL+vrmZPWVmW8xsk5ndb2ap4eeuNrP3zOyPZrYDuLcmfwFmdq+Z/Sv8fefwz/AaM9sQfs83mNkgM/vYzHZX/nsIv+ba8N/FLjObZWadanJ/EZHaUIAWkWR2JfBc+OtsM2sLEA6HrwFfAJ2BDsCU8HNj8ULilUAm3sj1jgjv1w5ohTcafB3ef4OfDj8+BigCKgfEfwIZQB+gDfDH8PFngcsrnXcesMU590EV9zwTeMM5t++g4y8D6Xij0lOA88ysWaX3fynw7/C5zwClQFegP3AWULnt4kTgc6At8MChfwQRORHoBlwGPAT8FDgD7+dwqZkND9c5GrgLuARoDbwLTI7C/UVEDkkBWkSSkpmdihdcX3DOLQU+A74dfnowcDRwu3Nun3Ou2Dn33/Bz3wN+55xb7DxrnXNfRHjbEHCPc26/c67IObfDOfeyc67QObcHL3yWh8P2wLnADc65XeGR43nh6/wLL/Bmhh9fgRe2q5IFbDn4oHOuFNgOZIXrXwZcHH76dKDQObcw/KHiPGBi+GexDS/Ij6t0uc3OuT8550qdc0UR/iwO5Zfhn/mbwD5gsnNum3NuE15ILh9pvwH4tXNuZfj9/Arop1FoEfGbArSIJKurgDedc9vDj//N120c2cAX4VB2sGy8sF0b+c654vIHZpZhZn8zsy/MrACYD7QIjwBnAzudc7sOvohzbjPwHjDGzFrgBe3nqrnndqD9wQfDvcpZ4efBe//jw99/m69HnzsBDYAt4RaK3cDf8EbEy204/Fuvka2Vvi+q4nHTSrU9XKmunYDh/cZARMQ3MZnEIiJSn5hZY7wWhdRwPzJAI7zwmoMXCI8xs7QqQvQG4LhqLl2I13JRrh2wsdJjd9D5PwZ6ACc6574ys37AB3ghcAPQysxaOOd2V3Gvf+CNhqcB74dHZ6syG/iVmTU5qI1jDLAfrwcc4EXg/8I92BfjtXaUv9/9eCPVVX2gqOp9xcoG4AHnXHUfHkREfKERaBFJRhcBZUBvoF/4qxdee8CVwCK8toffmFkTM0s3s1PCr30SuM3MBpqna6WWgQ+Bb5tZqpmdQ7gd4xCa4Y2o7jazVsA95U8457YAbwCPhicbNjCzYZVeOxUYAPwQrye6Ov/EC/EvhifpNTCzs4FHgHudc4Hw/fKBuXg92euccysr1fEmXrjONLMUMzuuvA85zv4K3GlmfaBisuPYONckIklAAVpEktFVwNPOuS+dc1+Vf+FN4PsO3gjwhXiT5r7EC6CXATjnXsTrVf43sAcvyLYKX/eH4dftDl9n6mHqeAhojNdGsRCYedDzVwBBYBWwDZhY/kS41/hloAvwn+pu4JzbjzcBbwPwP6AA+APwU+fc7w86/d/hc/990PErgYZAHrALeIkq2kJizTn3CvBbYEq4BWY5XjuLiIivzLl4/eZNRETqwszuBro75y4/7MkiIhI16oEWEUlA4ZaP7+KNUouISAyphUNEJMGY2QS8low3nHPz412PiEiyUQuHiIiIiEgNaARaRERERKQGFKBFRERERGog4SYRZmVluc6dO8e7DBERERE5wi1dunS7c671wccTLkB37tyZJUuWxLsMERERETnCmdkXVR1XC4eIiIiISA0oQIuIiIiI1IACtIiIiIhIDShAi4iIiIjUgAK0iIiIiEgNKECLiIiIiNSAArSIiIiISA0oQIuIiIiI1IACtIiIiIhIDfgWoM3s72a2zcyWV/O8mdkjZrbWzD42swF+1SIiIiIiEi1+jkA/A5xziOfPBbqFv64DHvOxFhERERGRqEjz68LOuflm1vkQp4wGnnXOOWChmbUws/bOuS1+1SQikih27ivh4427cfEuRESkHjitR5t4l3AA3wJ0BDoAGyo93hg+9o0AbWbX4Y1Sc8wxx8SkOBGRWAuFHO99tp0pizbwZt5XBMsUn0VEUgw+//X58S7jAPEM0BFzzj0OPA6Qm5ur/6OIyBFlS6CIF5ds5PnFG9i0u4gWGQ24YkhnzurTlkZpmustIlLfxDNAbwKyKz3uGD4mInLEC5aFmLNqG1MWfcm8NfmEHJzaNYtJ5/YMB+fUeJcoIiLViGeAng7cZGZTgBOBgPqfReRIt277Pp5fvIGXlm5k+979tM1sxPdHdOXS3GyOOSoj3uWJiEgEfAvQZjYZGAFkmdlG4B6gAYBz7q/ADOA8YC1QCFzjVy1SfxWWlPLqR5spDoZq9Lqe7ZoxuEsrzMynyiLnnOODDbv5ZGMg3qVIPRYsCzF75VYWfr6T1BTj9J5tGDcom+HdW5OWqjYNEZFE4ucqHOMP87wDfuDX/aX++zx/Lzf+axmrt+6p1euPzWrCpYOyGTOgI62bNYpydYe3c18Jr3ywiecXf8marXtjfn9JPJ2OyuCOc3rwrQEdaZOZHu9yRESklszLsYkjNzfXLVmyJN5lSB29ueIrfvzCR6SlGn+4tB852S0ifm1ZyDF/TT5TFn/J4vW7SEsxRvZqw7jBxzCsW2tSU/wblQ6FHAs+28GUxV/y5oqtlJSF6JfdgnGDsjm9ZxuNJMohtcxoUC9+ayIiIpExs6XOudxvHFeAllgqCzn+783VPDr3M07o2JxHvzOAji1r3/e5dtteXliygZeXbmTHvhLaN09nbG42Ywd2JLtV9PpJvwoU8+KSDTy/ZAMbdxXRvHEDLhnQgcsGZdOzXWbU7iMiIiL1hwK0xN3OfSXcMvkD/rt2O+MGZXPvqD6kN4jOSgMlpSHeXrmVKYs3MP/TfMBb0WDcoGM4s3dbGtZiKbBgWYh3Vm3j+cUbeGf1NkIOTj7uKC4blM3ZfdpFrXYRERGpnxSgJa4+2rCb7z+3jPy9+7lvVB/GDfZvQ5xNu4t4YfEGXlyygc2BYlo1achZvdvSuGHkgXd/aYi38raSv2c/bZo1YmxuRy7NzabTUU18q1tERETqFwVoiZspi77k7mkraN2sEY9dPoATOkbe71wXZSHHu5/m8/ziDSz4bAehGvxbN2BQ51aMG3wMp/XQKgkiIiLJqLoAnRA7EUpiKg6Wcc+0FTy/ZANDu2Xx8Lj+tGrSMGb3T00xRvRow4gebWJ2TxERETnyKUCLLzbsLOT7zy3jk00BbjqtK7ee2d3X1TFEREREYkUBWqJu/pp8bpnyAWVljieuzOXM3m3jXZKIiIhI1ChAS1Q9/d467nstj+5tmvHXKwbSJUuT7kREROTIogAtUfP6x1v4xat5nNW7LQ+N60dGQ/3zEhERkSOPEo5ExQdf7uJHL3zIwE4teWR8f62RLCIiIkcsrc0ldbZhZyETnl1Cm8xGPH7FQIVnEREROaIpQEudFBQH+e4/FrO/NMTTVw/iqKaN4l2SiIiIiK8UoKXWSstC3PTvD/g8fx9/vXwgXds0i3dJIiIiIr5TD7TUinOOe19dwfw1+fz6kr6c0jUr3iWJiIiIxIRGoKVWnn5vPf9a+CXXDzuW8YOPiXc5IiIiIjGjAC01NjtvK798PY+z+7TlJ+f0jHc5IiIiIjGlAC01snxTgFumfEDfDs156LL+pGh7bhEREUkyCtASsa8CxXzvH0to3rgBT16ZS+OGWq5OREREko8CtERk3/5SvvuPxewpDvLUVYNok5ke75JERERE4kKrcMhhlYUcP5zyISu3FPDkVbn0Pjoz3iWJiIiIxI1GoOWwfvPGSmav3MrdF/Tm9J5t412OiIiISFwpQMshPfe/L3ji3XVcdVInrj6lS7zLEREREYk7BWip1ruf5nP3tBWM6NGan1/QO97liIiIiNQLCtBSpU+37uH7/1pGtzZN+dP4/qSl6p+KiIiICChASxW2793PNc8splGDVJ66ehDN0hvEuyQRERGRekMBWg5QHCxjwrNL2L53P09dlUuHFo3jXZKIiIhIvaJl7KRCKOS47cWP+ODL3Tz2nQHkZLeId0kiIiIi9Y5GoKXCH2ev4bWPtzDp3J6c27d9vMsRERERqZcUoAWAl5du5E9z1nJZbjbXDzs23uWIiIiI1FsK0ML/Pt/BpP98zMnHHcUvLzoeM4t3SSIiIiL1lgJ0klu3fR/X/2sp2a0yeOw7A2mYpn8SIiIiIoeitJTEdheWcO0zizHg6asH0TxDy9WJiIiIHI5W4UhSJaUhrv/nUjbtKuK5CSfS6agm8S5JREREJCEoQCch5xx3/ucT/rduJw9d1o9BnVvFuyQRERGRhKEWjiT06NzPeHnZRiae0Y2L+neIdzkiIiIiCUUBOsm89vFmfj9rNaP7Hc0PR3aLdzkiIiIiCUctHEmipDTEzBVfcfuLH5HbqSW/HXOClqsTERERqQUF6CPcZ/l7eWHxBl5etpHte0vo2qYpf7tiIOkNUuNdmoiIiEhCUoA+AhWVlPHG8i1MWbSBRet3kppijOzZhvGDj2FY99akpmjkWURERKS2FKCPIMs3BXh+8QamfriJPcWldD4qg5+c05MxAzvQpll6vMsTEREROSIoQCe4guIg0z/czJTFX7J8UwGN0lI4r297LhuUzYldWqnPWURERCTKFKATVGlZiF++lsfzSzZQHAzRs10zfjGqDxf166AdBUVERER8pACdoO5/fSX/eP8Lxg7syBUndaJvh+YabRYRERGJAQXoBPTMe+t4ZsF6vndqF352Qe94lyMiIiKSVLSRSoJ5Z9U27nstjzN6teXO83rFuxwRERGRpKMAnUBWbingpn8vo1f7TB4e10/L0YmIiIjEgQJ0gthWUMx3n1lMs/QGPHXVIJo0UveNiIiISDwohSWAwpJSvvuPJewuCvLC9SfRrrnWdBYRERGJFwXoei4Uctz6/Ics3xzgiStyOb5D83iXJCIiIpLU1MJRz/121ipmrdjKz87vzRm928a7HBEREZGkpwBdj01Z9CV/m/c5lw85hmtP6RzvckREREQEBeh667212/nZ1OUM7ZbFvRf20SYpIiIiIvWEAnQ9tHbbHm7411KObd2Ev3xnAGmp+msSERERqS+UzOqZHXv3c80zi2mUlsJTVw0iM71BvEsSERERkUq0Ckc9Uhws47p/LmVbwX4mXzeE7FYZ8S5JRERERA7i6wi0mZ1jZqvNbK2ZTari+U5m9raZfWxmc82so5/11GfOOe546WOWfrGL/7s0hwHHtIx3SSIiIiJSBd8CtJmlAn8BzgV6A+PNrPdBpz0IPOucOwG4D/i1X/XUd9M/2sz0jzZz+9k9uOCEo+NdjoiIiIhUw88R6MHAWufc5865EmAKMPqgc3oDc8Lfv1PF80njzRVbaZvZiO+POC7epYiIiIjIIfgZoDsAGyo93hg+VtlHwCXh7y8GmpnZUQdfyMyuM7MlZrYkPz/fl2LjqbQsxLuf5jO8e2stVyciIiJSz8V7FY7bgOFm9gEwHNgElB18knPucedcrnMut3Xr1rGu0XcfbNhNQXEpI3q0iXcpIiIiInIYfq7CsQnIrvS4Y/hYBefcZsIj0GbWFBjjnNvtY0310rzV+aSmGKd0zYp3KSIiIiJyGH6OQC8GuplZFzNrCIwDplc+wcyyzKy8hjuBv/tYT701d802BhzTguaNteaziIiISH3nW4B2zpUCNwGzgJXAC865FWZ2n5mNCp82AlhtZmuAtsADftVTX23bU8zyTQVq3xARERFJEL5upOKcmwHMOOjY3ZW+fwl4yc8a6rt312wHYHj3I6+3W0RERORIFO9JhElv7pp8spo2onf7zHiXIiIiIiIRUICOo7KQq1i+LiVFy9eJiIiIJAIF6Dj6aONudhcGGdFD7RsiIiIiiUIBOo7mrs4nxWBoNy1fJyIiIpIoFKDjaN7qbfTLbkGLjIbxLkVEREREIqQAHSc79u7n400BLV8nIiIikmAUoOPk3U+345yWrxMRERFJNArQcTJ39TaOatKQvh2ax7sUEREREakBBeg4CIUc8z/dzjAtXyciIiKScBSg4+CTTQF27ivR8nUiIiIiCUgBOg7mrs7HDIZ2U4AWERERSTQK0HEwd802TujYglZNtHydiIiISKJRgI6xXftK+GjDbkZo9Q0RERGRhKQAHWPvrt1OyMFw9T+LiIiIJCQF6Bibu3obLTMakNOxRbxLEREREZFaUICOoVDIMX/NdoZ2a02qlq8TERERSUgK0DGUt6WA7Xv3a/k6ERERkQSmAB1Dc1dvA7R8nYiIiEgiU4COoXlr8unboTmtmzWKdykiIiIiUksK0DESKAqy7Mvdat8QERERSXAK0DHy30+3UxZyDNf6zyIiIiIJTQE6Ruat2UZmehr9srV8nYiIiEgiU4COAecc89bkM7R7a9JS9SMXERERSWRKczGwcssethbsj//23aEy2LcjvjVUZfeGeFdQd8Fi2Lst3lWIiIhIDChAx8C8NfkA8e9/fulaeDgHdn0R3zoq+/QteOh4+OSleFdSe87BC1fAX0+FsmC8qxERERGfKUDHwNzV2+jdPpM2menxK2LFVMibCiV74NVbvNBXH3zyovfnG3fAvu3xraW2PpoCn74Je7fC+nfjXY2IiIj4TAHaZ3uKgyz9Yld8l68r3AkzboP2OXDOb+HzufDBv+JXT7nS/bB6JhxzMhQXeCE60ezZCjMnQYdcaNAE8qbHuyIRERHxmQK0z95bu53SeC9fN/NOKNoFo/8Cg6+DTqfArJ9CwZb41QTw+TzYH4BTJ8Kw22H5y7BqRnxrqqkZt0GwCC56DLqfBStf9XrNRURE5IilAO2zeWvyadYojQGdWsangDVvwsdT4NRboV1fSEmBUX+Csv3w+o/i28qRNw0aZcKxI7z62vSB126Fot3xq6kmVkyFldNhxE+gdXfoPRoKt8MXC+JdmYiIiPhIAdpHzjnmrs7n1G5ZNIjH8nXFBfDaRGjd0xvhLXfUcXDaT2H1DG/UNx7KgrD6deh+DqQ1grSGMPrPsG8bvPnT+NRUE+VtMe1OgJNv8Y51PRPS0r1QLSIiIkcsBWgfrdm6ly2B4vj1P791N+zZ4rVupDU68Lkh34ejB8Rv8t76d722kt6jvz7WYYAXRj/4F3w2J/Y11UTltpjUBt6xRk2h6xleH3QoFN/6RERExDcK0D6at8ZbF3hYPPqf170LS5/2gnLH3G8+n5rmhb/iAnjjJ7GvL2+6N+mu68gDj4+YBEd1hek/hP17Y19XJCq3xbQ/4cDnel8Ee7+CjYvjU5uIiIj4TgHaR3NX59OzXTPaN28c2xuX7IPpN0HLLl6rRnXa9g5P3nsptpP3QmXeZLvuZ0GDg342DRrDqD9DYAO8fV/saopUdW0x5bqfDakNvf5uEREROSIpQPtk7/5SFq/fyfB4tG/MeQB2rfd6ihtmHPrceEze+2KBN9mucvtGZZ1OgsETYNHf4Iv3Y1NTpN66Gwo2eyH/4LYYgPRMOO50rw+6vqy1LSIiIlGlAO2T9z/bQbAsDsvXbVgMCx+F3Guh86mHP/+AyXs/878+8MJlWmNv0l11Rt4DzY/xRtKDRbGp63DK22JO+gFkD6r+vN6jvRH0zctiV5uIiIjEjAK0T+au3kaThqnkdmoVu5uW7odpP4DMDnDGLyJ/XcXkvX/CZ+/4Vx94k+vypnu9z42aVn9eo6Yw6mHYsRbm/sbfmiJRUgjTbz58WwxAj3MhJU1tHCIiIkcoBWifLP1iF4O6tKJhWgx/xPN/D9tXw4UPea0ENVE+ee/VW/ydvLdxkTfJrvdFhz/3uNOh/+Ww4E+w+QP/aorEOw/ArnXeGtqHa4tp3BK6DPcCtNo4REREjjgK0D7ZXRikTbMqemT9suVjePcPkDMeuh2iNaI65ZP3dvs8eS9vujfJrvvZkZ1/1gPQpDVMuwlKS/yr61DK22IGXgNdhkb2mt6jvT70rz7xtTQRERGJPQVonwSKgjRv3CA2NysLeq0bGUfB2b+q/XU6neRt9b3ocfhyYfTqK+ec1/983OmRj5A3bgEX/BG2Lof//jH6NR1OeVtMs/ZwZg0+WPQ8HyxFbRwiIiJHIAVoH5SUhigKlsUuQC94BL76GM7/P8ioY8/1yLuhRbY34hssjk595TYv8ybXVbf6RnV6ngfHj/FaVLbmRbemw6loi3m4Zm0xTbK8SZxq4xARETniKED7oKA4CBCbAJ2/Gub+1gulvUfV/XqNmnphccenMC/Kk/fypnmT63qcW/PXnvs7L8BOv8lbRzoWvvrEG/U+YVzt2mJ6jfJ+jvmrol+biIiIxI0CtA8CRV6AzvQ7QIfKvJHihhlw3oPRu2755L33Hone5D3nvADdZbg3ya6mmmR5IXrTUq8f2W9lQZj6fWjcCs75de2u0etCwNTGISIicoRJi3cBR6KYBehFj3urWlz8N2jaJrrXPusB+HS2F9AnvOOtF10XX33iTao79Ue1v8bxY2D5yzDnfmjbp3ZBPFJ50722mEufrX1bTLN2cMxJ3rVGTIpufZHa/imU1GRVFYO2x3tbvftlz1ewZ4t/16+t1j2/uTOmiIhIFRSgfVAeoH1t4Sgp9IJk1zPhhMuif/3yyXtTxsN7D8HwO+p2vbxp3qS6nufX/hpmcP4f4C8nwj8vrls9keg1qub92gfrPQpmTvKCbFa36NQVqf89Dm9Usd344XQ7C779gvfzjravPoEnRkLZ/uhfu67anQAT5kBqjOYuiIhIwlKA9kFBLAL02tneyOLJN/sTdODryXvzfue1I7TpVbvrlLdvdD7Va8Woi8z2cON73qocfkpJgy7D6n6dXhd6ATpvGgy7re7Xi9TOdTD7Hjh2BJx4Q+Sv+3Kh94Hpo8nQ79vRrams1FvRJD0TLngIUlKje/262LHW24nzvw/B8Fp86BARkaSiAO2DmATovGnesnWdTvHvHuD1HX8+1ws+332rdqEnf5U3mW5IDYLcobTI9r4SQfOO0HGQt3xfrAK0c96GOJYKo//i1RCpbmd7IXrmJK8Xvlm76NW14BHY8pHXFtPrguhdN1o2fwDzfuvVVtsPiyIikhQ0idAHFT3Q6T4F6GAxrJnptUP42asK0Zm8lzcNMOh5YVRLSxi9RnnBcee62Nxv2T9g3Xw4676ahWeAlBQY/Wfv39jrP47eEnz5a7wt2aPRFuOX8pVepsVwpRcREUlICtA+CBQFadwg1b9tvD9/x2vfiFUQOX4M9DjP67ne8VnNX5833ZtM16xt9GtLBOXLC6581f97BTbBmz+HzkNhwNW1u0ZWNzjtTlj1GuRNrXtNoTJv+cEGjaO7Wky0VXxYXAILH4t3NSIiUo8pQPvA910I86ZBegtvSbhYKJ+8l9oIpt8CoVDkr93+KWxbUX9HHWOhZWdo38//5eycg9d/5C3BN+oRbzS5tk662at5xu2wb0fd6lr0BGz4H5z72/r/Ier4MdD93Np/WBQRkaSgAO2DgqJS/wJ0aQmsnuGNCMdytYDM9nD2/fDFf2Hp05G/rjw09krS9o1yvUd5I5uBjf7d45OXvNaekT+HVsfW7VqpaV7/dNEurx+6tnath7d/4d9qMdFmBhf8AVIb1vzDooiIJA0FaB8EioJkNvapN3ndfCgOxGdEt/8V3qoOb90NuzdE9pqV071JdM07+FlZ/dcr/PflVxvH3nx44w7vZ12TVTcOpd3xMPTH8MkLsGZWzV/vnBdCLRUufMi/1WKiLfPo2n1YFBGRpKEA7QNfWzjypkLDZnDcaf5c/1DM4MJHvGD02sTDTzDbuc6bPNcrCluMJ7qsrtCmj39tHG/c7vXFj/pzdJeHG3obtO4Fr070PrjVxLJnYd08OPMXNZ/MGG8VHxbv8fe3BiIikpAUoH3gjUD7EKDLSmHV69DjHEhrFP3rR6JlJzjjHm8d6o+mHPrcldO9P3srQAPebw2+XOjtxBdNK1+DFa94m9206Rnda6c19Fo59n7l/eYhUgWbvXWVOw+FgddEt6ZYMIMLHwZX5n14iNZqJCIickRQgPZBgV8j0F/8F4p2xn9C3qAJkD3E643ds7X68/KmexPRWnaOWWn1Wu/RgItuG0fRLm/iYLu+cMrE6F23so4D4aQfwNJn4PN5hz/fOXgtSpMZ46llZxh5D6x96/AfFkVEJKkk6P/Z6q+ykGPPfp8mEeZNgwYZcNzI6F+7JirWCi6CGdVsDhLY6E2a0+jz19r0hKzu0W3jmPUz2LfdGyX2c1LpiLu8iYnTb4aSfYc+d/nLsOYNOP1ndZ/MGG+Dr4vsw6KIiCQVXwO0mZ1jZqvNbK2ZfWMqv5kdY2bvmNkHZvaxmZ3nZz2xsKfYp01UQmXer+q7nQUNM6J77dooXyt45XRYUcVaweWjrL2SePm6qvQeDV+854Xeulo7Gz78F5zyQ2ifU/frHUrDDK+/evcX3vod5H4AACAASURBVBJv1dmb7y191yEXhtzob02xEMmHRRERSTq+BWgzSwX+ApwL9AbGm1nvg077GfCCc64/MA6o5VZ39UfAr228v1wI+7bFv32jsoq1gm+Dwp0HPpc3Ddoe702ek6/1Hg0u5G1SUhf793i9uVndYfhPolPb4XQ+BQZ9z9tkZMOiqs954w5vMuPoKE9mjKesbjBiUvUfFkVEJOkcNkCb2YVmVpugPRhY65z73DlXAkwBDk5/DsgMf98c2FyL+9QrvgXoldMhLd0bga4vUtO8oFS0C2be+fXxPV95gV+rb3xT2+OhZZe6t3HM/oXXJjP6L9AgPTq1ReKMe70VNab9wNvuu7JVr8OK/8CwO6BNr9jVFAsn3+KN8lf1YVFERJJOJMH4MuBTM/udmdVkin8HoPJiwRvDxyq7F7jczDYCM4Cba3D9eqmgqBSA5hlRDNChkDchr+sZ0Khp9K4bDe36wqk/go+nwJo3vWMrXwVc/Rotry/MvJ/Luvm1D2JfLIDFT3jrPWcPjm59h9Oombem8/Y1MP93Xx8v2u1NHGzbF071aTJjPB2wscydhz9fRESOaIcN0M65y4H+wGfAM2b2vpldZ2bNonD/8cAzzrmOwHnAP6sa7Q7fb4mZLcnPz4/Cbf1TPgId1R7oTUtgz+b6G0iHhdcKfm0iFBd4o6tZPaK/pNqRovdoCJXC6jdq/tpgEUy7CVp08nYcjIeuZ0C/78B/H/LW+QZ486ewL9/7jUQsd8iMpao+LIqISFKKqDXDOVcAvITXhtEeuBhYZmaHGjHeBGRXetwxfKyy7wIvhO/xPpAOZFVx/8edc7nOudzWrVtHUnLc+NLCkTcNUhpA97Ojd81oSmvkjc7t2eKt0vDFe1p941CO7g/Nj6ldG8c7v4Kdn8GoP0HDJtGvLVJnPwBNsrxWjjWz4IPwZMaj+8WvplgYdhu07vn1h0UREUlKh91v2sxGAdcAXYFngcHOuW1mlgHkAX+q5qWLgW5m1gUvOI8Dvn3QOV8CI/FGtnvhBej6PcR8GFEP0M557RvHnQ7pzaNzTT90HAhDvg/v/9l7XF9Hy+sDM+8DxqLHYeNSb7OSSOze4P18B1wFxw73t8bDadwSzv8/eP5ymPKd2E5mjKfyD4tPnektbXckrDQiIpII2vWNdwUHOGyABsYAf3TOza980DlXaGbfre5FzrlSM7sJmAWkAn93zq0ws/uAJc656cCPgSfM7Fa8CYVXO5fYW34FioI0TE0hvUGUFjjZ8iEEvoQRCRBOTvsprJ4BluJNlpPq9bnYC8NPnl6z1zU7Gs76pT811VSvC733sWKqt8RdLCczxlPH3K8/LH74XLyrERE58lkK3LMr3lUcIJIAfS+wpfyBmTUG2jrn1jvn3j7UC51zM/AmB1Y+dnel7/OAU2pScH1XUBwks3EaZhadC+ZNg5Q06JEAS2Q3zICrZ0BZiTfKKtXrmAtXvQbFu2v4ukH16zcRF/8NRtwJrXvEu5LYOvM+OO40ryddRESSTiQB+kXg5EqPy8LHBvlSUYILFAXJjGr7xjToMgwyWkXnmn7LbB/vChJHl6HxrqDu0holX3gGb43rrmfEuwoREYmTSPoM0sLrOAMQ/j7Cps3kU1AUjF7/89YVsPNzracsIiIiUo9EEqDzwxMJATCz0UAU9iE+MgWiGaDzpnl9Pz0viM71RERERKTOImnhuAF4zsz+DBje5ihX+lpVAisoCtIlK0rLi+VNg06nQNP6vXSfiIiISDI5bIB2zn0GDDGzpuHHe32vKoEFioLR2URl2yrYvhoGT6j7tUREREQkaiIZgcbMzgf6AOnlq0s45+7zsa6E5JyjoLg0Oi0cK6cDpvYNERERkXrmsD3QZvZX4DLgZrwWjrFAJ5/rSkh795dSFnLRCdB50yH7RK1qISIiIlLPRDKJ8GTn3JXALufcL4CTgO7+lpWYorYL4Y7PYOsn2s1PREREpB6KJEAXh/8sNLOjgSCgYdEqFBSVApDZOKLOmOrlTfP+7HVhHSsSERERkWiLJOm9amYtgN8Dy/C23H7C16oSVPkIdJ03Ulk5HToMhBbZUahKRERERKLpkAHazFKAt51zu4GXzew1IN05F4hJdQkmKi0cu76AzR94WwWLiIiISL1zyBYO51wI+Eulx/sVnqtXEI0AvXK696d2HxQRERGplyLpgX7bzMZY+fp1Uq2C4igE6Lzp0O4EaNUlSlWJiIiISDRFEqCvB14E9ptZgZntMbMCn+tKSIGiICkGTRrWchJhYBNsXKTVN0RERETqsUh2ImwWi0KOBIGiIJmNG5CSUsvB+lWveX8qQIuIiIjUW4cN0GY2rKrjzrn50S8nsQWKgnVs35gGbXpDVrfoFSUiIiIiURVJr8Htlb5PBwYDS4HTfakogdUpQO/ZCl8sgBGToluUiIiIiERVJC0cB+zmYWbZwEO+VZTACoqCZKbXMkAvfRpwWn1DREREpJ6LZBLhwTYCvaJdyJGg1iPQ21bC/Afh+DHQtnf0CxMRERGRqImkB/pPeLsPghe4++HtSCgHCRSV1nwXwlAZTPsBpGfCub/zpzARERERiZpIeqCXVPq+FJjsnHvPp3oSlnOOgtqMQC98FDYthTFPQZMsf4oTERERkaiJJEC/BBQ758oAzCzVzDKcc4X+lpZY9peGKCkLkdm4BmtA7/gM5twPPc7z2jdEREREpN6LaCdCoHGlx42B2f6Uk7gCNd3GOxSC6TdDaiM4/w+gjR5FREREEkIkw6Xpzrm95Q+cc3vNLMPHmhJSjQP00r/DF+/BqD9BZnsfKxMRERGRaIpkBHqfmQ0of2BmA4Ei/0pKTDUK0Ls3wFv3wLEjoP8VvtYlIiIiItEVyQj0ROBFM9sMGNAOuMzXqhJQQaQB2jl4baL354WPqHVDREREJMFEspHKYjPrCfQIH1rtnAv6W1biKR+BPuxGKh9NhrWzvSXrWnaKQWUiIiIiEk2HbeEwsx8ATZxzy51zy4GmZvZ9/0tLLBG1cOzZCjPvhOwhMGhCjCoTERERkWiKpAd6gnNud/kD59wuQOnvIBUj0NUFaOfg9R9BsAhG/xlSarMJpIiIiIjEWyQpLtXs60ZdM0sFGvpXUmIKFAVp1iiN1JRqeprzpsKq1+C0OyGrW2yLExEREZGoiWQS4UzgeTP7W/jx9cAb/pWUmAoOtY134U6YcTu07wcn3RzbwkREREQkqiIJ0D8BrgNuCD/+GG8lDqkkUBSsPkDPnARFu+CKqZBag50KRURERKTeOWwLh3MuBPwPWA8MBk4HVvpbVuIpKArSvKptvNfMgo+fh6E/hnbHx74wEREREYmqaodDzaw7MD78tR14HsA5d1psSkssgaIgnbMO2qCxOACvToTWvWDobfEpTERERESi6lD9BKuAd4ELnHNrAczs1phUlYAKioPfXMLurbth71dw2b8gTfMuRURERI4EhwrQlwDjgHfMbCYwBW8nwuRTUgiBjYc8pVXRerq4/ZDf2DuwLQ+WPgMn3wwdB/pfo4iIiIjERLUB2jk3FZhqZk2A0Xhbercxs8eAV5xzb8aoxvjb8iE8fe4hT3k9BVgR/irX6lgYcZeflYmIiIhIjEWylfc+4N/Av82sJTAWb2WO5AnQWd1hzFPVPl2wv5Sf/ucTxuZ2ZFi31l8/0WUYNMyo9nUiIiIiknhqtKZaeBfCx8NfyaNJFvT9VrVP5+fv5dVQM844th/07RDDwkREREQk1rSfdBQUlG/jnV7NOtAiIiIicsRQgI6CQHmArm4jFRERERE5YihAR0F5gP7GMnYiIiIicsRRgI6CAgVoERERkaShAB0FBcWlAGRWtZW3iIiIiBxRFKCjIFAUJL1BCo3SUuNdioiIiIj4TAE6CgKFVWzjLSIiIiJHJAXoKAgUKUCLiIiIJAsF6ChQgBYRERFJHgrQUVBQHNQmKiIiIiJJQgE6CjQCLSIiIpI8FKCjIFAU1C6EIiIiIklCAbqOykKOPcWlGoEWERERSRIK0HW0t2ITFQVoERERkWSgAF1HAW3jLSIiIpJUFKDrSAFaREREJLkoQNeRArSIiIhIcvE1QJvZOWa22szWmtmkKp7/o5l9GP5aY2a7/azHDwrQIiIiIsklza8Lm1kq8BfgTGAjsNjMpjvn8srPcc7dWun8m4H+ftXjl4JiL0BnNvbtRykiIiIi9YifI9CDgbXOuc+dcyXAFGD0Ic4fD0z2sR5faARaREREJLn4GaA7ABsqPd4YPvYNZtYJ6ALMqeb568xsiZktyc/Pj3qhdREoCtIg1WjcIDXepYiIiIhIDNSXSYTjgJecc2VVPemce9w5l+ucy23dunWMSzu08m28zSzepYiIiIhIDPgZoDcB2ZUedwwfq8o4ErB9A6CgKEhmuto3RERERJKFnwF6MdDNzLqYWUO8kDz94JPMrCfQEnjfx1p8EygKahdCERERkSTiW4B2zpUCNwGzgJXAC865FWZ2n5mNqnTqOGCKc875VYufCsItHCIiIiKSHHxde805NwOYcdCxuw96fK+fNfgtUBSk01FN4l2GiIiIiMRIfZlEmLACGoEWERERSSoK0HXgnKOguFSbqIiIiIgkEQXoOthXUkZZyGkEWkRERCSJKEDXgXYhFBEREUk+CtB1EChUgBYRERFJNgrQdVBQ7AVobaQiIiIikjwUoOugvIVDG6mIiIiIJA8F6DpQD7SIiIhI8lGAroOC8gCdoQAtIiIikiwUoOsgUBTEDJo21DrQIiIiIslCAboOCoqCZKY3ICXF4l2KiIiIiMSIAnQdaBtvERERkeSjAF0HCtAiIiIiyUcBug4UoEVERESSjwJ0HRQUl5LZWBMIRURERJKJAnQdaARaREREJPkoQNdBoCioXQhFREREkowCdC0VB8soKQ1pBFpEREQkyShA11L5Nt6Z6QrQIiIiIslEAbqWKrbx1gi0iIiISFJRgK6lgAK0iIiISFJSgK4lBWgRERGR5KQAXUsK0CIiIiLJSQG6lsp7oLWMnYiIiEhyUYCupUBRKQCZ6dqJUERERCSZKEDXUqAoSNNGaaSl6kcoIiIikkyU/mpJ23iLiIiIJCcF6FoKFAVppvYNERERkaSjAF1LBcUagRYRERFJRgrQtVSgFg4RERGRpKQAXUvqgRYRERFJTgrQtRQoCmoNaBEREZEkpABdC8GyEIUlZRqBFhEREUlCCtC1UKBtvEVERESSlgJ0LQQUoEVERESSlgJ0LShAi4iIiCQvBehaKA/QmY21kYqIiIhIslGAroWC4lJAI9AiIiIiyUgBuha+HoFWgBYRERFJNgrQtVC+CkdmugK0iIiISLJRgK6FQFGQRmkppDdIjXcpIiIiIhJjCtC1UKBtvEVERESSlgJ0LQQUoEVERESSlgJ0LShAi4iIiCQvBehaUIAWERERSV4K0LUQKApqCTsRERGRJKUAXQuaRCgiIiKSvBSgaygUcuzZX6oRaBEREZEkpQBdQ3uKS3FO23iLiIiIJCsF6Bqq2MY7PS3OlYiIiIhIPChA11BBsRegNQItIiIikpwUoGuofARaAVpEREQkOSlA11BFgM5QgBYRERFJRgrQNfR1D7QCtIiIiEgyUoCuIbVwiIiIiCQ3BegaKigKkpZiZDRMjXcpIiIiIhIHvgZoMzvHzFab2Vozm1TNOZeaWZ6ZrTCzf/tZTzQEwrsQmlm8SxERERGROPBtMWMzSwX+ApwJbAQWm9l051xepXO6AXcCpzjndplZG7/qiZaAtvEWERERSWp+jkAPBtY65z53zpUAU4DRB50zAfiLc24XgHNum4/1REWgKEgzBWgRERGRpOVngO4AbKj0eGP4WGXdge5m9p6ZLTSzc6q6kJldZ2ZLzGxJfn6+T+VGpqC4VCPQIiIiIkks3pMI04BuwAhgPPCEmbU4+CTn3OPOuVznXG7r1q1jXOKBCtTCISIiIpLU/AzQm4DsSo87ho9VthGY7pwLOufWAWvwAnW95fVA+9Y6LiIiIiL1nJ8BejHQzcy6mFlDYBww/aBzpuKNPmNmWXgtHZ/7WFOdOOcIFAW1iYqIiIhIEvMtQDvnSoGbgFnASuAF59wKM7vPzEaFT5sF7DCzPOAd4Hbn3A6/aqqrfSVllIWcWjhEREREkpivvQjOuRnAjIOO3V3pewf8KPxV7xVoF0IRERGRpBfvSYQJRdt4i4iIiIgCdA0oQIuIiIiIAnQNlAfoTAVoERERkaSlAF0D6oEWEREREQXoGtAItIiIiIgoQNdAQVEQM2jWSBupiIiIiCQrBegaCBQFadYojZQUi3cpIiIiIhInCtA1ECgK0jxD7RsiIiIiyUwBugYKiks1gVBEREQkyamZtwYCRUEFaBERkQQWDAbZuHEjxcXF8S5F6pH09HQ6duxIgwaR5TwF6BoIFAVpm9k03mWIiIhILW3cuJFmzZrRuXNnzDSnScA5x44dO9i4cSNdunSJ6DVq4aiBQFGQzHSNQIuIiCSq4uJijjrqKIVnqWBmHHXUUTX6rYQCdA0UqIVDREQk4Sk8y8Fq+m9CATpCxcEy9peGtImKiIiI1NqOHTvo168f/fr1o127dnTo0KHicUlJySFfu2TJEm655ZbD3uPkk0+OVrkATJw4kQ4dOhAKhaJ63USmHugIaRtvERERqaujjjqKDz/8EIB7772Xpk2bctttt1U8X1paSlpa1fEsNzeX3Nzcw95jwYIF0SkWCIVCvPLKK2RnZzNv3jxOO+20qF27skO97/pII9AR0jbeIiIi4oerr76aG264gRNPPJE77riDRYsWcdJJJ9G/f39OPvlkVq9eDcDcuXO54IILAC98X3vttYwYMYJjjz2WRx55pOJ6TZs2rTh/xIgRfOtb36Jnz5585zvfwTkHwIwZM+jZsycDBw7klltuqbjuwebOnUufPn248cYbmTx5csXxrVu3cvHFF5OTk0NOTk5FaH/22Wc54YQTyMnJ4Yorrqh4fy+99FKV9Q0dOpRRo0bRu3dvAC666CIGDhxInz59ePzxxyteM3PmTAYMGEBOTg4jR44kFArRrVs38vPzAS/od+3ateKx3xIn6sdZQCPQIiIiR5RfvLqCvM0FUb1m76MzuefCPjV+3caNG1mwYAGpqakUFBTw7rvvkpaWxuzZs7nrrrt4+eWXv/GaVatW8c4777Bnzx569OjBjTfe+I1l2D744ANWrFjB0UcfzSmnnMJ7771Hbm4u119/PfPnz6dLly6MHz++2romT57M+PHjGT16NHfddRfBYJAGDRpwyy23MHz4cF555RXKysrYu3cvK1as4P7772fBggVkZWWxc+fOw77vZcuWsXz58orVL/7+97/TqlUrioqKGDRoEGPGjCEUCjFhwoSKenfu3ElKSgqXX345zz33HBMnTmT27Nnk5OTQunXrGv7ka0cj0BEqKFaAFhEREX+MHTuW1NRUAAKBAGPHjuX444/n1ltvZcWKFVW+5vzzz6dRo0ZkZWXRpk0btm7d+o1zBg8eTMeOHUlJSaFfv36sX7+eVatWceyxx1aE1uoCdElJCTNmzOCiiy4iMzOTE088kVmzZgEwZ84cbrzxRgBSU1Np3rw5c+bMYezYsWRlZQHQqlWrw77vwYMHH7B03COPPEJOTg5Dhgxhw4YNfPrppyxcuJBhw4ZVnFd+3WuvvZZnn30W8IL3Nddcc9j7RYtGoCOkEWgREZEjS21Giv3SpEmTiu9//vOfc9ppp/HKK6+wfv16RowYUeVrGjVqVPF9amoqpaWltTqnOrNmzWL37t307dsXgMLCQho3blxtu0d10tLSKiYghkKhAyZLVn7fc+fOZfbs2bz//vtkZGQwYsSIQy4tl52dTdu2bZkzZw6LFi3iueeeq1FddaER6AgFCsM90On6zCEiIiL+CQQCdOjQAYBnnnkm6tfv0aMHn3/+OevXrwfg+eefr/K8yZMn8+STT7J+/XrWr1/PunXreOuttygsLGTkyJE89thjAJSVlREIBDj99NN58cUX2bFjB0BFC0fnzp1ZunQpANOnTycYDFZ5v0AgQMuWLcnIyGDVqlUsXLgQgCFDhjB//nzWrVt3wHUBvve973H55ZcfMIIfCwrQEQoUeZ/YNIlQRERE/HTHHXdw55130r9//xqNGEeqcePGPProo5xzzjkMHDiQZs2a0bx58wPOKSwsZObMmZx//vkVx5o0acKpp57Kq6++ysMPP8w777xD3759GThwIHl5efTp04ef/vSnDB8+nJycHH70ox8BMGHCBObNm0dOTg7vv//+AaPOlZ1zzjmUlpbSq1cvJk2axJAhQwBo3bo1jz/+OJdccgk5OTlcdtllFa8ZNWoUe/fujWn7BoCVz8ZMFLm5uW7JkiUxv+8vX8tjyqIvWXHfOTG/t4iIiETHypUr6dWrV7zLiLu9e/fStGlTnHP84Ac/oFu3btx6663xLqvGlixZwq233sq7775b52tV9W/DzJY6576xdqBGoCMU0C6EIiIicoR44okn6NevH3369CEQCHD99dfHu6Qa+81vfsOYMWP49a9/HfN7q6E3QoGioNo3RERE5Ihw6623JuSIc2WTJk1i0qRJcbm3RqAjpAAtIiIiIqAAHbECtXCIiIiICArQEVOAFhERERFQgI6YJhGKiIiICChARyRYFmJfSRmZ6QrQIiIiUnunnXZaxXbY5R566KGKbbGrMmLECMqX8D3vvPPYvXv3N8659957efDBBw9576lTp5KXl1fx+O6772b27Nk1Kf+QJk6cSIcOHSp2HTySKUBHoKBiG28tWiIiIiK1N378eKZMmXLAsSlTpjB+/PiIXj9jxgxatGhRq3sfHKDvu+8+zjjjjFpd62ChUIhXXnmF7Oxs5s2bF5VrVsWPjWVqQwE6AgXF3l9W8wyNQIuIiEjtfetb3+L111+npKQEgPXr17N582aGDh3KjTfeSG5uLn369OGee+6p8vWdO3dm+/btADzwwAN0796dU089ldWrV1ec88QTTzBo0CBycnIYM2YMhYWFLFiwgOnTp3P77bfTr18/PvvsM66++mpeeuklAN5++2369+9P3759ufbaa9m/f3/F/e655x4GDBhA3759WbVqVZV1zZ07lz59+nDjjTcyefLkiuNbt27l4osvJicnh5ycHBYsWADAs88+ywknnEBOTg5XXHEFwAH1ADRt2rTi2kOHDmXUqFH07t0bgIsuuoiBAwfSp08fHn/88YrXzJw5kwEDBpCTk8PIkSMJhUJ069aN/Px8wAv6Xbt2rXhcWxpSjUCgYgRaAVpEROSI8cYk+OqT6F6zXV849zfVPt2qVSsGDx7MG2+8wejRo5kyZQqXXnopZsYDDzxAq1atKCsrY+TIkXz88ceccMIJVV5n6dKlTJkyhQ8//JDS0lIGDBjAwIEDAbjkkkuYMGECAD/72c946qmnuPnmmxk1ahQXXHAB3/rWtw64VnFxMVdffTVvv/023bt358orr+Sxxx5j4sSJAGRlZbFs2TIeffRRHnzwQZ588slv1DN58mTGjx/P6NGjueuuuwgGgzRo0IBbbrmF4cOH88orr1BWVsbevXtZsWIF999/PwsWLCArK4udO3ce9se6bNkyli9fTpcuXQD4+9//TqtWrSgqKmLQoEGMGTOGUCjEhAkTmD9/Pl26dGHnzp2kpKRw+eWX89xzzzFx4kRmz55NTk4OrVu3Puw9D0Uj0BFomJrC0G5ZtG/eON6liIiISIKr3MZRuX3jhRdeYMCAAfTv358VK1Yc0G5xsHfffZeLL76YjIwMMjMzGTVqVMVzy5cvZ+jQofTt25fnnnuOFStWHLKe1atX06VLF7p37w7AVVddxfz58yuev+SSSwAYOHAg69ev/8brS0pKmDFjBhdddBGZmZmceOKJFX3ec+bMqejvTk1NpXnz5syZM4exY8eSlZUFeB8qDmfw4MEV4RngkUceIScnhyFDhrBhwwY+/fRTFi5cyLBhwyrOK7/utddey7PPPgt4wfuaa6457P0ORyPQEeh9dCb//O6J8S5DREREoukQI8V+Gj16NLfeeivLli2jsLCQgQMHsm7dOh588EEWL15My5YtufrqqykuLq7V9a+++mqmTp1KTk4OzzzzDHPnzq1TvY0aNQK8AFxVD/KsWbPYvXs3ffv2BaCwsJDGjRtzwQUX1Og+aWlpFRMQQ6FQRZsLQJMmTSq+nzt3LrNnz+b9998nIyODESNGHPJnlZ2dTdu2bZkzZw6LFi3iueeeq1FdVdEItIiIiEgMNW3alNNOO41rr722YvS5oKCAJk2a0Lx5c7Zu3cobb7xxyGsMGzaMqVOnUlRUxJ49e3j11VcrntuzZw/t27cnGAweEBabNWvGnj17vnGtHj16sH79etauXQvAP//5T4YPHx7x+5k8eTJPPvkk69evZ/369axbt4633nqLwsJCRo4cyWOPPQZAWVkZgUCA008/nRdffJEdO3YAVLRwdO7cmaVLlwIwffp0gsFglfcLBAK0bNmSjIwMVq1axcKFCwEYMmQI8+fPZ926dQdcF+B73/sel19+OWPHjiU1NTXi91YdBWgRERGRGBs/fjwfffRRRYDOycmhf//+9OzZk29/+9uccsoph3z9gAEDuOyyy8jJyeHcc89l0KBBFc/98pe/5MQTT+SUU06hZ8+eFcfHjRvH73//e/r3789nn31WcTw9PZ2nn36asWPH0rdvX1JSUrjhhhsieh+FhYXMnDmT888/v+JYk/9v7/5D7a7rOI4/X8wrVzTSdIl4tRUOmmPelUNWXdAGxSqZYdEUAwkxkAjTft3yjygSrD+qWSKssvzDfkhlaX+EY441aGgz56+tyGSjyXRzZTUIa/buj/OdHZeyfeOce7rf83zA4Xw+n/O93/u+vO/93Pf93M/3fE88kZmZGe655x7WrVvHjKbEqAAABoNJREFUpk2bWLZsGeeffz47duxg6dKl3HDDDVx44YVMT09z/fXXA3D11VezefNmpqen2bp160tWnfutXr2aQ4cOsWTJEmZnZ1m5ciUACxcuZP369Vx66aVMT0+zdu3aFz9mzZo1HDx4cCDbNwBSVQM50VxZsWJFHX4vREmSpDZ27tzJkiVLRh2G5ti2bdu47rrr2LJlyyse83LfG0kerKoVRx7rHmhJkiR11k033cStt946kL3Ph7mFQ5IkSZ01OzvL7t27mZmZGdg5LaAlSZKkFiygJUnSWJlv139p+Np+T1hAS5KksTE5OcmBAwcsovWiquLAgQNMTk4e88d4EaEkSRobU1NT7Nmzh/379486FP0fmZycZGpq6piPt4CWJEljY2Ji4iW3hJb+F27hkCRJklqwgJYkSZJasICWJEmSWph3t/JOsh/YPaJPfxrw7Ig+t+ae+R4v5nu8mO/xYr7Hz6By/rqqWnjk4LwroEcpybaXux+6usl8jxfzPV7M93gx3+Nn2Dl3C4ckSZLUggW0JEmS1IIFdDvrRx2A5pT5Hi/me7yY7/FivsfPUHPuHmhJkiSpBVegJUmSpBYsoI9BktVJfpfkiSSzo45Hg5fktiT7kjzWN/aaJBuS/L55PmWUMWowkpyVZFOSHUkeT3JtM26+OyrJZJIHkjzc5Pzzzfjrk9zfzO0/THL8qGPV4CRZkOShJD9v+ua7o5LsSvJoku1JtjVjQ53TLaCPIskC4BbgXcC5wOVJzh1tVBqC7wKrjxibBTZW1WJgY9PX/HcI+HhVnQusBD7S/Eyb7+56HlhVVdPAcmB1kpXAl4CvVtU5wJ+Bq0YYowbvWmBnX998d9vbq2p531vXDXVOt4A+uguAJ6rqyar6B/AD4JIRx6QBq6pfAn86YvgS4PamfTvw3jkNSkNRVXur6jdN+2/0fsGeifnurOo52HQnmkcBq4AfNePmvEOSTAHvAb7V9IP5HjdDndMtoI/uTOCPff09zZi67/Sq2tu0nwZOH2UwGrwki4A3Afdjvjut+Xf+dmAfsAH4A/BcVR1qDnFu75avAZ8C/tX0T8V8d1kB9yZ5MMmHm7GhzunHDfJkUldVVSXxLWs6JMlJwI+Bj1XVX3sLVD3mu3uq6gVgeZKTgbuAN444JA1JkouBfVX1YJKLRh2P5sRMVT2V5LXAhiS/7X9xGHO6K9BH9xRwVl9/qhlT9z2T5AyA5nnfiOPRgCSZoFc831FVP2mGzfcYqKrngE3AW4CTkxxeSHJu7463AWuS7KK37XIVsA7z3VlV9VTzvI/eH8gXMOQ53QL66H4NLG6u3j0euAy4e8QxaW7cDVzZtK8EfjbCWDQgzV7IbwM7q+orfS+Z745KsrBZeSbJCcA76O193wS8vznMnHdEVX2mqqaqahG939n3VdUVmO9OSnJiklcdbgPvBB5jyHO6N1I5BkneTW8/1QLgtqq6ccQhacCSfB+4CDgNeAb4HPBT4E7gbGA38IGqOvJCQ80zSWaALcCj/Gd/5Gfp7YM23x2U5Dx6FxEtoLdwdGdVfSHJG+itUL4GeAj4YFU9P7pINWjNFo5PVNXF5rubmrze1XSPA75XVTcmOZUhzukW0JIkSVILbuGQJEmSWrCAliRJklqwgJYkSZJasICWJEmSWrCAliRJklqwgJakeSTJC0m29z1mB3juRUkeG9T5JKmrvJW3JM0vf6+q5aMOQpLGmSvQktQBSXYl+XKSR5M8kOScZnxRkvuSPJJkY5Kzm/HTk9yV5OHm8dbmVAuSfDPJ40nube7cJ0nqYwEtSfPLCUds4Vjb99pfqmoZ8A16d08F+Dpwe1WdB9wB3NyM3wxsrqpp4M3A4834YuCWqloKPAe8b8hfjyTNO96JUJLmkSQHq+qklxnfBayqqieTTABPV9WpSZ4Fzqiqfzbje6vqtCT7gan+WxknWQRsqKrFTf/TwERVfXH4X5kkzR+uQEtSd9QrtNt4vq/9Al4rI0n/xQJakrpjbd/z1qb9K+Cypn0FsKVpbwSuAUiyIMmr5ypISZrvXFmQpPnlhCTb+/q/qKrDb2V3SpJH6K0iX96MfRT4TpJPAvuBDzXj1wLrk1xFb6X5GmDv0KOXpA5wD7QkdUCzB3pFVT076lgkqevcwiFJkiS14Aq0JEmS1IIr0JIkSVILFtCSJElSCxbQkiRJUgsW0JIkSVILFtCSJElSCxbQkiRJUgv/BmClruk9X0eRAAAAAElFTkSuQmCC","text/plain":["<Figure size 864x432 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["plt.figure(figsize=(12, 6))\n","\n","plt.plot(range(epochs), history.history['accuracy'], label=\"Training Accuracy\")\n","plt.plot(range(epochs), history.history['val_accuracy'], label=\"Validation Accuracy\")\n","\n","plt.xlabel(\"Epoch\")\n","plt.ylabel(\"Accuracy\")\n","\n","plt.legend()\n","plt.title(\"Accuracy Over Time\")\n","\n","plt.show()"]},{"cell_type":"code","execution_count":43,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":514,"status":"ok","timestamp":1670143782493,"user":{"displayName":"Hamza ROUAISSI","userId":"09320853917146431941"},"user_tz":-60},"id":"9kyl3XBpMv4q","outputId":"0a44d6f2-7684-4d3d-e880-66a7b9805808"},"outputs":[{"name":"stdout","output_type":"stream","text":["2/2 [==============================] - 0s 8ms/step - loss: 0.7236 - accuracy: 0.7619 - auc: 0.8588\n"]},{"data":{"text/plain":["[0.7235907316207886, 0.761904776096344, 0.8588235378265381]"]},"execution_count":43,"metadata":{},"output_type":"execute_result"}],"source":["model.evaluate(X_test, y_test)"]},{"cell_type":"markdown","metadata":{"id":"7Sem_zWRLmRt"},"source":["##Results Interpretation"]},{"cell_type":"code","execution_count":44,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":32,"status":"ok","timestamp":1670143782495,"user":{"displayName":"Hamza ROUAISSI","userId":"09320853917146431941"},"user_tz":-60},"id":"43ohyG-cLFNY","outputId":"9c275874-57aa-4931-efc0-1bdf8475680f"},"outputs":[{"name":"stdout","output_type":"stream","text":["7/7 [==============================] - 0s 3ms/step\n"]}],"source":["# make a prediction\n","ynew = model.predict(X)\n"]},{"cell_type":"code","execution_count":45,"metadata":{"executionInfo":{"elapsed":24,"status":"ok","timestamp":1670143782495,"user":{"displayName":"Hamza ROUAISSI","userId":"09320853917146431941"},"user_tz":-60},"id":"roJVTjKJM4Q_"},"outputs":[],"source":["predicted=[]\n","for i in range(data.shape[0]):\n","    if ynew[i]>0.5:\n","        predicted.append(1)\n","    else:\n","        predicted.append(0)"]},{"cell_type":"code","execution_count":46,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":23,"status":"ok","timestamp":1670143782496,"user":{"displayName":"Hamza ROUAISSI","userId":"09320853917146431941"},"user_tz":-60},"id":"MMQ0rNytM5FO","outputId":"5a6c7070-9ddf-4b81-e216-5879300a1d60"},"outputs":[{"data":{"text/plain":["0.9186602870813396"]},"execution_count":46,"metadata":{},"output_type":"execute_result"}],"source":["#test_labels=[1,0,1,0,1,0]\n","test_labels=y\n","cm = confusion_matrix(y_true=test_labels, y_pred=predicted)\n","f1 = f1_score(y_true=test_labels, y_pred=predicted)\n","f1"]},{"cell_type":"code","execution_count":47,"metadata":{"executionInfo":{"elapsed":18,"status":"ok","timestamp":1670143782496,"user":{"displayName":"Hamza ROUAISSI","userId":"09320853917146431941"},"user_tz":-60},"id":"FAb8EN_VM5yO"},"outputs":[],"source":["def plot_confusion_matrix(cm, classes,\n","                        normalize=False,\n","                        title='Confusion matrix',\n","                        cmap=plt.cm.Blues):\n","    \"\"\"\n","    This function prints and plots the confusion matrix.\n","    Normalization can be applied by setting `normalize=True`.\n","    \"\"\"\n","    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n","    plt.title(title)\n","    plt.colorbar()\n","    tick_marks = np.arange(len(classes))\n","    plt.xticks(tick_marks, classes, rotation=45)\n","    plt.yticks(tick_marks, classes)\n","    if normalize:\n","        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n","        print(\"Normalized confusion matrix\")\n","    else:\n","        print('Confusion matrix, without normalization')\n","\n","    print(cm)\n","\n","    thresh = cm.max() / 2.\n","    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n","        plt.text(j, i, cm[i, j],\n","            horizontalalignment=\"center\",\n","            color=\"white\" if cm[i, j] > thresh else \"black\")\n","\n","    plt.tight_layout()\n","    plt.ylabel('True label')\n","    plt.xlabel('Predicted label')"]},{"cell_type":"code","execution_count":48,"metadata":{"executionInfo":{"elapsed":18,"status":"ok","timestamp":1670143782497,"user":{"displayName":"Hamza ROUAISSI","userId":"09320853917146431941"},"user_tz":-60},"id":"kmKSFcpFNAb9"},"outputs":[],"source":["cm_plot_labels = ['Not Good!','Good!']\n"]},{"cell_type":"code","execution_count":49,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":363},"executionInfo":{"elapsed":345,"status":"ok","timestamp":1670143782826,"user":{"displayName":"Hamza ROUAISSI","userId":"09320853917146431941"},"user_tz":-60},"id":"o-7DEJivNBTb","outputId":"872eef34-3070-4aef-cc08-49ce41aaebb2"},"outputs":[{"name":"stdout","output_type":"stream","text":["Confusion matrix, without normalization\n","[[96  8]\n"," [ 9 96]]\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["plot_confusion_matrix(cm=cm, classes=cm_plot_labels, title='Confusion Matrix')"]},{"cell_type":"markdown","metadata":{"id":"DyNsQ7QmUK07"},"source":["##conclusions"]},{"cell_type":"code","execution_count":56,"metadata":{"executionInfo":{"elapsed":391,"status":"ok","timestamp":1670143917337,"user":{"displayName":"Hamza ROUAISSI","userId":"09320853917146431941"},"user_tz":-60},"id":"jtOto-FgUMUQ"},"outputs":[],"source":["model = models['Random Forest']\n","y_pred = model.predict(X_test)"]},{"cell_type":"code","execution_count":57,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":363},"executionInfo":{"elapsed":554,"status":"ok","timestamp":1670143919226,"user":{"displayName":"Hamza ROUAISSI","userId":"09320853917146431941"},"user_tz":-60},"id":"3AkbUpFiUXFB","outputId":"19ca5b8e-768d-4b0b-fab5-ba8e7935ad27"},"outputs":[{"name":"stdout","output_type":"stream","text":["Confusion matrix, without normalization\n","[[21  4]\n"," [ 6 11]]\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["cm = confusion_matrix(y_test,predicted)\n","plot_confusion_matrix(cm=cm, classes=cm_plot_labels, title='Confusion Matrix')"]},{"cell_type":"markdown","metadata":{},"source":["We decided to go with the Naive Bayes because we discovered that the DL Model is overfitting."]}],"metadata":{"colab":{"authorship_tag":"ABX9TyPDgF3ecuDP+BaFRoro6J8s","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}