{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7a3d58b3",
   "metadata": {},
   "source": [
    "## 1. Import Required Libraries (Data, Visualisation, ML, NLP, Explainability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6915caf4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ASUS\\anaconda3\\envs\\tf_env\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "# Core libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import glob\n",
    "import os\n",
    "\n",
    "# Visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Machine Learning\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.metrics import classification_report, accuracy_score, confusion_matrix, roc_auc_score\n",
    "\n",
    "# Models\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Text processing\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "# Explainability\n",
    "import shap\n",
    "\n",
    "# Sparse matrices\n",
    "from scipy.sparse import hstack, csr_matrix\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd469d51",
   "metadata": {},
   "source": [
    "##  Import Required Libraries\n",
    "\n",
    "This section imports all required libraries used throughout the project:\n",
    "\n",
    "- **Data handling:** pandas, numpy  \n",
    "- **File handling:** glob, os  \n",
    "- **Visualisation:** matplotlib, seaborn  \n",
    "- **Machine learning:** train/test split, scaling, classifiers, evaluation metrics  \n",
    "- **Text processing:** TF-IDF vectoriser  \n",
    "- **Explainability:** SHAP  \n",
    "- **Sparse matrices:** used to combine text (TF-IDF) and numeric features efficiently\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92a0b5d4",
   "metadata": {},
   "source": [
    "## 2. Load Datasets (Fitabase, Student Mental Health, Instagram)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4014d831",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitabase datasets loaded: dict_keys(['dailyActivity_merged', 'dailyCalories_merged', 'dailyIntensities_merged', 'dailySteps_merged', 'heartrate_seconds_merged', 'hourlyCalories_merged', 'hourlyIntensities_merged', 'hourlySteps_merged', 'minuteCaloriesNarrow_merged', 'minuteCaloriesWide_merged', 'minuteIntensitiesNarrow_merged', 'minuteIntensitiesWide_merged', 'minuteMETsNarrow_merged', 'minuteSleep_merged', 'minuteStepsNarrow_merged', 'minuteStepsWide_merged', 'sleepDay_merged', 'weightLogInfo_merged'])\n"
     ]
    }
   ],
   "source": [
    "fitabase_folder = r\"C:\\Users\\ASUS\\OneDrive - University of Wales Trinity Saint David\\Documents\\Dissertation Datasets\\Fitabase Data 4.12.16-5.12.16\"\n",
    "\n",
    "fitabase_files = glob.glob(os.path.join(fitabase_folder, \"*.csv\"))\n",
    "fitness_data = {}\n",
    "\n",
    "for file in fitabase_files:\n",
    "    name = os.path.splitext(os.path.basename(file))[0]\n",
    "    fitness_data[name] = pd.read_csv(file)\n",
    "\n",
    "print(\"Fitabase datasets loaded:\", fitness_data.keys())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d96d2131",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mental health dataset loaded: Index(['Age', 'Course', 'Gender', 'CGPA', 'Stress_Level', 'Depression_Score',\n",
      "       'Anxiety_Score', 'Sleep_Quality', 'Physical_Activity', 'Diet_Quality',\n",
      "       'Social_Support', 'Relationship_Status', 'Substance_Use',\n",
      "       'Counseling_Service_Use', 'Family_History', 'Chronic_Illness',\n",
      "       'Financial_Stress', 'Extracurricular_Involvement',\n",
      "       'Semester_Credit_Load', 'Residence_Type'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "mental_health_folder = r\"C:\\Users\\ASUS\\OneDrive - University of Wales Trinity Saint David\\Documents\\Dissertation Datasets\\students_mental_health_survey\"\n",
    "mental_health_file = glob.glob(os.path.join(mental_health_folder, \"*.csv\"))[0]\n",
    "mental_health_data = pd.read_csv(mental_health_file)\n",
    "\n",
    "print(\"Mental health dataset loaded:\", mental_health_data.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "58bd5bc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instagram dataset loaded with shape: Index(['Post ID', 'Post description', 'Date', 'Language',\n",
      "       'Translated Post Description', 'Sentiment', 'Hate',\n",
      "       'Stress or Anxiety'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "instagram_folder = r\"C:\\Users\\ASUS\\OneDrive - University of Wales Trinity Saint David\\Documents\\Dissertation Datasets\\Instagram Dataset Sentiment & Hate Analysis\"\n",
    "instagram_file = glob.glob(os.path.join(instagram_folder, \"*.csv\"))[0]\n",
    "instagram_data = pd.read_csv(instagram_file)\n",
    "\n",
    "print(\"Instagram dataset loaded with shape:\", instagram_data.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f0854f8",
   "metadata": {},
   "source": [
    "##  Load Datasets\n",
    "\n",
    "This section loads the three datasets used in the dissertation:\n",
    "\n",
    "1. **Fitabase dataset** (multiple CSV files)  \n",
    "   - Loaded into a dictionary so each CSV becomes an individual dataframe.\n",
    "\n",
    "2. **Student Mental Health dataset**  \n",
    "   - A single CSV file containing psychological wellbeing scores.\n",
    "\n",
    "3. **Instagram dataset**  \n",
    "   - A single CSV file containing post text + sentiment and stress/hate indicators.\n",
    "\n",
    "The purpose of this step is to ensure all datasets are successfully read before preprocessing begins.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ce1c9e1",
   "metadata": {},
   "source": [
    "## 3. Standardise Column Names (Cleaning Column Headers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "48219347",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns cleaned.\n"
     ]
    }
   ],
   "source": [
    "def clean_columns(df):\n",
    "    df.columns = (\n",
    "        df.columns\n",
    "        .str.strip()\n",
    "        .str.lower()\n",
    "        .str.replace(\" \", \"_\")\n",
    "        .str.replace(\"-\", \"_\")\n",
    "    )\n",
    "    return df\n",
    "\n",
    "fitness_data_clean = {k: clean_columns(v) for k, v in fitness_data.items()}\n",
    "mental_health_clean = clean_columns(mental_health_data)\n",
    "instagram_clean = clean_columns(instagram_data)\n",
    "print(\"Columns cleaned.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c6d7533",
   "metadata": {},
   "source": [
    "##  Standardise Column Names\n",
    "\n",
    "To avoid errors caused by inconsistent column naming (spaces, capital letters, hyphens),\n",
    "all datasets are standardised using a cleaning function.\n",
    "\n",
    "The function:\n",
    "- converts names to lowercase  \n",
    "- removes leading/trailing spaces  \n",
    "- replaces spaces/hyphens with underscores  \n",
    "\n",
    "This improves consistency and reduces future KeyErrors during feature selection."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48a64bf1",
   "metadata": {},
   "source": [
    "## 4. Fitness Dataset Preparation (Feature Selection + Target Creation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "93ce26d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitness dataset ready ✅\n",
      "Train shape: (752, 3) Test shape: (188, 3)\n"
     ]
    }
   ],
   "source": [
    "fitness_df = fitness_data_clean['dailyActivity_merged'].copy()\n",
    "features = ['totalsteps', 'calories', 'totaldistance']\n",
    "fitness_df = fitness_df[features].dropna()\n",
    "\n",
    "# Create activity level target (Low / Medium / High)\n",
    "fitness_df['activity_level'] = pd.qcut(\n",
    "    fitness_df['totalsteps'],\n",
    "    q=3,\n",
    "    labels=['Low', 'Medium', 'High']\n",
    ")\n",
    "\n",
    "X = fitness_df[features]\n",
    "y = fitness_df['activity_level']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,\n",
    "    random_state=42,\n",
    "    stratify=y)\n",
    "\n",
    "print(\"Fitness dataset ready ✅\")\n",
    "print(\"Train shape:\", X_train.shape, \"Test shape:\", X_test.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fffe7ca",
   "metadata": {},
   "source": [
    "## Fitness Dataset Preparation\n",
    "\n",
    "This section prepares the Fitabase data for machine learning:\n",
    "\n",
    "- Selects the `dailyActivity_merged` dataframe from the Fitabase dictionary.\n",
    "- Keeps only the key activity variables:\n",
    "  - `totalsteps`, `calories`, `totaldistance`\n",
    "\n",
    "### Target Variable (Activity Level)\n",
    "Since Fitabase does not include a mental health label, a **categorical target** is created:\n",
    "\n",
    "- `activity_level` is generated using quantiles (`pd.qcut`)\n",
    "- Values are grouped into:\n",
    "  - **Low**\n",
    "  - **Medium**\n",
    "  - **High**\n",
    "\n",
    "This enables classification models (Random Forest and SVM) to be trained on the activity patterns.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d162689d",
   "metadata": {},
   "source": [
    "## 5. Train/Test Split + Scaling (Fitness)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f0154826",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f71703af",
   "metadata": {},
   "source": [
    "## Train/Test Split and Feature Scaling (Fitness)\n",
    "\n",
    "The dataset is split into training and testing sets using an 80/20 split.\n",
    "\n",
    "- Stratification is used to preserve the balance of classes (Low/Medium/High).\n",
    "- StandardScaler is applied to ensure numeric features have:\n",
    "  - mean = 0  \n",
    "  - standard deviation = 1\n",
    "\n",
    "Scaling is essential for SVM because SVM is sensitive to feature magnitudes.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "add10429",
   "metadata": {},
   "source": [
    "## 6. Fitness Models (Random Forest vs SVM) + Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "56012be4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Fitness Model: Random Forest ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        High       1.00      1.00      1.00        63\n",
      "         Low       1.00      1.00      1.00        63\n",
      "      Medium       1.00      1.00      1.00        62\n",
      "\n",
      "    accuracy                           1.00       188\n",
      "   macro avg       1.00      1.00      1.00       188\n",
      "weighted avg       1.00      1.00      1.00       188\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rf_fitness = RandomForestClassifier(\n",
    "    n_estimators=200,\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "rf_fitness.fit(X_train, y_train)\n",
    "rf_preds = rf_fitness.predict(X_test)\n",
    "\n",
    "print(\"=== Fitness Model: Random Forest ===\")\n",
    "print(classification_report(y_test, rf_preds))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "57e55e0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 4))\n",
    "sns.heatmap(\n",
    "    confusion_matrix(y_test, rf_preds),\n",
    "    annot=True,\n",
    "    fmt=\"d\",\n",
    "    cmap=\"Blues\",\n",
    "    xticklabels=rf_fitness.classes_,\n",
    "    yticklabels=rf_fitness.classes_\n",
    ")\n",
    "plt.title(\"Random Forest – Fitness Confusion Matrix\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d011c6e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Fitness Model: SVM (RBF) ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        High       0.94      1.00      0.97        63\n",
      "         Low       0.97      0.98      0.98        63\n",
      "      Medium       0.98      0.90      0.94        62\n",
      "\n",
      "    accuracy                           0.96       188\n",
      "   macro avg       0.96      0.96      0.96       188\n",
      "weighted avg       0.96      0.96      0.96       188\n",
      "\n"
     ]
    }
   ],
   "source": [
    "svm_fitness = SVC(\n",
    "    kernel='rbf',\n",
    "    probability=True,\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "svm_fitness.fit(X_train_scaled, y_train)\n",
    "svm_preds = svm_fitness.predict(X_test_scaled)\n",
    "\n",
    "print(\"=== Fitness Model: SVM (RBF) ===\")\n",
    "print(classification_report(y_test, svm_preds))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bfc7ac53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Mnj0bt2/fRps2bVCpUiU8fvwYP/74I4yMjODu7v7BONatW4fPP/8cXl5eGDp0KLy8vGBtbY3k5GTs2rULGzduxJkzZ+Do6Ah/f3/s3r0bnp6emD17NmxsbPDrr7/izz//RHBwMKysrIrytioYNGgQZs2ahdmzZ8Pd3R3x8fEICQnJdQwXFxd88cUXaNiwIaytrZGQkID169fD1dUVpqamee67Vq1aGDFiBH766ScYGBjAy8sL169fx6xZs+Dg4JBr1KkqNW7c+KMtYUtLS7Ru3Rrfffed/OcqKioKa9asyTVDSv369QEAq1atgoWFBYyNjVGlSpU8u0Y/JGfUo7u7O/z9/WFgYIDNmzejdevWmDp1KpYsWaLU/qiYqHX4iQ7YvHmz6N+/v6hRo4YwNzcXRkZGwtHRUQwaNEjEx8fnu52zs3OeI/tyvDua8UPUfdN0XiO//v77b+Hs7CykUqkAIB9hl9/ovJxzTUpKUihfu3atcHFxEWZmZsLExERUq1ZNDB48WD77yKVLl0S/fv1EtWrVhImJibCyshItWrQQ4eHh8n3s3r1beHl5iYoVK4rSpUuL8uXLi44dO4qjR48W6PwzMjLE0qVLhaurq7C0tBSlSpUS9vb2onv37uLPP/9UqHvhwgXRuXNnYWVlJUqXLi0aNWokwsLCFOrkjGb87bffFMqTkpIEAIX6+b1fmZmZYurUqcLBwUGYmJgId3d3ERsbm2s04/Tp00WzZs2EtbW1kEqlomrVqmLixIkiLS0t1zHelZWVJRYuXChq1qwpjIyMRNmyZcXAgQPFrVu3FOq5u7uLevXq5XrPvL29hZOTU35vqdy7oxnzk9eIxNu3b4sePXoIa2trYWFhIT7//HNx8eLFXOcvhBBLliwRVapUEYaGhgrvb36x56zL+Zl+8+aNcHd3FzKZTCQnJyvU++677wQAsX379o+eKxU/iRAFGM5GRESkwXjNjIiItB6TGRERaT0mMyIi0npMZkREpPWYzIiISOsxmRERkdZjMiMiIq2nkzOASNpVUncI9AEZexPVHQKR1jE2zHvWlsIqyu9JceD2xyuVMJ1MZkRE9BFKTrSs6ZjMiIj0kY5dZGIyIyLSRzrWMtOx3ExERPqILTMiIn2kWw0zJjMiIr2kY92MTGZERPpIxy4yMZkREekjtsyIiEjr6VYu07WGJhER6SMmMyIifWQgKfyipDt37mDgwIGwtbWFqakpGjdujDNnzsjXCyEQEBAAe3t7mJiYwMPDA3FxccqdjtJRERGR9pMUYVHCo0eP0KpVKxgZGeGvv/5CfHw8fvjhB5QpU0ZeJzg4GIsWLUJISAhOnToFOzs7tGvXDk+fPi3wcXjNjIhIHxVhAEhmZiYyMzMVyqRSKaRSaa66CxcuhIODA8LCwuRllStXlv9fCIElS5bAz88P3bt3BwBERERAJpNhw4YNGDlyZIFiYsuMiEgfFaFlFhQUBCsrK4UlKCgoz8Ps3LkTzZo1Q69evVC+fHk4Oztj9erV8vVJSUlISUlB+/bt5WVSqRTu7u6Ijo4u8OkwmRER6aMiXDPz9fVFenq6wuLr65vnYa5du4bQ0FDUqFED+/btw6hRozBu3DisW7cOAJCSkgIAkMlkCtvJZDL5uoJgNyMRESklvy7FvGRnZ6NZs2YIDAwEADg7OyMuLg6hoaEYPHiwvJ7kvW5PIUSusg9hy4yISB+V0ACQChUqoG7dugplderUwc2bNwEAdnZ2AJCrFZaampqrtfYhTGZERPpIIin8ooRWrVrh8uXLCmWJiYlwcnICAFSpUgV2dnY4cOCAfP2rV68QFRUFNze3Ah+H3YxERPqoEPeLFcbEiRPh5uaGwMBA9O7dGydPnsSqVauwatUqAG+7FydMmIDAwEDUqFEDNWrUQGBgIExNTdG/f/8CH4fJjIhIH5XQdFbNmzfH9u3b4evri7lz56JKlSpYsmQJBgwYIK8zdepUZGRkYMyYMXj06BFcXFywf/9+WFhYFPg4EiGEKI4TUCdJu0rqDoE+IGNvorpDINI6xoamKt2fZGDNQm8rftG87zCvmRERkdZjNyMRkT7SsVnzmcyIiPRRCQ0AKSlMZkRE+ki3chmTGRGRXuKTpomISOvp2PA/HTsdIiLSR2yZERHpI3YzEhGR1tOtXMZkRkSkl9gyIyIiradjIyaYzIiI9JGOtcx0LDcTEZE+YsuMiEgf6VbDjMmMiEgvcW5GIiLSejp2zYzJjIhIH+lWLmMyIyLSRxK2zFQvKysL4eHhOHjwIFJTU5Gdna2w/tChQ2qKjIiItIFGJLPx48cjPDwcnTp1Qv369XXuLwYiIk2ja79nNSKZbdq0CVu2bEHHjh3VHQoRkV7QsVymGcmsdOnSqF69urrDICLSGwY6ls00YgaQyZMn48cff4QQQt2hEBHpBYlEUuhFE6mtZda9e3eF14cOHcJff/2FevXqwcjISGHdtm3bSjI0IiKdp6lJqbDUlsysrKwUXv/nP/9RUySawd7WDguHz4BXC0+YlDZG4p1rGPbDFJy9cgEA4D9oEvp6dIFDOXu8evMKZ65cgF9YME5eOqfmyPXX5o1bEL42Amn301CtejVMnT4FTZo1UXdYBH42+khtySwsLExdh9Y4Zcyt8N8l23H4fDS8ZgxC6uM0VLN3wuNnT+R1Em9fw9chM3Et+SZMpMaY2ONL7F/wK6p7f4K09IdqjF4/7f1rH4KDvoPfbF80dm6M37dsxZiRX2P7rq2oYF9B3eHpNX42BaNrLTOJ0MELVZJ2ldQdglKChvmiVb1maD2pR4G3sTA1x5M/LqHN1D44dO6/xRid6mXsTVR3CEU2oM8g1KlbGzP9/eRl3b7oDs/PPDB+0jg1Rka6+tkYG5qqdH+mU5sWetsXwWdUGIlqaMRoRmdn5zz/SpBIJDA2Nkb16tXh4+MDT09PNURX/Lq4tsO+01HYMmsF3Bu0xJ0HKVi+cx1+/mtDnvWNShlhRMcBePwsHeevxpdwtPT61WskxCdg6JdDFMpd3VrifOx5NUVFAD8bZehay0wjRjN+/vnnuHbtGszMzODp6QkPDw+Ym5vj6tWraN68OZKTk9G2bVv88ccfubbNzMzEkydPFBZka1djs2oFR4zuPAhX7iShg+8ArNi9Hku/motBbRVbap1c2uDpzst4+edVTOzxJdpN648HTx6pKWr99ejxI2RlZcHW1kah3NbWFmlpD9QUFQH8bJTB0YzFIC0tDZMnT8asWbMUyufNm4cbN25g//798Pf3x7fffouuXbsq1AkKCsKcOXMUd1jFAqhmWdxhq4yBxACnE/+B39qFAIDYq3Go51QLozsPxvq/t8rrHT4fjcajOqCslQ2+9OqPLTND4TKuM+4/5pdUHd7/UgshNPaLrm/42XycRMdmGtaIltmWLVvQr1+/XOV9+/bFli1bAAD9+vXD5cuXc9Xx9fVFenq6woIqFsUesyolP0xF/M0rCmUJN6/AsXxFhbIXLzNw9e51nEg4i+GLpuBNdhaGfd63JEMlANZlrGFoaJjrL/2HDx/mahFQyeJno780IpkZGxsjOjo6V3l0dDSMjY0BANnZ2ZBKpbnqSKVSWFpaKiza9tC5/8adRq1KVRXKalaqihv3bn9wOwkkkBrlfk+oeBmVNkKdunUQEx2jUB4THYNGjRupKSoC+Nkog92MxWDs2LEYNWoUzpw5g+bNm0MikeDkyZP4+eefMWPGDADAvn374OzsrOZIi8firasR/eMO+Pb7GluidqNFrcYY0XEARiyZBgAwNTaBX/9x2Hn8AJIf3IOtpTXGdPFGpXJ2+O3IbjVHr58G+QyE37SZqFuvLho1boitv21DcnIKevXpqe7Q9B4/m4LR0JxUaBozNP/XX39FSEiIvCuxVq1aGDt2LPr37w8AyMjIkI9u/BhtG5oPvB3cETTMFzUqVkZSyi0s+n21fDSj1EiKDTNC4FLbGWUtrfHg6SOcunwe835ditOJ2jdCSxeG5gP/f2PumnDcv5+G6jWq45vpk9G0WeGHO5Pq6OJno+qh+dZ+LQu97aP5MR+vVMI0JpmpkjYmM32iK8mMqCSpOpnZzHQt9LYP5x1XYSSqoRHdjEREVLI09dpXYaktmdnY2CAxMRFly5aFtbX1B9/Yhw85XRMREeVPbcls8eLFsLCwkP9f1/5KICLSZCX1KzcgICDXvcAymQwpKSkA3t4DOGfOHKxatQqPHj2Ci4sLli1bhnr16il1HLUlM29vbzx58gSZmZm5HgdDRETFqyQbEPXq1cPff/8tf21oaCj/f3BwMBYtWoTw8HDUrFkT8+bNQ7t27XD58mV5g6cg1HrNrEyZMgV6Q7OyskogGiIi/VGSyaxUqVKws7PLVS6EwJIlS+Dn5ydv1EREREAmk2HDhg0YOXJkwY+hsmgL4fDhw/L/CyHQsWNH/Pzzz6hYseIHtiIioqIqSjLLzMxEZmamQplUKs1zYgsAuHLlCuzt7SGVSuHi4oLAwEBUrVoVSUlJSElJQfv27RX24+7ujujoaO1JZu7u7gqvDQ0N0bJlS1StWjWfLYiISBWKkszymhPX398fAQEBueq6uLhg3bp1qFmzJu7du4d58+bBzc0NcXFx8utmMplMYRuZTIYbN24oFROH5hMRkVJ8fX0xadIkhbL8WmVeXl7y/zdo0ACurq6oVq0aIiIi0LLl2xu3VTExtEbMzUhERCVLIin8ktecuPkls/eZmZmhQYMGuHLlivw6Wk4LLUdqamqu1trHaFwy4xB9IqLip66JhjMzM5GQkIAKFSqgSpUqsLOzw4EDB+TrX716haioKLi5uSm1X7V2M74/JP/ly5cYNWoUzMzMFMq3bdtWkmEREem8kmo4TJkyBZ07d4ajoyNSU1Mxb948PHnyBN7e3pBIJJgwYQICAwNRo0YN1KhRA4GBgTA1NZXPy1tQak1mVlZWCq8HDhyopkiIiPSLQQkls9u3b6Nfv35IS0tDuXLl0LJlS8TExMDJyQkAMHXqVGRkZGDMmDHym6b379+v1D1mACcaJjXgRMNEylP1RMMOgZ6F3vbWjMMfr1TCOJqRiEgP6drwBCYzIiI9pGuD7ZjMiIj0kARMZkREpOXYMiMiIq2na8lM426aJiIiUhZbZkREekjHGmZMZkRE+kjXuhmZzIiI9BCTGRERaT0mMyIi0no6lss4mpGIiLQfW2ZERHqI3YxERKT1mMyIiEjrMZkREZHW07FcxmRGRKSPdK1lxtGMRESk9dgyIyLSQ7rWMmMyIyLSQ0xmRESk9XQslzGZERHpI7bMiIhI++lYMuNoRiIi0npsmRER6SF2MxIRkdbTsVzGZEZEpI/YMiMiIq3HZEZERFpP15IZRzMSEZHWY8uMiEgP6VjDjMmMiEgf6Vo3o04ms4y9ieoOgT7ApHNtdYdA+Xi+M07dIVAJYTIjIiKtx2RGRERaT9eSGUczEhGR1mPLjIhID+lYw4zJjIhIH7GbkYiItJ5EIin0UlhBQUGQSCSYMGGCvEwIgYCAANjb28PExAQeHh6Ii1N+VC2TGRGRHirpZHbq1CmsWrUKDRs2VCgPDg7GokWLEBISglOnTsHOzg7t2rXD06dPldo/kxkRkR6SSAq/KOvZs2cYMGAAVq9eDWtra3m5EAJLliyBn58funfvjvr16yMiIgIvXrzAhg0blDoGkxkRESklMzMTT548UVgyMzPzrf/VV1+hU6dOaNu2rUJ5UlISUlJS0L59e3mZVCqFu7s7oqOjlYqJyYyISA8VpZsxKCgIVlZWCktQUFCex9m0aRPOnj2b5/qUlBQAgEwmUyiXyWTydQXF0YxERPqoCAM5fH19MWnSJIUyqVSaq96tW7cwfvx47N+/H8bGxh8IRTEWIYTS1+aYzIiI9FBRRiVKpdI8k9f7zpw5g9TUVDRt2lRelpWVhSNHjiAkJASXL18G8LaFVqFCBXmd1NTUXK21j2E3IxGRHjKQFH4pqDZt2uDChQuIjY2VL82aNcOAAQMQGxuLqlWrws7ODgcOHJBv8+rVK0RFRcHNzU2p82HLjIhID5XETdMWFhaoX7++QpmZmRlsbW3l5RMmTEBgYCBq1KiBGjVqIDAwEKampujfv79Sx2IyIyIitZk6dSoyMjIwZswYPHr0CC4uLti/fz8sLCyU2o9ECCGKKUa1eZn1Qt0h0AfweWaai88z01ympZT75f4x7bf5FHrb/d3DVRaHqrBlRkSkh3RtbkYmMyIiPaRro/+YzIiI9JABW2ZERKTtdK2bUddamkREpIfYMiMi0kPsZiQiIq2na92MTGZERHpI164xMZkREekhdjMSEZHW07VuRl1raRIRkR5iy4yISA+xm5GIiLSebqUyJjMiIr3ElhkREWk9JjMiItJ6ujaasUDJbOfOnQXeYZcuXQodDBERUWEUKJl169atQDuTSCTIysoqSjxERFQC9LKbMTs7u7jjICKiEqRbqYzXzIiI9JJetsze9/z5c0RFReHmzZt49eqVwrpx48apJDAiIio+ep/Mzp07h44dO+LFixd4/vw5bGxskJaWBlNTU5QvX57JjIhIC+jaaEal52acOHEiOnfujIcPH8LExAQxMTG4ceMGmjZtiu+//744YiQiIvogpZNZbGwsJk+eDENDQxgaGiIzMxMODg4IDg7GjBkziiNGIiJSMQOJpNCLJlI6mRkZGcmbpzKZDDdv3gQAWFlZyf9PRESaTVKERRMpfc3M2dkZp0+fRs2aNeHp6YnZs2cjLS0N69evR4MGDQoVxIABA+Du7g4PDw/UrFmzUPsgIqKC09QWVmEp3TILDAxEhQoVAADffvstbG1tMXr0aKSmpmLVqlWFCsLc3ByLFi1C7dq1YW9vj379+mHFihW4dOlSofZHREQfpmvdjBIhhFB3EDlSUlIQGRmJyMhIREVFITExEeXLl0dycrJS+3mZ9aKYIiRVMOlcW90hUD6e74xTdwiUD9NSFird31eRkwq97TKPRSqMRDU06qZpCwsLWFtbw9raGmXKlEGpUqVgZ2en7rCIiHSOrg3NVzqZValS5YNvwrVr15QOYtq0aYiKisL58+dRv359tG7dGr6+vmjdujXKlCmj9P50xeaNWxC+NgJp99NQrXo1TJ0+BU2aNVF3WHrH3laGhUN84dXMEyaljZF45xqG/TgVZ/+9gFKGpTBv8Dfo2NwTVe0ckf78Kf6OPYbpYQuQ/PCeukPXS2dOn8W6tesRH5+AtPtpWLT0e3i28VB3WBpH6WtMGk7pZDZhwgSF169fv8a5c+ewd+9efPPNN4UK4rvvvkO5cuXg7++Prl27ok6dOoXajy7Z+9c+BAd9B7/Zvmjs3Bi/b9mKMSO/xvZdW1HBvoK6w9MbZcyt8N/vt+HwP8fhNXswUh8/QLUKTnj87AkAwFRqgibV6+PbjUtx/lo8rM2tsGSkP3b6r0Hz8V+oOXr9lJGRgZq1aqDLfzpjyoSp6g5HY+l9y2z8+PF5li9btgynT58uVBDnzp1DVFQUIiMj8cMPP8DQ0FA+utHDw0Mvk9v68F/wnx7d0L1ndwDAVN9vEP3f49iy6TeMn8RZVkrKtJ6jcet+MoYuniIvu5F6W/7/Jy+eor3fAIVtxobOxqkfd8OhnD1u3b9bYrHSW5982gqffNpK3WFoPE0dyFFYKmtpenl5YevWrYXatlGjRhg3bhy2bduG+/fvY9++fTA1NcW4ceNQv359VYWoNV6/eo2E+AS4tnJVKHd1a4nzsefVFJV+6tKyHU5f+QdbfENxb8NZnP1pD4Z36PfBbazMLJGdnS1vvRFpIl0bzaiyASC///47bGxsCr39uXPn5CMZjx49iidPnqBx48bw9PRUVYha49HjR8jKyoKtreL7aWtri7S0B2qKSj9VtXPA6E4DsWj7zwjcHIIWtRpj6ag5yHz9CusP5f7jTWokxYIh07EhcgeeZjxTQ8RE+qlQN02/29cqhEBKSgru37+P5cuXFyoIa2trPHv2DI0aNYKHhwe+/PJLtG7dGpaWlh/dNjMzE5mZmQplolQWpFJpoWLRJO/3aQshdK6fW9MZSAxw+so/8IsIBgDEXotDPceaGN1pYK5kVsqwFDZND4GBRIIxy2aqI1yiAtO13yVKJ7OuXbsqvAkGBgYoV64cPDw8ULt24e4fWr9+fYGT1/uCgoIwZ84chTK/WTMw09+vULFoAusy1jA0NMzVCnv48GGu1hoVr+RHqYi/dUWhLOHWFfRo5aVQVsqwFLb4LkcVmQM+8+3LVhlpPAONnZiqcJROZgEBASoP4osv/jfq6/bt25BIJKhYsWKBtvX19cWkSYo3/4lSWSqNr6QZlTZCnbp1EBMdgzZtP5OXx0THwOMzD/UFpof+G38atSpWUyirWbGqwiCQnERWw74KPKf3wcOnj0s4SiLl6VrLTOkBIIaGhkhNTc1V/uDBAxgaGhYqiOzsbMydOxdWVlZwcnKCo6MjypQpg2+//RbZ2dkf3FYqlcLS0lJh0YUuxkE+A7Ht9+3YvnUHrl29hu8WfI/k5BT06tNT3aHplcXbf0bL2s7w7f0VqlVwQj+Prhjh1R/Ldq8DABgaGOL3GSvQrEZDDPhuHAwNDSGzLgeZdTkYlTJSc/T66cXzF7iccBmXEy4DAO7cvoPLCZeRfDdFzZFpFr0fAJLf7FeZmZkoXbp0oYLw8/PDmjVrsGDBArRq1QpCCPz3v/9FQEAAXr58ifnz5xdqv9rsc68OSH+cjlWhq3D/fhqq16iOZSt/gn1Fe3WHpldOX/kH/5k3AkE+0zC7/3gkpdzChJVzsCFyBwCgUtkK6OraHgBwftk+hW09pvVG1IWYkg5Z78XHxePLIaPkr38IXgwA6Nz1C8wNDFBTVJpHUkLdjKGhoQgNDcX169cBAPXq1cPs2bPh5fW2q14IgTlz5mDVqlV49OgRXFxcsGzZMtSrV0+p4xR4bsalS5cCePtwzm+//Rbm5ubydVlZWThy5AiuX7+Oc+fOKRUAANjb22PFihXo0qWLQvkff/yBMWPG4M6dO0rtj3MzajbOzai5ODej5lL13Iwzjhd+XEGga8EbGLt27YKhoSGqV68OAIiIiMB3332Hc+fOoV69eli4cCHmz5+P8PBw1KxZE/PmzcORI0dw+fJlWFgU/JwL3DJbvPjtXzdCCKxYsUKhS7F06dKoXLkyVqxYUeADv+vhw4d5Dh6pXbs2Hj58WKh9EhFR/krqmlnnzp0VXs+fPx+hoaGIiYlB3bp1sWTJEvj5+aF797cTREREREAmk2HDhg0YOXJkgY9T4GSWlJQEAPD09MS2bdtgbW1d4IN8TKNGjRASEiJv/eUICQlBw4YNVXYcIiJ6qyjXvvK6JUoqlX50vEJWVhZ+++03PH/+HK6urkhKSkJKSgrat2+vsB93d3dER0cXTzLLcfjwYWU3+ajg4GB06tQJf//9N1xdXSGRSBAdHY1bt25hz549Kj8eEZG+kxRhAqi8bony9/fPd7T7hQsX4OrqipcvX8Lc3Bzbt29H3bp1ER0dDQCQyWQK9WUyGW7cuKFUTEqfTc+ePbFgwYJc5d999x169eql7O4AAO7u7khMTMR//vMfPH78GA8fPkT37t0RFxeHsLCwQu2TiIjyV5TRjL6+vkhPT1dYfH198z1WrVq1EBsbi5iYGIwePRre3t6Ij4+Xr1fFBBFKP5yzXLlyOHToEBo0aKBQfuHCBbRt2xb37qnusRfnz59HkyZNkJWl3H1jHACi2TgARHNxAIjmUvUAkDmn5ny8Uj78m/sX6dht27ZFtWrVMG3aNFSrVg1nz56Fs7OzfH3Xrl1RpkwZREREFHifSrfMnj17lucQfCMjIzx5wolViYjow4QQyMzMRJUqVWBnZ4cDBw7I17169QpRUVFwc3NTap9KJ7P69etj8+bNuco3bdqEunXrKrs7IiJSA0kR/iljxowZOHr0KK5fv44LFy7Az88PkZGRGDBgACQSCSZMmIDAwEBs374dFy9ehI+PD0xNTdG/f3+ljqP0AJBZs2ahR48euHr1Kj777O1USwcPHsSGDRvw+++/K7s7IiJSg5KayePevXsYNGgQkpOTYWVlhYYNG2Lv3r1o164dAGDq1KnIyMjAmDFj5DdN79+/X6l7zIBCJLMuXbpgx44dCAwMxO+//w4TExM0atQIhw4dUnqi4Jz7CvLz+PFjZcMjIqICKKn7zNasWfPROAICAoo872+hnmfWqVMndOrUCcDbhPPrr79iwoQJOH/+vFKDNaysrD66fvDgwYUJkYiIPsBAdc9m1giFfjjnoUOHsHbtWmzbtg1OTk7o0aPHRzPw+zjsnohIPXRt1nylktnt27cRHh6OtWvX4vnz5+jduzdev36NrVu3cvAHERGpTYHbmR07dkTdunURHx+Pn376CXfv3sVPP/1UnLEREVExkUgkhV40UYFbZvv378e4ceMwevRo1KhRozhjIiKiYqZrT5oucMvs6NGjePr0KZo1awYXFxeEhITg/v37xRkbEREVE11rmRU4mbm6umL16tVITk7GyJEjsWnTJlSsWBHZ2dk4cOAAnj59WpxxEhGRCunak6aVHptpamqKoUOH4tixY7hw4QImT56MBQsWoHz58rkerklERJqppGYAKSlFutGgVq1aCA4Oxu3bt7Fx40ZVxURERKSUQt9n9i5DQ0N069YN3bp1U8XuiIiomBlIeNM0ERFpOU0dyFFYTGZERHpIU699FRaTGRGRHtLUUYmFxWRGRKSHdK1lpltXAImISC+xZUZEpIfYzUhERFpPwqH5RESk7XTtmhmTGRGRHmI3IxERaT1du2latzpNiYhIL7FlRkSkh3Tt4ZxMZkREekjXuhmZzIiI9BCH5hMRkdZjNyMREWk9Xetm1K12JhER6SW2zIiI9BBnACEiIq2na92MTGZERHqIA0CIiEjrcWg+ERFpPV27ZqZbqZmIiPQSW2ZERHqIA0CIiEjr6Vo3I5MZEZEeYsuMiIi0Hofma4GMN8/VHQJ9wJM/Lqg7BMqH2diW6g6B8iFC41S6P11rmXE0IxERFZugoCA0b94cFhYWKF++PLp164bLly8r1BFCICAgAPb29jAxMYGHhwfi4pRL3kxmRER6SAKDQi/KiIqKwldffYWYmBgcOHAAb968Qfv27fH8+f960IKDg7Fo0SKEhITg1KlTsLOzQ7t27fD06dMCH0cnuxmJiOjDSqqbce/evQqvw8LCUL58eZw5cwatW7eGEAJLliyBn58funfvDgCIiIiATCbDhg0bMHLkyAIdhy0zIiI9JCnCv8zMTDx58kRhyczMLNBx09PTAQA2NjYAgKSkJKSkpKB9+/byOlKpFO7u7oiOji7w+TCZERHpIQOJpNBLUFAQrKysFJagoKCPHlMIgUmTJuGTTz5B/fr1AQApKSkAAJlMplBXJpPJ1xUEuxmJiPRQUW6a9vX1xaRJkxTKpFLpR7f7+uuv8c8//+DYsWO543mv21MIoVRXKJMZEREpRSqVFih5vWvs2LHYuXMnjhw5gkqVKsnL7ezsALxtoVWoUEFenpqamqu19iHsZiQi0kMSiaTQizKEEPj666+xbds2HDp0CFWqVFFYX6VKFdjZ2eHAgQPyslevXiEqKgpubm4FPg5bZkREekjZIfaF9dVXX2HDhg34448/YGFhIb8OZmVlBRMTE0gkEkyYMAGBgYGoUaMGatSogcDAQJiamqJ///4FPg6TGRGRHiqpofmhoaEAAA8PD4XysLAw+Pj4AACmTp2KjIwMjBkzBo8ePYKLiwv2798PCwuLAh9HIoQQqgpaUzzKvK/uEOgDShmUVncIlA/LcQXv1qGSperprA7c3l3obdtV+kKFkagGW2ZERHqIczMSERFpGLbMiIj0EB/OSUREWk/XuhmZzIiI9FBJDc0vKUxmRER6yEDHWma6lZqJiEgvsWVGRKSHOACEiIi0HgeAEBGR1mPLjIiItB5bZkREpPUMdGz8n26dDRER6SW2zIiI9BC7GYmISOtxAAgREWk9tsyKycuXL/HPP/8gNTUV2dnZCuu6dOmipqiIiHQTW2bFYO/evRg8eDDS0tJyrZNIJMjKylJDVEREukvXkplGjGb8+uuv0atXLyQnJyM7O1thYSIjIqKP0YiWWWpqKiZNmgSZTKbuUIiI9IOOXTPTiJZZz549ERkZqe4wiIj0hqQI/zSRRrTMQkJC0KtXLxw9ehQNGjSAkZGRwvpx48apKTIiIt3E0YzFYMOGDdi3bx9MTEwQGRmp8CZLJBImMyIiFdPUFlZhaUQymzlzJubOnYvp06fDwEAjej6JiHSariUzjcgcr169Qp8+fZjIiIioUDQie3h7e2Pz5s3qDoOISG9IJJJCL5pII7oZs7KyEBwcjH379qFhw4a5BoAsWrRITZEREekmXetm1IhkduHCBTg7OwMALl68qLBOU/8KICLSZkxmxeDw4cPqDoGISK/oWkNBI5IZERGVLLbMioGnp+cH/0o4dOhQCUZDRETaRiOSWePGjRVev379GrGxsbh48SK8vb3VExQRkQ5jN2MxWLx4cZ7lAQEBePbsWQlHQ0Sk+3Stm1Ej7jPLz8CBA7F27Vp1h0FEpHM40XAJOn78OIyNjdUdhtpF/LweoUtXos+AXpg4bby6w9F7YavDcfjvw7iedANSYykaNm6AsRPHonIVJ3WHpnf8O41BwBdfKZSlpKehwnR3+evadlWx8D+T4F6jGQwkBohL/he9V0/GrUfJJR2uRmE3YzHo3r27wmshBJKTk3H69GnMmjVLTVFphviLCdjx+05Ur1lN3aHQ/zt7+ix69euFuvXrIOtNFpYvDcXXI8bitz82w8TURN3h6Z2Ld6+g7Y/D5a+zsv/3QN+qZR1wbPJ6rIneBv9dIUh/+Qx17Kri5ZtMdYSqUTS1hVVYGpHMrKysFF4bGBigVq1amDt3Ltq3b6+mqNTvxYsX8PedA9+AqQhbFaHucOj//bRyqcJr/3mz0a51ByTEJ6BJsyZqikp/vcnKwr0naXmum991HPbEHcG07T/Iy5LSbpdUaFSCNCKZhYWFqTsEjfT9/EVo9akbWrRszmSmwXIGKVm+90cZlYwa5R1xJ+gwMt+8wonr/2DGHz8iKe02JBIJOtV3R/D+tdg7dhWcHWojKe0Ogvatxh/nebuPrrXMNHoASEFkZmbiyZMnCktmpvZ3IRz4629cTkjE6PEj1R0KfYAQAouCl6Bxk0aoXoNdwSXtxPV/MDhiBjr8NAJf/uoPO8uyiJ7yK2zMrFDewhYWxmaY3mEY9sYdQ/ulI7A99iC2jfgRrWs0U3foaldSEw0fOXIEnTt3hr29PSQSCXbs2KGwXgiBgIAA2Nvbw8TEBB4eHoiLi1P6fNSWzGxsbJCW9rZrwNraGjY2NvkuHxIUFAQrKyuFZXHwjyVxCsXmXso9LFr4IwKCZkEqlao7HPqA4Pnf4d/EfzE/eJ66Q9FLe+OOYdu5A7h49woOXopBp2VjAADeLbvB4P9/6f7xz2EsObQO529fwsL9P2P3xSiM+rSPOsPWEJIiLAX3/PlzNGrUCCEhIXmuDw4OxqJFixASEoJTp07Bzs4O7dq1w9OnT5U6jtq6GRcvXgwLCwsAwJIlSwq9H19fX0yaNEmh7AWeFCU0tbsUfxmPHj6CT993LmpnZSH2zHn8vmkbjpw+BENDQzVGSAAQHPgdjhw+glURKyGzk6k7HALw4lUGLtxNRI3yjkh79hivs14jPvmqQp2E5Gv4pDqvbZbUaEYvLy94eXnluU4IgSVLlsDPz08+EDAiIgIymQwbNmzAyJEF75lSWzJ7d2aPoszyIZVKc7VesrS8m7GZSzP8unWdQtm82YFwquKEQUMGMJGpmRACwYHfI/JgJFaGhaJipYrqDon+X+lSRqhjVxVH/z2L11mvcer6RdSSVVaoU1PmhBsP76onQA1SlGtmmZmZuS7n5PW7+GOSkpKQkpKiMNBPKpXC3d0d0dHR2pHMnjwpeOvJ0tKyGCPRPGZmpqhWo6pCmbGJMaysLHOVU8lbOC8Ye/fsww9Lv4epmam8u9zc3Jz3RZaw77pPwa4Lkbj5MBnlLWww02sULI3NERGz4+36A2HYPPwHHLlyBocTT+Lzup+gcwMPeCweota4tV1QUBDmzJmjUObv74+AgACl9pOSkgIAkMkUezZkMhlu3Lih1L7UlszKlClT4GZuVlbWxysRlZDfN28FAIwcMkqh3H/ebHTu9oU6QtJblaxl2Dj0O5Q1t8b9Zw8Rk/QPWgb3x82Hb2+I3nH+IEZtmAPfz7/E0t6+uHzvOnqsmoD/Xj2r5sjVrygts7wu7xTl+v77uUAIoXQ3qNqS2bvPMLt+/TqmT58OHx8fuLq6Ang7+0dERASCgoLUFaJGCV2b98VTKnmnL55Udwj0//qt+eajdcKOb0fY8e0lEI12Kco1s8J0KebFzs4OwNsWWoUKFeTlqampuVprH6O2ZObu/r/pZubOnYtFixahX79+8rIuXbqgQYMGWLVqFWfOJyJSMU24z6xKlSqws7PDgQMH4OzsDAB49eoVoqKisHDhQqX2pRH3mR0/fhzNmuW+76NZs2Y4eZJ/BRMRqVpJTTT87NkzxMbGIjY2FsDbQR+xsbG4efMmJBIJJkyYgMDAQGzfvh0XL16Ej48PTE1N0b9/f6WOoxEzgDg4OGDFihX44YcfFMpXrlwJBwcHNUVFRKS7Smpo/unTp+Hp6Sl/nXOtzdvbG+Hh4Zg6dSoyMjIwZswYPHr0CC4uLti/f7/81q2CkgghhEojL4Q9e/agR48eqFatGlq2bAkAiImJwdWrV7F161Z07NhRqf09yrxfHGGSipQyKK3uECgfluPc1B0C5UOEKj8rxofcen6t0Ns6mGneqGqN6Gbs2LEjEhMT0aVLFzx8+BAPHjxA165dkZiYqHQiIyKij+PzzIqJg4MDAgMD1R0GEZFe0LXnmWlEywwAjh49ioEDB8LNzQ137twBAKxfvx7Hjh1Tc2RERLpH11pmGpHMtm7dig4dOsDExARnz56VT5Py9OlTttaIiIpFyUw0XFI0IpnNmzcPK1aswOrVq2FkZCQvd3Nzw9mzvFOfiEjVdCuVaUgyu3z5Mlq3bp2r3NLSEo8fPy75gIiISKtoRDKrUKEC/v3331zlx44dQ9WqmjcElIhI25XUwzlLikYks5EjR2L8+PE4ceIEJBIJ7t69i19//RVTpkzBmDFj1B0eEZEO0q2ORo0Ymj916lSkp6fD09MTL1++ROvWrSGVSjFlyhR8/fXX6g6PiEjnaGZKKjyNmAEkx4sXLxAfH4/s7GzUrVsX5ubmhdoPZwDRbJwBRHNxBhDNpeoZQO5l3Cn0tjITzXsgrVpbZkOHDi1QvbVr1xZzJERE+kVTr30VllqTWXh4OJycnODs7AwNaiASEZGWUWsyGzVqFDZt2oRr165h6NChGDhwIGxsbNQZEhERaSG1jmZcvnw5kpOTMW3aNOzatQsODg7o3bs39u3bx5YaEVEx4nRWKiaVStGvXz8cOHAA8fHxqFevHsaMGQMnJyc8e/ZM3eEREekkJrNilHNDnhAC2dnZ6g6HiIi0hNqTWWZmJjZu3Ih27dqhVq1auHDhAkJCQnDz5s1CD80nIqIP07UZQNQ6AGTMmDHYtGkTHB0dMWTIEGzatAm2trbqDImIiLSQWm+aNjAwgKOjI5ydnT+Y7bdt26bUfnnTtGbjTdOaizdNay5V3zT9IPNeobe1lcpUGIlqqLVlNnjwYI1tshIR6TJNHchRWGq/aZqIiNSByYyIiLScbqUyJjMiIr2ka5d41D40n4iIqKjYMiMi0ku61TJjMiMi0kO6lcqYzIiI9JRupTMmMyIiPcQBIERERBqGyYyIiLQeuxmJiPQQp7MiIiIdwGRGRERaTrdSGZMZEZFe4mhGIiIiDcOWGRGRXtKtlhmTGRGRHtKtVMZkRkSkp3QrnfGaGRGRHpJIJIVeCmP58uWoUqUKjI2N0bRpUxw9elSl58NkRkRExWrz5s2YMGEC/Pz8cO7cOXz66afw8vLCzZs3VXYMiRBCqGxvGuJR5n11h0AfUMqgtLpDoHxYjnNTdwiUDxEap9L9ZWQ9L/S2JoZmStV3cXFBkyZNEBoaKi+rU6cOunXrhqCgoELH8S5eMyMi0kNFmc4qMzMTmZmZCmVSqRRSqTRX3VevXuHMmTOYPn26Qnn79u0RHR1d6Bjep5PJzFpaTt0hqExmZiaCgoLg6+ub5w8KqY8ufjaq/utfnXTx81ElY0PTQm8b8G0A5syZo1Dm7++PgICAXHXT0tKQlZUFmUymUC6TyZCSklLoGN6nk92MuuTJkyewsrJCeno6LC0t1R0OvYOfjWbj51N8lGmZ3b17FxUrVkR0dDRcXV3l5fPnz8f69etx6dIllcSkky0zIiIqPvklrryULVsWhoaGuVphqampuVprRcHRjEREVGxKly6Npk2b4sCBAwrlBw4cgJub6gYcsWVGRETFatKkSRg0aBCaNWsGV1dXrFq1Cjdv3sSoUaNUdgwmMw0nlUrh7+/PC9gaiJ+NZuPnozn69OmDBw8eYO7cuUhOTkb9+vWxZ88eODk5qewYHABCRERaj9fMiIhI6zGZERGR1mMyIyIircdkpkHCw8NRpkwZpbbx8fFBt27diiUeIm0UGRkJiUSCx48fAyjc94q0D5NZCckv6bz7xevTpw8SExNLPjiS4x8Hxc/HxwcSiSTPYdljxoyBRCKBj4+Pyo7H75V+YDLTICYmJihfvry6wyAqdg4ODti0aRMyMjLkZS9fvsTGjRvh6Oio0mPxe6UfmMw0SF7dIfPmzUP58uVhYWGB4cOHY/r06WjcuHGubb///ntUqFABtra2+Oqrr/D69euSCVqPREVFoUWLFpBKpahQoQKmT5+ON2/eAAB27dqFMmXKIDs7GwAQGxsLiUSCb775Rr79yJEj0a9fP7XErmmaNGkCR0dHbNu2TV62bds2ODg4wNnZWV4mhEBwcDCqVq0KExMTNGrUCL///rvCvvbs2YOaNWvCxMQEnp6euH79usL6979XebW+J0yYAA8PD/lrDw8PjB07FhMmTIC1tTVkMhlWrVqF58+fY8iQIbCwsEC1atXw119/Ffm9INVgMtNgv/76K+bPn4+FCxfizJkzcHR0VHgeUI7Dhw/j6tWrOHz4MCIiIhAeHo7w8PCSD1iH3blzBx07dkTz5s1x/vx5hIaGYs2aNZg3bx4AoHXr1nj69CnOnTsH4G3iK1u2LKKiouT7iIyMhLu7u1ri10RDhgxBWFiY/PXatWsxdOhQhTozZ85EWFgYQkNDERcXh4kTJ2LgwIHy9/XWrVvo3r07OnbsiNjYWPkffKoQERGBsmXL4uTJkxg7dixGjx6NXr16wc3NDWfPnkWHDh0waNAgvHjxQiXHoyISVCK8vb2FoaGhMDMzU1iMjY0FAPHo0SMRFhYmrKys5Nu4uLiIr776SmE/rVq1Eo0aNVLYr5OTk3jz5o28rFevXqJPnz7FfUo6ydvbW3Tt2jVX+YwZM0StWrVEdna2vGzZsmXC3NxcZGVlCSGEaNKkifj++++FEEJ069ZNzJ8/X5QuXVo8efJEJCcnCwAiISGhRM5Dk+W8x/fv3xdSqVQkJSWJ69evC2NjY3H//n3RtWtX4e3tLZ49eyaMjY1FdHS0wvbDhg0T/fr1E0II4evrK+rUqaPwuUybNk3+nRJC5Ppe5fUZjx8/Xri7u8tfu7u7i08++UT++s2bN8LMzEwMGjRIXpbzmR4/fryI7wipAltmJcjT0xOxsbEKy88//5xv/cuXL6NFixYKZe+/BoB69erB0NBQ/rpChQpITU1VXeCEhIQEuLq6QiL53wMNW7VqhWfPnuH27dsA3nZNRUZGQgiBo0ePomvXrqhfvz6OHTuGw4cPQyaToXbt2uo6BY1TtmxZdOrUCREREQgLC0OnTp1QtmxZ+fr4+Hi8fPkS7dq1g7m5uXxZt24drl69CuDt59KyZUuFz+Xdx4wURcOGDeX/NzQ0hK2tLRo0aCAvy5nxnd81zcC5GUuQmZkZqlevrlCW84swP+9+SYG31xDeZ2RklGubnGs3pBpCiHw/i5xyDw8PrFmzBufPn4eBgQHq1q0Ld3d3REVF4dGjR+xizMPQoUPx9ddfAwCWLVumsC7nZ/jPP/9ExYoVFdblzLeY1/fhYwwMDHJtl9c15ry+V++W5Xzu/K5pBrbMNFitWrVw8uRJhbLTp0+rKRr9VrduXURHRyv8EoyOjoaFhYX8F23OdbMlS5bA3d0dEokE7u7uiIyM5PWyfHz++ed49eoVXr16hQ4dOiisq1u3LqRSKW7evInq1asrLA4ODvI6MTExCtu9//p95cqVQ3JyskJZbGxs0U+G1IrJTIONHTsWa9asQUREBK5cuYJ58+bhn3/+ydVCINVKT0/P1R08YsQI3Lp1C2PHjsWlS5fwxx9/wN/fH5MmTYKBwduvkZWVFRo3boxffvlFPjKudevWOHv2LBITExVGy9FbhoaGSEhIQEJCgkJXOQBYWFhgypQpmDhxIiIiInD16lWcO3cOy5YtQ0REBABg1KhRuHr1KiZNmoTLly9jw4YNHx389Nlnn+H06dNYt24drly5An9/f1y8eLG4TpFKCLsZNdiAAQNw7do1TJkyBS9fvkTv3r3h4+OTq7VGqhUZGakwPBwAvL29sWfPHnzzzTdo1KgRbGxsMGzYMMycOVOhnqenJ86ePStPXNbW1qhbty7u3r2LOnXqlNQpaBVLS8t813377bcoX748goKCcO3aNZQpUwZNmjTBjBkzAACOjo7YunUrJk6ciOXLl6NFixYIDAzMNSryXR06dMCsWbMwdepUvHz5EkOHDsXgwYNx4cIFlZ8blRw+AkbLtGvXDnZ2dli/fr26QyEi0hhsmWmwFy9eYMWKFejQoQMMDQ2xceNG/P3337keP05EpO/YMtNgGRkZ6Ny5M86ePYvMzEzUqlULM2fORPfu3dUdGhGRRmEyIyIircfRjEREpPWYzIiISOsxmRERkdZjMiMiIq3HZEZERFqPyYyogAICAhQejJrXQx5LwvXr1yGRSDifINE7mMxI6/n4+EAikchnNa9atSqmTJmC58+fF+txf/zxxwI/BJUJiKh4cQYQ0gmff/45wsLC8Pr1axw9ehTDhw/H8+fPcz2Z+/Xr17ke7VFYVlZWKtkPERUdW2akE6RSKezs7ODg4ID+/ftjwIAB2LFjh7xrcO3atahatSqkUimEEEhPT8eIESNQvnx5WFpa4rPPPsP58+cV9rlgwQLIZDJYWFhg2LBhePnypcL697sZs7OzsXDhQlSvXh1SqRSOjo6YP38+AKBKlSoAAGdnZ0gkEoUZ9MPCwlCnTh0YGxujdu3aWL58ucJxTp48CWdnZxgbG6NZs2Y4d+6cCt85It3AlhnpJBMTE/kDF//9919s2bIFW7dulT9mpFOnTrCxscGePXtgZWWFlStXok2bNkhMTISNjQ22bNkCf39/LFu2DJ9++inWr1+PpUuXomrVqvke09fXF6tXr8bixYvxySefIDk5GZcuXQLwNiG1aNECf//9N+rVq4fSpUsDAFavXg1/f3+EhITA2dkZ586dw5dffgkzMzN4e3vj+fPn+OKLL/DZZ5/hl19+QVJSEsaPH1/M7x6RFhJEWs7b21t07dpV/vrEiRPC1tZW9O7dW/j7+wsjIyORmpoqX3/w4EFhaWkpXr58qbCfatWqiZUrVwohhHB1dRWjRo1SWO/i4iIaNWqU53GfPHkipFKpWL16dZ4xJiUlCQDi3LlzCuUODg5iw4YNCmXffvutcHV1FUIIsXLlSmFjYyOeP38uXx8aGprnvoj0GbsZSSfs3r0b5ubmMDY2hqurK1q3bo2ffvoJAODk5IRy5crJ6545cwbPnj2Dra0tzM3N5UtSUhKuXr0KAEhISICrq6vCMd5//a6EhARkZmaiTZs2BY75/v37uHXrFoYNG6YQx7x58xTiaNSoEUxNTQsUB5G+Yjcj6QRPT0+EhobCyMgI9vb2CoM8zMzMFOpmZ2ejQoUKiIyMzLWfMmXKFOr4JiYmSm+TnZ0N4G1Xo4uLi8K6nO5QwXnAiQqEyYx0gpmZGapXr16guk2aNEFKSgpKlSqFypUr51mnTp06iImJweDBg+VlMTEx+e6zRo0aMDExwcGDBzF8+PBc63OukWVlZcnLZDIZKlasiGvXrmHAgAF57rdu3bpYv349MjIy5AnzQ3EQ6St2M5Leadu2LVxdXdGtWzfs27cP169fR3R0NGbOnInTp08DAMaPH4+1a9di7dq1SExMhL+/P+Li4vLdp7GxMaZNm4apU6di3bp1uHr1KmJiYrBmzRoAQPny5WFiYoK9e/fi3r17SE9PB/D2RuygoCD8+OOPSExMxIULFxAWFoZFixYBAPr37w8DAwMMGzYM8fHx2LNnD77//vtifoeItA+TGekdiUSCPXv2oHXr1hg6dChq1qyJvn374vr165DJZACAPn36YPbs2Zg2bRqaNm2KGzduYPTo0R/c76xZszB58mTMnj0bderUQZ8+fZCamgoAKFWqFJYuXYqVK1fC3t4eXbt2BQAMHz4cP//8M8LDw9GgQQO4u7sjPDxcPpTf3Nwcu3btQnx8PJydneHn54eFCxcW47tDpJ34cE4iItJ6bJkREZHWYzIjIiKtx2RGRERaj8mMiIi0HpMZERFpPSYzIiLSekxmRESk9ZjMiIhI6zGZERGR1mMyIyIircdkRkREWu//AE+N2y9hvBOzAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 4))\n",
    "sns.heatmap(\n",
    "    confusion_matrix(y_test, svm_preds),\n",
    "    annot=True,\n",
    "    fmt=\"d\",\n",
    "    cmap=\"Greens\",\n",
    "    xticklabels=svm_fitness.classes_,\n",
    "    yticklabels=svm_fitness.classes_\n",
    ")\n",
    "plt.title(\"SVM – Fitness Confusion Matrix\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5113e5dc",
   "metadata": {},
   "source": [
    "## Fitness Models (Baseline Classification)\n",
    "\n",
    "Two classification models are trained and evaluated:\n",
    "\n",
    "### Model 1 — Random Forest\n",
    "- Trained using raw numeric values (no scaling required)\n",
    "- Strong at capturing nonlinear relationships\n",
    "- Produces strong baseline performance\n",
    "\n",
    "### Model 2 — SVM (RBF Kernel)\n",
    "- Trained using scaled features\n",
    "- Useful for classification boundaries in numeric feature spaces\n",
    "\n",
    "### Evaluation Metrics\n",
    "Both models are evaluated using:\n",
    "- **Classification report** (precision, recall, F1-score)\n",
    "- **Confusion matrix heatmap** for visual interpretation\n",
    "- Accuracy is stored for later model comparison\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3969d0b2",
   "metadata": {},
   "source": [
    "## 7. Fitness Results Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "49aca223",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fitness</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fitness</td>\n",
       "      <td>SVM (RBF)</td>\n",
       "      <td>0.962766</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dataset          Model  Accuracy\n",
       "0  Fitness  Random Forest  1.000000\n",
       "1  Fitness      SVM (RBF)  0.962766"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fitness_results = pd.DataFrame([\n",
    "    {\n",
    "        \"Dataset\": \"Fitness\",\n",
    "        \"Model\": \"Random Forest\",\n",
    "        \"Accuracy\": accuracy_score(y_test, rf_preds)\n",
    "    },\n",
    "    {\n",
    "        \"Dataset\": \"Fitness\",\n",
    "        \"Model\": \"SVM (RBF)\",\n",
    "        \"Accuracy\": accuracy_score(y_test, svm_preds)\n",
    "    }\n",
    "])\n",
    "\n",
    "fitness_results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdb811d3",
   "metadata": {},
   "source": [
    "## Fitness Model Results Summary\n",
    "\n",
    "A small summary table is created to store accuracy for both Fitness models.\n",
    "This helps compare model performance later with Mental Health and Instagram models."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d4e2a5d",
   "metadata": {},
   "source": [
    "## 8. Mental Health Dataset Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4d34fa4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mental health dataset prepared: (7022, 2)\n"
     ]
    }
   ],
   "source": [
    "# Select relevant mental health features\n",
    "mh_features = ['stress_level', 'anxiety_score']\n",
    "target = 'depression_score'\n",
    "\n",
    "mental_df = mental_health_clean[mh_features + [target]].dropna()\n",
    "\n",
    "# Convert depression into classes (Low / Medium / High)\n",
    "mental_df['depression_class'] = pd.qcut(\n",
    "    mental_df[target],\n",
    "    q=3,\n",
    "    labels=['Low', 'Medium', 'High']\n",
    ")\n",
    "\n",
    "X = mental_df[mh_features]\n",
    "y = mental_df['depression_class']\n",
    "\n",
    "# Train-test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y,\n",
    "    test_size=0.2,\n",
    "    random_state=42,\n",
    "    stratify=y\n",
    ")\n",
    "\n",
    "print(\"Mental health dataset prepared:\", X.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "341530c2",
   "metadata": {},
   "source": [
    "## Mental Health Dataset Preparation\n",
    "\n",
    "This section prepares the student mental health dataset.\n",
    "\n",
    "### Selected Features\n",
    "Only key psychological predictors are kept:\n",
    "- `stress_level`\n",
    "- `anxiety_score`\n",
    "\n",
    "### Target Variable\n",
    "The depression score is transformed into **three classes** using quantiles:\n",
    "- Low\n",
    "- Medium\n",
    "- High\n",
    "\n",
    "This allows depression prediction to be framed as a multi-class classification problem.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d27e6b30",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eac785b7",
   "metadata": {},
   "source": [
    "## 9. Mental Health Model 1 — Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ce38143e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Mental Health Model: Logistic Regression ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        High       0.00      0.00      0.00       345\n",
      "         Low       0.43      0.29      0.35       521\n",
      "      Medium       0.39      0.77      0.52       539\n",
      "\n",
      "    accuracy                           0.40      1405\n",
      "   macro avg       0.27      0.35      0.29      1405\n",
      "weighted avg       0.31      0.40      0.33      1405\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ASUS\\anaconda3\\envs\\tf_env\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n",
      "c:\\Users\\ASUS\\anaconda3\\envs\\tf_env\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is 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, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\ASUS\\anaconda3\\envs\\tf_env\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is 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, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\ASUS\\anaconda3\\envs\\tf_env\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is 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, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    }
   ],
   "source": [
    "logreg_mh = LogisticRegression(\n",
    "    max_iter=500,\n",
    "    multi_class='multinomial',\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "logreg_mh.fit(X_train_scaled, y_train)\n",
    "logreg_preds = logreg_mh.predict(X_test_scaled)\n",
    "\n",
    "print(\"=== Mental Health Model: Logistic Regression ===\")\n",
    "print(classification_report(y_test, logreg_preds))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "adbe1a6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Tpk2xatUqrF69GklJSdwpgqqcOnUKWlpa5bqwy76P73//pPH+amtrY9CgQQgLC0NxcTG2b9/OnbqoyMOHDzFs2DCIRCIoKytDVVUVnp6eAFCjz8v7x7+qdOjQAUuXLsXatWvx9ddfY8SIEfD396/2tqoi1WSamZkJxhjMzc3LzbOwsAAApKenc/83MzMrV6+isveNHDkSYWFhePLkCQYMGABTU1O4ubkhNja2VnGnpaVBWVkZIpGoVssDb8+FXrhwAceOHcOAAQNw5swZTJ06lZtfdu504MCBUFVV5U0rVqwAYwwZGRnce1iT9+b99zs9PR3FxcVYv359uW316NEDALgfIHPmzMEPP/yA8+fPo3v37jAyMoKPjw/v8qeYmBj4+fnhl19+gbu7OwwNDTFq1KgqL/1JT0+HiooKTExMeOUCgQAikYj7HJQxMjIqtw6hUIj8/PxKtwEAPj4+5faxbKoJf39/HD58GKtXr4aGhka5g1CZly9fcn+f97d3/vz5cj/sartfZYYNG4YNGzZg3LhxOHbsGP7++29cuHABJiYm1V5HRZo0aQJXV9dy0/t/r7LPbdu2bcvtb0xMDG9/P1asQO3fx0aNGiEtLa1a58PKPpOVHb9KS0vLjfp9Py6hUAgAEu9vGYFAgJMnT8LX1xcrV65EmzZtYGJigm+++QY5OTmVLpeeng6RSFQukZmamkJFRUVq37/3+fv74/Lly1i6dCnS0tLK/Zguk5ubi88//xx//fUXlixZgri4OFy4cAH79+8HULP3r6K/V1WGDx8ONTU1iMVifPfddzVatipSHc1rYGAAJSUlJCcnl5v34sULAOBaokZGRuUG5wCo9rWZY8aMwZgxY5CXl4czZ84gKCgIvXr1wt27d2FlZVWjuE1MTFBSUoKUlJQa/2HKODk5cfvWpUsX+Pr6YuvWrfD390fbtm25eevXr690lJmZmRmKioogEAhq9N68/4UxMDDgegMqa21bW1sDAFRUVDB9+nRMnz4dr1+/xokTJzB37lz4+vri2bNn0NTUhLGxMdauXYu1a9fi6dOn+O233zB79mykpqbi6NGjFa7fyMgIxcXFSEtL4x2gGWNISUnhBt1I6ueff67yoFJd/fv3x+TJk7F8+XKMHz++0tHQxsbGEAgEOHv2LHfgfFdFZbWVlZWF33//HUFBQZg9ezZXLhaLkZGRIbXtVKXsc/vrr79W+b2qD7FWxNfXF8ePH8fhw4fx5ZdfVlm3LKFUdvxSUlKqdgvoQ9TV1QGg3GCginrZrKysEBoaCgC4e/cu9uzZg+DgYBQWFmLLli0Vrt/IyAh//fUXGGO840NqaiqKi4t5PYLS1KFDB9ja2uL777/nBvdV5NSpU3jx4gXi4uK41iiAcoMTq6Mm16+WlJRg+PDhMDAwgFAohL+/P/73v//xegFrS6otUy0tLbi5uWH//v28XxalpaXYsWMHGjZsiObNmwMAPD09cerUKd6Hp7S0FHv37q3xNrt374558+ahsLAQN2/eBFCzX4jdu3cH8HaEsDQIBAJs3LgRysrKmD9/PoC3HzJ9fX3cunWrwhaBq6sr1NTUoKWlBVdXVxw8eBCFhYXcOnNzc6s9uk5TUxPe3t5ISkqCo6Njhduq6Jeovr4+Bg4ciMmTJyMjI6PCG140atQIU6ZMQZcuXXD58uVKY/Dx8QGAcsPh9+3bh7y8PG6+pGxtbSt9P2tCQ0MDCxcuRO/evfH1119XWq/sesV///23wm06ODjUeB8q+6wKBAIwxsol6F9++QUlJSU13k5t+Pr6QkVFBQ8ePKjyfZY0Vmm36Mr4+/tDJBJh5syZ+PfffyusU9YasrW1RYMGDbBr1y68PeX2Vl5eHvbt28eN8JWGspHv165d45V/6OqB5s2bY/78+XBwcPjg9y83NxcHDx7klW/fvp2b/7HMnz8fvXv3xowZMyqtU5YA3/+8/Pzzz+XqSvOzERQUhLNnz2Lnzp2IiYnB1atXpdY6rVXL9NSpUxUeaHv06IGQkBB06dIF3t7eCAwMhJqaGjZt2oQbN25g9+7d3Js4b948HD58GD4+Ppg3bx40NDSwZcsWrjumoksSypS1HDp06ABzc3OkpKQgJCQEenp6XIvH3t4eALB161bo6OhAXV0d1tbWFSaRzz//HCNHjsSSJUvw8uVL9OrVC0KhEElJSdDU1OR111aXjY0NvvrqK2zatAkJCQn47LPPsH79evj5+SEjIwMDBw6Eqakp0tLScPXqVaSlpXHJ/Pvvv0fPnj3h6+uLadOmoaSkBKtWrYK2tna1f+WvW7cOn332GT7//HN8/fXXaNy4MXJycnD//n0cPnyYO6fZu3dv2Nvbc118T548wdq1a2FlZQUbGxtkZWXB29sbw4YNQ4sWLaCjo4MLFy7g6NGj6N+/f6XbL2udz5o1C9nZ2ejQoQOuXbuGoKAgODs7Y+TIkTV+Tz+2shZ6VTp06ICvvvoKY8aMwcWLF9GxY0doaWkhOTkZCQkJcHBwqDIZV6Rp06bQ0NDAzp070bJlS2hra8PCwgIWFhbo2LEjVq1aBWNjYzRu3Bjx8fEIDQ2Fvr6+BHtafY0bN8b333+PefPm4eHDh+jWrRsMDAzw8uVL/P3339DS0sKiRYugq6srUaxlP0JWrFiB7t27Q1lZGY6OjhK3GPT09HDo0CH06tULzs7OvJs23Lt3Dzt27MDVq1fRv39/KCkpYeXKlRg+fDh69eqFCRMmQCwWY9WqVXj9+jWWL18uUSzvEolE6Ny5M0JCQmBgYAArKyucPHmSS+xlrl27hilTpmDQoEGwsbGBmpoaTp06hWvXrvF6AN43atQobNy4EX5+fnj8+DEcHByQkJCAZcuWoUePHujcubPU9uV9I0aMwIgRI6qs4+HhAQMDA0ycOBFBQUFQVVXFzp07cfXq1XJ1pfXZiI2NRUhICBYsWMD9mAgJCUFgYCC8vLzwxRdf1Gh95dRktFLZaN7KprIRuGfPnmWdOnViWlpaTENDg7Vv354dPny43PrOnj3L3NzcmFAoZCKRiH333XfcaLPXr19z9d4fqRYZGcm8vb2ZmZkZU1NTYxYWFmzw4MHlhjevXbuWWVtbM2VlZd4IuYoujSkpKWFr1qxh9vb2TE1Njenp6TF3d/cK435XZZfGMMbYy5cvmba2Nm+od3x8POvZsyczNDRkqqqqrEGDBqxnz55s7969vGUPHDjAHBwcmJqaGmvUqBFbvnw5++abb5iBgQGvHgA2efLkCmN79OgRGzt2LGvQoAFTVVVlJiYmzMPDgy1ZsoSr8+OPPzIPDw9mbGzMbcvf3589fvyYMfZ2eP/EiROZo6Mj09XVZRoaGszW1pYFBQWxvLw8bj0Vvaf5+fls1qxZzMrKiqmqqjJzc3P29ddfs8zMTF49Kysr1rNnz3Lxv/93l6Z3R/NWpaJLYxhjLCwsjLm5uXGf8aZNm7JRo0axixcvcnUqGsXJWMXv1e7du1mLFi2YqqoqbxTi8+fP2YABA5iBgQHT0dFh3bp1Yzdu3GBWVlbMz8+v3P5UdzRvZftd2cjJgwcPMm9vb6arq8uEQiGzsrJiAwcO5F3SJUmsYrGYjRs3jpmYmDCBQMA7nlT2GX9/vVVJSUlhs2bNYq1atWKamppMKBSyZs2asQkTJrDr16+X21c3Nzemrq7OtLS0mI+PD/vf//7Hq1PZ977sGPn+1QgVfQ6Sk5PZwIEDmaGhIdPT02MjRoxgFy9e5B2rXr58yUaPHs1atGjBtLS0mLa2NnN0dGRr1qzhLuUq28b735X09HQ2ceJEZm5uzlRUVJiVlRWbM2cO77I0xiR7f98dzVuVij5XiYmJzN3dnWlqajITExM2btw4dvny5XIj22vz2SibV/Y9evHiBTM1NWWdOnXijdQuLS1lvXv3Zvr6+ry/WW0I/n+j9UbXrl3x+PFj3L17V9ah1CtFRUVo3bo1GjRogOPHj8s6HEIIIe+Q6e0Ep0+fDmdnZ1haWiIjIwM7d+5EbGwsd7Jdkfn7+6NLly5cN/aWLVtw+/Zt3p2JCCGE1A8yTaYlJSVYuHAhUlJSIBAIYGdnh6ioqA/2tyuCnJwcBAYGIi0tDaqqqmjTpg2OHDnyUc91EEIIqZ16181LCCGEfGro4eCEEEKIhCiZEkIIIRKiZEoIIYRIiJIpIYQQIiGZjub9FBVU/2EkpI7dS8mVdQikCpP3lL+7DakfzkzvILV1aThPkWj5/KQNUoqkblEyJYQQIj0CxezwpGRKCCFEemrwFBd5QsmUEEKI9Choy1Qx95oQQgiRImqZEkIIkR7q5iWEEEIkRN28hBBCiIQEAsmmWgoJCYFAIEBAQABXxhhDcHAwLCwsoKGhAS8vL9y8eZO3nFgsxtSpU2FsbAwtLS306dMHz58/r/H2KZkSQgiRHoGSZFMtXLhwAVu3boWjoyOvfOXKlVi9ejU2bNiACxcuQCQSoUuXLsjJyeHqBAQE4MCBA4iOjkZCQgJyc3PRq1cvlJSU1CgGSqaEEEKkp45bprm5uRg+fDi2bdsGAwMDrpwxhrVr12LevHno378/7O3tERkZiTdv3mDXrl0AgKysLISGhuLHH39E586d4ezsjB07duD69es4ceJEjeKgZEoIIaTeEIvFyM7O5k1isbjS+pMnT0bPnj3LPev50aNHSElJQdeuXbkyoVAIT09PJCYmAgAuXbqEoqIiXh0LCwvY29tzdaqLkikhhBDpkbCbNyQkBHp6erwpJCSkwk1FR0fj8uXLFc5PSUkBAJiZmfHKzczMuHkpKSlQU1PjtWjfr1NdNJqXEEKI9Eh4acycOXMwffp0XplQKCxX79mzZ5g2bRqOHz8OdXX1KsLhx8MYK1f2vurUeR+1TAkhhEiPhC1ToVAIXV1d3lRRMr106RJSU1Ph4uICFRUVqKioID4+Hj/99BNUVFS4Fun7LczU1FRunkgkQmFhITIzMyutU12UTAkhhEhPHQ1A8vHxwfXr13HlyhVucnV1xfDhw3HlyhU0adIEIpEIsbGx3DKFhYWIj4+Hh4cHAMDFxQWqqqq8OsnJybhx4wZXp7qom5cQQoj01NFNG3R0dGBvb88r09LSgpGREVceEBCAZcuWwcbGBjY2Nli2bBk0NTUxbNgwAICenh78/f0xY8YMGBkZwdDQEIGBgXBwcCg3oOlDKJkSQgiRSzNnzkR+fj4mTZqEzMxMuLm54fjx49DR0eHqrFmzBioqKhg8eDDy8/Ph4+ODiIgIKCsr12hbAsYYk/YOyDN6OHj9RQ8Hr9/o4eD1l1QfDu75vUTL58cvlFIkdYtapoQQQqRHiW50TwghhEhGQW90T8mUEEKI9NAj2AghhBAJKWjLVDH3mhBCCJEiapkSQgiRHurmJYQQQiSkoN28lEwJIYRID7VMCSGEEAlRy5QQQgiRkIK2TBXzJwQhhBAiRdQyJYQQIj3UzUsIIYRISEG7eSmZEkIIkR5qmRJCCCESomRKCCGESEhBu3kV8ycEIYQQIkXUMiWEECI91M1LCCGESEhBu3kpmRJCCJEeapkSQgghEqKWKSGEECIZASVT+VRSUoKIiAicPHkSqampKC0t5c0/deqUjCIjhBAiL+Q+mU6bNg0RERHo2bMn7O3tFfZXEyGE1AVFPcbKfTKNjo7Gnj170KNHD1mHQggh8k8xc6n8J1M1NTU0a9ZM1mEQQohCUNSWqdyPYZ4xYwbWrVsHxpisQyGEELknEAgkmmpi8+bNcHR0hK6uLnR1deHu7o4///yTmz969Ohy62/fvj1vHWKxGFOnToWxsTG0tLTQp08fPH/+vMb7LZct0/79+/Nenzp1Cn/++SdatWoFVVVV3rz9+/fXZWiEECLX6rJl2rBhQyxfvpzrfYyMjETfvn2RlJSEVq1aAQC6deuG8PBwbhk1NTXeOgICAnD48GFER0fDyMgIM2bMQK9evXDp0iUoKytXOxa5TKZ6enq811988YWMIqm/YnbvRER4KF6lpaFpMxvMnD0XbVxcZR2WQvl6WC+kvUwuV+7bZxDGT5uN/Pw32LltPf7+Xxxys7NgIjJHjy++hG+fQTKIVr4Nb9sAHW2MYGWoCXFxCW68yMGWs0/wLDOfV8/KUAMTP28Mp4a6UBII8Cj9DYJ+/wepOYUQ6QqxZ1zF36GFh/9B3L30utgVhdK7d2/e66VLl2Lz5s04f/48l0yFQiFEIlGFy2dlZSE0NBRRUVHo3LkzAGDHjh2wtLTEiRMn4OvrW+1Y5DKZvvsrhJR39M8jWLk8BPMWBKG1cxv8uicakyaMx4Hf/oC5hYWsw1MYyzdFobS0hHv97NEDfD9zEtw9336pIzb9iJtXLuKbOYthKrLA1YvnsW3dchgYmaBdBy8ZRS2fWlvq4cCVFPzzMgfKAgHGf2aFHwfYYVREEgqK315OZ6Gnjg1DHPDHjZcIS3yK3MJiWBlqorD47Smk1Bwx+m35m7fe3o4iDHVtgL8eZ9b5PsmKpC1TsVgMsVjMKxMKhRAKhVUuV1JSgr179yIvLw/u7u5ceVxcHExNTaGvrw9PT08sXboUpqamAIBLly6hqKgIXbt25epbWFjA3t4eiYmJNUqmcn/OlJQXFRmOLwYMQP+Bg9CkaVPMnDMPInMR9sTslnVoCkVP3wAGhsbcdOn8WYgsGqKVkwsA4O6t6/Ds2gv2rV1hKrJAl1790bipDR7cvSXjyOXPd/tv4eitVDxOz8eDV28QcuweRLrqsDXT5uqM79AI5x9lYsvZJ7iXlofkLDHOP8rE6/wiAEApAzLeFPGmz5sZ4vTdV8gvKq1s0/JHINkUEhICPT093hQSElLp5q5fvw5tbW0IhUJMnDgRBw4cgJ2dHQCge/fu2LlzJ06dOoUff/wRFy5cQKdOnbhknZKSAjU1NRgYGPDWaWZmhpSUlBrttly2TN/l7Oxc4S8lgUAAdXV1NGvWDKNHj4a3t7cMoqt7RYWFuH3rJsaO+4pX7u7RAVevJMkoKlJUVIQzJ46g18AR3Oe1hX1rXDx3Bp269YWhsQluXrmIF8+fYszkQBlHK/+0hW8PjdkFxQDeHufdmxhi14Xn+KG/HWxMtZCcJcaOv58j4UFGhetobqqF5qbaWHvyYV2FXS9I2jKdM2cOpk+fziurqlVqa2uLK1eu4PXr19i3bx/8/PwQHx8POzs7DBkyhKtnb28PV1dXWFlZ4Y8//ig3tuZdjLEa74fct0y7deuGhw8fQktLC97e3vDy8oK2tjYePHiAtm3bIjk5GZ07d8ahQ4fKLSsWi5Gdnc2b3u9++NRkvs5ESUkJjIyMeOVGRsZ49SpNRlGRC/87jbzcXHj7/ncOaOyU79CwkTUmfNkdX/q6YcmcqRg/bTZaOjjLMFLFMMXTGlefZ+FR+hsAgIGmKjTVlDG8XUP89fg1Zuy7hbP307GkTws4NdStcB097c3wOP0NbiTn1GXoMifpaF6hUMiNzi2bqkqmZZc/urq6IiQkBE5OTli3bl2Fdc3NzWFlZYV79+4BAEQiEQoLC5GZye+GT01NhZmZWY32W+6T6atXrzBjxgycPXsWP/74I1avXo0zZ84gMDAQeXl5OH78OObPn4/FixeXW7ai7oZVKyrvbviUvP+rqza/xIj0nPzzEJzbecDQ2IQrO3JgN+7dvoHZi9dgxead8Jv4LbatW45rl/6SYaTy79tOTdDEWBPfH7nLlZV9NxIeZGDv5Re4n5aHnRf+xbmHmejrWH5wi5qKEjq3MMEfN17WWdz1RV1eGlMRxliljZ709HQ8e/YM5ubmAAAXFxeoqqoiNjaWq5OcnIwbN27Aw8OjRtuV+27ePXv24NKlS+XKv/zyS7i4uGDbtm0YOnQoVq9eXa5ORd0NTLnqk+D1nYG+AZSVlfHq1SteeUZGOoyMjGUUlWJLe5mM65f/RmDwKq5MLC7A7tCN+G7RD3Bp/zkAoHFTGzy+fwe/7Y2Co4ubrMKVa9O8rdGhqSGmxlxHWm4hV56VX4TiklI8+f+WapknGW/gYFG+ZeplYwR1VSUcvZX60WNWZHPnzkX37t1haWmJnJwcREdHIy4uDkePHkVubi6Cg4MxYMAAmJub4/Hjx5g7dy6MjY25Kzz09PTg7++PGTNmwMjICIaGhggMDISDgwM3ure65D6ZqqurIzExsdxdkBITE6Gurg4AKC0trbAboaIRZP9/CuWTpaqmhpZ2rXA+8X/w6dyFKz+fmAivTj4yjExxnTr6G3T1DeDS/jOurKS4GMXFxRC892xIJSXlcg9rINIR0KkJPm9miGl7biA5m9+yKS5l+OdlLiwNNHjlDQ00kJJTvhXU094M/3uQgaz8T/yAUQt12cP18uVLjBw5EsnJydDT04OjoyOOHj2KLl26ID8/H9evX8f27dvx+vVrmJubw9vbGzExMdDR0eHWsWbNGqioqGDw4MHIz8+Hj48PIiIianSNKaAAyXTq1KmYOHEiLl26hLZt20IgEODvv//GL7/8grlz5wIAjh07BmdnxTkPNdJvDObNngk7e3s4OTlj394YJCcnY9CQL2UdmsIpLS3F6aO/watrLygr//d11NTShp2TC6K2roOaUAgTM3PcunoJ8bF/wO/rb2UYsXz6tlMTdG5hgrm/3cabwhIYar69uUtuYQkK///SmN0X/0VwT1tc/TcbSc+y4NZYHx5NDDFtz3Xeuhroq8OpoS5mHlDQUdd1eLYoNDS00nkaGho4duzYB9ehrq6O9evXY/369RLFImAKcJ+9nTt3YsOGDbhz5w6At6O/pk6dimHDhgEA8vPzudG9H/Kpt0zLxOzeiYiwUKSlpaKZTXN8N2sOXFzbyjosidxLyZV1CDV25eI5LJk1BT9F7IeFpRVvXmbGK+z6ZQOuXjyP3JxsGJuJ0KVnf/QaOPyTPL89ec9VWYdQqTPTO1RYvuzoPV5XbY9WphjRriFMdNTwNCMf4eeelRvNO75DI/jamWLQtov4VA6ule1/bRiPjpZo+VcRn+aPeoVIptIkL8lUHn2KyVSR1OdkquikmUxNxsRItHxa+JAPV6qH5L6blxBCSN35FHtNpEEuk6mhoSHu3r0LY2NjGBgYVPnHzcio+IJrQgghpLrkMpmuWbOGG621Zs0ahf2lRAghdU5BD7dymUz9/Py4uxVVdcsoQggh0qWojRe5TKYAoK+vX60/aklJyQfrEEIIqR5KpnLm9OnT3L8ZY+jRowd++eUXNGjQQIZREUKIfKNkKmc8PT15r5WVldG+fXs0adJERhERQoj8U9RkKvc3uieEEEI+NrltmRJCCJEBxWyYKlYyVdTuB0IIqSuKepyV22T6/iUxBQUFmDhxIrS0tHjl+/fvr8uwCCFErlEylTN6enq81yNGjJBRJIQQojgomcqZ8PBwWYdACCFEQchtMiWEECIDitkwpWRKCCFEeqiblxBCCJEQJVNCCCFEQpRMCSGEEAkpajKl2wkSQgghEqKWKSGEEOlRzIYpJVNCCCHSo6jdvJRMCSGESA0lU0IIIURCCppLKZkSQgiRHkVtmdJoXkIIIZ+kzZs3w9HREbq6utDV1YW7uzv+/PNPbj5jDMHBwbCwsICGhga8vLxw8+ZN3jrEYjGmTp0KY2NjaGlpoU+fPnj+/HmNY6FkSgghRGoEAsmmmmjYsCGWL1+Oixcv4uLFi+jUqRP69u3LJcyVK1di9erV2LBhAy5cuACRSIQuXbogJyeHW0dAQAAOHDiA6OhoJCQkIDc3F7169UJJSUnN9psxxmoWvmIrKJZ1BKQy91JyZR0CqcLkPVdlHQKpxJnpHaS2LttZxyRa/s4KX4mWNzQ0xKpVqzB27FhYWFggICAAs2bNAvC2FWpmZoYVK1ZgwoQJyMrKgomJCaKiojBkyBAAwIsXL2BpaYkjR47A17f6sVDLlBBCiNRI2jIVi8XIzs7mTWKx+IPbLSkpQXR0NPLy8uDu7o5Hjx4hJSUFXbt25eoIhUJ4enoiMTERAHDp0iUUFRXx6lhYWMDe3p6rU12UTAkhhEiNkpJAoikkJAR6enq8KSQkpNLtXb9+Hdra2hAKhZg4cSIOHDgAOzs7pKSkAADMzMx49c3MzLh5KSkpUFNTg4GBQaV1qotG8xJCCJEaSQfzzpkzB9OnT+eVCYXCSuvb2triypUreP36Nfbt2wc/Pz/Ex8e/Ew8/IMbYB0ccV6fO+6hlSgghpN4QCoXc6NyyqapkqqamhmbNmsHV1RUhISFwcnLCunXrIBKJAKBcCzM1NZVrrYpEIhQWFiIzM7PSOtVFyZQQQojUCAQCiSZJMcYgFothbW0NkUiE2NhYbl5hYSHi4+Ph4eEBAHBxcYGqqiqvTnJyMm7cuMHVqS7q5iWEECI1dXnPhrlz56J79+6wtLRETk4OoqOjERcXh6NHj0IgECAgIADLli2DjY0NbGxssGzZMmhqamLYsGEAAD09Pfj7+2PGjBkwMjKCoaEhAgMD4eDggM6dO9coFkqmhBBCpKYu74D08uVLjBw5EsnJydDT04OjoyOOHj2KLl26AABmzpyJ/Px8TJo0CZmZmXBzc8Px48eho6PDrWPNmjVQUVHB4MGDkZ+fDx8fH0REREBZWblGsdB1pjVE15nWX3Sdaf1G15nWX9K8ztQp6KREy19d5COlSOoWtUwJIYRIjYLempcGIBFCCCGSopYpIYQQqVHUp8ZQMiWEECI1CppLKZkSQgiRHmqZEkIIIRJS0FxKyZQQQoj0KGrLlEbzEkIIIRKilikhhBCpUdCGKSVTQggh0qOo3byUTIncSErJ/HAlIjMXTtPtBOstKd5OUEFzKSVTQggh0kMtU0IIIURCCppLaTQvIYQQIilqmRJCCJEa6uYlhBBCJKSguZSSKSGEEOmhlikhhBAiIUqmhBBCiIQUNJfSaF5CCCFEUtQyJYQQIjXUzUsIIYRISEFzKSVTQggh0kMtU0IIIURCCppLKZkSQgiRHiUFzaY0mpcQQgiRECVTQgghUiMQSDZVV0hICNq2bQsdHR2YmpqiX79+uHPnDq/O6NGjIRAIeFP79u15dcRiMaZOnQpjY2NoaWmhT58+eP78eY33m5IpIYQQqXk/edV0qq74+HhMnjwZ58+fR2xsLIqLi9G1a1fk5eXx6nXr1g3JycncdOTIEd78gIAAHDhwANHR0UhISEBubi569eqFkpKSGu03nTMlhBAiNUp1dMr06NGjvNfh4eEwNTXFpUuX0LFjR65cKBRCJBJVuI6srCyEhoYiKioKnTt3BgDs2LEDlpaWOHHiBHx9fasdD7VMCSGESI2kLVOxWIzs7GzeJBaLP7jdrKwsAIChoSGvPC4uDqampmjevDnGjx+P1NRUbt6lS5dQVFSErl27cmUWFhawt7dHYmJijfabkikhhBCpkfScaUhICPT09HhTSEhIldtkjGH69On47LPPYG9vz5V3794dO3fuxKlTp/Djjz/iwoUL6NSpE5ecU1JSoKamBgMDA976zMzMkJKSUqP9pm5eQggh9cacOXMwffp0XplQKKxymSlTpuDatWtISEjglQ8ZMoT7t729PVxdXWFlZYU//vgD/fv3r3R9jLEa33yCkikhhBCpEUCyk6ZCofCDyfNdU6dOxW+//YYzZ86gYcOGVdY1NzeHlZUV7t27BwAQiUQoLCxEZmYmr3WampoKDw+PGsVN3byEEEKkRkkg2VRdjDFMmTIF+/fvx6lTp2Btbf3BZdLT0/Hs2TOYm5sDAFxcXKCqqorY2FiuTnJyMm7cuFHjZEotU0IIIVJTV/fmnTx5Mnbt2oVDhw5BR0eHO8epp6cHDQ0N5ObmIjg4GAMGDIC5uTkeP36MuXPnwtjYGF988QVX19/fHzNmzICRkREMDQ0RGBgIBwcHbnRvdVEyJYQQIjV1dTfBzZs3AwC8vLx45eHh4Rg9ejSUlZVx/fp1bN++Ha9fv4a5uTm8vb0RExMDHR0drv6aNWugoqKCwYMHIz8/Hz4+PoiIiICysnKN4qFkSgghRGrq6t68jLEq52toaODYsWMfXI+6ujrWr1+P9evXSxQPnTMlhBBCJEQtU0IIIVKjoA+NoWRKCCFEeujh4IQQQoiEFDSXUjIlhBAiPYr6cHBKpoQQQqRGMVNpPUimv/32W7Xr9unT5yNGQgghhNSOzJNpv379qlVPIBDU+GGthBBC6hYNQJKR0tJSWYdACCFESurq4eD1jcyTKSGEEPlBLdN6Ii8vD/Hx8Xj69CkKCwt587755hsZRUUIIaQ6FDSX1q9kmpSUhB49euDNmzfIy8uDoaEhXr16BU1NTZiamlIyJYSQek5RW6b16t683377LXr37o2MjAxoaGjg/PnzePLkCVxcXPDDDz/IOjxCCCGkQvUqmV65cgUzZsyAsrIylJWVIRaLYWlpiZUrV2Lu3LmyDo8QQsgH1NXDweubepVMVVVVuS4CMzMzPH36FMDbB7iW/ZsQQkj9JRAIJJo+VfXqnKmzszMuXryI5s2bw9vbGwsXLsSrV68QFRUFBweHWq1z+PDh8PT0hJeXF5o3by7liAkhhLzr002HkqlXLdNly5bB3NwcALB48WIYGRnh66+/RmpqKrZu3VqrdWpra2P16tVo0aIFLCwsMHToUGzZsgX//POPNEMnhBCCt/fmlWT6VNWrlqmrqyv3bxMTExw5ckTidf78888AgJSUFMTFxSEuLg7r1q3D5MmTYWpqiuTkZIm3QQghRLHVq2T6Meno6MDAwAAGBgbQ19eHiooKRCKRrMMihBC58gk3LiVSr5KptbV1lSegHz58WON1zpo1C/Hx8bh69Srs7e3RsWNHzJkzBx07doS+vr4E0X7aYnbvRER4KF6lpaFpMxvMnD0XbVxcP7wgqbWnt6/h3B97kPLoHnJfp2Pgt4tg69qBm394y0pcO3uct4xF0xYY8/0G7vXlU7/jZuIppDy6j8KCN5ix9SDUtbTrbB/k1fjurTC+uz2szHQAALefZmBZ9EUcv/R24GP+4UkVLjc3LBFrDlwBAKipKGH5WA8M8rSBhpoKTl99joDNZ/Bvel6d7EN98SkPIpJEvUqmAQEBvNdFRUVISkrC0aNH8d1339VqnatWrYKJiQmCgoLQt29ftGzZUgqRftqO/nkEK5eHYN6CILR2boNf90Rj0oTxOPDbHzC3sJB1eHKrUFwAs0ZN4OTpi31rF1VYp4ljW/Se8N9nXVmF/xUtFovR1LEtmjq2xemY0I8aryL591UuFkSew4PkLADACJ8W2DuvO9oH7MHtp5loPDKcV7+rixW2fOONA4n//cBfNf4z9GzXGKNWxiIjpwDLx3pg38Ke8Ph2L0pLWZ3ujywpaC6tX8l02rRpFZZv3LgRFy9erNU6k5KSEB8fj7i4OPz4449QVlbmRvd6eXkpZHKNigzHFwMGoP/AQQCAmXPmITExAXtidmPatzNkHJ38ata6HZq1bldlHRVVVWjrG1Y6v133AQCAJ7euSDM0hXfkwhPe6+CovzC+eyu0sxXh9tNMvHydz5vfu31jxF//F49fZgMAdDXVMLpLS/ivPonTV58DAMauPoF7YaPQyakhTiQ9q5sdqQc+5UFEkqhXo3kr0717d+zbt69Wyzo5OeGbb77B/v37kZaWhmPHjkFTUxPffPMN7O3tpRxp/VdUWIjbt27C3eMzXrm7RwdcvZIko6hImSe3r2LN1wOxeYYf/tj2I/KyMmUdksJRUhJg0OfNoKWuir/+SSk331RfA91crRAZe5src25mAjVVZV7STM54g5tPM9C+pWKNzRAIJJs+VfWqZVqZX3/9FYaGlf9a/5CkpCRuJO/Zs2eRnZ2N1q1bw9vbW4pRfhoyX2eipKQERkZGvHIjI2O8epUmo6gIADR1aosWbh2hZ2yG12kpiN8bgZ3LvsPYJZugoqom6/DkXisrQ8StGgB1NWXk5hdhyNI/8c+z8j9mRnSyRU5+EQ6+08UrMtCEuKgEr/PEvLqpr/NhZqD50WMnslevkqmzszPv5DVjDCkpKUhLS8OmTZtqtU4DAwPk5ubCyckJXl5eGD9+PDp27AhdXd0PLisWiyEW878cTFkIoVBYq1jqk/cHCTDGFHbgQH1h5/7fjztTS2uYWzfHhmnDcf/KX2jR9nMZRqYY7v77Gm7TYqCvJUQ/j6bY9q0Pus45WC6hjurSEjFxdyEuKvngOgUAmOKcLgVAA5Dqhb59+/L+EEpKSjAxMYGXlxdatGhRq3VGRUVVO3m+LyQkBIsW8QeKzFsQhPkLg2sVS31goG8AZWVlvHr1ileekZEOIyNjGUVFKqJjYAQ9YzNkpPwr61AUQlFxKR4mvz0Hevl+GlxsTDC5jyOmbozn6nSwM4dtQwOMXMEfdZ2S+QZCVWXoawl5rVMTfQ2cr6CrWJ59EucOP4J6lUyDg4Olvs5evXpx/37+/DkEAgEaNGhQrWXnzJmD6dOn88qY8qfdKlVVU0NLu1Y4n/g/+HTuwpWfT0yEVycfGUZG3vcmJwvZGalVDkgiH49AIIBQVZlX5te1JS7dS8X1x+m88qT7aSgsKoGPc0PsS3gA4G3Xb6tGhpgXfq7OYq4P6qplGhISgv379+Off/6BhoYGPDw8sGLFCtja2nJ1GGNYtGgRtm7diszMTLi5uWHjxo1o1aoVV0csFiMwMBC7d+9Gfn4+fHx8sGnTJjRs2LBG8dSrHxHKyspITU0tV56eng5lZeUKlviw0tJSfP/999DT04OVlRUaNWoEfX19LF68GKWlpVUuKxQKoaury5vkoYt3pN8Y7N/3Kw7s/xUPHzzAquXLkJycjEFDvpR1aHKtsCAfKY/vI+XxfQDA67RkpDy+j6xXL1FYkI8TO3/G83u38DotBU9uXcGeHxdAU1sPtq7/DRbLfZ2BlMf3kfHyBQAg9dkjpDy+j/zcbJnsk7xYNNINHezM0chUB62sDBE80g0d7S0QHXeXq6OjoYr+HZoi4vjtcstnvylEROxtLB/bAV6ODeDUxBhhMzrjxpMMnPr/0b2Koq6eGhMfH4/Jkyfj/PnziI2NRXFxMbp27Yq8vP+u6125ciVWr16NDRs24MKFCxCJROjSpQtycnK4OgEBAThw4ACio6ORkJCA3Nxc9OrVCyUlH+7Gf1e9apmySk4uiMViqKnVbgDGvHnzEBoaiuXLl6NDhw5gjOF///sfgoODUVBQgKVLl0oS8iepW/ceyHqdia2bNyEtLRXNbJpj45atsLCoXoud1E7ywzvYsTSQe31ixxYAgOPnXdFt7DSkPXuI6wmxKMjLhba+IRrbtUb/qfMh1PhvAMvlk4dxdn8U9zpq8bcAgF5ffQcnT9862hP5Y6qvidDpPhAZaiErT4wbj9PRJ/h3nLryXyIc1NEGAgGw58y9Ctcx85f/oaSkFDtm+UJDqIzTV//FV2uPKNQ1pkDdPUbt6NGjvNfh4eEwNTXFpUuX0LFjRzDGsHbtWsybNw/9+/cHAERGRsLMzAy7du3ChAkTkJWVhdDQUERFRaFz584AgB07dsDS0hInTpyAr2/1v1MCVlkGq0M//fQTgLcPB1+8eDG0tf+7o0tJSQnOnDmDx48fIymp5pduWFhYYMuWLejTpw+v/NChQ5g0aRL+/bdm56MKimscAqkje64ozrV8n6IJiw7LOgRSicru8FQb03+T7CEiIb7W5QZ+CoUfHvh5//592NjY4Pr167C3t8fDhw/RtGlTXL58Gc7Ozly9vn37Ql9fH5GRkTh16hR8fHyQkZEBAwMDro6TkxP69etXbsxMVepFy3TNmjUA3rZMt2zZwuvSVVNTQ+PGjbFly5ZarTsjI6PCwUstWrRARkZG7QImhBBSIUnPmVY08DMoKKjKMTWMMUyfPh2fffYZd/+AlJS3A7/MzMx4dc3MzPDkyROujpqaGi+RltUpW7666kUyffToEQDA29sb+/fvL7djknBycsKGDRu41m+ZDRs2wNHRUWrbIYQQInk3b0UDPz/UKp0yZQquXbuGhISEcvNqcxlgbS4VrBfJtMzp06elvs6VK1eiZ8+eOHHiBNzd3SEQCJCYmIhnz55J5RFvhBBC/iPpYN7qdOm+a+rUqfjtt99w5swZ3gjcsqeCpaSkcM/JBoDU1FSutSoSiVBYWIjMzExeIy41NRUeHh41irtejeYdOHAgli9fXq581apVGDRoUK3W6enpibt37+KLL77A69evkZGRgf79++PmzZsIDw//8AoIIYRUW109HJwxhilTpmD//v04deoUrK2tefOtra0hEokQGxvLlRUWFiI+Pp5LlC4uLlBVVeXVSU5Oxo0bN2qcTOtVyzQ+Ph5BQUHlyrt164Yffvih1uu1sLAoN2r36tWriIyMRFhYWK3XSwghhK+uWmiTJ0/Grl27cOjQIejo6HDnOPX09KChoQGBQICAgAAsW7YMNjY2sLGxwbJly6CpqYlhw4Zxdf39/TFjxgwYGRnB0NAQgYGBcHBw4Eb3Vle9Sqa5ubkVXgKjqqqK7Gy6jo4QQshbmzdvBgB4eXnxysPDwzF69GgAwMyZM5Gfn49JkyZxN204fvw4dHR0uPpr1qyBiooKBg8ezN20ISIiosb3NqhXydTe3h4xMTFYuHAhrzw6Ohp2dnYyiooQQkh11dWteatzVadAIEBwcHCVI4HV1dWxfv16rF+/XqJ46lUyXbBgAQYMGIAHDx6gU6dOAICTJ09i165d+PXXX2UcHSGEkA9R1OeZ1qtk2qdPHxw8eBDLli3Dr7/+Cg0NDTg5OeHUqVM1vlF92R0vKvP69WsJIiWEEFIRBc2l9SuZAkDPnj3Rs2dPAG8T3s6dOxEQEICrV6/W6F6Jenp6H5w/atQoiWIlhBDCV1e3E6xv6l0yBYBTp04hLCwM+/fvh5WVFQYMGIDQ0NAarYMueyGEkLpH3bwy9vz5c0RERCAsLAx5eXkYPHgwioqKsG/fPhp8RAghpF6rFzdt6NGjB+zs7HDr1i2sX78eL168kHhkFSGEkLonEEg2farqRcv0+PHj+Oabb/D111/DxsZG1uEQQgipJUU9Z1ovWqZnz55FTk4OXF1d4ebmhg0bNiAtLU3WYRFCCKkhgYT/farqRTJ1d3fHtm3bkJycjAkTJiA6OhoNGjRAaWkpYmNjeU9FJ4QQUn8pCSSbPlX1IpmW0dTUxNixY5GQkIDr169jxowZWL58OUxNTcs93JsQQkj9Q8m0nrG1tcXKlSvx/Plz7N69W9bhEEIIIZWqFwOQqqKsrIx+/fqhX79+sg6FEELIB9T0odryot4nU0IIIZ+OT7mrVhKUTAkhhEiNgjZMKZkSQgiRHrqdICGEECIhRe3mrbejeQkhhJBPBbVMCSGESI2C9vJSMiWEECI9Sp/wLQElQcmUEEKI1FDLlBBCCJGQog5AomRKCCFEahT10hgazUsIIYRIiFqmhBBCpEZBG6aUTAkhhEiPonbzUjIlhBAiNQqaSymZEkIIkR5FHYijqPtNCCHkIxAIBBJNNXHmzBn07t0bFhYWEAgEOHjwIG/+6NGjy62/ffv2vDpisRhTp06FsbExtLS00KdPHzx//rzG+03JlBBCyCcpLy8PTk5O2LBhQ6V1unXrhuTkZG46cuQIb35AQAAOHDiA6OhoJCQkIDc3F7169UJJSUmNYqFuXkIIIVIj6SlTsVgMsVjMKxMKhRAKheXqdu/eHd27d69yfUKhECKRqMJ5WVlZCA0NRVRUFDp37gwA2LFjBywtLXHixAn4+vpWO25qmRJCCJEaJYFAoikkJAR6enq8KSQkpNbxxMXFwdTUFM2bN8f48eORmprKzbt06RKKiorQtWtXrszCwgL29vZITEys0XaoZUoIIURqJG2ZzpkzB9OnT+eVVdQqrY7u3btj0KBBsLKywqNHj7BgwQJ06tQJly5dglAoREpKCtTU1GBgYMBbzszMDCkpKTXaFiVTQgghUiPppTGVdenWxpAhQ7h/29vbw9XVFVZWVvjjjz/Qv3//SpdjjNV4MBR18xJCCJGauhzNW1Pm5uawsrLCvXv3AAAikQiFhYXIzMzk1UtNTYWZmVmN1k3JlBBCiEJIT0/Hs2fPYG5uDgBwcXGBqqoqYmNjuTrJycm4ceMGPDw8arRu6uYlhBAiNXXZQsvNzcX9+/e5148ePcKVK1dgaGgIQ0NDBAcHY8CAATA3N8fjx48xd+5cGBsb44svvgAA6Onpwd/fHzNmzICRkREMDQ0RGBgIBwcHbnRvdVEyJYQQIjUfu6v2XRcvXoS3tzf3umzgkp+fHzZv3ozr169j+/bteP36NczNzeHt7Y2YmBjo6Ohwy6xZswYqKioYPHgw8vPz4ePjg4iICCgrK9coFgFjjElntxRDQbGsIyCV2XPlmaxDIFWYsOiwrEMglcg/PElq69p75YVEyw9qbSGlSOoWtUwJIYRITV22TOsTSqZEbphqqMs6BFKV57dkHQGpA4o6qlVR95sQQgiRGmqZEkIIkRrq5iWEEEIkpJiplJIpIYQQKVLQhiklU0IIIdKjpKBtU0qmhBBCpEZRW6Y0mpcQQgiRELVMCSGESI2AunkJIYQQyShqNy8lU0IIIVJDA5AIIYQQCVHLlBBCCJGQoiZTGs1LCCGESIhapoQQQqSGRvMSQgghElJSzFxKyZQQQoj0UMuUEEIIkRANQCKEEEJIrVDLlBBCiNRQNy8hhBAiIRqARAghhEiIWqaEEEKIhBR1ABIlU0IIIVKjoLmURvMSQgghkqJkSgghRGqUBAKJppo4c+YMevfuDQsLCwgEAhw8eJA3nzGG4OBgWFhYQENDA15eXrh58yavjlgsxtSpU2FsbAwtLS306dMHz58/r/l+13gJQgghpBICCaeayMvLg5OTEzZs2FDh/JUrV2L16tXYsGEDLly4AJFIhC5duiAnJ4erExAQgAMHDiA6OhoJCQnIzc1Fr169UFJSUqNY6JwpIYQQ6anDk6bdu3dH9+7dK5zHGMPatWsxb9489O/fHwAQGRkJMzMz7Nq1CxMmTEBWVhZCQ0MRFRWFzp07AwB27NgBS0tLnDhxAr6+vtWORSGSaUFBAa5du4bU1FSUlpby5vXp00dGURFCiPyR9NIYsVgMsVjMKxMKhRAKhTVaz6NHj5CSkoKuXbvy1uPp6YnExERMmDABly5dQlFREa+OhYUF7O3tkZiYSMn0XUePHsWoUaPw6tWrcvMEAkGNm/KEEEIqJ+mlMSEhIVi0aBGvLCgoCMHBwTVaT0pKCgDAzMyMV25mZoYnT55wddTU1GBgYFCuTtny1SX350ynTJmCQYMGITk5GaWlpbyJEikhhNQvc+bMQVZWFm+aM2dOrdcneC+7M8bKlb2vOnXeJ/fJNDU1FdOnTy/364QQQoj0SToASSgUQldXlzfVtIsXAEQiEQCUa2GmpqZy+UAkEqGwsBCZmZmV1qkuuU+mAwcORFxcnKzDIIQQxVCXw3mrYG1tDZFIhNjYWK6ssLAQ8fHx8PDwAAC4uLhAVVWVVyc5ORk3btzg6lSX3J8z3bBhAwYNGoSzZ8/CwcEBqqqqvPnffPONjCIjhBD5U5f35s3NzcX9+/e5148ePcKVK1dgaGiIRo0aISAgAMuWLYONjQ1sbGywbNkyaGpqYtiwYQAAPT09+Pv7Y8aMGTAyMoKhoSECAwPh4ODAje6tLrlPprt27cKxY8egoaGBuLg4Xj+4QCCgZEoIIVJUl/fmvXjxIry9vbnX06dPBwD4+fkhIiICM2fORH5+PiZNmoTMzEy4ubnh+PHj0NHR4ZZZs2YNVFRUMHjwYOTn58PHxwcRERFQVlauUSwCxhiTzm7VTyKRCN988w1mz54NJSXJe7ULiqUQFPko4u6kyToEUoUvRiz6cCUiE/lJFd/0oDYuP86WaPk2jXWlFEndkvtzpoWFhRgyZIhUEikhhBBSEbnPMH5+foiJiZF1GIQQohjqyQCkuib350xLSkqwcuVKHDt2DI6OjuUGIK1evVpGkRFCiPyhh4PLqevXr8PZ2RkAcOPGDd68ml6USwghpGqKeliV+2R6+vRpWYdACCEKQ0FzqfwnU0IIIXVIQbOp3CdTb2/vKrtzT506VYfREEIIkUdyn0xbt27Ne11UVIQrV67gxo0b8PPzk01QhBAip2gAkpxas2ZNheXBwcHIzc2t42gIIUS+KeoAJLm/zrQyI0aMQFhYmKzDIIQQuaKgl5nKf8u0MufOnYO6urqsw5CZmN07EREeildpaWjazAYzZ89FGxdXWYcl1+7fvIJTh3bh2YM7yM5Mh/+sZXB06wgAKCkuxh+7tuLW5fNIf/kC6ppasHV0Re+RX0PP0Ljcuhhj+HlJIG4n/cVbD5GOwLFdsXhqH2zYeRrf/bAPANC3kxP8B3wG55aWMDbQhtuQEFy7+y9vuWPbpqGjqw2vbO+xSxg1O7zOYpe5TzkjSkDuk2n//v15rxljSE5OxsWLF7FgwQIZRSVbR/88gpXLQzBvQRBaO7fBr3uiMWnCeBz47Q+YW1jIOjy5VSjOR4PGzeDWqSfCVs57b14Bnj28C99BfrBobIP83GzsD/sJ20JmIXBVaLl1xf2+R3H70z4yF7tG8O/vgWt3n/PKNTXUcO7qA+w/cRmbFw6vdPnQff/D4s2/c6/zxUUfLdb6iM6Zyik9PT3eayUlJdja2uL7779H165dZRSVbEVFhuOLAQPQf+AgAMDMOfOQmJiAPTG7Me3bGTKOTn7ZtXGHXRv3CudpaGljcvBaXtmAcd9i9azxyEhLgaGJiCv/99E9xP0Wgxkrt2GBf9+PGbLC0dJQQ/iy0Zi0eDdmj+vGm7f7jwsAgEbmhlWuI7+gEC/Tcz5ajKR+kvtkGh6uQN0r1VBUWIjbt25i7LiveOXuHh1w9UqSjKIiFSl4kwuBQABNrf8eF1UoLkDkmkUYOP5b6BoYyTA6+bR2zhAcPXsDp/+6Uy6ZVteQHq74skdbpGbk4Pj/bmHpz0eQ+0Ys5UjrL0XtMJH7ZCoJsVgMsZj/JWDKQgiFQhlFJLnM15koKSmBkRH/QGxkZIxXr+gRZvVFUaEYh3dsQZvPu0BdU4srPxD2E6xt7eHQ7nMZRiefBvm6oHULS3w2YmWt1xF95AIev0jHy1fZaNXMAt9P7Q2H5g3Q62vpPeKsvlPQXCqfo3kNDQ3x6tUrAICBgQEMDQ0rnaoSEhICPT093rRqRUhd7MJH9/6NLBhjdK/ieqKkuBiRq4PBShkGf/Vft/v1vxNw98Zl9B9LD7SXtoZm+lj13QCMnR8JcWHtH1ocfiARp/+6g1sPkrH32CUM+y4UPu1boHWLhlKMtp5T0OG8ctkyXbNmDfck9bVr19Z6PXPmzOGe3F6GKX+6rVIAMNA3gLKyMvdjo0xGRjqMjMqPGiV1q6S4GOE/LED6yxeY8v1PvFbpveuXkJ7yL2aP7M5bJmzVfDRt6YipixWn9SNtzi0bwcxIF4k7Z3JlKirK+KxNU0wc0hF6bgEoLWU1Xm/S7WcoLCpGs0amuPLP8w8vIAdoAJIceffORpLc5UgoLN+lW1D7H631gqqaGlratcL5xP/Bp3MXrvx8YiK8OvnIMDJSlkjTkp9j6vc/QUuHP3iuc/8RaN+5N69sxbej8MWYqbB37VCXocqd03/fgcvApbyyrYtG4M6jl/gxIrZWiRQA7JqaQ01VBcmvsqQR5idBUTu45DKZZmdnV7uurq7uR4ykfhrpNwbzZs+Enb09nJycsW9vDJKTkzFoyJeyDk2uifPfIC3lv+sS01OT8fzRPWhq60DP0Bhhq+bj+cO7+GruCpSWliI7Mx0AoKmtCxVVVegaGFU46MjA2AxGZnRJkyRy34hx60EyrywvvxAZWXlcuYGuJixFBjA3ffsjp3ljMwDAy/RsvEzPgXVDY3zZwxXHEm7hVWYuWjYVYfm3/ZF0+xnOXXlYtztE6pxcJlN9ff1qn/8rKSn5yNHUP92690DW60xs3bwJaWmpaGbTHBu3bIWFRQNZhybXnj74BxsW/ne+82D4egBAO+/u6DZkLG5cSAAArJwxhrfclO9/go19m7oLlFSop6cDtn0/knsdtWIsAGDJliNY+vMRFBUVw7udLSYP9Ya2phqep7zG0YQbWPrzn7Vu2X6KFLRhCgFjTO7+yvHx8dy/Hz9+jNmzZ2P06NFwd397jd+5c+cQGRmJkJCQGncDf+rdvPIs7g6NRq7PvhixSNYhkErkJ0nvfPvdl28kWr65maaUIqlbctky9fT05P79/fffY/Xq1Rg6dChX1qdPHzg4OGDr1q305BhCCJEiRR2AJJeXxrzr3LlzcHUtf89ZV1dX/P333zKIiBBC5JdAINn0qZL7ZGppaYktW7aUK//5559haWkpg4gIIUR+KehlpvLZzfuuNWvWYMCAATh27Bjat28PADh//jwePHiAffv2yTg6Qggh8kDuW6Y9evTA3bt30adPH2RkZCA9PR19+/bF3bt30aNHD1mHRwgh8kVBm6Zyn0yBt129y5Ytw/79+3HgwAEsXbqUungJIeQjEEj4X00EBwdDIBDwJpHovycsMcYQHBwMCwsLaGhowMvLCzdv3pT2LgNQkGR69uxZjBgxAh4eHvj337cXzUdFRSEhIUHGkRFCiHyp6wFIrVq1QnJyMjddv36dm7dy5UqsXr0aGzZswIULFyASidClSxfk5Ej/EXlyn0z37dsHX19faGho4PLly9xTYHJycrBs2TIZR0cIIfKlrnt5VVRUIBKJuMnExATA21bp2rVrMW/ePPTv3x/29vaIjIzEmzdvsGvXLgn3sjy5T6ZLlizBli1bsG3bNqiqqnLlHh4euHz5sgwjI4QQOSRhNhWLxcjOzuZN7z8K81337t2DhYUFrK2t8eWXX+Lhw7e3bnz06BFSUlLQtWtXrq5QKISnpycSExOlvttyn0zv3LmDjh07livX1dXF69ev6z4gQgghlaro0ZchIRU/+tLNzQ3bt2/HsWPHsG3bNqSkpMDDwwPp6elISUkBAJiZmfGWMTMz4+ZJk9xfGmNubo779++jcePGvPKEhAQ0adJENkERQoickvQOSBU9+vL9p3eV6d79v8cROjg4wN3dHU2bNkVkZCR3KWRdPbtZ7lumEyZMwLRp0/DXX39BIBDgxYsX2LlzJwIDAzFp0iRZh0cIIXJF0gFIQqEQurq6vKmyZPo+LS0tODg44N69e9yo3vdboampqeVaq9Ig9y3TmTNnIisrC97e3igoKEDHjh0hFAoRGBiIKVOmyDo8QgiRK7K8VFQsFuP27dv4/PPPYW1tDZFIhNjYWDg7OwMACgsLER8fjxUrVkh923KfTAFg6dKlmDdvHm7duoXS0lLY2dlBW1tb1mERQojcqcv76wYGBqJ3795o1KgRUlNTsWTJEmRnZ8PPzw8CgQABAQFYtmwZbGxsYGNjg2XLlkFTUxPDhg2Teixym0zHjh1brXphYWEfORJCCFEkdZdNnz9/jqFDh+LVq1cwMTFB+/btcf78eVhZWQF42zOZn5+PSZMmITMzE25ubjh+/Dh0dHSkHotcPs8UAJSUlGBlZQVnZ2dUtYsHDhyo0Xrpeab1Fz3PtH6j55nWX9J8nunzzEKJlm9ooCalSOqW3LZMJ06ciOjoaDx8+BBjx47FiBEjYGhoKOuwCCFErn3Kj1GThNyO5t20aROSk5Mxa9YsHD58GJaWlhg8eDCOHTtWZUuVEEJI7Snofe7lN5kCb4dYDx06FLGxsbh16xZatWqFSZMmwcrKCrm5ubIOjxBC5I6iPhxcbrt531f2RAHGGEpLS2UdDiGEyCVJb9rwqZLrlqlYLMbu3bvRpUsX2Nra4vr169iwYQOePn1Kl8YQQsjHoKD9vHLbMp00aRKio6PRqFEjjBkzBtHR0TAyMpJ1WIQQQuSQ3CbTLVu2oFGjRrC2tkZ8fDzi4+MrrLd///46jowQQuTXJ9y4lIjcJtNRo0Z9lJsZE0IIqZyiHnblNplGRETIOgRCCFE4ijoASW6TKSGEEBlQzFxKyZQQQoj0KGgule9LYwghhJC6QC1TQgghUkMDkAghhBAJ0QAkQgghREKK2jKlc6aEEEKIhKhlSgghRGqoZUoIIYSQWqGWKSGEEKmhAUiEEEKIhBS1m5eSKSGEEKlR0FxKyZQQQogUKWg2pQFIhBBCiISoZUoIIURqaAASIYQQIiEagEQIIYRISEFzKZ0zJYQQIkUCCaca2rRpE6ytraGurg4XFxecPXtWCjtRc5RMCSGESI1Awv9qIiYmBgEBAZg3bx6SkpLw+eefo3v37nj69OlH2rvKUTIlhBDySVq9ejX8/f0xbtw4tGzZEmvXroWlpSU2b95c57HQOVNCCCFSI+kAJLFYDLFYzCsTCoUQCoW8ssLCQly6dAmzZ8/mlXft2hWJiYmSBVELlExrSF2O3jGxWIyQkBDMmTOn3Af1U9StlYmsQ5AaefvbAEB+0gZZhyAV8vi3kSZJj5HBS0KwaNEiXllQUBCCg4N5Za9evUJJSQnMzMx45WZmZkhJSZEsiFoQMMZYnW+V1AvZ2dnQ09NDVlYWdHV1ZR0OeQf9beov+tt8XNVtmb548QINGjRAYmIi3N3dufKlS5ciKioK//zzT53EW0aO2lmEEEI+dRUlzooYGxtDWVm5XCs0NTW1XGu1LtAAJEIIIZ8cNTU1uLi4IDY2llceGxsLDw+POo+HWqaEEEI+SdOnT8fIkSPh6uoKd3d3bN26FU+fPsXEiRPrPBZKpgpMKBQiKCiIBlHUQ/S3qb/ob1N/DBkyBOnp6fj++++RnJwMe3t7HDlyBFZWVnUeCw1AIoQQQiRE50wJIYQQCVEyJYQQQiREyZQQQgiRECVTBREREQF9ff0aLTN69Gj069fvo8RDyKcoLi4OAoEAr1+/BlC77xWRT5RM5UBlSe/dL/6QIUNw9+7dug+OcOjHycc3evRoCASCCi+NmDRpEgQCAUaPHi217dH3ipShZKogNDQ0YGpqKuswCPnoLC0tER0djfz8fK6soKAAu3fvRqNGjaS6LfpekTKUTBVERd1RS5YsgampKXR0dDBu3DjMnj0brVu3LrfsDz/8AHNzcxgZGWHy5MkoKiqqm6AVSHx8PNq1awehUAhzc3PMnj0bxcXFAIDDhw9DX18fpaWlAIArV65AIBDgu+++45afMGEChg4dKpPY65s2bdqgUaNG2L9/P1e2f/9+WFpawtnZmStjjGHlypVo0qQJNDQ04OTkhF9//ZW3riNHjqB58+bQ0NCAt7c3Hj9+zJv//veqot6HgIAAeHl5ca+9vLwwdepUBAQEwMDAAGZmZti6dSvy8vIwZswY6OjooGnTpvjzzz8lfi9I3aFkqqB27tyJpUuXYsWKFbh06RIaNWpU4TMAT58+jQcPHuD06dOIjIxEREQEIiIi6j5gOfbvv/+iR48eaNu2La5evYrNmzcjNDQUS5YsAQB07NgROTk5SEpKAvA28RobGyM+Pp5bR1xcHDw9PWUSf300ZswYhIeHc6/DwsIwduxYXp358+cjPDwcmzdvxs2bN/Htt99ixIgR3Pv67Nkz9O/fHz169MCVK1e4H5zSEBkZCWNjY/z999+YOnUqvv76awwaNAgeHh64fPkyfH19MXLkSLx580Yq2yN1gJFPnp+fH1NWVmZaWlq8SV1dnQFgmZmZLDw8nOnp6XHLuLm5scmTJ/PW06FDB+bk5MRbr5WVFSsuLubKBg0axIYMGfKxd0ku+fn5sb59+5Yrnzt3LrO1tWWlpaVc2caNG5m2tjYrKSlhjDHWpk0b9sMPPzDGGOvXrx9bunQpU1NTY9nZ2Sw5OZkBYLdv366T/ajPyt7jtLQ0JhQK2aNHj9jjx4+Zuro6S0tLY3379mV+fn4sNzeXqaurs8TERN7y/v7+bOjQoYwxxubMmcNatmzJ+7vMmjWL+04xxsp9ryr6G0+bNo15enpyrz09Pdlnn33GvS4uLmZaWlps5MiRXFnZ3/TcuXMSviOkrlDLVE54e3vjypUrvOmXX36ptP6dO3fQrl07Xtn7rwGgVatWUFZW5l6bm5sjNTVVeoET3L59G+7u7hC881TlDh06IDc3F8+fPwfwtmswLi4OjDGcPXsWffv2hb29PRISEnD69GmYmZmhRYsWstqFesfY2Bg9e/ZEZGQkwsPD0bNnTxgbG3Pzb926hYKCAnTp0gXa2trctH37djx48ADA279L+/bteX+Xdx/1JQlHR0fu38rKyjAyMoKDgwNXVvbUE/qufTro3rxyQktLC82aNeOVlR2IK/PuQQJ4ew7pfaqqquWWKTt3R6SDMVbp36Ks3MvLC6Ghobh69SqUlJRgZ2cHT09PxMfHIzMzk7p4KzB27FhMmTIFALBx40bevLLP8B9//IEGDRrw5pXdc7ei78OHKCkplVuuojEGFX2v3i0r+7vTd+3TQS1TBWVra4u///6bV3bx4kUZRaPY7OzskJiYyDsIJyYmQkdHhzvQl503Xbt2LTw9PSEQCODp6Ym4uDg6X1qJbt26obCwEIWFhfD19eXNs7Ozg1AoxNOnT9GsWTPeZGlpydU5f/48b7n3X7/PxMQEycnJvLIrV65IvjOk3qNkqqCmTp2K0NBQREZG4t69e1iyZAmuXbtWroVEpCsrK6tcd/xXX32FZ8+eYerUqfjnn39w6NAhBAUFYfr06VBSevsV1dPTQ+vWrbFjxw5uZGjHjh1x+fJl3L17lzdalLylrKyM27dv4/bt27xTFQCgo6ODwMBAfPvtt4iMjMSDBw+QlJSEjRs3IjIyEgAwceJEPHjwANOnT8edO3ewa9euDw6+69SpEy5evIjt27fj3r17CAoKwo0bNz7WLpJ6hLp5FdTw4cPx8OFDBAYGoqCgAIMHD8bo0aPLtVaJdMXFxfEuzwAAPz8/HDlyBN999x2cnJxgaGgIf39/zJ8/n1fP29sbly9f5hKngYEB7Ozs8OLFC7Rs2bKuduGToqurW+m8xYsXw9TUFCEhIXj48CH09fXRpk0bzJ07FwDQqFEj7Nu3D99++y02bdqEdu3aYdmyZeVGBb/L19cXCxYswMyZM1FQUICxY8di1KhRuH79utT3jdQv9Ag2wunSpQtEIhGioqJkHQohhHxSqGWqoN68eYMtW7bA19cXysrK2L17N06cOIHY2FhZh0YIIZ8capkqqPz8fPTu3RuXL1+GWCyGra0t5s+fj/79+8s6NEII+eRQMiWEEEIkRKN5CSGEEAlRMiWEEEIkRMmUEEIIkRAlU0IIIURClEwJIYQQCVEyJaQeCA4O5j2YvaKHTNeFx48fQyAQ0P1kCakhSqaEVGH06NEQCATcUz2aNGmCwMBA5OXlfdTtrlu3rtoPYacESIjs0R2QCPmAbt26ITw8HEVFRTh79izGjRuHvLw8bN68mVevqKio3KO1aktPT08q6yGE1A1qmRLyAUKhECKRCJaWlhg2bBiGDx+OgwcPcl2zYWFhaNKkCYRCIRhjyMrKwldffQVTU1Po6uqiU6dOuHr1Km+dy5cvh5mZGXR0dODv74+CggLe/Pe7eUtLS7FixQo0a9YMQqEQjRo1wtKlSwEA1tbWAABnZ2cIBALeE2TCw8PRsmVLqKuro0WLFti0aRNvO3///TecnZ2hrq4OV1dXJCUlSfGdI0RxUMuUkBrS0NDgHvh8//597NmzB/v27eMe89WzZ08YGhriyJEj0NPTw88//wwfHx/cvXsXhoaG2LNnD4KCgrBx40Z8/vnniIqKwk8//YQmTZpUus05c+Zg27ZtWLNmDT777DMkJyfjn3/+AfA2IbZr1w4nTpxAq1atoKamBgDYtm0bgoKCsGHDBjg7OyMpKQnjx4+HlpYW/Pz8kJeXh169eqFTp07YsWMHHj16hGnTpn3kd48QOcUIIZXy8/Njffv25V7/9ddfzMjIiA0ePJgFBQUxVVVVlpqays0/efIk09XVZQUFBbz1NG3alP3888+MMcbc3d3ZxIkTefPd3NyYk5NThdvNzs5mQqGQbdu2rcIYHz16xACwpKQkXrmlpSXbtWsXr2zx4sXM3d2dMcbYzz//zAwNDVleXh43f/PmzRWuixBSNermJeQDfv/9d2hra0NdXR3u7u7o2LEj1q9fDwCwsrKCiYkJV/fSpUvIzc2FkZERtLW1uenRo0d48OABAOD27dtwd3fnbeP91++6ffs2xGIxfHx8qh1zWloanj17Bn9/f14cS5Ys4cXh5OQETU3NasVBCKkcdfMS8gHe3t7YvHkzVFVVYWFhwRtkpKWlxatbWloKc3NzxMXFlVuPvr5+rbavoaFR42VKS0sBvO3qdXNz480r645m9IwLQqSGkikhH6ClpYVmzZpVq26bNm2QkpICFRUVNG7cuMI6LVu2xPnz5zFq1Ciu7Pz585Wu08bGBhoaGjh58iTGjRtXbn7ZOdKSkhKuzMzMDA0aNMDDhw8xfPjwCtdrZ2eHqKgo5Ofncwm7qjgIIZWjbl5CpKhz585wd3dHv379cOzYMTx+/BiJiYmYP38+Ll68CACYNm0awsLCEBYWhrt37yIoKAg3b96sdJ3q6uqYNWsWZs6cie3bt+PBgwc4f/48QkNDAQCmpqbQ0NDA0aNH8fLlS2RlZQF4eyOIkJAQrFu3Dnfv3sX169cRHh6O1atXAwCGDRsGJSUl+Pv749atWzhy5Ah++OGHj/wOESKfKJkSIkUCgQBHjhxBx44dMXbsWDRv3hxffvklHj9+DDMzMwDAkCFDsHDhQsyaNQsuLi548uQJvv766yrXu2DBAsyYMQMLFy5Ey5YtMWTIEKSmpgIAVFRU8NNPP+Hnn3+GhYUF+vbtCwAYN24cfvnlF0RERMDBwQGenp6IiIjgLqXR1tbG4cOHcevWLTg7O2PevHlYsWLFR3x3CJFf9HBwQgghRELUMiWEEEIkRMmUEEIIkRAlU0IIIURClEwJIYQQCVEyJYQQQiREyZQQQgiRECVTQgghREKUTAkhhBAJUTIlhBBCJETJlBBCCJEQJVNCCCFEQv8HW8FYgzyeD7QAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mental Health</td>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.402847</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Dataset                Model  Accuracy\n",
       "0  Mental Health  Logistic Regression  0.402847"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 4))\n",
    "sns.heatmap(\n",
    "    confusion_matrix(y_test, logreg_preds),\n",
    "    annot=True,\n",
    "    fmt=\"d\",\n",
    "    cmap=\"Blues\",\n",
    "    xticklabels=logreg_mh.classes_,\n",
    "    yticklabels=logreg_mh.classes_\n",
    ")\n",
    "plt.title(\"Logistic Regression – Mental Health Confusion Matrix\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.show()\n",
    "mh_results = pd.DataFrame([\n",
    "    {\n",
    "        \"Dataset\": \"Mental Health\",\n",
    "        \"Model\": \"Logistic Regression\",\n",
    "        \"Accuracy\": accuracy_score(y_test, logreg_preds)\n",
    "    }\n",
    "])\n",
    "mh_results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d8fc4ff",
   "metadata": {},
   "source": [
    "##  Mental Health Model 1: Logistic Regression\n",
    "\n",
    "Logistic Regression is used as a strong baseline model for multi-class classification.\n",
    "\n",
    "Steps:\n",
    "- Scale features using StandardScaler\n",
    "- Train logistic regression using multinomial strategy\n",
    "- Predict depression category\n",
    "- Evaluate using:\n",
    "  - classification report\n",
    "  - confusion matrix\n",
    "  - accuracy summary table\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c648191",
   "metadata": {},
   "source": [
    "## 10. Mental Health Model 2 — Random Forest + Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5e9811b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Mental Health Model: Random Forest ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        High       0.31      0.03      0.05       345\n",
      "         Low       0.37      0.31      0.34       521\n",
      "      Medium       0.39      0.68      0.49       539\n",
      "\n",
      "    accuracy                           0.38      1405\n",
      "   macro avg       0.36      0.34      0.30      1405\n",
      "weighted avg       0.36      0.38      0.33      1405\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rf_mh = RandomForestClassifier(\n",
    "    n_estimators=200,\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "rf_mh.fit(X_train, y_train)\n",
    "rf_preds = rf_mh.predict(X_test)\n",
    "\n",
    "print(\"=== Mental Health Model: Random Forest ===\")\n",
    "print(classification_report(y_test, rf_preds))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "91b03c78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mental Health</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.382206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Dataset          Model  Accuracy\n",
       "0  Mental Health  Random Forest  0.382206"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 4))\n",
    "sns.heatmap(\n",
    "    confusion_matrix(y_test, rf_preds),\n",
    "    annot=True,\n",
    "    fmt=\"d\",\n",
    "    cmap=\"Greens\",\n",
    "    xticklabels=rf_mh.classes_,\n",
    "    yticklabels=rf_mh.classes_\n",
    ")\n",
    "plt.title(\"Random Forest – Mental Health Confusion Matrix\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.show()\n",
    "mh_results = pd.DataFrame([\n",
    "    {\n",
    "        \"Dataset\": \"Mental Health\",\n",
    "        \"Model\": \"Random Forest\",\n",
    "        \"Accuracy\": accuracy_score(y_test, rf_preds)\n",
    "    }\n",
    "])\n",
    "mh_results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c748f12",
   "metadata": {},
   "source": [
    "##  Mental Health Model 2: Random Forest\n",
    "\n",
    "Random Forest is trained as an alternative model to capture nonlinear patterns.\n",
    "\n",
    "Steps:\n",
    "- Train Random Forest directly on numeric features\n",
    "- Predict depression class\n",
    "- Evaluate using:\n",
    "  - classification report\n",
    "  - confusion matrix\n",
    "  - model accuracy summary\n",
    "\n",
    "This allows a direct comparison against logistic regression.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e1e6618c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mental Health</td>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.402847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mental Health</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.382206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Dataset                Model  Accuracy\n",
       "0  Mental Health  Logistic Regression  0.402847\n",
       "1  Mental Health        Random Forest  0.382206"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mental_health_results = pd.DataFrame([\n",
    "    {\n",
    "        \"Dataset\": \"Mental Health\",\n",
    "        \"Model\": \"Logistic Regression\",\n",
    "        \"Accuracy\": accuracy_score(y_test, logreg_preds)\n",
    "    },\n",
    "    {\n",
    "        \"Dataset\": \"Mental Health\",\n",
    "        \"Model\": \"Random Forest\",\n",
    "        \"Accuracy\": accuracy_score(y_test, rf_preds)\n",
    "    }\n",
    "])\n",
    "\n",
    "mental_health_results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8fcb2be",
   "metadata": {},
   "source": [
    "## 11. Explainable AI (SHAP) — Mental Health Model Interpretation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bea5e30b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x230 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import shap\n",
    "\n",
    "# SHAP Explainer for Random Forest\n",
    "explainer = shap.TreeExplainer(rf_mh)\n",
    "shap_values = explainer.shap_values(X_test)\n",
    "\n",
    "# Summary plot for multiclass\n",
    "shap.summary_plot(\n",
    "   shap_values[:, :, 1], X_test, feature_names=mh_features, class_names=rf_mh.classes_\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "239e74ad",
   "metadata": {},
   "source": [
    "##  Explainable AI (SHAP) for Mental Health Model\n",
    "\n",
    "To increase interpretability, SHAP is applied on the Random Forest mental health model.\n",
    "\n",
    "SHAP provides:\n",
    "- Feature contribution explanations\n",
    "- Global importance ranking of features\n",
    "\n",
    "A SHAP summary plot is produced to show which features\n",
    "(stress or anxiety) contribute most strongly to depression class predictions.\n",
    "\n",
    "This strengthens the dissertation by providing transparency and model explainability.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b80b050c",
   "metadata": {},
   "source": [
    "## 12. Instagram Dataset Preparation (Text + Labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a738769e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select relevant columns\n",
    "insta_cols = ['post_description', 'sentiment', 'stress_or_anxiety', 'hate']\n",
    "instagram_df = instagram_clean[insta_cols].dropna()\n",
    "\n",
    "# Encode target sentiment\n",
    "le_sent = LabelEncoder()\n",
    "instagram_df['sentiment_encoded'] = le_sent.fit_transform(instagram_df['sentiment'])\n",
    "\n",
    "y = instagram_df['sentiment_encoded']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0316129c",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf = TfidfVectorizer(\n",
    "    max_features=3000,\n",
    "    stop_words='english'\n",
    ")\n",
    "\n",
    "X_text = tfidf.fit_transform(instagram_df['post_description'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4381de8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_cols = ['stress_or_anxiety', 'hate']\n",
    "\n",
    "X_numeric = instagram_df[numeric_cols].copy()\n",
    "\n",
    "for col in X_numeric.columns:\n",
    "    if X_numeric[col].dtype == 'O':\n",
    "        X_numeric[col] = LabelEncoder().fit_transform(X_numeric[col])\n",
    "    else:\n",
    "        X_numeric[col] = X_numeric[col].fillna(0)\n",
    "\n",
    "X_numeric_sparse = csr_matrix(X_numeric.values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "de89682d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instagram feature matrix shape: (60127, 3002)\n"
     ]
    }
   ],
   "source": [
    "X = hstack([X_text, X_numeric_sparse])\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y,\n",
    "    test_size=0.2,\n",
    "    random_state=42,\n",
    "    stratify=y\n",
    ")\n",
    "\n",
    "print(\"Instagram feature matrix shape:\", X.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd578f28",
   "metadata": {},
   "source": [
    "## Instagram Dataset Preparation (Text + Sentiment Labels)\n",
    "\n",
    "This section prepares Instagram data for text-based sentiment classification.\n",
    "\n",
    "### Selected Columns\n",
    "Only the key columns are used:\n",
    "- post_description (text)\n",
    "- sentiment (target)\n",
    "- stress_or_anxiety (numeric/categorical feature)\n",
    "- hate (numeric/categorical feature)\n",
    "\n",
    "### Text Feature Engineering\n",
    "Text is converted into numeric form using:\n",
    "- TF-IDF Vectorisation (max_features=3000)\n",
    "\n",
    "### Additional Numeric Features\n",
    "Stress/hate columns are encoded and converted into a sparse matrix.\n",
    "\n",
    "Finally:\n",
    "- TF-IDF features + numeric features are merged using `hstack`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d5641be",
   "metadata": {},
   "source": [
    "## 13. Instagram Model 1 — SVM + Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7fd00ecb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instagram SVM Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       anger       0.70      0.09      0.16       323\n",
      "     disgust       0.73      0.10      0.18       184\n",
      "        fear       1.00      1.00      1.00      3361\n",
      "         joy       0.67      0.45      0.54      1045\n",
      "     neutral       0.79      0.98      0.87      6090\n",
      "     sadness       0.65      0.28      0.39       714\n",
      "    surprise       1.00      0.23      0.37       309\n",
      "\n",
      "    accuracy                           0.84     12026\n",
      "   macro avg       0.79      0.45      0.50     12026\n",
      "weighted avg       0.83      0.84      0.81     12026\n",
      "\n"
     ]
    }
   ],
   "source": [
    "svm_text = SVC(kernel='linear')\n",
    "svm_text.fit(X_train, y_train)\n",
    "pred = svm_text.predict(X_test)\n",
    "\n",
    "print(\"Instagram SVM Report\")\n",
    "print(classification_report(y_test, pred, target_names=le_sent.classes_))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b9fecfc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Instagram</td>\n",
       "      <td>SVM (Linear)</td>\n",
       "      <td>0.838932</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Dataset         Model  Accuracy\n",
       "0  Instagram  SVM (Linear)  0.838932"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,4))\n",
    "sns.heatmap(\n",
    "    confusion_matrix(y_test, pred),\n",
    "    annot=True,\n",
    "    fmt=\"d\",\n",
    "    cmap=\"Blues\",\n",
    "    xticklabels=le_sent.classes_,\n",
    "    yticklabels=le_sent.classes_\n",
    ")\n",
    "plt.title(\"SVM Confusion Matrix — Instagram Sentiment\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.show()\n",
    "instagram_results = pd.DataFrame([\n",
    "    {\n",
    "        \"Dataset\": \"Instagram\",\n",
    "        \"Model\": \"SVM (Linear)\",\n",
    "        \"Accuracy\": accuracy_score(y_test, pred)\n",
    "    }\n",
    "])\n",
    "instagram_results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "555624f7",
   "metadata": {},
   "source": [
    "## Instagram Model 1: SVM (Text Classification)\n",
    "\n",
    "A linear SVM classifier is trained on TF-IDF features to predict sentiment.\n",
    "\n",
    "Why SVM?\n",
    "- Performs strongly in high-dimensional text spaces (TF-IDF)\n",
    "- Works well for multi-class classification\n",
    "\n",
    "Evaluation includes:\n",
    "- classification report\n",
    "- confusion matrix heatmap\n",
    "- accuracy result stored for comparison\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bda3f8c6",
   "metadata": {},
   "source": [
    "## 14. Instagram Model 2 — Random Forest + Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "af0ce459",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Instagram Model: Random Forest ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       anger       0.87      0.24      0.38       323\n",
      "     disgust       0.70      0.18      0.29       184\n",
      "        fear       0.98      1.00      0.99      3361\n",
      "         joy       0.77      0.51      0.61      1045\n",
      "     neutral       0.80      0.97      0.88      6090\n",
      "     sadness       0.76      0.32      0.46       714\n",
      "    surprise       0.95      0.29      0.45       309\n",
      "\n",
      "    accuracy                           0.85     12026\n",
      "   macro avg       0.83      0.50      0.58     12026\n",
      "weighted avg       0.85      0.85      0.83     12026\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rf_insta = RandomForestClassifier(\n",
    "    n_estimators=200,\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "rf_insta.fit(X_train, y_train)\n",
    "rf_preds = rf_insta.predict(X_test)\n",
    "\n",
    "print(\"=== Instagram Model: Random Forest ===\")\n",
    "print(classification_report(y_test, rf_preds, target_names=le_sent.classes_))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d733ef5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Instagram</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.852819</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Dataset          Model  Accuracy\n",
       "0  Instagram  Random Forest  0.852819"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,4))\n",
    "sns.heatmap(\n",
    "    confusion_matrix(y_test, rf_preds),\n",
    "    annot=True,\n",
    "    fmt=\"d\",\n",
    "    cmap=\"Greens\",\n",
    "    xticklabels=le_sent.classes_,\n",
    "    yticklabels=le_sent.classes_\n",
    ")\n",
    "plt.title(\"Random Forest Confusion Matrix — Instagram Sentiment\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.show()\n",
    "instagram_results = pd.DataFrame([\n",
    "    {\n",
    "        \"Dataset\": \"Instagram\",\n",
    "        \"Model\": \"Random Forest\",\n",
    "        \"Accuracy\": accuracy_score(y_test, rf_preds)\n",
    "    }\n",
    "])\n",
    "instagram_results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b95a42fb",
   "metadata": {},
   "source": [
    "##  Instagram Model 2: Random Forest (Text + Numeric)\n",
    "\n",
    "Random Forest is trained on the combined feature matrix:\n",
    "- TF-IDF text vectors\n",
    "- stress/hate numeric features\n",
    "\n",
    "This model is evaluated and compared against SVM.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ab23a2d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Instagram</td>\n",
       "      <td>SVM (TF-IDF)</td>\n",
       "      <td>0.838932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Instagram</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.852819</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Dataset          Model  Accuracy\n",
       "0  Instagram   SVM (TF-IDF)  0.838932\n",
       "1  Instagram  Random Forest  0.852819"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instagram_results = pd.DataFrame([\n",
    "    {\n",
    "        \"Dataset\": \"Instagram\",\n",
    "        \"Model\": \"SVM (TF-IDF)\",\n",
    "        \"Accuracy\": accuracy_score(y_test, pred)\n",
    "    },\n",
    "    {\n",
    "        \"Dataset\": \"Instagram\",\n",
    "        \"Model\": \"Random Forest\",\n",
    "        \"Accuracy\": accuracy_score(y_test, rf_preds)\n",
    "    }\n",
    "])\n",
    "\n",
    "instagram_results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12f19847",
   "metadata": {},
   "source": [
    "## 15. Final Model Comparison (All Datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "630d55b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fitness</td>\n",
       "      <td>SVM (RBF)</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fitness</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mental Health</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mental Health</td>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Instagram</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Instagram</td>\n",
       "      <td>SVM</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Dataset                Model  Accuracy  F1-score\n",
       "0        Fitness            SVM (RBF)      0.96      0.96\n",
       "1        Fitness        Random Forest      0.94      0.94\n",
       "2  Mental Health        Random Forest      0.38      0.33\n",
       "3  Mental Health  Logistic Regression      0.40      0.33\n",
       "4      Instagram        Random Forest      0.85      0.83\n",
       "5      Instagram                  SVM      0.84      0.81"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# ============================================\n",
    "# MODEL PERFORMANCE COMPARISON TABLE\n",
    "# ============================================\n",
    "\n",
    "results = [\n",
    "    # Fitness models\n",
    "    {\"Dataset\": \"Fitness\", \"Model\": \"SVM (RBF)\", \"Accuracy\": 0.96, \"F1-score\": 0.96},\n",
    "    {\"Dataset\": \"Fitness\", \"Model\": \"Random Forest\", \"Accuracy\": 0.94, \"F1-score\": 0.94},\n",
    "\n",
    "    # Mental Health models\n",
    "    {\"Dataset\": \"Mental Health\", \"Model\": \"Random Forest\", \"Accuracy\": 0.38, \"F1-score\": 0.33},\n",
    "    {\"Dataset\": \"Mental Health\", \"Model\": \"Logistic Regression\", \"Accuracy\": 0.40, \"F1-score\": 0.33},\n",
    "\n",
    "    # Instagram models\n",
    "    {\"Dataset\": \"Instagram\", \"Model\": \"Random Forest\", \"Accuracy\": 0.85, \"F1-score\": 0.83},\n",
    "    {\"Dataset\": \"Instagram\", \"Model\": \"SVM\", \"Accuracy\": 0.84, \"F1-score\": 0.81},\n",
    "]\n",
    "\n",
    "results_df = pd.DataFrame(results)\n",
    "results_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc1c760b",
   "metadata": {},
   "source": [
    "##  Final Cross-Dataset Model Comparison\n",
    "\n",
    "This section produces a unified comparison of model performance across:\n",
    "\n",
    "- Fitness baseline models\n",
    "- Mental health baseline models\n",
    "- Instagram sentiment models\n",
    "\n",
    "Two key evaluation metrics are stored:\n",
    "- Accuracy\n",
    "- F1-score\n",
    "\n",
    "This provides a clear, examiner-friendly summary of performance trends.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "424ab29c",
   "metadata": {},
   "source": [
    "## 16. Visual Comparison (Bar Charts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3a240ad2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HixYtJCndX+IzonDhwgoLC7N5PMrd2Bs2bGhT191/kPD19dVzzz2nDz74QFu2bNHu3btVoEABDR061Lr8+M8//3zgTa7Onz+f7vL0ggULWrf/3d19U68NHzhwYJrXMioqStL9X8siRYpIko4ePXrfOiXpr7/+kmEYD1Vvnjx5bJ67uLjcs/3mzZtpjnv3eye1LTExUVevXpV0Z7nv22+/rbZt22r58uXasmWLtm7dqooVK6Yb9O51OcDfWSwW/fDDD2ratKnGjx+vKlWqKF++fOrbt691ee/9ru8vWLBgmtfi7p8LSXJ1dc1QGE29Hvs///mPLl68aL2b/JNPPqmvvvrK5j0nKUM3V0uv7oy+H7t06aLo6GgdP35c7du3V/78+VWjRg2tWbPGus/UqVP1xhtvaOnSpWrQoIHy5Mmjtm3b6uDBgw+s7UHi4+OtNUkP/x5IT6dOnfTRRx+pZ8+eWr16tX755Rdt3bpV+fLlsznGg8b1uD+T0p3Pl9jYWFWqVElDhgxR2bJlVbBgQQ0fPtzm3gcAYCau6QaATBAREaFhw4ZpxowZevfdd+/Zz8/PT1u2bJFhGDbB++zZs0pKSrJe6+jn56ekpCSdP3/eJmDcfb1s7ty5rbOj95qRCwkJeZyhPbZPPvnE5trJv1+bm56yZcvq2Wef1ZQpU3TgwAFVr15d+fLle+BNj/z8/JSQkJCm/fTp0+me9+4/fKRuHzx4sNq1a5fuOe6+pvnvmjZtqv/+979aunSp3nzzzfvWmjt3bjk4ODxUvY8rvWutz5w5IxcXF+ts++eff66uXbtqzJgxNv3OnTunXLlypdk/o9f3BwUFWcPugQMHtGjRIo0YMUKJiYmaMWOG9T2ekJCQJuSePn06016L1JscSrLeCOxu8+fPV1RUlPLlyydJOnnypAIDA+973PReh4d5P3bv3l3du3fXtWvXtH79eg0fPlytWrXSgQMHFBQUJE9PT40cOVIjR47UH3/8YZ0dbt26tX7//feMDT4dv/zyi86cOaMePXpY2x72PXC3S5cu6ZtvvtHw4cNtfg5u3bqlCxcu2PR90Lge92cyVfny5fXFF1/IMAzt3r1bMTExGjVqlNzd3R/4swoAmYGZbgDIBIUKFdKgQYPUunVr69dopadhw4a6evWqli5datM+d+5c63bpzh14JWnevHk2/ebPn2/z3MPDQw0aNNCOHTtUoUKFNDPTYWFh6c4KZqWSJUva1JP6Hbrnz59XYmJiuvukBonUGbjmzZtr7dq1911K2rBhQ+3bt0+//vqrTfvcuXNlsVisr+n96ixevLh27dqV7uv4oJn+Nm3aqHz58ho7dqz27t2bbp/Vq1fr+vXr8vT0VI0aNbRkyRKbmb+UlBR9/vnnKly4sEqUKHHfeh/WkiVLbGbAr1y5ouXLl6tOnTrWZdYWiyXN16Z9++23OnXqVKbVUaJECb311lsqX7689d8qdWn2559/btN369atiouLs/5cPK758+frxo0bGj16tNauXZvmkTdvXusS8yZNmsjR0VHTp09/pHM9yvvR09NTzZs319ChQ5WYmKjffvstTZ8CBQooIiJCzz33nPbv36/r168/Un0XLlxQ79695ezsrP79+1vbM/oeSO1z9+y3xWKRYRhpjjFz5kzrZQzpSW9cD/Mzea967q6tYsWKmjx5snLlypXm3wYAzMJMNwBkkvfee++Bfbp27aqPP/5Y3bp107Fjx1S+fHlt3LhRY8aMUYsWLdSoUSNJd37hr1u3rl5//XVdu3ZNYWFh2rRpkz777LM0x/zggw/05JNPqk6dOnrppZcUHBysK1eu6NChQ1q+fHmaa5kzy/Xr17VixQpJsl43Hhsbq3PnzlnDw/2sXbtWr776qjp37qxatWrJz89PZ8+e1YIFC7Rq1Sp17drVOus5atQorVy5UnXr1tWQIUNUvnx5Xbx4UatWrdKAAQNUqlQp9e/fX3PnzlXLli01atQoBQUF6dtvv9W0adP00ksvZSjEfvLJJ2revLmaNm2qiIgIFSpUSBcuXFBcXJx+/fVXLV68+J77Ojo66uuvv1aTJk1Us2ZNvfTSS2rQoIE8PT11/Phx63eZ//XXX5KksWPHqnHjxmrQoIEGDhwoFxcXTZs2TXv37tWCBQsy7S7xf6+vcePGGjBggFJSUjRu3DhdvnxZI0eOtPZp1aqVYmJiVKpUKVWoUEHbt2/XhAkTHuv7q3fv3q0+ffromWeeUfHixeXi4qIff/xRu3fvts4ylixZUi+88II+/PBDOTg4qHnz5ta7lwcGBtqEwscxa9Ys5c6dWwMHDrS5vj1V165dNWnSJO3atUsVK1bUkCFDNHr0aN24cUPPPfecfH19tW/fPp07d87mdUtPRt+PvXr1kru7u2rXrq2AgACdOXNGY8eOla+vr3U2vkaNGmrVqpUqVKig3LlzKy4uTp999plq1qyZoe8DP3jwoH7++WelpKTo/Pnz2rJli2bNmqXLly9r7ty5Klu2rLVvRt8DxYoVk7u7u+bNm6fSpUvLy8tLBQsWVMGCBVW3bl1NmDBBefPmVXBwsGJjYzVr1qw0M+UZGVdGfyZTv6bvv//9r7y9veXm5qaQkBD99NNPmjZtmtq2bauiRYvKMAwtWbJEFy9eVOPGjR/42gFAprDfPdwAIPv6+93L7+fuu5cbhmGcP3/e6N27txEQEGA4OTkZQUFBxuDBg9Pc9frixYtGZGSkkStXLsPDw8No3Lix8fvvv6e5e7lh3LlDdWRkpFGoUCHD2dnZyJcvn1GrVi3jnXfesemjh7h7+YQJEzLUL71HUFDQffc1DMM4ceKE8dZbbxm1a9c2/P39DScnJ8Pb29uoUaOG8eGHH1rvLP33/pGRkYa/v7/h7OxsFCxY0OjYsaPxxx9/WPscP37c6NSpk+Hn52c4OzsbJUuWNCZMmGBz1/EHjW/Xrl1Gx44djfz58xvOzs6Gv7+/8dRTTxkzZsx44JgM486/2+jRo40qVaoYXl5ehrOzs1GkSBHj+eefNzZt2mTTd8OGDcZTTz1leHp6Gu7u7sYTTzxhLF++3KbPvd5rqXfc/vPPP23au3XrZnh6eqYZ77hx44yRI0cahQsXNlxcXIzKlSsbq1evttn3r7/+Mnr06GHkz5/f8PDwMJ588kljw4YNRr169Wzex/e7K/bddy//448/jIiICKNUqVKGp6en4eXlZVSoUMGYPHmyzb9xcnKyMW7cOKNEiRKGs7OzkTdvXuP55583Tpw4YXP8evXqGWXLlk1z3m7dut33fbdr1y5DktGvX7979kn9+fr7neLnzp1rVKtWzXBzczO8vLyMypUr2/wM3asew8jY+3HOnDlGgwYNjAIFChguLi7W9/Xu3butfd58800jLCzMyJ07t+Hq6moULVrU6N+/v3Hu3Ll7jsUw/u/fIvXh5ORk+Pn5GTVr1jSGDBliHDt2LM0+GX0PGIZhLFiwwChVqpTh7Oxs87l08uRJo3379kbu3LkNb29vo1mzZsbevXuNoKAgo1u3bg89roz+TE6ZMsUICQkxHB0drZ91v//+u/Hcc88ZxYoVM9zd3Q1fX1+jevXqRkxMzH1fOwDITBbDuOu2lQAAIMc4duyYQkJCNGHCBA0cONDe5QAA8K/DNd0AAAAAAJiE0A0AAAAAgElYXg4AAAAAgEnsOtO9fv16tW7dWgULFpTFYknzFTrpiY2NVdWqVeXm5qaiRYtqxowZ5hcKAAAAAMAjsGvovnbtmipWrKiPPvooQ/2PHj2qFi1aqE6dOtqxY4eGDBmivn376quvvjK5UgAAAAAAHt4/Znm5xWLR119/rbZt296zzxtvvKFly5YpLi7O2ta7d2/t2rVLP/30UxZUCQAAAABAxjnZu4CH8dNPP6lJkyY2bU2bNtWsWbN0+/ZtOTs7p9nn1q1bunXrlvV5SkqKLly4ID8/P1ksFtNrBgAAAADkPIZh6MqVKypYsKAcHO69iDxbhe4zZ86oQIECNm0FChRQUlKSzp07p4CAgDT7jB07ViNHjsyqEgEAAAAA/yInTpxQ4cKF77k9W4VuSWlmp1NXx99r1nrw4MEaMGCA9fmlS5dUpEgRnThxQj4+PuYVCgAAAADIsS5fvqzAwEB5e3vft1+2Ct3+/v46c+aMTdvZs2fl5OQkPz+/dPdxdXWVq6trmnYfHx9CNwAAAADgsTzosmW73r38YdWsWVNr1qyxafvuu+8UFhaW7vXcAAAAAADYk11D99WrV7Vz507t3LlT0p2vBNu5c6fi4+Ml3Vka3rVrV2v/3r176/jx4xowYIDi4uIUHR2tWbNmaeDAgfYoHwAAAACA+7Lr8vJt27apQYMG1uep115369ZNMTExSkhIsAZwSQoJCdGKFSvUv39/ffzxxypYsKCmTp2q9u3bZ3ntAAAAAAA8yD/me7qzyuXLl+Xr66tLly5xTTcAZFPJycm6ffu2vcsAHpuzs7McHR3tXQYA4BFkNFtmqxupAQD+3QzD0JkzZ3Tx4kV7lwJkmly5csnf3/+BN+IBAGRPhG4AQLaRGrjz588vDw8PQgqyNcMwdP36dZ09e1aSFBAQYOeKAABmIHQDALKF5ORka+C+19dEAtmNu7u7pDtfgZo/f36WmgNADpStvjIMAPDvlXoNt4eHh50rATJX6nua+xQAQM5E6AYAZCssKUdOw3saAHI2QjcAAAAAACYhdAMAgAxZt26dLBbLQ909Pjg4WFOmTDGtJgAA/ukI3QAA5BARERGyWCzq3bt3mm1RUVGyWCyKiIjI+sIAAPgXI3QDAJCDBAYG6osvvtCNGzesbTdv3tSCBQtUpEgRO1YGAMC/E6EbAIAcpEqVKipSpIiWLFlibVuyZIkCAwNVuXJla9utW7fUt29f5c+fX25ubnryySe1detWm2OtWLFCJUqUkLu7uxo0aKBjx46lOd/mzZtVt25dubu7KzAwUH379tW1a9dMGx8AANkNoRsAgByme/fumj17tvV5dHS0IiMjbfq8/vrr+uqrrzRnzhz9+uuvCg0NVdOmTXXhwgVJ0okTJ9SuXTu1aNFCO3fuVM+ePfXmm2/aHGPPnj1q2rSp2rVrp927d2vhwoXauHGj+vTpY/4gAQDIJgjdAADkMF26dNHGjRt17NgxHT9+XJs2bdLzzz9v3X7t2jVNnz5dEyZMUPPmzVWmTBl9+umncnd316xZsyRJ06dPV9GiRTV58mSVLFlSnTt3TnM9+IQJE9SpUyf169dPxYsXV61atTR16lTNnTtXN2/ezMohAwDwj+Vk7wIAAEDmyps3r1q2bKk5c+bIMAy1bNlSefPmtW4/fPiwbt++rdq1a1vbnJ2dVb16dcXFxUmS4uLi9MQTT9h8h3TNmjVtzrN9+3YdOnRI8+bNs7YZhqGUlBQdPXpUpUuXNmuIAABkG4RuAAByoMjISOsy748//thmm2EYkmQTqFPbU9tS+9xPSkqKXnzxRfXt2zfNNm7aBgDAHYTuLFZ10Fx7l5Dltk/oau8SAOBfp1mzZkpMTJQkNW3a1GZbaGioXFxctHHjRnXq1EmSdPv2bW3btk39+vWTJJUpU0ZLly612e/nn3+2eV6lShX99ttvCg0NNWcQAADkAFzTDQBADuTo6Ki4uDjFxcXJ0dHRZpunp6deeuklDRo0SKtWrdK+ffvUq1cvXb9+XT169JAk9e7dW4cPH9aAAQO0f/9+zZ8/XzExMTbHeeONN/TTTz/p5Zdf1s6dO3Xw4EEtW7ZMr7zySlYNEwCAfzxCNwAAOZSPj498fHzS3fbee++pffv26tKli6pUqaJDhw5p9erVyp07t6Q7y8O/+uorLV++XBUrVtSMGTM0ZswYm2NUqFBBsbGxOnjwoOrUqaPKlSvr7bffVkBAgOljAwAgu7AYGbloKwe5fPmyfH19denSpXv+ImImlpcDwKO5efOmjh49qpCQELm5udm7HCDT8N4GgOwpo9mSmW4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkTvYuADlf/Kjy9i7BLooM22PvEgAAAADYGTPdAAAAAACYhNANAAAAAIBJCN0AAPzLzJo1S02aNLHLuffs2aPChQvr2rVrdjk/AABZjWu6AQDZXtVBc7PsXNsndH3ofc6ePau3335bK1eu1B9//KHcuXOrYsWKGjFihKpWraqCBQuqX79+euutt9LsO3bsWE2cOFGnT5/W/Pnz1b17d5UqVUpxcXE2/RYtWqTw8HAFBQXp2LFj96zl1q1bGjZsmL744gtr24gRIzRy5EhJksVikb+/vxo0aKD33ntPgYGB1n7169dXbGystV++fPlUt25dvf/++woKCpIkHTt2TCEhIWnO27lzZ33++ecqX768qlevrsmTJ6c7XgAAchpmugEAMFn79u21a9cuzZkzRwcOHNCyZctUv359XbhwQS4uLnr++ecVExMjwzDS7Dt79mx16dJFLi4ukiRPT0+dPXtWP/30k02/6OhoFSlS5IG1fPXVV/Ly8lKdOnVs2suWLauEhASdPHlSCxcu1J49e9SxY8c0+/fq1UsJCQk6deqU/ve//+nEiRN6/vnn0/T7/vvvlZCQYH18/PHH1m3du3fX9OnTlZyc/MB6AQDI7gjdAACY6OLFi9q4caPGjRunBg0aKCgoSNWrV9fgwYPVsmVLSVKPHj10+PBhrV+/3mbfDRs26ODBg+rRo4e1zcnJSZ06dVJ0dLS17eTJk1q3bp06der0wHq++OILPf3002nanZyc5O/vr4IFC6pOnTrq1auXfv75Z12+fNmmn4eHh/z9/RUQEKAnnnhCL7/8sn799dc0x/Pz85O/v7/14evra93WtGlTnT9/3jprDgBATkboBgDARF5eXvLy8tLSpUt169atdPuUL19e1apV0+zZs23ao6OjVb16dZUrV86mvUePHlq4cKGuX78uSYqJiVGzZs1UoECBB9azYcMGhYWF3bfPmTNntGTJEjk6OsrR0fGe/S5cuKDFixerRo0aDzzv37m4uKhixYrasGHDQ+0HAEB2ROgGAMBETk5OiomJ0Zw5c5QrVy7Vrl1bQ4YM0e7du236RUZG6ssvv9TVq1clSVevXtXixYttZrlTVapUScWKFdOXX34pwzAUExOjyMjIB9Zy8eJFXbx4UQULFkyzbc+ePfLy8pKHh4cCAgK0bt06vfzyy/L09LTpN23aNHl5ecnT01N+fn7av3+/zax7qlq1aln/4ODl5aUdO3bYbC9UqNB9rz0HACCnIHQDAGCy9u3b6/Tp01q2bJmaNm2qdevWqUqVKoqJibH2ee6555SSkqKFCxdKkhYuXCjDMPTss8+me8zIyEjNnj1bsbGxunr1qlq0aPHAOm7cuCFJcnNzS7OtZMmS2rlzp7Zu3ap3331XlSpV0rvvvpumX+fOnbVz507t2rVLGzduVGhoqJo0aaIrV67Y9Fu4cKF27txpfZQpU8Zmu7u7u3WmHgCAnIzQDQBAFnBzc1Pjxo01bNgwbd68WRERERo+fLh1u6+vrzp06GBdYj579mx16NBBPj4+6R6vc+fO+vnnnzVixAh17dpVTk4P/kISPz8/WSwW/fXXX2m2ubi4KDQ0VGXLltWQIUNUqVIlvfTSS2n6+fr6KjQ0VKGhoapdu7ZmzZqlgwcPWv9YkCowMNDaLzQ0VK6urjbbL1y4oHz58j2wZgAAsjtCNwAAdlCmTJk031Xdo0cPbdq0Sd988402bdqU7tLyVHny5NHTTz+t2NjYDC0tl+4E6zJlymjfvn0P7Pv2229rwYIF6d4k7e9Sr/lOnUXPqL1796py5coPtQ8AANkRoRsAABOdP39eTz31lD7//HPt3r1bR48e1eLFizV+/Hi1adPGpm+9evUUGhqqrl27KjQ0VHXr1r3vsWNiYnTu3DmVKlUqw/U0bdpUGzdufGC/okWLqk2bNho2bJhN+/Xr13XmzBmdOXNGu3btUlRUlNzc3NSkSZMM13Ds2DGdOnVKjRo1yvA+AABkV4RuAABM5OXlpRo1amjy5MmqW7euypUrp7ffflu9evXSRx99lKZ/ZGSk/vrrrwzNXru7u8vPz++h6unVq5dWrFihS5cuPbDva6+9pm+//VZbtmyxtn366acKCAhQQECAGjRooD///FMrVqxQyZIlM1zDggUL1KRJEwUFBT1U7QAAZEcWwzAMexeRlS5fvixfX19dunTpntfJmanqoLlZfk57+9p7gr1LsIsiw/bYuwQgR7l586aOHj2qkJCQdG8Ehozr2LGjKleurMGDB2f5uW/duqXixYtrwYIFql27dpaf/5+I9zYAZE8ZzZbMdAMA8C8zYcIEeXl52eXcx48f19ChQwncAIB/jQff6hQAAOQoQUFBeuWVV+xy7hIlSqhEiRJ2OTcAAPbATDcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAkMMFBwdrypQp9i4DAIB/JSd7FwAAwOOKH1U+y85VZNieh94nIiJCc+bMkSQ5OjqqYMGCatmypcaMGaPcuXNndon/GCNGjNDIkSPTtK9Zs0aNGjWyQ0V3alq6dKl27txpl/MDAP59CN0AAGSBZs2aafbs2UpKStK+ffsUGRmpixcvasGCBfYuzVRly5bV999/b9OWJ0+eRzpWYmKiXFxcMqMsAACyDMvLAQDIAq6urvL391fhwoXVpEkThYeH67vvvrNuT05OVo8ePRQSEiJ3d3eVLFlSH3zwgc0xIiIi1LZtW73//vsKCAiQn5+fXn75Zd2+fdva5+zZs2rdurXc3d0VEhKiefPmpaklPj5ebdq0kZeXl3x8fNSxY0f98ccf1u0jRoxQpUqVFB0drSJFisjLy0svvfSSkpOTNX78ePn7+yt//vx69913HzhuJycn+fv72zxSg/OePXv01FNPyd3dXX5+fnrhhRd09erVNOMdO3asChYsqBIlSkiSTp06pfDwcOXOnVt+fn5q06aNjh07Zt1v3bp1ql69ujw9PZUrVy7Vrl1bx48fV0xMjEaOHKldu3bJYrHIYrEoJibmgWMAAOBxMNMNAEAWO3LkiFatWiVnZ2drW0pKigoXLqxFixYpb9682rx5s1544QUFBASoY8eO1n5r165VQECA1q5dq0OHDik8PFyVKlVSr169JN0JqidOnNCPP/4oFxcX9e3bV2fPnrXubxiG2rZtK09PT8XGxiopKUlRUVEKDw/XunXrrP0OHz6slStXatWqVTp8+LA6dOigo0ePqkSJEoqNjdXmzZsVGRmphg0b6oknnnjo1+D69etq1qyZnnjiCW3dulVnz55Vz5491adPH5sg/MMPP8jHx0dr1qyRYRi6fv26GjRooDp16mj9+vVycnLSO++8o2bNmmn37t1ycHBQ27Zt1atXLy1YsECJiYn65ZdfZLFYFB4err1792rVqlXW2XdfX9+Hrh0AgIdB6AYAIAt888038vLyUnJysm7evClJmjRpknW7s7OzzfXPISEh2rx5sxYtWmQTunPnzq2PPvpIjo6OKlWqlFq2bKkffvhBvXr10oEDB7Ry5Ur9/PPPqlGjhiRp1qxZKl26tHX/77//Xrt379bRo0cVGBgoSfrss89UtmxZbd26VdWqVZN0548A0dHR8vb2VpkyZdSgQQPt379fK1askIODg0qWLKlx48Zp3bp19w3de/bskZeXl/V5mTJl9Msvv2jevHm6ceOG5s6dK09PT0nSRx99pNatW2vcuHEqUKCAJMnT01MzZ860zo5HR0fLwcFBM2fOlMVikSTNnj1buXLl0rp16xQWFqZLly6pVatWKlasmCTZjN/Ly8s6+w4AQFYgdAMAkAUaNGig6dOn6/r165o5c6YOHDigV155xabPjBkzNHPmTB0/flw3btxQYmKiKlWqZNOnbNmycnR0tD4PCAjQnj13bu4WFxcnJycnhYWFWbeXKlVKuXLlsj6Pi4tTYGCgNXBLd4Jwrly5FBcXZw3dwcHB8vb2tvYpUKCAHB0d5eDgYNP291n09JQsWVLLli2zPnd1dbXWUbFiRWvglqTatWsrJSVF+/fvt4bu8uXL21zHvX37dh06dMimNkm6efOmDh8+rCZNmigiIkJNmzZV48aN1ahRI3Xs2FEBAQH3rRMAALNwTTcAAFnA09NToaGhqlChgqZOnapbt27ZzGwvWrRI/fv3V2RkpL777jvt3LlT3bt3V2Jios1x/r4kXZIsFotSUlIk3Vk6ntp2L4ZhpLv97vb0znO/c9+Li4uLQkNDrY/UsH+vOu6u/++hXLozA1+1alXt3LnT5nHgwAF16tRJ0p2Z759++km1atXSwoULVaJECf3888/3rRMAALMQugEAsIPhw4fr/fff1+nTpyVJGzZsUK1atRQVFaXKlSsrNDRUhw8ffqhjli5dWklJSdq2bZu1bf/+/bp48aL1eZkyZRQfH68TJ05Y2/bt26dLly7ZLMM2W5kyZbRz505du3bN2rZp0yY5ODhYb5iWnipVqujgwYPKnz+/TZgPDQ21uT67cuXKGjx4sDZv3qxy5cpp/vz5ku78ESA5Odm8gQEAcBdCNwAAdlC/fn2VLVtWY8aMkSSFhoZq27ZtWr16tQ4cOKC3335bW7dufahjlixZUs2aNVOvXr20ZcsWbd++XT179pS7u7u1T6NGjVShQgV17txZv/76q3755Rd17dpV9erVs1mWbrbOnTvLzc1N3bp10969e7V27Vq98sor6tKli3Vp+b32y5s3r9q0aaMNGzbo6NGjio2N1auvvqqTJ0/q6NGjGjx4sH766ScdP35c3333nQ4cOGD9g0JwcLCOHj2qnTt36ty5c7p161ZWDRkA8C9F6AYAwE4GDBigTz/9VCdOnFDv3r3Vrl07hYeHq0aNGjp//ryioqIe+pizZ89WYGCg6tWrp3bt2umFF15Q/vz5rdstFouWLl2q3Llzq27dumrUqJGKFi2qhQsXZubQHsjDw0OrV6/WhQsXVK1aNXXo0EENGzbURx999MD91q9fryJFiqhdu3YqXbq0IiMjdePGDfn4+MjDw0O///672rdvrxIlSuiFF15Qnz599OKLL0qS2rdvr2bNmqlBgwbKly9fjv+edACA/VmM1AvA/iUuX74sX19fXbp0ST4+Pll+/qqD5mb5Oe3ta+8J9i7BLooM22PvEoAc5ebNmzp69KhCQkLk5uZm73KATMN7GwCyp4xmS2a6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAgBwgODhYU6ZMeeT9Y2JilCtXrkyrJyepX7+++vXrZ+8yAADZlJO9CwAA4HHV/rB2lp1r0yubHnqfiIgIXbx4UUuXLs38gv6/rVu3ytPTM0N9g4OD1a9fP5sgGR4erhYtWjzy+WNiYtS9e3fr8/z586t69ep67733VLZs2Uc+7j/BkiVL5OzsbO8yAADZFDPdAADkAPny5ZOHh8cj7+/u7q78+fM/Vg0+Pj5KSEjQ6dOn9e233+ratWtq2bKlEhMTH+u4D3L79m1Tj58nTx55e3ubeg4AQM5F6AYAwM5iY2NVvXp1ubq6KiAgQG+++aaSkpKs269cuaLOnTvL09NTAQEBmjx5cpolz3cvLx8xYoSKFCkiV1dXFSxYUH379pV0Z6n08ePH1b9/f1ksFlksFknpLy9ftmyZwsLC5Obmprx586pdu3b3HYfFYpG/v78CAgIUFham/v376/jx49q/f7+1z+bNm1W3bl25u7srMDBQffv21bVr16zbExIS1LJlS7m7uyskJETz589PMzaLxaIZM2aoTZs28vT01DvvvCNJWr58uapWrSo3NzcVLVpUI0eOtHkd7/WaSNK0adNUvHhxubm5qUCBAurQoYN1292v9V9//aWuXbsqd+7c8vDwUPPmzXXw4EHr9tTXcvXq1SpdurS8vLzUrFkzJSQk3Pf1AwDkTIRuAADs6NSpU2rRooWqVaumXbt2afr06Zo1a5Y1SErSgAEDtGnTJi1btkxr1qzRhg0b9Ouvv97zmF9++aUmT56sTz75RAcPHtTSpUtVvnx5SXeWShcuXFijRo1SQkLCPYPgt99+q3bt2qlly5basWOHfvjhB4WFhWV4XBcvXtT8+fMlybo0e8+ePWratKnatWun3bt3a+HChdq4caP69Olj3a9r1646ffq01q1bp6+++kr//e9/dfbs2TTHHz58uNq0aaM9e/YoMjJSq1ev1vPPP6++fftq3759+uSTTxQTE6N33333ga/Jtm3b1LdvX40aNUr79+/XqlWrVLdu3XuOLSIiQtu2bdOyZcv0008/yTAMtWjRwmbG/fr163r//ff12Wefaf369YqPj9fAgQMz/PoBAHIOrukGAMCOpk2bpsDAQH300UeyWCwqVaqUTp8+rTfeeEPDhg3TtWvXNGfOHM2fP18NGzaUJM2ePVsFCxa85zHj4+Pl7++vRo0aydnZWUWKFFH16tUl3Vkq7ejoKG9vb/n7+9/zGO+++66effZZjRw50tpWsWLF+47l0qVL8vLykmEYun79uiTp6aefVqlSpSRJEyZMUKdOnayzxsWLF9fUqVNVr149TZ8+XceOHdP333+vrVu3WgP+zJkzVbx48TTn6tSpkyIjI63Pu3TpojfffFPdunWTJBUtWlSjR4/W66+/ruHDh9/3NYmPj5enp6datWolb29vBQUFqXLlyumO8eDBg1q2bJk2bdqkWrVqSZLmzZunwMBALV26VM8884ykO0veZ8yYoWLFikmS+vTpo1GjRt339QMA5EzMdAMAYEdxcXGqWbOmdZm3JNWuXVtXr17VyZMndeTIEd2+fdsaECXJ19dXJUuWvOcxn3nmGd24cUNFixZVr1699PXXX9sss86InTt3WkN+Rnl7e2vnzp3avn27NXDOmDHDun379u2KiYmRl5eX9dG0aVOlpKTo6NGj2r9/v5ycnFSlShXrPqGhocqdO3eac9096759+3aNGjXK5ti9evVSQkKCrl+/ft/XpHHjxgoKClLRokXVpUsXzZs3z/pHg7vFxcXJyclJNWrUsLb5+fmpZMmSiouLs7Z5eHhYA7ckBQQEpDtjDwDI+QjdAADYkWEYNoE7tU26c+3y3/87vT7pCQwM1P79+/Xxxx/L3d1dUVFRqlu37kPdcMzd3T3DfVM5ODgoNDRUpUqV0osvvqguXbooPDzcuj0lJUUvvviidu7caX3s2rVLBw8eVLFixe45pvTa775Te0pKikaOHGlz7D179ujgwYNyc3O772vi7e2tX3/9VQsWLFBAQICGDRumihUr6uLFixmqJbX97/9Gd9/t/O//lgCAfxdCNwAAdlSmTBlt3rzZJpBt3rxZ3t7eKlSokIoVKyZnZ2f98ssv1u2XL1+2uXFXetzd3fX0009r6tSpWrdunX766Sft2bNHkuTi4qLk5OT77l+hQgX98MMPjzEyqX///tq1a5e+/vprSVKVKlX022+/KTQ0NM3DxcVFpUqVUlJSknbs2GE9xqFDh9INv3erUqWK9u/fn+6xHRzu/Lpzv9fEyclJjRo10vjx47V7924dO3ZMP/74Y5rzlClTRklJSdqyZYu17fz58zpw4IBKly79OC8XACCH4ppuAACywKVLl7Rz506btjx58igqKkpTpkzRK6+8oj59+mj//v0aPny4BgwYIAcHB3l7e6tbt24aNGiQ8uTJo/z582v48OFycHBIM/udKiYmRsnJyapRo4Y8PDz02Wefyd3dXUFBQZLu3Ol8/fr1evbZZ+Xq6qq8efOmOcbw4cPVsGFDFStWTM8++6ySkpK0cuVKvf766xkes4+Pj3r27Knhw4erbdu2euONN/TEE0/o5ZdfVq9eveTp6am4uDitWbNGH374oUqVKqVGjRrphRde0PTp0+Xs7KzXXntN7u7u9xxrqmHDhqlVq1YKDAzUM888IwcHB+3evVt79uzRO++8c9/X5JtvvtGRI0dUt25d5c6dWytWrFBKSkq6S/iLFy+uNm3aqFevXvrkk0/k7e2tN998U4UKFVKbNm0y/NoAAP49CN0AAGSBdevWpbk5V7du3RQTE6MVK1Zo0KBBqlixovLkyaMePXrorbfesvabNGmSevfurVatWsnHx0evv/66Tpw4ITc3t3TPlStXLr333nsaMGCAkpOTVb58eS1fvlx+fn6SpFGjRunFF19UsWLFdOvWrXSXPdevX1+LFy/W6NGj9d5778nHx+e+d/S+l1dffVVTp07V4sWL1bFjR8XGxmro0KGqU6eODMNQsWLFbJagz507Vz169FDdunXl7++vsWPH6rfffrvnWFM1bdpU33zzjUaNGqXx48fL2dlZpUqVUs+ePR/4muTKlUtLlizRiBEjdPPmTRUvXlwLFixQ2bJl0z3X7Nmz9eqrr6pVq1ZKTExU3bp1tWLFijRLyoF/mvhR5e1dQpYrMmyPvUsAZDH+ZRcYXb58Wb6+vrp06ZJ8fHyy/PxVB83N8nPa29feE+xdgl3wIQ9krps3b+ro0aMKCQl5YADL6a5du6ZChQpp4sSJ6tGjh73LMdXJkycVGBio77///qFv7JZd8N5GViF0A5kro9mSmW4AAP7hduzYod9//13Vq1fXpUuXrF89lROXM//444+6evWqypcvr4SEBL3++usKDg5+pFl2AAD+CQjdAABkA++//772798vFxcXVa1aVRs2bEj3Wuzs7vbt2xoyZIiOHDkib29v1apVS/PmzWPpNgAg2yJ0AwDwD1e5cmVt377d3mVkiaZNm6pp06b2LgMAgEzDV4YBAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmsftXhk2bNk0TJkxQQkKCypYtqylTpqhOnTr37D9v3jyNHz9eBw8elK+vr5o1a6b3339ffn5+WVg1AAAAAPzzxNatZ+8Ssly99bH2LuG+7Bq6Fy5cqH79+mnatGmqXbu2PvnkEzVv3lz79u1TkSJF0vTfuHGjunbtqsmTJ6t169Y6deqUevfurZ49e+rrr7+2wwgAAP8EWfkLxqP8j/3s2bN6++23tXLlSv3xxx/KnTu3KlasqCFDhqh9+/bq16+f3nrrrTT7jR07VhMnTtTp06c1f/58de/eXaVKlVJcXJxNv0WLFik8PFxBQUE6duzYow4NAACYwK7LyydNmqQePXqoZ8+eKl26tKZMmaLAwEBNnz493f4///yzgoOD1bdvX4WEhOjJJ5/Uiy++qG3btmVx5QAAZFz79u21a9cuzZkzRwcOHNCyZctUv359Xb16Vc8//7xiYmJkGEaa/WbPnq0uXbrIxcVFkuTp6amzZ8/qp59+sukXHR2d7h+rAQCA/dktdCcmJmr79u1q0qSJTXuTJk20efPmdPepVauWTp48qRUrVsgwDP3xxx/68ssv1bJly6woGQCAh3bx4kVt3LhR48aNU4MGDRQUFKTq1atr8ODBatmypXr06KHDhw9r/fr1Nvtt2LBBBw8eVI8ePaxtTk5O6tSpk6Kjo61tJ0+e1Lp169SpU6csGxMAAMg4u4Xuc+fOKTk5WQUKFLBpL1CggM6cOZPuPrVq1dK8efMUHh4uFxcX+fv7K1euXPrwww/veZ5bt27p8uXLNg8AALKKl5eXvLy8tHTpUt26dSvN9vLly6tatWqaPXu2TXt0dLSqV6+ucuXK2bT36NFDCxcu1PXr1yVJMTExatasWZr/nwIAgH8Gu9+93GKx2Dw3DCNNW6p9+/apb9++GjZsmLZv365Vq1bp6NGj6t279z2PP3bsWPn6+lofgYGBmVo/AAD34+TkpJiYGM2ZM0e5cuVS7dq1NWTIEO3evdvaJzIyUl9++aWuXr0qSbp69aoWL15sM8udqlKlSipWrJi+/PJLGYahmJgYRUZGZtl4AADAw7Fb6M6bN68cHR3TzGqfPXv2nn+tHzt2rGrXrq1BgwapQoUKatq0qaZNm6bo6GglJCSku8/gwYN16dIl6+PEiROZPhYAAO6nffv2On36tJYtW6amTZtq3bp1qlKlimJiYiRJzz33nFJSUrRw4UJJd240ahiGnn322XSPFxkZqdmzZys2NlZXr15VixYtsmooAADgIdktdLu4uKhq1apas2aNTfuaNWtUq1atdPe5fv26HBxsS3Z0dJSkdG9AI0murq7y8fGxeQAAkNXc3NzUuHFjDRs2TJs3b1ZERISGDx8uSfL19VWHDh2sS8xnz56tDh063PP/WZ07d9bPP/+sESNGqGvXrnJysvs3gAIAgHuw6/LyAQMGaObMmYqOjlZcXJz69++v+Ph463LxwYMHq2vXrtb+rVu31pIlSzR9+nQdOXJEmzZtUt++fVW9enUVLFjQXsMAAOChlSlTRteuXbM+79GjhzZt2qRvvvlGmzZtSndpeao8efLo6aefVmxsLEvLAQD4h7Prn8bDw8N1/vx5jRo1SgkJCSpXrpxWrFihoKAgSVJCQoLi4+Ot/SMiInTlyhV99NFHeu2115QrVy499dRTGjdunL2GAADAfZ0/f17PPPOMIiMjVaFCBXl7e2vbtm0aP3682rRpY+1Xr149hYaGqmvXrgoNDVXdunXve9yYmBhNmzZNfn5+Zg8BAAA8BruvR4uKilJUVFS621Kvdfu7V155Ra+88orJVQEAkDm8vLxUo0YNTZ48WYcPH9bt27cVGBioXr16aciQITZ9IyMjNWTIEA0aNOiBx3V3d5e7u7tZZQMAgExiMe51MXQOdfnyZfn6+urSpUt2ub676qC5WX5Oe/vae4K9S7CLIsP22LsEIEe5efOmjh49qpCQELm5udm7HCDT8N5GVokfVd7eJWS5f+PvY7F169m7hCxXb32sXc6b0Wxp968MAwAAAAAgpyJ0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAIFv5l93/E/8CvKcBIGcjdAMAsgVnZ2dJ0vXr1+1cCZC5Ut/Tqe9xAEDOYvfv6QYAICMcHR2VK1cunT17VpLk4eEhi8Vi56qAR2cYhq5fv66zZ88qV65ccnR0tHdJAAATELoBANmGv7+/JFmDN5AT5MqVy/reBgDkPIRuAEC2YbFYFBAQoPz58+v27dv2Lgd4bM7OzsxwA0AOR+gGAGQ7jo6OBBUAAJAtcCM1AAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMYvfQPW3aNIWEhMjNzU1Vq1bVhg0b7tv/1q1bGjp0qIKCguTq6qpixYopOjo6i6oFAAAAACDjnOx58oULF6pfv36aNm2aateurU8++UTNmzfXvn37VKRIkXT36dixo/744w/NmjVLoaGhOnv2rJKSkrK4cgAAAAAAHsyuoXvSpEnq0aOHevbsKUmaMmWKVq9erenTp2vs2LFp+q9atUqxsbE6cuSI8uTJI0kKDg7OypIBAAAAAMgwuy0vT0xM1Pbt29WkSROb9iZNmmjz5s3p7rNs2TKFhYVp/PjxKlSokEqUKKGBAwfqxo0bWVEyAAAAAAAPxW4z3efOnVNycrIKFChg016gQAGdOXMm3X2OHDmijRs3ys3NTV9//bXOnTunqKgoXbhw4Z7Xdd+6dUu3bt2yPr98+XLmDQIAAAAAgPuw+43ULBaLzXPDMNK0pUpJSZHFYtG8efNUvXp1tWjRQpMmTVJMTMw9Z7vHjh0rX19f6yMwMDDTxwAAAAAAQHrsFrrz5s0rR0fHNLPaZ8+eTTP7nSogIECFChWSr6+vta106dIyDEMnT55Md5/Bgwfr0qVL1seJEycybxAAAAAAANyH3UK3i4uLqlatqjVr1ti0r1mzRrVq1Up3n9q1a+v06dO6evWqte3AgQNycHBQ4cKF093H1dVVPj4+Ng8AAAAAALKCXZeXDxgwQDNnzlR0dLTi4uLUv39/xcfHq3fv3pLuzFJ37drV2r9Tp07y8/NT9+7dtW/fPq1fv16DBg1SZGSk3N3d7TUMAAAAAADSZdevDAsPD9f58+c1atQoJSQkqFy5clqxYoWCgoIkSQkJCYqPj7f29/Ly0po1a/TKK68oLCxMfn5+6tixo9555x17DQEAAAAAgHuya+iWpKioKEVFRaW7LSYmJk1bqVKl0ixJBwAAAADgn8judy8HAAAAACCnInQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgkscK3YcOHdLq1at148YNSZJhGJlSFAAAAAAAOcEjhe7z58+rUaNGKlGihFq0aKGEhARJUs+ePfXaa69laoEAAAAAAGRXjxS6+/fvLycnJ8XHx8vDw8PaHh4erlWrVmVacQAAAAAAZGdOj7LTd999p9WrV6tw4cI27cWLF9fx48czpTAAAAAAALK7R5rpvnbtms0Md6pz587J1dX1sYsCAAAAACAneKTQXbduXc2dO9f63GKxKCUlRRMmTFCDBg0yrTgAAAAAALKzR1pePmHCBNWvX1/btm1TYmKiXn/9df3222+6cOGCNm3alNk1AgAAAACQLT3STHeZMmW0e/duVa9eXY0bN9a1a9fUrl077dixQ8WKFcvsGgEAAAAAyJYeeqb79u3batKkiT755BONHDnSjJoAAAAAAMgRHnqm29nZWXv37pXFYjGjHgAAAAAAcoxHWl7etWtXzZo1K7NrAQAAAAAgR3mkG6klJiZq5syZWrNmjcLCwuTp6WmzfdKkSZlSHAAAAAAA2dkjhe69e/eqSpUqkqQDBw7YbGPZOQAAAAAAdzxS6F67dm1m1wEAAAAAQI7zSNd0/93Jkyd16tSpzKgFAAAAAIAc5ZFCd0pKikaNGiVfX18FBQWpSJEiypUrl0aPHq2UlJTMrhEAAAAAgGzpkZaXDx06VLNmzdJ7772n2rVryzAMbdq0SSNGjNDNmzf17rvvZnadAAAAAABkO48UuufMmaOZM2fq6aeftrZVrFhRhQoVUlRUFKEbAAAAAAA94vLyCxcuqFSpUmnaS5UqpQsXLjx2UQAAAAAA5ASPFLorVqyojz76KE37Rx99pIoVKz52UQAAAAAA5ASPtLx8/Pjxatmypb7//nvVrFlTFotFmzdv1okTJ7RixYrMrhEAAAAAgGzpkWa669Wrp/379+s///mPLl68qAsXLqhdu3bav3+/6tSpk9k1AgAAAACQLT3STLckFSpUiBumAQAAAABwH4800z179mwtXrw4TfvixYs1Z86cxy4KAAAAAICc4JFC93vvvae8efOmac+fP7/GjBnz2EUBAAAAAJATPFLoPn78uEJCQtK0BwUFKT4+/rGLAgAAAAAgJ3ik0J0/f37t3r07TfuuXbvk5+f32EUBAAAAAJATPFLofvbZZ9W3b1+tXbtWycnJSk5O1o8//qhXX31Vzz77bGbXCAAAAABAtvRIdy9/5513dPz4cTVs2FBOTncOkZKSoq5du3JNNwAAAAAA/98jhW4XFxctXLhQ77zzjnbu3Cl3d3eVL19eQUFBmV0fAAAAAADZ1iN/T7ckFS9eXMWLF1dycrL27NkjHx8f5c6dO7NqAwAAAAAgW3uka7r79eunWbNmSZKSk5NVr149ValSRYGBgVq3bl1m1gcAAAAAQLb1SKH7yy+/VMWKFSVJy5cv15EjR/T777+rX79+Gjp0aKYWCAAAAABAdvVIofvcuXPy9/eXJK1YsUIdO3ZUiRIl1KNHD+3ZsydTCwQAAAAAILt6pNBdoEAB7du3T8nJyVq1apUaNWokSbp+/bocHR0ztUAAAAAAALKrR7qRWvfu3dWxY0cFBATIYrGocePGkqQtW7aoVKlSmVogAAAAAADZ1SOF7hEjRqhcuXI6ceKEnnnmGbm6ukqSHB0d9eabb2ZqgQAAAAAAZFeP/JVhHTp0kCSdPHlSKSkpcnBwULdu3TKtMAAAAAAAsrtHuqb778qUKaNjx45lQikAAAAAAOQsjx26DcPIjDoAAAAAAMhxHjt0AwAAAACA9D126B4yZIjy5MmTGbUAAAAAAJCjPPKN1FINHjw4M+oAAAAAACDHydTl5SdOnFBkZGRmHhIAAAAAgGwrU0P3hQsXNGfOnMw8JAAAAAAA2dZDLS9ftmzZfbcfOXLksYoBAAAAACAneajQ3bZtW1kslvt+TZjFYnnsogAAAAAAyAkeanl5QECAvvrqK6WkpKT7+PXXX82qEwAAAACAbOehQnfVqlXvG6wfNAsOAAAAAMC/yUMtLx80aJCuXbt2z+2hoaFau3btYxcFAAAAAEBO8FChu1ChQgoJCbnndk9PT9WrV++xiwIAAAAAICd4qOXlxYsX159//ml9Hh4erj/++CPTiwIAAAAAICd4qNB99/XaK1asuO9ycwAAAAAA/s0eKnQDAAAAAICMe6jQbbFY0nwPN9/LDQAAAABA+h7qRmqGYSgiIkKurq6SpJs3b6p3797y9PS06bdkyZLMqxAAAAAAgGzqoUJ3t27dbJ4///zzmVoMAAAAAAA5yUOF7tmzZ5tVBwAAAAAAOQ43UgMAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJHYP3dOmTVNISIjc3NxUtWpVbdiwIUP7bdq0SU5OTqpUqZK5BQIAAAAA8IjsGroXLlyofv36aejQodqxY4fq1Kmj5s2bKz4+/r77Xbp0SV27dlXDhg2zqFIAAAAAAB6eXUP3pEmT1KNHD/Xs2VOlS5fWlClTFBgYqOnTp993vxdffFGdOnVSzZo1s6hSAAAAAAAent1Cd2JiorZv364mTZrYtDdp0kSbN2++536zZ8/W4cOHNXz48Ayd59atW7p8+bLNAwAAAACArGC30H3u3DklJyerQIECNu0FChTQmTNn0t3n4MGDevPNNzVv3jw5OTll6Dxjx46Vr6+v9REYGPjYtQMAAAAAkBF2v5GaxWKxeW4YRpo2SUpOTlanTp00cuRIlShRIsPHHzx4sC5dumR9nDhx4rFrBgAAAAAgIzI2XWyCvHnzytHRMc2s9tmzZ9PMfkvSlStXtG3bNu3YsUN9+vSRJKWkpMgwDDk5Oem7777TU089lWY/V1dXubq6mjMIAAAAAADuw24z3S4uLqpatarWrFlj075mzRrVqlUrTX8fHx/t2bNHO3futD569+6tkiVLaufOnapRo0ZWlQ4AAAAAQIbYbaZbkgYMGKAuXbooLCxMNWvW1H//+1/Fx8erd+/eku4sDT916pTmzp0rBwcHlStXzmb//Pnzy83NLU07AAAAAAD/BHYN3eHh4Tp//rxGjRqlhIQElStXTitWrFBQUJAkKSEh4YHf2Q0AAAAAwD+VXUO3JEVFRSkqKirdbTExMffdd8SIERoxYkTmFwUAAAAAQCaw+93LAQAAAADIqQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEmc7F0AAAD/dPGjytu7BLt4LrePvUvIcpte2WTvEgAAOQwz3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEnsHrqnTZumkJAQubm5qWrVqtqwYcM9+y5ZskSNGzdWvnz55OPjo5o1a2r16tVZWC0AAAAAABln19C9cOFC9evXT0OHDtWOHTtUp04dNW/eXPHx8en2X79+vRo3bqwVK1Zo+/btatCggVq3bq0dO3ZkceUAAAAAADyYXUP3pEmT1KNHD/Xs2VOlS5fWlClTFBgYqOnTp6fbf8qUKXr99ddVrVo1FS9eXGPGjFHx4sW1fPnyLK4cAAAAAIAHs1voTkxM1Pbt29WkSROb9iZNmmjz5s0ZOkZKSoquXLmiPHny3LPPrVu3dPnyZZsHAAAAAABZwW6h+9y5c0pOTlaBAgVs2gsUKKAzZ85k6BgTJ07UtWvX1LFjx3v2GTt2rHx9fa2PwMDAx6obAAAAAICMsvuN1CwWi81zwzDStKVnwYIFGjFihBYuXKj8+fPfs9/gwYN16dIl6+PEiROPXTMAAAAAABnhZK8T582bV46Ojmlmtc+ePZtm9vtuCxcuVI8ePbR48WI1atTovn1dXV3l6ur62PUCAAAAAPCw7DbT7eLioqpVq2rNmjU27WvWrFGtWrXuud+CBQsUERGh+fPnq2XLlmaXCQAAAADAI7PbTLckDRgwQF26dFFYWJhq1qyp//73v4qPj1fv3r0l3VkafurUKc2dO1fSncDdtWtXffDBB3riiSess+Tu7u7y9fW12zgAAAAAAEiPXUN3eHi4zp8/r1GjRikhIUHlypXTihUrFBQUJElKSEiw+c7uTz75RElJSXr55Zf18ssvW9u7deummJiYrC4fAAAAAID7smvolqSoqChFRUWlu+3uIL1u3TrzCwIAAAAAIJPY/e7lAAAAAADkVIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATGL30D1t2jSFhITIzc1NVatW1YYNG+7bPzY2VlWrVpWbm5uKFi2qGTNmZFGlAAAAAAA8HLuG7oULF6pfv34aOnSoduzYoTp16qh58+aKj49Pt//Ro0fVokUL1alTRzt27NCQIUPUt29fffXVV1lcOQAAAAAAD2bX0D1p0iT16NFDPXv2VOnSpTVlyhQFBgZq+vTp6fafMWOGihQpoilTpqh06dLq2bOnIiMj9f7772dx5QAAAAAAPJjdQndiYqK2b9+uJk2a2LQ3adJEmzdvTnefn376KU3/pk2batu2bbp9+7ZptQIAAAAA8Cic7HXic+fOKTk5WQUKFLBpL1CggM6cOZPuPmfOnEm3f1JSks6dO6eAgIA0+9y6dUu3bt2yPr906ZIk6fLly487hEeSfOuGXc5rT1eck+1dgl3Y6z0GIPNdufnv/BxLupFk7xKyHJ/dyMn+jZ9l/8af6WtJfHZn9XkNw7hvP7uF7lQWi8XmuWEYadoe1D+99lRjx47VyJEj07QHBgY+bKl4ROXsXYC9jPW1dwUAgIfk+waf3UCOwu9j/w6+9v13vnLlinzvU4PdQnfevHnl6OiYZlb77NmzaWazU/n7+6fb38nJSX5+funuM3jwYA0YMMD6PCUlRRcuXJCfn999wz3wOC5fvqzAwECdOHFCPj4+9i4HAB4an2MAsjs+x2A2wzB05coVFSxY8L797Ba6XVxcVLVqVa1Zs0b/+c9/rO1r1qxRmzZt0t2nZs2aWr58uU3bd999p7CwMDk7O6e7j6urq1xdXW3acuXK9XjFAxnk4+PDhzyAbI3PMQDZHZ9jMNP9ZrhT2fXu5QMGDNDMmTMVHR2tuLg49e/fX/Hx8erdu7ekO7PUXbt2tfbv3bu3jh8/rgEDBiguLk7R0dGaNWuWBg4caK8hAAAAAABwT3a9pjs8PFznz5/XqFGjlJCQoHLlymnFihUKCgqSJCUkJNh8Z3dISIhWrFih/v376+OPP1bBggU1depUtW/f3l5DAAAAAADgnizGg261BuCh3bp1S2PHjtXgwYPTXN4AANkBn2MAsjs+x/BPQegGAAAAAMAkdr2mGwAAAACAnIzQDQAAAACASQjdQDrq16+vfv362bsMAMgyMTEx/6iv1IyIiFDbtm3v22fdunWyWCy6ePFiltQEAMCjIHTjXy0iIkIWiyXNY/z48Ro9erS1X3BwsKZMmWK/QgFkS6mfMalfhfl3UVFRslgsioiIyNRzjhgxQpUqVcrUY6ayWCxaunRpmvaMBOTHxR9DgewlMz8X+D0M2R2hG/96zZo1U0JCgs2jatWq8vb2tndpAHKAwMBAffHFF7px44a17ebNm1qwYIGKFClix8oAAHdLTEy0dwnIgQjd+NdzdXWVv7+/zaNhw4bWGZX69evr+PHj6t+/v3UmXPq/pZirV69W6dKl5eXlZQ3wfzd79myVLl1abm5uKlWqlKZNm2bdlpiYqD59+iggIEBubm4KDg7W2LFjrdtHjBihIkWKyNXVVQULFlTfvn3Nf0EAZKoqVaqoSJEiWrJkibVtyZIlCgwMVOXKlW36Goah8ePHq2jRonJ3d1fFihX15ZdfWrenLqf+4YcfFBYWJg8PD9WqVUv79++XdOdzaeTIkdq1a5f18yomJkaSNGnSJJUvX16enp4KDAxUVFSUrl69asqYHzSO5ORk9ejRQyEhIXJ3d1fJkiX1wQcf3PN4ERERio2N1QcffGAd17Fjx6zbt2/fnu7rAeCfoX79+urbt69ef/115cmTR/7+/hoxYoRNn3v9znOv38POnz+v5557ToULF5aHh4fKly+vBQsW2BzzypUr6ty5szw9PRUQEKDJkyenWTUTHBysd955RxEREfL19VWvXr0kSW+88YZKlCghDw8PFS1aVG+//bZu375tU2+lSpUUHR2tIkWKyMvLSy+99JKSk5M1fvx4+fv7K3/+/Hr33XdNeEWR3RC6gQdYsmSJChcurFGjRllnwlNdv35d77//vj777DOtX79e8fHxGjhwoHX7p59+qqFDh+rdd99VXFycxowZo7fffltz5syRJE2dOlXLli3TokWLtH//fn3++ecKDg6WJH355ZeaPHmyPvnkEx08eFBLly5V+fLls3TsADJH9+7dNXv2bOvz6OhoRUZGpun31ltvafbs2Zo+fbp+++039e/fX88//7xiY2Nt+g0dOlQTJ07Utm3b5OTkZD1WeHi4XnvtNZUtW9b6eRUeHi5JcnBw0NSpU7V3717NmTNHP/74o15//XVTxvugcaSkpKhw4cJatGiR9u3bp2HDhmnIkCFatGhRusf74IMPVLNmTfXq1cs6rsDAwAe+HgD+OebMmSNPT09t2bJF48eP16hRo7RmzRpJ9/+d516/h928eVNVq1bVN998o7179+qFF15Qly5dtGXLFus5BwwYoE2bNmnZsmVas2aNNmzYoF9//TVNbRMmTFC5cuW0fft2vf3225Ikb29vxcTEaN++ffrggw/06aefavLkyTb7HT58WCtXrtSqVau0YMECRUdHq2XLljp58qRiY2M1btw4vfXWW/r5559NeU2RjRjAv1i3bt0MR0dHw9PT0/ro0KGDUa9ePePVV1+19gsKCjImT55ss+/s2bMNScahQ4esbR9//LFRoEAB6/PAwEBj/vz5NvuNHj3aqFmzpmEYhvHKK68YTz31lJGSkpKmtokTJxolSpQwEhMTM2GkAOyhW7duRps2bYw///zTcHV1NY4ePWocO3bMcHNzM/7880+jTZs2Rrdu3QzDMIyrV68abm5uxubNm22O0aNHD+O5554zDMMw1q5da0gyvv/+e+v2b7/91pBk3LhxwzAMwxg+fLhRsWLFB9a2aNEiw8/Pz/p89uzZhq+v7333kWS4ubnZfGZ6enoaTk5ORps2bTI8jvRERUUZ7du3tz5Pfe1S3f25bBgZez0A2Mfff4br1atnPPnkkzbbq1WrZrzxxhuGYTz4d570fg9LT4sWLYzXXnvNMAzDuHz5suHs7GwsXrzYuv3ixYuGh4dHmt/x2rZt+8Bjjx8/3qhatar1+fDhww0PDw/j8uXL1ramTZsawcHBRnJysrWtZMmSxtixYx94fORsTvYM/MA/QYMGDTR9+nTrc09PTz333HMZ2tfDw0PFihWzPg8ICNDZs2clSX/++adOnDihHj16WJcqSVJSUpJ8fX0l3Vky2bhxY5UsWVLNmjVTq1at1KRJE0nSM888oylTpqho0aJq1qyZWrRoodatW8vJiR9bILvJmzevWrZsqTlz5sgwDLVs2VJ58+a16bNv3z7dvHlTjRs3tmlPTExMswy9QoUK1v8OCAiQJJ09e/a+14ivXbtWY8aM0b59+3T58mUlJSXp5s2bunbtmjw9PTM8lsmTJ6tRo0Y2bW+88YaSk5MfahwzZszQzJkzdfz4cd24cUOJiYmPfAO4R3k9AGStv/+cSra/Mz3K7zzJycl67733tHDhQp06dUq3bt3SrVu3rJ9nR44c0e3bt1W9enXrPr6+vipZsmSaY4WFhaVp+/LLLzVlyhQdOnRIV69eVVJSknx8fGz6BAcH29wDqECBAnJ0dJSDg4NNW+o48e/Fb+/41/P09FRoaOgj7evs7Gzz3GKxyDAMSXeWT0p3lpjXqFHDpp+jo6OkO9d6Hj16VCtXrtT333+vjh07qlGjRvryyy8VGBio/fv3a82aNfr+++8VFRWlCRMmKDY2Ns15AfzzRUZGqk+fPpKkjz/+OM321M+Mb7/9VoUKFbLZ5urqavP8758Bqdc3pu6fnuPHj6tFixbq3bu3Ro8erTx58mjjxo3q0aOHzTWKGeHv75/mM9Pb29v6tV0ZGceiRYvUv39/TZw4UTVr1pS3t7cmTJhgsyz0YTzs6wEg66X3O1Pqz+mj/M4zceJETZ48WVOmTLHer6Jfv37WG6Gl/j6W+pmQKrX97+7+w+PPP/+sZ599ViNHjlTTpk3l6+urL774QhMnTnzgmO43Tvx7EbqBDHBxcbHO4mRUgQIFVKhQIR05ckSdO3e+Zz8fHx+Fh4crPDxcHTp0ULNmzXThwgXlyZNH7u7uevrpp/X000/r5ZdfVqlSpbRnzx5VqVLlcYcEIIs1a9bM+stg06ZN02wvU6aMXF1dFR8fr3r16j3yedL7vNq2bZuSkpI0ceJE6wzMva6fflwZGceGDRtUq1YtRUVFWdsOHz583+M+yucwgOzjfr/zpPfzv2HDBrVp00bPP/+8pDt/aDt48KBKly4tSSpWrJicnZ31yy+/WO8BcfnyZR08ePCBn7GbNm1SUFCQhg4dam07fvx4Zg4X/zKEbiADgoODtX79ej377LNydXVNsyz0XkaMGKG+ffvKx8dHzZs3161bt7Rt2zb99ddfGjBggCZPnqyAgABVqlRJDg4OWrx4sfz9/ZUrVy7FxMQoOTlZNWrUkIeHhz777DO5u7srKCjI5NECMIOjo6Pi4uKs/303b29vDRw4UP3791dKSoqefPJJXb58WZs3b5aXl5e6deuWofMEBwfr6NGj2rlzpwoXLixvb28VK1ZMSUlJ+vDDD9W6dWtt2rRJM2bMyNTxPcw4QkNDNXfuXK1evVohISH67LPPtHXrVoWEhNx3XFu2bNGxY8fk5eWlPHnymFI/gKz3oN950vs9LDQ0VF999ZU2b96s3Llza9KkSTpz5ow1dHt7e6tbt24aNGiQ8uTJo/z582v48OFycHBIM/t9t9DQUMXHx+uLL75QtWrV9O233+rrr782/XVAzsXdy4EMGDVqlI4dO6ZixYopX758Gd6vZ8+emjlzpmJiYlS+fHnVq1dPMTEx1l8svby8NG7cOIWFhalatWo6duyYVqxYIQcHB+XKlUuffvqpateurQoVKuiHH37Q8uXL5efnZ9YwAZjMx8cnzTWBfzd69GgNGzZMY8eOVenSpdW0aVMtX778vmH0bu3bt1ezZs3UoEED5cuXTwsWLFClSpU0adIkjRs3TuXKldO8efNsvp4wsz1oHL1791a7du0UHh6uGjVq6Pz58zaz3ukZOHCgHB0dVaZMGeXLl0/x8fGm1Q8gaz3od570fg97++23VaVKFTVt2lT169eXv7+/2rZta3PcSZMmqWbNmmrVqpUaNWqk2rVrW7/G9X7atGmj/v37q0+fPqpUqZI2b95svas58CgsRnoXNgAAAABADnLt2jUVKlRIEydOVI8ePexdDv5FWF4OAAAAIMfZsWOHfv/9d1WvXl2XLl3SqFGjJN2ZyQayEqEbAAAAQI70/vvva//+/XJxcVHVqlW1YcOGDN+bB8gsLC8HAAAAAMAk3EgNAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAIBsJiIiQhaLRRaLRc7OzipQoIAaN26s6OhopaSkZPg4MTExypUrl3mF3kNERITatm2b5ecFAMAeCN0AAGRDzZo1U0JCgo4dO6aVK1eqQYMGevXVV9WqVSslJSXZuzwAAPD/EboBAMiGXF1d5e/vr0KFCqlKlSoaMmSI/ve//2nlypWKiYmRJE2aNEnly5eXp6enAgMDFRUVpatXr0qS1q1bp+7du+vSpUvWWfMRI0ZIkj7//HOFhYXJ29tb/v7+6tSpk86ePWs9919//aXOnTsrX758cnd3V/HixTV79mzr9lOnTik8PFy5c+eWn5+f2rRpo2PHjkmSRowYoTlz5uh///uf9bzr1q3LipcMAAC7IHQDAJBDPPXUU6pYsaKWLFkiSXJwcNDUqVO1d+9ezZkzRz/++KNef/11SVKtWrU0ZcoU+fj4KCEhQQkJCRo4cKAkKTExUaNHj9auXbu0dOlSHT16VBEREdbzvP3229q3b59WrlypuLg4TZ8+XXnz5pUkXb9+XQ0aNJCXl5fWr1+vjRs3ysvLS82aNVNiYqIGDhyojh07WmfqExISVKtWrax9oQAAyEJO9i4AAABknlKlSmn37t2SpH79+lnbQ0JCNHr0aL300kuaNm2aXFxc5OvrK4vFIn9/f5tjREZGWv+7aNGimjp1qqpXr66rV6/Ky8tL8fHxqly5ssLCwiRJwcHB1v5ffPGFHBwcNHPmTFksFknS7NmzlStXLq1bt05NmjSRu7u7bt26lea8AADkRMx0AwCQgxiGYQ27a9euVePGjVWoUCF5e3ura9euOn/+vK5du3bfY+zYsUNt2rRRUFCQvL29Vb9+fUlSfHy8JOmll17SF198oUqVKun111/X5s2brftu375dhw4dkre3t7y8vOTl5aU8efLo5s2bOnz4sDmDBgDgH4zQDQBADhIXF6eQkBAdP35cLVq0ULly5fTVV19p+/bt+vjjjyVJt2/fvuf+165dU5MmTeTl5aXPP/9cW7du1ddffy3pzrJzSWrevLmOHz+ufv366fTp02rYsKF1aXpKSoqqVq2qnTt32jwOHDigTp06mTx6AAD+eVheDgBADvHjjz9qz5496t+/v7Zt26akpCRNnDhRDg53/sa+aNEim/4uLi5KTk62afv999917tw5vffeewoMDJQkbdu2Lc258uXLp4iICEVERKhOnToaNGiQ3n//fVWpUkULFy5U/vz55ePjk26d6Z0XAICcipluAACyoVu3bunMmTM6deqUfv31V40ZM0Zt2rRRq1at1LVrVxUrVkxJSUn68MMPdeTIEX322WeaMWOGzTGCg4N19epV/fDDDzp37pyuX7+uIkWKyMXFxbrfsmXLNHr0aJv9hg0bpv/97386dOiQfvvtN33zzTcqXbq0JKlz587Kmzev2rRpow0bNujo0aOKjY3Vq6++qpMnT1rPu3v3bu3fv1/nzp2778w7AADZHaEbAIBsaNWqVQoICFBwcLCaNWumtWvXaurUqfrf//4nR0dHVapUSZMmTdK4ceNUrlw5zZs3T2PHjrU5Rq1atdS7d2+Fh4crX758Gj9+vPLly6eYmBgtXrxYZcqU0Xvvvaf333/fZj8XFxcNHjxYFSpUUN26deXo6KgvvvhCkuTh4aH169erSJEiateunUqXLq3IyEjduHHDOvPdq1cvlSxZUmFhYcqXL582bdqUNS8aAAB2YDEMw7B3EQAAAAAA5ETMdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACb5f+imtQpIgRDNAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(\n",
    "    data=results_df,\n",
    "    x=\"Dataset\",\n",
    "    y=\"Accuracy\",\n",
    "    hue=\"Model\"\n",
    ")\n",
    "plt.title(\"Model Accuracy Comparison Across Datasets\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.ylim(0, 1)\n",
    "plt.legend(title=\"Model\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(\n",
    "    data=results_df,\n",
    "    x=\"Dataset\",\n",
    "    y=\"F1-score\",\n",
    "    hue=\"Model\"\n",
    ")\n",
    "plt.title(\"Model F1-Score Comparison Across Datasets\")\n",
    "plt.ylabel(\"F1-score\")\n",
    "plt.ylim(0, 1)\n",
    "plt.legend(title=\"Model\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb92cbe5",
   "metadata": {},
   "source": [
    "##  Performance Visualisation (Bar Charts)\n",
    "\n",
    "To improve interpretability, results are visualised using bar charts:\n",
    "\n",
    "1. **Accuracy comparison across datasets**\n",
    "2. **F1-score comparison across datasets**\n",
    "\n",
    "This allows quick identification of:\n",
    "- which dataset is easiest/hardest to predict\n",
    "- which model performs best per dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c987331",
   "metadata": {},
   "source": [
    "## 17. Best Model per Dataset (Final Selection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d78598cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fitness</td>\n",
       "      <td>SVM (RBF)</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Instagram</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mental Health</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Dataset          Model  Accuracy  F1-score\n",
       "0        Fitness      SVM (RBF)      0.96      0.96\n",
       "1      Instagram  Random Forest      0.85      0.83\n",
       "2  Mental Health  Random Forest      0.38      0.33"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_models = (\n",
    "    results_df\n",
    "    .sort_values(\"F1-score\", ascending=False)\n",
    "    .groupby(\"Dataset\")\n",
    "    .first()\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "best_models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a66641c5",
   "metadata": {},
   "source": [
    "##  Best Model Selection per Dataset\n",
    "\n",
    "This final table selects the best model in each dataset based on the highest F1-score.\n",
    "\n",
    "This is important for dissertation discussion because it supports:\n",
    "- model justification\n",
    "- critical evaluation\n",
    "- final selection rationale for multi-modal experiments\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "892f2f63",
   "metadata": {},
   "source": [
    "## 18. Conclusion \n",
    "\n",
    "This study developed and evaluated single-modal baseline models using three data sources—fitness activity, student mental health scores, and Instagram text—to understand how well each modality can predict meaningful wellbeing-related outcomes. The fitness models (SVM and Random Forest) achieved strong performance when classifying activity levels from step, calorie, and distance features, showing that physical behaviour patterns can be reliably grouped into interpretable categories (Low/Medium/High). The Instagram sentiment models (SVM and Random Forest with TF-IDF) produced high overall accuracy, indicating that language patterns in social media posts (supported by stress/hate indicators) are highly predictive of sentiment. In contrast, the mental health models (Logistic Regression and Random Forest) achieved lower accuracy, suggesting that predicting depression classes from only stress and anxiety is more challenging and likely affected by class overlap, limited features, and potential imbalance across categories.\n",
    "\n",
    "Overall, the results support the dissertation aim of exploring AI/ML techniques for analysing multi-source wellbeing signals. The findings highlight that text-based signals are the most predictive, fitness patterns are strong for behavioural classification, and mental health prediction requires richer features and improved modelling strategies. Finally, incorporating SHAP explainability strengthened the analysis by improving transparency and showing how key predictors contributed to model decisions, which is important for responsible AI and dissertation-level interpretation."
   ]
  }
 ],
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