{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 🛡️ Cybersecurity Threat Detection - Advanced ML Training\n",
    "\n",
    "This notebook demonstrates advanced machine learning techniques for cybersecurity threat detection using multiple algorithms and real-world datasets.\n",
    "\n",
    "## 📊 What we'll cover:\n",
    "- **Dataset Loading & Preprocessing** - Multiple cybersecurity datasets\n",
    "- **Feature Engineering** - Advanced feature extraction and selection\n",
    "- **Model Training** - Random Forest, XGBoost, Neural Networks, Deep Learning\n",
    "- **Model Evaluation** - Comprehensive performance metrics\n",
    "- **Ensemble Methods** - Combining multiple models for better performance\n",
    "- **Real-time Prediction** - Deployment-ready inference pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🚀 Libraries loaded successfully!\n",
      "TensorFlow version: 2.20.0\n",
      "Pandas version: 2.3.2\n",
      "NumPy version: 2.3.2\n"
     ]
    }
   ],
   "source": [
    "# Import essential libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Machine Learning libraries\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score, roc_curve\n",
    "from sklearn.feature_selection import SelectKBest, f_classif\n",
    "\n",
    "# Deep Learning libraries\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, BatchNormalization\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
    "\n",
    "# Advanced ML libraries\n",
    "import xgboost as xgb\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "# Set visualization style\n",
    "plt.style.use('dark_background')\n",
    "sns.set_palette('husl')\n",
    "\n",
    "print(\"🚀 Libraries loaded successfully!\")\n",
    "print(f\"TensorFlow version: {tf.__version__}\")\n",
    "print(f\"Pandas version: {pd.__version__}\")\n",
    "print(f\"NumPy version: {np.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 📁 Dataset Loading and Exploration\n",
    "\n",
    "We'll work with multiple cybersecurity datasets to train comprehensive threat detection models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📊 Dataset manager initialized!\n"
     ]
    }
   ],
   "source": [
    "# Import our advanced dataset manager\n",
    "import sys\n",
    "sys.path.append('../app/services')\n",
    "from advanced_dataset_manager import AdvancedDatasetManager\n",
    "from ml_trainer import CyberSecurityMLTrainer\n",
    "\n",
    "# Initialize dataset manager\n",
    "dataset_manager = AdvancedDatasetManager('../datasets')\n",
    "ml_trainer = CyberSecurityMLTrainer('../models')\n",
    "\n",
    "print(\"📊 Dataset manager initialized!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📥 Downloading cybersecurity datasets...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Failed to download from https://raw.githubusercontent.com/Haggai-dev665/cybersecurity-datasets/main/malware_detection_sample.csv: 404 Client Error: Not Found for url: https://raw.githubusercontent.com/Haggai-dev665/cybersecurity-datasets/main/malware_detection_sample.csv\n",
      "Failed to download from https://github.com/fabriceyhc/SilkRoad/raw/master/data/sr2_drugs_A_Z.csv: 404 Client Error: Not Found for url: https://github.com/fabriceyhc/SilkRoad/raw/master/data/sr2_drugs_A_Z.csv\n",
      "Failed to download from https://raw.githubusercontent.com/shreyagopal/Phishing-Website-Detection-by-Machine-Learning-Techniques/master/dataset.csv: 404 Client Error: Not Found for url: https://raw.githubusercontent.com/shreyagopal/Phishing-Website-Detection-by-Machine-Learning-Techniques/master/dataset.csv\n",
      "Failed to download from https://github.com/ebubekirbbr/pdd/raw/master/Phishing_Legitimate_full.csv: 404 Client Error: Not Found for url: https://github.com/ebubekirbbr/pdd/raw/master/Phishing_Legitimate_full.csv\n",
      "All download attempts failed for phishing_detection. Creating synthetic dataset...\n",
      "Failed to download from https://raw.githubusercontent.com/MWiechmann/enron_spam_data/master/enron_spam_data.csv: 404 Client Error: Not Found for url: https://raw.githubusercontent.com/MWiechmann/enron_spam_data/master/enron_spam_data.csv\n",
      "Failed to download from https://github.com/jbrownlee/Datasets/raw/master/spambase.csv: 404 Client Error: Not Found for url: https://github.com/jbrownlee/Datasets/raw/master/spambase.csv\n",
      "All download attempts failed for spam_detection. Creating synthetic dataset...\n",
      "Failed to download from https://raw.githubusercontent.com/AhmadMamduhh/CICIDS2017-Dataset/main/MachineLearningCVE/Friday-WorkingHours-Afternoon-DDos.pcap_ISCX.csv: 404 Client Error: Not Found for url: https://raw.githubusercontent.com/AhmadMamduhh/CICIDS2017-Dataset/main/MachineLearningCVE/Friday-WorkingHours-Afternoon-DDos.pcap_ISCX.csv\n",
      "Failed to download from https://github.com/RUCD/CICIDS2017_ML/raw/master/dataset/CICIDS2017_sample.csv: 404 Client Error: Not Found for url: https://github.com/RUCD/CICIDS2017_ML/raw/master/dataset/CICIDS2017_sample.csv\n",
      "All download attempts failed for botnet_detection. Creating synthetic dataset...\n",
      "Failed to download from https://raw.githubusercontent.com/Yara-Rules/rules/master/malware/APT_DNS_Tunneling.yar: 404 Client Error: Not Found for url: https://raw.githubusercontent.com/Yara-Rules/rules/master/malware/APT_DNS_Tunneling.yar\n",
      "Failed to download from https://raw.githubusercontent.com/cure53/HTTPLeaks/master/payloads.txt: 404 Client Error: Not Found for url: https://raw.githubusercontent.com/cure53/HTTPLeaks/master/payloads.txt\n",
      "Failed to download from https://github.com/foospidy/payloads/raw/master/other/injection/sql_injection/payloads.txt: 404 Client Error: Not Found for url: https://github.com/foospidy/payloads/raw/master/other/injection/sql_injection/payloads.txt\n",
      "All download attempts failed for web_attack_detection. Creating synthetic dataset...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Downloaded 10 datasets\n",
      "❌ Failed: 0 datasets\n",
      "\n",
      "📋 Available Datasets:\n",
      "  🔸 malware_detection: Malware Detection Dataset (10,000 samples)\n",
      "  🔸 network_intrusion: Network Intrusion Detection (125,973 samples)\n",
      "  🔸 phishing_detection: Phishing Website Detection (11,055 samples)\n",
      "  🔸 spam_detection: Spam Email Detection (4,601 samples)\n",
      "  🔸 botnet_detection: Botnet Traffic Detection (80,000 samples)\n",
      "  🔸 vulnerability_assessment: CVE Vulnerability Database (5,000 samples)\n",
      "  🔸 threat_intelligence: Threat Intelligence Feeds (15,000 samples)\n",
      "  🔸 anomaly_detection: System Anomaly Detection (25,000 samples)\n",
      "  🔸 dns_tunneling: DNS Tunneling Detection (10,000 samples)\n",
      "  🔸 web_attack_detection: Web Application Attack Detection (8,000 samples)\n"
     ]
    }
   ],
   "source": [
    "# Download and prepare cybersecurity datasets\n",
    "print(\"📥 Downloading cybersecurity datasets...\")\n",
    "download_results = await dataset_manager.download_cybersecurity_datasets()\n",
    "\n",
    "print(f\"✅ Downloaded {download_results['successful_downloads']} datasets\")\n",
    "print(f\"❌ Failed: {len(download_results['failed'])} datasets\")\n",
    "\n",
    "# Display available datasets\n",
    "available_datasets = dataset_manager.get_available_datasets()\n",
    "print(\"\\n📋 Available Datasets:\")\n",
    "for dataset_id, info in available_datasets.items():\n",
    "    print(f\"  🔸 {dataset_id}: {info.get('name', 'Unknown')} ({info.get('samples', 0):,} samples)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🔍 Dataset Analysis and Preprocessing\n",
    "\n",
    "Let's start with malware detection dataset as our primary example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📊 Loaded malware_detection dataset with 10000 samples\n",
      "📈 Features: 8 columns\n",
      "\n",
      "📋 Dataset Info:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 8 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   file_hash      10000 non-null  object \n",
      " 1   file_size      10000 non-null  float64\n",
      " 2   entropy        10000 non-null  float64\n",
      " 3   pe_sections    10000 non-null  int64  \n",
      " 4   imports        10000 non-null  int64  \n",
      " 5   exports        10000 non-null  int64  \n",
      " 6   strings_count  10000 non-null  int64  \n",
      " 7   is_malware     10000 non-null  int64  \n",
      "dtypes: float64(2), int64(5), object(1)\n",
      "memory usage: 625.1+ KB\n",
      "None\n",
      "\n",
      "🔍 First 5 rows:\n"
     ]
    },
    {
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       "      <td>50</td>\n",
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      "text/plain": [
       "     file_hash     file_size   entropy  pe_sections  imports  exports  \\\n",
       "0  hash_000000  59481.957248  5.855952            7      122       82   \n",
       "1  hash_000001  16705.134357  2.669669            8      152        2   \n",
       "2  hash_000002  35108.216772  0.006230            1      460       57   \n",
       "3  hash_000003  27889.044618  2.329833           10      476       59   \n",
       "4  hash_000004  34343.148047  3.059696            3      485       50   \n",
       "\n",
       "   strings_count  is_malware  \n",
       "0           4436           0  \n",
       "1           1695           0  \n",
       "2           6406           0  \n",
       "3           9177           1  \n",
       "4           8848           0  "
      ]
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "⚖️ Class Distribution:\n",
      "is_malware\n",
      "0    5069\n",
      "1    4931\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load malware detection dataset\n",
    "dataset_id = 'malware_detection'\n",
    "df = await dataset_manager.load_dataset(dataset_id)\n",
    "\n",
    "if df is not None:\n",
    "    print(f\"📊 Loaded {dataset_id} dataset with {len(df)} samples\")\n",
    "    print(f\"📈 Features: {df.shape[1]} columns\")\n",
    "    \n",
    "    # Display basic information\n",
    "    print(\"\\n📋 Dataset Info:\")\n",
    "    print(df.info())\n",
    "    \n",
    "    # Display first few rows\n",
    "    print(\"\\n🔍 First 5 rows:\")\n",
    "    display(df.head())\n",
    "    \n",
    "    # Check class distribution\n",
    "    print(\"\\n⚖️ Class Distribution:\")\n",
    "    target_col = 'is_malware'\n",
    "    if target_col in df.columns:\n",
    "        class_dist = df[target_col].value_counts()\n",
    "        print(class_dist)\n",
    "        \n",
    "        # Visualize class distribution\n",
    "        plt.figure(figsize=(8, 6))\n",
    "        class_dist.plot(kind='bar', color=['#4ecdc4', '#ff6b6b'])\n",
    "        plt.title('Malware vs Benign Distribution', fontsize=16, color='white')\n",
    "        plt.xlabel('Class (0=Benign, 1=Malware)', color='white')\n",
    "        plt.ylabel('Count', color='white')\n",
    "        plt.xticks(rotation=0)\n",
    "        plt.grid(True, alpha=0.3)\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "else:\n",
    "    print(\"❌ Failed to load dataset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Advanced feature analysis\n",
    "if df is not None:\n",
    "    # Correlation matrix\n",
    "    numeric_features = df.select_dtypes(include=[np.number]).columns\n",
    "    correlation_matrix = df[numeric_features].corr()\n",
    "    \n",
    "    # Plot correlation heatmap\n",
    "    plt.figure(figsize=(12, 10))\n",
    "    mask = np.triu(np.ones_like(correlation_matrix, dtype=bool))\n",
    "    sns.heatmap(correlation_matrix, mask=mask, annot=False, cmap='coolwarm', \n",
    "                center=0, square=True, linewidths=0.5)\n",
    "    plt.title('Feature Correlation Matrix', fontsize=16, color='white')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    # Feature distributions\n",
    "    fig, axes = plt.subplots(2, 3, figsize=(15, 10))\n",
    "    axes = axes.ravel()\n",
    "    \n",
    "    for i, feature in enumerate(numeric_features[:6]):\n",
    "        if feature != target_col:\n",
    "            df[feature].hist(bins=30, ax=axes[i], alpha=0.7, color='#4ecdc4')\n",
    "            axes[i].set_title(f'{feature} Distribution', color='white')\n",
    "            axes[i].set_xlabel(feature, color='white')\n",
    "            axes[i].set_ylabel('Frequency', color='white')\n",
    "            axes[i].grid(True, alpha=0.3)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🛠️ Advanced Feature Engineering\n",
    "\n",
    "Let's create advanced features and prepare data for machine learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔧 Feature engineering complete: 8 → 19 features\n",
      "\n",
      "🆕 New features created: ['entropy_squared', 'row_std', 'row_max', 'log_file_size', 'row_range', 'sqrt_file_size', 'size_entropy_ratio', 'entropy_cubed', 'row_min', 'size_entropy_product', 'row_mean']\n",
      "\n",
      "📊 Top 10 features correlated with target:\n",
      "is_malware              1.000000\n",
      "imports                 0.491451\n",
      "row_min                 0.482505\n",
      "entropy                 0.477186\n",
      "entropy_squared         0.463514\n",
      "entropy_cubed           0.438443\n",
      "pe_sections             0.300981\n",
      "size_entropy_product    0.037089\n",
      "exports                 0.015833\n",
      "strings_count           0.014418\n",
      "Name: is_malware, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "def advanced_feature_engineering(df, target_col):\n",
    "    \"\"\"\n",
    "    Advanced feature engineering for cybersecurity data\n",
    "    \"\"\"\n",
    "    df_engineered = df.copy()\n",
    "    \n",
    "    # Create interaction features\n",
    "    if 'file_size' in df.columns and 'entropy' in df.columns:\n",
    "        df_engineered['size_entropy_ratio'] = df_engineered['file_size'] / (df_engineered['entropy'] + 1e-8)\n",
    "        df_engineered['size_entropy_product'] = df_engineered['file_size'] * df_engineered['entropy']\n",
    "    \n",
    "    # Create statistical features\n",
    "    numeric_cols = df.select_dtypes(include=[np.number]).columns\n",
    "    numeric_cols = [col for col in numeric_cols if col != target_col]\n",
    "    \n",
    "    # Row-wise statistics\n",
    "    if len(numeric_cols) > 0:\n",
    "        df_engineered['row_mean'] = df_engineered[numeric_cols].mean(axis=1)\n",
    "        df_engineered['row_std'] = df_engineered[numeric_cols].std(axis=1)\n",
    "        df_engineered['row_max'] = df_engineered[numeric_cols].max(axis=1)\n",
    "        df_engineered['row_min'] = df_engineered[numeric_cols].min(axis=1)\n",
    "        df_engineered['row_range'] = df_engineered['row_max'] - df_engineered['row_min']\n",
    "    \n",
    "    # Polynomial features for key attributes\n",
    "    if 'entropy' in df.columns:\n",
    "        df_engineered['entropy_squared'] = df_engineered['entropy'] ** 2\n",
    "        df_engineered['entropy_cubed'] = df_engineered['entropy'] ** 3\n",
    "    \n",
    "    if 'file_size' in df.columns:\n",
    "        df_engineered['log_file_size'] = np.log1p(df_engineered['file_size'])\n",
    "        df_engineered['sqrt_file_size'] = np.sqrt(df_engineered['file_size'])\n",
    "    \n",
    "    print(f\"🔧 Feature engineering complete: {df.shape[1]} → {df_engineered.shape[1]} features\")\n",
    "    return df_engineered\n",
    "\n",
    "# Apply feature engineering\n",
    "if df is not None:\n",
    "    df_engineered = advanced_feature_engineering(df, target_col)\n",
    "    \n",
    "    # Show new features\n",
    "    new_features = set(df_engineered.columns) - set(df.columns)\n",
    "    print(f\"\\n🆕 New features created: {list(new_features)}\")\n",
    "    \n",
    "    # Display correlation of new features with target (only numeric columns)\n",
    "    if target_col in df_engineered.columns:\n",
    "        # Select only numeric columns for correlation\n",
    "        numeric_df = df_engineered.select_dtypes(include=[np.number])\n",
    "        \n",
    "        if target_col in numeric_df.columns and len(numeric_df.columns) > 1:\n",
    "            target_corr = numeric_df.corr()[target_col].abs().sort_values(ascending=False)\n",
    "            print(\"\\n📊 Top 10 features correlated with target:\")\n",
    "            print(target_corr.head(10))\n",
    "        else:\n",
    "            print(f\"\\n⚠️ Target column '{target_col}' not found in numeric columns or insufficient numeric data\")\n",
    "            print(f\"Available numeric columns: {list(numeric_df.columns)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🤖 Advanced Model Training\n",
    "\n",
    "Now let's train multiple advanced machine learning models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🎯 Training data shape: (10000, 17)\n",
      "🎯 Target distribution: {0: 5069, 1: 4931}\n",
      "✅ Data prepared for training\n",
      "   Training set: 8000 samples\n",
      "   Test set: 2000 samples\n",
      "✅ Data prepared for training\n",
      "   Training set: 8000 samples\n",
      "   Test set: 2000 samples\n",
      "\n",
      "⚖️ After SMOTE balancing:\n",
      "   Training set: 8110 samples\n",
      "   Class distribution: [4055 4055]\n",
      "\n",
      "⚖️ After SMOTE balancing:\n",
      "   Training set: 8110 samples\n",
      "   Class distribution: [4055 4055]\n"
     ]
    }
   ],
   "source": [
    "# Prepare data for training\n",
    "if df_engineered is not None and target_col in df_engineered.columns:\n",
    "    # Separate features and target\n",
    "    feature_cols = [col for col in df_engineered.columns if col not in [target_col, 'file_hash']]\n",
    "    X = df_engineered[feature_cols]\n",
    "    y = df_engineered[target_col]\n",
    "    \n",
    "    print(f\"🎯 Training data shape: {X.shape}\")\n",
    "    print(f\"🎯 Target distribution: {y.value_counts().to_dict()}\")\n",
    "    \n",
    "    # Split data\n",
    "    X_train, X_test, y_train, y_test = train_test_split(\n",
    "        X, y, test_size=0.2, random_state=42, stratify=y\n",
    "    )\n",
    "    \n",
    "    # Scale features\n",
    "    scaler = StandardScaler()\n",
    "    X_train_scaled = scaler.fit_transform(X_train)\n",
    "    X_test_scaled = scaler.transform(X_test)\n",
    "    \n",
    "    print(f\"✅ Data prepared for training\")\n",
    "    print(f\"   Training set: {X_train.shape[0]} samples\")\n",
    "    print(f\"   Test set: {X_test.shape[0]} samples\")\n",
    "    \n",
    "    # Handle class imbalance with SMOTE\n",
    "    smote = SMOTE(random_state=42)\n",
    "    X_train_balanced, y_train_balanced = smote.fit_resample(X_train_scaled, y_train)\n",
    "    \n",
    "    print(f\"\\n⚖️ After SMOTE balancing:\")\n",
    "    print(f\"   Training set: {X_train_balanced.shape[0]} samples\")\n",
    "    print(f\"   Class distribution: {np.bincount(y_train_balanced)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🌲 Training Random Forest...\n",
      "Fitting 5 folds for each of 81 candidates, totalling 405 fits\n",
      "🎯 Best Random Forest parameters: {'max_depth': 10, 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 300}\n",
      "🎯 Best cross-validation F1 score: 0.8594\n",
      "🎯 Best Random Forest parameters: {'max_depth': 10, 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 300}\n",
      "🎯 Best cross-validation F1 score: 0.8594\n",
      "\n",
      "📊 Random Forest Test Results:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.86      0.86      1014\n",
      "           1       0.85      0.86      0.85       986\n",
      "\n",
      "    accuracy                           0.86      2000\n",
      "   macro avg       0.86      0.86      0.86      2000\n",
      "weighted avg       0.86      0.86      0.86      2000\n",
      "\n",
      "AUC-ROC: 0.9427\n",
      "\n",
      "📊 Random Forest Test Results:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.86      0.86      1014\n",
      "           1       0.85      0.86      0.85       986\n",
      "\n",
      "    accuracy                           0.86      2000\n",
      "   macro avg       0.86      0.86      0.86      2000\n",
      "weighted avg       0.86      0.86      0.86      2000\n",
      "\n",
      "AUC-ROC: 0.9427\n"
     ]
    }
   ],
   "source": [
    "# Train Random Forest with hyperparameter tuning\n",
    "print(\"🌲 Training Random Forest...\")\n",
    "\n",
    "rf_param_grid = {\n",
    "    'n_estimators': [100, 200, 300],\n",
    "    'max_depth': [10, 20, None],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4]\n",
    "}\n",
    "\n",
    "rf = RandomForestClassifier(random_state=42, n_jobs=-1)\n",
    "rf_grid = GridSearchCV(rf, rf_param_grid, cv=5, scoring='f1', n_jobs=-1, verbose=1)\n",
    "rf_grid.fit(X_train_balanced, y_train_balanced)\n",
    "\n",
    "print(f\"🎯 Best Random Forest parameters: {rf_grid.best_params_}\")\n",
    "print(f\"🎯 Best cross-validation F1 score: {rf_grid.best_score_:.4f}\")\n",
    "\n",
    "# Evaluate on test set\n",
    "rf_pred = rf_grid.predict(X_test_scaled)\n",
    "rf_pred_proba = rf_grid.predict_proba(X_test_scaled)[:, 1]\n",
    "\n",
    "print(\"\\n📊 Random Forest Test Results:\")\n",
    "print(classification_report(y_test, rf_pred))\n",
    "print(f\"AUC-ROC: {roc_auc_score(y_test, rf_pred_proba):.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🚀 Training XGBoost...\n",
      "Fitting 5 folds for each of 54 candidates, totalling 270 fits\n",
      "🎯 Best XGBoost parameters: {'learning_rate': 0.01, 'max_depth': 6, 'n_estimators': 100, 'subsample': 0.8}\n",
      "🎯 Best cross-validation F1 score: 0.8621\n",
      "\n",
      "📊 XGBoost Test Results:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.86      0.87      1014\n",
      "           1       0.86      0.87      0.87       986\n",
      "\n",
      "    accuracy                           0.87      2000\n",
      "   macro avg       0.87      0.87      0.87      2000\n",
      "weighted avg       0.87      0.87      0.87      2000\n",
      "\n",
      "AUC-ROC: 0.9475\n",
      "🎯 Best XGBoost parameters: {'learning_rate': 0.01, 'max_depth': 6, 'n_estimators': 100, 'subsample': 0.8}\n",
      "🎯 Best cross-validation F1 score: 0.8621\n",
      "\n",
      "📊 XGBoost Test Results:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.86      0.87      1014\n",
      "           1       0.86      0.87      0.87       986\n",
      "\n",
      "    accuracy                           0.87      2000\n",
      "   macro avg       0.87      0.87      0.87      2000\n",
      "weighted avg       0.87      0.87      0.87      2000\n",
      "\n",
      "AUC-ROC: 0.9475\n"
     ]
    }
   ],
   "source": [
    "# Train XGBoost\n",
    "print(\"🚀 Training XGBoost...\")\n",
    "\n",
    "xgb_param_grid = {\n",
    "    'n_estimators': [100, 200],\n",
    "    'max_depth': [6, 8, 10],\n",
    "    'learning_rate': [0.01, 0.1, 0.2],\n",
    "    'subsample': [0.8, 0.9, 1.0]\n",
    "}\n",
    "\n",
    "xgb_model = xgb.XGBClassifier(random_state=42, n_jobs=-1)\n",
    "xgb_grid = GridSearchCV(xgb_model, xgb_param_grid, cv=5, scoring='f1', n_jobs=-1, verbose=1)\n",
    "xgb_grid.fit(X_train_balanced, y_train_balanced)\n",
    "\n",
    "print(f\"🎯 Best XGBoost parameters: {xgb_grid.best_params_}\")\n",
    "print(f\"🎯 Best cross-validation F1 score: {xgb_grid.best_score_:.4f}\")\n",
    "\n",
    "# Evaluate on test set\n",
    "xgb_pred = xgb_grid.predict(X_test_scaled)\n",
    "xgb_pred_proba = xgb_grid.predict_proba(X_test_scaled)[:, 1]\n",
    "\n",
    "print(\"\\n📊 XGBoost Test Results:\")\n",
    "print(classification_report(y_test, xgb_pred))\n",
    "print(f\"AUC-ROC: {roc_auc_score(y_test, xgb_pred_proba):.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🧠 Training Deep Neural Network...\n",
      "Epoch 1/100\n",
      "Epoch 1/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 35ms/step - accuracy: 0.8087 - loss: 0.4161 - precision: 0.7763 - recall: 0.8549 - val_accuracy: 0.8557 - val_loss: 0.4155 - val_precision: 0.9167 - val_recall: 0.8088 - learning_rate: 0.0010\n",
      "Epoch 2/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 35ms/step - accuracy: 0.8087 - loss: 0.4161 - precision: 0.7763 - recall: 0.8549 - val_accuracy: 0.8557 - val_loss: 0.4155 - val_precision: 0.9167 - val_recall: 0.8088 - learning_rate: 0.0010\n",
      "Epoch 2/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 22ms/step - accuracy: 0.8466 - loss: 0.3311 - precision: 0.8445 - recall: 0.8411 - val_accuracy: 0.8483 - val_loss: 0.3582 - val_precision: 0.9143 - val_recall: 0.7964 - learning_rate: 0.0010\n",
      "Epoch 3/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 22ms/step - accuracy: 0.8466 - loss: 0.3311 - precision: 0.8445 - recall: 0.8411 - val_accuracy: 0.8483 - val_loss: 0.3582 - val_precision: 0.9143 - val_recall: 0.7964 - learning_rate: 0.0010\n",
      "Epoch 3/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step - accuracy: 0.8463 - loss: 0.3239 - precision: 0.8451 - recall: 0.8395 - val_accuracy: 0.8594 - val_loss: 0.3106 - val_precision: 0.8952 - val_recall: 0.8405 - learning_rate: 0.0010\n",
      "Epoch 4/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 24ms/step - accuracy: 0.8463 - loss: 0.3239 - precision: 0.8451 - recall: 0.8395 - val_accuracy: 0.8594 - val_loss: 0.3106 - val_precision: 0.8952 - val_recall: 0.8405 - learning_rate: 0.0010\n",
      "Epoch 4/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 33ms/step - accuracy: 0.8483 - loss: 0.3136 - precision: 0.8466 - recall: 0.8423 - val_accuracy: 0.8681 - val_loss: 0.2759 - val_precision: 0.8851 - val_recall: 0.8710 - learning_rate: 0.0010\n",
      "Epoch 5/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 33ms/step - accuracy: 0.8483 - loss: 0.3136 - precision: 0.8466 - recall: 0.8423 - val_accuracy: 0.8681 - val_loss: 0.2759 - val_precision: 0.8851 - val_recall: 0.8710 - learning_rate: 0.0010\n",
      "Epoch 5/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 40ms/step - accuracy: 0.8573 - loss: 0.3034 - precision: 0.8544 - recall: 0.8534 - val_accuracy: 0.8502 - val_loss: 0.2877 - val_precision: 0.8991 - val_recall: 0.8167 - learning_rate: 0.0010\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 40ms/step - accuracy: 0.8573 - loss: 0.3034 - precision: 0.8544 - recall: 0.8534 - val_accuracy: 0.8502 - val_loss: 0.2877 - val_precision: 0.8991 - val_recall: 0.8167 - learning_rate: 0.0010\n",
      "Epoch 6/100\n",
      "Epoch 6/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 34ms/step - accuracy: 0.8511 - loss: 0.2999 - precision: 0.8503 - recall: 0.8439 - val_accuracy: 0.8687 - val_loss: 0.2722 - val_precision: 0.8879 - val_recall: 0.8688 - learning_rate: 0.0010\n",
      "Epoch 7/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 34ms/step - accuracy: 0.8511 - loss: 0.2999 - precision: 0.8503 - recall: 0.8439 - val_accuracy: 0.8687 - val_loss: 0.2722 - val_precision: 0.8879 - val_recall: 0.8688 - learning_rate: 0.0010\n",
      "Epoch 7/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 39ms/step - accuracy: 0.8505 - loss: 0.3027 - precision: 0.8442 - recall: 0.8512 - val_accuracy: 0.8557 - val_loss: 0.2720 - val_precision: 0.9022 - val_recall: 0.8247 - learning_rate: 0.0010\n",
      "Epoch 8/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 39ms/step - accuracy: 0.8505 - loss: 0.3027 - precision: 0.8442 - recall: 0.8512 - val_accuracy: 0.8557 - val_loss: 0.2720 - val_precision: 0.9022 - val_recall: 0.8247 - learning_rate: 0.0010\n",
      "Epoch 8/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 42ms/step - accuracy: 0.8516 - loss: 0.2989 - precision: 0.8584 - recall: 0.8338 - val_accuracy: 0.8613 - val_loss: 0.2727 - val_precision: 0.8994 - val_recall: 0.8394 - learning_rate: 0.0010\n",
      "Epoch 9/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 42ms/step - accuracy: 0.8516 - loss: 0.2989 - precision: 0.8584 - recall: 0.8338 - val_accuracy: 0.8613 - val_loss: 0.2727 - val_precision: 0.8994 - val_recall: 0.8394 - learning_rate: 0.0010\n",
      "Epoch 9/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 41ms/step - accuracy: 0.8567 - loss: 0.2896 - precision: 0.8646 - recall: 0.8379 - val_accuracy: 0.8631 - val_loss: 0.2700 - val_precision: 0.8940 - val_recall: 0.8495 - learning_rate: 0.0010\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 41ms/step - accuracy: 0.8567 - loss: 0.2896 - precision: 0.8646 - recall: 0.8379 - val_accuracy: 0.8631 - val_loss: 0.2700 - val_precision: 0.8940 - val_recall: 0.8495 - learning_rate: 0.0010\n",
      "Epoch 10/100\n",
      "Epoch 10/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 34ms/step - accuracy: 0.8565 - loss: 0.2909 - precision: 0.8556 - recall: 0.8499 - val_accuracy: 0.8674 - val_loss: 0.2693 - val_precision: 0.8996 - val_recall: 0.8518 - learning_rate: 0.0010\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 34ms/step - accuracy: 0.8565 - loss: 0.2909 - precision: 0.8556 - recall: 0.8499 - val_accuracy: 0.8674 - val_loss: 0.2693 - val_precision: 0.8996 - val_recall: 0.8518 - learning_rate: 0.0010\n",
      "Epoch 11/100\n",
      "Epoch 11/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 51ms/step - accuracy: 0.8519 - loss: 0.2965 - precision: 0.8576 - recall: 0.8357 - val_accuracy: 0.8681 - val_loss: 0.2723 - val_precision: 0.8815 - val_recall: 0.8756 - learning_rate: 0.0010\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 51ms/step - accuracy: 0.8519 - loss: 0.2965 - precision: 0.8576 - recall: 0.8357 - val_accuracy: 0.8681 - val_loss: 0.2723 - val_precision: 0.8815 - val_recall: 0.8756 - learning_rate: 0.0010\n",
      "Epoch 12/100\n",
      "Epoch 12/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 33ms/step - accuracy: 0.8556 - loss: 0.2939 - precision: 0.8499 - recall: 0.8556 - val_accuracy: 0.8650 - val_loss: 0.2714 - val_precision: 0.8907 - val_recall: 0.8575 - learning_rate: 0.0010\n",
      "Epoch 13/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 33ms/step - accuracy: 0.8556 - loss: 0.2939 - precision: 0.8499 - recall: 0.8556 - val_accuracy: 0.8650 - val_loss: 0.2714 - val_precision: 0.8907 - val_recall: 0.8575 - learning_rate: 0.0010\n",
      "Epoch 13/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 36ms/step - accuracy: 0.8522 - loss: 0.2932 - precision: 0.8516 - recall: 0.8448 - val_accuracy: 0.8736 - val_loss: 0.2731 - val_precision: 0.8670 - val_recall: 0.9072 - learning_rate: 0.0010\n",
      "Epoch 14/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 36ms/step - accuracy: 0.8522 - loss: 0.2932 - precision: 0.8516 - recall: 0.8448 - val_accuracy: 0.8736 - val_loss: 0.2731 - val_precision: 0.8670 - val_recall: 0.9072 - learning_rate: 0.0010\n",
      "Epoch 14/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 39ms/step - accuracy: 0.8564 - loss: 0.2943 - precision: 0.8506 - recall: 0.8565 - val_accuracy: 0.8625 - val_loss: 0.2672 - val_precision: 0.8794 - val_recall: 0.8665 - learning_rate: 0.0010\n",
      "Epoch 15/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 39ms/step - accuracy: 0.8564 - loss: 0.2943 - precision: 0.8506 - recall: 0.8565 - val_accuracy: 0.8625 - val_loss: 0.2672 - val_precision: 0.8794 - val_recall: 0.8665 - learning_rate: 0.0010\n",
      "Epoch 15/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 42ms/step - accuracy: 0.8542 - loss: 0.2925 - precision: 0.8504 - recall: 0.8515 - val_accuracy: 0.8588 - val_loss: 0.2776 - val_precision: 0.8941 - val_recall: 0.8405 - learning_rate: 0.0010\n",
      "Epoch 16/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 42ms/step - accuracy: 0.8542 - loss: 0.2925 - precision: 0.8504 - recall: 0.8515 - val_accuracy: 0.8588 - val_loss: 0.2776 - val_precision: 0.8941 - val_recall: 0.8405 - learning_rate: 0.0010\n",
      "Epoch 16/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 36ms/step - accuracy: 0.8576 - loss: 0.2885 - precision: 0.8611 - recall: 0.8448 - val_accuracy: 0.8656 - val_loss: 0.2632 - val_precision: 0.8881 - val_recall: 0.8620 - learning_rate: 0.0010\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 36ms/step - accuracy: 0.8576 - loss: 0.2885 - precision: 0.8611 - recall: 0.8448 - val_accuracy: 0.8656 - val_loss: 0.2632 - val_precision: 0.8881 - val_recall: 0.8620 - learning_rate: 0.0010\n",
      "Epoch 17/100\n",
      "Epoch 17/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 26ms/step - accuracy: 0.8548 - loss: 0.2877 - precision: 0.8594 - recall: 0.8404 - val_accuracy: 0.8631 - val_loss: 0.2669 - val_precision: 0.9017 - val_recall: 0.8405 - learning_rate: 0.0010\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 26ms/step - accuracy: 0.8548 - loss: 0.2877 - precision: 0.8594 - recall: 0.8404 - val_accuracy: 0.8631 - val_loss: 0.2669 - val_precision: 0.9017 - val_recall: 0.8405 - learning_rate: 0.0010\n",
      "Epoch 18/100\n",
      "Epoch 18/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 38ms/step - accuracy: 0.8582 - loss: 0.3009 - precision: 0.8620 - recall: 0.8452 - val_accuracy: 0.8711 - val_loss: 0.2617 - val_precision: 0.8705 - val_recall: 0.8971 - learning_rate: 0.0010\n",
      "Epoch 19/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 38ms/step - accuracy: 0.8582 - loss: 0.3009 - precision: 0.8620 - recall: 0.8452 - val_accuracy: 0.8711 - val_loss: 0.2617 - val_precision: 0.8705 - val_recall: 0.8971 - learning_rate: 0.0010\n",
      "Epoch 19/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 39ms/step - accuracy: 0.8570 - loss: 0.2884 - precision: 0.8491 - recall: 0.8603 - val_accuracy: 0.8619 - val_loss: 0.2694 - val_precision: 0.8938 - val_recall: 0.8473 - learning_rate: 0.0010\n",
      "Epoch 20/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 39ms/step - accuracy: 0.8570 - loss: 0.2884 - precision: 0.8491 - recall: 0.8603 - val_accuracy: 0.8619 - val_loss: 0.2694 - val_precision: 0.8938 - val_recall: 0.8473 - learning_rate: 0.0010\n",
      "Epoch 20/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 48ms/step - accuracy: 0.8557 - loss: 0.2950 - precision: 0.8592 - recall: 0.8430 - val_accuracy: 0.8742 - val_loss: 0.2603 - val_precision: 0.8761 - val_recall: 0.8959 - learning_rate: 0.0010\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 48ms/step - accuracy: 0.8557 - loss: 0.2950 - precision: 0.8592 - recall: 0.8430 - val_accuracy: 0.8742 - val_loss: 0.2603 - val_precision: 0.8761 - val_recall: 0.8959 - learning_rate: 0.0010\n",
      "Epoch 21/100\n",
      "Epoch 21/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 49ms/step - accuracy: 0.8596 - loss: 0.2830 - precision: 0.8538 - recall: 0.8600 - val_accuracy: 0.8705 - val_loss: 0.2639 - val_precision: 0.8830 - val_recall: 0.8790 - learning_rate: 0.0010\n",
      "Epoch 22/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 49ms/step - accuracy: 0.8596 - loss: 0.2830 - precision: 0.8538 - recall: 0.8600 - val_accuracy: 0.8705 - val_loss: 0.2639 - val_precision: 0.8830 - val_recall: 0.8790 - learning_rate: 0.0010\n",
      "Epoch 22/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 51ms/step - accuracy: 0.8568 - loss: 0.2882 - precision: 0.8534 - recall: 0.8537 - val_accuracy: 0.8693 - val_loss: 0.2682 - val_precision: 0.9019 - val_recall: 0.8529 - learning_rate: 0.0010\n",
      "Epoch 23/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 51ms/step - accuracy: 0.8568 - loss: 0.2882 - precision: 0.8534 - recall: 0.8537 - val_accuracy: 0.8693 - val_loss: 0.2682 - val_precision: 0.9019 - val_recall: 0.8529 - learning_rate: 0.0010\n",
      "Epoch 23/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 26ms/step - accuracy: 0.8588 - loss: 0.2872 - precision: 0.8608 - recall: 0.8483 - val_accuracy: 0.8650 - val_loss: 0.2624 - val_precision: 0.8963 - val_recall: 0.8507 - learning_rate: 0.0010\n",
      "Epoch 24/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 26ms/step - accuracy: 0.8588 - loss: 0.2872 - precision: 0.8608 - recall: 0.8483 - val_accuracy: 0.8650 - val_loss: 0.2624 - val_precision: 0.8963 - val_recall: 0.8507 - learning_rate: 0.0010\n",
      "Epoch 24/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 30ms/step - accuracy: 0.8545 - loss: 0.2909 - precision: 0.8633 - recall: 0.8344 - val_accuracy: 0.8650 - val_loss: 0.2652 - val_precision: 0.8889 - val_recall: 0.8597 - learning_rate: 0.0010\n",
      "Epoch 25/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 30ms/step - accuracy: 0.8545 - loss: 0.2909 - precision: 0.8633 - recall: 0.8344 - val_accuracy: 0.8650 - val_loss: 0.2652 - val_precision: 0.8889 - val_recall: 0.8597 - learning_rate: 0.0010\n",
      "Epoch 25/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 42ms/step - accuracy: 0.8599 - loss: 0.2879 - precision: 0.8634 - recall: 0.8474 - val_accuracy: 0.8724 - val_loss: 0.2670 - val_precision: 0.8825 - val_recall: 0.8835 - learning_rate: 0.0010\n",
      "Epoch 26/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 42ms/step - accuracy: 0.8599 - loss: 0.2879 - precision: 0.8634 - recall: 0.8474 - val_accuracy: 0.8724 - val_loss: 0.2670 - val_precision: 0.8825 - val_recall: 0.8835 - learning_rate: 0.0010\n",
      "Epoch 26/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 42ms/step - accuracy: 0.8599 - loss: 0.2834 - precision: 0.8554 - recall: 0.8584 - val_accuracy: 0.8668 - val_loss: 0.2663 - val_precision: 0.8787 - val_recall: 0.8767 - learning_rate: 2.0000e-04\n",
      "Epoch 27/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 42ms/step - accuracy: 0.8599 - loss: 0.2834 - precision: 0.8554 - recall: 0.8584 - val_accuracy: 0.8668 - val_loss: 0.2663 - val_precision: 0.8787 - val_recall: 0.8767 - learning_rate: 2.0000e-04\n",
      "Epoch 27/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.8602 - loss: 0.2759 - precision: 0.8626 - recall: 0.8493 - val_accuracy: 0.8668 - val_loss: 0.2645 - val_precision: 0.8787 - val_recall: 0.8767 - learning_rate: 2.0000e-04\n",
      "Epoch 28/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.8602 - loss: 0.2759 - precision: 0.8626 - recall: 0.8493 - val_accuracy: 0.8668 - val_loss: 0.2645 - val_precision: 0.8787 - val_recall: 0.8767 - learning_rate: 2.0000e-04\n",
      "Epoch 28/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 41ms/step - accuracy: 0.8573 - loss: 0.2842 - precision: 0.8551 - recall: 0.8524 - val_accuracy: 0.8662 - val_loss: 0.2665 - val_precision: 0.8847 - val_recall: 0.8676 - learning_rate: 2.0000e-04\n",
      "Epoch 29/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 41ms/step - accuracy: 0.8573 - loss: 0.2842 - precision: 0.8551 - recall: 0.8524 - val_accuracy: 0.8662 - val_loss: 0.2665 - val_precision: 0.8847 - val_recall: 0.8676 - learning_rate: 2.0000e-04\n",
      "Epoch 29/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 45ms/step - accuracy: 0.8634 - loss: 0.2778 - precision: 0.8598 - recall: 0.8609 - val_accuracy: 0.8699 - val_loss: 0.2644 - val_precision: 0.8802 - val_recall: 0.8812 - learning_rate: 2.0000e-04\n",
      "Epoch 30/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 45ms/step - accuracy: 0.8634 - loss: 0.2778 - precision: 0.8598 - recall: 0.8609 - val_accuracy: 0.8699 - val_loss: 0.2644 - val_precision: 0.8802 - val_recall: 0.8812 - learning_rate: 2.0000e-04\n",
      "Epoch 30/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 44ms/step - accuracy: 0.8597 - loss: 0.2783 - precision: 0.8606 - recall: 0.8508 - val_accuracy: 0.8662 - val_loss: 0.2648 - val_precision: 0.8794 - val_recall: 0.8744 - learning_rate: 2.0000e-04\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 44ms/step - accuracy: 0.8597 - loss: 0.2783 - precision: 0.8606 - recall: 0.8508 - val_accuracy: 0.8662 - val_loss: 0.2648 - val_precision: 0.8794 - val_recall: 0.8744 - learning_rate: 2.0000e-04\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 23ms/step\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 23ms/step\n",
      "\n",
      "📊 Deep Neural Network Test Results:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.85      0.87      1014\n",
      "           1       0.85      0.88      0.87       986\n",
      "\n",
      "    accuracy                           0.87      2000\n",
      "   macro avg       0.87      0.87      0.87      2000\n",
      "weighted avg       0.87      0.87      0.87      2000\n",
      "\n",
      "AUC-ROC: 0.9511\n",
      "\n",
      "📊 Deep Neural Network Test Results:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.85      0.87      1014\n",
      "           1       0.85      0.88      0.87       986\n",
      "\n",
      "    accuracy                           0.87      2000\n",
      "   macro avg       0.87      0.87      0.87      2000\n",
      "weighted avg       0.87      0.87      0.87      2000\n",
      "\n",
      "AUC-ROC: 0.9511\n"
     ]
    }
   ],
   "source": [
    "# Train Deep Neural Network\n",
    "print(\"🧠 Training Deep Neural Network...\")\n",
    "\n",
    "def create_advanced_nn(input_dim, dropout_rate=0.3):\n",
    "    model = Sequential([\n",
    "        Dense(256, activation='relu', input_shape=(input_dim,)),\n",
    "        BatchNormalization(),\n",
    "        Dropout(dropout_rate),\n",
    "        \n",
    "        Dense(128, activation='relu'),\n",
    "        BatchNormalization(),\n",
    "        Dropout(dropout_rate),\n",
    "        \n",
    "        Dense(64, activation='relu'),\n",
    "        BatchNormalization(),\n",
    "        Dropout(dropout_rate * 0.5),\n",
    "        \n",
    "        Dense(32, activation='relu'),\n",
    "        Dropout(dropout_rate * 0.5),\n",
    "        \n",
    "        Dense(1, activation='sigmoid')\n",
    "    ])\n",
    "    \n",
    "    model.compile(\n",
    "        optimizer=Adam(learning_rate=0.001),\n",
    "        loss='binary_crossentropy',\n",
    "        metrics=['accuracy', 'precision', 'recall']\n",
    "    )\n",
    "    \n",
    "    return model\n",
    "\n",
    "# Create and train model\n",
    "nn_model = create_advanced_nn(X_train_balanced.shape[1])\n",
    "\n",
    "# Callbacks\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n",
    "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=1e-6)\n",
    "\n",
    "# Train model\n",
    "history = nn_model.fit(\n",
    "    X_train_balanced, y_train_balanced,\n",
    "    epochs=100,\n",
    "    batch_size=64,\n",
    "    validation_split=0.2,\n",
    "    callbacks=[early_stopping, reduce_lr],\n",
    "    verbose=1\n",
    ")\n",
    "\n",
    "# Evaluate\n",
    "nn_pred_proba = nn_model.predict(X_test_scaled).flatten()\n",
    "nn_pred = (nn_pred_proba > 0.5).astype(int)\n",
    "\n",
    "print(\"\\n📊 Deep Neural Network Test Results:\")\n",
    "print(classification_report(y_test, nn_pred))\n",
    "print(f\"AUC-ROC: {roc_auc_score(y_test, nn_pred_proba):.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 📈 Model Comparison and Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 Model Performance Summary:\n",
      "==================================================\n",
      "\n",
      "Random Forest:\n",
      "  Accuracy:  0.8565\n",
      "  Precision: 0.8520\n",
      "  Recall:    0.8580\n",
      "  F1-Score:  0.8550\n",
      "  AUC-ROC:   0.9427\n",
      "\n",
      "XGBoost:\n",
      "  Accuracy:  0.8680\n",
      "  Precision: 0.8603\n",
      "  Recall:    0.8742\n",
      "  F1-Score:  0.8672\n",
      "  AUC-ROC:   0.9475\n",
      "\n",
      "Deep Neural Network:\n",
      "  Accuracy:  0.8660\n",
      "  Precision: 0.8527\n",
      "  Recall:    0.8803\n",
      "  F1-Score:  0.8663\n",
      "  AUC-ROC:   0.9511\n"
     ]
    }
   ],
   "source": [
    "# Compare all models\n",
    "models_results = {\n",
    "    'Random Forest': {\n",
    "        'predictions': rf_pred,\n",
    "        'probabilities': rf_pred_proba,\n",
    "        'auc': roc_auc_score(y_test, rf_pred_proba)\n",
    "    },\n",
    "    'XGBoost': {\n",
    "        'predictions': xgb_pred,\n",
    "        'probabilities': xgb_pred_proba,\n",
    "        'auc': roc_auc_score(y_test, xgb_pred_proba)\n",
    "    },\n",
    "    'Deep Neural Network': {\n",
    "        'predictions': nn_pred,\n",
    "        'probabilities': nn_pred_proba,\n",
    "        'auc': roc_auc_score(y_test, nn_pred_proba)\n",
    "    }\n",
    "}\n",
    "\n",
    "# Plot ROC curves\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "for model_name, results in models_results.items():\n",
    "    fpr, tpr, _ = roc_curve(y_test, results['probabilities'])\n",
    "    plt.plot(fpr, tpr, label=f\"{model_name} (AUC = {results['auc']:.4f})\", linewidth=2)\n",
    "\n",
    "plt.plot([0, 1], [0, 1], 'k--', label='Random Classifier')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate', color='white', fontsize=12)\n",
    "plt.ylabel('True Positive Rate', color='white', fontsize=12)\n",
    "plt.title('🎯 ROC Curves Comparison - Cybersecurity Threat Detection', color='white', fontsize=14)\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Model performance summary\n",
    "print(\"\\n📊 Model Performance Summary:\")\n",
    "print(\"=\" * 50)\n",
    "for model_name, results in models_results.items():\n",
    "    from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "    \n",
    "    acc = accuracy_score(y_test, results['predictions'])\n",
    "    prec = precision_score(y_test, results['predictions'])\n",
    "    rec = recall_score(y_test, results['predictions'])\n",
    "    f1 = f1_score(y_test, results['predictions'])\n",
    "    \n",
    "    print(f\"\\n{model_name}:\")\n",
    "    print(f\"  Accuracy:  {acc:.4f}\")\n",
    "    print(f\"  Precision: {prec:.4f}\")\n",
    "    print(f\"  Recall:    {rec:.4f}\")\n",
    "    print(f\"  F1-Score:  {f1:.4f}\")\n",
    "    print(f\"  AUC-ROC:   {results['auc']:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🎭 Ensemble Model Creation\n",
    "\n",
    "Let's create an ensemble model that combines all our trained models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📊 Ensemble weights: {'Random Forest': np.float64(0.3317898200638655), 'XGBoost': np.float64(0.33347120463909363), 'Deep Neural Network': np.float64(0.33473897529704083)}\n",
      "\n",
      "🎭 Ensemble Model Results:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.86      0.86      1014\n",
      "           1       0.85      0.87      0.86       986\n",
      "\n",
      "    accuracy                           0.86      2000\n",
      "   macro avg       0.86      0.86      0.86      2000\n",
      "weighted avg       0.86      0.86      0.86      2000\n",
      "\n",
      "AUC-ROC: 0.9498\n",
      "\n",
      "🏆 Final Model Rankings (by AUC-ROC):\n",
      "========================================\n",
      "1. Deep Neural Network  AUC: 0.9511\n",
      "2. Ensemble             AUC: 0.9498\n",
      "3. XGBoost              AUC: 0.9475\n",
      "4. Random Forest        AUC: 0.9427\n"
     ]
    }
   ],
   "source": [
    "# Create ensemble predictions\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "\n",
    "# Weighted ensemble based on AUC scores\n",
    "weights = [results['auc'] for results in models_results.values()]\n",
    "weights = np.array(weights) / sum(weights)  # Normalize weights\n",
    "\n",
    "print(f\"📊 Ensemble weights: {dict(zip(models_results.keys(), weights))}\")\n",
    "\n",
    "# Weighted average of probabilities\n",
    "ensemble_proba = (\n",
    "    weights[0] * rf_pred_proba +\n",
    "    weights[1] * xgb_pred_proba +\n",
    "    weights[2] * nn_pred_proba\n",
    ")\n",
    "\n",
    "ensemble_pred = (ensemble_proba > 0.5).astype(int)\n",
    "ensemble_auc = roc_auc_score(y_test, ensemble_proba)\n",
    "\n",
    "print(f\"\\n🎭 Ensemble Model Results:\")\n",
    "print(classification_report(y_test, ensemble_pred))\n",
    "print(f\"AUC-ROC: {ensemble_auc:.4f}\")\n",
    "\n",
    "# Compare ensemble with individual models\n",
    "models_results['Ensemble'] = {\n",
    "    'predictions': ensemble_pred,\n",
    "    'probabilities': ensemble_proba,\n",
    "    'auc': ensemble_auc\n",
    "}\n",
    "\n",
    "# Final comparison\n",
    "print(\"\\n🏆 Final Model Rankings (by AUC-ROC):\")\n",
    "print(\"=\" * 40)\n",
    "sorted_models = sorted(models_results.items(), key=lambda x: x[1]['auc'], reverse=True)\n",
    "for i, (model_name, results) in enumerate(sorted_models, 1):\n",
    "    print(f\"{i}. {model_name:20} AUC: {results['auc']:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 💾 Model Saving and Deployment\n",
    "\n",
    "Save the best performing models for production use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "💾 Saving trained models...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Random Forest saved\n",
      "✅ XGBoost saved\n",
      "✅ Neural Network saved\n",
      "✅ Feature scaler saved\n",
      "✅ Feature names saved\n",
      "✅ Ensemble configuration saved\n",
      "\n",
      "🎉 All models saved successfully!\n",
      "📁 Models saved to: ../models/malware_detection/\n",
      "✅ Neural Network saved\n",
      "✅ Feature scaler saved\n",
      "✅ Feature names saved\n",
      "✅ Ensemble configuration saved\n",
      "\n",
      "🎉 All models saved successfully!\n",
      "📁 Models saved to: ../models/malware_detection/\n"
     ]
    }
   ],
   "source": [
    "import joblib\n",
    "import os\n",
    "\n",
    "# Create models directory\n",
    "os.makedirs('../models/malware_detection', exist_ok=True)\n",
    "\n",
    "# Save the best models\n",
    "print(\"💾 Saving trained models...\")\n",
    "\n",
    "# Save Random Forest\n",
    "joblib.dump(rf_grid.best_estimator_, '../models/malware_detection/random_forest.pkl')\n",
    "print(\"✅ Random Forest saved\")\n",
    "\n",
    "# Save XGBoost\n",
    "joblib.dump(xgb_grid.best_estimator_, '../models/malware_detection/xgboost.pkl')\n",
    "print(\"✅ XGBoost saved\")\n",
    "\n",
    "# Save Neural Network\n",
    "nn_model.save('../models/malware_detection/neural_network.h5')\n",
    "print(\"✅ Neural Network saved\")\n",
    "\n",
    "# Save scaler\n",
    "joblib.dump(scaler, '../models/malware_detection/scaler.pkl')\n",
    "print(\"✅ Feature scaler saved\")\n",
    "\n",
    "# Save feature names\n",
    "joblib.dump(feature_cols, '../models/malware_detection/feature_names.pkl')\n",
    "print(\"✅ Feature names saved\")\n",
    "\n",
    "# Save ensemble weights\n",
    "ensemble_config = {\n",
    "    'weights': weights.tolist(),\n",
    "    'models': list(models_results.keys())[:-1],  # Exclude ensemble itself\n",
    "    'threshold': 0.5,\n",
    "    'performance': {model: results['auc'] for model, results in models_results.items()}\n",
    "}\n",
    "joblib.dump(ensemble_config, '../models/malware_detection/ensemble_config.pkl')\n",
    "print(\"✅ Ensemble configuration saved\")\n",
    "\n",
    "print(\"\\n🎉 All models saved successfully!\")\n",
    "print(f\"📁 Models saved to: ../models/malware_detection/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🚀 Real-time Prediction Function\n",
    "\n",
    "Create a function for real-time threat detection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔧 Feature engineering complete: 6 → 17 features\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step\n",
      "🧪 Test Prediction Result:\n",
      "   Prediction: Malware\n",
      "   Confidence: 0.6950\n",
      "   Risk Level: Medium\n",
      "   Model Scores: {'random_forest': 0.617122198390176, 'xgboost': 0.5822932720184326, 'neural_network': 0.8844788074493408}\n",
      "\n",
      "🎉 Training pipeline completed successfully!\n",
      "📦 Models ready for deployment in real-time threat detection system.\n",
      "🧪 Test Prediction Result:\n",
      "   Prediction: Malware\n",
      "   Confidence: 0.6950\n",
      "   Risk Level: Medium\n",
      "   Model Scores: {'random_forest': 0.617122198390176, 'xgboost': 0.5822932720184326, 'neural_network': 0.8844788074493408}\n",
      "\n",
      "🎉 Training pipeline completed successfully!\n",
      "📦 Models ready for deployment in real-time threat detection system.\n"
     ]
    }
   ],
   "source": [
    "def predict_malware_threat(file_features, use_ensemble=True):\n",
    "    \"\"\"\n",
    "    Real-time malware threat prediction\n",
    "    \n",
    "    Args:\n",
    "        file_features (dict): Dictionary containing file features\n",
    "        use_ensemble (bool): Whether to use ensemble prediction\n",
    "    \n",
    "    Returns:\n",
    "        dict: Prediction results with confidence scores\n",
    "    \"\"\"\n",
    "    try:\n",
    "        # Convert features to DataFrame\n",
    "        sample_df = pd.DataFrame([file_features])\n",
    "        \n",
    "        # Apply feature engineering\n",
    "        sample_engineered = advanced_feature_engineering(sample_df, target_col=None)\n",
    "        \n",
    "        # Ensure we have all required features\n",
    "        for feature in feature_cols:\n",
    "            if feature not in sample_engineered.columns:\n",
    "                sample_engineered[feature] = 0\n",
    "        \n",
    "        # Select and scale features\n",
    "        X_sample = sample_engineered[feature_cols]\n",
    "        X_sample_scaled = scaler.transform(X_sample)\n",
    "        \n",
    "        if use_ensemble:\n",
    "            # Ensemble prediction\n",
    "            rf_prob = rf_grid.predict_proba(X_sample_scaled)[0, 1]\n",
    "            xgb_prob = xgb_grid.predict_proba(X_sample_scaled)[0, 1]\n",
    "            nn_prob = nn_model.predict(X_sample_scaled)[0, 0]\n",
    "            \n",
    "            # Weighted ensemble\n",
    "            ensemble_prob = (\n",
    "                weights[0] * rf_prob +\n",
    "                weights[1] * xgb_prob +\n",
    "                weights[2] * nn_prob\n",
    "            )\n",
    "            \n",
    "            prediction = \"Malware\" if ensemble_prob > 0.5 else \"Benign\"\n",
    "            confidence = ensemble_prob if ensemble_prob > 0.5 else 1 - ensemble_prob\n",
    "            \n",
    "            return {\n",
    "                'prediction': prediction,\n",
    "                'confidence': float(confidence),\n",
    "                'threat_probability': float(ensemble_prob),\n",
    "                'model_scores': {\n",
    "                    'random_forest': float(rf_prob),\n",
    "                    'xgboost': float(xgb_prob),\n",
    "                    'neural_network': float(nn_prob)\n",
    "                },\n",
    "                'risk_level': 'High' if ensemble_prob > 0.8 else 'Medium' if ensemble_prob > 0.5 else 'Low'\n",
    "            }\n",
    "        else:\n",
    "            # Single model prediction (best performing)\n",
    "            best_model = rf_grid  # Assuming RF is best\n",
    "            prob = best_model.predict_proba(X_sample_scaled)[0, 1]\n",
    "            prediction = \"Malware\" if prob > 0.5 else \"Benign\"\n",
    "            confidence = prob if prob > 0.5 else 1 - prob\n",
    "            \n",
    "            return {\n",
    "                'prediction': prediction,\n",
    "                'confidence': float(confidence),\n",
    "                'threat_probability': float(prob),\n",
    "                'model': 'Random Forest',\n",
    "                'risk_level': 'High' if prob > 0.8 else 'Medium' if prob > 0.5 else 'Low'\n",
    "            }\n",
    "            \n",
    "    except Exception as e:\n",
    "        return {\n",
    "            'error': str(e),\n",
    "            'prediction': 'Unknown',\n",
    "            'confidence': 0.0\n",
    "        }\n",
    "\n",
    "# Test the prediction function\n",
    "test_features = {\n",
    "    'file_size': 1048576,  # 1MB\n",
    "    'entropy': 7.5,        # High entropy\n",
    "    'pe_sections': 8,      # Multiple sections\n",
    "    'imports': 150,        # Many imports\n",
    "    'exports': 5,          # Few exports\n",
    "    'strings_count': 500   # Moderate strings\n",
    "}\n",
    "\n",
    "result = predict_malware_threat(test_features)\n",
    "print(\"🧪 Test Prediction Result:\")\n",
    "print(f\"   Prediction: {result['prediction']}\")\n",
    "print(f\"   Confidence: {result['confidence']:.4f}\")\n",
    "print(f\"   Risk Level: {result['risk_level']}\")\n",
    "if 'model_scores' in result:\n",
    "    print(f\"   Model Scores: {result['model_scores']}\")\n",
    "\n",
    "print(\"\\n🎉 Training pipeline completed successfully!\")\n",
    "print(\"📦 Models ready for deployment in real-time threat detection system.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 📋 Summary and Next Steps\n",
    "\n",
    "### ✅ What we accomplished:\n",
    "\n",
    "1. **📊 Data Preparation**\n",
    "   - Downloaded and processed cybersecurity datasets\n",
    "   - Applied advanced feature engineering\n",
    "   - Handled class imbalance with SMOTE\n",
    "\n",
    "2. **🤖 Model Training**\n",
    "   - Random Forest with hyperparameter tuning\n",
    "   - XGBoost with grid search optimization\n",
    "   - Deep Neural Network with advanced architecture\n",
    "   - Ensemble model combining all approaches\n",
    "\n",
    "3. **📈 Model Evaluation**\n",
    "   - Comprehensive performance metrics\n",
    "   - ROC curve analysis\n",
    "   - Model comparison and ranking\n",
    "\n",
    "4. **💾 Production Deployment**\n",
    "   - Saved all trained models\n",
    "   - Created real-time prediction function\n",
    "   - Prepared for integration with live system\n",
    "\n",
    "### 🚀 Next Steps:\n",
    "\n",
    "1. **Integration**: Connect models to the desktop/mobile applications\n",
    "2. **Monitoring**: Implement model performance monitoring\n",
    "3. **Updates**: Set up automated retraining pipeline\n",
    "4. **Scaling**: Deploy models to production infrastructure\n",
    "5. **Enhancement**: Add more sophisticated deep learning models\n",
    "\n",
    "The trained models are now ready to be integrated into the Cyber Forge AI platform for real-time threat detection! 🛡️"
   ]
  }
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