diff --git "a/pokemon/pokemon.ipynb" "b/pokemon/pokemon.ipynb"
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Classification for Pokemon Dataset\n",
+    "## Random forest , Logistic Refression, Gaussian Naive Bayes "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(r\"C:\\Users\\ACER\\Desktop\\Intelligence System\\pokemon\\pokemon.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 800 entries, 0 to 799\n",
+      "Data columns (total 12 columns):\n",
+      " #   Column      Non-Null Count  Dtype \n",
+      "---  ------      --------------  ----- \n",
+      " 0   #           800 non-null    int64 \n",
+      " 1   Name        799 non-null    object\n",
+      " 2   Type 1      800 non-null    object\n",
+      " 3   Type 2      414 non-null    object\n",
+      " 4   HP          800 non-null    int64 \n",
+      " 5   Attack      800 non-null    int64 \n",
+      " 6   Defense     800 non-null    int64 \n",
+      " 7   Sp. Atk     800 non-null    int64 \n",
+      " 8   Sp. Def     800 non-null    int64 \n",
+      " 9   Speed       800 non-null    int64 \n",
+      " 10  Generation  800 non-null    int64 \n",
+      " 11  Legendary   800 non-null    bool  \n",
+      "dtypes: bool(1), int64(8), object(3)\n",
+      "memory usage: 69.7+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "#               0\n",
+       "Name            1\n",
+       "Type 1          0\n",
+       "Type 2        386\n",
+       "HP              0\n",
+       "Attack          0\n",
+       "Defense         0\n",
+       "Sp. Atk         0\n",
+       "Sp. Def         0\n",
+       "Speed           0\n",
+       "Generation      0\n",
+       "Legendary       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ACER\\AppData\\Local\\Temp\\ipykernel_49656\\1106589682.py:1: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df = df.fillna(method='ffill')\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = df.fillna(method='ffill') "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "#             0\n",
+       "Name          0\n",
+       "Type 1        0\n",
+       "Type 2        0\n",
+       "HP            0\n",
+       "Attack        0\n",
+       "Defense       0\n",
+       "Sp. Atk       0\n",
+       "Sp. Def       0\n",
+       "Speed         0\n",
+       "Generation    0\n",
+       "Legendary     0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Convert categorical columns to numerical"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Type 1'] = df['Type 1'].astype('category').cat.codes\n",
+    "df['Type 2'] = df['Type 2'].astype('category').cat.codes\n",
+    "df['Legendary'] = df['Legendary'].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 800 entries, 0 to 799\n",
+      "Data columns (total 12 columns):\n",
+      " #   Column      Non-Null Count  Dtype \n",
+      "---  ------      --------------  ----- \n",
+      " 0   #           800 non-null    int64 \n",
+      " 1   Name        800 non-null    object\n",
+      " 2   Type 1      800 non-null    int8  \n",
+      " 3   Type 2      800 non-null    int8  \n",
+      " 4   HP          800 non-null    int64 \n",
+      " 5   Attack      800 non-null    int64 \n",
+      " 6   Defense     800 non-null    int64 \n",
+      " 7   Sp. Atk     800 non-null    int64 \n",
+      " 8   Sp. Def     800 non-null    int64 \n",
+      " 9   Speed       800 non-null    int64 \n",
+      " 10  Generation  800 non-null    int64 \n",
+      " 11  Legendary   800 non-null    int32 \n",
+      "dtypes: int32(1), int64(8), int8(2), object(1)\n",
+      "memory usage: 61.1+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Normalize columns using MinMax Scaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "num_cols = ['HP', 'Attack', 'Defense', 'Sp. Atk', 'Sp. Def', 'Speed']\n",
+    "df[num_cols] = scaler.fit_transform(df[num_cols])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save cleaned dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv(\"pokemon_cleaned.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   #           Name  Type 1  Type 2        HP    Attack   Defense   Sp. Atk  \\\n",
+      "0  1      Bulbasaur       9      13  0.173228  0.237838  0.195556  0.298913   \n",
+      "1  2        Ivysaur       9      13  0.232283  0.308108  0.257778  0.380435   \n",
+      "2  3       Venusaur       9      13  0.311024  0.416216  0.346667  0.489130   \n",
+      "3  4  Mega Venusaur       9      13  0.311024  0.513514  0.524444  0.608696   \n",
+      "4  5     Charmander       6      13  0.149606  0.254054  0.168889  0.271739   \n",
+      "\n",
+      "    Sp. Def     Speed  Generation  Legendary  \n",
+      "0  0.214286  0.228571           1          0  \n",
+      "1  0.285714  0.314286           1          0  \n",
+      "2  0.380952  0.428571           1          0  \n",
+      "3  0.476190  0.428571           1          0  \n",
+      "4  0.142857  0.342857           1          0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Prediction Task"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tasks = {\n",
+    "    \"Predict Type 1\": (\"Type 1\", \"multi\"),\n",
+    "    \"Predict Legendary\": (\"Legendary\", \"binary\"),\n",
+    "    \"Predict Generation\": (\"Generation\", \"multi\"),\n",
+    "    \"Predict Physical vs. Special\": (\"Physical Special\", \"binary\")\n",
+    "}\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a new column for Physical vs. Special Attacker (Binary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Physical Special'] = df.apply(lambda row: 1 if row['Attack'] > row['Sp. Atk'] else 0, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Models (Random Forest, Logistic Regression, Naive Bayes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.linear_model import LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "models = {\n",
+    "    \"Random Forest\": RandomForestClassifier(n_estimators=100, random_state=42),\n",
+    "    \"Naïve Bayes\": GaussianNB(),\n",
+    "    \"Logistic Regression\": LogisticRegression(max_iter=1000)\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loop through tasks and train models separately"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "🔹 Training for: Predict Type 1 (Target: Type 1)\n",
+      "\n",
+      "🔹 Training for: Predict Legendary (Target: Legendary)\n",
+      "\n",
+      "🔹 Training for: Predict Generation (Target: Generation)\n",
+      "\n",
+      "🔹 Training for: Predict Physical vs. Special (Target: Physical Special)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for task_name, (target, task_type) in tasks.items():\n",
+    "    print(f\"\\n🔹 Training for: {task_name} (Target: {target})\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Prepare features and target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = df.select_dtypes(include=['number']).drop(columns=[target])\n",
+    "y = df[target]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Split data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "🔹 Model: Random Forest\n",
+      "\n",
+      "🔹 Model: Naïve Bayes\n",
+      "\n",
+      "🔹 Model: Logistic Regression\n"
+     ]
+    }
+   ],
+   "source": [
+    "for model_name, model in models.items():\n",
+    "        print(f\"\\n🔹 Model: {model_name}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train model "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(X_train, y_train)\n",
+    "y_pred = model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Accuracy and classification report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Accuracy: 0.9313\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      0.80      0.89        56\n",
+      "           1       0.90      1.00      0.95       104\n",
+      "\n",
+      "    accuracy                           0.93       160\n",
+      "   macro avg       0.95      0.90      0.92       160\n",
+      "weighted avg       0.94      0.93      0.93       160\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "acc = accuracy_score(y_test, y_pred)\n",
+    "print(f\"✅ Accuracy: {acc:.4f}\")\n",
+    "print(classification_report(y_test, y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Confusion matrix for models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Logistic Regression Confusion Matrix and feature importance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🔹 Accuracy: 0.9313\n",
+      "\n",
+      "🔹 Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      0.80      0.89        56\n",
+      "           1       0.90      1.00      0.95       104\n",
+      "\n",
+      "    accuracy                           0.93       160\n",
+      "   macro avg       0.95      0.90      0.92       160\n",
+      "weighted avg       0.94      0.93      0.93       160\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "cm = confusion_matrix(y_test, y_pred)\n",
+    "\n",
+    "plt.figure(figsize=(6, 4))\n",
+    "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=[\"Non\", \"Yes\"], yticklabels=[\"Non\", \"Yes\"])\n",
+    "plt.xlabel(\"Predicted\")\n",
+    "plt.ylabel(\"Actual\")\n",
+    "plt.title(f\"Confusion Matrix - {task_name} ({model_name})\")\n",
+    "plt.show()\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "report = classification_report(y_test, y_pred)\n",
+    "\n",
+    "print(f\"🔹 Accuracy: {accuracy:.4f}\\n\")\n",
+    "print(\"🔹 Classification Report:\")\n",
+    "print(report)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Random Forest Confusion Matrix and Feature Importance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fit Random Forest model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomForestClassifier(random_state=42)"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "models[\"Random Forest\"].fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Evaluate Random Forest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred_rf = models[\"Random Forest\"].predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🔹 Model: Random Forest\n",
+      "🔹 Accuracy: 0.9750\n",
+      "\n",
+      "🔹 Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      0.93      0.96        56\n",
+      "           1       0.96      1.00      0.98       104\n",
+      "\n",
+      "    accuracy                           0.97       160\n",
+      "   macro avg       0.98      0.96      0.97       160\n",
+      "weighted avg       0.98      0.97      0.97       160\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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JGD9+fJr6U8ft5MmTaZ5Ht5L6O5sxY4Zx8uRJ4+jRo8YPP/xgBAcHGzabzf58vtPfrWEYRrVq1YyqVas6PM5XX31lSDKWLVtmGIZhfP3112n+XtJz47ZEREQYLi4u6d4vdQzu9Pl6q9eN6w0cONDImTOncebMGXtbYmKikSdPHqNz5872Nl9fX+Oll1665bruVFxcnOHv729IMkqVKmW88MILxieffGKcO3cuTd/Uv+lx48Y51FehQgUjICDA/toxd+5cw8XFxfjtt98c7j9lyhRDkrFq1SrDMAxj7969houLi/HUU0+lGcfr/z5vfH5evXrV4blhGNde7/Lnz+8wToaR9veanl9++cWQZHz33XfpbnOpUqXsf6+7d+82+vfvb0gyGjdu7ND3Tt9fUt8bbqz1qaeeMh544AH7z5s3bzYkGS+++KJDv/bt26fZrhYtWhhubm7G/v377W1Hjx41cufObTz++OP2ttTX+vDwcIcxrlatmmGz2YwXXnjB3nb16lWjUKFC6b5W3yj1uXHjrWPHjg7b0rVrV4f79evXz5BkLF261N5WpEgRQ5KxaNEih77Dhw83vLy8jD///NOhfcCAAYarq6sRGxtrGIZh9OrVy/Dx8TGuXr1603q/+OILh9eJ9Li5uRk9evS47bYj++GwQDiNDz74QD///LPDTbr2LdW5c+fUrl07nTp1yn5zdXVV1apVHQ4Vuf5b+8uXL+vUqVP2w2EyckjNnbjxHJqvvvpKKSkpat26tUO9gYGBKl68+H86tKVr1672/+fJk0clS5aUl5eXWrdubW8vWbKk8uTJk+4Mbd27d3fY89SjRw/lyJFDP/74oyTpl19+UVJSknr37i0Xl39ffrp16yYfHx/98MMPDutzd3dXZGTkHdfv7u5uX29ycrJOnz4tb29vlSxZMt3fT2RkpMP5GLVq1ZIk+7Zt2rRJBw4cUO/evdPsDUz9FvnMmTNaunSpWrdurQsXLth/H6dPn1Z4eLj27t2rI0eO3PE23IklS5bI399f/v7+Kl++vL744gs999xzDt9YS9LTTz9tP/zubmv98ccf9dhjj6lKlSr2+/v7+6tDhw63re/LL7+Un5+f/ve//6VZ9l/PP+jcubP8/f0VFBSkxo0b6+LFi5o9e3aa8ylv97uVrp2rtnbtWvveL0maN2+eChcurNq1a0uS/ff+/fff68qVK3dUY0pKihYuXKimTZume55n6hjc7fP1dtq0aaMrV6447FVbsmSJzp07pzZt2tjb8uTJo7Vr1+ro0aN3/Rg3yp8/v7Zs2aIXXnhBZ8+e1ZQpU9S+fXsFBARo+PDhaQ4Jy5Ejh55//nn7z25ubnr++ed14sQJbdiwQZL0xRdfqHTp0ipVqpTDa1zqIaCpr3ELFy5USkqKBg8e7PB6It36eebq6mp/bqSkpOjMmTO6evWqKleunKFxTz1sL2/evOku3717t/3vtVSpUnrrrbfUrFmzNIeH3+37y43vDbVq1dLp06cVHx8vSfbX3Z49ezr06927t8PPycnJWrJkiVq0aKHQ0FB7e4ECBdS+fXutXLnSvs5UXbp0cRjjqlWryjAMdenSxd7m6uqqypUr3/FsnsHBwWneo1OnNE/dlj59+jjcJ3VinxvfO0JCQux7alN98cUXqlWrlvLmzevwvAoLC1NycrJWrFgh6drfx8WLF+2fETIq9XHgfDgsEE6jSpUq6X7Q2bt3r6R/z924kY+Pj/3/Z86cUVRUlD777DOHY+Wla4cd3gs3Hnq3d+9eGYah4sWLp9v/+nBzNzw8PBw+iEvXTjAuVKhQmg8qvr6+6Z5LdWNN3t7eKlCggH262tTrv5QsWdKhn5ubm0JDQ9NcH6ZgwYJ3dTJ6SkqK3n33XU2aNEkHDhxQcnKyfdmNh41J0oMPPujwc+qHo9RtS/3QfatZJfft2yfDMDRo0CANGjQo3T4nTpxQwYIF01128uRJhzq9vb1vO7ta1apVNWLECNlsNuXKlUulS5dO91DQG587d1ProUOH0hwKJKX93aVn//79Klmy5D2ZPW/w4MGqVauWXF1d5efnp9KlS6f7OLf73UrXwkjv3r01b948DR48WOfPn9f333+vV155xf6cr127tp5++mlFRUXpnXfeUZ06ddSiRQu1b99e7u7u6dZ48uRJxcfH33Y20rt9vt5O+fLlVapUKc2fP9/+IXf+/Pny8/NzeH0bO3asOnbsqMKFC6tSpUpq1KiRIiIiHD5Y340CBQpo8uTJmjRpkvbu3avFixdrzJgxGjx4sAoUKODwpU1QUFCaSXlKlCgh6dqhco899pj27t2rXbt2pXk9SpX62rt//365uLioTJkyd13z7NmzNW7cOO3evdshNN/4N3M3bgySqYKDgzVt2jSlpKRo//79GjlypE6ePJlmEpS7fX+51XPcx8dHhw4dkouLi4oWLerQ78a/4ZMnT+rSpUvp/m2XLl1aKSkp+vvvv/XQQw/d9LF9fX0lSYULF07Tnt57RXq8vLwUFhaW7rLUbSlWrJhDe2BgoPLkyZPmvSO93+PevXu1devW2z6vXnzxRX3++edq2LChChYsqPr166t169Zq0KDBHW1HKsMwmMzCSRGu4PRST2SdO3euAgMD0yy//oNb69attXr1avXv318VKlSQt7e3UlJS1KBBgzu6ZsnNXmiv/1B1oxvPcUlJSZHNZtNPP/2U7ixTGZ32+GYzVt2s/WYfJMx0t7M7jRo1SoMGDVLnzp01fPhw5cuXTy4uLurdu3e6vx8zti11vf369UvzTWmqGz8QXO/RRx91+GAwZMiQ207s4Ofnd9MPIddL77nzX2rNCsqWLXtH234nv9u8efOqSZMm9nC1YMECJSYmOsxYlnpB4t9//13fffedFi9erM6dO2vcuHH6/fff/9M043f7fL0Tbdq00ciRI3Xq1Cnlzp1b3377rdq1a5fmdSz12mhLlizRW2+9pTFjxuirr75Sw4YNM7w9NptNJUqUUIkSJdS4cWMVL15c8+bNcwhXdyIlJUVly5bV+PHj011+4wf4u/Xxxx+rU6dOatGihfr376+AgAC5uroqOjraYS/mnUoNwjcLETeGhho1aqhixYp6/fXX9d5779nb7/b9xcrX5rt5vzCznjsNK+m9d6SkpOjJJ5+86QV+U0N+QECANm/erMWLF+unn37STz/9pJkzZyoiIiLdCWNu5ty5c/ZzruFcCFdweqnf6gUEBNzyQ9vZs2cVExOjqKgoDR482N6euufrejd7A0j9ZvHGmfFu/NbtdvUahqGQkBD7m0FWsXfvXj3xxBP2nxMSEnTs2DE1atRIkuwX19yzZ4/Dt+RJSUk6cODAHX1olm4+vgsWLNATTzyh6dOnO7Rn9E0u9bmROglDelK3I2fOnHdc//XmzZunf/75J8367oW7qbVIkSLpPrf37Nlz28cpWrSo1q5dqytXrtx0T2pW+UY3IiJCzZs31x9//KF58+bpkUcecfiGPtVjjz2mxx57TCNHjtQnn3yiDh066LPPPks3OPj7+8vHx0fbt2+/5WOb/XyVroWrqKgoffnll8qfP7/i4+PVtm3bNP0KFCigF198US+++KJOnDihihUrauTIkf8pXF0vNDRUefPm1bFjxxzajx49muaSEn/++ack2ScOKlq0qLZs2aJ69erd8nlStGhRpaSkaOfOnXc1CcWCBQsUGhqqr776ymH9Q4YMueN1XK9UqVKS0s5YeTPlypXTs88+q6lTp6pfv3568MEH7+r95U4VKVLEvrfs+r1SN/4N+/v7K1euXOn+be/evVsuLi7/OdD+V6nbsnfvXoeJI44fP65z5845XLj5ZooWLaqEhIQ7ep12c3NT06ZN1bRpU6WkpOjFF1/U1KlTNWjQIBUrVuy2r19HjhxRUlKSQ61wHpxzBacXHh4uHx8fjRo1Kt1zKlJn+Ev9Ru7Gb+FSZxi6XuoHhxtDlI+Pj/z8/OzHdqeaNGnSHdfbsmVLubq6KioqKk0thmGkmbY3M3344YcOYzh58mRdvXrV/oEtLCxMbm5ueu+99xxqnz59us6fP5/uLFbp8fLySjO20rXf0Y1j8sUXX2T4nKeKFSsqJCREEyZMSPN4qY8TEBCgOnXqaOrUqWk+SEq67QyRNWrUUFhYmP12L8PV3dTaqFEj/f7771q3bp3D8nnz5t32cZ5++mmdOnVKEydOTLMsddxSr1eW3u8xMzVs2FB+fn4aM2aMfv311zTX2Tl79mya51TqB/n0pgWXrl3npkWLFvruu++0fv36NMtT12f281W6dhhX2bJlNX/+fM2fP18FChTQ448/bl+enJyc5hCzgIAABQUFOWzPqVOntHv37jRTe99o7dq1unjxYpr2devW6fTp02kONbt69ap9Wmzp2hcrU6dOlb+/v32GtdatW+vIkSOaNm1amvX+888/9sdr0aKFXFxcNGzYsDR7dm61tyS91/K1a9dqzZo1t9zWmylYsKAKFy6c7u/6Zl599VVduXLFvnfubt5f7lTq6+71e8fSW6erq6vq16+vb775xn4It3QtuHzyySeqWbOmw+HxVkj9gu7G2lPH707eO1q3bq01a9Zo8eLFaZadO3dOV69elaQ076EuLi72mUxT/0Zu9h6fKvX8wRuv/QbnwJ4rOD0fHx9NnjxZzz33nCpWrKi2bdvK399fsbGx+uGHH1SjRg1NnDhRPj4+9mnKr1y5ooIFC2rJkiXpfluZ+iHhjTfeUNu2bZUzZ041bdpUXl5e6tq1q0aPHq2uXbuqcuXKWrFihf2b2ztRtGhRjRgxQgMHDtTBgwfVokUL5c6dWwcOHNDXX3+t7t27q1+/fqaNz91ISkpSvXr11Lp1a+3Zs0eTJk1SzZo11axZM0nXviEdOHCgoqKi1KBBAzVr1sze79FHH73jC0hWqlRJkydP1ogRI1SsWDEFBASobt26atKkiYYNG6bIyEhVr15d27Zt07x58zIcWFxcXDR58mQ1bdpUFSpUUGRkpAoUKKDdu3drx44d9jfpDz74QDVr1lTZsmXVrVs3hYaG6vjx41qzZo0OHz6coesW3St3Wuurr76quXPnqkGDBurVq5d9KvYiRYpo69att3yMiIgIzZkzR3369NG6detUq1YtXbx4Ub/88otefPFFNW/eXJ6enipTpozmz5+vEiVKKF++fHr44Ydve56S2XLmzKm2bdtq4sSJcnV1Vbt27RyWz549W5MmTdJTTz2lokWL6sKFC5o2bZp8fHzsH/jSM2rUKC1ZskS1a9e2Tyd+7NgxffHFF1q5cqXy5Mlj+vM1VZs2bTR48GB5eHioS5cuDpM9XLhwQYUKFVKrVq1Uvnx5eXt765dfftEff/yhcePG2ftNnDhRUVFRWrZsWZprmF1v7ty5mjdvnp566ilVqlRJbm5u2rVrl2bMmCEPDw+9/vrrDv2DgoI0ZswYHTx4UCVKlND8+fO1efNmffjhh/a9nM8995w+//xzvfDCC1q2bJlq1Kih5ORk7d69W59//rn9+kXFihXTG2+8oeHDh6tWrVpq2bKl3N3d9ccffygoKEjR0dHp1tykSRN99dVXeuqpp9S4cWMdOHBAU6ZMUZkyZZSQkJChMW/evLm+/vrrOz7PpkyZMmrUqJE++ugjDRo0SA888MAdv7/cqQoVKqhdu3aaNGmSzp8/r+rVqysmJibda/mNGDFCP//8s2rWrKkXX3xROXLk0NSpU5WYmKixY8dmuAazlC9fXh07dtSHH36oc+fOqXbt2lq3bp1mz56tFi1aOBwxcTP9+/fXt99+qyZNmqhTp06qVKmSLl68qG3btmnBggU6ePCg/Pz81LVrV505c0Z169ZVoUKFdOjQIb3//vuqUKGCfU9UhQoV5OrqqjFjxuj8+fNyd3dX3bp1FRAQIOnaRFkPPvgg07A7q0yalRCwTHpTj6dn2bJlRnh4uOHr62t4eHgYRYsWNTp16mSsX7/e3ufw4cPGU089ZeTJk8fw9fU1nnnmGePo0aPpTtc7fPhwo2DBgoaLi4vD1NWXLl0yunTpYvj6+hq5c+c2WrdubZw4ceKmU7GfPHky3Xq//PJLo2bNmoaXl5fh5eVllCpVynjppZeMPXv23PV4dOzY0fDy8krTt3bt2sZDDz2Upr1IkSIO0winrvPXX381unfvbuTNm9fw9vY2OnToYJw+fTrN/SdOnGiUKlXKyJkzp5E/f36jR48eaaY6v9ljG8a16Z8bN25s5M6d25Bkn+r38uXLRt++fY0CBQoYnp6eRo0aNYw1a9akmYo5dbruL774wmG9N5vyeuXKlcaTTz5p5M6d2/Dy8jLKlStnvP/++w599u/fb0RERBiBgYFGzpw5jYIFCxpNmjQxFixYkO42ZNSNY5+e1O1466230l1+p7Vu3brVqF27tuHh4WEULFjQGD58uDF9+vTbTsVuGNee52+88YYREhJi5MyZ0wgMDDRatWrlMNXz6tWrjUqVKhlubm63nfL6Zr+zO+13q+nM161bZ0gy6tevn2bZxo0bjXbt2hkPPvig4e7ubgQEBBhNmjRxeF0wjPSn7D506JARERFh+Pv7G+7u7kZoaKjx0ksv2acBv9Pn651OxZ5q79699qmsV65c6bAsMTHR6N+/v1G+fHn787l8+fLGpEmTHPqlvv7caqppw7j2HOnfv79RsWJFI1++fEaOHDmMAgUKGM8884yxceNGh76pf9Pr1683qlWrZnh4eBhFihQxJk6cmGa9SUlJxpgxY4yHHnrIcHd3N/LmzWtUqlTJiIqKMs6fP+/Qd8aMGcYjjzxi71e7dm3j559/dnjc68czJSXFGDVqlFGkSBHD3d3deOSRR4zvv//e6Nixo1GkSBGHdd/ueZlq48aNhqQ008ff6nVs+fLlDuu/0/eXm703pL4OX/+3+c8//xg9e/Y0HnjgAcPLy8to2rSp8ffff6e7XRs3bjTCw8MNb29vI1euXMYTTzxhrF69Ot3HuPH99GY13ey95Ua3GqdUV65cMaKiouyvKYULFzYGDhzocFkJw7j1a+SFCxeMgQMHGsWKFTPc3NwMPz8/o3r16sbbb79tvxTAggULjPr16xsBAQGGm5ub8eCDDxrPP/+8cezYMYd1TZs2zQgNDTVcXV0d/laSk5ONAgUKGG+++eZttxvZk80wMuHMRwDZ2qxZsxQZGak//vgj3RkZgaxsy5YtqlChgubMmaPnnnvO6nKyrTp16ujUqVO3PRftflWvXj0FBQVp7ty5VpcCCy1cuFDt27fX/v37VaBAAavLgQU45woA4NSmTZsmb29vtWzZ0upScB8bNWqU5s+ff1cTFCH7GTNmjF5++WWClRPjnCsAgFP67rvvtHPnTn344Yd6+eWX01x/CbgbVatWVVJSktVlwGIZnRgF2QfhCgDglP73v//p+PHjatSokaKioqwuBwCQDXDOFQAAAACYgHOuAAAAAMAEhCsAAAAAMAHnXKUjJSVFR48eVe7cue/oYoAAAAAAsifDMHThwgUFBQU5XJg9PYSrdBw9elSFCxe2ugwAAAAAWcTff/+tQoUK3bIP4SoduXPnlnRtAH18fCyuBgAAAIBV4uPjVbhwYXtGuBXCVTpSDwX08fEhXAEAAAC4o9OFmNACAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABDmsLgC3FzzgB6tLMNXB0Y2tLgEAAAAwHXuuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATZIlw9cEHHyg4OFgeHh6qWrWq1q1bd9O+X331lSpXrqw8efLIy8tLFSpU0Ny5cx36GIahwYMHq0CBAvL09FRYWJj27t17rzcDAAAAgBOzPFzNnz9fffr00ZAhQ7Rx40aVL19e4eHhOnHiRLr98+XLpzfeeENr1qzR1q1bFRkZqcjISC1evNjeZ+zYsXrvvfc0ZcoUrV27Vl5eXgoPD9fly5cza7MAAAAAOBmbYRiGlQVUrVpVjz76qCZOnChJSklJUeHChfW///1PAwYMuKN1VKxYUY0bN9bw4cNlGIaCgoLUt29f9evXT5J0/vx55c+fX7NmzVLbtm1vu774+Hj5+vrq/Pnz8vHxyfjGmSR4wA9Wl2Cqg6MbW10CAAAAcEfuJhtYuucqKSlJGzZsUFhYmL3NxcVFYWFhWrNmzW3vbxiGYmJitGfPHj3++OOSpAMHDiguLs5hnb6+vqpatepN15mYmKj4+HiHGwAAAADcDUvD1alTp5ScnKz8+fM7tOfPn19xcXE3vd/58+fl7e0tNzc3NW7cWO+//76efPJJSbLf727WGR0dLV9fX/utcOHC/2WzAAAAADghy8+5yojcuXNr8+bN+uOPPzRy5Ej16dNHy5cvz/D6Bg4cqPPnz9tvf//9t3nFAgAAAHAKOax8cD8/P7m6uur48eMO7cePH1dgYOBN7+fi4qJixYpJkipUqKBdu3YpOjpaderUsd/v+PHjKlCggMM6K1SokO763N3d5e7u/h+3BgAAAIAzs3TPlZubmypVqqSYmBh7W0pKimJiYlStWrU7Xk9KSooSExMlSSEhIQoMDHRYZ3x8vNauXXtX6wQAAACAu2HpnitJ6tOnjzp27KjKlSurSpUqmjBhgi5evKjIyEhJUkREhAoWLKjo6GhJ186Pqly5sooWLarExET9+OOPmjt3riZPnixJstls6t27t0aMGKHixYsrJCREgwYNUlBQkFq0aGHVZgIAAADI5iwPV23atNHJkyc1ePBgxcXFqUKFClq0aJF9QorY2Fi5uPy7g+3ixYt68cUXdfjwYXl6eqpUqVL6+OOP1aZNG3ufV199VRcvXlT37t117tw51axZU4sWLZKHh0embx8AAAAA52D5da6yIq5zdW9xnSsAAADcL+6b61wBAAAAQHZBuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAAT5LC6AEn64IMP9NZbbykuLk7ly5fX+++/rypVqqTbd9q0aZozZ462b98uSapUqZJGjRrl0L9Tp06aPXu2w/3Cw8O1aNGie7cRuKeCB/xgdQmmOzi6sdUlAAAAwESW77maP3+++vTpoyFDhmjjxo0qX768wsPDdeLEiXT7L1++XO3atdOyZcu0Zs0aFS5cWPXr19eRI0cc+jVo0EDHjh2z3z799NPM2BwAAAAATsrycDV+/Hh169ZNkZGRKlOmjKZMmaJcuXJpxowZ6fafN2+eXnzxRVWoUEGlSpXSRx99pJSUFMXExDj0c3d3V2BgoP2WN2/ezNgcAAAAAE7K0nCVlJSkDRs2KCwszN7m4uKisLAwrVmz5o7WcenSJV25ckX58uVzaF++fLkCAgJUsmRJ9ejRQ6dPnza1dgAAAAC4nqXnXJ06dUrJycnKnz+/Q3v+/Pm1e/fuO1rHa6+9pqCgIIeA1qBBA7Vs2VIhISHav3+/Xn/9dTVs2FBr1qyRq6trmnUkJiYqMTHR/nN8fHwGtwgAAACAs8oSE1pk1OjRo/XZZ59p+fLl8vDwsLe3bdvW/v+yZcuqXLlyKlq0qJYvX6569eqlWU90dLSioqIypWYAAAAA2ZOlhwX6+fnJ1dVVx48fd2g/fvy4AgMDb3nft99+W6NHj9aSJUtUrly5W/YNDQ2Vn5+f9u3bl+7ygQMH6vz58/bb33//fXcbAgAAAMDpWRqu3NzcVKlSJYfJKFInp6hWrdpN7zd27FgNHz5cixYtUuXKlW/7OIcPH9bp06dVoECBdJe7u7vLx8fH4QYAAAAAd8Py2QL79OmjadOmafbs2dq1a5d69OihixcvKjIyUpIUERGhgQMH2vuPGTNGgwYN0owZMxQcHKy4uDjFxcUpISFBkpSQkKD+/fvr999/18GDBxUTE6PmzZurWLFiCg8Pt2QbAQAAAGR/lp9z1aZNG508eVKDBw9WXFycKlSooEWLFtknuYiNjZWLy78ZcPLkyUpKSlKrVq0c1jNkyBANHTpUrq6u2rp1q2bPnq1z584pKChI9evX1/Dhw+Xu7p6p2wYAAADAeVgeriTp5Zdf1ssvv5zusuXLlzv8fPDgwVuuy9PTU4sXLzapMgAAAAC4M5YfFggAAAAA2QHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAEyQJcLVBx98oODgYHl4eKhq1apat27dTftOmzZNtWrVUt68eZU3b16FhYWl6W8YhgYPHqwCBQrI09NTYWFh2rt3773eDAAAAABOzPJwNX/+fPXp00dDhgzRxo0bVb58eYWHh+vEiRPp9l++fLnatWunZcuWac2aNSpcuLDq16+vI0eO2PuMHTtW7733nqZMmaK1a9fKy8tL4eHhunz5cmZtFgAAAAAnY3m4Gj9+vLp166bIyEiVKVNGU6ZMUa5cuTRjxox0+8+bN08vvviiKlSooFKlSumjjz5SSkqKYmJiJF3bazVhwgS9+eabat68ucqVK6c5c+bo6NGjWrhwYSZuGQAAAABnYmm4SkpK0oYNGxQWFmZvc3FxUVhYmNasWXNH67h06ZKuXLmifPnySZIOHDiguLg4h3X6+vqqatWqd7xOAAAAALhbOax88FOnTik5OVn58+d3aM+fP7927959R+t47bXXFBQUZA9TcXFx9nXcuM7UZTdKTExUYmKi/ef4+Pg73gYAAAAAkLLAYYH/xejRo/XZZ5/p66+/loeHR4bXEx0dLV9fX/utcOHCJlYJAAAAwBlYGq78/Pzk6uqq48ePO7QfP35cgYGBt7zv22+/rdGjR2vJkiUqV66cvT31fnezzoEDB+r8+fP2299//52RzQEAAADgxCwNV25ubqpUqZJ9MgpJ9skpqlWrdtP7jR07VsOHD9eiRYtUuXJlh2UhISEKDAx0WGd8fLzWrl1703W6u7vLx8fH4QYAAAAAd8PSc64kqU+fPurYsaMqV66sKlWqaMKECbp48aIiIyMlSRERESpYsKCio6MlSWPGjNHgwYP1ySefKDg42H4elbe3t7y9vWWz2dS7d2+NGDFCxYsXV0hIiAYNGqSgoCC1aNHCqs0EAAAAkM1ZHq7atGmjkydPavDgwYqLi1OFChW0aNEi+4QUsbGxcnH5dwfb5MmTlZSUpFatWjmsZ8iQIRo6dKgk6dVXX9XFixfVvXt3nTt3TjVr1tSiRYv+03lZAAAAAHArNsMwDKuLyGri4+Pl6+ur8+fPZ4lDBIMH/GB1CaY6OLrxXd8nu42BlLFxAAAAQOa6m2xwX88WCAAAAABZBeEKAAAAAExAuAIAAAAAE2Q4XM2dO1c1atRQUFCQDh06JEmaMGGCvvnmG9OKAwAAAID7RYbC1eTJk9WnTx81atRI586dU3JysiQpT548mjBhgpn1AQAAAMB9IUPh6v3339e0adP0xhtvyNXV1d5euXJlbdu2zbTiAAAAAOB+kaFwdeDAAT3yyCNp2t3d3XXx4sX/XBQAAAAA3G8yFK5CQkK0efPmNO2LFi1S6dKl/2tNAAAAAHDfyZGRO/Xp00cvvfSSLl++LMMwtG7dOn366aeKjo7WRx99ZHaNAAAAAJDlZShcde3aVZ6ennrzzTd16dIltW/fXkFBQXr33XfVtm1bs2sEAAAAgCwvQ+FKkjp06KAOHTro0qVLSkhIUEBAgJl1AQAAAMB9JUPh6sCBA7p69aqKFy+uXLlyKVeuXJKkvXv3KmfOnAoODjazRgAAAADI8jI0oUWnTp20evXqNO1r165Vp06d/mtNAAAAAHDfyVC42rRpk2rUqJGm/bHHHkt3FkEAAAAAyO4yFK5sNpsuXLiQpv38+fNKTk7+z0UBAAAAwP0mQ+Hq8ccfV3R0tEOQSk5OVnR0tGrWrGlacQAAAABwv8jQhBZjxozR448/rpIlS6pWrVqSpN9++03x8fFaunSpqQUCAAAAwP0gQ3uuypQpo61bt6p169Y6ceKELly4oIiICO3evVsPP/yw2TUCAAAAQJaX4etcBQUFadSoUWbWAgAAAAD3rQyHq3PnzmndunU6ceKEUlJSHJZFRET858IAAAAA4H6SoXD13XffqUOHDkpISJCPj49sNpt9mc1mI1wBAAAAcDoZOueqb9++6ty5sxISEnTu3DmdPXvWfjtz5ozZNQIAAABAlpehcHXkyBH17NlTuXLlMrseAAAAALgvZShchYeHa/369WbXAgAAAAD3rQydc9W4cWP1799fO3fuVNmyZZUzZ06H5c2aNTOlOAAAAAC4X2QoXHXr1k2SNGzYsDTLbDabkpOT/1tVAAAAAHCfyVC4unHqdQAAAABwdhk65woAAAAA4CjDFxG+ePGifv31V8XGxiopKclhWc+ePf9zYQAAAABwP8lQuNq0aZMaNWqkS5cu6eLFi8qXL59OnTqlXLlyKSAggHAFAAAAwOlk6LDAV155RU2bNtXZs2fl6emp33//XYcOHVKlSpX09ttvm10jAAAAAGR5GQpXmzdvVt++feXi4iJXV1clJiaqcOHCGjt2rF5//XWzawQAAACALC9D4Spnzpxycbl214CAAMXGxkqSfH199ffff5tXHQAAAADcJzJ0ztUjjzyiP/74Q8WLF1ft2rU1ePBgnTp1SnPnztXDDz9sdo0AAAAAkOVlaM/VqFGjVKBAAUnSyJEjlTdvXvXo0UMnT57U1KlTTS0QAAAAAO4HGdpzVblyZfv/AwICtGjRItMKAgAAAID7UYb2XNWtW1fnzp1L0x4fH6+6dev+15oAAAAA4L6ToXC1fPnyNBcOlqTLly/rt99++89FAQAAAMD95q4OC9y6dav9/zt37lRcXJz95+TkZC1atEgFCxY0rzoAAAAAuE/cVbiqUKGCbDabbDZbuof/eXp66v333zetOAAAAAC4X9xVuDpw4IAMw1BoaKjWrVsnf39/+zI3NzcFBATI1dXV9CIBAAAAIKu7q3BVpEgRXblyRR07dtQDDzygIkWK3Ku6AAAAAOC+ctcTWuTMmVNff/31vagFAAAAAO5bGZotsHnz5lq4cKHJpQAAAADA/StDFxEuXry4hg0bplWrVqlSpUry8vJyWN6zZ09TigMAAACA+0WGwtX06dOVJ08ebdiwQRs2bHBYZrPZCFcAAAAAnE6GwtWBAwfMrgMAAAAA7msZOufqeoZhyDAMM2oBAAAAgPtWhsPVnDlzVLZsWXl6esrT01PlypXT3LlzzawNAAAAAO4bGToscPz48Ro0aJBefvll1ahRQ5K0cuVKvfDCCzp16pReeeUVU4sEAAAAgKwuQ+Hq/fff1+TJkxUREWFva9asmR566CENHTqUcAUAAADA6WTosMBjx46pevXqadqrV6+uY8eO/eeiAAAAAOB+k6FwVaxYMX3++edp2ufPn6/ixYv/56IAAAAA4H6TocMCo6Ki1KZNG61YscJ+ztWqVasUExOTbugCAAAAgOwuQ3uunn76aa1du1Z+fn5auHChFi5cKD8/P61bt05PPfWU2TUCAAAAQJaXoT1XklSpUiV9/PHHZtYCAAAAAPetDIer5ORkff3119q1a5ckqUyZMmrevLly5MjwKgEAAADgvpWhJLRjxw41a9ZMcXFxKlmypCRpzJgx8vf313fffaeHH37Y1CIBAAAAIKvL0DlXXbt21UMPPaTDhw9r48aN2rhxo/7++2+VK1dO3bt3v6t1ffDBBwoODpaHh4eqVq2qdevW3bTvjh079PTTTys4OFg2m00TJkxI02fo0KGy2WwOt1KlSt3tJgIAAADAXclQuNq8ebOio6OVN29ee1vevHk1cuRIbdq06Y7XM3/+fPXp00dDhgzRxo0bVb58eYWHh+vEiRPp9r906ZJCQ0M1evRoBQYG3nS9Dz30kI4dO2a/rVy58s43DgAAAAAyIEPhqkSJEjp+/Hia9hMnTqhYsWJ3vJ7x48erW7duioyMVJkyZTRlyhTlypVLM2bMSLf/o48+qrfeektt27aVu7v7TdebI0cOBQYG2m9+fn53XBMAAAAAZESGwlV0dLR69uypBQsW6PDhwzp8+LAWLFig3r17a8yYMYqPj7ffbiYpKUkbNmxQWFjYv8W4uCgsLExr1qzJSFl2e/fuVVBQkEJDQ9WhQwfFxsb+p/UBAAAAwO1kaEKLJk2aSJJat24tm80mSTIMQ5LUtGlT+882m03JycnpruPUqVNKTk5W/vz5Hdrz58+v3bt3Z6QsSVLVqlU1a9YslSxZUseOHVNUVJRq1aql7du3K3fu3OneJzExUYmJifafbxUKAQAAACA9GQpXy5YtM7sO0zRs2ND+/3Llyqlq1aoqUqSIPv/8c3Xp0iXd+0RHRysqKiqzSgQAAACQDWUoXNWuXfs/P7Cfn59cXV3TnLt1/PjxW05Wcbfy5MmjEiVKaN++fTftM3DgQPXp08f+c3x8vAoXLmxaDQAAAACyvwxf8ffy5cvaunWrTpw4oZSUFIdlzZo1u+393dzcVKlSJcXExKhFixaSpJSUFMXExOjll1/OaFlpJCQkaP/+/Xruuedu2sfd3f2WE2QAAAAAwO1kKFwtWrRIEREROnXqVJpltzrP6kZ9+vRRx44dVblyZVWpUkUTJkzQxYsXFRkZKUmKiIhQwYIFFR0dLenaJBg7d+60///IkSPavHmzvL297bMU9uvXT02bNlWRIkV09OhRDRkyRK6urmrXrl1GNhUAAAAA7kiGwtX//vc/PfPMMxo8eHCaCSnuRps2bXTy5EkNHjxYcXFxqlChghYtWmRfZ2xsrFxc/p3Q8OjRo3rkkUfsP7/99tt6++23Vbt2bS1fvlySdPjwYbVr106nT5+Wv7+/atasqd9//13+/v4ZrhMAAAAAbsdmpE7zdxd8fHy0adMmFS1a9F7UZLn4+Hj5+vrq/Pnz8vHxsbocBQ/4weoSTHVwdOO7vk92GwMpY+MAAACAzHU32SBD17lq1aqVfU8RAAAAACCDhwVOnDhRzzzzjH777TeVLVtWOXPmdFjes2dPU4oDAAAAgPtFhsLVp59+qiVLlsjDw0PLly+3X0hYujahBeEKAAAAgLPJULh64403FBUVpQEDBjhMOAEAAAAAzipDySgpKUlt2rQhWAEAAADA/8tQOurYsaPmz59vdi0AAAAAcN/K0GGBycnJGjt2rBYvXqxy5cqlmdBi/PjxphQHAAAAAPeLDIWrbdu22S/mu337dlMLAgAAAID7UYbC1bJly8yuAwAAAADua3cVrlq2bHnbPjabTV9++WWGCwIAAACA+9FdhStfX997VQcAAAAA3NfuKlzNnDnzXtUBAAAAAPc1LlQFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALLw9UHH3yg4OBgeXh4qGrVqlq3bt1N++7YsUNPP/20goODZbPZNGHChP+8TgAAAAAwg6Xhav78+erTp4+GDBmijRs3qnz58goPD9eJEyfS7X/p0iWFhoZq9OjRCgwMNGWdAAAAAGAGS8PV+PHj1a1bN0VGRqpMmTKaMmWKcuXKpRkzZqTb/9FHH9Vbb72ltm3byt3d3ZR1AgAAAIAZLAtXSUlJ2rBhg8LCwv4txsVFYWFhWrNmTaauMzExUfHx8Q43AAAAALgbloWrU6dOKTk5Wfnz53doz58/v+Li4jJ1ndHR0fL19bXfChcunKHHBwAAAOC8LJ/QIisYOHCgzp8/b7/9/fffVpcEAAAA4D6Tw6oH9vPzk6urq44fP+7Qfvz48ZtOVnGv1unu7n7Tc7gAAAAA4E5YtufKzc1NlSpVUkxMjL0tJSVFMTExqlatWpZZJwAAAADcCcv2XElSnz591LFjR1WuXFlVqlTRhAkTdPHiRUVGRkqSIiIiVLBgQUVHR0u6NmHFzp077f8/cuSINm/eLG9vbxUrVuyO1gkAAAAA94Kl4apNmzY6efKkBg8erLi4OFWoUEGLFi2yT0gRGxsrF5d/d64dPXpUjzzyiP3nt99+W2+//bZq166t5cuX39E6AQAAAOBesBmGYVhdRFYTHx8vX19fnT9/Xj4+PlaXo+ABP1hdgqkOjm581/fJbmMgZWwcAAAAkLnuJhswWyAAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGCCHFYXAODOBQ/4weoSTHVwdGOrSwAAADANe64AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwQQ6rCwCAuxE84AerSzDVwdGNrS4BAACYhD1XAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJshhdQEAgLsTPOAHq0sw1cHRja0uAQAAU7DnCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATMBU7AOC+k92mo5eYkh4AsgP2XAEAAACACQhXAAAAAGACwhUAAAAAmCBLhKsPPvhAwcHB8vDwUNWqVbVu3bpb9v/iiy9UqlQpeXh4qGzZsvrxxx8dlnfq1Ek2m83h1qBBg3u5CQAAAACcnOXhav78+erTp4+GDBmijRs3qnz58goPD9eJEyfS7b969Wq1a9dOXbp00aZNm9SiRQu1aNFC27dvd+jXoEEDHTt2zH779NNPM2NzAAAAADgpy8PV+PHj1a1bN0VGRqpMmTKaMmWKcuXKpRkzZqTb/91331WDBg3Uv39/lS5dWsOHD1fFihU1ceJEh37u7u4KDAy03/LmzZsZmwMAAADASVkarpKSkrRhwwaFhYXZ21xcXBQWFqY1a9ake581a9Y49Jek8PDwNP2XL1+ugIAAlSxZUj169NDp06dvWkdiYqLi4+MdbgAAAABwNywNV6dOnVJycrLy58/v0J4/f37FxcWle5+4uLjb9m/QoIHmzJmjmJgYjRkzRr/++qsaNmyo5OTkdNcZHR0tX19f+61w4cL/ccsAAAAAOJtseRHhtm3b2v9ftmxZlStXTkWLFtXy5ctVr169NP0HDhyoPn362H+Oj48nYAEAAAC4K5buufLz85Orq6uOHz/u0H78+HEFBgame5/AwMC76i9JoaGh8vPz0759+9Jd7u7uLh8fH4cbAAAAANwNS/dcubm5qVKlSoqJiVGLFi0kSSkpKYqJidHLL7+c7n2qVaummJgY9e7d2972888/q1q1ajd9nMOHD+v06dMqUKCAmeUDAGCp4AE/WF2CqQ6Obmx1CQDwn1g+W2CfPn00bdo0zZ49W7t27VKPHj108eJFRUZGSpIiIiI0cOBAe/9evXpp0aJFGjdunHbv3q2hQ4dq/fr19jCWkJCg/v376/fff9fBgwcVExOj5s2bq1ixYgoPD7dkGwEAAABkf5afc9WmTRudPHlSgwcPVlxcnCpUqKBFixbZJ62IjY2Vi8u/GbB69er65JNP9Oabb+r1119X8eLFtXDhQj388MOSJFdXV23dulWzZ8/WuXPnFBQUpPr162v48OFyd3e3ZBsBAAAAZH+WhytJevnll296GODy5cvTtD3zzDN65pln0u3v6empxYsXm1keAAAAANyW5YcFAgAAAEB2QLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwARZ4jpXAAAAGRE84AerSzDVwdGNrS4BwH/AnisAAAAAMAHhCgAAAABMQLgCAAAAABNwzhUAAMB9jPPOgKyDPVcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJclhdAAAAAPBfBA/4weoSTHdwdGOrS0AGsOcKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMkMPqAgAAAAD8d8EDfrC6BFMdHN3Y6hLuGnuuAAAAAMAEhCsAAAAAMAHhCgAAAABMkCXC1QcffKDg4GB5eHioatWqWrdu3S37f/HFFypVqpQ8PDxUtmxZ/fjjjw7LDcPQ4MGDVaBAAXl6eiosLEx79+69l5sAAAAAwMlZHq7mz5+vPn36aMiQIdq4caPKly+v8PBwnThxIt3+q1evVrt27dSlSxdt2rRJLVq0UIsWLbR9+3Z7n7Fjx+q9997TlClTtHbtWnl5eSk8PFyXL1/OrM0CAAAA4GQsD1fjx49Xt27dFBkZqTJlymjKlCnKlSuXZsyYkW7/d999Vw0aNFD//v1VunRpDR8+XBUrVtTEiRMlXdtrNWHCBL355ptq3ry5ypUrpzlz5ujo0aNauHBhJm4ZAAAAAGdi6VTsSUlJ2rBhgwYOHGhvc3FxUVhYmNasWZPufdasWaM+ffo4tIWHh9uD04EDBxQXF6ewsDD7cl9fX1WtWlVr1qxR27Zt06wzMTFRiYmJ9p/Pnz8vSYqPj8/wtpkpJfGS1SWYKiPjmt3GQGIcJMZAYgwkxiAV48AYSIyBxBikYhyyzmfx1DoMw7htX0vD1alTp5ScnKz8+fM7tOfPn1+7d+9O9z5xcXHp9o+Li7MvT227WZ8bRUdHKyoqKk174cKF72xDcFd8J1hdQdbAODAGEmMgMQapGAfGQGIMJMYgFeOQ9cbgwoUL8vX1vWUfLiIsaeDAgQ57w1JSUnTmzBk98MADstlsFlaWeeLj41W4cGH9/fff8vHxsbocyzAOjIHEGEiMQSrGgTGQGAOJMZAYg1TOOA6GYejChQsKCgq6bV9Lw5Wfn59cXV11/Phxh/bjx48rMDAw3fsEBgbesn/qv8ePH1eBAgUc+lSoUCHddbq7u8vd3d2hLU+ePHezKdmGj4+P0/yh3ArjwBhIjIHEGKRiHBgDiTGQGAOJMUjlbONwuz1WqSyd0MLNzU2VKlVSTEyMvS0lJUUxMTGqVq1auvepVq2aQ39J+vnnn+39Q0JCFBgY6NAnPj5ea9euvek6AQAAAOC/svywwD59+qhjx46qXLmyqlSpogkTJujixYuKjIyUJEVERKhgwYKKjo6WJPXq1Uu1a9fWuHHj1LhxY3322Wdav369PvzwQ0mSzWZT7969NWLECBUvXlwhISEaNGiQgoKC1KJFC6s2EwAAAEA2Z3m4atOmjU6ePKnBgwcrLi5OFSpU0KJFi+wTUsTGxsrF5d8dbNWrV9cnn3yiN998U6+//rqKFy+uhQsX6uGHH7b3efXVV3Xx4kV1795d586dU82aNbVo0SJ5eHhk+vbdL9zd3TVkyJA0h0c6G8aBMZAYA4kxSMU4MAYSYyAxBhJjkIpxuDWbcSdzCgIAAAAAbsnyiwgDAAAAQHZAuAIAAAAAExCuAAAAAMAEhCukwWl4AAAAwN0jXDmpt956K9325ORktW/fPpOrQWarWLGizp49K0kaNmyYLl26ZHFFAAAA9z/ClZN66623NH36dIe25ORktW3bVps3b7amKAvEx8ffdNm+ffsysZLMtWvXLl28eFGSFBUVpYSEBIsrAoCsb8uWLXJ1dbW6jHvuxx9/VNeuXfXqq69q9+7dDsvOnj2runXrWlQZkPVZfp0rWOOHH35Q/fr15evrq1atWunq1atq3bq1du/erWXLllldXqZp3LixfvnllzTXatizZ4/q1aunw4cPW1TZvVWhQgVFRkaqZs2aMgxDb7/9try9vdPtO3jw4EyuzjpXr17V8uXLtX//frVv3165c+fW0aNH5ePjc9PxyW5efvllDRs2TPny5bO6FEsYhqF9+/YpKSlJJUuWVI4czvM2uXXr1jvuW65cuXtYSdaW3Q+d/+STTxQREaEGDRpoz549ev/99/XRRx+pQ4cOkqSkpCT9+uuvFleZuc6dO6d169bpxIkTSklJcVgWERFhUVWZ6+LFi/Ly8rK6jPsC17lyYkuXLlWLFi308ccfa/r06dq3b5+WLl1qv4CzM2jYsKFsNpu+/fZb+4eoXbt2qW7dumrdurXeffddiyu8N/bs2aMhQ4Zo//792rhxo8qUKZPuh0ibzaaNGzdaUGHmO3TokBo0aKDY2FglJibqzz//VGhoqHr16qXExERNmTLF6hLvmcOHD6tQoUKSpHz58mnz5s168MEHVbZsWf34448qXLiwxRVmjgMHDqhZs2bauXOnJKlQoUL68ssvVblyZYsryxwuLi6y2WwyDEM2m+2WfZOTkzOpqszVsmXLWy4/f/68li9fnm23X5IeeeQRRUZGqmfPnpKkzz//XJ07d9a7776rLl266Pjx4woKCsrWY3C97777Th06dFBCQoJ8fHwc/jZsNpvOnDljYXWZx9vbW61bt1bnzp1Vs2ZNq8vJ0ghXTm7hwoV65plnVLp0aS1dulR+fn5Wl5Sp/vnnH4WFhalQoUL67LPPtGPHDtWrV08dOnTQ+PHjrS4vU7i4uCguLk4BAQFWl2KpFi1aKHfu3Jo+fboeeOABbdmyRaGhoVq+fLm6deumvXv3Wl3iPePt7a0HHnhANWrU0MKFC/Xzzz+rRo0ayp07t30cnEGrVq20Y8cODR48WB4eHnr77bd1+fJlbdiwwerSMsWhQ4fs/9+0aZP69eun/v37q1q1apKkNWvWaNy4cRo7dqxatGhhUZX3Vs6cOfXkk0/e9EvGM2fO6Pvvv8/WwcLb21vbtm1TSEiIvW3ZsmVq1qyZ3nrrLT311FNOFa5KlCihRo0aadSoUcqVK5fV5Vhm4cKFmjVrln788UcFBwerc+fOioiIUFBQkNWlZTmEKydys2/kfv/9dxUrVswhWH311VeZVZblzp07pzp16qh48eJasWKFIiIibjrhB7KvBx54QKtXr1bJkiUdQsXBgwdVpkyZbD3px9WrV7Vx40b99ttveuONN+Tu7q78+fPr4MGDevfdd9WyZUun2KMdGBioBQsW2L+VPXbsmAoVKqT4+HinOxymSpUqGjp0qBo1auTQ/uOPP2rQoEHZNnCWK1dOvXr1UpcuXdJdvnnzZlWqVClbB4ugoCB99dVXeuyxxxzaf/31VzVp0kS9evVSdHR0th6D63l5eWnbtm1O8yXT7Zw8eVJz587VrFmztGvXLoWHh6tz585q1qyZUx1GfStMaOFEfH19072Fh4eraNGiDm3ZWXx8vMPNxcVF8+fP19q1a/X0009r0KBB9mXOYu7cuapRo4aCgoLs316/8847+uabbyyuLPOkpKSk+2Hh8OHDyp07twUVZZ4rV66oSpUq6tu3rzw9PbVp0ybNnDlTrq6umjFjhkJCQlSyZEmry7znTpw4oeLFi9t/LlCggDw9PXXixAkLq7LGjXsuUoWEhNgPm8yOKlWqdMtDod3d3fXggw9mYkWZr0qVKvrpp5/StNeuXVvfffedJkyYkPlFWSg8PFzr16+3uowsw9/fX3369NHWrVs1fvx4/fLLL2rVqpWCgoI0ePDgbP1F5J0iYjqRmTNnWl1ClpAnT550zycwDENTpkzR1KlT7eccOMM3c5MnT9bgwYPVu3dvjRw50r7NefPm1YQJE9S8eXOLK8wc9evX14QJE/Thhx9KunYsfUJCgoYMGZLm2/vsJk+ePKpQoYJq1KihpKQk/fPPP6pRo4Zy5Mih+fPnq2DBgvrjjz+sLvOeS/2de3p62ttcXFx04cIFhy9bfHx8rCgvU5UuXVrR0dH66KOP5ObmJunaRAbR0dEqXbq0xdXdO1OmTLnl637p0qV14MCBTKwo873yyitavXp1usvq1Kmj7777TnPmzMnkqqzTuHFj9e/fXzt37lTZsmWVM2dOh+XNmjWzqDJrHD9+XLNnz9asWbN06NAhtWrVSl26dNHhw4c1ZswY/f7771qyZInVZVqKwwKdVN26dfXVV18pT548Du3x8fFq0aKFli5dak1hmeBuZjmqXbv2PawkayhTpoxGjRplP+co9XC47du3q06dOjp16pTVJWaKw4cPKzw8XIZhaO/evapcubL27t0rPz8/rVixIlufk3bq1CmtWbNGq1ev1vjx42Wz2fToo49q3bp1mjdvnpo1a2b/gJ2dpU7ocL3rJ3dwpi9d1q1bp6ZNm8owDPvMgFu3bpXNZtN3332nKlWqWFwhkDlcXG5+kJezvB5I104XmTlzphYvXqwyZcqoa9euevbZZx0+R+7fv1+lS5dWUlKSdYVmAYQrJ3WzSQxOnDihggUL6sqVKxZVhszm6emp3bt3q0iRIg7hau/evSpXrpz++ecfq0vMNFevXtX8+fO1ZcsWJSQkqGLFiurQoYPDnozsLm/evFqxYoV27dqliIgIBQYG6vjx46pSpUq2n375TrfPGb50ka5NvTxv3jz7dY5Kly6t9u3bO935ZwCunVrSrl07denSRY8++mi6ff755x+NHTtWQ4YMyeTqshYOC3Qy11/HZOfOnYqLi7P/nJycrEWLFqlgwYJWlGaJmTNnytvbW88884xD+xdffKFLly6pY8eOFlWWeUJCQrR582YVKVLEoX3RokXZ+vCf9OTIkUMdOnSwX8/FWfn6+qp169bq0qWLli5dqly5cmX7YCVdm4Ia//Ly8lL37t2tLgOAxa5evaro6Gg9/fTTt5zcyNPT0+mDlSTJgFOx2WyGi4uL4eLiYthstjS3XLlyGR999JHVZWaa4sWLG0uXLk3Tvnz5cqNEiRIWVJT5pk2bZhQsWND47LPPDC8vL+PTTz81RowYYf+/s5g1a5bx/fff23/u37+/4evra1SrVs04ePCghZVlrtjYWCM5OdkwDMN46KGHjNjYWIsryjzXvz7e6uYs5syZY9SoUcMoUKCA/W9g/PjxxsKFCy2uDMhcy5cvN5o0aWIULVrUKFq0qNG0aVNjxYoVVpeVqTw9PZ3qvfC/YM+Vkzlw4IAMw1BoaKjWrVsnf39/+zI3NzcFBATI1dXVwgozV2xsbLozYhUpUkSxsbEWVJT5unbtKk9PT7355pu6dOmS2rdvr6CgIL377rtq27at1eVlmlGjRmny5MmSrl3PZ+LEiZowYYK+//57vfLKK05zeYLrLxi8fft2CyvJfMuWLbP/3zAMNWrUSB999JFT7c1Pdf1ENyNGjHDaiW6Ajz/+WJGRkWrZsqX9wsqrVq1SvXr1NGvWLLVv397iCjNHlSpVtGnTpjRHuSAtzrmCg127dmn69Ol6++23rS4lUzz44IOaOHFimtl+vvnmG7300ks6fPiwRZVZ49KlS0pISMjWkzfcTK5cubR79249+OCDeu2113Ts2DHNmTNHO3bsUJ06dXTy5EmrS0Qmc7aLKF+PiW6u2bdvn/bv36/HH39cnp6eDhOcOAtnH4PSpUure/fueuWVVxzax48fr2nTpmnXrl0WVZa5Pv/8cw0cOFCvvPKKKlWqlObcy9SJb8B1rqBrJy1Pnz5d1atX10MPPaRFixZZXVKmadeunXr27Klly5YpOTlZycnJWrp0qXr16uVUe21OnTql9evXa9euXU615/J63t7eOn36tCRpyZIlevLJJyVJHh4eTjWpByBdO8ohvXPQ3N3ddfHiRQsqylynT59WWFiYSpQooUaNGunYsWOSpC5duqhv374WV5c5GINr/vrrLzVt2jRNe7NmzbL9tPzXa9u2rQ4cOKCePXuqRo0aqlChgh555BH7v/gX4cqJrVq1Sp07d1b+/PnVvXt3Va9eXTt37nSqQ4GGDx+uqlWrql69evL09JSnp6fq16+vunXratSoUVaXd8/t2LFDjz/+uPLnz6+qVauqSpUqCggIUN26dbVnzx6ry8tUTz75pLp27aquXbvqzz//tF/baseOHQoODra2OCCTpU50cyNnmejmlVdeUY4cORQbG6tcuXLZ29u0aeM0X0AyBtcULlxYMTExadp/+eUXh8Oos7sDBw6kuf3111/2f/EvzrlyMidOnNCsWbM0Y8YMnT9/Xu3atdPy5ctVrVo1de7cWaVKlbK6xEzl5uam+fPna/jw4dqyZYs8PT1VtmxZpzimOC4uTrVr15a/v7/Gjx+vUqVKyTAM7dy5U9OmTVOtWrW0fft2pzlE8IMPPtCbb76pv//+W19++aUeeOABSdKGDRvUrl07i6uDVZzp8Kfr9enTRy+99JIuX74swzC0bt06ffrpp/YLC2d3S5Ys0eLFi1WoUCGH9uLFi+vQoUMWVZW5GINr+vbtq549e2rz5s2qXr26pGtfTs+aNUvvvvuuxdVlHmf4XGQWwpWTKVKkiFq1aqV3331XTz755C0vjudMSpQooRIlSlhdRqZ65513VKRIEa1atUoeHh729gYNGqhHjx6qWbOm3nnnHUVHR1tYZebJkyePJk6cmKY9KirKgmpghZYtWzr8fPnyZb3wwgtpzi1whslNnH2im4sXLzrsrUl15swZubu7W1BR5mMMrunRo4cCAwM1btw4ff7555KunYc1f/58p5zYZefOnYqNjU1zoeAbz113Zkxo4WRKlSqlxMREtW/fXs8995x9T1XOnDm1ZcsWlSlTxuIKM9/hw4f17bffpvtiMX78eIuquvcqVqyoAQMGqHXr1uku/+yzzzR27Fht3Lgxkyuzzrlz57Ru3TqdOHFCKSkp9nabzabnnnvOwsqQGSIjI++o38yZM+9xJVmLM05006hRI1WqVEnDhw9X7ty5tXXrVhUpUkRt27ZVSkqKFixYYHWJ9xxjgOv99ddfeuqpp7Rt2zbZbDalxofUvfupM4qCPVdOZ/fu3Vq1apWmT5+uRx99VCVKlNCzzz4ryTkPf4mJiVGzZs0UGhqq3bt36+GHH9bBgwdlGIYqVqxodXn31F9//XXLbaxcubJTHUf93XffqUOHDkpISJCPj4/D3wPhyjk4W2i6natXr2r58uXav3+/fbrpo0ePysfHR97e3hZXd2+NHTtW9erV0/r165WUlKRXX31VO3bs0JkzZ7Rq1Sqry8sUjAGu16tXL4WEhCgmJkYhISFat26dTp8+rb59+zrNDNN3ij1XTiwhIUGffvqpZs6cqd9//121a9dW+/bt1aJFC4frX2VnVapUUcOGDRUVFWWfbjggIEAdOnSwHx6XXbm6uurYsWM3/Tb6+PHjKliwoK5evZrJlVkjdUasUaNGpXsoDOBMDh06pAYNGig2NlaJiYn6888/FRoaql69eikxMVFTpkyxusR77vz585o4caK2bNmihIQEVaxYUS+99JIKFChgdWmZxlnHIF++fPrzzz/l5+envHnz3vLL5zNnzmRiZdbx8/PT0qVLVa5cOfn6+mrdunUqWbKkli5dqr59+2rTpk1Wl5hlEK4g6d/rW82dO1dnzpzRlStXrC4pU+TOnVubN29W0aJFlTdvXq1cuVIPPfSQtmzZoubNm+vgwYNWl3jPuLq66s8//7xpkD5+/LhKlSrlNLv6vby8tG3bNqe8phFwo9TrW02fPl0PPPCA/TpXy5cvV7du3bR3716rSwTumdmzZ6tt27Zyd3fXrFmzbhmuOnbsmImVWSdv3rzauHGjQkJCVLRoUX300Ud64okntH//fpUtW1aXLl2yusQsg8MCIenayZlvv/22Ro8erW+//dbqcjKNl5eX/TyrAgUKaP/+/XrooYckKdtfJNMwjFtO4uFsF4oMDw/X+vXrCVeApN9++02rV6+Wm5ubQ3twcLCOHDliUVWZ6+zZs5o+fbr9IrFlypRRZGSk8uXLZ3FlmcdZx+D6wNSpUyfrCslCHn74YW3ZskUhISGqWrWqxo4dKzc3N3344Ye8b96AcAUHOXLkSDNjVnb22GOPaeXKlSpdurQaNWqkvn37atu2bfrqq6/02GOPWV3ePbVs2TKrS8hSGjdurP79+2vnzp0qW7ascubM6bCcmZDgTFJSUtLda3348GHlzp3bgooy14oVK9S0aVP5+vqqcuXKkqT33ntPw4YN03fffafHH3/c4grvPcbgmpsdQn/69GkFBAQ4zdEdb775pv0C4sOGDVOTJk1Uq1YtPfDAA5o/f77F1WUtHBYIp/bXX38pISFB5cqV08WLF9W3b1+tXr1axYsX1/jx47mugxO51WUJbDab07yBAtK1C8X6+vrqww8/tM8U5+/vr+bNm+vBBx/M9pN/lC1bVtWqVdPkyZPl6uoq6dpsaC+++KJWr16tbdu2WVzhvccYXOPi4qK4uLg04ero0aMqWrSo/vnnH4sqs96ZM2due06aMyJcAQAAB4cPH1Z4eLgMw9DevXtVuXJl7d27V35+flqxYkW2n5bd09NTmzdvVsmSJR3a9+zZowoVKjjFB2pnH4P33ntPkvTKK69o+PDhDjNkJicna8WKFTp48CATOSANDguEUwsNDdUff/yhBx54wKH93LlzqlixolNNRY5/Xb582eHCyoCzKVSokLZs2aLPPvtMW7duVUJCgrp06aIOHTrI09PT6vLuuYoVK2rXrl1pgsWuXbtUvnx5i6rKXM4+Bu+8846ka+cfT5kyxb73TpLc3NwUHByc7WfNvJvTRJzh4up3inAFp3bw4MF0D/dKTEx0mpO2cU1ycrJGjRqlKVOm6Pjx4/appwcNGqTg4GB16dLF6hKBTJUjRw77dRCdTc+ePdWrVy/t27fPfv7t77//rg8++ECjR4/W1q1b7X3LlStnVZn3lLOPwYEDByRJTzzxhL766ivlzZvX4ooyn6+vr/3/hmHo66+/djgHb8OGDTp37pxTnat/JzgsEA4mTZqkU6dOafDgwVaXck+lzojYokULzZ492+EFJDk5WTExMfr555+1Z88eq0pEJhs2bJhmz56tYcOGqVu3btq+fbtCQ0M1f/58TZgwQWvWrLG6RCBT7dmzR++//759prjSpUvr5ZdfVqlSpSyu7N671TmY0rXzMFNnVM2u52MyBrjea6+9pjNnzjjsxUs9B8/Hx0dvvfWWxRVmHYQrOKhXr54OHDiQ7Q+Hu9WbRs6cORUcHKxx48apSZMmmVgVrFSsWDFNnTpV9erVs19QOjQ0VLt371a1atV09uxZq0sEMs2XX36ptm3bqnLlyqpWrZqka3st/vjjD3322Wd6+umnLa7w3jp06NAd982uEx8xBv86fPiwvv32W8XGxtov35Jq/PjxFlWVufz9/bVy5cp0z8GrXr26Tp8+bVFlWQ+HBcJBTEyM1SVkipSUFElSSEiI/vjjD/n5+VlcUdbjLHsxUx05ckTFihVL056SkuI0F9UGUr366qsaOHCghg0b5tA+ZMgQvfrqq9k+XPn5+cnLy8vqMizFGFwTExOjZs2a2b9se/jhh3Xw4EEZhqGKFStaXV6muXr1qnbv3p0mXO3evdv+mQrX3HqfL5DNRUVFpXvNlqSkJM2ZM8eCirKOL7/8UrNmzbK6jExTpkwZ/fbbb2naFyxYoEceecSCigDrHDt2TBEREWnan332WR07dsyCijJX/vz51blzZ61cudLqUizDGFwzcOBA9evXT9u2bZOHh4e+/PJL/f3336pdu7aeeeYZq8vLNJGRkerSpYvGjx+vlStXauXKlRo3bpy6du2qyMhIq8vLWgw4rd27dxsvvfSSUbduXaNu3brGSy+9ZOzatcvqsjKVi4uLcfz48TTtp06dMlxcXCyoCFZZuHCh4evra4wePdrIlSuX8dZbbxldu3Y13NzcjCVLllhdHpCpGjZsaMyYMSNN+4wZM4z69etbUFHm+vrrr43mzZsbOXPmNIoXL25ER0cbR44csbqsTMUYXOPt7W3s27fPMAzDyJMnj7F9+3bDMAxj8+bNRpEiRSysLHMlJycbY8aMMYKCggybzWbYbDYjKCjIGDNmjHH16lWry8tSOOfKSTn78fSpXFxcdPz4cfn7+zu0b9myRU888YTOnDljUWWwwm+//aZhw4Zpy5YtSkhIUMWKFTV48GDVr1/f6tKATDVlyhQNHjxYrVu3dpgp7osvvlBUVJSCgoLsfZs1a2ZVmffcyZMnNXfuXM2aNUu7du1SeHi4OnfurGbNmilHDuc4s8LZxyAwMFDLli1T6dKlVaZMGY0ePVrNmjXTli1bVKNGDSUkJFhdYqaLj4+XJPn4+FhcSdZEuHJSRYsWVYcOHdI9nv7jjz/W/v37LaosczzyyCOy2WzasmWLHnroIYc3iOTkZB04cEANGjTQ559/bmGVmcdZZwV777331L17d3l4eCg2NlaFCxfmSvOAbj9TXCpnminu/fffV//+/ZWUlCQ/Pz+98MILGjBggHLlymV1aZnGGcegRYsWaty4sbp166Z+/frpm2++UadOnezTs//yyy9Wl4gshnDlpHLlyqWtW7emOYF/7969Kl++vC5dumRRZZkjKirK/m/fvn0drryeenHAhx9+WGXLlrWqxEzjzHsxc+TIoaNHjyogIECurq46duyYAgICrC4LQBZx/PhxzZ49W7NmzdKhQ4f01FNPqUuXLjp8+LDGjBmjoKAgLVmyxOoy7ylnH4O//vpLCQkJKleunC5evKi+fftq9erVKl68uMaPH5/tZ0pMdfz4cfXr108xMTE6ceKEbowPzvIly53I/vtzka46derot99+SxOuVq5cqVq1allUVeYZMmSIJCk4OFht2rSRh4eHJOnChQv69NNP9c4772jDhg1O8WLhzLOCBQUF6csvv1SjRo1kGIYOHz6sy5cvp9v3wQcfzOTqgMy3Zs0anT592uEyFHPmzNGQIUN08eJFtWjRQu+//77c3d0trPLeGTZsmPr166dFixZp5syZWrx4scqUKaMXX3xRzz77rPLkyWPvW716dZUuXdq6Yu8RxuBfycnJOnz4sP0iyV5eXpoyZYrFVVmjU6dOio2N1aBBg1SgQAGO8rgF9lw5KY6nd7RixQpNnz5dX375pYKCgtSyZUs9/fTTevTRR60u7Z5z5r2YH374of73v//p6tWrN+1jcJFMOJGGDRuqTp06eu211yRJ27ZtU8WKFdWpUyeVLl1ab731lp5//nkNHTrU2kLvkdQ92MWLF1fbtm3VtWvXm74P/PPPPxo7dqz9y7rsgjFw5OHhoV27dikkJMTqUiyVO3du/fbbb6pQoYLVpWR5hCsnxfH0UlxcnGbNmqXp06crPj5erVu31pQpU7RlyxaVKVPG6vIyTaNGjfTMM8+kmUp15syZ+uyzz7R48WKLKsscFy5c0KFDh1SuXDn98ssveuCBB9LtV758+UyuDMh8BQoU0HfffafKlStLkt544w39+uuv9um4v/jiCw0ZMkQ7d+60ssx7xsXFRXFxcfL29s7W5xHdCmPgqHLlyhozZozq1atndSmWKlOmjObNm8elSe4AhwU6KWe/4FvTpk21YsUKNW7cWBMmTFCDBg3k6urqlLv7mzVrptdee00bNmxIdy/mt99+69A3u8mdO7cefvhhzZw5UzVq1Mi2hzsBd+Ls2bPKnz+//edff/1VDRs2tP/86KOP6u+//7aitExjs9mcPlQwBv8aMWKE+vXrp+HDh6tSpUppLqzsLDPmTZgwQQMGDNDUqVMVHBxsdTlZGnuu4JRy5Mihnj17qkePHipevLi9PWfOnE6354q9mP86d+6cFixYoP3796t///7Kly+fNm7cqPz586tgwYJWlwfcc0WKFNHcuXP1+OOPKykpSXny5NF3331n/9Z+27Ztql27dra9TIWLi4t8fX1vez5Jdt1+iTG40fXvkdePibMdMp43b15dunRJV69eVa5cuZQzZ06H5c7yfLgT7LlyMs5+snKqlStXavr06apUqZJKly6t5557Tm3btrW6LEs4+17MVFu3blVYWJh8fX118OBBdevWTfny5dNXX32l2NhYzZkzx+oSgXuuUaNGGjBggMaMGaOFCxcqV65cDpMcbd26VUWLFrWwwnsvKipKvr6+VpdhKcbgX8uWLbO6hCxhwoQJVpdw32DPlZNx9pOVb3Tx4kXNnz9fM2bM0Lp165ScnKzx48erc+fOyp07t9XlIRPVq1dPlSpV0tixY5U7d25t2bJFoaGhWr16tdq3b6+DBw9aXSJwz506dUotW7bUypUr5e3trdmzZ+upp56yL69Xr54ee+wxjRw50sIq753U842c+ZIMjAHw39zZ8UDINjZv3uxwUuZnn32mqlWratq0aerTp4/ee+89p7lwrnRtWtXOnTtr5cqV2rZtm/r27avRo0crICAgW55fdL01a9bo+++/d2ibM2eOQkJCFBAQoO7duysxMdGi6jLf+vXr9fzzz6dpL1iwoOLi4iyoCMh8fn5+WrFihc6ePauzZ886BCvp3wktsiuml2YM0vPbb7/p2WefVfXq1XXkyBFJ0ty5c+0TvTiL/fv3680331S7du104sQJSdJPP/2kHTt2WFxZ1kK4cjKcrHxzJUuW1NixY3X48GF9+umnVpdzzw0bNszhBXHbtm3q0qWLwsLCNGDAAH333XeKjo62sMLM5e7urvj4+DTtf/75p/z9/S2oCLCOr6+vXF1d07Tny5dPbm5uFlSUOTiYhzG40Zdffqnw8HB5enpq48aN9i8dz58/r1GjRllcXeb59ddfVbZsWa1du1ZfffWVEhISJElbtmzJ1l+4ZAThysnkz59fBw4ckCQlJSVp48aN9hnipGvTUt94kqKzcXV1VYsWLRxmycuO2IvpqFmzZho2bJiuXLki6dq3t7GxsXrttdey9YWUAfwrJSXF6Q+HYwwcjRgxQlOmTNG0adMcPh/VqFFDGzdutLCyzDVgwACNGDFCP//8s8MXLHXr1tXvv/9uYWVZD+HKyaSerPzbb79p4MCBTnmyMq5hL6ajcePGKSEhQf7+/vrnn39Uu3ZtFStWTLlz586255cAAG5tz549evzxx9O0+/r66ty5c5lfkEW2bduW5jBhSQoICNCpU6csqCjrYrZAJzN8+HC1bNlStWvXtp+sfP03EDNmzFD9+vUtrBCZJXUvZuHChe17MaOiouzLnW0vpq+vr37++WetWrVKW7ZsUUJCgipWrKiwsDCrSwMAWCQwMFD79u1Lc22nlStXKjQ01JqiLJAnTx4dO3ZMISEhDu2bNm3iUiU3IFw5mdSTlc+fPy9vb+80x9R/8cUX8vb2tqg6ZCamXP5XSkqKZs2apa+++koHDx6UzWZTSEiIAgMD7dcyAQA4n27duqlXr16aMWOGbDabjh49qjVr1qhfv34aNGiQ1eVlmrZt2+q1117TF198IZvNppSUFK1atUr9+vVTRESE1eVlKUzFDjgpZ59yOZVhGGratKl+/PFHlS9fXqVKlZJhGNq1a5e2bdumZs2aaeHChVaXCQCwgGEYGjVqlKKjo3Xp0iVJ1yZA6tevn4YPH25xdZknKSlJL730kmbNmqXk5GTlyJFDycnJat++vWbNmpXuBDjOinAFOLmb7cU8c+aMvL29s/XMYJI0c+ZM9erVS998842eeOIJh2VLly5VixYtNHHiRL6ZAwAnlpSUpH379ikhIUFlypRx2qN8YmNjtX37diUkJOiRRx5R8eLFrS4pyyFcAXBq9evXV926dTVgwIB0l48aNUq//vqrFi9enMmVAQCA+w3hCoBTCwwM1KJFi1ShQoV0l2/atEkNGzbkQsIA4IQuXryo0aNHKyYmRidOnFBKSorD8r/++suiyjJXnz590m232Wzy8PBQsWLF1Lx5c+XLly+TK8t6CFcAnJqbm5sOHTqkAgUKpLv86NGjCgkJsV84EgDgPNq1a6dff/1Vzz33nAoUKJBmgqNevXpZVFnmeuKJJ7Rx40YlJyerZMmSkqQ///xTrq6uKlWqlPbs2SObzaaVK1eqTJkyFldrLWYLBODUUk/MvRlXV1ddvXo1EysCAGQVP/30k3744QfVqFHD6lIslbpXaubMmfLx8ZF07Zztrl27qmbNmurWrZvat2+vV155xekPo2fPFQCn5uLiooYNG8rd3T3d5YmJiVq0aJGSk5MzuTIAgNVCQkL0448/qnTp0laXYqmCBQvq559/TrNXaseOHapfv76OHDmijRs3qn79+k5/UWEXqwsAACt17NhRAQEB8vX1TfcWEBDATIEA4KSGDx+uwYMH26dhd1bnz5/XiRMn0rSfPHlS8fHxkq5daDgpKSmzS8tyOCwQgFObOXOm1SUAALKocePGaf/+/cqfP7+Cg4OVM2dOh+UbN260qLLM1bx5c3Xu3Fnjxo3To48+Kkn6448/1K9fP7Vo0UKStG7dOpUoUcLCKrMGwhUAAACQjtTg4OymTp2qV155RW3btrWfh5wjRw517NhR77zzjiSpVKlS+uijj6wsM0vgnCsAAAAAt5WQkGCffj40NNRpL6Z8K5xzBQAAANzEuXPn9NFHH2ngwIE6c+aMpGuHAx45csTiyjJfXFycjh07puLFi8vb21vso0mLPVcAAABAOrZu3aqwsDD5+vrq4MGD2rNnj0JDQ/Xmm28qNjZWc+bMsbrETHH69Gm1bt1ay5Ytk81m0969exUaGqrOnTsrb968GjdunNUlZhnsuQIAAADS0adPH3Xq1El79+6Vh4eHvb1Ro0ZasWKFhZVlrldeeUU5c+ZUbGyscuXKZW9v06aNFi1aZGFlWQ8TWgAAAADp+OOPPzR16tQ07QULFlRcXJwFFVljyZIlWrx4sQoVKuTQXrx4cR06dMiiqrIm9lwBAAAA6XB3d7dfx+l6f/75p/z9/S2oyBoXL1502GOV6syZM3J3d7egoqyLcAUAAACko1mzZho2bJiuXLkiSbLZbIqNjdVrr72mp59+2uLqMk+tWrUczi+z2WxKSUnR2LFjVadOHesKy4KY0AIAAABIx/nz59WqVSutX79eFy5cUFBQkOLi4vTYY4/pp59+kpeXl9UlZort27erXr16qlixopYuXapmzZppx44dOnPmjFatWqWiRYtaXWKWQbgCAAAAbmHVqlXasmWLEhISVLFiRYWFhVldUqY7f/68Jk6c6DAO3bt314gRI/Thhx9aXV6WQbgCAAAArvPPP/8oJiZGTZo0kSQNHDhQiYmJ9uU5cuTQsGHDHGYQdEZbtmxRxYoVlZycbHUpWQazBQIAAADXmT17tn744Qd7uJo4caIeeugheXp6SpJ2796tAgUK6JVXXrGyTGRBTGgBAAAAXGfevHnq3r27Q9snn3yiZcuWadmyZXrrrbf0+eefW1QdsjLCFQAAAHCdffv2qWzZsvafPTw85OLy78fmKlWqaOfOnVaUhiyOwwIBAACA65w7d87hHKuTJ086LE9JSXFYnl21bNnylsvPnTuXOYXcRwhXAAAAwHUKFSqk7du3q2TJkuku37p1qwoVKpTJVWU+X1/f2y6PiIjIpGruD8wWCAAAAFynV69e+uWXX7Rhw4Y0MwL+888/qly5ssLCwvTuu+9aVCGyKsIVAAAAcJ3jx4+rQoUKcnNz08svv6wSJUpIkvbs2aOJEyfq6tWr2rRpk/Lnz29xpchqCFcAAADADQ4cOKAePXro559/VurHZZvNpieffFKTJk1SaGioxRUiKyJcAQAAADdx5swZ7du3T5JUrFgx5cuXz+KKkJURrgAAAADABFznCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAcN/q1KmTbDZbmlvqzF7/xaxZs5QnT57/XiQAwGnksLoAAAD+iwYNGmjmzJkObf7+/hZVk74rV64oZ86cVpcBALjH2HMFALivubu7KzAw0OHm6uqqb775RhUrVpSHh4dCQ0MVFRWlq1ev2u83fvx4lS1bVl5eXipcuLBefPFFJSQkSJKWL1+uyMhInT9/3r43bOjQoZKuXUR04cKFDjXkyZNHs2bNkiQdPHhQNptN8+fPV+3ateXh4aF58+ZJkj766COVLl1aHh4eKlWqlCZNmnTPxwcAkHnYcwUAyHZ+++03RURE6L333lOtWrW0f/9+de/eXZI0ZMgQSZKLi4vee+89hYSE6K+//tKLL76oV199VZMmTVL16tU1YcIEDR48WHv27JEkeXt731UNAwYM0Lhx4/TII4/YA9bgwYM1ceJEPfLII9q0aZO6desmLy8vdezY0dwBAABYgnAFALivff/99w7Bp2HDhjp79qwGDBhgDy2hoaEaPny4Xn31VXu46t27t/0+wcHBGjFihF544QVNmjRJbm5u8vX1lc1mU2BgYIbq6t27t1q2bGn/eciQIRo3bpy9LSQkRDt37tTUqVMJVwCQTRCuAAD3tSeeeEKTJ0+2/+zl5aVy5cpp1apVGjlypL09OTlZly9f1qVLl5QrVy798ssvio6O1u7duxUfH6+rV686LP+vKleubP//xYsXtX//fnXp0kXdunWzt1+9elW+vr7/+bEAAFkD4QoAcF/z8vJSsWLFHNoSEhIUFRXlsOcolYeHhw4ePKgmTZqoR48eGjlypPLly6eVK1eqS5cuSkpKumW4stlsMgzDoe3KlSvp1nV9PZI0bdo0Va1a1aGfq6vr7TcSAHBfIFwBALKdihUras+ePWlCV6oNGzYoJSVF48aNk4vLtbmdPv/8c4c+bm5uSk5OTnNff39/HTt2zP7z3r17denSpVvWkz9/fgUFBemvv/5Shw4d7nZzAAD3CcIVACDbGTx4sJo0aaIHH3xQrVq1kouLi7Zs2aLt27drxIgRKlasmK5cuaL3339fTZs21apVqzRlyhSHdQQHByshIUExMTEqX768cuXKpVy5cqlu3bqaOHGiqlWrpuTkZL322mt3NM16VFSUevbsKV9fXzVo0ECJiYlav369zp49qz59+tyroQAAZCKmYgcAZDvh4eH6/vvvtWTJEj366KN67LHH9M4776hIkSKSpPLly2v8+PEaM2aMHn74Yc2bN0/R0dEO66hevbpeeOEFtWnTRv7+/ho7dqwkady4cSpcuLBq1aql9u3bq1+/fnd0jlbXrl310UcfaebMmSpbtqxq166tWbNmKSQkxPwBAABYwmbceOA4AAAAAOCusecKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwwf8BIiRBOkcw4WkAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "cm_rf = confusion_matrix(y_test, y_pred_rf)\n",
+    "plt.figure(figsize=(6, 4))\n",
+    "sns.heatmap(cm_rf, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=[\"Non\", \"Yes\"], yticklabels=[\"Non\", \"Yes\"])\n",
+    "plt.xlabel(\"Predicted\")\n",
+    "plt.ylabel(\"Actual\")\n",
+    "plt.title(f\"Confusion Matrix - {task_name} (Random Forest)\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Accuracy and Classification Report\n",
+    "accuracy_rf = accuracy_score(y_test, y_pred_rf)\n",
+    "report_rf = classification_report(y_test, y_pred_rf)\n",
+    "\n",
+    "print(f\"🔹 Model: Random Forest\")\n",
+    "print(f\"🔹 Accuracy: {accuracy_rf:.4f}\\n\")\n",
+    "print(\"🔹 Classification Report:\")\n",
+    "print(report_rf)\n",
+    "\n",
+    "# Feature Importance for Random Forest\n",
+    "feature_importances = models[\"Random Forest\"].feature_importances_\n",
+    "sorted_indices = np.argsort(feature_importances)[::-1]\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(range(len(feature_importances)), feature_importances[sorted_indices], align=\"center\")\n",
+    "plt.xticks(range(len(feature_importances)), X_test.columns[sorted_indices], rotation=90)\n",
+    "plt.xlabel(\"Feature\")\n",
+    "plt.ylabel(\"Importance\")\n",
+    "plt.title(f\"Feature Importance - {task_name} (Random Forest)\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Naive Bayes Confusion Matrix and Feature Importance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Evaluate Naïve Bayes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fit the Naïve Bayes model\n",
+    "models[\"Naïve Bayes\"].fit(X_train, y_train)\n",
+    "\n",
+    "# Predict using the fitted Naïve Bayes model\n",
+    "y_pred_nb = models[\"Naïve Bayes\"].predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🔹 Model: Naïve Bayes\n",
+      "🔹 Accuracy: 0.8063\n",
+      "\n",
+      "🔹 Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.79      0.61      0.69        56\n",
+      "           1       0.81      0.91      0.86       104\n",
+      "\n",
+      "    accuracy                           0.81       160\n",
+      "   macro avg       0.80      0.76      0.77       160\n",
+      "weighted avg       0.80      0.81      0.80       160\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "cm_nb = confusion_matrix(y_test, y_pred_nb)\n",
+    "plt.figure(figsize=(6, 4))\n",
+    "sns.heatmap(cm_nb, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=[\"Non\", \"Yes\"], yticklabels=[\"Non\", \"Yes\"])\n",
+    "plt.xlabel(\"Predicted\")\n",
+    "plt.ylabel(\"Actual\")\n",
+    "plt.title(f\"Confusion Matrix - {task_name} (Naïve Bayes)\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Accuracy and Classification Report\n",
+    "accuracy_nb = accuracy_score(y_test, y_pred_nb)\n",
+    "report_nb = classification_report(y_test, y_pred_nb)\n",
+    "\n",
+    "print(f\"🔹 Model: Naïve Bayes\")\n",
+    "print(f\"🔹 Accuracy: {accuracy_nb:.4f}\\n\")\n",
+    "print(\"🔹 Classification Report:\")\n",
+    "print(report_nb)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import roc_curve, auc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Assign models to variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_lr = models[\"Logistic Regression\"]\n",
+    "model_rf = models[\"Random Forest\"]\n",
+    "model_nb = models[\"Naïve Bayes\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loop through each model and plot ROC curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.figure(figsize=(6, 4))\n",
+    "\n",
+    "# Store AUC scores\n",
+    "auc_scores = {}\n",
+    "\n",
+    "# Loop through each model and plot ROC curve\n",
+    "for model_name, model in models.items():\n",
+    "    if hasattr(model, \"predict_proba\"):  # Ensure model supports probability predictions\n",
+    "        y_scores = model.predict_proba(X_test)[:, 1]  # Get predicted probabilities\n",
+    "        fpr, tpr, _ = roc_curve(y_test, y_scores)\n",
+    "        roc_auc = auc(fpr, tpr)\n",
+    "        auc_scores[model_name] = roc_auc  # Store AUC score\n",
+    "        plt.plot(fpr, tpr, label=f\"{model_name} (AUC = {roc_auc:.2f})\")\n",
+    "\n",
+    "# Plot baseline\n",
+    "plt.plot([0, 1], [0, 1], linestyle=\"--\", color=\"gray\")\n",
+    "\n",
+    "plt.xlabel(\"False Positive Rate\")\n",
+    "plt.ylabel(\"True Positive Rate\")\n",
+    "plt.title(f\"ROC Curve - {task_name}\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### RESULTS AND DISCUSSION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🔹 Random Forest AUC Score: 0.9963\n",
+      "🔹 Naïve Bayes AUC Score: 0.8944\n",
+      "🔹 Logistic Regression AUC Score: 0.9902\n",
+      "\n",
+      "Based on the ROC Curve, the model with the highest AUC score is **Random Forest**, meaning it has the best overall performance in distinguishing between the two classes.\n",
+      "The Random Forest model performed the best, likely due to its ability to capture complex patterns in the data.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print AUC Scores\n",
+    "for model_name, auc_score in auc_scores.items():\n",
+    "    print(f\"🔹 {model_name} AUC Score: {auc_score:.4f}\")\n",
+    "\n",
+    "# Discussion\n",
+    "best_model = max(auc_scores, key=auc_scores.get)\n",
+    "print(f\"\\nBased on the ROC Curve, the model with the highest AUC score is **{best_model}**, meaning it has the best overall performance in distinguishing between the two classes.\")\n",
+    "\n",
+    "if auc_scores[\"Random Forest\"] > auc_scores[\"Logistic Regression\"] and auc_scores[\"Random Forest\"] > auc_scores[\"Naïve Bayes\"]:\n",
+    "    print(\"The Random Forest model performed the best, likely due to its ability to capture complex patterns in the data.\")\n",
+    "elif auc_scores[\"Logistic Regression\"] > auc_scores[\"Random Forest\"] and auc_scores[\"Logistic Regression\"] > auc_scores[\"Naïve Bayes\"]:\n",
+    "    print(\"Logistic Regression performed the best, indicating that the dataset may have a more linear decision boundary.\")\n",
+    "elif auc_scores[\"Naïve Bayes\"] > auc_scores[\"Logistic Regression\"] and auc_scores[\"Naïve Bayes\"] > auc_scores[\"Random Forest\"]:\n",
+    "    print(\"Naïve Bayes performed the best, which suggests that the dataset follows a strong assumption of independence between features.\")\n",
+    "else:\n",
+    "    print(\"The AUC scores are close, indicating that the models have similar performance.\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Precision-Recall (PR) Curve for 3 Models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import precision_recall_curve, auc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "models = {\n",
+    "    \"Logistic Regression\": model_lr,\n",
+    "    \"Random Forest\": model_rf,\n",
+    "    \"Naïve Bayes\": model_nb\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(6, 4))\n",
+    "\n",
+    "# Store AUC-PR scores\n",
+    "pr_auc_scores = {}\n",
+    "\n",
+    "# Loop through each model and plot Precision-Recall curve\n",
+    "for model_name, model in models.items():\n",
+    "    if hasattr(model, \"predict_proba\"):  # Ensure model supports probability predictions\n",
+    "        y_scores = model.predict_proba(X_test)[:, 1]  # Get predicted probabilities\n",
+    "        precision, recall, _ = precision_recall_curve(y_test, y_scores)\n",
+    "        pr_auc = auc(recall, precision)  # Calculate AUC for PR curve\n",
+    "        pr_auc_scores[model_name] = pr_auc  # Store PR AUC score\n",
+    "        plt.plot(recall, precision, marker=\".\", label=f\"{model_name} (AUC = {pr_auc:.2f})\")\n",
+    "\n",
+    "# Labels and title\n",
+    "plt.xlabel(\"Recall\")\n",
+    "plt.ylabel(\"Precision\")\n",
+    "plt.title(f\"Precision-Recall Curve - {task_name}\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### RESULTS AND DISCUSSION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "RESULTS & DISCUSSION \n",
+      "\n",
+      "🔹 Logistic Regression AUC-PR Score: 0.9937\n",
+      "🔹 Random Forest AUC-PR Score: 0.9980\n",
+      "🔹 Naïve Bayes AUC-PR Score: 0.9482\n",
+      "\n",
+      " The model with the highest AUC-PR score is **Random Forest**, meaning it is the best at handling class imbalance.\n",
+      " The Random Forest model performed the best, likely due to its ability to learn complex patterns and handle imbalanced datasets effectively.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\nRESULTS & DISCUSSION \\n\")\n",
+    "\n",
+    "# Print AUC-PR Scores\n",
+    "for model_name, pr_auc_score in pr_auc_scores.items():\n",
+    "    print(f\"🔹 {model_name} AUC-PR Score: {pr_auc_score:.4f}\")\n",
+    "\n",
+    "# Discussion\n",
+    "best_model = max(pr_auc_scores, key=pr_auc_scores.get)\n",
+    "print(f\"\\n The model with the highest AUC-PR score is **{best_model}**, meaning it is the best at handling class imbalance.\")\n",
+    "\n",
+    "if pr_auc_scores[\"Random Forest\"] > pr_auc_scores[\"Logistic Regression\"] and pr_auc_scores[\"Random Forest\"] > pr_auc_scores[\"Naïve Bayes\"]:\n",
+    "    print(\" The Random Forest model performed the best, likely due to its ability to learn complex patterns and handle imbalanced datasets effectively.\")\n",
+    "elif pr_auc_scores[\"Logistic Regression\"] > pr_auc_scores[\"Random Forest\"] and pr_auc_scores[\"Logistic Regression\"] > pr_auc_scores[\"Naïve Bayes\"]:\n",
+    "    print(\" Logistic Regression performed the best, suggesting that a linear decision boundary is effective for this dataset.\")\n",
+    "elif pr_auc_scores[\"Naïve Bayes\"] > pr_auc_scores[\"Logistic Regression\"] and pr_auc_scores[\"Naïve Bayes\"] > pr_auc_scores[\"Random Forest\"]:\n",
+    "    print(\" Naïve Bayes performed the best, which could indicate strong independence between features in the dataset.\")\n",
+    "else:\n",
+    "    print(\" The AUC-PR scores are close, meaning the models have similar performance.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Distribution of Predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import accuracy_score, classification_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "models = {\n",
+    "    \"Logistic Regression\": (model_lr, \"blue\"),\n",
+    "    \"Random Forest\": (model_rf, \"green\"),\n",
+    "    \"Naïve Bayes\": (model_nb, \"purple\")\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "==================================================\n",
+      "🔹 Model: Logistic Regression\n",
+      "==================================================\n",
+      "🔹 Accuracy: 0.9313\n",
+      "\n",
+      "🔹 Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      0.80      0.89        56\n",
+      "           1       0.90      1.00      0.95       104\n",
+      "\n",
+      "    accuracy                           0.93       160\n",
+      "   macro avg       0.95      0.90      0.92       160\n",
+      "weighted avg       0.94      0.93      0.93       160\n",
+      "\n",
+      "\n",
+      "==================================================\n",
+      "🔹 Model: Random Forest\n",
+      "==================================================\n",
+      "🔹 Accuracy: 0.9750\n",
+      "\n",
+      "🔹 Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      0.93      0.96        56\n",
+      "           1       0.96      1.00      0.98       104\n",
+      "\n",
+      "    accuracy                           0.97       160\n",
+      "   macro avg       0.98      0.96      0.97       160\n",
+      "weighted avg       0.98      0.97      0.97       160\n",
+      "\n",
+      "\n",
+      "==================================================\n",
+      "🔹 Model: Naïve Bayes\n",
+      "==================================================\n",
+      "🔹 Accuracy: 0.8063\n",
+      "\n",
+      "🔹 Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.79      0.61      0.69        56\n",
+      "           1       0.81      0.91      0.86       104\n",
+      "\n",
+      "    accuracy                           0.81       160\n",
+      "   macro avg       0.80      0.76      0.77       160\n",
+      "weighted avg       0.80      0.81      0.80       160\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 6))\n",
+    "\n",
+    "# Plot histograms for each model\n",
+    "for model_name, (model, color) in models.items():\n",
+    "    y_pred = model.predict(X_test)  # Get predictions\n",
+    "    sns.histplot(y_pred, kde=True, bins=20, color=color, label=f\"Predicted ({model_name})\", alpha=0.6)\n",
+    "\n",
+    "# Overlay actual values in red\n",
+    "sns.histplot(y_test, kde=True, bins=20, color=\"red\", label=\"Actual\", alpha=0.5)\n",
+    "\n",
+    "# Labels and title\n",
+    "plt.legend()\n",
+    "plt.xlabel(\"Class\")\n",
+    "plt.ylabel(\"Count\")\n",
+    "plt.title(f\"Prediction Distribution - {task_name}\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Print results for each model\n",
+    "for model_name, (model, _) in models.items():\n",
+    "    y_pred = model.predict(X_test)\n",
+    "    accuracy = accuracy_score(y_test, y_pred)\n",
+    "    report = classification_report(y_test, y_pred)\n",
+    "\n",
+    "    print(\"\\n\" + \"=\" * 50)\n",
+    "    print(f\"🔹 Model: {model_name}\")\n",
+    "    print(\"=\" * 50)\n",
+    "    print(f\"🔹 Accuracy: {accuracy:.4f}\\n\")\n",
+    "    print(\"🔹 Classification Report:\")\n",
+    "    print(report)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Convert to PKL format"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Models saved successfully!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import joblib\n",
+    "\n",
+    "# Save each trained model\n",
+    "joblib.dump(models[\"Logistic Regression\"], \"logistic_regression.pkl\")  # Logistic Regression\n",
+    "joblib.dump(models[\"Random Forest\"], \"random_forest.pkl\")   # Random Forest\n",
+    "joblib.dump(models[\"Naïve Bayes\"], \"naive_bayes.pkl\")  # Naïve Bayes\n",
+    "\n",
+    "print(\"Models saved successfully!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.6.1\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sklearn\n",
+    "print(sklearn.__version__)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}