05_ml_students_dropout.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e5985020",
   "metadata": {},
   "source": [
    "## Predict Students Dropout - Machine Learning Models\n",
    "\n",
    "This notebook is responsible for building and evaluating machine learning models to predict student dropout. The models used logestic regession, random forest and support vector machine\n",
    "\n",
    "### Steps\n",
    "\n",
    "1. Read in Prepared Data: Load the prepared data from the previously saved CSV file.\n",
    "2. Handle Missing Values: Handle any missing values in the dataset.\n",
    "3. Split Features and Target Variables: Separate the feature variables (X) and the target variable (y).\n",
    "4. Split Training and Testing Data: Split the dataset into training and testing sets to evaluate model performance.\n",
    "5. Build and Evaluate Machine Learning Models: Construct and evaluate various machine learning models to predict student dropout.\n",
    "6. Compare Model Performance: Compare the performance of different models to select the best one.\n",
    "7. Visualise Model Performance: Visualise the accuracy and F1 score of the models.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e965bff4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' install the required package to fetch the dataset for this project\\nfrom the UCI Machine Learning Repository\\xa0'"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\" install the required package to fetch the dataset for this project\n",
    "from the UCI Machine Learning Repository \"\"\"\n",
    "\n",
    "#%pip install ucimlrepo\n",
    "#%pip install xgboost\n",
    "#%pip install cmake\n",
    "#%pip install catboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "654c5810",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import required packages\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score, f1_score, classification_report, confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a45d947b",
   "metadata": {},
   "source": [
    "### Step 1: Read in Prepared Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "5807b9a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in Prepared Data\n",
    "df = pd.read_csv('data/prep_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "830f00fe",
   "metadata": {},
   "source": [
    "### Step 2: Handle Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "359d57ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handle Missing Values\n",
    "df.fillna(df.mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e0f7523",
   "metadata": {},
   "source": [
    "### Step 3: Split Features and Target Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "44b7dea7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split Features and Target Variables\n",
    "X = df.drop(columns=['Target_encoded'])\n",
    "y = df['Target_encoded']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cd5e45d",
   "metadata": {},
   "source": [
    "### Step 4: Split Training and Testing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "50fec5d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split Training and Testing Data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71050246",
   "metadata": {},
   "source": [
    "### Step 5: Build and Evaluate Machine Learning Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "f6d21fee",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/youssef/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "logisticRegr = LogisticRegression(max_iter=1000)\n",
    "logisticRegr_model = logisticRegr.fit(X_train, y_train)\n",
    "y_pred_lr = logisticRegr_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b85149d8",
   "metadata": {},
   "source": [
    "### Evaluate Logistic Regression Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "2d2a47bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression - Accuracy: 0.90%\n",
      "Logistic Regression - F1 Score: 0.90\n",
      "Logistic Regression - Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.83      0.86       277\n",
      "           1       0.90      0.94      0.92       449\n",
      "\n",
      "    accuracy                           0.90       726\n",
      "   macro avg       0.90      0.88      0.89       726\n",
      "weighted avg       0.90      0.90      0.90       726\n",
      "\n",
      "Logistic Regression - Confusion Matrix:\n",
      " [[229  48]\n",
      " [ 27 422]]\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate Logistic Regression Performance\n",
    "lr_accuracy = accuracy_score(y_test, y_pred_lr)\n",
    "lr_f1 = f1_score(y_test, y_pred_lr, average='weighted')\n",
    "print(f\"Logistic Regression - Accuracy: {lr_accuracy:.2f}%\")\n",
    "print(f\"Logistic Regression - F1 Score: {lr_f1:.2f}\")\n",
    "print(\"Logistic Regression - Classification Report:\\n\", classification_report(y_test, y_pred_lr))\n",
    "print(\"Logistic Regression - Confusion Matrix:\\n\", confusion_matrix(y_test, y_pred_lr))\n",
    "\n",
    "# Confusion Matrix for Logistic Regression\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.heatmap(confusion_matrix(y_test, y_pred_lr), annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('Confusion Matrix - Logistic Regression')\n",
    "plt.ylabel('Actual')\n",
    "plt.xlabel('Predicted')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3cbdf22",
   "metadata": {},
   "source": [
    "### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "41a27812",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random Forest Classifier\n",
    "rf_model = RandomForestClassifier(random_state=42)\n",
    "rf_model.fit(X_train, y_train)\n",
    "y_pred_rf = rf_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f22dbaf",
   "metadata": {},
   "source": [
    "### Evaluate Random Forest Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "986218d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest - Accuracy: 0.90%\n",
      "Random Forest - F1 Score: 0.90\n",
      "Random Forest - Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.82      0.86       277\n",
      "           1       0.90      0.95      0.92       449\n",
      "\n",
      "    accuracy                           0.90       726\n",
      "   macro avg       0.90      0.89      0.89       726\n",
      "weighted avg       0.90      0.90      0.90       726\n",
      "\n",
      "Random Forest - Confusion Matrix:\n",
      " [[228  49]\n",
      " [ 23 426]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate Random Forest Performance\n",
    "rf_accuracy = accuracy_score(y_test, y_pred_rf)\n",
    "rf_f1 = f1_score(y_test, y_pred_rf, average='weighted')\n",
    "print(f\"Random Forest - Accuracy: {rf_accuracy:.2f}%\")\n",
    "print(f\"Random Forest - F1 Score: {rf_f1:.2f}\")\n",
    "print(\"Random Forest - Classification Report:\\n\", classification_report(y_test, y_pred_rf))\n",
    "print(\"Random Forest - Confusion Matrix:\\n\", confusion_matrix(y_test, y_pred_rf))\n",
    "\n",
    "# Confusion Matrix for Random Forest\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.heatmap(confusion_matrix(y_test, y_pred_rf), annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('Confusion Matrix - Random Forest')\n",
    "plt.ylabel('Actual')\n",
    "plt.xlabel('Predicted')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b318c9c",
   "metadata": {},
   "source": [
    "### Support Vector Machine Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "d92abd18",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Support Vector Machine Classifier\n",
    "svc_model = SVC(random_state=42)\n",
    "svc_model.fit(X_train, y_train)\n",
    "y_pred_svc = svc_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85dcb021",
   "metadata": {},
   "source": [
    "### Evaluate Support Vector Machine Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "36ce217e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Machine - Accuracy: 0.62%\n",
      "Support Vector Machine - F1 Score: 0.47\n",
      "Support Vector Machine - Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00       277\n",
      "           1       0.62      1.00      0.76       449\n",
      "\n",
      "    accuracy                           0.62       726\n",
      "   macro avg       0.31      0.50      0.38       726\n",
      "weighted avg       0.38      0.62      0.47       726\n",
      "\n",
      "Support Vector Machine - Confusion Matrix:\n",
      " [[  0 277]\n",
      " [  0 449]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/youssef/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: 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",
      "/Users/youssef/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: 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",
      "/Users/youssef/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: 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"
     ]
    },
    {
     "data": {
      "image/png": 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gbdu2Je5Do9GgsLAQVlZWxUY7jFWRz0F5vzeKRjjS0tKKjexVNA8PD6hUKnz//fclTqIuqY0sT8qKHwCmT58OIQRee+01w7DigwoKCvDVV18BADp27AgA2Lhxo6JPcnIyzpw5Y9QMXmMVzeR++GYuRbGUxMrKCm3atMGyZcsAAD///HOpfTt16oQDBw4YEn2RDRs2wMHBodQP07KqUaMGpk6dil69eiEsLKzUfiqVCtbW1rCysjK05eXlITY2tljf8hpFKSwsxJAhQ6BSqbB7925ER0djyZIl2LZt2yO3s7W1RatWrRTLo669vnr1qmK29IPHP3v2LBwcHFCtWjUA918HGRkZhgoXAPLz8/HNN9+UuO/9+/cr+hYWFmLr1q2oW7dusT9WN23apPg5KSkJFy9eNFxPHhgYCHt7+2Kv97S0NMOpImOY8/uxs7PD0KFDsXfvXixYsAAFBQWKoe927dqhWrVqOH36dLHnvmixtbWFvb09goOD8fnnnz+yqixtBKu8ngNjlPd7IzQ0FFZWVoiJiSm3GM3Vs2dPCCHw119/lfi7MuZ0GpU/KSt+4P4bOyYmBuPGjUNAQABef/11NG3aFAUFBTh27BhWrVoFPz8/9OrVCw0bNsTo0aOxZMkSVKlSBd27d8eFCxcwa9Ys+Pj44M033yy3uF588UW4ubkhIiICb7/9NqytrbFu3TpcvnxZ0W/FihU4cOAAevTogZo1a+LOnTtYs2YNAKBz586l7n/OnDn4+uuv0aFDB8yePRtubm7YtGkTdu7ciYULF8LFxaXcHsvD5s+f/9g+PXr0wKJFizB06FCMHj0amZmZ+O9//1tiZeDv74+4uDhs3boVderUgZ2dnVkfJHPmzMH333+PvXv3QqvVYvLkyUhMTERERARatmwJX19fk/dZktjYWKxcuRJDhw5F69at4eLigrS0NHz66ac4deoUZs+eDVtbWwDAoEGDMHv2bAwePBhTp07FnTt38PHHH6OwsLDEfXt4eKBjx46YNWsWHB0dsXz5cvz222/FLukDgKNHj+LVV1/Fyy+/jMuXL2PmzJmoUaMGxo0bBwCoVq0aZs2ahRkzZmDEiBEYMmQIMjMzMXfuXNjZ2WHOnDlGPV5/f39s27YNMTExCAgIQJUqVdCqVavHbhcREYFly5Zh0aJFaNSoEYKCggzrnJycsGTJEoSFheHGjRsYMGAAPD09ce3aNfzyyy+4du2aIeEtWrQIzz//PNq0aYN///vfqFevHq5evYodO3Zg5cqVcHZ2Nty5btWqVXB2doadnR18fX3h7u5eLs+BscrzvVG7dm3MmDED77zzDvLy8gyX250+fRrXr1/H3LlzyzX2R2nXrh1Gjx6NkSNH4ujRo2jfvj0cHR2Rnp6OQ4cOwd/fH6+//nqFxUP/X6VOLXwCHD9+XISFhYmaNWsKW1tb4ejoKFq2bClmz56tmA1cWFgoFixYIBo0aCBsbGyEh4eHeOWVV8Tly5cV+yvtphglzdJGCbP6hRDip59+EkFBQcLR0VHUqFFDzJkzR3z66aeKWf2HDx8WL730kqhVq5ZQq9XC3d1dBAcHix07dhQ7xoOz+oUQ4sSJE6JXr17CxcVF2NraiubNmytm7Arxf7PfH7wcTIiSZ/iW5MGZy49S0szlNWvWiIYNGwq1Wi3q1KkjoqOjxerVqxWPXwghLly4IEJDQ4Wzs7MAYHh+S4v9wXVFs7j37t0rqlSpUuw5yszMFDVr1hStW7cWer3+kY/BWKdPnxaTJ08WrVq1EtWrVxfW1tbC1dVVBAcHi9jY2GL9d+3aJVq0aCHs7e1FnTp1xNKlS0ud1T9+/HixfPlyUbduXWFjYyMaNWokNm3apOhX9DvZu3evGD58uKhWrZph5vrZs2eLHf/TTz8VzZo1E7a2tsLFxUX06dNHnDp1StEnLCxMODo6lvh4b9y4IQYMGCCqVasmVCpViVerlKZly5YlzhovkpiYKHr06CHc3NyEjY2NqFGjhujRo0ex3/np06fFyy+/LNzd3YWtra2oWbOmCA8PV9y068MPPxS+vr7Cysqq2Gu7rM9BSSrivSGEEBs2bBCtW7cWdnZ2wsnJSbRs2VLx2Iz9rHrUrP4HL2V+8LE9HMuaNWtEmzZthKOjo7C3txd169YVI0aMEEePHn3kc0CWoRLiMdOQieiJplKpMH78eCxduvSR/datW4eRI0ciOTnZqMqbiP6ZpD3HT0REJCMmfiIiIolwqJ+IiEgirPiJiIgkwsRPREQkESZ+IiIiiTDxExERSeQfeee+O3crOwIiy+u98khlh0BkcXvHW+Y24kXsW04we9u8Y4++d8aT6h+Z+ImIiIyikm/gm4mfiIjkVY7fLvm0YOInIiJ5SVjxy/eIiYiIJMaKn4iI5MWhfiIiIolIONTPxE9ERPJixU9ERCQRVvxEREQSkbDil+9PHSIiIomx4iciInlxqJ+IiEgiEg71M/ETEZG8WPETERFJhBU/ERGRRCSs+OV7xERERBJjxU9ERPKSsOJn4iciInlV4Tl+IiIiebDiJyIikghn9RMREUlEwopfvkdMREQkMVb8REQkLw71ExERSUTCoX4mfiIikhcrfiIiIomw4iciIpKIhBW/fH/qEBERSYyJn4iI5KWqYv5ipujoaKhUKkRGRhrahBCIioqCt7c37O3tERISglOnTim20+v1mDhxIjw8PODo6IjevXsjLS3N5OMz8RMRkbxUKvMXMyQnJ2PVqlVo1qyZon3hwoVYtGgRli5diuTkZGi1WnTp0gW3b9829ImMjER8fDzi4uJw6NAh5OTkoGfPnigsLDQpBiZ+IiKSVwVW/Dk5ORg2bBg++eQTuLq6GtqFEPjwww8xc+ZM9OvXD35+fli/fj3+/vtvbN68GQCQnZ2N1atX44MPPkDnzp3RsmVLbNy4ESdOnMC+fftMioOJn4iI5FWGxK/X63Hr1i3FotfrSz3U+PHj0aNHD3Tu3FnRnpqaCp1Oh9DQUEObWq1GcHAwkpKSAAApKSkoKChQ9PH29oafn5+hj7GY+ImISF5lGOqPjo6Gi4uLYomOji7xMHFxcfj5559LXK/T6QAAGo1G0a7RaAzrdDodbG1tFSMFD/cxFi/nIyIiMsP06dMxadIkRZtarS7W7/Lly3jjjTewd+9e2NnZlbo/1UPzBoQQxdoeZkyfh7HiJyIieZVhqF+tVqNq1aqKpaTEn5KSgoyMDAQEBMDa2hrW1tZITEzExx9/DGtra0Ol/3DlnpGRYVin1WqRn5+PrKysUvsYi4mfiIjkVQGz+jt16oQTJ07g+PHjhqVVq1YYNmwYjh8/jjp16kCr1SIhIcGwTX5+PhITExEUFAQACAgIgI2NjaJPeno6Tp48aehjLA71ExGRvCrglr3Ozs7w8/NTtDk6OsLd3d3QHhkZiXnz5qF+/fqoX78+5s2bBwcHBwwdOhQA4OLigoiICEyePBnu7u5wc3PDlClT4O/vX2yy4OMw8RMRkbyekFv2Tps2DXl5eRg3bhyysrLQpk0b7N27F87OzoY+ixcvhrW1NQYOHIi8vDx06tQJ69atg5WVlUnHUgkhRHk/gMp2525lR0Bkeb1XHqnsEIgsbu/4thbdv0P/NWZv+/eXo8oxkorDc/xEREQS4VA/ERFJy9RL4f4JmPiJiEhe8uV9Jn4iIpIXK34iIiKJMPETERFJRMbEz1n9REREEmHFT0RE0pKx4mfiJyIiecmX95n4iYhIXqz4iYiIJMLET0REJBEZEz9n9RMREUmEFT8REUlLxoqfiZ+IiOQlX95n4iciInmx4iciIpIIEz8REZFEZEz8nNVPREQkEVb8REQkL/kKfiZ+IiKSl4xD/Uz8REQkLSZ+IiIiiTDxExERSUTGxM9Z/URERBYWExODZs2aoWrVqqhatSoCAwOxe/duw/rw8HCoVCrF0rZtW8U+9Ho9Jk6cCA8PDzg6OqJ3795IS0szORYmfiIikpeqDIsJnnnmGcyfPx9Hjx7F0aNH0bFjR/Tp0wenTp0y9OnWrRvS09MNy65duxT7iIyMRHx8POLi4nDo0CHk5OSgZ8+eKCwsNCkWDvUTEZG0Kmqov1evXoqf33vvPcTExODIkSNo2rQpAECtVkOr1Za4fXZ2NlavXo3Y2Fh07twZALBx40b4+Phg37596Nq1q9GxsOInIiJpPTy8bsqi1+tx69YtxaLX6x97zMLCQsTFxSE3NxeBgYGG9oMHD8LT0xMNGjTAa6+9hoyMDMO6lJQUFBQUIDQ01NDm7e0NPz8/JCUlmfSYmfiJiEhaZUn80dHRcHFxUSzR0dGlHuvEiRNwcnKCWq3G2LFjER8fjyZNmgAAunfvjk2bNuHAgQP44IMPkJycjI4dOxr+kNDpdLC1tYWrq6tinxqNBjqdzqTHzKF+IiIiM0yfPh2TJk1StKnV6lL7N2zYEMePH8fNmzfx5ZdfIiwsDImJiWjSpAkGDRpk6Ofn54dWrVqhVq1a2LlzJ/r161fqPoUQJp+uYOInIiJ5leEUv1qtfmSif5itrS3q1asHAGjVqhWSk5Px0UcfYeXKlcX6enl5oVatWjh79iwAQKvVIj8/H1lZWYqqPyMjA0FBQSbFzcRPZbZ1yyasW7sa169dQ9169THt3zPwbECryg6L6LEGP+uNdnXc4ONqj/y793BadxufHr6EtJt3DH32jm9b4rafJF3E58fSoXFWI3ZEyxL7vLPnD3z/5w2LxE7lozKv4xdClDonIDMzE5cvX4aXlxcAICAgADY2NkhISMDAgQMBAOnp6Th58iQWLlxo0nGZ+KlM9uzehYXzozFz1hy0aPksvvgsDuPGvIb4HTvh5e1d2eERPZK/d1XsOHkVf2TkwEqlQnhbH0T3bozXNv+CO3fvAQAGrU1RbNO6ZjVM6ljHkNCv5eiL9XmxiScGPuuN5Es3K+RxkPkqKvHPmDED3bt3h4+PD27fvo24uDgcPHgQe/bsQU5ODqKiotC/f394eXnhwoULmDFjBjw8PPDSSy8BAFxcXBAREYHJkyfD3d0dbm5umDJlCvz9/Q2z/I3FxE9lErt+LV7q3x/9BrwMAJg2fSaSkg7hs61b8Mabkys5OqJHm/n1b4qfP9j/Jz6PaIX61R1xIv02ACDr7wJFnyBfV/zy1y3obt2v1O6J4n3a1XFD4tlM3Cm4Z8HoqTxUVOK/evUqhg8fjvT0dLi4uKBZs2bYs2cPunTpgry8PJw4cQIbNmzAzZs34eXlhQ4dOmDr1q1wdnY27GPx4sWwtrbGwIEDkZeXh06dOmHdunWwsrIyKRYmfjJbQX4+zpw+hVGvjla0Bwa1wy/Hj1VSVETmc1Tf/wC9rb9b4vpq9jZ4rlY1vL//z1L3Ub+6I+pVd8TS71ItEiOVr4pK/KtXry51nb29Pb755pvH7sPOzg5LlizBkiVLyhRLpSb+tLQ0xMTEICkpCTqdDiqVChqNBkFBQRg7dix8fHwqMzx6jKybWSgsLIS7u7ui3d3dA9evX6ukqIjMN6ZdLZy4cgsXbuSVuL5LIw/8XXAPh86Xft6+W+PquHjjb5zW5VgqTKIyqbTEf+jQIcP5jtDQUISGhkIIgYyMDGzfvh1LlizB7t270a5du0fuR6/XF5scIaxMm2lJZfPwX8zmXF5CVNkmtK8NX3dHTNp2qtQ+3Rp74sAf11FQKEpcb2ulQocGHth09C9LhUnlTcKPqkpL/G+++SZeffVVLF68uNT1kZGRSE5OfuR+oqOjMXfuXEXbzFlz8J/ZUeUVKpXCtZorrKyscP36dUX7jRuZcHf3qKSoiEw37oXaCKztisnxp3E9N7/EPn5ezvBxtcd735wtdT8v1HWH2roK9v3GEa+nhYxFSqXdue/kyZMYO3ZsqevHjBmDkydPPnY/06dPR3Z2tmKZ+tb08gyVSmFja4vGTZriSNIPivYjSUlo3qLky5uInjTjX6iN5+u4Yer/zkB3u/TbrXZr7Ik/MnJwPvPv0vs08cSR1Cxk3yl5jgA9ecpy576nVaUlfi8vr0feX/jw4cOG6xcfRa1WG77msGjhMH/FGR42Etu+/ALx277A+T//xPvz5yE9PR0vDxpc2aERPdbE9rXRqaEHohPOIq+gEK4ONnB1sIGtlfJD3cHGCu3ruWH36YxS9gR4u6jh7+2M3WdK70NPHpXK/OVpVWlD/VOmTMHYsWORkpKCLl26QKPRQKVSQafTISEhAZ9++ik+/PDDygqPjNSt+4vIvpmFVTHLce1aBurVb4BlK1bB27tGZYdG9Fi9/O9/E9oHLzVVtL+//08kPDBcH1L//gTWb89mlrqvro09kZmTj5RL2RaIlCzlaa7czaUSQpQ8S6UCbN26FYsXL0ZKSorh+4StrKwQEBCASZMmGe5OZCqOspEMeq88UtkhEFlcaXdOLC/1p+4xe9uz73crx0gqTqVezjdo0CAMGjQIBQUFhgliHh4esLGxqcywiIhIEhIW/E/GDXxsbGyMOp9PRERUnmQc6n8iEj8REVFlkDDvM/ETEZG8qlSRL/Mz8RMRkbRkrPgr7Tp+IiIiqnis+ImISFqc3EdERCQRCfM+Ez8REcmLFT8REZFEmPiJiIgkImHe56x+IiIimbDiJyIiaXGon4iISCIS5n0mfiIikhcrfiIiIolImPc5uY+IiOSlUqnMXkwRExODZs2aoWrVqqhatSoCAwOxe/duw3ohBKKiouDt7Q17e3uEhITg1KlTin3o9XpMnDgRHh4ecHR0RO/evZGWlmbyY2biJyIisrBnnnkG8+fPx9GjR3H06FF07NgRffr0MST3hQsXYtGiRVi6dCmSk5Oh1WrRpUsX3L5927CPyMhIxMfHIy4uDocOHUJOTg569uyJwsJCk2JRCSFEuT66J8Cdu5UdAZHl9V55pLJDILK4vePbWnT/z807aPa2P80IKdOx3dzc8P7772PUqFHw9vZGZGQk3nrrLQD3q3uNRoMFCxZgzJgxyM7ORvXq1REbG4tBgwYBAK5cuQIfHx/s2rULXbt2Nfq4rPiJiEhaZRnq1+v1uHXrlmLR6/WPPWZhYSHi4uKQm5uLwMBApKamQqfTITQ01NBHrVYjODgYSUlJAICUlBQUFBQo+nh7e8PPz8/Qx1hM/EREJC2VyvwlOjoaLi4uiiU6OrrUY504cQJOTk5Qq9UYO3Ys4uPj0aRJE+h0OgCARqNR9NdoNIZ1Op0Otra2cHV1LbWPsTirn4iIpFWWy/mmT5+OSZMmKdrUanWp/Rs2bIjjx4/j5s2b+PLLLxEWFobExMRSYxFCPDY+Y/o8jImfiIikVZbL+dRq9SMT/cNsbW1Rr149AECrVq2QnJyMjz76yHBeX6fTwcvLy9A/IyPDMAqg1WqRn5+PrKwsRdWfkZGBoKAgk+LmUD8REVElEEJAr9fD19cXWq0WCQkJhnX5+flITEw0JPWAgADY2Ngo+qSnp+PkyZMmJ35W/EREJK2KunPfjBkz0L17d/j4+OD27duIi4vDwYMHsWfPHqhUKkRGRmLevHmoX78+6tevj3nz5sHBwQFDhw4FALi4uCAiIgKTJ0+Gu7s73NzcMGXKFPj7+6Nz584mxcLET0RE0qqoO/ddvXoVw4cPR3p6OlxcXNCsWTPs2bMHXbp0AQBMmzYNeXl5GDduHLKystCmTRvs3bsXzs7Ohn0sXrwY1tbWGDhwIPLy8tCpUyesW7cOVlZWJsXC6/iJnlK8jp9kYOnr+F/44JDZ234/+flyjKTisOInIiJp8Ut6iIiIJCJh3uesfiIiIpmw4iciImlxqJ+IiEgiEuZ9Jn4iIpIXK34iIiKJSJj3mfiJiEheVSTM/JzVT0REJBFW/EREJC0JC34mfiIikhcn9xEREUmkinx5n4mfiIjkxYqfiIhIIhLmfc7qJyIikgkrfiIikpYK8pX8TPxERCQtTu4jIiKSCCf3ERERSUTCvM/ET0RE8uK9+omIiOgfjRU/ERFJS8KCn4mfiIjkJePkPg71ExGRtFQq8xdTREdHo3Xr1nB2doanpyf69u2L33//XdEnPDwcKpVKsbRt21bRR6/XY+LEifDw8ICjoyN69+6NtLQ0k2Jh4iciImlVUanMXkyRmJiI8ePH48iRI0hISMDdu3cRGhqK3NxcRb9u3bohPT3dsOzatUuxPjIyEvHx8YiLi8OhQ4eQk5ODnj17orCw0OhYONRPRETSqqiB/j179ih+Xrt2LTw9PZGSkoL27dsb2tVqNbRabYn7yM7OxurVqxEbG4vOnTsDADZu3AgfHx/s27cPXbt2NSoWoxL/jh07jNoZAPTu3dvovkRERE8rvV4PvV6vaFOr1VCr1Y/dNjs7GwDg5uamaD948CA8PT1RrVo1BAcH47333oOnpycAICUlBQUFBQgNDTX09/b2hp+fH5KSkso38fft29eonalUKpOGG4iIiCpTWSb3RUdHY+7cuYq2OXPmICoq6pHbCSEwadIkPP/88/Dz8zO0d+/eHS+//DJq1aqF1NRUzJo1Cx07dkRKSgrUajV0Oh1sbW3h6uqq2J9Go4FOpzM6bqMS/71794zeIRER0dOiLPfqnz59OiZNmqRoM6banzBhAn799VccOnRI0T5o0CDD//38/NCqVSvUqlULO3fuRL9+/UrdnxDCpD9geI6fiIikVZaK39hh/QdNnDgRO3bswHfffYdnnnnmkX29vLxQq1YtnD17FgCg1WqRn5+PrKwsRdWfkZGBoKAgo2MwK/Hn5uYiMTERly5dQn5+vmLdv/71L3N2SUREVOEq6jJ+IQQmTpyI+Ph4HDx4EL6+vo/dJjMzE5cvX4aXlxcAICAgADY2NkhISMDAgQMBAOnp6Th58iQWLlxodCwmJ/5jx47hxRdfxN9//43c3Fy4ubnh+vXrcHBwgKenJxM/ERE9NSrqBj7jx4/H5s2b8b///Q/Ozs6Gc/IuLi6wt7dHTk4OoqKi0L9/f3h5eeHChQuYMWMGPDw88NJLLxn6RkREYPLkyXB3d4ebmxumTJkCf39/wyx/Y5h8Hf+bb76JXr164caNG7C3t8eRI0dw8eJFBAQE4L///a+puyMiIvrHi4mJQXZ2NkJCQuDl5WVYtm7dCgCwsrLCiRMn0KdPHzRo0ABhYWFo0KABDh8+DGdnZ8N+Fi9ejL59+2LgwIFo164dHBwc8NVXX8HKysroWFRCCGFK8NWqVcOPP/6Ihg0bolq1ajh8+DAaN26MH3/8EWFhYfjtt99M2Z1F3Llb2REQWV7vlUcqOwQii9s7vu3jO5VB+JZfzd523ZBm5RhJxTG54rexsTEMjWg0Gly6dAnA/SGIov8TERE9DR6+Ra4py9PK5HP8LVu2xNGjR9GgQQN06NABs2fPxvXr1xEbGwt/f39LxEhERGQRT2/6Np/JFf+8efMMMwzfeecduLu74/XXX0dGRgZWrVpV7gESERFZSkXdq/9JYnLF36pVK8P/q1evXuwLBIiIiOjJxRv4EBGRtJ7iwt1sJid+X1/fR05qOH/+fJkCIiIiqihP8yQ9c5mc+CMjIxU/FxQU4NixY9izZw+mTp1aXnERERFZnIR53/TE/8Ybb5TYvmzZMhw9erTMAREREVWUp3mSnrlMntVfmu7du+PLL78sr90RERFZnEpl/vK0KrfE/8UXX8DNza28dkdEREQWYNYNfB6cDCGEgE6nw7Vr17B8+fJyDY6IiMiSOLnPCH369FE8UVWqVEH16tUREhKCRo0alWtwRFS67z/dWNkhEFmehe/VX27D3k8RkxN/VFSUBcIgIiKqeDJW/Cb/sWNlZYWMjIxi7ZmZmSZ9LSAREVFlq6Iyf3lamVzxl/Ytvnq9Hra2tmUOiIiIqKI8zQncXEYn/o8//hjA/WGRTz/9FE5OToZ1hYWF+O6773iOn4iI6AlndOJfvHgxgPsV/4oVKxTD+ra2tqhduzZWrFhR/hESERFZiIzn+I1O/KmpqQCADh06YNu2bXB1dbVYUERERBWBQ/1G+Pbbby0RBxERUYWTsOA3fVb/gAEDMH/+/GLt77//Pl5++eVyCYqIiKgiVFGpzF6eViYn/sTERPTo0aNYe7du3fDdd9+VS1BEREQVoUoZlqeVybHn5OSUeNmejY0Nbt26VS5BERERkWWYnPj9/PywdevWYu1xcXFo0qRJuQRFRERUEWT8dj6TJ/fNmjUL/fv3x59//omOHTsCAPbv34/Nmzfjiy++KPcAiYiILOVpPldvLpMr/t69e2P79u04d+4cxo0bh8mTJ+Ovv/7CgQMHULt2bQuESEREZBkVVfFHR0ejdevWcHZ2hqenJ/r27Yvff/9d0UcIgaioKHh7e8Pe3h4hISE4deqUoo9er8fEiRPh4eEBR0dH9O7dG2lpaSbFYtb8hB49euCHH35Abm4uzp07h379+iEyMhIBAQHm7I6IiKhSVNS9+hMTEzF+/HgcOXIECQkJuHv3LkJDQ5Gbm2vos3DhQixatAhLly5FcnIytFotunTpgtu3bxv6REZGIj4+HnFxcTh06BBycnLQs2dPFBYWGh2LSpR28/3HOHDgANasWYNt27ahVq1a6N+/P/r374+WLVuas7tydeduZUdAZHmurSdUdghEFpd3bKlF9/92wjmzt53dpZ7Z2167dg2enp5ITExE+/btIYSAt7c3IiMj8dZbbwG4X91rNBosWLAAY8aMQXZ2NqpXr47Y2FgMGjQIAHDlyhX4+Phg165d6Nq1q1HHNqniT0tLw7vvvos6depgyJAhcHV1RUFBAb788ku8++67T0TSJyIiqgh6vR63bt1SLHq93qhts7OzAQBubm4A7t8dV6fTITQ01NBHrVYjODgYSUlJAICUlBQUFBQo+nh7e8PPz8/QxxhGJ/4XX3wRTZo0wenTp7FkyRJcuXIFS5YsMfpARERET5qynOOPjo6Gi4uLYomOjn7sMYUQmDRpEp5//nn4+fkBAHQ6HQBAo9Eo+mo0GsM6nU4HW1vbYrfMf7CPMYye1b93717861//wuuvv4769esbfQAiIqInVVnu1T9t+nRMmjRJ0aZWqx+73YQJE/Drr7/i0KFDxdY9/KVBQojHfpGQMX0eZHTF//333+P27dto1aoV2rRpg6VLl+LatWtGH4iIiOhJoyrDP7VajapVqyqWxyX+iRMnYseOHfj222/xzDPPGNq1Wi0AFKvcMzIyDKMAWq0W+fn5yMrKKrWPMYxO/IGBgfjkk0+Qnp6OMWPGIC4uDjVq1MC9e/eQkJCgmHVIRET0NKioWf1CCEyYMAHbtm3DgQMH4Ovrq1jv6+sLrVaLhIQEQ1t+fj4SExMRFBQEAAgICICNjY2iT3p6Ok6ePGnoY9RjNi10wMHBAaNGjcKhQ4dw4sQJTJ48GfPnz4enpyd69+5t6u6IiIgqTUUl/vHjx2Pjxo3YvHkznJ2dodPpoNPpkJeXB+D+EH9kZCTmzZuH+Ph4nDx5EuHh4XBwcMDQoUMBAC4uLoiIiMDkyZOxf/9+HDt2DK+88gr8/f3RuXNn4x+zaaErNWzYEAsXLkRaWhq2bNlSll0RERH9Y8XExCA7OxshISHw8vIyLA/eAn/atGmIjIzEuHHj0KpVK/z111/Yu3cvnJ2dDX0WL16Mvn37YuDAgWjXrh0cHBzw1VdfwcrKyuhYzL6O/0nG6/hJBryOn2Rg6ev43z943uxtp4bUKcdIKo7J9+onIiL6pyjLrP6nFRM/ERFJS8Lv6GHiJyIiecn47XxM/EREJC0Zh/rLNKufiIiIni6s+ImISFoSjvQz8RMRkbyqQL7Mz8RPRETSYsVPREQkERkn9zHxExGRtGS8nI+z+omIiCTCip+IiKQlYcHPxE9ERPKScaifiZ+IiKQlYd5n4iciInnJONGNiZ+IiKSlkrDkl/GPHSIiImmx4iciImnJV+8z8RMRkcQ4q5+IiEgi8qV9Jn4iIpKYhAU/Ez8REcmLs/qJiIjoH42Jn4iIpFWlDIspvvvuO/Tq1Qve3t5QqVTYvn27Yn14eDhUKpViadu2raKPXq/HxIkT4eHhAUdHR/Tu3RtpaWmmPmQmfiIiktfDydaUxRS5ublo3rw5li5dWmqfbt26IT093bDs2rVLsT4yMhLx8fGIi4vDoUOHkJOTg549e6KwsNCkWHiOn4iIpFVRZ/i7d++O7t27P7KPWq2GVqstcV12djZWr16N2NhYdO7cGQCwceNG+Pj4YN++fejatavRsbDiJyIiaZWl4tfr9bh165Zi0ev1Zsdy8OBBeHp6okGDBnjttdeQkZFhWJeSkoKCggKEhoYa2ry9veHn54ekpCSTjsPET0RE0irLOf7o6Gi4uLgolujoaLPi6N69OzZt2oQDBw7ggw8+QHJyMjp27Gj4Q0Kn08HW1haurq6K7TQaDXQ6nUnH4lA/ERGRGaZPn45JkyYp2tRqtVn7GjRokOH/fn5+aNWqFWrVqoWdO3eiX79+pW4nhDB5vgETPxERSass1/Gr1WqzE/3jeHl5oVatWjh79iwAQKvVIj8/H1lZWYqqPyMjA0FBQSbtm0P9REQkLVUZFkvKzMzE5cuX4eXlBQAICAiAjY0NEhISDH3S09Nx8uRJkxM/K34iIpJWRd24LycnB+fOnTP8nJqaiuPHj8PNzQ1ubm6IiopC//794eXlhQsXLmDGjBnw8PDASy+9BABwcXFBREQEJk+eDHd3d7i5uWHKlCnw9/c3zPI3FhM/ERFJq0oFXdB39OhRdOjQwfBz0dyAsLAwxMTE4MSJE9iwYQNu3rwJLy8vdOjQAVu3boWzs7Nhm8WLF8Pa2hoDBw5EXl4eOnXqhHXr1sHKysqkWFRCCFE+D+vJceduZUdAZHmurSdUdghEFpd3rPQb3pSHr09eNXvbnn6acoyk4vAcPxERkUQ41E9ERNJSVdi9+54cTPxERCQtCb+Vl4mfiIjkVVGT+54kTPxERCQtVvxEREQSkTHxc1Y/ERGRRFjxExGRtDirn4iISCJV5Mv7TPxERCQvVvxEREQS4eQ+IiIi+kdjxU9ERNKScaifFT+V2dYtm9A9tCNat/TH4Jf74eeUo5UdEpFZpowKRd6xpXh/Sv8S1y+ZORh5x5ZiwtAQRbvvMx7Y+sFruHQgGle/fx8bF4yCp5tzifugJ0sVlfnL04qJn8pkz+5dWDg/Gq+Nfh1bv9iOZ58NwLgxryH9ypXKDo3IJAFNaiKiXxB+/SOtxPW9QpqhtX9tXMm4qWh3sLPF18vHQwiB7qOXoOPIxbC1scKXH42BSsYTyE8ZVRn+Pa2Y+KlMYtevxUv9+6PfgJdRp25dTJs+E1ovLT7buqWyQyMymqO9LdbOC8e4d7bg5q28Yuu9q7tg8b9fxsgZ61Bwt1CxLrBFHdTydsdrczbi1LkrOHXuCkbP2YhWfrUR8lyDinoIZCaVyvzlacXET2YryM/HmdOnEBj0vKI9MKgdfjl+rJKiIjLdh9MHYc/3J/Htj78XW6dSqbD63RFYvH4/zpzXFVuvtrWGEAL6/LuGtjv5d1FYeA9BLepaNG4qO1UZlqcVEz+ZLetmFgoLC+Hu7q5od3f3wPXr1yopKiLTvNw1AC0a+WDWkh0lrp88sgvuFt7Dsi0HS1z/04kLyM3Lx3tv9IG9nQ0c7GwRHdkXVlZVoPWoasHIiczzRCf+y5cvY9SoUY/so9frcevWLcWi1+srKEICUOw8phCC5zbpqfCMphren9ofo/6zXlGxF2nZ2Afjh4Rg9JyNpe7jelYOhk1bjRfb++H6Dx/g6vfvo6qTPX4+fQmF9+5ZMnwqB1VUKrOXp9UTfTnfjRs3sH79eqxZs6bUPtHR0Zg7d66ibeasOfjP7CgLR0eu1VxhZWWF69evK9pv3MiEu7tHJUVFZLyWjWtC414VSZumGdqsra3w/LN1MXZQe/zn4//B080Jf+x6W7F+/qR+mDCsAxr1mAMA2H/kNzTtPRfu1Rxx9+49ZOfkITVhHi7+lVnhj4lM8/Smb/NVauLfsaPkobUi58+ff+w+pk+fjkmTJinahJW6THGRcWxsbdG4SVMcSfoBnTp3MbQfSUpCSMdOlRgZkXG+/el3BAx4T9G2au4r+D31Kj5YlwDd9VtISDqjWP/V8vHYvPMnbPjfkWL7y7yZCwAIbt0Anm5O+DrxhOWCp/IhYeav1MTft29fqFQqCCFK7fO4IWO1Wg21Wpno7xQfsSMLGR42EjP/PQ1N/PzQvHlLfPn5VqSnp+PlQYMrOzSix8r5W4/Tf6Yr2nLz8nEjO9fQfiM7V7G+4G4hrl6/hbMXMwxtw3u3xe+pOlzLykGbZr7479QBWLLpW0UfejI9zZflmatSE7+XlxeWLVuGvn37lrj++PHjCAgIqNigyCTdur+I7JtZWBWzHNeuZaBe/QZYtmIVvL1rVHZoRBWmQW1PvD2xN9xcHHDxyg0sXP0NPt54oLLDIiM8xafqzaYSjyq3Lax3795o0aIF3n777RLX//LLL2jZsiXumThBhhU/ycC19YTKDoHI4vKOLbXo/n86n232ts/VcTG673fffYf3338fKSkpSE9PR3x8vKLoFUJg7ty5WLVqFbKystCmTRssW7YMTZs2NfTR6/WYMmUKtmzZgry8PHTq1AnLly/HM888Y1LclTqrf+rUqQgKCip1fb169fDtt99WYERERCSTirqOPzc3F82bN8fSpSX/IbNw4UIsWrQIS5cuRXJyMrRaLbp06YLbt28b+kRGRiI+Ph5xcXE4dOgQcnJy0LNnTxQWFpa4z1Ifc2VW/JbCip9kwIqfZGDpij851fyKv7Wv8RX/g1QqlaLiF0LA29sbkZGReOuttwDcr+41Gg0WLFiAMWPGIDs7G9WrV0dsbCwGDRoEALhy5Qp8fHywa9cudO3a1ejjP9HX8RMREVlSWe7VX173kUlNTYVOp0NoaKihTa1WIzg4GElJSQCAlJQUFBQUKPp4e3vDz8/P0MdYTPxERCStstyrPzo6Gi4uLoolOjra5Bh0uvu3gtZoNIp2jUZjWKfT6WBrawtXV9dS+xjrib6BDxERkSWVZVJ/SfeRefjycpNiMeMuqObcKZUVPxERkRnUajWqVq2qWMxJ/FqtFgCKVe4ZGRmGUQCtVov8/HxkZWWV2sdYTPxERCSvJ+Dr+Xx9faHVapGQkGBoy8/PR2JiouHKt4CAANjY2Cj6pKen4+TJk4+8Oq4kHOonIiJpVdSd+3JycnDu3DnDz6mpqTh+/Djc3NxQs2ZNREZGYt68eahfvz7q16+PefPmwcHBAUOHDgUAuLi4ICIiApMnT4a7uzvc3NwwZcoU+Pv7o3PnzibFwsRPRETSqqg79x09ehQdOnQw/Fw0NyAsLAzr1q3DtGnTkJeXh3Hjxhlu4LN37144Ozsbtlm8eDGsra0xcOBAww181q1bBysrK5Ni4XX8RE8pXsdPMrD0dfy/XLr9+E6laF7T+fGdnkCs+ImISF4S3qufk/uIiIgkwoqfiIikxa/lJSIikoiMX8vLxE9ERNKSMO8z8RMRkcQkzPxM/EREJC0Zz/FzVj8REZFEWPETEZG0OLmPiIhIIhLmfSZ+IiKSmISZn4mfiIikJePkPiZ+IiKSlozn+Dmrn4iISCKs+ImISFoSFvxM/EREJDEJMz8TPxERSYuT+4iIiCQi4+Q+Jn4iIpKWhHmfs/qJiIhkwoqfiIjkJWHJz8RPRETS4uQ+IiIiiXByHxERkUQkzPuc3EdERBJTlWExQVRUFFQqlWLRarWG9UIIREVFwdvbG/b29ggJCcGpU6fK/PBKwsRPRERUAZo2bYr09HTDcuLECcO6hQsXYtGiRVi6dCmSk5Oh1WrRpUsX3L59u9zj4FA/ERFJqyyT+/R6PfR6vaJNrVZDrVaX2N/a2lpR5RcRQuDDDz/EzJkz0a9fPwDA+vXrodFosHnzZowZM8bsGEvCip+IiKSlUpm/REdHw8XFRbFER0eXeqyzZ8/C29sbvr6+GDx4MM6fPw8ASE1NhU6nQ2hoqKGvWq1GcHAwkpKSyv0xs+InIiJplWVy3/Tp0zFp0iRFW2nVfps2bbBhwwY0aNAAV69exbvvvougoCCcOnUKOp0OAKDRaBTbaDQaXLx4sQwRloyJn4iIpFWWy/keNaz/sO7duxv+7+/vj8DAQNStWxfr169H27Zt/38symCEEMXaygOH+omISGIVNK3/IY6OjvD398fZs2cN5/2LKv8iGRkZxUYBygMTPxERUQXT6/U4c+YMvLy84OvrC61Wi4SEBMP6/Px8JCYmIigoqNyPzaF+IiKSVkXduW/KlCno1asXatasiYyMDLz77ru4desWwsLCoFKpEBkZiXnz5qF+/fqoX78+5s2bBwcHBwwdOrTcY2HiJyIiaVXUnfvS0tIwZMgQXL9+HdWrV0fbtm1x5MgR1KpVCwAwbdo05OXlYdy4ccjKykKbNm2wd+9eODs7l3ssKiGEKPe9VrI7dys7AiLLc209obJDILK4vGNLLbr/9Ox8s7f1crEtx0gqDit+IiKSFr+dj4iISCby5X3O6iciIpIJK34iIpKWhAU/Ez8REcmroi7ne5Iw8RMRkbQ4uY+IiEgm8uV9Jn4iIpKXhHmfs/qJiIhkwoqfiIikxcl9REREEuHkPiIiIonIWPHzHD8REZFEWPETEZG0WPETERHRPxorfiIikhYn9xEREUlExqF+Jn4iIpKWhHmfiZ+IiCQmYebn5D4iIiKJsOInIiJpcXIfERGRRDi5j4iISCIS5n2e4yciIompyrCYYfny5fD19YWdnR0CAgLw/fffl/URmIyJn4iIpKUqwz9Tbd26FZGRkZg5cyaOHTuGF154Ad27d8elS5cs8MhKpxJCiAo9YgW4c7eyIyCyPNfWEyo7BCKLyzu21LL7LzB/W3sb0/q3adMGzz77LGJiYgxtjRs3Rt++fREdHW1+ICbiOX4iIpJWWSb36fV66PV6RZtarYZarS7WNz8/HykpKfj3v/+taA8NDUVSUpL5QZjhH5n47f6Rj+rJpdfrER0djenTp5f4gifLsHQlREp8nf8zlSVfRL0bjblz5yra5syZg6ioqGJ9r1+/jsLCQmg0GkW7RqOBTqczPwgz/COH+qli3bp1Cy4uLsjOzkbVqlUrOxwii+DrnB5mSsV/5coV1KhRA0lJSQgMDDS0v/fee4iNjcVvv/1m8XiLsDYmIiIyQ2lJviQeHh6wsrIqVt1nZGQUGwWwNM7qJyIisjBbW1sEBAQgISFB0Z6QkICgoKAKjYUVPxERUQWYNGkShg8fjlatWiEwMBCrVq3CpUuXMHbs2AqNg4mfykytVmPOnDmc8ET/aHydU1kNGjQImZmZePvtt5Geng4/Pz/s2rULtWrVqtA4OLmPiIhIIjzHT0REJBEmfiIiIokw8RMREUmEiZ+IiEgiTPxUZk/C10wSWcp3332HXr16wdvbGyqVCtu3b6/skIjKhImfyuRJ+ZpJIkvJzc1F8+bNsXQpvxuB/hl4OR+VyZPyNZNEFUGlUiE+Ph59+/at7FCIzMaKn8xW9DWToaGhivbK+JpJIiIyDhM/me1J+ppJIiIyDhM/lZlKpVL8LIQo1kZERE8GJn4y25P0NZNERGQcJn4y25P0NZNERGQcfjsflcmT8jWTRJaSk5ODc+fOGX5OTU3F8ePH4ebmhpo1a1ZiZETm4eV8VGbLly/HwoULDV8zuXjxYrRv376ywyIqFwcPHkSHDh2KtYeFhWHdunUVHxBRGTHxExERSYTn+ImIiCTCxE9ERCQRJn4iIiKJMPETERFJhImfiIhIIkz8REREEmHiJyIikggTPxERkUSY+ImeAlFRUWjRooXh5/DwcPTt27fC47hw4QJUKhWOHz9e4ccmovLBxE9UBuHh4VCpVFCpVLCxsUGdOnUwZcoU5ObmWvS4H330kdG3i2WyJqIH8Ut6iMqoW7duWLt2LQoKCvD999/j1VdfRW5uLmJiYhT9CgoKYGNjUy7HdHFxKZf9EJF8WPETlZFarYZWq4WPjw+GDh2KYcOGYfv27Ybh+TVr1qBOnTpQq9UQQiA7OxujR4+Gp6cnqlatio4dO+KXX35R7HP+/PnQaDRwdnZGREQE7ty5o1j/8FD/vXv3sGDBAtSrVw9qtRo1a9bEe++9BwDw9fUFALRs2RIqlQohISGG7dauXYvGjRvDzs4OjRo1wvLlyxXH+emnn9CyZUvY2dmhVatWOHbsWDk+c0RUGVjxE5Uze3t7FBQUAADOnTuHzz77DF9++SWsrKwAAD169ICbmxt27doFFxcXrFy5Ep06dcIff/wBNzc3fPbZZ5gzZw6WLVuGF154AbGxsfj4449Rp06dUo85ffp0fPLJJ1i8eDGef/55pKen47fffgNwP3k/99xz2LdvH5o2bQpbW1sAwCeffII5c+Zg6dKlaNmyJY4dO4bXXnsNjo6OCAsLQ25uLnr27ImOHTti48aNSE1NxRtvvGHhZ4+ILE4QkdnCwsJEnz59DD//+OOPwt3dXQwcOFDMmTNH2NjYiIyMDMP6/fv3i6pVq4o7d+4o9lO3bl2xcuVKIYQQgYGBYuzYsYr1bdq0Ec2bNy/xuLdu3RJqtVp88sknJcaYmpoqAIhjx44p2n18fMTmzZsVbe+8844IDAwUQgixcuVK4ebmJnJzcw3rY2JiStwXET09ONRPVEZff/01nJycYGdnh8DAQLRv3x5LliwBANSqVQvVq1c39E1JSUFOTg7c3d3h5ORkWFJTU/Hnn38CAM6cOYPAwEDFMR7++UFnzpyBXq9Hp06djI752rVruHz5MiIiIhRxvPvuu4o4mjdvDgcHB6PiIKKnA4f6icqoQ4cOiImJgY2NDby9vRUT+BwdHRV97927By8vLxw8eLDYfqpVq2bW8e3t7U3e5t69ewDuD/e3adNGsa7olIQQwqx4iOjJxsRPVEaOjo6oV6+eUX2fffZZ6HQ6WFtbo3bt2iX2ady4MY4cOYIRI0YY2o4cOVLqPuvXrw97e3vs378fr776arH1Ref0CwsLDW0ajQY1atTA+fPnMWzYsBL326RJE8TGxiIvL8/wx8Wj4iCipwOH+okqUOfOnREYGIi+ffvim2++wYULF5CUlIT//Oc/OHr0KADgjTfewJo1a7BmzRr88ccfmDNnDk6dOlXqPu3s7PDWW29h2rRp2LBhA/78808cOXIEq1evBgB4enrC3t4ee/bswdWrV5GdnQ3g/k2BoqOj8dFHH+GPP/7AiRMnsHbtWixatAgAMHToUFSpUgURERE4ffo0du3ahf/+978WfoaIyNKY+IkqkEqlwq5du9C+fXuMGjUKDRo0wODBg3HhwgVoNBoAwKBBgzB79my89dZbCAgIwMWLF/H6668/cr+zZs3C5MmTMXv2bDRu3BiDBg1CRkYGAMDa2hoff/wxVq5cCW9vb/Tp0wcA8Oqrr+LTTz/FunXr4O/vj+DgYKxbt85w+Z+TkxO++uornD59Gi1btsTMmTOxYMECCz47RFQRVIIn8oiIiKTBip+IiEgiTPxEREQSYeInIiKSCBM/ERGRRJj4iYiIJMLET0REJBEmfiIiIokw8RMREUmEiZ+IiEgiTPxEREQSYeInIiKSyP8D7WHh1ou+Bl0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate Support Vector Machine Performance\n",
    "svc_accuracy = accuracy_score(y_test, y_pred_svc)\n",
    "svc_f1 = f1_score(y_test, y_pred_svc, average='weighted')\n",
    "print(f\"Support Vector Machine - Accuracy: {svc_accuracy:.2f}%\")\n",
    "print(f\"Support Vector Machine - F1 Score: {svc_f1:.2f}\")\n",
    "print(\"Support Vector Machine - Classification Report:\\n\", classification_report(y_test, y_pred_svc))\n",
    "print(\"Support Vector Machine - Confusion Matrix:\\n\", confusion_matrix(y_test, y_pred_svc))\n",
    "\n",
    "# Confusion Matrix for Support Vector Machine\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.heatmap(confusion_matrix(y_test, y_pred_svc), annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('Confusion Matrix - Support Vector Machine')\n",
    "plt.ylabel('Actual')\n",
    "plt.xlabel('Predicted')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1bb2307",
   "metadata": {},
   "source": [
    "### Step 6: Compare Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "d96e0743",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    Model  Accuracy  F1 Score\n",
      "0     Logistic Regression  0.896694  0.895839\n",
      "1           Random Forest  0.900826  0.899780\n",
      "2  Support Vector Machine  0.618457  0.472659\n"
     ]
    }
   ],
   "source": [
    "# Compare Model Performance\n",
    "model_performance = {\n",
    "    'Model': ['Logistic Regression', 'Random Forest', 'Support Vector Machine'],\n",
    "    'Accuracy': [lr_accuracy, rf_accuracy, svc_accuracy],\n",
    "    'F1 Score': [lr_f1, rf_f1, svc_f1]\n",
    "}\n",
    "\n",
    "performance_df = pd.DataFrame(model_performance)\n",
    "print(performance_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8cf1a30",
   "metadata": {},
   "source": [
    "### Step 7: Visualize Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "cfe52c23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Accuracy\n",
    "plt.figure(figsize=(10, 5))\n",
    "bars = sns.barplot(x='Model', y='Accuracy', data=performance_df, palette='viridis')\n",
    "plt.title('Model Comparison - Accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Model')\n",
    "plt.ylim(0, 1)\n",
    "for bar in bars.patches:\n",
    "    bars.annotate(f'{bar.get_height()*100:.2f}%',\n",
    "                  (bar.get_x() + bar.get_width() / 2, bar.get_height()),\n",
    "                  ha='center', va='center', size=12, xytext=(0, 8),\n",
    "                  textcoords='offset points')\n",
    "plt.show()\n",
    "\n",
    "# Plot F1 Score\n",
    "plt.figure(figsize=(10, 5))\n",
    "bars = sns.barplot(x='Model', y='F1 Score', data=performance_df, palette='viridis')\n",
    "plt.title('Model Comparison - F1 Score')\n",
    "plt.ylabel('F1 Score')\n",
    "plt.xlabel('Model')\n",
    "plt.ylim(0, 1)\n",
    "for bar in bars.patches:\n",
    "    bars.annotate(f'{bar.get_height()*100:.2f}%',\n",
    "                  (bar.get_x() + bar.get_width() / 2, bar.get_height()),\n",
    "                  ha='center', va='center', size=12, xytext=(0, 8),\n",
    "                  textcoords='offset points')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd672042",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}