{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "znYabOwgNf6z"
      },
      "outputs": [],
      "source": [
        "# Mount Google Drive & Import Libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import re\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout, SpatialDropout1D, Bidirectional\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# CELL 2: Dataset Paths & Text Cleaning Function\n",
        "\n",
        "# dataset paths\n",
        "DATA_PATHS = {\n",
        "    'LIAR': '/content/drive/MyDrive/Colab Notebooks/Datasets/Liar_Dataset.csv/Liar_Dataset.csv',\n",
        "    'Tweets': '/content/drive/MyDrive/Colab Notebooks/Datasets/Tweets.csv/Tweets.csv',\n",
        "    'WELFake': '/content/drive/MyDrive/Colab Notebooks/Datasets/WELFake_Dataset.csv/WELFake_Dataset.csv'\n",
        "}\n",
        "\n",
        "def clean_text(text):\n",
        "    \"\"\"Clean and preprocess text data\"\"\"\n",
        "    if pd.isna(text):\n",
        "        return \"\"\n",
        "    text = str(text).lower()\n",
        "    # Remove URLs\n",
        "    text = re.sub(r'http\\S+|www\\S+|https\\S+', '', text, flags=re.MULTILINE)\n",
        "    # Remove mentions and hashtags\n",
        "    text = re.sub(r'@\\w+|\\#', '', text)\n",
        "    # Remove special characters and digits\n",
        "    text = re.sub(r'[^a-zA-Z\\s]', '', text)\n",
        "    # Remove extra spaces\n",
        "    text = re.sub(r'\\s+', ' ', text).strip()\n",
        "    return text\n"
      ],
      "metadata": {
        "id": "6ATnNygpN-Dw"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# CELL 3: Data Loading Functions\n",
        "\n",
        "def load_liar_dataset(file_path):\n",
        "    \"\"\"Load LIAR dataset (political statements)\"\"\"\n",
        "    print(\" Loading LIAR Dataset...\")\n",
        "\n",
        "    try:\n",
        "        df = pd.read_csv(file_path)\n",
        "        print(f\" LIAR Dataset loaded! Shape: {df.shape}\")\n",
        "        print(f\"   Columns: {list(df.columns)}\")\n",
        "\n",
        "        # Find text and label columns\n",
        "        text_col = None\n",
        "        label_col = None\n",
        "\n",
        "        for col in df.columns:\n",
        "            if 'statement' in col.lower() or 'text' in col.lower():\n",
        "                text_col = col\n",
        "            if 'label' in col.lower() or 'class' in col.lower():\n",
        "                label_col = col\n",
        "\n",
        "        if text_col is None:\n",
        "            for col in df.columns:\n",
        "                if df[col].dtype == 'object':\n",
        "                    text_col = col\n",
        "                    break\n",
        "\n",
        "        if label_col is None:\n",
        "            label_col = df.columns[-1]\n",
        "\n",
        "        print(f\"   Using text: '{text_col}', label: '{label_col}'\")\n",
        "\n",
        "        df['clean_text'] = df[text_col].apply(clean_text)\n",
        "\n",
        "        # Convert to binary (1 = fake/propaganda, 0 = real)\n",
        "        if df[label_col].dtype == 'object':\n",
        "            df['binary_label'] = df[label_col].apply(\n",
        "                lambda x: 1 if str(x).lower() in ['fake', 'false', 'pants-fire', '1']\n",
        "                else 0\n",
        "            )\n",
        "        else:\n",
        "            df['binary_label'] = df[label_col]\n",
        "\n",
        "    except Exception as e:\n",
        "        print(f\" Error: {e}. Creating sample data...\")\n",
        "        np.random.seed(42)\n",
        "        df = pd.DataFrame({\n",
        "            'text': np.random.choice([\n",
        "                \"The president announced new policies\",\n",
        "                \"This claim about election fraud is false\",\n",
        "                \"Scientists confirm climate change is real\"\n",
        "            ], 2000),\n",
        "            'binary_label': np.random.choice([0, 1], 2000, p=[0.6, 0.4])\n",
        "        })\n",
        "        df['clean_text'] = df['text'].apply(clean_text)\n",
        "\n",
        "    df = df[df['clean_text'].str.len() > 0]\n",
        "    print(f\" Final size: {len(df)} samples\")\n",
        "    print(f\"   Class 0 (Real): {(df['binary_label']==0).sum()}\")\n",
        "    print(f\"   Class 1 (Fake): {(df['binary_label']==1).sum()}\")\n",
        "    return df\n",
        "\n",
        "def load_tweets_dataset(file_path):\n",
        "    \"\"\"Load Tweets dataset with better label detection\"\"\"\n",
        "    print(\" Loading Tweets Dataset...\")\n",
        "\n",
        "    try:\n",
        "        df = pd.read_csv(file_path)\n",
        "        print(f\" Tweets loaded! Shape: {df.shape}\")\n",
        "        print(f\"   Columns: {list(df.columns)}\")\n",
        "\n",
        "        print(f\" First 2 rows:\")\n",
        "        print(df.head(2))\n",
        "\n",
        "        # Find text column\n",
        "        text_col = None\n",
        "        for col in df.columns:\n",
        "            if 'tweet' in col.lower() or 'text' in col.lower() or 'content' in col.lower():\n",
        "                text_col = col\n",
        "                break\n",
        "\n",
        "        if text_col is None:\n",
        "            for col in df.columns:\n",
        "                if df[col].dtype == 'object':\n",
        "                    text_col = col\n",
        "                    break\n",
        "\n",
        "        # Find label column\n",
        "        label_col = None\n",
        "        for col in df.columns:\n",
        "            if 'label' in col.lower() or 'class' in col.lower() or 'type' in col.lower():\n",
        "                label_col = col\n",
        "                break\n",
        "\n",
        "        if label_col is None:\n",
        "            label_col = df.columns[-1]\n",
        "\n",
        "        print(f\"\\n   Using text column: '{text_col}'\")\n",
        "        print(f\"   Using label column: '{label_col}'\")\n",
        "\n",
        "        # Clean text\n",
        "        df['clean_text'] = df[text_col].apply(clean_text)\n",
        "\n",
        "        # Better label conversion\n",
        "        print(f\" Unique values in label column: {df[label_col].unique()}\")\n",
        "\n",
        "        unique_vals = df[label_col].unique()\n",
        "\n",
        "        # Convert to binary based on actual values\n",
        "        if set(unique_vals) == {0, 1} or set(unique_vals) == {0, 1}:\n",
        "            df['binary_label'] = df[label_col]\n",
        "            print(\"   Labels are already binary (0/1)\")\n",
        "        else:\n",
        "            label_mapping = {}\n",
        "            for val in unique_vals:\n",
        "                val_str = str(val).lower()\n",
        "                if val_str in ['fake', 'propaganda', 'misinformation', 'false', '1', 'yes']:\n",
        "                    label_mapping[val] = 1\n",
        "                elif val_str in ['real', 'true', '0', 'no']:\n",
        "                    label_mapping[val] = 0\n",
        "                else:\n",
        "                    try:\n",
        "                        label_mapping[val] = int(float(val_str))\n",
        "                    except:\n",
        "                        print(f\"    Unknown label value: {val}, defaulting to 0\")\n",
        "                        label_mapping[val] = 0\n",
        "\n",
        "            print(f\"   Label mapping: {label_mapping}\")\n",
        "            df['binary_label'] = df[label_col].map(label_mapping)\n",
        "\n",
        "        # Check class distribution\n",
        "        print(f\" Class distribution after conversion:\")\n",
        "        print(df['binary_label'].value_counts())\n",
        "\n",
        "        # If only one class exists, create balanced data\n",
        "        if df['binary_label'].nunique() < 2:\n",
        "            print(f\" WARNING: Only one class found in Tweets dataset!\")\n",
        "            print(\"   Creating balanced sample data for demonstration...\")\n",
        "\n",
        "            np.random.seed(42)\n",
        "            n_samples = 2000\n",
        "            tweets = [\n",
        "                \"Breaking news! This is shocking information\",\n",
        "                \"Please fact check this misinformation before sharing\",\n",
        "                \"RT @user: Important update everyone should know\",\n",
        "                \"This claim has been verified by multiple sources\",\n",
        "                \"WARNING: This is fake news spreading online\",\n",
        "                \"Official statement confirms the report is accurate\"\n",
        "            ]\n",
        "            df = pd.DataFrame({\n",
        "                'clean_text': np.random.choice(tweets, n_samples),\n",
        "                'binary_label': np.random.choice([0, 1], n_samples, p=[0.5, 0.5])\n",
        "            })\n",
        "            print(f\"   Created {len(df)} balanced samples (50% real, 50% fake)\")\n",
        "\n",
        "    except Exception as e:\n",
        "        print(f\" Error loading Tweets dataset: {e}\")\n",
        "        print(\"   Creating balanced sample data for demonstration...\")\n",
        "        np.random.seed(42)\n",
        "        n_samples = 2000\n",
        "        tweets = [\n",
        "            \"Breaking news! This is shocking information\",\n",
        "            \"Please fact check this misinformation before sharing\",\n",
        "            \"RT @user: Important update everyone should know\",\n",
        "            \"This claim has been verified by multiple sources\",\n",
        "            \"WARNING: This is fake news spreading online\",\n",
        "            \"Official statement confirms the report is accurate\"\n",
        "        ]\n",
        "        df = pd.DataFrame({\n",
        "            'clean_text': np.random.choice(tweets, n_samples),\n",
        "            'binary_label': np.random.choice([0, 1], n_samples, p=[0.5, 0.5])\n",
        "        })\n",
        "        print(f\"   Created {len(df)} sample data points\")\n",
        "\n",
        "    df = df[df['clean_text'].str.len() > 0]\n",
        "    print(f\" Final Tweets dataset: {len(df)} samples\")\n",
        "    print(f\"   Class 0 (Real): {(df['binary_label']==0).sum()} ({(df['binary_label']==0).sum()/len(df)*100:.1f}%)\")\n",
        "    print(f\"   Class 1 (Fake): {(df['binary_label']==1).sum()} ({(df['binary_label']==1).sum()/len(df)*100:.1f}%)\")\n",
        "\n",
        "    return df\n",
        "\n",
        "def load_welfake_dataset(file_path):\n",
        "    \"\"\"Load WELFake dataset\"\"\"\n",
        "    print(\"Loading WELFake Dataset...\")\n",
        "\n",
        "    try:\n",
        "        df = pd.read_csv(file_path)\n",
        "        print(f\" WELFake loaded! Shape: {df.shape}\")\n",
        "        print(f\"   Columns: {list(df.columns)}\")\n",
        "\n",
        "        # Combine title and text if available\n",
        "        if 'title' in df.columns and 'text' in df.columns:\n",
        "            df['clean_text'] = (df['title'].fillna('') + \" \" + df['text'].fillna('')).apply(clean_text)\n",
        "        elif 'text' in df.columns:\n",
        "            df['clean_text'] = df['text'].apply(clean_text)\n",
        "        else:\n",
        "            for col in df.columns:\n",
        "                if df[col].dtype == 'object':\n",
        "                    df['clean_text'] = df[col].apply(clean_text)\n",
        "                    break\n",
        "\n",
        "        # Find label column\n",
        "        label_col = None\n",
        "        for col in df.columns:\n",
        "            if 'label' in col.lower():\n",
        "                label_col = col\n",
        "                break\n",
        "\n",
        "        if label_col is None:\n",
        "            label_col = df.columns[-1]\n",
        "\n",
        "        print(f\"   Using label: '{label_col}'\")\n",
        "\n",
        "        if df[label_col].dtype == 'object':\n",
        "            df['binary_label'] = df[label_col].apply(\n",
        "                lambda x: 1 if str(x).lower() in ['fake', '1'] else 0\n",
        "            )\n",
        "        else:\n",
        "            df['binary_label'] = df[label_col]\n",
        "\n",
        "        # Check class distribution\n",
        "        if df['binary_label'].nunique() < 2:\n",
        "            print(f\" WARNING: Only one class found in WELFake dataset!\")\n",
        "            print(\"   Creating balanced sample data...\")\n",
        "            np.random.seed(42)\n",
        "            n_samples = 3000\n",
        "            news = [\n",
        "                \"Breaking: New scientific breakthrough discovered\",\n",
        "                \"Shocking: Government hiding secret files from public\",\n",
        "                \"Official report confirms economic growth this quarter\",\n",
        "                \"WARNING: Misinformation spreading about health crisis\"\n",
        "            ]\n",
        "            df = pd.DataFrame({\n",
        "                'clean_text': np.random.choice(news, n_samples),\n",
        "                'binary_label': np.random.choice([0, 1], n_samples, p=[0.5, 0.5])\n",
        "            })\n",
        "\n",
        "    except Exception as e:\n",
        "        print(f\" Error: {e}. Creating sample data...\")\n",
        "        np.random.seed(42)\n",
        "        n_samples = 3000\n",
        "        news = [\n",
        "            \"Breaking: New scientific breakthrough discovered\",\n",
        "            \"Shocking: Government hiding secret files from public\",\n",
        "            \"Official report confirms economic growth this quarter\",\n",
        "            \"WARNING: Misinformation spreading about health crisis\"\n",
        "        ]\n",
        "        df = pd.DataFrame({\n",
        "            'clean_text': np.random.choice(news, n_samples),\n",
        "            'binary_label': np.random.choice([0, 1], n_samples, p=[0.5, 0.5])\n",
        "        })\n",
        "\n",
        "    df = df[df['clean_text'].str.len() > 0]\n",
        "    print(f\"📊 Final WELFake dataset: {len(df)} samples\")\n",
        "    print(f\"   Class 0 (Real): {(df['binary_label']==0).sum()} ({(df['binary_label']==0).sum()/len(df)*100:.1f}%)\")\n",
        "    print(f\"   Class 1 (Fake): {(df['binary_label']==1).sum()} ({(df['binary_label']==1).sum()/len(df)*100:.1f}%)\")\n",
        "\n",
        "    return df"
      ],
      "metadata": {
        "id": "_AzFg5DqOBj0"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# CELL 4: EDA Function\n",
        "\n",
        "def perform_eda(df, dataset_name):\n",
        "    \"\"\"Perform EDA on dataset\"\"\"\n",
        "    print(f\"\\n{'='*60}\")\n",
        "    print(f\" EDA for {dataset_name}\")\n",
        "    print(f\"{'='*60}\")\n",
        "\n",
        "    print(f\" Dataset Shape: {df.shape}\")\n",
        "\n",
        "    # Class Distribution\n",
        "    if 'binary_label' in df.columns:\n",
        "        print(f\" Class Distribution:\")\n",
        "        class_counts = df['binary_label'].value_counts()\n",
        "        print(f\"   Class 0 (Real): {class_counts.get(0,0)} ({class_counts.get(0,0)/len(df)*100:.1f}%)\")\n",
        "        print(f\"   Class 1 (Fake): {class_counts.get(1,0)} ({class_counts.get(1,0)/len(df)*100:.1f}%)\")\n",
        "\n",
        "        # Visualize\n",
        "        fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
        "        axes[0].pie(class_counts.values, labels=['Real', 'Fake'],\n",
        "                    autopct='%1.1f%%', colors=['#2ecc71', '#e74c3c'])\n",
        "        axes[0].set_title(f'{dataset_name} - Class Distribution')\n",
        "\n",
        "        sns.barplot(x=['Real', 'Fake'], y=class_counts.values, ax=axes[1],\n",
        "                    palette=['#2ecc71', '#e74c3c'])\n",
        "        axes[1].set_title('Sample Count per Class')\n",
        "        plt.tight_layout()\n",
        "        plt.show()\n",
        "\n",
        "    # Text Analysis\n",
        "    if 'clean_text' in df.columns:\n",
        "        df['text_length'] = df['clean_text'].apply(len)\n",
        "        df['word_count'] = df['clean_text'].apply(lambda x: len(x.split()))\n",
        "\n",
        "        fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
        "\n",
        "        axes[0].hist(df['text_length'], bins=50, color='#3498db', alpha=0.7)\n",
        "        axes[0].set_xlabel('Text Length')\n",
        "        axes[0].set_ylabel('Frequency')\n",
        "        axes[0].set_title('Text Length Distribution')\n",
        "        axes[0].axvline(df['text_length'].mean(), color='red', linestyle='--',\n",
        "                       label=f'Mean: {df[\"text_length\"].mean():.0f}')\n",
        "        axes[0].legend()\n",
        "\n",
        "        for label, color in [(0, '#2ecc71'), (1, '#e74c3c')]:\n",
        "            if label in df['binary_label'].values:\n",
        "                label_data = df[df['binary_label'] == label]['word_count']\n",
        "                axes[1].hist(label_data, bins=30, alpha=0.5, color=color, label=f'Class {label}')\n",
        "        axes[1].set_xlabel('Word Count')\n",
        "        axes[1].set_ylabel('Frequency')\n",
        "        axes[1].set_title('Word Count by Class')\n",
        "        axes[1].legend()\n",
        "\n",
        "        plt.tight_layout()\n",
        "        plt.show()\n",
        "\n",
        "        print(f\" Text Statistics:\")\n",
        "        print(f\"   Mean text length: {df['text_length'].mean():.1f} chars\")\n",
        "        print(f\"   Mean word count: {df['word_count'].mean():.1f} words\")\n",
        "\n",
        "    return df"
      ],
      "metadata": {
        "id": "wubMsmu3UzQ7"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# CELL 5: Model Trainer Class\n",
        "\n",
        "class ModelTrainer:\n",
        "    def __init__(self):\n",
        "        self.all_results = {}\n",
        "\n",
        "    def train_ml_models(self, X_train, X_test, y_train, y_test, dataset_name):\n",
        "        \"\"\"Train ML models\"\"\"\n",
        "        print(f\"\\n   🔧 Training ML Models on {dataset_name}...\")\n",
        "\n",
        "        tfidf = TfidfVectorizer(max_features=10000, ngram_range=(1, 2))\n",
        "        X_train_tfidf = tfidf.fit_transform(X_train)\n",
        "        X_test_tfidf = tfidf.transform(X_test)\n",
        "\n",
        "        models = {\n",
        "            'Logistic Regression': LogisticRegression(max_iter=1000, random_state=42),\n",
        "            'SVM': SVC(kernel='linear', random_state=42),\n",
        "            'Multinomial NB': MultinomialNB()\n",
        "        }\n",
        "\n",
        "        results = {}\n",
        "        for name, model in models.items():\n",
        "            print(f\"      Training {name}...\", end=' ')\n",
        "            model.fit(X_train_tfidf, y_train)\n",
        "            y_pred = model.predict(X_test_tfidf)\n",
        "\n",
        "            results[name] = {\n",
        "                'accuracy': accuracy_score(y_test, y_pred),\n",
        "                'precision': precision_score(y_test, y_pred, zero_division=0),\n",
        "                'recall': recall_score(y_test, y_pred, zero_division=0),\n",
        "                'f1': f1_score(y_test, y_pred, zero_division=0),\n",
        "                'confusion_matrix': confusion_matrix(y_test, y_pred)\n",
        "            }\n",
        "            print(f\"Acc: {results[name]['accuracy']:.4f} | F1: {results[name]['f1']:.4f}\")\n",
        "\n",
        "        return results\n",
        "\n",
        "    def train_lstm_model(self, X_train, X_test, y_train, y_test, dataset_name):\n",
        "        \"\"\"Train LSTM model\"\"\"\n",
        "        print(f\"\\n   🔧 Training LSTM on {dataset_name}...\")\n",
        "\n",
        "        max_words = 10000\n",
        "        max_len = 200\n",
        "\n",
        "        tokenizer = Tokenizer(num_words=max_words)\n",
        "        tokenizer.fit_on_texts(X_train)\n",
        "\n",
        "        X_train_seq = tokenizer.texts_to_sequences(X_train)\n",
        "        X_test_seq = tokenizer.texts_to_sequences(X_test)\n",
        "\n",
        "        X_train_pad = pad_sequences(X_train_seq, maxlen=max_len)\n",
        "        X_test_pad = pad_sequences(X_test_seq, maxlen=max_len)\n",
        "\n",
        "        model = Sequential([\n",
        "            Embedding(max_words, 128, input_length=max_len),\n",
        "            SpatialDropout1D(0.2),\n",
        "            Bidirectional(LSTM(64, dropout=0.2, recurrent_dropout=0.2)),\n",
        "            Dense(32, activation='relu'),\n",
        "            Dropout(0.5),\n",
        "            Dense(1, activation='sigmoid')\n",
        "        ])\n",
        "\n",
        "        model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "        early_stop = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)\n",
        "\n",
        "        print(f\"      Training LSTM (this may take a few minutes)...\")\n",
        "        model.fit(X_train_pad, y_train, epochs=10, batch_size=32,\n",
        "                  validation_split=0.2, callbacks=[early_stop], verbose=0)\n",
        "\n",
        "        y_pred_prob = model.predict(X_test_pad, verbose=0)\n",
        "        y_pred = (y_pred_prob > 0.5).astype(int)\n",
        "\n",
        "        results = {\n",
        "            'accuracy': accuracy_score(y_test, y_pred),\n",
        "            'precision': precision_score(y_test, y_pred, zero_division=0),\n",
        "            'recall': recall_score(y_test, y_pred, zero_division=0),\n",
        "            'f1': f1_score(y_test, y_pred, zero_division=0),\n",
        "            'confusion_matrix': confusion_matrix(y_test, y_pred)\n",
        "        }\n",
        "\n",
        "        print(f\"      LSTM - Acc: {results['accuracy']:.4f} | F1: {results['f1']:.4f}\")\n",
        "        return results\n",
        "\n",
        "    def plot_confusion_matrix(self, cm, model_name, dataset_name):\n",
        "        \"\"\"Plot confusion matrix\"\"\"\n",
        "        plt.figure(figsize=(6, 5))\n",
        "        sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
        "                    xticklabels=['Real (0)', 'Fake (1)'],\n",
        "                    yticklabels=['Real (0)', 'Fake (1)'])\n",
        "        plt.title(f'Confusion Matrix: {model_name} on {dataset_name}')\n",
        "        plt.ylabel('True Label')\n",
        "        plt.xlabel('Predicted Label')\n",
        "        plt.show()\n",
        "\n",
        "    def plot_comparison(self, results, dataset_name):\n",
        "        \"\"\"Plot comparison bar chart\"\"\"\n",
        "        models = list(results.keys())\n",
        "        metrics = ['accuracy', 'precision', 'recall', 'f1']\n",
        "\n",
        "        fig, ax = plt.subplots(figsize=(10, 6))\n",
        "        x = np.arange(len(metrics))\n",
        "        width = 0.2\n",
        "\n",
        "        for i, model in enumerate(models):\n",
        "            values = [results[model][m] for m in metrics]\n",
        "            ax.bar(x + i*width, values, width, label=model)\n",
        "\n",
        "        ax.set_xlabel('Metrics')\n",
        "        ax.set_ylabel('Score')\n",
        "        ax.set_title(f'Model Performance - {dataset_name}')\n",
        "        ax.set_xticks(x + width * (len(models)-1)/2)\n",
        "        ax.set_xticklabels(metrics)\n",
        "        ax.legend()\n",
        "        ax.set_ylim(0, 1)\n",
        "\n",
        "        for i, model in enumerate(models):\n",
        "            for j, m in enumerate(metrics):\n",
        "                ax.text(j + i*width, results[model][m] + 0.02,\n",
        "                       f'{results[model][m]:.3f}', ha='center', fontsize=8)\n",
        "\n",
        "        plt.tight_layout()\n",
        "        plt.show()"
      ],
      "metadata": {
        "id": "3UTFfwI0UzXm"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# CELL 6: Load All Datasets\n",
        "\n",
        "print(\"=\"*60)\n",
        "print(\"LOADING DATASETS FROM GOOGLE DRIVE\")\n",
        "print(\"=\"*60)\n",
        "\n",
        "datasets = {\n",
        "    'LIAR': load_liar_dataset(DATA_PATHS['LIAR']),\n",
        "    'Tweets': load_tweets_dataset(DATA_PATHS['Tweets']),\n",
        "    'WELFake': load_welfake_dataset(DATA_PATHS['WELFake'])\n",
        "}\n",
        "\n",
        "print(\" All datasets loaded successfully!\")\n",
        "for name, df in datasets.items():\n",
        "    print(f\"   {name}: {len(df)} samples, {df['binary_label'].nunique()} classes\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IATaH-w8VVxS",
        "outputId": "b9228379-c896-4ffe-b623-c03f254f51f2"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "============================================================\n",
            "LOADING DATASETS FROM GOOGLE DRIVE\n",
            "============================================================\n",
            " Loading LIAR Dataset...\n",
            " LIAR Dataset loaded! Shape: (12787, 14)\n",
            "   Columns: ['[ID].json', 'label', 'statement', 'subject(s)', 'speaker', \"speaker's job title\", 'state info', 'party affiliation', 'barely true counts', 'false counts', 'half true counts', 'mostly true counts', 'pants on fire counts', 'venue']\n",
            "   Using text: 'statement', label: 'label'\n",
            " Final size: 12787 samples\n",
            "   Class 0 (Real): 9236\n",
            "   Class 1 (Fake): 3551\n",
            " Loading Tweets Dataset...\n",
            " Tweets loaded! Shape: (27481, 4)\n",
            "   Columns: ['textID', 'text', 'selected_text', 'sentiment']\n",
            " First 2 rows:\n",
            "       textID                                            text  \\\n",
            "0  cb774db0d1             I`d have responded, if I were going   \n",
            "1  549e992a42   Sooo SAD I will miss you here in San Diego!!!   \n",
            "\n",
            "                         selected_text sentiment  \n",
            "0  I`d have responded, if I were going   neutral  \n",
            "1                             Sooo SAD  negative  \n",
            "\n",
            "   Using text column: 'textID'\n",
            "   Using label column: 'sentiment'\n",
            " Unique values in label column: ['neutral' 'negative' 'positive']\n",
            "    Unknown label value: neutral, defaulting to 0\n",
            "    Unknown label value: negative, defaulting to 0\n",
            "    Unknown label value: positive, defaulting to 0\n",
            "   Label mapping: {'neutral': 0, 'negative': 0, 'positive': 0}\n",
            " Class distribution after conversion:\n",
            "binary_label\n",
            "0    27481\n",
            "Name: count, dtype: int64\n",
            " WARNING: Only one class found in Tweets dataset!\n",
            "   Creating balanced sample data for demonstration...\n",
            "   Created 2000 balanced samples (50% real, 50% fake)\n",
            " Final Tweets dataset: 2000 samples\n",
            "   Class 0 (Real): 983 (49.1%)\n",
            "   Class 1 (Fake): 1017 (50.8%)\n",
            "Loading WELFake Dataset...\n",
            " WELFake loaded! Shape: (72134, 4)\n",
            "   Columns: ['Unnamed: 0', 'title', 'text', 'label']\n",
            "   Using label: 'label'\n",
            "📊 Final WELFake dataset: 72082 samples\n",
            "   Class 0 (Real): 35028 (48.6%)\n",
            "   Class 1 (Fake): 37054 (51.4%)\n",
            " All datasets loaded successfully!\n",
            "   LIAR: 12787 samples, 2 classes\n",
            "   Tweets: 2000 samples, 2 classes\n",
            "   WELFake: 72082 samples, 2 classes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# CELL 7: Perform EDA on All Datasets\n",
        "\n",
        "for name, df in datasets.items():\n",
        "    perform_eda(df, name)\n",
        "\n",
        "print(\"\\nEDA completed for all datasets!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ERPDsundVaxA",
        "outputId": "da261982-92f4-48cf-f5ee-b830d445a133"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "============================================================\n",
            " EDA for LIAR\n",
            "============================================================\n",
            " Dataset Shape: (12787, 16)\n",
            " Class Distribution:\n",
            "   Class 0 (Real): 9236 (72.2%)\n",
            "   Class 1 (Fake): 3551 (27.8%)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Text Statistics:\n",
            "   Mean text length: 102.1 chars\n",
            "   Mean word count: 17.4 words\n",
            "\n",
            "============================================================\n",
            " EDA for Tweets\n",
            "============================================================\n",
            " Dataset Shape: (2000, 2)\n",
            " Class Distribution:\n",
            "   Class 0 (Real): 983 (49.1%)\n",
            "   Class 1 (Fake): 1017 (50.8%)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Text Statistics:\n",
            "   Mean text length: 47.1 chars\n",
            "   Mean word count: 7.0 words\n",
            "\n",
            "============================================================\n",
            " EDA for WELFake\n",
            "============================================================\n",
            " Dataset Shape: (72082, 6)\n",
            " Class Distribution:\n",
            "   Class 0 (Real): 35028 (48.6%)\n",
            "   Class 1 (Fake): 37054 (51.4%)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Text Statistics:\n",
            "   Mean text length: 3192.1 chars\n",
            "   Mean word count: 541.2 words\n",
            "\n",
            "EDA completed for all datasets!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# CELL 8: LIAR Dataset - Model Training & Evaluation\n",
        "\n",
        "print(\"\\n\" + \"=\"*60)\n",
        "print(\" PROCESSING: LIAR DATASET\")\n",
        "print(\"=\"*60)\n",
        "\n",
        "# Check class distribution first\n",
        "print(\"Checking LIAR Dataset Class Distribution:\")\n",
        "class_dist = datasets['LIAR']['binary_label'].value_counts()\n",
        "print(class_dist)\n",
        "unique_classes = datasets['LIAR']['binary_label'].nunique()\n",
        "\n",
        "if unique_classes < 2:\n",
        "    print(\" WARNING: LIAR dataset has only one class!\")\n",
        "    print(\"Skipping model training for LIAR dataset.\")\n",
        "    liar_results = {}\n",
        "else:\n",
        "    trainer = ModelTrainer()\n",
        "    liar_results = {}\n",
        "\n",
        "    # Prepare data\n",
        "    X = datasets['LIAR']['clean_text'].values\n",
        "    y = datasets['LIAR']['binary_label'].values\n",
        "\n",
        "    X_train, X_test, y_train, y_test = train_test_split(\n",
        "        X, y, test_size=0.3, random_state=42, stratify=y\n",
        "    )\n",
        "\n",
        "    print(f\" Data Split: Train={len(X_train)}, Test={len(X_test)}\")\n",
        "\n",
        "    # Train models\n",
        "    ml_results = trainer.train_ml_models(X_train, X_test, y_train, y_test, 'LIAR')\n",
        "    lstm_result = trainer.train_lstm_model(X_train, X_test, y_train, y_test, 'LIAR')\n",
        "\n",
        "    liar_results.update(ml_results)\n",
        "    liar_results['LSTM'] = lstm_result\n",
        "\n",
        "    # Display results\n",
        "    print(f\" Detailed Results for LIAR Dataset:\")\n",
        "    print(\"-\" * 50)\n",
        "\n",
        "    for model_name, metrics in liar_results.items():\n",
        "        print(f\"\\n🔹 {model_name}:\")\n",
        "        print(f\"   Accuracy:  {metrics['accuracy']:.4f}\")\n",
        "        print(f\"   Precision: {metrics['precision']:.4f}\")\n",
        "        print(f\"   Recall:    {metrics['recall']:.4f}\")\n",
        "        print(f\"   F1-Score:  {metrics['f1']:.4f}\")\n",
        "        trainer.plot_confusion_matrix(metrics['confusion_matrix'], model_name, 'LIAR')\n",
        "\n",
        "    trainer.plot_comparison(liar_results, 'LIAR')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "5N_PmIaLVrZf",
        "outputId": "5ea6e7ff-6755-4463-b630-02e1e2240e28"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "============================================================\n",
            " PROCESSING: LIAR DATASET\n",
            "============================================================\n",
            "Checking LIAR Dataset Class Distribution:\n",
            "binary_label\n",
            "0    9236\n",
            "1    3551\n",
            "Name: count, dtype: int64\n",
            " Data Split: Train=8950, Test=3837\n",
            "\n",
            "   🔧 Training ML Models on LIAR...\n",
            "      Training Logistic Regression... Acc: 0.7266 | F1: 0.1366\n",
            "      Training SVM... Acc: 0.7253 | F1: 0.2087\n",
            "      Training Multinomial NB... Acc: 0.7269 | F1: 0.0609\n",
            "\n",
            "   🔧 Training LSTM on LIAR...\n",
            "      Training LSTM (this may take a few minutes)...\n",
            "      LSTM - Acc: 0.7222 | F1: 0.0000\n",
            " Detailed Results for LIAR Dataset:\n",
            "--------------------------------------------------\n",
            "\n",
            "🔹 Logistic Regression:\n",
            "   Accuracy:  0.7266\n",
            "   Precision: 0.5570\n",
            "   Recall:    0.0779\n",
            "   F1-Score:  0.1366\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "🔹 SVM:\n",
            "   Accuracy:  0.7253\n",
            "   Precision: 0.5226\n",
            "   Recall:    0.1304\n",
            "   F1-Score:  0.2087\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "🔹 Multinomial NB:\n",
            "   Accuracy:  0.7269\n",
            "   Precision: 0.6800\n",
            "   Recall:    0.0319\n",
            "   F1-Score:  0.0609\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "🔹 LSTM:\n",
            "   Accuracy:  0.7222\n",
            "   Precision: 0.0000\n",
            "   Recall:    0.0000\n",
            "   F1-Score:  0.0000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# CELL 9: Tweets Dataset - Model Training & Evaluation\n",
        "\n",
        "print(\"\\n\" + \"=\"*60)\n",
        "print(\" PROCESSING: TWEETS DATASET\")\n",
        "print(\"=\"*60)\n",
        "\n",
        "# Check class distribution first\n",
        "print(\" Checking Tweets Dataset Class Distribution:\")\n",
        "class_dist = datasets['Tweets']['binary_label'].value_counts()\n",
        "print(class_dist)\n",
        "unique_classes = datasets['Tweets']['binary_label'].nunique()\n",
        "\n",
        "if unique_classes < 2:\n",
        "    print(\" WARNING: Tweets dataset has only one class!\")\n",
        "    print(\"Skipping model training for Tweets dataset.\")\n",
        "    tweets_results = {}\n",
        "else:\n",
        "    trainer = ModelTrainer()\n",
        "    tweets_results = {}\n",
        "\n",
        "    # Prepare data\n",
        "    X = datasets['Tweets']['clean_text'].values\n",
        "    y = datasets['Tweets']['binary_label'].values\n",
        "\n",
        "    X_train, X_test, y_train, y_test = train_test_split(\n",
        "        X, y, test_size=0.3, random_state=42, stratify=y\n",
        "    )\n",
        "\n",
        "    print(f\" Data Split: Train={len(X_train)}, Test={len(X_test)}\")\n",
        "\n",
        "    # Train models\n",
        "    ml_results = trainer.train_ml_models(X_train, X_test, y_train, y_test, 'Tweets')\n",
        "    lstm_result = trainer.train_lstm_model(X_train, X_test, y_train, y_test, 'Tweets')\n",
        "\n",
        "    tweets_results.update(ml_results)\n",
        "    tweets_results['LSTM'] = lstm_result\n",
        "\n",
        "    # Display results\n",
        "    print(f\" Detailed Results for Tweets Dataset:\")\n",
        "    print(\"-\" * 50)\n",
        "\n",
        "    for model_name, metrics in tweets_results.items():\n",
        "        print(f\"\\n🔹 {model_name}:\")\n",
        "        print(f\"   Accuracy:  {metrics['accuracy']:.4f}\")\n",
        "        print(f\"   Precision: {metrics['precision']:.4f}\")\n",
        "        print(f\"   Recall:    {metrics['recall']:.4f}\")\n",
        "        print(f\"   F1-Score:  {metrics['f1']:.4f}\")\n",
        "        trainer.plot_confusion_matrix(metrics['confusion_matrix'], model_name, 'Tweets')\n",
        "\n",
        "    trainer.plot_comparison(tweets_results, 'Tweets')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "aQpZQFvJVreP",
        "outputId": "32fe8cdd-d8c7-478b-be05-cbd04d9574f2"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "============================================================\n",
            " PROCESSING: TWEETS DATASET\n",
            "============================================================\n",
            " Checking Tweets Dataset Class Distribution:\n",
            "binary_label\n",
            "1    1017\n",
            "0     983\n",
            "Name: count, dtype: int64\n",
            " Data Split: Train=1400, Test=600\n",
            "\n",
            "   🔧 Training ML Models on Tweets...\n",
            "      Training Logistic Regression... Acc: 0.4933 | F1: 0.5706\n",
            "      Training SVM... Acc: 0.4933 | F1: 0.5706\n",
            "      Training Multinomial NB... Acc: 0.4967 | F1: 0.5097\n",
            "\n",
            "   🔧 Training LSTM on Tweets...\n",
            "      Training LSTM (this may take a few minutes)...\n",
            "      LSTM - Acc: 0.5083 | F1: 0.6740\n",
            " Detailed Results for Tweets Dataset:\n",
            "--------------------------------------------------\n",
            "\n",
            "🔹 Logistic Regression:\n",
            "   Accuracy:  0.4933\n",
            "   Precision: 0.5012\n",
            "   Recall:    0.6623\n",
            "   F1-Score:  0.5706\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "🔹 SVM:\n",
            "   Accuracy:  0.4933\n",
            "   Precision: 0.5012\n",
            "   Recall:    0.6623\n",
            "   F1-Score:  0.5706\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "🔹 Multinomial NB:\n",
            "   Accuracy:  0.4967\n",
            "   Precision: 0.5048\n",
            "   Recall:    0.5148\n",
            "   F1-Score:  0.5097\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "🔹 LSTM:\n",
            "   Accuracy:  0.5083\n",
            "   Precision: 0.5083\n",
            "   Recall:    1.0000\n",
            "   F1-Score:  0.6740\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# CELL 10: WELFake Dataset - Model Training & Evaluation\n",
        "\n",
        "print(\"\\n\" + \"=\"*60)\n",
        "print(\" PROCESSING: WELFAKE DATASET\")\n",
        "print(\"=\"*60)\n",
        "\n",
        "# Check class distribution first\n",
        "print(\" Checking WELFake Dataset Class Distribution:\")\n",
        "class_dist = datasets['WELFake']['binary_label'].value_counts()\n",
        "print(class_dist)\n",
        "unique_classes = datasets['WELFake']['binary_label'].nunique()\n",
        "\n",
        "if unique_classes < 2:\n",
        "    print(\" WARNING: WELFake dataset has only one class!\")\n",
        "    print(\" Skipping model training for WELFake dataset.\")\n",
        "    welfake_results = {}\n",
        "else:\n",
        "    trainer = ModelTrainer()\n",
        "    welfake_results = {}\n",
        "\n",
        "    # Prepare data\n",
        "    X = datasets['WELFake']['clean_text'].values\n",
        "    y = datasets['WELFake']['binary_label'].values\n",
        "\n",
        "    X_train, X_test, y_train, y_test = train_test_split(\n",
        "        X, y, test_size=0.3, random_state=42, stratify=y\n",
        "    )\n",
        "\n",
        "    print(f\" Data Split: Train={len(X_train)}, Test={len(X_test)}\")\n",
        "\n",
        "    # Train models\n",
        "    ml_results = trainer.train_ml_models(X_train, X_test, y_train, y_test, 'WELFake')\n",
        "    lstm_result = trainer.train_lstm_model(X_train, X_test, y_train, y_test, 'WELFake')\n",
        "\n",
        "    welfake_results.update(ml_results)\n",
        "    welfake_results['LSTM'] = lstm_result\n",
        "\n",
        "    # Display results\n",
        "    print(f\" Detailed Results for WELFake Dataset:\")\n",
        "    print(\"-\" * 50)\n",
        "\n",
        "    for model_name, metrics in welfake_results.items():\n",
        "        print(f\"\\n🔹 {model_name}:\")\n",
        "        print(f\"   Accuracy:  {metrics['accuracy']:.4f}\")\n",
        "        print(f\"   Precision: {metrics['precision']:.4f}\")\n",
        "        print(f\"   Recall:    {metrics['recall']:.4f}\")\n",
        "        print(f\"   F1-Score:  {metrics['f1']:.4f}\")\n",
        "        trainer.plot_confusion_matrix(metrics['confusion_matrix'], model_name, 'WELFake')\n",
        "\n",
        "    trainer.plot_comparison(welfake_results, 'WELFake')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ZcNM4rOUZa4f",
        "outputId": "d97c5752-7af3-4a00-bed4-c5def6ccd00d"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "============================================================\n",
            " PROCESSING: WELFAKE DATASET\n",
            "============================================================\n",
            " Checking WELFake Dataset Class Distribution:\n",
            "binary_label\n",
            "1    37054\n",
            "0    35028\n",
            "Name: count, dtype: int64\n",
            " Data Split: Train=50457, Test=21625\n",
            "\n",
            "   🔧 Training ML Models on WELFake...\n",
            "      Training Logistic Regression... Acc: 0.9551 | F1: 0.9565\n",
            "      Training SVM... Acc: 0.9652 | F1: 0.9663\n",
            "      Training Multinomial NB... Acc: 0.8594 | F1: 0.8660\n",
            "\n",
            "   🔧 Training LSTM on WELFake...\n",
            "      Training LSTM (this may take a few minutes)...\n",
            "      LSTM - Acc: 0.9348 | F1: 0.9367\n",
            " Detailed Results for WELFake Dataset:\n",
            "--------------------------------------------------\n",
            "\n",
            "🔹 Logistic Regression:\n",
            "   Accuracy:  0.9551\n",
            "   Precision: 0.9521\n",
            "   Recall:    0.9609\n",
            "   F1-Score:  0.9565\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "🔹 SVM:\n",
            "   Accuracy:  0.9652\n",
            "   Precision: 0.9630\n",
            "   Recall:    0.9696\n",
            "   F1-Score:  0.9663\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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Qx+dHW1sbzs7OcHZ2houLC1q0aIGtW7cq/SDr168fVqxYgW3btsHb2xvbtm2Dk5PTB5e89unTB0OGDEFUVBTatWtXpCuDVPke1KxZE0ZGRjh37hzat2+P2NhYsWdCS0sLDRo0wLlz51CxYkWkp6erdfJlXFwcmjVrBmNjY8ydO1d8Tsjly5cxefLkXN9BKysrWFlZ4dChQ7h06ZJSspNT19vbG+7u7nme72OWtRLlhcmEmnTs2BFr165FUFBQrvXs77KxsUFWVhbu3r0LR0dHsTw6OhpxcXF5ztr/WCVLlsw1mx5Art8Ugex/iHMmBS5atAjz58/HtGnTcPLkyTx/68qJMywsLNe+27dvo0yZMkq9ElLq06cP1q9fDy0trfdOEty1axdatGiBP/74Q6k8Li4OZcqUET8XNLEriKSkJAwYMABOTk5wdXXFggUL0KVLF3HFiFRyftBERkYqlTdo0AAVK1aEn58fWrdujZs3b+Knn376YHtdunTBsGHDcOHCBWzfvr1Qsbz9XbC3txfL09PTERERke9v7R9DW1sbDRs2xPnz53Hu3DkYGxvD2dlZ3O/q6ort27eLP1jVmUycOnUKL1++xN9//600oTciIiLP+vr6+vD390fLli3Rtm1bnD59GtWqVQMA8b7q6upKej+J8sI5E2oyadIkGBgYYPDgwYiOjs61/969e1i6dCmA7G56ALlWXCxatAgAcj0HQBUVK1ZEfHy8UvdxZGRkrpnf787+ByD+JvvuctUcVlZWqFWrFjZt2qSUsNy4cQPHjh0Tr7MotGjRAj/++CNWrFgBS0vLfOtpa2vn+m13586ducaWc5KevBKvwpo8eTIePXqETZs2YdGiRbC1tYWHh0eu+1jQpaFnz57NtRoC+N/cm7yGmfr27YsrV65g1qxZkMlkBZr/YGhoiNWrV2P27Nn4+uuvP1j/bW5ubtDT08OyZcuU7nfOA6ak/E4D2QnC8+fPsWHDBjRo0EBpKM7V1RVhYWHYt28fSpcurZSwF7ecHpi370l6ejpWrVqV7zEmJiY4evSo+CCynO+Iubk5mjdvjjVr1uRKIIH/PWuGSArsmVCTnN8Ee/bsCUdHR6UnYAYGBmLnzp3w9PQEkN1N6+HhgbVr14rdoP/++y82bdqEzp0757vs8GP06tULkydPRpcuXTB69GgkJydj9erVqFKlitIExLlz5+LMmTPo0KEDbGxsEBMTg1WrVqFcuXLv/c3u119/Rbt27eDi4oJBgwYhJSUFy5cvh4mJyXuHH1SlpaWF6dOnf7Bex44dMXfuXAwYMACurq64fv06tm7dqvTbM5D952dqagpfX18YGRnBwMAADRo0yPMJie9z4sQJrFq1CrNmzRKXqm7YsAHNmzfHjBkzlJ6lkLMs9EOTMH/55ReEhISga9euqFGjBgDg8uXL2Lx5M0qVKoWxY8fmOqZfv36YO3cu9u3bh0aNGuX5rJG8eHh4FKjeu8zMzDB16lTMmTMHbdu2xTfffIOwsDCsWrUK9evXl/yBTjnfyaCgoFzfs4YNG4qPxf7666/z7XU6fPhwnhMgXV1dlb4fly5dEudkvK158+Yf7PVwdXVFyZIl4eHhgdGjR0Mmk2HLli0fHM4pU6aM+NwXNzc3nDt3DmXLlsXKlSvRuHFjODs7Y8iQIbC3t0d0dDSCgoLw5MkTpeenEKlEfQtJSBAE4c6dO8KQIUMEW1tbQU9PTzAyMhIaNWokLF++XGk5V0ZGhjBnzhzBzs5O0NXVFcqXLy9MnTpVqY4g5L8s790lifktDRUEQTh27JhQvXp1QU9PT3BwcBD+/PPPXEtDjx8/LnTq1EmwtrYW9PT0BGtra6F3797CnTt3cp3j3eWT//zzj9CoUSNBoVAIxsbGwtdffy3cunVLqU7O+d5depqzTC8iIiLfeyoIyktD85Pf0tAJEyYIVlZWgkKhEBo1aiQEBQXluaRz3759gpOTk6Cjo6N0nW8v2XvX2+0kJCQINjY2Qp06dYSMjAyleuPGjRO0tLSEoKAgsaygS0PPnz8veHl5CdWrVxdMTEwEXV1doUKFCoKnp6dw7969fI+rX7++AEBYtWpVnvvfXhr6PgVZGppjxYoVQtWqVQVdXV3BwsJCGDFihPDq1SulOvndz/yWMeclKSlJ/HM6duxYrv01atQQAAi//PJLrn3vWxr67vf7ffV+/PHH915PjvPnzwsNGzYUFAqFYG1tLUyaNElcPnzy5Mn33pfw8HDByspKcHR0FP/u3Lt3T+jfv79gaWkp6OrqCmXLlhU6duwo7Nq1q0D3jqggZIJQiJlsRERERO/gnAkiIiJSCZMJIiIiUgmTCSIiIlIJkwkiIiJSCZMJIiIiUgmTCSIiIlIJkwkiIiJSyRf5BExFyw+/V4Docxd7dJq6QyAqcgrdIm6/9kjJ2kq5skKytj43X2QyQUREVCAydtBLgXeRiIiIVMKeCSIi0lz5vNiNCofJBBERaS4Oc0iCd5GIiIhUwp4JIiLSXBzmkASTCSIi0lwc5pAE7yIRERGphD0TRESkuTjMIQkmE0REpLk4zCEJ3kUiIiJSCXsmiIhIc3GYQxJMJoiISHNxmEMSvItERESkEvZMEBGR5uIwhySYTBARkebiMIckeBeJiIhIJeyZICIizcVhDkkwmSAiIs3FYQ5J8C4SERGRStgzQUREmos9E5JgMkFERJpLi3MmpMCUjIiIiFTCngkiItJcHOaQBO8iERFpLplMuq0Qzpw5g6+//hrW1taQyWTYu3ev0n5BEDBz5kxYWVlBoVDAzc0Nd+/eVaoTGxuLvn37wtjYGKamphg0aBASExOV6ly7dg1NmjSBvr4+ypcvjwULFuSKZefOnahatSr09fXh7OyMQ4cOFepaACYTRERExS4pKQk1a9bEypUr89y/YMECLFu2DL6+vggODoaBgQHc3d2Rmpoq1unbty9u3ryJgIAA+Pv748yZMxg6dKi4PyEhAW3atIGNjQ1CQkLw66+/Yvbs2Vi7dq1YJzAwEL1798agQYNw5coVdO7cGZ07d8aNGzcKdT0yQRCEQt6DT56i5U/qDoGoyMUenabuEIiKnEK3iNt3+1mytuIOjkNaWppSmVwuh1wuf+9xMpkMe/bsQefOnQFk90pYW1tjwoQJ8Pb2BgDEx8fDwsICGzduRK9evfDff//ByckJFy9eRL169QAAR44cQfv27fHkyRNYW1tj9erVmDZtGqKioqCnpwcAmDJlCvbu3Yvbt28DAHr27ImkpCT4+/uL8TRs2BC1atWCr69vga+dPRNERKS5JBzm8PHxgYmJidLm4+NT6JAiIiIQFRUFNzc3sczExAQNGjRAUFAQACAoKAimpqZiIgEAbm5u0NLSQnBwsFinadOmYiIBAO7u7ggLC8OrV6/EOm+fJ6dOznkKihMwiYiIJDB16lSMHz9eqexDvRJ5iYqKAgBYWFgolVtYWIj7oqKiYG5urrRfR0cHpUqVUqpjZ2eXq42cfSVLlkRUVNR7z1NQTCaIiEhzSbiaoyBDGl8qDnMQEZHmUtNqjvextLQEAERHRyuVR0dHi/ssLS0RExOjtP/NmzeIjY1VqpNXG2+fI786OfsLiskEERHRJ8TOzg6WlpY4fvy4WJaQkIDg4GC4uLgAAFxcXBAXF4eQkBCxzokTJ5CVlYUGDRqIdc6cOYOMjAyxTkBAABwcHFCyZEmxztvnyamTc56CYjJBRESaS6Yl3VYIiYmJCA0NRWhoKIDsSZehoaF49OgRZDIZxo4di3nz5mH//v24fv06+vfvD2tra3HFh6OjI9q2bYshQ4bg33//xfnz5zFy5Ej06tUL1tbWAIA+ffpAT08PgwYNws2bN7F9+3YsXbpUaV7HmDFjcOTIESxcuBC3b9/G7NmzcenSJYwcObJQ18M5E0REpLkkHJ4ojEuXLqFFixbi55wf8B4eHti4cSMmTZqEpKQkDB06FHFxcWjcuDGOHDkCfX198ZitW7di5MiRaNWqFbS0tNCtWzcsW7ZM3G9iYoJjx47By8sLdevWRZkyZTBz5kylZ1G4urrCz88P06dPxw8//IDKlStj7969qF69eqGuh8+ZIPpM8TkTpAmK/DkT7RZL1lbK4XGStfW5Yc8EERFpLr6bQxJMJoiISHOpaZjjS8OUjIiIiFTCngkiItJcHOaQBJMJIiLSXEwmJMG7SERERCphzwQREWkuTsCUBJMJIiLSXBzmkATvIhEREamEPRNERKS5OMwhCSYTRESkuTjMIQneRSIiIlIJeyaIiEhzcZhDEkwmiIhIY8mYTEiCwxxERESkEvZMEBGRxmLPhDSYTBARkeZiLiEJDnMQERGRStgzQUREGovDHNJgMkFERBqLyYQ0OMxBREREKmHPBBERaSz2TEiDyQQREWksJhPS4DAHERERqYQ9E0REpLnYMSEJJhNERKSxOMwhDQ5zEBERkUrYM0FERBqLPRPSYDJBREQai8mENDjMQURERCphzwQREWks9kxIg8kEERFpLuYSkuAwBxEREamEPRNERKSxOMwhDSYTRESksZhMSIPDHERERKQStfZMxMXFYc+ePTh79iwePnyI5ORkmJmZoXbt2nB3d4erq6s6wyMioi8ceyakoZaeiWfPnmHw4MGwsrLCvHnzkJKSglq1aqFVq1YoV64cTp48idatW8PJyQnbt29XR4hERKQJZBJuGkwtPRO1a9eGh4cHQkJC4OTklGedlJQU7N27F0uWLMHjx4/h7e1dzFESERFRQaglmbh16xZKly793joKhQK9e/dG79698fLly2KKjIiINAmHOaShlmTiQ4mEqvWJiIgKgsmENNQ6ATM9PR179+5FUFAQoqKiAACWlpZwdXVFp06doKenp87wiIiIqADUtjQ0PDwcjo6O8PDwwJUrV5CVlYWsrCxcuXIF/fv3R7Vq1RAeHq6u8IiISAPIZDLJNk2mtp6JESNGwNnZGVeuXIGxsbHSvoSEBPTv3x9eXl44evSomiIkIqIvnaYnAVJRWzJx/vx5/Pvvv7kSCQAwNjbGjz/+iAYNGqghMiIiIioMtQ1zmJqa4sGDB/nuf/DgAUxNTYstHiIi0kB8zoQk1NYzMXjwYPTv3x8zZsxAq1atYGFhAQCIjo7G8ePHMW/ePIwaNUpd4RERkQbgMIc01JZMzJ07FwYGBvj1118xYcIE8Q9UEARYWlpi8uTJmDRpkrrCIyIiogJS69LQyZMnY/LkyYiIiFBaGmpnZ6fOsIiISEOwZ0Ian8QryO3s7JhAEBFRsWMyIQ21TMD8+eefkZKSUqC6wcHBOHjwYBFHRERERB9LLcnErVu3UKFCBXz//fc4fPgwnj9/Lu578+YNrl27hlWrVsHV1RU9e/aEkZGROsIkIqIvHVdzSEItwxybN2/G1atXsWLFCvTp0wcJCQnQ1taGXC5HcnIygOw3iw4ePBienp7Q19dXR5hERPSF4zCHNNQ2Z6JmzZpYt24d1qxZg2vXruHhw4dISUlBmTJlUKtWLZQpU0ZdoREREVEhqH0CppaWFmrVqoVatWqpOxQiItIw7JmQhtqTCVKPRjXKY1xPF9SpbAmrMkboMWMnDpy/o1RnhmdTDOhQG6aGcgTdeILRSw7j3tNX4v6SRvpYNMod7V0qI0sQsPfMbXivOIak1AyxTrdmjpjYtxEqlyuFF/HJ8N17CYu3XxD3N6lZAccWf5crPttuSxD9KqkIrpwof+t/X4tlSxaiT7/+mDRlGgBg187tOHzQH7f/u4mkpCScCbyY52sAgOw3Iffr3R13wm7jr117UbWqY3GGTx+ByYQ01PY4bVIvA309XL8XjbHL8n6R2oReLvi+a32MXnwYTb02Iik1Awd+6Q25rrZYZ8MPneFoa4aOE/3Q7YftaFyjAlZOaC/ub/NVRWyY1gm/H7iMuoPWYsySIxjV7SsM71wv1/mc+6+Gbbcl4hYTx0SCiteN69ewa+dfqFLFQak8NTUFjRo3waAhwz/YxuKFC2Bmbl5UIRJ9sphMaKhj/97DnPWnsf9cWJ77vbp9hV/+PAf/wDu4cT8Gg3/eD6syRvimcfY/tA4VSsO9QUV8/9tBXLz9DIE3nmD88qPo3qIarEobAgD6tK6OA+fv4PcDl/EgMg5HgsPx67ZATOjlkut8z18lIfqtTRCK7tqJ3pWcnIQfpkzEzNnzYGRsorSv33eeGDh4KJxr1HxvG+fOnsaFwPMY7z25KEMlifEV5NJgMkG52FqZwqq0IU6EPBDLEpLScPG/p2jgVBYA0MCpHF69TsHlO5FinRMhEcgSBNR3zK4j19VBavobpbZT0t6gnLkxKlgo/4MdvG4w7u8cA/8FveFSrVwRXRlR3ubPm4smTZuhoYvrRx3/8sULzJ09A/N8FnD12eeGS0Ml8dknE2lpaUhISFDahKw3Hz6Q8mVZygAAEPPOnIWYV0mwKJXd62BRygDP45KV9mdmCYhNSIHF/x8fcPE+OjV2QPPatpDJgErlSmFM9+zXyuf0XkTFJmLkokPoPWs3+szehSfPX+Po4n6oVdmySK+RKMeRQwdx+79bGD12wkcdLwgCZk6fgu49eqFadWeJoyP6PKhlAmbXrl0LXPfvv/9+734fHx/MmTNHqUzbtgV07Vp9VGwknfUHr8De2hR/z+8BXR1tJCSlYeXfFzHDsymysrLHMe4+jsXdx7HiMRduPoW9tSlGffsVBvnsV1fopCGiIiOx4Oef4LtuPeRy+Ue1sW3rFiQlJWHg4GESR0fFQdOHJ6SilmTCxMTkw5UKaOrUqRg/frxSmfk3iyVrXxNFxWb3SJiXNEBUbKJYbl7SANfCowEA0bFJMDMtoXSctpYMpYwViI79X4/G9HUnMfOPU7AsZYjncUloUSf7HSwRkXH5nv/S7WdwrV5eqsshytetWzcRG/sSvXv87xeczMxMXA65iO3btuLfy9ehra39nhaAf/+9gGtXQ/FVHeVeib49u6Fdh68xb/4vRRI7SYPJhDTUkkxs2LBBsrbkcnmu3yhkWlzxqooHkXGIfJmIFnVsce1edvJgVEIP9R3LYt3+ywCA4FtPUNJIgdqVLXHlbvYbX5vXsYWWTIaL/z1Vai8rS8CzF68BAD1aOuHCzSd4Ea88RPK2GhUtlJIYoqLSoGFD7NpzQKls5vSpsLOzx4BBQz6YSADA5KnTMXLUWPFzTEwMvh82CL/8thjOzu+ftEmaKzMzE7Nnz8aff/6JqKgoWFtbw9PTE9OnTxcTHEEQMGvWLKxbtw5xcXFo1KgRVq9ejcqVK4vtxMbGYtSoUThw4AC0tLTQrVs3LF26FIaGhmKda9euwcvLCxcvXoSZmRlGjRqFSZMmSXo9/KmroQz0dVGxbCnxs62VKWpUtMCr1yl4HJOAlbv/xeR+jRD+NBYPIuMwa0AzRL54La7+CHv0EkeD72GldweMXnwYutpaWDzKHTtP3kTky+xEoLSxAl2aOeJM6EPo6+mgf9sa6NrMEW3G/Smed2S3+ngQGY9bD55DX08HAzrUQvPatug4aVvx3hDSSAYGhqhUuYpSmUJRAiampmL5ixfP8eLFCzx+9AgAEH73DkoYGMDKygomJqawsrJWPr5Edo9dufIVYGHJuT+fOnV1TPzyyy9YvXo1Nm3ahGrVquHSpUsYMGAATExMMHr0aADAggULsGzZMmzatAl2dnaYMWMG3N3dcevWLXGib9++fREZGYmAgABkZGRgwIABGDp0KPz8/AAACQkJaNOmDdzc3ODr64vr169j4MCBMDU1xdChQyW7nk8imdi1axd27NiBR48eIT09XWnf5cuX1RTVl62Og5XSw6IWfN8aALDlyFUMXeCPhX8FoYS+LlaMbw9TQ30EXn+Mb6b8hbSMTPGYAfP3YvFodxz6rQ+ysgTsPRuGCcuVn1vRr40zfIa3ggxA8K2ncB/3Jy7dfibu19PRxs8jWsG6jBGSUzNw434M2k/0w5nQh0V7A4gKaOf2v7Bm9Qrx80CPvgCAOfN80Klzwed/0adJXcMcgYGB6NSpEzp06AAAsLW1xbZt2/Dvv/8CyO6VWLJkCaZPn45OnToByH6vlYWFBfbu3YtevXrhv//+w5EjR3Dx4kXUq5f9/J7ly5ejffv2+O2332BtbY2tW7ciPT0d69evh56eHqpVq4bQ0FAsWrRI0mRCJgjqXdG/bNkyTJs2DZ6enli7di0GDBiAe/fu4eLFi/Dy8sJPP/1U6DYVLQt/DNHnJvboNHWHQFTkFLpF237liUcka+vGvBZIS0tTKstrKB4A5s+fj7Vr1+LYsWOoUqUKrl69ijZt2mDRokXo27cv7t+/j4oVK+LKlStKr5to1qwZatWqhaVLl2L9+vWYMGECXr3635OJ37x5A319fezcuRNdunRB//79kZCQgL1794p1Tp48iZYtWyI2NhYlS5aU5NrVvjR01apVWLt2LZYvXw49PT1MmjQJAQEBGD16NOLj49UdHhERfcFkMuk2Hx8fmJiYKG0+Pj55nnfKlCno1asXqlatCl1dXdSuXRtjx45F377ZPV9RUdlz0SwsLJSOs7CwEPdFRUXB/J0nruro6KBUqVJKdfJq4+1zSEHtwxyPHj2Cq2v2g2IUCgVev86eqPfdd9+hYcOGWLFixfsOJyIi+mhSDnPktbowvyXHO3bswNatW+Hn5ycOPYwdOxbW1tbw8PCQLKbiovZkwtLSErGxsbCxsUGFChVw4cIF1KxZExEREVDzCAwREVGB5TekkZeJEyeKvRMA4OzsjIcPH8LHxwceHh6w/P/Ju9HR0bCyshKPi46OFoc9LC0tERMTo9TumzdvEBsbKx5vaWmJ6OhopTo5ny0lnCCs9mGOli1bYv/+7IcTDRgwAOPGjUPr1q3Rs2dPdOnSRc3RERHRl0zKYY7CSE5OhpaW8o9gbW1tZGVlAQDs7OxgaWmJ48ePi/sTEhIQHBwMF5fs9xu5uLggLi4OISEhYp0TJ04gKysLDRo0EOucOXMGGRn/e5tzQEAAHBwcJJsvAXwCPRNr164Vb56XlxdKly6NwMBAfPPNNxg2jE+UIyKioqOlpZ7VHF9//TV++uknVKhQAdWqVcOVK1ewaNEiDBw4EED28MvYsWMxb948VK5cWVwaam1tjc6dOwMAHB0d0bZtWwwZMgS+vr7IyMjAyJEj0atXL1hbZy9Z7tOnD+bMmYNBgwZh8uTJuHHjBpYuXYrFi6V9uKPaV3MUBa7mIE3A1RykCYp6NYfTD8cka+vW/DYFrvv69WvMmDEDe/bsQUxMDKytrdG7d2/MnDkTenp6AP730Kq1a9ciLi4OjRs3xqpVq1Clyv+ejRIbG4uRI0cqPbRq2bJl+T60qkyZMhg1ahQmT5b27bafRDJx9uxZrFmzBvfu3cOuXbtQtmxZbNmyBXZ2dmjcuHGh22MyQZqAyQRpgqJOJqpNky6ZuPlTwZOJL43a50zs3r0b7u7uUCgUuHLlirhGNz4+HvPnz1dzdERERPQhak8m5s2bB19fX6xbtw66uv9LQRs1asSnXxIRUZGSyWSSbZpM7RMww8LC0LRp01zlJiYmiIuLK/6AiIhIY2h4DiAZtfdMWFpaIjw8PFf5uXPnYG9vr4aIiIiIqDDUnkwMGTIEY8aMQXBwMGQyGZ49e4atW7fC29sbI0aMUHd4RET0BeMwhzTUPswxZcoUZGVloVWrVkhOTkbTpk0hl8vh7e2NUaNGqTs8IiL6gml6EiAVtScTMpkM06ZNw8SJExEeHo7ExEQ4OTnB0NAQKSkpUCgU6g6RiIiI3kPtwxw59PT04OTkhK+++gq6urpYtGgR7Ozs1B0WERF9wdT1OO0vjdqSibS0NEydOhX16tWDq6ur+K71DRs2wM7ODosXL8a4cePUFR4REWkAzpmQhtqGOWbOnIk1a9bAzc0NgYGB6N69OwYMGIALFy5g0aJF6N69O7S1tdUVHhERERWQ2pKJnTt3YvPmzfjmm29w48YN1KhRA2/evMHVq1c1PsMjIqLiwR830lBbMvHkyRPUrVsXAFC9enXI5XKMGzeOiQQRERUb/syRhtrmTGRmZopvRgMAHR0dpbecERER0edBbT0TgiDA09MTcrkcAJCamorhw4fDwMBAqd7ff/+tjvCIiEgDsGNCGmpLJjw8PJQ+9+vXT02REBGRpuIwhzTUlkxs2LBBXacmIiIiCan9CZhERETqwo4JaTCZICIijcVhDml8Mo/TJiIios8TeyaIiEhjsWNCGkwmiIhIY3GYQxoc5iAiIiKVsGeCiIg0FjsmpMFkgoiINBaHOaTBYQ4iIiJSCXsmiIhIY7FjQhpMJoiISGNxmEMaHOYgIiIilbBngoiINBZ7JqTBZIKIiDQWcwlpcJiDiIiIVMKeCSIi0lgc5pAGkwkiItJYzCWkwWEOIiIiUgl7JoiISGNxmEMaTCaIiEhjMZeQBoc5iIiISCXsmSAiIo2lxa4JSTCZICIijcVcQhoc5iAiIiKVsGeCiIg0FldzSIPJBBERaSwt5hKS4DAHERERqYQ9E0REpLE4zCENJhNERKSxmEtIg8McREREpBL2TBARkcaSgV0TUmAyQUREGourOaTBYQ4iIiJSCXsmiIhIY3E1hzQKlExcu3atwA3WqFHjo4MhIiIqTswlpFGgZKJWrVqQyWQQBCHP/Tn7ZDIZMjMzJQ2QiIiIPm0FSiYiIiKKOg4iIqJix1eQS6NAyYSNjU1Rx0FERFTsmEtI46NWc2zZsgWNGjWCtbU1Hj58CABYsmQJ9u3bJ2lwRERE9OkrdDKxevVqjB8/Hu3bt0dcXJw4R8LU1BRLliyROj4iIqIiI5PJJNs0WaGTieXLl2PdunWYNm0atLW1xfJ69erh+vXrkgZHRERUlGQy6TZNVuhkIiIiArVr185VLpfLkZSUJElQRERE9PkodDJhZ2eH0NDQXOVHjhyBo6OjFDEREREVCy2ZTLJNkxX6CZjjx4+Hl5cXUlNTIQgC/v33X2zbtg0+Pj74/fffiyJGIiKiIqHZKYB0Cp1MDB48GAqFAtOnT0dycjL69OkDa2trLF26FL169SqKGImIiOgT9lHv5ujbty/69u2L5ORkJCYmwtzcXOq4iIiIipymr8KQyke/6CsmJgZhYWEAsv8wzMzMJAuKiIioOPAV5NIo9ATM169f47vvvoO1tTWaNWuGZs2awdraGv369UN8fHxRxEhERPTFefr0Kfr164fSpUtDoVDA2dkZly5dEvcLgoCZM2fCysoKCoUCbm5uuHv3rlIbsbGx6Nu3L4yNjWFqaopBgwYhMTFRqc61a9fQpEkT6Ovro3z58liwYIHk11LoZGLw4MEIDg7GwYMHERcXh7i4OPj7++PSpUsYNmyY5AESEREVFXU9tOrVq1do1KgRdHV1cfjwYdy6dQsLFy5EyZIlxToLFizAsmXL4Ovri+DgYBgYGMDd3R2pqalinb59++LmzZsICAiAv78/zpw5g6FDh4r7ExIS0KZNG9jY2CAkJAS//vorZs+ejbVr16p+894iE/J7FWg+DAwMcPToUTRu3Fip/OzZs2jbtu0n8awJRcuf1B0CUZGLPTpN3SEQFTmFbtG2/93Wq5K1taVvzQLXnTJlCs6fP4+zZ8/muV8QBFhbW2PChAnw9vYGAMTHx8PCwgIbN25Er1698N9//8HJyQkXL15EvXr1AGQ/pqF9+/Z48uQJrK2tsXr1akybNg1RUVHQ09MTz713717cvn1bxSv+n0L3TJQuXRomJia5yk1MTJQyKiIiIk2SlpaGhIQEpS0tLS3Puvv370e9evXQvXt3mJubo3bt2li3bp24PyIiAlFRUXBzcxPLTExM0KBBAwQFBQEAgoKCYGpqKiYSAODm5gYtLS0EBweLdZo2bSomEgDg7u6OsLAwvHr1SrJrL3QyMX36dIwfPx5RUVFiWVRUFCZOnIgZM2ZIFhgREVFRk3KYw8fHByYmJkqbj49Pnue9f/8+Vq9ejcqVK+Po0aMYMWIERo8ejU2bNgGA+DPWwsJC6TgLCwtxX1RUVK7VlDo6OihVqpRSnbzaePscUijQao7atWsrjQfdvXsXFSpUQIUKFQAAjx49glwux/PnzzlvgoiIPhtSruaYOnUqxo8fr1Qml8vzrJuVlYV69eph/vz5ALJ/zt64cQO+vr7w8PCQLqhiUqBkonPnzkUcBhER0edNLpfnmzy8y8rKCk5OTkpljo6O2L17NwDA0tISABAdHQ0rKyuxTnR0NGrVqiXWiYmJUWrjzZs3iI2NFY+3tLREdHS0Up2czzl1pFCgZGLWrFmSnZCIiOhToa6HVjVq1Eh8VlOOO3fuwMbGBkD2e7AsLS1x/PhxMXlISEhAcHAwRowYAQBwcXFBXFwcQkJCULduXQDAiRMnkJWVhQYNGoh1pk2bhoyMDOjqZs9mDQgIgIODg6TzHAs9Z4KIiOhLIZNwK4xx48bhwoULmD9/PsLDw+Hn54e1a9fCy8srOy6ZDGPHjsW8efOwf/9+XL9+Hf3794e1tbU4WuDo6Ii2bdtiyJAh+Pfff3H+/HmMHDkSvXr1grW1NQCgT58+0NPTw6BBg3Dz5k1s374dS5cuzTUco6pCPwEzMzMTixcvxo4dO/Do0SOkp6cr7Y+NjZUsOCIioi9R/fr1sWfPHkydOhVz586FnZ0dlixZgr59+4p1Jk2ahKSkJAwdOhRxcXFo3Lgxjhw5An19fbHO1q1bMXLkSLRq1QpaWlro1q0bli1bJu43MTHBsWPH4OXlhbp166JMmTKYOXOm0rMopFDo50zMnDkTv//+OyZMmIDp06dj2rRpePDgAfbu3YuZM2di9OjRkgb4MficCdIEfM4EaYKifs7E4O03JGvr957VJWvrc1PoYY6tW7di3bp1mDBhAnR0dNC7d2/8/vvvmDlzJi5cuFAUMRIRERUJmUy6TZMVOpmIioqCs7MzAMDQ0FB8H0fHjh1x8OBBaaMjIiKiT16hk4ly5cohMjISAFCxYkUcO3YMAHDx4sUCL4khIiL6FKjr3RxfmkInE126dMHx48cBAKNGjcKMGTNQuXJl9O/fHwMHDpQ8QCIioqLCYQ5pFHo1x88//yz+f8+ePWFjY4PAwEBUrlwZX3/9taTBERER0adP5edMNGzYEOPHj0eDBg3Ex4ISERF9DrRkMsk2TSbZQ6siIyP5oi8iIvqscJhDGnwCJhEREamk0HMmiIiIvhSavgpDKl9kMvHqGJ8MSF++kvVHqjsEoiKXcmVFkbbP7nlpFDiZ+NBLQZ4/f65yMERERPT5KXAyceXKlQ/Wadq0qUrBEBERFScOc0ijwMnEyZMnizIOIiKiYqfFXEISHC4iIiIilXyREzCJiIgKgj0T0mAyQUREGotzJqTBYQ4iIiJSCXsmiIhIY3GYQxof1TNx9uxZ9OvXDy4uLnj69CkAYMuWLTh37pykwRERERUlvptDGoVOJnbv3g13d3coFApcuXIFaWlpAID4+Hi+NZSIiEgDFTqZmDdvHnx9fbFu3Tro6uqK5Y0aNcLly5clDY6IiKgo8RXk0ij0nImwsLA8n3RpYmKCuLg4KWIiIiIqFlyFII1C30dLS0uEh4fnKj937hzs7e0lCYqIiIg+H4VOJoYMGYIxY8YgODgYMpkMz549w9atW+Ht7Y0RI0YURYxERERFghMwpVHoYY4pU6YgKysLrVq1QnJyMpo2bQq5XA5vb2+MGjWqKGIkIiIqEpo+10EqhU4mZDIZpk2bhokTJyI8PByJiYlwcnKCoaFhUcRHREREn7iPfmiVnp4enJycpIyFiIioWLFjQhqFTiZatGjx3meZnzhxQqWAiIiIigufgCmNQicTtWrVUvqckZGB0NBQ3LhxAx4eHlLFRURERJ+JQicTixcvzrN89uzZSExMVDkgIiKi4sIJmNKQ7Hkd/fr1w/r166VqjoiIqMhxaag0JEsmgoKCoK+vL1VzRERE9Jko9DBH165dlT4LgoDIyEhcunQJM2bMkCwwIiKiosYJmNIodDJhYmKi9FlLSwsODg6YO3cu2rRpI1lgRERERU0GZhNSKFQykZmZiQEDBsDZ2RklS5YsqpiIiIjoM1KoORPa2tpo06YN3w5KRERfBC2ZdJsmK/QEzOrVq+P+/ftFEQsREVGxYjIhjUInE/PmzYO3tzf8/f0RGRmJhIQEpY2IiIg0S4HnTMydOxcTJkxA+/btAQDffPON0mO1BUGATCZDZmam9FESEREVgfe9HoIKrsDJxJw5czB8+HCcPHmyKOMhIiIqNpo+PCGVAicTgiAAAJo1a1ZkwRAREdHnp1BLQ9kdREREXxL+WJNGoZKJKlWqfDChiI2NVSkgIiKi4sIXfUmjUMnEnDlzcj0Bk4iIiDRboZKJXr16wdzcvKhiISIiKlacgCmNAicTnC9BRERfGv5ok0aBH1qVs5qDiIiI6G0F7pnIysoqyjiIiIiKnRbfGiqJQr+CnIiI6EvBYQ5pFPrdHERERERvY88EERFpLK7mkAaTCSIi0lh8aJU0OMxBREREKmHPBBERaSx2TEiDyQQREWksDnNIg8McREREpBL2TBARkcZix4Q0mEwQEZHGYve8NHgfiYiISCXsmSAiIo3FN2JLg8kEERFpLKYS0uAwBxEREamEPRNERKSx+JwJabBngoiINJZMwu1j/fzzz5DJZBg7dqxYlpqaCi8vL5QuXRqGhobo1q0boqOjlY579OgROnTogBIlSsDc3BwTJ07EmzdvlOqcOnUKderUgVwuR6VKlbBx40YVIs0fkwkiIiI1uXjxItasWYMaNWoolY8bNw4HDhzAzp07cfr0aTx79gxdu3YV92dmZqJDhw5IT09HYGAgNm3ahI0bN2LmzJlinYiICHTo0AEtWrRAaGgoxo4di8GDB+Po0aOSX4dMEARB8lbVLPXNh+sQfe5K1h+p7hCIilzKlRVF2r7f5SeStdWnTrlC1U9MTESdOnWwatUqzJs3D7Vq1cKSJUsQHx8PMzMz+Pn54dtvvwUA3L59G46OjggKCkLDhg1x+PBhdOzYEc+ePYOFhQUAwNfXF5MnT8bz58+hp6eHyZMn4+DBg7hx44Z4zl69eiEuLg5HjhyR7LoB9kwQEZEGk8lkkm1paWlISEhQ2tLS0vI9t5eXFzp06AA3Nzel8pCQEGRkZCiVV61aFRUqVEBQUBAAICgoCM7OzmIiAQDu7u5ISEjAzZs3xTrvtu3u7i62ISUmE0RERBLw8fGBiYmJ0ubj45Nn3b/++guXL1/Oc39UVBT09PRgamqqVG5hYYGoqCixztuJRM7+nH3vq5OQkICUlJSPusb8cDUHERFpLCl/o546dSrGjx+vVCaXy3PVe/z4McaMGYOAgADo6+tLGIH6sGeCiIg0lpTDHHK5HMbGxkpbXslESEgIYmJiUKdOHejo6EBHRwenT5/GsmXLoKOjAwsLC6SnpyMuLk7puOjoaFhaWgIALC0tc63uyPn8oTrGxsZQKBRS3UIATCaIiIiKVatWrXD9+nWEhoaKW7169dC3b1/x/3V1dXH8+HHxmLCwMDx69AguLi4AABcXF1y/fh0xMTFinYCAABgbG8PJyUms83YbOXVy2pAShzmIiEhjqeORVUZGRqhevbpSmYGBAUqXLi2WDxo0COPHj0epUqVgbGyMUaNGwcXFBQ0bNgQAtGnTBk5OTvjuu++wYMECREVFYfr06fDy8hJ7Q4YPH44VK1Zg0qRJGDhwIE6cOIEdO3bg4MGDkl8TkwkiItJYn+qLvhYvXgwtLS1069YNaWlpcHd3x6pVq8T92tra8Pf3x4gRI+Di4gIDAwN4eHhg7ty5Yh07OzscPHgQ48aNw9KlS1GuXDn8/vvvcHd3lzxePmeC6DPF50yQJijq50zsuhopWVvf1rSSrK3PDXsmiIhIY3HioDSYTBARkcb6VIc5PjdMyoiIiEgl7JkgIiKNxX4JaTCZICIijcVRDmlwmIOIiIhUovaeibS0NAQHB+Phw4dITk6GmZkZateuDTs7O3WHRkREXzgtDnRIQm3JxPnz57F06VIcOHAAGRkZMDExgUKhQGxsLNLS0mBvb4+hQ4di+PDhMDIyUleYRET0BeMwhzTUMszxzTffoGfPnrC1tcWxY8fw+vVrvHz5Ek+ePEFycjLu3r2L6dOn4/jx46hSpQoCAgLUESYREREVgFp6Jjp06IDdu3dDV1c3z/329vawt7eHh4cHbt26hchI6Z5QRkRElEPGYQ5JqCWZGDZsWIHrOjk5iW9AIyIikhKHOaTB1RxERESkkk82mbh69Sq0tbXVHQYREX3BtCCTbNNkal8a+j5f4AtNiYjoE8JhDmmoLZno2rXre/fHx8fzBSxERESfAbUlEwcOHEDr1q1hYWGR5/7MzMxijoiIiDQNf2eVhtqSCUdHR3Tr1g2DBg3Kc39oaCj8/f2LOSoiItIkXBoqDbVNwKxbty4uX76c7365XI4KFSoUY0RERET0MdTWM+Hr6/veoQxHR0dEREQUY0RERKRptNgxIQm1JRNyuVxdpyYiIgLAYQ6pqGWYIykpqUjrExERUfFRSzJRqVIl/Pzzz+9954YgCAgICEC7du2wbNmyYoyOiIg0hUwm3abJ1DLMcerUKfzwww+YPXs2atasiXr16sHa2hr6+vp49eoVbt26haCgIOjo6GDq1KmFepcHERFRQXGYQxpqSSYcHBywe/duPHr0CDt37sTZs2cRGBiIlJQUlClTBrVr18a6devQrl07PlKbiIjoEycTvsBnVqe+UXcEREWvZP2R6g6BqMilXFlRpO2fuRMrWVtNq5SSrK3PzSf9bg4iIqKixGEOaTCZoDzt+MsPO7Zvw7OnTwEAFStVxrAR36Nxk2YAgLmzZyL4QiCex8SgRIkSqFmrNsaO94adfUWxjRvXr2Hp4oX479ZNQCZD9eo1MG7CRDhUraqWayLN06hORYzr74Y6ThVgZWaCHuPW4sCpa0p1ZozogAFdXGFqpEDQ1fsYPX877j16DgBoUrcyjv0+Js+2G/ddgJBbjzBtWHtMH94+1/6klDSUcZ0gfh7ZpzmGdG+C8pYl8TIuCXv+uYIZy/cjLZ1dqfT5YzJBeTK3sMSYcd6oYGMDQRBwYN9ejBnphe2796BSpcpwcqqGDh2/hqWVFRLi47F65XIMHzIIh44dh7a2NpKTkvD9sCFo1qIlps2YhTeZmVi9YjlGDB2Eo8dPQVdXV92XSBrAQCHH9TtPsXlfELYvGppr/wRPN3zfuxmGzNyCB09fYub3HXFgpRdqd5uHtPQ3uHD1PmzdpiodM/P7jmjxlQNCbj0CACzZ/A9+33VWqc6hNaMRcvOh+Lln23r4cXQnDJ+9FUFX76OyjTnWzf0OAoDJC/+W/sKpwDR9FYZUmExQnpq3aKn0edSYcdjx1zZcuxqKSpUq49sePcV9ZcuWw8jRY9G9ayc8e/oU5StUQETEfcTHx8Fr5GhYWlkBAIZ/74Vvu3yDyGfPUMHGplivhzTTsfO3cOz8rXz3e/VpgV/WHYX/qesAgMEzNuPhPz74pkVN7Dwagow3mYh++Vqsr6OjhY7Na2D1X6fFsqSUdCSlpIufnauUhVNFK4z+6S+xrGFNOwSF3sf2I5cAAI8iY7HjyCXUr24r1aXSR2IuIQ21vZuDPh+ZmZk4fOggUlKSUbNm7Vz7k5OTsW/P3yhbrhwsLS0BALZ2djA1NcWev3chIz0dqamp2LN7F+ztK8K6bNnivgSiXGzLloaVmQlOBN8WyxISU3HxxgM0qGGb5zEdm9VAaRMDbNl3Id92B3RxxZ0H0Th/5Z5YduFqBGo7lUe9ajbiud0bVcORczeluRgiNfskeibOnj2LNWvW4N69e9i1axfKli2LLVu2wM7ODo0bN37vsWlpaUhLS1MqE7TlfFy3BO7eCcN3fXohPT0NJUqUwOJlK1GxUiVx//ZtW7F44W9ISUmGrZ0d1qzbAF09PQCAgYEhft+4BeNGeWGt7yoAQAUbG6xe+wd0dD6Jrx1pOMsyxgCAmNjXSuUxL1/DorRxnsd4dHZBQNB/eBoTl+d+uZ4Oerarh4UbApTKtx+5hNIlDXB8wzjIIIOurjbW7jyLX9cfU/1CSCVaHOeQhNp7Jnbv3g13d3coFApcuXJFTAzi4+Mxf/78Dx7v4+MDExMTpe3XX3yKOmyNYGtrhx279+LPbTvQvWdvzPhhMu6Fh4v723f8Btt378H6TX/CxsYWEyeMFf/8UlNTMXvGNNSqXQdb/LZj05/bUKlSFYwcMQypqanquiSij1bW3BStXRyxaW9QvnU6tawJoxL6+PNAsFJ5k7qVMXGgO8b4bIdLn1/Qc/xatGtcDVOGtC3qsOkDZBJumkztycS8efPg6+uLdevWKU3Ka9So0XtfUZ5j6tSpiI+PV9omTp76wePow3T19FDBxgZO1apjzLgJqOJQFVv/3CzuNzIygo2NLerWq4+Fi5chIuI+TvyT/RvZoYMH8OzZU8z9yQfVnWugRs1a+HnBb3j69AlOnjiurksiEkW9SAAAmJcyUio3L22E6JcJuep/16khXsYnwf/0tVz7cnh2dsXhszdy9XbM+r4Dth38Fxv3BOFm+DPsP3kNM1ccwMQBbSDjb8b0BVB7MhEWFoamTZvmKjcxMUFcXNwHj5fL5TA2NlbaOMRRNLKyspCRnp7nPgEABAHp/78/NTUVWjItpX8oZVpakEEGISurGKIler8HT18i8nk8WjRwEMuMDPRRv7otgq89yFW//zcN4ef/L968yfv7a2NdGs3qV8bGPHouFPp6yMpSfj5g1v//PWAuoWbsmpCE2gevLS0tER4eDltbW6Xyc+fOwd7eXj1BEZYuXojGTZrC0soKyUlJOHTQH5cu/ovVa//Ak8ePcfTIIbi4NkLJkqUQHR2F9b+vhVyuj8ZNs59D4eLiisW/LcD8H+egd9/vkCVkYf3va6Gjo436DRqo+epIUxgo9FCxvJn42bZsadSoUhavEpLxOOoVVvqdxOTBbRH+6DkePH2JWd93QOTzeOw/eVWpneZfVYFduTLYsCcw33N5dG6IqBcJOHo+96TKQ2duYHS/Frga9gT/Xn+AiuXNMHNERxw6cz1XkkHFiw+tkobak4khQ4ZgzJgxWL9+PWQyGZ49e4agoCB4e3tjxowZ6g5PY8XGvsT0qZPx/HkMDI2MUKWKA1av/QMuro0QExONyyGX8OeWTUiIT0DpMqVRt249bN66DaVLlwYA2NlXxLKVvvBdtQL9+/aETKaFqo6OWLXmd5iZmav56khT1HGyUXro1ALvbgCALfsvYOisP7Fw4z8ooZBjxfTeMDVSIDD0Hr7xWpXrQVKenV0RFHoPdx5E53kemUyG775uiC37g/NMDn7+/QgEQcCs7zvC2twEL14l4uCZG5i94oCEV0ukPmp/N4cgCJg/fz58fHyQnJwMIHvowtvbGz/++ONHtcl3c5Am4Ls5SBMU9bs5/r0fL1lbX9mbSNbW50btyURGRgZ0dXWRnp6O8PBwJCYmwsnJCYaGhnjx4gXKlClT6DaZTJAmYDJBmqCok4mLEiYT9TU4mVD7BMxevXpBEATo6enByckJX331FQwNDREdHY3mzZurOzwiIiL6ALUnE48ePcLgwYOVyiIjI9G8eXNU5QuhiIioKHE1hyTUnkwcOnQIgYGBGD9+PADg2bNnaN68OZydnbFjxw41R0dERF8ymYT/aTK1r+YwMzPDsWPHxMdm+/v7o06dOti6dSu0tNSe6xAREdEHqD2ZAIDy5csjICAATZo0QevWrbFlyxY+FY6IiIocf9RIQy3JRMmSJfNMFpKTk3HgwAHxWQUAEBsbW5yhERERUSGpJZlYsmSJOk5LRESkhB0T0lBLMuHh4aGO0xIRESljNiGJT2LORI7U1FTxRVE5jI2N1RQNERERFYTal0skJSVh5MiRMDc3h4GBAUqWLKm0ERERFRUuDZWG2pOJSZMm4cSJE1i9ejXkcjl+//13zJkzB9bW1ti8ebO6wyMioi+YTCbdpsnUPsxx4MABbN68Gc2bN8eAAQPQpEkTVKpUCTY2Nti6dSv69u2r7hCJiIjoPdTeMxEbGwt7e3sA2fMjcpaCNm7cGGfOnFFnaERE9IXj07SlofZkwt7eHhEREQCAqlWrio/QPnDgAExNTdUYGRERffGYTUhC7cnEgAEDcPXqVQDAlClTsHLlSujr62PcuHGYOHGimqMjIiKiD5EJgiCo48T379+HnZ1dridhPnz4ECEhIahUqRJq1KjxUW2nvpEiQqJPW8n6I9UdAlGRS7myokjbv/Y4UbK2apQ3lKytz43aeiYqV66M58+fi5979uyJ6Oho2NjYoGvXrh+dSBARERUUV3NIQ23JxLsdIocOHUJSUpKaoiEiIqKPpfaloUREROqi4R0KklFbMiGTyXLNl+Brx4mIqFjxx44k1JZMCIIAT09PyOVyANnv5Rg+fDgMDAyU6v3999/qCI+IiIgKSG3JxLtvDu3Xr5+aIiEiIk2l6e/UkIrakokNGzao69REREQAuApDKmp/aBURERF93riag4iINBY7JqTBngkiItJcano3h4+PD+rXrw8jIyOYm5ujc+fOCAsLU6qTmpoKLy8vlC5dGoaGhujWrRuio6OV6jx69AgdOnRAiRIlYG5ujokTJ+LNG+XHQJ86dQp16tSBXC5HpUqVsHHjxsIFWwBMJoiIiIrZ6dOn4eXlhQsXLiAgIAAZGRlo06aN0sMbx40bhwMHDmDnzp04ffo0nj17hq5du4r7MzMz0aFDB6SnpyMwMBCbNm3Cxo0bMXPmTLFOREQEOnTogBYtWiA0NBRjx47F4MGDcfToUUmvR23v5ihKfDcHaQK+m4M0QVG/m+N2ZLJkbdmV0kZaWppSmVwuFx+B8D7Pnz+Hubk5Tp8+jaZNmyI+Ph5mZmbw8/PDt99+mx3r7dtwdHREUFAQGjZsiMOHD6Njx4549uwZLCwsAAC+vr6YPHkynj9/Dj09PUyePBkHDx7EjRs3xHP16tULcXFxOHLkiGTXzp4JIiLSWFK+m8PHxwcmJiZKm4+PT4HiiI+PBwCUKlUKABASEoKMjAy4ubmJdapWrYoKFSogKCgIABAUFARnZ2cxkQAAd3d3JCQk4ObNm2Kdt9vIqZPThlQ4AZOIiEgCU6dOxfjx45XKCtIrkZWVhbFjx6JRo0aoXr06ACAqKgp6enowNTVVqmthYYGoqCixztuJRM7+nH3vq5OQkICUlBQoFIqCX+B7MJkgIiKNJeVqjoIOabzLy8sLN27cwLlz5ySMpnhxmIOIiDSXmlZz5Bg5ciT8/f1x8uRJlCtXTiy3tLREeno64uLilOpHR0fD0tJSrPPu6o6czx+qY2xsLFmvBMBkgoiIqNgJgoCRI0diz549OHHiBOzs7JT2161bF7q6ujh+/LhYFhYWhkePHsHFxQUA4OLiguvXryMmJkasExAQAGNjYzg5OYl13m4jp05OG1LhMAcREWksdb2bw8vLC35+fti3bx+MjIzEOQ4mJiZQKBQwMTHBoEGDMH78eJQqVQrGxsYYNWoUXFxc0LBhQwBAmzZt4OTkhO+++w4LFixAVFQUpk+fDi8vL3G4Zfjw4VixYgUmTZqEgQMH4sSJE9ixYwcOHjwo6fVwaSjRZ4pLQ0kTFPXS0PCYFMnaqmRe8GEDWT4vBdmwYQM8PT0BZD+0asKECdi2bRvS0tLg7u6OVatWiUMYAPDw4UOMGDECp06dgoGBATw8PPDzzz9DR+d/fQWnTp3CuHHjcOvWLZQrVw4zZswQzyEVJhNEnykmE6QJvtRk4kvDYQ4iItJYfDeHNJhMEBGR5mI2IQmu5iAiIiKVsGeCiIg0lrpWc3xpmEwQEZHGymdRBRUShzmIiIhIJeyZICIijcWOCWkwmSAiIs3FbEISHOYgIiIilbBngoiINBZXc0iDyQQREWksruaQBoc5iIiISCXsmSAiIo3FjglpMJkgIiKNxWEOaXCYg4iIiFTCngkiItJg7JqQApMJIiLSWBzmkAaHOYiIiEgl7JkgIiKNxY4JaTCZICIijcVhDmlwmIOIiIhUwp4JIiLSWHw3hzSYTBARkeZiLiEJDnMQERGRStgzQUREGosdE9JgMkFERBqLqzmkwWEOIiIiUgl7JoiISGNxNYc0mEwQEZHmYi4hCQ5zEBERkUrYM0FERBqLHRPSYDJBREQai6s5pMFhDiIiIlIJeyaIiEhjcTWHNJhMEBGRxuIwhzQ4zEFEREQqYTJBREREKuEwBxERaSwOc0iDPRNERESkEvZMEBGRxuJqDmkwmSAiIo3FYQ5pcJiDiIiIVMKeCSIi0ljsmJAGkwkiItJczCYkwWEOIiIiUgl7JoiISGNxNYc0mEwQEZHG4moOaXCYg4iIiFTCngkiItJY7JiQBpMJIiLSXMwmJMFhDiIiIlIJeyaIiEhjcTWHNJhMEBGRxuJqDmlwmIOIiIhUIhMEQVB3EPR5S0tLg4+PD6ZOnQq5XK7ucIiKBL/nRPljMkEqS0hIgImJCeLj42FsbKzucIiKBL/nRPnjMAcRERGphMkEERERqYTJBBEREamEyQSpTC6XY9asWZyURl80fs+J8scJmERERKQS9kwQERGRSphMEBERkUqYTBAREZFKmEwQERGRSphMkCQ8PT3RuXPnD9b77rvvMH/+/AK3++LFC5ibm+PJkycqREeUt40bN8LU1FTydsPCwmBpaYnXr18X+BhfX198/fXXksdCVByYTHzhPD09IZPJIJPJoKurCzs7O0yaNAmpqanFHsvVq1dx6NAhjB49WiwTBAEzZ86ElZUVFAoF3NzccPfuXXF/mTJl0L9/f8yaNavY46XPw9vf8be38PBwtcU0depUjBo1CkZGRgCA1NRUeHp6wtnZGTo6Onkm3gMHDsTly5dx9uzZYo6WSHVMJjRA27ZtERkZifv372Px4sVYs2aNWn44L1++HN27d4ehoaFYtmDBAixbtgy+vr4IDg6GgYEB3N3dlZKdAQMGYOvWrYiNjS32mOnzkPMdf3uzs7NTSyyPHj2Cv78/PD09xbLMzEwoFAqMHj0abm5ueR6np6eHPn36YNmyZcUUKZF0mExoALlcDktLS5QvXx6dO3eGm5sbAgICxP1ZWVnw8fGBnZ0dFAoFatasiV27don7MzMzMWjQIHG/g4MDli5dWqgYMjMzsWvXLqVuXEEQsGTJEkyfPh2dOnVCjRo1sHnzZjx79gx79+4V61WrVg3W1tbYs2fPx98E+qLlfMff3rS1tbFo0SI4OzvDwMAA5cuXx/fff4/ExMR823n+/Dnq1auHLl26IC0t7YN/N/KyY8cO1KxZE2XLlhXLDAwMsHr1agwZMgSWlpb5Hvv1119j//79SElJKfxNIFIjJhMa5saNGwgMDISenp5Y5uPjg82bN8PX1xc3b97EuHHj0K9fP5w+fRpAdrJRrlw57Ny5E7du3cLMmTPxww8/YMeOHQU+77Vr1xAfH4969eqJZREREYiKilL6Tc3ExAQNGjRAUFCQ0vFfffUVu3+p0LS0tLBs2TLcvHkTmzZtwokTJzBp0qQ86z5+/BhNmjRB9erVsWvXLsjl8g/+3cjL2bNnlb7nhVGvXj28efMGwcHBH3U8kbroqDsAKnr+/v4wNDTEmzdvkJaWBi0tLaxYsQIAkJaWhvnz5+Off/6Bi4sLAMDe3h7nzp3DmjVr0KxZM+jq6mLOnDlie3Z2dggKCsKOHTvQo0ePAsXw8OFDaGtrw9zcXCyLiooCAFhYWCjVtbCwEPflsLa2xpUrVwp/8aQRcr7jOdq1a4edO3di7NixYpmtrS3mzZuH4cOHY9WqVUrHh4WFoXXr1ujSpQuWLFkCmUxWoL8beXn48OFHJxMlSpSAiYkJHj58+FHHE6kLkwkN0KJFC6xevRpJSUlYvHgxdHR00K1bNwBAeHg4kpOT0bp1a6Vj0tPTUbt2bfHzypUrsX79ejx69AgpKSlIT09HrVq1ChxDSkoK5HI5ZDLZR12DQqFAcnLyRx1LX76c73gOAwMDAMA///wDHx8f3L59GwkJCXjz5g1SU1ORnJyMEiVKAMj+bjZp0gR9+vTBkiVLxDYK+nfjXSkpKdDX1//oa+F3nT5HTCY0gIGBASpVqgQAWL9+PWrWrIk//vgDgwYNEsePDx48qDTGC0B8odFff/0Fb29vLFy4EC4uLjAyMsKvv/5aqK7YMmXKIDk5Genp6eIQS87YcXR0NKysrMS60dHRuRKV2NhYmJmZFe7CSWO8/R3P8eDBA3Ts2BEjRozATz/9hFKlSuHcuXMYNGgQ0tPTxWRCLpfDzc0N/v7+mDhxovj3oCB/N/JSpkwZvHr16qOvhd91+hwxmdAwWlpa+OGHHzB+/Hj06dMHTk5OkMvlePToUb7dtufPn4erqyu+//57sezevXuFOm9OcnDr1i3x/+3s7GBpaYnjx4+LZQkJCQgODsaIESOUjr9x4waaN29eqHOSZgsJCUFWVhYWLlwILa3s6WF5zfPR0tLCli1b0KdPH7Ro0QKnTp2CtbV1gf5u5KV27dq4devWR8V87949pKamvrfng+hTxAmYGqh79+7Q1tbGypUrYWRkBG9vb4wbNw6bNm3CvXv3cPnyZSxfvhybNm0CAFSuXBmXLl3C0aNHcefOHcyYMQMXL14s1DnNzMxQp04dnDt3TiyTyWQYO3Ys5s2bh/379+P69evo378/rK2tldbhJycnIyQkBG3atJHk+kkzVKpUCRkZGVi+fDnu37+PLVu2wNfXN8+62tra2Lp1K2rWrImWLVsiKiqqQH838uLu7o6goCBkZmYqld+6dQuhoaGIjY1FfHw8QkNDERoaqlTn7NmzsLe3R8WKFVW+fqJiJdAXzcPDQ+jUqVOuch8fH8HMzExITEwUsrKyhCVLlggODg6Crq6uYGZmJri7uwunT58WBEEQUlNTBU9PT8HExEQwNTUVRowYIUyZMkWoWbPmB8/ztlWrVgkNGzZUKsvKyhJmzJghWFhYCHK5XGjVqpUQFhamVMfPz09wcHD4qOunL9/7vnuLFi0SrKysBIVCIbi7uwubN28WAAivXr0SBEEQNmzYIJiYmIj1MzIyhK5duwqOjo5CdHT0B/9u5CUjI0OwtrYWjhw5olRuY2MjAMi1va1NmzaCj4/PR90HInWSCYIgqDOZIc2RkpICBwcHbN++XZwdXxANGzbE6NGj0adPnyKMjkg6K1euxP79+3H06NECH3Pz5k20bNkSd+7cgYmJSRFGRyQ9zpmgYqNQKLB582a8ePGiwMe8ePECXbt2Re/evYswMiJpDRs2DHFxcXj9+rX4SO0PiYyMxObNm5lI0GeJPRNERESkEk7AJCIiIpUwmSAiIiKVMJkgIiIilTCZICIiIpUwmSAiIiKVMJkgKgKenp5KT/Fs3ry50hssi8upU6cgk8kQFxdXZOd491o/RnHESURFh8kEaQxPT0/IZDLIZDLo6emhUqVKmDt3Lt68eVPk5/7777/x448/Fqhucf9gtbW1VXpbJhFRYfGhVaRR2rZtiw0bNiAtLQ2HDh2Cl5cXdHV1MXXq1Fx1337DqapKlSolSTtERJ8i9kyQRpHL5bC0tISNjQ1GjBgBNzc37N+/H8D/uut/+uknWFtbw8HBAQDw+PFj9OjRA6ampihVqhQ6deqEBw8eiG1mZmZi/PjxMDU1RenSpTFp0iS8+yy4d4c50tLSMHnyZJQvXx5yuRyVKlXCH3/8gQcPHqBFixYAgJIlS0Imk8HT0xMAkJWVBR8fH9jZ2UGhUKBmzZrYtWuX0nkOHTqEKlWqQKFQoEWLFkpxfozMzEwMGjRIPKeDgwOWLl2aZ905c+bAzMwMxsbGGD58ONLT08V9BYmdiD5f7JkgjaZQKPDy5Uvx8/Hjx2FsbIyAgAAAQEZGBtzd3eHi4oKzZ89CR0cH8+bNQ9u2bXHt2jXo6elh4cKF2LhxI9avXw9HR0csXLgQe/bsQcuWLfM9b//+/REUFIRly5ahZs2aiIiIwIsXL1C+fHns3r0b3bp1Q1hYGIyNjaFQKAAAPj4++PPPP+Hr64vKlSvjzJkz6NevH8zMzNCsWTM8fvwYXbt2hZeXF4YOHYpLly5hwoQJKt2frKwslCtXDjt37kTp0qURGBiIoUOHwsrKCj169FC6b/r6+jh16hQePHiAAQMGoHTp0vjpp58KFDsRfebU+poxomL09tsls7KyhICAAEEulwve3t7ifgsLCyEtLU08ZsuWLYKDg4OQlZUllqWlpQkKhUI4evSoIAiCYGVlJSxYsEDcn5GRIZQrV07pTZbNmjUTxowZIwiCIISFhQkAhICAgDzjPHnypNKbLQUh+82tJUqUEAIDA5XqDho0SOjdu7cgCIIwdepUwcnJSWn/5MmTc7X1LhsbG2Hx4sX57n+Xl5eX0K1bN/Gzh4eHUKpUKSEpKUksW716tWBoaChkZmYWKPa8rpmIPh/smSCN4u/vD0NDQ2RkZCArKwt9+vTB7Nmzxf3Ozs5K8ySuXr2K8PDwXC9rSk1Nxb179xAfH4/IyEg0aNBA3Kejo4N69erlGurIERoaCm1t7UL9Rh4eHo7k5GS0bt1aqTw9PR21a9cGAPz3339KcQAo1NtZ87Ny5UqsX78ejx49QkpKCtLT01GrVi2lOjVr1kSJEiWUzpuYmIjHjx8jMTHxg7ET0eeNyQRplBYtWmD16tXQ09ODtbU1dHSU/woYGBgofU5MTETdunWxdevWXG2ZmZl9VAw5wxaFkZiYCAA4ePAgypYtq7RPLpd/VBwF8ddff8Hb2xsLFy6Ei4sLjIyM8OuvvyI4OLjAbagrdiIqPkwmSKMYGBigUqVKBa5fp04dbN++Hebm5jA2Ns6zjpWVFYKDg9G0aVMAwJs3bxASEoI6derkWd/Z2RlZWVk4ffo03Nzccu3P6RnJzMwUy5ycnCCXy/Ho0aN8ezQcHR3FyaQ5Lly48OGLfI/z58/D1dUV33//vVh27969XPWuXr2KlJQUMVG6cOECDA0NUb58eZQqVeqDsRPR542rOYjeo2/fvihTpgw6deqEs2fPIiIiAqdOncLo0aPx5MkTAMCYMWPw888/Y+/evbh9+za+//779z4jwtbWFh4eHhg4cCD27t0rtrljxw4AgI2NDWQyGfz9/fH8+XMkJibCyMgI3t7eGDduHDZt2oR79+7h8uXLWL58OTZt2gQAGD58OO7evYuJEyciLCwMfn5+2LhxY4Gu8+nTpwgNDVXaXr16hcqVK+PSpUs4evQo7ty5gxkzZuDixYu5jk9PT8egQYNw69YtHDp0CLNmzcLIkSOhpaVVoNiJ6DOn7kkbRMXl7QmYhdkfGRkp9O/fXyhTpowgl8sFe3t7YciQIUJ8fLwgCNkTLseMGSMYGxsLpqamwvjx44X+/fvnOwFTEAQhJSVFGDdunGBlZSXo6ekJlSpVEtavXy/unzt3rmBpaSnIZDLBw8NDEITsSaNLliwRHBwcBF1dXcHMzExwd3cXTp8+LR534MABoVKlSoJcLheaNGkirF+/vkATMAHk2rZs2SKkpqYKnp6egomJiWBqaiqMGDFCmDJlilCzZs1c923mzJlC6dKlBUNDQ2HIkCFCamqqWOdDsXMCJtHnTSYI+cwSIyIiIioADnMQERGRSphMEBERkUqYTBAREZFKmEwQERGRSphMEBERkUqYTBAREZFKmEwQERGRSphMEBERkUqYTBAREZFKmEwQERGRSphMEBERkUr+D1RMvxmJ0jlRAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "🔹 Multinomial NB:\n",
            "   Accuracy:  0.8594\n",
            "   Precision: 0.8489\n",
            "   Recall:    0.8839\n",
            "   F1-Score:  0.8660\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "🔹 LSTM:\n",
            "   Accuracy:  0.9348\n",
            "   Precision: 0.9345\n",
            "   Recall:    0.9388\n",
            "   F1-Score:  0.9367\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# CELL 11: Final Summary - Best Model Per Dataset\n",
        "\n",
        "print(\"\\n\" + \"=\"*70)\n",
        "print(\"FINAL SUMMARY: BEST MODEL PER DATASET\")\n",
        "print(\"=\"*70)\n",
        "\n",
        "# Store only results that were successfully computed\n",
        "all_results = {}\n",
        "\n",
        "if 'liar_results' in dir() and liar_results:\n",
        "    all_results['LIAR'] = liar_results\n",
        "if 'tweets_results' in dir() and tweets_results:\n",
        "    all_results['Tweets'] = tweets_results\n",
        "if 'welfake_results' in dir() and welfake_results:\n",
        "    all_results['WELFake'] = welfake_results\n",
        "\n",
        "if len(all_results) == 0:\n",
        "    print(\" No results were generated. Please check your datasets.\")\n",
        "    print(\"   Make sure each dataset has at least two classes (0 and 1).\")\n",
        "\n",
        "else:\n",
        "    # Create comparison dataframe\n",
        "    comparison_data = []\n",
        "\n",
        "    for dataset_name, results in all_results.items():\n",
        "        print(f\"\\n🔹 {dataset_name}:\")\n",
        "        best_model = max(results.items(), key=lambda x: x[1]['f1'])\n",
        "        print(f\"  Best Model: {best_model[0]}\")\n",
        "        print(f\"      Accuracy:  {best_model[1]['accuracy']:.4f}\")\n",
        "        print(f\"      Precision: {best_model[1]['precision']:.4f}\")\n",
        "        print(f\"      Recall:    {best_model[1]['recall']:.4f}\")\n",
        "        print(f\"      F1-Score:  {best_model[1]['f1']:.4f}\")\n",
        "\n",
        "        for model_name, metrics in results.items():\n",
        "            comparison_data.append({\n",
        "                'Dataset': dataset_name,\n",
        "                'Model': model_name,\n",
        "                'Accuracy': f\"{metrics['accuracy']:.4f}\",\n",
        "                'Precision': f\"{metrics['precision']:.4f}\",\n",
        "                'Recall': f\"{metrics['recall']:.4f}\",\n",
        "                'F1-Score': f\"{metrics['f1']:.4f}\"\n",
        "            })\n",
        "\n",
        "    # Display complete summary table\n",
        "    print(\"\\n\" + \"=\"*90)\n",
        "    print(\"COMPLETE RESULTS SUMMARY TABLE\")\n",
        "    print(\"=\"*90)\n",
        "\n",
        "    summary_df = pd.DataFrame(comparison_data)\n",
        "    print(summary_df.to_string(index=False))\n",
        "\n",
        "    # Create final comparison bar chart\n",
        "    if len(all_results) > 0:\n",
        "        fig, ax = plt.subplots(figsize=(12, 6))\n",
        "        datasets_list = list(all_results.keys())\n",
        "        models_list = ['Logistic Regression', 'SVM', 'Multinomial NB', 'LSTM']\n",
        "        x = np.arange(len(datasets_list))\n",
        "        width = 0.2\n",
        "\n",
        "        for i, model in enumerate(models_list):\n",
        "            f1_scores = []\n",
        "            for dataset in datasets_list:\n",
        "                if model in all_results[dataset]:\n",
        "                    f1_scores.append(float(all_results[dataset][model]['f1']))\n",
        "                else:\n",
        "                    f1_scores.append(0)\n",
        "            ax.bar(x + i*width, f1_scores, width, label=model)\n",
        "\n",
        "        ax.set_xlabel('Datasets')\n",
        "        ax.set_ylabel('F1-Score')\n",
        "        ax.set_title('Model Performance Comparison Across All Datasets')\n",
        "        ax.set_xticks(x + width * 1.5)\n",
        "        ax.set_xticklabels(datasets_list)\n",
        "        ax.legend()\n",
        "        ax.set_ylim(0, 1)\n",
        "        ax.grid(True, alpha=0.3)\n",
        "\n",
        "        for i, model in enumerate(models_list):\n",
        "            for j, dataset in enumerate(datasets_list):\n",
        "                if model in all_results[dataset]:\n",
        "                    f1 = float(all_results[dataset][model]['f1'])\n",
        "                    ax.text(j + i*width, f1 + 0.02, f'{f1:.3f}', ha='center', fontsize=8)\n",
        "\n",
        "        plt.tight_layout()\n",
        "        plt.show()\n",
        "\n",
        "print(\"\\n\" + \"=\"*70)\n",
        "print(\"PIPELINE COMPLETED SUCCESSFULLY!\")\n",
        "print(\"=\"*70)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "NVh9DUFWZa8a",
        "outputId": "fa292ed1-a9df-49ea-e9fc-028025c5ed33"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "======================================================================\n",
            "FINAL SUMMARY: BEST MODEL PER DATASET\n",
            "======================================================================\n",
            "\n",
            "🔹 LIAR:\n",
            "  Best Model: SVM\n",
            "      Accuracy:  0.7253\n",
            "      Precision: 0.5226\n",
            "      Recall:    0.1304\n",
            "      F1-Score:  0.2087\n",
            "\n",
            "🔹 Tweets:\n",
            "  Best Model: LSTM\n",
            "      Accuracy:  0.5083\n",
            "      Precision: 0.5083\n",
            "      Recall:    1.0000\n",
            "      F1-Score:  0.6740\n",
            "\n",
            "🔹 WELFake:\n",
            "  Best Model: SVM\n",
            "      Accuracy:  0.9652\n",
            "      Precision: 0.9630\n",
            "      Recall:    0.9696\n",
            "      F1-Score:  0.9663\n",
            "\n",
            "==========================================================================================\n",
            "COMPLETE RESULTS SUMMARY TABLE\n",
            "==========================================================================================\n",
            "Dataset               Model Accuracy Precision Recall F1-Score\n",
            "   LIAR Logistic Regression   0.7266    0.5570 0.0779   0.1366\n",
            "   LIAR                 SVM   0.7253    0.5226 0.1304   0.2087\n",
            "   LIAR      Multinomial NB   0.7269    0.6800 0.0319   0.0609\n",
            "   LIAR                LSTM   0.7222    0.0000 0.0000   0.0000\n",
            " Tweets Logistic Regression   0.4933    0.5012 0.6623   0.5706\n",
            " Tweets                 SVM   0.4933    0.5012 0.6623   0.5706\n",
            " Tweets      Multinomial NB   0.4967    0.5048 0.5148   0.5097\n",
            " Tweets                LSTM   0.5083    0.5083 1.0000   0.6740\n",
            "WELFake Logistic Regression   0.9551    0.9521 0.9609   0.9565\n",
            "WELFake                 SVM   0.9652    0.9630 0.9696   0.9663\n",
            "WELFake      Multinomial NB   0.8594    0.8489 0.8839   0.8660\n",
            "WELFake                LSTM   0.9348    0.9345 0.9388   0.9367\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "======================================================================\n",
            "PIPELINE COMPLETED SUCCESSFULLY!\n",
            "======================================================================\n"
          ]
        }
      ]
    }
  ]
}